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Author: Schrauth, Chris, P Title: Verification Process for Implementation of Robotic-Tended Sheet Metal

Forming Cell The accompanying research report is submitted to the University of Wisconsin-Stout, Graduate School in partial

completion of the requirements for the

Graduate Degree/ Major: MS Manufacturing Engineering

Research Adviser: Dr. Annamalai Pandian

Submission Term/Year: Fall, 2012

Number of Pages: 104

Style Manual Used: American Psychological Association, 6th edition

I understand that this research report must be officially approved by the Graduate School and that an electronic copy of the approved version will be made available through the University Library website

I attest that the research report is my original work (that any copyrightable materials have been used with the permission of the original authors), and as such, it is automatically protected by the laws, rules, and regulations of the U.S. Copyright Office.

My research adviser has approved the content and quality of this paper. STUDENT:

NAME DATE:

ADVISER: (Committee Chair if MS Plan A or EdS Thesis or Field Project/Problem):

NAME DATE:

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This section for MS Plan A Thesis or EdS Thesis/Field Project papers only Committee members (other than your adviser who is listed in the section above) 1. CMTE MEMBER’S NAME: DATE:

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Schrauth, Chris P. Verification Process for Implementation of Robotic-Tended Sheet Metal

Forming Cell

Abstract

This field project study was performed at a manufacturer of residential heating products.

This study implemented sheet metal forming and fabrication machine tools that were tended by a

robotic manipulator. This equipment was necessary to support the new product launch of a

relatively large appliance. The robotic automation capability of the new cell was justified

because the physical size of component parts exceeded the safe working capacity of a human

operator. The purpose of this study was to verify that the machine tool system would be capable

of meeting the engineering specifications for the product design. The quality planning tools,

including FMEA, Process Capability Studies, and Statistical Process Control, were executed as a

part of the verification methodology. The results provide an estimate of the new machine cell

capability to meet design specifications and insight to key opportunities for improvements.

Based on the results, the benefits and limitations of the verification process are also presented.

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Acknowledgements

I would like to thank my family for their understanding and encouragement during my

work towards this degree. I would also like to thank my field project advisor, Dr. Pandian, for

his timely review and the helpful suggestions with completing this paper.

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Table of Contents

.................................................................................................................................................... Page

Abstract ............................................................................................................................................2

List of Tables ...................................................................................................................................7

List of Figures ..................................................................................................................................8

Chapter I: Introduction ....................................................................................................................9

Statement of the Problem ...................................................................................................11

Purpose of the Study ..........................................................................................................11

Assumptions of the Study ..................................................................................................12

Definition of Terms............................................................................................................12

Limitations of the Study.....................................................................................................16

Methodology .....................................................................................................................16

Chapter II: Literature Review ........................................................................................................17

Robotic Tending of Press Brakes .......................................................................................17

Benefits and drawbacks .........................................................................................17

Machine tool design considerations .......................................................................19

Control and sensing considerations .......................................................................20

Manufacturing Process Verification ..................................................................................21

Predictive Techniques ............................................................................................23

Failure Mode and Effects Analysis ........................................................................23

First Article Inspection ..........................................................................................25

Production Part Approval Process .........................................................................26

Advanced Product Quality Planning ......................................................................28

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Process capability studies ......................................................................................29

Sustained Control Methods ....................................................................................32

Statistical Process Control .....................................................................................32

Automatic verification ...........................................................................................33

Summary ............................................................................................................................34

Chapter III: Methodology ..............................................................................................................35

Product Design and Manufacturing Process Overview .....................................................35

Failure Mode and Effects Analysis ....................................................................................42

Data Requirements .............................................................................................................43

Measurement System .........................................................................................................43

Sample Measurement Approach ........................................................................................44

Data Acquisition ................................................................................................................45

Data Analysis .....................................................................................................................46

Advantages and Limitations ..............................................................................................47

Chapter IV: Results ........................................................................................................................48

Failure Mode and Effects Analysis ....................................................................................50

Product design ........................................................................................................50

Process design ........................................................................................................52

Machine cell equipment design .............................................................................53

Measurement System Analysis ..........................................................................................54

Machine Cell Verification..................................................................................................57

Tolerance intervals .................................................................................................57

Potential process performance studies ...................................................................59

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Statistical Process Control .....................................................................................62

Long-term process capability .................................................................................64

Summary ............................................................................................................................67

Chapter V: Discussion ...................................................................................................................69

Conclusions ........................................................................................................................72

Recommendations ..............................................................................................................74

References ......................................................................................................................................75

Appendix A: Sheet Metal Material Specifications .......................................................................78

Appendix B: Process Failure Mode and Effects Analysis ............................................................79

Appendix C: Design Drawings .....................................................................................................83

Appendix D: Detailed Gage R&R Results ....................................................................................87

Appendix E: Pilot Production Tolerance Interval Results ............................................................89

Appendix F: Pilot Production Process Performance Studies ........................................................93

Appendix G: Statistical Process Control .......................................................................................97

Appendix H: Production Process Capability Studies ..................................................................101

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List of Tables

Table 1: Ten overall requirements of a PPAP ..............................................................................28

Table 2: Five phases of APQP ......................................................................................................29

Table 3: Product design specifications and critical requirements .................................................39

Table 4: Anatomy of robotic-tended forming and fabrication cell ...............................................41

Table 5: Summary of methods and results for project objectives .................................................49

Table 6: Measurement tool capability and Gage R&R Results ....................................................57

Table 7: Process mean, sigma, and tolerance interval estimates for firebox wrap characteristics ..................................................................................................................59

Table 8: Process mean, sigma, and tolerance interval estimates for firebox top

and bottom characteristics ..............................................................................................59 Table 9: Estimated process performance for firebox wrap characteristics ...................................61

Table 10: Estimated process performance for firebox top and bottom characteristics .................62

Table 11: Estimated long-term process characteristics and expected defect potential .................66

Table 12: Estimated long-term process capability indices and overall expected defective PPM ................................................................................................................67

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List of Figures

Figure 1: Failure Mode and Effects Analysis template ................................................................25

Figure 2: Individual part model views ..........................................................................................36

Figure 3: Hem channel and crimped hem .....................................................................................37

Figure 4: Crimping fabrication fixture ..........................................................................................38

Figure 5: Combustion chamber subassembly ...............................................................................39

Figure 6: Combustion chamber forming and fabrication cell .......................................................40

Figure 7: Process flow chart for combustion chamber subassembly ............................................42

Figure 8: Product design changes implemented to reduce crimping failure modes .....................52

Figure 9: Flowchart for verification of manufacturing equipment ...............................................73

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Chapter I: Introduction

The purpose of this project is to develop a verification strategy for a new machine tool

cell implemented by a manufacturer of residential heating appliances. These appliances include

gas and solid fuel stoves and fireplaces supplied primarily in markets in the United States,

Europe, and Australia.

Sheet steel is a major material used in the manufacture of these products. While some

sheet metal parts are utilized at a sufficient volume to benefit from dedicated hard-tooling, a

large portion of component parts are processed by press brake forming of cut blanks. This

manufacturer depends on the flexibility of various sizes and capacities of press brakes to produce

appliances and accessories. In most cases, press brakes are part of assembly lines where the

parts are formed on immediate demand with minimal batching. Many assembly lines produce

mixed models, a scenario which requires frequent press brake setup events. This approach to

blank forming requires operators skilled in tooling and machine setup, inspection, and rapid

blueprint interpretation. Above all, many operators need to have the ability to be inherently

familiar with a wide variety of assigned products.

Sheet metal forming with press brakes allows minimized tooling investment for new

product design, relatively easy work cell layout changes, and overall long-term flexibility in

asset utilization. However, this approach does present three challenges to this organization.

First, it requires qualified machine operators whose skill depends on adequate training and

experience. Second, it presents inherent opportunity for forming quality defects, especially

given the frequent tooling and machine setup. Finally, design applications are limited to the

physical size of the parts that can be safety handled by a human operator.

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This organization is approaching the launch of a new product line of gas heating

appliances. This new product chassis is unique due to its requirement of a large combustion

chamber constructed of formed sheet steel. Due to its physical size, some formed components of

the combustion chamber exceed the safe capacity of single press brake operator. To overcome

this constraint, investment is required in a new machine cell to perform forming and fabrication

of the subassembly. A robot-tended cell is proposed in this study. This machine cell will consist

of a press brake with fixed tooling, tended by a robotic manipulator, end-of arm tooling,

fabrication fixture, and sensing control system. Instead of a human operator, the robot will

manipulate the largest sheet metal part through the forming process, and then perform a crimping

process to mechanically fasten the three parts into a single subassembly. Because there will be

three appliance chassis sizes, the equipment will be capable of fabricating three subassemblies.

This cell will complete a finished cycle within the TAKT time of the active assembly line, which

will pull directly from the cell per cycle time demand.

This forming and fabrication cell will be the first implementation of a robotic press brake

in this operation. This manufacturer has had mixed success in similar past projects because

machines and tooling have not been properly verified. In many projects, the new equipment did

not fully perform the intended function. This resulted in additional debugging, unforeseen

equipment modifications, and production loss. Consequently, the process yield and equipment

uptime have been marginal overall. A deficiency of the current state is a lack of an effective

system to verify that new equipment is capable of meeting the design requirements on a

sustained basis.

Because marginal yields and product quality issues have occurred in the past with

equipment introductions, this project work aims to improve implementation of new

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manufacturing process equipment and tooling. This study considers quality engineering tools,

such as Failure Mode and Effects Analysis, as a part of a system to ensure preparation associated

with new manufacturing processes. This robotic production cell represents new technology that

could be leveraged in many other areas within the operation. Therefore, this study is taken up to

research and propose an effective verification system that can be implemented to ensure the new

machine cell will achieve the design specifications. The study will ensure product quality,

equipment performance, and safety of personnel. The remainder of this chapter will present the

problem statement, objectives, and significance of the study.

Statement of the Problem

Handling the large sheet metal parts during forming and crimping exceeds the safe

capacity of a human operator. A robotic press brake forming and crimping machine cell is

proposed to manufacture the large sheet metal components. There is not a defined plan to verify

the machine cell safety, reliability, and capability of consistently meeting the engineering

specifications. If the machine cell design is not properly verified, its implementation will

potentially result in quality defects, downtime, and unsafe conditions. This project study is

necessary to verify that the new machine cell will be capable of meeting the requirements.

Purpose of the Study

The objectives of this study are to:

1. Define the product design specifications and critical characteristics.

2. Analyze the new machine cell to identify preventative action for potential failure

modes to maximize equipment reliability and capacity. Minimize human interface

safety concerns that may result from process equipment design or operation.

3. Define and qualify gages for measuring critical part dimensions and characteristics.

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4. Verify that the new equipment is stable and has long-term capability to meet the

product design specifications and produce the critical characteristics.

5. Research verification methodologies used by manufacturing industries and identify a

system that can used for future launch of new process equipment and tooling in order

to consistently meet the requirements.

Assumptions of the Study

The assumptions of this study are:

1. The product design specifications and cycle time requirements will not change

significantly during this project. However, minor product design or tolerance

modifications may be justified based on the results of this project.

2. The design of the new production cell will be improved if justified by the results of

this study.

3. This new machine cell will be dedicated to this new line of products.

4. The press brake tooling will be fixed with no change-over requirement.

Definition of Terms

Air Bending. Press brake forming method that uses acute angle tooling capable of

forming acute, obtuse, and 90-degree bends by accurate advancement of the ram position to

control the depth of the upper die punch advancement into the bottom v-die.

Anderson-Darling Test. A statistical test, based on the size and shape of a distribution

representing a given data set, used to evaluate if the normal distribution is a reasonable model for

the given variable.

Back Gauge. An adjustable device on a press brake that accurately locates the work

piece in relation to the dies so that the bend position can be controlled.

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Control Plan. Documentation of a process and necessary evaluation program to ensure

that it remains in control and produces to specifications.

Crimp. Mechanical joining of multiple metal components by deforming one or both of

them under pressure in order to fasten them together

Failure Mode and Effects Analysis (FMEA). A procedure used to evaluate a product or

process for potential failure modes and classification of their effects based on severity,

occurrence probability, and detectability.

Gage Repeatability and Reproducibility (GR&R). An analysis of variable

measurement tools or methods to determine if measurement variability is low enough that it does

not interfere with the ability to detect non-conforming parts, or differences between parts.

ISO9000. A family of international quality standards.

Measurement System Analysis (MSA). Tools and techniques used to evaluate and

improve the method(s) associated with measurement systems.

Natural Process Tolerance Limits. The natural limits of long-term process performance

defined as three standard deviations from each side of the process average, determined by a SPC

control chart.

Natural Process Tolerance Limits = µ±3σ

Where µ is the overall mean and σ is the point estimate for process standard deviation calculated

by dividing the average of all subgroup ranges by the d2 Control Chart Constant for the subgroup

size.

Normal Distribution. A continuous probability distribution that fits a symmetrical bell-

shaped curve centered about the estimated mean.

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Normality. A statistical assumption that can be tested to determine if specific data should

be modeled by the normal distribution.

Pin Gage. Precision ground cylinders that function as reference gages for measurement

of bore diameters or gaps between surfaces.

Parts Per Million (PPM). Potential defect rate based on its concentration within a

population of one million units.

Pre-Control. A monitoring technique used in new and existing processes to evaluate if

process output occurs within specification limits.

Press Brake. A machine tool used to form bends into sheet or plate material.

Process Capability, Cp. Statistical estimate of process capability that compares the

engineering tolerance with the natural process tolerance, and assumes that the process mean is

centered between the engineering tolerance.

Cp = (USL – LSL) ÷ 6 σ

Where σ is the true process standard deviation estimated from a normally distributed sample

standard deviation or stable process history provided by a control chart.

Process Capability Index, Cpk. Statistical estimate of process capability that accounts

for the relative centering of the process mean within the engineering tolerance.

Cpk = min [(USL - µ) ÷ 3σ, (µ - LSL) ÷ 3σ]

Where σ is the process standard deviation, and µ is the process mean from control chart history.

Process Capability Study. An engineering study used to estimate the ability of a process

to produce within the specification limits.

Process Performance, Pp. Statistical estimate of process capability recommended for

use when a process is not in statistical control. It compares the engineering tolerance with the

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natural process tolerance, and assumes that the process mean is centered between the engineering

tolerance.

Pp = (USL – LSL) ÷ 6 ѕ

Where ѕ is the sample standard deviation

Process Performance Index, Ppk. Statistical estimate of process capability

recommended for use when a process is not in statistical control. It accounts for the process

mean relative centering within the engineering tolerance.

Ppk = min[(USL – Xbar) ÷ 3ѕ , (Xbar - LSL) ÷ 3ѕ]

Where ѕ is the sample standard deviation and Xbar is the sample mean.

Process Yield. The percentage of acceptable parts among all parts produced in a

specified period of time.

Risk Priority Number (RPN). Numeric risk assessment assigned to a failure mode

during Failure Mode an Effects Analysis (FMEA). It accounts for the likelihood of occurrence,

likelihood of detection, and severity of the failure mode.

Statistical Process Control (SPC). System for monitoring a process to determine if its

output is stable and identify influence of variation that may warrant action to prevent it from

going out of control.

TAKT Time. The pace of a manufacturing system adjusted to produce at a rate

equivalent to current customer demand.

Tolerance Interval. Estimate of statistical limits within which a stated proportion of the

population is expected to occur, at a given confidence level.

Two-Sided Tolerance Interval = Xbar +/- K2 ѕ

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Where K2 is a two-sided tolerance interval factor for a normal distribution, and ѕ is the sample

standard deviation.

Springback. Sheet metal rebound on either side of the bend after the force from the

forming tool has been removed.

Stability. Consistency of a process over a period of time such that its mean and variation

remain unchanged and are constant during the timeframe under study.

Total Productive Maintenance (TPM). Method of improving reliability of

manufacturing equipment through proactive involvement of machine operator in routine

preventative maintenance.

Limitations of the Study

The results and recommendations of this study apply specifically to this new machine cell

and the products it will produce.

Methodology

This report will present an overview of literature related to robotic press tending and

techniques for verifying new manufacturing equipment. The report will also provide a more

detailed summary of this field study, including the forming and fabrication cell, along with

definition of the product design specifications. This section will also summarize the

methodology of verification, including FMEA, MSA, run-off pilot capability studies, and SPC

analysis used to estimate long-term capability. Finally, the results and analysis of these methods

will be presented in support of the conclusions and suggested improvements.

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Chapter II: Literature Review

The overall goal of this project is successful implementation of the new forming and

fabrication cell. This study will ensure that the process output will consistently conform to

product design specifications. This study will also evaluate the new equipment to improve its

reliability and safety. The outcome will be a machine cell with maximized process yield,

equipment uptime, and safe human interface.

This chapter will review the benefits, drawbacks, and special considerations associated

with robotic tending of press brakes. This chapter will also review several process verification

systems and supporting methods that are applied in the field of manufacturing. The review will

include literature covering short and long-term predictive verification techniques, and available

methods that help sustain long-term capability and control. The literature referenced in this

discussion includes manufacturing and quality engineering, as well as statistical analysis sources.

Robotic-Tending of Press Brakes

Robotic manipulators provide flexible automation capability to manufacturing processes

including assembly, welding, painting, grinding, palletizing, and machine tending. Robotics

technology has typically been applied to applications that are hazardous to human operators or in

scenarios where production volume is sufficient to justify the cost of soft automation. Robotic

tending of press brakes can easily be justified for medium to high volume applications.

However, even lower volume applications can be justified in certain situations (Glaser, 2009).

Benefits and drawbacks. According to Glaser (2009) and LeTang (2012), robotic

automation can provide several important benefits to manufacturers. An investment in robotic

automation is often a preferred alternative to hard automation due to its flexibility as an asset.

An advantage is that once the intended application is obsolete, the robot can be redeployed to

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another application. In higher volume situations, robotic press brakes can result in lower defects,

higher productivity, and lower costs compared to those tended by human operators. In these

high volume scenarios, the system can run “lights-out” on off-shifts, further reducing labor costs.

Even in lower volume situations, automation of press brake tending frees up human operators to

perform value-added tasks such as secondary operations, quality checks, external tooling setup,

or maintenance. While not necessarily faster than skilled human operators, robotic-automated

press brakes result in more consistent cycle times with less unplanned downtime. Newer robots,

when integrated with properly designed machine tool and control systems, are extremely reliable.

Thus, machine capacity and utilization rate are higher, leading to potentially lower per part cost

and faster return on investment.

Another key advantage is that a robot can be sized with available payload capacity to

safety handle large or heavy work pieces that cannot be handled safely by a human operator.

Along with improved safety, robotic tending can also lead to quality benefits. Sensing and

controls can be added to the system to compliment the repeatability of the robotic manipulator.

Human-invoked variables can be removed from the process. As a result, forming variation can

be decreased, and process defects can be more easily detected and diagnosed.

Robotic tending of press brake also presents several drawbacks. First, it requires a

greater capital investment to fulfill its requirements of tooling and controls. Second, it carries

higher setup costs in terms of programming time, debugging, and setup scrap material. In some

cases, this may present less flexibility, such as batch manufacturing scenarios. LeTang (2012)

provided an example of a batch manufacturer setting up for a single run of parts where the

tooling and programming setup would be more difficult because a robot does not have the

capability to recognize parts and tooling as quickly as a human operator. Another key drawback

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is that robotic tending may require more floor space than a human-operated press brake. A

robotic press brake requires space for conveyors, reference table, and cell perimeter guarding. It

is common for the floor space requirement to be up to five times that of a traditional human-

operated machine cell (Glaser, 2009).

Machine tool design considerations. There are several methods that can be applied to

the machine tooling to effectively integrate robotic tending of a press brake. The sheet inbound

conveyor or staging pallets need to be designed to contain and control the appropriate blanks for

the program (Glaser, 2009). Most systems depend on a reference or squaring table as one of the

initial steps in the forming process. This hardware is a tilted flat surface that uses gravity to

establish the blank at the robot point of reference. This ensures that the robot has the sheet

properly orientated and located at the start of the processing sequence.

In most robot-tended press brakes, the robot manipulator is fitted with an end-of-arm

gripper. This is the most critical component of the entire system as it is the primary interface

between the robot and the work piece. The design of the end-of arm gripper must achieve two

critical functions. First, it must have the ability to securely grip the surfaces of the work piece

despite surface oils and sheet deflection (Glaser, 2009). Second, the gripper must be properly

sized and configured to adapt to the work piece as its form evolves during the bending sequence.

Clamps, magnets, or vacuum cups, sometimes used in combination, must be configured to grip

the part shape as it changes during the forming sequence. To accomplish the ability to adapt to

changing part geometry, a regripping station can be added to the cell. This added hardware is a

special pedestal that allows the gripper to release the work piece, reorient it, and then regrip the

work piece to facilitate subsequent forming (Glaser, 2009). As an alternative to a regripping

station, some applications employ sheet follower plates on the press brake. These accessories

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can also be fitted with grippers to support sheets at the proper angle in conjunction with the

tooling and back gauge engagement with the work piece.

The press brake back gauge is another critical element within the system. It functions as

a reference point for the work piece relative to the tooling. Most new press brakes employ CNC

multi-axis back gauges that automatically adjust to the bend sequence program. It is possible for

the back gauge position to also compensate for variation in sheet metal thickness and stiffness.

This may be especially useful in operations where the sheet metal material specifications do not

narrowly define alloy or sheet thickness. The back gauge can be a primary locator of robot-

positioned blank into the brake prior to each step in forming (Glaser, 2009)

Control and sensing considerations. Several sensing and control tactics can be applied

to enhance the capability of robotic press brakes. In many applications, use of special sensors

provides in-process feedback between the work piece, the press brake, and the robot manipulator.

On the inbound conveyor or pallet, double-blank detection can be used to prevent more than one

work piece from being griped by the robot (LeTang, 2012). The sensors function to identify and

stop the process if multiple blanks are picked by the gripper. Blank size and orientation must be

verified on the reference table prior to execution of the bend program. Capacitive or proximity

sensors can be incorporated in the table to verify correct blank size and position (Glaser, 2009).

It is also useful to fit the end-of arm sheet gripper with sensing capability. These sensors

can function to ensure that the work piece is properly fixed to the gripper, and stop the process if

the blank is not held securely. One example is part-present sensing, where a sensor accompanies

each individual or set of vacuum cups, to detect if sections of large parts are securely held by the

gripper (Part Present Sensing, n.d.).

The press brake can also be fitted with sensing capability to provide forming process

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input to the control system. This enhances the capability for the robot and the press brake to

compensate, or stop the process, in order to prevent subsequent defects. Laser or mechanical

back gauge sensors can verify blank installation into the tooling, and the control can direct the

back gauge to adjust the sheet position before actuating the press ram. This is a preferred

approach as the robot can be more easily programmed to manipulate the parts into the

approximate location, while the work piece is indexed to the final position by the intelligent back

gauge. This approach can help ensure accuracy of bend location (Part Present Sensing n.d.).

Another step towards adaptive control is automated gauging for bend angle. Laser vision

modules are available that can be mounted adjacent to press tooling to measure the bend angles

on parts in-process. These sensors provide input to the controller that allows it to adjust bend

angles by making continuous process adjustments to the back gage, manipulator, or follower

table (Laser Bend Angle Sensing, n.d.). A possible alternative to laser vision is material

thickness sensing that actively measures blank thickness to compensate the back gauge position

and ram force (Bend Angle Sensing, n.d.). These types of sensing enhance the capability to

offset the effects of changes in tool condition, or variation in material thickness, hardness, tensile

strength, grain orientations, heat-affected zones, or springback. These options become more

relevant in air-bending scenarios that provide less accurate bend angles than other press brake

tooling options. Air bending is more flexible and requires lower tonnage, so it is favored by

many manufacturers. Thus, in-process gauging of bend angle can be an important advancement

for an automated forming operation.

Manufacturing Process Verification

Verification of products, machines, and processes is a key element of manufacturing,

both in support of new process development and ongoing quality assurance. Verification is a

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broadly-used term, but in this context can be defined as the evaluation of tooling, machines, and

manufacturing processes to confirm that the subject of study is capable of meeting the targeted

design specifications (Berger et al., 2007). Verification is often confused, or used

interchangeably, with the term validation. Examples of the overlapping use of these terms for

similar activities include the pharmaceutical and medical device industries. Processes within

these industries are subjected to standardized qualification protocol as required validation to

maintain regulatory compliance (Mitu, 2011). In quality planning and process development by

broader commercial manufacturing, verification and validation are two separate and independent

tasks. A distinction between the terms is that verification confirms that the specifications can be

achieved, while validation determines if the customer expectations are achieved by the

specifications (Berger et al., 2007). In other words, validation is an evaluation of the design,

while verification is an evaluation of the process that is intended to manufacture the design. In

manufacturing systems, verification supports several key objectives and requirements. First, it is

used during late stages of development of new equipment, tooling, and processes to predict if

their output will adequately meet the specifications. Verification activities in these scenarios

may find deficiencies or other opportunities for improvement of future performance. As a result,

verification in such cases may need to occur more than once to ensure that the finalized subject

of study improved, capable, and reliable. With existing equipment, tooling, and processes,

similar evaluation is often utilized to qualify modifications or improvements made during their

life within the manufacturing system.

Second, ongoing verification of the ability to continuously meet the specifications is an

important function of manufacturing quality. Output from equipment, tooling, and processes is

monitored both in continuous and interval manufacturing. This verification is accomplished by

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an array of applicable methods, ranging from sophisticated on-machine verification (OMV), to

simple measurement tools used in tandem with a statistical process control (SPC) chart. Finally,

verification is often a requirement as part of contractual or regulatory compliance (Omar, 2011).

Original equipment manufacturers (OEM’s) of medical, aerospace, and automotive components

are often subjected to strict verification processes that provide scientific evidence of long-term

capability to meet specifications with very low probability of a defect (Mitu, 2011). In some

cases, this verification is conducted by a third party that is responsible for making the assessment

of the OEM for the customer (Omar, 2011). In all cases, the primary purpose of the verification

effort is to confirm that the given subject is capable of sufficiently conforming to the

specifications.

Predictive techniques. Verification often achieves its greatest return when it is used as a

tool to predict the future performance of new equipment, tooling, and processes. This

developmental work creates opportunities to identify future deficiencies that can be prevented

once actual manufacturing demand exists. There are several established methodologies adopted

for assessing future process verification.

Failure Mode and Effects Analysis. An important predictive tool that closely supports

the verification process is Failure Mode and Effects Analysis (FMEA). This evaluation is a

formal part of APQP, PPAP, and ISO9000 (Berger et al., 2007). It is also widely used as an

independent tool by many organizations during product and process development. During

FMEA, new product designs, machines, and processes are evaluated for possible failures, along

with their causes and effects. The overall goal of this evaluation system to identify, prevent, or

at the least, minimize negative effects of potential failures before they occur in component,

systems, product, or processes. FMEA is a team-based activity where brainstorming identifies

24

failure modes, and each mode is assigned a ranking based on its potential severity, occurrence

probability, and likelihood of being detected. The three ranked values are multiplied to calculate

the Risk Priority Number (RPN) for each mode of failure identified. The RPN becomes the basis

for assigning and prioritizing action to mitigate the potential failures.

According to McDermott and Mikulak (2009), an effective FMEA is organized into five

overall stages. The initial step is to define the team, evaluation scope, and gather relevant inputs

including prints, test data, and warranty data. The second step is to systematically review the

subject to identify potential failure modes, along with the potential causes and effects of each

mode. The third stage is to evaluate each potential failure, while utilizing a specific ranking

system to quantify the risk that accounts for severity, occurrence, and detection. The fourth step

is to determine the RPN, which accounts for the multiplied product of severity, occurrence, and

detection rankings. The final stage includes development of an action plan to reduce overall

RPN of design or system. Figure 1 provides an example of a template form that is commonly

utilized to guide and document Process FMEA (FMEA Template, n.d.)

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FAILURE MODE AND EFFECTS ANALYSIS

Item: Drill Hole Responsibility:

J. Doe

FMEA number:

Model: Current Prepared by: J. Doe

Page : 1 of 1

Core Team: J. Doe (Engineering), J. Smith (Production), B. Jones (Quality)

FMEA Date (Orig):

Rev: 1

Process Function

Potential Failure Mode

Potential Effect(s) of Failure

Se

v

Potential Cause/ Mechanism of Failure

Occu

r

Current Process Controls

Dete

c

RP

N

Recommended Action(s)

Responsibility and Target Completion Date

Action Results

Actions Taken

Se

v

Occ

Det

RP

N

Drill Blind Hole

Hole to deep

Break through bottom of plate

7 Improper machine set up

3 Operator training and instructions

3 63

0

Hole not deep enough

Incomplete thread form

5 Improper machine set up

3 Operator training and instructions

3 45

0

5 Broken Drill 5 None 9 ##

Install Tool Detectors

J. Doe 3/1/2008

5 5 1 25

0 0

Figure 1. Failure Mode and Effects Analysis template

Often suppliers, OEMs, and end customers participate as a cross-functional team in

FMEA events. Stamatis (1998) defined two primary types including Design FMEA (DFMEA)

and Process FMEA (PFMEA). DFMEA is executed during the design conception and prototype

phase. PFMEA is a strategic part of manufacturing preparedness, and is done once the product is

defined and the associated manufacturing processes are conceived and developed. PFMEA is

one of the primary inputs in the development of the final process control plan (Stamatis, 1998).

Both types of FMEA are often completed in sequential phases to repeatedly scrutinize the

product or process design during its development.

First Article Inspection. Another predictive verification technique is First Article

Inspection (FAI), which in some cases is referred to as the First Article Report. According to

Berger et al. (2007), it is a highly detailed inspection of an initial physical sample against the

26

OEM specifications and drawings. It is required for new components, processes, or revisions

resulting from tooling, process, or design changes. FAI is widely used as verification of

manufacturing components supplied to the military or aerospace industries. Converse to the

automotive industry use of PPAP with large statistical-based samples, FAI is used in aerospace

sector when large quantity samples are relatively costly to produce (Berger et al., 2007). The

International Aerospace Quality Group has developed the International Standard for First Article

Inspection. According to the Automotive Industry Action Group (2006), many PPAP systems

also include FAI in cases where multi-unit samples are not available or justified financially.

While widely used, FAI does not provide data to measure the process distribution or stability.

Consequently, its drawback is that it is not provide verification of sustained process capability to

meet the design specifications.

Production Part Approval Process. Many manufacturing organizations, including those

within the automotive industry, use the Production Part Approval Process (PPAP) (Stamatic,

2003). It serves as a comprehensive and standardized approach to verification. The PPAP

process is widely recognized and has been adopted as a part of the ISO 9000 standard to support

qualification of new products, tooling, and revisions to existing products (Berger et al., 2007).

Omar (2011) summarized the Automotive Industry Action Group specific conditions of

production under which the PPAP is conducted. The conditions include a specified minimum

pilot production time, a minimum sample quantity of sequential parts, at the given production

rate on the subject machines and tooling. The output of the controlled production run, along with

other specific requirements, must occur to satisfy the PPAP.

According to the Automotive Industry Action Group (2006), a PPAP should include the

ten elements presented in Table 1. These elements include requirements of documentation on

27

the process, product design specifications, applicable testing agency certifications, along with

critical customer requirements. The PPAP requires that the process under study should be stable

enough to predict short-term capability, and if not then an approved plan and timeline to reach

such a state should be documented.

In most cases, a major requirement of the PPAP includes measurements from a

production sample (Production Part Approval Process, 2006). There must be adequate evidence

that the sample measurements are obtained with a system or tools demonstrating repeatability

and reproducibility. Typically, a Gage R&R study is utilized to fulfill this PPAP requirement.

Once a sample is measured, the data is analyzed to determine if the production output will be

acceptable. PPAP employs short-term process capability estimates that indicate the ability of the

process, under expected production conditions, to meet the specifications. The estimates include

process capability or performance for each separate operation. If this is a sustained process and

the data can be obtained from SPC showing normality and stability, then Cp and Cpk capability

indices should be used. If the process is new or otherwise without evidence of stability, then Pp

and Ppk indices should be used to describe the potential capability (Relyea, 2011).

28

Table 1

Ten overall requirements of a PPAP

Element Requirements

1 Design documents specifications and drawings

2 Failure Mode and Effects Analysis

3 Process flow chart of manufacturing process and supply chain

4 Measurement System Analysis

5 Measurement data from the sample manufacturing run

6 Sufficient evidence of process stability

7 Short-term process capability estimates

8 Applicable laboratory testing and certification

9 Process or Product Control Plan

10 Customer requirements and OEM specifications

Advanced Product Quality Planning. While PPAP is often used by OEMs to verify

sources of components and materials, many organizations also employ internal qualification,

verification, and validation steps during product or process development. According to Omar

(2011), Advanced Product Quality Planning (APQP) is a system used to develop a new product

or service that will be properly supported with an effective plan for achieving high quality.

Stamatis (1998) pointed out that the APQP process is a formal system within Ford, Chrysler,

GM, while also required by Tier I suppliers to these three organizations. The main premise of

APQP is that during the product development process, quality is built into the components,

systems, and processes associated with the launch. APQP puts an emphasis on project

29

management to reduce the timeline needed to achieve quality excellence. While this type of

quality planning can vary by industry, APQP specifically includes five phases shown in Table 2.

Table 2

Five phases of APQP

Phase Activities

1 Concept development, project approval, and project planning

2 Program approval, prototyping development, and prototype qualification

3 Prototype testing, product/process verification, and production process planning

4 Pilot short runs, PPAP, production system verification and product validation

5 Launch, monitor, control, improve

Process capability studies. Process capability studies are an important tool used in both

PPAP and APQP, as well an independent tool in manufacturing and quality engineering (Berger

et al., 2007). The purpose of a process capability study is to estimate the ability of a process to

produce products that fall within the specifications. Process capability studies are widely used,

and recognized by many manufacturing sectors. When conducted specifically on machines, the

same type of technique is sometimes termed a machine capability study (Relyea, 2011). While

practices vary to some extent, the common approach includes completely defining the

specification requirements and conducting the study in five steps (Berger et al., 2007 & Relyea,

2011).

In the initial step, the machine or process is setup to operate in a constant state where it

can be monitored for sources of special variation, such as equipment failure, human error,

operator adjustments, or other abnormal event that would influence the outcome. If such an

30

event occurs, the study should be truncated and repeated at another time. Once a machine or

process can operate under stable conditions, the intended sample can be produced.

The second step involves qualification of the measurement tools and equipment used to

evaluate the sample produced in the study. Stamatic (2003) recommended that whenever

possible, measurement resolution should be at least 10 data categories within the specification

limits. Except in scenarios where the tools or measurements are very simple, a minimum

requirement is that Measurement System Analysis (MSA) be properly executed. MSA usually

involves verification of measurement tool accuracy, along with Gage R&R studies that function

to ensure repeatability and reproducibility of the measurement tools and techniques (Berger et

al., 2007).

In the third step, the process should operate to produce a sample that will provide

adequate confidence in the capability estimate. For studies that assess potential capability of a

new system, the sample quantity is typically a minimum 25 to 40 units (Relyea, 2011). Kapadia

(2000) cautioned that the sample quantity must be accounted for in the subsequent analysis of the

study data. For studies that focus on the historical or long-term capability, such as those based

on SPC data, the minimum recommended sample size is 30 subgroups (Relyea, 2011).

In the fourth step, the units within the sample are measured using the tools and techniques

previously qualified through MSA. Sample statistics such as mean, range, and standard

deviation are calculated. If long term SPC subgroup data is available, then the process mean and

standard deviation is inferred based on the control chart data (Berger, 2006). The grand mean, or

overall mean of the control chart subgroup means, is assumed to represent the process mean.

The process standard deviation is estimated by dividing the average of the subgroup ranges by

d2, which is an SPC constant selected based on the subgroup sample size.

31

In the fifth step, the measurements should be analyzed to confirm that the process output

follows a normal distribution (Kotz & Johnson, 2002). At a minimum, the analysis should

include review of a histogram or normal probability plot of the data. After the study, sample

statistics and the capability indices are calculated and interpreted. Important indices include

Capability Ratio, Process Potential, and Process Capability. The Capability Ratio (Cr) is the

ratio between the specification range and the range in actual production measurements. Relyea

(2011) indicated that the preference is a Cr of at least 1.33, in which case the product variation

consumes no more than 75% of the total specification range. Process Capability (Cp) estimates

the precision, or distribution range, of the process output. This estimate of dispersion depends on

the standard deviation and is fully independent of the specification limits (Relyea, 2011).

Process Capability Index (Cpk) estimates the location of the process output distribution relative

to the specification range. This index considers both the estimate of the process mean and

standard deviation. Stamatic (2003) indicated that a Cpk value of 1.33 is the minimum

requirement for most organizations.

While these capability indices are widely recognized and used to make assessment of

suppliers and internal processes, they must be interpreted carefully. The accuracy of the

aforementioned indices is highly dependent the process output being normally distributed. If the

histogram or normal probability plot of the data does not confirm a normal distribution, then the

sample data must be subjected to alternative analysis (Berger et al., 2007). It is critical that the

analyst use the proper approach to data sets that are not confirmed to be normal. Alternative

methods exist to support analysis of non-normal data sets. For example, Krisnamoorthi &

Khatwani (2000) presented methodology for using the Wiebull distribution as an adaptable basis

for computing the indices for many non-normal data sets.

32

Another key assumption is that the process under study must be stable and in a state of

statistical control. Comments by Kapadia (2000) acknowledge a common tendency for

practitioner to attempt to use the methods to estimate capability before actual stability and

control are established. It is critical that the process history be documented and demonstrate that

special causes of variation are not present, but that common causes are represented and can be

accounted for in the capability estimates. Thus, accurate estimate of the capability indices

depend on data from long-term process operation, primarily including SPC data.

Sustained control methods. As a machine or process is implemented into full

manufacturing, there is a need to ensure that it remains in a state of consistently achieving the

specifications. Ongoing verification that the machine or process is producing the desired result

is critical for most manufacturing organizations. The most basic approach to control is some

level of planned inspection with measurement tools and gages. Quality engineering methods

have evolved that depend on statistical analysis to detect and diagnose abnormal conditions that

may lead to defects. Conversely, increasing automation of manufacturing has expanded to

include sensing and measurement that allows affected processes to self-detect and react to

defects immediately as they occur.

Statistical Process Control. Many machines and processes are monitored and evaluated

by a variety of quality tools, such as Statistical Process Control and Pre-Control. According to

Berger et al. (2007), these tools are the basis of detection, diagnosis, and ongoing verification of

machine or process capability to produce within the specifications over the long term.

A key advantage of SPC and Pre-Control is that they can be effective at detecting

conditions or time periods in which abnormal variation affects the process or machine. In many

cases, these special events or conditions cause subtle or infrequent effects. However, if they

33

become more frequent or sustained, they can induce process drift where the process or machine

output has decreased potential to meet its target specifications. Thus, SPC and Pre-Control

function to monitor for unnatural abnormalities that may cause increased potential for defects.

An important function of SPC is that it can be used to assess process performance during

production. Stamatis (2003) emphasized that the relationship between the control limits and the

subgroup mean and ranges can be monitored to assess whether the process is operating in at state

of statistical control. Additional analysis of the historical control chart data can be leveraged to

make predictions of future process yield, the probability of a defective dimension above or below

the specification limits, and process capability indices. Unlike short-term studies during

development that yield predictions of potential performance, analysis of SPC historical data

allows are more accurate assessment of stability and long-term process or machine capability.

Automatic verification. A more advanced approach is in-process verification, or on-

machine verification (OMV), that is built into tooling and processing equipment. This has been

most widely implemented in high-volume manufactured components, such as the automotive and

electronics sectors. One example is Automated Optical Inspection (AOI), which is an

autonomous and non-contact visual inspection of continuous manufacturing (Hewitt, 2009).

AOI can be effective in screening manufactured components that have specific defect or flaw

outside the limits of an acceptable part. A similar type of in-process verification is the

automated in-circuit test where test probes inspect a printed circuit board for the specified

component layout, short or open circuits, and solder condition. OMV is also being applied to

machine tools and CNC machining equipment. According to Hewitt (2009), many modern

machine tools either come with or can be retrofitted with probing capabilities to assist in

machine setup. It is possible to use the setup probing to perform in-process measurement

34

verification. The outcome is that the machine can perform certain verification measurements on

the affected part before it is discharged from processing. This type of OMV is valuable to

operations that lack traditional inspection equipment, or occasionally process components

beyond the physical limits of such equipment. These scenarios are prevalent in the aerospace and

energy industries. In summary, OMV allows high inspection verification coverage, and can be

used to detect problems early in the manufacturing process. Thus, a key outcome of OHV is that

it allows defects to be contained and problems resolved rapidly with minimal scrap.

Summary

There are several verification methods that can be applied to the implementation of this

sheet metal forming process. Elements of the APQP framework and PPAP are appropriate for

pre-production assessment of the process. The preparatory evaluation should also include

FMEA of tooling, equipment, and human interface. The outcome of such evaluation will be to

eliminate or minimize existing deficiencies that may result in potential defects or equipment

failure.

Applicable methods include process capability assessment, based on sample runs during

pilot production and SPC subgroups taken from the long-term continuous production population.

These methods will provide guidance for improvement during equipment and tooling

development. In addition, they also provide a system to closely monitor output and yield during

the initial phase of production of the new forming cell. Finally, the history from the initial

months of production will allow an accurate assessment of the ability of the machine cell to meet

the specifications.

35

Chapter III: Methodology

There was insufficient evidence that the new sheet metal forming equipment was capable

of consistently producing combustion chambers that conform to the design requirements. The

purpose of this study was to use process equipment verification methodologies to ensure that the

equipment will be properly qualified to meet the design requirements. This chapter will provide

an explanation of the selected approach to this study: An overview of the product design and

manufacturing process illustrates the background for selection of the required data. The

application of Process Failure Mode and Effects Analysis is summarized. The measurement

system and Gage R&R is reviewed. The data acquisition plan and subsequent analysis methods

are summarized. The advantages and limitations of the methodology are presented.

Product Design and Manufacturing Overview

The combustion chamber is fabricated from sheet steel blanks previously processed by a

CNC punch press. The blank material is cold-rolled commercial quality (CRCQ) steel. The

steel is coated with a hot-dipped aluminized coating that provides high-temperature corrosion

resistance. Appendix A provides more detail on the sheet material specified for the product

design. The design of the combustion chamber includes three formed sheet metal parts. Figure 2

shows model views of the three parts. Each of the parts is formed separately before they are

fabricated into the final combustion chamber. The three parts include the firebox wrap, firebox

top, and firebox bottom. Blue prints for the three parts, and final subassembly, are provided in

Appendix C.

36

Figure 2. Individual part model views left to right, firebox top, firebox wrap, and firebox bottom

The firebox top and bottom are very similar in design and function. Both parts are

rectangular with four edges. Three edges are formed into open hem channels, and the fourth

edge is two bends used to form a flat and rigid glass seal surface. The hem channels function to

align with the firebox wrap and are closed by a subsequent crimping operation to seal the corners

of the combustion chamber. Figure 3 shows the open hem channel before and after it is closed

by the crimping process. The glass seal flange serves a critical function of sealing against the

transparent ceramic glass panel that closes the viewing opening of the combustion chamber.

Both the firebox top and bottom contain the formed hem channels and the glass seal flange

features. Both parts are formed on human-operated press brakes.

37

Figure 3. Hem channel and crimped hem The firebox wrap is formed by a series of four bends. The two center bends form the

back corners of the combustion chamber, while the two bends towards the part edge form the

vertical glass seal flange. The glass seal flanges are critical for the same reason stated for the

firebox top and bottom. The firebox wrap is formed by a ten-foot hydraulic press brake and the

part is manipulated robotically.

The combustion chamber assembly and fabrication is accomplished in a specialized

fixture. The fabrication fixture is shown in Figure 4. The robot loads the formed firebox wrap

into the fixture. A human operator installs the formed firebox top and bottom so that their hem

channels align with the edges of the firebox wrap. The fixture automatically clamps the three

parts. The robot then engages the fixtured parts with a crimping device which compresses the

hem channels along their length to assemble the sealed combustion chamber. Figure 3 shows the

closed hem after the completed crimping operation.

38

Figure 4. Crimping fabrication fixture The combustion chamber serves several functions. First, it must be within the

dimensional tolerance to align with other subassemblies and parts within the appliance. Second,

the mechanically crimped edges must be fastened to provide sufficient strength and seal against

air leakage. Third, the four-sided glass flange perimeter, as formed by the assembly of three

parts, must be flat within +/- 0.06” to create an adequate seal to exist with the glass panel. Figure

5 shows the finished combustion chamber subassembly. A summary of the design specifications

and critical requirements are shown in Table 3.

39

`

Figure 5. Combustion chamber subassembly

Table 3

Product design specifications and critical requirements

Characteristic Specification

Firebox Top Glass Flange Return 90 +/- 2° at 0.53 +/- 0.03” Glass Flange 90 +/- 2° at 0.81 +/- 0.03” Open Hem Channel 45 +/- 2° at 0.5 +/- 0.03”

90 +/-2° at 0.42 +/- 0.03” Firebox Bottom Glass Flange Return 90 +/- 2° at 0.53 +/- 0.03” Glass Flange 90 +/- 2° at 0.81 +/- 0.03 Open Hem Channel 45 +/- 2° at 0.5 +/- 0.03”

90 +/-2° at 0.42 +/- 0.03” Firebox Wrap Front Corner/Glass Flange Left and Right 108 +/- 2° at 1.63 +/- 0.03” Back Corners Left and Right 108 +/- 2° at 21.08 +/- 0.03” Combustion Chamber Assembly

Overall Height 36.54 +/- 0.03” Overall Width 45.85 +/- 0.03” Glass Flange Flatness +/- 0.06”

40

The manufacturing process occurs in the forming and fabrication cell. The machine cell

is shown in Figure 6. The sheet metal blanks are staged at cell and automatically picked for

processing by the robot. The primary equipment in the cell layout includes the press brake, the

robot manipulator, and the fabrication fixture. The robot automatically moves the finished

component from the fabrication fixture to the outbound conveyor. The components and their

functions are summarized in Table 4. The process flow sequence is provided in Figure 7.

Figure 6. Combustion chamber forming and fabrication cell

41

Table 4

Anatomy of robotic-tended forming and fabrication cell

Component Function

Control Human Machine Interface, Programming Interface, Sensing Inputs, Robot Outputs

Inbound Sheet Pallet Queue blanks for robot gripper pull

Squaring/Reference Table Identify blank type, locate and orientate blank

Press Brake Force and motion control for forming bends

Fixed Air-Bend Tooling Bend tooling for press brake

Follower Plate Bend Support Platforms Support work piece during press brake motion

Quick-Disconnect Tool Changer Allows compatibility for multiple End-of Arm Tools for robot manipulator

Robot Manipulator Transfers and position blank through forming and crimping processes

Vacuum Pump Central source of vacuum pressure for suction grippers on End of Arm Sheet Gripper and Follow Plate Bend Support Platforms

End of Arm Tool Sheet Gripper Adaptive tool that grips blank through forming, fixturing, and outbound transfer of components

End of Arm Tool Crimper Tool with hydraulic crimping head that fabricates firebox top and bottom to firebox wrap

Crimping Fixture Locates and clamps components of combustion chamber during crimping fabrication process

Sensors Provide input to controller of status of operations and verification whether critical conditions exist

Outbound Conveyor Queue fabricated combustion chamber subassembly

42

Figure 7. Process flowchart for combustion chamber subassembly

Failure Mode and Effects Analysis

An FMEA event was conducted on the new forming and fabrication cell. This event

occurred once the equipment was functional, programming was complete, and limited trials had

been completed. The timing of the event was significant in that it allowed the FMEA to account

for the overall cell design, yet still permit improvements to be made before full production. A

multi-functional FMEA team consisted of representatives from engineering, quality,

maintenance, tooling, programming, and manufacturing. During this event, the process was

systematically evaluated in the terms, “How can the process fail such that it produces a defect?”.

The team identified 17 potential failure modes during the event that warranted improvement

Blank Staged on Inbound Pallet

Robot transfers blank from Inbound Pallet to

Squaring Table

Blank size, orientation, and location verified by

sensors on Squaring Table

Robot transfers blank from Squaring Table to

Press Brake

Form Bend #1 - Left Flange

Robot removes workpiece, rotates, and

repositions in Press Brake

Form Bend #2 - Right Flange

Robot repositions workpiece

Form Bend #3 - Interior corner angle

Rebot removes workpiece, rotates, and

repositions in Press Brake

Form Bend #4 - Interior corner angle

Robot transfers formed Firebox Wrap from Press

Brake to Crimp Fixture

Formed Firebox Wrap staged in Crimp Fixture

Operator loads formed Firebox Top into Crimp

Fixture

Fixture rotates workpiece

Operator loads formed firebox bottom into

Crimp Fixture

Robot changes end-of-arm from sheet gripper

tool to crimping tool

Robot crimps Firebox Top to Firebox Wrap

Fixture rotates workpiece

Robot crimps Firebox Top to Firebox Wrap

Robot changes end-of-arm from crimping tool

to sheet gripper tool

Robot transfers fabricated

subassembly to Outbound Conveyor

43

action. These included possible causes of quality defects, unplanned machine downtime, or

unsafe human interface conditions.

During the FMEA, the failure modes were each assigned a Risk Priority Number (RPN).

Subsequent work related to the action items took place over several weeks to make the

improvements to reduce the RPN of each failure mode. The team placed priority on reducing

failure modes with either high likelihood or severity. The team collaboration was based on

engineering judgment to develop a financially justified improvement plan to reduce the RPN of

each mode. The team goal was for the resulting RPN of each mode to be reduced to 100 or less.

The outcome of the initial FMEA became a working document as the FMEA Action Item

Register provided in detail in Appendix B.

Data Requirements

The data required in the study included measurements of the design specifications

summarized in Table 4. For the firebox bottom and top, the required data include location and

angle measurements for the bends forming the hem channels and glass mating flange. For the

firebox wrap, the required data include location and angle measurements for the bends required

to form the three walls and two glass flanges. For the final combustion chamber assembly, the

required data include the glass seal flange dimensions.

Measurement System

Common measurement tools for the trade of sheet metal bending were utilized in this

study. The bend angles were measured with a vernier protractor, with a measurement accuracy

of 2 minutes, or 0.033 degrees. The bend locations and dimension, along with the glass flange

perimeter dimensions, were measured with either 12 and 24-inch digital calipers, or 60-inch

vernier caliper, each with a measurement accuracy of 0.001 inches. The 12-inch and 24-inch

44

digital calipers had been previously qualified for this project application during previous

measurement system analysis.

While the measurement tools and methods were relatively simple, the vernier protractor

and 60-inch vernier caliper did require operator skill to be accurate and reliable. The operators

were trained on how to take accurate measurements with the tools. Once the training was

complete, both of these tools were subjected to a gage repeatability and reproducibility (R&R)

study to qualify them as a part of the measurement system. Adhering to AIAG guidelines, a

crossed gage R&R study was setup using three appraisers, ten parts, and three trials. The ten

parts selected for the study included several that measured outside the central 50% of the

tolerance range. The measurements were randomized between operators. Each gage was

calibrated prior each of the nine measurement sequences. A total of 90 measurements were

taken with each tool. During each trial, the ten parts were measured in random order and

measurement data was logged by an observer. Due to its size and weight, the 60-inch vernier

caliper required two operators, with a single appraiser making measurement judgment.

Sample Measurement Approach

This study was conducted in two phases. The initial phase occurred during the

production equipment installation and development timeframe, prior to actual manufacturing

launch. During this phase, pilot production trials were conducted to predict the short-term

capability of the manufacturing process to produce to the design specifications. Programming,

tooling, and machine modifications were made during this time. The sample size of these runs

was limited to between 5 and 30 units. Several trials were truncated due to equipment

malfunction or defective parts. Several trials were repeated as the data indicated improvement

was necessary and the equipment was modified. The trials ultimately resulted in estimates of

45

tolerance intervals and process capability for important characteristics of the product. This

information was used to make final adjustments to the product and machine cell design.

The second phase of the study occurred during the first three months of continuous

production. Measurements were obtained from samples drawn from in-process inventory of

production runs over the course of a 13-week period. A sample of the firebox top, firebox

bottom, firebox wrap, and fabricated chamber assembly was drawn for measurement of the

selected dimensions. The sample size for each type of part was three units. The sample

frequency was approximately two per week, for a total of 29 subgroups for each part type and

finished chamber subassembly. These subgroups were selected to be drawn from units operated

in series in a relatively short period of time within the selected sampling day. This was done to

minimize within-sample variation, while providing maximum detection of process shifts over

time.

Data Acquisition

During the initial pilot production trials, the series of parts was produced under closely

monitored conditions. The resulting parts were immediately measured and the data was

analyzed. During the 13-week period of initial production, the measurement data acquired from

periodic samples was documented within the framework of Statistical Process Control (SPC). A

Mean and range control chart was utilized to keep track of sample measurements from the three

individual parts and the final subassembly. Data acquired with the supporting control chart

provided an opportunity to also assess the state of process control, stability, and presence of

abnormalities. It also was important in assessing normality as a consideration for subsequent

analysis.

46

Data Analysis

Data from the pilot production runs was analyzed to predict the process tolerance limits,

expected defect rate, and the process performance indices Ppk and Pp. These estimates were

based on the descriptive sample statistics including the mean, standard deviation, range, and

sample size. Assessment of normality, which is an underlying requirement to these analysis

techniques, was limited to graphical analysis with a histogram. The performance indices, not

thought to be valid for long spans of time, were used primarily for comparing the process

performance as improvements were made to the equipment. More emphasis was placed on

analysis of the estimates of process tolerance limits and the probability of defective forming

characteristics outside the specification limits. These estimates were obtained using the sample

statistics along with the appropriate Z-score from the standard normal distribution. In summary,

analysis from these short-term studies was used to understand the capability of the future process

and to identify and prioritize needed improvements before production could launch.

The data from the SPC control charts was used to assess process performance during

initial production and also predict future capability. The subgroup sampling design, along with

the mean and range chart, allowed calculation of control limits. The relationship between the

control limits and the subgroup mean and ranges was monitored to assess whether the process

was operating in at state of statistical control. This monitoring was based on the several

applicable rules for determining statistical control. This was a key benefit of the SPC approach

as it allowed the analysis to account for the requirement that process capability predictions only

be made on processes operating in a state of control. It also allowed detection of special-cause

variation that may affect the analysis.

47

Analysis of the SPC, representing a 13-week period and 29 subgroup samples for each

selected measurement, provided a timeframe from which to estimate the process capability.

These estimates included predictions of future probability of defective forming characteristics

outside the specification limits, and process capability indices. The process mean was estimated

by the calculating the overall mean of the 29 sample means represented for each selected

measurement. The process standard deviation was estimated by dividing the overall mean of the

29 subgroup ranges, by the d2 constant for subgroup size of 3. Once the process mean and

standard deviation were estimated, the standard normal distribution was used to predict the

probability of defective production. These techniques were similar to the short-term analysis in

that they were dependent on the process population exhibiting an approximate normal

distribution.

Advantages and Limitations

This methodology had the advantage of drawing upon verification techniques recognized

by many industries and organizations. The methods and statistical analysis were established and

understood to be effective when used properly. The use of SPC control chart data provided an

ideal process history from which to derive measures of past performance and future capability.

The primary limitation of this methodology was that it was based on a sample of the

production population. It may not account for the total population and how it may have been

affected by machine malfunction, or other variables such as press brake or tooling variability.

The analysis techniques can predict, but not state with certainty, the long-term ability of the

machine cell to produce within the specification limits.

48

Chapter IV: Results

The research of related literature revealed existing techniques that could be applied to

methodology defined for this project. The machine cell was not yet proven to be capable of

meeting the requirements, so the purpose of this study was to verify that the process capability of

producing within the product design specifications. The methodology of this study was

developed to meet the five main objectives of the project. The objectives, and corresponding

methods and results, are summarized in Table 5. This chapter presents the results of the

verification methodology that was executed during this project.

49

Table 5

Summary of methods and results for project objectives

# Objective Methods Results

1 Define the product design specifications and critical requirements.

Engineering drawings and performance requirements were reviewed.

Seven characteristics were identified that were critical to final dimensions and function of the combustion chamber. These seven characteristics were the basis for evaluating the long-term process capability.

2 Analyze the new machine cell to identify preventative action for potential failure modes to maximize equipment reliability and capacity. Minimize human interface safety concerns that may result from process equipment design or operation.

Design Failure Mode and Effects Analysis was conducted on the new machine cell equipment and processes.

Action was taken to reduce the likelihood and/or effects of 17 potential failure modes identified by the analysis. The RPN score of each failure mode was reduced. These improvements were in place by the time that final programming, tooling, and pilot production runs were completed.

3 Define and qualify gages for measuring critical part dimensions and characteristics.

Measurement tools with required accuracy were defined for the study. Gage R&R study and Measurement System Analysis were conducted to ensure adequate precision of two new tools.

The accuracy, repeatability, and reproducibility were found to be adequate for this project. The Gage R&R, as a percentage of tolerance, was found to be acceptable.

4 Verify that the new equipment is stable and has long-term capability to meet the product design specifications and critical requirements to maximize process yield.

Capability was assessed in two phases. The pilot production phase focused on estimating process performance with tolerance intervals, and reducing the expected defect rate, for eight characteristics. The production phase used SPC process history to base estimates of long-term process capability for seven characteristics.

For the eight characteristics evaluated during the pilot production, the process tolerance intervals were fully contained within the specification range. The expected defect potential ranged from 0-21 PPM. During full production, the SPC history provided estimates of Cpk ranging from 1.19 to 2.69. Expected defect potential ranged from 0 to 229 PPM for the seven characteristics monitored during full production.

5 Research verification methodologies used by manufacturing industries and identify a system that can used for future launch of new process equipment and tooling in order to consistently meet the requirements.

Quality engineering and manufacturing verification literature was reviewed.

The verification of the machine cell closely followed the Production Part Approval Process. The literature also was referenced in defining the valid use of SPC process history as a basis for estimating process capability of meeting the requirements.

50

Failure Mode and Effects Analysis

FMEA occurred once the machine cell installation and programming was initially

complete. This event was timed so that it could fully analyze the machine cell design, yet still

permit any needed improvements to be made before full production. The scope of the FMEA

included the equipment and processes associated with the forming and crimping of the

combustion chamber. The FMEA team identified 17 potential failure modes during the event

that warranted action to prevent quality defects, unplanned machine downtime, or unsafe human

interface conditions. The improvement action targeted three main areas including the product

design, the manufacturing process, and the machine cell equipment design.

Product design. The PFMEA of the crimping assembly process revealed several

possible failure modes that could cause defects, scrap, and downtime. There was no automatic

detection of several potential defects at the press brake or at the crimping fixture. First, the

firebox top and firebox bottom were similar in overall dimension and forming profile. These two

parts were formed and installed into the crimping assembly fixture by a human operator. While

the two parts each had some exclusive geometry, it was possible for each part to be installed on

either end of the firebox wrap. The crimping fixture design did not prevent the firebox top and

bottom from being installed in reversed positions on the wrap. Second, the FMEA identified that

the hem channel could be formed backwards on the firebox top and bottom. The design of the

parts permitted human installation of the deformed parts on the firebox wrap, and the subsequent

crimping would lead to a defective combustion chamber assembly. Third, the FMEA revealed a

deficiency in the way that the crimping fixture secured the parts during processing. The initial

method consisted of right-angle welding magnets placed by the operator part edges inside the

combustion chamber. This was intended to hold the parts in the fixture location as they were

51

fastened by the robotic crimping head. It was likely that the magnets would be used

inconsistently by the human operator. It was foreseeable that there would be inconsistent

engagement of the hem channel with the edges of the firebox wrap. This would be a source of

subsequent dimensional variability, defects, and scrap.

To address these concerns, the product design was modified to include four additional

sheet metal brackets. The added brackets aligned with holes that were CNC punched in the

blanks. The bracket location was established so that the firebox top and bottom had to be

properly orientated, and located on the correct ends of the firebox wrap. This design change also

ensured the sufficient engagement of the hem channels with the edge of the wrap. Once the

formed parts were installed correctly in the crimping fixture, the brackets were riveted in place.

A riveted bracket is shown in Figure 8. This replaced the operator interpretation associated with

the welding magnets. This added step helped tie the parts together at their specified orientation

and position, before robotic crimping occurred.

Another possible defect was that the part design and crimping fixture allowed the firebox

top and bottom to be installed such their lap joints were reversed on the firebox. The lap joints

existed where tabs on the front corners of the firebox top and bottom mated against the face of

the firebox wrap. These joints create the four corners of the glass seal flange, and a defective

overlap between parts could result in deficient appliance performance. To prevent this possible

defect, a dimple feature was added to tab on the front corners of the firebox top and bottom. The

dimple feature is shown in Figure 8. This allowed the crimping fixture to be capable of

preventing reversed lap joint overlap as the formed parts were loaded into the crimping fixture.

52

Figure 8. Product design changes implemented to reduce crimping failure modes

Process design. The FMEA identified several failure modes that were addressed by

making process changes. There was potential for several failure modes at the press brakes and

squaring table. To prevent repeated forming defects at the press brake forming workstations,

process controls were implemented. Both the human operated and robot tended press brakes

were affected by these control activities. These included press brake setup checklist, 1st Part

Inspection, and TPM checklist. These became the responsibility of the press brake machine

operator. TPM and daily cleaning were also implemented for the squaring table. These control

procedures were intended reduce the probability of defects, scrap, and downtime resulting of

forming errors, tooling wear, press variability, and sheet metal variability.

Another process control was implemented to prevent failure modes associated with

incorrect queue of firebox wrap blanks at the inbound conveyor of the robotic machine cell. An

operating system was established to ensure communication between the assembly line schedule

and the CNC punch process. This functioned to ensure that the correct blank sequence was

queued at the inbound conveyor. In addition, a reaction plan was established to allow the

inbound conveyor to be safely accessed in the unlikely event that the blanks needed to be

resequenced.

53

Machine cell equipment design. The FMEA identified nine modes of failure that were

mitigated by improving specific equipment within the machine cell. These improvements are

summarized in Appendix B. Several improvements were accomplished with additional sensing

to control system. Sensors were added to the inbound pallet and outbound conveyor to ensure

correct flow of incoming blanks and unload of the fabricated part. Sensing was also added to the

squaring table to prevent the robot from processing incorrectly orientated blanks. Pressure

sensors were added to the system to ensure gripper received adequate vacuum pressure and allow

it to automatically shut down in the event of vacuum failure. In addition, a check valve was

added to the vacuum system to minimize failure modes associated with sudden loss of vacuum

pressure due to an event such as a power outage.

Several improvements were also made to the tooling. The tooling that formed the crimp

dimples was modified to optimize the dimple depth for the sheet thickness. This minimized any

distorting or insufficient seal along the crimped seam. The squaring table surfaces that located

the sheet edges were also modified with replaceable hardened steel plates to minimize wear-

related drift of the zero location. The FMEA revealed that the crimping fixture did not have

adequate feature to consistently control the position of the firebox wrap. The formed part did not

always fully engage the fixture when it was released from the robot gripper. This deficiency was

addressed by adding a chamfer feature to its locating blocks to lead the part fully into the fixture.

Improvements were made to the press brake used for forming the hem channels in the

firebox top and bottom. The FMEA team suspected that the original press lacked adequate

tonnage and control for the application. This was verified by a capability study. Consequently,

the assembly line acquired a press brake with sufficient capacity and controls. The base of this

54

press was fitted with a sheet support table to facilitate full back stop engagement of the large

parts.

The results of FMEA action and RPN reduction are detailed in Appendix A. The goal

was for the RPN of each mode to be reduced to 100 or less, which was accomplished with

exception of the mode associated with defects caused during the crimping process. The RPN of

this mode was 126 after the improvements. The team formulated options to reduce the RPN of

this mode further, but consensus was that none of the options provided additional reduction that

was cost-justified.

The FMEA justified action that was effective at reducing the effects of 17 potential

failure modes. Action was taken on each of the failure modes, with priority applied based on

RPN and severity rankings. After the improvement action was taken, the team adjusted the RPN

of each potential failure mode. Once the improvement plan was executed, each of the individual

RPN scores was reduced from the original estimates ranging from 165 to 800. The resulting

RPN for reach of the modes ranged from 24 to 126 after work was complete. These

improvements were in place by the time that final programming, tooling, and pilot production

runs were completed.

Measurement System Analysis

Accurate and precise tools were required to measure characteristics of formed parts

during setup, programming, and verification of the machine cell. The four measurement tools

used for the project included a 24-inch digital caliper, 12-inch digital caliper, 60-inch vernier

caliper, and vernier protractor. Accuracy of each tool was verified by calibration. The precision

of the tools was qualified by Gage R&R studies. The 24-inch and 12-inch digital calipers were

deemed precise by previous Gage R&R studies carried out on similar sheet metal parts. For this

55

project, the vernier protractor and 60 inch vernier caliper were new gages that required

qualification.

The Gage R&R data was analyzed with Minitab16 software. The Analysis of Variance

(ANOVA) option of analysis was used for this project. This is recommended by the software

because this option is more sensitive to smaller effects and the interaction between operator and

the part (Sleeper, 2012). Minitab16 analysis of Gage R&R requires an estimate of the process

variation, and recommends that this be an estimate of the historical standard deviation. For this

project analysis, the historical standard deviation of similar sheet metal assemblies was utilized.

For the venier protractor, the analysis utilized a standard deviation of 0.1 ° for bend angle. For

the vernier caliper, the analysis utilized a standard deviation of 0.005” for bend location.

The detailed results of the Minitab16 analysis are provided in Appendix D. These results

include a Gage Run Chart, Variation Report, Summary Report. The Gage Run Charts provide

visual indication that the variation by operators is less than the variation between parts. This is a

desired result indicating that the measurement gages can reliably distinguish between different

parts. In addition, graphical analysis also indicates that there is not likely significant variation

between the operators chosen for this project.

The Variation Report provides graphical and statistical information. The X-bar Chart by

Operator indicates that the measurement system can reliably distinguish between parts. The

Operator Main Effects graph indicates that different operators who use the gages should be able

to achieve reproducible measurements. A key result for this project exists in the R-Chart for

operator repeatability. With the exception of one part-operator combination for the vernier

protractor, all plot points are located within the lines. This indicates that the variation is

expected to be relatively consistent for operators and parts.

56

The Summary Report provides two important estimates. First, it provides an estimate of

the percentage of process variation that can be the result of measurement system variation.

Second, it provides an estimate of the measurement system variation as a percentage of the

engineering tolerance. For both estimates, the general rule for determining measurement system

capability is a result less than 10%. A result of 10% to 30% for either estimate is considered

marginal. An estimate of greater than 30% is considered unacceptable. A measurement system

with greater than 30% variation would likely contribute excess error. This excessive

measurement error would inhibit the ability of the tool or system to distinguish between good

and bad parts, or accurately assess process performance (Sleeper, 2009).

A summary of the Gage R&R results is provided in Table 6. The third and fourth column

show the key estimates from the Minitab16 Summary Report. For the 60-inch vernier caliper,

the results can be classified as acceptable. For the vernier protractor, the results can be classified

as marginal. The fifth and sixth columns estimate the repeatability and reproducibility as a

percentage of the engineering tolerance. These percentages are the basis of the total Gage

R&R% estimate provided in the seventh column. For the 60-inch vernier caliper, the 8.34%

indicates an acceptable measurement system. For the vernier protractor, the 14.32% indicates a

marginal measurement system. Alternative tools were researched as possible improved methods

of angle measurement. These tools did not present inherent advantage, so additional training was

provided on proper interpretation of the vernier scale. While less than ideal, the vernier

protractor was implemented for angle measurements of the parts.

57

Table 6 Measurement tool capability and Gage R&R Results

Gage Measurement Accuracy

Process Variation

Attributed to Measurement

System

Measurement System

Variation as a Percentage of

Tolerance

Repeatability as a

Percentage of Tolerance

Reproducibility as a Percentage

of Tolerance

Total Gage R&R as a

Percentage of

Tolerance Vernier

Protractor 0.033°

9.5%

14.3%

13.54%

4.61%

14.32%

60 inch Vernier Caliper

0.001”

5.6%

8.3%

8.34%

0%

8.34%

Machine Cell Verification

Once the FMEA action items were completed, several short pilot runs consisting of ten

parts or less were completed. These short runs allowed final adjustments of tooling and

programming to produce part characteristics within specifications. Once these adjustments were

complete, and the measurement system was qualified, the pilot run-off was executed on the

machine cell. During this event, the process was closely monitored as it produced 30 finished

combustion chamber assemblies. This pilot production sample was measured during processing

and after final fabrication. The selected measurements accounted for characteristics that were

relatively important in determining either the dimensions, or critical function, of the fully

fabricated combustion chamber. The measurement data was analyzed to predict the potential

stability and capability of the process.

Tolerance intervals. The measurement data from the 30 unit sample was analyzed to

estimate a statistical tolerance interval for each selected characteristic. The analysis was

performed with Minitab16 software, which provided accurate calculations for the sample

statistics and tolerance intervals. The validity of the tolerance interval estimate is dependent on

the sample data following a normal distribution. Consequently, Minitab16 also provided a

58

histogram, normal probability plot, and statistical test for normality. The project was analyzed

with the software to estimate a two-sided tolerance interval that covered 95% of the population,

at a 95% confidence level. The detailed results of the Minitab16 analysis are provided in

Appendix E.

The results were evaluated based on two objectives. First, the histogram and normal

probability plot were evaluated to determine the validity of the tolerance interval. At this stage

of the project, it was desirable for the process to show very limited variation due to special

causes. The histogram and normal probability plot indicated that the process was relatively

stable during the 30-unit run. Despite failure of the statistical normality tests for three of eight

characteristics sampled, the results were considered satisfactory based on the histogram graphical

analysis. Second, the normal two-sided tolerance interval for each characteristic was compared

to its specification range. Verification of the process required that the tolerance range,

accounting for 95% of the population at 95% confidence, fell within the specification range.

This requirement was achieved for all eight characteristics. The tolerance interval estimates for

each characteristic are summarized in Table 7 and 8.

59

Table 7

Process mean, sigma, and tolerance interval estimates for firebox wrap characteristics

Characteristic

Gage

LSL

Target

USL

Process Mean

Process Sigma

Process Lower

Tolerance Limit

Process Upper

Tolerance Limit

Side Depth

24” Digital Caliper

21.05

21.08

21.11

21.083

0.007

21.067

21.1

Front Corner Seal Flange Angle

12” Digital Caliper

106 108 110 107.19 0.245 106.55 107.83

Rear Corner Angle

Vernier Protractor

106 108 110 107.26 0.236 106.66 107.86

Seal Flange Width

12” Vernier Caliper

1.6 1.63 1.66 1.627 0.004 1.618 1.636

Table 8 Process mean, sigma, and tolerance interval estimates for firebox top and bottom characteristics

Characteristic

Gage

LSL

Target

USL

Process Mean

Process Sigma

Process Lower

Tolerance Limit

Process Upper

Tolerance Limit

Overall Depth

24” Digital Caliper

19.91

19.97

20.03

19.978

0.006

19.963

19.994

Seal Flange Angle

Vernier Protractor

88 90 92 89.5 0.298 88.74 90.27

Hem Depth 12” Digital Caliper

0.5 0.53 0.56 0.536 0.003 0.528 0.543

Seal Flange Width

12” Digital Caliper

1.03 1.06 1.09 1.065 0.004 1.054 1.076

Potential process performance studies. The process seemed to operate without any

identified abnormalities or malfunction during the pilot run. The tolerance interval estimates for

the eight characteristics fell within their specification ranges. The next step was to look at the

60

potential capability of the process relative to the eight characteristics. This analysis was also

executed with the aid of Minitab16 software. Since this was a short-term pilot run, the results

were analyzed with the Capability Snapshot function provided by Minitab16. Detailed software

results are provided in Appendix F.

The Minitab results were analyzed with emphasis on the graphical assessment of the

process distribution, its normality, its relation to the specification range, and the expected defect

rate for each characteristic. The software provides a statistical test and normal probability plot

that determines the normality of the distribution. The histogram provided a visual representation

of the accuracy and precision of the process. The accuracy, relative to target specification, was

apparent. The precision, or spread of the measurements, was also apparent. The software output

did include process performance estimates of Pp and Ppk, but this was not considered vital

information for this short-term run.

Process normality for each characteristic was adequate, as indicated by the Anderson-

Darling test executed by Minitab16. For all eight characteristics, the sample mean was not

accurately aligned with the specification target. This indicated that opportunity existed to make

additional adjustments to the machine cell to allow it to produce closer to the nominal

specification. This was recognized as the ideal approach to leverage this study, and also produce

higher quality parts in guarding against potential effect of tolerance stack-up. Despite the mean

being off target, expected defects were relatively low. This was because the variation, or

precision, of the eight samples was such that the probability of defects was small. The highest

expected defect rate for the eight parts was for the Firebox Wrap side depth, which had an expect

21 PPM defect rate. These results are summarized in Table 9 and 10.

The results indicated that there were opportunities for minor programming to adjust

61

forming closer to specification target. However, the process appeared to have the ability to

operate without excess variation or instability. The machine cell needed to support the new

product launch and there was not time the risk of making adjustments to optimize accuracy.

Short-term capability was considered to be verified with these results and the machine cell

design was frozen for production startup.

Table 9

Estimated process performance for firebox wrap characteristics

Characteristic

Target

Process Mean

Pp

Ppk

Expected Defective

PPM Side Depth

21.08

21.083

1.54

1.37

21

Front Corner Seal Flange Angle

108

107.19

2.72

1.62

1

Rear Corner Angle

108 107.26 2.83 1.78 0

Seal Flange Width

1.63 1.627 2.77 2.5 0

62

Table 10 Estimated process performance for firebox top and bottom characteristics

Characteristic

Target

Process Mean

Pp

Ppk

Expected Defective

PPM Overall Depth

19.97

19.978

3.32

2.86

0

Seal Flange Angle

90 89.5 2.24 1.68 0

Hem Depth 0.53 0.536 3.32 2.71 0

Seal Flange Width

1.06 1.065 2.32 1.9 0

Statistical Process Control. The second phase of the machine cell verification occurred

during the initial period of full production. Statistical Process Control (SPC) was utilized to

document enough process history to provide a valid estimate of long-term process capability.

Sample measurements of select characteristics were taken from production of the firebox top,

firebox bottom, firebox wrap, and fabricated chamber assembly. Seven characteristics, which

were considered important to tolerance stack-up and product function, were monitored during

production sampling. The samples were drawn as rational subgroups to minimize within-sample

variation and maximize detection of process shifts. The sample size for each type of part was

three units. The sample frequency was approximately two per week, for a total of 29 subgroups

for each part type and finished chamber subassembly. In all, 87 measurements were taken for

each characteristic. The subgroup samples were documented with a mean and range control

chart for each characteristic. For this project, the SPC control charts were analyzed with

Minitab16. The specific function employed was the Stability Report for Mean-R control chart.

63

Detailed output from the software is provided in Appendix G. This application of SPC control

charting provided results that allow assessment of long-term process capability.

The first step was to determine if the process was stable in producing the seven

characteristics. The control charts were analyzed for presence of special cause variation during

the 29 plotted points for each characteristic. For this project, the criteria for stability included

three of the Typical Special Cause Criteria from the AIAG 2nd Edition SPC manual:

1. One point more than 3σ from either side of the centerline

2. Seven points in a row on one side of the centerline

3. Six points in a row all increasing or all decreasing

Six of the seven characteristics met all three of the selected criteria for stability. The

Firebox Wrap Flange Width was the one characteristic that did not meet all the criteria. The

control chart exhibited seven points above the centerline, which violates the second selected

criterion. This is a sign that the process may not have been stable in producing this specific

characteristic. As a result, extra caution was taken during interpretation and use of process

capability estimates associated with this characteristic.

The second step in using the SPC to conduct the capability study was to determine if it

was a reasonable to assume that the selected characteristics were represented by the normal

distribution. The normality assumption had to be satisfied for the analysis to yield valid

estimates of capability indices. The assessment of normality came by histogram analysis and the

Anderson-Darling normality test on the 87 individual measurement values for each

characteristic. These graphical and statistical test results are detailed for the seven characteristics

in Appendix H.

First, the histograms did appear to show an approximately normal distribution for the

64

seven characteristics. However, graphical analysis as a sole determination is limited to data sets

that produce a symmetrical, single peak, histograms. Several characteristics seemed to exhibit

possible skewed or bimodal distributions. Consequently, the second step in assessing the

normality assumption was the Anderson-Darling test. For this test, the p-value was interpreted in

assessing the assumption that the sample fits a normal distribution model. If the p-value is less

than 0.05, the assumption of normality should be rejected. For the selected characteristics, the p-

value ranged from 0.089 to 0.477. Because the p-value exceeded 0.05 for all seven

characteristics, the normal assumption was not rejected.

Long-term process capability. The next step was to estimate the process average,

sigma, and capability to produce within the specification. The control chart overall mean was

used to estimate the process average, or point estimate for µ. The control chart average range,

divided by the d2 control chart constant, was used to estimate the process sigma, or point estimate

for σ. From these estimates, the natural process tolerance limits were calculated and compared to

the specification range for each characteristic. This comparison was the basis of the estimates of

expected defective PPM outside of the specification limits. These results are summarized for

each characteristic in Table 13. These calculations and estimates were accomplished with the

assistance of Minitab16 software. The Between/Within Capability function of the software was

employed to provide graphical and statistical results. The detailed software results are provided

in Appendix H. The proportion defective relative to the specification was calculated using the

following equations to determine z-values.

Zupper = (USL + Overall Mean) / σ Zlower= (Overall Mean – LSL) / σ

Where σ = Mean Subgroup Range (R) / d2 d2 = 1.693 is constant for subgroup size of 3

65

The z-values defined areas represented by the Standard Normal Distribution Table to

infer the expected proportion of production that should fall outside the specification limit (Berger

et al., 2007). These results indicated that the process mean was located within reasonable

proximity to the center of tolerance for the seven characteristics. The process sigma was such

that the natural tolerance limits were fully contained within the specification range for each

characteristic. Based on the calculated z-values, all but one characteristic exhibited a low

probability of being defective. The one exception was the Combustion Chamber Outer Width,

which was expected to be 0.01% defective beyond its upper and lower specification limit.

Assuming that the machine cell were to maintain a similar level of stability without significant

process shifts, the probability of a defect occurring within the selected characteristics ranged

from 0 to 0.02%.

The SPC process history was also the basis of process capability indices and overall

expected defective PPM. These estimates were provided by the Between/Within Capability

Sixpack function of Mintab16 software. These results are provided in detail in Appendix H and

summarized in Table 14. A Cpk of at least 1 is considered to be the minimum requirement for a

process to be considered capable (Berger et al., 2007). For the seven characteristics, Cpk

ranged from 1.19 to 2.69, indicating the machine cell would likely be capable as long as it

remained stable and did not develop process shifts. The expected defective PPM in Table 11

reflects the Minitab16 estimate of PPM-B/W, which is represents the number of defects expected

in a population of one million. The estimate for the seven characteristics ranged from 0 to

roughly 229 PPM. As expected, these PPM estimates follow a similar pattern to the percentage

defective estimates shown in Table 12.

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Table 11

Estimated long-term process characteristics and expected defect potential

Characteristic

Specification

Process Mean

µ

Process Sigma

Σ

Natural Process Tolerance

Limits µ+/-3σ

%

Defective Above USL

%

Defective Below LSL

Firebox Wrap Front Corner Angle

106 – 100 °

108

0.4513

107.85 – 108.15

0

0

Firebox Wrap Flange Width

1.6 – 1.66”

1.632

0.0058

1.615 – 1.649

0

0

Top/Bottom Hem Depth

0.5 – 0.56”

0.5361

0.003

0.527 – 0.545

0

0

Top/Bottom Seal Flange Width

1.03 – 1.09”

1.063

0.0055

1.047 – 1.08

0

0

Top/Bottom Seal Flange Angle

88 - 92°

90.17

0.3666

89.07 – 91.27

0

0

Combustion Chamber Outer Width

45.82 – 45.88”

45.85

0.0073

45.82 – 45.87

0.01%

0.01%

Combustion Chamber Inner Width

36.51 – 36.57”

36.54

0.0067

36.52 – 36.56

0

0

67

Table 12 Estimated long-term process capability indices and overall expected defective PPM

Characteristic

Cp

Cpk

Expected Defective PPM

Firebox Wrap Front Corner Angle

1.27

1.26

139 Firebox Wrap Flange Width

2.04

1.93

0

Top/Bottom Hem Depth

3.38

2.69

0

Top/Bottom Seal Flange Width

1.87

1.71

0.14

Top/Bottom Seal Flange Angle

1.66

1.52

2.72

Combustion Chamber Outer Width

1.24

1.19

228.78 Combustion Chamber Inner Width

1.44

1.33

34.34

Summary

The new machine cell was designed and installed, but its ability to meet the requirements

had not been evaluated. The purpose of this study was to use process equipment verification

methodologies to ensure that the equipment would be capable of consistently meeting the design

requirements. This chapter presents the results of the verification methodology.

After the equipment was installed, it was evaluated by a FMEA team. The team

prescribed action that was effective at reducing the effects of 17 potential failure modes. These

improvements reduced the individual RPN scores to where they ranged between 24 and 126. The

goal was for the RPN of each mode to be reduced to 100 or less. This was accomplished with

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exception the robotic crimping process, which retained an assigned RPN of 126. These

improvements set the stage for final programming, testing, and verification of the machine cell.

The critical product characteristics were identified for both phases of the machine cell

verification. The appropriate tools were selected for measuring each characteristic. Two new

tools selected specifically for this project, protractor and 60-inch caliper with vernier scale, were

evaluated by Measurement System Analysis. The Gage R&R results indicated that both tools

should be able to detect differences between parts, and process changes, without inducing excess

measurement error. The selected measurement tools were utilized for both phases of the

machine cell verification

Verification of the machine cell was broken into two phases. The first phase included a

series of pilot production runs that resulted in estimates of tolerance intervals and process

potential performance associated with eight product characteristics. The results estimated Ppk

ranging between 1.37 and 2.56 for the eight characteristics. The expected defect rate for the

eight characteristics ranged from 0 to 21 PPM.

The second phase used SPC control charts on actual production measurements as a basis

for projecting long-term capability. The seven design characteristics measured during this phase

were selected as key process indicators and accounted for potential effects of tolerance stack-up.

The SPC control charts for the seven characteristics indicated that the process output was stable

and followed an approximate normal distribution. The results estimated Cpk ranging from 1.19

to 2.69 for the seven characteristics. The expected defect rate for the seven characteristics

ranged between 0 and 229 PPM. These results provide a picture of the machine cell capability to

meet the design requirements. The results also indicate possible opportunities to improve the

process to reduce the probability of defective production.

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Chapter V: Discussion

The robotic press brake forming and crimping machine cell was implemented in this

application for two primary reasons. First, it provided machine capacity to accommodate the

large sheet metal work pieces that could not be handled safely by a human operator. Second, the

repeatability of the robotic manipulator was expected to lead to reduced forming variation, and

better detection and diagnosis of defects. Despite its potential benefits, implementation without

testing and verification of the machine cell would potentially result in quality defects, downtime,

and unsafe conditions. This project study was necessary to verify that the new machine cell

would be safe, reliable, and capable of production consistently meeting specifications. To

accomplish its objectives, the project was executed during three phases, including production

preparation, pilot production, and full production.

During the first phase, the project defined the product design specifications and

characteristics that were critical to final dimensions and function of the combustion chamber.

These specific characteristics were measured during pilot production testing and full production

to assess the stability and quantify capability of machine cell performance.

As a result of research conducted during the initial phase, this project identified several

verification methodologies used to verify new equipment by similar manufacturing industries.

The verification of the robotic press brake forming and crimping machine cell was executed

similar to methodology applied by the Production Part Approval Process. The literature also was

referenced in defining the valid use of SPC process history as a basis for estimating process

capability. Because the machine cell performance could be modeled by the normal distribution,

these methodologies did provide valid results in which to accomplish its verification.

The first phase of the project also utilized FMEA of the new machine cell to identify

70

preventative action for potential failure modes. This project accomplished specific actions that

helped minimize human interface safety concerns, along with potential causes of machine

downtime or quality defects. These improvements were timed so that subsequent verification

testing would be not affected by the effects of the potential causes. Furthermore, it was

important to make these improvements before the verification phase to increase the probability

that pilot production represented the future state of the process.

Finally, the initial phase of the project defined and qualified gages for measuring selected

part dimensions and characteristics. Gage R&R studies were conducted to ensure adequate

precision of the vernier caliper and protractor used to measure dimensions and forming angles on

the combustion chambers. The Gage R&R results indicated that the selected measurement

system would represent and account for the process output during the verification during pilot

and full production. This helped ensure that measurement error would not significantly influence

results, which established conditions for reasonable and valid analysis of the process output.

During its final two phases, the project evaluated the machine cell performance stability

and its capability to produce product meeting the design requirements. The pilot production

phase focused on estimating process performance with tolerance intervals, and reducing the

expected defect rate, for eight selected characteristics. For all eight characteristics, the sample

mean was not accurately aligned with the specification target. However, the variation of the

eight samples was sufficiently low that despite the mean being off target, expected defects were

relatively low ranging from 0 to 21 PPM. The process appeared to possess the ability to operate

without excessive variation or instability. The results indicated that opportunity existed to make

additional adjustments to the machine cell to allow it to produce closer to the nominal

specification targets. This approach was recognized to produce higher quality parts and make

71

production more resistant to potential effects of tolerance stack-up. However, there was not

time to take on risk of making programming and tooling adjustments to optimize accuracy before

the final phase.

During the final production phase, SPC control charts provided process history from

which to base estimates of long-term process capability for seven characteristics. This approach

leveraged the preparatory research as it provided for statistically valid estimates of capability

indices and expected defect rates. Overall, the machine cell output during the SPC monitoring

period was stable and not influenced by assignable special causes of variation. In addition, the

measurement data followed an approximate normal distribution so that Cpk and expected PPM

could be estimated. Control chart history during initial production provided estimates of Cpk

ranging from 1.19 to 2.69 for the seven characteristics.

Expected defect potential ranged from 0 to 229 PPM for the seven characteristics

monitored during full production. These statistical estimates indicate the extent to which the

machine cell can potentially produce a part outside of either specification limit. Closer analysis

reveals the cause of the defect potential. For example, the process measurements of combustion

chamber overall width exhibits relatively greater variation than some of the other measured

characteristics. The added variation accounts for the higher expected defect potential of 229

PPM. Conversely, relatively low process variability exists for the hem channel depth formed in

the firebox top and bottom. The machine cell has a very low potential to produce a defect in this

characteristic. In addition to process variability, the relationship between the process mean and

the target specification is also important. The process mean is relatively close to the target

specification for the selected characteristics, with the exception of the formed hem depth on the

72

firebox top and bottom. In the case of the formed hem channel depth, even though the process

mean is off target, the variability in forming is so small that defect potential is low.

Conclusions

This project study accomplished three main outcomes towards verification of the robotic-

tended sheet metal forming cell. First, the machine cell and related product design was improved

by application of FMEA. This analysis, along with the improvements it helped justify, ensured

that the machine cell was ready for performance testing and subsequent production.

Second, the machine cell was tested during pilot and full production to measure its output

of selected characteristics. These activities helped monitor machine cell performance, reliability,

stability, and detection of potential major abnormal variation. The results provide estimates of

the ability of the cell to meet specifications in the short-term, while inferring the degree to which

specifications will be met over the long-term.

Analysis of these results provides valuable information on where the machine cell can be

modified to further improve its capabilities. Because the Cpk index accounts for both the

process sigma and centering of its mean relative to the tolerance, it can be an effective guide for

future improvements. The machine cell currently meets the minimum Cpk requirement of 1 for

its ability to produce the selected characteristics. This means that in the short-term, the machine

natural tolerance limits exist entirely within the product specification limits. It needs to be

considered that even if process sigma remains relatively constant, it is possible for the process

mean to shift over time. For this reason, a Cpk of 1 is considered a minimum requirement, and

higher values of this index are desirable. Many sources recommend that a Cpk of at least 1.33 be

achieved to help ensure that the process remains capable despite potential shifts in the mean over

the long-term.

73

Finally, an effective verification strategy was identified for this field project. It can

potentially be effective during implementation of similar manufacturing process equipment. The

process that was used during this project study is summarized in Figure 9. The primary

limitation of this methodology is that is based on a sample of the production population. It may

not account for how the entire population may be affected by intermittent machine malfunction,

press brake and tooling variability, or other causes of potential defects. The analysis techniques

can predict, but not state with certainty, the long-term ability of the machine cell to meet the

critical product requirements.

Figure 9. Flowchart for verification of manufacturing equipment

Determine critical product, performance, or customer

requirements.

Document machine/process map or flowchart.

Conduct Failure Mode and Effects Analysis on all steps of

the machine/process.

Improve the machine/process to achieve acceptable Risk Priority Number for each

failure mode.

Identify measurement tool/system that meet

accuracy requirements for each specified requirement

Complete Measurement System Analysis to ensure

selected tool/system exhibits adequate repeatibility and

reproducability.

Conduct machine/process pilot production run and

measure its output.

Analyze pilot production results to determine

machine/process short-term performance characteritics and esrimated capability to

meet requirements.

If necessary, improve machine/process

performance and capability of producing applicable

characteristics.

Repeat pilot production run and analyze results to verify

machine/process performance and capability

improvement.

Measure initial production with SPC variable control

charting for process output of critical requirements.

Utilize SPC to moniter machine/process stability and

potential influence of abnormal variation relative to

control limits, and adjust process if necessary.

Estimate machine/process mean, sigma, capability, and

expected defect potential relative to the specification

limits.

If necessary, improve machine/process

performance and capability of producting applicable

characteristics.

Complete verification and use approprtiate control plan to

ensure machine/process capability is sustained

74

Recommendations

Based on the project conclusions, several recommendations can be provided. First, the

machine cell should be modified to improve the capability to a minimum Cpk of 1.33 for the

seven selected characteristics. Specifically, this improvement should target the firebox wrap

front corner angle and the combustion chamber outer width. This improvement should reduce

the expected defect PPM in the short-term, and prevent significant increases in defects if subtle

process shifts occur over the long-term.

Second, continued use of SPC is recommended to ensure that the robotic sheet metal

forming and fabrication remains stable and is not influenced by abnormal variables. This will

also help identify potential process shifts so that appropriate adjustment can take place. In

addition, the SPC can serve as process history to verify improvements recommended for the

firebox wrap front corner angle and combustion chamber outer width dimension. Use of SPC

should only be eliminated if on-machine verification can be verified to make its use unnecessary.

Finally, the organization should consider further application of the verification

methodology identified and successful applied by this project. When properly applied, this

verification methodology may help support implementation of similar manufacturing process

equipment.

75

References

Automotive Industry Action Group. (2006). Production Part Approval Process (Version 4).

Southfield, MI.

Bend Angle Sensing (n.d.). Material Thickness Sensing System. Retrieved from

http://www.accurpress.com/innovation.html#robotics

FMEA Template (n.d.). FMEA Template. Retrieved from asq.org/learn-about-quality/data-

collection-analysis-tools/overview/asq-fmea-template.xls

Glaser, P. (2000). Industrial Robotics: How to Implement the Right System for Your Plant. New

York, NY: Industrial Press, Inc.

Hewitt, P. (2009). Examining on-machine verification. Quality Digest. Retrieved from

http://www.qualitydigest.com/magazine/2009/may/article/examining-machine-

verification.html

Kapadia, Mehernosh. Measuring your process capability. Retrieved from

http://www.symphonytech.com/articles/processcapability.htm

Khatwani, S., Krishnamoorthi, K.S. (2000). A capability index for all occasions. ASQ’s 54th

Annual Quality Congress Proceedings. Indianapolis, IN.

Kotz, S., Johnson, N. (2002). Process capability indices-a review, 1992-2000. Journal of Quality

Technology, 34(1), 2-6.

Laser Bend Angle Sensing (n.d.). Amada HFB 1003 Down-Acting Press Brake Incorporates

New Automatic Bend Indicator. Retrieved from

http://mfgnewsweb.com/archives/metalforming/dec98/amada.htm

76

LeTang, P.K. (2012). Justifying a robotic press brake: Per-piece cost should not be the only

factor. Retrieved from http://www.thefabricator.com/article/bending/justifying-a-robotic-

press-brake

McDermott, R., Mikulak, R.J. (2009). Basics of FMEA, 2nd Edition. New York, NY: Productivity

Press.

Mitu, R.K. (2011). Medical Validation: Process Validation, Principles, Practices, and Strategies

for Medical Devices. United States of America: Rofri Med Corporation.

Omar, Mohammed A. (2011). The Automotive Body Manufacturing Systems and Processes.

United Kingdom: John Wiley and Sons, Ltd.

Parts Present Sensing. (n.d.). Motoman Robots Handling and Press Tending Appliance Panels.

Retrieved from http://www.motoman.com/casestudies/acs-061.php

Relyea, Douglas B. (2011). Practical Application of Process Capability Study. Boca Raton, FL:

CRC Press.

Robotic Press Brake. (n.d.). Manual Press Brake Operation Goes from Bottle Neck to “Lights

Out” with Robotics. Retrieved from http://www.motoman.com/casestudies/acs-

midwestmachine.php

Roger W, Berger, Donald W. Benbow, Ahmad K. Elshennawy, & H. Fred Walker. (Eds.).

(2007). The Certified Quality Engineer Handbook, 2nd Edition. Milwaukee, WI:

American Society for Quality Press.

Sleeper, A. (2012). MiniTab DeMYSTiFieD. United States of America: McGraw-Hill

Companies, Inc.

Stamatis, D.H. (1998). Advanced Quality Planning: A Common Sense Guide to AQP and APQP.

Portland, OR: Productivity Press.

77

Stamatis, D.H. (2003). Six Sigma and Beyond: Statistical Process Control. Boca Raton, FL:

CRC Press.

78

Appendix A: Sheet Metal Material Specifications

Material: Aluminized Cold-Rolled Commercial Steel Type B (CS- B)

Length: 96.0 +/- 0.5”

Width: 48 +/- 0.5”

Diagonal: 107.33 +/- 0.062

Thickness 0.03 – 0.034”

Max # of Waves: 3

Max Height of Waves: 0.188”

Coating: Type 1 Aluminized T1-25 minimum both sides

Coating Weight: Minimum 0.25 oz/sqft surface per ASTM A463-10

Surface Finish: Regular matte finish, 60 Ra Max

Surface Lubrication: Light rust preventative oil compatible with removal by alkaline cleaner

79

Appendix B: Process Failure Mode and Effects Analysis

B.1 FMEA Action Items Register

Item # RPN Potential Failure Mode and Cause Completed Actions Updated

RPN

1 800

Crimped assembly unloaded incorrectly on empty outbound conveyor, OR unloaded when there is already a part in queue. Due to unload conveyor misplaced, or not in position, or there is already a firebox in that position.

Sensor(s) add to verify unload zone for finished part transfer from robot - it is in position, empty, and ready to accept finished subassembly, and it is guarded.

56

2 600 Buckled or unevenly formed crimps. Due to excessive dimple depth in tool.

Reduce dimple depth in crimp tool and verify that distorted crimps are minimized.

126

3 336

Defective hem. Due to part "jumps" back gage - inserted too deep (back gage relatively short). Debris contamination on tooling. Worn back gage (not hardened).

Implemented support table to help with part handling and facilitate proper engagement with the existing backstop. Implement a TPM on the press that includes daily cleaning of the tooling.

56

4 336

Firebox top and bottom can be installed with lap joint at front corner of firebox backwards. Design allows assembly error. Fixture station does not control correct assembly.

Change part design to add a dimple to prevent misalignment. 36

5 300

Fail to hold part securely per the squaring table. Due to vacuum pump failure, power failure, multiple suction cup failures.

Implement check valve to prevent sudden pressure loss, low pressure sensor(s), TPM/PM (cleaning) of suction cups & hoses. Protectors over all cables, hoses, fragile equipment that could be potentially damaged by a dropped part.

90

6 294

Hem formed towards incorrect face of blank. Due to machine operator error, no visual queue, not controlled by design or process.

Change part design so that an additional L-bracket can be added to the back corners of each part to poke-yoke top/bottom location and alignment on the wrap. This will also aid in holding the part during the crimping process.

24

7 240 Blanks drawn from bin incorrectly by robot. Due to part loading not controlled.

Photo-eye sensor on squaring table detects incorrectly orientated parts and robot will be programmed to reposition as a result - auto correcting mistake. Incorrect blank is returned to inbound pallet, operation halted, and fault identified at HMI. Photo-eye sensor also added to the robot end of arm tooling to detect the height of the stack of blanks. This ensures proper location for blank pick-up.

24

80

8 240

Firebox top and bottom can be installed upside down. Due to design allows assembly error. Fixture station does not control correct assembly.

Change part design so that an additional L-bracket can be added to the back corners of each part to poke-yoke top/bottom location and alignment on the wrap. This will also aid in holding the part during the crimping process.

24

9 240

Firebox top and bottom reverse order or two tops/two bottoms vise-versa. Due to design allows assembly error. Fixture station does not control correct assembly.

Sensing added to crimping head to inspect for exhaust collar location and height dimension, pilot grommet cutout, and hem feature. Probability of this type of defect also mitigated by L-bracket (Item #8).

24

10 224

Defective hem. Due to no support table for relatively large parts. No "RAM ADJUST" to tune parallelism of ram - especially if we need to compensate for worn bearing. Press is "short on power" - possibly insufficient force for these parts?

Capability study complete determine hem dimension tolerance limits and document on prints. This study an also be used to determine if the press is capable of producing this design. Results indicated that press was incapable of meeting specifications. The equipment replaced with a press that is capable of meeting specifications for the hem channel forming.

24

11 200 Wrap not located properly in fixture. Due to fixture blocks are not fixed.

Modified fixture blocks so they are fixed and chamfered so as to engage the notches in the glass flange.

80

12 168

Firebox components do not stay in intended position after located and installed by operator. Due to welding magnets lost, missing, or provide insufficient force. Operator may forget to use magnets or position them improperly. Defective hem formed may lead to increased manipulation of force by operator.

Change part design so that an additional L-bracket can be added to the back corners of each part to poke-yoke top/bottom. This will ensure location and alignment on the wrap. This will also aid in holding the part during the crimping process. (Item #8 & 9)

18

13 144

Defective Bend Angle/Location. Due to Die wear, debris on dies, ram not parallel, suction failure on end-of-arm, robot unexpectedly adjusts coordinate path, loss of hydraulic function on support table, ram linear travel indicator drift.

Implemented tooling setup checklist, 1st Part Inspection, and machine operator TPM

48

14 128

Defective Bend (including bend formed backwards in relation to hem or cut-out profile). Due to machine operator error, design and machine does not control Short cycle - did not hold foot pedal down for required time (until machine beeps). Part not aligned against backstop. Ram parallelism out of tolerance. Incorrect tooling, machine setup, wrong program, programmed not updated per engineering change, prints & part # not on PC/HMI screen. Tooling wear and/or contamination.

Implemented tooling setup checklist, 1st Part Inspection, and machine operator TPM

56

81

15 98

Blank does not end up in correct position on squaring table. Due to wear on the locating bars on the edges of the table, debris falling and/or accumulating on the edges surfaces of the table.

TPM to clean table daily. Procedure an operator access added to allow cleaning. Hardened steel added to locating bars to reduce probability of wear from blank installation.

24

16 60

Parts queued out of sequence with assembly schedule. Due to parts not staged per current schedule, schedule change

Assembly line schedule and Amada will communicate using proven system used by assembly line and its other internal customers. Ensure access to inbound conveyor so parts can be shifted as needed.

40

17 56

Program fails to properly recognize correct blank loaded in squaring table. Due to proximity sensor failure, or adjustment due to repeated vibration

TPM/PM on proximity sensor location & depth on mounting rail. Mark reference locations on proximity sensor mounting rail

24

B.2 Machine Cell Improvement Summary

Potential Failure Mode and Cause Quality Downtime Safety Machine Cell Improvement Crimped assembly unloaded incorrectly on empty outbound conveyor, and/or unloaded when there is already a part in queue. Result of unload conveyor misplaced, or not in position at all, or there is already a firebox in that position at time of robot unload

X X X

Added sensors to unload zone to verify status of unload zone as to its position, access, and ability accept finished subassembly. Also added guarding around unload zone to prevent human access during unload zone.

Buckled or unevenly formed crimps due to excessive dimple depth X

Reduced dimple depth in crimp tool and verify that distorted crimps are minimized.

Defective hem resulting from blank inserted too deep. Back gauge is short such that it may not properly stop blanks. Back gauge steel not hardened. Debris contamination on tooling.

X

Implemented support table to help with part handling and facilitate proper engagement with the existing backstop.

Failure of robot blank gripper to hold part securely per the squaring table, resulting from insufficient vacuum pressure failure, power failure, and/or suction cup failures. Failure of blank gripper part-part present sensors to detect adjustment of gripped blank position during manipulation.

X X X

Implemented check valve to prevent sudden pressure loss, low pressure sensor(s), TPM/PM (cleaning) of suction cups & hoses. Protectors added over all cables, hoses, fragile equipment that could be potentially damaged by a dropped part.

Blanks drawn from inbound pallet incorrectly by robot as a result of part loading not controlled.

X X

Photo-eye sensor added to squaring table to detect incorrectly orientated blanks. Robot programmed to detect and react to incorrect blank orientation. Incorrect blank is returned to inbound pallet, operation halted, and fault identified at HMI. Photo-eye sensor also added to the robot end of arm tooling to detect the height of the stack of blanks. This ensured proper location for blank pick-up.

82

Defective hem channel formed in firebox top and firebox bottom. Result of no support table for relatively large parts. Press does not have adequate adjustment to tune parallelism of ram - Press tonnage capacity marginal for this application.

X

Results of capability study indicated that press was incapable of meeting specifications. The press brake was replaced with a one capable of meeting specifications for the hem channel forming.

Wrap not located properly in crimping fixture as a result of insufficient guidance.

X

Modified fixture blocks so they are fixed and chamfered to engage the notches in the glass flange feature of the firebox wrap.

Blank does not end up in correct position on squaring table. Due to wear on the locating bars on the edges of the table and/or debris falling and/or accumulating on the edges surfaces of the table.

X

Implemented TPM to clean table daily. Developed procedure and operator access added to allow cleaning. Hardened steel added to locating bars to reduce probability of wear from blank installation.

Program fails to properly recognize correct blank loaded in squaring table, resulting from potential sensor failure, and/or adjustment due to vibration.

X

Implemented proximity sensor location and depth on mounting rail and reference locations on proximity sensor mounting rail.

83

Appendix C: Design Drawings

Figure C1 - Subassembly consisting of three formed parts crimped into combustion chamber

84

Figure C2 - Firebox wrap consisting of four bends

•

0 ·:..--- ---. --­_...,.....

14---- 2,.68------,

10( Ill

85

Figure C3 - Firebox bottom consisting of eight bends

86

Figure C4 - Firebox top consisting of eight bends

OETAi l 6 SC•LE lt l U FLACESI

1-----------u.n tEF-----------l

0 1!.91

·D ··D ··D · SEE DETAi l

87

Appendix D: Detailed Gage R&R Results

D.1 Vernier Protractor 2 min (0.33 Degree) Accuracy

109

108

107

1 2 3

0.4

0.2

0.0

109

108

107

321

110

108

106

Variation by Source

Total Gage 0.095 8.82 9.55 14.32

Repeatability 0.090 8.34 9.03 13.54

Reproducibility 0.031 2.87 3.11 4.67

Operator 0.000 0.00 0.00 0.00

Operator by Part 0.031 2.87 3.11 4.67

Part-to-Part 1.078 99.61 107.79 161.68

Study Variation 1.082 100.00 108.21 162.32

Process Variation 1.000 92.41 100.00 150.00

Tolerance (upper spec - lower spec): 4

Source StDev Variation

%Study

Variation

%Process

%Tolerance

Xbar Chart of Part Averages by OperatorAt least 50% should be outside the limits. (actual: 80.0%)

R Chart of Test-Retest Ranges by Operator (Repeatability)Operators and parts with larger ranges have less consistency.

Reproducibility — Operator by Part InteractionLook for abnormal points or patterns.

Reproducibility — Operator Main EffectsLook for operators with higher or lower averages.

Gage R&R Study for MeasurementsVariation Report

process variation.

variation. A historical standard deviation is used to estimate the

The measurement system variation equals 9.5% of the process

100%30%10%0%

NoYes

9.5%

tolerance.

The measurement system variation equals 14.3% of the

100%30%10%0%

NoYes

14.3%

ReprodRepeatTotal Gage

45

30

15

0

30

10

%Process Var

%Tolerance

and is 3.1% of the total variation in the process.

same item. This equals 32.6% of the measurement variation

The variation that occurs when different people measure the

-- Operator and Operator by Part components (Reproducibility):

9.0% of the total variation in the process.

times. This equals 94.5% of the measurement variation and is

occurs when the same person measures the same item multiple

-- Test-Retest component (Repeatability): The variation that

reproducibility to guide improvements:

total gage variation is unacceptable, look at repeatability and

Examine the bar chart showing the sources of variation. If the

>30%: unacceptable

10% - 30%: marginal

<10%: acceptable

General rules used to determine the capability of the system:

Number of parts in study 10

Number of operators in study 3

Number of replicates 3

Study Information

Variation by Source

(Replicates: Number of times each operator measured each part)

Comments

Gage R&R Study for MeasurementsSummary Report

Can you adequately assess process performance?

Can you sort good parts from bad?

110

109

108

107

110

109

108

107

1

Operators

Me

asu

re

me

nts

Mean

2 3 4 5

6 7 8 9 10

Mean

1

2

3

O perators

Gage name:

Date of study :

Reported by :

Tolerance:

Misc:

Panel variable: Parts

Gage Run Chart of Measurements by Parts, Operators

v v \ ?" • v v \ 1--- ,Avl\/ "-\. 1

~ ~~ Ll S2 : .... . . . .,J S I "- . . . . ......... ·I

.. --~~~---

--- ......... l r=-l ...... :.;r ...... --... ............. --------- --------- l±____j

.......... -------- --------- ----- ~-........... --- ~-.~ ---------

88

D.2 60 inch Vernier Caliper 0.001” Accuracy

36.50

36.45

36.40

1 2 3

0.04

0.02

0.00

36.50

36.45

36.40

321

36.50

36.45

36.40

Variation by Source

Total Gage 0.008 17.21 5.56 8.34

Repeatability 0.008 17.21 5.56 8.34

Reproducibility 0.000 0.00 0.00 0.00

Operator 0.000 0.00 0.00 0.00

Part-to-Part 0.048 98.51 31.83 47.74

Study Variation 0.048 100.00 32.31 48.46

Process Variation 0.150 309.51 100.00 150.00

removed from the table.

The Operator by Part interaction was not statistically significant and was

Tolerance (upper spec - lower spec): 0.6

Source StDev Variation

%Study

Variation

%Process

%Tolerance

Xbar Chart of Part Averages by Operator

At least 50% should be outside the limits. (actual: 80.0%)

R Chart of Test-Retest Ranges by Operator (Repeatability)

Operators and parts with larger ranges have less consistency.

Reproducibility — Operator by Part Interaction

Look for abnormal points or patterns.

Reproducibility — Operator Main Effects

Look for operators with higher or lower averages.

Gage R&R Study for Measurements

Variation Report

process variation.

variation. A historical standard deviation is used to estimate the

The measurement system variation equals 5.6% of the process

100%30%10%0%

NoYes

5.6%

tolerance.

The measurement system variation equals 8.3% of the

100%30%10%0%

NoYes

8.3%

ReprodRepeatTotal Gage

45

30

15

0

30

10

%Process Var

%Tolerance

total variation in the process.

equals 0.0% of the measurement variation and is 0.0% of the

occurs when different people measure the same item. This

-- Operator component (Reproducibility): The variation that

5.6% of the total variation in the process.

times. This equals 100.0% of the measurement variation and is

occurs when the same person measures the same item multiple

-- Test-Retest component (Repeatability): The variation that

reproducibility to guide improvements:

total gage variation is unacceptable, look at repeatability and

Examine the bar chart showing the sources of variation. If the

>30%: unacceptable

10% - 30%: marginal

<10%: acceptable

General rules used to determine the capability of the system:

Number of parts in study 10

Number of operators in study 3

Number of replicates 3

Study Information

Variation by Source

(Replicates: Number of times each operator measured each part)

Comments

Gage R&R Study for Measurements

Summary Report

Can you adequately assess process performance?

Can you sort good parts from bad?

36.52

36.48

36.44

36.40

36.52

36.48

36.44

36.40

1

Operators

Me

asu

re

me

nts

Mean

2 3 4 5

6 7 8 9 10

Mean

1

2

3

O perators

Gage name:

Date of study :

Reported by :

Tolerance:

Misc:

Panel variable: Parts

Gage Run Chart of Measurements by Parts, Operators

j p ·--v· l~v,_ · lr ·-v ·l j o>6 • • "''"""'/-=..---= l.c=--....,L::.. '=""" I ;:>""'L,._, ~ I

: I

• .... /\." J /\.." ~-~~ ,.. ........ D --------

• ....'\. ...

E rc::::J ~.L-~ \...2J.

A ..-. ... v~~ --------

\...--

89

Appendix E: Pilot Production Tolerance Interval Results

90.390.089.789.489.188.8

Nonparametric

Normal

90.290.089.889.689.489.289.088.8

90.290.089.889.689.489.289.088.8

99

90

50

10

1

Pe

rce

nt

N 30

Mean 89.503

StDev 0.298

Lower 88.742

Upper 90.265

Lower 89.000

Upper 90.100

AD 0.297

P-Value 0.568

Statistics

Normal

Nonparametric

Normality Test

Normal Probability Plot

Tolerance Interval Plot for Top/Bottom Seal Flange Angle95% Tolerance Interval

At Least 95% of Population Covered

20.0019.9919.9819.9719.96

Nonparametric

Normal

20.0019.9919.9819.9719.96

20.0019.9919.9819.9719.96

99

90

50

10

1

Pe

rce

nt

N 30

Mean 19.978

StDev 0.006

Lower 19.963

Upper 19.994

Lower 19.969

Upper 19.997

AD 0.395

P-Value 0.351

Statistics

Normal

Nonparametric

Normality Test

Normal Probability Plot

Tolerance Interval Plot for Top/Bottom Part Depth Dimension95% Tolerance Interval

At Least 95% of Population Covered

I

j

: I

I I I I I I I --~-~--~--,--T--T--r

I I I I I I I

-- .. --r---,----1

I I

I

I I I I I I

I

~ ---r-----T----~---1 I . I I ,---- -r-- ---

1 I I ,.

:

I I I ,--T--T--r--

1 I I I I I I I I I

r--r--~--~--,--T--T--r--

1 I I I I I I I I I I I I I I I

I I

I

I • I I I -~----T

I I I I I

--~-----~----T 1 I I I I I I I

-~----T----~-----~--1 I I I I ~ R R

I

I

90

0.5440.5400.5360.5320.528

Nonparametric

Normal

0.5450.5400.5350.530

0.54250.54000.53750.53500.53250.5300

99

90

50

10

1

Pe

rce

nt

N 30

Mean 0.536

StDev 0.003

Lower 0.528

Upper 0.543

Lower 0.529

Upper 0.541

AD 0.522

P-Value 0.170

Statistics

Normal

Nonparametric

Normality Test

Normal Probability Plot

Tolerance Interval Plot for Top/Bottom Hem Depth Dimension95% Tolerance Interval

At Least 95% of Population Covered

1.0751.0701.0651.0601.055

Nonparametric

Normal

1.0751.0701.0651.0601.055

1.0751.0701.0651.0601.055

99

90

50

10

1

Pe

rce

nt

N 30

Mean 1.065

StDev 0.004

Lower 1.054

Upper 1.076

Lower 1.057

Upper 1.073

AD 0.296

P-Value 0.572

Statistics

Normal

Nonparametric

Normality Test

Normal Probability Plot

Tolerance Interval Plot for Top/Bottom Seal Flange Width Dimension95% Tolerance Interval

At Least 95% of Population Covered

l.c=t=r=O ~.1 I I I I I I I I I I I I

---~--~---r--~---~---r-1 I I I ' ' I I I I I

---~--~--- t"--~----t--1 I I 1 I I

I I I I I -1---r---1- ~-

1 I I 1 . . . I

I .cdlJ l.b. I

I I I I I

I I I I I -;-----!----;-----!----~

I I I I I I -;-----!----1 I

---t--1

I ·---~----~---

1 I I I

--~--1

91

21.09621.08821.08021.072

Nonparametric

Normal

21.10021.09521.09021.08521.08021.07521.07021.065

21.10021.09521.09021.08521.08021.07521.070

99

90

50

10

1

Pe

rce

nt

N 30

Mean 21.083

StDev 0.007

Lower 21.067

Upper 21.100

Lower 21.072

Upper 21.098

AD 0.254

P-Value 0.707

Statistics

Normal

Nonparametric

Normality Test

Normal Probability Plot

Tolerance Interval Plot for Wrap Side Depth Dimension95% Tolerance Interval

At Least 95% of Population Covered

108.0107.8107.6107.4107.2107.0106.8106.6

Nonparametric

Normal

108.00107.75107.50107.25107.00106.75106.50

107.75107.50107.25107.00106.75106.50

99

90

50

10

1

Pe

rce

nt

N 26

Mean 107.189

StDev 0.245

Lower 106.548

Upper 107.831

Lower 106.800

Upper 107.640

AD 0.351

P-Value 0.442

Statistics

Normal

Nonparametric

Normality Test

Normal Probability Plot

Tolerance Interval Plot for Wrap Front Corner Angle95% Tolerance Interval

At Least 95% of Population Covered

j : I

I I I I I I I -,---r--r--T--,---1 I I I ' I I I I I I I -,---r-- ,- -,- --r --r 1 I I I I

I I I I I I --r--r--T--,--,---r--r I I I I I I I

d 1. HI I tw I I

j : I I I

I I I I I I I _, ___ , ___ , ___ , _____ _ I I I I I

I I I • I ·- __ , ___ , __ - ---r---r-I 1 I

I I I I -r---r---r---r---1 I I I

92

108.0107.7107.4107.1106.8106.5

Nonparametric

Normal

108.00107.75107.50107.25107.00106.75106.50

108.0107.8107.6107.4107.2107.0106.8106.6

99

90

50

10

1

Pe

rce

nt

N 30

Mean 107.260

StDev 0.236

Lower 106.658

Upper 107.862

Lower 106.880

Upper 107.880

AD 0.395

P-Value 0.352

Statistics

Normal

Nonparametric

Normality Test

Normal Probability Plot

Tolerance Interval Plot for Wrap Rear Corner Angle95% Tolerance Interval

At Least 95% of Population Covered

1.6361.6321.6281.6241.620

Nonparametric

Normal

1.6361.6321.6281.6241.620

1.63751.63501.63251.63001.62751.62501.62251.6200

99

90

50

10

1

Pe

rce

nt

N 30

Mean 1.627

StDev 0.004

Lower 1.618

Upper 1.636

Lower 1.621

Upper 1.634

AD 0.279

P-Value 0.622

Statistics

Normal

Nonparametric

Normality Test

Normal Probability Plot

Tolerance Interval Plot for Wrap Flange Width Dimension95% Tolerance Interval

At Least 95% of Population Covered

I

j

I

I

rl

I I I I I I I ,--,--,--,--,---1 I I I I I I ,--,--, I I

I

I

I I I I I I I --~--~-~--;--~--~--~-

1 I I I I I I I I

--~--~-~-1 I

~

bu I

I

• I

1 I I I ~--,--,--,--,

I I I I I I I I I I I I

I

I

,--,--,--,--,--,--,--, I I I I I I I I

I

I ·-~·

I I , I I I

• -,-- .. -- t- --t--~· I I I I I

I I I I I I t---t--"1 t-1 I I I .

93

Appendix F: Pilot Production Process Performance Studies

109.8109.2108.6108.0107.4106.8106.2

LSL Target USL

measures represent long-term performance, may not apply.

time. Therefore, the usual interpretation, that the capability

sources of variation that may appear over a longer period of

However, the data collection method used may not capture all

The capability measures use the overall standard deviation.

Normality Test

Results Pass

P-value 0.442

(Anderson-Darling)

Lower Spec 106

Target 108

Upper Spec 110

Customer Requirements

Z.Bench 4.86

% Out of spec (observed) 0.00

% Out of spec (expected) 0.00

PPM (DPMO) (observed) 0

PPM (DPMO) (expected) 1

Total N 26

Mean 107.19

Mean off target Yes

P-value 0.000

Standard deviation 0.24490

Capability statistics

Pp 2.72

Ppk 1.62

Process Characterization

Histogram

Are the data inside the limits and close to the target?

Points should be close to line.

Normality Plot Comments

Capability Snapshot for Wrap Front Corner Angle

Summary Report

109.8109.2108.6108.0107.4106.8106.2

LSL Target USL

measures represent long-term performance, may not apply.

time. Therefore, the usual interpretation, that the capability

sources of variation that may appear over a longer period of

However, the data collection method used may not capture all

The capability measures use the overall standard deviation.

Normality Test

Results Pass

P-value 0.352

(Anderson-Darling)

Lower Spec 106

Target 108

Upper Spec 110

Customer Requirements

Z.Bench 5.35

% Out of spec (observed) 0.00

% Out of spec (expected) 0.00

PPM (DPMO) (observed) 0

PPM (DPMO) (expected) 0

Total N 30

Mean 107.26

Mean off target Yes

P-value 0.000

Standard deviation 0.23553

Capability statistics

Pp 2.83

Ppk 1.78

Process Characterization

Histogram

Are the data inside the limits and close to the target?

Points should be close to line.

Normality Plot Comments

Capability Snapshot for Wrap Rear Corner Angle

Summary Report

! I !

\

/

1

1/ \ 1

I \

i J rn i

•

94

21.10421.09621.08821.08021.07221.06421.056

LSL Target USL

measures represent long-term performance, may not apply.

time. Therefore, the usual interpretation, that the capability

sources of variation that may appear over a longer period of

However, the data collection method used may not capture all

The capability measures use the overall standard deviation.

Normality Test

Results Pass

P-value 0.707

(Anderson-Darling)

Lower Spec 21.05

Target 21.08

Upper Spec 21.11

Customer Requirements

Z.Bench 4.10

% Out of spec (observed) 0.00

% Out of spec (expected) 0.00

PPM (DPMO) (observed) 0

PPM (DPMO) (expected) 21

Total N 30

Mean 21.083

Mean off target Yes

P-value 0.009

Standard deviation 0.0065091

Capability statistics

Pp 1.54

Ppk 1.37

Process Characterization

Histogram

Are the data inside the limits and close to the target?

Points should be close to line.

Normality Plot Comments

Capability Snapshot for Wrap Side Depth Dimension

Summary Report

1.6561.6481.6401.6321.6241.6161.6081.600

LSL Target USL

measures represent long-term performance, may not apply.

time. Therefore, the usual interpretation, that the capability

sources of variation that may appear over a longer period of

However, the data collection method used may not capture all

The capability measures use the overall standard deviation.

Normality Test

Results Pass

P-value 0.622

(Anderson-Darling)

Lower Spec 1.6

Target 1.63

Upper Spec 1.66

Customer Requirements

Z.Bench 7.50

% Out of spec (observed) 0.00

% Out of spec (expected) 0.00

PPM (DPMO) (observed) 0

PPM (DPMO) (expected) 0

Total N 30

Mean 1.6271

Mean off target Yes

P-value 0.000

Standard deviation 0.0036173

Capability statistics

Pp 2.76

Ppk 2.50

Process Characterization

Histogram

Are the data inside the limits and close to the target?

Points should be close to line.

Normality Plot Comments

Capability Snapshot for Wrap Flange Width Dimension

Summary Report

95

0.5600.5520.5440.5360.5280.5200.5120.504

LSL Target USL

measures represent long-term performance, may not apply.

time. Therefore, the usual interpretation, that the capability

sources of variation that may appear over a longer period of

However, the data collection method used may not capture all

The capability measures use the overall standard deviation.

Normality Test

Results Pass

P-value 0.170

(Anderson-Darling)

Lower Spec 0.5

Target 0.53

Upper Spec 0.56

Customer Requirements

Z.Bench 8.12

% Out of spec (observed) 0.00

% Out of spec (expected) 0.00

PPM (DPMO) (observed) 0

PPM (DPMO) (expected) 0

Total N 30

Mean 0.53553

Mean off target Yes

P-value 0.000

Standard deviation 0.0030141

Capability statistics

Pp 3.32

Ppk 2.71

Process Characterization

Histogram

Are the data inside the limits and close to the target?

Points should be close to line.

Normality Plot Comments

Capability Snapshot for Top/Bottom Hem Depth

Summary Report

1.091.081.071.061.051.041.03

LSL Target USL

measures represent long-term performance, may not apply.

time. Therefore, the usual interpretation, that the capability

sources of variation that may appear over a longer period of

However, the data collection method used may not capture all

The capability measures use the overall standard deviation.

Normality Test

Results Pass

P-value 0.572

(Anderson-Darling)

Lower Spec 1.03

Target 1.06

Upper Spec 1.09

Customer Requirements

Z.Bench 5.70

% Out of spec (observed) 0.00

% Out of spec (expected) 0.00

PPM (DPMO) (observed) 0

PPM (DPMO) (expected) 0

Total N 30

Mean 1.0654

Mean off target Yes

P-value 0.000

Standard deviation 0.0043126

Capability statistics

Pp 2.32

Ppk 1.90

Process Characterization

Histogram

Are the data inside the limits and close to the target?

Points should be close to line.

Normality Plot Comments

Capability Snapshot for Top/Bottom Seal Flange Width

Summary Report

96

91.891.290.690.089.488.888.2

LSL Target USL

measures represent long-term performance, may not apply.

time. Therefore, the usual interpretation, that the capability

sources of variation that may appear over a longer period of

However, the data collection method used may not capture all

The capability measures use the overall standard deviation.

Normality Test

Results Pass

P-value 0.568

(Anderson-Darling)

Lower Spec 88

Target 90

Upper Spec 92

Customer Requirements

Z.Bench 5.05

% Out of spec (observed) 0.00

% Out of spec (expected) 0.00

PPM (DPMO) (observed) 0

PPM (DPMO) (expected) 0

Total N 30

Mean 89.503

Mean off target Yes

P-value 0.000

Standard deviation 0.29790

Capability statistics

Pp 2.24

Ppk 1.68

Process Characterization

Histogram

Are the data inside the limits and close to the target?

Points should be close to line.

Normality Plot Comments

Capability Snapshot for Top/Bottom Seal Flange Angle

Summary Report

20.0220.0019.9819.9619.9419.92

LSL Target USL

measures represent long-term performance, may not apply.

time. Therefore, the usual interpretation, that the capability

sources of variation that may appear over a longer period of

However, the data collection method used may not capture all

The capability measures use the overall standard deviation.

Normality Test

Results Pass

P-value 0.351

(Anderson-Darling)

Lower Spec 19.91

Target 19.97

Upper Spec 20.03

Customer Requirements

Z.Bench 8.59

% Out of spec (observed) 0.00

% Out of spec (expected) 0.00

PPM (DPMO) (observed) 0

PPM (DPMO) (expected) 0

Total N 30

Mean 19.978

Mean off target Yes

P-value 0.000

Standard deviation 0.0060269

Capability statistics

Pp 3.32

Ppk 2.86

Process Characterization

Histogram

Are the data inside the limits and close to the target?

Points should be close to line.

Normality Plot Comments

Capability Snapshot for Top/Bottom Depth Dimension

Summary Report

/

97

Appendix G: Statistical Process Control

109

108

107

Mean

__X=108.023

UCL=108.814

LCL=107.233

28252219161310741

2

1

0

Subgroup

Ran

ge

_R=0.773

UCL=1.990

LCL=0

XBar Unusually small mean 4

Chart Reason Out-of-Control Subgroups

Xbar-R Chart of Wrap Front CornerStability Report

Subgroups omitted from the calculations: 4

Is the process stable?Investigate out-of-control subgroups. Look for patterns and trends.

28252219161310741

1.640

1.635

1.630

1.625

Subgroup

Mean __

X=1.63161

UCL=1.63979

LCL=1.62343

28252219161310741

0.02

0.01

0.00

Subgroup

Ran

ge

_R=0.00799

UCL=0.02058

LCL=0

Xbar-R Chart of Wrap Flange WidthStability Report

Is the process mean stable?

Investigate out-of-control subgroups. Look for patterns and trends.

Is the process variation stable?Investigate out-of-control subgroups. Look for patterns and trends.

98

28252219161310741

0.540

0.535

0.530

Subgroup

Mean

__X=0.53610

UCL=0.54120

LCL=0.53100

28252219161310741

0.012

0.008

0.004

0.000

Subgroup

Ran

ge

_R=0.00499

UCL=0.01283

LCL=0

Xbar-R Chart of Top/Bottom Hem DepthStability Report

Is the process mean stable?

Investigate out-of-control subgroups. Look for patterns and trends.

Is the process variation stable?Investigate out-of-control subgroups. Look for patterns and trends.

28252219161310741

1.070

1.065

1.060

1.055

Subgroup

Mean __

X=1.06253

UCL=1.07069

LCL=1.05437

28252219161310741

0.02

0.01

0.00

Subgroup

Ran

ge

_R=0.00797

UCL=0.02052

LCL=0

Xbar-R Chart of Top/Bottom Seal Flange WidthStability Report

Is the process mean stable?

Investigate out-of-control subgroups. Look for patterns and trends.

Is the process variation stable?Investigate out-of-control subgroups. Look for patterns and trends.

99

28252219161310741

90.8

90.4

90.0

Subgroup

Mean __

X=90.171

UCL=90.677

LCL=89.666

28252219161310741

1.2

0.8

0.4

0.0

Subgroup

Ran

ge

_R=0.494

UCL=1.272

LCL=0

Xbar-R Chart of Top/Bottom Seal Flange AngleStability Report

Is the process mean stable?

Investigate out-of-control subgroups. Look for patterns and trends.

Is the process variation stable?Investigate out-of-control subgroups. Look for patterns and trends.

28252219161310741

45.86

45.85

45.84

Subgroup

Mean __

X=45.85125

UCL=45.86519

LCL=45.83731

28252219161310741

0.03

0.02

0.01

0.00

Subgroup

Ran

ge

_R=0.01363

UCL=0.03508

LCL=0

Xbar-R Chart of Combustion Chamber Outer WidthStability Report

Is the process mean stable?

Investigate out-of-control subgroups. Look for patterns and trends.

Is the process variation stable?Investigate out-of-control subgroups. Look for patterns and trends.

100

28252219161310741

36.55

36.54

36.53

Subgroup

Mean __

X=36.54220

UCL=36.55410

LCL=36.53029

28252219161310741

0.03

0.02

0.01

0.00

Subgroup

Ran

ge

_R=0.01164

UCL=0.02996

LCL=0

Xbar-R Chart of Combustion Chamber Inner HeightStability Report

Is the process mean stable?

Investigate out-of-control subgroups. Look for patterns and trends.

Is the process variation stable?Investigate out-of-control subgroups. Look for patterns and trends.

101

Appendix H: Production Process Capability Studies

28252219161310741

109

108

107In

div

idua

l Va

lue

_X=107.988

UCL=109.109

LCL=106.867

28252219161310741

1.0

0.5

0.0

Movin

g R

an

ge

__MR=0.421

UCL=1.377

LCL=0

28252219161310741

2

1

0

Sa

mp

le R

an

ge

_R=0.764

UCL=1.968

LCL=0

109.8109.2108.6108.0107.4106.8106.2

LSL USL

LSL 106

USL 110

Specifications

110108106

B/W

Overall

Specs

Btw 0.2676

Within 0.4515

B/W 0.5249

Overall 0.5096

StDevCp 1.27

Cpk 1.26

PPM-B/W 139.34

Pp 1.31

Ppk 1.30Cpm *

PPM-O 87.35

Capa Stats

Between/Within Capability Sixpack of Wrap Front Corner AngleIndividuals Chart of Subgroup Means

Moving Range Chart of Subgroup Means

Range Chart of All Data

Capability Histogram

Normal Prob PlotA D: 0.509, P: 0.193

Capability Plot

28252219161310741

1.64

1.63

1.62

In

div

idua

l Va

lue

_X=1.63161

UCL=1.64073

LCL=1.62249

28252219161310741

0.010

0.005

0.000

Movin

g R

an

ge

__MR=0.00343

UCL=0.01120

LCL=0

28252219161310741

0.02

0.01

0.00

Sa

mp

le R

an

ge

_R=0.00799

UCL=0.02058

LCL=0

1.656

1.648

1.640

1.632

1.624

1.616

1.608

1.600

LSL USL

LSL 1.60

USL 1.66

Specifications

1.6561.6441.6321.620

B/W

Overall

Specs

Btw 0.001344

Within 0.004722

B/W 0.004909

Overall 0.005812

StDevCp 2.04

Cpk 1.93

PPM-B/W 0.00

Pp 1.72

Ppk 1.63Cpm *

PPM-O 0.54

Capa Stats

1

Between/Within Capability Sixpack of Wrap Flange WidthIndividuals Chart of Subgroup Means

Moving Range Chart of Subgroup Means

Range Chart of All Data

Capability Histogram

Normal Prob PlotA D: 0.646, P: 0.089

Capability Plot

I~::~ I 1.~1 I~ I

:D

i I I ----r-----T--J ----~-----·-- L I I I 1 ·--- --· I I 1 I 1 T-----~------r J - ~-----~------~ ft I I I

0~--------~

I! Ail. 1° I I I r--T--,-­

- L--.1.--__. I I I

_.,---1 I I I ,

.J

102

28252219161310741

0.540

0.535

0.530

In

div

idua

l Va

lue

_X=0.53610

UCL=0.54130

LCL=0.53091

28252219161310741

0.0050

0.0025

0.0000

Movin

g R

an

ge

__MR=0.001952

UCL=0.006379

LCL=0

28252219161310741

0.010

0.005

0.000

Sa

mp

le R

an

ge

_R=0.00499

UCL=0.01283

LCL=0

0.560

0.552

0.544

0.536

0.528

0.520

0.512

0.504

LSL USL

LSL 0.50

USL 0.56

Specifications

0.550.540.53

B/W

Overall

Specs

Btw 0.0003242

Within 0.002945

B/W 0.002963

Overall 0.002985

StDevCp 3.38

Cpk 2.69

PPM-B/W 0.00

Pp 3.35

Ppk 2.67Cpm *

PPM-O 0.00

Capa Stats

Between/Within Capability Sixpack of Top/Bottom Hem DepthIndividuals Chart of Subgroup Means

Moving Range Chart of Subgroup Means

Range Chart of All Data

Capability Histogram

Normal Prob PlotA D: 0.544, P: 0.158

Capability Plot

28252219161310741

1.07

1.06

1.05

In

div

idua

l Va

lue

_X=1.06253

UCL=1.07367

LCL=1.05138

28252219161310741

0.010

0.005

0.000

Movin

g R

an

ge

__MR=0.00419

UCL=0.01369

LCL=0

28252219161310741

0.02

0.01

0.00

Sa

mp

le R

an

ge

_R=0.00797

UCL=0.02052

LCL=0

1.088

1.080

1.072

1.064

1.056

1.048

1.040

1.032

LSL USL

LSL 1.03

USL 1.09

Specifications

1.081.071.061.05

B/W

Overall

Specs

Btw 0.002531

Within 0.004709

B/W 0.005347

Overall 0.005464

StDevCp 1.87

Cpk 1.71

PPM-B/W 0.14

Pp 1.83

Ppk 1.68Cpm *

PPM-O 0.25

Capa Stats

Between/Within Capability Sixpack of Top/Bottom Seal Flange WidthIndividuals Chart of Subgroup Means

Moving Range Chart of Subgroup Means

Range Chart of All Data

Capability Histogram

Normal Prob PlotA D: 0.483, P: 0.224

Capability Plot

1~1

I~ I 1.~1

I Jl jD -1-----i-----r-.1..----1--I . -t---

D :

I! Jl !D I I I - .---,---,---,--· --~--~--~--· -

I I ---1- .,... --

. I I I I , --,---r--r--r--

--!- --!- --!- --!- --

103

28252219161310741

91

90

89

In

div

idua

l Va

lue

_X=90.171

UCL=91.143

LCL=89.200

28252219161310741

1.0

0.5

0.0

Movin

g R

an

ge

__MR=0.365

UCL=1.193

LCL=0

28252219161310741

1.0

0.5

0.0

Sa

mp

le R

an

ge

_R=0.494

UCL=1.272

LCL=0

91.891.290.690.089.488.888.2

LSL USL

LSL 88

USL 92

Specifications

919089

B/W

Overall

Specs

Btw 0.2764

Within 0.2918

B/W 0.4019

Overall 0.3666

StDevCp 1.66

Cpk 1.52

PPM-B/W 2.72

Pp 1.82

Ppk 1.66Cpm *

PPM-O 0.31

Capa Stats

Between/Within Capability Sixpack of Top/Bottom Seal Flange AngleIndividuals Chart of Subgroup Means

Moving Range Chart of Subgroup Means

Range Chart of All Data

Capability Histogram

Normal Prob PlotA D: 0.345, P: 0.477

Capability Plot

28252219161310741

45.86

45.85

45.84

In

div

idua

l Va

lue

_X=45.85125

UCL=45.86021

LCL=45.84229

28252219161310741

0.010

0.005

0.000

Movin

g R

an

ge

__MR=0.00337

UCL=0.01101

LCL=0

28252219161310741

0.04

0.02

0.00

Sa

mp

le R

an

ge

_R=0.01363

UCL=0.03508

LCL=0

45.8845.8745.8645.8545.8445.8345.82

LSL USL

LSL 45.82

USL 45.88

Specifications

45.8845.8645.8445.82

B/W

Overall

Specs

Btw 0

Within 0.008048

B/W 0.008048

Overall 0.007277

StDevCp 1.24

Cpk 1.19

PPM-B/W 228.78

Pp 1.37

Ppk 1.32Cpm *

PPM-O 47.78

Capa Stats

Between/Within Capability Sixpack of Combustion Chamber Outer WidthIndividuals Chart of Subgroup Means

Moving Range Chart of Subgroup Means

Range Chart of All Data

Capability Histogram

Normal Prob PlotA D: 0.528, P: 0.174

Capability Plot

1~!1 1.~1 1~1

:D

' ' ' - - -,- -- - T-- - -,- -.

--~----~---~--- -I I I

--""'1-- ....fl --+-1 I I

~ ----T---~-- -____ ,~. ___ _. __

01--------1

: :D I __J ~ I

' ' 1 -,----,----r-- r. 1 -~---~----~-- ~

I I I 1---- ---i I I I I ~ ---~---r---T l ___ L ___ L ___ l

01-----------i

104

28252219161310741

36.55

36.54

36.53In

div

idua

l Va

lue

_X=36.54220

UCL=36.55454

LCL=36.52985

28252219161310741

0.016

0.008

0.000

Movin

g R

an

ge

__MR=0.00464

UCL=0.01517

LCL=0

28252219161310741

0.030

0.015

0.000

Sa

mp

le R

an

ge

_R=0.01164

UCL=0.02996

LCL=0

36.5736.5636.5536.5436.5336.5236.51

LSL USL

LSL 36.51

USL 36.57

Specifications

36.5636.5436.52

B/W

Overall

Specs

Btw 0.001089

Within 0.006875

B/W 0.006961

Overall 0.006691

StDevCp 1.44

Cpk 1.33

PPM-B/W 34.30

Pp 1.49

Ppk 1.39Cpm *

PPM-O 16.96

Capa Stats

Between/Within Capability Sixpack of Combustion Chamber Inner HeightIndividuals Chart of Subgroup Means

Moving Range Chart of Subgroup Means

Range Chart of All Data

Capability Histogram

Normal Prob PlotA D: 0.427, P: 0.306

Capability Plot

1::::<'~1 1.~1 1::0+Y:Y±I

--;.----+----· ·-~----~----r--· ~ ---+- --· .

- .,. ~