Laser Solutions Short Courses - … 4 - Arzu Ozkan.pdfOptimizing Laser Machining using Design of...

Laser Solutions Short Courses

Short Course #4

Optimizing Laser Machining Processes for Yield and Throughput

in Manufacturing using DOE (Design of Experiments)

Arzu Ozkan Course Instructor

Sunday, September 26 2:00PM Room: Orange County 1

Optimizing Laser Machining using Design of Experiments (DOE)

Principles

A Case Study: Application of Shainin Techniques in Medical Device

Manufacturing

Arzu Ozkan, Lumyn Technologies LLC

2220 Oakland Rd, San Jose, CA 95131650.733.6060 www.lumyntech.comICALEO 2010, Anaheim CA

2220 Oakland Rd, San Jose, CA 95131

650.733.6060 www.lumyntech.comICALEO 2010, Anaheim CA

Outline

Part I: INTRODUCTION

1. D.O.E.: The Need and The Benefits

2. Decision-making

3. Summary of D.O.E Tools and Their Relative Effectiveness

Part II: APPROACHES TO DESIGN OF EXPERIMENTS

4. Possible Objectives of Experiments

5. Cause and Effect Diagram

6. Controlled Experiments

7. Responses

8. Example I: Material Removal Rate

9. Example II: Feature Size

10. Example III: Hole Diameter

11. Example IV: Taper

12. Example V: Surface Finish

13. Example VI: Heat Affected Zone (HAZ)

14. Factors

15. Example I: Laser Wavelength

16. Example II: Laser Power

17. Example III: Laser Pulse Width

18. Variables Held Constant

19. Blocking Concept

20. Randomization Concept

21. Confounding Concept

PART III: SHAININ DESIGN OF EXPERIMENTS

22. Dorian Shainin

23. Paretos Law

24. The Green Y

25. Likert Scale

26. Seven Step Problem Solving Framework

27. Finding the Red X

28. Multi-Vari Analysis

29. Separating Important Parameters: Paired Comparison

30. Process Characterization: Variables Search

31. Validation: B vs. C

32. Process Optimization: Scatter Plot

PART IV: CASE STUDY

33. Laser Optimization--A Case Study



Part I: INTRODUCTION



1a: Design Of Experiments

D.O.E. is a systematic approach to engineering problem-solving

Experimental based modeling

Proposed by Ronald A. Fisher, in his book The Design of

Experiments in 1935.

Some pioneers of D.O.E: R. Fisher, G. Taguchi, D. Montgomery,

R. Myers, and D. Shainin.



1b: The Need for D.O.E.

Your time is valuable.

Are you in the market for a laser? Which laser is the best?

Are you a researcher in academia or a member of an applications

laboratory for a laser manufacturer? How to generate concise defendable data in a short time?

Are you a member of senior management in a laser company? How to determine specification of a new laser product?

Are you a laser or manufacturing engineer at a medical device

manufacturing, electronics, semiconductor, or solar company? While best complying with FDA or other regulatory agencies, how can you differentiate your

product from the competition and run your laser production maintenance-free?

Are you an integrator or OEM? Which laser is the best? Second source?



1c: The Benefits of D.O.E

Most knowledge gained with minimal

expenditure of time and money

Reduce time to market

Identify important and unimportant

variables and open up tolerances to

reduce costs

Ensures generation of valid and

defensible engineering conclusions



2a: Decision-making

Speculation

Opinions

Ideas

Thoughts

Theory

Assumption

Supposition

Creativity

Brainstorming

Knowledge

Facts

Truth

Evidence

Confirmation

Verification

Proof

Validation

Do

Check

Act

Plan



2b: Problem Solving Process

In Six Sigma, the PDSA cycle is called "define, measure, analyze,

improve, control" (DMAIC).

Experiment

HypothesisImplementation

Evaluation



Source: http://timoelliott.com/blog/2007/06/intestine_based_decision_makin.html

3: Summary of D.O.E Tools and Their Effectiveness

Classical Taguchi Shainin

Technique

Effectiveness

Cost

Complexity

StatisticalValidity

Applicability

Ease ofImplementation

Several Approaches Fractional Factorials,EVOP..

Moderate (3:1 Improvement) Retrogression Possible

Moderate Average of 50 experiments

Moderate Full ANOVA required

Low Higher order & 2nd order interaction effects confounded with main effects To a lesser extent, even 2nd order interaction effects confounded

Requires hardware Main use in production

Moderate Engineering and statistical knowledge required

One Approach Orthogonal Arrays

Low to Moderate (Up to 2:1 Improvement) Retrogression Likely

High Average of 50 to 100 experiments

High Inner and outer array multiplication S/N,ANOVA

Poor No randomization Even 2nd order interaction effects confounded with main effects

Primary use as a substitute for Monte Carlo Simulation - Questionable Results

Difficult Engineers not likely to use technique

Characteristics

Minimum of 8 Approaches

Extremely powerful (100% to Up to 50:1 improvement) No Retrogression

Low Average of 20 to experiments

Low Experiments can be Implemented by line operator.

High Excellent separation and quantification of main and interaction effects

Requires hardware Can be used as early as prototype and engineering run stage

Easy Even line workers conduct experiments



Part II: APPROACHES TO DESIGN OF

EXPERIMENTS



4: Possible Objectives of the Experiment

Find the variables that have a significant effect on the response

Find where to set the significant variables so that a desired

response is obtained

Find where to set the significant variables so that the response

has small variability

Find where to set the significant variables so that the effect of

nuisance variables on the response is small



5: Cause and Effect Diagram

A brainstorming tool developed by Ishikawa to find possible

causes for a defined effect.

Sometimes referred to as a fishbone diagram

Material Laser Galvo Scanner

power

pulse width

wavelength

repetition rate

M^2flatness

coating

roll direction

alloy type

thickness

duty cycle

divergence

power stabilitypulse rise time

pulse fall time

polarization

beam spot size

scanner speed

laser on delay

laser off delay

jump speed

field size

Beam perpendicularity

Rayleigh Range

Working Distance

Contr

ast R

atio

Of M

ark

ed C

hara

cte

rs



6: Designed (Controlled) Experiment

Systematic controlled changes of the inputs (factors) to a

process in order to observe corresponding changes (effects) in

the output (response).

Controlled experiments can be used to establish cause and

effect relationships.



7: Responses

Response is the measured variable of interest

There may be more than one response

Plan how you will measure the response variable(s)

Examples: material removal rate (MRR), kerf width, hole diameter,

taper, circularity, scribe depth, even layer by layer removal, surface

finish (roughness, recast, flash, debris), metallurgical

characteristics & Heat Affected Zone (HAZ)



11: Taper

Courtesy of Mr. Xinbing Lui of Panasonic Boston Laboratory

Nozzle diameter standarddeviation not to exceed 2%

Stringent taper tolerancerequired to fabricate inkjetnozzle plates



12b: Surface Finish: Micro-cracking

UV grade fused silica Infrasil Fused quartz

266 nm DPSS

Pulsed CO2

Ultrafast

Q-switched CO2



13: Heat Affected Zone (HAZ)

Disk laser cutting of Nitinol



14a: Factors

Factors are the controlled variables of interest in the experiment

They are the variables that are changed during the experiment

in a controlled manner

Examples: Laser, beam delivery, motion system, assist gas,

material parameters, etc.



14b: List of Possible Factors

Laser

Power Power Stability Wavelength Spectral Width Repetition Rate Pulse Width Duty Cycle Pulse Rise & Fall time Beam Divergence Beam M^2 Focused Spot Size Focused Spot Rayleigh Range Beam perpendicular to

processing plane

Process Gas and Delivery

Assist Gas Type Assist Gas Purity Level Assist Gas Pressure Nozzle Diameter Nozzle Design Nozzle Beam Alignment Nozzle Stand-off Exhaust Rate Exhaust Location Water Assist

Motion System

Linear Motion Speed(Cutting Speed)

Acceleration andDeceleration

Motion System Tuning Corner Angle & Radius Scanner delay times Cut Program Direction

Material

Type (Alloy, Composite) Mechanical Properties Optical Properties Surface Condition Thickness Uniformity Tube Circularity Flatness Waviness Roll Direction

Ambient

Temperature Humidity



18: Variables Held Constant

These are the variables controlled at fixed levels

Their effect on the response is not of interest in the experiment

Examples: parameters fixed due to laser, beam delivery or

motion system architecture, assist gas type and pressure,

nozzle diameter, focused spot size, etc



19: Blocking Concept

It is possible to have small differences in: Composition of various batches of raw material

Methods used by various operators

Temperature, humidity, etc on various days

Blocking is a technique to define groupings with homogeneous

conditions of: raw material, operators, days, etc that may affect

the experiment



20a: Randomization Concept

Randomization is done to balance out the effect of noise and

extraneous variables that may affect the response

Randomization should include:

The order (sequence) of conducting the experimental runs

The selection and assignment of materials, operators, machines,

subjects, locations, etc. to experimental conditions



20b: Table of Random Numbers

80 14 67 29 70 44 69 53 51 58 40 45 4 31 85 25 6 31 74 14 55 13 34 95 3448 58 6 90 36 35 19 94 38 13 25 42 21 79 44 94 13 4 56 70 27 67 42 34 3969 63 85 3 17 82 5 22 26 54 84 78 47 0 91 29 87 90 47 74 32 27 54 83 6639 65 78 11 40 48 40 23 30 25 45 32 15 9 3 12 14 4 28 68 89 49 73 50 8761 18 41 7 27 3 83 48 10 88 22 66 22 32 45 30 6 86 5 80 33 72 10 21 7

15 66 33 12 4 90 82 6 33 70 83 57 49 96 12 47 9 73 18 89 80 80 95 24 7379 12 39 88 47 37 8 18 99 69 31 89 46 64 6 50 48 47 81 51 66 16 10 83 5027 95 81 3 65 75 84 46 62 60 92 95 15 44 89 41 61 31 28 11 56 61 47 62 3934 62 68 17 22 27 56 90 53 45 21 84 83 43 71 57 86 34 64 31 55 72 44 19 7557 16 83 35 96 13 39 71 72 93 42 3 71 92 50 63 24 59 37 34 49 80 31 87 49

3 74 9 96 37 29 11 25 26 30 44 85 78 39 31 50 75 7 35 22 78 66 71 82 3021 49 58 38 12 72 74 55 91 52 59 25 79 39 10 73 73 13 38 19 56 79 10 23 611 8 72 1 8 11 19 88 12 53 3 46 91 4 72 58 26 90 69 37 96 69 43 77 717 92 88 46 16 1 14 31 9 43 85 28 54 31 99 1 21 42 89 87 90 5 10 66 170 35 91 61 58 51 71 83 74 61 91 8 15 42 95 96 23 86 42 82 44 16 97 91 51

69 65 46 7 6 41 49 47 49 35 47 5 54 15 36 8 80 8 71 18 28 87 3 32 6791 11 32 74 42 38 72 55 49 63 27 68 23 4 70 8 52 87 6 76 45 25 35 4 6690 12 32 72 44 80 14 83 88 71 74 88 72 99 80 46 29 2 19 95 90 4 84 79 9739 91 70 7 15 72 84 78 86 96 33 50 5 30 39 55 86 65 96 26 55 90 14 49 7742 16 79 69 40 1 93 70 59 12 30 30 45 26 5 67 29 77 7 2 7 14 59 57 49

16 49 20 58 56 75 44 82 68 78 34 55 25 55 37 96 71 4 43 34 21 37 49 68 108 73 64 39 27 99 97 54 58 63 98 71 95 15 19 90 55 54 11 34 10 72 30 18 3885 2 70 67 40 94 74 38 49 33 29 82 94 51 6 8 89 74 42 81 95 25 29 27 018 45 98 50 14 3 57 15 14 90 52 60 45 92 97 33 44 90 94 76 95 81 33 17 4977 27 24 53 8 73 76 28 93 74 49 62 57 47 67 55 47 33 23 3 43 47 19 9 73

43 40 76 93 60 45 2 82 51 24 56 89 90 75 88 1 13 31 66 69 45 60 7 7 765 67 50 60 7 69 77 74 54 37 32 28 7 96 40 37 38 57 53 63 73 0 96 7 1930 35 40 31 60 53 58 76 92 77 86 97 4 13 34 29 59 96 9 75 54 54 85 24 9138 40 85 73 33 27 79 42 41 54 39 73 48 45 4 32 62 9 1 70 37 75 20 71 3126 53 35 39 64 82 61 1 55 35 71 77 76 41 17 23 60 78 37 37 61 9 73 92 72

56 83 50 74 40 22 50 35 34 40 35 7 41 34 35 14 66 78 87 83 43 77 88 59 5737 47 15 8 1 65 9 41 94 52 40 19 62 84 64 43 89 21 77 54 56 94 57 17 723 93 15 95 92 40 20 5 92 91 97 99 45 4 43 87 80 30 32 52 96 97 84 7 6632 66 85 76 53 14 4 51 43 11 69 70 35 32 11 39 91 95 55 55 85 36 5 79 082 82 59 19 21 24 71 64 65 81 11 45 14 31 73 97 11 66 62 5 67 87 68 89 20

42 57 30 94 10 98 25 52 45 93 69 16 76 34 62 9 32 93 6 11 69 36 79 37 1341 56 71 3 9 35 21 28 22 8 74 78 81 76 21 83 3 93 54 37 76 35 43 53 5020 24 77 27 5 9 21 7 20 52 14 11 1 89 54 22 96 29 26 82 73 94 85 32 019 62 31 92 88 76 14 49 65 8 71 69 91 66 86 56 66 50 13 74 55 54 25 78 2348 40 52 61 27 67 1 4 20 62 52 33 44 51 79 40 45 74 83 59 83 32 80 43 12

Source: K. Bhote, World Class Quality



21: Confounding Concept

If two or more factors are confounded and show significanteffect, we will not be able to distinguish and single out thesignificant factor.

Experiment:

Response: Increased gas mileage

Possible factors: expensive higher octane gas, higher quality oil

Treatment: switch to both at the same time

Results: Mileage improves significantly

Results are confounded, it is not possible to tell is the improvementis due to gas or oil.



PART III: SHAININ DESIGN OF EXPERIMENTS



22a: Dorian Shainin

A quality engineering pioneer, Dorian Shainin (19142000), isresponsible for the development of over 20 statistical engineeringtechniques.

He worked to improve the quality and reliability of an array of products,including paper, printing, textiles, rubber, nuclear energy, airplanes,automobiles, cassette decks, space ships, light bulbs, representingover 200 different industries, ranging from the U.S. Department ofDefense, Rolls Royce Ltd. and Exxon to Polaroid, Hewlett-Packard,AT&T and Ford Motor.

During the 1960s Shainin worked for Grumman Aerospace as areliability consultant for NASA's Apollo Lunar Module. Theeffectiveness of his approach was demonstrated by zero failures ineleven manned missions, six of which featured moon landings. Whenthe command module became uninhabitable during the failed Apollo 13mission, the Lunar Module became the lifeboat that brought the Apollo13 astronauts to lunar orbit and back to Earth.

During the years that Shainin served as a reliability consultant for Pratt& Whitney Aircraft, he worked on the hydrogen-oxygen fuel cell thatpowered Apollo environmental life support in addition to the RL-10cryogenic liquid rocket engine. The RL-10 soon became America'smost reliable space engine, at one point logging 128 ignitions in spacewithout a single failure.

My particular technique is to say to people, Lets stop guessing.Instead, lets find cluessources of knowledge that you just would nothave otherwise.Dorian Shainin --Wikipedia

Talk to the parts.--Dorian Shainin



22b: Weakness of Classical/Taguchi D.O.E.

Interaction are effects confused with main effects

Engineering guesses instead of talking to the parts or process

One or two approaches vs. several different approaches

Higher cost

Difficult for engineers to understand and implement

Longer time

Poorer results - no breakthrough



23: Paretos Law and The Green Y, Red X

Effect/Output

Response/Green Y

50%

0%

1 2 3 4 5 6 7 20...

The Vital Few The Trivial Many

Red X

Pink X

Pale Pink X



24: Defining the Problem: The Green Y

Before the start of D.O.E., it is important to describe, define and

quantify the problem, the Green Y.

1. State problem in one sentence or one paragraph as a maximum.

2. Quantify the Green Y in terms of i.e. defect levels (# of laser

drilled holes with flash etc)?

3. Can the Green Y be transformed into an variable on a scale, say,

of 1 to 10 - with 1 being the worst and 10 being the best. This is

known as a Likert scale.



0 1 2

3 4 5

25: Likert Scale

best

worst




Define the problem to be solved - The GreenY

Time-to-Time(Temporal)

Unit-to-Unit(Cyclical)

Within Unit(Positional)

Week-to-Week Day-to-Day Shift-to-Shift Hour-to-Hour

Variation bw/consecutive unitsVariation amonggroup of unitsLot-to-Lot

PositionComponentMachine-to-MachineTester-to-Tester

6:1Ratio

Determine Measurement Accuracy

Do Multi-Variance Analysis

26: Finding the Red X



1. Define the Problem (The Green Y)

2. Quantify and measure the Green Y

3. Review problem history

4. Generate clues Multi-Variance analysis Components Search Paired Comparisons Product/Process Search

5. Design of Experiments Variables Search Full Factorials B vs C

6. Turn the problem on and off - B vs C

7. Establish realistic specifications andtolerances

27a: Seven Step Problem Solving Framework



27b: Clue Generating Tools

Multi-Variance Chart

Component Search

Paired Comparison

Product & Process Search



28: Finding the Red X: Multi-Variance Analysis

Objective: Reduces a large number of unrelated, unmanageable

potential causes to a family of fewer and related ones, such as

time-to-time, part-to-part within part, machine-to-machine, test

position-to-test position. Detects non-random trends.

Where: Determines how a product/process is running; a quick snapshot

without massive historical data that is of very limited usefulness.

Replaces process capability studies in some white collar

applications.

When: At engineering pilot run, production pilot run or in production.

Study Size: Min. 9-15 or until 80% of historic variation is captured.



29a: Paired Comparison

Objective: Provides clues to the Red X by determining a repetitive

difference between pairs of differently performing products.

Separating important parameters that distinguishes bad from good.

Where: There are matched sets of differently performing products

(labeled good and bad) with that cannot be disassembled.

When: At engineering pilot run, production pilot run or in production.

Study Size: 6 to 8 pairs.



29b: Paired Comparison

Procedure:

1. Select one best unit and one worst unit from a number of good

and bad units. Call this pair one. Observe and note differences

between these two units. Differences can be visual,

dimensional, electrical, mechanical, chemical including x-ray,

electron scanning microscopes or any other method.

2. Select a second pair of the 2nd best and 2nd worst unit.

Observe and note differences, as in step one.

3. Repeat the search process with a third, forth and fifth pair, etc.

4. Usually by the fifth or sixth pair of observations, the difference

will be repeated three or four times, providing a strong clue for

the major cause of variation.



29c: Rank Analysis (Tukey Test)

Procedure:

1. Rank all readings of each quality characteristic from lowest to

highest or vice-versa.

2. Determine if each reading is good or bad.

3. Draw a line near the top of the readings where the all bad

change good or visa-versa. This is the top end count.

4. Draw a line near the bottom of the readings where the all good

changes to bad or visa versa. This is the bottom end count.

5. Add the top and bottom end counts.

6. A minimum of six total end counts is needed for a 90%

confidence that there is a significant difference between the

good and bad for that particular quality characteristic.



30: Variables Search

STAGE OBJECTIVE

1Ball Park

2Elimination

3Capping Run

4Factorial Analysis

To determine Red X. Pink X areamong the causes being considered.Assures repeatability of the disassembly and re-assembly process.

To eliminate all unimportant causes and their associated interaction effects.

To verify that the important causes aretruly important and that the unimportantcauses are truly unimportant.

To quantify the magnitudes of the important main causes and their interaction effect.



31a: Validation: Better B vs. Current C

4 Distributions of B vs. C Processes

Worse Better

1 C B Null Hypothesis(No Difference)

C = Current Process B = Better (?) Process

RESULTS

2

C BPink X

3C B

Red X

4

CB

Super Red X




K = Difference between means of C and B,expressed in number of standard deviations

Minimum Spacing: XB - XC K C

Where:XB is the average of the B product/process.Xc is the average of the C product/process.

B & C is the standard deviations of the B & C product/process respectively.K is 2.9 for a 90% confidence if B = C.K is 3.7 for a 90% confidence if B C.

Meanof C

Meanof B

C B

K

31b: Permanency of Improvement




When You Expect:

An improvement

Design Change Process Improvement Yield Improvement Mfg. Method Change Improved Reliability, Life Use of New Equipment

An Economy Gain

Cost Improvement Cycle Time Improvement Safety Improvement Less Variation Open a Tolerance Ease of Manufacturing Space Reduction Eliminate an Operation/Test Faster Set-up Time Less Expensive Tooling Machine Efficiency Environmental Improvement Reduced Equipment Maintenance Increased Up-Time Use of an Alternate Vendor Use of an Alternate Component

31c: Applications of B vs. C Trials




32a: Process Optimization: Scatter Plots

Y

XPositive Correlation

(A) (B)

Y

XUnclear Positive Correlation

Y

XNegative Correlation

(C)

Y

XNo Correlation

(D)




Y

XNon-Linear Correlation

Y

XInsufficient Data Range

(E) (F)

Y

XCorrelation Through Stratification

(G)

32b: Process Optimization: Scatter Plots




Y

XiRealistic

Manufacturing Tolerance

Regression Lineor Curve

30 Random Examplesof Units Made to theCurrent Tolerance95% of Total Effect

of Mfg Factors OtherThan X

Cu

sto

me

r R

eq

uir

em

en

ts

Hi

Lo

33c: Realistic Tolerances




PART IV: CASE STUDY



33a: Definition of The Case Study Scenario

A medical device company manufactures a polymer mesh used

to wrap broken bones after they are set

This polymer mesh is cut and marked with laser

Polymer meshes have been observed to crack in testing

It is very important that time and cost efficient Shainin

techniques are used to identify & quickly solve the problem

because the time to create a single usable mesh is several days

Milestone deadline is approaching



33b: Bone Mesh Manufacturing Process

1. Custom expansion of polymer using a balloon

2. Laser cutting of mesh and marking with a serial code

3. Coating with a substance that makes the mesh neutral to the

immune system

4. Slicing the ends

5. Sterilization

6. Packaging



Process # of

Parameters

Balloon Expansion 11

Laser Cutting 9

Drying 5

Anti-immune

Coating

12

Slicing the Ends 8

Sterilization 9

Packaging 7

Total Parameters 61

33c: Application

Goal: To find the Red X or keyparameter that affects polymer meshcracking and propose process tominimize cracks.

Problem: There are 55 parameters inthe whole process chain. No time toproduce enough meshes for testingand evaluating each processcondition.

Solution: Use Shainin Techniques toreduce sample quantity and still getabove 99% confidence in processchanges.



Best Marginal

End Slicing Temp. ( C) 30 50

Anti-Immune Coating Thickness

(um)

15 25

Coating Drying Temp. Low High

Sterilization Temp. ( C) 45 50

Laser Power (W) 3.0 3.5

Polymer Purity > 90% < 80%

Wall Thickness (mm) 1 1.4

Sample Units 3 3

33d: Step 1-- Confirmation of Key Parameters

Assume:

Wall thickness is the Red X for radial strength.

Sterilization is another important factor affecting cracking.

Packaging conditions will affect cracking.



Sample After Testing I After Testing II

Visual (# cracks ) Visual (# cracks)

1T 34 49

2T 28 55

4T 37 54

3C No cracks No cracks



D/d =54:3=18 > 1.25, No overlap between the groups. This

suggests that the Red X is in the selected process parameters.

33e: Total # of cracks at one day after packaging



Sample After Testing I After Testing II

Visual (# cracks) Visual (# cracks)

1Ta 46 78

2Ta 27 43

4Ta 34 50

3Ca 2 3

5Ca 2 2

6Ca 1 4

D/d =49:18.5= 2.65 > 1.25, No overlap between the groups. This

suggests again that the Red X is in the selected process parameters.

33f: Number of cracks 14 days after packaging



Run 1 Run 2 Run 3 Run 4 Run 5 Run 6 Run 7 Run 8 Run 9Run

10

Run

11

Run

12

End Slicing

Temp 50 C 30 C 50 C 30 C 50 C 30 C 50 C 30 C 50 C 30 C 50 C 30 C

Anti-Immune

Coating

Thickness

15 25 15 25 25 15 25 15 25 15 25 15

Coating

Drying TempLow High High Low Low High High Low High Low High Low

Sterilization

Temperature45 50 C 50 C 45 50 C 45 45 45 50 C 45 50 C 45

Cutting

Power3 W 3.5 W 3.5 W 3 W 3.5 W 3 W 3.5 W 3 W 3 W 3.5 W 3.5 W 3 W

Wall

thickness1 mm 1 mm 1 mm 1 mm 1 mm 1 mm 1 mm 1 mm 1 mm 1 mm 1 mm 1 mm

Polymer

Purity> 90% < 80% < 80% > 90% < 80% > 90% < 80% > 90% < 80% > 90% > 90% < 80%

Sample Units 4 4 4 4 4 4 4 4 4 4 4 4

33g: Step 2--Variables Search for Finding the Red X



Run 1 Run 2

End Slicing Temperature ( C) 30 50

Anti-Immune Coating Thickness

(um)

17 20

Coating Drying Temp. Low High

Sterilization Temp. ( C) 45 50

Cutting Power (W) 3.5 3

Polymer Purity < 80% > 90%

Wall Thickness (mm) 1 1

Sample Units 4 4

33h: Step 3--Capping Run or Confirming the Effects



Stent Crack Variable Search

-10

0

10

20

30

40

50

60

70

80

0 2 4 6 8 10 12

Parameters

Nu

mb

er

of

Cra

cks

Center Line

Decision

Limits

Marginal

Decision

Limits Best

Crack Variable Search

Parameters

Decision

Limits

Marginal

Decision

Limits

Best

33j: Chart for Step 1, 2 and Capping Run



33k: Conclusions from Step 1, 2 and Capping Run

Key Parameters affecting mesh cracking:

Red X End Slicing Temperature

Pink X Coating Drying Temperature

Laser cutting power, anti-immunity coating thickness and

polymer purity at t=3 days have no effect on the number of

mesh cracks tested.



33l: Coating Drying Temperature Study

-2

8

18

28

38

48

58

68

Nu

mb

er

of

Cra

cks

Day 1

Day 7

Day 26

Effect of Coating Drying Temperature on Product Life

Low temp High temp



Process Coating Drying

Temp

Process Time

Best Conditions Low 30 min

Current Conditions High 60 min

33m: Step 4--Confirming the New Process is Better

Number of cracks at testing counted at day 1 and day 14 after

sterilization.

Radial strength tested at day 1 and day 14 after sterilization

Implant performance is tested



33n: B vs C Study-- Summary of Results

Mesh inspections and crack counts confirmed that the B

process is better

Implant evaluation for B process is equivalent to C process

Proposed implementation of B process

Low Temp Coating Drying Temperature

Reduce drying process time



Number Process Total Cracks

1 B 1

2 B 3

3 B 4

4 B 5

5 B 5

6 B 5

7 B 5

8 B 6

9 B 7

10 C 7

11 C 8

12 B 10

13 C 12

14 C 13

15 C 14

16 C 14

17 C 17

18 C 17

19 C 23

20 C 30

1. All samples were testedrandomly.

2. Total end count = 16.5Confidence level > 99%

(more than 10 end counts)

3. Beta Risk

Xb - Xc = 15.5 -5.1 = 10.4

K = 1.03

K x number of C samples= 10.3 < 10.4

Beta Risk of 5% or Confidence level >

95%

4. Conclusion: B process is betterthan C process.

33o: B vs. C Study--Cracks at Day 1: Tukey Test



33p: B vs. C-- Implant Testing

Drug Released Rate

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 hr 2 hrs 4 hrs 24 hrs

Pe

rfo

rma

nce

Current

Best

Time (hour)



33q: Reported Testing Conclusions

With limited time and sample size, it was found that end slicing and

coating drying temperatures are important factors that affect mesh

cracking.

By adding a coating drying study, coating drying temperature was

identified to have more effects on mesh cracking than process time.

The proposed coating drying process from this study will benefit all

polymer products since it significantly minimizes mesh cracks and

extents life time of implant.

IP rights have been created for new process tested by Shainin

technique.

Shainin D.O.E. techniques save resources and time for process

optimization and is recommended for future process development.

ICALEO 2010 Laser Solutions Short Course Evaluation

Course #4: Optimizing Laser Machining Processes for Yield and Throughput in Manufacturing using DOE (Design of Experiments) Course Instructor: Arzu Ozkan Please rate the following: (circle) Very Course Excellent Good Good Fair Poor Overall Course 5 4 3 2 1 Course Instructor 5 4 3 2 1 Presentation of material 5 4 3 2 1 Organization of material 5 4 3 2 1 Course well paced 5 4 3 2 1 Would you recommend this course to others in your profession? yes no

What was the strongest feature of the course? What was not covered that you felt should have been covered (if anything)? What would you like to hear more about next time? What was covered that left an impression/impact on you? Suggestions & Comments (for this course or courses you would like in the future): Name: (optional)

Please Use Reverse Side for Additional Comments.

Please return evaluation form to the Registration Desk by Thursday afternoon

or fax 407.380.5588 to LIA upon your return home.

Laser Solutions Short Courses - … 4 - Arzu Ozkan.pdfOptimizing Laser Machining using Design of...

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Transcript of Laser Solutions Short Courses - … 4 - Arzu Ozkan.pdfOptimizing Laser Machining using Design of...