A Case Study on Optimizing an Industrial Robot cell using ...

School of Innovation, Design and Engineering

A Case Study on Optimizing an Industrial

Robot cell using Simulation as a tool

Master thesis work

30 credits, Advanced level

Product and process development Production and Logistics

Varun Krishnappa

2020

Report code: xxxx Commissioned by: Tutor (company): Elias Häggström Tutor (university): Mikael Hedelind Examiner: Antti Salonen

ABSTRACT

The dynamic changes in the manufacturing sector have increased the competition between the

industries. To sustain these disruptive changes and maintain competitiveness in the global

market, companies need to continually improve their production performance, successfully

developing and implementing innovative production practices, and producing high-quality

products in shorter lead times at optimum costs. The variations and fluctuations in customer

demands can be satisfied by introducing new technologies into manufacturing systems, which

brings in varying flexibility and agility character into production. Introduction of robots in

manufacturing system have increased the productivity and quality of the processes. Effective

and efficient programming helps achieve flexibility in the production process because the robot

programming aids the robot to perform various tasks and motion. ABB RobotStudio is one

type of offline programming and simulation software that helps in stimulating the robot using

a Virtual Robot controller. The simulation tool lets users recreate the production environment

and program a robot and calculate the cycle time without a real robot.

This case study's objective is used to evaluate the aspect of simulation to be integrated into the

case company's production development process and the use of simulation in optimizing the

robot cell. Therefore, a case study was conducted at LEAX Group AB, a manufacturing

industry. ABB RobotStudio was a tool used in this case study to simulate the existing

production system.

The empirical findings have shown the mapping of flow and process mapping of the robot cell

in LEAX Group. The empirical part highlights the building of the existing simulation model of

the robot cell. The challenges faced while simulating the model are also discussed. The analysis

part highlights the optimization of the robot cell and the integration of the simulation model

into the production development process. Finally, a conclusion has been drawn by answering

two research questions, and a recommendation is given. The conclusion highlights the

integration of simulation in the production development process and the process of optimizing

the robot cell.

Keywords: Manufacturing Industry, Production development system, Simulation,

RobotStudio.

ACKNOWLEDGEMENTS.

I would like to express my gratefulness to LEAX group AB in Köping for giving me an

opportunity to conduct this project. I would like to express my appreciation to Elias Häggström,

my company supervisor who endorsed me through the project by providing the required data

through study visit, meetings, and discussion. I would even like to thank the employees in the

LEAX Group AB in Latvia, who created a good environment and provided me with the

required data.

Finally, I would like to express my humble and sincere gratitude to my academic supervisor

Mikael Hedelind, and Victor Azamfire (PHD student) who guided and supported me with the

feedback to improve the quality of the research.

Lastly, I would like to convey my gratitude to my family and friends who stood beside me and

supported me during this thesis.

Contents

1. INTRODUCTION ....................................................................................................................................... 9

1.1. BACKGROUND........................................................................................................................................ 9 1.2. PROBLEM FORMULATION ..................................................................................................................... 10 1.3. AIM AND RESEARCH QUESTIONS .......................................................................................................... 10

2. RESEARCH METHOD ............................................................................................................................ 12

2.1. RESEARCH PROCESS ............................................................................................................................ 12 2.2. DATA COLLECTION .............................................................................................................................. 12 2.3. CASE STUDY ........................................................................................................................................ 14 2.4. SIMULATION STUDY ............................................................................................................................ 15 2.5. DATA ANALYSIS .................................................................................................................................. 17 2.6. QUALITY OF RESEARCH ....................................................................................................................... 18

3. THEORETIC FRAMEWORK ................................................................................................................. 19

3.1. INDUSTRY 4.0 ...................................................................................................................................... 19 3.2. MANUFACTURING INDUSTRY ............................................................................................................... 21 3.3. PRODUCTION SYSTEM DEVELOPMENT .................................................................................................. 21 3.4. INDUSTRIAL ROBOT ............................................................................................................................. 22 3.5. ROBOT SIMULATION ............................................................................................................................. 24 3.6. PROCESS MAPPING .............................................................................................................................. 28 3.6.1. PROCESS MAPPING TECHNIQUES ...................................................................................................... 28

4. EMPIRICAL FINDINGS .......................................................................................................................... 30

4.1. COMPANY BACKGROUND .................................................................................................................... 30 4.2. PRODUCTION DEVELOPMENT PROCESS ................................................................................................ 30 4.3. CURRENT SITUATION ........................................................................................................................... 31 4.4. CURRENT STATE ANALYSIS.................................................................................................................. 31 4.4.1. RING GEAR MECHANISM ................................................................................................................. 32 4.4.2. SUN GEAR MECHANISM .................................................................................................................. 36 4.5. FUTURE STATE OF RING GEAR MECHANISM. ....................................................................................... 39 4.5.1. RING GEAR MECHANISM (IMPROVEMENT) ...................................................................................... 39 4.6. SIMULATION OF ROBOT CELL .............................................................................................................. 43 4.7. SIMULATION ........................................................................................................................................ 44 4.8. CHALLENGES FACED WHILE SIMULATING THE ROBOT CELL ................................................................. 47

5. ANALYSIS ................................................................................................................................................. 48

5.1. OPTIMIZATION OF THE RING GEAR ...................................................................................................... 48 5.2. SIMULATION INTEGRATION .................................................................................................................. 51 5.2.1. CURRENT VS FUTURE STATE PRODUCTION SYSTEM DEVELOPMENT PROCESS AT LEAX. ................. 52 5.2.4. ONLINE VS OFFLINE PROGRAMMING ................................................................................................ 54

6. CONCLUSION ANS RECOMENDATION ............................................................................................ 55

7. REFERENCE ............................................................................................................................................. 57

8. APPENDICES ............................................................................................................................................ 63

List of Figures

Figure 1: Research Process ...................................................................................................... 12 Figure 2: Simulation Steps (Law, 2009) .................................................................................. 15

Figure 3: The nine pillars of Industry 4.0 (Rubmann, et al., 2015) ........ Error! Bookmark not

defined. Figure 4: Production System Development (Bruch & Bellgran, 2013) ................................... 22 Figure 5: Industrial Robot (ABB, 2020) .................................................................................. 22 Figure 6: IRB 6600-175/2.8 (ABB, 2020) ............................................................................... 23

Figure 7: RobotStudio simulation window (ABB, 2020) ........................................................ 25 Figure 8: Summary of the map ................................................................................................ 34 Figure 9: Mapping of flow ....................................................................................................... 35

Figure 10: Summary of the map .............................................................................................. 38 Figure 11: Summary of the map .............................................................................................. 41 Figure 12: Mapping of flow ..................................................................................................... 42 Figure 13: Conceptual Model .................................................................................................. 43

Figure 14: Simulation of Existing robot cell............................................................................ 44 Figure 15: Robot cell virtual environment ............................................................................... 45 Figure 16: Station logic ............................................................................................................ 46 Figure 17: Debugging of program ........................................................................................... 47

Figure 18: Utilization ............................................................................................................... 49 Figure 19: KPIs ........................................................................................................................ 50

Figure 20: One cycle of the robot (before and after) ............................................................... 50 Figure 21: Utilization ............................................................................................................... 51

Figure 21: Creating empty station in RobotStudio .................................................................. 63 Figure 22: Creating System from backup ................................................................................ 64

Figure 23: Creating new virtual controller............................................................................... 65 Figure 24: Adding existing virtual controller .......................................................................... 66 Figure 25: Smart component .................................................................................................... 66

Figure 26: Graphical representation of the task ....................................................................... 70 Figure 27: Graphical representation of utilization ................................................................... 71 Figure 28: Graphical representation of sun gear mechanism ................................................... 72

Figure 29: Graphical representation of utilization ................................................................... 73 Figure 30: Graphical representation of ring gear mechanism .................................................. 74

Figure 31: Graphical representation of utilization ................................................................... 75

List of Table

Table 1: Process of robot action in ring gear mechanism ........................................................ 32 Table 2: Overview of signal flow for ring gear ....................................................................... 33

Table 3: process of robot action in sun gear mechanism ......................................................... 36 Table 4: Overview of signal flow for sun gear ........................................................................ 37 Table 5: Ring gear process ....................................................................................................... 39 Table 6: overview of future state flow of information for ring gear ........................................ 40 Table 7: Online vs offline programming ................................................................................. 54

Table 8: Ring Gear Mechanism ............................................................................................... 68 Table 9: Operation time ........................................................................................................... 69

Table 10: Total time and Working time ................................................................................... 70 Table 11: Utilization ................................................................................................................ 70

Table 12: Sun Gear Mechanism ............................................................................................... 71 Table 13: Operation Time ........................................................................................................ 72 Table 14: Total Time and Working Time ................................................................................ 72 Table 15: Utilization ................................................................................................................ 72

Table 16: Ring Gear Mechanism ............................................................................................. 73 Table 17: Operation Time ........................................................................................................ 74 Table 18: Total time and Working Time ................................................................................. 74 Table 19: Utilization ................................................................................................................ 75

ABBREVIATIONS

CAD Computer Aided Design

IR Industrial Robot

I/O Input Output signal

IDT School of Innovation, Design and Engineering

Mdh Mälardalens University

OLP Offline Programming

PSD Production System Development

RS RobotStudio

TCP Tool Center Point

VSM Value Stream Mapping

1. INTRODUCTION

1.1. Background

The ever-changing dynamic manufacturing environment has caused disruptive changes in

various manufacturing perspectives, such as operations, management, human resources,

research and development, sustainability, technology, and digitization (Garcia, et al., 2018).

These changes are caused due to constantly emerging customer demands, varying market

opportunities, technological advancements, and the need for customer-specific customization

(Bruch & Rosio, 2015), creating a sense of competition among the industries which are under

severe pressure to increase their competitiveness (Andersson & Bellgran, 2015). To sustain

these disruptive changes and maintain competitiveness in the global market, companies need

to continually work towards improving their production performance, successfully developing

and implementing innovative production practices, and producing high-quality products in

shorter lead times at optimum costs (Bellgran & Safsten, 2009). For instance, manufacturing

companies failing to change and improve themselves can result in an increasing gap between

market requirements and production performance, leading to lost competitiveness, ultimately

losing market share and profitability (Andersson & Bellgran, 2015). The variations and

fluctuations in customer demands can be satisfied by introducing new technologies into

manufacturing systems, which brings in varying flexibility and agility character into production

(Kivikunnas, et al., 2010). The introduction of robotization into manufacturing systems has

fully automated the manual, repetitive, strenuous, and hazardous tasks and has performed at

high efficiency and safety. Literature shows that using robots in production lines has relatively

decreased 50% of production cost, productivity increase by 30%, and 85% utilization rate in

few manufacturing sectors (Golda, et al., 2018). Industry 4.0 concept also helps in increases

efficiency through digitalization (Stancioiu, 2017).

Industrial robots in manufacturing systems increase the productivity and quality of the process.

Robots replace humans in performing a wide range of repetitive tasks, which would otherwise

be hazardous, tedious, and time-consuming (Li & Zhang, 2011). A robot can perform a variant

process, which supports the flexibility of the cell's alternative configuration. The robot can

efficiently carry out various operations in an organized sequence, such as transportation,

material handling, and machining (Papakostas, et al., 2011). The robot's effective and efficient

programming helps achieve flexibility in the production process because the robot's

programming aids the robot to perform various tasks and motions. The robot's programming

has two approaches, which are classified as online and offline programming. Usually, the

programming of robot tasks is performed by teaching each position to the robot in the real work

cell, known as online programming (Li & Zhang, 2011)The involvement of real robots is not

necessary for an offline programming method. This approach benefits in reducing the robot

downtime, and the production line will not be affected (Jen Yap, et al., 2014).

The offline programming is a simulation software that helps in simulating the robot using a

Virtual Robot controller. The simulation tool lets users recreate the production environment

and program a robot and calculate the cycle time without a real robot. Accurate representation

of the real world is essential because most of the offline programming information sent to the

virtual robot is positional. Offline Programming is a software component that offers an

application-specific tool that helps generate a robot program. The user can load their CAD

drawing into a simulation tool with the help of a software component that can generate a robot

path by joining points in a 3D space or selecting the whole space and letting the simulation

software generate a path. The generated path inside the CAD-based programming is usually

customized to a specific application such as paint, weld, pick and place, etc. The robot

simulation is user-friendly, wherein a user can generate a robot path for a particular process

with minimum robot knowledge (Rossano, et al., 2013).

1.2. Problem formulation

As a result of rapid technology development around the world, automation of industries has

become prominent. Industry 4.0 provides tools essential for production efficiencies, changing

manufacturing relationships traditionally to achieve the global market requirement. Robot-

based production systems have been a vital part of industrial manufacturing strategy. However,

the increasing complexity of integration, demand fluctuations, and planning has created a

need for the manufacturing industry to be more flexible and agile. On the contrary, robots are

meant to perform standard repetitive works, making it difficult for the robotised production

lines to adjust to variations. The integration of simulation aims to increase efficiency, ramp-up

time, and optimize design, which could also be achieved by estimating an automated system's

critical characteristics before its physical existence (Laemmle & Gust, 2019). To simulate the

robot system, one can visualize the robot system in a realistic way, where the different scenarios

can be tested to optimize the work cell and increase productivity(Kumar & Phrommathed,

2006) in their research shows that data analysis, process mapping, and computer simulation

can be beneficial because a change in the information flow, system, procedure, etc., can be

analyzed without disturbing the entire system (Zlajpah, 2008). Therefore the thesis focus on

integrating simulation tool into the company production cell and display the use of simulation

when optimizing the robot cell. Two research questions have been framed and listed below to

fulfill the purpose of the thesis.

1.3. Aim and Research questions

The aim of the project is to investigate simulation as a tool to optimize a robot cell and to

incorporate simulation as part of production system development in a manufacturing company.

Thus, two research questions have been formulated to achieve the aim of the thesis.

RQ 1: How can simulation be used when optimizing the work of a robot in a

workstation?

RQ 2: How can simulation be integrated in a company’s production development

process?

1.4. Limitations

The thesis research area was to optimize the robot and integrate the simulation into company’s

production development process. The case study will only focus on a specific cell, where ring

gear and sun gear are machined individually. Further, only the ring gear mechanism is taken

into consideration for future studies. The simulation tool used in this case study is ABB

RobotStudio. The company uses ABB Robots in the cell for production. Hence, ABB

RobotStudio was selected to simulate the robot cell. The data collected for building the

simulation is through observation, interview, and documentation. All the collected data is

provided by LEAX GROUP.

2. RESEARCH METHOD

This part of the thesis represents the research methodology where the research process, the

method used to collect data is discussed. Further the process of data collection, case study,

simulation study, data analysis and quality of the data is presented.

2.1. Research Process

The research process for this thesis is started by formulating the problems by identifying the

broader field of the particular area for investigation and then the broader area has been divided

to understand the relationship between the sub-areas, followed by formulating two research

questions which help in achieving the aim of the research. Further, the required data is collected

through semi-structured interview with company personnel, observations, and company’s data

logs. Simultaneously, a literature for the relevant topic was reviewed by focusing on an area of

optimization of a robot using simulation as a tool and integration of the simulation for

production development. Later, the process mapping and simulation model concept was

developed using the data collected, and then the verification and validation of the simulation

model were conducted. Further, the empirical findings' result was analysed with the collected

research literature to achieve the research objective. The overview of the research process

highlighted in Figure 1.

Figure 1: Research Process

2.2. Data Collection

Data collection helps in drawing inferences and conclusion for the research study. The data

collection method is divided into two categories such as primary and secondary data (Kothari,

2004).

2.2.1. Primary Data

Data gathering by direct means or in-person by interview or observation is referred to as

primary data. The data acquired from prior work such as journal, thesis report etc. are referred

to as secondary data (Kothari, 2004). Due to improved technology, the availability of secondary

data has been more accessible, and the easy accessibility of the research publication database

helped the researcher attain secondary data. There are different source and technic used to

collect the required data for the case study (Bell, 2014). The type of technique used in this

thesis to collect data is through observation, documentation, and interview.

2.2.1.1. Observation method (Primary data)

Observation method is one of the most used method for data collection. Observation acts as a

scientific tool and a data collection method when performing a formulate research objective,

which is recorded and planned systematically. The observation was conducted to test and

manage validity and reliability (Kothari, 2004). A visit to the LEAX group in Rezekne aims

for data collection. A small plant tour and company presentation were given to the researcher

and allowed to examine and observe the process of Robot cell three during the first day of the

study visit. The study aims in understand the robot process flow from picking up raw material

until placing the finished part on to the conveyor.

2.2.1.2. Documentation (Primary data)

The collected data regarding documentation includes the CAD model of the robot cell, and the

data collected from an observation is also documented for further use. The collected CAD

model helped in building the virtual environment in the simulation software.

2.2.1.3. Interviews method (Primary data)

The data collection for the interview method involves the presentation of oral-verbal

questioning and response (Kothari, 2004). Personal interviews and telephonic interviews are

used to collect the required data in this case study and is explained below.

2.2.1.3.1. Personal interview (Primary data)

A personal interview is one such interview methods where the person must ask questions to

the other person in a face-to-face contact (Kothari, 2004). The interviews were conducted with

the Automation engineer in LEAX Rezekne during the study visit. On the second day of the

study visit, an interview was conducted to understand the robot cell's process. The questions

answer in the interview were written on a sheet of paper and then transferred into an excel

sheet. The data collected from the interview helped in building a robot process map. During

the interview, the Robot cell backup folder from the flex pendant was requested and collected.

2.2.1.3.2. Telephonic interview

A telephonic interview is a method of collecting information or data through the telephone.

This method is not widely used but plays a vital role in the industry, specifically in development

areas. This method benefits in flexibility, quick response, cheaper, etc (Kothari, 2004). During

the thesis work, many questions were raised, and the raised question was cleared by conducting

several skype call meetings. Most of the information regarding sensors were collected through

a skype call and documented to continue with the simulation model. Even the progress of the

project was also displayed through skype meetings.

2.2.2. Secondary Data

Even though the research is dependent on the primary data, it is essential to study secondary

data to understand the theory behind the research topic. The secondary data can be used to

analyse the obtained research work, which is collected from primary data. The researcher in

the research work uses both primary and secondary data. The secondary data is collected by

accessing the database through Mälardalens University (MDH) website.

2.2.2.1. Literature review (Secondary data)

The literature review deals with the three main topics: industrial robot, simulation, and

optimization in the manufacturing industry. The three main topics in the literature review aim

to determining the relationship between the research problem and the body of knowledge in

the specific field to understand the researcher's knowledge broadly, improve research

methodology, and clarify the research problem. The literature review explains the nine pillars

of industry 4.0 and then narrowed down to industrial robot, simulation, and offline

programming.

The extraction of scientific papers and the books used in a literature study was collected from

a database such as Scopus, Emerald Insight, IEEE explorer, DiVA Research Gate, and Google

Scholar. The keyword used to search the specific area of research are “Industry 4.0”,

“simulation,” “offline programming,” “Industrial robot,” “process mapping”, and “robot

studio.” Based on the keyword the paper was selected, further numerous filers were used to

collect the relevant papers. The literature search was limited to the past 30 years. First, the

abstract was thoroughly examined to know the paper outline and then a relevant article was

selected for the study. Further, the snowball technique was followed to collect more topic-

related articles. Finally, the scientific articles and the books on the relevant topic was collected.

2.3. Case Study

The case study is performed to understand the complexity of the situation in a better way. The

researcher can also maintain the holistic and significant properties of real-life events by

conducting case studies (Yin, 2013). A case study can be defined as a detailed, multifaceted

examination with a qualitative research method. The study can be conducted in detail and can

be trusted on a certain data source (Orum, et al., 1991). The case study approach implemented

for this thesis comprises of acquisition of data for simulating the robot cell, analysing the

simulation study, and drawing conclusion. This thesis employs a single case study, which

benefits in more profound observation of the study. The case study research includes multiple

data collection methods such as observation, interviews, questionnaires, and relevant

documents from multiple sources. The implementation of multiple data collection techniques

and sources manipulate the outcome of credibility and give different clarification (Graeme &

Nargiza, 2018). The required data for this thesis was collected by conducting interviews with

concerned company employees, documentation and by observation.

There are two main focal research methodologies in academic which includes qualitative, and

quantitative research methods. Qualitative research is a scientific method of observation to

collect a non-numerical data, whereas quantitative research is the observation of empirical

investigation through mathematical, statistical, and computational techniques (Giver, 2008)

(Seale, 2004). This research is a qualitative study because the process includes the procedure

and the emerging questions with data collection and analysing the data to present clarification

for the data (Creswell, 2013).

2.4. Simulation Study

The simulation was built based on the data collected through observation, interviews, and

documentation. The outcome of the process mapping discussed in section4 also supported in

building the simulation model. The process of simulation on this case study if followed by

several steps, which are listed in Figure 2.

Figure 2: Simulation Steps (Law, 2009)

Step-1: Formulating the problem.

The first and the most important stage of the simulation journey is problem formulation. The

team must state the problem and the problem formulated must be shared with the people who

is involved in the study (Law, 2009). The problem formulation has begun through meeting with

concerned manager at LEAX. In the meeting, the problem was explained, understood, and

discussed to lay foundation for scope, boundary conditions, expected goal, limitations of the

problem and outcomes from the research. After a meeting, a decision has been made for visiting

production facility of LEAX in Latvia for physical visualization and comparison from theory

to practicality for checking discrepancies in formulated problem.

Step-2: Collect information/ data and construct conceptual model

The building of the conceptual model and data collection are the two steps followed in this

process. Inputs are the factors used for building the model, and the output is the company's

required goal. Hence, the better way is to start the model in a simple way and through the

process of data collection complexity of the model increases gradually. (Banks, et al., 2010).

The conceptual model was built to visualize and understand the input and output steps while

building the model. The conceptual model was built after the first meeting in the company.

The data collection process and techniques for simulation model was described in section 2.2

and the conceptual model for the conducted simulation is shown in Figure 13

Step-3: Is the conceptual model valid?

In the simulation Journey this step is considered as a gate of checkpoint before moving to next

step. The purpose of this step is to validate the result from the previous step for ensuring no

errors by discussing with concerned people (Law, 2009). To ensure the good result of the

simulation model the researcher need to go back to the previous step, clear the errors and then

move forward to the next phase. Several meeting with the company employees was conducted

through skype call for the collection of required data.

Step-4: Program the model.

If both the formulated problem and the conceptual model is validated based on the data

collected. The next step is to model a cell using one of the simulation tools (Law, 2009). In this

thesis, ABB Robot Studio is used as a simulation tool to build the model.

Step-5: Is the program model Valid?

This process is considered as one of the checkpoints in the flow. After the completion of the

program, the next step is to check the program. The checking of the program is known as

verification. Validation is also taken place parallelly. Validation is the comparison of the

simulation program with the real word system (Shannon , 1998). The verification of the

simulation model was conducted by running the model for multiple times. This process is

conducted to know the robot performance and to verify the proper running of smart component

and sensor used. Later the model was validated by comparing the outcome of simulation model

with a video of robot test run.

Step-6: Design, Conduct and analyse Experiments.

When the above process is completed without any errors, the next step is to try the model with

the possible scenarios and comparing the result (Law, 2009). Further scenarios were not

simulated because any more optimization efforts would overburden the robot by pushing its

utilization to 100% making it redundant to changes and even the two machines in the robot cell

are running with least possible cycle time.

Step-7: Documentation and presenting the simulation result.

This is the last step in the simulation process where the result is documented and presented to

stakeholders. Animation, tables, chart, and pictures are different ways of presentation (Law,

2009). The documentation for simulation tool used in this case study is by taking a backup

folder form the simulation software, which is explained in appendices and the presentation of

the simulation result is done by taking a screen recording of the running simulation model.

2.5. Data Analysis

The data analysis comprises of data collection and their method in this case study. The

collection of data through observation, documentation and interviews for empirical findings

and the data collected for building the theoretical findings are analysed thoroughly and

categorised to answer the research question. The process of data analysis follows the steps of

data reduction, data display and verification/conclusion drawings which is suggested by (Miles

& Huberman, 1994).

2.5.1. Data Reduction

The process of simplifying, focusing, selecting, abstracting, and transforming the data is

referred to as data reduction. When the researcher decides the case, research question and the

data collection approach, the data reduction comes into picture (Miles & Huberman, 1994). In

this thesis the data collected from observation, documentation, and interview were used to serve

the research questions and to know the answer for the framed questions.

2.5.2. Data Display

Data display is the second most process of data analysis activities. Basically, display is an

organized, compressed information that end up in drawing conclusion and action. Displaying

of data helps researcher in understanding the scenario and even helps in proposing an improved

scenario (Miles & Huberman, 1994). In this case study the data of the process mapping was

displayed through Excel sheet and the simulation model result was displayed through screen

recording. The process mapping helped in understanding the process and suggesting the future

improved scenario and the simulation model helps in visualization of the process and testing

the different scenarios without disturbing the real production cell. The data displayed in the

empirical findings is compared with theoretical framework to drawing the conclusion.

2.5.3. Conclusion drawing and verification.

The third step of data analysis is conclusion drawing and verification. Conclusion drawing is

the process of considering the data collected and to review their suggestion for the questions

framed. Whereas, verification is reviewing the data for many times to cross verify their

conclusion (Miles & Huberman, 1994). Using the result of the collected data the research

question was answered and the conclusion was drawn. The simulation model was reverified

for several time to know whether the model is imitating the real world. The outcome of

simulation model and the process mapping of the robot cell was compared with the recorded

video of the robot cell production line.

2.6. Quality of Research

Based on validity and reliability, the quality of the research can be measured. The right

performance can be measured using the right tool for the right task, which becomes a vital

factor for validity. The similar result obtained by the repetitive test experiment is estimated in

reliability. Hence, validity and reliability play a crucial role in evaluating the quality of the

research (Yin, 2009). The research quality of the case study lies in earning credibility, where

the method can be useful to another robot cell in the manufacturing industry. In general, there

are four criteria to address the quality of the research, namely: constructed validity, external

validity, internal validity, and reliability.

One way of testing the validity is through constructed validity. It is used to ensure that the

actual tool is used for the proposed research, which can be reached by triangulation (Yin, 2009).

The thesis essential tool for collecting data is through observation, documentation, and

interview, which is explained in section 2.2. To improve the quality of the gathered data,

regular supervision was done by the MDH supervisor and several meetings was conducted for

the employee of the company. Triangulation is a method used in this thesis to compare and

verify the various collected data used in this research with reviewed literature.

External validity is used to compare the empirical findings with the existing research method

or case study in a similar body of knowledge (Yin, 2009). The thesis's external validity is

conducted by performing the empirical research and comparing it with the findings of the

literature review. The opportunity for a more detailed analysis of the data is possible because

the research study is based on one single case study.

Internal validity helps perform and build a relationship of an optimal triangulation pattern

between the collected data and aim to clarify and explain how to connect the data with each

other (Yves-Chantal, 2010). The interview conducted with the case company’s employee

highlights the collected data for process mapping and simulation of the robot cell. The collected

data was further strengthened by the researcher's onsite observation and examining the

recorded video of the robot cell process.

The explanation of every part of the research with transparency to fully understand the executed

study is known as reliability (Yin, 2009). The data collected in this thesis was transferred to an

excel spread sheet and was further used to build the simulation model. The developed process

mapping and the robot cell simulation is described in the chapter4. The verification and

validation of the model are tested by running the model for several times, and the outcome of

the model is compared with the real world. This helps in achieving the reliability of the model.

The model can even be used for testing the different scenarios in the robot cell.

3. THEORETIC FRAMEWORK

Here the theoretical knowledge is built based on the scientific article, books, and conference

paper. This section highlights Industry 4.0, the manufacturing industry, and its production

system development. The section highlights the introduction of the robot and ways of

programming the robot and concludes by explaining the process of mapping.

3.1. Industry 4.0

Due to the increase in competition between many manufacturing companies, it is necessary to

improve their production system's efficiency and effectiveness (Jayachitra & Prasad, 2010). A

company must change rapidly in digital technology since it is one of the crucial factors in

developing its production system (Roll, et al., 2019). The fourth industrial revolution helps

achieve digitalization in many manufacturing industrial areas such as production, planning, and

logistic. The concept of industry 4.0 aims in fulfilling the needs for a more flexible, reliable,

and efficient process of the industry using digital technology (Damiani et al., 2018). The

industrial revolution started with the mechanisation of the steam power and cotton gin, which

played an essential role during the first industrial revolution in the 1700s. During the second

industrial revolution, steel production, electricity, and petroleum bought many changes to

society. Electronics, telecommunications, and computers, the third Industrial revolution

(Chitiba, 2018). The concept of industry 4.0 or the fourth industrial revolution helps increase

the resource's efficiency through digitalization. Industry 4.0 is one of many industries' steps to

be more competitive and improve their efficiency (Stancioiu, 2017).

Nine Pillars of Industry 4.0

The nine pillars of Industry 4.0 is shown in Error! Reference source not found. and a short

explanation is given below. These 9 pillars of Industry 4.0 helps in optimizing, automating,

and integrating the flow of production cell, which enhances in increasing the efficiency of the

company (Vaidya, et al., 2018).

Figure 3: The nine pillars of Industry 4.0 (Rubmann, et al., 2015)

3.1.1. Big Data and Analysis

The collection and evaluation of various data from a different source which are stored in

different structure to gain value and even supports real-time decision making (Gerbert, et al.,

2015).

3.1.2. Autonomous Robot

Nowadays, tackling complex tasks has become more accessible by using robots in

manufacturing industries. Robots have become more flexible, cooperative, and autonomous.

They even create a safe environment and work with humans parallelly (Gerbert, et al., 2015).

3.1.3. Simulation

Simulation is one of the tool established to evaluate and predict the complex and sophisticated

performance of the system (Xu, et al., 2016). Simulation in production process not only reduce

the down time but also helps in optimizing the process in production system (Simons, et al.,

2017).

3.1.4. Horizontal and vertical integration

The two crucial mechanisms used in the industrial organization are self-optimization and

integration. The industry used horizontal and vertical integration as one of the strategies in their

business. The acquiring of the company from other companies with the same business is

referred to as horizontal integration. Whereas in vertical integration, the company take control

of production stages or product distribution (Magidel, et al., 2018).

3.1.5. The Industrial Internet of Things

In modern wireless technology, the Internet of Things (IoT) gains a broader interest. Things

such as sensors, actuators, radio frequency identification, mobile phone, etc. are capable of

interacting with each other and unite their fellow mates to achieve their common goal (Hozdić,

2015).

3.1.6. Cyber Physical system

Cyber means communication, computation, and control that are switched, discrete, and logical.

Whereas the physical refers to human-made and natural system that is controlled by a law of

physics and continuously operated. The management of an interconnected system between

computational ability and physical skills of transformative technology is known as a cyber-

physical system (Wang & Wang, 2016).

3.1.7. Additive Manufacturing

The technology of building the 3D object by adding layer by layer material is known as additive

manufacturing. Additive manufacturing is a method which is commonly used for producing a

small quantity of product which is complex and light weight in design. The transportation

distance and stock on hand can be reduced by a high-performance and reorganized additive

manufacturing system (Gerbert, et al., 2015).

3.1.8. Augmented Reality

A technology that connects reality with the virtual environment is known as augmented reality.

There are various services supported by augmented reality, such as providing repair instruction

through mobile devices and picking material in a warehouse. The decision making and work

procedure can be improved through augmented reality by providing real-time information for

workers (Gerbert, et al., 2015).

3.2. Manufacturing industry

The transformation of material and information into goods for the fulfilment of customer need

is known as manufacturing. Manufacturing industries are more focussed on transforming their

production process towards flexibility (Dimitris, et al., 2014). Environmental change and

customer demand have led the company to revise its production strategy. The variations and

fluctuations in customer demands can be met by introducing new technologies into

manufacturing systems (Kivikunnas, et al., 2010). The increase in the trend towards

decentralization and globalization of the manufacturing system entails exchanging and

collaboration of real-time information between the various production development nodes such

as setup planning, designing, machining, assembly, production scheduling, etc. This

collaboration can be achieved by employing industry 4.0 in the production system. Digital

technology helps in fulfilling the needs for more reliable, flexible, and optimized industrial

processes. Employing Industry 4.0 and implementing digital tools such as 3D modelling,

virtual reality, and simulation in the manufacturing companies can pave the way for developing

the production system and making different optimal decisions (Monostori, et al., 2016). These

digital tools play a vital role in improving the production processes in manufacturing

companies, which leads to achieve flexibility in the production process and reach the customer

demand (Monostori, et al., 2016).

3.3. Production System Development

The companies now a day’s urge to develop their production system. (Bellgran & Safsten,

n.d.)mentioned that the need for an increase in capacity, introduction of the new product or

change in a product, improving the work environment, etc. are some of the reasons for

production system development. The development of the production system process has been

divided into three steps: design, building, and evaluation. In the design phase, the relevant data

and information are collected and then the production's conceptual model is made and

evaluated. The design phase even identifies the process of improving the current state and

identifying the appropriate solution. Figure 4 indicates the different steps of the design phase.

After the design phase, the building of the production system and then followed by evaluation

of the implemented solution in the production system arise (Bruch & Bellgran, 2013).

Figure 4: Production System Development (Bruch & Bellgran, 2013)

3.4. Industrial Robot

The International Organization for Standardization defines industrial robot as “automatically

controlled, reprogrammable, multipurpose manipulator programmable in three or more axes”

(Manipulating Industrial Robots, n.d.). An industrial robot is a programmable robot which is

typically used in industrial application. Based on the application and the workspace the

configuration of the robot is differed. The customer requirement decides the design process of

the robot. The requirement includes workspace, application of the robot, reach, accuracy,

repeatability, payload, resolution, and degree of freedom. The robot can be equipped to perform

various applications such as material handling, assembly, welding, gluing, and painting (Reddy

& Brioso, 2011). The robot cell is equipped with a controller and flex pendant, where the

controlling decision and logic are made in the controller, and flex pendant is used to load and

operate the program. The industrial robot's physical construction includes several jointed links

with an electric motor used to activate the link. The basic joint is either a revolute or prismatic

joint. A revolute joint is typically a servo, whereas a prismatic joint is usually a pneumatic or

hydraulic system (Morten, et al., 2015) (Johan, 2007). Figure 5 indicates the six-axis industrial

robot.

Figure 5: Industrial Robot (ABB, 2020)

An industrial robot can be used to increase the productivity and quality of the production

process. The wide range of repetitive tasks, which is tedious, time-consuming, and dangerous

by humans, has been replaced by robots. Hence industrial robot is beneficial in achieving

flexibility and quality in the production system. However, the system's flexibility can only be

achieved through effective and efficient programming of the industrial robot. Online

programming is the traditional way of programming the robot, which is tedious and time

consumption (Li & Zhang, 2011). The other way of programming is through offline

programming, where a real robot's involvement is not necessary. This programming benefits

in risk reduction, increase productivity, and reduces robot downtime (Fang, et al., 2018).

3.4.1. IRB 6600

The robot used in this thesis is IRB 6600, which is a model in ABB’s robot family. The robot

comes in several versions, with different arm length and machining handling capacity. As the

robot can bend fully backward, the range of working is extended greatly, and the robot can be

well fitted into the dense production line. The typical application area of the robot is material

handling, machine tending, and spot welding. The motion and load of the machine can be

monitored using the built-in service information system. The robot's active safety features

protect the workers in the unlikely event of an accident and the robot itself. Collision detection

reduces the collision force significantly, especially when managing a high payload. The active

brake system controls the breaking while ensuring the robot maintains its path but allows rapid

recovery. The version, arm length, and machining handling capacity of the robot used in this

thesis are IRB 6600-175/2.8. Where IRB 6600 is a robot type, 175 is a handling capacity in

kilograms, and 2.8 is a reach of the robot in the meter. The other way of programming is offline

programming, where a real robot's involvement is not necessary. This programming benefits

in risk reduction, increase productivity and reduces robot downtime (ABB, 2020). Figure 6

shows the ABB Robot which is used in this thesis work.

Figure 6: IRB 6600-175/2.8 (ABB, 2020)

3.4.2. Online Programming

The guiding of the robot through the desired path using a teach pendant with the help of a

skilled operator is known as online programming. The online programming includes jogging

the robot, recording a specific point in the robot controller, and creating the movement

command by utilizing the recorded point. Programming the robot requires an operator

responsible for guiding the robot, orienting the robot in six-degree-of-freedom, and maintaining

the desired position. Despite using online programming is simple and widely used, it has

several drawbacks. The robot which is jogged using a teach pendant is not intuitive as the robot

system is usually defined by many coordinate systems. Jogging the robot accurately through

the desired position without any collision is very difficult and time-consuming, especially when

there is a complect workpiece geometry or in a complicated process. Besides, many drawbacks

testing for the generated program must be done for reachability and safety reasons before the

program is convincing. The robot program that is generated using the lead-through method is

not much flexible and reusable. The slight change in the process will demand repetition of the

process, which is tedious and time consumption. The quality of the created robot motion will

depend on the operator skill level (Pan, et al., 2012).

3.5. Robot simulation

Simulation is one of the nine pillars of Industry 4.0, which drives innovation and helps visualize

and forecast for producing flawless products, assembly lines, and real-world design systems,

which minimizes cost and maximizes output. (Banks, 1998) defines Simulation as the imitation

of the operation of a real-world process or system over time. A simulation is a powerful tool

that supports planning, design, analysis, and decision-making in different production

development areas. Simulation is widely used in all fields, especially in the manufacturing

field. Simulation, which is recognized as an essential tool in robotics, contributes to designing

new products, investigating the performance, and designing the process. The structural,

characteristics, and functional study of the robot system can be allowed in Simulation. The role

of the Simulation becomes more critical as the complexity of the system increases. Therefore,

the simulation tool can surely improve the system's design, development, and operation

(Zlajpah, 2008). This can be viewed through animation and graphical means in a real fashion

on the computer. Hence, an operation's manufacturing outcome can easily be observed without

utilizing any actual equipment, which results in cost-efficient and minimizing risk. Simulation

has various commercial software to provide solutions for facility layout planning involving

robot work cells. Specifically, for industrial robot simulation and visualization, various

commercial software has been developed. The simulation software helps check reachability,

safety issues, workspace, and other industrial robot aspects (Fauadi & Jumali, 2008). There are

many simulation concepts available in today's environment, including discreet event

simulation, continuous or geometric Simulation, etc.

The idea of discreet event simulation is based on facility layout planning. The overall picture

of simulating robot work cells can be achieved in discreet even Simulation, wherein the

industrial robot is considered an event inside the robot cell. The detailed information such as

path planning and robot programming is unavailable in this simulation type since the robot's

command cannot be automatically generated from this simulation result. Besides, the unique

constraints and ergonomic issues are not considered in this simulation environment

(Jahangirian, et al., 2010). Whereas in geometric or continuous Simulation, graphical

representation within the constant time interval is available. During the manufacturing process,

geometric Simulation is more appropriate for 3D visualization, collision detection, and offline

programming. However, robot manufacturers developed various simulation software (Yap, et

al., 2014). One such simulation software developed from the robot manufacturers in the ABB

RobotStudio, which is used as a simulation tool in this thesis work.

Figure 7: RobotStudio simulation window (ABB, 2020)

ABB RobotStudio is a commercial software application for simulating and offline

programming using the ABB robot and its application. This application consists of a virtual

robot model that with basic functionality. The application comes with virtual control for a robot

that resembles the robot’s real controller. Offline Programming (OLP) is a method where the

flexible robot program can be generated for complex robot paths. Offline Programming and

robot simulation are powerful tools used to save money and time for end-users when designing

the work cell. OLP method can be used to analyse and test various improvements planned to

increase the robotised production system's efficiency without hindering the operations. OLP

shifts the robot’s burden of programming in the workshop to a computer model environment,

where the robot can be jogged to its desired position using a simulated robot (Pan, et al.,2012).

Figure 7 indicates the robot's offline programming in a manufacturing cell, including the

simulation window, rapid language test programming window (a high-level programming

language used for controlling ABB industrial robot), signal control panel, and command

ribbon. The automatic path generation of the robot is possible using the 3D CAD model. The

virtual teach pendant can even be used to jog and record some robot's configuration and

position (Cristoiu & Nicolescu, 2017). By using graphical programming, the movement of the

robot can be created, editing, and debugging. This tool is widely used in many automation

industries by robot programmers and mechanical designers. It is even used in troubleshooting

and remote maintenance by taking a virtual copy of the live system and then moving it offline

to know the situation and study the system in depth. RobotStudio can verify the accessibility,

reach, and collision between each path can be examined (Connolly, 2009).

Creating a workstation in a virtual environment will help in following:

• Perceive conceptual design as a complex workplace.

• Possibility of interactive correction of the position of the workstations.

• Integration of CAD model into Robot Studio environment and recognition of edges and

points which define the exact target of the robot,

• If the simulation is based on the use of the virtual controller, the kinematic motion of the

robot can be visualized, which is equivalent to the real controller.

• Simulation of the material flow in the workplace.

• The whole simulation of the workplace can reduce the total cost of investment, as long as

it is possible to determine the optimal solution in terms of material flow and the overall

layout of particular workplaces (Holubek, et at.,2014).

3.5.1.1. Key Steps of offline programming and simulation

• 3D CAD model generation

The indispensable design phase for a production system is Computer-Aided Design (CAD).

Offline programming starts from creating a 3D CAD model of a workstation and workpiece

(Pan, et al., 2012). The subsequent integration of the various CAD data into the simulation

environment often creates different 3D rendering and compatibility issues. Hence, it is essential

to use various CAD converters where different CAD formats can be obtained, which allows

integration of CAD model into the simulation environment. The 3D CAD model, used in the

simulation environment, helps in testing reachability and visualization of the virtual robot cell

environment (Holubek1, et al., 2014).

• Trajectory planning

The robot configuration needs to be selected by considering issues such as reachability,

collision avoidance, minimising configuration transition, etc because the inverse kinematics of

industrial robots usually have multiple solutions in cartesian space. By utilizing the CAD

model, the path can be automatically generated in the RobotStudio. The cycle time can even

be reduced in RobotStudio by optimizing acceleration, speed, etc. OLP can even deal with the

issue such as reachability, transition, collision avoidance, etc (Marcos, et al., 2013).

• I/O signals

The process includes the necessary I/O (input and output) control signals adding to the work

cell's equipment. This process is used to achieve the interaction between the robot and the

external equipment. The animation effect in RobotStudio can be achieved through an effective

tool known as a smart component. I/O boards provide a common signal such as digital input,

analog input, digital output, analog output, and conveyor chain tracking. I/O board is a device

located in the field bus, used to connect the I/O signal of the smart component with the I/O

signal of the robot, where the input signal of the robot end in the output signal of the smart

component, and the output signal of the robot end is the input signal of the smart component

(Li & Liu, 2019).

• Synchronization

After generating a path and connecting every signal to the robot, synchronization of the

network of signals is necessary. The simulation and modelling part and the RAPID

programming and controller parts are the two separate parts in RobotStudio. In the simulation

and modelling part, the work object, target, path, signals, and the virtual environment are

created. After modelling the simulation, the created target, path, and signals are generated to

represent close to the actual system. The results are a generation on RAPID programming,

where the code can be used to run both the virtual and real robot (Persson & Norrman, 2018).

• Calibration

(Hayani, 2014) in his study, says that the offline program can be downloaded after satisfaction

from the robot's result and performance. The program can then be run using the real robot.

Before running the program, it is necessary to calibrate the program. The synchronization of

TCP and workpiece position can be seen in this procedure. He even mentioned that the robot's

TCP should be defined in the real robot and then brought back to the simulation environment

to modify the actual definition. This process helps in programming the robot in a perfect

position, and then the target and path using the CAD model can be generated in the virtual

environment. The created target and path generated in the virtual environment are brought back

into the real world and modified (Hayani, 2014).

3.5.2. Advantages and disadvantages of Offline Programming (OLP).

There are many advantages to the offline programming method.

• In offline programming, one does not require a real robot for the process. The downtime

of the robot can be reduced using OLP. The development of the robot's program can be

carried on in OLP rather than programming it on the production site. It is more flexible

for generating programs offline rather than using the jog-and-teach method.

• The program's integration is quicker by picking the required part of the program, and the

routine developed earlier can also be included easily in the new program.

• OLP method can be incorporated into the simulation, which results in the pre-checking

of the program. Thereby the movement of the robot can be confirmed. This even

improves the minimization of the errors and hence increases safety and productivity.

• It is even possible to optimize the workspace layout, and the robot task can also be

planned.

Disadvantages of OLP:

• As the OLP package is quite expensive, it is difficult to perform OLP in a small product

volume because it is difficult to justify economically.

• The associated errors when calibrating robot requires expensive software, measurement

hardware, and technical knowledge.

• When the robot is programmed offline, the next step is to test the program with the real

robot to verify the correctness of the work. Due to calibration error, it may lead to a crash

of a robot.

• Accurate modeling of the robot cell is required in the OLP method (Neto & Mendes, 2013).

3.6. Process Mapping

Process mapping is one of the tools which efficiently helps in modelling simulation. The

sequence of activities that are represented in a diagrammatic manner is referred to as a process

map. The map helps in visualizing all the processes in a sequence using graphical design (Heher

& Chen, 2017). Process mapping is a pair of analytical tools and process intervention, where

in-process intervention, the errors are reduced to improve the performance. Whereas in the

analytical tool, the task's analysis is done by a graphical diagram, which includes the

performance and activities of work. Process mapping helps in analysing and improving the

cycle time, workflow, cost, and job satisfaction (Kalman, 2002).

3.6.1. Process mapping Techniques

There are various techniques used for process mapping. The views and perspectives are

different in each process mapping. The techniques used for process mapping are listed below:

1. A Block diagram is one of the techniques which provides a quick summary of process

flow.

2. Decision American National Standard Institute (ANSI) flow chart, which alternates the

process steps and identifies the decision steps.

3. Functional flow, which demonstrates the relationship between the process among

departments.

4. The flowcharts show the physical flow of the activities called a String diagram or

geographical flowchart.

5. Quality Process Language Diagram, which shows the interaction of information with a

process.

6. Operational charts, where the value-added and non-value-added steps in the process are

shown (Kalman, 2002).

3.6.2. Value Stream Mapping

Value Stream Mapping (VSM) is one of the tools used in mapping the process. An enterprise

improvement tool that helps visualize the entire process flow, including information and

material flow, is called VSM. VSM can be defined as collecting all value and non-value-added

activities, which include the flow of material from raw material to end-users using the same

resources. VSM is conducted in three steps, starting with constructing the current state of the

process map and then followed by constructing the future state VSM and developing the action

plan (Singh, et al.,2011). VSM management involves measuring, understanding, and

improving the material and information flow and the collaboration of all tasks. This helps

improve the companies' cost, quality, and service of the product where the company can stay

competitive. It is one of the valuable tools that help understand the current state of the process

and improve the process (Dal Forn, et al.,2014). There are many advantages of VSM, such as

displaying the product and information flow, information related inventory level, etc.

Unfortunately, many disadvantages can also occur when using VSM because it is a Paper and

pencil base technique and hence will be a limit in accuracy level. If the production system is

complex, then there will be a failure in the mapping of flow (Braglia, et al.,2006).

4. EMPIRICAL FINDINGS

4.1. Company Background

The case company for this thesis work is LEAX Group AB, a manufacturing company. Lennart

Berggren and Axel Seger founded LEAX Group in the year 1982. LEAX Group is one of the

fastest-growing, privately-owned business groups with its origin in Köping, Sweden. From the

beginning of the 1990s, the company has grown through acquisition and organic growth. There

is a growth of 35% every year, and its turnover is more than SEK1,5 billion. About 1200

employees are working in the company. The case company's customers are mainly within the

commercial vehicles, Mining and construction, Agriculture, General Industry, and Automotive

Industry. Today the company has extended his territory holding five factories in Sweden, two

in Latvia, one in Germany, one in Hungary, one in Brazil, and one in China. The company's

vast territory enables them to meet their customer demand and needs of both closeness and low

cost. The company's mission is to produce advanced components and subsystems for

demanding customers. They provide flexible machining, assembly, and testing of subsystems

and services in Management consultancy and measuring Technique. Its central vision is to

become the world's most admired supplier of advanced machining and industrialization.

LEAX Group uses automation with an industrial robot as a standard way of working to secure

quality and production output. Automation also reduces ergonomy, workload and free up time

for machine operators. The company used in this case study is located in Rezekne. In LEAX

Rezekne, a new production area of 2000 meter square have been built during 2019 to support

one Swedish automotive customer to produce gear components for a new electrical

transmission. About 30 machines, together with automation equipment, are installed for this

purpose. Three-part numbers are produced in the cell, and the yearly volume capacity is above

600,000 gears components.

4.2. Production Development Process

At the case company, a production development process starts by planning a development

project. Then the right persons to perform the development process are roped in to form a cross-

functional team.

The next step is defining operation performance and the machines involved in operating.

Further, the team will go deeper into an investigation for calculating the cycle time for each

part and then see if machines are available in the company. In parallel, the team looks for a

suitable combination of the machines to perform tasks if the automation occurs. Then the team

decides to use one robot for two or three machines in the cell. If there is a time for more

automation, the company then goes with more machines as possible in the robot cell.

Then the team finalises the level of automation needed in the cell, the number of robots needed

to perform the task, the machine required for operation, and the number of people required to

operate the robot cell. The team then looks at the robot's suitable size to perform the task and

then investigate whether the specified robot is available in the company. If not, the company

invests the money in the specific robot to perform operations. The way of lift, reach of the

robot, and handling the robot's weight are also considered while investing in the robot.

Depending on the cycle time and the machine's loading style, the team decides whether to use

a single gripper or double gripper for the robot to perform the task. For example, the case study

performed in the robot cell has Liebherr machine and Haas machine. In the Liebherr machine,

there is a ring loader to load the part, whereas in the Haas machine, the parts in placed directly

on the fixture, which requires more time and demand in the double gripper.

After finalizing the gripper and machines, designing the robot cell's layout in CAD software is

conducted. The top view of the design highlights the reach of the robot. The detailed gripper is

assigned, and the team waits until the gripper is ready. After the gripper is ready, the machine

and the robot are placed in the real world's desired position.

The next step is programming the robot, where the team program the robot but not in a detailed

manner. The movement of the robot is not programmed in the real world. The code for

programming the robot is done RAPID using RobotStudio software by the company. After

generating the program for the robot, it is tested on the real robot. The robot program and the

communication between the robot and the machine are tested. Several weeks are required to

see the proper working of the robot cell. After finalizing the proper working of the robot cell,

the product development process is completed.

4.3. Current situation

One of the robot cells in Rezekne is handling two types of parts known as ring gear and sun

gear, machined individually. The robot cell layout includes one ABB robot with a double

gripper, two metalworking machines, one orientation stand, and one conveyor. The cycle time

in the metalworking machine is short, and robot handling is the limitation for cell output.

Capacity in the robot cell is one of the bottlenecks in the production line and must be utilized

optimally. Production engineers at LEAX often focus on cycle time in metalworking machines.

The robot's cycle time is often secondary due to a lack of experience, routines, and tools to

optimize this.

Hence the purpose of this case study is to analyse a robot cell in the production line and use

process mapping and simulation as a tool to optimize the existing robot cell. In this robot cell,

two types of the part are known as the ring gear, and the sun gear produced individually. The

process mapping for the robot cell was conducted, and the simulation of the robot cell was

performed. The case study focuses on the integration of the simulation in the company’s

production system development.

4.4. Current State analysis

Process mapping:

For mapping the process, the primary input is to understand and collect relevant data of the

process's actual steps. At LEAX, the robot cell's current process is understood through a semi-

structured interview with the operator in charge of the robot cell. During the interview, the

operator has explained step by step process. It is a cyclic process repeating throughout one type

of part. The parts machined in this robot cell are ring gear and sun gear. Additional to the

interview, the process has been recorded, and individual times for the steps have been clocked

using a stopwatch for obtaining time data. The recorded videos were used to visualize the steps

and compare the process map with the actual working process.

4.4.1. Ring Gear Mechanism

The ring gear machining involves several steps. These steps are divided into two parts. The

first part consists of first initial steps which will be performed during the start of the new ring

gear machining and involves setting up of the parts in the CNC machining. These steps are

performed only at the start of ring gear machining batch and are not repeated. The latter steps

are cyclic which are repeated all over the batch and listed in Table 1. Relevant times are noted

through clocking time using stopwatch and compared with recorded videos.

The process for mapping the robot cell was conducted by following the below steps.

• Identifying the customer.

Here in cell 3 the customer was an operator, who was loading the raw part to conveyor and

unloading the finished part from the conveyor.

• Define valuable output.

The output from the cell 3 is the machined part which is kept on to the conveyor.

• Define input.

The input is the raw part which is needed to be machined in the cell. The input part on the

conveyor in semi-finished ring gear.

• Describe the process.

The below Table 1 highlights the operation and the task time of the robot used in ring gear

mechanism.

Table 1: Process of robot action in ring gear mechanism

Steps Operation

Task Time

(sec)

1 Pick the part from conveyor with Gripper-2 0

2 Goes to the orient stand to orient 6

3 Process of orientation stand 5

4 Towards waiting position 2

5 Waiting Time of robot 34

6 Pick the part from machine-1 with Gripper-1 4

7 Place the part in Machine-1(Haas) 8

8 Place the finished part on to the Conveyor with Gripper-1 6

9 Pick the part from conveyor with Gripper-2 5







16 Pick the part from Machine-2 with Gripper-2 8

17 Place the part in Machine-2 with Gripper-1 5

18 Goes to the orient stand and flip the part 10







25 Pick the part from Machine-2 with Gripper-2 8

26 Place the part in Machine-2 with Gripper-1 5

27 Goes to the orient stand and flip the part 10







34 Place the finished part on to the Conveyor with Gripper-1 6

• Document flow of information.

The below Table 2 indicates the information of the signals used to interact between the robot

and the surroundings in robot cell.

Table 2: Overview of signal flow for ring gear

Information Going from Going to Format How it is used

Pick part

from

conveyor

Conveyor Robot Digital

I/O

When there is a

"conveyorpartunload" signal from

the conveyor, and gripper1status

with empty part. It is time to pick

the part from the conveyor

Place the

part to

Liebherr

Machine

Liebherr

Machine Robot

Digital

I/O

When there is a "Liebherr part load"

and "Liebherr door open" sign from

the Liebherr machine and the

gripper1status with raw part. It is

time to place the part to Liebherr

machine.

Pick the part

from

LiebherrMac

hine

Liebherr

Machine Robot

Digital

I/O

When there is a

"Liebherrpartunload" and

"liebherrdooropen" signal from the

Liebherr machine and the

gripper2status with empty part. Then

it is time to pick the part from the

Liebherr machine.

Flipping and

orientation of

the part

robot Orientati

on Stand

Digital

I/O

When there is a Liebherr machined

part on Gripper2. Then the next

cycle of the robot is to flip the part

and orient the part for further

operation.

Place the

part to Haas

Machine

Haas

Machine Robot

Digital

I/O

When there is a "HaasPart load" and

"HaasDoorOpen" sign from the

Haas machine and the gripper2status

with oriented part. Then it is time to

place the part to the Haas machine

for machining.

Pick the part

from Haas

Machine

Haas

Machine Robot

Digital

I/O

when there is a "HaasPartUnload"

and "HaasDoorOpen" sign from the

Haas Machine and Gripper1status

which empty part and Gripper2status

with Oriented part. Then it is time to

pick the part from Haas Machine.

Place the

finished part

to the

conveyor


I/O

When there is a

"ConveyorPartLoad" sign and the

Gripper1status with

HaasMachinedPart. Then it is time

for the robot to Place the machined

part on to the conveyor.

• Target Cell KPIs

Target cell KPIs are the number of parts produced. In this cell for the ring gear mechanism,

approximately 28 parts are produced in one hour.

• Summarize the map

Figure 8 indicates the summary of the process mapping for the ring gear mechanism.

Figure 8: Summary of the map

• Mapping of flow (Ring Gear Mechanism)

Figure 9 indicates the ring gear mechanism mapping of flow. The Haas machine is used for

machining ring gear twice (Chamfering and brushing) and the Liebherr machine is used once

(Teeth hobbing) in one complete cycle. Therefore, in the mapping of flow it is mentioned that

Gripper= Haas part 2 Or Haas Part 1. This means the gripper is holding first machined ring

gear (Brushing) from the Haas or second machined ring gear (Chamfering) from the Haas.

After brushing, hobbing and chamfering, the finished part is kept on the conveyor. The oriented

part 1 and 2 is mentioned in the mapping of flow. This means the same ring gear part is oriented

for two times in different time interval.

Figure 9: Mapping of flow

The robot cell consists of one orientation stand, conveyor, and two machining equipment

known as Hass and Liebherr. In the production process, robot will pick one ring gear and orient

it and places in Haas machine. In Haas machine, the ring gear will be placed two times. At first,

the brushing is done on the ring gear to take off the left-over chips on the ring gear machined

in the previous machining cell, and then the chamfering is done on the same ring gear. The

Liebherr machine is used only for teeth hobbing operation on the ring gear. The current

machining process has drawbacks. Excessive chip formation on the ring gear requires an

additional non-value-added process of removing these chip formations. To overcome this

drawback further improvement is proposed with two scenarios, which are listed below.

Scenario 1: Use special tools in the Haas machine to minimize the cycle time. By reducing the

cycle time of the Haas machine, the waiting time of the robot can be reduced.

Scenario 2: By implementing the special mechanism to clear the chips formed on the ring gear

before entering the robot cell.

4.4.2. Sun Gear Mechanism

The process mapping was conducted by following the below steps:


Here in cell 3 the customer was an operator, who was loading the raw part to conveyor and




• Define input.

The input is the raw part which is needed to be machined in the cell 3. Semifinished sun gear

will be placed on the conveyor for further machining.


Table 3 indicates the operation and the task time of the robot.

Table 3: process of robot action in sun gear mechanism

Steps Operations Task Time (sec)

1 Pick the part from conveyor from Gripper-2 0

2 Picks the parts from Gripper-1 from Machine-2 7

3 Puts the part in Machine-2(Liebherr) 5

4 Goes near the orientation stand and flip the part 10

5 Goes to the orientation stand and orient the part 7

6 Picks the part from Machine-1 in Gripper-2 10

7 Place the part in Machine-1 from Gripper-1 7

8 Place the finished part to conveyor 6

• Document flow of information.

The below Table 5 highlights the information of signal flow between the robot and

surroundings for sun gear mechanism.

Table 4: Overview of signal flow for sun gear


Pick part from

conveyor Conveyor Robot Digital I/O

when there is a

"conveyorpartunload" signal from

the conveyor, and gripper1status

with empty part. It is time to pick

the part from the conveyor

Place the part

to Liebherr

Machine

Liebherr

Machine Robot Digital I/O

when there is a "Liebherr part

load" and "Liebherr door open"

sign from the Liebherr machine

and the gripper1status with raw

part. It is time to place the part to

Liebherr machine.

Pick the part

from Liebherr

Machine

Liebherr


When there is a


"liebherrdooropen" signal from the

Liebherr machine and the

gripper2status with empty part.

Then it is time to pick the part

from the Liebherr machine.

Flipping and

orientation of

the part

robot Orientati

on Stand Digital I/O

When there is a Liebherr machined

part on Gripper2. Then the next

cycle of the robot is to flip the part

and orient the part for further

operation.

Place the part

to Haas

Machine

Haas


When there is a "HaasPart load"

and "HaasDoorOpen" sign from

the Haas machine and the

gripper2status with oriented part.

Then it is time to place the part to

the Haas machine for machining.

Pick the part

from Haas

Machine

Haas


when there is a "HaasPartUnload"


the Haas Machine and

Gripper1status which empty part

and Gripper2status with Oriented

part. Then it is time to pick the part

from Haas Machine.

Place the

finished part

to the

conveyor

Conveyor Robot Digital I/O

When there is a


Gripper1status with

HaasMachinedPart. Then it is time

for the robot to Place the machined

part on to the conveyor.


Target cell KPIs are the number of parts produced. In this cell for the Sun gear mechanism,

approximately 70 parts are produced per hour.

• Summarize the map.

Figure 10 indicates the summary of the process mapping for sun gear mechanism.


4.5. Future state of Ring Gear Mechanism.

After analysing the two scenarios from the current state of ring gear mechanism. The second

scenario for implementing the special mechanism to clear the chips formed on the ring gear

before entering the robot cell was selected. The result of implementing the scenario 2 is

explained below.

4.5.1. Ring Gear Mechanism (Improvement)

The process mapping was conducted by following the below steps.


Here in robot cell the customer was an operator, who was loading the raw part to conveyor and




• Define input.

The input is the raw part which is needed to be machined in the cell 3. Semi-finished ring gear

will be placed on the conveyor for further machining.


Table 5 highlights the operation and the task time of the robot.

Table 5: Ring gear process

Steps Operations Task Time

1 Pick the part from conveyor from Gripper-1 0

2 Picks the parts from Gripper-2 from Machine-2 5

3 Puts the part in Machine-2(Liebherr) from G1 6

4 Goes near the orientation stand and flip the part 9

5 Goes to the orientation stand and orient the part 11

6 Towards Waiting Position 2

7 Waiting time of Robot 16

8 Picks the part from Machine-1 in Gripper-1 3

9 Place the part in Machine-1 from Gripper2 9

10 Place the finished part to conveyor from G1 5

• Documentation flow of information

Table 6 indicates the signal flow in robot cell between robot and surroundings.

Table 6: overview of future state flow of information for ring gear


Pick part from

conveyor Conveyor Robot

Digital

I/O

when there is a

"conveyorpartunload" signal

from the conveyor, and

gripper1status with empty part. It

is time to pick the part from the

conveyor

Place the part to

Liebherr Machine

Liebherr

Machine Robot

Digital

I/O

when there is a "Liebherr part

load" and "Liebherr door open"

sign from the Liebherr machine

and the gripper1status with raw

part. It is time to place the part to

Liebherr machine.

Pick the part from

LiebherrMachine

Liebherr

Machine Robot

Digital

I/O

When there is a


"liebherrdooropen" signal from

the Liebherr machine and the

gripper2status with empty part.

Then it is time to pick the part

from the Liebherr machine.

Flipping and

orientation of the

part

robot Orientation

Stand

Digital

I/O

When there is a Liebherr

machined part on Gripper2. Then

the next cycle of the robot is to

flip the part and orient the part for

further operation.

Place the part to

Haas Machine

Haas

Machine Robot

Digital

I/O

When there is a "HaasPart load"


the Haas machine and the

gripper2status with oriented part.

Then it is time to place the part to

the Haas machine for machining.

Pick the part from

Haas Machine

Haas

Machine Robot

Digital

I/O

when there is a

"HaasPartUnload" and

"HaasDoorOpen" sign from the

Haas Machine and Gripper1status

which empty part and

Gripper2status with Oriented

part. Then it is time to pick the

part from Haas Machine.

Place the finished

part to the

conveyor


I/O

When there is a


Gripper1status with

HaasMachinedPart. Then it is

time for the robot to Place the

machined part on to the

conveyor.


Target cell KPIs are the number of parts produced. In this cell for the Sun gear mechanism,

approximately 58 parts are produced per hour.

• Summarize the map

The below Figure 11 indicates the summary of the process mapping for the ring gear

mechanism.


• Mapping of flows

Figure 12 indicates the mapping of flow for the ring gear mechanism.

Figure 12: Mapping of flow

No

4.6. Simulation of Robot Cell

4.6.1. Current situation in the company

At present, the case company is using online programming to program the robot in the cell. In

the case of operational changes or reprogramming of the robot, the company needs to stop the

production line, and the changes to the robot movement are made. The company uses ABB

RobotStudio software to visualize the program in a RAPID tab and write the code for the robot.

Therefore, the simulation was conducted to know the importance of RobotStudio and integrate

it into the production system development. The simulation model's current state was done to

imitate the real environment of the production system into a virtual simulation model. This

simulation was conducted in order to visualize the optimized version of the ring gear

mechanism. The simulated robot cell can even be used in operational changes or

reprogramming the robot without any stoppage of the production line. If the management

accepts the improved model, it can be implemented into reality.

4.6.2. Conceptual Model

The conceptual model helps in visualizing and understanding the input and output steps while

simulating the model. Therefore, it is better to start the model in a simple way and as the process

continues the complexity of the model increases. Hence it is necessary to construct the

conceptual model before simulation of the process to obtain a brief idea about the total process

and provide a foundation for simulation model. Figure 13 indicates the conceptual model,

which gives an overview of the simulation model. The model input indicated the necessity for

simulating model. The model content indicates the demand need to be reached by the company,

the model output indicates the result need to be achieved after simulation and the assumptions

part indicates the excluded data while simulating the model.

Figure 13: Conceptual Model

4.6.3. Data collection

Before constructing the simulation model, it is necessary to collect certain data required to

simulate a model. A backup folder is a set of basic instructions of the robot which is present in

the software. The first thing is to collect a backup folder from the robot cell. The backup folder

was collected with the help of one of the Engineers in LEAX Rezekne. To build a virtual

environment, it is necessary to collect a 3D CAD model. The company shared all the CAD

model, which is used in the simulation. The model contains a double gripper, two metalworking

machines, a fence, one orientation stand, and one conveyor. Some parts of the CAD model on

the orientation stand were missed from the company. Therefore, it was used just for

visualization purpose.

4.7. Simulation

After collection of the data the next step is to simulate a robot cell. The steps simulation for

the existing robot cell is listed in Figure 14 and explained further in details.

Figure 14: Simulation of Existing robot cell

4.7.1. Creating system from backup

The backup folder which is collected from the company was to create a system. The procedure

for creating a system from backup is listed in appendices. The backup folder contains all the

RAPID program and the I/O signal used in the robot cell. After creating a backup folder, all

the TCP, wojb, target, and path used in the robot cell will be transferred to RobotStudio.

4.7.2. Loading of Robot and CAD model

The robot which is used in the robot cell is a 6-axis robot and the specification of the robot is

listed below:

Type: IRB 6600-175/2.8

Net Weight: 1780KG

After loading the robot, the next step is to load a CAD model shared by the company. The

STEP file of the CAD model was converted to an SAT file using a CAD exchanger tool because

RobotStudio supports the SAT file. The virtual environment's main aim is to look for a better

configuration of the layout and provide the reach and kinematics of the robot to execute the

given task. Figure 15 which includes the conveyor, fence, double gripper, two metalworking

machines (Haas and Liebherr machine), and orientation stand, indicate the virtual environment

of the Robot cell 3. After importing the CAD model to RobotStudio, it is essential to place the

robot on a designed steel base in the right position. For the right placement of the robot, it is

necessary to analyse the kinematics and reach of the robot. Then the physical behaviour of the

real environment like opening and closing of the door etc are created in the simulation software.

Figure 15: Robot cell virtual environment

4.7.3. Signals and sensor

Various signals and sensors were added into the robot cell. A two-line sensor was added to the

conveyor to convey the loaded and unloaded sign for the robot. Some line sensor was added

on to the two-metal machine to deliver the message of load and unload the part and open and

close the robot's door. Pose mover, such as gripper open and close and door open and close,

was added. The attach and detach command were added in the RobotStudio to pick and place

the machining part using a gripper. Then the sensor, signal logic, and pose mover were

connected to the desired position. This support in reaching the real-world situation in a virtual

environment.

4.7.4. Connections

All the I/O signal used in the real world is displayed in the system one, as shown in Figure 16.

I\O signal is used to understand the interaction between the robot and the external equipment.

The output signal of the system supports in triggering the input signal. Based on the collected

data and the process mapping, the signal's connection to the desired path was made. After

connecting all the smart components to station logic, the next step is to run the program and

see whether the programming is running as it is in the real world.

Figure 16: Station logic

4.7.5. Simulation validation

A screen recording of the running robot cell in the simulation tool was taken. Then the

recording of the simulation tool was compared with the video of the real robot cell. The time

of each robot movement was compared with the process mapping, and the simulation was

validated.

4.8. Challenges faced while simulating the robot cell

There are many challenges faced while simulating the model which are listed below.

4.8.1. CAD Model

After creating system from backup and loading the robot and CAD model. The positioning of

the robot in the robot studio software was not matching to actual robot position. Hence, the

model was adjusted to the position of the robot target and continued the process. As this was a

backup system the CAD model was adjusted accordingly.

4.8.2. Sensor attachment

The biggest challenge faced while simulating the robot cell was to synchronize each signal

used in the robot cell. The signals were attached properly by collected data and by debugging

the program. After many trials, the sensors and signals were properly equipped. The sensor and

signal attachment will help in running the program smoother in a simulation model.

4.8.3. Debugging of program

To gain a more profound knowledge of robot working procedures and collect information about

the I/O signals debugging process was conducted. The process involves creation of “toggle

break point”. This helps in stopping the programming at a particular break point. After stopping

the program, the next step was to use the step out command. The step out command helps in

reading the program line by line and understand the rapid programming and easily highlight

possible programming errors. This debugging helped in know the robot process and helped in

understanding the I/O signals. Figure 17 the debugging process steps used in this thesis work.

Figure 17: Debugging of program

5. ANALYSIS

In this chapter, an analysis of empirical findings is made and compared with the theoretical

framework. The two framed research questions, such as optimizing the robot cell using

simulation as a tool and integration of the simulation into the company’s production system

development, are explained.

5.1. Optimization of the Ring Gear

Based on the literature review, it is known that the process mapping is an analytical tool that

helps in reducing the error, improving the performance and improve the workflow (Kalman,

2002). The process mapping in the empirical finding helps in giving out different scenarios to

improve the performance and even helped in building the simulation model. (Heher & Chen,

2017) says that process mapping is one of the tools which efficiently helps in modelling

simulation.

The empirical result obtained from the process mapping of the current state and the future states

is explained below. The comparison of the obtained result from the current and future state is

explained below.

5.1.1. Ring Gear Mechanism

As seen in the empirical findings, the sequence of robot task mapping helped in understand the

process, and the decision used in the process. The empirical findings highlight the process

mapping of the robot cell of the ring gear mechanism. Figure 8 shows the summary of the map,

where the KPIs of the ring gear mechanism are 28 parts per hour. Figure 9 indicates the

mapping of ring gear mechanism flow. The mapping of flow aided to understand the process

and guided in analysing the further improvement of the process. By analysing the mapping of

flow, two scenarios for further improvement is presented.

5.1.2. Results

From the standard operating procedure of ring gear, it is known that the Haas machine's

performance directly impacts the robot's waiting time. Hence a first scenario was presented to

use a special tool in the Haas machine to reduce the cycle time. Reduction of cycle time in the

Haas machine will directly impact the robot performance and decrease the robot's waiting time

and impact KPIs.

• Scenario 1: use special tools in the machine to minimize the cycle time or optimize the

cycle time of the machine.

The first scenario was to reduce the machine's cycle time by using the specific tool in the

machines or optimizing cycle time. The implementation of the specific tool in the Haas

machine decreases the cycle time of the machine and reduces the robot waiting time. The

further optimization of the cycle time is not possible because the case company mentioned that

the machine is running at its optimal cycle time. This scenario can be implemented, but the

impact of the outcome is less.

• Scenario 2: By implementing the special mechanism to clear the chips formed on the ring

gear before entering the robot cell.

From the mapping of flow in the ring gear, it is observed that the ring gear is machined twice

in the Haas machine. By interviewing the company employee, it is known that the Haas

machine is used for chamfering and cleaning the chips formed from the previous robot cell.

Therefore, it was clear that a nonvalue added work is performed in the Haas machine by

cleaning the chips. The above process impacted the robot performance and affects the KPIs.

The second scenario presented help to overcome these issues.

Further to gain the scenario's usefulness, the simulation can be conducted for programming the

robot with the help of the cross-functional team by considering the boundary condition of the

robot. The reprogramming of the robot based on the scenario is possible through ABB

RobotStudio. Using simulation while programming the robot has a huge advantage. The

company is now using online programming to reprogram the robot. The pros and cons of using

online and offline programming is listed in section5.2.4. The reprogramming of the robot by

considering the scenario through the ABB RobotStudio was not possible in this case study

because the company already programmed the robot considering scenario 2. Hence the

simulation was built just to imitate the real robot cell. The process of building the simulation

is explained in section 4.6. The below result was extracted and calculated from the process

mapping of the current and future state of the robot cell. Further the company can use the

constructed simulation model for reprogramming the robot and trying out different scenarios

and layout planning.

The implementation of specific mechanism for clearing the chips before the robot cell has a

significant impact on the robot and the cell's overall performance. Implementing the specific

mechanism has decrease the cycle of the robot from 31 steps to 10 steps and is shown in Figure

20. The robot cell's KPIs has also increased from 28 parts per hour to 58 parts per hour after

implementing a specific mechanism and is shown in Figure 19. This scenario has a huge impact

on KPIs, and the utilization of the machine and the robot is presented in Figure 18: Utilization.

The utilization of the robot has slightly decreased, but the utilization of the two machines has

increased. Further, the robot can be optimized with the help of a simulation tool by trying

different scenarios such as speed, configuration, movement, etc.

Figure 18: Utilization

83.82

51.1

79.7775.7584.84

95.45

0

20

40

60

80

100

120

Robot Haas Machine Liebherr Machine

Utilization

Before Optimization After Optimization

Figure 19: KPIs

Figure 20: One cycle of the robot (before and after)

28

58

0

10

20

30

40

50

60

70

Before Optimization After Optimization

Par

ts p

er H

ou

rKPIs

Steps Operations

Cycle

Time

1

Pick the part from conveyor

from Gripper-1 0

2

Picks the parts from

Gripper-2 from Machine-2 5

3

Puts the part in Machine-

2(Liebherr) from G1 6

4

Goes near the orientation

stand and flip the part 9

5

Goes to the orientation

stand and orient the part 11

6 Towards Waiting Position 2

7 Waiting time of Robot 16

8

Picks the part from

Machine-1 in Gripper-1 3

9

Place the part in Machine-1

from Gripper2 9

10

Place the finished part to

conveyor from G1 5

5.1.3. Sun Gear mechanism

The sun gear mechanism in robot cell was visualised with the help of the recorded video

provided by the company. The movement of the robot was written down, and the cycle time of

each moment was noted down using the recorded video, which is seen in Table 3. The

documented flow of information of the sun gear mechanism was then collected by conducting

skype call meetings. With the help of collected data the utilization of the robot and the machine

was calculated.

Figure 21: Utilization

Figure 21 indicates the utilization of the two machine and robot. The further simulation process

is not conducted for sun gear mechanism due to time restriction.

5.2. Simulation Integration

A rapid change in the company towards digital technology is one of the crucial factors in

developing production system (Roll, et al., 2019). The concept of industry 4.0 aims in fulfilling

the needs for reliable, flexible, and efficient process of the industry using digital technology

(Damiani et al., 2018). Simulation is one of the nine pillars of industry 4.0, which helps in

visualising the process of the real environment into reality. Simulation is considered as one of

the essential tool in robotics, contributes in designing the process, investigating the

performance etc (Zlajpah, 2008). The current state of the production system development

approach at LEAX is studied and analysed to answer this research question. A set of potential

improvements is suggested to improve the current state, which would help the company

transform into a future state involving simulation software to analyse the production system

development process.

100

67.30769231

46.15384615

0

20

40

60

80

100

120

Utilization of the Robot Utilization of the HaasMachine

Utilization of LiebherrMachine

Utilization

5.2.1. Current vs future state production system development process at LEAX.

As seen in the empirical findings, LEAX Group starts the process by collecting all the

information about the parts, machine, and the robot. In the building phase, the company uses a

CAD model to design the layout and use RobotStudio (RAPID) to program the robot. In the

evaluation phase, the company works for weeks to finalize the robot cell's proper running. (Pan,

et al., 2012) says that online programming is a simple and widely used process, with several

drawback. He mentioned that it is difficult and time consuming to jog the robot without

collision. Especially, the jogging get complicated when there is a complex workpiece geometry

or in a complicated process. The robot programming using online programming is not much

flexible and reusable. The changes in the process will demand in repetition of the process and

the production is also stopped (Pan, et al., 2012).

According to (Bellgran & Safsten, n.d.) the production system development has been divided

into three steps namely design, building and evaluation. The future state production

development process at LEAX is obtained by transforming from the above-mentioned

approach to a simulation-based approach. The new approach would bring effective changes in

the company's development process. In the building phase, a conceptual model is created based

on the desired outputs and available inputs, bringing the development team into a common

understanding. The input data is generally obtained in terms of the process map, cycle time,

utilization, current layout, production planning, and capacity. The process of abstracting a

model in a real system is known as conceptual model. It is the most important aspect while

modelling simulation. The aspect of the study such as data requirement, developing speed of

model, model validity and the confidence in the model result will be impacted by conceptual

model (Robinson, 2008).

In the design phase, depending on the type of project, whether it is designing a new workstation

or developing an existing workstation, the virtual model that fulfils all the conceptual model

requirements is designed. This virtual model generally includes a CAD model of the

workstation layout, robot of choice based on the application. The cross-functional team

employed would develop the boundary conditions for the workstation. These boundary

conditions are in terms of maximum speed, control volumes, and I/O signals. With the input

data, operating procedure, and boundary condition, the modeler provides a sequence of target

positions and a generated path. Now the model can be simulated in the software with the

provided inputs. The model's result is synchronised to the RAPID programming extension in

the software, which provides the total program for given input conditions. This difference in

automatically generating the robot program, which significantly reduced human work. Multiple

scenarios can be obtained by varying input and output conditions, and corresponding robot

programs can be generated quickly by eliminating human errors. The developed program for

various scenarios is evaluated in the software and compared to select the optimal scenario. The

computational speed of evaluation is far high compare to the current system at LEAX.

(Connolly, 2009) in his study says that graphical programming helps in creating, editing, and

debugging the robot movement. He even says that the tool is used widely in many automotive

industries by robot programmer and mechanical designer. The simulation tool can be used in

troubleshooting and remote maintenance (Connolly, 2009).

The simulation tool used in this case study helps in verifying the accessibility, reach, and

collision between the robot and the surroundings (Connolly, 2009). Integration of right CAD

model into simulation environment helps in defining the exact target of the robot. The

kinematic motion can even be visualised in the simulation environment, which resembles the

real controller (Holubek, et al., 2014). (Persson & Norrman, 2018) says that the simulation

model including the robot path, target and signals which is created in the RobotStudio can be

synchronised into RAPID tab. He says that the generated code in the RAPID tab can be used

to run both the virtual robot and the real robot (Persson & Norrman, 2018). Hence, the selected

scenario can be physically tested to refine target positions. The refinement means making sure

that the robot arm reaches the exact position and precisely performs the process. (Hayani, 2014)

in his study says that the calibration of the programme is necessary before running it in reality.

5.2.2. Simulation

Simulation leads the innovation and helps to visualize and forecast the flawless production

product before implementing it into the real world. This results in minimizing the cost and

maximizing the output (Dimitris, et al., 2014). (Velíšek, et al., 2017) in his study explains the

modelling of workstation in the RobotStudio environment. He started with importing the CAD

model into RobotStudio environment by converting the STEP file into SAT file. The main idea

behind importing the CAD file is to look for a better configuration of the layout and prove the

reach and kinematics of the robot. He says that the robot was place on the steel base plate after

importing all files. Now the target was created in offline based on the requirement and was

finally tested to see the proper running of the robot (Velíšek, et al., 2017).The simulation model

for the existing robot cell is explained in section 4.7. The simulation model for the new robot

cell is explained in section 3.5. The simulation model is started by observation and

understanding the production process. Then the conceptual model containing the necessary

input, output, assumption, and layout design for simulating model is presented. Then the

detailed process mapping was drawn, which is shown in section 4.4.1. After collecting the

required data, the simulation model is built. The simulation model result was then compared to

real world to achieve validation.

5.2.3. Training of the software

To simulate any workstation, it is necessary to get educated in modelling simulation. Good

knowledge and experience within the simulation software are required while simulating the

process. The simulation software used in this thesis is ABB RobotStudio. The effort invested

in practicing the simulation software would aid in improving the knowledge and experience of

the employees at LEAX. The training of simulation modelling parallel serve the purpose of

practical training and modelling the current state. The brainstorming session can even be

conducted to know the way of employees thinking towards digitalization.

5.2.4. Online vs offline programming

The pros and cons which is listed below is extracted from the empirical part and discussion

with the company personals.

---3

Online Programming

Offline Programming

Programming Option Pros Cons

Pros Cons • Time Consuming

• Tedious

• Least flexible

• Skilled labour is

required

• Cost and risk

maintenance are high.

• No productivity when

programming

• Low cost

• More flexible

• Quicker integration of

the program

• Increase safety

• Reduces time when

programming and

reprogramming

• Optimization of the

robot workspace

• Quite expensive

• Time consumption in

building phase.

• Training of software

is necessary

• Target position

accuracy depend on

CAD model.

Table 7: Online vs offline programming

6. CONCLUSION ANS RECOMENDATION

The conclusion for the findings and the summarised analysis is presented in this section. This

thesis aims in integrating the simulation in the production system process and optimization of

the robot cell using simulation software. Based on the problem stated by the company two

research questions was formulated.

RQ 1: How can simulation be used when optimizing the work of a robot in a

workstation?

RQ 2: How simulation can be integrated in a company’s production development

process?

The company is now using the traditional way of programming and optimizing the robot in a

workstation. This current process was time-consuming and negatively affected the production

system. The simulation software currently used in the company is a limited version that only

allows usage of the RAPID program extension, which can be used only to write and edit a

robot's program. The thesis was conducted to evaluate the potential of using a simulation

modelling approach for optimising the robot cell using the simulation tool and to know the

importance of using simulation tools in the company's production development process.

Regarding the first research question, how can simulation be used when optimizing the robot's

work in a workstation? Based on the analysis, the conclusion is to first build the process

mapping, analyse the process, and suggest a different scenario. The process mapping helps

understand the cell's logical step flow, and the reason for the waiting time of the robot can even

be analysed. Then the company can try different, with the help of the simulation model. The

company can build the simulation based on the mapping of flow. After simulating the whole

process, the company can further change the process by altering the robot path or speed by

brainstorming sessions within a cross-functional team. The best scenario suitable for improving

the production system can be selected based on the scenario's best output. After implementing

the scenario into reality, the company can achieve optimization of the robot cell. Simulation

software even provides a collision detection tool where the collision between two objects is

detected.

The second research question: how simulation can be integrated into the company's production

system process. From the analysis, the recommendation for the company is to use process

mapping in the design phase of the production development process. The process mapping

helped understand the cell's process, and the improvement can be recommended by suggesting

different scenarios. Whereas in the building phase, the company can use simulation as a tool to

program the robot. The simulation integrating into the company's production development

process helps in trying out different scenarios without disturbing the production line.

Simulation software helps in trying out different layout planning, trying out different robots,

and trying out different optimal robot paths in the case company. The integration of simulation

into the company's production system development is detailly explained in section 5.2. In the

evaluation phase, the company can implement the result of simulation into reality by

calibration.

Further, the case company can implement simulation modelling across the workstations to

check the feasibility of implementation in terms of capital investment and expected output. The

concerned cross-functional team must be employed at the respective workstation. This would

improve the overall factory virtually and integrate all the workstations to obtain a holistic view

of improvement.

7. Discussion

Generally, robot simulation software is used to program the robot in the company. Offline

programming as simulation tool which helps in imitating the real-world system and program

the robot as in reality in virtual environment. This case study is a good example of simulating

existing robot cell. Since, the company is trying to find out the routeing and tool to optimize

the robot cycle time. Robot simulation helps in optimizing the robot cycle time by trying out

different scenarios such as speed, configuration, and path. The process mapping in the

empirical findings helps in building the simulation model in an easier was. This approach of

optimizing the robot cell and integrating the simulation software into company’s production

development process can even be adapted to other manufacturing companies. Further, the

research can be conducted to simulate the new robot cell from the scratch. The study on cost

analysis between the online and offline programming can be considered to know the best

feasible option to consider according to the requirement. The pros and cons described at the

end of the analysis part will help the manufacturing companies to know the usefulness of

utilizing the offline programming in their production process. Further, by conducting

brainstorming activity, the employee’s opinion on online and offline programming can also be

analysed.

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9. APPENDICES

Creating an empty station in robot studio.

Creating a solution with an empty station can be done by following steps:

1. Click on the file tab in Robot studio, then the backstage view appears and then click on

New.

2. Select “Solution with Empty station” under the station.

3. By clicking on “Create” the new solution is created in the robot studio. The solution is

saved by default.

Figure 22: Creating empty station in RobotStudio

Creating System from backup

The system, which is created from the backup, creates the new system from the controller

system backup, which is launched from the system builder. Additionally, you can even change

the program revision and options.

Creating the system from backup follows the following steps:

1. By clicking on “System Builder” a new box is appeared. Select “Create from Backup” to

continue further.

2. The welcome page is appeared on the screen, by reading the information click on next to

complete the steps.

3. Enter the name for the system you are creating in the Name box.

4. In the “Path” box enter the path where you want to store the system and click next.

5. In the “Backup Folder” enter the path where the backup folder is located.

6. In the “media pool” box enter the path where the appropriate Robot Ware is present and

click next and click finish.

Figure 23: Creating System from backup

Creating New Virtual Controller

1. By clicking on “New Controller” the dialog box is opened.

2. Entre the controller name in the Name box below the Controller group.

3. Below Create new group, required robot ware version can be selected or distribution

package and media pool location can be set by clicking on location. The required robot

model in the list to create a controller can be selected.

4. Select “Create from Backup” to create from backup, then select the required backup folder

by clicking on browse. The robot ware version can even be selected by robot ware add-in

version. By ticking on restore backup on the checkbox, the backup is restored on the new

controller.

5. Under the mechanism group. Select either import library or the use of existing station

library and click ok.

Figure 24: Creating new virtual controller

Adding an Existing Virtual Controller.

1. By clicking on the “Existing Controller”, a dialog box is appeared.

2. Select a suitable folder in the “Location” list.

3. Select a controller under the “Virtual Controller” list.

4. Either select import library on to use the existing station library under the “Option Group”

and click ok.

Figure 25: Adding existing virtual controller

Smart Component.

Smart Component are nothing but an object of RobotStudio with inbuilt properties and logic

for simulating components, where this won’t be a part of virtual controller. By default, robot

studio recommends a set of Base Smart Components for signal logic, arithmetic, basic motions,

sensor, parametric modelling and so on. Base Smart Component can even be used to build a

user defined smart component with more complicated properties. Some of the complex

properties includes gripper motion, object moving on conveyor, logic and so on. Smart

Component can even be saved as a library life for any further reuse.

Figure 26: Smart component

Create Mechanism.

By create mechanism it is possible to simulate external object such as turn table, positioners,

grippers etc. The steps to create a mechanism is listed below.

1. Click on create mechanism, then the create mechanism window is opened.

2. Enter the mechanism name under the “Mechanism Model Name” box.

3. Select a mechanism type from the “Mechanism Type list”

4. A tree like structure can be seen, right click on the link and click on add link, “Create

Link” dialog box is appeared.

5. Under the select component list, select a required component and click on arrow to add

the component in the component list. If any more component is available, then the list is

automatically selected. Add the component if it is required.

6. Enter a required value under Selected Component group boxes and click “Apply to

Component”. Repeat it for each component as required and click ok.

7. Right click on Joints in the tree structure and click on Add Joints, then the Create Joint

dialog box appears.

8. Click ok after completing the “Create Joint” dialog box.

9. Right click on tool data/Frame in the tree structure and the click on add frame/tool to

bring the create tool/frame data.

10. After completing the Create frame/tool dialog box clock ok and complete the step.

11. Right click on calibration and click on “Add Calibration” in the tree structure to bring up

the “Create Calibration” dialog box.

12. By completing the “Create Dependency” dialog box, click on ok and complete the step.

13. Compile the mechanism if all nodes are valid.

14. To add a Pose for the Mechanism click add and create pose in the dialog box and click

Apply followed by “Ok”.

15. To edit a transition time, click on edit Transition Time” and click close.

Create a Backup

The following steps should be followed to create a backup.

1. From the Controller browser, the system to create a backup is selected.

2. Select Create backup by right clicking on the system. Create backup dialog box is appeared

on the screen.

3. Enter a name under backup name, select a suitable location under the location.

4. Click on checkbox Backup to archive file, so that the backup is created in .tar file and click

OK.

Process mapping

Ring Gear Mechanism

Operation and the cycle time of the robot is listed below

Table 8: Ring Gear Mechanism

Steps Operation Duration

Cycle

Time

1 Pick the part from conveyor with Gripper-2 3 0

2 Goes to the orient stand to orient 9 6

3 Process of orientation stand 14 5

4 Towards waiting position 16 2

5 Waiting Time of robot 50 34

6 Pick the part from machine-1 with Gripper-1 54 4

7 Place the part in Machine-1(Haas) 62 8

8 Place the finished part on to the Conveyor with Gripper-1 68 6








16 Pick the part from Machine-2 with Gripper-2 139 8

17 Place the part in Machine-2 with Gripper-1 144 5

18 Goes to the orient stand and flip the part 154 10




















38 Towards Home 292 2






















60 Puts the part in Machine-2 with Gripper-1 489 5









Table 9: Operation time

Robot Operation Haas Machine Operation Liebherr Machine Operation

Start time End Time Start time End Time Start time End Time

3 16 0 49 0 17

50 85 64 119 150 222

119 165 134 189 222 294

188 234 202 257 427 499

257 292 272 327 499 571

326 361 341 396

395 441 411 466

464 510 481 536

510 … 551 606

Figure 27: Graphical representation of the task

Utilization

Below table highlights the calculation of the utilization of robot and the two machines.

Table 10: Total time and Working time

Total Time of the robot cycle 272

Working Time of Robot 228

Working Time of Haas Machine 139

Working Time of Liebherr Machine 217

Table 11: Utilization

Utilization of the Robot =

Working Time of Robot/Total Time of

Robot*100 83.824

Utilization of the Haas Machine=

Working Time of Haas Machine/Total Time

of Robot*100 51.103

Utilization of Liebherr Machine=

Working Time of Liebherr Machine/Total

Time of Robot*100 79.779

Figure 28: Graphical representation of utilization

Sun Gear Mechanism

The below table highlights the operation and the task time of the robot. Table 12: Sun Gear Mechanism

Steps Operations Duration Cycle Time

1 Pick the part from conveyor from Gripper-2 18 0

2 Picks the parts from Gripper-1 from Machine-2 25 7

3 Puts the part in Machine-2(Liebherr) 30 5

4 Goes near the orientation stand and flip the part 40 10

5 Goes to the orientation stand and orient the part 47 7

6 Picks the part from Machine-1 in Gripper-2 57 10

7 Place the part in Machine-1 from Gripper-1 64 7

8 Place the finished part to conveyor 70 6

9 Pick the part from conveyor from Gripper-2 74 4

10 Picks the parts from Gripper-1 from Machine-2 81 7

11 Puts the part in Machine-2(Liebherr) 86 5

12 Goes near the orientation stand and flip the part 96 10

13 Goes to the orientation stand and orient the part 103 7

14 Picks the part from Machine-1 in Gripper-2 113 10

15 Place the part in Machine-1 from Gripper-1 120 7

16 Place the finished part to conveyor 126 6

83.82352941

51.10294118

79.77941176

0

10

20

30

40

50

60

70

80

90


Utilization

Table 13: Operation Time


Start Time End Time Start Time End Time Start Time End Time

18 126 10 45 0 8

66 101 36 64

122 157 92 120

148 176

Figure 29: Graphical representation of sun gear mechanism

Utilization

Below table highlights the calculation of the utilization for the robot and the two machines. Table 14: Total Time and Working Time






Utilization of the Robot


Robot*100 100

Utilization of the Haas Machine

Working Time of Haas Machine/Total

Time of Robot*100 67.30769231

Utilization of Liebherr Machine

Working Time of Liebherr

Machine/Total Time of Robot*100 46.15384615


Ring Gear Mechanism (Optimization)

The below table highlight the operation and the task time of the robot.

Table 16: Ring Gear Mechanism

Steps Operations Duration

Cycle

Time Column1

1 Pick the part from conveyor from Gripper-1 7 0 0

2 Picks the parts from Gripper-2 from Machine-2 12 5 5

3 Puts the part in Machine-2(Liebherr) from G1 18 6 6

4 Goes near the orientation stand and flip the part 27 9 9

5 Goes to the orientation stand and orient the part 38 11 11

6 Towards Waiting Position 40 2 2

7 Waiting time of Robot 56 16 16

8 Picks the part from Machine-1 in Gripper-1 59 3 3

9 Place the part in Machine-1 from Gripper2 68 9 9

10 Place the finished part to conveyor from G1 73 5 5

11 Pick the part from conveyor from Gripper-1 78 5 5

12 Picks the parts from Gripper-2 from Machine-2 83 5 5

13 Puts the part in Machine-2(Liebherr) from G1 89 6 6

14 Goes near the orientation stand and flip the part 98 9 9

15 Goes to the orientation stand and orient the part 109 11 11

16 Towards Waiting Position 111 2 2

17 Waiting time of Robot 127 16 16

18 Picks the part from Machine-1 in Gripper-1 130 3 3

19 Place the part in Machine-1 from Gripper-2 139 9 9

20 Place the finished part to conveyor 144 5 5

100

67.30769231

46.15384615

0

20

40

60

80

100

120

Utilization of the Robot Utilization of the HaasMachine

Utilization of LiebherrMachine

Utilization

Table 17: Operation Time


Start Time End Time Start Time End Time Start Time End Time

7 40 2 57 0 22

56 111 72 127 32 104

127

Figure 31: Graphical representation of ring gear mechanism

Utilization

Below table highlights the calculation of utilization for robot and the two machines.

Table 18: Total time and Working Time







75.75757576

84.84848485

95.45454545

0

20

40

60

80

100

120


Utilization

Utilization of the Robot


Robot*100 75.75758

Utilization of the Haas Machine

Working Time of Haas Machine/Total

Time of Robot*100 84.84848

Utilization of Liebherr Machine

Working Time of Liebherr

Machine/Total Time of Robot*100 95.45455

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