Azizi Control Engineering in Mechatronics

1. Auflage 2023
ISBN: 978-981-16-7775-5
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 202 Seiten, eBook

Reihe: Intelligent Technologies and Robotics

This book provides an in-depth understanding of the fundamental scientific principles and technologies used in the design of modern computer-controlled machines and processes. It emphasizes the synergies in the design process and explores the challenges and opportunities for integrating diverse engineering disciplines. The book consists of six chapters that cover a wide range of topics related to mechatronics and control system engineering. Overall, the book is an excellent resource for professionals, engineers, researchers, and students who want to gain a comprehensive understanding of the trans-disciplinary field of mechatronics and control systems engineering.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Azizi, Aydin

Inhaltsverzeichnis

INTRODUCTION                

1.1           Background         

1.2           Industry 4.0         

1.3           Material handling systems in intelligent production systems   

1.4           Types of control systems  

1.4.1        Centralized control system              

1.4.2       Decentralized Control System        

1.4.3        Distributed Control System             

1.4.4       Control Agents   

1.4.5        Agent-Based Control System          

1.4.6       Master and Slaves agents 

1.5           Lean Six Sigma approach for performance evaluation and improvement of the manufacturing system       

1.5.1        Lean Six Sigma “Define” Phase       

1.5.2        Lean Six Sigma “Measure” Phase   

1.5.3        Lean Six Sigma “Analyze” Phase    

1.5.4        Lean Six Sigma “Improve” Phase

1.5.5        Lean Six Sigma “Control” Phase     

1.6           Overall equipment effectiveness (OEE)        

1.7           Lead time and Time study in OEE and LSS  

1.8           The simulation for OEE and LSS     

1.9           The objective of the research          

2              LITERATURE REVIEW     

2.1           Overview              

2.2          Agent-Based control architecture for the industry 4.0 implementation 19

2.3           Agent-Based control architectures for Manufacturing system (Material Handling Systems approach)         

2.4          Importance of manufacturing system performance evaluation

2.5           Lean Six Sigma approach for manufacturing system performance evaluation and improvement

2.6          Time study techniques for Lean Six Sigma and manufacturing system performance evaluation and improvements.   

2.6.1       Classic time study techniques         

2.6.2       Method Time Measurement as a Predetermined time study technique

2.6.3       Maynard Operation Sequence technique time study

2.7           Overall Equipment Effectiveness for Lean Six Sigma and manufacturing system performance evaluation and improvements

2.8          Overall Equipment Effectiveness and time study relationship 

2.9          Computer-based simulation for Lean Six Sigma and manufacturing system performance evaluation and improvements    

2.10        Literature review conclusion           

3              METHODOLOGY               

3.1           Overview              

3.2           Design of the agent-based control architecture with Maser-Slave Mechanism

3.2.1        Structural definition of the proposed control system

3.2.1.1     Physical resource layer of the proposed control architecture  

3.2.1.2    Resource control layer of the proposed control architecture   

3.2.1.2.1 Slave Agents in the resource control layer    

3.2.1.2.2 Master Agents in the resource control layer 

3.2.1.3     Management layer of the proposed control architecture         

3.2.1.4    Communication between control architecture layers

3.3           Case Study System description with the agent-based control architecture         

3.4           System description with the agent-based control  architecture              

3.5           Lean Six Sigma Strategies to evaluate and improve the performance of the target system after agent-based control architecture           

3.5.1        “Define” phase of Lean Six Sigma  

3.5.2        “Measure” phase of Lean Six Sigma               

3.5.2.1     Time Study methodology and data collection            

3.5.3        "Analyze" Phase of Six Sigma          

3.5.3.1     Overall Equipment Effectiveness and performance evaluation

3.5.4        “Improve” Phase of Lean Six Sigma               

3.5.4.1     The Simulation model to verify the solutions for improving the system performance       

3.5.5        “Control” Phase of Six Sigma          

4              RESULT AND DISCUSSION          

4.1           Overview              

4.2          Discrete time study results for each phase of the system         

4.2.1       Time study result of the Main conveyor        

4.2.1.1    Identified problem and limitation for the main conveyor by time study and possible solutions

4.2.2       Time study result of the robot arm 

4.2.2.1    Identified problem and limitation for robot arm by time study and possible solutions

4.2.3       Time study result of the right side-conveyor

4.2.3.1    Identified problem and limitation for right side-conveyor by time study and possible solutions     

4.2.4       Time study result of the right slider unit       

4.2.4.1    Identified problem and limitation for right slider by time study and possible solutions     

4.2.5       Time study result of the Left side-conveyor and left slider unit

4.2.6       Comparison between left and right side-conveyor time study results

             4.2.7       Comparison between left and right slider unit time study results
                   4.2.8       Overall time study result of the entire system            
                    4.3           Utilization rate report to identify the resources with more Idle time                    4.4          Simulation result of the system by Arena     
                    4.5           OEE Analysis before optimization
                    4.6          OEE Analysis after optimization   

CONCLUSION   
REFERENCES       APPENDIX

Über Autor(innen)

Dr. Aydin Azizi holds a Ph.D. degree in Mechanical Engineering-Mechatronics, an M.Sc. in Mechatronics and a B.Sc. in Mechanical Engineering-Heat & Fluids. Certified as the Fellow of the Higher Education Academy (FHEA), official instructor for the Siemens Mechatronic Certification Program (SMSCP) and the editor of the book series Emerging Trends in Mechatronics publishing by Springer Nature Group, he currently serves as a Senior Lecturer at the Oxford Brookes University. His current research focuses on investigating and developing novel techniques to model, control and optimize complex systems. Dr. Azizi’s areas of expertise include Control & Automation, Artificial Intelligence and Simulation Techniques. Dr. Azizi is the recipient of the National Research Award of Oman for his AI-focused research, DELL EMC’s “Envision the Future” competition award in IoT for “Automated Irrigation System”, and ‘Exceptional Talent’ recognition by the British Royal Academy of Engineering.