Analysis of assembly-time performance (ATP) in manufacturing operations with collaborative robots: a systems approach

Abstract

Reducing station processing times has a significant importance in manufacturing assembly systems. In recent years, there has been a growing interest in using collaborative robots to assist human operators in many manufacturing systems, which can not only improve ergonomics measures but also reduce processing time and increase throughput. In this paper, a system-theoretic approach is introduced to analyse the assembly-time performance (ATP) of assembly systems with collaborative robots, where ATP is defined as the probability to finish all the assembly operations in a station within a desired time interval. Specifically, the assembly operations are described by stochastic processes with both individual (human operator and robot) preparation tasks and joint collaboration tasks, characterised by general or arbitrary distributions of task times. Then an efficient algorithm is presented by using gamma distributions to approximate task times and aggregate multiple interacting tasks to calculate ATP. High accuracy in ATP evaluation is obtained through such an approximation method. In addition, system properties, such as monotonicity and sensitivity, i.e. bottlenecks, are investigated. Finally, a case study at an automotive powertrain assembly plant is introduced to illustrate the applicability of the method and the effectiveness for assembly time reduction through using collaborative robots.

Keywords:

• Assembly-time performance
• collaborative robots
• human operators
• gamma distribution
• coefficient of variation

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, JL, upon reasonable request.

Additional information

Notes on contributors

Nan Chen

Nan Chen is currently a lecturer in the Department of Management Science and Engineering, School of Management, Shanghai University, Shanghai, China. Chen received his BSc in Industrial Engineering and PhD in Management Science and Engineering from Tsinghua University, Beijing, China, in 2014 and 2019, respectively. His research interests include stochastic modelling, analysis and design of systems with primary focus on manufacturing and healthcare industry.

Ningjian Huang

Ningjian Huang is a lab group manager at General Motors Global R&D Center. He received his PhD in Systems Engineering from Oakland University in 1991. He has 25 years experience in automotive manufacturing. He has been managing the company's advanced manufacturing technology portfolios for many years, overseeing the technology transfer in every manufacturing areas. He led a project on Plant of Future where new potential game changing technologies were assessed and explored. In his current capacity, he is responsible for strategy, architecture, research and development in robotics and automation, with a focus on smart manufacturing. He has 30t records of inventions and 100t publications.

Robert Radwin

Robert G. Radwin is the Duane H. and Dorothy M. Blumke professor in Industrial and Systems Engineering and Biomedical Engineering at UW-Madison. He received a BS in Electrical Engineering from New York University in 1975, and MS degrees in Electrical and Computer Engineering and in Bioengineering from the University of Michigan in 1979. He earned a PhD in Industrial and Operations Engineering from the University of Michigan in 1986. He received a Presidential Young Investigator Award from the National Science Foundation and a Special Emphasis Research Career Award from the National Institute for Occupational Safety and Health in 1991. He has seven filed or granted US Patents. He has received several awards as an innovator and researcher, is a fellow of five professional societies, and has served on numerous national committees. He is the reviews track editor for the journal Human Factors and associate editor for the journal IISE Transactions on Occupational Ergonomics and Human Factors. He is founding chair of the UW-Madison Department of Biomedical Engineering and is a discovery fellow at the Wisconsin Institute for Discovery. His primary research interests are in instruments and analytical methods to assess physical stress in the workplace; causes and prevention of work-related musculoskeletal disorders; and ergonomics of manually operated machinery, equipment, medical instruments and hand tools. His research is supported by NSF, NIH, NIOSH, NASA, companies and foundations.

Jingshan Li

Jingshan Li received BS and MS degrees in Automation and a PhD in Electrical Engineering from Tsinghua University, Chinese Academy of Sciences, and University of Michigan, in 1989, 1992 and 2000, respectively. He was with General Motors Research & Development Center from 2000 to 2006, the University of Kentucky from 2006 to 2010, and Department of Industrial and Systems Engineering, University of Wisconsin, Madison, from 2010 to 2021. He is now the Gavriel Salvendy Chair Professor in Department of Industrial Engineering, Tsinghua University, Beijing, China. He received the 2010 NSF Career Award, 2006 IEEE Early Career Award, and multiple best paper awards in IIE Transactions, IEEE Transactions on Automation Science and Engineering, and multiple prestigious international conferences. He is the senior editor, department editor, area editor and associate editor of multiple IEEE and IISE Transactions and leading journals in manufacturing and service systems. He is an IEEE fellow and an IISE fellow, and an IEEE distinguished lecturer in robotics and automation, and organisational chairs of multiple flagship international conferences. He is also the editor-in-chief of IEEE International Conference on Automation Science and Engineering. His primary research interests are in modelling, analysis and control of manufacturing and healthcare systems. His research has been supported by NSF, DOE, NIST, PCORI, AHRQ, manufacturing companies and healthcare organisations.