Modeling, analysis, and improvement of integrated productivity and quality system in battery manufacturing

Abstract

A battery manufacturing system typically includes a serial production line with multiple inspection stations and repair processes. In such systems, productivity and quality are tightly coupled. Variations in battery quality may add up along the line so that the upstream quality may impact the downstream operations. The repair process after each inspection can also affect downstream quality behavior and may further impose an effect on the throughput of conforming batteries. In this article, an analytical model of such an integrated productivity and quality system is introduced. Analytical methods based on an overlapping decomposition approach are developed to estimate the production rate of conforming batteries. The convergence of the method is analytically proved and the accuracy of the estimation is numerically justified. In addition, bottleneck identification methods based on the probabilities of blockage, starvation, and quality statistics are investigated. Indicators are proposed to identify the downtime and quality bottlenecks that remove the need to calculate throughput and quality performance and their sensitivities. These methods provide a quantitative tool for modeling, analysis, and improvement of productivity and quality in battery manufacturing systems and can be applied to other manufacturing systems ameanable to investigation using integrated productivity and quality models.

Keywords:

• Quality
• productivity
• integration
• serial line
• repair
• bottleneck
• battery manufacturing

Additional information

Notes on contributors

Feng Ju

Feng Ju received his B.S. from the Department of Electrical Engineering, Shanghai Jiao Tong University, China, in 2010, and an M.S. in Electrical and Computer Engineering, and a Ph.D. in Industrial and Systems Engineering from the University of Wisconsin, Madison, in 2011 and 2015, respectively. Starting from August 2015, he will be an Assistant Professor with the School of Computing, Informatics & Decision Systems Engineering at Arizona State University. His research interests include modeling, analysis, continuous improvement and optimization of manufacturing systems. Dr. Ju is a member of the Institute of Electrical and Electronics Engineers (IEEE), Institute for Operations Research and the Management Sciences (INFORMS), and Institute of Industrial Engineers (IIE).

Jingshan Li

Jingshan Li received a B.S. degree from Tsinghua University, Beijing, China, the M.S. degree from Chinese Academy of Sciences, Beijing, and a Ph.D. from University of Michigan, Ann Arbor, in 1989, 1992, and 2000, respectively. He was a Staff Research Engineer at General Motors Research and Development Center, Warren, MI from 2000 to 2006, and was with University of Kentucky, Lexington, KY from 2006 to 2010. He is now a Professor in the Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI. His primary research interests are in modeling, analysis and control of manufacturing and healthcare systems. He received the NSF Career Award, IEEE Early Industry/ Government Career Award in Robotics and Automation, and multiple awards in IIE Transactions, IEEE Transactions, and international conferences. He is a Senior Editor of IEEE Robotics and Automation Letters, Department Editor of IIE Transactions and Associate Editor of IEEE Transactions on Automation Science and Engineering, International Journal of Production Research, Flexible Service and Manufacturing, and International Journal of Automation Technology, and was an Associate Editor of Mathematical Problems in Engineering.

Guoxian Xiao

Guoxian Xiao received a B.S. degree in mechanical engineering and an M.S. degree in manufacturing engineering from Northeastern University, Shenyang, China, in 1982 and 1984, respectively, and the Ph.D. degree in mechanical engineering from the University of Massachusetts, Amherst, in 1995. He is a Technical Fellow at General Motors (GM) Research and Development Center. With GM since 1997, he leads several projects on the research and development of advanced technologies in machining process, real-time plant floor system, and remanufacturing system. He has six U.S. patents and has authored more than 40 technical papers for journals and conferences. He received five GM’s Boss Kettering Innovation Awards, John Campbell Award in GM, and the ASME Blackall Machine Tool and Gage Tool Award (2003), in machining process, production and maintenance system research.

Jorge Arinez

Jorge Arinez received a B.A.Sc. degree from the University of Toronto, Toronto, ON, Canada, and M.S. and Ph.D. degrees in mechanical engineering from the Massachusetts Institute of Technology, Cambridge, MA, USA. He is a Lab Group Manager with the Manufacturing Systems Research Lab, General Motors R&D, Warren, MI, USA. His main responsibilities involve strategically defining and managing portfolios of advanced manufacturing technology projects. He has also led their development and implementation throughout GM’s global manufacturing operations. His research is focused on the development of analytical tools for real-time production monitoring and control with a focus on energy efficiency and sustainability of manufacturing systems.

Weiwen Deng

Weiwen Deng is a distinguished professor and Executive Associate Director of Automotive Research Institute of Jilin University of China since 2010. Prior to that, he was a staff researcher at General Motors R&D Center in USA. He holds over 40 US and Chinese patents with another 18 applications pending, and is the author/co-author of over 100 peer reviewed international journal and conference papers. Currently, he also serves as Editor-in-Chief of International Journal of Vehicle Autonomous Systems and Associate Editors of International Journal of Vehicle Design. His primary research interests are in dynamics and controls, modeling and simulation on intelligent and electric vehicles, battery manufacturing and management.