Product quality prediction based on process data: Taking the gearbox of Company D as an example

Abstract

This paper aims to show a case study of gearbox quality improvement in Company D. First, quality improvement actions are prioritized through quality function deployment methodology. Second, product performance prediction models are established based on process failure mode and effect analysis and Monte Carlo simulation. Torque and angle methods are used to further improve process prediction. Then, support vector machine based real-time prediction mechanism is built for dynamic prediction of new processes. Finally, a quality prediction information management system is developed. This study effectively addresses the actual problems of Company D and provides reference for quality prediction of similar products.

Keywords:

• d
• eviation warning
• process data
• product performance prediction
• product quality prediction system
• quality management

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (grant numbers 72171166, 72032005).

Notes on contributors

Zhen He

Zhen He is a Professor in College of Management and Economics, Tianjin University. His research interests focus on quality engineering and quality management. He is the recipient of Outstanding Research Young Scholar of National Natural Science Foundation of China. He is also an academician of International Academy for Quality. He has published more than 100 refereed articles in various prestigious journals including European Journal of Operational Research, Journal of Quality Technology, International Journal of Production Research, Computers and Industrial Engineering, et al.

Hao Hu

Hao Hu is a PhD candidate in College of Management and Economics, Tianjin University, China. His research interests focus on operations management and quality management.

Min Zhang

Min Zhang is a Professor in College of Management and Economics, Tianjin University, China. Her research interests focus on operations management and quality management. She has published more than 50 refereed articles in research journals including Journal of Business Ethics, International Journal of Production Economics, International Journal of Production Research, Production Planning & Control, Quality and Reliability Engineering International, et al.

Xiaofei Yin

Xiaofei Yin is an electric drive system integration manager in Shanghai Nio automative Co, Ltd.