Statistical Process Control for Monitoring Nonlinear Profiles: A Six Sigma Project on Curing Process

ABSTRACT

Curing duration and target temperature are the most critical process parameters for high-pressure hose products. The air temperature collected in the curing chamber is represented in the form of a profile. A proper statistical process control (SPC) implementation needs to consider both numeric as well as profile quality characteristics. This article describes a successful Six Sigma project in the context of statistical engineering for integrating SPC, a statistical method, to the existing practice of engineering process control (EPC) according to science. A case study on a real production curing process is thoroughly investigated. It is shown that the new findings could potentially result in significant energy savings. The solutions provided in this study can be generalized into other curing processes and applications subjected to both EPC and SPC.

KEYWORDS:

• autoclave
• biplot chart
• DMAIC
• Hotelling T ² chart
• profile analysis

ACKNOWLEDGMENTS

We are grateful to the guest editors and two referees whose constructive comments were extremely helpful.

Additional information

Notes on contributors

Shing I. Chang

Dr. Shing I. Chang is an associate professor in the Department of Industrial and Manufacturing Systems Engineering at Kansas State University. His main research interests include multivariate statistical process control for manufacturing and health care, nonlinear profile monitoring, neural networks and fuzzy set applications in quality engineering, and multivariate experimental designs. He is a senior member of both Institute of Industrial Engineers and American Associate of Quality. He served as department editor of IIE Transactions in 2003 to 2009. He was a NASA summer fellow in 2004 and a recipient of SME young manufacturing engineer award in 1997.

Tzong-Ru Tsai

Dr. Tzong-Ru Tsai is a professor in the Department of Statistics at Tamkang University. His main research interests include quality control and reliability analysis. He has served as an executive editor for the International Journal of Intelligent Technologies and Applied Statistics since 2008 and served in the editorial board of the Journal of the Chinese Institute of Industrial Engineers from 2007 to 2011. Dr. Tsai also served as a senior consultant for the Electronics and Optoelectronics Research Lab. of the Industrial Technology Research Institute in 2010 to 2011.

Dennis K. J. Lin

Dr. Dennis K. J. Lin is a university distinguished professor of supply chain and statistics at Penn State University. His research interests are quality assurance, industrial statistics, data mining, and response surface. He has published over 150 papers in a wide variety of journals. He currently serves or has served as associate editor for near 10 journals and was coeditor for Applied Stochastic Models for Business and Industry. Dr. Lin is an elected fellow of ASA and ASQ, an elected member of ISI, a lifetime member of ICSA, and a fellow of RSS. He is an honorary chair professor for various universities, including a Chang-Jiang Scholar of China at Renmin University, National Chengchi University (Taiwan), Fudan University, and XiAn Statistical Institute (China). He is also the recipient of the 2004 Faculty Scholar Medal Award at Penn State University and the Youden Address speaker in 2010.

Shih-Hsiung Chou

Shih-Hsiung Chou is a Ph.D. candidate and a system administrator in the Department of Industrial and Manufacturing Systems Engineering at Kansas State University. His main research interests include multivariate statistical process control, high-dimensional data visualization, data mining, machine learning, and artificial intelligence.

Yu-Siang Lin

Yu-Siang Lin is a Ph.D. candidate in the Department of Industrial Management of National Taiwan University of Science and Technology in Taiwan. His advisor is Dr. Kung-Jeng Wang. He has a double major in industrial management and computer science. His current research interests are in the areas of supply chain management, warehouse configuration, production planning, inventory systems, and gene algorithms.