Variation analysis for custom manufacturing processes

Abstract

Discovering and addressing unknown, including unanticipated, part-to-part variation sources is an important, yet challenging problem in manufacturing variation reduction. The state-of-art methods for solving this problem have focused solely on traditional mass manufacturing settings, in which abundant measurement data of parts with the same design are available. Applying these methods to custom manufacturing processes is problematic because the number of parts with the same design in custom manufacturing is often small. This paper proposes a new variation model that considers custom manufacturing parameters to aggregate measurement data across all custom parts. We also propose to estimate this model via a conditional autoencoder. The advantages of the proposed approach are demonstrated with a simulated toy-building brick example and a real cylindrical machining example. The approach successfully reveals unknown variation patterns even with a relatively small number of parts in these examples. Our approach is also generally applicable to any mainstream manufacturing processes that produce multiple part designs.

Keywords:

• Blind discovery
• conditional autoencoder
• convolutional neural networks
• deep learning
• statistical process control

Acknowledgments

The authors thank the anonymous reviewers for their helpful comments to improve this paper.

Additional information

Notes on contributors

Linxi Li

Linxi Li received the B.S. degree in Mathematics and Physics from Warwick University, Coventry, U.K., the M.S. degree in Statistics from West Virginia University, Morgantown, West Virginia. She is currently a Ph.D. candidate in the Systems Modeling and Analysis program held by the Department of Mathematics & Applied Mathematics and the Department of Statistical Sciences & Operations Research at Virginia Commonwealth University, Richmond, Virginia. Her research interests lie in statistics and machine learning, especially dimension reduction methodology.

Anh Tuan Bui

Anh Tuan Bui received the B.S. degree in electrical engineering from Hanoi University of Science and Technology, Hanoi, Vietnam, the M.S. degree in industrial and management engineering from Pohang University of Science and Technology, Pohang, South Korea, and the Ph.D. degree in industrial engineering & management sciences from Northwestern University, Evanston, Illinois. He is currently an assistant professor in the Department of Statistical Sciences & Operations Research at Virginia Commonwealth University, Richmond, Virginia. His research interests lie in statistics and machine learning, for analyzing data from manufacturing, healthcare, and other enterprise systems. Dr. Bui is a recipient of the Lloyd S. Nelson Award from the American Society for Quality.