Phase I Analysis for Monitoring Nonlinear Profiles in Manufacturing Processes

Abstract

Phase I analysis of nonlinear profiles aims at identifying the data from an in-control process as accurately as possible so that quality engineers can have a good reference to establish the control charts for a future process. Unlike linear profiles, which can be represented by a linear regression model with its model parameters used for monitoring and detection, nonlinear profiles are often sampled into high-dimensional data vectors and analyzed by nonparametric methods. Meanwhile, automatic in-process data-collection devices generate huge historical data sets, which must be analyzed for the presence of observations from out-of-control process conditions. The high dimensionality and data contamination present a challenge to the Phase I analysis of nonlinear profiles. This paper presents a strategy that consists of two major components: a data-reduction component that projects the original data into a lower dimension subspace while preserving the data-clustering structure and a data-separation technique that can detect single and multiple shifts as well as outliers in the data. Simulated data sets as well as nonlinear profile signals from a forging process are used to illustrate the effectiveness of the proposed strategy.

• Change-Point Detection
• Clustering
• Control Chart
• Multivariate Analysis

Additional information

Notes on contributors

Yu Ding

Dr. Ding is an Assistant Professor in the Department of Industrial and Systems Engineering, Texas A&M University. His email address is [email protected].

Li Zeng

Ms. Zeng is a Graduate Student in the Department of Industrial and Systems Engineering, The University of Wisconsin at Madison. Her email address is [email protected].

Shiyu Zhou

Dr. Zhou is an Assistant Professor in the Department of Industrial and Systems Engineering, The University of Wisconsin at Madison. His email address is [email protected].