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Phase I Analysis for Monitoring Nonlinear Profiles in Manufacturing Processes

Summary: [This abstract is based on the authors' abstract.]Unlike linear profiles that can be represented by a linear regression model, nonlinear profiles are frequently sampled into high-dimensional data vectors and analyzed by nonparametric methods, meanwhile generating huge historical data sets that must be analyzed. High dimensionality and data contamination create problems for the Phase I analysis of nonlinear profiles. A two-component strategy is proposed: a data-reduction component that projects original data into a lower dimension subspace, and a data-separation technique that can detect single and multiple shifts as well as outliers in the data. Simulated data and nonlinear profile signals from a forging process illustrate the proposed strategy.

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  • Topics: Process Management, Change Management
  • Keywords: Control charts,Manufacturing process,Multivariate analysis,Nonlinear models,Change agent,Clustering
  • Author: Ding, Yu; Zeng, Li; Zhou, Shiyu
  • Journal: Journal of Quality Technology