Multivariate statistical process monitoring and control of machining process using principal component-based Hotelling T 2 charts: a machine vision approach

Ketaki Joshi and Bhushan Patil

International Journal of Productivity and Quality Management, 2022, vol. 35, issue 1, 40-56

Abstract: Machine vision offers image-based inspection and quality control. Principal component-based multivariate statistical process monitoring (MSPM) and control facilitates monitoring of production typically involves several quality characteristics with a single control chart that identifies and diagnoses faults by signal decomposition. The paper presents principal component-based MSPM and control of the machining process using machine vision for industrial components manufactured on conventional lathe machines. It involves extraction of critical dimensions and surface characteristics using image-processing techniques, data dimensionality reduction using principal component analysis (PCA), process monitoring, and control using principal components based Hotelling T² chart. Fault diagnosis involves decomposition of T² statistic into contribution by individual principal components and their combinations, identification of out-of-control scenarios using decision tree and their physical interpretation to detect possible causes of errors for further analysis and control. The approach potentially offers an industry-ready solution to automated, economic and 100% process monitoring and control.

Keywords: quality inspection; quality control; machine vision; MSPM; MSPC; principal component analysis; PCA; Hotelling T 2 chart. (search for similar items in EconPapers)
Date: 2022
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