Title: Multivariate statistical process monitoring and control of machining process using principal component-based Hotelling T2 charts: a machine vision approach

Authors: Ketaki Joshi; Bhushan Patil

Addresses: Fr. Conceicao Rodrigues College of Engineering, Affiliated to University of Mumbai, Bandra (W), Mumbai-400050, Maharashtra, India ' Fr. Conceicao Rodrigues College of Engineering, Affiliated to University of Mumbai, Bandra (W), Mumbai-400050, Maharashtra, India

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 T2 chart.

DOI: 10.1504/IJPQM.2022.120709

International Journal of Productivity and Quality Management, 2022 Vol.35 No.1, pp.40 - 56

Received: 01 Nov 2019
Accepted: 20 Feb 2020

Published online: 04 Feb 2022 *

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