A synergistic Mahalanobis–Taguchi system and support vector regression based predictive multivariate manufacturing process quality control approach. (October 2020)

• Record Type: Journal Article
• Authors: Sikder, Sagar; Mukherjee, Indrajit; Panja, Subhash Chandra
• Is Part Of: Journal of manufacturing systems. Volume 57(2020)
• Page Start: 323
• Page End: 337
• Publication Date: 2020-10
• Subjects: Quality control -- Prediction-based multivariate process control -- Process capability -- Mahalanobis-Taguchi system -- Manufacturing process -- Support vector regression -- Bootstrap prediction interval; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2020.10.003
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: This paper proposes a synergistic prediction-based multivariate process quality control approach. The proposed approach considered influence of inputs (or covariates) for process adjustment. The proposed approach considered response uncertainties to predict, diagnose and adjust process. The proposed multivariate process control approach is proactive, nonparametric and distribution-free. Case studies demonstrated suitability of the approach and improvement in process performance. Abstract: The primary objective of this study is to propose and verify a new synergistic prediction-based multivariate process quality control (MPQC) approach for manufacturing processes. The proposed approach considers the influence of covariates (e.g. uncontrollable inputs) and output (or response) uncertainties to predict, monitor, diagnose, and adjust for out-of-control scenarios. The prediction-based real-time synergistic approach integrates off-line and on-line multivariate quality control strategies. In this approach, based on a current state prediction of responses, process control variables are adjusted to prevent any out-of-control or abnormal situations in the process. The unique approach is designed based on a Mahalanobis–Taguchi System (MTS), support vector regression (SVR), bootstrap prediction interval (PI), and derivative-free Nelder-Mead (NM) optimisation strategy. Two real-life case studies demonstrate the suitability of the proposed approach and show improvements in process performance. This easy-to-implement distribution-free predictive quality control approach provides the necessary flexibility to industry practitioners for real-life implementation in discrete or continuous manufacturing processes.