A synergistic Mahalanobis–Taguchi system and support vector regression based predictive multivariate manufacturing process quality control approach

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.