A synergic multivariate statistical process control framework for monitoring, diagnosis, and adjustment of multiple response abrasive machining processes

Sagar Sikder, Indrajit Mukherjee and Subhash Chandra Panja

International Journal of Industrial and Systems Engineering, 2019, vol. 33, issue 3, 314-345

Abstract: In various abrasive machining processes, output quality is defined in terms of multiple critical responses and their deviations from target values. These multiple responses are often interacting and changing the process conditions for improving or controlling one response may deteriorate the quality of another. Thus, there is a need to simultaneously consider all responses and recommend a trade-off operating condition for process control and optimisation. Two important fields that consider simultaneous control and optimisation of multiple responses are the multivariate statistical process control (MSPC) and multiple response optimisation (MRO). Although various MSPC and MRO approaches have been proposed by researchers, there is limited prior research on the integration of MSPC and MRO approaches to ensure the stability and process capability. In this study, a synergic MSPC and MRO approach is proposed based on Mahalanobis-Taguchi system and nonlinear optimisation to ensure the stability and capability of the abrasive machining process.

Keywords: abrasive machining process; multivariate statistical process control; MSPC; multiple response optimisation; MRO; Mahalanobis-Taguchi system; MTS; r-control chart; multivariate process capability. (search for similar items in EconPapers)
Date: 2019
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