Abstract
Purpose – The purpose of this paper is to build a monitoring scheme in order to detect and subsequently eliminate abnormal behavior of the concerned casting process so as to produce worm wheels with good quality characteristics. Design/methodology/approach – In this a study, a process monitoring strategy has been devised for a centrifugal casting process using data-based multivariate statistical technique, namely, partial least squares regression (PLSR). Findings – Based on a case study, the PLSR model constructed for this study seems to mimic the actual process quite well which is evident from the various performance criteria (predicted and analysis of variance results). Practical implications – The practical implication of the study involves development of a software application with a back-end database which would be interfaced with a computer program based on PLSR algorithm for estimation of model parameters and the control limit for the monitoring chart. It would help in easy and real-time detection of faults. Originality/value – This study concerns the application of a PLSR-based monitoring strategy to a centrifugal casting process engaged in the production of worm wheel.
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