Multivariate statistical monitoring strategy for an automotive manufacturing part facility

Abstract

In this article an attempt has been made to devise a statistical monitoring strategy based on multivariate projection based method. The projection based method that was employed for development for the strategy was Principle Component Analysis (PCA) which is primarily used as a dimension reduction technique. The strategy thus devised was applied to a connecting rod manufacturing facility. Connecting rods are primarily produced via hot forging process accompanied by a litany of other finishing processes. Each process in turn is characterized by their own set of process characteristics which requires simultaneous monitoring so as to produce good quality end product. Simultaneous monitoring of characteristics and moreover correlated characteristics can be achieved via the employment of multivariate statistical control technique. Projection based techniques viz., PCA and partial least squares regression fall under special category MSPC techniques owning to their ability to deal with highly correlated and ill-conditioned data. The PCA based monitoring strategy thus devised in this article was found to be efficient in detection of faults indicated by out-of-control observations and thus aiding in producing good quality connecting rods.