Application of principal component analysis in machine-part cell formation

Abstract

The present work applies Principal Component Analysis (PCA) for grouping of machines and parts so that the part families could be processed in the cells formed by those associated machines. An incidence matrix with binary entries has been chosen to apply this methodology. After performing the eigenanalysis of the principal component and observing the component loading plot of the principal components, the machine groups and part families are identified and arranged to form machine-part cells. Later, the same methodology is extended and it is applied to nine other machine-part matrices collected from literature for the validation of the proposed methodology. The goodness of cell formation is compared using the grouping efficacy and the potential of eigenanalysis in cell formation is established over the best available results using the various established methodologies. The result shows that in 70% of the problems there is an increase in grouping efficacy and in 30% problem, the performance measure of cell formation is as good as the best result from literature.

Development of a cell formation heuristic by considering realistic data using principal component analysis and Taguchi’s method

“…For more details of PCA method, and its application in cell formation relevant literature such as (Albadawi et al 2005;Chattopadhyay et al 2012;Gupta et al 2012;Hachicha et al 2006Hachicha et al , 2008aLlin et al 2010;Kumar and Jain 2010;Mehrjoo and Bashiri 2013;Min et al 2014) and others can be referred.…”