Machine–part cell formation through visual decipherable clustering of self-organizing map

Abstract

Machine–part cell formation is used in cellular manufacturing in order to process large varieties, improve quality, and lower work-in-process levels, reducing manufacturing lead time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In cellular manufacturing, the fundamental problem is the formation of part families and machine cells. The present paper deals with the self-organizing map (SOM) method, an unsupervised learning algorithm in artificial intelligence which has been used as a visually decipherable clustering tool of machine–part cell formation. The objective of the paper is to cluster the binary machine–part matrix through visually decipherable cluster of SOM color coding and labeling via the SOM map nodes in such a way that the part families are processed in that machine cell. The U-matrix, component plane, principal component projection, scatter plot, and histogram of SOM have been reported in the present work for the successful visualization of the machine–part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and was found to be immensely useful to both industry practitioners and researchers.