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Machine-part cell formation is used in cellular manufacturing in order toprocess a large variety, quality, lower work in process levels, reducingmanufacturing lead-time and customer response time while retaining flexibilityfor new products. This paper presents a new and novel approach for obtainingmachine cells and part families. In the cellular manufacturing the fundamentalproblem is the formation of part families and machine cells. The present paperdeals with the Self Organising Map (SOM) method an unsupervised learningalgorithm in Artificial Intelligence, and has been used as a visuallydecipherable clustering tool of machine-part cell formation. The objective ofthe paper is to cluster the binary machine-part matrix through visuallydecipherable cluster of SOM color-coding and labelling via the SOM map nodes insuch a way that the part families are processed in that machine cells. TheUmatrix, component plane, principal component projection, scatter plot andhistogram of SOM have been reported in the present work for the successfulvisualization of the machine-part cell formation. Computational result with theproposed algorithm on a set of group technology problems available in theliterature is also presented. The proposed SOM approach produced solutions witha grouping efficacy that is at least as good as any results earlier reported inthe literature and improved the grouping efficacy for 70% of the problems andfound immensely useful to both industry practitioners and researchers.

Machine-Part cell formation through visual decipherable clustering of Self Organizing Map