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

TL;DR

This paper introduces a novel visual clustering approach using Self Organizing Map (SOM) for machine-part cell formation in manufacturing, improving clustering efficacy and providing clear visual insights.

Contribution

The paper presents a new SOM-based method for machine-part clustering that enhances visualization and improves grouping efficacy over previous techniques.

Findings

SOM approach achieves at least comparable grouping efficacy to existing methods.

The method improves grouping efficacy in 70% of tested problems.

Visual tools like Umatrix and component planes aid in interpreting clusters.

Abstract

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, 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 the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and 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 labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, 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 found immensely useful to both industry practitioners and researchers.