Formation of part family in reconfigurable manufacturing system using principle component analysis and K-means algorithm

Abstract

The Reconfigurable Manufacturing Systems (RMS) is the next step in manufacturing, allowing the production of any quantity of highly customised and complex parts together with the benefits of mass production. In RMSs, parts are grouped into families, each of which requires a specific system configuration. Initially system is configured to produce the first family of parts. Once it is finished, the system is reconfigured in order to produce the second family, and so forth. The effectiveness of a RMS depends on the formation of the optimum set of part families addressing various reconfigurability issues. The aim of this work is to establish a methodology for grouping parts into families for effective working of Reconfigurable Manufacturing Systems (RMSs). The methodology carried out in three phases. In the first phase, the correlation matrix is used as similarity coefficient matrix. In the second phase, Principal Component Analysis (PCA) is applied to find the eigenvalues and eigenvectors on the correlation similarity matrix. A scatter plot analysis as a cluster analysis is applied to make parts groups while maximizing correlation between parts. In the third phase, Agglomerative Hierarchical K-means algorithm improved the parts family formation using Euclidean distance resulting in an optimum set of part families for reconfigurable manufacturing system. Copyright © (2012) by Danube Adria Association for Automation and Manufacturing (DAAAM).