Surface Mount Technology Line Optimisation using Modified k-means with Feature Weight Constraints

Abstract

The common setup problem for chip mounters in a Surface Mount Technology (SMT) line is to group different device models, each of them consisting of different components, into minimum number of clusters where each cluster has a maximum component size. This type of setup problem is classified as a clustering problem with feature weight constraints which cannot be fulfilled by traditional data clustering. In this study, we introduce some vital modifications on standard k-means algorithm such that it can incorporate feature weight constraints adapted from cluster size constraints. We also propose a modification to the elbow method to determine the number of clusters of the clustering problem with feature weight constraints. The results show that the proposed algorithm (modified k-means with feature weight constraints) is able to fulfill the feature weight constraints and solve the common setup problem. The results verify the proposed algorithm has superior performance over standard k-means algorithm in clustering problem with feature weight constraints. For the common setup problem on a given data set, the analysis shows that 1000 runs of the proposed algorithm implemented using MATLAB is able to obtain at least one valid clustering result within a reasonable run time.