A typical process route discovery method based on clustering analysis

Abstract

A typical process route is a sample of planning the process route. It is a kind of the process planning knowledge. In order to discover the typical process route in the process planning database from the Computer Aided Process Planning (CAPP), Knowledge Discovery in Database (KDD) is applied. Process data selection, process data purge and process data transformation are employed to get optimized process data. The clustering analysis is adopted as the algorithm mining the typical process route. A mathematics model describing the process route was built by the data matrix. There are three similarities in process route clustering: the similarity between operations was measured by the Manhattan distance based on operation code; the similarity between process routes was calculated by the Euclidean distance and expressed as a dissimilarity matrix; the similarity between process route clusters was evaluated by the average distance based on the dissimilarity matrix. Then, the process route clusters were eventually merged by the agglomerative hierarchical clustering method. And the process routes clustering result was determined by the clustering granularity of process route. This method has been applied successfully to discovering the typical process route of a kind of axle sleeves.