Milling tool wear state recognition based on partitioning around medoids (PAM) clustering

Abstract

This paper presents a partitioning around medoid (PAM)-based novel method to realize the recognition of the tool wear state in milling. In PAM, the representative objects called medoids are used to define clusters and average dissimilarities are applied to assess the medoids, which make PAM robust to outliers and therefore improve the clustering performance. Meanwhile, locality preserving projections (LPP) method is utilized to further increase the clustering accuracy by dimension reduction. To show the effectiveness of the proposed method, end milling experiment of Ti-6Al-4V alloy were carried out and the commonly used k-means and fuzzy c-means (FCM) algorithm are introduced to make a comparison with PAM algorithm by using five clustering evaluation indicators. The results show that PAM performs higher accuracy and robustness than the other two clustering algorithm.