A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques

作者：Teresa Garcia Ordas Maria; Alegre Enrique; Gonzalez Castro Victor; Alaiz Rodriguez Rocio

来源：International Journal of Advanced Manufacturing Technology, 2017, 90(5-8): 1947-1961.

DOI：10.1007/s00170-016-9541-0

• Tool wear classification
• B-ORCHIZ
• aZIBO
• ZMEG
• Shape description

In this paper, we present a new approach to categorize the wear of cutting tools used in edge profile milling processes. It is based on machine learning and computer vision techniques, specifically using B-ORCHIZ, a novel shape-based descriptor computed from the wear region image. A new Insert dataset with 212 images of tool wear has been created to evaluate our approach. It contains two subsets: one with images of the main cutting edge and the other one with the edges that converge to it (called Insert-C and Insert-I, respectively). The experiments were conducted trying to discriminate between two (low-high) and three (low-medium-high) different wear levels, and the classification stage was carried out using a support vector machine (SVM). Results show that B-ORCHIZ outperforms other shape descriptors (aZIBO and ZMEG) achieving accuracy values between 80.24 and 88.46 % in the different scenarios evaluated. Moreover, a hierarchical cluster analysis was performed, offering prototype images for wear levels, which may help researchers and technicians to understand how the wear process evolves. These results show a very promising opportunity for wear monitoring automation in edge profile milling processes.

• 出版日期2017-5