Evaluation and prediction of drilling wear based on machine vision

Surface wear of drilling bit in the drilling process is complex, and the traditional wear index is not suitable to reflect the wear condition of drilling bit well; it is necessary to investigate on the appropriate wear testing and evaluation method. In this paper, a visual measurement system is set up to obtain the drilling bit wear characteristics based on machine vision. The wear area image is grayscale processed, and the gray image is thresholding segmentation processed, then the boundary feature is extracted based on the chain code method. A comprehensive evaluation system is established, in which the wear perimeter, the equivalent wear diameter, and maximum wear width of different areas are proposed as the wear indicators. The wear fusion feature based on the principal component analysis is proposed to characterize the drilling bit wear degree, the wear indicators of different regions are characterized and fused as the wear degree of different regions, then the prediction model of the drilling bit wear degree is established based on support vector machine. The results show that the predicted results are mainly consistent with the experimental results. The research of the drilling bit wear characteristics based on principal component analysis can provide guidance for the study of drilling bit wear mechanism.