Wear detection of WC-Cu based impregnated diamond bit matrix based on SEM image and deep learning

The working efficiency and lifetime of impregnated diamond tools are closely related to their wear conditions, among which different wear modes of the metal matrix play an essential role. Since traditional qualitative description cannot meet the requirement of mathematical relationship establishment, a deep learning method, Mask R-CNN, was applied for the quantitative determination of the matrix wear based on scanning electron microscope (SEM) images. A series of WC-Cu based metal matrix composite (MMC) samples had been prepared by hot-pressed sintering, followed by a pin-on-disc wear test to obtain the wear surface images, and the datasets were established based on a normal wear classification principle where classification is of four basic types: abrasive wear, adhesive wear, fatigue wear and corrosion wear (corrosion wear is not involved in this study). After training, validation, and test based on the SEM wear image datasets, the wear segmentation results from the trained model indicated that Mask R-CNN could automatically identify the wear of metal matrices efficiently and accurately, which was in good agreement with the results obtained by manual labelling. By modifying the algorithm codes, the masks of abrasive, adhesive, and fatigue wear were extracted and counted for model effectiveness evaluation. Moreover, the wear condition values (i.e., wear region areas) obtained from extracted masks would be easily applied for correlation analysis between cutting tool qualities and drilling efficiencies in future research as well. In comparison with statistic results by artificial cognition, the three types of wear showed an average wear region mask IoU over 70%, and an average wear region area loss of less than 3%. In the process of wear detection on similar wear images in published work, the Mask R-CNN model also presented good performances. All related codes and SEM image datasets are available at https://github.com/sunwucheng/IDB_matrix_wear.