Tool wear monitoring system in belt grinding based on image-processing techniques

Tool wear monitoring is a major concern in securing surface quality of workpieces and performance of the machining process. Most existing tool wear monitoring techniques seem to have looked at places other than the tool itself for solution or simply inapplicable in the highly automated industries. With insights from these techniques, this paper thus proposes an image-processing-based tool wear monitoring method that combines random forest classifier (RFC) and a multiple linear regression (MLR) model to detect different wear conditions and evaluate the remaining grinding ability for robotic belt grinding. Through a non-contact digital microscope capturing images of belt surfaces, the correlation between abrasive grain area and grinding belt life is established, the tree-based RFC method is applied for belt condition monitoring, and a MLR model to grinding ability evaluation. Results from training and testing verify the validity of the proposed monitoring method: the total prediction accuracy of RFC is over 90% under different grinding belt conditions, and the mean absolute percentage error of the MLR model is less than 3.39%.