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%.

监控刀具磨损是确保工件表面质量和加工过程性能的主要考虑因素。大多数现有的工具磨损监测技术似乎都在寻找工具本身以外的地方以寻求解决方案，或者根本不适用于高度自动化的行业。因此，利用这些技术的见识，本文提出了一种基于图像处理的刀具磨损监测方法，该方法结合了随机森林分类器（RFC）和多元线性回归（MLR）模型来检测不同的磨损条件并评估机器人的剩余磨削能力砂带打磨。通过非接触式数字显微镜捕获砂带表面图像，建立了磨粒面积与砂带寿命之间的相关性，将基于树的RFC方法应用于砂带状态监测，并用MLR模型进行磨削能力评估。训练和测试的结果证明了所提出的监控方法的有效性：在不同的砂带条件下，RFC的总预测准确性超过90％，MLR模型的平均绝对百分比误差小于3.39％。