In-process belt-image-based material removal rate monitoring for abrasive belt grinding using CatBoost algorithm

Abstract

A reliable material removal rate (MRR) prediction method significantly optimizes the grinding surface quality and improves the processing efficiency for robotic abrasive belt grinding. Using worn-belt image features to predict MRR is a direct and reliable method; however, this method is rarely reported at present. This paper proposes an MRR prediction method for Inconel 718 grinding based on the abrasive belt image analysis and categorical boosting (CatBoost) algorithm. During belt grinding, four wear types of abrasive belts, namely fracture, adhesion, rubbing wear, and fall-off, are identified and analyzed. Under various grinding parameters, the experimental MRR rapidly decreases at first, then in a gradual manner. For an effective evaluation of belt wear severity, cutting grain area ratio, color moments, and texture features are extracted from belt images. MRR and abrasive belt image features are strongly correlated after normalization. All image features are taken into account for MRR prediction model training. Verification experiments indicate that the predicted data is in good agreement with the experimental data. The maximum absolute error, mean absolute error, root mean square error, and determination coefficient of the MRR prediction model are 0.17 μm, 0.4 μm, 0.2 μm, and 99.42%, respectively, which are superior to those of other popular machine learning algorithms. In this study, we present a comprehensive understanding of the relationship between MRR and abrasive belt characteristics, as well as demonstrate the feasibility of accurately predicting MRR using the CatBoost algorithm.