Abstract
The study aims to explore quality prediction in ceramic bearing grinding processing, with particular focus on the effect of grinding parameters on surface roughness. The study uses active learning regression model for model construction and optimization, and empirical analysis of surface quality under different grinding conditions. At the same time, various deep learning models are utilized to conduct experiments on quality prediction in grinding processing. The experimental setup covers a variety of grinding parameters, including grinding wheel linear speed, grinding depth and feed rate, to ensure the accuracy and reliability of the model under different conditions. According to the experimental results, when the grinding depth increases to 21 μm, the average training loss of the model further decreases to 0.03622, and the surface roughness Ra value significantly decreases to 0.1624 μm. In addition, the experiment also found that increasing the grinding wheel linear velocity and moderately adjusting the grinding depth can significantly improve the machining quality. For example, when the grinding wheel linear velocity is 45 m/s and the grinding depth is 0.015 mm, the Ra value drops to 0.1876 μm. The results of the study not only provide theoretical support for the grinding processing of ceramic bearings, but also provide a basis for the optimization of grinding parameters in actual production, which has an important industrial application value.