# Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction
## 1 Introduction  
Precision bearings serve as critical load-bearing interfaces in mechanical systems, where the outer ring's machining quality dictates performance metrics including dynamic load capacity, operational stability, and service lifespan. Grinding plays a vital role in bearing production and is the most important process in manufacturing high-precision, high-quality bearings. Therefore, the bearing outer ring grinding process directly influences the performance of the bearing ring, making it highly important to enhance precision grade. The topographical features of ground surfaces govern multiple functional aspects, from interfacial contact mechanics to long-term reliability indicators such as assembly precision, fatigue strength, corrosion resistance, and contact stiffness. They serve as a crucial indicator for evaluating the grinding process. Consequently, to improve the quality of bearing grinding, it is very important to predict the surface roughness of the bearing outer ring during the grinding process. Conventional post-process surface characterization techniques present notable limitations in terms of throughput and operational economics, often relying on roughness meters for measurement after grinding. This approach may lead to a significant number of workpieces being deemed unqualified, thereby increasing costs. However, roughness issues may arise in the early stages of the grinding process. Early detection and handling can effectively reduce scrap rates, improve processing efficiency, and also play a crucial role in intelligent processing. Therefore, the importance of real-time prediction of bearing ring surface roughness is increasingly prominent, as it not only enhances production efficiency and reduces production costs but also improves product quality levels. As an important cutting process, grinding has attracted extensive research from many scholars focusing on monitoring and prediction during the grinding process. The monitoring parameters are also quite diverse, ranging from workpiece roundness [1; 2; 3] and grinding burns [4; 5; 6] to abrasive belt wear [7; 8; 9], as well as thermal damage in cylindrical rough grinding [10]. Developing models that can accurately predict the surface roughness of workpieces has always been a challenge in the field of grinding. State-of-the-art approaches for surface topography prediction in grinding operations typically adopt one of three methodological frameworks: prediction models based on machining theory, empirical models based on experimental design and analysis, and prediction models based on signal data. Prediction modeling strategies derive surface generation predictions from fundamental abrasive-workpiece interaction mechanisms. [PERSON] et al. [11], based on a normal distribution model of the grinding wheel surface and grit size, and considering the interaction between the grinding wheel and the workpiece, established a three-dimensional theoretical model for the surface topography of inner-diameter ground bearing rings. The theoretical model for the grinding surface topography was validated by examining the surface morphology of bearing rings under various machining parameters. [PERSON] et al. [12] defined the surface characteristics of the grinding wheel using grinding wheel parameters. Based on the relationship between bearing raceway grinding and surface grinding, as well as the movement trajectory of the abrasive grains on the grinding wheel surface during processing, a prediction model for the surface roughness of bearing raceway grinding was established. Based on the experimental and simulation results of grinding surface roughness, the effects of grinding wheel speed, workpiece speed, and grinding depth on surface roughness were investigated, and the surface roughness at high grinding wheel line speeds was predicted. [PERSON] et al. [13] considered the ultrasonic vibration of the workpiece and the shape of abrasive grains, and they established a surface equation for abrasive grain trajectories as a function of time.