# Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction
## 1 Introduction
Subsequently, by subdividing the workpiece into a grid, and based on the minimum grain size at each grid point, they proposed a novel simulation model to describe the surface topography during the grinding process. This model utilizes simulation techniques to predict the surface roughness in ultrasonic vibration-assisted grinding and has been experimentally validated. Furthermore, an in-depth discussion was conducted on the influence of ultrasonic amplitude on surface roughness. Empirical modeling paradigms establish process-property relationships through systematic experimental investigations. [PERSON] et al. [14] studied multi-objective optimization of the external circular grinding process, selected cutting speed, feed speed, and cutting depth as input parameters of the experimental process, designed the experimental matrix using the Taguchi method, and applied the data envelopment-based sorting (DEAR) method to determine the values of the input parameters. At the same time, both minimum surface roughness and maximum material removal rate were ensured. [PERSON] et al. [15] conducted orthogonal experiments to investigate the impact of grinding parameters (wheel speed, workpiece speed, and grinding depth) on the surface quality of ceramic internal cylindrical grinding. As a result of these experiments, two mathematical models for predicting surface roughness were established. The surface roughness prediction model derived from empirical equations exhibited better predictive performance compared to the theoretical prediction model. The empirical model based on experimental design and analysis requires large cost and time investment in experimental design, data acquisition, and processing, especially in the complex grinding process, which also requires a large number of tests and data processing. At the same time, the experimental data are affected by experimental conditions, environment, equipment, and other factors, and they may have certain limitations. The proliferation of industrial IoT systems has enabled comprehensive process digitization through multi-modal sensor networks. Commonly used monitoring signals include force signal [16; 17], vibration signal [18; 19], acoustic emission signal [20; 21], current signal [22; 23], etc. These signals contain dynamic multi-physical field properties in the cutting process. [PERSON] et al. [24] proposed a time-spatial spectrum analysis method based on the monitored grinding force signal and the grinding surface texture curve, established a Chi-square distribution model, and predicted the amplitude-frequency response of the surface texture through the monitoring of the force signal. They found that the surface roughness (Ra) could be calculated from the surface texture curve. [PERSON] et al. [25] established a surface roughness prediction model that introduced cutting power as a decision variable and considered the dynamic changes of grinding parameters and grinding wheel state. Compared with common models, the prediction accuracy was improved, especially in the case of grinding wheel wear. [PERSON] et al. [26] successfully improved the accuracy of online prediction of nodular cast iron surface roughness by extracting 13 parameters that reflect the characteristics of grinding acoustic emission signals and combining a genetic algorithm and BP neural network optimized using particle swarm optimization. [PERSON] et al. [27] proposed a multi-sensor signal fusion method based on principal component analysis to extract fusion features from the force and vibration signals in the grinding process, which retains the physical significance of the original features and achieves stable and high-precision surface roughness prediction. [PERSON] et al.