# Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction
## 6 Conclusions  
This research proposes a feature extraction-based surface roughness prediction method for precision grinding of bearing outer rings, which integrally considers the grinding processing parameters and a variety of sensor signals and extracts the combination of sensor signal features in the grinding process to realize the prediction of the surface roughness of precision grinding of the bearing outer ring. This improves the universality and accuracy of the prediction. The specific conclusions are as follows:  
1. For the small sample dataset, the grinding signal data are preprocessed accordingly, and the prediction model of surface roughness of precision grinding of the bearing outer ring based on feature extraction is established. The vibration and acoustic emission signals during the grinding process are decomposed using the CEEMDAN algorithm, and the signal components are retained according to the size of the correlation coefficient. The retained signal components are subjected to multi-transform domain feature extraction in the time-, frequency- and entropy-domains, then fused and downgraded via principal component analysis. The grinding parameters and the downgraded signal features are combined to form a total feature set reflecting the surface roughness of the grinding surface, and the grinding surface roughness of the outer ring is predicted using the support vector extraction model based on the total feature set reflecting the surface roughness of the grinding surface. Based on the total feature set reflecting the grinding surface roughness, the support vector regression model is used to realize the monitoring of the grinding surface roughness of bearing rings with small samples and multiple working conditions. Figure 7: Prediction results of different models. 2. Multi-case thin-walled bearing collar cylindrical grinding experiments are conducted to experimentally verify the established prediction method. In the small sample data set, the comparative experimental results show that, compared with simply extracting the signal time-domain and frequency-domain features, the introduction of entropy features proposed in this research can extract signals from the grinding vibration and acoustic emission that contain more information related to the surface roughness of the grinding surface. At the same time, the grinding parameters also contain information related to the surface roughness of the grinding surface, which in turn can be used to obtain regression prediction of a better degree of fit and higher accuracy. As a result, the \(R^{2}\), \(MAE\), \(RMSE\), and \(MAPE\) values of the model are 0.9953, 0.0020, 0.0050, and 0.0187, respectively. 3. The experimental results show that support vector regression is more applicable to small sample sizes, which improves the accuracy of the prediction model of precision grinding surface roughness of the bearing outer ring. The values of \(R^{2}\), \(MAE\), \(RMSE\), and \(MAPE\) of the model are improved from 0.9473, 0.0088, 0.0167, and 0.0612 to 0.9953, 0.0020, 0.0050, and 0.0187. 4. The prediction method of surface roughness of bearing outer ring precision grinding based on feature extraction proposed in this research can effectively carry out the prediction of surface roughness of bearing collar outer ring grinding with small samples and multiple working conditions, improve the accuracy of the prediction model, and provide a basis for the development of relevant optimization decisions for bearing collar precision grinding. This paper employs feature selection and SVR for predicting surface roughness, achieving promising results. However, there remains significant room for improvement in specific algorithmic aspects such as feature selection methods, parameter optimization, and model architecture to enhance prediction accuracy and robustness, thereby further advancing surface roughness prediction technology.