# Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction
## 1 Introduction
[28] studied the influence of vibration characteristic frequency on surface roughness by extracting the vibration characteristics of fluorophlogoptic in speed point grinding, and they analyzed the influence of five process parameters such as grinding speed, table feed speed, grinding depth, deflection angle, and inclination angle on the deviation rate of characteristic frequency and surface roughness. On this basis, A modified model containing vibration characteristics was proposed, which successfully realizes the goal of real-time vibration signal monitoring and surface roughness control. [PERSON] et al. [29] analyzed the correlation between the characteristic values in different frequency bands of grinding acoustic emission signals and the grinding surface roughness values. They selected the best sensitive frequency band and the characteristic matrix of grinding acoustic emission signals as the input parameters of a CNN-BiLSTM neural network. A method for predicting the surface roughness of PSZ ceramic grinding based on correlation analysis and a convolutional bidirectional long short-term memory neural network (CNN-BiLSTM) was proposed. [PERSON] et al. [30] established three surface roughness prediction models based on vibration signals, namely a fast Fourier transform-deep neural network, fast Fourier transform-long short-term memory network, and one-dimensional convolutional neural network (1D-CNN). Through comparative analysis, it was shown that the 1D-CNN had strong feature extraction ability. [PERSON] et al. [31] designed a suitable artificial neural network to predict grinding surface roughness and grinding force by installing a single integrated acoustic emission sensor on the machine tool. They trained and tested two models, one using only grinding parameters and the other using acoustic emission signals and grinding parameters as input data. A feedforward neural network was selected to model Bayesian backpropagation, and the model was verified using experiments with different grinding parameters and neural network parameters. The experimental results show that the acoustic emission signal, as an additional input parameter to the grinding parameters, can significantly improve the efficiency of neural network prediction of grinding force and surface roughness. The surface roughness prediction method based on signal data avoids strict mathematical derivation of complex mechanisms in the grinding process. The signal data in the grinding process can be collected in real time, which can reflect changes in the machining process in real time so the model can be quickly adjusted and optimized to adapt to dynamic changes in the grinding process. In summary, the monitoring and prediction of grinding processes have been widely studied, but the following issues remain:  
1. Most surface roughness prediction models are established using a single sensor, and the influence of process parameters and other factors on surface roughness is rarely considered at the same time, which greatly limits the stability and universality of prediction models. 2. In actual processing, the number of signal samples is small, and the deep learning model does not provide clear advantages in the case of small samples and strong noise due to the excessive number of parameters, the need for a large amount of data for training, complex parameter adjustment processes, and other reasons. To address the aforementioned issues, this study proposes an innovative feature engineering method that integrates CEEMDAN decomposition, multi-domain feature extraction, and grinding process parameters. It adaptively decomposes signals to screen sensitive components and effectively captures the nonlinear dynamics of the grinding process through entropy-based features, demonstrating significant advantages over traditional single-sensor or single-domain feature analysis methods. This enables high-precision monitoring of grinding surface roughness for bearing outer rings under conditions of small sample sizes and multiple operations.