# Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction
## 5 Experimental Results and Discussion  
The hardware setup consisted of a KELLENBERGER CNC internal and external cylindrical grinding machine (AG UR225/1500, Kellenberger, St. Gallen, Switzerland, with roundness accuracy for fly grinding of 0.2 \(\upmu\)m, max. grinding diameter of 432 mm, grinding length of 1500 mm, and center height of 225 mm). The grinding wheel was made of CBN (cubic boron nitride) with a diameter of 400 mm. The workpiece as a thin-walled bearing ring (model 719-182B), manufactured from GCr15 bearing steel, and featured an outer ring diameter of 214 mm [35]. This configuration ensured high-precision grinding for the bearing ring. Grinding surface roughness is affected by many factors, such as grinding wheel type, workpiece parameters, grinding parameters, and so on. Compared with other processing, grinding processing usually takes a long time, and the feed speed is slow. If all the influencing factors are considered in the experimental scheme, the experimental workload will become huge. In order to explore the grinding surface roughness law of the outer ring of the bearing ring and comprehensively consider the experimental cost, the orthogonal experiment design included 16 test conditions with 3 factors and 4 levels. The varying parameters for grinding wheel linear speed (m/s), workpiece speed (r/min), and grinding depth (\(\upmu\)m) are shown in Table 4. While the parameter ranges in Table 4 reflect current production standards, existing data typically provide local optima within fixed operational constraints. Our model aims to generalize process behavior beyond historical parameter combinations (e.g., predicting untested scenarios), as well as to quantify interactions between parameters (e.g., nonlinear effects of wheel speed and feed rate on Ra) that may not be evident in routine production records. The method of reverse grinding was used in the experiment. At the same time, considering that electrical components such as sensors and signal data lines are easy to damage, each processing time is short, and the bearing surface temperature rise is low, the experiment adopts a dry grinding method. The grinding repeats 5 times and holds for 10 s under the same parameters. According to [PERSON]'s sampling law, in order to avoid aliasing, the sampling frequency should be greater than twice the highest frequency in the signal. Therefore, the \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & **Grinding Wheel** & **Workpiece** & **Grinding** \\  & **Linear** & **Speed/r\(\cdot\)min\({}^{-1}\)** & **Depth/\(\upmu\)m** \\ \hline
1 & 25 & 20 & 20 \\
2 & 25 & 40 & 30 \\
3 & 25 & 60 & 40 \\
4 & 25 & 80 & 50 \\
5 & 30 & 40 & 40 \\
6 & 30 & 60 & 50 \\
7 & 30 & 80 & 20 \\
8 & 30 & 20 & 30 \\
9 & 35 & 60 & 50 \\
10 & 35 & 80 & 20 \\
11 & 35 & 20 & 30 \\
12 & 35 & 40 & 40 \\
13 & 40 & 80 & 30 \\
14 & 40 & 20 & 40 \\
15 & 40 & 40 & 50 \\
16 & 40 & 60 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Three-factor four-level L16 orthogonal experiment table. sampling frequency set by the vibration sensor acquisition system is 51.2 KHz, and the sampling frequency set by the acoustic emission sensor data acquisition card is 4 MHz. The probe sensor of the roughness meter was placed at the highest point of the bearing outer ring, and five points were randomly selected along the vertical direction of the grinding direction for measurement. The sampling length was 0.8 mm, the evaluation length was 0.8 \(\times\) 5 mm, and the measurement parameter was the arithmetic average deviation Ra value of the evaluation profile. After measurement, the average value of 5 points is taken as the surface roughness value. The test equipment layout and test site are shown in Figures 2 and 3. An acoustic emission sensor and vibration sensor were placed on the specially designed bearing ring fixture, and the acoustic emission signal and vibration signal were collected at the same time.