A New Prediction Method for the Preload Drag Force of Linear Motion Rolling Bearing
Abstract
:1. Introduction
2. Experiment
2.1. Loading Test
2.2. Preload Drag Force Measurement
2.3. Raceway Topography Measurement
2.4. Test Procedure
3. Results
3.1. Preload Drag Force Degradation
3.2. Change in Raceway Topography
3.3. Two-Dimensional Profile of a Raceway Surface
3.3.1. Statistical Parameters
3.3.2. Fractal Parameters
3.3.3. Recursive Analysis
4. Correlation Study
4.1. Establishing Correlation
4.2. Gray Correlation
5. Preload Prediction
5.1. Gaussian Regression Process Model of Preload Drag Force
5.2. Gaussian Regression Model Training and Prediction Analysis
5.2.1. Gaussian Regression Model Training
5.2.2. Prediction and Test Comparison of Preload Drag Force
6. Conclusions
- The degradation in preload drag force of the LMRB is divided into three stages: rapid descent, slow descent, and failure.
- Comparing the variation in preload drag force with the microscopic images and two-dimensional profile of the raceway surface shows that the raceway morphology reflects the degradation state of its preload drag force.
- The four parameters Ra, Rt, D, and Rr effectively represent the variational law of raceway morphology. The variational trends of Ra and Rt are “bathtub shaped”, and the variational trends of the fractal dimension D and recurrence rate Rf show an “inverted bathtub shape”. This conclusion provides a new way to monitor the degradation of the preload drag force of an LMRB.
- Gray correlation analysis of the degradation trend of the preload drag force yields correlations of 0.645, 0.657, 0.718, and 0.722 for the four characteristic parameters Ra, Rt, D, and Rr, respectively. Rr was recognized as optimal characterizing parameter.
- By using the Gaussian process regression theory, a regression model of the preload drag force is constructed on the basis of the rolling track topography of LMRB. The accuracies of prediction results of the three sets are 93.75%, 98.5%, and 98.8%, respectively, which validates the accuracy of the proposed model. This model describe in this paper can be utilized efficiently for the prediction of LMRB preload drag force degradation based on rolling morphology.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Parameter | Symbol | Value/Unit |
---|---|---|
Basic dynamic load rating | C | 58 kN |
Diameter of the rolling element | D | 4 mm |
Length of rolling element | l | 6 mm |
Basic static load rating | 135 kN | |
Hardness of raceway | H | 58 HRC |
Guide material | \ | GCr15 |
Carriage material | \ | GCr15 |
Rolling element material | \ | GCr15 |
Length of the tested rail | L | 1480 mm |
Loading and Running Text | |
---|---|
Loading | 34.8 kN |
Running speed | 50 m/min |
Measurement interval | 5 km (0–30 km), 10 km (>30 km) |
Lubrication method | Grease lubrication(glp-500) |
Experiment temperature | 20 ± 0.5 °C |
Running distance | 1000 mm |
Safety distance | 480 mm |
Preload drag force measurement text | |
Sliding table moving speed | 0.7 m/min |
Measuring length | 1000 mm |
Sampling frequency | 10 Hz |
Raceway topography measurement text | |
Range of surface profiler | 100 μm |
Gaussian filter cutoff point of surface profiler | 0.25 mm |
Resolution of surface profiler | 0.01 μm |
Measuring length | 2.5 mm |
Sampling points | 4500 |
Region | Region 1 | Region 2 | Region 3 | Region 4 | Region 5 | Region 6 | Average Value | |
---|---|---|---|---|---|---|---|---|
γ | ||||||||
γ (F, Ra) | 0.649 | 0.648 | 0.634 | 0.647 | 0.656 | 0.635 | 0.645 | |
γ (F, Rt) | 0.668 | 0.652 | 0.658 | 0.626 | 0.678 | 0.659 | 0.657 | |
γ (F, D) | 0.718 | 0.717 | 0.725 | 0.711 | 0.721 | 0.717 | 0.718 | |
γ (F, Rr) | 0.730 | 0.733 | 0.726 | 0.696 | 0.720 | 0.725 | 0.722 |
Parameters | Base Function | Kernel Function | RMSE | R2 | |
---|---|---|---|---|---|
Category | |||||
GPR | Constant | Matern 5/2 | 0.00935 | 1.00 |
Test Groups | Region 1 | Region 2 | Region 3 | |
---|---|---|---|---|
Data | ||||
Measured value | 5.76 | 6.66 | 9.30 | |
Predicted value | 6.12 | 6.76 | 9.41 | |
Accuracy | 93.75% | 98.5% | 98.8% |
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Liu, L.; Chen, H.; Li, Z.; Li, W.-P.; Liang, Y.; Feng, H.-T.; Zhou, C.-G. A New Prediction Method for the Preload Drag Force of Linear Motion Rolling Bearing. Metals 2022, 12, 2139. https://doi.org/10.3390/met12122139
Liu L, Chen H, Li Z, Li W-P, Liang Y, Feng H-T, Zhou C-G. A New Prediction Method for the Preload Drag Force of Linear Motion Rolling Bearing. Metals. 2022; 12(12):2139. https://doi.org/10.3390/met12122139
Chicago/Turabian StyleLiu, Lu, Hu Chen, Zhuang Li, Wan-Ping Li, Yi Liang, Hu-Tian Feng, and Chang-Guang Zhou. 2022. "A New Prediction Method for the Preload Drag Force of Linear Motion Rolling Bearing" Metals 12, no. 12: 2139. https://doi.org/10.3390/met12122139