Prediction Model of Sound Signal in High-Speed Milling of Wood–Plastic Composites
Abstract
:1. Introduction
2. Experimental Scheme and Design
2.1. Experimental Conditions
2.2. Experimental Design
3. Milling Sound Signal Pre-Processing
4. Regression Model and Verification of Milling Sound Signal in Full-Factor Test
5. Prediction Model of Milling Sound Signal Based on BP Neural Network Optimized by PSO
5.1. Construction of BP Neural Network Prediction Model
5.2. Optimization of BP Neural Network by PSO
5.3. Network Training and Verification
6. Conclusions
- (1)
- The mathematical regression model (Equation (3)) of the sound signal in the WPC milling process was established by using the full-factor test method. The test results show that the prediction error of the regression model is less than 4.6%, indicating good prediction ability.
- (2)
- The PSO algorithm was used to optimize the BP neural network prediction model of the sound signal in the WPC milling process, obtaining the PSO–BP prediction model. Compared with the BP neural network, the determination coefficient (R2) of the PSO–BP neural network prediction model increased from 0.83 to 0.93 and the prediction accuracy improved significantly. Therefore, during the high-speed milling of WPCs, the PSO–BP neural network prediction model has a better global approximation ability than the BP neural network model.
- (3)
- In the sound signal prediction model for the WPC milling process, the total mean values of the prediction error of the PSO–BP model decreased by 59% and 28%, respectively, compared with the regression and traditional BP neural network models. Therefore, the PSO–BP neural network model can effectively improve the prediction accuracy of WPCs’ milling sound signal and provide a highly precise mathematical model for predicting the variation law of the sound signal in the high-speed milling of WPCs.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Material Composition | Wood Flour | Calcium Carbonate | PE Used Material | Phase Solvent | Lubricant |
---|---|---|---|---|---|
Proportion | 54.70% | 13.70% | 27.35% | 2.74% | 1.51% |
Density (g/cm3) | Flexural Modulus (MPa) | Shore Hardness (HD) |
---|---|---|
1.19 | 28 | 58 |
Name | Unit | Low Level | High Level |
---|---|---|---|
ap | mm | 3 | 5 |
ae | mm | 6 | 10 |
v | m/min | 250 | 350 |
f | mm/r | 0.1 | 0.5 |
Horizontal Number | ap | ae | v | f |
---|---|---|---|---|
(mm) | (mm) | (m/min) | (mm/r) | |
1 | 1 | 2 | 150 | 0.1 |
2 | 2 | 4 | 200 | 0.2 |
3 | 3 | 6 | 250 | 0.3 |
4 | 4 | 8 | 300 | 0.4 |
5 | 5 | 10 | 350 | 0.5 |
Std | Test Number | ap | ae | v | f | SPL |
---|---|---|---|---|---|---|
(mm) | (mm) | (m/min) | (mm/r) | dB | ||
8 | 1 | 5 | 10 | 350 | 0.1 | 77.7 |
4 | 2 | 5 | 10 | 250 | 0.1 | 74.1 |
13 | 3 | 3 | 6 | 350 | 0.5 | 80.2 |
12 | 4 | 5 | 10 | 250 | 0.5 | 90.2 |
16 | 5 | 5 | 10 | 350 | 0.5 | 72.8 |
9 | 6 | 3 | 6 | 250 | 0.5 | 82.2 |
15 | 7 | 3 | 10 | 350 | 0.3 | 80.8 |
10 | 8 | 5 | 6 | 250 | 0.3 | 83.3 |
5 | 9 | 3 | 6 | 350 | 0.1 | 81.9 |
17 | 10 | 4 | 8 | 300 | 0.1 | 83.5 |
19 | 11 | 4 | 8 | 300 | 0.5 | 82.3 |
3 | 12 | 3 | 10 | 250 | 0.5 | 93.6 |
1 | 13 | 3 | 6 | 250 | 0.1 | 77.1 |
6 | 14 | 5 | 6 | 350 | 0.1 | 68.3 |
14 | 15 | 5 | 6 | 350 | 0.3 | 82.5 |
7 | 16 | 3 | 10 | 350 | 0.5 | 85.5 |
11 | 17 | 3 | 10 | 250 | 0.1 | 89.1 |
18 | 18 | 4 | 8 | 300 | 0.5 | 79.4 |
2 | 19 | 5 | 6 | 250 | 0.1 | 68.5 |
Test Number | ap | ae | v | f | SPL |
---|---|---|---|---|---|
(mm) | (mm) | (m/min) | (mm/r) | dB | |
1 | 1 | 2 | 150 | 0.1 | 67.2 |
2 | 1 | 4 | 200 | 0.2 | 69.4 |
3 | 1 | 6 | 250 | 0.3 | 73.9 |
4 | 1 | 8 | 300 | 0.4 | 84.9 |
5 | 1 | 10 | 350 | 0.5 | 82.4 |
6 | 2 | 2 | 200 | 0.3 | 80.5 |
7 | 2 | 4 | 250 | 0.4 | 81.3 |
8 | 2 | 6 | 300 | 0.5 | 81.5 |
9 | 2 | 8 | 350 | 0.1 | 77.7 |
10 | 2 | 10 | 150 | 0.2 | 79.7 |
11 | 3 | 2 | 250 | 0.5 | 83.8 |
12 | 3 | 4 | 300 | 0.1 | 78.3 |
13 | 3 | 6 | 350 | 0.2 | 84.1 |
14 | 3 | 8 | 150 | 0.3 | 82.6 |
15 | 3 | 10 | 200 | 0.4 | 87.7 |
16 | 4 | 2 | 300 | 0.2 | 91.8 |
17 | 4 | 4 | 350 | 0.3 | 75.6 |
18 | 4 | 6 | 150 | 0.4 | 85.4 |
19 | 4 | 8 | 200 | 0.5 | 89.2 |
20 | 4 | 10 | 250 | 0.1 | 79.9 |
21 | 5 | 2 | 350 | 0.4 | 84.1 |
22 | 5 | 4 | 150 | 0.5 | 85.6 |
23 | 5 | 6 | 200 | 0.1 | 80.4 |
24 | 5 | 8 | 250 | 0.2 | 84.8 |
25 | 5 | 10 | 300 | 0.3 | 88.1 |
Number | ap | ae | v | f | Prediction Results | Real Values | Error Percentage |
---|---|---|---|---|---|---|---|
(mm) | (mm) | (m/min) | (mm/r) | dB | dB | % | |
18 | 4 | 6 | 150 | 0.4 | 81.7 | 85.4 | 4.6 |
19 | 4 | 8 | 200 | 0.5 | 86.4 | 89.2 | 3.3 |
20 | 4 | 10 | 250 | 0.1 | 81.5 | 79.9 | 1.9 |
21 | 5 | 2 | 350 | 0.4 | 85.7 | 84.1 | 1.8 |
22 | 5 | 4 | 150 | 0.5 | 83.4 | 85.6 | 2.7 |
23 | 5 | 6 | 200 | 0.1 | 81.2 | 80.4 | 1.1 |
24 | 5 | 8 | 250 | 0.2 | 85.3 | 84.8 | 0.6 |
25 | 5 | 10 | 300 | 0.3 | 90.0 | 88.0 | 2.3 |
m | n | Number of Hidden Layers | H | Transfer Function | Training Function | |
---|---|---|---|---|---|---|
Hidden Layer | Output Layer | |||||
4 | 1 | 1 | 3 | logsig | purelin | trainlm |
Population Size | Evolution Algebra | c1 | c2 |
---|---|---|---|
20 | 40 | 1.49618 | 1.49618 |
Number | ap | ae | v | f | BP Prediction Values | PSO–BP Prediction Values | Real Values | BP Error Percentage | PSO–BP Error Percentage |
---|---|---|---|---|---|---|---|---|---|
(mm) | (mm) | (m/min) | (mm/r) | (dB) | (dB) | (dB) | (%) | (%) | |
18 | 4 | 6 | 150 | 0.4 | 87.1 | 86.7 | 85.4 | 1.9 | 1.50 |
19 | 4 | 8 | 200 | 0.5 | 87.6 | 89.5 | 89.2 | 1.8 | 0.40 |
20 | 4 | 10 | 250 | 0.1 | 81.3 | 78.2 | 79.9 | 1.7 | 2.10 |
21 | 5 | 2 | 350 | 0.4 | 83.4 | 84.3 | 84.1 | 0.9 | 0.13 |
22 | 5 | 4 | 150 | 0.5 | 87.5 | 86.1 | 85.6 | 2.2 | 0.60 |
23 | 5 | 6 | 200 | 0.1 | 80.8 | 80.8 | 80.4 | 0.5 | 0.51 |
24 | 5 | 8 | 250 | 0.2 | 84.7 | 83.1 | 84.8 | 0.2 | 2.00 |
25 | 5 | 10 | 300 | 0.3 | 86.8 | 87.8 | 88.0 | 1.3 | 0.30 |
Mean error percentage | 1.3 | 0.94 |
Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
---|---|---|---|---|
BP | 0.91 | 0.83 | 0.80 | 1.24 |
PSO–BP | 0.96 | 0.93 | 0.92 | 1.08 |
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Wei, W.; Shang, Y.; Peng, Y.; Cong, R. Prediction Model of Sound Signal in High-Speed Milling of Wood–Plastic Composites. Materials 2022, 15, 3838. https://doi.org/10.3390/ma15113838
Wei W, Shang Y, Peng Y, Cong R. Prediction Model of Sound Signal in High-Speed Milling of Wood–Plastic Composites. Materials. 2022; 15(11):3838. https://doi.org/10.3390/ma15113838
Chicago/Turabian StyleWei, Weihua, Yunyue Shang, You Peng, and Rui Cong. 2022. "Prediction Model of Sound Signal in High-Speed Milling of Wood–Plastic Composites" Materials 15, no. 11: 3838. https://doi.org/10.3390/ma15113838