Prediction Method for High-Speed Laser Cladding Coating Quality Based on Random Forest and AdaBoost Regression Analysis
Abstract
:1. Introduction
2. Experimental Design
2.1. Experimental Equipment and Materials
2.2. Experimental Principle
2.3. Experimental Design
2.4. Experimental Method
3. Model for Predicting the Quality of the Cladding Layer
3.1. Establishment of Prediction Model
3.1.1. The RF Model
- A training set with n samples was selected from the high-speed laser cladding parameter combination sample set using the Bootstrap method. The selected samples were used to train a decision tree as the sample of the decision tree node.
- m features were randomly selected from the 6 features of the sample, satisfying the condition m << 6. Then, 1 feature was chosen from the m features as the splitting feature of the node; steps 1 to 2 were repeated 100 times, where 100 is the number of decision trees in the RF.
- The trained RF was used to predict the test sample, and the prediction result was obtained using the voting method.
3.1.2. The AB Model
- m samples were selected from the 25 sample sets of high-speed laser cladding as a training set D, D = ((x1, y1), (x2, y2), …, (xm, ym)).
- A sampling weight D1(x) = wj was assigned to each training sample, with the initial wj = 1/m, i = 1. A training set was generated by sampling with replaceability, with an equal volume and weight. A weak learner was assigned for each training round, and the weight of the weak learner was calculated.
- Weak learners were combined into a strong learner: ).
3.2. Tests and Results
4. Importance Evaluation and Prediction Model Verification
4.1. Evaluation of the Importance of High-Speed Laser Cladding Process Parameters
4.2. Verification of High-Speed Laser Cladding Coating Quality Using the AB Prediction Model
5. Conclusions
- (1)
- The prediction process of high-speed laser cladding coating quality is complicated, and the RF and AB algorithms have strong mapping ability and nonlinear relationships. Therefore, when solving complex multivariate nonlinear problems, they can be adopted to address the issue of high-precision fitting, which is difficult for multiple-regression analysis, thereby achieving effective prediction of cladding quality. The AB prediction model captures volatile data points, and it is more accurate and more sensitive to abnormal data (maximum or minimum) than the RF in the prediction of multiple response values.
- (2)
- The AB algorithm was also used to evaluate the importance of process parameters during the training of the learner. The most effective method to change the height and surface roughness of the cladding layer is to adjust the scanning speed. On the other hand, the overlap rate is the most important factor for controlling the dilution ratio and near-surface grain size of high-speed laser cladding. Alongside that, the microhardness of the coating and the thermal effect of the substrate can be effectively enhanced by adjusting the laser power and scanning speed. These observations provide a basis for the adjustment of process parameters in the stability control of the melting process.
- (3)
- The experimental results indicate that the prediction errors of the AB model on the response values of cladding layer height, molten pool depth, dilution ratio, grain size, surface roughness and microhardness are all less than 6%. Therefore, the quality of the high-speed laser cladding layer can be predicted by this prediction model during machining. The prediction results indicate that the application of machine learning methods based on the AB algorithm has a certain reference value and practical significance in parameter prediction and performance optimization of the high-speed laser cladding process. It provides a new idea for the process parameter control in high-speed laser cladding.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Parameter Type | Inversion |
---|---|
Power | 500–2000 W |
Wave length | 1080 nm |
Spot diameter | 1.2 mm |
Powder feeding method | Three-way coaxial powder feeding |
Scanning speed | 0–20 m |
Gas flow | 20–25 L/min |
Maximum spindle speed | 200 r/min |
Machine stroke (X-axis) | 3222 mm |
Machine stroke (Y-axis) | 400 mm |
Machine stroke (Z-axis) | 300 mm |
Chemical Composition | C | Si | Cr | Ni | Mo | B | Fe |
Value (wt.%) | 0.15 | 4.5 | 22 | 13 | 2 | 1.6 | Bal. |
No. | Laser Power/ | Scanning Speed/ | Overlap Ratio/ | Powder Flow Rate/ |
---|---|---|---|---|
P (W) | Ss (mm/min) | Or (%) | Vp (r/min) | |
1 | 660 | 3600 | 20 | 2.5 |
2 | 880 | 7200 | 35 | 3 |
3 | 1100 | 10,800 | 50 | 3.5 |
4 | 1320 | 14,400 | 65 | 4 |
5 | 1540 | 18,000 | 80 | 4.5 |
No. | Process Parameter | Response | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Laser Power/ | Scanning Speed | Overlap Ratio | Powder Feeding Rate | Height | Depth | Dilution Rate | Grain Size | Roughness | Hardness | |
P (W) | Ss (mm/min) | Or (%) | Vp (r/min) | H (μm) | D (μm) | H (%) | Ds (μm) | Ra (μm) | HV0.2 | |
1 | 660 | 3600 | 20 | 2.5 | 245.15 | 27.24 | 10 | 2.34 | 34.63 | 764.83 |
2 | 660 | 7200 | 35 | 3 | 131.01 | 51.8 | 28.33 | 2.17 | 25.63 | 769.87 |
3 | 660 | 10,800 | 50 | 3.5 | 170.3 | 29.07 | 14.58 | 1.03 | 6.71 | 915.10 |
4 | 660 | 14,400 | 65 | 4 | 90.29 | 9.03 | 9.09 | 0.75 | 3.03 | 969.22 |
5 | 660 | 18,000 | 80 | 4.5 | 105.51 | 25.12 | 19.23 | 0.78 | 9.96 | 987.57 |
6 | 880 | 3600 | 35 | 3.5 | 431.22 | 36.7 | 7.84 | 1.99 | 26.63 | 707.07 |
7 | 880 | 7200 | 50 | 4 | 293.37 | 27.5 | 8.57 | 1.1 | 24.8 | 838.93 |
8 | 880 | 10,800 | 65 | 4.5 | 222.01 | 14.17 | 6 | 0.8 | 4.49 | 862.75 |
9 | 880 | 14,400 | 80 | 2.5 | 237.17 | 12.48 | 5 | 0.89 | 4.62 | 822.71 |
10 | 880 | 18,000 | 20 | 3 | 101.63 | 31.54 | 23.68 | 1.39 | 24.05 | 803.18 |
11 | 1100 | 3600 | 50 | 4.5 | 514.41 | 45.92 | 8.2 | 1.15 | 25.75 | 869.69 |
12 | 1100 | 7200 | 65 | 2.5 | 272.33 | 31.25 | 10.29 | 0.87 | 17.9 | 819.93 |
13 | 1100 | 10,800 | 80 | 3 | 467.05 | 10.86 | 2.27 | 0.95 | 10.75 | 857.91 |
14 | 1100 | 14,400 | 20 | 3.5 | 109.78 | 34.1 | 23.68 | 1.51 | 14.26 | 797.77 |
15 | 1100 | 18,000 | 35 | 4 | 72.38 | 16.51 | 18.57 | 1.22 | 22.2 | 876.60 |
16 | 1320 | 3600 | 65 | 3 | 755.01 | 64.45 | 7.87 | 1.16 | 18.9 | 692.25 |
17 | 1320 | 7200 | 80 | 3.5 | 340.56 | 31.79 | 8.54 | 1.09 | 20.89 | 802.23 |
18 | 1320 | 10,800 | 20 | 4 | 184.97 | 33.63 | 15.38 | 1.83 | 21.38 | 756.58 |
19 | 1320 | 14,400 | 35 | 4.5 | 137.86 | 27.57 | 16.67 | 1.25 | 18.55 | 811.26 |
20 | 1320 | 18,000 | 50 | 2.5 | 165.23 | 18.36 | 10 | 1.01 | 18.82 | 822.14 |
21 | 1540 | 3600 | 80 | 4 | 948.57 | 102.4 | 9.75 | 1.31 | 26.57 | 624.48 |
22 | 1540 | 7200 | 20 | 4.5 | 190.51 | 51.29 | 21.21 | 1.72 | 26.57 | 729.75 |
23 | 1540 | 10,800 | 35 | 2.5 | 226.66 | 65.8 | 22.5 | 1.28 | 17.65 | 770.49 |
24 | 1540 | 14,400 | 50 | 3 | 208.09 | 31 | 12.96 | 1.03 | 4.49 | 771.56 |
25 | 1540 | 18,000 | 65 | 3.5 | 171.21 | 21.95 | 11.36 | 0.71 | 5.53 | 849.48 |
No. | Process Parameter | Response | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Laser Power/ | Scanning Speed | Overlap Ratio | Powder Feeding Rate | Height | Depth | Dilution Rate | Grain Size | Roughness | Hardness | |
P (W) | Ss (mm/min) | Or (%) | Vp (r/min) | H (μm) | D (μm) | H (%) | Ds (μm) | Ra (μm) | HV0.2 | |
1 | 660 | 14,400 | 65 | 3.5 | 85.78 | 9.57 | 10.04 | 0.79 | 5.83 | 903.29 |
2 | 660 | 18,000 | 50 | 4.5 | 95.35 | 25.15 | 20.87 | 1.03 | 6.23 | 927.11 |
3 | 880 | 3600 | 65 | 2.5 | 401.57 | 35.9 | 8.21 | 1.15 | 16.82 | 758.64 |
4 | 880 | 18,000 | 65 | 3.5 | 170.66 | 19.7 | 10.35 | 0.75 | 4.69 | 881.57 |
5 | 1100 | 18,000 | 65 | 3.5 | 180.59 | 20.51 | 10.20 | 0.91 | 6.09 | 837.64 |
6 | 1540 | 3600 | 80 | 3 | 985.15 | 100.5 | 9.25 | 1.35 | 18.82 | 658.49 |
Model | Height (H) | Depth (D) | Dilution (η) | ||||||
---|---|---|---|---|---|---|---|---|---|
MAE | RMSE | R2 | MAE | RMSE | R2 | MAE | RMSE | R2 | |
AdaBoost | 39.372 | 45.009 | 0.979 | 1.603 | 1.678 | 0.997 | 0.578 | 0.819 | 0.963 |
Model | Dendrite size (Ds) | Roughness (Ra) | Hardness (Hv0.2) | ||||||
MAE | RMSE | R2 | MAE | RMSE | R2 | MAE | RMSE | R2 | |
AdaBoost | 0.035 | 0.038 | 0.966 | 0.723 | 0.972 | 0.971 | 17.992 | 21.011 | 0.949 |
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Xv, Y.; Sun, Y.; Zhang, Y. Prediction Method for High-Speed Laser Cladding Coating Quality Based on Random Forest and AdaBoost Regression Analysis. Materials 2024, 17, 1266. https://doi.org/10.3390/ma17061266
Xv Y, Sun Y, Zhang Y. Prediction Method for High-Speed Laser Cladding Coating Quality Based on Random Forest and AdaBoost Regression Analysis. Materials. 2024; 17(6):1266. https://doi.org/10.3390/ma17061266
Chicago/Turabian StyleXv, Yifei, Yaoning Sun, and Yuhang Zhang. 2024. "Prediction Method for High-Speed Laser Cladding Coating Quality Based on Random Forest and AdaBoost Regression Analysis" Materials 17, no. 6: 1266. https://doi.org/10.3390/ma17061266