Optimized XGBoost Model with Small Dataset for Predicting Relative Density of Ti-6Al-4V Parts Manufactured by Selective Laser Melting
Abstract
:1. Introduction
2. Method
2.1. Experimental Dataset
2.2. XGBoost Model
2.3. Hyperparameter Optimization with GridsearchCV Method
3. Results and Discussion
3.1. Performance of the Optimized XGBoost Model
3.2. Influence of Dataset Sizes
3.3. Comparing the Predictive Ability with That of Other ML Models under Small Dataset
4. Conclusions
- (1)
- The trained optimized XGBoost model can effectively provide accurate correspondence between the relative density of the SLMed Ti-6Al-4V parts by SLM and the processing parameters.
- (2)
- As the dataset size decreases, when the size of the test dataset is larger than 541, the prediction accuracy changes slightly, but when the size of the test dataset is smaller than 541, the prediction accuracy drops sharply, at which point the model has lost its predictive ability.
- (3)
- The present optimized XGBoost model outperforms the ANN and SVR models with respect to the accuracy and generality in predicting the relative density of the SLMed Ti-6Al-4V parts under a small dataset.
- (4)
- The optimized XGBoost model has strong practicability under a small dataset. Using this method, the SLM operators can accurately estimate the relative density of the products based on the input processing parameters before printing, without spending a great deal of experience and time.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Element | Al | V | Fe | C | N | O | H | Ti | Others |
---|---|---|---|---|---|---|---|---|---|
wt. % | 5.50–6.50 | 3.50–4.50 | 0.25 | 0.08 | 0.03 | 0.13 | 0.0125 | Balance | 0.50 |
Process Parameters | Unit | Value |
---|---|---|
Laser scanning speed | mm/s | 800, 900, 1000, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000, 2100, 2200, 2300, 2400, 2500 |
Laser power | W | 80, 90, 95, 100, 105, 110, 115, 120, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180 |
Hatch distance | μm | 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100 |
Power layer thickness | μm | 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80 |
Item | Range of Values | Tolerance |
---|---|---|
1–10 | 1 | |
0.01–0.3 | 0.02 | |
K | 100–600 | 50 |
0–0.05 | 0.01 | |
0–1 | 0.1 |
Training Dataset (Set) | Test Dataset (Set) | MAE | RMSE | |
---|---|---|---|---|
48,648 | 10,811 | 0.4768 | 0.6245 | 0.9699 |
27,027 | 6757 | 0.4815 | 0.6344 | 0.9696 |
16,216 | 4055 | 0.5194 | 0.7179 | 0.9643 |
8108 | 2028 | 0.6001 | 0.9917 | 0.9513 |
4324 | 1082 | 0.6871 | 1.1797 | 0.9428 |
2594 | 649 | 0.8011 | 1.7171 | 0.9184 |
2162 | 541 | 0.8889 | 2.1495 | 0.8930 |
1621 | 406 | 0.9870 | 2.2707 | 0.8840 |
486 | 122 | 1.5577 | 5.1405 | 0.7632 |
Test | SVR | DNN | Optimized XGBoost |
---|---|---|---|
MAE | 1.3344 | 0.8576 | 0.8011 |
RMSE | 4.8646 | 1.7316 | 1.7171 |
0.7687 | 0.7849 | 0.9184 |
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Zou, M.; Jiang, W.-G.; Qin, Q.-H.; Liu, Y.-C.; Li, M.-L. Optimized XGBoost Model with Small Dataset for Predicting Relative Density of Ti-6Al-4V Parts Manufactured by Selective Laser Melting. Materials 2022, 15, 5298. https://doi.org/10.3390/ma15155298
Zou M, Jiang W-G, Qin Q-H, Liu Y-C, Li M-L. Optimized XGBoost Model with Small Dataset for Predicting Relative Density of Ti-6Al-4V Parts Manufactured by Selective Laser Melting. Materials. 2022; 15(15):5298. https://doi.org/10.3390/ma15155298
Chicago/Turabian StyleZou, Miao, Wu-Gui Jiang, Qing-Hua Qin, Yu-Cheng Liu, and Mao-Lin Li. 2022. "Optimized XGBoost Model with Small Dataset for Predicting Relative Density of Ti-6Al-4V Parts Manufactured by Selective Laser Melting" Materials 15, no. 15: 5298. https://doi.org/10.3390/ma15155298
APA StyleZou, M., Jiang, W.-G., Qin, Q.-H., Liu, Y.-C., & Li, M.-L. (2022). Optimized XGBoost Model with Small Dataset for Predicting Relative Density of Ti-6Al-4V Parts Manufactured by Selective Laser Melting. Materials, 15(15), 5298. https://doi.org/10.3390/ma15155298