Machine learning enabling prediction in mechanical performance of Ti6Al4V fabricated by large-scale laser powder bed fusion via a stacking model

Abstract

Determining appropriate process parameters in large-scale laser powder bed fusion (LPBF) additive manufacturing pose formidable challenges that necessitate advanced approaches to minimize trial-and-error during experimentation. This work proposed a data-driven approach based on stacking ensemble learning to predict the mechanical properties of Ti6Al4V alloy fabricated by large-scale LPBF for the first time. This method can adapt to the complexity of large-scale LPBF data distribution and exhibits a more generalized predictive capability compared to base models. Specifically, the stacking model utilized artificial neural network (ANN), gradient boosting regressor, kernel ridge regression, and elastic net as base models, with the Lasso model serving as the meta-model. Bayesian optimization and cross-validation were utilized for model optimization and training based on a limited data set, resulting in higher predictive accuracy compared to traditional artificial neural network model. The statistical analysis of the ANN and stacking models indicates that the stacking model exhibits superior performance on the test set, with a coefficient of determination value of 0.944, mean absolute percentage error of 2.51%, and root mean squared error of 27.64, surpassing that of the ANN model. All statistical metrics demonstrate superiority over those obtained from the ANN model. These results confirm that by integrating the base models, the stacking model exhibits superior predictive stability compared to individual base models alone, thereby providing a reliable assessment approach for predicting the mechanical properties of metal parts fabricated by the LPBF process.

Graphical abstract

Keywords

machine learning / laser powder bed fusion / ensemble learning / stacking algorithm / additive manufacturing

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant No. 52305358), the Fundamental Research Funds for the Central Universities, China (Grant No. 2023ZYGXZR061), the Guangdong Basic and Applied Basic Research Foundation, China (Grant No. 2022A1515010304), the Young Elite Scientists Sponsorship Program by China Association for Science and Technology, China (Grant No. 2023QNRC001), and the Young Talent Support Project of Guangzhou, China (Grant No. QT-2023-001).

Conflict of Interest

The authors declare that they have no conflict of interest.

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