Machine learning assisted prediction and optimization of mechanical properties for laser powder bed fusion of Ti6Al4V alloy

Due to the complex physical metallurgy phenomena and enormous parameter combination, the traditional trialand-error method makes the microstructure tailoring of laser additive manufactured (LAM) for exceptional performance still a major challenge. Here, we presented a machine learning-based model to facilitate the parameter optimization and microstructure tailoring of laser powder bed fused (L-PBF) Ti6Al4V alloy with enhanced strength-ductility synergy. Initially, a database was constructed based on the 173 data sets from 31 related literature, with an in-depth analysis of key parameters such as laser power, laser speed, and powder size using the Pearson correlation coefficient (PCC). K-Means clustering was integrated into the Clustering Integrated Regression Model (CIRM), enhancing the cohesion of similar data groups based on process parameters. This strategic clustering significantly increases the precision of tailored predictive models for each group, markedly improving overall prediction accuracy. Additionally, combined with non-dominated sorting Genetic Algorithm II (NSGA-II), the CIRM model ensures rapid optimization and achieves the balance between strength and ductility during multi-objective optimization. L-PBF experiments, based on optimized parameters provided by the NSGA-II model, demonstrated an excellent combination of strength and ductility, compared to existing literature. Moreover, the Shapley additive explanation (SHAP) was introduced to interpret the prediction model, which indicates that adjusting the grain size distribution of martensite through laser-related parameters is critical for simultaneously enhancing strength and ductility. Essentially, our work provides a robust approach for the accurate prediction and multi-objective optimization of mechanical properties in LAM metallic materials.