Explainable machine learning for hardness prediction of laser powder bed fused Ti-6Al-4V and assisting in understanding effects of process parameters

Abstract

Rapid fabrication of the Ti-6Al-4 V alloy with the target hardness via laser powder bed fusion (LPBF) is essential to meet the requirements of specific applications. Herein, the explainable machine learning (xML) models were employed to predict the hardness of LPBF-ed parts and to provide insights into the mechanisms linking process-microstructure-hardness. Results indicate that gradient boosting decision tree (GBDT) model outperforms others on test set, achieving an R square (R2) of 0.963, a mean absolute error (MAE) of 2.059 HV, and a root mean squared error (RMSE) of 3.844 HV. The contributions of process parameters were evaluated using SHapley Additive exPlanations (SHAP) values. The results demonstrate that the scanning speed (v) has the most significant influence on hardness, with the mean |SHAP value| accounting for approximately 51.2 % of the overall value, and shows an overall positive correlation with hardness. Experimental validation is consistent with the prediction results that S6 fabricated under the high v displayed a high hardness of 404.58 HV. The high v results in a large number of dislocations in as-built S6, impeding dislocation slip during loading and thus increasing resistance to deformation. The {10—11}<—1012> compression twins, nano-stacking faults and numerous fine α’-colonies provide extra grain boundary strengthening. Furthermore, the substantial volume fractions of interfaces between randomly oriented α’ martensite act as a strong barrier to dislocation slip transmission, thereby offering effective strengthening. This work offers guidance for predicting properties and exploring the underlying mechanisms of process parameters on material properties.