Machine learning in additive manufacturing——NiTi alloy’s transformation behavior

• By integrating the feature screening strategy, the feature descriptors that had a substantial influence on the LPBF-NiTi alloy were extracted. • Six machine learning models were thoroughly evaluated, and the results showed that the GRNN model had the best prediction accuracy (R 2 = 0.97). • Controllable phase transition processing windows of LPBF-NiTi alloys with varying compositions were created using the improved GRNN model. The laser powder bed fused NiTi alloys (LPBF-NiTi) demonstrate shape memory functionality and superelasticity as a result of their distinctive phase transition characteristics. Nevertheless, achieving precise control and regulation of the phase transition temperature poses a challenge, influenced by factors like powder composition and process parameter. In this study, a feature screening strategy and machine learning (ML) method were employed to predict and regulate the phase transition temperature of LPBF-NiTi alloy, offering a more efficient and cost-effective alternative to traditional experimental methods of regulation and control. Specifically, accuracy analysis was performed on LPBF-NiTi phase transition datasets with varying compositions and process conditions utilizing generalized regression neural network (GRNN), and other methods. The findings indicate that the interpretable features extracted through the selection strategy outlined in this study when combined with the GRNN model, demonstrate a high level of prediction accuracy (R 2 = 0.97). To investigate the accuracy of the model, this study utilized various process parameters to fabricate NiTi alloys with different compositions from Ni 50.8 Ti 49.2 alloy powder. Using this model, the study identified a novel, larger window of optimal LPBF processing that allows for controllable complex phase transitions.