The prediction model for additively manufacturing of NiTiHf high-temperature shape memory alloy

NiTi-based alloys are one of the most well-known alloys among shape memory alloys having a wide range of applications from biomedical to aerospace areas. Adding a third element to the binary alloys of NiTi changes the thermomechanical properties of the material remarkably. Two unique features of stability and high transformation temperature have turned NiTiHf as a suitable ternary shape memory alloys in various applications. Selective laser melting (SLM) as a layer-based fabrication method addresses the difficulties and limitations of conventional methods. Process parameters of SLM play a prominent role in the properties of the final parts so that by using the different sets of process parameters, different thermomechanical responses can be achieved. In this study, different sets of process parameters (PPs) including laser power, hatch space, and scanning speed were defined to fabricate the NiTiHf samples. Changing the PPs is a powerful tool for tailoring the thermomechanical response of the fabricated parts such as transformation temperature (TTs), density, and mechanical response. In this work, an artificial neural network (ANN) was developed to achieve a prediction tool for finding the effect of the PPs on the TTs and the size deviation of the printed parts.