Additive manufacturing of shape memory alloys has been of interest for almost a decade now. However, many challenges have hindered the full potential of AM in SMAs. One reason could be due to the small window of process parameters to print defect-free parts while maintaining the suitable composition. On the other hand, the high cost of the material makes it impossible to solve all of these problems through trial and error. More recently Integrated Computational Material Engineering is proposed to overcome the high cost of experiments through multi-scale multi-physics modeling to find the process-structure-property-performance relationships. In this work, an ICME framework will be presented where a wide variety of alloys are screened to come up with a few options that are suitable for laser powder bed fusion (LPBF). To this end, ThermoCalc is used to assess the solidification behavior of the selected alloys. The results are then validated through single-track and subsequent experiments. As the next step, the material properties are collected from Thermo-Calc and fed into a multi-physics LPBF process modeling to find melt pool sizes, printability maps, and temperatures histories which can be validated through single-track experiments and in-situ thermal monitoring using a dual-wavelength pyrometer. Once validated, the temperature profile is used to predict the microstructures under different processing parameters and compared against EBSD images. In the next step, using a crystal plasticity model, microstructure, and melt pool size, mechanical properties are calculated. As the final step, the fatigue life of the fabricated part is modeled through a self-heating approach which is a relatively fast method to assess the endurance limits.
Learning Objectives:
- Identify the main steps in the ICME of LPBF of SMAs
- Assemble the tools needed for performing ICME of LPBF of SMAs
- Plan experimental studies for ICME of LPBF of SMAs
