Shape memory alloys (SMAs) cover a wide variety of applications from biomedical to aerospace, automotive to oil industries. Both shape memory and/or superelasticity behavior make these alloys suitable for such applications, which is a result of a phase transformation between solid- state phases. However, SMAs are currently available in very simple geometries due to difficulties in their processing and machining. Hence, additive manufacturing (AM) of SMAs as a way to achieve complex shapes has been of interest to researchers. University of Toledo is among the pioneering groups working on laser powder bed fusion of SMAs and have done extensive process optimization. There are multiple process parameters (PPs) involved in AM which needs to be optimized to achieve a successful built. As for successful built, different criteria were defined: density, dimensional accuracies, microstructural features, impurity pickups, mechanical behavior-recoverable, and irrecoverable strains, strength, modulus of elasticity-, and transformation temperatures to name a few. However, one missing part of AM of SMAs is designing an alloy(s) that best suits the modality of manufacturing process. In other words, the recent works has mainly focused on process optimization on the currently available alloys, therefore, resulting in many challenges toward AM such as solidification cracking and composition variation. As such, our team has established an Advanced Manufacturing Institute toward expanding the knowledge of AM of SMAs. In this presentation, an optimization framework will be presented toward designing new SMAs that are suitable for AM followed by process and post-processing of the AM- fabricated parts. This involves a combined computational and experimental process-structure-property assessments across wide variety of alloys and processing techniques (e.g., Laser Powder Bed fusion, Binder Jetting, and Direct Energy Deposition). In addition, a summary of the current state of the art in AM of SMAs, challenges, and path forward will be part of the talk.
Learning Objectives:
- Identify opportunities in material design and AM of shape memory alloys
- Explain the current challenges in achieving functional AM parts from shape memory alloys
