Additively manufactured NiTi-based shape memory alloys (SMAs) offer transformative potential for aerospace and biomedical devices, combining unique functional properties with unparalleled design freedom. However, the intrinsic multi-physics complexity of additive manufacturing often leads to inconsistent microstructures, phase transformation behaviors, and thermomechanical properties across different batches and equipment, hindering reliable applications. This review comprehensively examines how artificial intelligence (AI) is overcoming these barriers. We analyze the usage of machine learning for optimizing process parameters, predicting phase transformation temperatures, and migrating defect mitigation. Furthermore, we explore AI-driven strategies for alloy design, highlighting how transfer learning leverages conventional processing data to accelerate the development of novel NiTi-based compositions with tailored transformation temperatures and functional characteristics. The discussion also covers AI-enabled multiscale structure optimization, including the use of computer vision and generative modeling to design complex lattice architectures with programmable and application-specific mechanical responses. Finally, we identify emerging opportunities, including multi-source data fusion, CALPHAD-integrated thermodynamic databases, and large language models, which promise to revolutionize the development paradigm for additively manufactured NiTi SMAs. These AI-enhanced approaches collectively address the critical challenges in transitioning laboratory innovations to industrial-scale applications while enabling unprecedented control over process-structure-property relationships in the multifunctional NiTi-based alloys. This review shows how artificial intelligence is accelerating the development of additively manufactured NiTi shape memory alloys. By optimizing processing, guiding alloy design, and enabling multiscale structural control, AI overcomes major challenges in consistency and performance, paving the way for reliable, high-precision NiTi components in aerospace and biomedical applications.
Li et al. (Sun,) studied this question.