Selective Laser Melting of NiTi Shape Memory Alloys: a Systematic Literature Review on Microstructure, Mechanical Properties, and Processing Parameter Optimisation

Additive manufacturing (AM) enables the fabrication of complex geometries with high precision, and selective laser melting (SLM) has emerged as a key technique for producing high-performance metallic components with tailored properties. The use of SLM for NiTi shape memory alloys (SMAs) has attracted growing interest due to their superelasticity, shape memory effect, and phase transformation behaviour, which are essential for advanced engineering and biomedical applications. However, achieving a reliable balance between mechanical performance, microstructural stability, and functional reliability through parameter optimisation remains challenging. The review critically examines the key factors influencing mechanical properties, phase transformation behaviour, and microstructural evolution of NiTi SMAs produced by SLM. Relevant studies were systematically identified, screened, and analysed from the Web of Science and Scopus databases following the PRISMA framework to ensure a transparent and objective selection process. The synthesis of current research shows that scanning speed, hatch spacing, and scanning strategy strongly influence grain morphology, texture development, defect formation, precipitation behaviour, and transformation temperatures. The wide scatter in reported tensile and functional properties is shown to arise from different optimisation priorities, where strength-oriented parameter sets promote slip-dominated deformation at the expense of ductility and recoverable strain, while transformation-preserving strategies maintain superior functional recovery even at comparable porosity levels. Despite significant progress, challenges related to porosity, anisotropy, compositional instability, and post-processing remain. Overall, this review highlights that optimisation of SLM NiTi must be treated as a system-level problem, integrating statistical design of experiments, predictive thermal–metallurgical modelling, and in situ process monitoring to define robust, geometry-aware processing windows for application-specific performance.