The adoption of laser powder bed fusion (L-PBF) for metals by the industry has been limited despite the significant progress made in the development of the process chain. One of the key obstacles is the inconsistency of the parts obtained from L-PBF. Due to its complexity, there are many potential fluctuations that can occur within the process chain which can lead to quality inconsistency in L-PBF parts. Machine learning (ML) has the possibility to overcome this obstacle by utilising datasets obtained at various stages of the L-PBF process chain. In this perspective article, the integration of ML into the different stages of L-PBF process chain, which potentially lead to better quality control, is explored. Prior to L-PBF, ML can be used for part designs and file preparation. Then, ML algorithms can be applied in the process parameter optimisation and in situ monitoring. Finally, ML can also be integrated into the post-processing.

NiTi shape memory alloys (SMAs) have the best combination of properties among the different SMAs. However, the limitations of conventional manufacturing processes and the poor manufacturability of NiTi have critically limited its full potential applicability. Thus, additive manufacturing, commonly known as 3D printing, has the potential to be a solution in fabricating complex NiTi smart structures. Recently, a number of studies on Selective Laser Melting (SLM) of NiTi were conducted to explore the various aspects of SLM-produced NiTi. Compared to producing conventional metals through the SLM process, the fabrication of NiTi SMA is much more challenging. Not only do the produced parts require a high density that leads to good mechanical properties, strict composition control is needed as well for the SLM NiTi to possess suitable phase transformation characteristics. Additionally, obtaining a good shape memory effect from the SLM NiTi samples is another challenging task that requires further understanding. This paper presents the results of the effects of energy density and SLM process parameters on the properties of SLM NiTi. Its shape memory properties and potential applications were then reviewed and discussed.

Three-dimensional (3D) printed Sc-modified Al alloy by powder bed fusion (PBF) provides significant strength and ductility without hot tearing during the process. This kind of 3D-printable high specific strength materials exhibits great potential in lightweight applications. Due to the lesser design limitation through the 3D printing process, the degree of lightweight is greatly affected by the specific strength of the materials. Hence, to further improve the mechanical properties of the material through process optimisation or post-treatment is of great importance. Microstructure feature variations due to different processing parameters are well known for traditional processes and materials. This study explores the parameter–microstructure–performance relationship of 3D printed Sc-modified Al alloys from the perspective of melt pool interactions. According to the stress concentration effect and Hall–Petch effect, the mechanical properties of the 3D printed materials vary greatly depending on the difference in defect size, shape and grain size.