Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing

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.

Automated summary

The adoption of laser powder bed fusion (L-PBF) for metals by the industry has been limited due to inconsistency in parts. Machine learning (ML) has the potential to overcome this obstacle by utilizing datasets obtained at various stages of the process chain, leading to better quality control.