Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing

The application of three-dimensional (3D) printing/Additive Manufacturing (AM) for developing multi-functional smart/intelligent composite materials is a highly promising area of engineering research. However, there is often no reliable means for predicting and modelling the material performance, and the wide-scale industrial adoption of AM is limited due to factors such as design barriers, limited materials library, processing defects and inconsistency in product quality. A comprehensive framework considering the generalised applicability of ML algorithms at sub-sequent stages of the AM process from the initial design to the post-processing stages in the literature is lacking. In this paper, the integration of various ML applications at various sub-processes is discussed, including pre-processing design stage, parameter optimisation, anomaly detection, in-situ monitoring, and the final post-processing stages. The challenges and potential solutions for standardising these integrated techniques have been identified. The article is promising for professionals and researchers in AM and AI/ML techniques.

Automated summary

The application of 3D printing/Additive Manufacturing in developing smart composite materials shows great potential, but faces challenges such as predicting material performance, design barriers, and inconsistency in product quality. This paper discusses integrating machine learning in various sub-processes of AM, from design to post-processing stages, and identifies challenges and potential solutions for standardizing these techniques. Professionals and researchers in AM and AI/ML techniques will find this article promising.