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.