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A Systematic Literature Review of Machine Learning Approaches for Optimization in Additive Manufacturing

The rapid expansion of additive manufacturing into more and more industries increases the need to improve productivity by optimizing the technological process chains. This paper reviews literature about machine learning approaches using big data on High-Performance Computing resources for optimizing additive manufacturing processes, starting from the parts' geometrical design. Based on the literature included in international peer-reviewed journals and conferences, we build a comprehensive overview of optimization methods in the three main stages geometrical design, process parameter configuration, and in-situ anomaly detection. Furthermore, we aim to foster the understanding and adaption of the identified machine learning approaches for optimizing additive manufacturing and identify future research needs.