Machine Learning for Advanced Additive Manufacturing

Increasing demand for the fabrication of components with complex designs has spurred a revolution in manufacturing methods. Additive manufacturing stands out as a promising technology when it comes to prototyping multi-functional and multi-material designs. However, challenges still exist in the additive manufacturing process, such as mismatched material properties, lack of build consistency, and pervasive imperfections in the printed part. These inherent challenges can be avoided by implementing algorithms to detect imperfections and modulate printing parameters in real time. In this paper, several algorithms, with a focus on machine learning methods, are reviewed and explored to systematically tackle the three main stages of the additive manufacturing process: geometrical design, process parameter configuration, and in situ anomaly detection. Current challenges and future opportunities for algorithmically driven additive manufacturing processes, as well as potential applications to other manufacturing methods, are also discussed.