A Review of the Applications of Machine Learning for Prediction and Analysis of Mechanical Properties and Microstructures in Additive Manufacturing

This article provides an insightful review of the recent applications of machine learning(ML) techniques in additive manufacturing (AM) for the prediction and amelioration ofmechanical properties, as well as the analysis and prediction of microstructures. AM isthe modern digital manufacturing technique adopted in various industrial sectorsbecause of its salient features, such as the fabrication of geometrically complex and custom-ized parts, the fabrication of parts with unique properties and microstructures, and the fab-rication of hard-to-manufacture materials. The functioning of the AM processes iscomplicated. Several factors such as process parameters, defects, cooling rates, thermalhistories, and machine stability have a prominent impact on AM products'propertiesand microstructure. It is difficult to establish the relationship between these AM factorsand the AM end product properties and microstructure. Several studies have utilized differ-ent ML techniques to optimize AM processes and predict mechanical properties and micro-structure. This article discusses the applications of various ML techniques in AM to predictmechanical properties and optimization of AM processes for the amelioration of mechanical properties of end parts. Also, ML applications for segmentation, prediction, and analysis ofAM-fabricated material's microstructures and acceleration of microstructure prediction procedures are discussed in this article.