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

Abstract

This article provides an insightful review of the recent applications of machine learning (ML) techniques in additive manufacturing (AM) for the prediction and amelioration of mechanical properties, as well as the analysis and prediction of microstructures. AM is the modern digital manufacturing technique adopted in various industrial sectors because of its salient features, such as the fabrication of geometrically complex and customized parts, the fabrication of parts with unique properties and microstructures, and the fabrication of hard-to-manufacture materials. The functioning of the AM processes is complicated. Several factors such as process parameters, defects, cooling rates, thermal histories, and machine stability have a prominent impact on AM products’ properties and microstructure. It is difficult to establish the relationship between these AM factors and the AM end product properties and microstructure. Several studies have utilized different ML techniques to optimize AM processes and predict mechanical properties and microstructure. This article discusses the applications of various ML techniques in AM to predict mechanical properties and optimization of AM processes for the amelioration of mechanical properties of end parts. Also, ML applications for segmentation, prediction, and analysis of AM-fabricated material’s microstructures and acceleration of microstructure prediction procedures are discussed in this article.