Abstract:Additive manufacturing (AM) is a revolutionary breakthrough in the manufacturing of modern high-end equipment. In order to promote the mass production and reliable applications of AM components, the major determining factors are manufacturing repeatability, quality reliability, and performance predictability. However, the combined effects of anisotropic microstructure, randomly distributed defects, internal residual stresses, and surface roughness pose a challenge for the prediction accuracy and efficiency of mechanical properties based on traditional empirical models and limited testing data. Recently, as an inevitable product of the development of big data and artificial intelligence to a certain stage, machine learning (ML) has demonstrated a great potential for modelling the complex nonlinear relationship among high-dimensional physical quantities, which has received continuous attention in the field of mechanical properties of AM materials. This paper reviews the research progress in predicting mechanical properties of additively manufactured metals and components by machine learning. First, the common ML algorithms and general ML procedures are briefly introduced, along with special studies on the characteristics and construction methods of the advanced physics-informed machine learning (PIML). Furthermore, the reasons for the formation of the four major influencing factors on the mechanical properties of AM materials and the current application status of ML in predicting these influencing factors are summarized. This paper focuses on the representative research results of ML and PIML in predicting the tensile and fatigue fracture properties of AM properties. Finally, the limitations of ML in predicting the mechanical properties of AM materials, as well as the hot topics and technological prospects, are pointed out.