Mechanistic models for additive manufacturing of metallic components

Abstract

Additive manufacturing (AM), also known as 3D printing, is gaining wide acceptance in diverse industries for the manufacturing of metallic components. The microstructure and properties of the components vary widely depending on printing process and process parameters, and prediction of causative variables that affect structure, properties and defects is helpful for their control. Since models are most useful when they can correctly predict experimental observations, we focus on the available mechanistic models of AM that have been adequately validated. Specifically, the applications of transport phenomena models in the studies of solidification, residual stresses, distortion, formation of defects and the evolution of microstructure and properties are critically reviewed. The functionality of AM models in understanding of the printability of commonly used AM alloys and the fabrication of functionally graded alloys are also assessed. Opportunities for future research are identified considering the gaps in knowledge in modeling. The uniqueness of this review includes substantive discussions of the rapid certification of the AM components aided by scale models, bidirectional models, cloud based big data, machine learning and digital twins of AM hardware.