Multiscale and Machine Learning Modeling for Additive Manufacturing

Abstract

Additive manufacturing (AM) techniques provide the opportunity to simultaneously design new materials and components with complex structures in less time, enabling faster material developments. Even though compositionally similar, the texture of the materials produced by such techniques is significantly different from conventionally manufactured materials. Additively manufactured materials produces highly heterogeneous microstructure within a single build. Such variations in the microstructure make qualifying AM products challenging for extreme environment applications. Understanding the AM process and its influence on the materials’ microstructures/properties is paramount for evaluating the workability and performance of the manufactured materials. The performance of AM materials for advanced nuclear reactor applications is of interest to the Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy. Hence, considering the microstructural variabilities in the AM products and their impact on the performance of the material, it is important to correlate the process conditions to the final product and establish a process-structure-property- performance (PSPP) correlation for AM materials.