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. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. The AMMT program aims to demonstrate its new accelerated development and qualification methods via laser powder bed fusion 316 stainless steel to aid the understanding of the the process variabilities on its performance under nuclear iii environments. Here, we lay the foundation to integrate multiscale mechanistic models with machine learning approaches for additively manufactured 316L stainless steel. We utilize and extend the existing capabilities of MOOSE and MALAMUTE to develop a multiscale model that can couple different physical aspects of the AM process in a modular way. This year, the necessary modeling capabilities and the computational frameworks are developed with an ultimate goal of establishing the PSPP correlation for AM. A multiphysics multiscale model is developed using level-set and Arbitrary Lagrangian Eulerian (ALE) approach to demonstrate the melt pool dynamics during the laser powder bed fusion method. A phase-field-based microstructural evolution model is developed to capture the solidification and epitaxial grain growth under moving heat source in 316L SS. The model demonstrates the elongated grain formation and Cr segregation observed in typical additively manufactured SS materials. Furthermore, various machine learning models are developed to simplify the outputs of the physics-based models and represent them with a simplified surrogate model. A genetic programming-based symbolic regression model is developed to correlate the process conditions to the cooling rate of the work-piece. In addition, a Proper Orthogonal Decomposition based dimensionality reduction workflow and a multi-output Gaussian processes model has been implemented in the Multiphysics Object-Oriented Simulation Environment (MOOSE). For initial verification, the models have been tested for efficiently predicting the temperature distribution surrounding the melt pool. This work can capture the microstructural variabilities that are often missing in the part-scale models. In fiscal year 2023, several unique modeling capabilities have been developed and tested to capture material evolution during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way.
