Matt Rolchigo

Under the Rapid Qualification technical area of the Advanced Materials and Manufacturing Technologies program, simulation-based approaches are being developed towards understanding the linkage between laser powder bed fusion part geometry, processing, material structure, and properties and potential sources of variability thereof. Previous modeling and simulation work under the program has focused on developing the simulation pipelines using a representative volume element approach, which allowed for understanding the process-structure-property relationships at specific locations of interest. However, these tools needed two key features to be able to determine microstructure uniformity across different component geometries, process conditions, and feedstock compositions: automatic selection the areas of interest via mapping of the heterogeneity in thermal conditions of a part and assessment of solidification microstructure composition-sensitivity within the Stainless Steel 316H specification. Here, heterogeneity in the part was assessed via using melt pool geometry as a surrogate parameter for solidification conditions, which was then fed into a machine learning clustering method and connected component analysis to find regions of interest. This method was applied to a "Challenge Problem"-type build, where process conditions were intentionally varied at different locations in the part, and the results of the clustering analysis showed the clusters agreed with the intuitive result that clusters are centered on the regions of varying process parameters. Including more complexity in the assumptions of the thermal simulations, namely layer-wise heat accumulation, did introduce more overlap in melt pool conditions between the regions of varying processing parameters, which was also captured by the clustering analysis This validation indicates that such analysis will be able to identify regions of heterogeneity in future parts where heterogeneity is unknown. Composition-dependent solidification pathways and their influence on microstructure morphology and texture were analyzed with via thermodynamic calculations of interface response functions and cellular automata predictions of grain growth. Analysis showed that for the three powder compositions that have been used within the program at the Manufacturing Demonstration Facility, an austenite-dominant solidification mode is present, which anecdotally matches with experimental observations. When considering the most susceptible composition to a mixed ferrite-austenite solidification mode within the ASME Boiler and Pressure Vessel Code Section III Stainless Steel 316H specification, it was shown that mixed-phase solidification can occur and result in significantly different grain morphology and texture. While there is uncertainty in the input parameters to the material models that generated this result, it suggests that experimental validation in the high Creq/Nieq end of the composition specification would be valuable.

Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. This study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.

The Advanced Materials and Manufacturing Technologies program aims to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. However, the characteristic process-structure-property relationships of additive manufacturing (AM) materials pose challenges for the qualification and certification of AM nuclear components. In particular, component-scale variations in microstructure and properties can be driven by localized changes in melt pool dynamics due to how process parameters interact with different part geometries. Computational modeling tools can play a crucial role in predicting and controlling this variability. This report presents final results on process modeling tools designed to predict microstructure variability in additively manufactured stainless steel 316 parts. It details the software packages and physical modeling approaches employed to simulate an AM component within an automated process modeling workflow. Results are demonstrated through comparisons between predicted microstructures and experimental measurements across various representative processing conditions. The report concludes by discussing identified challenges and future opportunities for connecting the developed simulation workflow with mechanics simulations for prediction of part performance.

AdditiveFOAM is a computational framework that simulates transport phenomena in Additive Manufacturing (AM) processes. It is built on OpenFOAM (Weller et al., 1998), the leading free, open-source software package for computational fluid dynamics (CFD). OpenFOAM offers an extensible platform for solving complex multiphysics problems using state-of-the-art finite volume methods. AdditiveFOAM leverages these capabilities to develop specialized tools aimed at addressing challenges in AM processing.