Myna: Connecting powder bed fusion build data to simulation tools for digital twin applications

Journal Article · Fri Jul 11 00:00:00 EDT 2025 · Computational Materials Science

DOI:https://doi.org/10.1016/j.commatsci.2025.114094· OSTI ID:3002570

Knapp, Gerald L. ^[1]; Reeve, Samuel Temple ^[1]; Coleman, John ^[1]; Rolchigo, Matthew ^[1]; Stump, Benjamin ^[1]; DeWitt, Stephen ^[1]; Plotkowski, Alex ^[1]

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1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Additive manufacturing (AM), as a digital process, can generate a detailed digital thread linking a part’s design and manufacturing to its operational performance. As AM systems advance, an increasing amount of process data is stored in manufacturing databases. In principle, this data can be utilized by simulation-based digital twin approaches, such as real-time process control and asynchronous post-processing guidance. However, few tools currently exist for systematically integrating digital thread data with computational tools. Here, in this study, we propose a software package, called Myna, for connecting data from powder bed fusion processes to simulation tools. The utility of such a platform is demonstrated using build data from the Oak Ridge National Laboratory Manufacturing Demonstration Facility “Peregrine v2023-10” public dataset to automatically configure and run 54 semi-analytical 3DThesis melt pool simulations, 78 numerical Additive FOAM melt pool simulations, and 3 ExaCA microstructure simulations. The simulated, spatially registered microstructures are then compared directly with electron backscatter diffraction characterization of the corresponding as-built part locations. The resulting simulated microstructure showed variation as a function of process parameters, particularly stripe width; however, the experimental data had little variation between the microstructure texture and grain size resulting from different processing conditions. Analysis of the discrepancies suggest that it is possible a two-phase ferritic-austenitic solidification model is needed to accurately predict grain size and texture for certain stainless steel 316L feedstock compositions under powder bed fusion conditions, providing direction for future research. As illustrated here, due to the number and complexity of the simulations involved in AM process-structure–property predictions, automated methods to connect process data and simulations will remain necessary tools for testing hypotheses and implementing digital twin applications.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Materials & Manufacturing Technologies Office (AMMTO); USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 3002570
• Journal Information: Computational Materials Science, Journal Name: Computational Materials Science Vol. 258; ISSN 0927-0256
• Publisher: Elsevier
• Country of Publication: United States
• Language: English