Abstract
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, 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.
| Original language | English |
|---|---|
| Article number | 114094 |
| Journal | Computational Materials Science |
| Volume | 258 |
| DOIs | |
| State | Published - Aug 2025 |
Funding
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05–00OR22725 with the U.S. Department of Energy (DOE). The development of Myna was sponsored by the DOE Advanced Materials & Manufacturing Technologies Office and utilized resources at the ORNL Manufacturing Demonstration Facility. Efforts for quantitative microstructure analysis capabilities in Myna were sponsored by the DOE Office of Nuclear Energy Advanced Materials & Manufacturing Technologies program. This research also used compute resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. DOE under Contract No. DE-AC05-00OR22725. The authors would also like to acknowledge the ORNL MDF Digital Factory team, specifically Vincent Paquit, Luke Scime, Zack Snow, and William Halsey, whose groundwork on building Peregrine and the digital infrastructure to extract and store powder bed fusion process data at the MDF was a key enabling technology for this work. Additional thanks to Sarah Graham and Julio Ortega Rojas for sample preparation and EBSD data collection.