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Meeting 2026 TMS Annual Meeting & Exhibition
Symposium Verification, Calibration, and Validation Approaches in Modeling the Mechanical Performance of Metallic Materials
Presentation Title 2025 NIST Additive Manufacturing Benchmark Tensile Challenge to Improve Computational Prediction Techniques: Prediction Trends
Author(s) Nik Hrabe, Jake Benzing, Newell Moser, Orion L. Kafka, Nicholas Derimow, Alec Saville, Cassidy Allen
On-Site Speaker (Planned) Orion L. Kafka
Abstract Scope AM Bench is a series of additive manufacturing (AM) benchmark measurements and challenge problems that enable modelers to test their simulations against benchmark data. For the macroscale quasi-static tensile challenge, we requested prediction of uniaxial tensile properties of as-built IN718, having provided 3D microstructural information. This talk will discuss the outcomes of the model predictions provided in response to our challenge after reviewing some details of the challenge formulation. We expect to review the different models applied to our challenge. We compare each proposed modeling result to experimental ground truth “answers,” which were withheld from modelers. We will also analyze the results provided comparatively to explore the relative strengths and weaknesses of the models. Limitations and gaps identified by the challenge will be debriefed. Finally, we will consider innovations for the next round of benchmark challenges, with audience input.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Mechanical Properties, Modeling and Simulation

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

2025 NIST Additive Manufacturing Benchmark Fatigue Challenge to Improve Computational Prediction Techniques: Measurement Description
2025 NIST Additive Manufacturing Benchmark Fatigue Challenge to Improve Computational Prediction Techniques: Prediction Trends
2025 NIST Additive Manufacturing Benchmark Tensile Challenge to Improve Computational Prediction Techniques: Measurement Description
2025 NIST Additive Manufacturing Benchmark Tensile Challenge to Improve Computational Prediction Techniques: Prediction Trends
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