SIM-AM 2025

In Situ Synchrotron Diffraction for Benchmarking Phase Transformations in Additive Manufacturing of Fe-Cr-Ni Alloys

  • Zhang, Fan (National Institute of Standards and Technolog)
  • Aroh, Joseph (National Institute of Standards and Technolog)
  • Chuang, Andrew (Argonne National Laboratory)

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Accurately predicting microstructural evolution in metal additive manufacturing (AM) remains a central challenge due to the rapid thermal cycles and far-from-equilibrium conditions inherent to the process. In particular, the phase transformations that occur during and after solidification govern final part properties but are difficult to observe directly. To address this gap, we conducted high-speed synchrotron X-ray diffraction measurements for AM Bench 2025 Challenge Problem AMB2025-08, capturing the real-time evolution of phases during laser melting of Fe-Cr-Ni alloys. These measurements provide one of the most detailed experimental datasets to date for benchmarking models of phase transformation sequence and kinetics under AM conditions. In this talk, we will describe the experimental design and execution of the high-speed synchrotron X-ray diffraction measurements performed at beamline 1-ID-E of the Advanced Photon Source. Diffraction patterns were acquired at 250 Hz during linear laser scans across custom-fabricated Fe-Cr-Ni alloys with varying Cr/Ni ratios. The experiments were tailored to isolate the effects of composition and processing parameters, such as laser power and scan speed, on phase evolution under highly non-equilibrium solidification conditions representative of metal AM. We will highlight the setup of the custom AM chamber, the X-ray optics and laser system, and the methodology for aligning, calibrating, and analysing the diffraction data. Additionally, we will introduce the curated datasets and access tools developed to facilitate model validation, supporting open collaboration across the AM community. Our broader aim is to accelerate integration among experimentalists, modelers, and data scientists to improve the fidelity and impact of predictive simulations in additive manufacturing. REFERENCES [1] Guo, Q. et al. Additive Manufacturing, 2022, 59, 103068. [2] König, H.-H. et al. Acta Materialia, 2023, 246, 118713. [3] Parab, N. D. et al. J. Synchrotron Rad., 2018, 25, 1467–1477.