Combining synchrotron X-ray diffraction, mechanistic modeling and machine learning for in situ subsurface temperature quantification during laser melting

Rachel E Lim; Tuhin Mukherjee; Chihpin Chuang; Thien Q Phan; Tarasankar DebRoy; Darren C Pagan

doi:10.1107/S1600576723005198

Combining synchrotron X-ray diffraction, mechanistic modeling and machine learning for in situ subsurface temperature quantification during laser melting

J Appl Crystallogr. 2023 Jul 20;56(Pt 4):1131-1143. doi: 10.1107/S1600576723005198. eCollection 2023 Aug 1.

Authors

Rachel E Lim¹, Tuhin Mukherjee¹, Chihpin Chuang², Thien Q Phan³, Tarasankar DebRoy¹, Darren C Pagan¹

Affiliations

¹ Pennsylvania State University, University Park, PA 16802, USA.
² Argonne National Laboratory, Lemont, IL 60439, USA.
³ Lawrence Livermore National Laboratory, Livermore, CA 14850, USA.

Abstract

Laser melting, such as that encountered during additive manufacturing, produces extreme gradients of temperature in both space and time, which in turn influence microstructural development in the material. Qualification and model validation of the process itself and the resulting material necessitate the ability to characterize these temperature fields. However, well established means to directly probe the material temperature below the surface of an alloy while it is being processed are limited. To address this gap in characterization capabilities, a novel means is presented to extract subsurface temperature-distribution metrics, with uncertainty, from in situ synchrotron X-ray diffraction measurements to provide quantitative temperature evolution data during laser melting. Temperature-distribution metrics are determined using Gaussian process regression supervised machine-learning surrogate models trained with a combination of mechanistic modeling (heat transfer and fluid flow) and X-ray diffraction simulation. The trained surrogate model uncertainties are found to range from 5 to 15% depending on the metric and current temperature. The surrogate models are then applied to experimental data to extract temperature metrics from an Inconel 625 nickel superalloy wall specimen during laser melting. The maximum temperatures of the solid phase in the diffraction volume through melting and cooling are found to reach the solidus temperature as expected, with the mean and minimum temperatures found to be several hundred degrees less. The extracted temperature metrics near melting are determined to be more accurate because of the lower relative levels of mechanical elastic strains. However, uncertainties for temperature metrics during cooling are increased due to the effects of thermomechanical stress.

Keywords: Gaussian process regression; additive manufacturing; elastic strains; heat-transfer and fluid-flow modeling; laser melting; machine learning; superalloys; synchrotron X-ray diffraction; temperature-distribution metrics; thermomechanical stress.

Grants and funding

REL and DCP were supported by the National Institute of Standards and Technology (NIST) under award 70NANB20H208. The work of TQP was performed under the auspices of the US Department of Energy (DOE) at Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. This research used resources of the Advanced Photon Source (APS), a US DOE Office of Science user facility operated for the DOE Office of Science by Argonne National Laboratory under contract No. DE-AC02-06CH11357.