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Laser melting, such as that encountered during additive manufacturing (AM),produces extreme gradients of temperature in both space and time, which in turninfluence microstructural development in the material. Qualification and modelvalidation of the process itself and resulting material produced necessitatesthe ability to characterize these temperature fields. However, well-establishedmeans to directly probe material temperature below the surface of an alloywhile it is being processed are limited. To address this gap incharacterization capabilities, we present a novel means to extract subsurfacetemperature distribution metrics, with uncertainty, from in situ synchrotronX-ray diffraction measurements to provide quantitative temperature evolutionduring laser melting. Temperature distribution metrics are determined usingGaussian Process Regression supervised machine learning surrogate modelstrained with a combination of mechanistic modeling (heat transfer and fluidflow) and X-ray diffraction simulation. Trained surrogate model uncertaintiesare found to range from 5% to 15% depending on the metric and currenttemperature. The surrogate models are then applied to experimental data toextract temperature metrics from an Inconel 625 nickel superalloy wall specimenduring laser melting. Maximum temperatures of the solid phase in thediffraction volume through melting and cooling are found to reach the solidustemperature as expected, with mean and minimum temperatures found to be severalhundred degrees less. The extracted temperature metrics near melting aredetermined to be more accurate due to the lower relative levels of mechanicalelastic strains. However, uncertainties for temperature metrics during coolingare increased due to the effects of thermomechanical stress.

Combining Synchrotron X-ray Diffraction, Mechanistic Modeling, and  Machine Learning for In Situ Subsurface Temperature Quantification during  Laser Melting