  Laser melting, such as that encountered during additive manufacturing (AM),
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 resulting material produced necessitates
the ability to characterize these temperature fields. However, well-established
means to directly probe material temperature below the surface of an alloy
while it is being processed are limited. To address this gap in
characterization capabilities, we present a novel means to extract subsurface
temperature distribution metrics, with uncertainty, from in situ synchrotron
X-ray diffraction measurements to provide quantitative temperature evolution
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. 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. Maximum temperatures of the solid phase in the
diffraction volume through melting and cooling are found to reach the solidus
temperature as expected, with mean and minimum temperatures found to be several
hundred degrees less. The extracted temperature metrics near melting are
determined to be more accurate due to the lower relative levels of mechanical
elastic strains. However, uncertainties for temperature metrics during cooling
are increased due to the effects of thermomechanical stress.
