Related Experiment Video
Updated: Jul 19, 2025
11:34
Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
Published on: May 15, 2017
11.2K
Combining synchrotron X-ray diffraction, mechanistic modeling and machine learning for in situ subsurface temperature
Rachel E Lim1, Tuhin Mukherjee1, Chihpin Chuang2
1Pennsylvania State University, University Park, PA 16802, USA.
Journal of Applied Crystallography
|August 9, 2023
Summary
This study introduces a new method using synchrotron X-ray diffraction and machine learning to measure subsurface temperatures during laser melting. This provides crucial data for qualifying additive manufacturing processes and materials.
Area of Science:
- Materials Science
- Manufacturing Engineering
- Computational Modeling
Background:
- Laser melting in additive manufacturing creates extreme thermal gradients impacting material microstructure.
- Accurate characterization of these temperature fields is essential for process and material validation.
- Directly measuring subsurface temperatures during laser melting remains a significant challenge.
Purpose of the Study:
- To develop a novel method for extracting subsurface temperature distribution metrics during laser melting.
- To quantify the uncertainty associated with these temperature measurements.
- To provide in situ quantitative temperature evolution data for laser melting processes.
Main Methods:
- Utilized in situ synchrotron X-ray diffraction measurements.
- Developed Gaussian process regression supervised machine-learning surrogate models.
- Trained models with mechanistic heat transfer, fluid flow, and X-ray diffraction simulations.
Main Results:
- Successfully extracted subsurface temperature metrics with uncertainties ranging from 5-15%.
- Applied models to Inconel 625 alloy, confirming maximum solid-phase temperatures reached the solidus.
- Observed increased uncertainty during cooling due to thermomechanical stress.
Conclusions:
- The developed method provides a viable approach for in situ subsurface temperature characterization during laser melting.
- Machine learning surrogate models offer a powerful tool for analyzing complex thermal processes.
- Understanding temperature evolution is critical for controlling microstructure and properties in additive manufacturing.
