|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Additive Manufacturing: Advanced Characterization with Synchrotron, Neutron, and In Situ Laboratory-scale Techniques II
||In-situ Temperature Quantification during Laser Powder Bed Fusion Additive Manufacturing
||Rachel Lim, Tuhin Mukherjee, Tarasankar DebRoy, Thien Phan, Darren Pagan
|On-Site Speaker (Planned)
Laser powder bed fusion additive manufacturing produces rapid changes and large gradients in temperature 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 bulk temperature fields. However, there are no proven means to directly probe material temperature in the bulk of an alloy (as opposed to the surface) while it is being processed. To address this gap in characterization capabilities, a novel means is presented to accurately extract bulk temperature metrics from in-situ synchrotron x-ray diffraction measurements to provide quantitative analysis of temperature evolution during laser powder bed fusion. Temperature metrics are determined using a supervised machine learning surrogate model trained with a combination of thermal modeling and x-ray diffraction simulation.
||Additive Manufacturing, ICME, Machine Learning