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About this Abstract
Meeting 2027 TMS Annual Meeting & Exhibition
Symposium Algorithms Development in Materials Science and Engineering
Presentation Title Rapid Part-Scale Temperature Prediction for Additive Manufacturing via Bayesian-Calibrated Physics-Based Modeling
Author(s) Brodan Richter, Joshua Pribe, George Weber, Evan Adcock
On-Site Speaker (Planned) Brodan Richter
Abstract Scope Significant community effort has been focused on modeling additive manufacturing processes to guide process parameter selection and defect avoidance. Part-scale temperature prediction, where the temperature of the entire part is simulated throughout the build process, is a critical element of this simulation chain. While part-scale temperature prediction techniques are increasingly available in commercial software, they typically take on the order of hours to days to run, making design-space exploration and optimization computationally expensive. In this work, a rapid, physics-based, part-scale temperature model for powder bed fusion is developed and incorporated into the open-source “Materialite” software package. The model's speed enables both Bayesian calibration of the simulation parameters and Monte Carlo estimation of temperature prediction uncertainty. The model is assessed through validation against numerous experimental measurements. Finally, the model's value is illustrated by optimizing the power-versus-layer profile to maintain a consistent temperature during the build process.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Modeling and Simulation, Computational Materials Science & Engineering

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