About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Modeling, Simulation and Artificial Intelligence
|
| Presentation Title |
Simulation Calibration and Process Optimization for DED |
| Author(s) |
Ashley Gannon, Stephen DeWitt, Bruno Turcksin, Callan Herberger, James Haley |
| On-Site Speaker (Planned) |
Ashley Gannon |
| Abstract Scope |
We present a two-stage framework that calibrates high-fidelity thermomechanical simulations against in-situ infrared (IR) measurements and optimizes interpass dwell and reheat parameters using Bayesian methods. In the calibration stage, we project spatially resolved IR temperature fields onto the simulation mesh and adjust key model parameters (laser absorption and penetration depth, convective heat-transfer coefficient) to minimize the discrepancy between simulated and measured thermal histories. Building on the calibrated model, the optimization stage partitions the part geometry and injects candidate dwell durations and reheat power settings into the toolpath. An ensemble of Adamantine simulations evaluates each candidate by quantifying the cumulative time points spend within critical phase-transformation windows for 17-4PH. Bayesian optimization then guides successive iterations toward parameter combinations that maximize time in the phase-transformation window across partitions. Our pipeline demonstrates how coupling IR-driven calibration with data-driven optimization can improve process development in DED and lays the groundwork for future closed-loop control. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, |