| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Spatially Tailored Materials: Processing-Structure-Properties
|
| Presentation Title |
Gradient Alloy Design Guidelines via High-Throughput CALPHAD-Based ICME Simulations With Machine Learning |
| Author(s) |
Jixuan Dong, S. Mohadeseh Taheri-Mousavi |
| On-Site Speaker (Planned) |
Jixuan Dong |
| Abstract Scope |
Additive manufacturing's ability to tailor material composition and microstructure at voxel size resolution offers unprecedented performance in structural alloys. However, varying the processing and compositional space to define the “menu” of materials solely by relying on the experiments is time-consuming and expensive. To address this challenge, simulation-based guidelines are essential. Here we developed process-structure-property relationships for compositional gradients between Inconel 718 and Monel K-500 using CALPHAD-based ICME techniques and machine learning. The target properties, including mechanical and thermal properties, are calculated under high-temperature conditions. A property diagram for IN718 and MK500 and gradient compositions was developed to illustrate the range of properties for each alloy and property transition between the two alloys. The observed non-linear property trend for the linear composition transition highlights the urgent need for simulation predictions to guide and complement experimental validations of the most potential candidates for the material menu. |
| Proceedings Inclusion? |
Planned: |