| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
|
| Presentation Title |
Generative AI for Inverse Design of Inconel 718 |
| Author(s) |
Jarvis Loh, Nigel Neo, Zhidong Leong, Wen Jun Wee, Yang Hao Lau, Xinyu Yang, Mark Jhon, Rajeev Ahluwalia, Robert Laskowski, Wei-Lin Tan |
| On-Site Speaker (Planned) |
Wei-Lin Tan |
| Abstract Scope |
A challenge in employing machine learning for the inverse design of materials is explainability – many frameworks use property as input and processing parameters as output, resulting in a process-property ‘black box’ AI, bypassing microstructural information. In this work we attempt to plug the process-structure-property gap by using a Continuous Conditional Generative Adversarial Network (CcGAN) architecture to inversely generate microstructures corresponding to specified yield strengths. A convolutional neural network is then employed to predict the requisite processing parameters to attain these generated microstructures. To demonstrate this architecture, we generated an extensive training set of microstructures using phase field simulations to model the precipitation of γ’ and γ” in Inconel 718, and a crystal plasticity model to calculate the corresponding yield strengths. Our methodology resulted in qualitatively well-aligned CcGAN-generated microstructures compared with those derived from physics-based calculations. Furthermore, the predictions for processing parameters achieve a root mean squared error within 7%. |
| Proceedings Inclusion? |
Undecided |