The global metal market, expected to exceed $18.5 trillion by 2030, faces costly inefficiencies from defects in alloy manufacturing. Although microstructure analysis has improved alloy performance, current numerical models struggle to accurately simulate solidification. In this research, we thus introduce AlloyGAN - the first domain-driven Conditional Generative Adversarial Network (cGAN) involving domain prior for generating alloy microstructures of previously not considered chemical and manufactural compositions. AlloyGAN improves cGAN process by involving prior factors from solidification reaction to generate scientifically valid images of alloy microstructure given basic alloy manufacturing compositions. It achieves a faster and equally accurate alternative to traditional material science methods for assessing alloy microstructures. We contribute (1) a novel Alloy-GAN design for rapid alloy optimization; (2) unique methods that inject prior knowledge of the chemical reaction into cGAN-based models; and (3) metrics from machine learning and chemistry for generation evaluation. Our approach highlights the promise of GAN-based models in the scientific discovery of materials. AlloyGAN has successfully transitioned into an AIGC startup with a core focus on model-generated metallography. We open its interactive demo at: https://deepalloy.com/
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This content will become publicly available on February 7, 2025
Simulating Castable Aluminum Alloy Microstructures with AlloyGAN Deep Learning Model
Material scientists have made progress in controlling alloy performance through microstructure quantification. However, attempts at numerically modeling microstructures have failed due to the complex nature of the solidification process. In this research, we present the AlloyGAN deep learning model to generate microstructures for castable aluminum alloys. This innovative model demonstrates its capacity to simulate the evolution of aluminum alloy microstructures in response to variations in composition and cooling rates. Specifically, it is successful to simulate various effects on castable aluminum, including: (1) the influence of Si and other elements on microstructures, (2) the relationship between cooling rate and Secondary Dendritic Arm Spacing, and (3) the impact of P/Sr elements on microstructures. Our model delivers results that match the accuracy and robustness of traditional computational materials science methods, yet significantly reduces computation time.
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- Award ID(s):
- 2021871
- PAR ID:
- 10523444
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- ISBN:
- 978-3-031-50348-1
- Page Range / eLocation ID:
- 804-811
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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