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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Interpretable and generative deep learning framework for predicting flow behavior and microstructure evolution in thermo-mechanical processing

Author(s)

Min Jik Kim, Woo Seok Yang, Sehyeok Oh, Da Seul Shin

On-Site Speaker (Planned)

Min Jik Kim

Abstract Scope

The application of data-driven deep learning has emerged as a promising approach in materials science. In thermo-mechanical processing (TMP), predicting flow behavior and microstructural evolution is critical for process optimization. Herein, we propose two deep learning models: an autoencoder-prediction network (AE-PN) and a conditional generative adversarial network (cGAN). The AE-PN predicts TMP responses of Inconel 718 based on TMP conditions such as temperature and strain rate, achieving high accuracy (RMSE = 4.19 and R2 = 0.9995) while offering interpretability through latent space analysis. In parallel, cGAN generates synthetic microstructure images conditioned on TMP variables, capturing statistical and morphological features of microstructural evolution. The generated images yielded a mean SSIM of approximately 0.88 compared to experimental images. Together, these models demonstrate a data-driven strategy that not only predicts thermomechanical properties but also enables condition-based microstructure generation, offering new insights and expanding deep learning applications in materials processing.

Proceedings Inclusion?

Planned:

Keywords

High-Temperature Materials, Machine Learning, Mechanical Properties

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