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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title AI-Enabled Microstructure Characterization of an Al–Si–Mg–Cu Die-Casting Alloy Under 490 °C Solution Treatment
Author(s) Namseok Kim, Kwangmin Choi , Seongho Ha, Bonghwan Kim, Hyunkyu Lim, Shae K Kim, Youngok Yoon
On-Site Speaker (Planned) Youngok Yoon
Abstract Scope This work applies computer vision to quantify microstructure–property relationships in a high-thermal-conductivity Al–Si–Mg–Cu die-casting alloy for electric-vehicle motor housing applications. As a model processing window, we investigate the as-cast condition and solution treatment at 490 °C for 30, 60, and 120 min. A convolutional neural network with a U-Net-like architecture is trained to segment eutectic Si from backscattered electron micrographs and automatically extract particle size distributions, aspect ratio, area fraction, and a fragmentation index. These AI-derived descriptors are then linked to measured mechanical properties and electrical and thermal conductivities, enabling a data-driven view of how solution time controls the dissolution, spheroidization, and partial coarsening of eutectic Si. The resulting framework demonstrates how deep learning-based microstructure characterization can support processing parameter selection and provides a transferable approach for AI-enabled, high-throughput evaluation of cast aluminum alloys and other structural materials in computer vision for materials science and applied AI for manufacturing.
Proceedings Inclusion? Undecided

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