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Meeting TMS Specialty Congress 2025
Symposium 3rd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2025)
Presentation Title Optimal Design Method Using High-Fidelity Surrogate Modeling Based on Finite Element analysis Data
Author(s) Seongtak Kim, Dongwoon Han, Hyokyu Kim
On-Site Speaker (Planned) Seongtak Kim
Abstract Scope This study proposes an optimal design method for photovoltaic (PV) module frames by developing a deep learning model based on finite element analysis (FEA) data. For decades, PV modules have maintained standardized structures, resulting in similar frame designs. However, the increasing size of PV cells, the emergence of building-integrated photovoltaic (BIPV) modules with diverse shapes, and the shift away from aluminum frames to reduce carbon emissions necessitate customized frame designs. To address this need, we apply a deep learning-based high-fidelity surrogate model with over 99% accuracy for frame optimization. This approach enables the design of lighter frames while enhancing performance compared to conventional designs.
Proceedings Inclusion? Undecided

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