Surrogate modeling of slot-die coating manifold for lithium-ion battery manufacturing using mode extraction methods
- Journal
- Chemical Engineering Science
- Page
- 122459
- Year
- 2026
- Link
- https://doi.org/10.1016/j.ces.2025.122459 52회 연결
In lithium-ion battery manufacturing, slurry flow within slot-die coating manifolds is influenced by viscosity changes from microstructure deformations or grade variations. Since the slurry flow within slot-die manifold directly impacts coating quality, understanding its behavior under varying viscosity conditions is important. However, traditional CFD simulations for such analyses are costly and time-consuming, necessitating efficient and accurate surrogate models.
This paper proposes a data-driven surrogate modeling method for the slot-die manifolds in lithium-ion battery applications, integrating mode extraction with machine learning and deep learning regression models. Proper orthogonal decomposition (POD) and kernel POD (KPOD) were applied to CFD data, with 4-fold incremental mode extraction tested for memory efficiency. Regression models were trained to predict feature coefficients of reduced bases from mode extraction. The optimal model, combining incremental POD and Gaussian process regression, achieved mean relative prediction errors of 0.03 %–0.05 % across velocity components and an average inference speed of 0.11 seconds per test sample. It also reduced peak memory usage from 120.64 GB to 53.47 GB. In contrast, KPOD-based model showed minimal memory gain (from 25.22 GB to 25.19 GB) and higher mean relative prediction errors (0.42 %–1.00 %), with similar inference speed to that of POD-based model. These results show that the proposed POD-based surrogate model is highly efficient and accurate for real-time control and optimization, in coating processes and other repetitive simulation tasks.