Hyperelastic and Stacked Ensemble-Driven Predictive Modeling of PEMFC Gaskets Under Thermal and Chemical Aging

This study comprehensively investigates the stress distribution and aging effects in Ethylene Propylene Diene Monomer (EPDM) and Liquid Silicone Rubber (LSR) gasket materials through a novel integration of hyperelastic modeling and advanced machine learning techniques. By employing the Mooney-Rivlin, Ogden, and Yeoh hyperelastic models, we evaluated the mechanical behavior of EPDM and LSR under conditions of no aging, heat aging, and combined heat- and sulfuric-acid exposure. Each model revealed distinct sensitivities to stress distribution and material deformation, with peak von Mises stress values indicating that LSR experiences higher internal stress than EPDM across all conditions. For instance, without aging, LSR shows a von Mises stress of 24.17 MPa compared to 14.96 MPa for EPDM, while under heat and sulfuric acid exposure, LSR still exhibits higher stress values, showcasing its resilience under extreme conditions. Additionally, the ensemble learning approach achieved a classification accuracy of 98% for LSR and 84% for EPDM in predicting aging effects, underscoring the robustness of our predictive framework. These findings offer practical implications for selecting suitable gasket materials and developing predictive maintenance strategies in industrial applications, such as fuel cells, where material integrity under stress and aging is paramount.