Enhancing Structural Reliability of Composite Adhesive Joints: An Integrated Experimental‐Numerical‐Machine Learning Framework for Material‐Geometric Co‐Optimization

ABSTRACT The reliability of composite adhesive joints is compromised by non‐linear dependencies between material properties and manufacturing uncertainties. This study addresses the trade‐off between structural performance and consistency by developing an integrated experimental‐numerical‐ML framework for multi‐objective optimization (MOP) within a 20‐dimensional design space. Static tensile tests validated the numerical baseline, showing a high correlation with errors below 8%. Experimental results revealed a critical interplay between adhesive ductility and bondline thickness ( t _a ); specifically, increasing t _a from 0.2 to 0.9 mm caused a 38.4% strength reduction in brittle epoxy (AV138) due to localized peel stresses and a transition from fiber tear to adhesive failure. Conversely, ductile adhesives (Sikaforce 7752) maintained high loads (up to 8554 N) by transitioning to a cohesive failure mode, demonstrating superior stress redistribution. Building on this physical foundation, a robust XGBoost surrogate model was developed ( R ² = 0.97), and SHAP/PDP analyses were employed to translate these complex material‐geometric interactions into physics‐informed design constraints. Finally, a novel MOP scheme balanced maximum force against manufacturing robustness (CV%) using Uncertainty Quantification. The framework identified a globally superior design with a load‐bearing capacity of 12,343 N and exceptional consistency (CV = 0.02%). Independent FEA validation confirmed the solution's reliability, with only a 9% conservative prediction error. This research provides a rigorous, data‐driven methodology that bridges the gap between fracture mechanics and automated design, ensuring structural safety in high‐performance composite assemblies.