Analysis of composite/titanium alloy hybrid bolted structures based on improved LaRC05 criterion and backpropagation neural network

Abstract

Reliable prediction of ultimate load in composite/titanium bolted joints is hindered by intricate, interacting damage modes under multi-axial stress. To overcome the limitations of current high-fidelity yet computationally expensive models, we present a hybrid data-physics framework that couples an enhanced shear-driven three-dimensional LaRC05 failure criterion with a streamlined backpropagation (BP) neural network. This synergistic coupling represents a paradigm shift, enabling a leap from high-cost, high-fidelity simulation to high-fidelity, low-cost prediction. The progressive damage process is embedded in ABAQUS through a user-defined material subroutine, accurately capturing damage initiation and evolution without resorting to excessive mesh refinement. Bayesian optimisation and Shapley Additive Explanations (SHAP)-based feature selection yield a compact network architecture that retains only the most influential inputs, ensuring robust generalisation across a broad design space. Validation against experimental data demonstrates markedly improved accuracy and computational efficiency, enabling rapid evaluation during early-stage design. The resulting tool is readily deployable, offering practitioners a swift and dependable route for the safety assessment of lightweight hybrid structures.