On a Multiphysics Digital Twin for Aging Prediction of Adhesive Step Lap Joints

The present work describes the early steps in creating a digital twin to predict aging for multi-material adhesive step lap joints (ASLJs), considering simultaneous mechanical and environmental influences. It begins by defining the digital twin context and its specific architecture known as the data-driven digital twin (D3T). For a D3T to work, a theoretical and computational framework must be established to understand how the properties of materials in ASLJs degrade due to environmental damage. The developed framework describes a thermodynamics-based theory for predicting material degradation. The computational implementation of the framework and its performance are evaluated using two models of multi-material ASLJs. The first model contains a single-step ASLJ made of titanium Ti-6Al-4V and carbon epoxy composite with FM-300K adhesive, featuring three variations to show different levels of homogenization. Studies on this model assess the impact of various parameters such as the finite element order, mesh density, damage parameters, and inclusion of damage models for the participating domains. The validation of this model is also provided against experimental data. The second model addresses a multistep ASLJ using the same materials. Predictions from this model are compared favorably with experimental results under two different environmental conditions to gain insights into the aging and performance degradation of the ASLJs. Finally, conclusions and plans close the present paper.