Machine Learning for Strength and Damage Prediction of Adhesive Joints

Abstract

In this study, machine learning (ML), a subdivision of artificial intelligence (AI), is implemented to study the mechanical behavior of adhesive single-lap joints (SLJs) subjected to tensile loading. The experimental data for training and testing the ML models are compiled from peer-reviewed journal papers to eliminate bias and increase the diversity within the data. The dataset is comprised of eight continuous SLJ parameters, which are used to predict the SLJ damage mode and failure strength. To accomplish this, regression and classification models are built using deep neural networks (DNN) and random forests (RF). Finite element (FE) modeling is conducted, and the performance is compared with the accuracy of the regression ML models. Results show ML models were able to predict strength with higher accuracy than FE modeling. Furthermore, both DNN and RF classified damage mode accurately without failure criteria, exposing limitations within FE modeling. As a result, this study introduces the use of ML for strength and damage mode prediction of adhesive SLJ, providing insights into their mechanical behavior, revealing hidden property performance patterns, and enhancing predictability.