Machine learning algorithms for deeper understanding and better design of composite adhesive joints

Abstract

In this study, machine learning (ML), a subdivision of artificial intelligence (AI), is implemented to study the mechanical behavior of composite 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 from various research groups to eliminate bias and increase the diversity within the dataset. The dataset consists of eight continuous SLJ input parameters, which are used to predict the SLJ damage mode and failure strength. Regression and classification models are built using deep neural networks (DNN) and random forests (RF). Finite element (FE) modeling is conducted, and the numerical performance is compared with the accuracy of the regression ML models. Results show that ML models can predict strength with high accuracy. Furthermore, both DNN and RF classify damage modes accurately without the need for complex failure criteria, which cannot be typically achieved using traditional FE methods. This study utilizes ML algorithms to gain a deeper understanding of the structure-property-performance relationships of SLJs, leading to better designs of composite adhesive joints with higher strength efficiency.