Prediction of Strength of Adhesive Bonded Joints Based on Machine Learning Algorithm and Finite Element Analysis

Abstract

Adhesive bonded joints are one of important joining technologies in supporting various manufacturing applications. It is important to predict the optimal strength of adhesive bonded joints in order to fit design requirements. Prediction on joint strengths is usually based on experimental tests and Finite Element Analysis (FEA). However, it is a time-consuming and expensive process. To improve computational efficiency and reduce experimental cost, in this chapter, a new approach based on a machine learning algorithm and a FEA method is proposed to predict the failure loads of joints. The innovations of the approach include the following aspects: (1) the FEA model for analyzing the strengths of joints is validated using experimental tests to generate a robust dataset for training a Deep Neural Networks (DNNs) algorithm, which is designed to predict the failure loads of various joint material combinations with high efficiency; (2) based on the trained DNNs algorithm, a Fruit Fly Algorithm (FFO) is proposed to identify the optimal material parameters of Adhesive bonded joints in a given geometry and an joint configuration. The proposed FEA method was successfully validated by conducting experiments with samples of single lap joints. 375 samples were generated by the validated FEA model for DNNs’ training, validation and testing. Case studies showed that the computational time of this approach was saved by 99.54% compared with that of the FEA model, and optimal parameters were identified within 9 iterations based on the proposed FFO optimization algorithm.