Free vibration analysis and optimal design of adhesively bonded double-strap joints by using artificial neural networks

Abstract

This study investigates the free vibration characteristics of an adhesively bonded double-strap joint with viscoelastic adhesive layer. To simplify the spatial finite element mesh generation and efficiently model the adhesively bonded joint, a layerwise plate finite element was extended to accommodate to the modeling of the joint, where the joint structure is treated as a special sandwich laminate. The proposed method was validated by three-dimensional finite element analysis and then applied to generate sampling points for training artificial neural networks (ANNs). The effects of the adhesive material properties and joint geometrical parameters on the joint dynamic characteristics were investigated in detail using the trained ANNs. The optimum design problem is defined as a multi-objective optimization problem considering maximizing the first natural frequency and corresponding loss factor while minimizing the total structural weight. The nondominated sorting genetic algorithm combined with the ANNs were employed to tackle the problem. The proposed method provides a computationally efficient alternative for analyzing and optimizing the adhesive double-strap joints.