A parametric study of adhesive bonded joints with composite material using black-box and grey-box machine learning methods: Deep neuron networks and genetic programming. (15th July 2021)

• Record Type: Journal Article
• Authors: Gu, Zewen; Liu, Yiding; Hughes, Darren J.; Ye, Jianqiao; Hou, Xiaonan
• Is Part Of: Composites. Number 217(2021)
• Publication Date: 2021-07-15
• Subjects: Adhesive bonded joint -- Composite material -- Finite element model -- Genetic programming -- Deep neuron network; Composite materials -- Periodicals; Materials science -- Periodicals; Composite materials; Periodicals; Electronic journals; 620.118
• DOI: 10.1016/j.compositesb.2021.108894
• Languages: English
• ISSNs: 1359-8368

Abstract

Abstract: The aerospace, automotive and marine industries have witnessed a rapid increase of using adhesive bonded joints due to their advantages in joining dissimilar and/or new engineering materials. Joint strength is the key property in evaluating the capability of the adhesive joint. In this paper, developments of black-box and grey-box machine learning (ML) models are presented to allow accurate predictions of the failure load of single lap joints by considering a mix of continuous and discrete design (geometry and material) variables. Firstly, the failure loads of 300 single lap joint samples with different geometry/material parameters are calculated by FE models to generate a data set of which accuracy is validated by experimental results. Then, a deep neuron network (black-box) and a genetic programming (grey-box) model are developed for accurately predicting the failure load of the joint. Based on both ML models, a case study is conducted to explore the relationships between specific design variables and overall mechanical performances of the single lap adhesive joint, and optimal designs of structure and material can be obtained. Graphical abstract: Image 1 Highlights: Development of both deep neuron networks and genetic programming models for predicting the strength of composite joints. Consideration of geometry (continuous) and material (discrete) variables in the development of machine learning models. Discussion of the optimal design and the effects of design variables on the overall performance of composite joints.