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

Gu, Zewen and Liu, Yiding and J.Hughes, Darren and Ye, Jianqiao and Hou, Xiaonan (2021) 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. Composites Part B: Engineering, 217. ISSN 1359-8368

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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.

Item Type:
Journal Article
Journal or Publication Title:
Composites Part B: Engineering
Additional Information:
This is the author’s version of a work that was accepted for publication in Composites Part B: Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Composites Part B: Engineering, 217, 2021 DOI: 10.1016/j.compositesb.2021.108894
ID Code:
154610
Deposited By:
Deposited On:
05 May 2021 14:15
Refereed?:
Yes
Published?:
Published
Last Modified:
16 Jun 2021 09:13