Rights statement: 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
Accepted author manuscript, 1.94 MB, PDF document
Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A parametric study of adhesive bonded joints with composite material using black-box and grey-box machine learning methods
T2 - Deep neuron networks and genetic programming
AU - Gu, Zewen
AU - Liu, Yiding
AU - J.Hughes, Darren
AU - Ye, Jianqiao
AU - Hou, Xiaonan
N1 - 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
PY - 2021/7/15
Y1 - 2021/7/15
N2 - 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.
AB - 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.
U2 - 10.1016/j.compositesb.2021.108894
DO - 10.1016/j.compositesb.2021.108894
M3 - Journal article
VL - 217
JO - Composites Part B: Engineering
JF - Composites Part B: Engineering
SN - 1359-8368
M1 - 108894
ER -