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
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- @Article{GU:2021:CPBE,
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author = "Zewen Gu and Yiding Liu and Darren J. Hughes and
Jianqiao Ye and Xiaonan Hou",
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title = "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",
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journal = "Composites Part B: Engineering",
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volume = "217",
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pages = "108894",
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year = "2021",
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ISSN = "1359-8368",
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DOI = "doi:10.1016/j.compositesb.2021.108894",
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URL = "https://www.sciencedirect.com/science/article/pii/S1359836821002857",
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keywords = "genetic algorithms, genetic programming, Adhesive
bonded joint, Composite material, Finite element model,
Deep neuron network",
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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",
- }
Genetic Programming entries for
Zewen Gu
Yiding Liu
Darren J Hughes
Jianqiao Ye
Xiaonan Hou
Citations