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,

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

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