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; Liu, Yiding; Hughes, Darren J.; Ye, Jianqiao; Hou, Xiaonan

doi:10.1016/j.compositesb.2021.108894

Cited by 39 publications

(12 citation statements)

References 31 publications

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“…Before the training process, all inputs and the eight regression targets P1–P8 are normalized between 0 and1 to improve the efficiency of training data‐based models. [ 20,21 ] As the CNN model individually seeks a set of viable weights to fit the neuron networks for simultaneously predicting the eight outputs, it can be treated as a multiregression CNN model. The size of the training group is 104 and the batch size of the CNN model is set to 30, thus allowing the model to study a part of the training data at a time, and the epoch is 500, which updates the weights of the neuron networks by 500 cycles.…”

Section: Resultsmentioning

confidence: 99%

A Novel Self‐Updating Design Method for Complex 3D Structures Using Combined Convolutional Neuron and Deep Convolutional Generative Adversarial Networks

Hou

Saafi

et al. 2022

Advanced Intelligent Systems

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Mechanical design is one of the essential disciplines in engineering applications, while inspirations of design ideas highly depend on the ability and prior knowledge of engineers or designers. With the rapid development of machine learning (ML) techniques, artificial intelligence (AI)‐based design methods are promising tools for the design of advanced engineering systems. So far, there have been some studies of 2D patterns and structural designs based on ML techniques. However, a particular challenge remains in allowing complex 3D mechanical designs using ML techniques. Herein, a novel and experience‐free method to equip ML models with 3D design capabilities by combining a convolutional neuron network (CNN) with a deep convolutional generative adversarial network (DCGAN) is developed. The model directly receives 2D image‐based training data that define the complex 3D structures of a specific machine part. After the training process, an infinite number of new 3D designs can be generated by the proposed model, with their geometric and mechanical properties being accurately predicted at the same time. Moreover, the generated new designs can be fed back to expand the original input datasets for further ML model training and updating.

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Section: Resultsmentioning

confidence: 99%

A Novel Self‐Updating Design Method for Complex 3D Structures Using Combined Convolutional Neuron and Deep Convolutional Generative Adversarial Networks

Hou

Saafi

et al. 2022

Advanced Intelligent Systems

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“…The civil engineering industry has witnessed a rapid growth in the use of externally bonded composite materials to strengthen structures across the world [1][2][3]. This has stimulated the use of structural epoxy adhesives in civil structures, as it can offer the advantages of low additional weight, more uniform stress distribution, and design flexibility, when compared to conventional joining techniques [4][5][6]. However, when the ambient temperature increases, adhesive joints may perform differently than at a normal temperature, as the glass transition behaviour of the adhesive layer will result in a reduction in its strength and stiffness, consequently reducing the stress transfer capacity of the joint [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Machine learning approach for analysing and predicting the modulus response of the structural epoxy adhesive at elevated temperatures

Wang

Stratford

et al. 2023

The Journal of Adhesion

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For bonded Fibre Reinforced Polymer (FRP) strengthening systems in civil engineering projects, the adhesive joint performance is a key factor in the effectiveness of the strengthening; however, it is known that the material properties of structural epoxy adhesives change with temperature. This present paper examines the implied relationship between the curing regimes and the storage modulus response of the adhesive using a Machine Learning (ML) approach.A dataset containing 157 experimental data collected from the scientific papers and academic theses was used for training and testing an Artificial Neural Network (ANN) model. The sensitivity analysis reveals that the curing conditions have a significant effect on the glass transition temperatures (Tg) of the adhesive, and consequently on the storage modulus response at elevated temperatures. Curing at an extremely high temperature for a long time does not, however, guarantee a better thermal performance. For the studied adhesive, curing in a warm (≥ 45 °C) and dry (near 0 % RH) environment for 21 days is recommended for practical applications. A software with a Graphical User Interface (GUI) was established, which can predict the storage modulus response of the adhesive, plot the corresponding response curve, and estimate the optimum curing condition.

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“…Recently, evidence-based machine learning techniques have been used to replace the conventional numerical and experimental methods for investigating the single/mutual effects of the design variables and to arrive at the optimal design solution. A Genetic Algorithm (GA) is one of the promising machine learning techniques for aiding engineering research and applications [15][16][17][18][19]. Concurrently, the use of finite element (FE) simulations is becoming prominent in composite materials to reduce the reliance on costly non-standard experimental iterations.…”

Section: Introductionmentioning

confidence: 99%

Design optimisation of braided composite beams for lightweight rail structures using machine learning methods

Hou

Liu

et al. 2022

Composite Structures

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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

Cited by 39 publications

References 31 publications

A Novel Self‐Updating Design Method for Complex 3D Structures Using Combined Convolutional Neuron and Deep Convolutional Generative Adversarial Networks

A Novel Self‐Updating Design Method for Complex 3D Structures Using Combined Convolutional Neuron and Deep Convolutional Generative Adversarial Networks

Machine learning approach for analysing and predicting the modulus response of the structural epoxy adhesive at elevated temperatures

Design optimisation of braided composite beams for lightweight rail structures using machine learning methods

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