An integrated micromechanical model and BP neural network for predicting elastic modulus of 3-D multi-phase and multi-layer braided composite

Ye, Xu; You, Tao; Du, Chenglie

doi:10.1016/j.compstruct.2014.11.052

Cited by 49 publications

(10 citation statements)

References 26 publications

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“…Xu et al [17] developed a multi-layers micromechanical model, in which the interfacial mechanical properties could be defined, to predict the elastic modulus of 3D multi-phase braided composites. Lei et al [18] established a representative unit-cell with interface zones to study the effect of cut-edge on the tensile properties of 3D braided composites by using nonlinear numerical analysis.…”

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confidence: 99%

A novel interface constitutive model for prediction of stiffness and strength in 3D braided composites

Zhang

Curiel-Sosa

Bui

2017

Composite Structures

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Please cite this article as: Zhang, C., Curiel-Sosa, J.L., Quoc Bui, T., A novel interface constitutive model for prediction of stiffness and strength in 3D braided composites, Composite Structures (2016), doi: http://dx.doi. org/ 10.1016/j.compstruct.2016.12.042 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Maximum stress criteria and a gradual degradation scheme are applied to predict the damage evolution of yarns and matrix. A user-material subroutine VUMAT based on finite element package ABAQUS/Explicit is developed for these constitutive models. The stiffness and strength properties of 3D braided composites are derived from the calculated stress-strain curves under typical loading cases. The damage mechanism of constituents especially the interface is revealed in these simulation processes. The effects of the interface parameters on the mechanical properties of composites are investigated, which provides a reference for optimizing design and control of the interface properties of 3D braided composites.

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confidence: 99%

A novel interface constitutive model for prediction of stiffness and strength in 3D braided composites

Zhang

Curiel-Sosa

Bui

2017

Composite Structures

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“…However, the main problem of the PID controller is the tuning parameters for higher perfect characters. Over the past years, many tuning methods such as optimal PSO [34], fuzzy tuning [35] and model-based optimal method [36,37] were developed. For a grouting system, the maximum pressure must be controlled dynamically.…”

Section: A Robust Tuning Methods Of the Pid Parameters Based On The Bpmentioning

confidence: 99%

A robust controlling methodology for a grouting process

Zhang

Guo

2019

Automatika

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The grouting technology is an effective and economic method in the grouting industry field. In this paper, a nonlinear model for the grouting dynamic process was established, and the controlling parameters were further modified through a robust method. Moreover, the grouting pressure system for the neural network was also modelled based on a sensitivity analysis algorithm, and in particular, the iterative learning algorithm and Lyapunov asymptotical theory. The results showed that such a robust controlling methodology was better than the normal manual operation method. The subsequent numerical simulations demonstrated that the tuning methodology could meet all the requirements for the grouting control with the maximum pressure variable in the range of 8.1%. The present study and the proposed method could be applied to various engineering projects and especially, to implement in the real control of damming grouting.

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“…The BP neural network is a kind of multilayer feed-forward network according to the back-propagation algorithm for errors, is currently one of the most widely used neural network models [9]. The recognition and classification of the face images is an important application for the BP neural network in the field of pattern recognition and classification.…”

Section: Single Bp Neural Network and Hybrid Bp Neural Network (Hbpnns)mentioning

confidence: 99%

Learning Hierarchical Representations for Face Recognition using Deep Belief Network Embedded with Softmax Regress and Multiple Neural Networks

Zhang¹

2015

Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences

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In face recognition and classification, feature extraction and classification based on insufficient labeled data is a well-known challenging problem. In this paper, a novel semi-supervised learning algorithm named deep belief network embedded with Softmax regress (DBNESR) is proposed to address this problem. DBNESR first learns hierarchical representations of feature by deep learning and then makes more efficient classification with Softmax regress. At the same time we design many kinds of classifiers based on supervised learning: BP, HBPNNs, RBF, HRBFNNs, SVM and multiple classification decision fusion classifier-hybrid HBPNNs-HRBFNNs-SVM classifier. The conducted experiments validate: Firstly, the proposed semi-supervised deep learning algorithm DBNESR is optimal for face recognition with the highest and most stable recognition rates; Second, the semi-supervised learning algorithm has better effect than all supervised learning algorithms; Third, hybrid neural networks has better effect than single neural network.

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An integrated micromechanical model and BP neural network for predicting elastic modulus of 3-D multi-phase and multi-layer braided composite

Cited by 49 publications

References 26 publications

A novel interface constitutive model for prediction of stiffness and strength in 3D braided composites

A novel interface constitutive model for prediction of stiffness and strength in 3D braided composites

A robust controlling methodology for a grouting process

Learning Hierarchical Representations for Face Recognition using Deep Belief Network Embedded with Softmax Regress and Multiple Neural Networks

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