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中文核心期刊
Duan Zunyi, Liu Yi, Zhang Haoxiang, Chen Zhiyuan, Xu Bin, Zhu Jihong, Yan Jun. Machine learning-based design optimization of variable stiffness fiber reinforced composites to minimize structural compliance. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(7): 1849-1860. DOI: 10.6052/0459-1879-23-521
Citation: Duan Zunyi, Liu Yi, Zhang Haoxiang, Chen Zhiyuan, Xu Bin, Zhu Jihong, Yan Jun. Machine learning-based design optimization of variable stiffness fiber reinforced composites to minimize structural compliance. Chinese Journal of Theoretical and Applied Mechanics, 2024, 56(7): 1849-1860. DOI: 10.6052/0459-1879-23-521

MACHINE LEARNING-BASED DESIGN OPTIMIZATION OF VARIABLE STIFFNESS FIBER REINFORCED COMPOSITES TO MINIMIZE STRUCTURAL COMPLIANCE

  • Received Date: November 01, 2023
  • Accepted Date: January 01, 2024
  • Available Online: February 21, 2024
  • Published Date: January 02, 2024
  • The variable stiffness design optimization of fiber-reinforced composite laminates optimizes the designability of fiber laying angles point by point to match the spatial variation of stress states in the structure and more efficiently exert the directionality of fiber-reinforced composite laminates in strength and stiffness performance, providing the designers with a broader design space and design flexibility. However, the traditional variable stiffness design optimization of composite material based on gradient algorithms inevitably faces large-scale computational challenges in structural and sensitivity analysis due to its large number of design variables. At the same time, there is a problem with the randomness of load conditions in the conceptual design stage of the structure, and how to formulate an efficient design scheme for random load conditions in the initial conceptual design stage has important engineering value. In recent years, with the rapid development of artificial intelligence and high-performance computing, it has become possible to build end-to-end machine learning models based on the datasets obtained by traditional optimization, which provides the possibility of achieving real-time variable stiffness optimization of composite material. In this paper, the back propagation (BP) neural network algorithm is used to establish a variable stiffness design optimization method for fiber-reinforced composites based on machine learning. Firstly, based on the normal distribution fiber optimization (NDFO) interpolation scheme, a composite material variable stiffness design optimization model is constructed with minimizing structural compliance as an objective function, and the sample datasets required for neural network model training are obtained by considering the randomness of load magnitude and direction. Secondly, the means square error (MSE) was used as the objective function to train the sample dataset using the BP neural network model. Finally, a model evaluation system based on the Pearson correlation coefficient and MSE is established to evaluate the generated neural network model. Numerical examples discuss the variable stiffness design optimization of MBB beam with round holes and C-type cantilever beam, elaborate the implementation process of the variable stiffness design optimization of composite based on machine learning, and systematically compare the differences between the variable stiffness design optimization of composite based on machine learning and the traditional variable stiffness design optimization results of composite based on NDFO in fiber laying trajectory and objective function, and verify the effectiveness of the proposed method.
  • [1]
    王琥, 李启迪, 李光耀. 变刚度复合材料结构设计方法及不确定性分析研究进展. 机械工程学报, 2019, 55(8): 46-55 (Wang Hu, Li Qidi, Li Guangyao. Research progress on structural design methods and uncertainty analysis of variable stiffness composite materials. Journal of Mechanical Engineering, 2019, 55(8): 46-55 (in Chinese) doi: 10.3901/JME.2019.08.046

    Wang Hu, Li Qidi, Li Guangyao. Research progress on structural design methods and uncertainty analysis of variable stiffness composite materials. Journal of Mechanical Engineering, 2019, 55(8): 46-55 (in Chinese) doi: 10.3901/JME.2019.08.046
    [2]
    Duan Z, Yan J, Zhao G. Integrated optimization of the material and structure of composites based on the Heaviside penalization of discrete material model. Structural and Multidisciplinary Optimization, 2015, 51: 721-732 doi: 10.1007/s00158-014-1168-x
    [3]
    Xu Y, Zhu J, Wu Z, et al. A review on the design of laminated composite structures: Constant and variable stiffness design and topology optimization. Advanced Composites and Hybrid Materials, 2018, 1: 460-477 doi: 10.1007/s42114-018-0032-7
    [4]
    Ghiasi H, Fayazbakhsh K, Pasini D, et al. Optimum stacking sequence design of composite materials. Part II: Variable stiffness design. Composite Structures, 2010, 93(1): 1-13 doi: 10.1016/j.compstruct.2010.06.001
    [5]
    Duan Z, Liu Y, Fan J, et al. Concurrent multi-material and multi-scale design optimization of fiber-reinforced composite material and structures for minimum structural compliance. Composite Structures, 2023, 311: 116796 doi: 10.1016/j.compstruct.2023.116796
    [6]
    Stegmann J, Lund E. Discrete material optimization of general composite shell structures. International Journal for Numerical Methods in Engineering, 2005, 62(14): 2009-2027 doi: 10.1002/nme.1259
    [7]
    Bruyneel M. SFP—A new parameterization based on shape functions for optimal material selection: Application to conventional composite plies. Structural and Multidisciplinary Optimization, 2011, 43(1): 17-27 doi: 10.1007/s00158-010-0548-0
    [8]
    Gao T, Zhang W, Duysinx P. A bi-value coding parameterization scheme for the discrete optimal orientation design of the composite laminate. International Journal for Numerical Methods in Engineering, 2012, 91(1): 98-114 doi: 10.1002/nme.4270
    [9]
    Niu B, Olhoff N, Lund E, et al. Discrete material optimization of vibrating laminated composite plates for minimum sound radiation. International Journal of Solids and Structures, 2010, 47(16): 2097-2114 doi: 10.1016/j.ijsolstr.2010.04.008
    [10]
    Yin L, Ananthasuresh GK. Topology optimization of compliant mechanisms with multiple materials using a peak function material interpolation scheme. Structural and Multidisciplinary Optimization, 2001, 23: 49-62 doi: 10.1007/s00158-001-0165-z
    [11]
    Kiyono CY, Silva ECN, Reddy JN. A novel fiber optimization method based on normal distribution function with continuously varying fiber path. Composite Structures, 2017, 160: 503-515 doi: 10.1016/j.compstruct.2016.10.064
    [12]
    Fayazbakhsh K, Nik MA, Pasini D, et al. Defect layer method to capture effect of gaps and overlaps in variable stiffness laminates made by automated fiber placement. Composite Structures, 2013, 97: 245-251 doi: 10.1016/j.compstruct.2012.10.031
    [13]
    Sun S, Han Z, Fu H, et al. Defect characteristics and online detection techniques during manufacturing of FRPs using automated fiber placement: A review. Polymers, 2020, 12(6): 1337 doi: 10.3390/polym12061337
    [14]
    Wang X, Jiang M, Zhou Z, et al. 3D printing of polymer matrix composites: A review and prospective. Composites Part B : Engineering 2017, 110: 442-458
    [15]
    Liu G, Xiong Y, Zhou L. Additive manufacturing of continuous fiber reinforced polymer composites: Design opportunities and novel applications. Composites Communications, 2021, 27: 100907 doi: 10.1016/j.coco.2021.100907
    [16]
    Van Campen JMJF, Kassapoglou C, Gürdal Z. Generating realistic laminate fiber angle distributions for optimal variable stiffness laminates. Composites Part B: Engineering, 2012, 43(2): 354-360 doi: 10.1016/j.compositesb.2011.10.014
    [17]
    Hao P, Feng S, Zhang K, et al. Adaptive gradient-enhanced kriging model for variable-stiffness composite panels using Isogeometric analysis. Structural and Multidisciplinary Optimization, 2018, 58: 1-16 doi: 10.1007/s00158-018-1988-1
    [18]
    Serhat G, Bediz B, Basdogan I. Unifying lamination parameters with spectral-Tchebychev method for variable-stiffness composite plate design. Composite Structures, 2020, 242: 112183 doi: 10.1016/j.compstruct.2020.112183
    [19]
    Brampton CJ, Wu KC, Kim HA. New optimization method for steered fiber composites using the level set method. Structural and Multidisciplinary Optimization, 2015, 52: 493-505 doi: 10.1007/s00158-015-1256-6
    [20]
    Tian Y, Shi T, Xia Q. A parametric level set method for the optimization of composite structures with curvilinear fibers. Computer Methods in Applied Mechanics and Engineering, 2022, 388: 114236 doi: 10.1016/j.cma.2021.114236
    [21]
    Niu X, Liu Y, Wu J, et al. Curvature-controlled trajectory planning for variable stiffness composite laminates. Composite Structures, 2020, 238: 111986 doi: 10.1016/j.compstruct.2020.111986
    [22]
    Shafighfard T, Demir E, Yildiz M. Design of fiber-reinforced variable-stiffness composites for different open-hole geometries with fiber continuity and curvature constraints. Composite Structures, 2019, 226: 111280 doi: 10.1016/j.compstruct.2019.111280
    [23]
    Duan Z, Yan J, Lee I, et al. A two-step optimization scheme based on equivalent stiffness parameters for forcing convexity of fiber winding angle in composite frames. Structural and Multidisciplinary Optimization, 2019, 59: 2111-2129 doi: 10.1007/s00158-018-2179-9
    [24]
    McCulloch, Warren S, Walter P. A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 1943, 5: 115-133
    [25]
    Rosenblatt F. The perceptron, a perceiving and recognizing automaton project para. Cornell Aeronautical Laboratory, 1957
    [26]
    Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature, 1986, 323(6088): 533-536
    [27]
    Hochreiter S. Untersuchungen zu dynamischen neuronalen Netzen. Diploma, Technische Universität München, 1991, 91(1): 31
    [28]
    Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science, 2006, 313(5786): 504-507
    [29]
    阎军, 许琦, 张起等. 人工智能在结构拓扑优化领域的现状与未来趋势. 力学学报, 2021, 38(4): 412-422 (Yan Jun, Xu Qi, Zhang Qi, et al. Current situation and future trend of artificial intelligence in the field of structural topology optimization. Chinese Journal of Theoretical and Applied Mechanics, 2021, 38(4): 412-422 (in Chinese)

    Yan Jun, Xu Qi, Zhang Qi, et al. Current situation and future trend of artificial intelligence in the field of structural topology optimization. Chinese Journal of Theoretical and Applied Mechanics, 2021, 38(4): 412-422 (in Chinese)
    [30]
    Chandrasekhar A, Suresh K. Tou NN: Topology optimization using neural networks. Structural and Multidisciplinary Optimization, 2021, 63: 1135-1149
    [31]
    Senhora FV, Chi H, Zhang Y, et al. Machine learning for topology optimization: Physics-based learning through an independent training strategy. Computer Methods in Applied Mechanics and Engineering, 2022, 398: 115116
    [32]
    张坤鹏, 郝鹏, 段于辉等. 基于深度学习的多级曲线加筋壁板布局优化设计. 中国舰船研究, 2021, 16(4): 86-95 (Zhang K, Hao P, Duan Y, et al. Optimal design of multi-level curve reinforced wall plate layout based on deep learning. Chinese Journal of Ship Research, 2021, 16(4): 86-95 (in Chinese)

    Zhang K, Hao P, Duan Y, et al. Optimal design of multi-level curve reinforced wall plate layout based on deep learning. Chinese Journal of Ship Research, 2021, 16(4): 86-95 (in Chinese)
    [33]
    史冬岩, 王立夫, 张博洋等. 基于Pyramid-Attention-U-Net深度学习模型的实时拓扑优化设计. 工程力学, 2024, 出版中 (Shi Dongyan, Wang Lifu, Zhang Boyang, et al. Real-time topology optimization design based on Pyramid-Attention-U-Net deep learning model. Engineering Mechanics, 2024, in press (in Chinese)

    Shi Dongyan, Wang Lifu, Zhang Boyang, et al. Real-time topology optimization design based on Pyramid-Attention-U-Net deep learning model. Engineering Mechanics, 2024, in press (in Chinese)
    [34]
    陈高勇, 何彬, 赵刚. 拓扑优化结构再设计中的神经网络求解方法. 机械设计与制造, 2023, 9: 282-285 (Chen Gaoyong, He Bin, Zhao Gang. Neural network solution method in topology optimization structure redesign. Mechanical Design and Manufacturing, 2023, 9: 282-285 (in Chinese)

    Chen Gaoyong, He Bin, Zhao Gang. Neural network solution method in topology optimization structure redesign. Mechanical Design and Manufacturing, 2023, 9: 282-285 (in Chinese)
    [35]
    Pitton SF, Ricci S, Bisagni C. Buckling optimization of variable stiffness cylindrical shells through artificial intelligence techniques. Composite Structures, 2019, 230: 111513 doi: 10.1016/j.compstruct.2019.111513
    [36]
    Liu X, Qin J, Zhao K, et al. Design optimization of laminated composite structures using artificial neural network and genetic algorithm. Composite Structures, 2023, 305: 116500 doi: 10.1016/j.compstruct.2022.116500
    [37]
    Kim C, Lee J, Yoo J. Machine learning-combined topology optimization for functionary graded composite structure design. Computer Methods in Applied Mechanics and Engineering, 2021, 387: 114158 doi: 10.1016/j.cma.2021.114158
    [38]
    Khalilpourazari S, Khalilpourazary S, Özyüksel Çiftçioğlu A, et al. Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence. Journal of Intelligent Manufacturing, 2021, 32: 1621-1647
    [39]
    Xu Y, Gao Y, Wu C, et al. Machine learning based topology optimization of fiber orientation for variable stiffness composite structures. International Journal for Numerical Methods in Engineering, 2021, 122(22): 6736-6755 doi: 10.1002/nme.6809
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