Strain-deformation Reconstruction of Carbon Fiber Composite Laminates Based on BP Neural Network
Strain-deformation Reconstruction of Carbon Fiber Composite Laminates Based on BP Neural Network
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DOI：
10.1590/1980-5373-mr-2019-0393
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Guoping Ding;Siyuan Jiang;Songchao Zhang;Jieliang Xiao
中科院分区：
文献类型：
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作者：
Guoping Ding;Siyuan Jiang;Songchao Zhang;Jieliang Xiao
碳纤维增强复合材料（CFRP）层合板结构件在航空航天和军事领域中的应用要求精度高、稳定性强。通常这些结构构件的变形在运行过程中很难直接测量，但CFRP层合结构的变形可以用应变信息重建。CFRP层合板结构可以通过变厚度的铺层设计以适应不同应用的要求。针对CFRP层合板结构中各层刚度和强度不连续的特点，提出将BP神经网络应用于CFRP层合板的变形重构。以应变为输入，变形为输出，在大量实验数据的基础上，通过训练得到应变与变形之间的BP神经网络模型。本文设计了等厚度和变厚度的CFRP试件，构建了相应的应变-变形重构实验系统。采用光纤光栅传感器测量CFRP试件表面的应变，采用激光位移传感器测量试件的变形。神经网络重构得到的预测挠度与实际测量挠度的对比分析表明，BP神经网络在一定误差范围内可以重构CFRP层合板的结构变形。
The Carbon Fiber Reinforced Polymer (CFRP) laminate structural components used in the aerospace and military domains require high precision and strong stability. Usually the deformation of these structural components is difficult to be measured directly during operation, but the deformation of the CFRP laminate structure can be reconstructed with strain information. The CFRP laminate structure can be designed to adapt to the requirements of different applications through layering of variable thickness. In this paper, aiming at the discontinuous stiffness and strength of the variable laminations within the CFRP laminate structure, the BP neural network is proposed to be applied to the deformation reconstruction of CFRP laminates. With strain as input and deformation as output, based on a large amount of experimental data, the BP neural network model between strain and deformation is obtained through training. In this paper, CFRP test piecs with equal thickness and variable thickness were designed, and the corresponding strain-deformation reconstruction experimental system was constructed. The strain on the surface of CFRP test piece was measured by the fiber grating sensor, and the deformation of the test piece was measured by the laser displacement sensor. The comparative analysis between the predicted deflection obtained by neural network reconstruction and the actual measured deflection shows that BP neural network can reconstruct the structural deformation of CFRP laminates within certain error range.