缝合复合材料面内刚度和强度的神经网络预测被引量：6

NEURAL NETWORK PREDICTING OF STIFFNESS AND STRENGTH IN PLANE OF STITCHED COMPOSITES

下载PDF

导出

摘要　基于神经网络的BP算法,建立了预测缝合复合材料刚度和强度性能的模型。根据试验所得的缝合复合材料的性能参数,训练人工神经网络,拟合出输入参数(各种缝纫参数与等效未缝纫层合板性能参数)与输出参数(缝合层合板性能参数)之间的非线性关系,设计完成了缝合复合材料弹性性能与强度分析软件,并以此软件分析计算在新的缝纫参数和等效未缝纫层合板性能参数情况下的缝合复合材料性能。与实验结果对比,两者符合较好。为缝合复合材料刚度强度预测开辟了一条新的有效途径。 Models estimating stiffness and strength of stitched composites were established based on BP algorithm of neural network, and the longitudinal stiffness and strength networks were trained according to experimental data. The nonlinear relationship between input parameters and output parameters was determined and the analysis software of stitched laminates was completed. The longitudinal stiffness and strength of stitched laminates with new stitching parameters and new equivalent unstitched laminate performance parameters were predicted using the software. The results of the prediction and the test data are in good agreement. A very effective approach to estimate the stiffness and strength of stitched composites has been found.

作者燕瑛刘兵山黄聪成传贤

机构地区北京航空航天大学航空科学与工程学院

出处《复合材料学报》 EI CAS CSCD 北大核心 2004年第6期179-183,共5页 Acta Materiae Compositae Sinica

基金国家自然科学基金资助(50073002)

关键词缝合复合材料神经网络 BP算法刚度强度预测 Backpropagation Forecasting Neural networks Stiffness Strength of materials

分类号 TB330.1 [一般工业技术—材料科学与工程]

引文网络
相关文献

参考文献7

1Mouritz A P, Leong K H, Herszberg I. A review of the effect of stitching on the in-plane mechanical properties of fibre-reinforced polymer composites[J]. Composites Part A: Applied Science and Manufacturing, 1997, 28 (12): 979-991.
2Theocaris P S. Panagiotopoulos P D. Neural network for computing in fracture mechanics: Methods and prospects of applications[J]. Computer Methods in Applied Mechanics and Engineering, 1993, 106: 213-228.
3Zhang Z, Klein P, Friedrich K. Dynamic mechanical properties of PTFE based short carbon fibre reinforced composites: Experiment and artificial neural network prediction[J]. Composites Science and Technology, 2002, 62 (7-8): 1001-1009.
4Rao H S, Mukherjee A. Artificial neural networks for predicting the macromechanical behavior of ceramic-matrix composites[J]. Computational Materials Science, 1996, 5 (4): 307-322.
5Zhang Z , Friedrich K. Artificial neural networks applied to polymer composites: A review[J]. Composites Science and Technology, 2003, 63 (14): 2029-2044.
6Lina J T, Bhattacharyyaa D, Kecmanb V. Multiple regression and neural networks analyses in composites machining[J]. Composites Science and Technology, 2003, 63 (3): 539-548.
7Victor A G, Tadanobu S, Abraham I B, et al. Neural computing of effective properties of random composite materials[J]. Computers and Structures, 2001, 79 (1): 1-6.