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.
参考文献
| [1] | 袁曾任. 人工神经元网络及其应用[M]. 北京:清华大学出版社,1999. |
| [2] | Mouritz 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. |
| [3] | 李水乡, 孙慧玉, 黄传奇. 用人工神经网络方法模拟三维编织复合材料的力学性能[J]. 南京航空航天大学学报, 1997, 29 (4): 397-401. |
| [4] | 华宏星, 陈小琳, 石银明. 运用神经网络识别复合材料板刚度[J]. 复合材料学报, 2000, 17 (1): 108-110. |
| [5] | 刘贵立, 张国英. 基于BP网络的材料设计方法[J]. 兵器材料科学与工程, 2000, 23 (2): 56-59. |
| [6] | Theocaris 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. |
| [7] | Zhang 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. |
| [8] | Rao 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. |
| [9] | Zhang Z , Friedrich K. Artificial neural networks applied to polymer composites: A review[J]. Composites Science and Technology, 2003, 63 (14): 2029-2044. |
| [10] | Lina 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. |
| [11] | Victor 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. |
上一张
下一张
上一张
下一张
计量
- 下载量()
- 访问量()
文章评分
- 您的评分：
-
10%
-
20%
-
30%
-
40%
-
50%