1. 南京航空航天大学 航空宇航学院， 南京 210016； 2. 中国商用飞机有限责任公司， 上海 200126
出版日期:2019-10-28发布日期:2019-11-01通讯作者:陈普会，男，教授，博士生导师，电话（Tel.）：025-84896256; E-mail:phchen@nuaa.edu.cn.作者简介:盛鸣剑（1981-），男，上海市人，博士生，主要研究方向为复合材料结构设计.基金资助:国家自然科学基金（11572152），江苏高校优势学科建设工程资助项目An Estimating Method of Compressive Strength of Composite Laminates After Low-Velocity Impact
SHENG Mingjian 1,2,CHEN Puhui 1,QIAN Yibin 21. College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China; 2. Commercial Aircraft Corporation of China, Ltd., Shanghai 200126, China
Online:2019-10-28Published:2019-11-01摘要/Abstract
摘要： 低速冲击后压缩强度是复合材料层合板的重要性能指标.在分析CCF300/5428复合材料层合板遭受不同能量等级低速冲击后压缩强度试验数据的基础上,提出一种基于广义回归神经网络技术的低速冲击后压缩强度估算模型.该模型以冲击能量、凹痕深度以及损伤面积为输入参数，以高斯函数为隐含层激励函数，使用部分试验数据训练，寻找最优光滑因子.此外，以最优光滑因子对所提模型进行重构并采用部分试验数据对该模型进行验证.结果表明：基于广义回归神经网络的模型具有较好的试验数据泛化能力，可用于估算低速冲击后复合材料的压缩强度.
关键词: 神经网络； 复合材料； 低速冲击； 压缩强度； 估算
Abstract: The compressive strength after low-velocity impact is a key performance index of the composite laminates. By analyzing data of compressive strength experiment of CCF300/5428 composite laminates after low-velocity impact (LVI) under different energy levels, a prediction model based on generalized regression neural network technology of compressive strength after LVI is proposed. This model uses the impact energy, the dent depth and the damage area as input parameters and the Gauss function as the hidden layer excitation function. The model uses part of the experimental data to train for finding the optimal smoothness coefficient. Then, the model is reconstructed with the optimal smoothness coefficient, and is simulated with part of the experimental data. Results show that the model has good generalization ability of experimental data and is feasible to estimate the compressive strength after low-velocity impact.
Key words: neural network; composite materials; low-velocity impact; compressive strength; estimation
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