| 基于BP神经网络的冷挤压孔疲劳寿命预测 |
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| 引用本文： | 李庆林,黎向锋,左敦稳.基于BP神经网络的冷挤压孔疲劳寿命预测[J].航空精密制造技术,2008,44(1):35-37. |
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| 作者姓名： | 李庆林 黎向锋 左敦稳 |
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| 作者单位： | 南京航空航天大学机电工程学院,南京,210016 |
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| 摘 要： | 利用超高强度300M、30CrMnSiA调质钢、A3普通碳素结构钢的试验数据作为训练样本,预测了LY12CZ铝合金的疲劳寿命,并与试验数据在疲劳寿命N、子样平均值X、中值疲劳寿命N50方面分别进行分析比较,结果表明模型的误差控制在2%以内,证明了模型预测方法的可行性和有效性,也为预测新材料的疲劳寿命提供了方法和依据。
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| 关 键 词： | 冷挤压孔 疲劳寿命 BP神经网络 |
| 文章编号： | 1003-5451(2008)01-0035-03 |
| 修稿时间： | 2007年9月4日 |
Fatigue Life Prediction for Cold-Expanded Hole Based on BP Neural Network |
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| LI Qing-lin,LI Xiang-feng,ZUO Dun-wen.Fatigue Life Prediction for Cold-Expanded Hole Based on BP Neural Network[J].Aviation Precision Manufacturing Technology,2008,44(1):35-37. |
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| Authors: | LI Qing-lin LI Xiang-feng ZUO Dun-wen |
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| Abstract: | Test data of 300M ultrahigh strength steel, 30CrMnSiA steel and A3 structural carbon steel are applied as training samples. Then using this model the fatigue life of LY12CZ aluminium alloy is predicted. By comparing fatigue life(N), average value of sample and median fatigue life (N50) in simulation results with those in test results, it is found that the error of the model is less than 2%. This model is proved feasible and effective, also a method and basis for fatigue life prediction of new materials are provided. |
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| Keywords: | cold-expanded hole fatigue life BP neural network |
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