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| Statistical Study on Corrosion Damage Distribution of Aircraft Structure Based on Neural Network |
| DaZhao Yu;Yueliang Chen; |
| 海军航空工程学院青岛分院 |
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Abstract Make use of available experimental data of LY12CZ aluminum alloy,a probabilistic neural network was developed to classify the maximum corrosion depth ranges based on the material failure mode.The output is in good agreement with
experiment result .Statistical study on classified corrosion damage was carried out.The results show that maximum pitting corrosion depth for aluminum alloy conforms to Gumbel distribution.The normal distribution and three parameters Weibull distribution fit well with period of intergranular corrosion and the maximum exfoliation corrosion depth is consistent with the three parameters Weibull distribution law.It may be necessary to use several distribution functions rather than a single distribution to represent corrosion damage characteristics due to the large distribution of maximum corrosion depth on aircraft materials.
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Received: 21 December 2004
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Corresponding Authors:
DaZhao Yu
E-mail: ytyudazhao@yahoo.com.cn
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