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首页> 外文会议>Sixth International Conference on Damage and Fracture Mechanics: Computer Aided Assessment and Control 2000 Nontreal, Canada, >Corrosion prediction in aging aircraft materials
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Corrosion prediction in aging aircraft materials

机译：飞机材料老化中的腐蚀预测

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摘要

An artificial nerual network is developed to predict the corrosion behavior of different series of aluminum alloys when exposed to a variety of corrosive substances, short term and long term aircraft carrier exposures. Given the corrosion environment and time of exposure the neural network predicts the ASTM G34 corrosion rating and the resulting material loss. The trained and limited test results predicted from the neural network are in good comparison to the experimental data. The effects of corrosion environment and material type from neural network simulation are presented to illustrate the trends. Based on the preliminary results, the neural network approach to corrosion predictions is envouraging and can be used for a variety of materials and environments if more data is available. It is possible to sue another neural network to predict the required exposure time to produce a particular corrosion classification in an environment. It is intended that the approach developed here will assist in the structural integrity evaluation of aging aircraft.
机译：建立了人工神经网络，以预测当暴露于各种腐蚀性物质，短期和长期航空母舰时不同系列铝合金的腐蚀行为。给定腐蚀环境和暴露时间，神经网络可以预测ASTM G34腐蚀等级以及由此造成的材料损失。从神经网络预测的训练有限的测试结果与实验数据具有很好的对比。通过神经网络仿真，给出了腐蚀环境和材料类型的影响，以说明趋势。基于初步结果，用于腐蚀预测的神经网络方法令人鼓舞，如果有更多数据可用，则可用于多种材料和环境。可以起诉另一个神经网络，以预测在环境中产生特定腐蚀分类所需的暴露时间。旨在在此开发的方法将有助于老化飞机的结构完整性评估。

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