| Fractal Characteristic of Pits Distribution on 304 Stainless Steel Corroded Surface and Its Application in Corrosion Diagnosis |
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| 作者姓名： | 梁成浩 |
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| 作者单位： | [1]Electromechanics ＆ Materials Engineering College, Dalian Maritime University, Dalian 116026, China [2]College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310032, China |
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| 摘 要： | Electrochemical techniques and fractal theory were employed to study the corrosion behaviors and pits distribution characteristics on the corroded surfaces of 304 stainless steel exposed in FeCl3 solution. Fractal features of pits distribution over the corroded surfaces were observed and described by the fractal dimension. A 5-8-2 back-propagation （BP） artificial neural network model for the diagnoses of the pitting corrosion rate and pits deepness of 304 stainless steel under various conditions was developed by considering the fractal dimension as a key parameter for describing the pitting corrosion characteristics. The predicted results are well in agreement with the experimental data of pitting corrosion rate and pit deepness. The max relative errors between their experimental and simulation data are 6.69% and 4.62%, respectively.
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| 关 键 词： | 304不锈钢 缺陷分布 分形特征 表面腐蚀 |
| 收稿时间： | 1 March 2006 |
| 修稿时间： | 2006-03-01 |
Fractal characteristic of pits distribution on 304 stainless steel corroded surface and its application in corrosion diagnosis |
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| Liang Chenghao,Zhang Wei.Fractal Characteristic of Pits Distribution on 304 Stainless Steel Corroded Surface and Its Application in Corrosion Diagnosis[J].Journal of Wuhan University of Technology. Materials Science Edition,2007,22(3):389-393. |
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| Authors: | Liang Chenghao Zhang Wei |
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| Affiliation: | (1) Electromechanics & Materials Engineering College, Dalian Maritime University, Dalian, 116026, China;(2) College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310032, China |
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| Abstract: | Electrochemical techniques and fractal theory were employed to study the corrosion behaviors and pits distribution characteristics on the corroded surfaces of 304 stainless steel exposed in FeCl3 solution. Fractal features of pits distribution over the corroded surfaces were observed and described by the fractal dimension. A 5-8-2 back-propagation (BP) artificial neural network model for the diagnoses of the pitting corrosion rate and pits deepness of 304 stainless steel under various conditions was developed by considering the fractal dimension as a key parameter for describing the pitting corrosion characteristics. The predicted results are well in agreement with the experimental data of pitting corrosion rate and pit deepness. The max relative errors between their experimental and simulation data are 6.69% and 4.62%, respectively. |
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| Keywords: | 304 stainless steel pits distribution fractal neural network diagnosis |
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