Please wait a minute...
J Chin Soc Corr Pro  2004, Vol. 24 Issue (2): 108-111     DOI:
Research Report Current Issue | Archive | Adv Search |
CONSULTATION AND FORECAST DIAGNOSIS SYSTEM OF METAL IN MARINE ENVIRONMENT
Shouyan Wang;
天津大学材料学院
Download:  PDF(166KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  A consultation and forecast diagnosis system of metal exposed in marine environment has been developed by using the object-oriented programming language, Visual Basic (VB). The system comprises three major modules: database and management system, corrosion prediction and corrosion morphology analysis. In the first part, the common data management function such as data input, data modify, data delete and data refresh are discussed. There are two different method used in the corrosion prediction module, the artificial neural network (ANN) and gray model. Using the ANN method, the corrosion depth of new materials in different marine environment can be predicted. The gray model method can give the corrosion rate of materials in the future. The corrosion morphology images of metallic material in seawater, which are acquired by scanner, are stored in the database. Taking the gray distribution or fractal characters of metallic samples and their corrosion morphology as the knowledge base respectively, the diagnosing system identifying corrosion morphology of metallic material in seawater was established according to the theory of fuzzy pattern recognition. The corrosion morphology of sample can be identified by gray distribution or fractal characters of corrosion images.
Key words:  database      prediction      corrosion morphology      
Received:  25 January 2003     
ZTFLH:  TG171  
Corresponding Authors:  天津大学材料学院   

Cite this article: 

Shouyan Wang. CONSULTATION AND FORECAST DIAGNOSIS SYSTEM OF METAL IN MARINE ENVIRONMENT. J Chin Soc Corr Pro, 2004, 24(2): 108-111 .

URL: 

https://www.jcscp.org/EN/     OR     https://www.jcscp.org/EN/Y2004/V24/I2/108

[1]YuRR ,CaiZZ .CorrosionandProtectionofUndergroundMetalPipeline[M ].Beijing:PetroleumIndustrialPress,1998:1(俞蓉蓉,蔡志章.地下金属管道的腐蚀与防护[M ].北京:石油工业出版社,1998:1)
[2]KongDY ,HouGY ,SongSZ .Studythecorrosiondatamanage mentandpredictionsystemofsteelusedinseawater[J].Corros.Sci.Prot.Technol.,2001,12:16(孔德英,侯国艳,宋诗哲.常用金属海水腐蚀数据管理及预测系统[J].腐蚀科学与防护技术,2001,12:16)
[3]TangYM ,ZhengXM ,WangGY .Designandestablishmentofametalcorrosionimagedatabase[J].J .Chin.Soc.Corros.Prot.,1999,19(1):60(唐聿明,郑晓梅,王光耀.金属腐蚀图文库的设计和实现[J].中国腐蚀与防护学报,1999,19(1):60)
[4]GaoLQ ,ZhuGW ,LinJ .Thedesigningandachievingofcorrosiondatabasequeryingsystemforinternet[J].J .Chin.Soc.Corros.Prot.,2001,21(5):306(高立群,朱国文,林建.网络腐蚀数据库查询系统的设计与实现[J].中国腐蚀与防护学报,2001,21(5):306)
[5]WangSY ,KongDY ,SongSZ .Diagnosingcorrosionmodalitysys temofmetallicmaterialinseawaterbasedonfuzzypatternrecogni tion[J].ActaMetall.Sin.,2001,37:517(王守琰,孔德英,宋诗哲.基于模糊模式识别的金属材料海水腐蚀形貌诊断系统[J].金属学报,2001,37:517)
[1] HU Zongwu, LIU Jianguo, XING Rui, YIN Fabo. Erosion-corrosion Behavior of 90o Horizontal Elbow in Single Phase Flow[J]. 中国腐蚀与防护学报, 2020, 40(2): 115-122.
[2] Xiangfeng KONG, Jing ZHANG, Yuanqing JIANG, Dongzhi CHU, Chunhu LI, Nan GAO, Jing LV, Yan ZOU. Corrosion Performance of Underwater Welded Joints of E40 Steel in Coastal Water of Qingdao via Mass-loss Method[J]. 中国腐蚀与防护学报, 2018, 38(3): 226-232.
[3] Baohui GUO, Youxu QIU, Hailong LI. Application of Artificial Neural Network for Preparation Process of Ni-SiC Composite Coatings on Ti-Alloy TA15[J]. 中国腐蚀与防护学报, 2017, 37(4): 389-394.
[4] Xiaodong LIN,Qunjia PENG,En-Hou HAN,Wei KE. Review of Thermal Aging of Nuclear Grade Stainless Steels[J]. 中国腐蚀与防护学报, 2017, 37(2): 81-92.
[5] Yanan NIE,Hao SHEN,Kunpeng GU,Chengqi WANG. Seawater Corrosion Resistance and Service Life Prediction of Glass Fiber Reinforced Plastic Composites[J]. 中国腐蚀与防护学报, 2016, 36(4): 357-362.
[6] Haixia LIU,Xuequn CHENG,Xiaogang LI,Kui XIAO,Chaofang DONG. Prediction Model for Corrosion of Aluminum 1060 in Marine Atmospheric Environments[J]. 中国腐蚀与防护学报, 2016, 36(4): 349-356.
[7] Xinsheng ZHANG,Naining CAO,Yayun LI. Residual Life Prediction of Buried Oil and Gas Pipelines Based on Gumbel Extreme Value Type I Distribution[J]. 中国腐蚀与防护学报, 2016, 36(4): 370-374.
[8] Jing LIU,Xiaolu LI,Chongwei ZHU,Tao ZHANG,Guanxin ZENG,Guozhe MENG,Yawei SHAO. Prediction of Critical Pitting Temperature of 316L Stainless Steel in Gas Field Environments by Artificial Neutral Network[J]. 中国腐蚀与防护学报, 2016, 36(3): 205-211.
[9] Zhian DENG,Shuyi LI,Xiaokun LI,Shan WANG,Xiaojun WANG. A Prediction Method Based on Fuzzy Neural Network for Corrosion Rate of Marine Pipelines[J]. 中国腐蚀与防护学报, 2015, 35(6): 571-576.
[10] Zhiping ZHU,Zhaohui YIN,Sen LIU,Jianfeng XIAO. Corrosion Behavior and Prediction Model for Copper Exposed in a Simulated High H2S Containing Environment[J]. 中国腐蚀与防护学报, 2015, 35(4): 333-338.
[11] YE Chao, DU Nan, TIAN Wenming, ZHAO Qing, ZHU Li. Effect of pH on Pitting Corrosion Process of 304 Stainless Steel in 3.5%NaCl Solution[J]. 中国腐蚀与防护学报, 2015, 35(1): 38-42.
[12] CHENG Congqian, CAO Tieshan, WANG Dongying, YAO Jingwen, WANG Jian, GUAN Meng, ZHAO Jie. Erosion-corrosion Morphology of Cr13 Stainless Steel Induced by Jet Flow of Hydrochloric Acid Solution[J]. 中国腐蚀与防护学报, 2014, 34(5): 439-444.
[13] WU Tangqing, YANG Pu, ZHANG Mingde, XU Jin, YAN Maocheng, YU Changkun, SUN Cheng. Microbiologically Induced Corrosion of X80 Pipeline Steel in an Acid Soil Solution: (II) Corrosion Morphology and Corrosion Product Analysis[J]. 中国腐蚀与防护学报, 2014, 34(4): 353-358.
[14] LIU Tao,AI Jun,ZHANG Lifang,ZHANG Pengfei,
YANG Zhaohui,XU Chunlin. Life Prediction of Anti-corrosion Coating for Steel Box Girder by In-door Simulation Tests Combined with #br#Image Processing Technique[J]. 中国腐蚀与防护学报, 2013, 33(5): 407-412.
[15] HAN Xiabing,GAO Zhiming,DANG Lihua,WANG Ying,BI Huichao. Wavelet Packet Analysis of Early Corrosion Image of Q235 Steel in Simulated Atmospheric Environment[J]. 中国腐蚀与防护学报, 2013, 33(3): 211-215.
No Suggested Reading articles found!