STUDY ON RELATIVITY BETWEEN CORROSION IMAGESAND DATA OF METALLIC SAMPLES IN SEAWATER

J Chin Soc Corr Pro

2001, Vol. 21

Issue (6): 352-356 DOI:

Research Report

Current Issue | Archive | Adv Search

STUDY ON RELATIVITY BETWEEN CORROSION IMAGESAND DATA OF METALLIC SAMPLES IN SEAWATER

;;

天津大学材料学院

Download:

PDF(117KB)
Export: BibTeX | EndNote (RIS)

Abstract Scanner is used to acquire corrosion images of carb on steel and low-alloy steel in seawater and in order to show the corrosion moda lity clearly the images are pre-processed by the average value filter and non-li near fuzzy enhancement methods. The gray relational analysis and canonical corre lation analysis are used to analyze the relations between grey data and corrosio n data of metallic samples. The results show that there is higher gray rel ational grade. The canonical correlation coefficient between grey data and uniform corrosion lo st-weight is 0.99 while the coefficient between grey data and localized corrosio n depth is 0.98. Using artificial neural network theory, the model between the g rey distribution of metallic samples and localized corrosion depth has been deve loped. According to this model, there are only 3.89 percent absolute error betwe en the predicted result and the real value and the error will decrease if normal samples increase. With a high correlative coefficient, 0.98, the linear relatio n al model between the grey distribution of metallic samples and uniform corrosion lost-weight has been studied, too.

Key words: carbon steel low-alloy steel gray relational analysi s canonical correlation analysis artificial neu

Received: 13 October 2000

ZTFLH:

TG171

	Service

	E-mail this article
	Add to citation manager
	E-mail Alert
	RSS
	（）
	Articles by authors

Cite this article:

. STUDY ON RELATIVITY BETWEEN CORROSION IMAGESAND DATA OF METALLIC SAMPLES IN SEAWATER. J Chin Soc Corr Pro, 2001, 21(6): 352-356 .

URL:

https://www.jcscp.org/EN/ OR https://www.jcscp.org/EN/Y2001/V21/I6/352

[1] Zhou X K. Applied Computer Image Processing[M].Beijing:Beijing University of Aeronautics and Astronautics Press, 1996
(周孝宽．实用微机图像处理[Ｍ]．北京:北京航空航天大学出版社,1996)
[2] Deng J L. Gray Control System[M]. Wuhan: Huazhong University of Science and Technology Press, 1985
(邓聚龙．灰色控制系统[Ｍ]．武汉:华中理工大学出版社,1985)
[3] An X Z, Lin X M.Applied Multianalysis Statistics Methods[M].Jilin:Jilin Science and Technology Press, 1992
(安希忠,林秀梅．实用多元统计方法[Ｍ]．吉林:吉林科学技术出版社,1992)
[4] Kong D Y, Song S Z. Analysis of corrosion data for carbon steel and low - alloy steel in seawater by artificial neural network[J]. Journal of Chinese Society for Corrosion and Protection, 1998,18(4) :289 - 296
(孔德英,宋诗哲．人工神经网络技术探讨碳钢、低合金钢实海腐蚀数据[Ｊ]．中国腐蚀与防护学报,1998,18(4):289-
296)

[1]	ZHANG Chen, LU Yuan, ZHAO Jingmao. Synergistic Inhibition Effect of Imidazoline Ammonium Salt and Three Cationic Surfactants in H₂S/CO₂ Brine Solution[J]. 中国腐蚀与防护学报, 2020, 40(3): 237-243.
[2]	Ping XU,Shuo ZHANG,Shuai SI,Yajun ZHANG,Changzheng WANG. Corrosion Mechanism of Carbon Steel Induced by Protein and Polysaccharide-the Main Components of EPS[J]. 中国腐蚀与防护学报, 2019, 39(2): 176-184.
[3]	Xiankang ZHONG,Junying HU. Corrosion Behavior of X65 Carbon Steel in CO₂Containing Liquids with Constant pH and Ferrous Ion Concentration[J]. 中国腐蚀与防护学报, 2018, 38(6): 573-578.
[4]	Li WANG, Chunyun GUO, Kui XIAO, Tuerxun·Silayiding, Chaofang DONG, Xiaogang LI. Corrosion Behavior of Carbon Steels Q235 and Q450 in Dry Hot Atmosphere at Turpan District for Four Years[J]. 中国腐蚀与防护学报, 2018, 38(5): 431-437.
[5]	Yue QIAO, Zhiping ZHU, Lei YANG, Zhifeng LIU. Monitoring and Simulated Experiments of Oxidation-Reduction Potential of Boiler Feedwater at High Temperatures[J]. 中国腐蚀与防护学报, 2018, 38(5): 487-494.
[6]	Wanjun PENG, Jiheng DING, Hao CHEN, Haibin YU. Corrosion Inhibition of Bio-based Inhibitor Furfuryl Glycidyl Ether[J]. 中国腐蚀与防护学报, 2018, 38(3): 303-308.
[7]	Guofu OU, Lulu ZHAO, Kai WANG, Kuanxin WANG, Haozhe JIN. Dew-Point Corrosion Behavior of 10^# Carbon Steel inHCl-H₂O Environment[J]. 中国腐蚀与防护学报, 2018, 38(1): 33-38.
[8]	Jie ZHANG, Xiuhua HU, Chuanbo ZHENG, Jizhou DUAN, Baorong HOU. Influence of Calcareous Deposit on Corrosion Behavior of Q235 Carbon Steel in Marine Microalgae Containing Medium[J]. 中国腐蚀与防护学报, 2018, 38(1): 18-25.
[9]	Xiaobo MENG,Wubin JIANG,Yongli LIAO,Ruihai LI,Zhijun ZHENG,Yan GAO. Investigation on Atmospheric Corrosion Behavior of Transmission Tower Materials in Simulated Industrial Environments[J]. 中国腐蚀与防护学报, 2017, 37(5): 460-466.
[10]	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.
[11]	Qingli CHENG,Bin TAO,Shuan LIU,Quanzhen LIU,Weihua ZHANG,Songbai TIAN,Liping WANG. Corrosion Behaviour of Q235B Carbon Steel in Sediment Water From Crude Oil[J]. 中国腐蚀与防护学报, 2017, 37(2): 126-134.
[12]	Yongsheng HAO,Abdullahi SANI Luqman,Lixin SONG,Guobao XU,Tiejun GE,Qinghong FANG. Corrosion Inhibition Effect of Phytic Acid Conversion Coating Formed on Q235 Carbon Steel in Acidic and Neutral Solutions[J]. 中国腐蚀与防护学报, 2016, 36(6): 549-558.
[13]	Jihui WANG,Huajie YAN,Wenbin HU. Preparation and Inhibition Behavior of Molybdate Intercalated ZnAlCe-hydrotalcite[J]. 中国腐蚀与防护学报, 2016, 36(6): 637-644.
[14]	Hongwei LIU,Fuping XIONG,Yalin LV,Chengxuan GE,Hongfang LIU,Yulong HU. CO₂ Corrosion Inhibition of Carbon Steel by Dodecylamine under Flow Conditions[J]. 中国腐蚀与防护学报, 2016, 36(6): 645-651.
[15]	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.

No Suggested Reading articles found!

Viewed

Full text

Abstract

Cited

Shared

Discussed