Prediction Model of Stress Corrosion Susceptibility of Stainless Steel Based on PSO-Hybrid

CAI Qiheng; LI Guanghai; WANG Qiang; CAO Luowei

doi:10.11973/fsyfh-202306015

Corrosion & Protection > 2023 > 44(6): 96-102. > DOI: 10.11973/fsyfh-202306015

CAI Qiheng, LI Guanghai, WANG Qiang, CAO Luowei. Prediction Model of Stress Corrosion Susceptibility of Stainless Steel Based on PSO-Hybrid[J]. Corrosion & Protection, 2023, 44(6): 96-102. DOI: 10.11973/fsyfh-202306015

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Prediction Model of Stress Corrosion Susceptibility of Stainless Steel Based on PSO-Hybrid

1. College of Quality and Safety Engineering, China Jiliang University, Hangzhou 310018, China;
2. China Special Equipment Inspection and Research Institute, Beijing 100029, China

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Received Date: April 01, 2021

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In order to improve the accuracy and scientificity of the prediction of stress corrosion cracking (SCC) sensitivity of stainless steel, the main influencing factors of SCC behavior of stainless steel were extracted by principal component analysis (PCA) as the input of the subsequent model, and then the representative algorithms of different schools of machine learning were mixed into Hybrid model, and optimized by particle swarm optimization (PSO) algorithm, and the prediction model PSO-Hybrid of SCC sensitivity of stainless steel was proposed. Taking the measured data of an austenitic stainless steel as an example, the predicted value and the actual value were compared to verify the reliability and superiority of the model. The results showed that the Hybrid idea was feasible and scientific, and after PSO optimization, the average accuracy of the Hybrid model and the Matthews correlation coefficient were increased by 3.3% and 8.3% respectively. The PSO-Hybrid model had high prediction accuracy and good stability.

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[1]	刘传森,李壮壮,陈长风. 不锈钢应力腐蚀开裂综述[J]. 表面技术,2020,49(3):1-13.
[2]	张克乾,唐占梅,胡石林,等. 核电用结构材料SCC裂纹扩展的研究现状[J]. 腐蚀与防护,2019,40(3):157-163,219.
[3]	吕战鹏. 高温水中应力腐蚀开裂机理及扩展模型[J]. 中国材料进展,2019,38(7):651-662.
[4]	骆正山,宋莹莹,毕傲睿. 基于GRA-RFR的油气集输管道内腐蚀速率预测[J]. 材料保护,2020,53(3):95-100.
[5]	李易安,骆正山. 基于KPCA-ICS-ELM算法的埋地管线土壤腐蚀深度预测[J]. 中国安全科学学报,2020,30(12):100-105.
[6]	者娜,杨剑锋,刘文彬,等. 基于KPCA和SVM的工艺管道腐蚀速率预测[J]. 腐蚀与防护,2019,40(1):56-60.
[7]	SMETS H M G,BOGAERTS W F L. SCC analysis of austenitic stainless steels in chloride-bearing water by neural network techniques[J]. Corrosion,1992,48(8):618-623.
[8]	赵景茂,胡瑞,左禹. 应用神经网络技术预测应力腐蚀开裂[J]. 腐蚀与防护,2004,25(11):501-502,506.
[9]	郭浩,吕战鹏,冯国强,等. 人工神经网络模型预测不锈钢在高温水中的应力腐蚀破裂[J]. 中国腐蚀与防护学报,2004,24(1):25-28.
[10]	JIANG P,CRAIG P,CROSKY A,et al. Risk assessment of failure of rock bolts in underground coal mines using support vector machines[J]. Applied Stochastic Models in Business and Industry,2018,34(3):293-304.
[11]	DOMINGOS P. The master algorithm:how the quest for the ultimate learning machine will remake our world[M]. New York:Basic Books,2015.
[12]	周星,丁立新,万润泽,等. 分类器集成算法研究[J]. 武汉大学学报(理学版),2015,61(6):503-508.
[13]	张钰,陈珺,王晓峰,等. 随机森林在滚动轴承故障诊断中的应用[J]. 计算机工程与应用,2018,54(6):100-104,114.
[14]	CHEN T Q,GUESTRIN C. XGBoost:a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:Association for Computing Machinery,2016:785-794.
[15]	全恺,梁伟,张来斌,等. 基于故障树与贝叶斯网络的川气东送管道风险分析[J]. 油气储运,2017,36(9):1001-1006.
[16]	HUANG G B,ZHU Q Y,SIEW C K. Extreme learning machine:theory and applications[J]. Neurocomputing,2006,70(1/2/3):489-501.
[17]	张学工. 关于统计学习理论与支持向量机[J]. 自动化学报,2000,26(1):32-42.
[18]	王盼锋,王寿喜,马钢,等. 基于PCA-PSO-SVM模型的海底多相流管道内腐蚀速率预测[J]. 安全与环境工程,2020,27(2):183-189.
[19]	印翔,黄一,李恺强,等. 基于粒子群优化算法的腐蚀预测灰色动态模型[J]. 腐蚀与防护,2020,41(1):18-22.
[20]	STURROCK C P,BOGAERTS W F. Empirical learning investigations of the stress corrosion cracking of austenitic stainless steels in high-temperature aqueous environments[J]. Corrosion,1997,53(4):333-343.

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