PREDICTION OF TIME TO FAILURE IN SCC OF 304 STAINLESS STEEL IN AQUEOUS CHLORIDE SOLUTION USING NEURAL NETWORK

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

SHAHRABI FARAHANI M.T.; BAYAGI V.; LAJEVARDI S.A.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: ESTEGHLAL Year:2008 | Volume:27 | Issue:1 Page(s): 135-141

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,043

Download:

Cites:

Information Journal Paper

Title

PREDICTION OF TIME TO FAILURE IN SCC OF 304 STAINLESS STEEL IN AQUEOUS CHLORIDE SOLUTION USING NEURAL NETWORK

Author(s)

SHAHRABI FARAHANI M.T. | BAYAGI V. | LAJEVARDI S.A. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORKQ2

STRESS CORROSION CRACKINGQ3

SENSITIVITY ANALYSISQ3

TIME TO FAILUREQ3

CHLORIDE CONCENTRATIONQ3

APPLIED STRESSQ3

Abstract

Prediction of SCC risk of austenitic stainless steels in aqueous chloride solution and estimation of the TIME TO FAILURE as a result of SCC form important and complicated topics for study. Despite the many studies reported in the literature, a formulation or a reliable method for the prediction of TIME TO FAILURE as a result of SCC is yet to be developed. This paper is an effort to investigate the capability of ARTIFICIAL NEURAL NETWORK in estimatiing the TIME TO FAILURE for SCC of 304 stainless steel in aqueous chloride solution and to provide a SENSITIVITY ANALYSIS thereof. The input parameters considered are temperature, chloride ion concentration, and APPLIED STRESS. The TIME TO FAILURE is defined as the output parameter and the key criterion to evaluate the effective parameters. The statistical performance of the neural network is expressed as the average of three learning and testing results. The SCC database is divided into two sections designated as the learning set and the testing set. The output results show that ARTIFICIAL NEURAL NETWORK can predict the TIME TO FAILURE for about 74% of the variance of SCC experimental data. Furthermore, the SENSITIVITY ANALYSIS also exhibits the effects of input parameters on SCC of 304 stainless steel in aqueous chloride solutions.

Cites

No record.

References

No record.

Cite

APA: Copy

SHAHRABI FARAHANI, M.T., BAYAGI, V., & LAJEVARDI, S.A.. (2008). PREDICTION OF TIME TO FAILURE IN SCC OF 304 STAINLESS STEEL IN AQUEOUS CHLORIDE SOLUTION USING NEURAL NETWORK. ESTEGHLAL, 27(1), 135-141. SID. https://sid.ir/paper/5946/en

Vancouver: Copy

SHAHRABI FARAHANI M.T., BAYAGI V., LAJEVARDI S.A.. PREDICTION OF TIME TO FAILURE IN SCC OF 304 STAINLESS STEEL IN AQUEOUS CHLORIDE SOLUTION USING NEURAL NETWORK. ESTEGHLAL[Internet]. 2008;27(1):135-141. Available from: https://sid.ir/paper/5946/en

IEEE: Copy

M.T. SHAHRABI FARAHANI, V. BAYAGI, and S.A. LAJEVARDI, “PREDICTION OF TIME TO FAILURE IN SCC OF 304 STAINLESS STEEL IN AQUEOUS CHLORIDE SOLUTION USING NEURAL NETWORK,” ESTEGHLAL, vol. 27, no. 1, pp. 135–141, 2008, [Online]. Available: https://sid.ir/paper/5946/en

Related Journal Papers