An automatic pitting corrosion detection approach for 316L stainless steel

作者: M.J. Jiménez–Come , I.J. Turias , F.J. Trujillo

DOI: 10.1016/J.MATDES.2013.11.045

关键词:

摘要: … In this sense, the objective of this paper was to present an automatic model based on artificial neural networks to predict pitting corrosion behaviour of austenitic stainless steel. The …

参考文章(44)
J H Payer, G H Koch, Mph Brongers, N G Thompson, Y P Virmani, CORROSION COST AND PREVENTIVE STRATEGIES IN THE UNITED STATES ,(2002)
David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams, Learning representations by back-propagating errors Nature. ,vol. 323, pp. 696- 699 ,(1988) , 10.1038/323533A0
Janez Demšar, Statistical Comparisons of Classifiers over Multiple Data Sets Journal of Machine Learning Research. ,vol. 7, pp. 1- 30 ,(2006)
L H Bennett, J Kruger, R L Parker, E Passaglia, C Reimann, A W Ruff, H Yakowitz, E B Berman, Economic effects of metallic corrosion in the United States Part I National Bureau of Standards. ,(1978) , 10.6028/NBS.SP.511-1
Christopher M. Bishop, Pattern Recognition and Machine Learning ,(2006)
K.V.S. Ramana, T. Anita, Sumantra Mandal, S. Kaliappan, H. Shaikh, P.V. Sivaprasad, R.K. Dayal, H.S. Khatak, Effect of different environmental parameters on pitting behavior of AISI type 316L stainless steel: Experimental studies and neural network modeling Materials & Design. ,vol. 30, pp. 3770- 3775 ,(2009) , 10.1016/J.MATDES.2009.01.039
M.J. Jiménez-Come, E. Muñoz, R. García, V. Matres, M.L. Martín, F. Trujillo, I. Turias, Pitting corrosion behaviour of austenitic stainless steel using artificial intelligence techniques Journal of Applied Logic. ,vol. 10, pp. 291- 297 ,(2012) , 10.1016/J.JAL.2012.07.005
K.L. Edwards, Y.-M. Deng, Supporting design decision-making when applying materials in combination Materials & Design. ,vol. 28, pp. 1288- 1297 ,(2007) , 10.1016/J.MATDES.2005.12.009
Robert P Wei, D Gary Harlow, Mechanistically based probability modelling, life prediction and reliability assessment Modelling and Simulation in Materials Science and Engineering. ,vol. 13, ,(2005) , 10.1088/0965-0393/13/1/R02