Modeling Corrosion Property of High Vanadium High Speed Steel (HVHSS) under H₃PO₄ Medium Condition Using Artificial Neural Network

Abstract:

The components in slurry pump suffer serious corrosion abrasion under H3PO4 medium. Presently, High chromium cast irons are often used to produce the components in slurry pump. New high vanadium high speed steel(HVHSS) has been proved to be a more excellent wear resistance material than high chromium cast irons under abrasive wear and rolling wear. In order to apply HVHSS in slurry pump to replace high chromium cast irons, the corrosion properties of HVHSS were tested under different H3PO4 medium concentration conditions. Using back-propagation (BP) neural network, the non-linear relationship between the corrosion weight losses (W) and H3PO4 concentration, corrosion time (C, t) is established on the base of the dealing with experimental data. The results show that the well-trained BP neural network can predict the wear weight loss precisely according to H3PO4 concentration and corrosion time. The prediction results reveal that the corrosion time and H3PO4 concentration have obvious effect on corrosion weight loss of HVHSS. And corrosion weight loss takes on linear relationship with corrosion time and H3PO4 concentration, respectively.

• Periodical: Applied Mechanics and Materials (Volumes 52-54)
• Pages: 1243-1246
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.52-54.1243
• Online since: March 2011
• Authors: Liu Jie Xu, Song Min Zhang, Shi Zhong Wei
• Keywords: Bayesian Regularization, BP Neural Network (BPNN), Corrosion, H₃PO₄, High Vanadium High Speed Steel

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