Prediction of pitting corrosion of surface treated AISI 316L stainless steel by artificial neural network

Abstract

This paper presents an artificial neural network based solution method for modelling the pitting resistance of AISI 316L stainless steel in various surface treated forms. Surface treatment is a promising technique for improving the corrosion resistance of stainless steels. In this study, cyclic polarisation tests were performed before and after surface treatment. Experimental results were modelled by the neural network. The artificial neural network model exhibited superior performance based on the fitness of the observed versus predicted data. The results showed that the predicted data from the neural network model were considerably similar to the experimental data. The model has been saved and can easily be used to predict the corrosion in different surface treatment methods.

Pitting corrosion of super martensitic stainless steel 00Cr13Ni5Mo2

“…In addition, its properties can be further optimized by high thermal stability and dispersive distribution between low-carbon lath martensite of the reversed austenitic structure (Li et al, 2007;Liu et al, 2011). Therefore, the corrosion resistance, including the pitting resistance, of super martensitic stainless steel was higher than that of conventional API 13 per cent Cr martensitic stainless steel, as shown in Figure 4, which is in good accordance with the previous reports (Moreira et al, 2004;Jafari et al, 2011). In addition, compared to the imported super martensitic stainless steel, the pitting potential of the domestic steel was higher, as shown in Figure 4, and the pitting rate at the standard condition (6 weight per cent FeCl 3 , 1 per cent HCl, 50 Ϯ 2°C, 72 h) also was lower.…”

“…At the present time, deep oil and gas wells are being operated owing to the increasing consumption of oil and gas resources, and drilling environments are becoming more and more severe, utilizing high pressures, high temperatures (Dong et al, 2010) and high flow rates (John et al, 2009). In consequence, super martensitic stainless steel 00Cr13Ni5Mo2 was developed and used successfully for down-hole strings (Takabe et al, 2009) because of the satisfactory combination of excellent mechanical properties, availability at a low cost and high corrosion resistance (Jafari et al, 2011). The term "super" is related to the improvement in the mechanical and the anti-corrosion properties when comparing them to conventional martensitic stainless steels (Aquino et al, 2010) and the stability of martensite at high temperature, which is achieved mainly by low-carbon and high-nickel contents.…”

Application of artificial intelligence (AI) in the area of corrosion protection

“…This can be used to adjust different parameters to prepare the desired films (Coşkun and Karahan, 2018; Mousavifard et al , 2015). Similar methods can also be used to predict the corrosion properties of stainless-steel surface treatment (Jafari et al , 2011). Moreover, AI can predict the corrosion performance of new alloy materials.…”

“…At present, AI is playing a more and more important role in the field of scientific research. In terms of corrosion science research, the application of AI mainly focuses on corrosion protection materials and methods (Belayadi et al , 2019; Coşkun and Karahan, 2018; Feiler et al , 2020; Jafari et al , 2011; Jiménez-Come et al , 2012; Mousavifard et al , 2015; Wen et al , 2009; Zadeh Shirazi and Mohammadi, 2017), corrosion image recognition (Ahuja and Shukla, 2018; Hoang and Tran, 2019; Petricca et al , 2016; Tian et al , 2019; Yao et al , 2019) and corrosion life prediction (Al-Shehri, 2019; Alani and Faramarzi, 2014; Chae et al , 2020; Guzman Urbina and Aoyama, 2018; Zhi et al , 2020). Compared with traditional methods, AI shows unique advantages (Boucherit et al , 2022; Boucherit et al , 2019; Boucherit and Arbaoui, 2021).…”

Incidence of Heat Treatment on the Corrosive Behavior of Aisi 316l Austenitic Stainless Steel

“…As mentioned in [2], the superior corrosion resistance of austenitic stainless steels can be attributed to the formation of a chromium oxide-hydroxide enriched passive layer, with a thickness ranging from 0.5 nm to 5 nm, when exposed to oxygen. This passive layer exhibits self-healing properties [3][4][5][6][7]. However, austenitic stainless steels are susceptible to degradation caused by thermal aging and external factors such as irradiation, stress, temperature, and coolant media, which can affect the reliability of components [8,9].…”