Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks


  • Rusul Kh. Abd Polymers And Petrochemical Engineering Dep. Basrah University For Oil And Gas, Iraq -Basrah
  • Nawal J. Hammadi College of Engineering – University of Basrah, Iraq –Basrah



Pitting Corrosion, Carbon Steel , Pits Depth , Pit Density , ANNs.


The purpose of this research is to investigate the impact of corrosive environment (corrosive ferric chloride of 1, 2, 5, 6% wt. at room temperature), immersion period of (48, 72, 96, 120, 144 hours), and surface roughness on pitting corrosion characteristics and use the data to build an artificial neural network and test its ability to predict the depth and intensity of pitting corrosion in a variety of conditions. Pit density and depth were calculated using a pitting corrosion test on carbon steel (C-4130). Pitting corrosion experimental tests were used to develop artificial neural network (ANN) models for predicting pitting corrosion characteristics. It was found that artificial neural network models were shown to be quite effective; the results were validated by the experimental agreement with those acquired from laboratory tests. Specifically, the correlation coefficient, R = 0.9944.



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How to Cite

al hamad, rusul khalid and Hammadi, N. J. . (2022) “Estimating Pitting Corrosion Depth and Density on Carbon Steel (C-4130) using Artificial Neural Networks”, Journal of Engineering, 28(5), pp. 11–24. doi: 10.31026/j.eng.2022.05.02.