Corrosion Science and Technology

The Corrosion Science Society of Korea (한국부식방식학회)
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An Experimental Investigation of the Application of Artificial Neural Network Techniques to Predict the Cyclic Polarization Curves of AL-6XN Alloy with Sensitization

  • Jung, Kwang-Hu (Mokpo branch, Korea institute of maritime and fisheries technology) ;
  • Kim, Seong-Jong (Division of Marine Engineering, Mokpo Maritime University)
  • Received : 2021.04.02
  • Accepted : 2021.04.08
  • Published : 2021.04.30
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Abstract

Artificial neural network techniques show an excellent ability to predict the data (output) for various complex characteristics (input). It is primarily specialized to solve nonlinear relationship problems. This study is an experimental investigation that applies artificial neural network techniques and an experimental design to predict the cyclic polarization curves of the super-austenitic stainless steel AL-6XN alloy with sensitization. A cyclic polarization test was conducted in a 3.5% NaCl solution based on an experimental design matrix with various factors (degree of sensitization, temperature, pH) and their levels, and a total of 36 cyclic polarization data were acquired. The 36 cyclic polarization patterns were used as training data for the artificial neural network model. As a result, the supervised learning algorithms with back-propagation showed high learning and prediction performances. The model showed an excellent training performance (R2=0.998) and a considerable prediction performance (R2=0.812) for the conditions that were not included in the training data.

Keywords

Acknowledgement

This research was a part of the project titled 'Demonstration of aftertreatment systems of Ship's air pollutant (NOx/SOx/PM) and establishment of their certification system', funded by the Ministry of Oceans and Fisheries, Korea.

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