[논문]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; Kim, Seong-Jong

doi:10.14773/cst.2021.20.2.62

[국내논문] An Experimental Investigation of the Application of Artificial Neural Network Techniques to Predict the Cyclic Polarization Curves of AL-6XN Alloy with Sensitization 원문보기

Corrosion science and technology, v.20 no.2, 2021년, pp.62 - 68

Jung, Kwang-Hu (Mokpo branch, Korea institute of maritime and fisheries technology) , Kim, Seong-Jong (Division of Marine Engineering, Mokpo Maritime University)

Abstract ▼ AI-Helper

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.

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표/그림 (9)

그림 Fig. 1. Schematic diagram for ANN architecture used for modeling polarization curves
그림 Fig. 2. The microstructure of AL-6XN alloy heat-treated at 800 ºC: (a) as-received, (b) 3 hours, (c) 12 hours, (d) electronically extracted secondary phase from Fig. 2(c)
그림 Fig. 3. Fitted curve for DOS value
표 Table 1. Designed factors and their levels
표 Table 2. Experiment design matrix for CPDP tests in 3.5% NaCl solution
그림 Fig. 4. Experimental and ANN predicted polarization curves for some train data. These conditions included in Table 2
표 Table 3. Comparison of performance of ANN structure with hidden neurons number ranging from 10 to 100
그림 Fig. 5. Diagram of actual values vs. predicted values for log(i): (a) training data set: 34,184 plots, (b) validation data set: 3,799 plots, (c) test data set: 4,279 plots, (d) total data set: 42,781 plots
그림 Fig. 6. Experimental and ANN predicted polarization curves. These conditions not included in Table 2

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