D. Saber1,*, Ibrahim B. M. Taha2, Kh. Abd El-Aziz3
Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 757-769, 2021, DOI:10.32604/iasc.2021.018516
Abstract In this study, optimal regression learner methods were used to predict the corrosion behavior of aluminum–silicon alloys (Al–Si) with various Si ratios in different media. Al–Si alloys with 0, 1%, 8%, 11.2%, and 15% Si were tested in different media with different pH values at different stirring speeds (0, 300, 600, 750, 900, 1050, and 1200 rpm). Corrosion behavior was evaluated via electrochemical potentiodynamic test. The corrosion rates (CRs) obtained from the corrosion tests were utilized in the formation of datasets of various machine regression learner optimization (MRLO) methods, namely, decision tree, support vector machine, Gaussian process regression, and ensemble… More >
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