Prediction of corrosion susceptibilities of Al-based metal matrix composites reinforced with SiC particles using artificial neural network

Abstract

In this theoretical study, the prediction of the corrosion resistance of Al-Si-Mg-based metal matrix composites reinforced with SiC particles has been studied, using an artificial neural network. Four input vectors were used in the construction of the proposed network; namely, volume fraction of SiC reinforcement, aging time of the composites, environmental conditions, and potential. Current was used as the one output in the proposed network. Test results indicate that the proposed network can be used efficiently for the prediction of the corrosion resistance of Al-Si-Mgbased metal matrix composites reinforced with SiC particles, and the methodology is suitable for engineers to study the corrosion of metal matrix composites. In addition, a few forecasts regarding the polarization response for different SiC volume fractions and aging conditions have also been generated without using any experimental data.

Test parameters optimization for constrained spray forming of aluminum alloy based on artificial neural network

“…ANN has been developed to predict porosity percent of Al-Si casting alloys, and is used to correlate chemical composition and cooling rate with porosity [20,21]. ANN accurately predicted the corrosion resistance of Al-Si-Mg-based metal matrix composites reinforced with SiC particles, average square of the Pearson product-moment correlation coefficient (R2), maximum mean square error(MSE), and minimum root mean squared deviation(RMSD) were calculated as 0.9904, 0.00002476, and 0.00157480,respectively, it can be seen that the experimental results are highly consistent with the ANN results [22]. ANN predicted the mechanical properties of A356 including yield stress, ultimate tensile strength, maximum force and elongation percentage, the prediction of ANN model was found to be in good agreement with experimental data [23,24].…”

An artificial neural network (ANN) solution to the prediction of age-hardening and corrosion behavior of an Al/TiC functional gradient material (FGM)

“…Then, these values can be used for the prediction of the output data corresponding to different input data. [14][15][16][17] Modeling with ANN has been used for the solution of many complex engineering problems such as nuclear engineering, 18 bioengineering, 19 thermal engineering, 20 metallurgical and materials engineering 21 or, automotive engineering, 22,23 and, in the analysis of many laminated or multilayer composites [24][25][26][27] until now. It is well known that corrosion is a dangerous and extremely costly problem in the widespread use of engineering materials.…”

An investigation on the aging responses and corrosion behaviour of A356/SiC composites by neural network: The effect of cold working ratio

“…Some research groups [11][12][13][14] studied to predict the corrosion and mechanical behaviour of the MMCs by using NN. Also, an MLP ANN model was employed aiming to predict low-carbon steel, copper and aluminium corrosion rates by Kenny et al 15 Tuntas and Dikici 16 discussed the ANN prediction of the corrosion resistance of AMMCs reinforced with SiC particles under different aging (T6) conditions. The theoretical study shows that the generated potentiodynamic polarization curves of the composites are certainly acceptable levels when compared with the experimental results.…”