Forthcoming and Online First Articles

International Journal of Computational Materials Science and Surface Engineering

International Journal of Computational Materials Science and Surface Engineering (IJCMSSE)

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International Journal of Computational Materials Science and Surface Engineering (2 papers in press)

Regular Issues

  • Predicting the tensile behaviour of friction stir welded AA2024 and AA5083 alloy based on artificial neural network and mayfly optimization algorithm   Order a copy of this article
    by P.M. Diaz, M. Julie Emerald Jiju 
    Abstract: In recent years, Metal Matrix Composites (MMCs) are developed as one of the functional materials with enhanced properties and wide range of applications Aluminium metal matrix composites are the type of MMCs where aluminium is taken as the base metal alloy The hybrid metal composite is fabricated by welding AA2024 and AA5083 alloys To predict the tensile behaviour of AA2024 and AA5083 alloys, a new approach has been proposed by integrating the artificial neural network with mayfly optimization algorithm To analyse the predicting efficiency of the proposed approach, it is compared with artificial neural networks and experimental test values The results from the analysis indicated that the proposed approach has enhanced predicting accuracy than artificial neural
    Keywords: Aluminium alloy; Mechanical properties; ANN; Mayfly optimization algorithm; Inertial weights.
    DOI: 10.1504/IJCMSSE.2023.10053477
     
  • Corrosion estimation of Cu and Br based automotive parts exposed to biodiesel environment : Case of RSM and ANN   Order a copy of this article
    by David Samuel, A. Taheri-Garavand, Marcus A. Amuche, Christopher C. Enweremadu 
    Abstract: It is critical to analyze the effects of operational variables on corrosion when selecting materials for the biodiesel and automotive industries. This was the first study to present an optimization strategy for minimizing corrosion rates (CRs) of automotive parts (APs) specifically copper and brass in a biodiesel environment, employing novel response surface methodology (RSM) and 5-fold cross-validation of an Artificial Neural Network (ANN). To model CRs, the RSM and ANN were used. The mechanical properties of APs were investigated, specifically their hardness number and tensile strength, as well as their surface morphologies. The optimum CRs for copper and brass were 0.01656 mpy and 0.008189 mpy at a B 3.91 biodiesel/diesel blend and 240.9-hour exposure. The established ANN model configuration (2-13-2) proved superior adaptability and nonlinearity. The ANN model had a higher coefficient of determination and lower values of root mean squared errors (RMSE), mean average error (MAE), and average absolute deviation (AAD) when compared to the RSM model; this validates the ANN model's superiority for predicting CRs of copper and brass.
    Keywords: Response Surface Methodology; Artificial Neural Network; Corrosion; Copper; Brass; Modelling; Biodiesel.
    DOI: 10.1504/IJCMSSE.2023.10056070
     

 

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