International Journal of Computational Materials Science and Surface Engineering (5 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  
  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  
  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
   
  
• Improving engine's lubrication based on optimized partial micro-textures  
  by Jili Zha, Vanliem Nguyen, Tianfeng Ye 
  Abstract: The partial textures (PT) used by spherical textures (ST), circular cylinder textures, conical textures, wedge-shaped textures, and square cylinder textures are designed on the crankpin-bearing (CB) to improve the lubrication of an engine. Based on the lubrication model of CB, the effect of PT's dimension/distribution densities on the lubrication performance is analyzed. Radius and depth of PT are then optimized to maximize lubrication of an engine. This study’s objective is the decrease Ff (CB’s friction force) and increase p (oil film’s pressure). Results indicate that engine's lubrication using PT is better than using full textures. Also, the lubrication in an engine using ST is also better than using other PT. Especially, with ST optimized, both maximum p and Ff are ameliorated by 8% and 25% in comparison with full textures. Consequently, CB’s bearing surface designed by optimized ST should be applied to further improve engine's lubrication.
  Keywords: Lubricaton model; crankpin bearing; spherical textures; conical textures; square cylinder textures; wedge shaped textures; optimization algorithm; lubrication performance.
  DOI: 10.1504/IJCMSSE.2023.10058111
   
  
• COMPUTATIONAL AND EXPERIMENTAL ANALYSIS OF PARTLY COATED HYDROPHOBIC AIRFOIL  
  by Suresh Chandra Khandai, Prasath S, Naveen B 
  Abstract: Hydrophobic materials are those which repel water molecules, the use of such material on aircraft surfaces can produce lower drag and higher lift compared to smooth finished surfaces, as the potential of reducing skin friction drag is much higher compared to other types of drag. The CFD studies for partly coated hydrophobic were carried out using ANSYS FLUENT software for different angles of attack such as 0
  Keywords: Hydrophobic; airfoil; lift; drag; surface coatings; anti-icing.
  DOI: 10.1504/IJCMSSE.2023.10058150
   
  
• Numerical simulation of SiC crystal growth during physical vapor transport using the lattice Boltzmann - phase field model  
  by Hu Zhao, Shilin Mao, Hui Xing, Dongke Sun 
  Abstract: The lattice Boltzmann-phase field (LB-PF) model is utilized to investigate SiC crystal growth in physical vapor transport (PVT). In the model, the magnetic vector potential equation is used to compute the magnetic field distribution of the growth environment, the thermal stress equation based on displacement is applied to solve the stress evolution in the crystals and the LB-PF equation is applied to describe the crystal growth. After model validation, the model is applied to study the temperature evolution and crystal growth under different coil positions, currents and pressures. The results show that moving up coils and increasing currents have little effect on axial temperature gradient in growth environment, the crystal growth rates could be increased by reducing the pressure. The present model has been demonstrated its potential in simulations of SiC crystal growth.
  Keywords: SiC; Crystal growth; Lattice Boltzmann; Phase eld; Physical vapor transport; Stress; Pressure.
  DOI: 10.1504/IJCMSSE.2023.10059324