Graphical Representations of Experimental and ANN Predicted Data for Mechanical and Electrical Properties of AlSiC Composite Prepared by Stir Casting Method

2nd International Conference on Engineering for Sustainable World

Babalola, Philip O.^1 ; Bolu, Christian A.^1 ; Inegbenebor, Anthony O.^1 ; Kilanko, Oluseun^1

Department of Mechanical Engineering, College of Engineering, Covenant University, Ota, Nigeria^1

关键词: Al-SiC composites;    Aluminium matrix composites;    Graphical illustrations;    Graphical representations;    Mechanical and electrical properties;    Silicon carbide powder;    Stir casting;    Ultimate tensile strength;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/413/1/012063/pdf
DOI  :  10.1088/1757-899X/413/1/012063

来源: IOP

【 摘 要 】

Artificial Neural network is a field of man-made intelligence that is able to undertake design prediction, mechanical property forecast, and process selection. In this paper, Aluminium Silicon Carbide composite was developed by reinforcing aluminium metal with silicon carbide powder using stir casting method. The produced aluminium matrix composites (AMC)were subjected to tensile, hardness and electrical tests to obtain tensile extension (mm), load (N), modulus (N/mm^2), yield strength (MPa), hardness (HV), ultimate tensile strength (MPa), tenacity at fracture (gf/tex), time at fracture (s), hardness (HV), conductivity(M/m), and tensile stress (MPa) data. Artificial Neural Network (ANN) was then used to train, test, and validate the obtained experimental data and then predict new set of data. The experimental and ANN predicted data were represented using graphical illustrations. The results showed that ANN could be used to replace rigorous, costly and time consuming experimental exercise with minimal loss in accuracy.