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An integrated artificial neural network and Taguchi approach to optimize the squeeze cast process parameters of AA6061/Al2O3/SiC/Gr hybrid composites prepared by novel encapsulation feeding technique
L. Natrayan,
Published in Elsevier Ltd
2020
Volume: 25
   
Abstract
Aluminum matrix composites known for their superior mechanical properties finds its use as liners in engine cylinders, discs, drum brakes, and pistons in automotive applications. This paper investigates the influence of squeeze casting process parameters on AA6061/Al2O3/SiC/Gr hybrid metal matrix composite using the encapsulated feeding technique. Four levels of factors selected for the L16 orthogonal array to optimize the process parameters were squeeze pressure (60, 80, 100, 120 MPa), melt temperature (700,750,800,850 °C), die temperature (100, 150, 200, 250 °C) and pressure holding time (5, 10, 15, 20 s). Hardness and tensile strength were measured for the designed experiments. Scanning electron microscope with energy-dispersive X-ray spectroscopy identified surface morphologies and elemental analysis. Optimized results were predicted using the artificial neural network. The melt temperature and squeeze pressure exhibited a significant contribution in controlling the mechanical behaviour of the hybrid composites. Taguchi analysis suggested that SP3, MT2, DT4 and HT2 casting conditions presented the optimal process parameter level that showed the maximum hardness of 131 HV and the tensile strength of 329 MPa. Scanning electron microscopy and energy-dispersive X-ray spectroscopy showed uniform distribution of reinforcement in the encapsulation process compared with the regular feeding technique. ANN predicted the hardness and tensile strength with 95 % accuracy. Compared with the regression model and experimental data, the ANN prediction was more accurate. The defined hybrid metal matrix composite stands out as the substitute for AA6061 alloy that meets the demands of the modern automotive industry in engine cylinder liner applications. © 2020 Elsevier Ltd
About the journal
JournalData powered by TypesetMaterials Today Communications
PublisherData powered by TypesetElsevier Ltd
ISSN23524928