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Experimental Investigation and Prediction Analysis on Granite/SiC Reinforced Al7050 and Al7075 Using Hybrid Deep Neural Network Based Salp Swarm Optimization

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Abstract

At present, industries require tremendous new technologies to solve the challenges faced in the production process. In the existing researches, Al7075 is frequently used but Al7050 is rare hence, in this study the effort has been made on the development of Al7050 and Al7075 alloys reinforced with Granite and Silicon carbide particles using stir casting technique. The nine different reinforcing mix ratios are fixed by using the Taguchi method performed in Minitab. Morphology of the reinforced particle is examined under optical microscopy and the mechanical properties such as hardness, impact, and wear of both Al7050 and Al7075 composites are studied. The Brinell hardness test is carried out to assess the hardness of both composites. Besides, Hybrid Deep Neural Network-based Salp Swarm Optimization (DNN-SSO) is performed to forecast and validate the experimented mechanical properties and compared with related neural networks. In which, the proposed DNN-SSO shows better outcomes by providing closer results to the experimented characteristics than the predicted DNN and ANN. From the overall study, in both Al7050 and Al7075 composites, hardness and wear rates are optimal when using 12% of granite and 6% of SiC particles. The optimal impact strength is achieved from 8% of granite and 4% of SiC particles. Besides, the reinforced composite of Al7050 possesses favorable impact energy and Al7075 possesses better hardness and wear rate. The superior mechanical characteristics observed for hardness is 141.22 BHN, wear rate is 0.00125 mm3/m and impact energy is 13.35 J. The predicted characteristics obtained using the proposed hybrid DNN-SSO achieve closer values to the experimented outcomes.

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  1. Department of Mechanical Engineering, Arunai Engineering College, Thiruvannamalai, Tamil Nadu, India

    Selvarasu Saminathan & Jayakumar Lakshmipathy

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  1. Selvarasu Saminathan
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  2. Jayakumar Lakshmipathy
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Saminathan, S., Lakshmipathy, J. Experimental Investigation and Prediction Analysis on Granite/SiC Reinforced Al7050 and Al7075 Using Hybrid Deep Neural Network Based Salp Swarm Optimization. Silicon 14, 5887–5903 (2022). https://doi.org/10.1007/s12633-021-01349-0

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