Stirring Squeeze of Aluminium Alloy metal matrix composites using optimal prediction process

Swathi, D.; Kalpana, V.; Selvarathi, C.; Rajendran, Sasikumar

doi:10.1016/j.matpr.2022.09.502

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…35 The proposed reinforced material is made into a 150 mm-tall, 50 mm-diameter-diameter cylindrical shape. A set of eight specimens is also produced while maintaining a constant die temperature of 400 C when the melt temperature is varied among 750, 800, 850 and 900 C. 36 Another 8 specimens are manufactured for secondary testing at various die temperatures of 250, 399, 350, and 400 C at a constant melt temperature of 800 C. Throughout the specimen fabrication process, the squeeze casting pressure is kept constant at 100 MPa.…”

Section: Experimentationmentioning

confidence: 99%

Prediction of nano metal matrix composites based on hybrid approach

Sudheer Kumar Varma,

Rajasekhar,

Ganesan

et al. 2024

Lubrication Science

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This manuscript proposes a hybrid method to predict the optimal nano‐metal matrix composites. The proposed hybrid technique is the wrapper of the Fire‐Hawk Optimizer (FHO) and Spiking Neural Network (SNN). Commonly it is known as FHO‐SNN method. The main objective of the proposed method is to improve the method parameters for better enhancement in mechanical properties. FHO approach is used to improve the process parameters of stirring squeeze casting method. The SNN predicts optimal parameters. Moreover, the problem based on the casting is reduced. By then the proposed hybrid technique performance is performed in the MATLAB platform and associated with various existing approaches. The proposed system shows the high tensile strength, impact energy and hardness compared with other existing methods.

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Section: Experimentationmentioning

confidence: 99%