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- W4280527441 abstract "In this present study, aluminium and its alloys are welded using friction stir welding technique (FSW) and the experimental work is ameliorated by Response Surface Methodology (RSM) and Artificial Neural Networking (ANN). FSW is exploited predominantly in many industries for welding of aluminium and its alloys. Moreover, the mechanical properties of the welded zone are prompted by the quality of the weld and the welding parameters. RSM and ANN are the various techniques are deployed to validate the tensile strength (TS) based on the welding parameters. Back propagation (BP) is instigated for the prediction of the error such as (Mean Absolute Error) MAE. Three different tools have been exploited for welding of aluminium alloys and comparison is also effectuated using ANN and RSM. In these techniques, the TS of the predicted value is consequential in the range of 202.64Mpa to 219.30Mpa at high Rotational Speed (RS). Also, the percentage of error is less than 1.5 % in ANN which ascertains the superior TS in design space." @default.
- W4280527441 created "2022-05-22" @default.
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- W4280527441 date "2022-01-01" @default.
- W4280527441 modified "2023-10-18" @default.
- W4280527441 title "Experimental analysis of friction stir welded of dissimilar aluminium 6061 and Titanium TC4 alloys using Response Surface Methodology (RSM)" @default.
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- W4280527441 doi "https://doi.org/10.1016/j.matpr.2022.04.822" @default.
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