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- W2789772561 abstract "Abstract Present study outlines the estimation of machining performances in the wire electric discharge machining of hybrid composite alloy using Multiple Regression Analysis (MRA) and Group Method of Data Handling (GMDH) technique. Al2024-5%TiC-5% flyash hybrid metal matrix composite synthesized by stir casting route was machined using different process parameters based on Taguchi’s L27 standard orthogonal array. Parameters such as pulse-on time, pulse-off time, current and bed speed were varied. The response variables measured for the analysis are Dimensional Error (DE), Surface Roughness (SR), Volumetric Material Removal Rate (VMRR) and Electrode Wear (EW). Machining performances have been compared using sophisticated mathematical models viz., MRA and GMDH. The GMDH algorithm is designed to learn the process by training the algorithm with the experimental data. Different GMDH models can be obtained by varying the percentage of data in the training set and the best model can be selected from these, viz., 50%, 62.5% & 75%. The best model is selected from the said percentages of data. Three different criterion functions, viz., Root Mean Square (Regularity or RMS) criterion, Unbiased criterion and Combined criterion were considered for estimation. Estimation and comparison of machining performances were carried out using MRA and GMDH techniques." @default.
- W2789772561 created "2018-03-29" @default.
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- W2789772561 date "2018-01-01" @default.
- W2789772561 modified "2023-09-24" @default.
- W2789772561 title "Estimation of Machining Performances using MRA and GMDH in Wire EDM of Al2024 based Hybrid MMC" @default.
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- W2789772561 doi "https://doi.org/10.1016/j.matpr.2018.01.111" @default.
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