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- W2792873996 abstract "Principal component analysis (PCA) method has been developed and has optimized the process parameters of Electro-chemical machining (ECM). The major performance characteristics selected are surface roughness i.e arithmetic average (Ra), average distance between the highest peak and lowest valley (Rz) and Electrolyte coating thickness. The corresponding machining parameters viz. voltage, feed rate, electrolyte concentration and electrode (Copper & Brass) are considered in this study. Initially, Full factorial (DOE) 24 has been used to perform the experimental runs. PCA is a technique of great practical importance in many applications of statistical inference, when it becomes difficult to relate a large number of independent variables with a number of dependent outcomes without having enough observations to carry out the analysis in a reliable way. Correlated responses are transformed into uncorrelated or independent quality indices called principal components.Thereafter, analysis of variance (ANOVA) is applied to determine the relative significant parameter and percentage of contribution of machining parameters.Finally, confirmation test have been used to compare the results of the proposed and predicted values." @default.
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- W2792873996 date "2018-01-01" @default.
- W2792873996 modified "2023-10-12" @default.
- W2792873996 title "Principal component analysis to optimize the ECM parameters of Aluminium alloy" @default.
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- W2792873996 doi "https://doi.org/10.1016/j.matpr.2017.12.126" @default.
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