|REVIEW OF PARAMETRIC OPTIMIZATION OF THE PRODUCTION IN MILLING OPERATION BY PRINCIPAL COMPONENT ANALYSIS (PCA)|
|SANTOSH JALTARE , DR. MANISH GANGIL|
|Cite This Article:|
REVIEW OF PARAMETRIC OPTIMIZATION OF THE PRODUCTION IN MILLING OPERATION BY PRINCIPAL COMPONENT ANALYSIS (PCA), International Journal of Innovative Research in Technology(www.ijirt.org) ,ISSN: 2349-6002 ,Volume 5 ,Issue 12 ,Page(s):620-624 ,May 2019 ,Available :IJIRT148203_PAPER.pdf
|MQL, surface roughness, nanofluid, feed,depth of cut and cutting speed.|
|Amid the considerable research has been studied on the parametric optimization of surface properties of the product in milling operation use of MQL. The most of the researches have been concentrated on geometric programming. However, the use of geometric programming in the previous studies was restricted for a specific cutting tool and work piece combination. Therefore, a separate solution procedure is required for different types of tool-work piece combinations. The grey–Taguchi method was adopted to optimize the milling parameters such as cutting speed, feed, depth of cut and MQL. In this paper, the cutting speed, feed rate, axial depth of cut and radial depth of cut and Zno MQL are given parameter to evaluate optimum output in terms of surface roughness and material removal rate restricted with some practical constraints of milling operations, are expressed in terms of variables which satisfy minimum production cost or maximum production rates of milling operations. Apart from optimizing a single response (process output), multi objective optimization problems have also been solved using Taguchi method followed by grey relation theory. However, this approach is based on the assumption that quality indices being uncorrelated or independent . But it is felt that, in practice, there may be some correlation among various quality indices (responses) under consideration. To overcome this limitation we apply grey based Taguchi approach L16 OA, the present paper proposes application of Principal Component Analysis (PCA) to convert correlated responses into uncorrelated quality indices called principal components. Finally based on grey relation theory Taguchi method has been applied to solve this optimization problem.|
|Unique Paper ID: 148203|
Publication Volume & Issue: Volume 5, Issue 12
Page(s): 620 - 624
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