Manuscript Number : IJSRSET173462
Surface Grinding Parameters Optimization of Austenitic Stainless Steel Sheet (AISI 304) by Taguchi Method
Authors(3) :-Tushar Khule, Prof. Mali M. S., Dr. Zope S. B.
The manufacturing process of surface grinding has been established in the mass production of slim, rotationally symmetrical components. Due to the complex set-up, which results from the large sensitivity of this grinding process to a multiplicity of geometrical, kinematical and dynamical influence parameters, surface grinding is rarely applied within limited-lot production. The substantial characteristics of this grinding process are the simultaneous guidance and machining of the work piece on its periphery. Surface grinding is an essential process for final machining of components requiring smooth surfaces and precise tolerances. As compared with other machining processes, grinding is costly operation that should be utilized under optimal conditions. Although widely used in industry, grinding remains perhaps the least understood of all machining processes. The proposed work takes the following input processes parameters namely Work speed, feed rate and depth of cut. The main objective of this work is to predict the grinding behavior and achieve optimal operating processes parameters. The main objective in any machining process is to maximize the Metal Removal Rate (MRR) and to minimize the surface roughness. In order to optimize these values Taguchi method, regression analysis and ANOVA is used.
Tushar Khule
ANOVA, Depth of Cut, Feed Rate, Surface Grinding, Taguchi Method, Work Speed
Publication Details
Published in :
Volume 3 | Issue 5 | July-August 2017 Article Preview
Department of Mechanical, SPPU PUNE /SVCET, RAJURI, PUNE, Maharashtra, India
Prof. Mali M. S.
Department of Mechanical, SPPU PUNE /SVCET, RAJURI, PUNE, Maharashtra, India
Dr. Zope S. B.
Department of Mechanical, SPPU PUNE /SVCET, RAJURI, PUNE, Maharashtra, India
Date of Publication :
2017-08-31
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) :
379-384
Manuscript Number :
IJSRSET173462
Publisher : Technoscience Academy
Journal URL :
https://ijsrset.com/IJSRSET173462