Optimization of machining parameters for end milling of Inconel 718 super alloy using Taguchi based grey relational analysis
Name:
Publisher version
View Source
Access full-text PDFOpen Access
View Source
Check access options
Check access options
Issue Date
2013-11-13Subjects
grey relational analysismachinability
Taguchi method
multi-response optimization
end milling
Inconel super alloy
Subject Categories::H710 Manufacturing Systems Engineering
Metadata
Show full item recordAbstract
This study investigated the parameter optimization of end milling operation for Inconel 718 super alloy with multi-response criteria based on the taguchi orthogonal array with the grey relational analysis. Nine experimental runs based on an L9 orthogonal array of Taguchi method were performed. Cutting speed, feed rate and depth of cut are optimized with considerations of multiple performance characteristics namely surface roughness and material removal rate. A grey relational grade obtained from the grey relational analysis is used to solve the end milling process with the multiple performance characteristics. Additionally, the analysis of variance (ANOVA) is also applied to identify the most significant factor. Finally, confirmation tests were performed to make a comparison between the experimental results and developed model. Experimental results have shown that machining performance in the end milling process can be improved effectively through this approach.Citation
Maiyar L, Ramanujam R, Venkatesan K, Jerald J (2013) 'Optimization of machining parameters for end milling of Inconel 718 super alloy using Taguchi based grey relational analysis', International Conference on Design and Manufacturing - Chennai, Elsevier Ltd.Publisher
Elsevier LtdAdditional Links
https://www.sciencedirect.com/science/article/pii/S1877705813017220Type
Conference papers, meetings and proceedingsLanguage
enae974a485f413a2113503eed53cd6c53
10.1016/j.proeng.2013.09.208
Scopus Count
Collections
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International