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Investigation of material removal rate and surface roughness using multi-objective optimization for micro-milling of inconel 718

Investigation of material removal rate and surface roughness using multi-objective optimization... The purpose of this study is to realize the multi-objective optimization for MRR and surface roughness in micro-milling of Inconel 718.Design/methodology/approachTaguchi method has been applied to conduct experiments, and the cutting parameters are spindle speed, feed per tooth and depth of cut. The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis. Genetic algorithm has been utilized to implement multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718.FindingsThis paper implemented the multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. And some conclusions can be summarized. Depth of cut is the major cutting parameter influencing surface roughness. Feed per tooth is the major cutting parameter influencing MRR. A number of cutting parameters have been obtained along with the set of pareto optimal solu-tions of MRR and surface roughness in micro-milling of Inconel 718.Originality/valueThere are a lot of cutting parameters affecting surface roughness and MRR in micro-milling, such as tool diameter, depth of cut, feed per tooth, spindle speed and workpiece material, etc. However, to the best our knowledge, there are no published literatures about the multi-objective optimization of surface roughness and MRR in micro-milling of Inconel 718. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Lubrication and Tribology Emerald Publishing

Investigation of material removal rate and surface roughness using multi-objective optimization for micro-milling of inconel 718

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References (19)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0036-8792
DOI
10.1108/ilt-07-2018-0259
Publisher site
See Article on Publisher Site

Abstract

The purpose of this study is to realize the multi-objective optimization for MRR and surface roughness in micro-milling of Inconel 718.Design/methodology/approachTaguchi method has been applied to conduct experiments, and the cutting parameters are spindle speed, feed per tooth and depth of cut. The first-order models used to predict surface roughness and MRR for micro-milling of Inconel 718 have been developed by regression analysis. Genetic algorithm has been utilized to implement multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718.FindingsThis paper implemented the multi-objective optimization between surface roughness and MRR for micro-milling of Inconel 718. And some conclusions can be summarized. Depth of cut is the major cutting parameter influencing surface roughness. Feed per tooth is the major cutting parameter influencing MRR. A number of cutting parameters have been obtained along with the set of pareto optimal solu-tions of MRR and surface roughness in micro-milling of Inconel 718.Originality/valueThere are a lot of cutting parameters affecting surface roughness and MRR in micro-milling, such as tool diameter, depth of cut, feed per tooth, spindle speed and workpiece material, etc. However, to the best our knowledge, there are no published literatures about the multi-objective optimization of surface roughness and MRR in micro-milling of Inconel 718.

Journal

Industrial Lubrication and TribologyEmerald Publishing

Published: Aug 22, 2019

Keywords: Multi-objective optimization; Surface roughness; Micro-milling; Inconel 718; Material removal rate

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