Issue |
Manufacturing Rev.
Volume 8, 2021
|
|
---|---|---|
Article Number | 17 | |
Number of page(s) | 14 | |
DOI | https://doi.org/10.1051/mfreview/2021015 | |
Published online | 24 June 2021 |
Research Article
Experimental investigation and optimization of wall deflection and material removal rate in milling thin-wall parts
1
Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576 104, Karnataka, India
2
Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781 039, Assam, India
* e-mail: gururaj.bolar@manipal.edu
Received:
27
November
2020
Accepted:
10
June
2021
The selection of optimal process parameters is essential while machining thin-wall parts since it influences the quality of the product and affects productivity. Dimensional accuracy affects the product quality, whereas the material removal rate alters the process productivity. Therefore, the study investigated the effect of tool diameter, feed per tooth, axial and radial depth of cut on wall deflection, and material removal rate. The selected process parameters were found to significantly influence the in-process deflection and thickness deviation due to the generation of unfavorable cutting forces. Further, an increase in the material removal rate resulted in chatter, thus adversely affecting the surface quality during the final stages of machining. Considering the conflicting nature of the two performance measures, Non-dominated Sorting Genetic Algorithm-II was adopted to solve the multi-objective optimization problem. The developed model could predict the optimal combination of process variables needed to lower the in-process wall deflection and maintain a superior surface finish while maintaining a steady material removal rate.
Key words: Thin–wall milling / part deflection / material removal rate / aluminum alloy 2024-T351 / surface roughness / multi-objective optimization / NSGA-II
© G. Bolar and S.N. Joshi, Published by EDP Sciences 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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