Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation

Parametric Optimization Using The Particle Swarm Optimization (PSO) Technique for Minimizing Tool Wear While Milling Inconel 718 Alloy Assisted by Minimum Quantity Lubrication (MQL)

Authors
Vishal S. Sharma, GurRaj Singh, Knut Sorby
Corresponding Author
Vishal S. Sharma
Available Online November 2016.
DOI
https://doi.org/10.2991/iwama-16.2016.38How to use a DOI?
Keywords
particle swarm optimization; Inconel 718; lubrication; surface roughness; depth of cut; MQL; tool wear
Abstract
In today's industrial scenario, the high cost involved in manufacturing is the major concern apart from the environmental factors. With the manufacturing cost reaching sky high levels, the use of a suitable optimization technique has become one major requirement while designing any manufacturing process. The current study involves a series of milling experiments on Inconel 718 alloy. Minimum quantity lubrication has been used as the cooling technique alongside the flood and the dry conditions. The combined objective functions were generated using ANOVA. Particle swarm optimization (PSO) technique was used to optimize the input parameters i.e. the cutting speed (Vc), cutting feed (F) and the depth of cut (ae) in order to minimize the tool wear (Vbmax). A series of validation experiments were performed and the PSO technique proved to be a highly effective method in predicting the tool wear (Vbmax), also allowing a simultaneous comparison amongst the cooling methods, thus, suggesting MQL to be a better cooling technique when compared to the dry and the flood cooling.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
6th International Workshop of Advanced Manufacturing and Automation
Part of series
Advances in Economics, Business and Management Research
Publication Date
November 2016
ISBN
978-94-6252-243-5
ISSN
2352-5428
DOI
https://doi.org/10.2991/iwama-16.2016.38How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Vishal S. Sharma
AU  - GurRaj Singh
AU  - Knut Sorby
PY  - 2016/11
DA  - 2016/11
TI  - Parametric Optimization Using The Particle Swarm Optimization (PSO) Technique for Minimizing Tool Wear While Milling Inconel 718 Alloy Assisted by Minimum Quantity Lubrication (MQL)
BT  - 6th International Workshop of Advanced Manufacturing and Automation
PB  - Atlantis Press
SP  - 202
EP  - 208
SN  - 2352-5428
UR  - https://doi.org/10.2991/iwama-16.2016.38
DO  - https://doi.org/10.2991/iwama-16.2016.38
ID  - Sharma2016/11
ER  -