Parametric Optimization Using The Particle Swarm Optimization (PSO) Technique for Minimizing Tool Wear While Milling Inconel 718 Alloy Assisted by Minimum Quantity Lubrication (MQL)
Vishal S. Sharma, GurRaj Singh, Knut Sorby
Vishal S. Sharma
Available Online November 2016.
- https://doi.org/10.2991/iwama-16.2016.38How to use a DOI?
- particle swarm optimization; Inconel 718; lubrication; surface roughness; depth of cut; MQL; tool wear
- 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.
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 - Proceedings of the 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 -