Investigation, optimising the MQL-Turning parameters of Nimonic 75 using weighted Mayfly algorithm

ABSTRACT

The present study is focused on the investigation and simultaneous optimisation of MQL-turning parameters with the application of the weighted Mayfly algorithm for sustainable machining of Nimonic 75. The nanofluid used for the experimental work is graphene oxide dispersed rice bran oil. The parameters of cutting velocity, feed rate, nozzle angle, and nozzle distance were considered for processing characteristics of force, roughness, and residual stress. The weight in the Mayfly algorithm is determined using a grey relational coefficient (GRC). The weight importance calculated for each response is compared with the different schemes in the algorithm. The result showed that the parameters with these schemes had different optimal values. At optimal factor levels determined using GRC in the Mayfly, the algorithm improved ‘F-value = 0.40’ compared to factor levels in other methods. Compared to experimental results, the optimised value reduces the surface roughness, cutting force, and residual stress by 30%, 3%, and 10%, respectively. The feed rate, cutting velocity, and nozzle distance are the most vital parameters for optimising the output response with the GRC-Mayfly process.

KEYWORDS:

• Superalloy
• metaheuristic
• MQL
• surface roughness
• cutting force
• residual stress

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors thank Vellore Institute of Technology for providing ”VIT SEED GRANT - SG20210179” for carrying out this research work.