Performance Evaluation of Al-SiC Nanofluids Based MQL Sustainable Cooling Techniques During Turning of EN-24 Steel

Thakur, Archana; Manna, Alakesh; Samir, Sushant

doi:10.1007/s12633-020-00875-7

Cited by 7 publications

(1 citation statement)

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“…Response surface methodology was employed for optimization of multiple responses and develops the empirical relationship. Response surface approach is a set of mathematical and statistical tools for modelling and analysing problems in which a response is affected by a variety of input characteristics, with the goal of optimising the response [21]. The analysis of variance was done to study the main effects of input parameters and their interactions on the performance characteristics.…”

Section: Design Of Experimentsmentioning

confidence: 99%

Multi-response optimization in turning of EN-24 steel under MQL

Thakur¹,

Guleria²,

Lal³

2022

Eng. Res. Express

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Cutting fluids used in manufacturing industry have environmental and economic drawbacks. In order to reduce the negative effects associated with cutting the fluids, it is now becoming indispensable to move towards more sustainable techniques. In this research work, the turning of EN-24 steel under minimum quantity lubrication (MQL) environment have been done. The study has been completed three phases. The first phase deals with planning of experiments based on Box-Behnken design. Theinfluence of input process parameters i.e., cutting speed, feed rate, depth of cut and MQL flow rate have been investigated on output parameters i.e., surface roughness, tool wear, cutting temperature and cutting force. The desirability approach strategy has been used for multi-response optimizationof input parameters in the second phase.The third step deals with analysis of results using SEM, 3D surface graphs and main effect graphs. The confirmation experiments have been carried out to verify the developed model. Experimental results revealed that, the turning performance under high MQL flow ratehas been found to be the most effective parameter on mean surface roughness, cutting temperature, tool wear and cutting force.

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Section: Design Of Experimentsmentioning

confidence: 99%