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<p>One of the most crucial aspects of machining processes is optimization of process parameters. In this paper, design of experiments and genetic algorithm techniques are used to develop mathematical equations and optimizing the turning operation while machining AISI 304 steel material using herbal based Al<sub>2</sub>O<sub>3</sub> nano cutting fluids with minimum quantity lubrication (MQL). The parameters such as flow quantity of nano cutting fluid, nano particles volume concentration, cutting speed, feed rate and depth of cut, each at three levels are taken into account to investigate their effects. The responses measured are cutting force, surface roughness and cutting temperature. A central composite face centered design was used to conduct the experiments. Model equations for each response have been developed using multiple linear regression and multi objective optimization has been performed. Genetic algorithm has been employed as an evolutionary computation tool for the optimization purpose and pareto optimal solutions for responses have been tabulated. Cutting force of 1.7932&#xa0;kgf, surface roughness of 1.1628&#xa0;µm, temperature of 52.46&#xa0;°C were observed as optimum response values. The cutting speed of 50.83&#xa0;m/min, feed rate of 0.088&#xa0;mm/rev, depth of cut of 1.21&#xa0;mm, MQL flow rate of 101.19&#xa0;ml/hr, volume concentration of nano particles of 0.55% are corresponding optimum values of input parameters.</p>

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Experimental investigation of herbal based Al2O3 nano cutting fluids with minimum quantity lubrication in optimization of turning

  • 期刊论文

  • 发表日期：

    2025年1月24日

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  • G. Srinivasarao,
  • V. Tarachand,
  • T. K. Nagaraja,
  • Harish Hanumanthappa,
  • D. Kondalarao,
  • M. Usha

摘要

One of the most crucial aspects of machining processes is optimization of process parameters. In this paper, design of experiments and genetic algorithm techniques are used to develop mathematical equations and optimizing the turning operation while machining AISI 304 steel material using herbal based Al2O3 nano cutting fluids with minimum quantity lubrication (MQL). The parameters such as flow quantity of nano cutting fluid, nano particles volume concentration, cutting speed, feed rate and depth of cut, each at three levels are taken into account to investigate their effects. The responses measured are cutting force, surface roughness and cutting temperature. A central composite face centered design was used to conduct the experiments. Model equations for each response have been developed using multiple linear regression and multi objective optimization has been performed. Genetic algorithm has been employed as an evolutionary computation tool for the optimization purpose and pareto optimal solutions for responses have been tabulated. Cutting force of 1.7932 kgf, surface roughness of 1.1628 µm, temperature of 52.46 °C were observed as optimum response values. The cutting speed of 50.83 m/min, feed rate of 0.088 mm/rev, depth of cut of 1.21 mm, MQL flow rate of 101.19 ml/hr, volume concentration of nano particles of 0.55% are corresponding optimum values of input parameters.