Parametric optimization of end milling process under minimum quantity lubrication with nanofluid as cutting medium using pareto optimality approach

Abstract

In this paper a genetic algorithm based multi-objective optimization approach is applied in order to predict the optimal machining parameters for the end milling process of aluminium alloy 6061 T6 combined with minimum quantity lubrication (MQL) conditions using waterbased TiO2 nanofluid as cutting fluid. The optimization is carried out employing a parametric model (in terms of input cutting parameters, i.e., cutting speed, feed rate, depth of cut, MQL flow rate and % volume concentration of nanofluid) and exploiting the capabilities of the MOGA-II algorithm applied to the constrained machining problem. The objective functions selected to optimize are: to minimize the surface roughness; to maximize the material removal rate; and to minimize the flank wear of the cutting tool. The output of the optimization includes several alternative optimal solutions, i.e., Pareto frontier, and the best compromised configuration of the cutting parameters is selected subject to weighted preference.

Effect of tri-hybridized metallic nano cutting fluids in end milling of AA7075 in minimum quantity lubrication environment

“…The statistical analysis and the experimented values fall within the range and goes well with the previous research works. [39][40][41]…”

Investigation of thermal and hydrodynamic behaviour of Al2O3-water nanofluid through a rough parallel plate

“…Nanofluids are popular heat transfer fluids made up of a base fluid and nanoparticles. The execution of nanoparticles into heat transfer base fluid significantly enhances the heat transfer performance of the working fluids [2][3][4][5][6][7]. Solid nanoparticles have a higher thermal conductivity which supports the overall thermal and hydrodynamic properties of the heat transfer fluid as investigated by Li et al [8] and Cheng et al [9].…”

Hafif Metallerde Minimum Miktarda Yağlama (MQL) Sisteminin Kullanımı

“…Najiha ve arkadaşlarının yaptığı çalışmada, [28] kesme sıvısı olarak su bazlı TiO2 nanoyakıt kullanılarak minimum miktarda yağlama (MQL) koşulları ile kombine edilmiş 6061 T6 alüminyum alaşımının uç frezeleme işlemi için uygun değer işleme parametrelerini tahmin etmek için genetik algoritmaya dayalı çok amaçlı bir optimizasyon yaklaşımı uygulanmıştır. Optimizasyon, uygulanan MOGA-II algoritmasının özelliklerini kullanarak parametrik bir model (giriş kesme parametreleri, kesme hızı, besleme hızı, kesme derinliği, MQL akış oranı ve % hacim konsantrasyonu açısından) kullanılarak gerçekleştirilmiştir.…”