Abstract—Surface roughness and Material Removal Rate (MRR) are two characteristic quantities for machining quality and productivity, respectively. The aim of this study is to optimize two objectives simultaneously: minimizing surface roughness and maximizing MRR in hard turning of SKD61 alloy steel under minimum cooling lubrication conditions using a nanofluid. The experiments were conducted on an EMCO Maxxturn 45 CNC lathe by applying the L27 orthogonal array of the Taguchi method. The Greybased Taguchi method combined with variance analysis was used to investigate the influence of four factors, including the concentration of nano SiO₂ particles in the cutting fluid and three turning process parameters (cutting speed, feed rate, and cutting depth), on the grey relation grade. Additionally, single-objective optimizations were also performed to assess the impact of the input factors on each output response. The results showed that to achieve the minimum surface roughness and maximum MRR simultaneously, a concentration of 4% nano SiO₂ particles in the cutting fluid should be applied. Furthermore, the optimal cutting conditions were determined to be a cutting speed of 80 m/min, a cutting depth of 0.6 mm, and a feed rate of 0.1 mm/rev. The factor that had the greatest influence on the overall grey relation grade was cutting speed, followed by the nanoparticle concentration. Cutting speed and nano-particle concentration contributed approximately 35% and 17% respectively to the overall impact. Another important conclusion is the effectiveness of cool lubrication in metal cutting, reaffirmed by the use of a cutting fluid containing solid nano SiO₂ particles.

Keywords—surface roughness, Material Removal Rate (MRR), hard turning, hardened SKD61 tool steel, SiO₂nanoparticles, Minimum Quantity Lubrication (MQL), Grey-based Taguchi method

Cite: Minh Hung Vu, Quoc Manh Nguyen, Minh Hue Pham Thi, and The Vinh Do, "Investigating the Impact of SiO₂ Nanoparticle Concentration and Cutting Parameters on Surface Roughness and MRR in Hard Turning—A Multiobjective Optimization Approach," International Journal of Mechanical Engineering and Robotics Research, Vol. 13, No. 6, pp. 586-594, 2024.

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