Optimal Approaches for Hard Milling of SKD11 Steel Under MQL Conditions Using SIO2 Nanoparticles

Abstract

Productivity and quality are always two goals in the production process. In metal cutting, two prominent representatives of quality and productivity are roughness and material removal rate (MRR). In this study, the Response Surface method was used to perform single-objective and multiobjective optimizations during the hard milling of SKD11 steel. From there, comparative analyzes are carried out to give effective advice for different approaches in actual production. The selected inputs are the nanoparticle concentration in the cutting oil and three typical cutting parameters including cutting velocity, depth of cut, and feed rate. Each input will have three levels including low, high and average. The L27 orthogonal array developed by Taguchi was applied to the experimental design. In addition, ANOVA was also used to evaluate the statistical indicators of the study. The results of single-objective optimization show that the feed rate is the main influencing factor for the roughness followed by the nanoparticle concentration. They contribute 51.2% and 21.12% of the total roughness effect, respectively. On the other hand, the main factors affecting the material removal rate are the depth of cut and feed rate. In multiobjective optimization, a compromise solution has also been proposed to achieve small roughness and high material removal rate. The minimum roughness was 0.1956 μm and the maximum material removal rate was 1479.8688 mm3/min when applying the multiobjective optimal machining condition.

Turning SKD 11 Hardened Steel: An Experimental Study of Surface Roughness and Material Removal Rate Using Taguchi Method

“…Te impact of these factors has statistical signifcance with a p value less than 0.05. Tese fndings indicate that feed rate has a greater infuence on achieving low surface roughness, which can be observed in the fndings of multiple researchers [72,73]. Additionally, the R 2 value of the model for surface roughness is 96.76%, suggesting a higher level of reliability and credibility for the model.…”

Multi-objective optimization for balancing surface roughness and material removal rate in milling hardened SKD11 alloy steel with SIO2 nanofluid MQL

“…Moreover, apart from their lubricating and heat transfer properties, the nanoparticles employed in the MQL process can also function as solid lubricants, minimizing the occurrence of adhesive wear between the cutting tool and workpiece. As a result, cutting operations become smoother, and chip evacuation is improved [21][22][23][24][25][26][27][28].…”