Multi-objective Optimization of Surface Roughness and MRR in Surface Grinding of Hardened SKD11 Using Grey-Based Taguchi Method

Hong, Tran Thi; Hung, Tran Vinh; Giang, Tran Ngoc; Tú, Nguyễn Thanh; Hưng, Lê Xuân; Danh, Bui Thanh; Tung, Luu Anh

doi:10.1007/978-3-030-64719-3_64

Cited by 1 publication

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“…e improvement of lubricating e ciency of nanouid is attributed to four mechanisms including the rolling of nanospheres [4][5][6], the self-repairing e ect [7][8][9], tribolm formation, and the polishing e ect [10]. e excellent e ectiveness of nano uid in improving roughness, reducing cutting force, cutting heat, reducing tool wear, and increasing tool life has been shown in machining processes such as grinding [11,12], turning [13][14][15], and milling [16][17][18][19]. e nano uid MQL approach is not economically expensive.…”

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confidence: 99%

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

Nguyen

Tam

2022

Advances in Materials Science and Engineering

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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.

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