Optimal Approaches for Hard Milling of SKD11 Steel Under MQL Conditions Using SIO2 Nanoparticles
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 optim...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/2627522 |
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| author | Quoc-Manh Nguyen The-Vinh Do |
| author_facet | Quoc-Manh Nguyen The-Vinh Do |
| author_sort | Quoc-Manh Nguyen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b4449f510724425e9e890fee3a3820ac |
| institution | Kabale University |
| issn | 1687-8442 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-b4449f510724425e9e890fee3a3820ac2025-02-03T01:07:56ZengWileyAdvances in Materials Science and Engineering1687-84422022-01-01202210.1155/2022/2627522Optimal Approaches for Hard Milling of SKD11 Steel Under MQL Conditions Using SIO2 NanoparticlesQuoc-Manh Nguyen0The-Vinh Do1Hung Yen University of Technology and EducationThai Nguyen University of TechnologyProductivity 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.http://dx.doi.org/10.1155/2022/2627522 |
| spellingShingle | Quoc-Manh Nguyen The-Vinh Do Optimal Approaches for Hard Milling of SKD11 Steel Under MQL Conditions Using SIO2 Nanoparticles Advances in Materials Science and Engineering |
| title | Optimal Approaches for Hard Milling of SKD11 Steel Under MQL Conditions Using SIO2 Nanoparticles |
| title_full | Optimal Approaches for Hard Milling of SKD11 Steel Under MQL Conditions Using SIO2 Nanoparticles |
| title_fullStr | Optimal Approaches for Hard Milling of SKD11 Steel Under MQL Conditions Using SIO2 Nanoparticles |
| title_full_unstemmed | Optimal Approaches for Hard Milling of SKD11 Steel Under MQL Conditions Using SIO2 Nanoparticles |
| title_short | Optimal Approaches for Hard Milling of SKD11 Steel Under MQL Conditions Using SIO2 Nanoparticles |
| title_sort | optimal approaches for hard milling of skd11 steel under mql conditions using sio2 nanoparticles |
| url | http://dx.doi.org/10.1155/2022/2627522 |
| work_keys_str_mv | AT quocmanhnguyen optimalapproachesforhardmillingofskd11steelundermqlconditionsusingsio2nanoparticles AT thevinhdo optimalapproachesforhardmillingofskd11steelundermqlconditionsusingsio2nanoparticles |