Optimal Approaches for Hard Milling of SKD11 Steel Under MQL Conditions Using SIO<sub>2</sub> Nanoparticles

Nguyen, Quoc-Manh; Do, The-Vinh

doi:https://doi.org/10.1155/2022/2627522

Advances in Materials Science and Engineering

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Abstract Introduction Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 2627522 | https://doi.org/10.1155/2022/2627522

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

Quoc-Manh Nguyen¹and The-Vinh Do²

Academic Editor: Yee-Wen Yen

Received29 Apr 2022

Revised08 Jul 2022

Accepted10 Oct 2022

Published21 Oct 2022

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.

1. Introduction

In this study, the hard milling of alloy steel SKD 11 was performed under MQL conditions using nanofluid. SKD11 alloy steel is a high carbon and chromium tool steel. After heat treatment, SKD 11 alloy steel has many outstanding properties such as good wear resistance, high hardness, and high strength. Because of its mechanical properties, SKD 11 steel is commonly used as a material for stamping dies, plastic molds, and cold-work dies. However, it is a difficult material to work especially after heat treatment. Therefore, it is very meaningful to study the effective cutting of SKD 11 steel.

Why choose MQL nanofluid? This is a new coolant and lubrication method in machining that only appeared about 10 years ago. The nanofluid MQL method has many advantages over traditional cooling lubrication methods including conventional MQL. The appearance of nanoparticles markedly improved the lubricating and cooling properties of the base cutting fluid. The cooling effect of nanofluids is explained by the mechanism of enhanced heat exchange by cutting oil containing nanoparticles. Another reason, the added nanoparticles have increased the wettability of the nanofluid compared to the base cutting fluid [1–3]. The improvement of lubricating efficiency of nanofluid is attributed to four mechanisms including the rolling of nanospheres [4–6], the self-repairing effect [7–9], tribo-film formation, and the polishing effect [10]. The excellent effectiveness of nanofluid 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 [13–15], and milling [16–19]. The nanofluid MQL approach is not economically expensive. At a low cost, a manufacturer can turn an ordinary machine into a machine with advanced lubrication. To convert a conventional machine into a machine with MQL nanofluid cooling, an MQL nozzle is installed on it. It is a low-cost solution with high efficiency in lubrication and cooling.

A single-objective optimization problem is to find the best solution for only one specific criterion. This problem only cares about a specific criterion but ignores other criteria. In fact, the desire of the manufacturers is to achieve the criteria simultaneously. These criteria are even contradictory. Therefore, a “win-win” solution will be found that achieves the criteria that conflict with each other. This is the goal of multiobjective optimization. In experimental research, the Response Surface Methodology (RSM) is used by many researchers because of its suitability. The goal of the RSM is to optimize the output responses that are influenced by the input variables (i.e., the independent variables) [19–22]. A series of targeted experiments will be conducted to determine the data set for statistical analysis. A quadratic regression model will be formed based on experimental data. The quadratic RSM model is capable of predicting the variations of the input response (dependent variable) that depend on the input variables (independent variable). Then, analysis of variance (ANOVA) will be used to evaluate the adequacy of the regression model. ANOVA will evaluate the effects of each input variable and their interactive effects on the output response.

In the study of optimization of MRR and surface roughness in turning of X20Cr13 by using Taguchi in combination with Grey [23], mono-objective optimizations for each criterion are also performed. The results show that the cutting mode with minimum cutting speed, minimum feed rate, and the minimum depth of cut will achieve optimal roughness. However, the cutting mode with the highest levels will achieve the largest MRR. On the other hand, a multiobjective optimization solution was performed to obtain the smallest roughness and the largest MRR. Accordingly, a cutting mode including maximum depth of cut, maximum cutting speed, and minimum feed is the optimal mode. In another study [13], Sahoo et al investigated the effect of cutting mode for multi-objective optimization in turning AISI 1040 steel. The authors also found an optimal cutting mode for two criteria including low roughness and high MRR. During end milling of alloy AL8112 under MQL nano-lubricant conditions, Okokpujie et al. found the optimal cutting mode for 3 criteria including roughness, MRR, and cutting force simultaneously [16]. In a study by Do and Phan [19], four parameters of the cutting mode including cutting speed, depth of cut, feed rate, and hardness of the workpiece were investigated. An optimal mode to achieve minimum roughness and maximum MRR has been proposed. Response Surface Methodology was used in a study by Dinesh et al. [24]. Cutting parameters such as cutting speed, depth of cut, feed rate, and tool nose radius were investigated to find small surface roughness and high material removal rate. Regression mathematical models describing the relationship between the cutting parameters and the output responses (roughness and MRR) are also proposed.

In general, classical cutting parameters such as the cutting speed, the feed rate, and the depth of cut have been investigated by many researchers [25–28]. However, many other factors need to be investigated in metal cutting. In machining with the application of MQL nanofluid, the nanoparticle concentration parameter is a parameter that has won much attention from researchers. This parameter has been concluded to have a great influence on roughness [18, 29–31], cutting force [31, 32], tool life, tool wear [33, 34], cutting temperature [35, 36], and so on.

In this study, the concentration of nanoparticles combined with three cutting parameters (cutting speed, feed amount, and depth of cut) will be investigated in the hard milling of SKD11 alloy steel. Response Surface Methodology (RSM) is used in mono-objective and multiobjective optimizations. Two typical criteria for quality and productivity including surface roughness and material removal rate are output responses. A regression mathematical model describing the relationship between input variables and output response has been built. ANOVA is applied to evaluate the reliability of the model.

2. Experiment Setup

In this study, the experiments were conducted on the 5-axis milling machine DMU50. The cutting tool is a TiAlN ϕ10 end mill. Figure 1 shows the milling tool used in this work. Technical specifications of the cutting tool are shown in Table 1. Each new tool is used for one experiment. The SKD11 alloy steel workpieces are 150 × 150 × 200 mm blocks with a hardness of 50HRC. The composition of the material is shown in Table 2. The workpiece is firmly fixed on the machine table by the universal vise. The MQL spray mounted on the machine is the MC1700 nozzle manufactured by Noga Engineering Ltd. The nozzle is provided with a compressed air pressure of 3 kg/cm². It sprays into the cutting area a quantity of cutting fluid with a flow rate of 50 ml/h. The machining settings are shown in Figure 2. The results of the surface roughness (Ra) were collected using the Mitutoyo SJ-401 surf-test. Each experiment was carried out 3 times, after that, the results will be taken as the average value. The information of the experiment can be seen in Table 3.

In this study work, the cutting parameters including cutting speed (V), feed rate (f), depth of cut (ap), and nanoparticle concentration (C) were selected as input parameters. Each input element consists of three levels including low, high and intermediate. Levels are selected based on machining conditions, recommended by the tool manufacturer. In addition, they are also considered based on references to the author’s own research and those of other authors. The experiments were organized according to the L27 orthogonal array of the Taguchi method. With four inputs and their three levels, twenty-seven experiments were performed.

3. Result and Discussions

Table 4 shows the result of the experiment. The roughness results were collected using the Mitutoyo SJ-401 surf-test equipment. The roughness of each machined surface is measured at three locations and is the average value. Meanwhile, the material removal rate (MMR) is calculated by the following formula:where a_p is the depth-of-cut (mm), a_e is the width-of-cut (mm), V is the cutting speed (m/min), f is the feed rate (mm/tooth), z is the flute of the cutter, and D is the diameter of the cutting tool (mm).

In this study, Minitab 17 software was used to provide statistical analysis. Based on the Response Surface method, Minitab 17 gave a regression equation that is represented in the following.

Analysis of variance was performed and shown in Table 5. Based on ANOVA, the effects of each input factor on the output response are determined. Accordingly, the feed rate has the largest dominant effect on surface roughness compared to other factors. This factor contributed 51.2% of the total impact, followed by the concentration of nanoparticles with 21.12%. The influence of three factors including the feed rate, the concentration, and the cutting speed has statistical significance with a value less than 0.05. The coefficient of determination R-sq = 87.77% means that 87.77% of the variation in surface texture can be explained by the inputs in this study.

The optimal plot of surface roughness is shown in Figure 3. It can be seen that a minimum surface roughness value of 0.1082 μm is obtained when machining with a maximum cutting speed of 80 m/min, a minimum feed rate of 0.01 mm/tooth, a minimum depth of cut of 0.2 mm under MQL cooling condition applying nano concentration of 4 wt%. It is clear that minimum roughness is achieved under machining conditions of a maximum cutting speed, a minimum feed rate, and a minimum depth of cut. This result is completely consistent with previously published studies [37–42]. According to the published studies, the explanations can be given by the following arguments.

Figure 4(a) depicts the interaction of two inputs including feed rate and depth of cut with the roughness. The other two factors including cutting velocity and concentration of nanoparticles are fixed with values of 60 m/min and 2 wt%, respectively. As shown in Figure 4(a), the feed rate has the greatest impact. The increase in the feed rate leads to a sharp increase in surface roughness. During cutting, furrows on the machined surface are created by the helicoidal movement of the cutting tool. Aouici et al. suggested that the increase in feed rate will create wide and deep furrows on the machined surface that leads to an increase in roughness [41]. Another reason, Revankar et al. argue that increasing the feed rate will lead to a reduction in heat dissipation time in the machining region, and an increase in chip accumulation in the cut zone. This is what causes the increase in roughness [42].

The increase in roughness with the corresponding increase in depth of cut (shown in Figure 4(a)) is explained by the effect of cutting force [37]. An increase in depth of cut results in a large chip load which leads to an increase in cutting force. According to Perez et al. [43], high cutting forces will create chatter vibrations that cause large roughness. This is also consistent with the statements of Colafemina et al. [44]. The authors claim that machining with a low depth of cut will significantly reduce chatter vibrations.

Figure 4(b) depicts the influence of two inputs including nanoparticle concentration and cutting speed on surface roughness. The depth of cut and the feed rate are fixed to 0.4 mm and 0.02 mm/tooth, respectively. As shown in Figure 4(b), it can be seen that an increase in the cutting speed causes a decrease in Ra. Çolak et al. suggested that the main reason for this was the formation of the built-up edge (BUE). The authors explain that BUE will form when machining at low speed and it will adversely affect roughness [39]. Another cause is thought to be the effect of temperature. It was concluded that the high temperature generated by high cutting velocities makes the surface of the workpiece softer. This facilitates the chip forming process leading to a reduction in roughness [45].

Figure 4(b) also shows that the increase of the nanoparticle concentration significantly improves the surface roughness. That proves the remarkable effect of reducing the roughness of SiO₂ nanoparticles when adding cutting oil compared with conventional MQL. This was covered in the introduction of this article. Mechanisms explaining the effectiveness of nanoparticle addition in cooling lubricating oil are shown in Figure 5.

The results shown in Figure 3 are the mono-objective optimization. This result ignores the other criteria of the machining process. Thus, mono-objective optimization will have many limitations when applied to actual production. In this study, a multiobjective optimization was performed to achieve the minimum roughness and maximum MRR. Based on the experimental data, the desirability function is used to extract the optimal values of the multiobjective problem by Minitab statistical software as shown in Figure 6.

With a composite desirability value of 0.7306, a compromise result was given where the minimum value of roughness was 0.1956 μm and the maximum value of material removal rate was 1479.8688 mm³/min. This optimal value can be achieved by applying the machining mode set with 80 m/min for cutting speed, 0.6 mm for depth of cut, 0.0249 mm/tooth for feed rate, and 4% for nanoparticle concentration.

Table 6 shows a comparison between different cutting modes to achieve some specific purposes. As shown in Table 6, the results of multiobjective optimization show that this is a win-win solution. The roughness value of this solution is larger than the roughness value of the optimized solution for roughness but less than the roughness value of the optimized solution for MRR. On the other hand, the MRR value of the multi-objective optimized solution is larger than the MRR value of the optimized solution for roughness but smaller than the MRR value of the optimized solution for MRR.

4. Conclusion

In this study, four parameters including nanoparticle concentration, cutting speed, depth of cut, and feed rate were investigated in the hard milling of alloy steel SKD11. The Response Surface method was used to perform single-objective and multiobjective optimizations for roughness and MRR. Some of the conclusions that can be made are as follows:

A regression equation describing the relationship between the roughness and the four inputs was built.

In the relationship between roughness and input factors, the two factors that have the strongest influence on roughness are feed rate and nanoparticle concentration. They contribute 51.2% and 21.12% of the total roughness effect, respectively.

The results of the paper have demonstrated the obvious effectiveness in improving the roughness of SiO2 nanoparticles when adding cutting oil compared with conventional MQL.

Multiobjective optimization to achieve minimum roughness and maximum material removal rate was performed. The results of multiobjective optimization show that this is a win-win solution. The compromised result was given where the minimum value of roughness was 0.1956 μm and the maximum value of material removal rate was 1479.8688 mm³/min with the composite desirability value of 0.7306.

Data Availability

The [roughness data in the experiments of this study] data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors would like to thank Thai Nguyen University of Technology. The study was supported by Thai Nguyen University of Technology.

References

S. Khandekar, M. R. Sankar, V. Agnihotri, and J. Ramkumar, “Nano-cutting fluid for enhancement of metal cutting per-formance,” Materials and Manufacturing Processes, vol. 27, no. 9, pp. 963–967, 2012.
View at: Publisher Site | Google Scholar
T. S. Srivatsan, A Review of “Contact Angle, Wettability, and Adhesion, Brill Aca-demic Press, PA Leiden, The Netherlands, vol. 5, 2008.
J. Hemalatha, S. K. Das, Sus. Choi, W. Yu, and T. Pradeep, A Review of: Nanofluids: Science and Technology, Taylor & Francis, Hoboken, NJ, USA, vol. 397, 2008.
L. Kong, J. Sun, and Y. Bao, “Preparation, characterization and tribological mechanism of nanofluids,” RSC Advances, vol. 7, no. 21, pp. 12599–12609, 2017.
View at: Publisher Site | Google Scholar
F. Chin˜ as-Castillo and H. A. Spikes, “Mechanism of action of colloidal solid dispersions,” Journal of Tribology, vol. 125, no. 3, pp. 552–557, 2003.
View at: Publisher Site | Google Scholar
J. Viesca, A. Hernández Battez, R. González, R. Chou, and J. Cabello, “Antiwear properties of carbon-coated copper nanoparti-cles used as an additive to a polyalphaolefin,” Tribology International, vol. 44, no. 7-8, pp. 829–833, 2011.
View at: Publisher Site | Google Scholar
G. Liu, X. Li, B. Qin, D. Xing, Y. Guo, and R. Fan, “Investigation of the mending effect and mechanism of copper nano-particles on a tribologically stressed surface,” Tribology Letters, vol. 17, no. 4, pp. 961–966, 2004.
View at: Publisher Site | Google Scholar
T. Shen, D. Wang, J. Yun, Q. Liu, X. Liu, and Z. Peng, “Tribological properties and triboch,” Tribology International, vol. 93, pp. 332–346, 2016.
View at: Publisher Site | Google Scholar
F. Wu, W. Liu, J. Qiu et al., “Enhanced photocatalytic degradation and adsorption of methylene blue via TiO₂ nanocrystals supported on graphene-like bamboo charcoal,” Applied Surface Science, vol. 358, pp. 425–435, 2015.
View at: Publisher Site | Google Scholar
D. X. Peng, C. H. Chen, Y. Kang, Y. P. Chang, and S. Y. Chang, “Size Effects of SiO2 Nanoparticles as Oil Additives on Tribology of Lubricant,” Industrial Lubrication and Tribology, vol. 62, no. 2, 2010.
View at: Publisher Site | Google Scholar
M. Janardhan and A. G. Krishna, “Multi-objective optimization of cutting parameters for surface roughness and metal removal rate in surface grinding using response surface methodology,” International Journal of Advances in Engineering & Technology, vol. 3, p. 270, 2012.
View at: Google Scholar
T. T. Hong, D. T. Vinh, T. V. Hung et al., “Multi-objective optimization of surface roughness and MRR in surface grinding of hardened SKD11 using grey-based Taguchi method,” International Conference on Engineering Research and Applications, Springer, Berlin, Germany, pp. 584–593, 2020.
View at: Google Scholar
A. K. Sahoo, A. N. Baral, A. K. Rout, and B. Routra, “Multi-objective optimization and predictive modeling of surface roughness and material removal rate in turning using grey relational and regression analysis,” Procedia Engineering, vol. 38, pp. 1606–1627, 2012.
View at: Publisher Site | Google Scholar
S. Raykar, D. D’Addona, and A. Mane, “Multi-objective optimization of high speed turning of Al 7075 using grey rela-tional analysis,” Procedia Cirp, vol. 33, pp. 293–298, 2015.
View at: Publisher Site | Google Scholar
R. Kumar, P. S. Bilga, and S. Singh, “Multi objective optimization using different methods of assigning weights to en-ergy consumption responses, surface roughness and material removal rate during rough turning operation,” Journal of Cleaner Production, vol. 164, pp. 45–57, 2017.
View at: Publisher Site | Google Scholar
I. P. Okokpujie, O. S. Ohunakin, and C. A. Bolu, “Multi-objective optimization of machining factors on surface roughness, material removal rate and cutting force on end-milling using MWCNTs nano-lubricant,” Progress in Additive Manufac-turing, vol. 6, no. 1, pp. 155–178, 2021.
View at: Publisher Site | Google Scholar
A. Kumbhar, R. Bhosale, A. Modi, S. Jadhav, S. Nipanikar, and A. Kulkarni, “Multi-objective optimization of machining parameters in CNC end milling of stainless steel 304,” International Journal of Innovative Research in Science, Engi-neering and Technology, vol. 4, pp. 8419–8426, 2015.
View at: Google Scholar
T.-D. Phan, T. V. Do, T.-L. Pham, and H.-L. Duong, “Optimization of cutting parameters and nanoparticle concentra-tion in hard milling for surface roughness of JIS SKD61 steel using linear regression and Taguchi method,” International Conference on Engineering Research and Applications, Springer, Berlin, Germany, pp. 628–635, 2020.
View at: Publisher Site | Google Scholar
T.-V. Do and T.-D. Phan, “Multi-objective optimization of surface roughness and MRR in milling of hardened SKD 11 steel under nanofluid MQL condition,” International Journal of Mechanical Engineering and Robotics Research, vol. 10, pp. 357–362, 2021.
View at: Publisher Site | Google Scholar
T. Singh, P. Singh, J. Dureja, M. Dogra, H. Singh, and M. S. Bhatti, “A review of near dry machining/minimum quantity lubrication machining of difficult to machine alloys,” International Journal of Machining and Machinability of Materials, vol. 18, no. 3, pp. 213–251, 2016.
View at: Publisher Site | Google Scholar
E. Kilickap, A. Yardimeden, and Y. H. Çelik, “Mathematical modelling and optimization of cutting force, tool wear and surface roughness by using artificial neural network and response surface methodology in milling of Ti-6242S,” Applied Sciences, vol. 7, no. 10, p. 1064, 2017.
View at: Publisher Site | Google Scholar
T.-V. Do and N.-A.-V. Le, “Optimization of surface roughness and cutting force in MQL hard-milling of AISI H13 steel,” Advances in Engineering Research and Application: Proceedings of the International Conference, ICERA 2018, Springer, Berlin, Germany, pp. 448–454, 2019.
View at: Google Scholar
L. Bouzid, S. Boutabba, M. A. Yallese, S. Belhadi, and F. Girardin, “Simultaneous optimization of surface roughness and material removal rate for turning of X20Cr13 stainless steel,” International Journal of Advanced Manufacturing Technology, vol. 74, no. 5-8, pp. 879–891, 2014.
View at: Publisher Site | Google Scholar
S. Dinesh, A. G. Antony, K. Rajaguru, and V. Vijayan, “Investigation and prediction of material removal rate and sur-face roughness in CNC turning of EN24 alloy steel,” Asian Journal of Research in Social Sciences and Humanities, vol. 6, no. 8, pp. 849–863, 2016.
View at: Publisher Site | Google Scholar
A. T. Abbas, D. Y. Pimenov, I. N. Erdakov, T. Mikolajczyk, M. S. Soliman, and M. M. El Rayes, “Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel,” International Journal of Advanced Manufacturing Technology, vol. 105, no. 5-6, pp. 2151–2165, 2019.
View at: Publisher Site | Google Scholar
A. P. Markopoulos, N. E. Karkalos, M. Mia et al., “Sustainability assessment, investigations, and modelling of slot milling characteristics in eco-benign machining of hardened steel,” Metals, vol. 10, no. 12, p. 1650, 2020.
View at: Publisher Site | Google Scholar
E. Salur, M. Kuntoğlu, A. Aslan, and D. Y. Pimenov, “The effects of MQL and dry environments on tool wear, cutting temperature, and power consumption during end milling of AISI 1040 steel,” Metals, vol. 11, no. 11, p. 1674, 2021.
View at: Publisher Site | Google Scholar
B. Sen, M. K. Gupta, M. Mia, D. Y. Pimenov, and T. Mikołajczyk, “Performance assessment of minimum quantity castor-palm oil mixtures in hard-milling operation,” Materials, vol. 14, no. 1, p. 198, 2021.
View at: Publisher Site | Google Scholar
Q.-M. Nguyen, H.-T. Nguyen, N.-A.-V. Le, T.-N. Nguyen, T.-H.-T. Dang, and T. V. Do, “Investigation on the effect of nanofluid-MQL parameters on surface roughness during milling of hardened 9CrSi steel,” International Confer-Ence on Material, Machines and Methods for Sustainable Development, Springer, Berlin, Germany, pp. 311–316, 2020.
View at: Google Scholar
T. V. Do and T.-D. Phan, “An empirical investigation of SIO2 nano concentration under MQL on surface roughness in hard milling of jis SKD61 steel,” Journal of Applied Engineering Science, vol. 18, no. 3, pp. 432–437, 2020.
View at: Publisher Site | Google Scholar
A. Kumar Sharma, A. Kumar Tiwari, A. Rai Dixit, and R. Kumar Singh, “Measurement of machining forces and surface roughness in turning of AISI 304 steel using alumina-MWCNT hybrid nanoparticles enriched cutting fluid,” Measurement, vol. 150, Article ID 107078, 2020.
View at: Publisher Site | Google Scholar
M. Sayuti, A. A. D. Sarhan, T. Tanaka, M. Hamdi, and Y. Saito, “Cutting force reduction and surface quality improvement in machining of aerospace duralumin AL-2017-T4 using carbon onion nanolubrication system,” International Journal of Advanced Manufacturing Technology, vol. 65, no. 9-12, pp. 1493–1500, 2013.
View at: Publisher Site | Google Scholar
H. Hegab, U. Umer, I. Deiab, and H. Kishawy, “Performance evaluation of Ti–6Al–4V machining using nano-cutting fluids under minimum quantity lubrication,” International Journal of Advanced Manufacturing Technology, vol. 95, no. 9-12, pp. 4229–4241, 2018.
View at: Publisher Site | Google Scholar
A. Raina and A. Anand, “Influence of surface roughness and nanoparticles concentration on the friction and wear char-acteristics of PAO base oil,” Materials Research Express, vol. 5, no. 9, Article ID 095018, 2018.
View at: Publisher Site | Google Scholar
R. K. Singh, A. K. Sharma, A. R. Dixit, A. Mandal, and A. K. Tiwari, “Experimental investigation of thermal conductivity and specific heat of nanoparticles mixed cutting fluids,” Materials Today Proceedings, vol. 4, no. 8, pp. 8587–8596, 2017.
View at: Publisher Site | Google Scholar
R. K. Singh, A. R. Dixit, A. Mandal, and A. K. Sharma, “Emerging application of nanoparticle-enriched cutting fluid in metal removal processes: a review,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 39, no. 11, pp. 4677–4717, 2017.
View at: Publisher Site | Google Scholar
H.-T. Nguyen and Q.-C. Hsu, “Surface roughness analysis in the hard milling of JIS SKD61 alloy steel,” Applied Sciences, vol. 6, p. 172, 2016.
View at: Publisher Site | Google Scholar
T.-V. Do and Q.-C. Hsu, “Optimization of minimum quantity lubricant conditions and cutting parameters in hard mill-ing of AISI H13 steel,” Applied Sciences, vol. 6, no. 3, p. 83, 2016.
View at: Publisher Site | Google Scholar
O. Çolak, C. Kurbanoğlu, and M. C. Kayacan, “Milling surface roughness prediction using evolutionary programming methods,” Materials & Design, vol. 28, no. 2, pp. 657–666, 2007.
View at: Publisher Site | Google Scholar
J. S. Dureja, V. K. Gupta, V. S. Sharma, and M. Dogra, “Design optimization of cutting conditions and analysis of their effect on tool wear and surface roughness during hard turning of AISI-H11 steel with a coated—mixed ceramic tool,” Proceedings of the Institution of Mechanical Engineers - Part B: Journal of Engineering Manufacture, vol. 223, no. 11, pp. 1441–1453, 2009.
View at: Publisher Site | Google Scholar
H. Aouici, H. Bouchelaghem, M. A. Yallese, M. Elbah, and B. Fnides, “Machinability investigation in hard turning of AISI D3 cold work steel with ceramic tool using response surface methodology,” International Journal of Advanced Manufacturing Technology, vol. 73, no. 9-12, pp. 1775–1788, 2014.
View at: Publisher Site | Google Scholar
G. D. Revankar, R. Shetty, S. S. Rao, and V. N. Gaitonde, “Analysis of surface roughness and hardness in titanium alloy machining with polycrystalline diamond tool under different lubricating modes,” Materials Research, vol. 17, no. 4, pp. 1010–1022, 2014.
View at: Publisher Site | Google Scholar
J. Pérez, L. López de lacalle, J. Llorente, and J. Sanchez, “Advanced cutting conditions for the milling of aeronautical alloys,” Journal of Materials Processing Technology, vol. 100, no. 1-3, pp. 1–11, 2000.
View at: Publisher Site | Google Scholar
J. P. Colafemina, R. G. Jasinevicius, and J. G. Duduch, “Surface integrity of ultra-precision diamond turned Ti (commercially pure) and Ti alloy (Ti-6Al-4V),” Proceedings of the Institution of Mechanical Engineers - Part B: Journal of Engineering Manufacture, vol. 221, no. 6, pp. 999–1006, 2007.
View at: Publisher Site | Google Scholar
N. Karkalos, N. Galanis, and A. Markopoulos, “Surface roughness prediction for the milling of Ti–6Al–4V ELI alloy with the use of statistical and soft computing techniques,” Measurement, vol. 90, pp. 25–35, 2016.
View at: Publisher Site | Google Scholar

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Copyright © 2022 Quoc-Manh Nguyen and The-Vinh Do. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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