Sage Journals: Discover world-class research

Abstract

The high cutting heat and cutting forces are still the big obstacles in hard machining technology, which puts more pressure to find out the alternative solution for these problems. The work content presents an experimental study on the effect of nanoparticle concentration and cutting parameters on surface roughness and cutting force during hard milling under MQL environment using Al₂O₃ nano-cutting oil. The Box-Behnken experimental designs for response surface methodology was used to evaluate the influence of the input parameters and determine the optimal values. The obtained results show that the nanoparticle concentration, cutting speed, and feed rate all have the great influences on surface roughness R_z and resultant cutting force F, so the study of the influence of these parameters on the efficiency of the hard milling process is very significant. The proposed reasonable value ranges will help technicians quickly choose to meet their demands for specific objective functions. Furthermore, the optimal parameter set of nanoparticle concentration of 1.27 wt%, cutting speed V_c = 103 m/min, and feed rate $f$ = 0.09 mm/tooth was determined. It revealed that the use of MQL with nano-cutting oils contributed to improve the lubrication and cooling performance in the cutting zone.

Keywords

Hard milling cutting parameter cutting force surface roughness nanofluid Minimal Quantity Lubrication

Introduction

Recently, the demand for finishing hardened materials along with the requirements for productivity as well as ensuring environmental friendliness has been increasing to put more pressure on finding out the novel technological solutions.¹ The traditional method for finishing heat-treated steels is the grinding process. Low productivity and the problem of environmental pollution from the use of coolant are still inherent disadvantages, so hard machining technology was researched and developed in the 1980s.² The use of cutting tools with geometric defined cutting edge for directly cutting heat-treated steels with high hardness has been proven to bring out the higher flexibility and productivity while ensuring the same/higher accuracy compared to grinding. In particular, hard milling has recently attracted the special attention to researchers and manufacturers all over the world, especially in the field of mold industry.² However, the enormous amount of cutting heat and large cutting forces are still the huge challenge in hard milling technology, as these factors accelerate tool wear adversely affecting machined surface quality and tool life. The phenomenon of rapid wear and low tool life has been recorded in dry cutting of AISI O2 cold work tool steel (∼58 HRC).³ Besides, the use of flood coolant technology in hard milling faces the difficulty due to thermal shock phenomenon.^1,4 Therefore, reducing friction and cutting heat is the main strategy to improve the machining efficiency of hard milling. Some of the proposed technological solutions to improve lubrication and cooling in the cutting zone are Minimum Quantity Lubrication (MQL),^5,6 Minimum Quantity Cooling Lubrication (MQCL),^7–9 nanofluid MQL (NF MQL),^10–12 etc. In particular, MQL using nano-cutting oil has attracted much attention and the obtained research results are very promising. MQL technology delivers a minimal amount of cutting oil in the mist form with high pressure directly into the cutting zone, and therefore the high lubrication efficiency is achieved. However, this technology is initially shown to be effective in processing materials with low hardness such as aluminum alloys, copper, and alloy steels that have not been heat-treated.¹³ The application of MQL technology in hard machining processes, especially hard milling, has not yielded the expected results. To further improve the efficiency, the recent research direction is to use nano cutting oil to improve the lubricating and cooling ability of the base cutting oil, thereby helping to enlarge the MQL applicability for hard milling.¹⁴Şirin and Kıvak concluded that the improvement in thermal conductivity and lubricity by using nano cutting oil contributes to the increase in milling performance of Inconel X-750 superalloy.¹⁵ On the other hand, the grain size, type, and concentration of nanoparticles as well as the based fluid types strongly influence the cooling and lubricating of nano cutting oil.¹⁶ The better thermal conductivity and the reduction of friction coefficient were reported in Al₂O₃ nanofluid MQL hard turning of AISI 1045 steel.¹¹ Uysal et al.¹⁰ showed that the longer tool life and better surface roughness were achieved due to the lubricity enhancement of MoS₂ nano cutting oil in MQL milling of AISI 420 stainless steel. Moreover, the authors pointed out that nanoparticle concentration has a significant effect on cutting performance and this parameter is needed to be studied and optimized. Sharma et al.¹⁷ indicated the four main mechanisms (rolling, sliding, polishing, and filming) of nanoparticles in cutting zone, but each type of nanoparticles has own characteristics in morphology, size, hardness, and so forth, which leads to the difference in cooling and lubricating effects. Maruda et al.¹⁸ made an extensive study on the effects of the copper nanoparticle sizes (22, 35, 65, 80 nm) and concentrations (0.25%, 0.5%, 0.75%, and 1.0%) on MQL turning of 316L stainless steel. The authors concluded that the smaller nanoparticle size is more favorable for better machined surface topography. Also, the copper nano concentration of 0.5% exhibited the better results in terms or surface quality, power consumption.¹⁹ In addition, the increase of copper nanoparticle concentration from 0.5% to 0.8% can lead to the agglomerations. As a result, friction coefficient, cutting force, and energy consumption go up and negatively affect the machining output. Among the most common used nanoparticles, Al₂O₃ nanoparticles possess the attractive characteristics of high harness, nearly spherical morphology, and good thermal conductivity.¹¹ Hence, when they penetrate into the cutting zone, they will create the rolling mechanism to reduce the friction coefficient significantly, so the grinding performance was improved.²⁰ On the other hand, the good resistance to high temperature of Al₂O₃ nanoparticles makes them suitable to the application generating high temperature like hard machining.²¹ Also, the presence of Al₂O₃ nanoparticles in the vegetable oils not only enhances the high heat resistance²² but also improves the cooling and lubricating effects of the based oil as well as enlarges the applicability of vegetable oil, an eco-friendly biodegradable oil.^23,24 Eltaggaz et al.²⁵ found that the better turning performance was achieved under MQL using Al₂O₃ nano cutting oil than the dry and wet conditions. Hegab et al.²⁶ found that the concentration of Al₂O₃ nanoparticles and multi-walled carbon nanotubes (MWCNTs) in MQL environment is closely related to the machining output, but this factor is a complicated function needed to be investigated and optimized for specific parameters and objectives.²⁷ Günan et al.¹³ evaluated the Al₂O₃ nanofluid MQL milling process of Hastelloy C276 alloy and pointed out that the increase in the Al₂O₃ concentrations contributes to reduce the cutting forces, but the higher value of nanoconcentration tends to cause the impedance phenomena, leading to increase the surface roughness values. From the literature review, it was noticeable that some initial research results on the application of NF MQL technology using vegetable oil for hard milling have shown positive results. However, this is a new research direction, so the publications are limited, so the authors are motivated to make an experimental study on the influence and optimization of the cutting parameters in hard milling of 60Si2Mn steel (50–52 HRC) under MQL environment using Al₂O₃ nano-cutting oil based on soybean oil. In the previous study,⁴ the authors investigated the efficiency of nano-cutting oil based on soybean oil and emulsion oil in hard milling of 60Si2Mn steel. The results show that MQL using nano-cutting oil gives better results in terms of cutting forces and surface roughness compared to cutting oil without nanoparticles. The improved lubrication efficiency by using nano cutting oils helps to reduce cutting forces, tool wear, and improves the machinability of carbide inserts. After that, the authors conducted a further study on the effects of flow rate and air flow pressure of MQL using nano-cutting oil on cutting forces, surface integrity, and determined the optimal values,^14,28 which are used for this study. The obtained results presented in this work aim to evaluate the effects of Al₂O₃ nanoparticle concentration, cutting speed, and feed rate on the surface roughness R_z and total cutting force, from which the appropriate value ranges and the optimal set can be determined for further studies and production practice.

Material and method

The diagram of the experimental set-up and measurements is shown in Figure 1. The experimental trials were conducted on a VMC 85S machining center. The BAP 400R-80-27-6T face mill head was used with APMT 1604 PDTR LT30 PVD submicron carbide inserts made by LAMINA Technologies (Switzerland). The technical specification of milling inserts consists of TiAlN coated material, lead angle of 90°, flank angle of 11°, and nose radius of 0.66 mm. The MQL system includes: NOGA MiniCool MC1700 combined with the air pressure regulator, and air flow rate valve, PT-0136 compressed air, soybean oil, and Al₂O₃ nanoparticles (30 nm).²⁸ The external MQL nozzle was chosen due to the ease to apply in practice and the modification of the machine tool is not required. It was mounted to directly spray on the flank face of cutting tool with spray angle of 12° and nozzle distance of 55 mm.²⁹ Ultrasons-HD (JP SELECTA in Spain) is used to create ultrasonic vibration in at least 50 min in advance in order to ensure the homogeneity of Al₂O₃ nanoparticles in the soybean oil.²⁸ Then, the obtained nano cutting oils were directly used for MQL system to avoid the precipitation of agglomerated nanoparticles during the long machining time. Kistler quartz three-component dynamometer (9257BA) and SJ-210 Mitutoyo (made by Japan) were used for measuring cutting forces and surface roughness (cut-off length of 0.08 mm). The 60Si2Mn steel samples were austenitized at 850°C, then tempered at 400°C, and finally were oil quenched to the room temperature to reach the hardness of 50–52 HRC. The chemical composition of steel samples is shown in Table 1.

Figure 1.

Experimental set-up and measurements.

Table 1.

Chemical composition of 60Si2Mn steel (wt%) according to JIS standard.

C	Si	Mn	P	S	Cr	Ni	Fe
0.56–0.64	1.50–2.00	0.60–0.90	≤0.035	≤0.035	0.35 max	0.35 max	Rest

Box-Behnken optimal experimental planning design with three input variables was used to study the influence of nanoparticle concentration (NC), cutting speed (V_c), and feed rate ( $f$ ) on the resultant cutting force F and surface roughness R_z (Table 2). The fixed parameters include the depth of cut a_p = 0.2 mm, air pressure p = 6 bar, and air flow rate Q = 200 l/min. The air flow rate and air pressure have the direct relationship with the flow rate, the oil droplet sizes, and the penetrability of cutting oil into contact zone. For the high air pressure and air flow rate, the oil particle formation is better and the droplet size is smaller, so the penetration into the cutting zone is more favorable.³⁰ Also, the values of air pressure and air flow rate were based on the previous work.¹⁴

Table 2.

Design of experiment.

No.	Input parameters	Symbol	Low level	High level	Responses
1	Nanoparticle concentration (wt%)	NC	0.5	1.5	- Resultant cutting force $F$ - Surface roughness R_z
2	Cutting speed (m/min)	V _c	90	130
3	Feed rate (mm/tooth)	$f$	0.08	0.16

The experimental matrix was built with the help of Minitab 19 software. The experimental trails were conducted by following RunOrder. Three repetitions were performed for each test. Three repetitions were performed for each cutting trial, and the average values were taken. The cutting tool was changed to the new one after each cutting trial. The values of R_z were measured three times by SJ-210 portable surface roughness tester and the average value was taken after each run. The feed force F_a, radial force F_r, and the tangential force F_t were measured directly during cutting process by Kistler quartz three-component dynamometer (9257BA). The resultant cutting force F is calculated by equation (1).

F = \sqrt{F_{a}^{2} + F_{r}^{2} + F_{t}^{2}}

(1)

Results and discussion

The ANOVA analysis with the confidence level of 95% (i.e. 5% significance level) was carried out. The regression models of surface roughness R_z and resultant cutting force F are given by the equations (2) and (3), respectively. The fit of the regression models were evaluated through the coefficients of determination R², which equal to 85.95% and 78.02% respectively, so the obtained regression models are in agreement with the experimental data.

\begin{matrix} R_{z} = 7.05 + 1.73 * NC - 0.1177 * V_{c} - 9.6 * f \\ - 1.057 * NC * NC + 0.000556 * V_{c} * V_{c} \\ + 106.4 * f * f + \\ 0.00392 * NC * V_{c} + 0.16 * NC * f - 0.095 * V_{c} * f \end{matrix}

(2)

\begin{matrix} F = 2219 - 1385 * NC - 18.6 * V_{c} - 78 * f + 498 * \\ NC * NC + 0.0998 * V_{c} * V_{c} + 15126 * f * f + \\ 2.15 * NC * V_{c} + 435 * NC * f - 32.7 * V_{c} * f \end{matrix}

(3)

Effects of investigated variables on the objective functions

The main effects of the investigated variables shown in Figures 2 and 3 shows that they all have the significant influences on R_z and F, in which, the biggest influence is still the nanoparticle concentration causes the strongest effect, followed by the cutting speed, and then the feed rate. For the increase of nanoparticle concentration from 0.5 wt% to about 1.1 wt%, the roughness value R_z increases but the resultant cutting force F decreases. The reason lying in here is that the density of Al₂O₃ nanoparticles in the cutting area increases in combination with the fresh cutting edge, so the so-called “appropriate wear land” on flank face does not exist,⁴ thus increasing the phenomenon of compression in the cutting area and causing scratches on the machined surface. Hence, the surface roughness tends to increase. In contrast, increasing the nanoparticle concentration in the cutting zone helps to reduce the friction coefficient, thereby reducing the cutting force. However, for increasing the particle concentration from about 1.2 wt% to 1.5 wt%, the cutting force increased due to the phenomenon of nanoparticle compression and collision in the cutting zone, causing the lubrication process to be intermittent.^13,17,25 When the cutting speed is increased from 90 to 110 m/min for R_z and 90 to 103 m/min for F, the values of R_z and F decrease due to thermal soften phenomena²; however, when increasing V_c to 130 m/min, both R_z and F values increase.² As the feed rate increases, R_z and F values go up, especially R_z due to the increase in cross-section of the cutting layer.¹³

Figure 2.

Main effect of investigated variables on surface roughness R_z.

Figure 3.

Main effect of investigated variables on resultant cutting force F.

The interaction effects shown in Figures 4 and 5 exhibit that the interaction between nanoparticle concentration and cutting speed (NC × V_c) is the largest influence, while other interactions have little impact. The main reason is that the influence of the nanoparticle concentration and the cutting speed on the interaction among the nanoparticles in the cutting zone. The obtained results show that Al₂O₃ nano cutting oil greatly influences the cutting performance when changing the cutting parameters. Thus, the study of the effect of nano-cutting oil is significant. Since the concentration of nanoparticles (NC), the cutting speed (V_c), and their interaction (NC × V_c) are the main influencing factors on the objective functions. This result has scientific and practical significance in that to improve cutting conditions, it should be focused on selecting and adjusting nanoparticle concentration (NC) and cutting speed (V_c), while the feed rate has little influence. Hence, in order to ensure both productivity and machined quality, it is possible to choose the largest possible feed rate $f$ .

Figure 4.

Interaction effect of among investigated variables on R_z.

Figure 5.

Interaction effect of among investigated variables on F.

The surface and contour plots in Figure 6 show the influence of the nanoparticle concentration and cutting speed on the objective functions R_z and F when the feed rate $f$ is fixed at 0.12 mm/tooth. Figure 6(a) shows that the optimal R_z value domain is the region with R_z < 0.8 µm when the nanoparticle concentration is from 0.50 to 0.55 wt% and the cutting speed V_c = 100–130 m/min. Figure 6(b) shows that the domain for the smaller F value is the region with F < 550 N when using NC = 1.00–1.25 wt% and V_c = 92–110 m/min.

Figure 6.

Effect of nanoparticle concentration and cutting speed on: (a) R_z and (b) F at $f$ = 0.12 mm/tooth.

The surface and contour plots in Figure 7 show the influence of nanoparticle concentration and feed rate on R_z and F in case of fixed cutting speed at 110 m/min. Figure 7(a) shows that the optimal R_z value domain (R_z < 0.8 µm) can be achieved with NC = 0.50–0.70 wt% and $f$ =0.08–0.13 mm/tooth. From Figure 7(b), NC = 1.00–1.25 wt% and $f$ = 0.09–0.12 mm/tooth should be chosen for smaller F (F < 550 N).

Figure 7.

Effect of nanoparticle concentration and feed rate on: (a) R_z and (b) F at V_c = 110 m/min.

The surface and contour plots in Figure 8 exhibit the influence of nanoparticle concentration and feed rate on R_z and F when NC is fixed at 1.0 wt%. Figure 8(a) shows that in order to achieve the optimal R_z value domain (R_z < 1.2 µm), V_c = 92–128 m/min and $f$ = 0.08–0.13 mm/tooth should be recommended. From Figure 8(b), for smaller F value (F < 550 N), it should be chosen with V_c = 90–110 m/min and $f$ = 0.08–0.12 mm/tooth.

Figure 8.

Effect of cutting speed and feed rate on: (a) R_z and (b) F at NC = 1.0 wt%.

The contour graphs (Figures 6 –8) help technicians quickly choose the investigated parameters in the optimal domain depending on the objective functions. Specifically, for smaller surface roughness R_z, the nanoparticle concentration NC = 0.5–0.55 wt%, cutting speed V_c = 92–128 m/min, feed rate $f$ = 0.08–0.13 mm/tooth should be chosen. For smaller resultant cutting force F, the nanoparticle concentration NC = 1.0–1.2 wt%, cutting speed V_c = 90–110 m/min; feed rate $f$ = 0.08–0.12 mm/tooth should be selected. In order to achieve the smaller values of both surface roughness R_z and the resultant cutting force F, the cutting speed can be selected in the range of 100–110 m/min. As a result, the cutting speed of the carbide insert has been improved from V_c = 50–55 m/min as recommended by the manufacturer to V_c = 100–110 m/min (an increase of about 200%).⁴ However, if the exact optimal values of the nanoparticle concentration, cutting speed, and feed rate, the more specific calculation will be needed and the obtained results are presented in the next section.

Optimization of the nanoparticle concentration, cutting speed, and feed rate

Based on the specific conditions and requirements of the machining process, it is possible to choose the objective function and the appropriate optimal criteria. If both objective functions of R_z and F are taken into account, the multi-objective optimization will be used with the same importance and weight. The result of multi-objective optimization is shown in Figure 9 and the optimal set of NC = 1.2677 wt%, V_c = 103.3333 m/min, and $f$ = 0.0921 mm/tooth. Hence, NC = 1.27 wt%, V_c = 103 m/min, and $f$ = 0.09 mm/tooth should be recommended for the optimal set of investigated variables. The obtained optimized results indicate the optimal Al₂O₃ nanoparticle concentration at 1.27 wt%, completely consistent with the experimental studies.^13,23 The increase of Al₂O₃ nanoparticle concentration from 0.5% to 1.0 wt% contributes to improve the efficiency of the lubrication process, thereby contribute to reduce the surface roughness and cutting forces. However, if it continues to rise to 1.5 wt%, the surface roughness values and cutting forces tend to increase,¹³ so the accurate determination of the optimal value has the important scientific and practical significance for further studies. The reasons are that the concentration of Al₂O₃ nanoparticles in cutting oil has a great influence on the machining output and the using cost but is a complex function. Furthermore, the specified optimal cutting speed and feed rate will add more technological guides for application in production practice. The cutting speed increased from 50 to 55 m/min as recommended by the manufacturer to 103 m/min has proven the improvement in lubrication and cooling efficiency in the cutting zone by using Al₂O₃ nano cutting oil.

Figure 9.

Multi-objective optimization of nanoparticle concentration, cutting speed, and feed rate.

Conclusion

The work has successfully applied the vegetable oil with nanoparticles as the base cutting oil for the MQL technology used for hard milling process. The improvement of lubricating and cooling capabilities was reported and the applicability of vegetable oils for hard milling has been enlarged. Besides, the lubricating and cooling efficiency of MQL method has been significantly improved, thereby improving the machinability of coated carbide inserts and the hard milling efficiency.

The influences of input parameters including nanoparticle concentration, cutting speed, and feed rate on the surface roughness R_z and resultant cutting force F were evaluated and the optimal values were also determined for the different optimization objectives and the specific technology conditions by ANOVA and Response surface methodology (RSM) analysis. These results also provide important technological guidelines for further studies and production practice.

When $f$ is fixed at 0.12 mm/tooth, NC = 0.50–0.55 wt% and V_c = 100–130 m/min for the optimal R_z value domain (R_z < 0.8 µm) or NC = 1.00–1.25 wt% and V_c = 92–110 m/min for the optimal F value domain (F < 550 N).

When V_c is fixed at 110 m/min, the optimal R_z value domain (R_z < 0.8 µm) can be achieved with NC = 0.50–0.70 wt% and $f$ = 0.08–0.13 mm/tooth. At the same time, NC = 1.00–1.25 wt% and $f$ = 0.09–0.12 mm/tooth should be chosen for the optimal F value domain (F < 550 N).

In case of NC fixed at 1.0 wt%. The optimal R_z value domain (R_z < 1.2 µm) can be achieved by using V_c = 92–128 m/min and $f$ = 0.08–0.13 mm/tooth. For the optimal F value domain (F < 550 N), it should be chosen with V_c = 90–110 m/min and $f$ = 0.08–0.12 mm/tooth.

From the obtained results, in order to achieve the smaller surface roughness R_z, NC = 0.5–0.55 wt%, V_c = 92–128 m/min, $f$ = 0.08–0.13 mm/tooth should be chosen. For smaller total cutting force F values, the NC = 1.0–1.2 wt%, V_c = 90–110 m/min; $f$ = 0.08–0.12 mm/tooth should be selected. The result of multi-optimization indicates that NC = 1.27 wt%, V_c = 103 m/min, and $f$ = 0.09 mm/tooth are the optimal set of investigated variables.

In further study, more in-depth assessment of the influence of Al₂O₃ nano-cutting oils on the cutting mechanism, wear mechanism, and machined surface integrity.

Footnotes

Appendix

Handling Editor: Sharmili Pandian

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work presented in this paper is supported by Thai Nguyen University of Technology, Thai Nguyen University, Vietnam.

ORCID iD

Tran The Long

References

Sharma

Kalsia

Uppal

, et al. Machining of hard and brittle materials: a comprehensive review. Mater Today Proc 2021; 50: 1048–1052.

Paulo Davim

. Machining of hard materials. London: Springer-Verlag London Limited, 2011.

Çalışkan

Kurbanoğlu

Panjan

, et al. Wear behavior and cutting performance of nanostructured hard coatings on cemented carbide cutting tools in hard milling. Tribol Int 2013; 62: 215–222.

Minh

The

Bao

. Performance of Al₂O₃ nanofluids in minimum quantity lubrication in hard milling of 60Si₂Mn steel using cemented carbide tools. Adv Mech Eng 2017; 9: 1687814017710618.

Rahim

Dorairaju

. Evaluation of mist flow characteristic and performance in Minimum Quantity Lubrication (MQL) machining. Measurement 2018; 123: 213–225.

Sen

Gupta

Mia

, et al. Performance assessment of minimum quantity castor-palm oil mixtures in hard-milling operation. Materials 2021; 14: 198.

Pervaiz

Deiab

Rashid

, et al. Minimal quantity cooling lubrication in turning of Ti6Al4V: influence on surface roughness, cutting force and tool wear. Proc IMechE, Part B: J Engineering Manufacture 2017; 231: 1542–1558.

Maruda

Krolczyk

Feldshtein

, et al. Tool wear characterizations in finish turning of AISI 1045 carbon steel for MQCL conditions. Wear 2017; 372: 54–67.

Liu

Jiang

, et al. Study on vibration and surface roughness in MQCL turning of stainless steel. J Manuf Processes 2021; 65: 343–353.

10.

Uysal

Demiren

Altan

. Applying Minimum Quantity Lubrication (MQL) method on milling of martensitic stainless steel by using nano MoS₂ reinforced vegetable cutting fluid. Procedia Soc Behav Sci 2015; 195: 2742–2747.

11.

Abbas

Gupta

Soliman

, et al. Sustainability assessment associated with surface roughness and power consumption characteristics in nanofluid MQL-assisted turning of AISI 1045 steel. Int J Adv Manuf Technol 2019; 105: 1311–1327.

12.

Garg

Sarma

Panda

, et al. Study of effect of nanofluid concentration on response characteristics of machining process for cleaner production. J Clean Prod 2016; 135: 476–489.

13.

Günan

Kıvak

Yıldırım

ÇV

, et al. Performance evaluation of MQL with Al₂O₃ mixed nanofluids prepared at different concentrations in milling of Hastelloy C276 alloy. J Mater Res Technol 2020; 9: 10386–10400.

14.

Duc

Long

Tuan

. Performance investigation of MQL parameters using nano cutting fluids in hard milling. Fluids 2021; 6: 248.

15.

Şirin

Kıvak

. Effects of hybrid nanofluids on machining performance in MQL-milling of Inconel X-750 superalloy. J Manuf Processes 2021; 70: 163–176.

16.

Pryazhnikov

Minakov

Rudyak

, et al. Thermal conductivity measurements of nanofluids. Int J Heat Mass Transf 2017; 104: 1275–1282.

17.

Sharma

Tiwari

Dixit

. Mechanism of nanoparticles functioning and effects in machining processes: a review. Mater Today Proc 2015; 2: 3539–3544.

18.

Maruda

Arkusz

Szczotkarz

, et al. Analysis of size and concentration of nanoparticles contained in cutting fluid during turning of 316L steel in minimum quantity lubrication conditions. J Manuf Processes 2023; 87: 106–122.

19.

Maruda

Szczotkarz

Leksycki

, et al. Analysis of power grid parameters depending on the variable concentration and size of copper nanoparticles and aerosol formation parameters in the minimum quantity lubrication method during turning of Ti6Al4V titanium alloy. Adv Sci Technol Res J 2023; 17: 315–324.

20.

Wang

Zhang

, et al. Experimental evaluation of the lubrication properties of the wheel/workpiece interface in MQL grinding with different nanofluids. Tribol Int 2016; 99: 198–210.

21.

Luo

Wei

Huang

, et al. Tribological properties of Al₂O₃ nanoparticles as lubricating oil additives. Ceram Int 2014; 40: 7143–7149.

22.

Duc

Long

Ngoc

. Effectiveness of alumina nanofluid on slotting end milling performance of SKD 11 tool steel. J Comput Appl Res Mech Eng 2020; 9: 359–369.

23.

Duc

Long

Dong

. Effect of the alumina nanofluid concentration on minimum quantity lubrication hard machining for sustainable production. Proc IMechE, Part C: J Mechanical Engineering Science 2019; 233: 5977–5988.

24.

Hegab

Kishawy

Umer

, et al. A model for machining with nano-additives based minimum quantity lubrication. Int J Adv Manuf Technol 2019; 102: 2013–2028.

25.

Eltaggaz

Zawada

Hegab

, et al. Coolant strategy influence on tool life and surface roughness when machining ADI. Int J Adv Manuf Technol 2018; 94: 3875–3887.

26.

Hegab

Umer

Soliman

, et al. Effects of nano-cutting fluids on tool performance and chip morphology during machining Inconel 718. Int J Adv Manuf Technol 2018; 96: 3449–3458.

27.

Rahmati

Sarhan

AAD

Sayuti

. Morphology of surface generated by end milling AL6061-T6 using molybdenum disulfide (MoS₂) nanolubrication in end milling machining. J Clean Prod 2014; 66: 685–691.

28.

Duc

Tuan

Long

, et al. Machining feasibility and sustainability study associated with air pressure, air flow rate, and nanoparticle concentration in nanofluid MQL-assisted hard milling process of 60Si2Mn steel. Proc IMechE, Part C: J Mech Eng Sci 2022; 236: 11256–11269.

29.

Park

Olortegui-Yume

Joshi

, et al. Measurement of droplet size and distribution for minimum quantity lubrication (MQL). In: 2008 international conference on smart manufacturing application, Goyangi, South Korea, 9–11 April 2008, pp.447–454. New York: IEEE.

30.

Jadhav

Deivanathan

. Numerical analysis of the effect of air pressure and oil flow rate on droplet size and tool temperature in MQL machining. Mater Today Proc 2021; 38: 2499–2505.

Optimization of Al 2 O 3 nanoparticle concentration and cutting parameters in hard milling under nanofluid MQL environment

Abstract

Keywords

Introduction

Material and method

Results and discussion

Effects of investigated variables on the objective functions

Optimization of the nanoparticle concentration, cutting speed, and feed rate

Conclusion

Footnotes

Appendix

Declaration of conflicting interests

Funding

ORCID iD

References