Skip to main content
SpringerLink
Log in

Optimization of MQL turning process considering the distribution and control of cutting fluid mist particles

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper presents the effect of cutting parameters on the mist particle diameters under the minimum quantity lubrication (MQL) for reducing the air pollution and harm to operators caused by the mist particles produced during the MQL process. In this study, turning experiments were conducted on AISI 304 stainless steel using MQL, for which 16 groups of Taguchi experiments were utilized. The results show that by using the MQL, the contribution rates of the cutting speed and air pressure to the mist particle diameters were 54.62% and 25.34%, respectively. The increase in air pressure caused an increase in the overall proportion of mist particles with diameters in the range of 2.5~10 μm, from 88 up to 96%. The increase in cutting speed aided the conversion of mist particles with a diameter in the range of 2.5~10 μm to a diameter of 2.5 μm, about 16 to 27% of the 2.5~10 μm. However, the overall proportion of the mist particles was not changed. Finally, by targeting the cutting efficiency, surface roughness, and deposition of the cutting fluid mist, the four parameters were optimized using a multi-objective optimization algorithm. Compared to the 14th group experiment with a similar cutting efficiency, the verification experiment results show that the surface roughness decreased 16% and the deposition of cutting fluid mist increased 173% by using the optimized parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The original data has been included as supplementary materials.

Code availability

Not applicable.

References

  1. Demirbas E, Kobya M (2017) Operating cost and treatment of metalworking fluid wastewater by chemical coagulation and electrocoagulation processes. Process Saf Environ Prot 105:79–90. https://doi.org/10.1016/j.psep.2016.10.013

    Article  Google Scholar 

  2. Kiss A, Fries E (2009) Occurrence of benzotriazoles in the rivers Main, Hengstbach, and Hegbach (Germany). Environ Sci Pollut Res 16(6):702–710. https://doi.org/10.1007/s11356-009-0179-4

    Article  Google Scholar 

  3. Weinert K, Inasaki I, Sutherland JW, Wakabayashi T (2004) Dry machining and minimum quantity lubrication. CIRP Ann Manuf Technol 53(2):511–537. https://doi.org/10.1016/s0007-8506(07)60027-4

    Article  Google Scholar 

  4. Sen B, Mia M, Krolczyk GM, Mandal UK, Mondal SP (2021) Eco-friendly cutting fluids in minimum quantity lubrication assisted machining: a review on the perception of sustainable manufacturing. INT J PR ENG MAN-GT 8(1):249–280. https://doi.org/10.1007/s40684-019-00158-6

  5. Dhar NR, Ahmed MT, Islam S (2007) An experimental investigation on effect of minimum quantity lubrication in machining AISI 1040 steel. Int J Mach Tool Manu 47(5):748–753. https://doi.org/10.1016/j.ijmachtools.2006.09.017

    Article  Google Scholar 

  6. Rahman M, Kumar AS, Salam MU (2002) Experimental evaluation on the effect of minimal quantities of lubricant in milling. Int J Mach Tool Manu 42(5):539–547. https://doi.org/10.1016/s0890-6955(01)00160-2

    Article  Google Scholar 

  7. Ozbek O, Saruhan H (2020) The effect of vibration and cutting zone temperature on surface roughness and tool wear in eco-friendly MQL turning of AISI D2. J Mater Res Technol -Jmr&T 9(3):2762–2772. https://doi.org/10.1016/j.jmrt.2020.01.010

    Article  Google Scholar 

  8. Maruda RW, Legutko S, Krolczyk GM, Raos P (2015) Influence of cooling conditions on the machining process under MQCL and MQL conditions. Tehnicki Vjesnik-Tech Gaz 22(4):965–970. https://doi.org/10.17559/tv-20140919143415

    Article  Google Scholar 

  9. Shah P, Khanna N, Zadafiya K, Bhalodiya M, Maruda RW, Krolczyk GM (2020) In-house development of eco-friendly lubrication techniques (EMQL, nanoparticles plus EMQL and EL) for improving machining performance of 15-5 PHSS. Tribol Int 151. https://doi.org/10.1016/j.triboint.2020.106476

  10. Javidikia M, Sadeghifar M, Songmene V, Jahazi M (2020) Effect of turning environments and parameters on surface integrity of AA6061-T6: experimental analysis, predictive modeling, and multi-criteria optimization. Int J Adv Manuf Technol 110(9-10):2669–2683. https://doi.org/10.1007/s00170-020-06027-w

    Article  Google Scholar 

  11. Ozgoren YO, Cetinkaya S, Saridemir S, Cicek A, Kara F (2013) Artificial neural network based modelling of performance of a beta-type Stirling engine. P I MECH ENG E-J PRO 227(3):166–177. https://doi.org/10.1177/0954408912455763

  12. Eser A, Ayyildiz EA, Ayyildiz M, Kara F (2021, 2021) Artificial intelligence-based surface roughness estimation modelling for milling of AA6061 alloy. Adv Mater Sci Eng. https://doi.org/10.1155/2021/5576600

  13. Ayyildiz EA, Ayyildiz M, Kara F (2021) Optimization of surface roughness in drilling medium-density fiberboard with a parallel robot. Adv Mater Sci Eng 2021:1–8. https://doi.org/10.1155/2021/6658968

    Article  Google Scholar 

  14. Shoshkes M, Banfield WG Jr, Rosenbaum SJ (1950) Distribution, effect and fate of oil aerosol particles retained in the lungs of mice. Arch Ind Hyg Occup Med 1(1):20–35

    Google Scholar 

  15. Thornburg J, Leith D (2000) Size distribution of mist generated during metal machining. Appl Occup Environ Hyg 15(8):618–628

    Article  Google Scholar 

  16. Park K-H, Olortegui-Yume J, Yoon M-C, Kwon P (2010) A study on droplets and their distribution for minimum quantity lubrication (MQL). Int J Mach Tool Manu 50(9):824–833. https://doi.org/10.1016/j.ijmachtools.2010.05.001

    Article  Google Scholar 

  17. Zhao W, He N, Li L, Yang Y, Shi Q (2014) Investigation on the influence of system parameters on ambient air quality in minimum quantity lubrication milling process. J Mech Eng 50(13):184–189

    Article  Google Scholar 

  18. Zhao W, Chen C, He N, Li L, Ren F, Liang X (2015) Effect of milling speed on oil mist concentration in minimum quantity lubrication. Journal of Nanjing University of Aeronautics and Astronautics 47(3):440–445

    Google Scholar 

  19. Lu T, Huang S, Hu X, Feng B, Xu X (2019) Study on aerosol characteristics of electrostatic minimum quantity lubrication and its turning performance. J Mech Eng 55(1):129–138

    Article  Google Scholar 

  20. Lv T, Huang S, Liu E, Ma Y, Xu X (2018) Tribological and machining characteristics of an electrostatic minimum quantity lubrication (EMQL) technology using graphene nano-lubricants as cutting fluids. J Manuf Process 34:225–237. https://doi.org/10.1016/j.jmapro.2018.06.016

    Article  Google Scholar 

  21. Lv T, Xu X, Yu A, Hu X (2021) Oil mist concentration and machining characteristics of SiO2 water-based nano-lubricants in electrostatic minimum quantity lubrication-EMQL milling. J Mater Process Technol 290. https://doi.org/10.1016/j.jmatprotec.2020.116964

  22. Xu XF, Lv T, Luan ZQ, Zhao YY, Wang MH, Hu XD (2019) Capillary penetration mechanism and oil mist concentration of Al2O3 nanoparticle fluids in electrostatic minimum quantity lubrication (EMQL) milling. Int J Adv Manuf Technol 104(5-8):1937–1951. https://doi.org/10.1007/s00170-019-04023-3

    Article  Google Scholar 

  23. Maruda RW, Krolczyk GM, Feldshtein E, Pusavec F, Szydlowski M, Legutko S, Sobczak-Kupiec A (2016) A study on droplets sizes, their distribution and heat exchange for minimum quantity cooling lubrication (MQCL). Int J Mach Tool Manu 100:81–92. https://doi.org/10.1016/j.ijmachtools.2015.10.008

    Article  Google Scholar 

  24. Maruda RW, Feldshtein E, Legutko S, Krolczyk GM (2016) Analysis of contact phenomena and heat exchange in the cutting zone under minimum quantity cooling lubrication conditions. Arab J Sci Eng 41(2):661–668. https://doi.org/10.1007/s13369-015-1726-6

    Article  Google Scholar 

  25. Kara F, Takmaz A (2019) Optimization of cryogenic treatment effects on the surface roughness of cutting tools. Mater Test 61(11):1101–1104. https://doi.org/10.3139/120.111427

    Article  Google Scholar 

  26. Ozturk B, Kara F (2020) Calculation and estimation of surface roughness and energy consumption in milling of 6061 alloy. Adv Mater Sci Eng 2020:1–12. https://doi.org/10.1155/2020/5687951

    Article  Google Scholar 

  27. Kara F, Karabatak M, Ayyildiz M, Nas E (2020) Effect of machinability, microstructure and hardness of deep cryogenic treatment in hard turning of AISI D2 steel with ceramic cutting. J Mater Res Technol -Jmr&T 9(1):969–983. https://doi.org/10.1016/j.jmrt.2019.11.037

    Article  Google Scholar 

  28. Gopal PM, Prakash KS (2018) Minimization of cutting force, temperature and surface roughness through GRA, TOPSIS and Taguchi techniques in end milling of Mg hybrid MMC. Measurement 116:178–192. https://doi.org/10.1016/j.measurement.2017.11.011

    Article  Google Scholar 

  29. Sokolović DS, Höflinger W, Šečerov Sokolović RM, Sokolović SM, Sakulski D (2013) Experimental study of mist generated from metalworking fluids emulsions. J Aerosol Sci 61:70–80. https://doi.org/10.1016/j.jaerosci.2013.03.010

    Article  Google Scholar 

  30. Sarikaya M, Gullu A (2014) Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL. J Clean Prod 65:604–616. https://doi.org/10.1016/j.jclepro.2013.08.040

    Article  Google Scholar 

  31. Zhang D, Pei X (2003) Effects of machining processes on surface roughness and fatigue life. China Mech Eng 14(16):1374–1377

    Google Scholar 

  32. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  33. Jensen MT (2003) Reducing the run-time complexity of multiobjective EAs: the NSGA-II and other algorithms. IEEE Trans Evol Comput 7(5):503–515. https://doi.org/10.1109/tevc.2003.817234

    Article  Google Scholar 

  34. Sen B, Mia M, Mandal UK, Dutta B, Mondal SP (2019) Multi-objective optimization for MQL-assisted end milling operation: an intelligent hybrid strategy combining GEP and NTOPSIS. Neural Comput Applic 31(12):8693–8717. https://doi.org/10.1007/s00521-019-04450-z

    Article  Google Scholar 

  35. La Fe PI, Quiza R, Haeseldonckx D, Rivas M (2020) Sustainability-focused multi-objective optimization of a turning process. INT J PR ENG MAN-GT 7(5):1009–1018. https://doi.org/10.1007/s40684-019-00122-4

  36. Dhanalakshmi S, Rameshbabu T (2020) Multi-aspects optimization of process parameters in CNC turning of LM 25 alloy using the taguchi-grey approach. Metals 10(4). https://doi.org/10.3390/met10040453

  37. Chen M-F, Tzeng G-H, Ding CG (2008) Combining fuzzy AHP with MDS in identifying the preference similarity of alternatives. Appl Soft Comput 8(1):110–117. https://doi.org/10.1016/j.asoc.2006.11.007

    Article  Google Scholar 

  38. Diakoulaki D, Mavrotas G, Papayannakis L (1995) Determining objective weights in multiple criteria problems - the critic method. Comput Oper Res 22(7):763–770. https://doi.org/10.1016/0305-0548(94)00059-h

    Article  MATH  Google Scholar 

  39. Chen TY, Li CH (2010) Determining objective weights with intuitionistic fuzzy entropy measures: a comparative analysis. Inf Sci 180(21):4207–4222. https://doi.org/10.1016/j.ins.2010.07.009

    Article  Google Scholar 

  40. Chen H (2004) Combination determining weights method for multiple attribute decision making based on maximizing deviations. Syst Eng Electron 26(2):194–197

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Sichuan Science and Technology Program (2019YFG0358), a research project of the Chengdu Science and Technology Bureau (2015-NY02-00285-NC), and the Key Technologies Research and Development Program (2020YFB2010500). The authors remain immensely grateful for their support and contribution.

Funding

1. The Sichuan Science and Technology Program (2019YFG0358)

2. Chengdu Science and Technology Bureau (2015-NY02-00285-NC)

3. The Key Technologies Research and Development Program (2020YFB2010500)

Author information

Authors and Affiliations

  1. School of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, People’s Republic of China

    Niancong Liu, Xing Zou, Jia Yuan, Hao Jiang & Yu Zhang

  2. Chengdu Tool Research Institute Co., Ltd., Chengdu, 610500, People’s Republic of China

    Yun Chen

Authors
  1. Niancong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xing Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jia Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

1. The distribution of mist particles of turning AISI 304 under MQL is studied, and the effects of cutting speed, feed rate, depth of cut, and air pressure on the diameters of mist particles are also discussed.

2. The cutting efficiency, surface roughness, and deposition of cutting fluid mist are innovatively taken as an optimization goal.

3. An improved gray relational analysis based on the combined weight is used for searching the best solution to achieve higher cutting efficiency, lower surface roughness, and more deposition of cutting fluid mist.

4. Reducing the emission of mist particles is achieved to protect the environment under MQL.

Corresponding author

Correspondence to Niancong Liu.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, N., Zou, X., Yuan, J. et al. Optimization of MQL turning process considering the distribution and control of cutting fluid mist particles. Int J Adv Manuf Technol 116, 1233–1246 (2021). https://doi.org/10.1007/s00170-021-07480-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-07480-x

Keywords

Search

Navigation