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Nanopartikül Takviyeli Minimum Miktarda Yağlama (MMY) Yönteminin Kesme Performansına Etkisinin Deneysel ve İstatistiki Araştırılması

Year 2024, Volume: 10 Issue: 1, 102 - 113, 30.04.2024

Fuat Kara

Abstract

Bu çalışmada, 20NiCrMo2 çeliğinin tornalanmasında kaplamalı karbür ve sermet olmak üzere iki farklı kesici takım kullanılmıştır. Bu takımlar ile üç farklı soğutma yöntemi (kuru, MQL, nano-MQL) üç farklı kesme hızı (80, 120, 160 m/dak) ve üç farklı ilerleme hızı (0,125, 0,167, 0,2 mm/dev) değerlerinde tornalama deneyleri gerçekleştirilmiştir. Deneyler sonucunda ortalama yüzey pürüzlülüğü (Ra) ve kesme bölgesi sıcaklığı (Ctemp) üzerinde kesme parametrelerinin, kesici takım türünün ve soğtma yöntemi türünün etkileri incelenmiştir. Çalışmada ayrıca deneysel Ra ve Ctemp sonuçlarına Taguchi optimizasyon metodu uygulanmıştır. Taguchi optimizasyonu sonucunda Ra ve Ctemp üzerinde en etkili kesme parametreleri tespit edilmiştir. ANOVA analizi ile bu sonuç doğrulanmıştır. Ra için optimum parametreler; sermet kesici takım, nano-mql soğutma yöntemi, 160 m/dak kesme hızı ve 0,12 mm/dev ilerleme hızı olarak bulunmuştur. Ctemp için optimum parametreler; karbür kesici takım, nano-mql soğutma yöntemi, 80 m/dak kesme hızı ve 0,12 mm/dev ilerleme hızı olarak bulunmuştur. Yapılan 18 tornalama deneyi içerisinde optimum parametrelere ait deneyler yer almadığı için hem Ra hem de Ctemp için 19. deneyler yapılmıştır. Optimum parametreler için ortalama yüzey pürüzlülük değeri 1,08 µm olarak ölçülmüştür. Bununla birlikte, optimum parametreler için kesme bölgesi sıcaklığı 122 °C olarak ölçülmüştür.

Keywords

ANOVA MQL nano-MQL Kesme sıcaklığı Taguchi analizi Yüzey pürüzlülüğü

References

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  • [16] O. Özbek, “Evaluation of Nano Fluids with Minimum Quantity Lubrication in Turning of Ni-Base Superalloy UDIMET 720,” Lubricants, vol. 11, no. 4, p. 159, Apr. 2023. doi:10.3390/lubricants11040159
  • [17] I. Ciftci and H. Gökçe, “Optimisation of cutting tool and cutting parameters in machining of molybdenum alloys through the Taguchi Method”. Journal of the Faculty of engineering and architecture of Gazi university, vol. 2018, no. 2018. Apr. 2018. doi:10.17341/gazimmfd.416482 [18] C. Liu et al., “Effects of process parameters on cutting temperature in dry machining of ball screw,” ISA Transactions, vol. 101, pp. 493–502, Jun. 2020. doi:10.1016/j.isatra.2020.01.031
  • [19] E. Nas, “Analysis of the electrical discharge machining (EDM) performance on Ramor 550 armor steel,” Materials Testing, vol. 62, no. 5, pp. 481–491, May 2020. doi:10.3139/120.111510 [20] Y. Pan et al., “New insights into the methods for predicting ground surface roughness in the age of digitalisation,” Precision Engineering-journal of The International Societies for Precision Engineering and Nanotechnology, vol. 67, pp. 393–418, Jan. 2021. doi:10.1016/j.precisioneng.2020.11.001
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  • [22] C. Xia, Z. Pan, J. Polden, H. Li, Y. Xu, and S. Chen, “Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning,” Journal of Intelligent Manufacturing, Jan. 2021. doi:10.1007/s10845-020-01725-4
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Experimental and Statistical Investigation of the Effect of Nanoparticle Minimum Quantity Lubrication (nano-MQL) Method on Cutting Performance

Year 2024, Volume: 10 Issue: 1, 102 - 113, 30.04.2024

Fuat Kara

Abstract

In this study, two distinc cutting tools, coated carbide and cermet, were used in turning 20NiCrMo2 case-hardened steel. Turning experiments were carried out with these tools at three distinc cooling methods (dry, MQL, nano-MQL), three distinc cutting speeds (80, 120, 160 m/min) and three distinc feed rates (0.125, 0.167, 0.2 mm/rev) has been carried out. As a result of the experiments, the effects of cutting parameters, cutting tool type and cooling method type on the average surface roughness (Ra) and cutting zone temperature (Ctemp) were examined. In the study, the Taguchi optimization method was also applied to the experimental Ra and Ctemp results. As a result of Taguchi optimization, the most effective cutting parameters on Ra and Ctemp were determined. This result was confirmed by ANOVA analysis. Optimum parameters for Ra; cermet cutting tool, nano-MQL cooling method, 160 m/min cutting speed and 0.12 mm/rev feed rate. Optimum parameters for Ctemp; carbide cutting tool, nano-MQL cooling method, 80 m/min cutting speed and 0.12 mm/rev feed rate. Ideal numbers for both Ra and Ctemp were not found in the 18 turning experiments performed. Therefore, the 19th experiment was conducted for both output parameters. The average surface roughness value for optimum parameters was measured as 1.08 µm. For optimum parameters, the cutting zone temperature was measured as 122 °C.

Keywords

ANOVA Cutting temperature MQL nano-MQL Surface Roughness Taguchi analyses

References

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  • [2] N. M.s, M. M. Rahman, and K. Kadirgama, “Parametric optimization of end milling process under minimum quantity lubrication with nanofluid as cutting medium using pareto optimality approach,” International Journal of Automotive and Mechanical Engineering, vol. 13, no. 2, pp. 3345–3360, 2016. doi:10.15282/ijame.13.2.2016.5.0277
  • [3] N. N. N. Hamran, J. A. Ghani, R. Ramli, and C. H. C. Haron, “A review on recent development of minimum quantity lubrication for sustainable machining,” Journal of Cleaner Production, p. 122165, May 2020. doi:10.1016/j.jclepro.2020.122165
  • [4] A. Marques, Mauro Paipa Suarez, Wisley Falco Sales, and Álisson Rocha Machado, “Turning of Inconel 718 with whisker-reinforced ceramic tools applying vegetable-based cutting fluid mixed with solid lubricants by MQL,” vol. 266, pp. 530–543, Apr. 2019. doi:10.1016/j.jmatprotec.2018.11.032
  • [5] K. A. Osman, H. Ö. Ünver, and U. Şeker, “Application of minimum quantity lubrication techniques in machining process of titanium alloy for sustainability: a review,” The International Journal of Advanced Manufacturing Technology, vol. 100, no. 9–12, pp. 2311–2332, Oct. 2018. doi:10.1007/s00170-018-2813-0
  • [6] U. M. R. Paturi, Y. R. Maddu, R. R. Maruri, and S. K. R. Narala, “Measurement and Analysis of Surface Roughness in WS2 Solid Lubricant Assisted Minimum Quantity Lubrication (MQL) Turning of Inconel 718,” Procedia CIRP, vol. 40, pp. 138–143, 2016. doi:10.1016/j.procir.2016.01.082
  • [7] M. N. Sharif, S. Pervaiz, and I. Deiab, “Potential of alternative lubrication strategies for metal cutting processes: a review,” The International Journal of Advanced Manufacturing Technology, vol. 89, no. 5–8, pp. 2447–2479, Aug. 2016. doi:10.1007/s00170-016-9298-5 [8] A. Eltaggaz, H. Hegab, I. Deiab, and H. A. Kishawy, “Hybrid nano-fluid-minimum quantity lubrication strategy for machining austempered ductile iron (ADI),” International Journal on Interactive Design and Manufacturing (IJIDeM), vol. 12, no. 4, pp. 1273–1281, Jun. 2018. doi:10.1007/s12008-018-0491-7 [9] M.A. Makhesana, K. M. Patel, G. M. Krolczyk, M. Danish, Anil Kumar Singla, and N. Khanna, “Influence of MoS2 and graphite-reinforced nanofluid-MQL on surface roughness, tool wear, cutting temperature and microhardness in machining of Inconel 625,” CIRP Journal of Manufacturing Science and Technology, vol. 41, pp. 225–238, Apr. 2023. doi:10.1016/j.cirpj.2022.12.015.
  • [10] R. Singh Rooprai, T. Singh, M. Singh, M. Rana, V. Kumar Sharma, and S. Sharma, “Multi-variable optimization for surface roughness and micro-hardness in MQL assisted face milling of EN31 steel using Taguchi based grey relational analysis,” Materials Today: Proceedings, vol. 43, pp. 3144–3147, 2021. doi:10.1016/j.matpr.2021.01.624
  • [11] O. Özbek, and H. Saruhan, “The effect of vibration and cutting zone temperature on surface roughness and tool wear in eco-friendly MQL turning of AISI D2,” Journal of Materials Research and Technology, vol. 9, no. 3, pp. 2762–2772, May 2020. doi:10.1016/j.jmrt.2020.01.010
  • [12] J. Ning and S. Liang, “Predictive Modeling of Machining Temperatures with Force–Temperature Correlation Using Cutting Mechanics and Constitutive Relation,” Materials, vol. 12, no. 2, p. 284, Jan. 2019. doi:10.3390/ma12020284
  • [13] Y.-C. Lin, K.-D. Wu, W.-C. Shih, P.-K. Hsu, and J.-P. Hung, “Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network,” Applied Sciences, vol. 10, no. 11, p. 3941, Jun. 2020. doi:10.3390/app10113941
  • [14] N. A. Özbek, “Effects of cryogenic treatment types on the performance of coated tungsten tools in the turning of AISI H11 steel,” Journal of Materials Research and Technology, vol. 9, no. 4, pp. 9442-9456, 2020. doi:10.1016/j.jmrt.2020.03.038
  • [15] A. Agrawal, S. Goel, W. B. Rashid, and M. Price, “Prediction of surface roughness during hard turning of AISI 4340 steel (69 HRC),” Applied Soft Computing, vol. 30, pp. 279–286, May 2015. doi:10.1016/j.asoc.2015.01.059
  • [16] O. Özbek, “Evaluation of Nano Fluids with Minimum Quantity Lubrication in Turning of Ni-Base Superalloy UDIMET 720,” Lubricants, vol. 11, no. 4, p. 159, Apr. 2023. doi:10.3390/lubricants11040159
  • [17] I. Ciftci and H. Gökçe, “Optimisation of cutting tool and cutting parameters in machining of molybdenum alloys through the Taguchi Method”. Journal of the Faculty of engineering and architecture of Gazi university, vol. 2018, no. 2018. Apr. 2018. doi:10.17341/gazimmfd.416482 [18] C. Liu et al., “Effects of process parameters on cutting temperature in dry machining of ball screw,” ISA Transactions, vol. 101, pp. 493–502, Jun. 2020. doi:10.1016/j.isatra.2020.01.031
  • [19] E. Nas, “Analysis of the electrical discharge machining (EDM) performance on Ramor 550 armor steel,” Materials Testing, vol. 62, no. 5, pp. 481–491, May 2020. doi:10.3139/120.111510 [20] Y. Pan et al., “New insights into the methods for predicting ground surface roughness in the age of digitalisation,” Precision Engineering-journal of The International Societies for Precision Engineering and Nanotechnology, vol. 67, pp. 393–418, Jan. 2021. doi:10.1016/j.precisioneng.2020.11.001
  • [21] M. Tomov, M. Kuzinovski, and P. Cichosz, “Development of mathematical models for surface roughness parameter prediction in turning depending on the process condition,” International Journal of Mechanical Sciences, vol. 113, pp. 120–132, Jul. 2016. doi:10.1016/j.ijmecsci.2016.04.015
  • [22] C. Xia, Z. Pan, J. Polden, H. Li, Y. Xu, and S. Chen, “Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning,” Journal of Intelligent Manufacturing, Jan. 2021. doi:10.1007/s10845-020-01725-4
  • [23] S. V. Alagarsamy, M. Ravichandran, M. Meignanamoorthy, S. Sakthivelu, and S. Dineshkumar, “Prediction of surface roughness and tool wear in milling process on brass (C26130) alloy by Taguchi technique,” Materials Today: Proceedings, vol. 21, pp. 189–193, 2020. doi:10.1016/j.matpr.2019.04.219 [24] F. Kara, N. Bulan, Mahir Akgün, and Uğur Köklü, “Multi-Objective Optimization of Process Parameters in Milling of 17-4 PH Stainless Steel using Taguchi-based Gray Relational Analysis,” Engineered science, Jan. 2023. doi:10.30919/es961 [25] H. Gokce, “Optimisation of Cutting Tool and Cutting Parameters in Face Milling of Custom 450 through the Taguchi Method,” Advances in Materials Science and Engineering, vol. 2019, pp. 1–10, Sep. 2019. doi:10.1155/2019/5868132
  • [26] I. Tlhabadira, I. A. Daniyan, R. Machaka, C. Machio, L. Masu, and L. R. VanStaden, “Modelling and optimization of surface roughness during AISI P20 milling process using Taguchi method,” The International Journal of Advanced Manufacturing Technology, vol. 102, no. 9–12, pp. 3707–3718, Feb. 2019. doi:10.1007/s00170-019-03452-4
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  • [28] F. Kara, “Taguchi optimization of surface roughness and flank wear during the turning of DIN 1.2344 tool steel,” Materials Testing, vol. 59, no. 10, pp. 903–908, Oct. 2017, doi:10.3139/120.111085
  • [29] Douglas Barrett, “Taguchi’s Quality Engineering Handbook,” Technometrics, vol. 49, no. 2, pp. 224–225, May 2007 doi:10.1198/tech.2007.s480
  • [30] M. Akgün, B. Özlü, and F. Kara, “Effect of PVD-TiN and CVD-Al2O3 Coatings on Cutting Force, Surface Roughness, Cutting Power, and Temperature in Hard Turning of AISI H13 Steel,” Journal of Materials Engineering and Performance, vol. 32, no. 3, pp. 1390–1401, Aug. 2022. doi:10.1007/s11665-022-07190-9
  • [31] E. Nas, O. Özbek, F. Bayraktar, and F. Kara, “Experimental and Statistical Investigation of Machinability of AISI D2 Steel Using Electroerosion Machining Method in Different Machining Parameters,” Advances in Materials Science and Engineering, vol. 2021, pp. 1–17, Oct. 2021. doi:10.1155/2021/1241797
  • [32] O. Özbek, N. Altan Özbek, F. Kara, and H. Saruhan, “Effect of vibration and cutting zone temperature on surface topography during hybrid cooling/lubrication assisted machining of Vanadis 10,” MP MATERIALPRUEFUNG - MP MATERIALS TESTING, vol. 65, no. 9, pp. 1437–1452, Aug. 2023. doi:10.1515/mt-2023-0057 [33] S. Yağmur, “The effects of cooling applications on tool life, surface quality, cutting forces, and cutting zone temperature in turning of Ni-based Inconel 625,” The International Journal of Advanced Manufacturing Technology, vol. 116, no. 3–4, pp. 821–833, Jun. 2021. doi:10.1007/s00170-021-07489-2
  • [34] N. A. Özbek, O. Özbek, and F. Kara, “Statistical Analysis of the Effect of the Cutting Tool Coating Type on Sustainable Machining Parameters,” Journal of Materials Engineering and Performance, vol. 30, no. 10, pp. 7783–7795, Aug. 2021. doi:10.1007/s11665-021-06066-8
There are 27 citations in total.

Details

Primary Language English
Subjects Tribology
Journal Section Research Articles
Authors

Fuat Kara 0000-0002-3811-3081

Early Pub Date April 6, 2024
Publication Date April 30, 2024
Submission Date December 20, 2023
Acceptance Date February 15, 2024
Published in Issue Year 2024 Volume: 10 Issue: 1

Cite

IEEE F. Kara, “Experimental and Statistical Investigation of the Effect of Nanoparticle Minimum Quantity Lubrication (nano-MQL) Method on Cutting Performance”, GJES, vol. 10, no. 1, pp. 102–113, 2024.
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