Startseite Technik Effect of cutting parameters on the machinability of X37CrMoV5-1 hot work tool steel
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Effect of cutting parameters on the machinability of X37CrMoV5-1 hot work tool steel

  • Mustafa Özdemir

    Mustafa Özdemir was born in 1984 in Sivas, Turkey. He completed his B.Sc. and M.Sc. degrees at the Faculty of Technical Education, Gazi University, Ankara, Turkey, followed by his Ph.D. at the Graduate School of Natural and Applied Sciences at Gazi University, Ankara, in 2015. He is working as an Associate Professor at Vocational High School of Bozok University. He has published many papers related to the machinability of steels. Research areas include bending dies, sheet metal dies, heat treatment, deformation of metals, drilling with electro erosion, computer-aided design, computer-aided manufacturing, and CNC.

     EMAIL logo
Veröffentlicht/Copyright: 16. März 2022
Veröffentlichen auch Sie bei De Gruyter Brill
Manuskript einreichen Informationen für Autor*innen
Materials Testing
Aus der Zeitschrift Materials Testing Band 64 Heft 3

Abstract

Hard turning was carried out on an X37CrMoV5-1 hot work tool steel with a hardness of 50 ± 2 HRC on a computer numerical control lathe, using a ceramic insert without the use of a coolant. The cutting parameters included three different cutting speeds, three different feed rates, and three different cutting depths. A full factorial design (FFD) was created, and 33=27 experiments were carried out. The effects of cutting parameters on cutting force (Fc), surface roughness (Ra), material removal rate (MRR), specific cutting energy (SCE), current (Cu), and sound intensity (SI) were investigated. As a result of the analysis of variance (ANOVA), the effect ratios of cutting parameters on Fc, Ra, MRR, SCE, Cu, and SI were examined, and important parameters were determined. As a result, the effective rates of the feed rate, which is the most effective parameter, on Fc, Ra, and MRR were determined as 61.72, 95.90, and 61.70%, respectively. The cutting depth was 54.81 and 34.37% on SCE and SI, respectively, and the cutting speed was effective on Cu by 79.87%. By using FFD and response surface methodology (RSM), the regression equations of the results of Fc, Ra, MRR, SCE, Cu, and SI were extracted, and r 2 values were examined. In the validation experiments performed after the optimization experiments, the experimental results were estimated using FFD, RSM, and Taguchi method, and the differences between them were analyzed.

Keywords: ANOVA; CNC turning; cutting parameters; machinability; X37CrMoV5-1 steel
Corresponding author: Mustafa Özdemir, Machine and Metal Technology Department, Yozgat Bozok University, Yozgat, Turkey, E-mail:

About the author

Mustafa Özdemir

Mustafa Özdemir was born in 1984 in Sivas, Turkey. He completed his B.Sc. and M.Sc. degrees at the Faculty of Technical Education, Gazi University, Ankara, Turkey, followed by his Ph.D. at the Graduate School of Natural and Applied Sciences at Gazi University, Ankara, in 2015. He is working as an Associate Professor at Vocational High School of Bozok University. He has published many papers related to the machinability of steels. Research areas include bending dies, sheet metal dies, heat treatment, deformation of metals, drilling with electro erosion, computer-aided design, computer-aided manufacturing, and CNC.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

[1] I. Y. Malik, U. Lorenz, A. Chugreev, and B. A. Behrens, “Microstructure and wear behaviour of high alloyed hot-work tool steels 1.2343 and 1.2367 under thermo-mechanical loading,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 629, pp. 1–7, 2019, https://doi.org/10.1088/1757-899X/629/1/012011.Suche in Google Scholar

[2] S. Mayer, C. Scheu, H. Leitner, H. Clemens, I. Siller, Influence of the cooling rate on the mechanical properties of a hot-work tool steel, BHM Berg-und Hüttenmännische Monatshefte, vol. 152, no. 5, pp. 132–136, 2007, https://doi.org/10.1007/s00501-007-0285-x.Suche in Google Scholar

[3] R. M’Saoubi, D. Axinte, S. L. Soo, et al.., High performance cutting of advanced aerospace alloys and composite materials, CIRP Ann., vol. 64, no. 2, pp. 557–580, 2015, https://doi.org/10.1016/j.cirp.2015.05.002.Suche in Google Scholar

[4] H. Koçak, “Improving the surface properties of Al 6013 and MS 58 materials by ball burnishing process for hole,” Gazi Univ. J. Sci. Part C: Des. Technol., vol. 8, no. 4, pp. 972–980, 2020, https://doi.org/10.29109/gujsc.821576.Suche in Google Scholar

[5] W. König, A. Berktold, and K. F. Koch, “Turning versus grinding a comparison of surface integrity aspects and attainable accuracies,” CIRP Ann., vol. 42, no. 1, pp. 39–43, 1993, https://doi.org/10.1016/S0007-8506(07)62387-7.Suche in Google Scholar

[6] T. Zhao, J. M. Zhou, V. Bushlya, and J. E. Ståhl, “Effect of cutting edge radius on surface roughness and tool wear in hard turning of AISI 52100 steel,” Int. J. Adv. Manuf. Technol., vol. 91, no. 9, pp. 3611–3618, 2017, https://doi.org/10.1007/s00170-017-0065-z.Suche in Google Scholar

[7] M. Akkaş, M. Yavuz, and A. Şahinoğlu, “Investigation of the relationship between power consumption and noise level during hard turning of CBN tools and DIN 1.2367 steel,” Gazi Univ. J. Sci. Part C: Des. Technol., vol. 9, no. 2, pp. 262–271, 2021, https://doi.org/10.29109/gujsc.891815.Suche in Google Scholar

[8] M. A. Shalaby, M. A. El Hakim, M. M. Abdelhameed, J. E. Krzanowski, S. C. Veldhuis, and G. K. Dosbaeva, “Wear mechanisms of several cutting tool materials in hard turning of high carbon–chromium tool steel,” Tribol. Int., vol. 70, pp. 148–154, 2014, https://doi.org/10.1016/j.triboint.2013.10.011.Suche in Google Scholar

[9] H. Aouici, M. A. Yallese, K. Chaoui, T. Mabrouki, and J. F. Rigal, “Analysis of surface roughness and cutting force components in hard turning with CBN tool: prediction model and cutting conditions optimization,” Measurement, vol. 45, pp. 344–353, 2012, https://doi.org/10.1016/j.measurement.2011.11.011.Suche in Google Scholar

[10] V. D. Patel and A. H. Gandhi, “Analysis and modeling of surface roughness based on cutting parameters and tool nose radius in turning of AISI D2 steel using CBN tool,” Measurement, vol. 138, pp. 34–38, 2019, https://doi.org/10.1016/j.measurement.2019.01.077.Suche in Google Scholar

[11] T. Özel, T. K. Hsu, and E. Zeren, “Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened AISI H13 steel,” Int. J. Adv. Manuf. Technol., vol. 25, no. 3, pp. 262–269, 2005, https://doi.org/10.1007/s00170-003-1878-5.Suche in Google Scholar

[12] S. Benlahmidi, H. Aouici, F. Boutaghane, A. Khellaf, B. Fnides, and M. A. Yallese, “Design optimization of cutting parameters when turning hardened AISI H11 steel (50 HRC) with CBN7020 tools,” Int. J. Adv. Manuf. Technol., vol. 89, nos 1–4, pp. 803–820, 2017, https://doi.org/10.1007/s00170-016-9121-3.Suche in Google Scholar

[13] A. Khellaf, M. Yallese, S. Boutabba, M. Habak, H. Aouici, and S. Smaiah, “Mathematical modeling and multi-objective optimization of technological parameters in Hard Turning operation using RSM and Genetic Algorithmic Approach,” in Congrès français de mécanique, AFM, Association Française de Mécanique, vol. 28, 2017, pp. 1–10.Suche in Google Scholar

[14] F. Karaaslan and A. Şahinoğlu, “Determination of ideal cutting conditions for maximum surface quality and minimum power consumption during hard turning of AISI 4140 steel using TOPSIS method based on fuzzy distance,” Arab. J. Sci. Eng., vol. 45, no. 11, pp. 9145–9157, 2020, https://doi.org/10.1007/s13369-020-04635-y.Suche in Google Scholar

[15] A. Şahinoğlu and M. Rafighi, “Optimization of cutting parameters with respect to roughness for machining of hardened AISI 1040 steel,” Mater. Test., vol. 62, no. 1, pp. 85–95, 2020, https://doi.org/10.3139/120.111458.Suche in Google Scholar

[16] A. Şahinoğlu and M. Rafighi, “Investigation of vibration, sound intensity, machine current and surface roughness values of AISI 4140 during machining on the lathe,” Arab. J. Sci. Eng., vol. 45, no. 2, pp. 765–778, 2020, https://doi.org/10.1007/s13369-019-04124-x.Suche in Google Scholar

[17] A. K. Parida and K. Maity, “Numerical and experimental analysis of specific cutting energy in hot turning of Inconel 718,” Measurement, vol. 133, pp. 361–369, 2019, https://doi.org/10.1016/j.measurement.2018.10.033.Suche in Google Scholar

[18] F. E. Pfefferkorn, S. Lei, Y. Jeon, and G. Haddad, “A metric for defining the energy efficiency of thermally assisted machining,” Int. J. Mach. Tools Manuf., vol. 49, no. 5, pp. 357–365, 2009, https://doi.org/10.1016/j.ijmachtools.2008.12.009.Suche in Google Scholar

[19] Y. T. Ic, G. E. Saraloğlu, C. Cabbaroğlu, Y. E. Dilan, and S. H. Maide, “Optimisation of cutting parameters for minimizing carbon emission and maximising cutting quality in turning process,” Int. J. Prod. Res., vol. 56, no. 11, pp. 4035–4055, 2008, https://doi.org/10.1080/00207543.2018.1442949.Suche in Google Scholar

[20] S. Neşeli, S. Yaldız, and E. Türkeş, “Optimization of tool geometry parameters for turning operations based on the response surface methodology,” Measurement, vol. 44, no. 3, pp. 580–587, 2011, https://doi.org/10.1016/j.measurement.2010.11.018.Suche in Google Scholar

[21] F. Googerdchian, A. Moheb, R. Emadi, and M. Asgari, “Optimization of Pb (II) ions adsorption on nanohydroxyapatite adsorbents by applying Taguchi method,” J. Hazard. Mater., vol. 349, pp. 186–194, 2018, https://doi.org/10.1016/j.jhazmat.2018.01.056.Suche in Google Scholar PubMed

[22] U. Şeker, A. Kurt, and I. Ciftci, “The effect of feed rate on the cutting forces when machining with linear motion,” J. Mater. Process. Technol., vol. 146, pp. 403–407, 2004, https://doi.org/10.1016/j.jmatprotec.2003.12.001.Suche in Google Scholar

[23] 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,” Appl. Sci., vol. 7, no. 10, p. 1064, 2017, https://doi.org/10.3390/app7101064.Suche in Google Scholar

[24] M. Mia, M. A. Khan, and N. R. Dhar, “Study of surface roughness and cutting forces using ANN, RSM, and ANOVA in turning of Ti-6Al-4V under cryogenic jets applied at flank and rake faces of coated WC tool,” Int. J. Adv. Manuf. Technol., vol. 93, no. 1, pp. 975–991, 2017, https://doi.org/10.1007/s00170-017-0566-9.Suche in Google Scholar

[25] M. Kuntoğlu and H. Sağlam, “Investigation of progressive tool wear for determining of optimized machining parameters in turning,” Measurement, vol. 140, pp. 427–436, 2019, https://doi.org/10.1016/j.measurement.2019.04.022.Suche in Google Scholar

[26] H. Tanabi and M. Rafighi, “Turning machinability of alloyed ductile iron compared to forged EN 1.7131 steel,” Mater. Test., vol. 62, pp. 1259–1264, 2020, https://doi.org/10.3139/120.111612.Suche in Google Scholar

[27] M. W. Azizi, O. Keblouti, L. Boulanouar, and M. A. Yallese, “Design optimization in hard turning of E19 alloy steel by analysing surface roughness, tool vibration and productivity,” Structural Engineering and Mechanics, vol. 73, no. 5, pp. 501–513, 2020, https://doi.org/10.12989/sem.2020.73.5.501.Suche in Google Scholar

[28] S. R. Das, D. Dhupal, and A. Kumar, “Experimental investigation into machinability of hardened AISI 4140 steel using TiN coated ceramic tool,” Measurement, vol. 62, pp. 108–126, 2015, https://doi.org/10.1016/j.measurement.2014.11.008.Suche in Google Scholar

[29] F. Kara, “Taguchi optimization of surface roughness and flank wear during the turning of DIN 1.2344 tool steel,” Mater. Test., vol. 59, no. 10, pp. 903–908, 2017, https://doi.org/10.3139/120.111085.Suche in Google Scholar

[30] M. Özdemir, “Optimization with Taguchi method of influences on surface roughness of cutting parameters in CNC turning processing,” Mechanics, vol. 25, no. 5, pp. 397–405, 2019, https://doi.org/10.5755/j01.mech.25.5.23005.Suche in Google Scholar

[31] V. V. D. Sahithi, T. Malayadrib, and N. Srilatha, “Optimization of turning parameters on surface roughness based on Taguchi technique,” Mater. Today: Proc., vol. 18, pp. 3657–3666, 2019, https://doi.org/10.1016/j.matpr.2019.07.299.Suche in Google Scholar

[32] O. Keblouti, L. Boulanouar, M. W. Azizi, and M. A. Yallese, “Effects of coating material and cutting parameters on the surface roughness and cutting forces in dry turning of AISI 52100 steel,” Struct. Eng. Mech., vol. 61, pp. 519–526, 2017, https://doi.org/10.12989/sem.2017.61.4.519.Suche in Google Scholar

[33] R. Tulasi, R. Singh, and M. I. Ali, “Optimizing surface roughness in turning operation using Taguchi technique,” Mater. Today: Proc., vol. 5, no. 9, pp. 19043–19048, 2018, https://doi.org/10.1016/j.matpr.2018.06.256.Suche in Google Scholar

[34] A. S. Mohruni, M. Yanis, and E. Kurniawan, “Development of surface roughness prediction model for hard turning on AISI D2 steel using Cubic Boron Nitride insert,” J. Teknol., vol. 80, pp. 173–178, 2018, https://doi.org/10.11113/jt.v80.10492.Suche in Google Scholar

[35] A. R. Motorcu, Y. Isik, A. Kus, and M. C. Cakir, “Analysis of the cutting temperature and surface roughness during the orthogonal machining of AISI 4140 alloy steel via the taguchi method,” Analysis, vol. 50, pp. 343–351, 2016, https://doi.org/620.181.4:621.9.015:669.15.10.17222/mit.2015.021Suche in Google Scholar

[36] A. Aggarwal, H. Singh, P. Kumar ve, and M. Singh, “Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi’s technique-A comparative analysis,” J. Mater. Process. Technol., vol. 200, pp. 373–384, 2008, https://doi.org/10.1016/j.jmatprotec.2007.09.041.Suche in Google Scholar

[37] G. Kant and S. S. Kuldip, “Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining,” J. Cleaner Prod., vol. 83, pp. 151–164, 2014, https://doi.org/10.1016/j.jclepro.2014.07.073.Suche in Google Scholar

[38] M. P. Sealy, Z. Y. Liu, D. Zhang, Y. B. Guo, and Z. Q. Liu, “Energy consumption and modeling in precision hard milling,” J. Cleaner Prod., vol. 135, pp. 1591–1601, 2016, https://doi.org/10.1016/j.jclepro.2015.10.094.Suche in Google Scholar

[39] F. Draganescu, M. Gheorghe, and C. V. Doicin, “Models of machine tool efficiency and specific consumed energy,” J. Mater. Process. Technol., vol. 141, no. 1, pp. 9–15, 2003, https://doi.org/10.1016/S0924-0136(02)00930-5.Suche in Google Scholar

[40] D. K. Patel and R. G. Jivani, “Experimental investigations on material removal rate, power consumption and surface roughness of EN19 steels in turing using Taguchi method-a review,” Int. J. Eng. Res. Technol., vol. 3, no. 2, pp. 2648–2651, 2014.Suche in Google Scholar

[41] J. M. Chambers, W. S. Cleveland, B. Kleiner, and P. A. Tukey, Graphical Methods for Data Analysis, UK, Chapman and Hall/CRC, 2018.10.1201/9781351072304Suche in Google Scholar

[42] F. F. Gan, K. J. Koehler, and J. C. Thompson, “Probability plots and distribution curves for assessing the fit of probability models,” Am. Stat., vol. 45, no. 1, pp. 14–21, 1991, https://doi.org/10.1080/00031305.1991.10475759.Suche in Google Scholar
Published Online: 2022-03-16
Published in Print: 2022-03-28

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Effect of water absorption and hydroxyapatite addition on mechanical and microstructural properties of dental luting cements
  3. Mechanical properties and corrosion behavior of a friction stir processed magnesium alloy composite AZ31B–SiC
  4. Multi-objective optimization of build orientation considering support structure volume and build time in laser powder bed fusion
  5. Fatigue properties and crack growth behavior of 7N01 and 6N01 aluminum alloys
  6. Surface roughness prediction of wire electric discharge machining (WEDM)-machined AZ91D magnesium alloy using multilayer perceptron, ensemble neural network, and evolving product-unit neural network
  7. Effect of FeTi-FeB inoculation on the shape of carbide reinforcements in hypoeutectic high chromium white cast iron
  8. Development of an eutectic-based self-healing in Al–Si cast alloy
  9. Effect of process parameters on mechanical properties of additive manufactured SMP structures based on FDM
  10. Effect of calcination and sintering temperature on porosity and microstructure of porcelain tiles
  11. Effect of particles on tensile and bending properties of jute epoxy composites
  12. Effect of cutting parameters on the machinability of X37CrMoV5-1 hot work tool steel
  13. Cutting forces during drilling of a SiCp reinforced Al-7075 matrix composite
  14. Modeling and simulation of tensile properties of r-LDPE/GSF composite using the response surface methododology
  15. Machinability of alloy ductile iron and forged 16MnCr5 steel
https://doi.org/10.1515/mt-2021-2029
Schlagwörter für diesen Artikel
ANOVA; CNC turning; cutting parameters; machinability; X37CrMoV5-1 steel

Artikel in diesem Heft

  1. Frontmatter
  2. Effect of water absorption and hydroxyapatite addition on mechanical and microstructural properties of dental luting cements
  3. Mechanical properties and corrosion behavior of a friction stir processed magnesium alloy composite AZ31B–SiC
  4. Multi-objective optimization of build orientation considering support structure volume and build time in laser powder bed fusion
  5. Fatigue properties and crack growth behavior of 7N01 and 6N01 aluminum alloys
  6. Surface roughness prediction of wire electric discharge machining (WEDM)-machined AZ91D magnesium alloy using multilayer perceptron, ensemble neural network, and evolving product-unit neural network
  7. Effect of FeTi-FeB inoculation on the shape of carbide reinforcements in hypoeutectic high chromium white cast iron
  8. Development of an eutectic-based self-healing in Al–Si cast alloy
  9. Effect of process parameters on mechanical properties of additive manufactured SMP structures based on FDM
  10. Effect of calcination and sintering temperature on porosity and microstructure of porcelain tiles
  11. Effect of particles on tensile and bending properties of jute epoxy composites
  12. Effect of cutting parameters on the machinability of X37CrMoV5-1 hot work tool steel
  13. Cutting forces during drilling of a SiCp reinforced Al-7075 matrix composite
  14. Modeling and simulation of tensile properties of r-LDPE/GSF composite using the response surface methododology
  15. Machinability of alloy ductile iron and forged 16MnCr5 steel
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2026 De Gruyter Brill
Heruntergeladen am 1.1.2026 von https://www.degruyterbrill.com/document/doi/10.1515/mt-2021-2029/pdf
Button zum nach oben scrollen