Startseite Optimization of cutting parameters with respect to roughness for machining of hardened AISI 1040 steel
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Optimization of cutting parameters with respect to roughness for machining of hardened AISI 1040 steel

  • Abidin Şahinoğlu und Mohammad Rafighi
Veröffentlicht/Copyright: 20. Dezember 2019
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Materials Testing
Aus der Zeitschrift Materials Testing Band 62 Heft 1

Abstract

Today, energy consumption and environmental issues are important topics in all industries around the globe. However, quality is in direct proportion with energy consumption, since better surface finish means more energy consumption. The main objective of this work is minimizing both surface roughness and power consumption by estimating the optimum machining parameters. In this study, turning tests were carried out on three different hardened AISI 1040 steels (10, 15, 20 HRC) at three different depths of cuts (1.2, 2.4, 3.6 mm), feed rates (0.15, 0.25, 0.35 mm × rev−1) and cutting speeds (120, 140, 160 m × min−1) without coolant. The effects of cutting parameters and workpieces hardness on surface roughness, sound level and power consumption were examined. These analyses were conducted using a full factorial experimental design method. The response surface methodology and analysis of variance were also used to determine the effects of input parameters on the response variables. Experimental results showed that an increase in the feed rate value causes an increase in the surface roughness, the sound level, and the power consumption values. The results of the presented work show that feed rate is the most effective machining parameter that affects surface roughness and power consumption. Following feed rate, depth of cut and cutting speed also have an important impact. Thus, decreasing the value of feed rate and depth of cut will reduce the amount of power consumption.

∗Correspondence Address, Abidin Şahinoğlu, Department of Mechanical and Metal Technology, Çankırı Karatekin University, Çankırı, Turkey, E-mail:

Dr. Şahinoğlu, born in 1981, completed his undergraduate and graduate education in manufacturing engineering at Gazi University. He works in the field of machine manufacturing and design. He has three patents in machine design and manufacture. One of them is the “intelligent tool machining design” which determines the cutting parameters according to sound and vibration analysis. He has published some papers related to machining operation. He has been working as instructor at Çankırı Karatekin University, department of mechanical and metal technology since 2012.

Dr. Rafighi, born in 1988, received his BSc degree in mechanical engineering from Islamic Azad University of Tabriz in 2010. He got his MS. and PhD degrees in manufacturing engineering from Gazi University in 2013, and 2018, respectively. He has been honored as a first ranked student of the term, with PhD CGPA (4.00). Gazi University Projects of Scientific Investigation (BAP) supported both of his graduate thesis studies. Dr. Rafighi has attended the Rolls-Royce the Jet Engine Design Project at Brandenburg University of Technology, Cottbus, Germany, as a researcher. He has published some papers related to machining operation. Since September 2018, he has been working as an assistant professor at the University of Turkish Aeronautical Association department of mechanical engineering.

References

1 V.Bedekar, P.Pauskar, R.Shivpuri, J.Howe: Microstructure and texture evolutions in AISI 1050 steel by flow forming, Procedia Engineering81 (2014), pp. 2355–236010.1016/j.proeng.2014.10.333Suche in Google Scholar

2 A.Khalil, M.Ali, A.Azmi: Effect of Al2O3 nanolubricant with SDBS on tool wear during turning process of AISI 1050 with minimal quantity lubricant, Procedia Manufacturing2 (2015), pp. 130–13410.1016/j.promfg.2015.07.023Suche in Google Scholar

3 S.Murugappan, S.Arul: Effect of cryogenic precooling on chip reduction co-efficient during turning of EN8 steel rod, Materials Today: Proceedings4 (2017), No. 8, pp. 8848–885510.1016/j.matpr.2017.07.235Suche in Google Scholar

4 A. K.Sharma, R. K.Singh, A. R.Dixit, A. K.Tiwari: Characterization and experimental investigation of Al2O3 nanoparticle based cutting fluid in turning of AISI 1040 steel under minimum quantity lubrication (MQL), Materials Today: Proceedings3 (2016), No. 6, pp. 1899–190610.1016/j.matpr.2016.04.090Suche in Google Scholar

5 K.Fang, N.Uhan, F.Zhao, J. W.Sutherland: A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction, Journal of Manufacturing Systems30 (2011), No. 4, pp. 234–24010.1016/j.jmsy.2011.08.004Suche in Google Scholar

6 K. S.Sangwan: Development of a multi criteria decision model for justification of green manufacturing systems, International Journal of Green Economics5 (2011), No. 3, pp. 285–30510.1504/IJGE.2011.044239Suche in Google Scholar

7 Y.He, B.Liu, X.Zhang, H.Gao, X.Liu: A modeling method of task-oriented energy consumption for machining manufacturing system, Journal of Cleaner Production23 (2012), No. 1, pp. 167–17410.1016/j.jclepro.2011.10.033Suche in Google Scholar

8 F.Pusavec, P.Krajnik, J.Kopac: Transitioning to sustainable production–Part I: application on machining technologies, Journal of Cleaner Production18 (2010), No. 2, pp. 174–18410.1016/j.jclepro.2009.08.010Suche in Google Scholar

9 C.Moganapriya, R.Rajasekar, K.Ponappa, R.Venkatesh, S.Jerome: Influence of coating material and cutting parameters on surface roughness and material removal rate in turning process using Taguchi method, Materials Today: Proceedings5 (2018), No. 2, pp. 8532–853810.1016/j.matpr.2017.11.550Suche in Google Scholar

10 Z.Hessainia, A.Belbah, M. A.Yallese, T.Mabrouki, J.-F.Rigal: On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations, Measurement46 (2013), No. 5, pp. 1671–168110.1016/j.measurement.2012.12.016Suche in Google Scholar

11 H.Aouici, M. A.Yallese, K.Chaoui, T.Mabrouki, J.-F.Rigal: Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization, Measurement45 (2012), No. 3, pp. 344–35310.1016/j.measurement.2011.11.011Suche in Google Scholar

12 G. C.Benga, A. M.Abrao: Turning of hardened 100Cr6 bearing steel with ceramic and PCBN cutting tools, Journal of materials processing technology143 (2003), pp. 237–24110.1016/S0924-0136(03)00346-7Suche in Google Scholar

13 P. V.Krishna, R.Srikant, D. N.Rao: Experimental investigation on the performance of nanoboric acid suspensions in SAE-40 and coconut oil during turning of AISI 1040 steel, International Journal of Machine Tools and Manufacture50 (2010), No. 10, pp. 911–91610.1016/j.ijmachtools.2010.06.001Suche in Google Scholar

14 R.Padmini, P. V.Krishna, G. K. M.Rao: Effectiveness of vegetable oil based nanofluids as potential cutting fluids in turning AISI 1040 steel, Tribology International94 (2016), pp. 490–50110.1016/j.triboint.2015.10.006Suche in Google Scholar

15 M. A.Yallese, K.Chaoui, N.Zeghib, L.Boulanouar, J.-F.Rigal: Hard machining of hardened bearing steel using cubic boron nitride tool, Journal of Materials Processing Technology209 (2009), No. 2, pp. 1092–110410.1016/j.jmatprotec.2008.03.014Suche in Google Scholar

16 Q.Zhong, R.Tang, T.Peng: Decision rules for energy consumption minimization during material removal process in turning, Journal of Cleaner Production140 (2017), pp. 1819–182710.1016/j.jclepro.2016.07.084Suche in Google Scholar

17 C.Camposeco-Negrete: Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA, Journal of Cleaner Production53 (2013), pp. 195–20310.1016/j.jclepro.2013.03.049Suche in Google Scholar

18 D.Salgado, F.Alonso: An approach based on current and sound signals for in-process tool wear monitoring, International Journal of Machine Tools and Manufacture47 (2007), No. 14, pp. 2140–215210.1016/j.ijmachtools.2007.04.013Suche in Google Scholar

19 L.Zhou, J.Li, F.Li, Q.Meng, J.Li, X.Xu: Energy consumption model and energy efficiency of machine tools: a comprehensive literature review, Journal of Cleaner Production112 (2016), pp. 3721–373410.1016/j.jclepro.2015.05.093Suche in Google Scholar

20 S. T.Newman, A.Nassehi, R.Imani-Asrai, V.Dhokia: Energy efficient process planning for CNC machining, CIRP Journal of Manufacturing Science and Technology5 (2012), No. 2, pp. 127–13610.1016/j.cirpj.2012.03.007Suche in Google Scholar

21 M. K.Pradhan: Estimating the effect of process parameters on MRR, TWR and radial overcut of EDMed AISI D2 tool steel by RSM and GRA coupled with PCA, The International Journal of Advanced Manufacturing Technology68 (2013), No. 1–4, pp. 591–60510.1007/s00170-013-4780-9Suche in Google Scholar

22 P. S.Bilga, S.Singh, R.Kumar: Optimization of energy consumption response parameters for turning operation using Taguchi method, Journal of cleaner production137 (2016), pp. 1406–141710.1016/j.jclepro.2016.07.220Suche in Google Scholar

23 R. K.Bhushan: Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites, Journal of Cleaner Production39 (2013), pp. 242–25410.1016/j.jclepro.2012.08.008Suche in Google Scholar

24 S.Chinchanikar, S.Choudhury: Effect of work material hardness and cutting parameters on performance of coated carbide tool when turning hardened steel: an optimization approach, Measurement46 (2013), No. 4, pp. 1572–158410.1016/j.measurement.2012.11.032Suche in Google Scholar

25 A.Pathak, R.Warghane, S.Deokar: Optimization of cutting parameters in dry turning of AISI A2 tool steel using carbide tool by Taguchi based fuzzy logics, Materials Today: Proceedings5 (2018), No. 2, pp. 5082–509010.1016/j.matpr.2017.12.087Suche in Google Scholar

26 S. A.Bagaber, A. R.Yusoff: Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316, Journal of Cleaner Production157 (2017), pp. 30–4610.1016/j.jclepro.2017.03.231Suche in Google Scholar

27 A.Aggarwal, H.Singh, P.Kumar, M.Singh: Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique – a comparative analysis, Journal of Materials Processing Technology200 (2008), No. 1–3, pp. 373–38410.1016/j.jmatprotec.2007.09.041Suche in Google Scholar

28 D.Fratila, C.Caizar: Application of Taguchi method to selection of optimal lubrication and cutting conditions in face milling of AlMg3, Journal of Cleaner Production19 (2011), No. 6–7, pp. 640–64510.1016/j.jclepro.2010.12.007Suche in Google Scholar

29 L.Abhang, M.Hameedullah: Power prediction model for turning EN-31 steel using response surface methodology, Journal of Engineering Science & Technology Review3 (2010), No. 1, pp. 116–12210.25103/jestr.031.20Suche in Google Scholar

30 G.Campatelli, L.Lorenzini, A.Scippa: Optimization of process parameters using a response surface method for minimizing power consumption in the milling of carbon steel, Journal of Cleaner Production66 (2014), pp. 309–31610.1016/j.jclepro.2013.10.025Suche in Google Scholar

31 M.Emami, M. H.Sadeghi, A. A. D.Sarhan, F.Hasani: Investigating the minimum quantity lubrication in grinding of Al2O3 engineering ceramic, Journal of Cleaner Production66 (2014), pp. 632–64310.1016/j.jclepro.2013.11.018Suche in Google Scholar

32 A.Bhattacharya, S.Das, P.Majumder, A.Batish: Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA, Production Engineering3 (2009), No. 1, pp. 31–4010.1007/s11740-008-0132-2Suche in Google Scholar

33 H.Tebassi, M.Yallese, R.Khettabi, S.Belhadi, I.Meddour, F.Girardin: Multi-objective optimization of surface roughness, cutting forces, productivity and power consumption when turning of Inconel 718, International Journal of Industrial Engineering Computations7 (2016), No. 1, pp. 111–13410.5267/j.ijiec.2015.7.003Suche in Google Scholar

34 D.Murat, C.Ensarioglu, N.Gursakal, A.Oral, M. C.Cakir: Surface roughness analysis of greater cutting depths during hard turning, Materials Testing59 (2017), No. 9, pp. 795–80210.3139/120.111074Suche in Google Scholar

35 K.Aldaş, I.Özkul, M.Eskil: Prediction of surface roughness in longitudinal turning process by a genetic learning algorithm, Materials Testing56 (2014), No. 5, pp. 375–38010.3139/120.110570Suche in Google Scholar

Published Online: 2019-12-20
Published in Print: 2020-01-07

© 2020, Carl Hanser Verlag, München

Artikel in diesem Heft

  1. Inhalt/Contents
  2. Contents
  3. Fachbeiträge/Technical Contributions
  4. Strain rate effect on the acoustic emission characteristics of concrete under uniaxial tension
  5. Characterization of thick carbon/basalt hybrid fiber polyester composites with graphene nanoplatelets
  6. Influence of powder nitriding on the mechanical behavior of laser-powder bed fusion processed tool steel X30CrMo7-2
  7. Consideration of imperfections and support effects in the fatigue assessment of welded cruciform joints
  8. Roll optimization via numerical modeling of stress distribution
  9. Submerged arc welding of Ramor 500 Steel and numerical modeling of the residual stress
  10. Life extension heat treatment of IN 783 bolts
  11. Increased load bearing capacity of mechanically joined FRP/metal joints using a pin structured auxiliary joining element
  12. Innovative characterization and mechanical properties of natural cellulosic Coccinia Indica fiber and its composites
  13. Post-weld heat treatment effects on the tensile properties of cold metal arc welded AA 6061-T6 aluminum joints
  14. Wear and corrosion behavior of coconut shell ash (CSA) reinforced Al6061 metal matrix composites
  15. Optimization of cutting parameters with respect to roughness for machining of hardened AISI 1040 steel
  16. Shunting effects on the resistance spot welding parameters of DP600
  17. Properties of P460-S355 submerged arc welds
  18. BEZUGSQUELLEN
  19. Materials Testing
https://doi.org/10.3139/120.111458

Artikel in diesem Heft

  1. Inhalt/Contents
  2. Contents
  3. Fachbeiträge/Technical Contributions
  4. Strain rate effect on the acoustic emission characteristics of concrete under uniaxial tension
  5. Characterization of thick carbon/basalt hybrid fiber polyester composites with graphene nanoplatelets
  6. Influence of powder nitriding on the mechanical behavior of laser-powder bed fusion processed tool steel X30CrMo7-2
  7. Consideration of imperfections and support effects in the fatigue assessment of welded cruciform joints
  8. Roll optimization via numerical modeling of stress distribution
  9. Submerged arc welding of Ramor 500 Steel and numerical modeling of the residual stress
  10. Life extension heat treatment of IN 783 bolts
  11. Increased load bearing capacity of mechanically joined FRP/metal joints using a pin structured auxiliary joining element
  12. Innovative characterization and mechanical properties of natural cellulosic Coccinia Indica fiber and its composites
  13. Post-weld heat treatment effects on the tensile properties of cold metal arc welded AA 6061-T6 aluminum joints
  14. Wear and corrosion behavior of coconut shell ash (CSA) reinforced Al6061 metal matrix composites
  15. Optimization of cutting parameters with respect to roughness for machining of hardened AISI 1040 steel
  16. Shunting effects on the resistance spot welding parameters of DP600
  17. Properties of P460-S355 submerged arc welds
  18. BEZUGSQUELLEN
  19. Materials Testing
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