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Effect of Tool Vibration on Flank Wear and Surface Roughness During High-Speed Machining of 1040 Steel

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Abstract

In recent years, the tool condition monitoring mechanism is necessary for analyzing the failure of the cutting tools in production practices. In a machining environment, steady and catastrophic failures of a tool are general faults associated with a machining process. The relationship between surface roughness, tool wear and vibration is explored during high-speed dry machining by using main input factor. L27 numbers of trials were performed in a CNC lathe with uncoated carbide CNMG120408 tool and alloy steel AISI 1040 workpiece. The predictable model is capable of expecting surface roughness (Ra), tool wear (VBc), and vibration of amplitude using observed data when turning alloy steel. The vibration was recorded only in the turning direction with a uniaxial accelerometer. Additionally, tool flank wear and finished work surface roughness are measured at various combinations of parameters. The outcomes of the work show that the axial feed rate is the main effective turning variable that influences surface roughness largely (91.97%). Optimization of turning process variables plays a significant role in turning to develop quality, manufacturing production rate and decrease production price. In this analysis, an advanced weighted principal component analysis strategy was initiated to optimize process variables in turning of 1040 alloy steel and the optimum relation was found to be d3 (0.5 mm)–f1 (0.06 mm/rev)–v3 (300 m/min). Higher depth of cutting along with largest cutting speed confirms the larger production rate which is desirable for industrial concern. Also, at the optimal setting, the excellent finish of surface with low wear and low acceleration is noticed with an improved S/N ratio of CQL from initial setting. However, the current work presented a better co-relation between tool vibrations, tool wear, and test surface finish which will be beneficial for the industrial uses.

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References

  1. M.A. Guvenc, M. Cakir, S. Mistikoglu, Experimental study on optimization of cutting parameters by using Taguchi method for tool vibration and surface roughness in dry turning of AA6013, in 10th International Symposium on Intelligent Manufacturing and Service Systems (2019), pp. 1032–1040

  2. T. Mohanraj, S. Shankar, R. Rajasekar, N.R. Sakthivel, A. Pramanik, Tool condition monitoring techniques in milling process—a review. J. Mater. Res. Technol. 9, 1032–1042 (2019)

    Article  Google Scholar 

  3. M.M. Faiz, M. Hairizal, A.B. Hadzley, M.F. Naim, T. Norfauzi, U.A.A. Umar, A.A. Aziz, S. Noorazizi, Effect of hydraulic pressure on hardness, density, tool wear and surface roughness in the fabrication of alumina based cutting tool. J. Adv. Manuf. Technol. (JAMT) 13(2(1)) (2019)

  4. A. Şahinoğlu, 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. 45(2), 765–778 (2020)

    Article  Google Scholar 

  5. 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. Mater. Today Proc. 5(2), 8532–8538 (2018)

    Article  CAS  Google Scholar 

  6. 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. Measurement 46(5), 1671–1681 (2013)

    Article  Google Scholar 

  7. G. Quintana, J. Ciurana, Chatter in machining processes: a review. Int. J. Mach. Tools Manuf. 51(5), 363–376 (2011)

    Article  Google Scholar 

  8. M. Siddhpura, R. Paurobally, A review of chatter vibration research in turning. Int. J. Mach. Tools Manuf. 61, 27–47 (2012)

    Article  Google Scholar 

  9. S. Karabulut, A. Sahinoglu, Effect of the cutting parameters on surface roughness, power consumption and machine noise in machining of R260 steel. J. Polytech. Politek. 21(1), 237–244 (2018)

    Google Scholar 

  10. A. Şahinoğlu, Ş. Karabulut, A. Güllü, Study on spindle vibration and surface finish in turning of Al 7075, in Solid State Phenomena, vol. 261 (Trans Tech publications Ltd, Rijeka, 2017), pp. 321–327

  11. R. Kishore, S.K. Choudhury, K. Orra, On-line control of machine tool vibration in turning operation using electro-magneto rheological damper. J. Manuf. Process. 31, 187–198 (2018)

    Article  Google Scholar 

  12. A. Şahinoğlu, A. Güllü, M.A. Dönertaş, GGG50 Malzemenin Torna Tezgâhında Farklı Kesme Parametrelerinde İşlenmesinde Titreşim, Ses Şiddetininve Yüzey Pürüzlülüğünün İncelenmesi. Sinop Üniv. Fen Bilim. Derg. 2(1), 67–79 (2017)

    Google Scholar 

  13. S.A. Bagaber, A.R. Yusoff, Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316. J. Clean. Prod. 157, 30–46 (2017)

    Article  Google Scholar 

  14. D.R. Salgado, F.J. Alonso, An approach based on current and sound signals for in-process tool wear monitoring. Int. J. Mach. Tools Manuf. 47(14), 2140–2152 (2007)

    Article  Google Scholar 

  15. 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. J. Clean. Prod. 112, 3721–3734 (2016)

    Article  Google Scholar 

  16. A. Şahinoğlu, A. Güllü, İ. Çiftçi, Analysis of surface roughness, sound level, vibration and current when machining AISI 1040 steel. Sigma J. Eng. Nat. Sci. Mühendis. Fen Bilim. Derg. 37(2), 423–437 (2019)

    Google Scholar 

  17. M.W. Azizi, S. Belhadi, M.A. Yallese, T. Mabrouki, J.F. Rigal, Surface roughness and cutting forces modeling for optimization of machining condition in finish hard turning of AISI 52100 steel. J. Mech. Sci. Technol. 26(12), 4105–4114 (2012)

    Article  Google Scholar 

  18. I. Meddour, M.A. Yallese, H. Bensouilah, A. Khellaf, M. Elbah, Prediction of surface roughness and cutting forces using RSM, ANN, and NSGA-II in finish turning of AISI 4140 hardened steel with mixed ceramic tool. Int. J. Adv. Manuf. Technol. 97(5–8), 1931–1949 (2018)

    Article  Google Scholar 

  19. A. Zerti, M.A. Yallese, I. Meddour, S. Belhadi, A. Haddad, T. Mabrouki, Modeling and multi-objective optimization for minimizing surface roughness, cutting force, and power, and maximizing productivity for tempered stainless steel AISI 420 in turning operations. Int. J. Adv. Manuf. Technol. 102(1–4), 135–157 (2019)

    Article  Google Scholar 

  20. S.R. Das, A. Kumar, D. Dhupal, Effect of machining parameters on surface roughness in machining of hardened AISI 4340 steel using coated carbide inserts. Int. J. Innov. Appl. Stud. 2(4), 445–453 (2013)

    Google Scholar 

  21. A.R. Motorcu, The optimization of machining parameters using the Taguchi method for surface roughness of AISI 8660 hardened alloy steel. J. Mech. Eng. 56(6), 391–401 (2010)

    Google Scholar 

  22. K. Bouacha, M.A. Yallese, T. Mabrouki, J.F. Rigal, Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool. Int. J. Refract. Met. Hard Mater. 28(3), 349–361 (2010)

    Article  CAS  Google Scholar 

  23. A.K. Sahoo, B. Sahoo, Performance studies of multilayer hard surface coatings (TiN/TiCN/Al2O3/TiN) of indexable carbide inserts in hard machining: part II (RSM, grey relational and techno economical approach). Measurement 46(8), 2868–2884 (2013)

    Article  Google Scholar 

  24. M.C. Cakir, C. Ensarioglu, I. Demirayak, Mathematical modeling of surface roughness for evaluating the effects of cutting parameters and coating material. J. Mater. Process. Technol. 209(1), 102–109 (2009)

    Article  CAS  Google Scholar 

  25. Ş. Karabulut, U. Gökmen, H. Çinici, Optimization of machining conditions for surface quality in milling AA7039-based metal matrix composites. Arab. J. Sci. Eng. 43(3), 1071–1082 (2018)

    Article  CAS  Google Scholar 

  26. V.N. Gaitonde, S.R. Karnik, L. Figueira, J.P. Davim, Machinability investigations in hard turning of AISI D2 cold work tool steel with conventional and wiper ceramic inserts. Int. J. Refract. Met. Hard Mater. 27(4), 754–763 (2009)

    Article  CAS  Google Scholar 

  27. S. Thamizhmanii, S. Saparudin, S. Hasan, Analyses of surface roughness by turning process using Taguchi method. J. Achiev. Mater. Manuf. Eng. 20(1–2), 503–506 (2007)

    Google Scholar 

  28. 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. Prod. Eng. Res. Dev. 3(1), 31–40 (2009)

    Article  Google Scholar 

  29. G. Kant, K.S. Sangwan, Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining. J. Clean. Prod. 83, 151–164 (2014)

    Article  Google Scholar 

  30. A. Labidi, H. Tebassi, S. Belhadi, R. Khettabi, M.A. Yallese, Cutting conditions modeling and optimization in hard turning using RSM, ANN and desirability function. J. Fail. Anal. Prev. 18(4), 1017–1033 (2018)

    Article  Google Scholar 

  31. M.K. Gupta, G. Singh, P.K. Sood, Experimental investigation of machining AISI 1040 medium carbon steel under cryogenic machining: a comparison with dry machining. J. Inst. Eng. Series (India) C 96(4), 373–379 (2015)

    Article  Google Scholar 

  32. N.R. Dhar, S. Islam, M. Kamruzzaman, S. Paul, Wear behavior of uncoated carbide inserts under dry, wet and cryogenic cooling conditions in turning C-60 steel. J. Braz. Soc. Mech. Sci. Eng. 28(2), 146–152 (2006)

    Article  Google Scholar 

  33. B.S. Prasad, Y.R. Reddy, Analysis of real-time vibration assisted tool condition monitoring in drilling. Int. J. Manuf. Res. 14(2), 101–117 (2019)

    Article  Google Scholar 

  34. R. Suresh, S. Basavarajappa, G.L. Samuel, Some studies on hard turning of AISI 4340 steel using multilayer coated carbide tool. Measurement 45(7), 1872–1884 (2012)

    Article  Google Scholar 

  35. A. Das, N. Tirkey, S.K. Patel, S.R. Das, B.B. Biswal, A comparison of machinability in hard turning of EN-24 alloy steel under mist cooled and dry cutting environments with a coated cermet tool. J. Fail. Anal. Prev. 19(1), 115–130 (2019)

    Article  Google Scholar 

  36. A. Panda, A. Sahoo, A. Rout, Statistical regression modeling and machinability study of hardened AISI 52100 steel using cemented carbide insert. Int. J. Ind. Eng. Comput. 8(1), 33–44 (2017)

    Google Scholar 

  37. A. Erçetin, Ü.A. Usca, An experimental investigation of effect of turning AISI 1040 steel at low cutting speed on tool wear and surface roughness steel. Turkish J. Nat. Sci. 5(1), 29–36 (2016)

    Google Scholar 

  38. L. Huang, J.C. Chen, A multiple regression model to predict in-process surface roughness in turning operation via accelerometer. J. Ind. Technol. 17(2), 1–8 (2001)

    Google Scholar 

  39. B.C. Routara, S.D. Mohanty, S. Datta, A. Bandyopadhyay, S.S. Mahapatra, Combined quality loss (CQL) concept in WPCA-based Taguchi philosophy for optimization of multiple surface quality characteristics of UNS C34000 brass in cylindrical grinding. Int. J. Adv. Manuf. Technol. 51(1–4), 135–143 (2010)

    Article  Google Scholar 

  40. D. Das, P. Mishra, S. Singh, A. Chaubey, B. Routara, Machining performance of aluminium matrix composite and use of WPCA based Taguchi technique for multiple response optimization. Int. J. Ind. Eng. Comput. 9(4), 551–564 (2018)

    Google Scholar 

  41. R. Kumar, A. Modi, A. Panda, A.K. Sahoo, A. Deep, P.K. Behra, R. Tiwari, Hard turning on JIS S45C structural steel: an experimental, modelling and optimisation approach. Int. J. Autom. Mech. Eng. 16(4), 7315–7340 (2019)

    Article  CAS  Google Scholar 

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Acknowledgments

The authors are grateful to KIIT Deemed to be University, Bhubaneswar, for providing the sufficient facilities to fulfill the current experimental research work.

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Correspondence to Amlana Panda.

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Swain, S., Panigrahi, I., Sahoo, A.K. et al. Effect of Tool Vibration on Flank Wear and Surface Roughness During High-Speed Machining of 1040 Steel. J Fail. Anal. and Preven. 20, 976–994 (2020). https://doi.org/10.1007/s11668-020-00905-x

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