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Investigation of surface roughness in face milling processes

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Abstract

This study aims to investigate the effects of dry, minimum quantity lubrication (MQL), and nanofluid cutting conditions on surface roughness (Ra) and material removal rate (MRR) for Al6082-T6. Three controllable factors, namely, feed rate (Fr), spindle speed (Vs), and depth of cut (Dc) are studied at three levels using Taguchi method. Single-response optimization is conducted using S/N ratio and contour plots. Empirical models of Ra and MRR for all cutting conditions are developed, and analysis of variance (ANOVA) is used to measure the adequacy of these models. Experimental results reveal that 26~30% improvement in Ra could be observed when experimental setup shifted from dry to MQL, and 13~16% improvement is recorded when further shifted to nanofluid cutting condition. No remarkable effect of cutting conditions (dry, MQL, and nanofluid) is observed on MRR. Additionally, Vs is observed insignificant for MRR in all cutting conditions. The appropriate cutting conditions and optimum values of input variables are proposed to the practitioners for industrial machining and production when contemplating face milling processes.

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Abbreviations

ANOVA:

Analysis of variance

MQL:

Minimum quantity lubrication

MRR:

Material removal rate

Ra:

Surface roughness

S/N ratio:

Signal-to-noise ratio

Fr:

Feed rate

Vs:

Spindle speed

Dc:

Depth of cut

References

  1. Benardos P, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robot Comput Integr Manuf 18(5-6):343–354

    Google Scholar 

  2. Lee T, Lin Y (2000) A 3D predictive cutting-force model for end milling of parts having sculptured surfaces. Int J Adv Manuf Technol 16(11):773–783

    Google Scholar 

  3. Korkut I, Donertas M (2007) The influence of feed rate and cutting speed on the cutting forces, surface roughness and tool–chip contact length during face milling. Mater Des 28(1):308–312

    Google Scholar 

  4. Baek DK, Ko TJ, Kim HS (2001) Optimization of feedrate in a face milling operation using a surface roughness model. Int J Mach Tools Manuf 41(3):451–462

    Google Scholar 

  5. Fratila D, Caizar C (2011) Application of Taguchi method to selection of optimal lubrication and cutting conditions in face milling of AlMg3. J Clean Prod 19(6-7):640–645

    Google Scholar 

  6. Ozcelik B, Oktem H, Kurtaran H (2005) Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm. Int J Adv Manuf Technol 27(3-4):234–241

    Google Scholar 

  7. Bhavsar SN, Aravindan S, Rao PV (2015) Investigating material removal rate and surface roughness using multi-objective optimization for focused ion beam (FIB) micro-milling of cemented carbide. Precis Eng 40:131–138

    Google Scholar 

  8. Parashar V, Purohit R (2017) Investigation of the effects of the machining parameters on material removal rate using Taguchi method in EndMilling of Steel Grade EN19. Mater Today Proc 4(2):336–341

    Google Scholar 

  9. Chen SH, Kuo CP, Ling CC (2007) On tool-chip interface stress distributions ploughing force and size effect in machining inconel-718 and AISI4340. J Chin Inst Eng 30(2):211–218

    Google Scholar 

  10. Yan J, Li L (2013) Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality. J Clean Prod 52:462–471

    Google Scholar 

  11. Kuram E, Ozcelik B, Bayramoglu M, Demirbas E, Simsek BT (2013) Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments. J Clean Prod 42:159–166

    Google Scholar 

  12. Sayuti M, Sarhan AAD, Tanaka T, Hamdi M, Saito Y (2013) Cutting force reduction and surface quality improvement in machining of aerospace duralumin AL-2017-T4 using carbon onion nanolubrication system. Int J Adv Manuf Technol 65(9-12):1493–1500

    Google Scholar 

  13. Wakabayashi T, Suda S (2008) Environmentally friendly machining of aluminum using minimal quantity lubrication system. In: Manufacturing Systems and Technologies for the New Frontier. Springer, pp 377–380

  14. Klocke F, Eisenblätter G (1997) Dry cutting. CIRP Ann 46(2):519–526

    Google Scholar 

  15. Tosun N, Huseyinoglu M (2010) Effect of MQL on surface roughness in milling of AA7075-T6. Mater Manuf Process 25(8):793–798

    Google Scholar 

  16. Zhang S, Li J, Wang Y (2012) Tool life and cutting forces in end milling Inconel 718 under dry and minimum quantity cooling lubrication cutting conditions. J Clean Prod 32:81–87

    Google Scholar 

  17. Li K-M, Chou S-Y (2010) Experimental evaluation of minimum quantity lubrication in near micro-milling. J Mater Process Technol 210(15):2163–2170

    Google Scholar 

  18. Liao Y, Lin H, Chen Y (2007) Feasibility study of the minimum quantity lubrication in high-speed end milling of NAK80 hardened steel by coated carbide tool. Int J Mach Tools Manuf 47(11):1667–1676

    Google Scholar 

  19. Liao Y, Lin H (2007) Mechanism of minimum quantity lubrication in high-speed milling of hardened steel. Int J Mach Tools Manuf 47(11):1660–1666

    Google Scholar 

  20. Da Silva R et al (2011) Tool wear analysis in milling of medium carbon steel with coated cemented carbide inserts using different machining lubrication/cooling systems. Wear 271(9-10):2459–2465

    Google Scholar 

  21. Hadi M, Atefi R (2015) Effect of minimum quantity lubrication with gamma-Al 2 O 3 nanoparticles on surface roughness in milling AISI D3 steel. Indian J Sci Technol 8(S3):130–135

    Google Scholar 

  22. Rapoport L, Leshchinsky V, Lvovsky M, Nepomnyashchy O, Volovik Y, Tenne R (2002) Mechanism of friction of fullerenes. Ind Lubr Tribol 54(4):171–176

    Google Scholar 

  23. Zhang B-S, Xu BS, Xu Y, Gao F, Shi PJ, Wu YX (2011) Cu nanoparticles effect on the tribological properties of hydrosilicate powders as lubricant additive for steel–steel contacts. Tribol Int 44(7-8):878–886

    Google Scholar 

  24. Sharma AK, Tiwari AK, Dixit AR (2015) Improved machining performance with nanoparticle enriched cutting fluids under minimum quantity lubrication (MQL) technique: a review. Mater Today Proc 2(4-5):3545–3551

    Google Scholar 

  25. Rahmati B, Sarhan AA, Sayuti M (2014) Morphology of surface generated by end milling AL6061-T6 using molybdenum disulfide (MoS2) nanolubrication in end milling machining. J Clean Prod 66:685–691

    Google Scholar 

  26. Raza MH et al (2020) Investigating the effects of gating design on mechanical properties of aluminum alloy in sand casting process. J King Saud Univ Eng Sci

  27. Tahir W, Jahanzaib M, Ahmad W, Hussain S (2019) Surface morphology evaluation of hardened HSLA steel using cryogenic-treated brass wire in WEDM process. Int J Adv Manuf Technol 104(9):4445–4455

    Google Scholar 

  28. Tsao C (2009) Grey–Taguchi method to optimize the milling parameters of aluminum alloy. Int J Adv Manuf Technol 40(1-2):41–48

    Google Scholar 

  29. Ali MA et al (2020) Mechanical characterization of aged AA2026-AA2026 overcast joints fabricated by squeeze casting. Int J Adv Manuf Technol

  30. Raza MH, Sajid M, Wasim A, Hussain S, Jahanzaib M (2019) Modeling of the mechanical properties of directionally solidified Al-4.3% Cu alloy using response surface methodology. Int J Adv Manuf Technol 103(9):3913–3925

    Google Scholar 

  31. Alharthi NH, Bingol S, Abbas AT, Ragab AE, el-Danaf EA, Alharbi HF (2017) Optimizing cutting conditions and prediction of surface roughness in face milling of AZ61 using regression analysis and artificial neural network. Adv Mater Sci Eng 2017:1–8

    Google Scholar 

  32. Akhtar MU, Raza MH, Shafiq M (2018) Role of batch size in scheduling optimization of flexible manufacturing system using genetic algorithm. J Ind Eng Int:1–12

  33. Montgomery DC (2017) Design and analysis of experiments. Wiley

  34. Sofuoğlu MA, Çakır FH, Kuşhan MC, Orak S (2019) Optimization of different non-traditional turning processes using soft computing methods. Soft Comput 23(13):5213–5231

    Google Scholar 

  35. Gürgen S, Çakır FH, Sofuoğlu MA, Orak S, Kuşhan MC, Li H (2019) Multi-criteria decision-making analysis of different non-traditional machining operations of Ti6Al4V. Soft Comput 23(13):5259–5272

    Google Scholar 

  36. Sofuoğlu MA, Arapoğlu RA, Orak S (2017) Multi objective optimization of turning operation using hybrid decision making analysis. Anadolu Univ Sci Technol A Appl Sci Eng 18(3)

  37. Kartal F, Yerlikaya Z, Gökkaya H (2017) Effects of machining parameters on surface roughness and macro surface characteristics when the machining of Al-6082 T6 alloy using AWJT. Measurement 95:216–222

    Google Scholar 

  38. Kumar S, Saravanan I, Patnaik L (2020) Optimization of surface roughness and material removal rate in milling of AISI 1005 carbon steel using Taguchi approach. Mater Today Proc 22:654–658

    Google Scholar 

  39. Nguyen T-T (2019) Prediction and optimization of machining energy, surface roughness, and production rate in SKD61 milling. Measurement 136:525–544

    Google Scholar 

  40. Kulkarni HB et al (2020) Investigations on effect of nanofluid based minimum quantity lubrication technique for surface milling of Al7075-T6 aerospace alloy. Mater Today Proc 27:251–256

    Google Scholar 

  41. Park S (1996) Robust design and analysis for quality engineering. Boom Koninklijke Uitgevers

  42. Taguchi G, Phadke MS (1989) Quality engineering through design optimization. In: Quality control, robust design, and the Taguchi Method. Springer, pp 77–96

  43. Abouelatta O, Madl J (2001) Surface roughness prediction based on cutting parameters and tool vibrations in turning operations. J Mater Process Technol 118(1-3):269–277

    Google Scholar 

  44. Özel T, Hsu T-K, Zeren E (2005) 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 25(3-4):262–269

    Google Scholar 

  45. Suresh P, Rao PV, Deshmukh S (2002) A genetic algorithmic approach for optimization of surface roughness prediction model. Int J Mach Tools Manuf 42(6):675–680

    Google Scholar 

  46. Suresh Kumar Reddy N (2005) Venkateswara Rao, A genetic algorithmic approach for optimization of surface roughness prediction model in dry milling. Mach Sci Technol 9(1):63–84

    Google Scholar 

  47. Raza MH, Wasim A, Ali MA, Hussain S, Jahanzaib M (2018) Investigating the effects of different electrodes on Al6061-SiC-7.5 wt% during electric discharge machining. Int J Adv Manuf Technol 99(9-12):3017–3034

    Google Scholar 

  48. Cheng K (2008) Machining dynamics: fundamentals, applications and practices. Springer Science & Business Media

  49. Altintas Y, Ber A (2001) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Appl Mech Rev 54(5):B84–B84

    Google Scholar 

  50. Okafor AC, Nwoguh TO (2020) Comparative evaluation of soybean oil–based MQL flow rates and emulsion flood cooling strategy in high-speed face milling of Inconel 718. Int J Adv Manuf Technol 107(9):3779–3793

    Google Scholar 

  51. Liu G, Li X, Qin B, Xing D, Guo Y, Fan R (2004) Investigation of the mending effect and mechanism of copper nano-particles on a tribologically stressed surface. Tribol Lett 17(4):961–966

    Google Scholar 

  52. Peng DX, Kang Y, Hwang RM, Shyr SS, Chang YP (2009) Tribological properties of diamond and SiO2 nanoparticles added in paraffin. Tribol Int 42(6):911–917

    Google Scholar 

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Authors and Affiliations

  1. Department of Industrial and Manufacturing System Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong

    Muhammad Huzaifa Raza & Ray Y. Zhong

  2. Department of Industrial Engineering, University of Engineering and Technology Taxila, Taxila, Pakistan

    Faisal Hafeez & Asif Imran

Authors
  1. Muhammad Huzaifa Raza
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  2. Faisal Hafeez
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  3. Ray Y. Zhong
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  4. Asif Imran
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Correspondence to Muhammad Huzaifa Raza.

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Raza, M.H., Hafeez, F., Zhong, R.Y. et al. Investigation of surface roughness in face milling processes. Int J Adv Manuf Technol 111, 2589–2599 (2020). https://doi.org/10.1007/s00170-020-06188-8

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