Skip to main content

Investigations of surface quality and energy consumption associated with costs and material removal rate during face milling of AISI 1045 steel

Abstract

Machining of AISI 1045 steel is prominent in several industries due to their good machining characteristics. In this study, the optimum conditions of fly (face) milling of parts made of AISI 1045 steel was analyzed. The generated surface quality, the cost of the cutting tool components, the energy consumption, the wearing of the cutting tool, and material removal rate are the main parameters in this study. Several cutting experiments over different cutting lengths have been conducted and analyzed statistically to determine the optimum targeted cutting conditions. A multilayer regression analysis was conducted on obtained experimental results and inducing non-linear mathematical equations with high coefficient of determination (R2 = 0.98). The influence of feed per tooth (fz), cutting speed (vc), flank wear (VB) to surface roughness (Rz), cutting power (Pc), material removal rate (MRR), sliding distance (ls), and the tool life (T/) has been considered. The overall results, estimated through Grey relational analysis (GRA), revealed that the optimum fly milling performance for a fast manufacturing (case 1) are obtained for feed per tooth fz = 0.25 mm/tooth, cutting speed vc = 392.6 m/min, and machined length l = 5 mm. While the optimum parameters for resource (tools) conservation (case 2) are feed per tooth fz = 0.125 mm/tooth, cutting speed vc = 392.6 m/min, and machined length l = 5 mm.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

References

  1. 1.

    Annual energy review - energy information administration, 2011: https://www.eia.gov/totalenergy/data/annual. Accessed on 2011

  2. 2.

    López De Lacalle LN, Lamikiz A, Sánchez JA, Fernández De Bustos I (2006) Recording of real cutting forces along the milling of complex parts. Mechatronics 16(1):21–32. https://doi.org/10.1016/j.mechatronics.2005.09.001

    Article  Google Scholar 

  3. 3.

    Araújo Junior AS, Sales WF, da Silva RB, Costa ES, Rocha Machado Á (2017) Lubri-cooling and tribological behavior of vegetable oils during milling of AISI 1045 steel focusing on sustainable manufacturing. J Clean Prod 156:635–647. https://doi.org/10.1016/j.jclepro.2017.04.061

    Article  Google Scholar 

  4. 4.

    Abbas AT, Pimenov DY, Erdakov IN, Mikolajczyk T, Soliman MS, El Rayes MM (2019) Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel. Int J Adv Manuf Technol 105(5–6):2151–2165. https://doi.org/10.1007/s00170-019-04327-4

    Article  Google Scholar 

  5. 5.

    Dennison MS, Meji MA, Nelson AJR, Balakumar S, Prasath K (2019) A comparative study on the surface finish achieved during face milling of AISI 1045 steel components using eco-friendly cutting fluids in near dry condition. Int J Mach Mach Mater 21(5–6):337–356. https://doi.org/10.1504/IJMMM.2019.103128

    Article  Google Scholar 

  6. 6.

    Uriarte L, Azcárate S, Herrero A, Lopez De Lacalle LN, Lamikiz A (2008) Mechanistic modelling of the micro end milling operation. Proc Inst Mech Eng P B J Eng Manuf 222(1):23–33. https://doi.org/10.1243/09544054JEM837

    Article  Google Scholar 

  7. 7.

    Srivatsan H, Purimetla U, Dattathreya V, Gangadhar V, Marimuthu P (2019) Effect of cutting conditions on the residual stresses induced by milling of AISI 1045 steel. Int J Recent Technol Eng 8(2):1462–1464. https://doi.org/10.35940/ijrte.B2106.078219

    Article  Google Scholar 

  8. 8.

    Padma Ooha DNV, Prakash Marimuthu K, Thenarasu M (2019) Effect of speed, feed and depth of cut on machining induced residual stresses in aisi 1045 steel. Int J Recent Technol Eng 8(2):3397–3400. https://doi.org/10.35940/ijrte.A1262.078219

    Article  Google Scholar 

  9. 9.

    D'Errico GE, Bugliosi S, Guglielmi E (1998) Tool-life reliability of cermet inserts in milling tests. J Mater Process Technol 300(3–4):337–343. https://doi.org/10.1016/S0924-0136(97)00437-8

    Article  Google Scholar 

  10. 10.

    Richetti A, Machado ÁR, Da Silva MB, Ezugwu EO, Bonney J (2005) Influence of the number of inserts for tool life evaluation in face milling of steels. Int J Mach Tool Manuf (7-8):695–700. https://doi.org/10.1016/j.ijmachtools.2004.02.007

  11. 11.

    Muñoz-Escalona P, Díaz N, Cassier Z (2012) Prediction of tool wear mechanisms in face milling AISI 1045 steel. J Mater Eng Perform 21(6):797–808. https://doi.org/10.1007/s11665-011-9964-6

    Article  Google Scholar 

  12. 12.

    Pimenov DY, Hassui A, Wojciechowski S, Mia M, Magri A, Suyama DI, Bustillo A, Krolczyk G, Gupta MK (2019) Effect of the relative position of the face milling tool towards the workpiece on machined surface roughness and milling dynamics. Appl Sci 9(5):842. https://doi.org/10.3390/app9050842

    Article  Google Scholar 

  13. 13.

    Ali RA, Mia M, Khan AM, Chen W, Gupta MK, Pruncu CI (2019) Multi-response optimization of face milling performance considering tool path strategies in machining of Al-2024. Materials 12(7):1013. https://doi.org/10.3390/ma12071013

    Article  Google Scholar 

  14. 14.

    Toledo JVR, Arruda EM, Júnior SSC, Diniz AE, Ferreira JR (2018) Performance of wiper geometry carbide tools in face milling of AISI 1045 steel. J Braz Soc Mech Sci Eng 40(10):478–415. https://doi.org/10.1007/s40430-018-1400-5

    Article  Google Scholar 

  15. 15.

    Pimenov DY (2014) Experimental research of face mill wear effect to flat surface roughness. J Frict Wear 35(3):250–254. https://doi.org/10.3103/S1068366614030118

    Article  Google Scholar 

  16. 16.

    Hu L, Peng C, Evans S, Peng T, Liu Y, Tang R, Tiwari A (2017) Minimising the machining energy consumption of a machine tool by sequencing the features of a part. Energy 121:292–305. https://doi.org/10.1016/j.energy.2017.01.039

    Article  Google Scholar 

  17. 17.

    Hu L, Liu Y, Peng C, Tang W, Tang R, Tiwari A (2018) Minimising the energy consumption of tool change and tool path of machining by sequencing the features. Energy 147:390–402. https://doi.org/10.1016/j.energy.2018.01.046

    Article  Google Scholar 

  18. 18.

    Li L, Yan J, Xing Z (2013) Energy requirements evaluation of milling machines based on thermal equilibrium and empirical modelling. J Clean Prod 52:113–121. https://doi.org/10.1016/j.jclepro.2013.02.039

    Article  Google Scholar 

  19. 19.

    Aramcharoen A, Mativenga PT (2014) Critical factors in energy demand modelling for CNC milling and impact of toolpath strategy. J Clean Prod 78:63–74. https://doi.org/10.1016/j.jclepro.2014.04.065

    Article  Google Scholar 

  20. 20.

    Garg A, Lam JSL, Gao L (2015) Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach. J Clean Prod 108:34–45. https://doi.org/10.1016/j.jclepro.2015.06.043

    Article  Google Scholar 

  21. 21.

    Albertelli P, Keshari A, Matta A (2016) Energy oriented multi cutting parameter optimization in face milling. J Clean Prod 137:1602–1618. https://doi.org/10.1016/j.jclepro.2016.04.012

    Article  Google Scholar 

  22. 22.

    Garg A, Lam JSL, Gao L (2016) Power consumption and tool life models for the production process. J Clean Prod 131:754–764. https://doi.org/10.1016/j.jclepro.2016.04.099

    Article  Google Scholar 

  23. 23.

    Shnfir M, Olufayo OA, Jomaa W, Songmene V (2019) Machinability study of hardened 1045 steel when milling with ceramic cutting inserts. Materials 12(23):3974. https://doi.org/10.3390/ma12233974

    Article  Google Scholar 

  24. 24.

    Khan AM, Jamil M, Salonitis K, Sarfraz S, Zhao W, He N, Mia M, Zhao G (2019) Multi-objective optimization of energy consumption and surface quality in nanofluid SQCl assisted face milling. Energies 12(4):710. https://doi.org/10.3390/en12040710

    Article  Google Scholar 

  25. 25.

    Yang W-A, Guo Y, Liao W (2011) Multi-objective optimization of multi-pass face milling using particle swarm intelligence. Int J Adv Manuf Technol 56(5):429–443. https://doi.org/10.1007/s00170-011-3187-8

    Article  Google Scholar 

  26. 26.

    Yang Y, Li X, Gao L, Shao X (2016) Modeling and impact factors analyzing of energy consumption in CNC face milling using GRASP gene expression programming. Int J Adv Manuf Technol 87(5):1247–1263. https://doi.org/10.1007/s00170-013-5017-7

    Article  Google Scholar 

  27. 27.

    Wang Y-C, Kim D-W, Katayama H, Hsueh W-C (2018) Optimization of machining economics and energy consumption in face milling operations. Int J Adv Manuf Technol 99(9–12):2093–2100. https://doi.org/10.1007/s00170-018-1848-6

    Article  Google Scholar 

  28. 28.

    Sales W, Becker M, Barcellos CS, Landre J Jr, Bonney J, Ezugwu EO (2009) Tribological behaviour when face milling AISI 4140 steel with minimum quantity fluid application. Ind Lubr Tribol 61(2):84–90. https://doi.org/10.1108/00368790910940400

    Article  Google Scholar 

  29. 29.

    Singh GR, Gupta MK, Mia M, Sharma VS (2018) Modeling and optimization of tool wear in MQL-assisted milling of Inconel 718 superalloy using evolutionary techniques. Int J Adv Manuf Technol 97(1–4):481–494. https://doi.org/10.1007/s00170-018-1911-3

    Article  Google Scholar 

  30. 30.

    Gupta MK, Pruncu CI, Mia M, Singh G, Singh S, Prakash C, Sood PK, Gill HS (2018) Machinability investigations of Inconel-800 super alloy under sustainable cooling conditions. Materials 11:2088. https://doi.org/10.3390/ma11112088

    Article  Google Scholar 

  31. 31.

    Siller HR, Vila C, Rodríguez CA, Abellán JV (2009) Study of face milling of hardened AISI D3 steel with a special design of carbide tools. Int J Adv Manuf Technol 40(1):12–25. https://doi.org/10.1007/s00170-007-1309-0

    Article  Google Scholar 

  32. 32.

    Cui X, Zhao J (2014) Cutting performance of coated carbide tools in high-speed face milling of AISI H13 hardened steel. Int J Adv Manuf Technol (9-12):1811–1824. https://doi.org/10.1007/s00170-014-5611-3

  33. 33.

    Houchuan Y, Zhitong C, ZiTong Z (2015) Influence of cutting speed and tool wear on the surface integrity of the titanium alloy Ti-1023 during milling. Int J Adv Manuf Technol 78(5–8):1113–1126. https://doi.org/10.1007/s00170-014-6593-x

    Article  Google Scholar 

  34. 34.

    Liu G, Zou B, Huang C, Wang X, Wang J, Liu Z (2016) Tool damage and its effect on the machined surface roughness in high-speed face milling the 17-4PH stainless steel. Int J Adv Manuf Technol 83(1–4):257–264. https://doi.org/10.1007/s00170-015-7564-6

    Article  Google Scholar 

  35. 35.

    Bruni C, d’Apolito L, Forcellese A, Gabrielli F, Simoncini M (2008) Surface roughness modelling in finish face milling under MQL and dry cutting conditions. Int J Mater Form 1(1):503–506. https://doi.org/10.1007/s12289-008-0151-8

    Article  Google Scholar 

  36. 36.

    Sahu NK, Andhare AB (2017) Modelling and multiobjective optimization for productivity improvement in high speed milling of Ti–6Al–4V using RSM and GA. J Braz Soc Mech Sci Eng 39(12):5069–5085. https://doi.org/10.1007/s40430-017-0804-y

    Article  Google Scholar 

  37. 37.

    Siwawut S, Saikaew C, Wisitsoraat A, Surinphong S (2018) Cutting performances and wear characteristics of WC inserts coated with TiAlSiN and CrTiAlSiN by filtered cathodic arc in dry face milling of cast iron. Int J Adv Manuf Technol 97(9):3883–3892. https://doi.org/10.1007/s00170-018-2200-x

    Article  Google Scholar 

  38. 38.

    Pimenov DY (2015) Mathematical modeling of power spent in face milling taking into consideration tool wear. J Frict Wear 36(1):45–48. https://doi.org/10.3103/S1068366615010110

    Article  Google Scholar 

  39. 39.

    Guzeev VI, Pimenov DY (2011) Cutting force in face milling with tool wear. Russ Eng Res 31(10):989–993. https://doi.org/10.3103/S1068798X11090139

    Article  Google Scholar 

  40. 40.

    Pimenov DY, Guzeev VI (2017) Mathematical model of plowing forces to account for flank wear using FME modeling for orthogonal cutting scheme. Int J Adv Manuf Technol 89(9–12):3149–3159. https://doi.org/10.1007/s00170-016-9216-x

    Article  Google Scholar 

  41. 41.

    D’yakonov AA (2012) Improvement of grinding speeds by assessing the machinability of materials. Russ Eng Res 32(7–8):604–607. https://doi.org/10.3103/S1068798X12060068

    Article  Google Scholar 

  42. 42.

    Bronshtein IN, Semendyaev KA (2010) Spravochnik po matematikedlyainzheneroviuchashchikhsyavtuzov: Uchebnoeposobie (A handbook on mathematics for engineers and students of High Educ. Tech. Inst.: a tutorial). St. Petersburg: Lan’

  43. 43.

    Abbas AT, Pimenov DY, Erdakov IN, Mikolajczyk T, El Danaf EA, Taha MA (2017) Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method. Int J Adv Manuf Technol 93(5–8):2375–2392. https://doi.org/10.1007/s00170-017-0678-2

    Article  Google Scholar 

Download references

Funding

This work was financially supported by the Deanship of Scientific Research at King Saud University through research group no. RGP-1439-020. The research was also supported through Act 211 Government of the Russian Federation, contract no. 02.A03.21.0011.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Danil Yu. Pimenov.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pimenov, D.Y., Abbas, A.T., Gupta, M.K. et al. Investigations of surface quality and energy consumption associated with costs and material removal rate during face milling of AISI 1045 steel. Int J Adv Manuf Technol 107, 3511–3525 (2020). https://doi.org/10.1007/s00170-020-05236-7

Download citation

Keywords

  • Face milling
  • Fly milling
  • Cost saving
  • Power consumption
  • Surface roughness
  • Tool wear
  • Non-linear regression analysis