Skip to main content
SpringerLink
Log in

Study of cutting forces using FE, ANOVA, and BPNN in elliptical vibration cutting of titanium alloy Ti-6Al-4V

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In this study, the finite element (FE) analysis, analysis of variance (ANOVA), and the back propagation neural network (BPNN) were employed to evaluate and predict the cutting forces of titanium alloy Ti-6Al-4V depending on vibration frequency, tangential amplitude, and thrust amplitude, as well as cutting speed during elliptical vibration cutting (EVC). A series of EVC simulations were conducted based on the verified FE model to evaluate the impacts of different EVC parameters on cutting forces. The results show that the tangential force decreases with increasing vibration frequency, tangential amplitude, and thrust amplitude, but with decreasing cutting speed. The positive and negative thrust forces decrease with increasing frequency and tangential amplitude, but with decreasing thrust amplitude and cutting speed. In addition, ANOVA results clearly indicated that the tangential amplitude is the dominant parameter affecting the cutting forces, and the percent contributions to cutting forces are 69.56%, 66.03%, and 62.83%, respectively. Further, the BPNN models with three different activation functions and different architectures are utilized to predict the cutting forces, and the best performance, in terms of agreement with the target outputs, can be achieved by the network using logarithmic sigmoid activation function and architecture with 15 neurons in one hidden layer. The correlation coefficients for training and testing the selected network are 0.99993 and 0.99916, and the mean square errors are 0.1963 and 2.6070, respectively, and these reveal that BPNN is fairly successful in predicting the cutting forces.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Pramanik A, Littlefair G (2015) Machining of titanium alloy (Ti-6Al-4V)—theory to application. Mach Sci Technol 19(1):1–49

    Article  Google Scholar 

  2. Arrazola PJ, Garay A, Iriarte LM, Armendia M, Marya S, Le Maitre F (2009) Machinability of titanium alloys (Ti6Al4V and Ti555. 3). J Mater Process Technol 209(5):2223–2230

    Article  Google Scholar 

  3. Shamoto E, Moriwaki T (1994) Study on elliptical vibration cutting. CIRP Ann Manuf Technol 43(1):35–38

    Article  Google Scholar 

  4. Zhou XQ, Zhao SX, Zhu ZW, Lin JQ, Luo D (2011) A study on elliptical vibration cutting by Finite Element Analysis. Adv Mater Res 230:1029–1033

    Google Scholar 

  5. Zhao HD, Li SG, Zou P, Kang D (2017) Process modeling study of the ultrasonic elliptical vibration cutting of Inconel 718. Int J Adv Manuf Technol 92(5-8):2055–2068

    Article  Google Scholar 

  6. Zhang CM, Li C, Zhang DY (2011) Study on the cutting force in ultrasonic elliptical vibration cutting of hardened stainless steel. Appl Mech Mater 55:327–331

    Article  Google Scholar 

  7. Shamoto E, Moriwaki T (1999) Ultaprecision diamond cutting of hardened steel by applying elliptical vibration cutting. CIRP Ann Manuf Technol 48(1):441–444

    Article  Google Scholar 

  8. Ma C, Shamoto E, Moriwaki T, Wang L (2004) Study of machining accuracy in ultrasonic elliptical vibration cutting. Int J Mach Tools Manuf 44(12-13):1305–1310

    Article  Google Scholar 

  9. Bai W, Sun R, Gao Y, Leopold J (2016) Analysis and modeling of force in orthogonal elliptical vibration cutting. Int J Adv Manuf Technol 83(5-8):1025–1036

    Article  Google Scholar 

  10. Kim GD, Loh BG (2007) Characteristics of elliptical vibration cutting in micro-V grooving with variations in the elliptical cutting locus and excitation frequency. J Micromech Microeng 18(2):025002

    Article  Google Scholar 

  11. Kim GD, Loh BG (2011) Direct machining of micro patterns on nickel alloy and mold steel by vibration assisted cutting. Int J Precis Eng Manuf 12(4):583–588

    Article  Google Scholar 

  12. He Y, Zou P, Zhu WL, Ehmann KF (2017) Ultrasonic elliptical vibration cutting of hard materials: simulation and experimental study. Int J Adv Manuf Technol 91(1-4):363–374

    Article  Google Scholar 

  13. Kong C, Wang D (2018) Numerical investigation of the performance of elliptical vibration cutting in machining of AISI 1045 steel. Int J Adv Manuf Technol 98(1-4):715–727

    Article  Google Scholar 

  14. Dai H, Chen J, Liu G (2019) A numerical study on subsurface quality and material removal during ultrasonic vibration assisted cutting of monocrystalline silicon by molecular dynamics simulation. Mater Res Express 6(6):065908

    Article  Google Scholar 

  15. D’addona DM, Teti R (2013) Genetic algorithm-based optimization of cutting parameters in turning processes. Procedia Cirp 7:323–328

    Article  Google Scholar 

  16. Santos MC, Machado AR, Barrozo MAS, Jackson MJ, Ezugwu EO (2015) Multi-objective optimization of cutting conditions when turning aluminum alloys (1350-O and 7075-T6 grades) using genetic algorithm. Int J Adv Manuf Technol 76(5-8):1123–1138

    Article  Google Scholar 

  17. Zendehboudi S, Ahmadi M, James L, Chatzis I (2012) Prediction of condensate-to-gas ratio for retrograde gas condensate reservoirs using artificial neural network with particle swarm optimization. Energy Fuel 26(6):3432–3447

    Article  Google Scholar 

  18. Crichigno Filho JM (2017) Applying extended Oxley’s machining theory and particle swarm optimization to model machining forces. Int J Adv Manuf Technol 89(1-4):1127–1136

    Article  Google Scholar 

  19. Xu S, Wang Y, Huang F (2017) Optimization of multi-pass turning parameters through an improved flower pollination algorithm. Int J Adv Manuf Technol 89(1-4):503–514

    Article  Google Scholar 

  20. Sortino M, Belfio S, Totis G, Di Gaspero L, Nali M (2015) An investigation on swarm intelligence methods for the optimization of complex part programs in CNC turning. Int J Adv Manuf Technol 80(1-4):657–672

    Article  Google Scholar 

  21. Akay B, Karaboga D (2012) Artificial bee colony algorithm for large-scale problems and engineering design optimization. J Intell Manuf 23(4):1001–1014

    Article  Google Scholar 

  22. Khorasani A, Yazdi MRS (2017) Development of a dynamic surface roughness monitoring system based on artificial neural networks (ANN) in milling operation. Int J Adv Manuf Technol 93(1-4):141–151

    Article  Google Scholar 

  23. Erkan Ö, Işık B, Çiçek A, Kara F (2013) Prediction of damage factor in end milling of glass fibre reinforced plastic composites using artificial neural network. Appl Compos Mater 20(4):517–536

    Article  Google Scholar 

  24. Rao KV, Murthy BSN, Rao NM (2014) Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network. Measurement 51:63–70

    Article  Google Scholar 

  25. Ali SM, Dhar NR (2010) Tool wear and surface roughness prediction using an artificial neural network (ANN) in turning steel under minimum quantity lubrication (MQL). World Acad Sci Eng Technol 62:830–839

    Google Scholar 

  26. Rao KV, Murthy PBGSN (2018) Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. J Intell Manuf 29(7):1533–1543

    Article  Google Scholar 

  27. Suksawat B (2010) Chip form classification and main cutting force prediction of cast nylon in turning operation using artificial neural network. In: International Conference on Computer Applications in Shipbuilding 2010, IEEE, pp 172–175

  28. Kara F, Aslantaş K, Cicek A (2015) ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316 L stainless steel. Neural Comput & Applic 26(1):237–250

    Article  Google Scholar 

  29. Mia M, Khan MA, Dhar NR (2017) 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 93(1-4):975–991

    Article  Google Scholar 

  30. Johnson GR, Cook WH (1983) A constitutive model and data for metals subjected to large strains, high strain rates and high temperatures. In: The 7th International Symposium on Ballistics, The Hague, The Netherlands, pp 541–547

  31. ABAQUS, Inc (2017) ABAQUS user’s manual. ABAQUS Inc., Palo Alto

    Google Scholar 

  32. Johnson GR, Cook WH (1985) Fracture characteristics of three metals subjected to various strains, strain rates, temperatures and pressures. Eng Fract Mech 21(1):31–48

    Article  Google Scholar 

  33. Hillerborg A, Modéer M, Petersson PE (1976) Analysis of crack formation and crack growth in concrete by means of fracture mechanics and finite elements. Cem Concr Res 6 6(6):773–781

    Article  Google Scholar 

  34. Liu S (2007) FEM simulation and experiment research of cutting temperature and force in orthogonal cutting of titanium alloys. Master's thesis of Nanjing University of Aeronautics & Astronautics 1

  35. Lesuer DR (2001) Experimental investigations of material models for Ti-6AL-4V titanium and 2024-T3 aluminium. Department of Transportation Federal Aviation Administration, Washington, D.C

    Google Scholar 

  36. Ambati R, Yuan H (2011) FEM mesh-dependence in cutting process simulations. Int J Adv Manuf Technol 53(1-4):313–323

    Article  Google Scholar 

  37. Chen G, Ren C, Yang X, Jin X, Guo T (2011) Finite element simulation of high-speed machining of titanium alloy (Ti–6Al–4V) based on ductile failure model. Int J Adv Manuf Technol 56(9-12):1027–1038

    Article  Google Scholar 

  38. Editor committee of China aeronautical materials handbook (2001) China aeronautical materials handbook. China Standard Press, Beijing, pp 104–107 (in Chinese)

    Google Scholar 

  39. Zhang YC, Mabrouki T, Nelias D, Gong YD (2011) Chip formation in orthogonal cutting considering interface limiting shear stress and damage evolution based on fracture energy approach. Finite Elem Anal Des 47(7):850–863

    Article  Google Scholar 

  40. Karpat Y (2011) Temperature dependent flow softening of titanium alloy Ti6Al4V: an investigation using finite element simulation of machining. J Mater Process Technol 211(4):737–749

    Article  Google Scholar 

  41. Cotterell M, Byrne G (2008) Characterisation of chip formation during orthogonal cutting of titanium alloy Ti–6Al–4V. CIRP J Manuf Sci Technol 1(2):81–85

    Article  Google Scholar 

  42. Malinov S, Sha W, McKeown JJ (2001) Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural network. Comput Mater Sci 21(3):375–394

    Article  Google Scholar 

  43. Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993

    Article  Google Scholar 

  44. MacKay DJ (1992) Bayesian interpolation. Neural Comput 4(3):415–447

    Article  MATH  Google Scholar 

  45. Ezugwu EO, Fadare DA, Bonney J, Da Silva RB, Sales WF (2005) Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. Int J Mach Tools Manuf 45(12-13):1375–1385

    Article  Google Scholar 

  46. Mia M, Dhar NR (2016) Prediction of surface roughness in hard turning under high pressure coolant using artificialneural network. Measurement 92:464–474

    Article  Google Scholar 

  47. Nielsen RH (1989) Theory of the back propagation neural network. In: Proceedings of the International Joint Conference on Neural Networks, 18–22 June 1989, vol 1. IEEE, New York, Washington, DC, pp 593–605

    Google Scholar 

Download references

Funding

Authors would like to express sincere gratitude to the support from the National Natural Science Foundation of China under 51775457 and Science Challenge Project TZ2018006-0101-04.

Author information

Authors and Affiliations

  1. Tribology Research Institute, Department of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Haibo Xie & Zhanjiang Wang

Authors
  1. Haibo Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhanjiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhanjiang Wang.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, H., Wang, Z. Study of cutting forces using FE, ANOVA, and BPNN in elliptical vibration cutting of titanium alloy Ti-6Al-4V. Int J Adv Manuf Technol 105, 5105–5120 (2019). https://doi.org/10.1007/s00170-019-04537-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-019-04537-w

Keywords

Search

Navigation