Skip to main content
SpringerLink
Log in

Investigation of parametric control method and model in abrasive belt grinding of nickel-based superalloy blade

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

Abstract

Abrasive belt grinding is gradually recognized as an effective machining technology for blade. However, the complex contact characteristics of this technology make the grinding quality of blade unable to be precisely controlled. In order to solve this problem, a parametric control method for precision abrasive belt grinding of blade was proposed, and a multi-parameter test platform for state parameters (grinding force, vibration, and temperature) was established. The grinding experiments of nickel-based superalloy samples were carried out, and the prediction model of state parameters based on back propagation neural network was constituted. The prediction accuracy of the model was 93.58%. On this basis, the grinding experiments of nickel-based superalloy blade were conducted. The influence of state parameters on the evaluation parameters (material removal, profile accuracy, surface quality) was analyzed and the optimal grinding parameters were determined. The experimental results showed that the machining quality of new-type aeroengine blades can be effectively improved by this parametric control method and model.

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
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Fu YZ, Wang XP, Gao H, Wei HB, Li SC (2016) Blade surface uniformity of blisk finished by abrasive flow machining. Int J Adv Manuf Technol 84(5-8):1725–1735. https://doi.org/10.1007/s00170-015-8270-0

    Article  Google Scholar 

  2. Klocke F, Zeis M, Klink A (2015) Interdisciplinary modelling of the electrochemical machining process for engine blades. CIRP Ann Manuf Technol 64(1):217–220. https://doi.org/10.1016/j.cirp.2015.04.071

    Article  Google Scholar 

  3. Li X, Meng FJ, Cui W, Ma S (2015) The CNC grinding of integrated impeller with electroplated CBN wheel. Int J Adv Manuf Technol 79(5-8):1353–1361. https://doi.org/10.1007/s00170-015-6904-x

    Article  Google Scholar 

  4. Cho SS, Ryu YK, Lee SY (2002) Curved surface finishing with flexible abrasive tool. Int J Mach Tools Manuf 42(2):229–236. https://doi.org/10.1016/S0890-6955(01)00106-7

    Article  Google Scholar 

  5. Axinte DA, Kritmanorot M, Axinte M, Gindy NNZ (2005) Investigations on belt polishing of heat-resistant titanium alloys. J Mater Process Technol 166(3):398–404. https://doi.org/10.1016/j.jmatprotec.2004.08.030

    Article  Google Scholar 

  6. Xiao GJ, Huang Y (2016) Equivalent self-adaptive belt grinding for the real-R edge of an aero-engine precision-forged blade. Int J Adv Manuf Technol 83(9-12):1697–1706. https://doi.org/10.1007/s00170-015-7680-3

    Article  Google Scholar 

  7. Pandiyan V, Tjahjowidodo T, Samy MP (2016) In-process surface roughness estimation model for compliant abrasive belt machining process. Proc CIRP 46:254–257. https://doi.org/10.1016/j.procir.2016.03.126

    Article  Google Scholar 

  8. Pandiyan V, Caesarendra W, Tjahjowidodo T, Praveen G (2017) Predictive modelling and analysis of process parameters on material removal characteristics in abrasive belt grinding process. Appl Sci Basel 7(4). https://doi.org/10.3390/app7040363

  9. Wang YJ, Huang Y, Chen YX, Yang ZS (2016) Model of an abrasive belt grinding surface removal contour and its application. Int J Adv Manuf Technol 82(9-12):2113–2122. https://doi.org/10.1007/s00170-015-7484-5

    Article  Google Scholar 

  10. Xiao GJ, Huang Y (2017) Adaptive belt precision grinding for the weak rigidity deformation of blisk leading and trailing edge. Adv Mech Eng 9(10):12. https://doi.org/10.1177/1687814017731705

    Article  Google Scholar 

  11. Serpin K, Mezghani S, El Mansori M (2015) Multiscale assessment of structured coated abrasive grits in belt finishing process. Wear 332:780–787. https://doi.org/10.1016/j.wear.2015.01.054

    Article  Google Scholar 

  12. Subrahmanya N, Shin YC (2008) Automated sensor selection and fusion for monitoring and diagnostics of plunge grinding. J Manuf Sci Eng-Trans ASME 130(3):11. https://doi.org/10.1115/1.2927439

    Article  Google Scholar 

  13. Nguyen D, Yin SH, Tang QC, Son PX, Duc LA (2019) Online monitoring of surface roughness and grinding wheel wear when grinding Ti-6Al-4 V titanium alloy using ANFIS-GPR hybrid algorithm and Taguchi analysis. Precis Eng J Int Soc Precis Eng Nanotechnol 55:275–292. https://doi.org/10.1016/j.precisioneng.2018.09.018

    Article  Google Scholar 

  14. Cheng C, Li JY, Liu YM, Nie M, Wang WX (2019) Deep convolutional neural network-based in-process tool condition monitoring in abrasive belt grinding. Comput Ind 106:1–13. https://doi.org/10.1016/j.compind.2018.12.002

    Article  Google Scholar 

  15. Pandiyan V, Caesarendra W, Tjahjowidodo T, Tan HH (2018) In-process tool condition monitoring in compliant abrasive belt grinding process using support vector machine and genetic algorithm. J Manuf Process 31:199–213. https://doi.org/10.1016/j.jmapro.2017.11.014

    Article  Google Scholar 

  16. Zou L, Liu X, Huang Y, Fei Y (2019) A numerical approach to predict the machined surface topography of abrasive belt flexible grinding. Int J Adv Manuf Technol 104(5-8):2961–2970. https://doi.org/10.1007/s00170-019-04032-2

    Article  Google Scholar 

  17. Guo MX, Li BZ, Ding ZS, Liang SY (2016) Empirical modeling of dynamic grinding force based on process analysis. Int J Adv Manuf Technol 86(9-12):3395–3405. https://doi.org/10.1007/s00170-016-8465-z

    Article  Google Scholar 

  18. Liu Y, Li Q, Xiao GJ, Huang Y (2019) Study of the vibration mechanism and process optimization for abrasive belt grinding for a Blisk-Blade. IEEE Access 7:24829–24842. https://doi.org/10.1109/Access.2019.2899495

    Article  Google Scholar 

  19. Yan SJ, Xu XH, Yang ZY, Zhu DH, Ding H (2019) An improved robotic abrasive belt grinding force model considering the effects of cut-in and cut-off. J Manuf Process 37:496–508. https://doi.org/10.1016/j.jmapro.2018.12.029

    Article  Google Scholar 

  20. Zou L, Huang Y, Zhang G, Cui X (2019) Feasibility study of a flexible grinding method for precision machining of the TiAl-based alloy. Mater Manuf Process 34(10):1160–1168. https://doi.org/10.1080/10426914.2019.1628255

    Article  Google Scholar 

  21. Ren X, Cabaravdic M, Zhang X, Kuhlenkotter B (2007) A local process model for simulation of robotic belt grinding. Int J Mach Tools Manuf 47(6):962–970. https://doi.org/10.1016/j.ijmachtools.2006.07.002

    Article  Google Scholar 

  22. Cheng J, Li YC, Zhou JH, Liu JZ, Cen KF (2010) Maximum solid concentrations of coal water slurries predicted by neural network models. Fuel Process Technol 91(12):1832–1838. https://doi.org/10.1016/j.fuproc.2010.08.007

    Article  Google Scholar 

  23. Arriandiaga A, Portillo E, Sanchez JA, Cabanes I, Zubizarreta A (2017) Recurrent ANN-based modelling of the dynamic evolution of the surface roughness in grinding. Neural Comput Applic 28(6):1293–1307. https://doi.org/10.1007/s00521-016-2568-1

    Article  Google Scholar 

  24. Klocke F, Soo SL, Karpuschewski B, Webster JA, Novovic D, Elfizy A, Axinte DA, Tonissen S (2015) Abrasive machining of advanced aerospace alloys and composites. CIRP Ann Manuf Technol 64(2):581–604. https://doi.org/10.1016/j.cirp.2015.05.004

    Article  Google Scholar 

  25. Dai CW, Ding WF, Zhu YJ, Xu JH, Yu HW (2018) Grinding temperature and power consumption in high speed grinding of Inconel 718 nickel-based superalloy with a vitrified CBN wheel. Precis Eng J Int Soc Precis Eng Nanotechnol 52:192–200. https://doi.org/10.1016/j.precisioneng.2017.12.005

    Article  Google Scholar 

  26. Erdik T, Sen Z (2009) Prediction of tool wear using regression and ANN models in end-milling operation a critical review. Int J Adv Manuf Technol 43(7-8):765–766. https://doi.org/10.1007/s00170-008-1758-0

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 51875064) and the Fundamental Research Funds for the Central Universities (Grant No. 2019CDJGFJX003).

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing, 400044, China

    Zhaorui Li, Lai Zou, Jiachao Yin & Yun Huang

Authors
  1. Zhaorui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lai Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiachao Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yun Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lai Zou.

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

Li, Z., Zou, L., Yin, J. et al. Investigation of parametric control method and model in abrasive belt grinding of nickel-based superalloy blade. Int J Adv Manuf Technol 108, 3301–3311 (2020). https://doi.org/10.1007/s00170-020-05607-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-020-05607-0

Keywords

Search

Navigation