Skip to main content
SpringerLink
Log in

A novel toolpath for 7-NC grinding of blades with force-position matching

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

Abstract

The characteristic of uneven machining allowance of blades and the flexible contact properties of the belt grinding makes the traditional toolpath of the grinding tool difficult to precisely control the machining profile. In this paper, a novel toolpath planning method based on force-position matching is proposed to perform an efficient grinding process for aero-engine blades. A material removal rate (MRR) model is established through the orthogonal grinding experiments of titanium alloy sample, and the point-by-point adjustment of the 7th axis is controlled based on this model and the machining allowance distribution. Subsequently, the step length is calculated based on the Taylor expansion method, and the post-processing generation of the self-developed 7th axis NC machining tool is carried out based on the double vector control method. On this basis, the comparative experimental results revealed that the average surface profile accuracy of blades of the proposed method was 0.019 mm, which was improved by 54.76% than that of the traditional method. Moreover, the average surface roughness and the variation range of surface roughness were achieved to 0.34 µm and 0.14 µm, which were improved by 27.7% and 33.3% than that of the former method. It is concluded that this research is beneficial to comprehensively improve the machined quality of blades with uneven machining allowance distribution in NC belt grinding.

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

Similar content being viewed by others

Availability of data and materials

The authors confirm that the data supporting the findings of this study are available within the article.

References

  1. Wang T, Zou L, Wan Q, Zhang X, Li Y, Huang Y (2021) A high-precision prediction model of surface roughness in abrasive belt flexible grinding of aero-engine blade. J Manuf Process 66:364–375. https://doi.org/10.1016/j.jmapro.2021.04.002

  2. Wang J, Zhang D, Wu B, Luo M, Zhang Y (2015) Kinematic analysis and feedrate optimization in six-axis NC abrasive belt grinding of blades. Int J Adv Manuf Technol 79(1–4):405–414. https://doi.org/10.1007/s00170-015-6824-9

  3. Zhou K, Liu J, Xiao G, Huang Y, Song K, Xu J, Chen B (2021) Probing residual stress evolution of titanium alloy due to belt grinding based on molecular dynamics method. J Manuf Process 66:446–459. https://doi.org/10.1016/j.jmapro.2021.04.043

  4. Wu S, Kazerounian K, Gan Z, Sun Y (2014) A material removal model for robotic belt grinding process. Mach Sci Technol 18(1):15–30. https://doi.org/10.1080/10910344.2014.863623

  5. Wei H, Peng C, Gao H, Wang X, Wang X (2019) On establishment and validation of a new predictive model for material removal in abrasive flow machining. Int J Mach Tools Manuf 138:66–79. https://doi.org/10.1016/j.ijmachtools.2018.12.003

  6. Qi J, Zhang D, Li S, Chen B (2016) A micro-model of the material removal depth for the polishing process. Int J Adv Manuf Technol 86(9–12):2759–2770. https://doi.org/10.1007/s00170-016-8385-y

  7. Ren L, Zhang G, Zhang L, Zhang Z, Huang Y (2019) Modelling and investigation of material removal profile for computer controlled ultra-precision polishing. Precis Eng 55:144–153. https://doi.org/10.1016/j.precisioneng.2018.08.020

  8. Yang Z, Chu Y, Xu X, Huang H, Zhu D, Yan S, Ding H (2021) Prediction and analysis of material removal characteristics for robotic belt grinding based on single spherical abrasive grain model Int J Mech Sci 190. https://doi.org/10.1016/j.ijmecsci.2020.106005

  9. Wang G, Wang Y, Xu Z (2009) Modeling and analysis of the material removal depth for stone polishing. J Mater Process Technol 209(5):2453–2463. https://doi.org/10.1016/j.jmatprotec.2008.05.041

  10. Li J, Wei Z, Wang T, Cheng J, He Q (2017) A theoretical model incorporating both the nano-scale material removal and wafer global uniformity during planarization process. Thin Solid Films 636:240–246. https://doi.org/10.1016/j.tsf.2017.06.020

  11. Fan C, Zhang L, Zhao Q, Zhao J, Zhao J, Sun L (2018) Modeling and optimization of material removal influenced by sliding velocity in polishing. Proc Inst Mech Eng Part B-J Eng Manuf 233(4):1127–1135. https://doi.org/10.1177/0954405418774589

  12. Fan W, Wang W, Wang J, Zhang X, Qian C, Ma T (2020) Microscopic contact pressure and material removal modeling in rail grinding using abrasive belt. Proc Inst Mech Eng Part B-J Eng Manuf 235(1–2):3–12. https://doi.org/10.1177/0954405420932419

  13. Satyarthi MK, Pandey PM (2013) Modeling of material removal rate in electric discharge grinding process. Int J Mach Tools Manuf 74:65–73. https://doi.org/10.1016/j.ijmachtools.2013.07.008

  14. Kumar S, Dvivedi A (2020) Development of material removal rate model and performance evaluation of ultrasonic turning process. Mater Manuf Process 35(14):1598–1611. https://doi.org/10.1080/10426914.2020.1784929

  15. Zarepour H, Yeo SH (2012) Predictive modeling of material removal modes in micro ultrasonic machining. Int J Mach Tools Manuf 62:13–23. https://doi.org/10.1016/j.ijmachtools.2012.06.005

  16. 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. Proc Inst Mech Eng Part B-J Eng Manuf 40:131–138. https://doi.org/10.1016/j.precisioneng.2014.10.014

  17. Fan W, Lee C, Chen J, Xiao Y (2015) Real-time Bezier interpolation satisfying chord error constraint for CNC tool path. Sci China-Technol Sci 59(2):203–213. https://doi.org/10.1007/s11431-015-5949-2

  18. Sarkar S, Dey PP (2015) Tool path planning for machining free-form surfaces. Trans FAMENA 39(1):65–78

    Google Scholar 

  19. Hu P, Chen L, Tang K (2017) Efficiency-optimal iso-planar tool path generation for five-axis finishing machining of freeform surfaces. Comput Aided Des 83:33–50. https://doi.org/10.1016/j.cad.2016.10.001

  20. Huang Z, Song R, Wan C, Wei P, Wang H (2019) Trajectory planning of abrasive belt grinding for aero-engine blade profile. Int J Adv Manuf Technol 102(1–4):605–614. https://doi.org/10.1007/s00170-018-3187-z

  21. Ma K, Han L, Sun X, Liang C, Zhang S, Shi Y, Wang X (2020) A path planning method of robotic belt grinding for workpieces with complex surfaces. IEEE-ASME Trans. Mechatron 25(2):728–738. https://doi.org/10.1109/tmech.2020.2974925

  22. Wen Y, Jaeger DJ, Pagilla PR (2022) Uniform coverage tool path generation for robotic surface finishing of curved surfaces. IEEE Robot Autom Lett 7(2):4931–4938. https://doi.org/10.1109/lra.2022.3152695

  23. Li W, Wang G, Zhang G, Pang C, Yin Z (2016) A novel path generation method of onsite 5-axis surface inspection using the dual-cubic NURBS representation. Meas Sci Technol 27(9). https://doi.org/10.1088/0957-0233/27/9/095003

  24. Wang G, Lv B, Liu B, Mu H (2019) Tool path generation method for three-dimensional vibration-assisted machining. IEEE International Conference on Mechatronics and Automation, pp. 1715–1720. https://doi.org/10.1109/ICMA.2019.8816456

  25. Zhao X, Zhao H, Li X, Ding H (2017) Path smoothing for five-axis machine tools using dual quaternion approximation with dominant points. Int J Precis Eng Manuf 18(5):711–720. https://doi.org/10.1007/s12541-017-0085-5

  26. Sun S, Altintas Y (2021) A G3 continuous tool path smoothing method for 5-axis CNC machining. CIRP J Manuf Sci Technol 32:529–549. https://doi.org/10.1016/j.cirpj.2020.11.002

  27. Yang J, Chen Y, Chen Y, Zhang D (2015) A tool path generation and contour error estimation method for four-axis serial machines. Mechatronics 31:78–88. https://doi.org/10.1016/j.mechatronics.2015.03.001

  28. Chaves-Jacob J, Linares JM, Sprauel JM (2013) Improving tool wear and surface covering in polishing via toolpath optimization. J Mater Process Technol 213(10):1661–1668. https://doi.org/10.1016/j.jmatprotec.2013.04.005

  29. Hatem N, Yusof Y, Kadir AZA, Latif K, Mohammed MA (2021) A novel integrating between tool path optimization using an ACO algorithm and interpreter for open architecture CNC system. Expert Syst Appl 178. https://doi.org/10.1016/j.eswa.2021.114988

  30. Tajima S, Sencer B (2017) Global tool-path smoothing for CNC machine tools with uninterrupted acceleration. Int J Mach Tools Manuf 121:81–95. https://doi.org/10.1016/j.ijmachtools.2017.03.002

  31. Huai W, Shi Y, Tang H, Lin X (2019) An adaptive flexible polishing path programming method of the blisk blade using elastic grinding tools. J Mech Sci Technol 33(7):3487–3495. https://doi.org/10.1007/s12206-019-0643-0

  32. Lv C, Zou L, Huang Y, Li H, Wang T, Mu Y (2022) A novel toolpath for robotic adaptive grinding of extremely thin blade edge based on dwell time model. IEEE-ASME Trans Mechatron 1–11. https://doi.org/10.1109/tmech.2022.3156804

Download references

Acknowledgements

We thank Revealer for their assistance in recording surface images of abrasive belts by high-speed camera 5F01.

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 52075059) and the Natural Science Foundation of Chongqing (Grant cstc2020jcyj-msxmX0266).

Author information

Authors and Affiliations

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

    Yilin Mu, Chong Lv, Heng Li, Lai Zou, Wenxi Wang & Yun Huang

Authors
  1. Yilin Mu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chong Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Heng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lai Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenxi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yun Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yilin Mu, Chong Lv and Lai Zou were responsible for planning this paper, Yilin Mu and Heng Li were responsible for experimental work. Yilin Mu, Chong Lv, Heng Li, Lai Zou,Wenxi Wang and Yun Huang were involved in the discussion and significantly contributed to making the final draft of the article. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Chong Lv.

Ethics declarations

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publish

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mu, Y., Lv, C., Li, H. et al. A novel toolpath for 7-NC grinding of blades with force-position matching. Int J Adv Manuf Technol 123, 259–270 (2022). https://doi.org/10.1007/s00170-022-10138-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-10138-x

Keywords

Search

Navigation