Skip to main content
SpringerLink
Log in

Modeling and monitoring the material removal rate of abrasive belt grinding based on vision measurement and the gene expression programming (GEP) algorithm

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

Abstract

Accurately predicting the material removal rate (MRR) in belt grinding is challenging because of the randomly distributed multiple cutting edges, flexible contact, and continuous wear of the abrasive grains, undermining the ability to achieve the expected machining requirements for belt grinding using the planned parameters. With the development of sensing technology, big data, and intelligent algorithms, online identification methods for material removal through sensing signals have gained traction. A vision-based material removal monitoring method in the belt grinding process was investigated by adopting the gene expression programming (GEP) algorithm. First, the relationship between the grinding parameters and MRR was investigated through a series of experiments. Second, methods of image shooting distance calibration and automatic image segmentation were established. Furthermore, the definition and quantification method of 11 features related to the color, texture, and energy of spark images are described, based on which the features are extracted. Then, the optimal feature subset was determined by analyzing the fluctuation degree and correlation with MRR by computing the coefficient of variation of the features and Pearson’s coefficient of features and MRR, respectively. Finally, a continuous function model including the selected features was obtained using the GEP method. The predicted results and testing time were compared with those of other methods such as LightGBM, convolutional neural network (CNN), support vector regression (SVR), and BP neural network. The results show that the MRR prediction model based on the GEP algorithm can obtain explicit function expressions and is highly effective in predicting accuracy and test time, which is of utmost significance for accurate and efficient acquisition of MRR data online.

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
Fig. 15

Similar content being viewed by others

Availability of data and materials

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Wang TT, Zou L, Wan QH, Zhang XH, Li YJ, 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

    Article  Google Scholar 

  2. Pandiyan V, Tjahjowidodo T, Samy M (2016) In-process surface roughness estimation model for compliant abrasive belt machining process. Procedia Cirp 46:254–257

    Article  Google Scholar 

  3. Li LF, Feng HJ, Chen HB, Chen XQ (2021) A novel material removal rate model based on single grain force for robotic belt grinding. J Manuf Process 68:1–12

    Article  Google Scholar 

  4. Zhe H, Li JY, Liu YM, Wang WY (2021) Investigation of conditions leading to critical transitions between abrasive belt wear modes for rail grinding. Wear 484–485(1–2):204048

  5. 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

    Article  Google Scholar 

  6. Zhe H, Li JY, Liu YM, Wang WX (2021) Investigation of conditions leading to critical transitions between abrasive belt wear modes for rail grinding. Wear 484–485(1–2):204048

  7. Yang ZY, Chu Y, Xu XH, Huang HJ, Zhu DH, Yan SJ, 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 190:106005

  8. Ren X, Cabaravdic M, Zhang X, Kuhlenk TB (2007) A local process model for simulation of robotic belt grinding. Int J Mach Tools Manuf 47(6):962–970

    Article  Google Scholar 

  9. Hammann G (1998) Modellierung des Abtragsverhaltens elastischer robot-ergefuehrter Schleifwerkzeuge, PhD Thesis, University Stuggart

  10. Zhang XQ, Chen HB, Xu JJ, Song XF, Wang JW, Chen XQ (2018) A novel sound-based belt condition monitoring method for robotic grinding using optimally pruned extreme learning machine. J Mater Process Technol 260:9–19

    Article  Google Scholar 

  11. Ren LJ, Zhang GP, Zhang L, Zhang Z, Huang YM (2018) Modelling and investigation of material removal profile for computer controlled ultra-precision polishing. Precis Eng 55:144–153

    Article  Google Scholar 

  12. Rabinowicz E, Tanner RI (1966) Friction and wear of materials. J Appl Mech 33(2):479

    Article  Google Scholar 

  13. Cabaravdic, Kuhlenköetter B (2005) Belt grinding processes optimization. Mo Metal loberfläche 4:44–47

    Google Scholar 

  14. 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 4:363

    Article  Google Scholar 

  15. Pandiyan V, Shevchik S, Wasmer K, Castagne S, Tjahjowidodo T (2020) Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: a review. J Manuf Process 57:114–135

    Article  Google Scholar 

  16. Gao K, Chen H, Zhang X, Ren X, Chen J, Chen X (2019) A novel material removal prediction method based on acoustic sensing and ensemble XGBoost learning algorithm for robotic belt grinding of Inconel 718. Int J Adv Manuf Technol 105:217–232

    Article  Google Scholar 

  17. Ren LJ, Zhang GP, Wang Y, Zhang Q, Wang F, Huang YM (2019) A new in-process material removal rate monitoring approach in abrasive belt grinding. Int J Adv Manuf Technol 104(5–8):2715–2726

    Article  Google Scholar 

  18. Wang NN, Zhang GP, Ren LJ, Pang WJ (2021) Vision and sound fusion-based material removal rate monitoring for abrasive belt grinding using improved LightGBM algorithm. J Manuf Process 66:281–292

    Article  Google Scholar 

  19. Rajmohan B, Radhakrishnan V (1994) On the possibility of process monitoring in grinding by spark intensity measurements. J Manuf Sci Eng 116(1):124–129

    Google Scholar 

  20. Rajmohan B, Radhakrishnan V (1992) A study on the thermal aspects of chips in grinding. Int J Mach Tools Manuf 32(4):563–569

    Article  Google Scholar 

  21. Ferreira C (2001) Gene Expression Programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    MathSciNet  MATH  Google Scholar 

  22. Wang NN, Zhang GP, Pang WJ, Ren LJ, Wang YP (2021) Novel monitoring method for material removal rate considering quantitative wear of abrasive belts based on LightGBM learning algorithm. Int J Adv Manuf Technol 114(11–12):1–13

    Article  Google Scholar 

Download references

Funding

This work was supported by the Shaanxi Province key projects (grant number 2017ZDXM-GY-133).

Author information

Authors and Affiliations

  1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Lijuan Ren, Nina Wang, Wanjing Pang, Yongchang Li & Guangpeng Zhang

Authors
  1. Lijuan Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nina Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wanjing Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongchang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guangpeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Lijuan Ren performed the analysis and summary of the experimental data and was a major contributor in writing the manuscript. Nina Wang, Zhijian Yang, Yongchang Li, and Wanjing Pang participate in carrying out grinding experiments. Guangpeng Zhang is responsible for the project management. All the authors read and approved the final manuscript.

Corresponding author

Correspondence to Lijuan Ren.

Ethics declarations

Ethics approval

All data in this paper comes from machining grinding experiments and does not involve ethical issues.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflicts of interest

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ren, L., Wang, N., Pang, W. et al. Modeling and monitoring the material removal rate of abrasive belt grinding based on vision measurement and the gene expression programming (GEP) algorithm. Int J Adv Manuf Technol 120, 385–401 (2022). https://doi.org/10.1007/s00170-022-08822-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-08822-z

Keywords

Search

Navigation