[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Predictive surface roughness model for robotic polishing considering initial surface quality

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

Abstract

The polishing process is complex and influenced by various parameters, making the construction of predictive models for polishing quality a significant area of research. Existing models primarily focus on four parameters: contact force, rotational speed, feed rate, and sandpaper grit, while neglecting the impact of initial surface quality, resulting in limited accuracy and applicability. This paper proposes a method for constructing a surface roughness prediction model that considers initial surface quality, which consists of two parts: First, through experimental polishing tests on workpieces with various initial surface qualities, it was shown that the initial surface quality has a significant effect on the final polishing result; second, the initial surface quality is classified into three grades based on roughness values, and a prediction model for post-polishing surface roughness is constructed by integrating the initial surface quality and the four process parameters using response surface methodology. Finally, a series of polishing experiments with different parameter combinations obtained model prediction errors ranging from 3.40 to 11.44% (average 7.48%), verifying the practicality and generality of the proposed prediction model.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Data Availability

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

References

  1. Wei Y, Xu Q (2022) Design of a new passive end-effector based on constant-force mechanism for robotic polishing. Robot Comput Integr Manuf 74:102278

    Article  MATH  Google Scholar 

  2. Lu L, Zhang L, Fan C, Wang H (2022) High-order joint-smooth trajectory planning method considering tool-orientation constraints and singularity avoidance for robot surface machining. J Manuf Process 80:789–804

    Article  MATH  Google Scholar 

  3. Zhang B, Wu S, Wang D, Yang S, Jiang F, Li C (2023) A review of surface quality control technology for robotic abrasive belt grinding of aero-engine blades. Measurement 220:113381

    Article  MATH  Google Scholar 

  4. Wang Z, Zou L, Duan L, Liu X, Lv C, Gong M, Huang Y (2021) Study on passive compliance control in robotic belt grinding of nickel-based superalloy blade. J Manuf Process 68:168–179

    Article  Google Scholar 

  5. Li J, Guan Y, Chen H, Wang B, Zhang T, Hong J, Wang D (2022) Real-time normal contact force control for robotic surface processing of workpieces without a priori geometric model. Int J Adv Manuf Technol 119(3):2537–2551

    MATH  Google Scholar 

  6. Zhang J, Liu J, Yang S, Ju C, Li J, Qiao Z (2022) A model for material removal of robot-assisted blade polishing using abrasive cloth wheel. Int J Adv Manuf Technol 123(7):2819–2831

    Article  MATH  Google Scholar 

  7. He Y, Zhang W, Li YF, Wang YL, Wang Y, Wang SL (2021) An approach for surface roughness measurement of helical gears based on image segmentation of region of interest. Measurement 183:109905

    Article  MATH  Google Scholar 

  8. Nagai S, Yoshida I, Oshiro K, Sakakibara R (2022) Acceleration of surface roughness evaluation using RANSAC and least squares method for Running-in wear process analysis of plateau surface. Measurement 203:111912

    Article  Google Scholar 

  9. Ke X, Yu Y, Li K, Wang T, Zhong B, Wang Z, Kong L, Guo J, Huang L, Idir M, Liu C, Wang C (2023) Review on robot-assisted polishing: status and future trends. Robot Comput Integr Manuf 80:102482

    Article  Google Scholar 

  10. Yu H, Wang J, Lu Y (2016) Simulation of grinding surface roughness using the grinding wheel with an abrasive phyllotactic pattern. Int J Adv Manuf Technol 84(5–8):861–871

    MATH  Google Scholar 

  11. Zhou W, Tang J, Chen H, Shao W (2019) A comprehensive investigation of surface generation and material removal characteristics in ultrasonic vibration assisted grinding. Int J Mech Sci 156:14–30

    Article  Google Scholar 

  12. Zhou W, Tang J, Shao W (2020) Study on surface generation mechanism and roughness distribution in gear profile grinding. Int J Mech Sci 187:105921

    Article  Google Scholar 

  13. Zheng Q, Xiao J, Wang C, Liu H, Huang T (2022) A robotic polishing parameter optimization method considering time-varying wear. Int J Adv Manuf Technol 121(9–10):6723–6738

    Article  MATH  Google Scholar 

  14. De Agustina B, Marín MM, Teti R, Rubio EM (2014) Surface roughness evaluation based on acoustic emission signals in robot assisted polishing. Sensors 14(11):21514–21522

    Article  Google Scholar 

  15. Beatriz DA, Marta M, Roberto T, Eva R (2018) Analysis of force signals for the estimation of surface roughness during robot-assisted polishing. Materials 11(8):1438

    Article  MATH  Google Scholar 

  16. Segreto T, Karam S, Teti R (2016) Signal processing and pattern recognition for surface roughness assessment in multiple sensor monitoring of robot-assisted polishing. Int J Adv Manuf Technol 90:1023–1033

    Article  Google Scholar 

  17. Qi J, Zhang D, Li S, Chen B (2018) Modeling and prediction of surface roughness in belt polishing based on artificial neural network. Proceedings of the institution of mechanical engineers part B journal of engineering manufacture 232(12):2154–2163

  18. Mohammad AEK, Hong J, Wang D (2017) Polishing of uneven surfaces using industrial robots based on neural network and genetic algorithm. Int J Adv Manuf Technol 93(1):1–9

    MATH  Google Scholar 

  19. Jia H, Lu X, Cai D, Xiang Y, Chen J, Bao C (2023) Predictive modeling and analysis of material removal characteristics for robotic belt grinding of complex blade. Appl Sci 13(7):4248–4264

  20. Zolpakar NA, Yasak MF, Pathak S (2021) A review: use of evolutionary algorithm for optimisation of machining parameters. Int J Adv Manuf Technol 115:31–47

    Article  MATH  Google Scholar 

  21. Zhang J, Zhao T, Duan J, Lin X, Sun P, Shi Y (2014) Surface roughness prediction and parameters optimization in grinding and polishing process for IBR of aero-engine. Int J Adv Manuf Technol 74(5):653–663

    Google Scholar 

  22. Huai W, Tang H, Shi Y, Lin X (2016) Prediction of surface roughness ratio of polishing blade of abrasive cloth wheel and optimization of processing parameters. Int J Adv Manuf Technol 90(1–4):1–10

    MATH  Google Scholar 

  23. Zhang J, Shi Y, Lin X, Li Z (2017) Parameter optimization of five-axis polishing using abrasive belt flap wheel for blisk blade. J Mech Sci Technol 31(10):4805–4812

    Article  MATH  Google Scholar 

  24. Yang A, Han Y, Pan Y, Xing H, Li J (2017) Optimum surface roughness prediction for titanium alloy by adopting response surface methodology. Results in Phys 7:1046–1050

    Article  MATH  Google Scholar 

  25. Pan Y, Zhou P, Yan Y, Agrawal A, Wang Y, Guo D, Goel S (2021) New insights into the methods for predicting ground surface roughness in the age of digitalisation. Precis Eng 67:393–418

    Article  MATH  Google Scholar 

  26. Hussein HI, Anwar S (2023) A review of optimization algorithms in SVM parameters. Adv Mater Scie Manuf Eng 14:1389–1414

    MATH  Google Scholar 

  27. Li J, Guan Y, Chen H, Wang B, Zhang T (2023) Robotic polishing of unknown-model workpieces with constant normal contact force control. IEEE/ASME Trans Mechatron 28(2):1093–1103

    Article  MATH  Google Scholar 

  28. Rahman MA, Saleh T, Jahan MP, McGarry C, Chaudhari A, Huang R, Tauhiduzzaman M, Ahmed A, Mahmud AA, Bhuiyan MS, Khan MF, Alam MS, Shakur MS (2023) Review of intelligence for additive and subtractive manufacturing: current status and future prospects. Micromachines 14(3):508–561

    Article  Google Scholar 

Download references

Funding

This work was supported in part by the Natural Science Foundation of Guangdong Province (Grant No. 2023A1515011253), the Higher Education Institution Featured Innovation Project of the Department of Education of Guangdong Province (Grant No. 2023KTSCX138), the Offshore Wind Power Joint Fund Project of Guangdong Basic and Applied Basic Research Foundation (Grant No. 2024A1515240007), the Shaoguan City Support for Researchers Project (Grant No. 230330098033679), the Natural Science Foundation of Chongqing (Grant No. CSTB2022NSCQMSX1386), and the Shaoguan University Ph.D. Initiation Project (Grant No. 440-9900064602).

Author information

Authors and Affiliations

  1. School of Intelligent Engineering, Shaoguan University, Shaoguan, 512005, China

    Jian Li, Han Hu, Dongchang Li, Weihui Wu & Jin Li

  2. Chongqing Research Institute of HIT, Chongqing, 401135, China

    Jian Li

  3. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, 510006, China

    Yisheng Guan & Zhiyun He

  4. School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China

    Hui Bi & Wenqiang Wu

  5. School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China

    Tao Zhang

Authors
  1. Jian Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yisheng Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiyun He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenqiang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Han Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dongchang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Weihui Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Tao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Equivalent contributions from all authors.

Corresponding authors

Correspondence to Wenqiang Wu or Tao Zhang.

Ethics declarations

Conflict 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

Springer Nature or its licensor (e.g. a society or other partner) 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

Li, J., Guan, Y., Bi, H. et al. Predictive surface roughness model for robotic polishing considering initial surface quality. Int J Adv Manuf Technol (2025). https://doi.org/10.1007/s00170-025-15235-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00170-025-15235-1

Keywords