Skip to main content
Log in

An error compensation method based on machine vision for laser-processing systems with galvanometers

  • Regular Paper
  • Published:
Applied Physics B Aims and scope Submit manuscript

Abstract

Laser-processing system plays an important role in modern processing. To improve the precision of a galvanometer laser-processing system, a novel compensation method based on machine vision is proposed. First, the various errors existing in the scanning process of the galvanometer system and the influence on the accuracy of the scanning system were analyzed. Second, processing a specific grid within the processing range of the laser-processing system, the machine vision method was employed to extract the skeleton image and obtain the sub-pixel coordinates of each grid corner point. By comparing them with their theoretical positions, the position and error of all corner points of the mesh were obtained. Finally, the position and error of the four corner points in the arbitrary grid region were analyzed by the weighted interpolation method, and the error value and compensation value of any point in the grid were derived, and an error compensation model was established to compensate the scanning system. To verify the effectiveness of the proposed compensation method, a compensation experiment was conducted, and the workpiece machining accuracy after compensation was improved. The method is simple in operation, offers good compensation effect, and has important theoretical significance and practical value for improving the precision of laser-processing systems.

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

Access this article

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

Instant access to the full article PDF.

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. Q. Zhao, C. Wang, J. Yang, Graphic distortion analysis and correction arithmetic research on laser galvanometer scanning. J. Changchun Univ. Sci. Technol. 35(4), 63–65 (2012)

    Google Scholar 

  2. A. Manakov, H. Peter, I. Ivo, A mathematical model and calibration procedure for galvanometric laser scanning systems, in 16th international workshop on vision, modeling and visualization (2011)

  3. H. Jigang, Q. Qin, W. Jie, F. Hui, Two dimensional laser galvanometer scanning technology for additive manufacturing. Int. J. Mater. Mech. Manuf. 6(5), 332–336 (2018)

    Google Scholar 

  4. G. Li, Y. Fang, R. Ji, Z. Zhang, H. Zhang, Mu. Jinzhen, T. Song, High-precision laser tracking system based on two-dimensional galvanometers and position sensitive detectors. Chin. J. Lasers 46(7), 0704007 (2019)

    Article  Google Scholar 

  5. S. Huilai, L. Shuzhong, Research on errors reasons in dual-galvanometric laser scanning manufacturing. Laser Infrared 35(3), 161–163 (2005)

    Google Scholar 

  6. Y. Zhao, L. Bingheng, Pillow-shaped distortion correction algorithm of galvanometric scanning system. Chin. J. Lasers 35(3), 161–163 (2005)

    Google Scholar 

  7. A. Lasagni, M.A.O. Delgado, M.A. Ortega, Reducing field of view distortion for galvanometer scanning system using a vision system. Opt. Lasers Eng. 86, 106–114 (2016)

    Article  Google Scholar 

  8. X. Zheng, X. Ni, Z. Lu et al., Detection of nonlinear scanning error for f-theta objective. Opto-Electron. Eng. 31(4), 9–12 (2004)

    Google Scholar 

  9. C. Zhibin, F. Lei, X. Wenjian et al., Directional error analysis of 2D galvanometer scanning system. J. Appl. Opt. 39(2), 180–186 (2018)

    Article  Google Scholar 

  10. A. Jagna, K.A. Kumar, V.K. Prasad, Rule-based order-independent parallel algorithm for binary image thinning. Int. J. Theor. Appl. Mech. 11(2), 171–185 (2016)

    Google Scholar 

  11. M. Shaomin, D. Haiyang, S. Ping et al., A New improved fast parallelthinning algorithm. Microelectron. Comput. 30(1), 53–55 (2013)

    Google Scholar 

  12. W. Wang, Y.P. Tang, J.L. Ren et al., An improved algorithm for Harris corner detection. Opt. Precis. Eng. 16(10), 1995–2001 (2008)

    Google Scholar 

  13. Y.-T. Liang, Y.-C. Chiou, An effective corner detection method using subpixel edge detector and Gaussian filter. Sensor Rev. 30(1), 51–61 (2010)

    Article  Google Scholar 

  14. Ai. Yufeng, Z. Min, Z. Qi et al., Corner detection of checkerboard based on sub-pixel edge. J. Xi’an Univ. Technol. 35(3), 333–337 (2019)

    Google Scholar 

  15. C. Nian, Z. Haiyuan, Z. Nan et al., Using bilinear interpolation and wavelet transformation to zoom images based on an error-amended sharp edge algorithm. Laser Infrared 40(5), 558–562 (2010)

    Google Scholar 

Download references

Acknowledgements

The project was supported by National Key Research & Development Programme of China (2017YFB1104600) and the Open Fund of the State Key Laboratory of Integrated Optoelectronics (IOSKL2018KF03).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Guangsheng Chen.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Additional information

Communicated by Dieter Meschede.

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

Chen, G., Zhang, Y. & Tian, P. An error compensation method based on machine vision for laser-processing systems with galvanometers. Appl. Phys. B 127, 3 (2021). https://doi.org/10.1007/s00340-020-07550-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00340-020-07550-0

Navigation