Skip to main content

Advertisement

Log in

Application of machine vision in improving safety and reliability for gear profile measurement

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

This research presents a camera-based measurement system which is developed to improve the safety and reliability for gear profile measurement system. Gear profile measurement is vital in precision engineering. To increase the safety and reliability of the precision measurement, application of camera or vision is very useful. Automatic control is also necessary to increase reliability of the measurement system. Normally, gear profiles are measured using contact-based stylus system. During gear profile measurement, human monitoring is required to avoid accident and sometimes we may face great danger regarding safety of our body especially eyes. The stylus is sharp and thin and if it is collided to the gear teeth there is high probability of breaking and scattering the stylus tip. To save time, if the measurement probe scans the gear shape with a speed of 10 mm/s then the issue of safety should be considered highly. The traditional methods for gear measurement are either time consuming or expensive. This paper presents the successful implementation of the camera system in precision measurement which saves time and increases safety and reliability of the measurement with the increment of the measurement performance by increasing production rate. Color-based stylus tracking algorithm is implemented to acquire better reliability of the complete system. Thus, the developed system with vision enhances safety and reliability of the precision measurement.

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Ali, MD. H., Kurokawa, S., Uesugi, K.: Vision based measurement system for gear profile. In: IEEE International Conference of Informatics, Electronics and Vision (2013) doi:10.1109/ICIEV.2013.6572652

  2. Marciniec, A., Budzik, G., Dziubek, T.: Automated Measurement of Bevel Gears of the air craft gearbox using GOM. J. KONES Power Train Transp. 18(2) 259–264 (2011)

  3. Tian, G.Y., Lu, R.S., Gledhill, D.: Surface measurement using active vision and light scattering. Opt. Lasers Eng. 45(1), 131–139 (2007)

  4. Al-Kindi, G.A., Shirinzadeh, B.: An evaluation of surface roughness parameters measurement using vision-based data. Int. J. Mach. Tools Manuf. 47(3–4), 697–708 (2007)

    Article  Google Scholar 

  5. Jeyapoovan, T., Murugan, M.: Surface roughness classification using image processing. Measurement 46(7), 2065–2072 (2013)

    Article  Google Scholar 

  6. Su, J.C., Huang, C.K., Tarng, Y.S.: An automated flank wear measurement of microdrills using machine vision. J. Mater. Process. Technol. 180(1—-3), 328–335 (2006). ISSN 0924–0136

    Article  Google Scholar 

  7. Ting-Fa, X., Peng, Z.: Precise perimeter measurement for 3D object with a light-pen vision measurement system. Opt. Laser Technol. 41(6), 815–819 (2009). ISSN 0030–3992

    Article  Google Scholar 

  8. Withrobot Lab. http://www.withrobot.com (2012)

  9. Fukuda, Y., Feng, M.Q., Shinozuka, M.: Cost-effective vision-based system for monitoring dynamic response of civil engineering structures. Struct. Control Health Monit. 17(8), 918–936 (2010)

    Article  Google Scholar 

  10. Lee, J.J., Shinozuka, M.: A vision-based system for remote sensing of bridge displacement. NDT Int. 39(5), 425–431 (2006)

    Article  Google Scholar 

  11. Choi, H.S., Cheung, J.H., Kim, S.H., Ahn, J.H.: Structural dynamic displacement vision system using image processing. NDT E Int. 44(7), 597–608 (2011)

    Article  Google Scholar 

  12. Jainy, S.: Machine vision based identification and dimensional measurement of electronic components. Master’s Thesis, p. 12 (2006)

  13. Dalsa technology: Precision measurements with machine vision, USA. https://www.teledynedalsa.com

  14. Muljowadidodo, K., Mohammad, A.R., SaptoAdi, N., Budiyono, A.: Vision based distance measurement system. Indian J. Mar. Sci. 38(3), 327 (2009)

    Google Scholar 

  15. Mohamed, A., Esa, A.H., Ayub, M.A.: Roundness measurement of cylindrical parts by machine vision. In: International Conference on Electrical, Control and Computer Engineering, pp. 486–490 (2011)

  16. Zhang, T., Luo, Y., Wang, X., Wang, M.: Machine vision technology for measurement of miniature parts in narrow space using borescope. In: International Conference on Digital manufacturing and Automation (ICDMA), China, pp. 904–907 (2010)

  17. Kondo, Y., Osawa, S., Sato, O., Komori, M., Takatsuji, T.: Evaluation of instruments for pitch measurement using a sphere artifact. Precis. Eng. 36(4), 604–611 (2012)

    Article  Google Scholar 

  18. http://www.codeproject.com/Articles/31104/Lego-Pan-Tilt-Camera-and-Objects-Tracking

  19. http://www.codeproject.com/Articles/8374/Tracking-an-object-from-a-live-video-input

  20. Kurada, S., Bradley, C.: A review of machine vision sensors for tool condition monitoring. Comput. Ind. 34(1), 55–72 (1997)

    Article  Google Scholar 

  21. http://processing.org/examples/histogram.html

  22. http://www.rpmechatronics.co.uk/en/Reliance-Cool-Muscle

  23. http://www.codeproject.com/Articles/91470/Computer-Vision-Laser-Range-Finder

  24. https://workspaces.codeproject.com/shellscript/computer-vision-laser-range-finder/article/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Md. Hazrat Ali.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ali, M.H., Kurokawa, S. & Uesugi, K. Application of machine vision in improving safety and reliability for gear profile measurement. Machine Vision and Applications 25, 1549–1559 (2014). https://doi.org/10.1007/s00138-014-0619-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-014-0619-0

Keywords

Navigation