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Prediction of depth of cut for robotic belt grinding

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Abstract

Robotic belt grinding systems can be used not only to replace low efficiency, high pollution manual finishing operations but also to improve production rate and manufacturing flexibility, especially for grinding small batches of workpieces with complicated features. The contact wheel is made from soft material with significant elasticity and is tensioned by a grinding belt. Soft contact between the workpiece and contact wheel provides the benefits of high surface quality but reduces the dimensional accuracy of the finished workpiece. This paper analyzes the contact wheel’s deformation caused by belt tension in order to accurately predict the depth of cut. The elastic mechanics based on the power series method is employed to establish and solve the tension model, and the deformation of the contact wheel is obtained. The validity of the analytical model is verified by a finite element software. Then, two modified models of grinding stress distribution are developed, and the distribution of depth of grinding is predicted. Tests are running and showing that the prediction error is less than 3.1% on a given grinding path. An accurate, fast method is thus developed to predict the depth of cut for belt grinding.

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References

  1. 2013–2016: High demand for industrial robots is continuing: http://www.ifr.org/industrial-robots/statistics/, April 22, 2016

  2. Chen Y, Dong F (2013) Robot machining: recent development and future research issues. Int J Adv Manuf Technol 66(9–12):1489–1497

    Article  Google Scholar 

  3. Whitney DE (1990) Development and control of an automated robotic weld bead grinding system. J Dyn Syst Meas Control 112(2):166–176

    Article  Google Scholar 

  4. Bogue R (2009) Finishing robots: a review of technologies and applications. Ind Robot 36(1):6–12

    Article  Google Scholar 

  5. Huang H, Zhou L, Chen XQ, Zhou L (2003) SMART robotic system for 3D profile turbine vane airfoil repair. Int J Adv Manuf Technol 21(4):275–283

    Article  Google Scholar 

  6. Kuhlenkoetter B, Zhang X (2006) A robot system for high quality belt grinding and polishing processes. Cutting Edge Robotics 2006:755–770

    Google Scholar 

  7. Pandremenos J, Doukas C, Stavropoulos P, Chryssolouris G (2011) Machining with robots: a critical review. Proc. 7th International Conference on Digital Enterprise Technology; Athens, Greece; Sept. 28–30 2011

  8. Domroes F, Rieger M, Kuhlenkoetter B (2014) Towards autonomous robot machining. Proc. IEEE 41st International Symposium on Robotics ISR/Robotik 2014; Munich, Germany; 2014 June 2–3. pp1–6

  9. Huang H, Gong ZM, Chen XQ (2002) Robotic grinding and polishing for turbine-vane overhaul. J Mater Process Technol 127(2):140–145

    Article  Google Scholar 

  10. Wang J, Sun Y, Kazerounian K (2003) Process modeling of flexible robotic grinding. Proc. IEEE Int. Symposium on Circuits and Systems; Bangkok Thailand; May 25–28 2003. pp 700–705

  11. Wang J, Zhang G, Zhang H (2008) Force control technologies for new robotic applications. Proc. IEEE Int. Conference on Technologies for Practical Robot Applications; Woburn, USA; Nov. 10–11 2008. pp 143–149

  12. Sun Y, Giblin DJ, Kazerounian K (2009) Accurate robotic belt grinding of workpieces with complex geometries using relative calibration techniques. Robot Comp-Int Manuf 25(1):204–210

    Article  Google Scholar 

  13. Zhang X, Krewet C, Kuhlenkötter B (2006) Automatic classification of defects on the product surface in grinding and polishing. Int J Mach Tool Manu 46(1):59–69

    Article  Google Scholar 

  14. Wang W, Yun C, Zhang L (2011) Designing and optimization of an off-line programming system for robotic belt grinding process. Chin J Mech Eng-En 24(4):647–652

    Article  Google Scholar 

  15. Zhao Y, Zhao J, Zhang L, Qi L, Tang Q (2009) Path planning for automatic robotic blade grinding. Proc. 2009 International Conference on of Mechatronics and Automation; Changchun China; 2009 Sept. 9–12. pp 1556–1560

  16. Wang W, Yun C (2011) A path planning method for robotic belt surface grinding. Chin J Aeronaut 24(7):520–526

    Article  Google Scholar 

  17. Lv H, Song Y, Jia P, Gan Z, Qi L (2010) An adaptive modeling approach based on ESN for robotic belt grinding. Proc. IEEE International Conference on Information and Automation; Harbin China; June 20–23, 2010. pp 787–792

  18. Liang W, Song Y, Lv H, Jia P, Gan Z, Qi L (2010) A novel control method for robotic belt grinding based on SVM and PSO algorithm. Proc. International Conference on Intelligent Computation Technology and Automation; Changsha China; May 11–12, 2010. pp 258–261

  19. Song Y, Liang W, Yang Y (2012) A method for grinding removal control of a robot belt grinding system. J Intell Manuf 23(5):1903–1913

    Article  Google Scholar 

  20. Song Y, Lv H, Yang Z (2012) An adaptive modeling method for a robot belt grinding process. IEEE-ASME T Mech 17(2):309–317

    Article  Google Scholar 

  21. Song Y, Yang H, Lv H (2013) Intelligent control for a robot belt grinding system. IEEE Trans Control Syst Technol 21(3):716–724

    Article  Google Scholar 

  22. Zhang X, Cabaravdic M, Kneupner K et al (2004) Real-time simulation of robot controlled belt grinding processes of sculptured surfaces. Int J Adv Robot Syst 1(2):109–114

    Article  Google Scholar 

  23. Zhang X, Kuhlenkötter B, Kneupner K (2005) An efficient method for solving the Signorini problem in the simulation of free-form surfaces produced by belt grinding. Int J Mach Tool Manu 45(6):641–648

    Article  Google Scholar 

  24. Ren X, Mueller H, Kuhlenkoetter B (2006) Surfel-based surface modeling for robotic belt grinding simulation. J Zhejiang Univ-Sc A 7(7):1215–1224

    Article  MATH  Google Scholar 

  25. Ren X, Kuhlenkötter B, Müller H (2006) Simulation and verification of belt grinding with industrial robots. Int J Mach Tool Manu 46(7):708–716

    Article  Google Scholar 

  26. Ren X, Cabaravdic M, Zhang X, Kuhlenkötter B (2007) A local process model for simulation of robotic belt grinding. Int J Mach Tool Manu 47(6):962–970

    Article  Google Scholar 

  27. Ren X, Kuhlenkötter B (2008) Real-time simulation and visualization of robotic belt grinding processes. Int J Adv Manuf Tech 35(11–12):1090–1099

    Article  Google Scholar 

  28. Wu S, Kazerounian K, Gan Z, Sun Y (2013) A simulation platform for optimal selection of robotic belt grinding system parameters. Int J Adv Manuf Tech 64(1–4):447–458

    Article  Google Scholar 

  29. Brady B, Brown E (2013) Rock mechanics: for underground mining. Springer Science & Business Media, Glasgow

    Google Scholar 

  30. Salerno VL, Mahoney JB (1968) Stress solution for an infinite plate containing two arbitrary circular holes under equal biaxial stresses. J Engi Ind 90(4):656–665

    Article  Google Scholar 

  31. Johnson KL (1985) Contact mechanics. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  32. Sommer JG (2009) Engineered rubber products. Hanser Pulications, Cincinnati

    Book  Google Scholar 

  33. Hammann G (1998) Modellierung des abtragsverhaltens elastischer robotergefuehrter schleifwerkzeuge. University of Stuttgart, Dissertation

    Book  Google Scholar 

Download references

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Correspondence to Wei Wang.

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Wang, W., Liu, F., Liu, Z. et al. Prediction of depth of cut for robotic belt grinding. Int J Adv Manuf Technol 91, 699–708 (2017). https://doi.org/10.1007/s00170-016-9729-3

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  • DOI: https://doi.org/10.1007/s00170-016-9729-3

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