Flexible robot-based cast iron deburring cell for small batch production using single-point laser sensor

  • E. Villagrossi
  • C. Cenati
  • N. Pedrocchi
  • M. Beschi
  • Lorenzo Molinari Tosatti


The presented work here is devoted to the definition of innovative methodologies to speed up the programming time of a robotized deburring task. The proposed solutions are defined in a standard cast iron foundry scenario, where the deburring workstations are equipped with flexible but inaccurate fixturing system, the working environment is dirty, and the production is characterized by small batches. The developed system exploits a 3D vision sensor, namely a single-point laser displacement sensor (SP-LS), in combination to a handshaking communication process for the robot-sensor information synchronization. Such approach enables the robot to be used as a measuring instrument allowing a fast reconstruction of 3D images extremely robust in hard working conditions. Adopting a two-stage methodology, the comparison of the reconstructed 3D point cloud with the nominal 3D point cloud allows the automatic adjustment of the robot deburring trajectories. An experimental campaign demonstrates the feasibility and the effectiveness of the proposed solutions.


Deburring with robots Deburring of small batch production Deburring trajectory adjustment 3D point cloud reconstruction Single-point displacement laser sensor 


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  1. 1.
  2. 2.
    Arvind K (1994) Probabilistic clock synchronization in distributed systems. IEEE Trans Parallel Distrib Syst 5(5):474–487. doi:10.1109/71.282558 CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Bellandi P, Docchio F, Sansoni G (2013) Roboscan: a combined 2D and 3D vision system for improved speed and flexibility in pick-and-place operation. Int J Adv Manuf Technol 69(5):1873–1886. doi:10.1007/s00170-013-5138-z CrossRefGoogle Scholar
  5. 5.
    Besl PJ, McKay HD (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256. doi:10.1109/34.121791 CrossRefGoogle Scholar
  6. 6.
    CAEF (2013) The European Foundry Industry Report. Tech. rep., CAEF - The European Foundry AssociationGoogle Scholar
  7. 7.
    Cheng K (2009) Machining dynamics. Springer-Verlag, London. doi:10.1007/978-1-84628-368-0 CrossRefGoogle Scholar
  8. 8.
    COMAU (2016) COMAU programming manualGoogle Scholar
  9. 9.
    Delcam (2015) CAM for robot multi-axes programming and simulation. http://www.delcam.com/it/software/robotics/
  10. 10.
    Duelen G, Munch H, Surdilovic D, Timm J (1992) Automated force control schemes for robotic deburring: development and experimental evaluation Proceedings of the International Conference on Industrial Electronics, Control, Instrumentation, and Automation, 1992. Power Electronics and Motion Control. doi:10.1109/IECON.1992.254483, pp 912–917CrossRefGoogle Scholar
  11. 11.
    Fanuc (2016) USING force sensors in your robotic applications. http://robot.fanucamerica.com/robotics-articles/force-sensors-in-robot-applications.aspx
  12. 12.
    Flexicast Consortium (2013) Robust and FLEXible CAST iron manufacturing. http://flexicast-euproject.com/expect-and-impact/
  13. 13.
    Jinno M, Uenohara M, Oaki J, Tatsuno K (1999) Teaching-less robot system for finishing workpieces of various shapes using force control and computer vision Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. doi:10.1109/IROS.1999.813065, vol 1, pp 573–578
  14. 14.
    Jonsson M, Stolt A, Robertsson A, Gegerfelt S, Nilsson K (2013) On force control for assembly and deburring of castings. Prod Eng 7(4):351–360. doi:10.1007/s11740-013-0459-1 CrossRefGoogle Scholar
  15. 15.
    Kang HS, Noh JW, Kwak SJ (2012) Synchronization method for laser scanner and robot Proceedings of the 12th International Conference on Control, Automation and Systems (ICCAS), pp 952–955Google Scholar
  16. 16.
  17. 17.
    Open Source Project (2016) Point Cloud Library. http://pointclouds.org/
  18. 18.
    Orbis Research Global Deburring Machines Market 2016-2020. Tech. rep., Orbis Research (2016). http://www.qyresearchgroup.com/report/48045
  19. 19.
    Pérez L, Rodríguez I, Rodríguez N, Usamentiaga R, García DF (2016) Robot guidance using machine vision techniques in industrial environments: a comparative review. Sensors 16:3. doi:10.3390/s16030335 CrossRefGoogle Scholar
  20. 20.
    Rajaraman M, Dawson-Haggerty M, Shimada K, Bourne D (2013) Automated workpiece localization for robotic welding 2013 IEEE International Conference on Automation Science and Engineering (CASE). doi:10.1109/CoASE.2013.6654062, pp 681–686CrossRefGoogle Scholar
  21. 21.
    Rundman K, Iacoviello F (2016) Cast irons Reference module in materials science and materials engineering. Elsevier. doi:10.1016/B978-0-12-803581-8.09803-9 Google Scholar
  22. 22.
    Rusu RB, Cousins S (2011) 3D is here: Point Cloud Library (PCL) Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Shanghai, ChinaGoogle Scholar
  23. 23.
  24. 24.
    Schützer K, Abele E, Güth S (2015) Simulation-based deburring tool and process development. CIRP Ann Manuf Technol 64(1):357–360. doi:10.1016/j.cirp.2015.04.099 CrossRefGoogle Scholar
  25. 25.
    Skotheim Ø, Lind M, Ystgaard P, Fjerdingen SA (2012) A flexible 3D object localization system for industrial part handling 2012 IEEE/RSJ international conference on intelligent robots and systems. doi:10.1109/IROS.2012.6385508, pp 3326–3333CrossRefGoogle Scholar
  26. 26.
    Song HC, Kim BS, Song JB (2012) Tool path generation based on matching between teaching points and cad model for robotic deburring Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). doi:10.1109/AIM.2012.6265921, pp 890–895Google Scholar
  27. 27.
    Tellaeche A, Arana R (2016) Robust 3D object model reconstruction and matching for complex automated deburring operations. J Imaging 2(1):8. doi:10.3390/jimaging2010008 CrossRefGoogle Scholar
  28. 28.
    Thomessen T, Elle OJ, Larsen JL, Andersen T, Pedersen JE, Lien TK (1993) Automatic programming of grinding robot. Model Identif Control 14(2):93–105. doi:10.4173/mic.1993.2.4 CrossRefGoogle Scholar
  29. 29.
    Wadhwa RS, Lien TK (2013) Manufacturing automation for environmentally sustainable foundries Re-engineering manufacturing for sustainability. doi:10.1007/978-981-4451-48-2_28. Springer Singapore, Singapore, pp 171–174CrossRefGoogle Scholar
  30. 30.
    Wang X, Wang Y, Xue Y (2006) Adaptive control of robotic deburring process based on impedance control Proceedings of the IEEE International Conference on Industrial Informatics. doi:10.1109/INDIN.2006.275700, pp 921–925Google Scholar
  31. 31.
    Zhang H, Chen H, Xi N, Zhang G, He J (2006) On-line path generation for robotic deburring of cast aluminum wheels 2006 IEEE/RSJ international conference on intelligent robots and systems. doi:10.1109/IROS.2006.281679, pp 2400–2405CrossRefGoogle Scholar
  32. 32.
    Ziliani G, Legnani G, Visioli A (2005) A mechatronic design for robotic deburring Proceedings of the IEEE International Symposium on Industrial Electronics (ISIE). doi:10.1109/ISIE.2005.1529167, vol 4, pp 1575–1580

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© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.National Research Council of Italy, Institute of Industrial Technologies and AutomationMilanItaly

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