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Improving robotic machining accuracy through experimental error investigation and modular compensation

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Abstract

Machining using industrial robots is currently limited to applications with low geometrical accuracies and soft materials. This paper analyzes the sources of errors in robotic machining and characterizes them in amplitude and frequency. Experiments under different conditions represent a typical set of industrial applications and allow a qualified evaluation. Based on this analysis, a modular approach is proposed to overcome these obstacles, applied both during program generation (offline) and execution (online). Predictive offline compensation of machining errors is achieved by means of an innovative programming system, based on kinematic and dynamic robot models. Real-time adaptive machining error compensation is also provided by sensing the real robot positions with an innovative tracking system and corrective feedback to both the robot and an additional high-dynamic compensation mechanism on piezo-actuator basis.

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References

  1. Tolio T, Urgo M (2013) Design of flexible transfer lines: a case-based reconfiguration cost assessment. J Manuf Syst 32(2):325–334

    Article  Google Scholar 

  2. International Federation of Robotics (2013) World Robotics 2012, Statistical Yearbook

  3. Liang J, Bi S (2010) Design and experimental study of an end effector for robotic drilling. Int J Adv Manuf Technol 50(1–4):399–407

    Article  Google Scholar 

  4. Schneider U, Ansaloni M, Drust M, Leali F, Verl A (2013) Experimental investigation of error sources in robot machining. In: International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), pp. 14-26, Porto, Portugal

  5. Standard ISO 9283 (1998) Manipulating industrial robots—performance criteria and related test methods

  6. Siciliano B, Khatib O (2008) Handbook of robotics. Springer, New York

    Book  MATH  Google Scholar 

  7. Shiakolas PS, Conrad KL, Yih TC (2002) On the accuracy, repeatability, and degree of influence of kinematics parameters for industrial robots. Int J Model Simul 22:245–254

    Google Scholar 

  8. Mustafa SK, Pey YT, Yang G, Chen I (2010) A geometrical approach for online error compensation of industrial manipulator. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 738-743, July 6-9, Montreal, Canada

  9. Breth JF, Vasselin E, Lefebvre D, Dakyo B (2005) Determination of the repeatability of a Kuka robot using the stochastic ellipsoid approach. In: IEEE International Conference on Robotics and Automation, pp. 4339-4344, Barcelona, Spain

  10. Heisel U, Richter F, Wurst K-H (1997) Thermal behavior of industrial robots and possibilities for errors compensation. CIRP Ann Manuf Technol 46:283–286

    Article  Google Scholar 

  11. Zhan Q, Wang X (2012) Hand-eye calibration and positioning for a robot drilling system. Int J Adv Manuf Technol 61(5–8):691–701

    Article  Google Scholar 

  12. Lehmann C, Pellicciari M, Drust M, Gunnink JW (2013) Machining with industrial robots: the COMET project approach. In: International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), pp. 27-36, Porto, Portugal

  13. Pan Z, Polden J, Larkin N, Van Duin S, Norrish J (2012) Recent progress on programming methods for industrial robots. Robot Comput Integr Manuf 28:87–94

    Article  Google Scholar 

  14. Tarn TJ, Song M, Xi M, Ghosh BJ (1996) Multi-sensor fusion scheme for calibration-free stereo vision in a manufacturing workcell. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, pp. 416-423, Washington DC, USA

  15. Legnani G, Tosi D, Fassi I, Giberti I, Cinquemani S (2010) The “point of isotropy” and other properties of serial and parallel manipulators. Mech Mach Theory 45:1407–1423

    Article  MATH  Google Scholar 

  16. Dietz T, Schneider U, Barho M, Oberer-Treitz S, Drust M, Hollmann R, Hägele M (2012) Programming system for efficient use of robots for deburring in SME environments. In: 7th German Conference on Robotics (Robotik), Munich, Germany

  17. Oh YT (2011) Influence of the joint angular characteristics on the accuracy of industrial robots. Ind Robot 38:406–418

    Article  Google Scholar 

  18. Erkaya S (2012) Investigation of joint clearance effects on welding robot manipulators. Robot Comput Integr Manuf 28:449–457

    Article  Google Scholar 

  19. Gong C, Yuan J, Ni J (2000) Nongeometric error identification and compensation for robotic system by inverse calibration. Int J Mach Tools Manuf 40:2119–2137

    Article  Google Scholar 

  20. Ruderman M, Hoffmann F, Bertram T (2009) Modeling and identification of elastic robot joints with hysteresis and backlash. IEEE Trans Ind Electron 56:3840–3847

    Article  Google Scholar 

  21. Kumagai S, Ohishi K, Miyazaki T (2009) high performance robot motion control based on zero phase error notch filter and D-PD control. In: IEEE International Conference on Mechatronics, pp. 1-6, Malaga, Spain

  22. Marton L, Lantos B (2009) Friction and backlash measurement and identification method for robotic arms. In: IEEE International Conference on Advanced Robotics, pp. 1-6, Munich, Germany

  23. Thomsen S, Fuchs FW (2009) Speed control of torsional drive systems with backlash. In: 13th European Conference on Power Electronics and Applications, pp. 1-10, Barcelona, Spain

  24. Carvalho Bittencourt A, Wernholt E, Sander-Tavallaey S, Brogardh T (2010) An extended friction model to capture load and temperature effects in robot joints. In: IEEE International Conference on Intelligent Robots and Systems, pp. 6161-6167, Taipei, Taiwan

  25. Jin M, Jin Y, Chang PH, Choi C (2009) High-accuracy trajectory tracking of industrial robot manipulators using time delay estimation and terminal sliding mode. In: 35th Annual Conference of IEEE Industrial Electronics, pp. 3095-3099, Porto, Portugal

  26. Merlet JP (2009) Interval analysis for certified numerical solution of problems in robotics. Int J Appl Math Comput Sci 19:399–412

    Article  MATH  Google Scholar 

  27. Zhang H, Wang J, Zhang G, Gan Z, Pan Z, Cui H, Zhu Z (2005) Machining with flexible manipulator: toward improving robotic machining performance. In: IEEE International Conference on Advanced Intelligent Mechatronics, pp. 1127-1132, Monterey, California, USA

  28. Zhang H, Pan Z (2008) Robotic machining: material removal rate control with a flexible manipulator. In: IEEE Conference on Robotics, Automation and Mechatronics, pp. 30-35, Chengdu, China

  29. Pan Z, Zhang H, Zhu Z, Wang J (2006) Chatter analysis of robotic machining process. J Mater Process Technol 173:301–309

    Article  Google Scholar 

  30. Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51:363–376

    Article  Google Scholar 

  31. Liu X-W, Cheng K, Webb D, Longstaf AP, Widiyarto MH (2004) Improved dynamic cutting force model in peripheral milling. Part II: experimental verification and prediction. Int J Adv Manuf Technol 24:794–805

    Article  Google Scholar 

  32. Surdilovic C, Dragoljub Zhao H, Schreck G, Krueger J (2012) Advanced methods for small batch robotic machining of hard materials. In: 7th German Conference on Robotics (Robotik), pp.1-6, 21-22 May, Munich, Germany

  33. Tolio T, Ceglarek D, ElMaraghy HA, Fischer A, Hu SJ, Laperrière L, Newman ST, Váncza J (2010) SPECIES—coevolution of products, processes and production systems. CIRP Ann Manuf Technol 59(2):672–693. doi:10.1016/j.cirp.2010.05.008

    Article  Google Scholar 

  34. Pellicciari M, Leali F, Andrisano AO, Pini F (2012) Enhancing changeability of automotive hybrid reconfigurable systems in digital environments. Int J Interact Des Manuf 6:251–263

    Article  Google Scholar 

  35. Lehmann C, Halbauer M, Euhus D, Overbeck D (2012) Milling with industrial robots: strategies to reduce and compensate process force induced accuracy influences. In: 17th IEEE International Conference on Emerging Technologies & Factory Automation (ETFA), pp. 1-4, Kraków, Poland

  36. Lehmann C, Olofsson B, Nilsson K, Halbauer M, Haage M, Robertsson A, Sörnmo O, Berger U (2013) Robot joint modeling and parameter identification using the clamping method. In: IFAC Conference on Manufacturing Modeling, Management and Control (MIM), pp. 813-818, Saint Petersburg, Russia

  37. Lehmann C, Halbauer M, van der Zwaag J, Schneider U, Berger U (2013) Offline path compensation to improve accuracy of industrial robots for machining applications. In: Proceedings of 14th Automation Congress, VDI-report 2209, pp. 147-152, Baden-Baden, Germany

  38. Puzik A (2011) Genauigkeitssteigerung bei der spanenden bearbeitung mit industrierobotern durch fehlerkompensation mit 3D Ausgleichsaktorik, Dissertation, University Stuttgart, Fraunhofer IPA

  39. Kienzle O (1952) Bestimmung von kräften an werkzeugmaschinen. VDI-Z 94:299–305

    Google Scholar 

  40. Bennett D, Hollerbach J, Henri P (1992) Kinematic calibration by direct estimation of the Jacobian matrix. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 351-357, Nice, France

  41. Nilsson K (2012) Patent application SE-1251196-0: method and system for determination of at least one property of a manipulator

  42. Wang Z, Mastrogiacomo L, Franceschini F, Maropoulos P (2011) Experimental comparison of dynamic tracking performance of iGPS and laser tracker. Int J Adv Manuf Technol 56(1–4):205–213

    Article  Google Scholar 

  43. Schneider U, Diaz Posada JR, Drust M, Verl A (2013) Position control of an industrial robot using an optical measurement system for machining purposes. In: International Conference on Manufacturing Research (ICMR), pp. 307-312, Cranfield University, United Kingdom

  44. Schneider U, Oloffson B, Sörnmo O, Drust M, Robertsson A, Hägele M, Johansson R (2013) Integrated approach to robotic machining with macro/micro actuation. In: International Journal of Robotics and Computer-Integrated Manufacturing, submitted

  45. Puzik A, Meyer C, Verl A (2010) Industrial robots for machining processes in combination with a 3D-piezo-compensation-mechanism. In: 7th CIRP International Conference on Intelligent Computation in Manufacturing Engineering (ICME), Capri, Italy

  46. Schneider U, Drust M, Puzik A, Verl A (2013) Compensation of errors in robot machining with a parallel 3D-piezo compensation mechanism. In: 46th CIRP Conference on Manufacturing Systems, pp. 305-310, Sesimbra, Portugal

  47. Olofsson B, Sörnmo O, Schneider U, Robertsson A, Puzik A, Johansson R (2011) Modeling and control of a piezo-actuated high-dynamic compensation mechanism for industrial robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4704-4709

  48. Sörnmo O, Olofsson B, Schneider U, Robertsson A, Johansson R (2012) Increasing the milling accuracy for industrial robots using a piezo-actuated high-dynamic micro manipulator. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp.104-110, Kaohsiung, Taiwan

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Author information

Authors and Affiliations

  1. Department of Robot and Assistive Systems, Fraunhofer Institute for Manufacturing Engineering and Automation (IPA), Nobelstr. 12, 70565, Stuttgart, Germany

    Ulrich Schneider, Manuel Drust & Alexander Verl

  2. DIEF Engineering Department Enzo Ferrari, University of Modena and Reggio Emilia, Via Vignolese 905/B, 41125, Modena, Italy

    Matteo Ansaloni, Marcello Pellicciari & Francesco Leali

  3. Chair of Automation Technology, Brandenburg University of Technology, Siemens-Halske-Ring 14, 03046, Cottbus, Germany

    Christian Lehmann

  4. Delcam PLC, Small Heath Business Park, Birmingham, B10 0HJ, UK

    Jan Willem Gunnink

Authors
  1. Ulrich Schneider
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  2. Manuel Drust
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  3. Matteo Ansaloni
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  4. Christian Lehmann
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  5. Marcello Pellicciari
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  6. Francesco Leali
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  7. Jan Willem Gunnink
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  8. Alexander Verl
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Correspondence to Ulrich Schneider.

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Schneider, U., Drust, M., Ansaloni, M. et al. Improving robotic machining accuracy through experimental error investigation and modular compensation. Int J Adv Manuf Technol 85, 3–15 (2016). https://doi.org/10.1007/s00170-014-6021-2

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  • DOI: https://doi.org/10.1007/s00170-014-6021-2

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