A human mimicking control strategy for robotic deburring of hard materials

References

• ABB. 2017. Function Package for Force Control. s.l.:s.n.
   Google Scholar
• Abele, E., et al. 2008. Prediction of the Tool Displacement by Coupled Models of the Compliant Industrial Robot and the Milling Process. s.l., s.n. 223–230. Proceedings of the CIRP 2nd International Conference Process Machine Interactions Vancouver,  Canada; 2010.
   Google Scholar
• Abele, E., M. Weigold, and S. Rothenbücher. 2007. “Modeling and Identification of an Industrial Robot for Machining Applications.” CIRP Annals - Manufacturing Technology 56 (1): 387–390. doi:10.1016/j.cirp.2007.05.090.
   Web of Science ®Google Scholar
• ATI. 2017. Robotic Deburring Tools. s.l.:s.n.
   Google Scholar
• Berselli, G., et al. 2016. “Engineering Methods and Tools Enabling Reconfigurable and Adaptive Robotic Deburring.” In A Cura Di Advances on Mechanics, Design Engineering and Manufacturing: Proceedings of the International Joint Conference on Mechanics, Design Engineering & Advanced Manufacturing (JCM 2016), edited by B. Eynard, et al., 655–664. Catania, Italy: Springer International Publishing.
   Google Scholar
• Bone, G. M., M. A. Elbestawi, R. R. Lingarkar, and L. L. Liu. 1991. “Force Control for Robotic Deburring.” ASME Journal of Dynamic Systems, Measurement, and Control 113 (3): 395–400. doi:10.1115/1.2896423.
   Web of Science ®Google Scholar
• Cen, L., and S. Melkote. 2017. “Effect of Robot Dynamics on the Machining Forces in Robotic Milling.” Procedia Manufacturing 10: 486–496. doi:10.1016/j.promfg.2017.07.034.
   Google Scholar
• Daniali, M. M., and G. Vossoughi. 2009. “Intelligent Active Vibration Control of Constrained Manipularors.” International Conference on Industrial Mechatronics and Automation, Chengdu, China.
   Google Scholar
• Duelen, G., H. Munch, D. Surdilovic, and J. Timm. 1992. Automated Force Control Schemes for Robotic Deburring: Development and Experimental Evaluation. Proceedings of the 1992 International Conference on Industrial Electronics, Control, Instrumentation, and Automation, vol. 2, San Diego, CA, 1992, pp. 912-917.
   Google Scholar
• Fanuc. 2017. Utilizing Force Sensors in Your Robotic Applications. s.l.:s.n.
   Google Scholar
• Iglesias, I., M. Sebastian, and J. Ares. 2015. “Overview of the State of Robotic Machining: Current Situation and Future Potential.” Procedia Engineering 132: 911–917. doi:10.1016/j.proeng.2015.12.577.
   Google Scholar
• Jonsson, M., A. Stolt, A. Robertsson, S. Von Gegerfelt, and K. Nilsson. 2013. “On Force Control for Assembly and Deburring of Castings.” Production Engineering 7 (4): 351–360. doi:10.1007/s11740-013-0459-1.
   Google Scholar
• Kazerooni, H. 1988. “Automated Robotic Deburring Using Impedance Control.” IEEE Control Systems Magazine 8 (1, Feb): 21–25. doi:10.1109/37.464.
   Google Scholar
• Kramer, B. M., and S. S. Shim. 1990. “Development of a System for Robotic Deburring.” Robotics and Computer Integrated Manufacturing 7 (3): 291–295. doi:10.1016/0736-5845(90)90013-X.
   Web of Science ®Google Scholar
• Kuss, A., M. Drust, and A. Verl, 2016. “Detection of Workpiece Shape Deviations for Tool Path Adaptation in Robotic Deburring Systems.” Procedia CIRP, Volume 57, 2016, Pages 545-550, ISSN 2212-8271,https://doi.org/10.1016/j.procir.2016.11.094.
   Google Scholar
• Lehmann, C., M. Pellicciari, M. Drust, and J. W. Gunnink. 2013. “Machining with Industrial Robots: The COMET Project Approach.” In A Cura Di Robotics in Smart Manufacturing: International Workshop, WRSM 2013, Co-Located with FAIM 2013, Porto, Portugal, June 26-28, 2013. Proceedings, edited by P. Neto and A. P. Moreira, 27–36. Berlin, Heidelberg: Springer Berlin Heidelberg.
   Google Scholar
• Leonesio, M., P. Parenti, A. Cassinari, G. Bianchi, and M. Monno. 2012. “A Time-Domain Surface Grinding Model for Dynamic Simulation.” Procedia CIRP 4: 166–171. doi:10.1016/j.procir.2012.10.030.
   Google Scholar
• Open Source Robotic Foundation (OSRF). 2017. ROS - Robot Operating System. www.ros.org, accessed July 3 2018
   Google Scholar
• Pandremenos, J., C. Doukas, P. Stavropoulos, and G. Chryssolouris. 2011. Machining with Robots: A Critical Review, 614–621. Proceedings of DET2011 7th International Conference on Digital Enterprise Technology Athens, Greece, 2011.
   Google Scholar
• Rea Minango, N., and C. Espindola Ferreira. 2017. “Combining the STEP-NC Standard and Forward and Inverse Kinematics Methods for Generating Manufacturing Tool Paths for Serial and Hybrid Robots.” International Journal of Computer Integrated Manufacturing 30 (11): 1203–1223. doi:10.1080/0951192X.2017.1305507.
   Web of Science ®Google Scholar
• Open Source Project. 2017. ROS Industrial. www.rosindustrial.org, accessed July 3 2018
   Google Scholar
• Simoni, L., et al. 2017. On the Use of a Temperature Based Friction Model for a Virtual Force Sensor in Industrial Robot Manipulators. 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, 2017, pp. 1-6.
   Google Scholar
• Song, H. C., B. S. Kim, and J. B. Song. 2012. Tool Path Generation Based on Matching between Teaching Points and CAD Model for Robotic Deburring. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Kachsiung, 2012, pp. 890-895. doi: 10.1109/AIM.2012.6265921
   Google Scholar
• Song, H., and J. Song. 2013. “Precision Robotic Deburring Based on Force Control for Arbitrarily Shaped Workpiece Using CAD Model Matching.” International Journal of Precision Engineering and Manufacturing 14 (1): 85–91. doi:10.1007/s12541-013-0013-2.
   Web of Science ®Google Scholar
• Thomessen, T., O. J. Elle, J. Lund Larsen, T. Andersen, J. E. Pedersen, and T. K. Lien. 1993. “Automatic Programming of Grinding Robot.” Modeling, Identification and Control 14 (2): 93–105. doi:10.4173/mic.1993.2.4.
   Web of Science ®Google Scholar
• Villagrossi, E., et al. 2017. “Flexible Robot-Based Cast Iron Deburring Cell for Small Batch Production Using Single-Point Laser Sensor.” The International Journal of Advanced Manufacturing Technology 92 (1-4):1425–1438.
   Web of Science ®Google Scholar
• Wang, X., Y. Wang, and Y. Xue. 2006. Adaptive Control of Robotic Deburring Process Based on Impedance Control. 2006 4th IEEE International Conference on Industrial Informatics, Singapore, 2006, pp. 921-925. doi: 10.1109/INDIN.2006.275700
   Google Scholar
• Xi, F. J., T. Chen, and S. Guo. 2017. “Robotic Polishing and Deburring.” In Comprehensive Materials Finishing, edited by MSJ Hashmi, Elsevier, Oxford, 2017, pp. 121-153, ISBN 9780128032497. doi:10.1016/B978-0-12-803581-8.09151-7.
   Google Scholar
• Zhang, H., et al. 2006. On-Line Path Generation for Robotic Deburring of Cast Aluminum Wheels. s.l., s.n. 2400–2405.
   Google Scholar
• Ziliani, G., A. Visioli, and G. Legnani. 2007. “A Mechatronic Approach for Robotic Deburring.” Mechatronics 17 (8): 431–441. doi:10.1016/j.mechatronics.2007.04.012.
   Web of Science ®Google Scholar