A Brief Review of Robotic Machining

Alexandru Bârsan 1
  • 1 Faculty of Engineering/Department of Industrial Machines and Equipment, “Lucian Blaga” University, Sibiu, Romania

Abstract

The approach of this paper was to analyze the technical borders of industrial robots and to provide an overview of current technology, technical constraints and the potential types of future research suggestion concerning robotic machining. These complex automation machines used in manufacturing processes are an emerging chapter of industrial engineering that contribute to automatically performing operation in subtractive manufacturing and sheet metal forming processes. Compared with CNC machines which have shape limitations and have the restricted working area, the industrial robot is a flexible, cost-saving alternative.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1. Altintas, Y., Ber, A. A., Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design, Appl. Mech. Rev., Vol. 54, No.5, pp. B84-B84., (2001).

  • 2. Cen, L., Melkote, S. N., Effect of robot dynamics on the machining forces in robotic milling. Procedia Manufacturing, Vol. 10, pp. 486-496, (2017).

  • 3. IFR Statistical Department. Executive Summary WR 2019 Industrial Robots, (2019).

  • 4. Klimchik, A., Ambiehl, A., Garnier, S., Furet, B., Pashkevich, A., Efficiency evaluation of robots in machining applications using industrial performance measure, Robotics and Computer-Integrated Manufacturing, Vol. 48, 12-29. (2017).

  • 5. Brunete, A., Gambao, E., Koskinen, J., Heikkilä, T., Kaldestad, K. B., Tyapin, I., ... & Anton, S., Hard material small-batch industrial machining robot, Robotics and Computer-Integrated Manufacturing, Vol. 54, pp. 185-199, (2018).

  • 6. Caro, S., Dumas, C., Garnier, S., Furet, B., Workpiece placement optimization for machining operations with a KUKA KR270-2 robot. 2013 IEEE International Conference on Robotics and Automation, IEEE, pp. 2921-2926, (2013).

  • 7. Racz, G. S., Oleksik, V. S., Breaz, R. E., Incremental forming–CAE/CAM approaches and results, IOP Conference Series: Materials Science and Engineering, Vol. 591, No. 1, pp. 012065. IOP Publishing, (2019).

  • 8. Breaz, R. E., Racz, S. G., Considerations Regarding the Industrial Implementation of Incremental Forming Process. Materials Science Forum, Trans Tech Publications Ltd., Vol. 957, pp. 111-119, (2019).

  • 9. Oleksik, V., Influence of geometrical parameters, wall angle and part shape on thickness reduction of single point incremental forming, Procedia Engineering, Vol. 81, pp. 2280-2285, (2014).

  • 10. Popp, Mihai., Rusu, Gabriela., Racz, Sever-Gabriel., Popp, Ilie Octavian., Force and thickness prediction with FEA of cranial implants manufactured through SPIF, MATEC Web of Conferences, No. 290, (2019).

  • 11. DePree, J., Gesswein, C., Robotic machining white paper project. Halcyon Development-Robotic Industries Association (RIA), (2008).

  • 12. Zhang, H., Wang, J., Zhang, G., Gan, Z., Pan, Z., Cui, H., Zhu, Z., Machining with flexible manipulator: toward improving robotic machining performance. Proceedings, 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, IEEE, pp. 1127-1132, (2005).

  • 13. Abele, E., Weigold, M., Rothenbücher, S., Modeling and identification of an industrial robot for machining applications. CIRP annals, Vol. 56, No. 1, pp. 387-390, (2007).

  • 14. Wu, Y., Klimchik, A., Caro, S., Furet, B., Pashkevich, A., Geometric calibration of industrial robots using enhanced partial pose measurements and design of experiments, Robotics and Computer-Integrated Manufacturing, Vol. 35, pp. 151-168, (2015).

  • 15. Diaz Posada, J., Schneider, U., Sridhar, A., Verl, A., Automatic motion generation for robotic milling optimizing stiffness with sample-based planning, Machines, Vol. 5, No. 1, (2017).

  • 16. Crenganiș, M., Bârsan, A., Racz, S. G., Iordache, M. D., SINGLE POINT INCREMENTAL FORMING USING KUKA KR6-2 INDUSTRIAL ROBOT-A DYNAMIC APPROACH, Proceedings in Manufacturing Systems, Vol. 13, No. 3, pp. 133-140, (2018).

  • 17. Taek Oh, Y., Influence of the joint angular characteristics on the accuracy of industrial robots. Industrial Robot: An International Journal, Vol. 38, No. 4, pp. 406-418, (2011).

  • 18. Erkaya, S., Investigation of joint clearance effects on welding robot manipulators, Robotics and Computer-Integrated Manufacturing, Vol. 28, No. 4, pp. 449-457, (2012).

  • 19. Gong, C., Yuan, J., Ni, J., Nongeometric error identification and compensation for robotic system by inverse calibration, International Journal of Machine Tools and Manufacture, Vol. 40, No. 14, pp. 2119-2137, (2000).

  • 20. Ruderman, M., Hoffmann, F., Bertram, T., Modeling and identification of elastic robot joints with hysteresis and backlash, IEEE Transactions on Industrial Electronics, Vol. 56, No. 10, pp. 3840-3847, (2009).

  • 21. Crenganis, M., Csiszar, A., A Dynamic Model for KUKA KR6 in SPIF Processes. Materials Science Forum, Trans Tech Publications Ltd., Vol. 957, pp. 156-166, (2019).

  • 22. ISO 9283, Manipulating industrial robots – Performance criteria and related test methods, International Organization for Standardization, Geneva, Switzerland, (1998).

OPEN ACCESS

Journal + Issues

Search