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Industrial robotic machining: a review

  • ORIGINAL ARTICLE
  • Open access
  • Published: 03 April 2019
  • volume 103, pages 1239–1255 (2019)
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Industrial robotic machining: a review
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  • Wei Ji  ORCID: orcid.org/0000-0002-9642-69831 &
  • Lihui Wang1 

  • 6563 Accesses

  • 179 Citations

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Abstract

For the past three decades, robotic machining has attracted a large amount of research interest owning to the benefit of cost efficiency, high flexibility and multi-functionality of industrial robot. Covering articles published on the subjects of robotic machining in the past 30 years or so; this paper aims to provide an up-to-date review of robotic machining research works, a critical analysis of publications that publish the research works, and an understanding of the future directions in the field. The research works are organised into two operation categories, low material removal rate (MRR) and high MRR, according their machining properties, and the research topics are reviewed and highlighted separately. Then, a set of statistical analysis is carried out in terms of published years and countries. Towards an applicable robotic machining, the future trends and key research points are identified at the end of this paper.

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References

  1. Cheng K (2008) Machining dynamics: fundamentals, applications and practices. Springer Science & Business Media

  2. Altintas Y (2012) Manufacturing automation: metal cutting mechanics, machine tool vibrations, and CNC design. Cambridge university press

  3. International Federation of Robotics I (2017) Executive summary world Robotics 2017 Industrial Robots

  4. Appleton E, Williams DJ (1987) Industrial robot applications. HALSTED PRESS, New York

    Book  Google Scholar 

  5. Hu YN, Chen YH (1999) Implementation of a robot system for sculptured surface cutting. Part 2. Finish machining. Int J Adv Manuf Technol 15:630–639. https://doi.org/10.1007/s001700050112

    Article  Google Scholar 

  6. Song Y, Chen YH (1999) Feature-based robot machining for rapid prototyping. Proc Inst Mech Eng Part B J Eng Manuf 213:451–459. https://doi.org/10.1243/0954405991516921

    Article  Google Scholar 

  7. Milutinovic D, Glavonjic M, Slavkovic N, Dimic Z, Zivanovic S, Kokotovic B, Tanovic L (2011) Reconfigurable robotic machining system controlled and programmed in a machine tool manner. Int J Adv Manuf Technol 53:1217–1229. https://doi.org/10.1007/s00170-010-2888-8

    Article  Google Scholar 

  8. Dimic Z, Milutinovic D, Zivanovic S, Kvrgic V (2016) Virtual environment in control and programming system for reconfigurable machining robot 9. Teh Vjesn - Tech Gaz 23:1821–1182. https://doi.org/10.17559/TV-20150210133556

    Google Scholar 

  9. Nagata F, Otsuka A, Watanabe K, Habib MK (2014) Fuzzy feed rate controller for a machining robot. IEEE Int Conf Mechatronics Autom IEEE ICMA 2014:198–203. https://doi.org/10.1109/ICMA.2014.6885695

    Google Scholar 

  10. Nagata F, Otsuka A, Watanabe K, Habib MK (2015) Machining robot for foamed polystyrene materials using fuzzy feed rate controller Fusaomi Nagata * and Akimasa Otsuka Keigo Watanabe. Int J Mechatronics Autom 5:34–43

    Article  Google Scholar 

  11. Nagata F, Habib MK, Otsuka A, Hayashi S, Nagatomi T, Watanabe K (2015) Vibrational motion control for foamed polystyrene machining robot and extraction of radius of curvature for fuzzy feed rate control. Artif Life Robot 20:197–202. https://doi.org/10.1007/s10015-015-0221-1

    Article  Google Scholar 

  12. Nagata F, Hayashi S, Nagatomi T, et al (2015) Robotic trajectory following controller with a capability for generating micro vibrational motion along curved surface. IECON 2015 - 41st Annu Conf IEEE Ind Electron Soc 765–770. doi: https://doi.org/10.1109/IECON.2015.7392191

  13. Nagata F, Watanabe K, Habib MK (2018) Machining robot with vibrational motion and 3D printer-like data interface. Int J Autom Comput 15:1–12. https://doi.org/10.1007/s11633-017-1101-z

    Article  Google Scholar 

  14. Vergeest JSM, Tangelder JWH (1996) Robot machines rapid prototype. Ind Robot An Int J 23:17–20. https://doi.org/10.1108/01439919610130328

    Article  Google Scholar 

  15. Pandremenos J, Doukas C, Stavropoulos P, Chryssolouris G (2011) Machining with robots: a critical review. 7th Int Conf Digit Enterp Technol

  16. Chen Y, Dong F (2013) Robot machining: recent development and future research issues. Int J Adv Manuf Technol 66:1489–1497. https://doi.org/10.1007/s00170-012-4433-4

    Article  Google Scholar 

  17. Karim A, Verl A (2013) Challenges and obstacles in robot-machining. 2013 44th Int Symp robot ISR 2013. doi: https://doi.org/10.1109/ISR.2013.6695731

  18. Bo H, Azhar M, Mohan DM, Campolo D (2015) Review of robotic control strategies for industrial finishing operations. In: 2015 10th International Symposium on Mechatronics and its Applications (ISMA). pp 1–6

  19. Iglesias I, Sebastián MA, Ares JE (2015) Overview of the state of robotic machining: current situation and future potential. Procedia Eng 132:911–917. https://doi.org/10.1016/j.proeng.2015.12.577

    Article  Google Scholar 

  20. Yuan L, Pan Z, Ding D, Sun S, Li W (2018) A review on chatter in robotic machining process regarding both regenerative and mode coupling mechanism. IEEE/ASME Trans Mechatronics 23:2240–2251. https://doi.org/10.1109/TMECH.2018.2864652

    Article  Google Scholar 

  21. Izumi T, Narikiyo T, Fukui Y (1987) Teachingless grinding robot depending on three force information. Adv Robot 2:55–67

    Article  Google Scholar 

  22. Muto S, Shimokura K (1994) Teaching and control of robot contour-tracking using contact point detection. Proc 1994 IEEE Int Conf Robot Autom:674–681. https://doi.org/10.1109/ROBOT.1994.351408

  23. Jinno M, Ozaki F, Yoshimi T et al (1995) Development of a force controlled robot for grinding, chamfering and polishing. Proc - IEEE Int Conf Robot Autom 2:1455–1460. https://doi.org/10.1109/ROBOT.1995.525481

    Google Scholar 

  24. Surdilovic D, Zhao H, Schreck G, Krueger J (2012) Advanced methods for small batch robotic machining of hard materials. Robot Proc Robot 2012; 7th Ger Conf 1–6

  25. Villagrossi E, Pedrocchi N, Beschi M, Molinari Tosatti L (2018) A human mimicking control strategy for robotic deburring of hard materials. Int J Comput Integr Manuf 31:869–880. https://doi.org/10.1080/0951192X.2018.1447688

    Article  Google Scholar 

  26. Domroes F, Krewet C, Kuhlenkoetter B (2013) Application and analysis of force control strategies to deburring and grinding. Mod Mech Eng 3:11–18. https://doi.org/10.4236/mme.2013.32A002

    Article  Google Scholar 

  27. Chen F, Zhao H, Li D, Chen L, Tan C, Ding H (2018) Robotic grinding of a blisk with two degrees of freedom contact force control. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-018-2925-6

  28. Ding Y, Min X, Fu W, Liang Z (2018) Research and application on force control of industrial robot polishing concave curved surfaces. Proc Inst Mech Eng Part B J Eng Manuf 0954405418802309. doi: https://doi.org/10.1177/0954405418802309

  29. Leali F, Pellicciari M, Pini F, Berselli G, Vergnano A (2013) An offline programming method for the robotic deburring of aerospace components. In: Neto P, Moreira AP (eds) Robotics in Smart Manufacturing. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 1–13

    Google Scholar 

  30. Rafieian F, Hazel B, Liu Z (2014) Regenerative instability of impact-cutting material removal in the grinding process performed by a flexible robot arm. Procedia CIRP 14:406–411. https://doi.org/10.1016/j.procir.2014.03.099

    Article  Google Scholar 

  31. Liu H, Wan Y, Zeng Z, et al (2016) Freeform surface grinding and polishing by CCOS based on industrial robot. In: 8th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Advanced Optical Manufacturing Technologies,

  32. Xie S, Li S, Chen B, Qi J (2017) Research on robot grinding technology considering removal rate and roughness. In: Huang Y, Wu H, Liu H, Yin Z (eds) Intelligent robotics and applications. Springer International Publishing, Cham, pp 79–90

    Chapter  Google Scholar 

  33. Sufian M, Chen X, Yu D (2017) Investigating the capability of precision in robotic grinding. In: Proceedings of the 23rd International Conference on Automation & Computing. Huddersfield, UK

  34. Li W, Xie H, Zhang G, Yan SJ, Yin ZP (2016) 3-D shape matching of a blade surface in robotic grinding applications. IEEE/ASME Trans Mechatronics 21:2294–2306

    Article  Google Scholar 

  35. Mao Y, Zhao H, Zhao X, Ding H (2017) Trajectory and force generation with multi-constraints for robotic belt grinding. In: Huang Y, Wu H, Liu H, Yin Z (eds) Intelligent robotics and applications. Springer International Publishing, Cham, pp 14–23

    Chapter  Google Scholar 

  36. Zhang T, Su J (2018) Collision-free planning algorithm of motion path for the robot belt grinding system. Int J Adv Robot Syst 15:172988141879377. https://doi.org/10.1177/1729881418793778

    Article  Google Scholar 

  37. Yan S, Xu X, Yang Z, Zhu D, Ding H (2019) An improved robotic abrasive belt grinding force model considering the effects of cut-in and cut-off. J Manuf Process 37:496–508. https://doi.org/10.1016/j.jmapro.2018.12.029

    Article  Google Scholar 

  38. Pandian J (1998) Automated fixture and robot aided deburring for light aircraft components. University of Manitoba, Winnipeg, Manitoba

    Google Scholar 

  39. Huang H, Gong ZM, Chen XQ, Zhou L (2003) SMART robotic system for 3D profile turbine vane airfoil repair. Int J Adv Manuf Technol 21:275–283. https://doi.org/10.1007/s001700300032

    Article  Google Scholar 

  40. Ricardo J, Posada D, Kumar S, et al (2016) Automatic programming and control for robotic deburring description of the robot deburring system. In: 47th international symposium on Robotics pp 688–695

  41. Ji W, Wang Y, Liu H, Wang L (2018) Interface architecture design for minimum programming in human-robot collaboration. Procedia CIRP 72:129–134. https://doi.org/10.1016/j.procir.2018.03.013

    Article  Google Scholar 

  42. Ji W, Li Y, Wang L (2018) A task-oriented cyber-physical system in manufacturing. In: The 48th international conference on computers and industrial engineering. Auckland, p 8

  43. Diao S, Chen X, Luo J (2018) Development and experimental evaluation of a 3D vision system for grinding robot. Sensors (Switzerland) 18:1–20. https://doi.org/10.3390/s18093078

    Google Scholar 

  44. KATIC D, VUKOBRATOVIC M (1997) Classification and learning of robot-environment dynamic models. In: proceedings of the 1997 EEE Intemational conference on robotics and automation. pp 2632–2637

  45. Katić D, Vukobratović M (1998) A neural network-based classification of environment dynamics models for compliant control of manipulation robots. IEEE Trans Syst Man, Cybern Part B Cybern 28:58–69. https://doi.org/10.1109/3477.658578

    Article  Google Scholar 

  46. Zhang H, Wang J, Zhang G, et al (2005) Machining with flexible manipulator: toward improving robotic machining performance. Proceedings, 2005 IEEE/ASME Int Conf Adv Intell Mechatronics 1127–1132 . doi: https://doi.org/10.1109/AIM.2005.1511161

  47. Dumas C, Caro S, Garnier S, Furet B (2011) Joint stiffness identification of six-revolute industrial serial robots. Robot Comput Integr Manuf 27:881–888. https://doi.org/10.1016/j.rcim.2011.02.003

    Article  Google Scholar 

  48. Denkena B, Bergmann B, Lepper T (2017) Design and optimization of a machining robot. In: Procedia Manufacturing. Elsevier B.V., pp 89–96

  49. Caro S, Dumas C, Garnier S, Furet B (2013) Workpiece placement optimization for machining operations with a KUKA KR270-2 robot. Proc - IEEE Int Conf Robot Autom 2921–2926 . doi: https://doi.org/10.1109/ICRA.2013.6630982

  50. Caro S, Dumas C, Garnier S, Furet B (2013) Workpiece placement optimization in robotic-based manufacturing. IFAC, Workpiece Placement Optimization in Robotic-based Manufacturing

  51. Caro S, Garnier S, Furet B, et al (2014) Workpiece placement optimization for machining operations with industrial robots. IEEE/ASME Int Conf Adv Intell Mechatronics, AIM 1716–1721. doi: https://doi.org/10.1109/AIM.2014.6878331

  52. Garnier S, Dumas C, Caro S, Furet B (2013) Quality certification and productivity optimization in robotic-based manufacturing. IFAC, Quality Certification and Productivity Optimization in Robotic-based Manufacturing

  53. Klimchik A, Ambiehl A, Garnier S, Furet B, Pashkevich A (2016) Experimental study of robotic-based machining. IFAC-PapersOnLine 49:174–179. https://doi.org/10.1016/j.ifacol.2016.07.591

    Article  MathSciNet  Google Scholar 

  54. Klimchik A, Ambiehl A, Garnier SS, Furet B, Pashkevich A (2017) Efficiency evaluation of robots in machining applications using industrial performance measure. Robot Comput Integr Manuf 48:12–29. https://doi.org/10.1016/j.rcim.2016.12.005

    Article  Google Scholar 

  55. Subrin K, Sabourin L, Gogu G, Mezouar Y (2012) Performance criteria to evaluate a kinematically redundant robotic cell for machining tasks. Appl Mech Mater 162:413–422. https://doi.org/10.4028/www.scientific.net/AMM.162.413

    Article  Google Scholar 

  56. Lin Y, Zhao H, Ding H (2017) Posture optimization methodology of 6R industrial robots for machining using performance evaluation indexes. Robot Comput Integr Manuf 48:59–72. https://doi.org/10.1016/j.rcim.2017.02.002

    Article  Google Scholar 

  57. Xiong G, Ding Y, Zhu LM (2019) Stiffness-based pose optimization of an industrial robot for five-axis milling. Robot Comput Integr Manuf 55:19–28. https://doi.org/10.1016/j.rcim.2018.07.001

    Article  Google Scholar 

  58. Xie H, Li W, Yin Z (2018) Posture Optimization Based on Both Joint Parameter Error and Stiffness for Robotic Milling. In: Chen Z, Mendes A, Yan Y, Chen S (eds) Intelligent robotics and applications. Springer International Publishing, Cham, pp 277–286

    Chapter  Google Scholar 

  59. Mousavi S, Gagnol V, Bouzgarrou BC, Ray P (2017) Dynamic modeling and stability prediction in robotic machining. Int J Adv Manuf Technol 88:3053–3065. https://doi.org/10.1007/s00170-016-8938-0

    Article  Google Scholar 

  60. Mousavi S, Gagnol V, Bouzgarrou BC, Ray P (2018) Stability optimization in robotic milling through the control of functional redundancies. Robot Comput Integr Manuf 50:181–192. https://doi.org/10.1016/j.rcim.2017.09.004

    Article  Google Scholar 

  61. Karim A, Hitzer J, Lechler A, Verl A (2017) Analysis of the dynamic behavior of a six-axis industrial robot within the entire workspace in respect of machining tasks. IEEE/ASME Int Conf Adv Intell Mechatronics, AIM:670–675. https://doi.org/10.1109/AIM.2017.8014094

  62. Bu Y, Liao W, Tian W, Zhang J, Zhang L (2017) Stiffness analysis and optimization in robotic drilling application. Precis Eng 49:388–400. https://doi.org/10.1016/j.precisioneng.2017.04.001

    Article  Google Scholar 

  63. Pan ZZ, Zhang H, Zhu Z, Wang J (2006) Chatter analysis of robotic machining process. J Mater Process Technol 173:301–309. https://doi.org/10.1016/j.jmatprotec.2005.11.033

    Article  Google Scholar 

  64. Abele E, Weigold M, Rothenbücher S (2007) Modeling and identification of an industrial robot for machining applications. CIRP Ann - Manuf Technol 56:387–390. https://doi.org/10.1016/j.cirp.2007.05.090

    Article  Google Scholar 

  65. Cordes M, Hintze W, Altintas Y (2019) Chatter stability in robotic milling. Robot Comput Integr Manuf 55:11–18. https://doi.org/10.1016/j.rcim.2018.07.004

    Article  Google Scholar 

  66. Safi SM, Amirabadi H, Lirabi I, Khalili K, Rahnama S (2013) A new approach for chatter prediction in robotic milling based on signal processing in time domain. Appl Mech Mater 346:45–51. https://doi.org/10.4028/www.scientific.net/AMM.346.45

    Article  Google Scholar 

  67. Vieler H, Karim A, Lechler A (2017) Drive based damping for robots with secondary encoders. Robot Comput Integr Manuf 47:117–122. https://doi.org/10.1016/j.rcim.2017.03.007

    Article  Google Scholar 

  68. Yuan L, Sun S, Pan Z, Ding D, Gienke O, Li W (2019) Mode coupling chatter suppression for robotic machining using semi-active magnetorheological elastomers absorber. Mech Syst Signal Process 117:221–237. https://doi.org/10.1016/j.ymssp.2018.07.051

    Article  Google Scholar 

  69. Yuan L (2017) A study of chatter in robotic machining and a semi- active chatter suppression method using magnetorheological elastomers (MREs). University of Wollongong

  70. Puzik A, Meyer C, Verl A (2010) Industrial robots for machining processes in combination with a 3D-piezo-compensation mechanism. CIRP Intell Comput Manuf Eng ICME

  71. Guo Y, Dong H, Wang G, Ke Y (2016) Vibration analysis and suppression in robotic boring process. Int J Mach Tools Manuf 101:102–110. https://doi.org/10.1016/j.ijmachtools.2015.11.011

    Article  Google Scholar 

  72. Puzik A, Meyer C, Verl A (2010) Results of robot machining with additional 3D-piezo-actuation-mechanism for error compensation.7th CIRP Int Conf, Intell Comput … 415–421

  73. Olofsson B, Sörnmo O, Schneider U, et al (2011) Modeling and control of a piezo-actuated high-dynamic compensation mechanism for industrial robots. In: 2011 IEEE/RSJ international conference on intelligent robots and systems. pp 4704–4709

  74. Lehmann C, Pellicciari M, Drust M, Gunnink JW (2013) Machining with industrial robots: the COMET project approach. In: Neto P, Moreira AP (eds) Robotics in smart manufacturing. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 27–36

    Chapter  Google Scholar 

  75. Schneider U, Ansaloni M, Drust M, Leali F, Verl A (2013) Experimental investigation of sources of error in robot machining. In: Neto P, Moreira AP (eds) Robotics in smart manufacturing. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 14–26

    Chapter  Google Scholar 

  76. Schneider U, Drust M, Puzik A, Verl A (2013) Compensation of errors in robot machining with a parallel 3D-piezo compensation mechanism. Procedia CIRP 7:305–310. https://doi.org/10.1016/j.procir.2013.05.052

    Article  Google Scholar 

  77. Olof S, Schneider U, Robertsson A, et al (2013) High-accuracy milling with industrial robots using a piezo-actuated high-dynamic compensation mechanism. Comet

  78. Schneider U, Momeni-K M, Ansaloni M, Verl A (2014) Stiffness modeling of industrial robots for deformation compensation in machining. IEEE Int Conf Intell Robot Syst 4464–4469 . doi: https://doi.org/10.1109/IROS.2014.6943194

  79. Schneider U, Drust M, Ansaloni M, Lehmann C, Pellicciari M, Leali F, Gunnink JW, Verl A (2016) Improving robotic machining accuracy through experimental error investigation and modular compensation. Int J Adv Manuf Technol 85:3–15. https://doi.org/10.1007/s00170-014-6021-2

    Article  Google Scholar 

  80. Haage M, Halbauer M, Lehmann C, Städter JP (2014) Increasing robotic machining accuracy using offline compensation based on joint-motion SIMULATION Proc Jt Conf ISR 2014 - 45th Int Symp Robot Robot 2014 - 8th Ger Conf Robot ISR/ROBOTIK 2014 347–354

  81. Schneider U, Diaz Posada JR, Verl A (2015) Automatic pose optimization for robotic processes. Proc - IEEE Int Conf robot autom 2054–2059. doi: https://doi.org/10.1109/ICRA.2015.7139468

  82. Halbauer M, Lehmann C, Städter JP et al (2013) Milling strategies optimized for industrial robots to machine hard materials. IEEE Int Conf Emerg Technol Fact Autom ETFA:1–4. https://doi.org/10.1109/ETFA.2013.6648124

  83. Leali F, Pini F, Ansaloni M (2013) Integration of CAM off-line programming in robot high-accuracy machining. In: proceedings of the 2013 IEEE/SICE international symposium on system Integration pp 580–585

  84. Wang G, Dong H, Guo Y, Ke Y (2016) Dynamic cutting force modeling and experimental study of industrial robotic boring. Int J Adv Manuf Technol 86:179–190. https://doi.org/10.1007/s00170-015-8166-z

    Article  Google Scholar 

  85. Wang G, Dong H, Guo Y, Ke Y (2017) Chatter mechanism and stability analysis of robotic boring. Int J Adv Manuf Technol 91:411–421. https://doi.org/10.1007/s00170-016-9731-9

    Article  Google Scholar 

  86. Guo Y, Dong H, Wang G (2018) A robotic boring system for intersection holes in aircraft assembly. Ind Robot An Int J 45:328–336. https://doi.org/10.1108/IR-09-2017-0176

    Article  Google Scholar 

  87. Dong S, Zheng K, Liao W (2018) Stability of lateral vibration in robotic rotary ultrasonic drilling. Int J Mech Sci 145:346–352. https://doi.org/10.1016/j.ijmecsci.2018.07.004

    Article  Google Scholar 

  88. Dong S, Liao W, Zheng K, Liu J, Feng J (2019) Investigation on exit burr in robotic rotary ultrasonic drilling of CFRP/aluminum stacks. Int J Mech Sci 151:868–876. https://doi.org/10.1016/j.ijmecsci.2018.12.039

    Article  Google Scholar 

  89. Huan J (1982) Bahnregelung zur Bahnerzeugung an numerisch gesteuerten Werkzeugrnaschinen. Un- iversity of Stuttgart

  90. Chin JH, Tsai HC (1993) A path algorithm for robotic machining. Robot Comput Integr Manuf 10:185–198. https://doi.org/10.1016/0736-5845(93)90054-N

    Article  Google Scholar 

  91. Krži P, Pušavec F, Kopa J (2013) Kinematic constraints and offline programming in robotic machining. Teh Vjesn 3651:117–124

    Google Scholar 

  92. Slamani M, Gauthier S, Chatelain JF (2014) Analysis of trajectory deviation during high speed robotic trimming of carbon-fiber reinforced polymers. Robot Comput Integr Manuf 30:546–555. https://doi.org/10.1016/j.rcim.2014.03.007

    Article  Google Scholar 

  93. Slamani M, Gauthier S, Chatelain JF (2015) A study of the combined effects of machining parameters on cutting force components during high speed robotic trimming of CFRPs. Measurement 59:268–283. https://doi.org/10.1016/j.measurement.2014.09.052

    Article  Google Scholar 

  94. Xiong G, Ding Y, Zhu L (2017) A feed-direction stiffness based trajectory optimization method for a milling robot. In: Huang Y, Wu H, Liu H, Yin Z (eds) Intelligent robotics and applications. Springer International Publishing, Cham, pp 184–195

    Chapter  Google Scholar 

  95. Villagrossi E (2016) robot dynamics modelling and control for machining applications. Università degli Studi di Brescia

  96. He F, Liu Y, Liu K (2018) A chatter-free path optimization algorithm based on stiffness orientation method for robotic milling. Int J Adv Manuf Technol doi: https://doi.org/10.1007/s00170-018-3099-y

  97. Owen WS, Croft EA, Benhabib B (2006) Real-time trajectory resolution for a two-manipulator machining system. J Robot Syst 22:51–63. https://doi.org/10.1002/rob.20151

    Article  MATH  Google Scholar 

  98. Owen WS, Croft EA, Benhabib B (2005) Acceleration and torque redistrubution for a dual-manipulator system. IEEE Trans Robot 21:1226–1230

    Article  Google Scholar 

  99. Owen WS, Croft EA, Benhabib B (2004) Real-time trajectory resolution for dual robot machining. IEEE Int Conf Robot Autom 2004 Proceedings ICRA 5:4332–4337. https://doi.org/10.1109/ROBOT.2004.1302399

    Google Scholar 

  100. Owen W, Croft E, Benhabib B (2008) Stiffness optimization for two-armed robotic sculpting. Ind Robot An Int J 35:46–57. https://doi.org/10.1108/01439910810843289

    Article  Google Scholar 

  101. Owen WS, Croft EA, Benhabib B (2008) A multi-arm robotic system for optimal sculpting. Robot Comput Integr Manuf 24:92–104. https://doi.org/10.1016/j.rcim.2006.08.001

    Article  Google Scholar 

  102. Owen WS, Croft EA, Benhabib B (2009) On-line trajectory resolution for two-armed systems with conflicting performance criteria. Mech Mach Theory 44:949–965. https://doi.org/10.1016/j.mechmachtheory.2008.06.001

    Article  MATH  Google Scholar 

  103. Ji W, Yin S, Wang L (2018) A big data analytics based machining optimisation approach. J Intell Manuf. https://doi.org/10.1007/s10845-018-1440-9

  104. Atmosudiro A, Keinert M, Karim A, et al (2014) Productivity increase through joint space path planning for robot machining. Proc - UKSim-AMSS 8th Eur model Symp Comput model simulation, EMS 2014 257–262 . doi: https://doi.org/10.1109/EMS.2014.46

  105. Chen C, Peng F, Yan R, Li Y, Wei D, Fan Z, Tang X, Zhu Z (2019) Stiffness performance index based posture and feed orientation optimization in robotic milling process. Robot Comput Integr Manuf 55:29–40. https://doi.org/10.1016/j.rcim.2018.07.003

    Article  Google Scholar 

  106. Brüning J, Denkena B, Dittrich MA, Park H-S (2016) Simulation based planning of machining processes with industrial robots. Procedia Manuf 6:17–24. https://doi.org/10.1016/j.promfg.2016.11.003

    Article  Google Scholar 

  107. Cen L, Melkote SN (2017) Effect of robot dynamics on the machining forces in robotic milling. Procedia Manuf 10:486–496. https://doi.org/10.1016/j.promfg.2017.07.034

    Article  Google Scholar 

  108. Wang Z, Keogh P (2017) Active vibration control for robotic machining. In: ASME 2017 International Mechanical Engineering Congress and Exposition

  109. Huynh HN, Riviere-Lorphevre E, Verlinden O (2018) Multibody modelling of a flexible 6-axis robot dedicated to robotic machining. In: the 5th joint international conference on multibody system Dynamics. pp 1–18

  110. Garnier S, Subrin K, Waiyagan K (2017) Modelling of robotic drilling. Procedia CIRP 58:416–421. https://doi.org/10.1016/j.procir.2017.03.246

    Article  Google Scholar 

  111. Tratar J, Pušavec F, Kopač J (2014) Tool wear performance evaluation in MDF machining with anthropomorphic robot. Tech Gaz 21:911–915

    Google Scholar 

  112. Schreck G, Surdilovic D, Krüger J (2014) HEPHESTOS: hard material small-batch industrial machining. Robot Scientif. pp 239–244

  113. Furtado LFF, Villani E, Trabasso LG, Sutério R (2017) A method to improve the use of 6-dof robots as machine tools. Int J Adv Manuf Technol 92:2487–2502. https://doi.org/10.1007/s00170-017-0336-8

    Article  Google Scholar 

  114. Matsuoka SI, Shimizu K, Yamazaki N, Oki Y (1999) High-speed end milling of an articulated robot and its characteristics. J Mater Process Technol 95:83–89. https://doi.org/10.1016/S0924-0136(99)00315-5

    Article  Google Scholar 

  115. Mejri S, Gagnol V, Le TP et al (2016) Dynamic characterization of machining robot and stability analysis. Int J Adv Manuf Technol 82:351–359. https://doi.org/10.1007/s00170-015-7336-3

    Article  Google Scholar 

  116. Tratar J, Kopač J (2013) Robot milling of welded structures. J Prod Eng 16:29–32

    Google Scholar 

  117. Klimchik A, Bondarenko D, Pashkevich A et al (2012) Compliance error compensation in robotic-based milling. In: Ferrier J-L, Bernard A, Gusikhin O, Madani K (eds) Informatics in control, automation and robotics: 9th international conference. Springer International Publishing, Cham, pp 197–216

    Google Scholar 

  118. Klimchik A, Bondarenko D, Pashkevich A, et al (2012) Compensation of tool deflection in robotic-based milling. Icinco 113–122

  119. Höfener M, Schüppstuhl T (2014) A method for increasing the accuracy of “on-workpiece” machining with small industrial robots for composite repair. Prod Eng 8:701–709. https://doi.org/10.1007/s11740-014-0570-y

    Article  Google Scholar 

  120. Höfener M, Schüppstuhl T (2014) Small industrial robots for on-aircraft repair of composite structures summary/abstract kinematics for on-aircraft machining of composites. In: Conference ISR ROBOTIK. pp 422–427

  121. Kothe S, Stürmer SPV, Schmidt HC, et al (2016) Accuracy analysis and error source identification for optimization of robot-based machining systems for aerospace production. SAE Tech Pap 2016–Octob. doi: https://doi.org/10.4271/2016-01-2137

  122. Kubela T, Pochyly A, Singule V (2016) Assessment of industrial robots accuracy in relation to accuracy improvement in machining processes. Proc - 2016 IEEE Int power Electron motion control Conf PEMC 2016 720–725. doi: doi:https://doi.org/10.1109/EPEPEMC.2016.7752083

  123. Cordes M, Hintze W (2017) Offline simulation of path deviation due to joint compliance and hysteresis for robot machining. Int J Adv Manuf Technol 90:1075–1083. https://doi.org/10.1007/s00170-016-9461-z

    Article  Google Scholar 

  124. Tang X, Yan R, Peng F, Liu G, Li H, Wei D, Fan Z (2018) Deformation error prediction and compensation for robot multi-axis milling. In: Chen Z, Mendes A, Yan Y, Chen S (eds) Intelligent robotics and applications. Springer International Publishing, Cham, pp 309–318

    Chapter  Google Scholar 

  125. Slamani M, Gauthier S, Chatelain J-F (2016) Comparison of surface roughness quality obtained by high speed CNC trimming and high speed robotic trimming for CFRP laminate. Robot Comput Integr Manuf 42:63–72. https://doi.org/10.1016/j.rcim.2016.05.004

    Article  Google Scholar 

  126. Slamani M, Chatelain JF (2019) Assessment of the suitability of industrial robots for the machining of carbon-fiber reinforced polymers (CFRPs). J Manuf Process 37:177–195. https://doi.org/10.1016/j.jmapro.2018.11.022

    Article  Google Scholar 

  127. Barnfather JD, Goodfellow MJ, Abram T (2016) A performance evaluation methodology for robotic machine tools used in large volume manufacturing. Robot Comput Integr Manuf 37:49–56. https://doi.org/10.1016/j.rcim.2015.06.002

    Article  Google Scholar 

  128. Barnfather JD, Goodfellow MJ, Abram T (2016) Development and testing of an error compensation algorithm for photogrammetry assisted robotic machining. Measurement 94:561–577. https://doi.org/10.1016/j.measurement.2016.08.032

    Article  Google Scholar 

  129. Lehmann C, Halbauer M, Euhus D, Overbeck D (2012) Milling with industrial robots: strategies to reduce and compensate process force induced accuracy influences. IEEE Int Conf Emerg Technol Fact Autom ETFA doi: https://doi.org/10.1109/ETFA.2012.6489741

  130. Denkena B, Litwinski K, Schönherr M (2013) Innovative drive concept for machining robots. Procedia CIRP 9:67–72. https://doi.org/10.1016/j.procir.2013.06.170

    Article  Google Scholar 

  131. Domrös DF (2014) Towards autonomous robot machining. In: Conference ISR ROBOTIK. pp 448–453

  132. Xie Y, Zou W, Yang Y, Lia J (2018) Design of robotic end-effector for milling force control. IOP Conf Ser Mater Sci Eng 423:012032. https://doi.org/10.1088/1757-899X/423/1/012032

    Article  Google Scholar 

  133. Diaz Posada JR, Schneider U, Pidan S, et al (2016) High accurate robotic drilling with external sensor and compliance model-based compensation. Proc - IEEE Int Conf robot autom 3901–3907 . doi: https://doi.org/10.1109/ICRA.2016.7487579

  134. Qin C, Tao J, Wang M, Liu C (2016) A novel approach for the acquisition of vibration signals of the end effector in robotic drilling. AUS 2016–2016 IEEE/CSAA Int Conf Aircr Util Syst 522–526. doi: https://doi.org/10.1109/AUS.2016.7748106

  135. Rosa DGG, Feiteira JFS, Lopes AM, de Abreu PAF (2017) Analysis and implementation of a force control strategy for drilling operations with an industrial robot. J Braz Soc Mech Sci Eng 39:4749–4756. https://doi.org/10.1007/s40430-017-0913-7

    Article  Google Scholar 

  136. Brunete A, Gambao E, Koskinen J, Heikkilä T, Kaldestad KB, Tyapin I, Hovland G, Surdilovic D, Hernando M, Bottero A, Anton S (2017) Hard material small-batch industrial machining robot. Robot Comput Integr Manuf 000:1–15. https://doi.org/10.1016/j.rcim.2017.11.004

    Google Scholar 

  137. Leali F, Vergnano A, Pini F, Pellicciari M, Berselli G (2016) A workcell calibration method for enhancing accuracy in robot machining of aerospace parts. Int J Adv Manuf Technol 85:47–55. https://doi.org/10.1007/s00170-014-6025-y

    Article  Google Scholar 

  138. Solvang B, Refsahl LK, Sziebig G (2009) STEP-NC based industrial robot CAM system. IFAC Proc 42:245–250. https://doi.org/10.3182/20090909-4-JP-2010.00043

    Article  Google Scholar 

  139. Lijin F, Li LI, Guoxun W (2017) Integration of cutting robot with CAD/CAM system based on STEP-NC. 868:93–98 . doi: https://doi.org/10.4028/www.scientific.net/AMM.868.93

  140. Rea Minango SN, Ferreira JCE (2017) Combining the STEP-NC standard and forward and inverse kinematics methods for generating manufacturing tool paths for serial and hybrid robots. Int J Comput Integr Manuf 30:1203–1223. https://doi.org/10.1080/0951192X.2017.1305507

    Article  Google Scholar 

  141. Zivanovic S, Slavkovic N, Milutinovic D (2018) An approach for applying STEP-NC in robot machining. Robot Comput Integr Manuf 49:361–373. https://doi.org/10.1016/j.rcim.2017.08.009

    Article  Google Scholar 

  142. Toquica JS, živanović S, Alvares AJ, Bonnard R (2018) A STEP-NC compliant robotic machining platform for advanced manufacturing. Int J Adv Manuf Technol 95:3839–3854. https://doi.org/10.1007/s00170-017-1466-8

    Article  Google Scholar 

  143. Huynh HN, Riviere-Lorphevre E, Verlinden O (2016) Milling simulations with a 3-DOF flexible. Int J Mech aerospace Ind Mechatron Manuf Eng 10:1543–1552

    Google Scholar 

  144. Huynh HN, Kouroussis G, Verlinden O (2018) Modal updating of a 6-axis robot for milling application. In: 25th international congress on sound and vibration. pp 1–8

  145. Uhlmann E, Reinkober S, Hollerbach T (2016) Energy efficient usage of industrial robots for machining processes. Procedia CIRP 48:206–211. https://doi.org/10.1016/j.procir.2016.03.241

    Article  Google Scholar 

  146. Denkena B, Brüning J, Windels L, Euhus D, Kirsch S, Overbeck D, Lepper T (2017) Holistic process planning chain for robot machining. Prod Eng 11:715–722. https://doi.org/10.1007/s11740-017-0771-2

    Article  Google Scholar 

  147. Lee R-S, Tsai J-P, Lee J-N, Kao YC, Lin GCI, Lu TF (2000) Collaborative virtual cutting verification and remote robot machining through the Internet. Proc Instn Mech Engre 214:635–644

    Article  Google Scholar 

  148. Choi S, Cho CN, Kim H-J (2015) Development of hexapod robot for machining. In: 15th international conference on control, automation and systems. pp 738–740

  149. Lei P, Zheng L (2017) An automated in-situ alignment approach for finish machining assembly interfaces of large-scale components. Robot Comput Integr Manuf 46:130–143. https://doi.org/10.1016/j.rcim.2017.01.004

    Article  Google Scholar 

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Acknowledgements

This work is supported by an EU project, COROMA: Cognitively Enhanced Robot for Flexible Manufacturing of Metal and Composite parts (H2020-IND-CE-2016-17).

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  1. KTH Royal Institute of Technology, 100 44, Stockholm, Sweden

    Wei Ji & Lihui Wang

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Ji, W., Wang, L. Industrial robotic machining: a review. Int J Adv Manuf Technol 103, 1239–1255 (2019). https://doi.org/10.1007/s00170-019-03403-z

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  • Received: 28 August 2018

  • Accepted: 29 January 2019

  • Published: 03 April 2019

  • Issue Date: 19 July 2019

  • DOI: https://doi.org/10.1007/s00170-019-03403-z

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Keywords

  • Robotic machining
  • Machining vibration
  • Trajectory planning
  • Machining process
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