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Robotized manufacturing equipment: A review from the perspective of mechanism topology

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Abstract

In recent years, robots have been extensively applied in the establishment of intelligent manufacturing systems. Many robotized manufacturing equipment have been developed, some of which have gained successful applications. A mechanism is the skeleton of a machine that transfers motion and energy. The topology of a mechanism is an important gene that determines the function and performance of a machine. Although several reviews have focused on robotized manufacturing, an up-to-date review of robotized manufacturing equipment from the perspective of mechanism topology is lacking. This motivated us to present a survey about existing robotics structures used in manufacturing equipment. Relevant studies are classified into three types: serial robot-based manufacturing equipment, parallel kinematic machines, and hybrid robot-based manufacturing equipment. The features and performance behaviour of these equipments, as determined by their mechanism topology, are analyzed. In particular, the influences of joint numbers, rotational axes of output motion, actuation schemes, and topological symmetry on equipment performance are discussed to provide insight into the new development of robotized manufacturing equipment, as well as future research directions on this topic.

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References

  1. Wang L. From intelligence science to intelligent manufacturing. Engineering, 2019, 5: 615–618

    Article  Google Scholar 

  2. Zhou J, Li P, Zhou Y, et al. Toward new-generation intelligent manufacturing. Engineering, 2018, 4: 11–20

    Article  Google Scholar 

  3. Day C P. Robotics in industry—Their role in intelligent manufacturing. Engineering, 2018, 4: 440–445

    Article  Google Scholar 

  4. Ji W, Wang L. Industrial robotic machining: A review. Int J Adv Manuf Technol, 2019, 103: 1239–1255

    Article  Google Scholar 

  5. Tao B, Zhao X W, Ding H. Mobile-robotic machining for large complex components: A review study. Sci China Tech Sci, 2019, 62: 1388–1400

    Article  Google Scholar 

  6. Kim S H, Nam E, Ha T I, et al. Robotic machining: A review of recent progress. Int J Precis Eng Manuf, 2019, 20: 1629–1642

    Article  Google Scholar 

  7. Yuan L, Pan Z, Ding D, et al. A review on chatter in robotic machining process regarding both regenerative and mode coupling mechanism. IEEE ASME Trans Mechatron, 2018, 23: 2240–2251

    Article  Google Scholar 

  8. Xie Z, Xie F, Zhu L, et al. Robotic mobile and mirror milling of large-scale complex structures. Natl Sci Rev, 2023, 10: doi: https://doi.org/10.1093/nsr/nwac188

  9. Kubela T, Pochyly A, Singule V. Assessment of industrial robots accuracy in relation to accuracy improvement in machining processes. In: Proceedings of 2016 IEEE International Power Electronics and Motion Control Conference (PEMC). Varna, 2016. 720–725

  10. Schneider U, Momeni-K M, Ansaloni M, et al. Stiffness modeling of industrial robots for deformation compensation in machining. In: Proceedings of 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. Chicago, 2014. 4464–4469

  11. Zhang Y, Guo K, Sun J. Investigation on the milling performance of amputating clamping supports for machining with industrial robot. Int J Adv Manuf Technol, 2019, 102: 3573–3586

    Article  Google Scholar 

  12. Huynh H N, Assadi H, Rivière-Lorphèvre E, et al. Modelling the dynamics of industrial robots for milling operations. Rob Comput Integr Manuf, 2020, 61: 101852

    Article  Google Scholar 

  13. Nguyen V, Melkote S N. Identification of industrial robot frequency response function for robotic milling using operational modal analysis. Procedia Manuf, 2020, 48: 154–158

    Article  Google Scholar 

  14. Nguyen V, Johnson J, Melkote S. Active vibration suppression in robotic milling using optimal control. Int J Mach Tools Manuf, 2020, 152: 103541

    Article  Google Scholar 

  15. Nguyen V, Melkote S. Hybrid statistical modelling of the frequency response function of industrial robots. Rob Comput Integr Manuf, 2021, 70: 102134

    Article  Google Scholar 

  16. Lehmann C, Halbauer M, Euhus D, et al. Milling with industrial robots: Strategies to reduce and compensate process force induced accuracy influences. In: Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012). Krakow, 2012. 1–4

  17. Cvitanic T, Nguyen V, Melkote S N. Pose optimization in robotic machining using static and dynamic stiffness models. Rob Comput Integr Manuf, 2020, 66: 101992

    Article  Google Scholar 

  18. Zhang H, Wang J, Zhang G, et al. Machining with flexible manipulator: toward improving robotic machining performance. In: Proceedings of 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Monterey, 2005. 1127–1132

  19. Wang J, Zhang H, Fuhlbrigge T. Improving machining acmcuracy with robot deformation compensation. In: Proceedings of 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. St. Louis, 2009. 3826–3831

  20. Pan Z, Zhang H, Zhu Z, et al. Chatter analysis of robotic machining process. J Mater Processing Tech, 2006, 173: 301–309

    Article  Google Scholar 

  21. Chen C, Peng F, Yan R, et al. Stiffness performance index based posture and feed orientation optimization in robotic milling process. Rob Comput Integr Manuf, 2019, 55: 29–40

    Article  Google Scholar 

  22. Pan Z, Zhang H. Robotic machining from programming to process control: A complete solution by force control. Ind Robot, 2008, 35: 400–409

    Article  Google Scholar 

  23. Mejri S, Gagnol V, Le T P, et al. Dynamic characterization of machining robot and stability analysis. Int J Adv Manuf Technol, 2016, 82: 351–359

    Article  Google Scholar 

  24. Leali F, Vergnano A, Pini F, et al. A workcell calibration method for enhancing accuracy in robot machining of aerospace parts. Int J Adv Manuf Technol, 2016, 85: 47–55

    Article  Google Scholar 

  25. Nagata F, Okada Y, Kusano T, et al. Reverse and forward post processors for a robot machining system. In: Proceedings of International Conference on Intelligent Robotics and Applications. Cham, 2017. 70–78

  26. Belchior J, Guillo M, Courteille E, et al. Off-line compensation of the tool path deviations on robotic machining: Application to incremental sheet forming. Rob Comput Integr Manuf, 2013, 29: 58–69

    Article  Google Scholar 

  27. Xiong G, Ding Y, Zhu L M. Stiffness-based pose optimization of an industrial robot for five-axis milling. Rob Comput Integr Manuf, 2019, 55: 19–28

    Article  Google Scholar 

  28. Chen S, Zhang T. Force control approaches research for robotic machining based on particle swarm optimization and adaptive iteration algorithms. Ind Robot, 2018, 45: 141–151

    Article  Google Scholar 

  29. Lin Y, Zhao H, Ding H. Real-time path correction of industrial robots in machining of large-scale components based on model and data hybrid drive. Rob Comput Integr Manuf, 2023, 79: 102447

    Article  Google Scholar 

  30. Guo Y, Dong H, Ke Y. Stiffness-oriented posture optimization in robotic machining applications. Rob Comput Integr Manuf, 2015, 35: 69–76

    Article  Google Scholar 

  31. Tchoń K, Muszyński R. Singularities of nonredundant robot kinematics. Int J Robotics Res, 1997, 16: 60–76

    Article  Google Scholar 

  32. Xiao W, Huan J. Redundancy and optimization of a 6R robot for five-axis milling applications: Singularity, joint limits and collision. Prod Eng Res Dev, 2012, 6: 287–296

    Article  Google Scholar 

  33. Lin Y, Zhao H, Ding H. Posture optimization methodology of 6R industrial robots for machining using performance evaluation indexes. Rob Comput Integr Manuf, 2017, 48: 59–72

    Article  Google Scholar 

  34. Merlet J P. Jacobian, manipulability, condition number, and accuracy of parallel robots. J Mech Des, 2006, 128: 199–206

    Article  Google Scholar 

  35. Tandirci M, Angeles J, Ranjbaran F. The characteristic point and the characteristic length of robotic manipulators. In: Proceedings of International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Scottsdale, 1992. 203–208

  36. Bu Y, Liao W, Tian W, et al. Stiffness analysis and optimization in robotic drilling application. Prec Eng, 2017, 49: 388–400

    Article  Google Scholar 

  37. Cordes M, Hintze W, Altintas Y. Chatter stability in robotic milling. Rob Comput Integr Manuf, 2019, 55: 11–18

    Article  Google Scholar 

  38. Celikag H, Sims N D, Ozturk E. Cartesian stiffness optimization for serial arm robots. Procedia CIRP, 2018, 77: 566–569

    Article  Google Scholar 

  39. Denkena B, Bergmann B, Lepper T. Design and optimization of a machining robot. Procedia Manuf, 2017, 14: 89–96

    Article  Google Scholar 

  40. Wu J, Ye H, Yu G, et al. A novel dynamic evaluation method and its application to a 4-DOF parallel manipulator. Mech Mach Theory, 2022, 168: 104627

    Article  Google Scholar 

  41. Saxena V, Liu D, Daniel C M, et al. A simulation study of the workspace and dexterity of a stewart platform based machine tool. In: ASME International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997, 18244: 617–623

    Google Scholar 

  42. Terrier M, Dugas A, Hascoët J Y. Qualification of parallel kinematics machines in high-speed milling on free form surfaces. Int J Mach Tools Manuf, 2004, 44: 865–877

    Article  Google Scholar 

  43. Falco J A, Kent E W. Virtual manufacturing tools for collaborative exploration of hexapod machine capabilities and applications. In: Proceedings of the 19th International CIRP Design Seminar on Multimedia Technologies for Collaborative Design & Manufacturing. Los Angeles, 1997. 8–10

  44. Ibaraki S, Yokawa T, Kakino Y, et al. Kinematic calibration on a parallel kinematic machine tool of the Stewart platform by circular tests. In: Proceedings of 2004 American Control Conference. Boston, 2004. 1394–1399

  45. Huang T, Wang J, Whitehouse D J. Closed form solution to workspace of hexapod-based virtual axis machine tools. J Mech Des, 1999, 121: 26–31

    Article  Google Scholar 

  46. Wang Z, Wang Z, Liu W, et al. A study on workspace, boundary workspace analysis and workpiece positioning for parallel machine tools. Mech Mach Theory, 2001, 36: 605–622

    Article  MATH  Google Scholar 

  47. Conti J P, Conti J P, Zhang G, et al. Workspace variation of a hexapod machine tool. US Department of Commerce, National Institute of Standards and Technology, Maryland, 1998

  48. Jiang Q, Gosselin C M. Determination of the maximal singularity-free orientation workspace for the Gough-Stewart platform. Mech Mach Theory, 2009, 44: 1281–1293

    Article  MATH  Google Scholar 

  49. Patel A J, Ehmann K F. Volumetric error analysis of a Stewart platform-based machine tool. CIRP Ann, 1997, 46: 287–290

    Article  Google Scholar 

  50. Ren X D, Feng Z R, Su C P. A new calibration method for parallel kinematics machine tools using orientation constraint. Int J Mach Tools Manuf, 2009, 49: 708–721

    Article  Google Scholar 

  51. Patel A J, Ehmann K F. Calibration of a hexapod machine tool using a redundant leg. Int J Mach Tools Manuf, 2000, 40: 489–512

    Article  Google Scholar 

  52. Harib K H, Sharif Ullah A M M, Hammami A. A hexapod-based machine tool with hybrid structure: Kinematic analysis and trajectory planning. Int J Mach Tools Manuf, 2007, 47: 1426–1432

    Article  Google Scholar 

  53. Grimm A, Schulze S, Silva A, et al. Friction stir welding of light metals for industrial applications. Mater Today-Proc, 2015, 2: S169–S178

    Article  Google Scholar 

  54. Schwaar M, Jaehnert T, Ihlenfeldt S. Mechatronic design, experimental property analysis and machining strategies for a 5-strut-PKM. In: Proceedings of 3rd Chemnitz Parallel Kinematics Seminar. Zwickau, 2002. 2002

  55. Weck M, Staimer D. Parallel kinematic machine tools-current state and future potentials. CIRP Ann, 2002, 51: 671–683

    Article  Google Scholar 

  56. Liu X J, Xie Z H, Xie F G, et al. Design and development of a portable machining robot with parallel kinematics. In: Proceedings of 2019 16th International Conference on Ubiquitous Robots (UR). Jeju, 2019. 133–136

  57. Xie Z, Xie F, Liu X J, et al. A parallel machining robot and its control method for high-performance machining of curved parts. Robot Comput Integr Manuf, 2023, 81: 102501

    Article  Google Scholar 

  58. Mei B, Xie F, Liu X J, et al. Elasto-geometrical error modeling and compensation of a five-axis parallel machining robot. Precision Eng, 2021, 69: 48–61

    Article  Google Scholar 

  59. Xie Z, Xie F, Liu X J, et al. Tracking error prediction informed motion control of a parallel machine tool for high-performance machining. Int J Mach Tools Manuf, 2021, 164: 103714

    Article  Google Scholar 

  60. Xu Y, Zhao Y, Yue Y, et al. Type synthesis of overconstrained 2R1T parallel mechanisms with the fewest kinematic joints based on the ultimate constraint wrenches. Mech Mach Theory, 2020, 147: 103766

    Article  Google Scholar 

  61. Neumanm K E. Robot. US Patent, 4732525, 1988

  62. Neumann K E. Parallel kinematic machine with an active measuring system. US Patent, WO2006062466, 2006

  63. Hennes N. Ecospeed, an innovative machinery concept for high performance 5-axis machining of large structural componenets in aircraft engineering. In: 3rd Chemnitz Parallel Kynematics Seminar. 2002. 763–774

  64. Liu H T, Huang T, Zhao X M, et al. Optimal design of the TriVariant robot to achieve a nearly axial symmetry of kinematic performance. Mech Mach Theory, 2007, 42: 1643–1652

    Article  MATH  Google Scholar 

  65. Liu Q, Tian W, Li B, et al. Kinematics of a 5-axis hybrid robot near singular configurations. Rob Comput Integr Manuf, 2022, 75: 102294

    Article  Google Scholar 

  66. Kanaan D, Wenger P, Chablat D. Kinematic analysis of a serial-parallel machine tool: The VERNE machine. Mech Mach Theory, 2009, 44: 487–498

    Article  MATH  Google Scholar 

  67. Li Y G, Liu H T, Zhao X M, et al. Design of a 3-DOF PKM module for large structural component machining. Mech Mach Theory, 2010, 45: 941–954

    Article  MATH  Google Scholar 

  68. Son S, Kim T, Sarma S E, et al. A hybrid 5-axis CNC milling machine. Precision Eng, 2009, 33: 430–446

    Article  Google Scholar 

  69. Xie F G, Liu X J, Zhang H, et al. Design and experimental study of the SPKM165, a five-axis serial-parallel kinematic milling machine. Sci China Tech Sci, 2011, 54: 1193–1205

    Article  MATH  Google Scholar 

  70. Huang P, Wang J, Wang L, et al. Kinematical calibration of a hybrid machine tool with Regularization method. Int J Mach Tools Manuf, 2011, 51: 210–220

    Article  Google Scholar 

  71. Chen S L, Chang T H, Inasaki I, et al. Post-processor development of a hybrid TRR-XY parallel kinematic machine tool. Int J Adv Manuf Tech, 2002, 20: 259–269

    Article  Google Scholar 

  72. Ye W, Li Q C, Chai X X. New family of 3-DOF UP-equivalent parallel mechanisms with high rotational capability. Chin J Mech Eng, 2018, 31: 1–12

    Article  Google Scholar 

  73. Xu P, Cheung C F, Li B, et al. Design, dynamic analysis, and experimental evaluation of a hybrid parallel-serial polishing machine with decoupled motions. J Mech Robotics, 2021, 13: 061008

    Article  Google Scholar 

  74. Tang T, Fang H, Zhang J. Hierarchical design, laboratory prototype fabrication and machining tests of a novel 5-axis hybrid serial-parallel kinematic machine tool. Rob Comput Integr Manuf, 2020, 64: 101944

    Article  Google Scholar 

  75. Wu J, Gao Y, Zhang B, et al. Workspace and dynamic performance evaluation of the parallel manipulators in a spray-painting equipment. Rob Comput Integr Manuf, 2017, 44: 199–207

    Article  Google Scholar 

  76. Neumann K E. Tricept application. In: Proceedings of the 3rd Chemnitz Parallel Kinematics Seminar. Zwickau, 2002. 547–551

  77. Siciliano B. The Tricept robot: Inverse kinematics, manipulability analysis and closed-loop direct kinematics algorithm. Robotica, 1999, 17: 437–445

    Article  Google Scholar 

  78. Neumann K E. System and method for controlling a robot. US Patent, 6301525, 2001

  79. Olazagoitia J L, Wyatt S. New PKM Tricept T9000 and its application to flexible manufacturing at aerospace industry. SAE Technical Paper, 2007, 2142: 37–48

    Google Scholar 

  80. Joshi S, Lung-Wen Tsai S. A comparison study of two 3-DOF parallel manipulators: One with three and the other with four supporting legs. IEEE Trans Robot Automat, 2003, 19: 200–209

    Article  Google Scholar 

  81. Zhang D, Gosselin C M. Kinetostatic analysis and design optimization of the tricept machine tool family. J Manuf Sci Eng, 2002, 124: 725–733

    Article  Google Scholar 

  82. Zhang D. On stiffness improvement of the Tricept machine tool. Robotica, 2005, 23: 377–386

    Article  Google Scholar 

  83. Dong C, Liu H, Huang T, et al. A screw theory-based semi-analytical approach for elastodynamics of the tricept robot. J Mech Robotics, 2019, 11: 031005

    Article  Google Scholar 

  84. Merlet J P. Parallel Robots. Dordrecht: Springer Science & Business Media, 2006. 31–36

  85. Mendes N, Neto P, Loureiro A, et al. Machines and control systems for friction stir welding: A review. Mater Des, 2016, 90: 256–265

    Article  Google Scholar 

  86. Huang T, Li M, Zhao X M, et al. Conceptual design and dimensional synthesis for a 3-DOF module of the TriVariant-a novel 5-DOF reconfigurable hybrid robot. IEEE Trans Robot, 2005, 21: 449–456

    Article  Google Scholar 

  87. Li M, Huang T, Chetwynd D G, et al. Forward position analysis of the 3-DOF module of the TriVariant: A 5-DOF reconfigurable hybrid robot. J Mech Des, 2006, 128: 319–322

    Article  Google Scholar 

  88. Li M, Huang T, Mei J, et al. Dynamic formulation and performance comparison of the 3-DOF modules of two reconfigurable PKM—The tricept and the trivariant. J Mech Des, 2005, 127: 1129–1136

    Article  Google Scholar 

  89. Hong Z. Error modeling and performance comparison of 3-DOF modules of two reconfigurable PKM-the Tricept and the TriVariant (in Chinese). J Tianjin Univ, 2007, 40: 1176–1182

    Google Scholar 

  90. Sun T, Song Y, Li Y, et al. Workspace decomposition based dimensional synthesis of a novel hybrid reconfigurable robot. J Mech Robotics, 2010, 2: 031009

    Article  Google Scholar 

  91. Sun T, Song Y M. Comparison between a 4-DOF Hybrid Module and Tricept module focusing on inverse kinematics and stiffness. In: Proceedings of 2009 IEEE International Conference on Robotics and Biomimetics. Guilin, 2009. 1597–1602

  92. Chen X, Liu X J, Xie F G, et al. A comparison study on motion/force transmissibility of two typical 3-DOF parallel manipulators: The sprint Z3 and A3 tool heads. Int J Adv Rob Syst, 2014, 11: 5

    Article  Google Scholar 

  93. Zhao Y Q, Zhang J, Ruan L Y, et al. A modified elasto-dynamic model based static stiffness evaluation for a 3-PRS PKM. Proc Inst Mech Eng Part C-J Mech Eng Sci, 2016, 230: 353–366

    Article  Google Scholar 

  94. Zhang J, Zhao Y Q, Ceccarelli M. Elastodynamic model-based vibration characteristics prediction of a three prismatic-revolute-spherical parallel kinematic machine. J Dynamic Syst Measurement Control, 2016, 138: 041009

    Article  Google Scholar 

  95. Carretero J A, Podhorodeski R P, Nahon M A, et al. Kinematic analysis and optimization of a new three degree-of-freedom spatial parallel manipulator. J Mech Des, 2000, 122: 17–24

    Article  Google Scholar 

  96. Pond G, Carretero J A. Architecture optimisation of three 3-RS variants for parallel kinematic machining. Rob Comput Integr Manuf, 2009, 25: 64–72

    Article  Google Scholar 

  97. Tsai M S, Shiau T N, Tsai Y J, et al. Direct kinematic analysis of a 3-PRS parallel mechanism. Mech Mach Theory, 2002, 38: 71–83

    Article  MATH  Google Scholar 

  98. Neumann K. Adaptive in-jig high load Exechon machining & assembly technology. SAE International, 2008, 08AMT-0044

  99. Zoppi M, Zlatanov D, Molfino R. Kinematics analysis of the Exechon tripod. In: Proceedings of the ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Montreal, 2010, 44106. 1381–1388

  100. Molfino R, Zoppi M, Zlatanov D. Reconfigurable swarm fixtures. In: Proceedings of 2009 ASME/IFToM International Conference on Reconfigurable Mechanisms and Robots. London, 2009

  101. López-Custodio P C, Fu R, Dai J S, et al. Compliance model of Exechon manipulators with an offset wrist. Mech Mach Theory, 2022, 167: 104558

    Article  Google Scholar 

  102. Bi Z M, Jin Y. Kinematic modeling of Exechon parallel kinematic machine. Rob Comput Integr Manuf, 2011, 27: 186–193

    Article  Google Scholar 

  103. Hu B. Kinematically identical manipulators for the Exechon parallel manipulator and their comparison study. Mech Mach Theory, 2016, 103: 117–137

    Article  Google Scholar 

  104. López-Custodio P C, Dai J S, Fu R, et al. Kinematics and constraints of the exechon robot accounting offsets due to errors in the base joint axes. J Mech Robotics, 2020, 12: 021109

    Article  Google Scholar 

  105. Bi Z M. Kinetostatic modeling of Exechon parallel kinematic machine for stiffness analysis. Int J Adv Manuf Technol, 2014, 71: 325–335

    Article  Google Scholar 

  106. Fu R, Curley P, Higgins C, et al. Double-sided milling of thin-walled parts by dual collaborative parallel kinematic machines. J Mater Processing Tech, 2022, 299: 117395

    Article  Google Scholar 

  107. Tang T, Luo H, Song Y, et al. Chebyshev inclusion function based interval kinetostatic modeling and parameter sensitivity analysis for Exechon-like parallel kinematic machines with parameter uncertainties. Mech Mach Theory, 2021, 157: 104209

    Article  Google Scholar 

  108. Huang T, Dong C L, Liu H T et al. A 5-DOF hybrid robot with multi-axis rotating support. Chinese Patent, 201510401279.9, 2015

  109. Dong C, Liu H, Yue W, et al. Stiffness modeling and analysis of a novel 5-DOF hybrid robot. Mech Mach Theory, 2018, 125: 80–93

    Article  Google Scholar 

  110. Dong C, Liu H, Xiao J, et al. Dynamic modeling and design of a 5-DOF hybrid robot for machining. Mech Mach Theory, 2021, 165: 104438

    Article  Google Scholar 

  111. Wu L, Wang G, Liu H, et al. An approach for elastodynamic modeling of hybrid robots based on substructure synthesis technique. Mech Mach Theory, 2018, 123: 124–136

    Article  Google Scholar 

  112. Zhao Y, Mei J, Jin Y, et al. A new hierarchical approach for the optimal design of a 5-dof hybrid serial-parallel kinematic machine. Mech Mach Theory, 2021, 156: 104160

    Article  Google Scholar 

  113. Yang X, Liu H, Xiao J, et al. Continuous friction feedforward sliding mode controller for a TriMule hybrid robot. IEEE ASME Trans Mechatron, 2018, 23: 1673–1683

    Article  Google Scholar 

  114. Dong C, Li J, Liu H, et al. Isotropy of tangential motion transmissibility and kinematic performance analysis of TriMule and Exechon robots (in Chinese). J Mech Eng, 2021, 57: 23–32

    Article  Google Scholar 

  115. Dong C, Yue W, Liu H, et al. Stiffness analysis and comparison of TriMule and Tricept robots (in Chinese). J Mech Eng, 2021, 57: 30–38

    Article  Google Scholar 

  116. Neumann K. The key to aerospace automation. SAE Technical Paper, 2006, 2006-01-3144

  117. Huang T, Dong C, Liu H, et al. A simple and visually orientated approach for type synthesis of overconstrained 1T2R parallel mechanisms. Robotica, 2019, 37: 1161–1173

    Article  Google Scholar 

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Correspondence to QinChuan Li.

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This work was supported by the National Natural Science Foundation of China (Grant Nos. 51935010, 51525504).

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Ye, W., Tang, T. & Li, Q. Robotized manufacturing equipment: A review from the perspective of mechanism topology. Sci. China Technol. Sci. 66, 1683–1697 (2023). https://doi.org/10.1007/s11431-022-2349-7

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