Eliminating the Effect of Uncertainties of Cutting Forces by Fuzzy Controller for Robots in Milling Process
Source abstract
Abstract
This study presents a method of controlling robots based on fuzzy logic to eliminate the effect of uncertainties that are generated by the cutting forces in milling process. The common method to control industrial robots is based on the robot dynamic model and the differential equations of motion to compute the control values. The quantities in the differential equations of the motion of robots are complex and difficult to determine fully and accurately. The interaction forces between the cutting tool and the workpiece are the cutting forces, which are generated during the machining process. It is difficult to calculate the cutting force because it depends on many factors such as material of the machining part, depth of cut, feed rate, etc. This article presents the fuzzy rule system and the selection of the physical value domain of input and output variables of the fuzzy controller. The fuzzy rules are applied in this article to allow us to compute the driving forces based on the errors of input and output signals of the joint positions and velocities, thereby avoiding the calculation of cutting forces. This article shows the simulation results of the fuzzy controller and comparison with the results of the conventional controller when the dynamic model is assumed to be correctly determined. The achieved results are reliable and facilitate the research and application of a fuzzy controller to mechanical processing robots in general and milling machining in particular.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced machining processes and optimization · Engineering Technology and Methodologies · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Hanoi University of Science and Technology, 01 Dai Co Viet, Hai Ba Trung, Hanoi 100000, Vietnam
College of Urban Works Construction, Yen Thuong, Gia Lam, Ha Noi 100000, Vietnam
References (81)
Show source-provided references
1. Appleton, E., and Williams, D.J. (2012). Industrial Robot Applications, Springer Science & Business Media.
2. Industrial robotic Machining: A review10.1007/s00170-019-03403-z ↗
3. Pandremenos, J., Doukas, C., Stavropoulos, P., and Chryssolouris, G. (2011, January 28–30). Machining with robots: A critical review. Proceedings of the DET 2011, Athens, Greece.
4. CNC system of the 5-axis hybrid robot for milling10.1016/j.mechatronics.2016.03.001 ↗
5. CCT-based mode coupling chatter avoidance in robotic milling10.1016/j.jmapro.2017.06.010 ↗
6. COMET, E.U (2010, September 01). FP7-Project: Plug-and-Produce Components and Methods for Adaptive Control of Industrial Robots Enabling Cost Effective, High Precision Manufacturing in Factories of the Future. Available online: http://www.cometproject.eu.
7. Trajectory tracking controller of the hybrid robot for milling10.1016/j.mechatronics.2016.03.012 ↗
8. Modelling of cutting force and robot load during machining. In Advanced Materials Research
9. Effect of robot dynamics on the machining forces in robotic milling10.1016/j.promfg.2017.07.034 ↗
10. Position/force control of robot manipulators using reinforcement learning
11. Evaluation of cutting forces and prediction of chatter vibrations in milling10.1590/S1678-58782004000100013 ↗
12. Vibration analysis of robotic milling tasks10.1016/j.procir.2017.12.210 ↗
13. Dynamic cutting force modeling and experimental study of industrial robotic boring10.1007/s00170-015-8166-z ↗
14. A new identification method of specific cutting coefficients for ball end milling10.1016/j.procir.2014.03.059 ↗
15. Specific cutting force and cutting condition interaction modeling for round insert face milling operation
16. Nan, C., and Liu, D. (2018). Analytical Calculation of Cutting Forces in Ball-End Milling with Inclination Angle. J. Manuf. Mater. Process., 2.10.3390/jmmp2020035 ↗
17. Unified cutting force model for turning, boring, drilling and milling operations10.1016/j.ijmachtools.2011.12.008 ↗
18. Davoudinejad, A., Tosello, G., Parenti, P., and Annoni, M. (2017). 3D finite element simulation of micro end-milling by considering the effect of tool run-out. Micromachines, 8.10.3390/mi8060187 ↗
19. Huo, D., Chen, W., Teng, X., Lin, C., and Yang, K. (2017). Modeling the influence of tool deflection on cutting force and surface generation in micro-milling. Micromachine, 8.10.3390/mi8060188 ↗
20. Luo, M., Chong, Z., and Liu, D. (2018). Cutting forces measurement for milling process by using working tables with integrated PVDF thin-film sensors. Sensors, 18.10.3390/s18114031 ↗
21. Prediction of cutting forces in three and five-axis ball-end milling with tool indentation effect10.1016/j.ijmachtools.2012.12.002 ↗
22. An examination of the fundamental mechanics of cutting force coefficients10.1016/j.ijmachtools.2013.10.008 ↗
23. Propagation of assembly errors in multitasking machines by the homogenous matrix method10.1007/s00170-012-4715-x ↗
24. Identification and modeling of cutting forces in ball-end milling based on two different finite element models with Arbitrary Lagrangian Eulerian technique10.1007/s00170-017-0229-x ↗
25. A mechanistic cutting force model for new barrel end mills10.1016/j.proeng.2015.12.532 ↗
26. Cutting force integration at the CAM stage in the high-speed milling of complex surfaces10.1080/09511920500069309 ↗
27. Prediction of specific force coefficients from a FEM cutting model10.1007/s00170-008-1717-9 ↗
28. Cutting force estimation in sculptured surface milling10.1016/j.ijmachtools.2004.05.004 ↗
29. The identification of the cutting force coefficients for ball-end finish milling10.1007/s00170-019-03481-z ↗
30. Modeling of cutting force, tool deflection, and surface error in micro-milling operation10.1007/s00170-018-2415-x ↗
31. A hybrid approach using machine learning to predict the cutting forces under consideration of the tool wear10.1016/j.procir.2019.04.031 ↗
32. Cutting forces modeling for micro flat end milling by considering tool run-out and bottom edge cutting effect10.1177/0954405417726811 ↗
33. Fuzzy sets10.1016/S0019-9958(65)90241-X ↗
34. Mamdani, E.H. (April, January 28). Twenty years of fuzzy control: Experiences gained and lessons learnt. Proceedings of the 1993 Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, USA.
35. A review on classical and fuzzy PID controllers
36. Hampel, R., Wagenknecht, M., and Chaker, N. (2013). Fuzzy Control: Theory and Practice, Springer Science & Business Media.
37. Boutalis, Y., Christodoulou, M.A., Theodoridis, D., and Kottas, T. (2014). System identification and adaptive control. Theory and Applications of the Neurofuzzy and Fuzzy Cognitive Network Models, Springer International Publishing.10.1007/978-3-319-06364-5 ↗
38. Matía, F., Marichal, G.N., and Jiménez, E. (2014). Fuzzy Modeling and Control: Theory and Applications, Atlantis Press.10.2991/978-94-6239-082-9 ↗
39. Hooda, D.S., and Raich, V. (2017). Fuzzy Logic Models and Fuzzy Control: An Introduction, Alpha Science International.
40. Sharma, K.D., Chatterjee, A., and Rakshit, A. (2018). Intelligent Adaptive Fuzzy Control. Intelligent Control, Springer.10.1007/978-981-13-1298-4_1 ↗
41. De Silva, C.W. (2018). Intelligent Control: Fuzzy Logic Applications, CRC press.10.1201/9780203750513 ↗
42. Lee, K.H. (2004). First Course on Fuzzy Theory and Applications, Springer Science & Business Media.
43. Barros, L.C.D., Bassanezi, R.C., and Lodwick, W.A. (2017). A First Course in Fuzzy Logic, Fuzzy Dynamical Systems, and Biomathematics: Theory and Applications, Springer-Verlag.
44. Liu, H., Wang, T., and Wang, D. (2015). Constant cutting force control for CNC machining using dynamic characteristic-based fuzzy controller. Shock Vib., 2015.10.1155/2015/406294 ↗
45. Kovač, P., Rodić, D., Gostimirović, M., Savković, B., and Ješić, D. (March, January 28). Application of adaptive neuro fuzzy systems for grinding process modeling. Proceedings of the 6th International Conference on Information Society and Technology ICIST, Society for Information Systems and Computer Networks Pub., Belgrade, Serbia.
46. Fuzzy logic for constant force control of end milling10.1109/41.744408 ↗
47. Fuzzy PI control design for an industrial weigh belt feeder10.1109/TFUZZ.2003.812686 ↗
48. Fuzzy unidirectional force control of constrained robotic manipulators10.1016/S0165-0114(02)00234-8 ↗
49. Fuzzy impedance and force control of a Stewart platform10.3906/elk-1208-54 ↗
50. Chen, C.H., Wang, C.C., Wang, Y.T., and Wang, P.T. (2017). Fuzzy logic controller design for intelligent robots. Math. Probl. Eng., 1–12.10.1155/2017/8984713 ↗
51. Stoian, V., and Ivanescu, M. (2008). Robot Control by Fuzzy Logic. Frontiers in Robotics, Automation and Control, IntechOpen Pub.10.5772/6329 ↗
52. Vashisth, H., and Woo, P.Y. (1996, January 9). Application of fuzzy logic to robotic control. Proceedings of the 1996 IEEE IECON. 22nd International Conference on Industrial Electronics, Control, and Instrumentation, Taipei, Taiwan.
53. Fuzzy PD+ I based hybrid force/position control of an industrial robot manipulator10.3182/20140313-3-IN-3024.00062 ↗
54. Applications of fuzzy logic in the control of robotic manipulators10.1016/0165-0114(94)00219-W ↗
55. An optimal fuzzy-PI force/motion controller to increase industrial robot autonomy10.1007/s00170-013-4741-3 ↗
56. Indirect adaptive fuzzy control for industrial robots: A solution for contact applications10.1016/j.eswa.2015.07.047 ↗
57. Hedge-Algebras-Based Controller for Mechanisms of Relative Manipulation10.1007/s12541-018-0045-8 ↗
58. Toan, N.V., and Khoi, P.B. (2019). Fuzzy-based-admittance controller for safe natural human–robot interaction. Adv. Robot., 1–9.10.1080/01691864.2019.1607551 ↗
59. Use of fuzzy logic in robotics10.1016/0166-3615(88)90046-2 ↗
60. Optimizing Neuro-Fuzzy System for Robot Control
61. Lin, C.H., Wang, S.H., and Lin, C.J. (2018). Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments. Sensors, 18.10.3390/s18124181 ↗
62. A control solution for closed-form mechanisms of relative manipulation based on fuzzy approach
63. Hsu, F.Y., and Fu, L.C. (1999, January 10–15). A new adaptive fuzzy hybrid force/position control for intelligent robot deburring. Proceedings of the 1999 IEEE International Conference on Robotics and Automation, Detroit, MI, USA.
64. Conventional and fuzzy force control in robotised machining10.4028/www.scientific.net/SSP.210.178 ↗
65. Fuzzy logic based tool condition monitoring for end-milling10.1016/j.rcim.2016.12.009 ↗
66. Intelligent robot deburring using adaptive fuzzy hybrid position/force control10.1109/70.864223 ↗
67. Application of fuzzy logic for controlling mechanisms of relative manipulation robot (MRM robot)
68. Calculation and simulation the program motion of mechanisms of relative manipulation (MRM)
69. Khoi, P.B. (2009, January 8–9). Kinematic problems of programming control of mechanism of relative manipulation. Proceedings of the National Conference on Mechanics, Hanoi, Vietnam.
70. Investigation of kinematics and motion planning for mechanical machining robots
71. Kinematic Modelling of a Robot in Form-shaping Milling Complex Surfaces10.24018/ejers.2019.4.11.1619 ↗
72. Khoi, P.B., and Huy, L.Q. (2017, January 8–9). Kinematic modeling of of the process of grinding turbine blades by using robots. Proceedings of the 10th National conference on Mechanics, Hanoi, Vietnam.
73. Afonin, V.L., and Khoi, P.B. (1997). Method for Calculating Action and Constrained Reactions Forces in Mechanisms of Relative Manipulation while Executing Programming Motion, IMASH, RAS.
74. Khoi, P.B. (1997). Applying Principle of Compatibility for Analyzing Forces of Mechanism of Relative Manipulation Robot. [PhD Thesis, Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAS)].
75. Khoi, P.B. (2004, January 8–9). Dynamical investigation of relation manipulation mechanisms in mechanical processing. Proceedings of the National Conference on Mechanics, Hanoi, Vietnam.
76. Robot dynamics in mechanical processing
77. Force analysis of a robot in machining process
78. Effect of cutting forces on the form-shaping motion in robotic milling
79. Inverse dynamic analysis of miling machining robot: Application in calibration of cutting force10.15625/2525-2518/57/6/13917 ↗
80. Control of inverse dynamics of robot in milling
81. Abele, E., Bauer, J., Pischan, M., Stryk, O.V., Friedmann, M., and Hemker, T. (2010, January 10–11). Prediction of the tool displacement for robot milling applications using coupled models of an industrial robot and removal simulation. Proceedings of the CIRP 2nd Inter Confernce on Process Machine Interactions, Vancouver, BC, Canada.