PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

An off-line application that determines the maximum accuracy of the realization of reference points from G-code for given parameters of CNC machine dynamics

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents an off-line application that determines the maximum accuracy of the reference points for the given dynamics parameters of a CNC machine. These parameters are maximum speed, acceleration, and JERK. The JERK parameter determines the rate of change of acceleration. These parameters are defined for each working axis of the machine. The main achievement of the algorithm proposed in the article is the determination of the smallest error specified for each reference point resulting from the implemented G-code for the considered dynamic parameters of the CNC machine. The solutions to this problem in industry consider the improvement in the accuracy of hitting the reference points, but they do not provide information on whether the obtained solution is optimal for such parameters of the machine dynamics. The algorithm makes the accuracy dependent on the adopted dynamic parameters of the machine and the parameters of the PLC controller used in the CNC machine.
Słowa kluczowe
Rocznik
Strony
art. no. e147345
Opis fizyczny
Bibliogr. 32 poz., rys.
Twórcy
  • Department of Electrical and Computer Engineering Fundamentals, Rzeszow University of Technology, ul. W. Pola 2, 35-959 Rzeszow, Poland
  • Institute of Technical Engineering, State University of Technology and Economics in Jaroslaw, ul. Czarnieckiego 16, 37-500 Jaroslaw, Poland
autor
  • Department of Electrical and Computer Engineering Fundamentals, Rzeszow University of Technology, ul. W. Pola 2, 35-959 Rzeszow, Poland
  • University of Rzeszow, ul. Rejtana 16C, Rzeszow, Poland
Bibliografia
  • [1] L. Zou, T. Zhu, and M. Nie, “Jerk-continuous Feedrate Optimization method for NURBS Interpolation,” IEEE Access, vol. 11, pp. 25664–25681, 2023, doi: 10.1109/ACCESS.2023.3248081.
  • [2] M. Langeron, E. Duc, C. Lartigue, and P. Bourdet, “A new format for 5-axis tool path computation using Bspline curves,” Comput.-Aided Des., vol. 36, pp. 1219–1229, 2004.
  • [3] K. Erwinski, A. Wawrzak, and M. Paprocki, “Real-Time Jerk Limited Feedrate Profiling and Interpolation for Linear Motor Multiaxis Machines Using NURBS Toolpaths,” IEEE Trans. Ind. Inform., vol. 18, no. 11, pp. 7560–7571, Nov. 2022, doi: 10.1109/TII.2022.3147806.
  • [4] Q. Bi, N. Huang, C. Sun, Y. Wang, L. Zhu, and H. Ding, “Identification and compensation of geometric errors of rotary axes on five-axis machine by on-machine measurement,” Int. J. Mach. Tools Manuf., vol. 89, pp. 182–191, 2015.
  • [5] H.J. Lee, Y. Liu, and S.H. Yang, “Accuracy improvement of miniaturized machine tool: Geometric error modelling and compensation,” Int. J. Mach. Tools Manuf., vol. 46, pp. 1508–1516, 2006.
  • [6] S. Garus, B. Błachowski, W. Sochacki, A. Jaskot, P. Kwiatoń, and M. Ostrowski, “Mechanical vibrations: recent trends and engineering applications” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, p. e140351, 2022.
  • [7] Y. Sun, S. Sun, J. Xu, and D. Guo, “A unified method of generating tool path based on multiple vector fields for CNC machining of compound NURBS surfaces,” Comput.-Aided Des., vol. 91, pp. 14–26, 2017.
  • [8] XF. Li, H. Zhao, X. Zhao, and H. Ding, “Interpolation-based contour error estimation and component-based contouring control for five-axis CNC machine tools,” Sci. China Tech. Sci., vol. 61, pp. 1666–1678, 2018.
  • [9] T.-Ch. Lu and S.-L. Chen, “Real-Time Local Optimal Bézier Corner Smoothing for CNC Machine Tools,” IEEE Access, vol. 9, pp. 152718–152727, 2021, doi: 10.1109/ACCESS.2021.3123329.
  • [10] M. Chen and Y. Sun, “ moving knot sequence-based feedrate scheduling method of parametric interpolator for CNC machining with contour error and drive constraints,” Int. J. Adv. Manuf. Technol., vol. 98, pp. 487–504, 2018.
  • [11] T. Kapitaniak, M. Šofer, B. Błachowski, W. Sochacki, and S. Garus, “Vibrations, mechanical waves, and propagation of heat in physical systems,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, p. e140149, 2022.
  • [12] S.Z. Mansour and R. Seethaler, “Feedrate optimization for computer numerically controlled machine tools using modelled and measured process constraints,” J. Manuf. Sci. Eng., vol. 139, p. 9, 2017, doi: 10.1115/1.4033933.
  • [13] A.M. Titu, A.B. Pop, M. Nabiałek, C. Cristina, D. Andrei, and V. Sandu, “Experimental modelling of the milling process of aluminium alloys used in the aerospace industry,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 5, p. e138565, 2021.
  • [14] M. Rahaman, R. Seethaler, and I. Yellowley, “A new approach to contour error control in high speed machining,” Int. J. Mach. Tools Manuf., vol. 88, pp. 42–50, 2015.
  • [15] N. Jacquod, R. Herzog, G. Pipeleers, and T. Mercy “Spline-Based Trajectory Generation for CNC Machines,” IEEE Trans. Ind. Electron., vol. 66, no. 8, pp. 6098–6107, Aug. 2019, doi: 10.1109/TIE.2018.2874617.
  • [16] J. Dong and J. A. Stori, “A generalized time-optimal bi-directional scan algorithm for con-strained feedrate optimization,” J. Dyn. Syst. Meas. Control-Trans. ASME, vol. 128, pp. 379–390, 2006.
  • [17] I. Rojek, D. Mikołajewski, P. Kotlarz, M. Macko, and J. Kopowski, “Intelligent system supporting technological process planning for machining and 3D printing,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 2, p. e136722, 2021.
  • [18] S.D. Timar, R.T. Farouki, T.S. Smitha, and C.L. Boyadjieff, “Algorithms for time-optimal control of CNC machines along curved tool paths,” Robot. Comput.-Integr. Manuf., vol. 21, pp. 37–53, 2005.
  • [19] Y. Jin, Y. He, J. Fu, Z-W. Lin, and W-F. Gang, “A fine-interpolation-based parametric interpolation method with a novel real-time look-ahead algorithm,” Comput.-Aided Des., vol. 55, pp. 37–48, 2014.
  • [20] Y. Wang, D. Yang, R. Gai, S. Wang, and S. Sun, “Design of trigonometric velocity scheduling algorithm based on pre-interpolation and look-ahead interpolation,” Int. J. Mach. Tools Manuf., vol. 96, pp. 94–105, 2015.
  • [21] B. Kaiser, A. Csiszar, and A. Verl, “Generative models for direct generation of CNC toolpaths,” 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 2018.
  • [22] M. Annoni, A. Bardine, S. Campanelli, P. Foglia, and C.A. Prete, “A real-time configurable NURBS interpolator with bounded acceleration jerk and chord error,” Comput.-Aided Des., vol. 44, pp. 509–521, 2012.
  • [23] X. Beudaert, S. Lavernhe, and C. Tournier, “Feedrate interpolation with axis jerk constraints on 5-axis NURBS and G1 tool path,” Int. J. Mach. Tools Manuf., vol. 57, pp. 73–82, 2012.
  • [24] J.-A. Lin, M.-T. Lin, Y.-Z. Li, and Y.-H. Wang, “CNC Interpolator Parameter Optimization using Deep Learning,” 2022 IEEE 4th Eurasia Conference on IOT, Communication and Engineering (ECICE), 2022.
  • [25] J. Dong, P. M. Ferreira, and J.A. Stori, “Feed-rate optimization with jerk constraints for generating minimum-time trajectories,” Int. J. Mach. Tools Manuf., vol. 47, pp. 1941–1955, 2007.
  • [26] Y. Sun, Y. Zhao, Y. Bao, and D. Guo, “A smooth curve evolution approach to the feed-rate planning on five-axis tool path with geometric and kinematic constraints,” Int. J. Mach. Tools Manuf., vol. 97, pp. 86–97, 2015.
  • [27] Q. Zhang, C-M. Yuan, X-S. Gao, and H. Li, “A greedy algorithm for feedrate planning of CNC machines along curved tool paths with confined jerk,” Robot. Comput.-Integr. Manuf., vol. 28, pp. 472–483, 2012.
  • [28] Q. Zhang and X.-S. Gao, “Practical feedrate optimization for planar high precision contouring,” 2016 IEEE International Conference on Information and Automation (ICIA), 2016.
  • [29] M.M. Emami and B. Arezoo, “A look-ahead command generator with control over trajectory and chord error for NURBS curve with unknown arc length,” Comput.-Aided Des., vol. 42, pp. 625–632, 2010.
  • [30] Y. Chen, X. Ji, Y. Tao, and H. Wei, “Look-Ahead Algorithm with whole S-Curve Acceleration and Deceleration,” Adv. Mech. Eng., vol. 5, 2013, doi: 10.1155/2013/974152.
  • [31] L. Zylka, J. Burek, and D. Mazur, “Diagnostic of peripheral longitudinal grinding by using acoustic emission signal,” Adv. Prod. Eng. Manag., vol. 12, no. 3, pp 221–232, Sep. 2017.
  • [32] B. Pękala, E. Rak, B. Kwiatkowski, A. Szczur, and R. Rak, “The use of concave and convex functions to optimize the feed-rate of numerically controlled machine tools,” 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), UK, 2020, pp. 1–8, doi: 10.1109/FUZZ48607.2020.9177569.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3dcc8645-1e34-46d1-af86-61f25e8f061b
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.