Calculating G-code for CNC machine using the Mamdani fuzzy logic inference system

• Autorzy: Kalandyk Dawid , Kwiatkowski Bogdan , Mazur Damian

Identyfikatory

DOI

10.24425/acs.2024.149669

• Języki publikacji: EN

Abstrakty

EN

The widespread desire to automate the CNC machine control process and optimize it is leading to the development of new algorithms. The article presents both a novel approach to this task based on a fuzzy decision-making system as well as an evaluation of the proposed solution on a large database containing data from multiple machining processes and a comparison with the Reference Points Realization Optimization (RPRO) algorithm used in industry. In addition to achieving the intended accuracy of the machining process, the presented system is also easily interpretable for the expert operating the machine. It is also possible to manipulate the presented system easily and shape it according to specific needs.

Słowa kluczowe

EN

CNC machine; G-code calculation; fuzzy logic

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2024
• Tom: Vol. 34, No. 3
• Strony: 501--511
• Opis fizyczny: Bibliogr. 14 poz., rys., tab.

Twórcy

autor

Kalandyk Dawid

• Doctoral School of the Rzeszow University of Technology, Rzeszów 35-959, Poland

• https://orcid.org/0000-0002-7317-5499

autor

Kwiatkowski Bogdan

• Department of Electrical Engineering and Fundamentals of Computer Science, Rzeszow University of Technology, Rzeszów 35-959, Poland

• https://orcid.org/0000-0001-5287-2191

autor

Mazur Damian

• Department of Electrical Engineering and Fundamentals of Computer Science, Rzeszow University of Technology, Rzeszów 35-959, Poland

• https://orcid.org/0000-0002-3247-5903

Bibliografia

• [1] J.M. Langeron, E. Duc, C. Lartigue and P. Bourdet: A new format for 5-axis tool path computation using Bspline curves. Comput-Aided Design, 36(12), (2004), 1219-1229. DOI: 10.1016/j.cad.2003.12.002
• [2] 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. International Journal of Machine Tools and Manufacture, 89 (2015), 182-191. DOI: 10.1016/j.ijmachtools.2014.11.008
• [3] H.J. Lee, Y. Liu and S.H. Yang: Accuracy improvement of miniaturized machine tool: Geometric error modelling and compensation. International Journal of Machine Tools and Manufacture, 46 (2006), 1508-1516. DOI: 10.1016/j.ijmachtools.2005.09.004
• [4] 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 Design, 91 (2017), 14-26. DOI: 10.1016/j.cad.2017.04.003
• [5] 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. Science China Technological Sciences, 61 (2018), 1666-1678. DOI: 10.1007/s11431-017-9204-y
• [6] B. Kwiatkowski, T. Kwater, D. Mazur and J. Bartman: 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. Bulletin of the Polish Academy of Sciences. Technical Sciences, 72(1), (2024). DOI: 10.24425/bpasts.2023.147345
• [7] M. Chen and Y. Sun: A moving knot sequence-based feedrate scheduling method of parametric interpolator for CNC machining with contour error and drive constraints. The International Journal of Advanced Manufacturing Technology, 98 (2018), 487-504, DOI: 10.1007/s00170-018-2279-0
• [8] 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. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, UK, (2020). DOI: 10.1109/FUZZ48607.2020.9177569
• [9] S.Z. Mansour and R. Seethaler: Feedrate optimization for computer numerically controlled machine tools using modeled and measured process constraints. Journal of Manufacturing Science and Engineering, 139(1), (2017). DOI: 10.1115/1.4033933
• [10] M. Rahaman, R. Seethaler and I. Yellowley: A new approach to contour error control in high speed machining. International Journal of Machine Tools and Manufacture, 88 (2015), 42-50. DOI: 10.1016/j.ijmachtools.2014.09.002
• [11] T. Kar, N.K. Mandal and N.K. Singh: Multi-response optimization and surface texture characterization for CNC milling of Inconel 718 Alloy. Arabian Journal for Science and Engineering, 45 (2020), 1265-1277. DOI: 10.1007/s13369-019-04324-5
• [12] S. Datta, S.S. Mahapatra, B.C. Routara, and A. Bandyopadhyay: The fuzzy inference system approach to a multi-performance characteristic index for surface quality improvement in CNC end milling. International Journal of Experimental Design and Process Optimisation, 2(3), (2011), 265-282. DOI: 10.1504/IJEDPO.2011.042747
• [13] A. Molina, H. Ponce, P. Ponce, G. Tello and M. Ramírez: Artificial hydrocarbon net-works fuzzy inference systems for CNC machines position controller. International Journal of Advanced Manufacturing Technology, 72 (2014), 1465-1479. DOI: 10.1007/s00170-014-5676-z
• [14] D. Kalandyk, B. Kwiatkowski and D. Mazur: Application of Mamdani fuzzy logic inference system to optimize CNC machine motion dynamics. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Incheon, Republic of Korea, (2023). DOI: 10.1109/FUZZ52849.2023.1030980