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Warianty tytułu
Języki publikacji
Abstrakty
The essential source of errors in machining on five-axis milling centres are errors caused by the improper calibration of a machine tool, setup errors and a process plan which is not designed optimally. For the multilateral machining of the complex, precise parts of machines, a process plan should include, before and during the process, coordinate measurements by means of a touch probe in order to verify previously made surfaces or to determine accurately the position and orientation of a local coordinate system. The uncertainty of these measurements is connected with the estimate precision of performing the manufactured parts. This paper presents a tool devised for determining the uncertainty of the results of coordinate measurements by a simulation method, which is also useful at the stage of machine tool calibration. A basis of this method is a bipartite graph with a tree structure. Transitions in the graph constitute a set of elementary measurement activities and analytical activities which determine the uncertainty of the position and orientation of respective geometric features and abstract objects. Based on the results of the conducted simulation tests it is possible to build analytical models for the rapid determination of measurement uncertainty. The tool devised is aimed at vector dimensioning and therefore it enables simple extension, including integration with geometric dimensioning and tolerancing. This paper includes an example of applying the method devised, which confirms its practicability.
Słowa kluczowe
Wydawca
Rocznik
Tom
Strony
449--464
Opis fizyczny
Bibliogr. 18 poz., fig.
Twórcy
autor
- Faculty of Mechanical Engineering and Computer Science, University of Bielsko-Biala, Willowa 2, 43-309 Bielsko-Biala, Poland
Bibliografia
- 1. Hasanov S., Alkunte S., Rajeshirke M., Gupta A. Huseynov O., Fidan I., Alifui-Segbaya F., Rennie A. Review on additive manufacturing of multi-material parts: Progress and Challenges. J. Manuf. Mater. Process. 2022, 6(1), 4. https://doi.org/10.3390/jmmp6010004.
- 2. Lauwers B., Klocke F., Klink A., Tekkaya A.E., Neugebauer R., Mcintosh D. Hybrid process in manufacturing. CIRP Annals 2014, 63(2), 561–583,https://doi.org/10.1016/j.cirp.2014.05.003.
- 3. Klocke F., Brummer C. Hybrid Cutting. In: The International Academy for Production (eds) CIRP Encyclopedia of Production Engineering 2018, 1–4. Springer. https://doi.org/10.1007/978-3-642-35950-7_6408-3.
- 4. Stryczek R. A metaheuristic for fast machining error compensation. J. Intell. Manuf. 2016, 27, 1209–1220. https://doi.org/10.1007/s10845-014-0945-0.
- 5. Adam A., Sam T.-H., Latif K., Yusof Y., Khan Z., Ali Memon D., Saif Y., Hatem N., Ahmed M.I., Kadir A.Z.A. Review on advanced CNC controller for manufacturing in industry 4.0. In: Yusof, Y. (ed.) Enabling Industry 4.0 through Advances in Manufacturing and Materials 2022, 261–269, Springer. https://doi.org/10.1007/978-981-19-2890-1_26.
- 6. Stryczek R., Szczepka W. Simulation tests of adaptive control strategies for CNC machine tools. J. Mach. Engin. 2019, 19(2), 73–82. https://doi.org/10.5604/01.3001.0013.2225.
- 7. Zhang Z., Jiang F., Luo M., Wu B., Zhang D., Tang K. Geometric error measuring, modeling, and compensation for CNC machine tools: A review. Chin. J. Aeronaut. 2022, 37(2), 163–198. https://doi.org/10.1016/j.cja.2023.02.035.
- 8. Gao W., Ibaraki S., Donmez M.A., Kono D., Mayer J.R.R., Chen Y.-L., Szipka K., Archenti A., Linares J.-M., Suzuki N. Machine tool calibration: Measurement, modeling, and compensation of machine tool errors. Int. J. Mach. Tools Manuf. 2023, 187, 104017. https://doi.org/10.1016/j.ijmachtools.2023.104017.
- 9. Chen J.X., Lin S.W., Zhou X.L. A comprehensive error analysis method for the geometric error of multiaxis machine tool. Int. J. of Mach. Tools Manuf. 2016, 106(2), 56–66. https://doi.org/10.1016/j.ijmachtools.2016.04.001.
- 10. Royer M., Anselmetti B. 3D manufacturing tolerancing with probing of a local work coordinate system. Int. J. Adv. Manuf. Technol. 2016, 84, 2151–2165. https://doi.org/10.1007/s00170-015-7797-4.
- 11. Wan H., Chen S., Zheng T., Jiang D., Zhang C., Yang G. Piecewise modeling and compensation of geometric errors in five-axis machine tools by local product of exponentials formula. Int. J. Adv. Manuf. Technol. 2022, 121, 2987–3004. https://doi.org/10.1007/s00170-022-09178-0.
- 12. Stryczek R. Alternative methods for estimating plane parameters based on a point cloud. Meas. Sci. Rev. 2017, 17(6), 282–289. https://doi.org/10.1515/msr-2017-0035.
- 13. Multliba U., Gomez-Acedo E., Vega S.A.I., Yagüe-Fabra J.A. Uncertainty assessment for on-machine tool measurement: An alternative approach to the ISO 15530-3 technical specification, Precis. Engin. 2019, 57, 45–53. https://doi.org/10.1016/j.precisioneng.2019.03.005.
- 14. Blecha P., Holub M., Marek T., Jankovych R., Misun F., Smolik J., Machalka M. Capability of measurement with a touch probe on CNC machine tools. Measurement 2022, 195, 111153. https://doi.org/10.1016/j.measurement.2022.111153.
- 15. Płowucha W. Point-plane distance model for uncertainty evaluation of coordinate measurement. Metrol. Meas. Syst. 2020, 27(4), 625–639. https://doi.org/10.24425/mms.2020.134843.
- 16. Płowucha W., Jakubiec W., Rosner P. Evaluation of measurement uncertainty – Monte Carlo method. Mechanik 2017, 90(12), 1152–1154. https://doi.org/10.17814/mechanik2017.12.195.
- 17. Sepahi-Boroujeni S., Mayer J.R.R., Khameneifar F. Efficient uncertainty estimation of indirectly measured geometric errors of five-axis machine tools via Monte-Carlo validated GUM framework, Precis. Engin. 2021, 67, 160– 171. https://doi.org/10.1016/j.precisioneng.2020.09.027.
- 18. Wojtyła M., Rosner P., Płowucha W., Forbes A.B., Savio E. Balsamo, A. Validation of the sensitivity analysis method of coordinate measurement uncertainty evaluation. Measurement 2022, 199(4), 111454. https://doi.org/10.1016/j.measurement.2022.1114.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-212d017c-7e5e-4761-9337-47bd64a3b2df