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A method to analyze the machining accuracy reliability sensitivity of machine tools based on Fast Markov Chain simulation

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PL
Podejście do analizy czułości niezawodnościowej dokładności obrabiarek oparte na symulacji metodą szybkich łańcuchów Markowa
Języki publikacji
EN
Abstrakty
EN
With the ever increasing demand of higher machining accuracies, the machining accuracy reliability has evolved into an indicator to evaluate the performance of a machine tool. Consequentially, methods for improving the machining accuracy reliability have become the focus of attention for both manufacturers and users. Generally, the intercoupling geometric errors are the main cause which may lead to a reduction of the machining accuracy of machine tools. In this paper, the machining accuracy reliability is defined as the ability of a machine tool to perform at its specified machining accuracy under the stated conditions for a given period of time, and a new approach for analyzing the machining accuracy reliability of machine tools based on fast Markov chain simulations is proposed. Using this method, seven different failure modes could be determined for a machine tool. An analysis of the machining accuracy reliability sensitivity was performed based on solving the integral of the failure probability of the machine tool, and the key geometric errors which most strongly affect the machining accuracy reliability were identified. Finally, in this study, a 4-axis machine tool was selected as an example to experimentally validate the effectiveness of the proposed method.
PL
Wraz z wciąż rosnącym zapotrzebowaniem na coraz to wyższą dokładność obróbki, niezawodność dokładności obróbki stała się wskaźnikiem pozwalającym na ocenę charakterystyk obrabiarek. W rezultacie, metody doskonalenia niezawodności dokładności obróbki znalazły się w centrum uwagi zarówno producentów jak i użytkowników tych maszyn. Na ogół, do zmniejszenia dokładności obróbki prowadzą nakładające się błędy geometryczne. W niniejszej pracy, niezawodność dokładności obróbki zdefiniowano jako zdolność obrabiarki do pracy z określoną dla niej dokładnością w zadanych warunkach przez dany okres czasu. Zaproponowano nowe podejście do analizy niezawodności dokładności obróbki oparte na symulacji metodą szybkich łańcuchów Markowa. Za pomocą tej metody, można ustalić siedem różnych przyczyn uszkodzeń obrabiarki. Analizę czułości niezawodnościowej dokładności obróbki przeprowadzono obliczając całkę prawdopodobieństwa uszkodzenia obrabiarki. Określono także kluczowe błędy geometryczne, które najsilniej wpływają na niezawodność dokładności obróbki. Wreszcie, efektywność proponowanej metody sprawdzono doświadczalnie na przykładzie obrabiarki czteroosiowej.
Rocznik
Strony
552--564
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
autor
  • Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
autor
  • Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
autor
  • Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
autor
  • Department of Mechatronics Engineering, Shantou University, Shantou, Guangdong, 515063, China
Bibliografia
  • 1. Avontuur GC, van der Werff K. Systems reliability analysis of mechanical and hydraulic drive systems. Reliability Engineering & System Safety 2002; 77(2):121-130, http://dx.doi.org/10.1016/S0951-8320(02)00039-X.
  • 2. Bohez EL, Ariyajunya B, Sinlapeecheewa C, et al. Systematic geometric rigid body error identification of 5-axis milling machines. Computeraided design 2007b; 39(4): 229-244.
  • 3. Cai L, Zhang Z, Cheng Q, Liu Z, Gu P. A geometric accuracy design method of multi-axis NC machine tool for improving machining accuracy reliability. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2015; 17(1): 143-155, http://dx.doi.org/10.17531/ein.2015.1.19.
  • 4. Çaydaş U, Ekici S. Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing 2012; 23(3): 639-650, http://dx.doi.org/10.1007/s10845-010-0415-2.
  • 5. Chen B, Chen X, Li B, et al. Reliability estimation for cutting tools based on logistic regression model using vibration signals. Mechanical Systems and Signal Processing 2011; 25(7): 2526-2537, http://dx.doi.org/10.1016/j.ymssp.2011.03.001.
  • 6. Chen G, Liang Y, Sun Y, et al. Volumetric error modeling and sensitivity analysis for designing a five-axis ultra-precision machine tool. The International Journal of Advanced Manufacturing Technology 2013; 68(9-12): 2525-2534, http://dx.doi.org/10.1007/s00170-013-4874-4.
  • 7. Cheng Q, Zhao H, Zhang G, et al. An analytical approach for crucial geometric errors identification of multi-axis machine tool based on global sensitivity analysis. The International Journal of Advanced Manufacturing Technology 2014, 75(1-4): 107-121, http://dx.doi.org/10.1007/s00170-014-6133-8.
  • 8. De-Lataliade A, Blanco S, Clergent Y. Monte Carlo method and sensitivity estimations. Journal of Quantitative Spectroscopy and Radiative Transfer 2007; 75(5): 529-538, http://dx.doi.org/10.1016/S0022-4073(02)00027-4.
  • 9. Du X, Sudjianto A, Huang B. Reliability-Based Design With the Mixture of Random and Interval Variables. Journal of Mechanical Design 2005; 127(6): 1068-1076, http://dx.doi.org/10.1115/1.1992510.
  • 10. Eman KF, Wu BT, DeVries MF. A Generalized Geometric Error Model for Multi-Axis Machines. Annals of the CIRP 1987; 36(07): 253–256, http://dx.doi.org/10.1016/S0007-8506(07)62598-0.
  • 11. Fu G, Fu J, Xu Y, et al. Product of exponential model for geometric error integration of multi-axis machine tools. The International Journal of Advanced Manufacturing Technology 2014; 71(9-12): 1653-1667, http://dx.doi.org/10.1007/s00170-013-5586-5.
  • 12. Ghosh R, Chakraborty S, Bhattacharyya B. Stochastic Sensitivity Analysis of Structures Using First-order Perturbation. Meccanica 2001; 36(3): 291-296, http://dx.doi.org/10.1023/A:1013951114519.
  • 13. Guo J, Du X. Reliability sensitivity analysis with random and interval variables. International Journal for Numerical Methods in Engineering 2009; 78(13): 1585-1617, http://dx.doi.org/10.1002/nme.2543.
  • 14. Habibi M, Arezoo B, Nojedeh MV. Tool deflection and geometrical error compensation by tool path modification. International Journal of Machine Tools and Manufacture 2011; 51(6): 439-449, http://dx.doi.org/10.1016/j.ijmachtools.2011.01.009.
  • 15. Homma T, Saltelli A. Importance measures in global sensitivity analysis of nonlinear models. Reliability Engineering & System Safety 1996; 52(1): 1-17, http://dx.doi.org/10.1016/0951-8320(96)00002-6.
  • 16. Jha BK, Kumar A. Analysis of geometric errors associated with five-axis machining center in improving the quality of cam profile. International Journal of Machine Tools and Manufacture 2003; 43(6): 629-636, http://dx.doi.org/10.1016/S0890-6955(02)00268-7.
  • 17. Kim K, Kim MK. Volumetric accuracy analysis based on generalized geometric error model in multi-axis machine tools. Mechanism and Machine Theory 1991; 26(2): 207-219, http://dx.doi.org/10.1016/0094-114X(91)90084-H.
  • 18. Lei WT, Hsu YY. Accuracy enhancement of five-axis CNC machines through real-time error compensation. International Journal of Machine Tools and Manufacture 2003; 43(9): 871-877. http://dx.doi.org/10.1016/S0890-6955(03)00089-0.
  • 19. Lin PD, Tzeng CS. Modeling and measurement of active parameters and workpiece home position of a multi-axis machine tool. International Journal of Machine Tools and Manufacture 2008; 48(3-4): 338-349, http://dx.doi.org/10.1016/j.ijmachtools.2007.10.004.
  • 20. Liu H, Li B, Wang X, Tan G. Characteristics of and measurement methods for geometric errors in CNC machine tools. The International Journal of Advanced Manufacturing Technology 2011; 54(1-4): 195-201, http://dx.doi.org/10.1007/s00170-010-2924-8.
  • 21. Liu YW. Applications of multi-body dynamics in the field of mechanical engineering. Chinese Journal of Mechanical Engineering 2000; 11(1): 144-149.
  • 22. Lin TR. Reliability and failure of face-milling tools when cutting stainless steel. Journal of Materials Processing Technology 1998; 79(1): 41-46, http://dx.doi.org/10.1016/S0924-0136(97)00451-2.
  • 23. Shin YC, Chin H, Brink MJ. Characterization of CNC machining centers. Journal of Manufacturing Systems 1991; 10(5): 407-421, http://dx.doi.org/10.1016/0278-6125(91)90058-A.
  • 24. Stryczek R. A metaheuristic for fast machining error compensation. Journal of Intelligent Manufacturing 2014; 1-12, http://dx.doi.org/10.1007/s10845-014-0945-0.
  • 25. Soons JA, Theuws FC, Schellekens PH. Modeling the errors of multi-axis machines: a general methodology. Precision Engineering 1992; 14(1): 5-19, http://dx.doi.org/10.1016/0141-6359(92)90137-L.
  • 26. Tang J. Mechanical system reliability analysis using a combination of graph theory and Boolean function. Reliability Engineering & System Safety 2001; 72(1): 21-30, http://dx.doi.org/10.1016/S0951-8320(00)00099-5.
  • 27. Tsutsumi M, Saito A. Identification of angular and positional deviations inherent to 5-axis machining centers with a tilting-rotary table by simultaneous four-axis control movements. International Journal of Machine Tools and Manufacture 2004; 44(12-13): 1333-1342, http://dx.doi.org/10.1016/j.ijmachtools.2004.04.013.
  • 28. Xiao NC, Huang HZ, Wang Z, et al. Reliability sensitivity analysis for structural systems in interval probability form. Structural and Multidisciplinary Optimization 2011; 44(5): 691-705, http://dx.doi.org/10.1007/s00158-011-0652-9.
  • 29. Xu C, Gertner G. Extending a global sensitivity analysis technique to models with correlated parameters. Computational Statistics & Data Analysis 2007; 51(12): 5579-5590, http://dx.doi.org/10.1016/j.csda.2007.04.003.
  • 30. Yan S, Li B, Hong J. Bionic design and verification of high-precision machine tool structures. The International Journal of Advanced Manufacturing Technology 2015; 1-13, http://dx.doi.org/10.1007/s00170-015-7155-6.
  • 31. Zhang Y M, Wen BC, Liu QL. Reliability sensitivity for rotor-stator systems with rubbing. Journal of Sound and Vibration 2003; 259(5): 1095-1107, http://dx.doi.org/10.1006/jsvi.2002.5117.
  • 32. Zhu S, Ding G, Qin S, et al. Integrated geometric error modeling, identification and compensation of CNC machine tools. International Journal of Machine Tools and Manufacture 2012; 52(1): 24-29, http://dx.doi.org/10.1016/j.ijmachtools.2011.08.011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-21790a51-6f0f-4b5e-baf8-dd8a143eddef
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