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Conception of a Durability Prediction Method for a Metal-elastomer Machine Mount

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Języki publikacji
EN
Abstrakty
EN
Elastic elements utilised in the support systems of machines are subjects to deterioration processes dependent on the character of their operational loads, work environment and construction. The extent of this change and work time after which it occurs is important information in the maintenance process, necessary for its planning. This paper presents the developed concept of durability prediction method of a four-joint metal-elastomer machine support based on the variation of working temperature of its elastomer elements. Therefore, the solution enables the estimation of significant indexes determining the life cycle of the mount. The developed simulation model is a useful tool for designers, allowing them to estimate the time of the machine mount’s correct work. It also helps the operator to schedule the inspections of technical condition (degree of wear) of the machine mount and prepare its replacement operations accordingly. This problem is significant because any failure to an element usually generates higher costs than carrying out a scheduled preventive replacement.
Bibliografia
  • 1. Yan H., Li Y., Yuan F., Peng F., Yang X., Hou X. Analysis of the Screening Accuracy of a Linear Vibrating Screen with a Multi-layer Screen Mesh. Strojniski Vestnik/Journal of Mechanical Engineering 2020; 66: 289–299, DOI: 10.5545/sv-jme.2019.6523.
  • 2. Harris C., Piersol A. Harris’ Shock and Vibration Handbook, McGraw-Hill handbooks. McGraw-Hill: 2002.
  • 3. Cieplok G. Verification of the nomogram for amplitudę determination of resonance vibrations in the run-down phase of a vibratory machine. Journal of Theoretical and Applied Mechanics 2009; 47: 295–306, https:// www.researchgate.net/publication/265919414.
  • 4. Neidhart H.J. Elastic joints. US Patent 2,712,742, 1955.
  • 5. Młynarski S., Pilch R., Smolnik M., Szybka J., Wiązania G. A model of an adaptive strategy of preventive main- tenance of complex technical objects. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2020; 22(1): 35–41, DOI:10.17531/ein.2020.1.5.
  • 6. Pilch R. A method for obtaining the required system reliability level by applying preventive maintenance. Simulation: Transactions of the Society for Modeling and Simulation International 2015; 91: 615–624, DOI:10.1177/2F0037549715592274.
  • 7. Kuraś Ł.M., Smolnik M. Modernisation of LEMACH 6 Design-Research Method as a Reliability Engineering Tool. Journal of KONBiN 2020; 50(2): 145–164, DOI:10.2478/jok-2020-0032.
  • 8. Guo F., Jia X., Huang L., Salant R.F., Wang Y. The effect of aging during storage on the performance of a radial lip seal. Polymer Degradation and Stability 2013; 98: 2193–2200.
  • 9. Pham H. (Ed.) Handbook of Reliability Engineering. London, Springer-Verlag: 2003.
  • 10. Pourmand P., Hedenqvist M., Furó I., Gedde U. Radiochemical ageing of highly filled EPDM seals as revealed by accelerated ageing and ageing in-service for 21 years. Polymer Degradation and Stability2017; 144: 473–484.
  • 11. Gac P.L., Saux V.L., Paris M., Marco Y.Ageing mechanism and mechanical degradation behaviour of polychloroprene rubber in a marine environment: Comparison of accelerated ageing and long term exposure. Polymer Degradation and Stability 2012; 97: 288–296.
  • 12. Kömmling A., Jaunich M., Wolff D. Effects of heterogeneous aging in compressed HNBR and EPDM o-ring seals. Polymer Degradation and Stability 2016; 126: 39–46, DOI:10.1016/j.polymdegradstab.2016.01.012.
  • 13. Celina M., Wise J., Ottesen D., Gillen K., Clough R. Correlation of chemical and mechanical property changes during oxidative degradation of neoprene. Polymer Degradation and Stability 2000; 68: 171–184.
  • 14. Chou H.W., Huang J.S. Effects of cyclic compression and thermal aging on dynamic properties of neoprene rubber bearings. Journal of Applied Polymer Science 2007; 107: 1635–1641.
  • 15. Saux V.L., Gac P.L., Marco Y., Calloch S. Limits in the validity of Arrhenius predictions for field ageing of asilica filled polychloroprene in a marine environment. Polymer Degradation and Stability 2014; 99: 254–261, DOI:10.1016/j.polymdegradstab.2013.10.027.
  • 16. Woo C.S., Park H.S. Useful lifetime prediction of rubber component. Engineering Failure Analysis 2011; 18: 1645–1651, DOI:10.1016/j.engfailanal.2011.01.003.
  • 17. Hassine M.B., Naït-Abdelaziz M., Zaïri F., Colin X., Tourcher C., Marque G. Time to failure prediction in rubber components subjected to thermal ageing: A combined approach based upon the intrinsic defect concept and the fracture mechanics. Mechanics of Materials 2014; 79: 15–24.
  • 18. Oldfield D., Symes T. Long term natural ageing of silicone elastomers. Polymer Testing 1996; 15: 115–128.
  • 19. ISO 188. Rubber, vulcanized or thermoplastic – accelerated ageing and heat resistance tests. Geneva, International Organization for Standardization: 2011.
  • 20. Gac P.Y.L., Celina M., Roux G., Verdu J., Davies P., Fayolle B. Predictive ageing of elastomers: Oxidation driven modulus changes for polychloroprene. Polymer Degradation and Stability 2016; 130: 348– 355, DOI:10.1016/j.polymdegradstab.2016.06.014.
  • 21. Davies P., Evrard G. Accelerated ageing of polyurethanes for marine applications. Polymer Degradation and Stability 2007; 92: 1455–1464.
  • 22. Ha-Anh T., Vu-Khanh T. Prediction of mechanical properties of polychloroprene during thermo-oxi-dative aging. Polymer Testing 2005; 24: 775–780.
  • 23. Bower A.F. Applied Mechanics of Solid. CRC Press: 2009.
  • 24. Tobias P.A., Trindade D.C. Applied Reliability. Third Edition. Boca Raton, CRC Press Taylor and Francis Group: 2012.
  • 25. Banic M., Stamenkovic D., Miltenovic V., Milosevic M., Miltenovic A., Djekic P., Rackov M. Prediction of heat generation in rubber or rubber-metal springs. Thermal Science 2012; 16: 527–539, DOI:10.2298/ TSCI120503189B.
  • 26. Luo R.K., Wu W.X., Mortel W.J. A method to predict the heat generation in a rubberspring used in the railway industry. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 2005; 219: 239–244.
  • 27. Luukkonen A., Sarlin E., Villman V., Hoikkanen M., Vippola M., Kallio M., Vuorinen J., Lepistö T. Heat generation in dynamic loading of hybrid rubbersteel composite structure. ICCM17 Edinburgh, 17th International Conference on Composite Materials, 27-31 July 2009, Edinburgh, UK. 2009.
  • 28. Faulin J., Juan Perez A.A., Martorell Alsina S.S., Ramirez-Marquez J.E. (Eds.) Simulation Methods for Reliability and Availability of Complex Systems. London, New York, Springer: 2010.
  • 29. Johnson P.E. Monte Carlo Analysis in Academic Research. In: Little TD. (ed.) The Oxford Handbook of Quantitative Methods in Psychology, 1. Oxford, Oxford University Press: 2013: 454–479.
  • 30. Sikora W., Michalczyk K., Machniewicz T. Numerical Modelling of Metal-Elastomer Spring Nonlinear Response for Low-Rate Deformations. Acta Mechanica et Automatica 2018; 12: 31–37.
  • 31. Bowling S.R., Khasawneh M.T., Kaewkuekool S., Cho B.R. A Logistic Approximation to the Cumulative Normal Distribution. Journal of Industrial Engineering and Management 2009; 2: 114–127.
  • 32. Hamasha M.M. Mathematical approximation of single- and double-sided truncated normal distribution using logistic function. International Journal of Industrial Engineering 2019; 26: 934–944, DOI:10.23055/ijietap.2019.26.6.3344.
  • 33. Villanueva D., Feijóo A. Comparison of logistic functions for modeling wind turbine power curves. Electric Power Systems Research 2018; 155: 281– 288, DOI:10.1016/j.epsr.2017.10.028.
  • 34. Zhang L., Wu A., Wang H., Wang L. Represen-tation of batch settling via fitting a logistic function. Minerals Engineering 2018; 128: 160–167, DOI:10.1016/j.mineng.2018.08.039.
  • 35. Selech J., Andrzejczak K. Identification of Reliability Models for Non-repairable Railway Component. In: Kabashkin I., Yatskiv (Jackiva) I., Prentkovskis O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2018. Lecture Notes in Networks and Systems, vol 68. Springer, Cham: 2019, https://doi.org/10.1007/978-3-030-12450-2_49.
  • 36. Świderski A., Jóźwiak A., Jachimowski R. Operational quality measures of vehicles applied for the transport services evaluation using artificial neural networks. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2018; 20(2): 292–299, http://dx.doi.org/10.17531/ein.2018.2.16.
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-5546953d-3e52-4411-b797-d1332d59d815
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