ICF13B

13th International Conference on Fracture June 16–21, 2013, Beijing, China -10- performed by use of MATLAB and the analytical crack propagation software NASGRO. Therefore, the significant parameters are stochastically modeled. To identify the significant parameters of the Forman/Mettu-equation a sensitivity analysis was performed. The results are inconsistence to the statistical analysis of crack propagation data used in literature. For instance the fracture toughness is insignificant to the scattering of the residual lifetime and can be neglected in the stochastic crack propagation simulation. As simulation method the basic Monte Carlo simulation was used. The stochastic input vectors are determined by a random number generator. After the simulation the residual lifetimes are statistically analyzed. Thus, it is possible to obtain residual lifetimes for pretended probabilities of survival. References [1] M. Sander, H. A. Richard, J. Lebahn, M. Wirxel, Fracture mechanical investigations on wheelset axles. Proceedings of 16th International Wheelset Congress, Cape Town, 2010. [2] P. Hübner, G. Pusch, U. Zerbst, Ableitung von Quantilrisswachstumskurven für Restlebensdauerberechnungen. DVM-Bericht 236, Berlin, 2004, pp. 121–130 [3] C. Theis, W. Kernbichler, Grundlagen der Monte Carlo Methoden. Institut für Theoretische Physik, TU Graz, 2002 [4] J. D. Sørensen, Notes in structural reliability theory and risk analysis. Institute of Building Technology and Structural Engineering, Aalborg University, 2004 [5] OptiSLang – Sensitivity Analysis, Multidisciplinary Optimization, Robustness Evaluation, Reliability Analysis and Robust Optimization. version 3.1, DYNARDO GmbH, Weimar, 2010 [6] J. P. C. Kleijnen, Sensitivity Analysis and Related Analysis. Schriftenreihe 706, Faculty of Economics and Business Administration, Tilburg University, 1995 [7] A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, S. Tarantola, Global Sensitivity Analysis: The Primer. Wiley, Chichester, 2008 [8] K. Siebertz, D. van Bebber, T. Hochkirchen, Statistische Versuchsplanung: Design of Experiments (DoE). Springer-Verlag, Berlin Heidelberg, 2010 [9] NASGRO – Fracture Mechanics and Fatigue Crack Growth Analysis Software. Reference manual, version 6.0, 2009 [10] S. Beretta,M. Carboni, Experiments and Stochastic Model for Propagation Lifetime of Railway Axles. Engineering Fracture Mechanics 73, 2006, pp. 2627–2641 [11] S. Henkel, P. Hübner, G. Pusch, Zyklisches Risswachstumsverhalten von Guss-Eisenwerkstoffen – Analytische und statistische Aufbereitung für die Nutzung mit dem Berechnungsprogramm ESACRACK. DVM-Bericht 240, Berlin, 2008, pp. 251–259 [12] P. Hübner, U. Zerbst, M. Berger, T. Brecht, The Fracture of a Wobbler in a Heavy Plate Mill. Engineering Failure Analysis 16, 2009, pp. 1097–1108 [13] Wheelset Integrated Design an Effective Maintenance (WIDEM). 6th Framework Program, Sustainable Development, project number TST-CT-2005-516196, 2005 [14] W. F. Wu, C. C. Ni, A Study of Stochastic Fatigue Crack Growth Modeling Through Experimental Data. Probabilistic Engineering Mechanics 18, 2003, pp. 107–118 [15] H. Döker, Fatigue crack growth threshold: implications, determination and data evaluation, Int. J. Fatigue 19, 1997, pp. 145–149 [16] J. Lebahn, M. Sander, Untersuchungen zur Restlebensdauerberechnung mit NASGRO an eigenspannungsbehafteten, abgesetzten Hohlwellen. DVM-Bericht 242, 2010, pp. 103–112 [17] C. Ronniger, Design of Experiments & Statistics. Visual-XSel 12.0, CRGRAPH, Munich, 2012

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