PSI - Issue 37

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Procedia Structural Integrity 37 (2022) 746–754

© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Pedro Miguel Guimaraes Pires Moreira Abstract The fatigue strength of shafts is always subjected to scatter. Knowledge of this scatter is essential — especially in the high-cycle fatigue (HCF) region — for producing safe shaft designs. Experimental determination is extremely time consuming and cost intensive. Therefore, this paper investigates the possibility of quantifying the scatter of (nominal) fatigue strength in the HCF region by means of a probabilistic model. The basis for establishing such a model is the identification of the parameters influencing the damage mechanism. In this context, the scatter-influencing parameters of external shape, surface and material condition were statistically modelled in a suitable manner. These influencing parameters act as input parameters in a local strength approach in addition to non-scattering parameters. Using a probabilistic model developed on the basis of Monte Carlo simulations, models of shafts and their surfaces can be randomly generated. The shafts so generated are submitted to local strength verifications by means of finite-element analyses of the entire failure-critical shaft surface. The stochastically generated shafts and the application of nominal stresses simulate the experimental testing at different nominal stress levels. By statistical evaluation of these fictitious nominal stress levels with respect to calculated failures and run-outs of the shafts, the probability distribution of the fatigue strength of the shaft population — and thus its scatter — can be determined. The probabilistic model is validated using a shaft population under non-scattering rotating bending load and compared with the experimentally determined scatter of the fatigue strength. © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Pedro Miguel Guimaraes Pires Moreira Keywords: fatigue strength, probabilistic method, scatter Abstract The fatigue strength of shafts is always subjected to scatter. Knowledge of this scatter is essential — especially in the high-cycle fatigue (HCF) re ion — for producing safe shaft de igns. Experimental determina ion is extremely time consuming and ost in ensive. Therefore, this pape investi ates the possibility of quant fyi g the scatter of (nominal) fatigue strength in the HCF regi n by means of a pr babil tic model. The basis for e tablishing such a model is th identification of the pa ameters influencin the damage mechanism. In his c ntext, the scatte -influencing parameters of ex ernal shape, surface and mat rial condition wer st tistically modelled i a uitabl manner. Th se influencing param ters act as i put parameters in a local strength approach in add tion to non-scatteri g parameters. Using a probabilistic model dev loped on the basis of Monte Carlo simulations, m dels of shaf s a d their urfaces can be randomly generated. The shafts so generated are submitted t local strength verifications by means of inite-element analy es of the entire failure-critical shaft urfac . Th stochastically generated shaf s and the application of nominal stresses simulat the exp rim nt l testing at different nominal stress levels. By statistical ev luation of these fictitious levels with resp ct to calcul ted failures and u -outs of the shafts, th probability distribution f the fatigue strength of the shaft population — and thus its s atter — can be dete mined. The probabilistic model s validated using a shaft population under non-scattering rotati g bending lo d and ompared with the exp rimental y determined sc tter of the fatigue strength. © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review u der responsibility of Pedro Miguel Guimara s Pires Moreira K ywords: fatigue strength, probabilistic method, scatter ICSI 2021 The 4th International Conference on Structural Integrity Probabilistic Method to Estimate the Scatter of the Fatigue Strength of Shafts in the HCF Region Sebastian Vetter a,* , Erhard Leidich a , Nils Becker b , Berthold Schlecht b , Alexander Hasse a a Technische Universität Chemnitz, Professorship Machine Elements and Product Development, ICSI 2021 The 4th International Conference on Structural Integrity Probabilistic Method to Estimate the Scatter of the Fatigue Strength of Shafts in the HCF Region Sebastian Vetter a,* , Erhard Leidich a , Nils Becker b , Berthold Schlecht b , Alexander Hasse a a Technische Universität Chemnitz, Professorship Machine Elements and Product Development, Institute of Engin eri g Design and Drive Technology, Reiche hain r Strasse 70, 09126 Chemnitz, Germany b Technische Universität Dresden, Professorship Machine Elem ts, Münchner Platz 3, 01187 Dresden, Germany Institute of Engineering Design and Drive Technology, Reichenhainer Strasse 70, 09126 Chemnitz, Germany b Technische Universität Dresden, Professorship Machine Elements, Münchner Platz 3, 01187 Dresden, Germany

* Corresponding author. Tel.: +49-371-531-32070; fax: +49-371-531-23319. E-mail address: sebastian.vetter@mb.tu-chemnitz.de * Corresponding author. Tel.: +49-371-531-32070; fax: +49-371-531-23319. E-mail ad ress: sebastian.vetter@mb.tu-chemnitz.de

2452-3216 © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Pedro Miguel Guimaraes Pires Moreira 2452-3216 © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review u der responsibility of Pedro Miguel Guimara s Pires Moreira

2452-3216 © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Pedro Miguel Guimaraes Pires Moreira 10.1016/j.prostr.2022.02.005