Low cycle fatigue lifetime prediction of superplastic shape memory alloy structures: Application to endodontic instruments.

In this paper we propose a methodology for a fast numerical determination of low cycle fatigue lifetime of superelastic shape memory alloy structures. This method is based on the observation that generally, in low cycle fatigue, shape memory alloy (SMA) structures are subject to loadings that lead to a confined non-linear behaviour at stress concentration points, such as notches. Numerical fatigue lifetime prediction requires the computation of the mechanical state at critical points. However, classical computational methods, like the non-linear finite element method, lead to a prohibitive computation time in a non-linear cyclic framework. To overcome this issue, we propose to use fast prediction methods, based on localization laws. Following the determination of the stabilized behaviour, an energetic fatigue criterion is applied. The numerical fatigue life prediction model is validated experimentally on SMA endodontic instruments. Copyright © 2023 Elsevier Ltd. All rights reserved.

Citation

Yoann Congard, Luc Saint-Sulpice, Laurent Pino, Mahmoud Barati, Julien Mordeniz, Shabnam Arbab Chirani, Sylvain Calloch. Low cycle fatigue lifetime prediction of superplastic shape memory alloy structures: Application to endodontic instruments. Journal of the mechanical behavior of biomedical materials. 2023 Nov;147:106122

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PMID: 37778169

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