A Walker-based mean strain correction models for low-cycle fatigue life prediction
https://doi.org/10.26896/1028-6861-2023-89-1-67-73
Аннотация
A Walker-based mean strain correction model of low-cycle fatigue (LFC) life prediction is proposed for high loaded parts. The model is based on a function depending on the strain range and strain ratio controlled in the strain-controlled LCF test of fatigue specimens and a constant reflecting the material sensitivity to strain ratio. The independence from the stress cycle parameters which can change during the strain-controlled LCF test is an obvious advantage of the model. The model was verified using the results of strain-controlled LCF tests of smooth titanium alloy Ti-6A1-4V ELI and iron-based alloy specimens conducted at room temperature. The proposed model was compared to the Smith - Watson - Topper and Walker models that take into account the mean stress effect. The proposed model provided the best prediction accuracy for titanium alloy. For Iron-based alloys the results obtained by the Walker model and the model proposed are close to each other. A simplified model based on the analysis of model parameters tailing into account the mean strain effect for predicting fatigue life of aeroengine critical parts is developed using a limited amount of experimental data when only the results of Rε = 0 tests are known. A comparison of the predicted life with the number of cycles to failure showed satisfactory results of fatigue life prediction for Ti-6A1-4V ELI and Iron-based alloys specimens.
Об авторе
A. N. Servetnik
Central Institute of Aviation Motors
Россия
Россия
Anton N. Servetnik.
2, Aviamotornaya Street, Moscow, 111116
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Рецензия
Для цитирования:
Servetnik A.N. A Walker-based mean strain correction models for low-cycle fatigue life prediction. Заводская лаборатория. Диагностика материалов. 2023;89(1):67-73. https://doi.org/10.26896/1028-6861-2023-89-1-67-73
For citation:
Servetnik A.N. A Walker-based mean strain correction models for low-cycle fatigue life prediction. Industrial laboratory. Diagnostics of materials. 2023;89(1):67-73. https://doi.org/10.26896/1028-6861-2023-89-1-67-73
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