PSI - Issue 22

Yang Ai et al. / Procedia Structural Integrity 22 (2019) 70–77 Y. Ai et al. / Structural Integrity Procedia 00 (2019) 000 – 000

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predictions and experimental results.

Acknowledgements The authors would like to acknowledge the financial support of the National Natural Science Foundation of China (No. 11672070), Sichuan Provincial Key Research and Development Program (No. 2019YFG0348) and Science and Technology Program of Guangzhou, China (No. 201904010463), and Open Project Program of Key Laboratory of Aero-engine Thermal Environment and Structure (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology (No. CEPE2018007). References [1] S. P. Zhu, S. Foletti, and S. Beretta, “Evaluation of size effect on strain -controlled fatigue behavior of a quench and tempered rotor steel: Experimental and numerical study,” Mater. Sci. Eng. A , vol. 735, pp. 423 – 435, 2018. [2] M. Shirani and G. Härkegår d, “Large scale axial fatigue testing of ductile cast iron for heavy section wind turbine components,” Eng. Fail. Anal. , vol. 18, no. 6, pp. 1496 – 1510, 2011. [3] D. Liao, S. P. Zhu, J. A. F. O. Correia, A. M. P. De Jesus, and R. Calçada, “Computational fra mework for multiaxial fatigue life prediction of compressor discs considering notch effects,” Eng. Fract. Mech. , vol. 202, pp. 423 – 435, 2018. [4] L. K. Song, G. C. Bai, and C. W. Fei, “Probabilistic LCF life assessment for turbine discs with DC strategy -based wavelet neural network regression,” Int. J. Fatigue , vol. 119, pp. 204 – 219, 2019. [5] M. Shirani and G. Härkegård, “Casting defects and fatigue behaviour of ductile cast iron for wind turbine components: A compr ehensive study,” Materwiss. Werksttech. , vol. 42, no. 12, pp. 1059 – 1074, 2011. [6] S. P. Zhu, S. Foletti, and S. Beretta, “Probabilistic framework for multiaxial LCF assessment under material variability,” Int. J. Fatigue , vol. 103, pp. 371 – 385, 2017. [7] S. P. Zhu, Q. Liu, W. Peng, and X. C. Zha ng, “Computational -experimental approaches for fatigue reliability assessment of turbine bladed disks,” Int. J. Mech. Sci. , vol. 142 – 143, pp. 502 – 517, 2018. [8] S. P. Zhu, Q. Liu, Q. Lei, and Q. Wang, “Probabilistic fatigue life prediction and reliability assessment of a high pressure turbine disc considering load variations,” Int. J. Damage Mech. , vol. 27, no. 10, pp. 1569 – 1588, 2018. [9] S. P. Zhu, H. Z. Huang, R. Smith, V. Ontiveros, L. P. He, and M. Modarres, “Bayesian framework for probabilistic low cy cle fatigue life prediction and uncertainty modeling of aircraft turbine disk alloys,” Probabilistic Eng. Mech. , vol. 34, pp. 114 – 122, 2013. [10] S. P. Zhu, Q. Liu, J. Zhou, and Z. Y. Yu, “Fatigue reliability assessment of turbine discs under multi -source uncertainties,” Fatigue Fract. Eng. Mater. Struct. , vol. 41, no. 6, pp. 1291 – 1305, 2018. [11] G. Härkegrd and G. Halleraker, “Assessment of methods for prediction of notch and size effects at the fatigue limit based on test data by Böhm and Magin,” Int. J. Fatigue , vol. 32, no. 10, pp. 1701 – 1709, 2010. [12] M. Leitner, M. Vormwald, and H. Remes, “Statistical size effect on multiaxial fatigue strength of notched steel components,” Int. J. Fatigue , vol. 104, pp. 322 – 333, 2017. [13] D. E. L. Khoukhi, F. Morel, N. Saintier, D. Bellett, and P. Osmond, “The effect of microstructural heterogeneities on the High Cycle Fatigue scatter of cast aluminium alloys : from an elementary volume to the structure,” in MATEC Web of Conferences , 2018, vol. 165. [14] R. Kuguel, “ A relation between theoretical stress concentration factor and fatigue notch factor deduced from the concept of highly stressed volume,” in proc. ASTM , 1961, vol. 61, pp. 732 – 748. [15] R. Wang, D. Li, D. Hu, F. Meng, H. Liu, and Q. Ma, “A combined critical distance and highly -stressed-volume model to evaluate the statistical size effect of the stress concentrator on low cycle fatigue of TA19 plate,” Int. J. Fatigue , vol. 95, pp. 8 – 17, 2017. [16] G. Qian, W. S. Lei, M. Niffenegger, and V. F. González- Albuixech, “On the temperature independence of statistical model parameters for cleavage fracture in ferritic steels,” Philos. Mag. , vol. 98, no. 11, pp. 959 – 1004, 2018. [17] C. Przybilla, A. Fernández- Canteli, and E. Castillo, “Maximum likelihood estimation for the three -parameter Weibull cdf of strength in presence of concurrent flaw populations,” J. Eur. Ceram. Soc. , vol. 33, no. 10, pp. 1721 – 1727, 2013. [18] M. J. Lamela et al. , “Probabilistic Characterization of Glass under Different Type of Testing,” Procedia Mater. Sci. , vol. 3, pp. 2111 – 2116, 2014. [19] M. Benedetti and C. Santus, “Mean stress and plasticity effect prediction on notch fatigue and crack growth threshold, combin ing the theory of critical distances and multiaxial fatigue criteria,” Fatigue and Fracture of Engineering Materials and Structures , 2018. [20] L. Susmel and D. Taylor, “The theory of critical distances to predict static strength of notched brittle components subject ed to mixed-mode