Fatigue failure mechanism and life prediction of a cast duplex stainless steel after thermal aging

Guo, Can; Yu, Dunji; Sun, Xingyue; Wang, Yu; Chen, Xu

doi:10.1016/j.ijfatigue.2021.106161

Cited by 18 publications

(3 citation statements)

References 39 publications

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“…For fatigue life prediction analysis, the BCM equation provides satisfactory results under uniaxial loading condition for many materials [4,39]. The equation is described in equation (3.2).…”

Section: Algorithmsmentioning

confidence: 99%

“…With a quantity of uniaxial fatigue experimental data, the semi-empirical Basquin-Coffin-Manson (BCM) equation provides a widely accepted relationship between strain amplitude and fatigue life [4,5]. Based on the BCM equation, a series of fatigue damage parameters [6][7][8][9][10] derived from strain and stress characteristics are proposed to make an equivalent damage calculation to predict multiaxial fatigue life.…”

Section: Introductionmentioning

confidence: 99%

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A physics-guided modelling method of artificial neural network for multiaxial fatigue life prediction under irregular loading

Zhou,

Sun,

Chen

2023

Phil. Trans. R. Soc. A.

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To improve the generalization of the artificial neural network (ANN) model on the prediction of multiaxial irregular cases, a physics-guided modelling method is proposed with inspiration from the Basquin–Coffin–Manson equation. The method suggested using two neurons in the last hidden layer of the ANN model and constraining the sign of weight and bias value. In this way, the prior physical knowledge of fatigue life distribution is introduced into the ANN model, which resulted in a satisfactory performance on the life prediction of multiaxial loading cases and better extrapolation ability. Furthermore, the physics-guided ANN model can also provide satisfactory prediction on irregular cases with the training of only regular cases. Compared with the conventional model, the average relative error and root mean squared error (RMSE) of prediction decreased by 33.29% and 44.29%, respectively. It greatly broadens the application scenarios of neural networks on multiaxial fatigue life prediction. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

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Section: Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A physics-guided modelling method of artificial neural network for multiaxial fatigue life prediction under irregular loading

Zhou,

Sun,

Chen

2023

Phil. Trans. R. Soc. A.

Self Cite

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“…However, it can be also detected after solidification of the heat-resistant austenitic stainless steel with mass-based composition Fe-0.4%C-25%Cr-20%Ni (Wieczerzak et al, 2017;Padilha and Rios, 2002). It is worth mentioning that although there are several published works on low carbon duplex stainless steels, such as the book of Robert Gunn (2003) and recently the articles of Li et al (2019) and Guo et al (2021), there is still a lack of publications that mention the high carbon ones. It draws the attention of the reader that even fundamental aspects of the high carbon duplex steels, like its microstructure evolution during solidification, are not well-known.…”

Section: Metalurgia E Materiaismentioning

confidence: 99%

Influence of carbon content on the microstructure formation of duplex stainless steels Fe-25Cr-5Ni

2021

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This study evaluated the as-cast microstructure of three alloys based on Fe-25%Cr-5%Ni weight (wt.) composition whose carbon content is 0.02 wt.%, 0.12 wt.%, and 0.37 wt.%, respectively. Thermocalc was applied to predict the possible phases that could be formed during the cooling of each alloy and then the cross-sections of the ingots were characterized. The first alloy, with 0.02 wt.%C, presented a microstructure composed of ferrite and austenite, which was formed through solid-state precipitation. The second alloy, with 0.12 wt.%C, showed a microstructure formed by ferrite, austenite from the peritectic reaction ( L + δ → γ ) and M 23 C 6 (M=Fe, Cr) carbide. Finally, the third alloy, with 0.37 wt.%C, exhibited a microstructure formed by dendrites of ferrite and austenite, also formed according to the peritectic reaction ( L + δ → γ ), with an M 23 C 6 (M = Fe, Cr) and M 7 C 3 (M = Fe, Cr) carbide network in the interdendritic regions.

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Effect of thermal aging on the low cycle fatigue behavior of Z3CN20.09M stainless steel in high-temperature pressurized water

Qiao,

Zheng,

Tan

et al. 2024

Corrosion Science

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Fatigue failure mechanism and life prediction of a cast duplex stainless steel after thermal aging

Cited by 18 publications

References 39 publications

A physics-guided modelling method of artificial neural network for multiaxial fatigue life prediction under irregular loading

A physics-guided modelling method of artificial neural network for multiaxial fatigue life prediction under irregular loading

Influence of carbon content on the microstructure formation of duplex stainless steels Fe-25Cr-5Ni

Effect of thermal aging on the low cycle fatigue behavior of Z3CN20.09M stainless steel in high-temperature pressurized water

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