Fatigue life prediction of notched components under size effect using stress gradient-based approach

Ye, Wen-Long; Zhu, Shun‐Peng; He, Jin-Chao; Wang, Hongtao

doi:10.1007/s10704-021-00580-5

Cited by 18 publications

(8 citation statements)

References 42 publications

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“…Therefore, the proposed model may be applicable to multiaxial fatigue life prediction under more complicated loading conditions. For notched members with geometric discontinuities, notch and size effects have shown significant influences on their fatigue performance 48,49 . The proposed deep learning model may well characterize the notch fatigue behavior under complex loading histories, which can be conducted as the next research work.…”

Section: Discussionmentioning

confidence: 99%

Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

Heng

Gao

et al. 2023

Fatigue Fract Eng Mat Struct

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A new algorithm optimization‐based hybrid neural network model is proposed in the present study for the multiaxial fatigue life prediction of various metallic materials. Firstly, a convolutional neural network (CNN) is applied to extract the in‐depth features from the loading sequence composed of the critical fatigue loading conditions. Meanwhile, the multiaxial historical loading information with time‐series features is retained. Then, a long short‐term memory (LSTM) network is adopted to capture the time‐series features and in‐depth features of the CNN output. Finally, a full connection layer is used to achieve dimensional transformation, which makes the fatigue life predictable. Herein, the hyperparameters of the LSTM network are automatically determined using the slime mold algorithm (SMA). The test results demonstrate that the proposed model has pleasant prediction performance and extrapolation capability, and it is suitable for the life prediction of various metallic materials under uniaxial, proportional multiaxial, nonproportional multiaxial loading conditions.

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Section: Discussionmentioning

confidence: 99%

Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

Heng

Gao

et al. 2023

Fatigue Fract Eng Mat Struct

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“…In this context, prognostics and health management (PHM) is playing an increasingly important role. PHM can be combined with multiple disciplines, such as machine learning [4,5] sensor technology [6,7], fault diagnosis [8], statistics [9][10][11][12] and reliability engineering [13][14][15][16], to realize an online evaluation of system health status and to predict the state of the system based on the current information. PHM converts signals and data detected by sensors into health status information about the system, and it alerts users so that the system can be maintained in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of the components in a system will gradually deteriorate over time [15][16][17]. Therefore, people need to maintain systems periodically to ensure the function.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Maintenance Strategy for Multi-Component Systems Using a Genetic Algorithm

Shi¹,

Ma²,

Ma³

2023

Computer Modeling in Engineering &Amp; Sciences

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In multi-component systems, the components are dependent, rather than degenerating independently, leading to changes in maintenance schedules. In this situation, this study proposes a grouping dynamic maintenance strategy. Considering the structure of multi-component systems, the maintenance strategy is determined according to the importance of the components. The strategy can minimize the expected depreciation cost of the system and divide the system into optimal groups that meet economic requirements. First, multi-component models are grouped. Then, a failure probability model of multi-component systems is established. The maintenance parameters in each maintenance cycle are updated according to the failure probability of the components. Second, the component importance indicator is introduced into the grouping model, and the optimization model, which aimed at a maximum economic profit, is established. A genetic algorithm is used to solve the non-deterministic polynomial (NP)-complete problem in the optimization model, and the optimal grouping is obtained through the initial grouping determined by random allocation. An 11-component series and parallel system is used to illustrate the effectiveness of the proposed strategy, and the influence of the system structure and the parameters on the maintenance strategy is discussed.

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“…In contrast, it is well known that stress distribution affects fatigue life in the HCF regime, and Peterson [4], Neuber [5], Ishibashi [6], and Siebel et al [7] formulated the fatigue limit taking the stress gradient into account. Recently, Ye et al [8] proposed a technique to predict the fatigue life of notched specimens taking into account stress and its gradient. They demonstrated that the fatigue life of notched specimens with different notch sizes could be evaluated using this technique.…”

Section: Introductionmentioning

confidence: 99%

Influence of Strain Gradient on Fatigue Life of Carbon Steel for Pressure Vessels in Low-Cycle and High-Cycle Fatigue Regimes

et al. 2022

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This paper discusses how the strain gradient influences the fatigue life of carbon steel in the low-cycle and high-cycle fatigue regimes. To obtain fatigue data under different strain distributions, cyclic alternating bending tests using specimens with different thicknesses and cyclic tension–compression tests were conducted on carbon steel for pressure vessels (SPV235). The crack initiation life and total failure life were evaluated via the strain-based approach. The experimental results showed that the crack initiation life became short with decreasing strain gradient from 102 to 106 cycles in fatigue life. On the other hand, the influence of the strain gradient on the total failure life was different from that on the crack initiation life: although the total failure life of the specimen subjected to cyclic tension–compression was also the shortest, the strain gradient did not affect the total failure life of the specimen subjected to cyclic bending from 102 to 106 cycles in fatigue life. This was because the crack propagation life became longer in a thicker specimen. Hence, these experimental results implied that the fatigue crack initiation life could be characterized by not only strain but also the strain gradient in the low-cycle and high-cycle fatigue regimes.

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Fatigue life prediction of notched components under size effect using stress gradient-based approach

Cited by 18 publications

References 42 publications

Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

Multiaxial fatigue life prediction for various metallic materials based on the hybrid CNN‐LSTM neural network

A Dynamic Maintenance Strategy for Multi-Component Systems Using a Genetic Algorithm

Influence of Strain Gradient on Fatigue Life of Carbon Steel for Pressure Vessels in Low-Cycle and High-Cycle Fatigue Regimes

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