A Review of Damage, Void Evolution, and Fatigue Life Prediction Models

Lee, Hsiao Wei; Basaran, Cemal

doi:10.3390/met11040609

Cited by 28 publications

(8 citation statements)

References 152 publications

(177 reference statements)

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“…UMT has been utilized in various fatigue tests, e.g., Lee et al 115 incorporated the two‐scale model shown in Figure 10 and described in section 2.4 into this method for test frequencies of 30 Hz and 20 kHz. A review paper offers a comprehensive summary of this theory and its applications 116 …”

Section: Fatigue Life Prediction Methodsmentioning

confidence: 99%

“…A review paper offers a comprehensive summary of this theory and its applications. 116 Yang et al 117 derived a 1D thermodynamic relationship to describe the temperature changes of a specimen N f F I G U R E 1 3 A represented predicted model for comparing the calculated S-N data with the experimental data of SAE1045 steel. 94 [Colour figure can be viewed at wileyonlinelibrary.com] by combining thermoelastic, inelastic, and heat transfer effects.…”

Section: Other Approachesmentioning

confidence: 99%

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Thermo‐based fatigue life prediction: A review

Teng

2023

Fatigue Fract Eng Mat Struct

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The main objective of this review paper is twofold: Recall the most captivating areas of research in predicting the fatigue life of metals with the application of thermal methodology, particularly for the stress‐controlled fatigue tests. The explicit lifetime models reviewed have been developed by scholars over the past two decades owing to the advancements in infrared thermal imaging technology. Introduce, discuss, and conclude a broad range of alternative theoretical frameworks in thermodynamics. Some investigations are made with the previous methods, including the most recent evolutions of lifetime models for structural integrity across various fields. Some future perspectives are provided in final, which could pave the way for a new realistic or potential capability.

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Section: Fatigue Life Prediction Methodsmentioning

confidence: 99%

Section: Other Approachesmentioning

confidence: 99%

Thermo‐based fatigue life prediction: A review

Teng

2023

Fatigue Fract Eng Mat Struct

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“…The study results highlighted that the techniques for predicting fatigue life, strain response, and natural frequency under strong noise excitation are still incomplete, requiring further development to enhance design practicality. Moreover, Lee et al [19][20][21] combined the second law of thermodynamics with Newton's laws to effectively predict high-cycle fatigue life based on Unified Mechanics Theory (UMT). The presented work has not relied on traditional empirical curve fitting, providing an important alternative perspective.…”

Section: Introductionmentioning

confidence: 99%

Acoustic and Vibration Response and Fatigue Life Analysis of Thin-Walled Connection Structures under Heat Flow Conditions

Sha,

Zhao,

Tang

et al. 2024

Aerospace

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Thin-walled connection structures are commonly used in the hot-end components of aerospace vehicles. Large deflection nonlinear responses and fatigue failure occur due to their discontinuous mass distribution and prominent cross-sectional changes under the action of complex thermal, aerodynamic, and noise loads. A thermoacoustic fatigue test was carried out to obtain the acoustic and vibration responses and fatigue life changes of the connection structure under heat flow conditions in engineering applications. The high-temperature acoustic fatigue test system of aviation thin-walled structures was used, taking the high-temperature alloy thin-walled plate-load-bearing frame bolted connection structure as the research object. As a result, the vibration response and fatigue life under different thermoacoustic loads were obtained. The contact finite element method was used to simulate the connection pre-tightening force, and the coupled finite element/boundary element method was used to calculate the acoustic and vibration response of the heat flow conditions. The changing rules of the frequency response peak value at the critical point of the thin-walled connection structure under the effects of different temperature fields, fluid fields, and sound fields were obtained through the processing and analysis of the calculation results. Considering the structural vibration fatigue damage mechanism, this study employed an improved rainflow counting method to compute the rainflow circulation matrix (RFM) and rainflow damage matrix (RFD) of the vibration stress time history at critical points within the structure framework. Said method was combined with Miner’s linear cumulative damage theory to estimate the fatigue life under various thermal-fluid-acoustic coupled loads. A comprehensive analysis validates the accuracy of the established numerical simulation calculation model in identifying critical connection points within structures subjected to pre-tightening forces. This model effectively characterizes thermal, aerodynamic, and acoustic loads on high-temperature alloy thin-walled-load-bearing frame bolted connection structures. It delineates the relationship between vibration response and fatigue life while assessing the impact of three distinct load parameters.

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“…Furthermore, most of the above models are again essentially based on fitting. Recently, the focus has been given to fatigue prediction using the unified mechanics theory (UMT) proposed by Basaran [9] , which is a purely physics-based approach that does not require the fitting of an empirical evolution function [10,11] . The UMT has been validated for the fatigue prediction of metals in studies by Noushad et al [12] and Egner et al [13] .…”

Section: Introductionmentioning

confidence: 99%

High-cycle fatigue S-N curve prediction of steels based on a transfer learning-guided convolutional neural network

Wei¹,

Wang²,

Jia³

et al. 2022

J Mater Inf

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The evaluation and prediction of fatigue properties are crucial for metallic materials. Although the determination of S-N curves represents the most important methods for evaluating such properties, its fatigue testing is costly and time-consuming. Furthermore, fatigue testing involves different test conditions, thereby complicating the evaluation of the fatigue properties. This study develops a transfer convolutional neural network (TR-CNN) framework, in which the prediction of the reversed torsion S-N curves of steels is transferred from rotating bending S-N curves. In the TR-CNN framework, the source CNN models for rotating-bending curve prediction are first trained based on the composition and process conditions. Subsequently, based on the source models, the reversed torsion S-N curves are estimated by training the TR-CNN models based on only a small dataset. After proving the rationality of the framework, its universality with respect to different amounts of data is further investigated. The reversed torsion curves under small-sample conditions (22 samples) are predicted accurately by the TR-CNN. Additionally, the TR-CNN models remain accurate under varying amounts of data (22-112 samples), showing excellent generality for different amounts of fatigue data. The predictive capability of the TR-CNN models is improved by introducing tensile properties into the source models. The proposed TR-CNN framework can significantly reduce the cost of evaluating fatigue properties, and the prediction of S-N curves can be optimized by combining the transfer framework and low-cost properties related to fatigue.

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A Review of Damage, Void Evolution, and Fatigue Life Prediction Models

Cited by 28 publications

References 152 publications

Thermo‐based fatigue life prediction: A review

Thermo‐based fatigue life prediction: A review

Acoustic and Vibration Response and Fatigue Life Analysis of Thin-Walled Connection Structures under Heat Flow Conditions

High-cycle fatigue S-N curve prediction of steels based on a transfer learning-guided convolutional neural network

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