2022
DOI: 10.1111/ffe.13640
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Fatigue modeling using neural networks: A comprehensive review

Abstract: Neural network (NN) models have significantly impacted fatigue‐related engineering communities and are expected to increase rapidly due to the recent advancements in machine learning and artificial intelligence. A comprehensive review of fatigue modeling methods using NNs is lacking and will help to recognize past achievements and suggest future research directions. Thus, this paper presents a survey of 251 publications between 1990 and July 2021. The NN modeling in fatigue is classified into five applications… Show more

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Cited by 135 publications
(50 citation statements)
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References 243 publications
(322 reference statements)
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“…Data-driven methods are increasingly used to predict fatigue of components and parts [28] but also to determine SCFs. In terms of computational efficiency, ANN, once trained, are clearly superior to other computer-based methods such as FE simulations [29]; however, ANN have very limited ability to make predictions outside the training data space [33][34][35].…”
Section: Discussion Of the Applicability Readiness Of The Presented M...mentioning
confidence: 99%
See 1 more Smart Citation
“…Data-driven methods are increasingly used to predict fatigue of components and parts [28] but also to determine SCFs. In terms of computational efficiency, ANN, once trained, are clearly superior to other computer-based methods such as FE simulations [29]; however, ANN have very limited ability to make predictions outside the training data space [33][34][35].…”
Section: Discussion Of the Applicability Readiness Of The Presented M...mentioning
confidence: 99%
“…Data-driven methods (i.e., machine learning) are increasingly used to predict fatigue of components and parts [28]. Machine learning using ANN is the most common approach [29] and is suitable when large, complex data sets are available and there is no accurate physical description of the phenomenon [30][31][32].…”
Section: Characterization Of Stress Concentrations Along Weld Seamsmentioning
confidence: 99%
“…where α and β are the regularization parameters; E w is the penalty term of the loss function; and M is the number of connection weights. Weight is modified as indicated in Equation (6). When updating the gradient to realize weight decay, the weight is multiplied by a constant coefficient of less than 1.…”
Section: Bayesian Regularizationmentioning
confidence: 99%
“…Later, the stress ratio R [3], the failure probability P [4], and the loading frequency [5] were gradually integrated for practical applications. Despite their extensiveness and complexity, the proposed equations cannot be applied to all fatigue analyses and it is difficult to ensure accuracy, and a unanimous consensus on quantitative measurement of these factors has not been achieved [6]. On the other hand, the backpropagation neural network (BPNN) has emerged as the most widely used soft computing method, automatically approximating the training data.…”
Section: Introductionmentioning
confidence: 99%
“…On the other hand, as reported in several recent studies [68,69], applications of DNN based modelling of fatigue behavior attracts lots of attention lately due to their higher performance and accuracy compared to the SNNs [70,71]. In addition, it should be mentioned that applications of NN-based models for fatigue behavior analyses are investigated in the comprehensive review study carried out by Chen and Liu [72].…”
Section: Introductionmentioning
confidence: 99%