Fatigue life estimation of an all aluminium alloy 1055 MCM conductor for different mean stresses using an artificial neural network

Badibanga, Remy Kalombo; Pestana, Miélle Silva; Júnior, Raimundo Carlos Silvério Freire; Ferreira, J.L.A.; Silva, Cosme Roberto Moreira; Veloso, Luís Augusto Conte Mendes; Cãmara, Eduardo César Bezerra; Araújo, J.A.

doi:10.1016/j.ijfatigue.2020.105814

Cited by 21 publications

(15 citation statements)

References 3 publications

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“…Generating data for ANN training, based on prior knowledge of the problem (trivial data such as the average stress and number of cycles when the alternating stress is zero) to train the ANN is an interesting solution. This is done in previous works 74,76–78,91,107,108 …”

Section: Discussionmentioning

confidence: 99%

“…The number of six stress ratios was presented in Júnior et al, 74 and there were significant improvements (using MNN) where it is possible to obtain generalization in the results and with three stress ratios 75 . A similar approach as Júnior et al's 74 approach is applied to predict that the fatigue lives can be seen in the work conducted by Pestana et al, 76 Kalombo et al, 77 and Cãmara et al 78 Jimenez‐Martinez and Alfaro‐Ponce 79 used FNN to predict the fatigue life of chassis component subjected to sequence loading and different temperatures. The fatigue life is used as one of the inputs.…”

Section: Review Of Nn Applications In Fatiguementioning

confidence: 99%

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Fatigue modeling using neural networks: A comprehensive review

Chen

Liu

2022

Fatigue Fract Eng Mat Struct

135

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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: fatigue life prediction, fatigue crack, fatigue damage diagnosis, fatigue strength, and fatigue load. A wide range of NN architectures are employed in the literature and are summarized in this review. An overview of important considerations and current limitations for the application of NNs in fatigue is provided. Statistical analysis for the past and the current trend is provided with representative examples. Existing gaps and future research directions are also presented based on the reviewed articles.

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Section: Discussionmentioning

confidence: 99%

Section: Review Of Nn Applications In Fatiguementioning

confidence: 99%

Fatigue modeling using neural networks: A comprehensive review

Chen

Liu

2022

Fatigue Fract Eng Mat Struct

135

View full text Add to dashboard Cite

show abstract

“…In order to overcome the drawbacks of existing physical process‐based models, many recent studies have been devoted to the development of data‐driven mean stress models 27–30 . One great advantage of data‐driven models is that they can automatically acquire the optimal input–output mapping relationship based on raw data information without disturbance by subjective factors.…”

Section: Introductionmentioning

confidence: 99%

Fatigue life prediction in presence of mean stresses using domain knowledge‐integrated ensemble of extreme learning machines

Gan

Zhong

2022

Fatigue Fract Eng Mat Struct

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An accurate and stable data-driven model is proposed in this work for fatigue life prediction in presence of mean stresses. Multiple independent extreme learning machines are integrated into the model with distinct neural network configurations to simulate the complex correlations among mean stress levels, material properties, and fatigue lives. Meanwhile, the theoretical prediction, as a representation of domain knowledge, is used to optimize the data-driven processes of model training and prediction. Extensive experimental data of 13 metallic materials with different mean stress levels are collected from the open literatures for model training and evaluation. The results demonstrate that the proposed model can achieve high accuracy and good stability in fatigue life prediction under mean stress loading conditions, even with a small training dataset, showing great applicability for fatigue life prediction.

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“…In recent years, attempts have been made so that S-N data can be obtained more efficiently within reasonable time and cost (Durodola et al, 2017;Kalombo et al, 2020). For composite materials, the effort is more challenging.…”

Section: Introductionmentioning

confidence: 99%

Efficient Fatigue Life Assessment of Composite Materials by Using a Hybrid Surrogate Modeling

Airlangga

Kida

Hidayat

2021

Materials Research Communications

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In this study, hybrid surrogate and nonlinear autoregressive with exogenous inputs (NARX) model is developed and presented as data-driven based predictive model for efficient fatigue life assessment of composite materials. Surrogate modeling based upon wavelet neural networks (WNN) is employed to efficiently unveil mathematical pattern in S-N data, but costly to get from experiments. Moreover, the NARX architecture is chosen in order to enable multi-step ahead prediction in fatigue life assessment of multivariable amplitude loadings. By observing fatigue data as dynamic data of stress ratio series, it is shown that the hybrid model produces reasonably accurate fatigue life prediction by using fatigue data from two stress ratio values only. The use of two stress ratio values also allows usage of more limited fatigue data in the lifetime prediction. The WNN-NARX surrogate model is tested with well-known fatigue data in literature. Several composite materials examined in this study show the efficacy and robustness of the proposed model.

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Fatigue life estimation of an all aluminium alloy 1055 MCM conductor for different mean stresses using an artificial neural network

Cited by 21 publications

References 3 publications

Fatigue modeling using neural networks: A comprehensive review

Fatigue modeling using neural networks: A comprehensive review

Fatigue life prediction in presence of mean stresses using domain knowledge‐integrated ensemble of extreme learning machines

Efficient Fatigue Life Assessment of Composite Materials by Using a Hybrid Surrogate Modeling

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