Prediction of Fatigue Crack Growth Process via Artificial Neural Network Technique

The ongoing effort to create methods for detecting and quantifying fatigue damage is motivated by the high levels of uncertainty in present fatigue-life prediction approaches and the frequently catastrophic nature of fatigue failure. The fatigue life of high strength aluminum alloy 2090-T83 is predicted in this study using a variety of artificial intelligence and machine learning techniques for constant amplitude and negative stress ratios (R ¼ À1). Artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), support-vector machines (SVM), a random forest model (RF), and an extreme-gradient tree-boosting model (XGB) are trained using numerical and experimental input data obtained from fatigue tests based on a relatively low number of stress measurements. In particular, the coefficients of the traditional force law formula are found using relevant numerical methods. It is shown that, in comparison to traditional approaches, the neural network and neuro-fuzzy models produce better results, with the neural network models trained using the boosting iterations technique providing the best performances. Building strong models from weak models, XGB helps to predict fatigue life by reducing model partiality and variation in supervised learning. Fuzzy neural models can be used to predict the fatigue life of alloys more accurately than neural networks and traditional methods.

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“…The semi-linear region's slope is managed by the constant g. Except for the input layer, all layers exhibit sigmoid nonlinearity [14].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

Abdullatef¹,

Alzubaidi²,

Al-Tamimi³

et al. 2023

Fluid Dynamics &Amp; Materials Processing

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“…That is, the crack length is monitored under working conditions without interrupting the operation. Cheng et al 151 and Nechval et al 152 used FNNs for this purpose. In their work, the crack length is used as input, and the output is the number of cycles.…”

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

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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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“…The ability of the ANNs to learn by example, makes them particularly useful for modeling highly complicated and non-linear processes, since the development of analytical models for such processes is extremely difficult. ANN modeling has been successfully used in a wide variety of engineering applications including automatic control [2], solar energy systems [3,4], traffic and transportation [5], image processing [6], biomechanics [7], materials science and engineering [8][9][10], manufacturing [11,12], fracture mechanics and fault detection [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks

2020

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An artificial neural network (ANN) extracts knowledge from a training dataset and uses this acquired knowledge to forecast outputs for any new set of inputs. When the input/output relations are complex and highly non-linear, the ANN needs a relatively large training dataset (hundreds of data points) to capture these relations adequately. This paper introduces a novel assisted-ANN modeling approach that enables the development of ANNs using small datasets, while maintaining high prediction accuracy. This approach uses parameters that are obtained using the known input/output relations (partial or full relations). These so called assistance parameters are included as ANN inputs in addition to the traditional direct independent inputs. The proposed assisted approach is applied for predicting the residual strength of panels with multiple site damage (MSD) cracks. Different assistance levels (four levels) and different training dataset sizes (from 75 down to 22 data points) are investigated, and the results are compared to the traditional approach. The results show that the assisted approach helps in achieving high predictions’ accuracy (<3% average error). The relative accuracy improvement is higher (up to 46%) for ANN learning algorithms that give lower prediction accuracy. Also, the relative accuracy improvement becomes more significant (up to 38%) for smaller dataset sizes.

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Prediction of Fatigue Crack Growth Process via Artificial Neural Network Technique

Cited by 3 publications

References 31 publications

Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

Fatigue Life Estimation of High Strength 2090-T83 Aluminum Alloy under Pure Torsion Loading Using Various Machine Learning Techniques

Fatigue modeling using neural networks: A comprehensive review

A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks

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