Using machine learning to predict lifetime under isothermal low-cycle fatigue and thermo-mechanical fatigue loading

Bartošák, Michal

doi:10.1016/j.ijfatigue.2022.107067

Cited by 29 publications

(2 citation statements)

References 49 publications

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“…Machine learning (ML) has powerful nonlinear processing and multivariate learning capabilities. It has been widely used for crack growth to solve complex nonlinear prediction problems [18][19][20][21][22][23][24][25]. Indeed, the radial basis function artificial neural network (RBF-ANN), backpropagation neural network (BPNN), extreme learning machine (ELM), fully connected neural network, random forest (RF), hidden Markov model (HMM), and long short-term memory (LSTM) all yield accurate life and crack growth predictions [26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Crack Growth Rate Model Derived from Domain Knowledge-Guided Symbolic Regression

Zhou

Yang

Xiao

et al. 2023

Chin. J. Mech. Eng.

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Machine learning (ML) has powerful nonlinear processing and multivariate learning capabilities, so it has been widely utilised in the fatigue field. However, most ML methods are inexplicable black-box models that are difficult to apply in engineering practice. Symbolic regression (SR) is an interpretable machine learning method for determining the optimal fitting equation for datasets. In this study, domain knowledge-guided SR was used to determine a new fatigue crack growth (FCG) rate model. Three terms of the variable subtree of ΔK, R-ratio, and ΔKth were obtained by analysing eight traditional semi-empirical FCG rate models. Based on the FCG rate test data from other literature, the SR model was constructed using Al-7055-T7511. It was subsequently extended to other alloys (Ti-10V-2Fe-3Al, Ti-6Al-4V, Cr-Mo-V, LC9cs, Al-6013-T651, and Al-2324-T3) using multiple linear regression. Compared with the three semi-empirical FCG rate models, the SR model yielded higher prediction accuracy. This result demonstrates the potential of domain knowledge-guided SR for building the FCG rate model.

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Section: Introductionmentioning

confidence: 99%

Crack Growth Rate Model Derived from Domain Knowledge-Guided Symbolic Regression

Zhou

Yang

Xiao

et al. 2023

Chin. J. Mech. Eng.

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show abstract

“…Sun et al [23] proposed an image recognition-based method with hysteresis loop images as input features of the convolution neural network (CNN) model, which demonstrates good prediction performance and flexibility in transferring applications. As for thermo-mechanical fatigue loading, the neural network with the gated recurrent unit (GRU) model [24] provided a good correlation between the experiment and the prediction. Besides, the machine learning models without neural network architecture can also provide good predicted results [25][26][27], such as support vector machine, random forest and so on.…”

Section: Introductionmentioning

confidence: 99%

A physics-guided modelling method of artificial neural network for multiaxial fatigue life prediction under irregular loading

Zhou,

Sun,

Chen

2023

Phil. Trans. R. Soc. A.

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To improve the generalization of the artificial neural network (ANN) model on the prediction of multiaxial irregular cases, a physics-guided modelling method is proposed with inspiration from the Basquin–Coffin–Manson equation. The method suggested using two neurons in the last hidden layer of the ANN model and constraining the sign of weight and bias value. In this way, the prior physical knowledge of fatigue life distribution is introduced into the ANN model, which resulted in a satisfactory performance on the life prediction of multiaxial loading cases and better extrapolation ability. Furthermore, the physics-guided ANN model can also provide satisfactory prediction on irregular cases with the training of only regular cases. Compared with the conventional model, the average relative error and root mean squared error (RMSE) of prediction decreased by 33.29% and 44.29%, respectively. It greatly broadens the application scenarios of neural networks on multiaxial fatigue life prediction. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

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An Introduction to the Probabilistic Linear Matching Method Framework for Structural Integrity Assessment Under Uncertain Design Conditions

Wang,

Chen

2023

Lecture Notes in Applied and Computational Mechanics

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Using machine learning to predict lifetime under isothermal low-cycle fatigue and thermo-mechanical fatigue loading

Cited by 29 publications

References 49 publications

Crack Growth Rate Model Derived from Domain Knowledge-Guided Symbolic Regression

Crack Growth Rate Model Derived from Domain Knowledge-Guided Symbolic Regression

A physics-guided modelling method of artificial neural network for multiaxial fatigue life prediction under irregular loading

An Introduction to the Probabilistic Linear Matching Method Framework for Structural Integrity Assessment Under Uncertain Design Conditions

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