A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation

Tognan, Alessandro; Patanè, Andrea; Laurenti, Luca; Salvati, Enrico

doi:10.1016/j.cma.2023.116521

Cited by 13 publications

(3 citation statements)

References 63 publications

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“…The time series method is adopted to fine tune the prediction of the above introduced model, in order to reinforce the confidence of prediction. The fine tuning of the time series can be viewed as a method of injecting physics information, albeit not as intricate as the approaches in Salvati et al 27 and Tognan et al 28 . For this fatigue crack length estimation problem, it is important to adopt the time series fine tuning to ensure that for the same specimen, the crack length predicted by the model will not decrease with the increasing of the cycle of fatigue loading.…”

Section: Methodsmentioning

confidence: 99%

A dilated convolution‐based method with time series fine tuning for data‐driven crack length estimation

Gao,

Hu,

Han

et al. 2024

Fatigue Fract Eng Mat Struct

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This paper presents a novel crack size estimation method based on dilated convolution and time series fine tuning using in‐situ Lamb wave. The contributions of this proposed method are twofold: (1) a dilated convolution‐based neural network which is an effective representation learning methods with larger receptive fields for periodic signals and (2) a crack length adjustment approach based on previous estimations with a time series fine tuning method. The time series fine tuning method utilizes the monotonicity of the crack length with respect to time. A new evaluation metric has been adopted, which considers prediction errors, time errors, and monotonicity errors. The proposed method outperforms the previous feature extraction‐based methods with or without time series fine tuning in this new evaluation metric.

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Section: Methodsmentioning

confidence: 99%

A dilated convolution‐based method with time series fine tuning for data‐driven crack length estimation

Gao,

Hu,

Han

et al. 2024

Fatigue Fract Eng Mat Struct

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show abstract

“…Furthermore, statistical post-processing allowed for the retrieval of EH's curve at different failure probabilities 28 . Given the relatively young age of the MAP approach, its potential has only been exploited in 29 , to the best of the authors' knowledge.…”

Section: Introductionmentioning

confidence: 99%

B-FADE: Bayesian-FAtigue moDel Estimator in Python and its Application to the Probabilistic Estimation of El Haddad Curves

Tognan,

Salvati

2024

Preprint

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Accurate calibration of semi-empirical fatigue models against experimental evidence is a critical step for achieving reliable predictions. Amongst many semi-empirical fatigue models, El Haddad’s (EH) curve is widely exploited to characterise the fatigue endurance limit of defect-laden and cracked metallic alloys. A few deterministic computational models exist in this respect, however, they lack a robust probabilistic perspective and their implementation code is not publicly accessible. The authors of the present work have recently exploited Maximum a Posteriori (MAP) to robustly and probabilistically estimate EH's curves, even in case of data scarcity or incomplete datasets, combining experimental evidence and prior knowledge taken from literature. Whilst the implementation scheme was published, the associated code was not made available. Hereby, the authors present B-FADE, a flexible open-source Python package, aimed at releasing the implementation of the MAP approach with improvements, as well as several pre- and post-processing utilities to facilitate its deployment. The package is conferred with a sufficient level of abstraction, thus turning out to be easily extensible to future implementation of other relevant fatigue models.

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“…One of the extra motivations for employing ML approaches was the lack of a simple physical-based solution for the mentioned work [17]. Nowadays, researchers demonstrate that because of the variation in fatigue lifetime and the experimental nature of fatigue, physics-based ML models can estimate the fatigue problem better than traditional ML methods, achieving high accuracy with low-trained data [18,19]. However, the present work aims to propose the interpretation of the impact of certain binary and continuous physical features, demonstrating their effect on the estimation of fatigue lifetime and its logarithm value directly, without using feature engineering, constraint enforcement, hybrid models, or optimizers, as represented in the literature [20], to build higher accuracy models.…”

Section: Introductionmentioning

confidence: 99%

Interpretation of fatigue lifetime prediction by machine learning modeling in piston aluminum alloys under different manufacturing and loading conditions

Azadi,

Matin

2024

Frattura Ed Integrità Strutturale

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Various input variables, including corrosion time, fretting force, stress, lubrication, heat-treating, and nano-particles, were evaluated by modeling of stress-controlled fatigue lifetimes in AlSi12CuNiMg aluminum alloy of the engine pistons with different machine learning (ML) techniques. Bending fatigue experiments were conducted through cyclic loading with zero mean stress, and then experimental data was predicted by five different ML-based models. Moreover, when the optimal ML prediction model was found, it was analyzed using the Shapley additive explanation (SHAP) values method. Results illustrated that extreme gradient boosting (XGBoost) had superior data for estimations, with average training coefficients of determination of at least 63% and 90%, respectively for fatigue lifetime and its logarithmic value. The SHAP values interpretation of the XGBoost model revealed that fretting force, stress, and corrosion time were the most significant inputs in estimating the logarithm values of fatigue lifetimes, respectively.

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A Bayesian defect-based physics-guided neural network model for probabilistic fatigue endurance limit evaluation

Cited by 13 publications

References 63 publications

A dilated convolution‐based method with time series fine tuning for data‐driven crack length estimation

A dilated convolution‐based method with time series fine tuning for data‐driven crack length estimation

B-FADE: Bayesian-FAtigue moDel Estimator in Python and its Application to the Probabilistic Estimation of El Haddad Curves

Interpretation of fatigue lifetime prediction by machine learning modeling in piston aluminum alloys under different manufacturing and loading conditions

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