An online‐offline prognosis model for fatigue life prediction under biaxial cyclic loading with overloads

Li, Guoyi; Datta, S. K.; Chattopadhyay, Aditi; Iyyer, Nagaraja; Phan, Nam

doi:10.1111/ffe.12983

Cited by 14 publications

(5 citation statements)

References 26 publications

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“…Stable graphene layers on the tribosurface further reduce the COF by allowing for a longer sliding distance with the same amount of effort. Sliding distance affects wear rate more than the COF, as shown by the ANOVA results for AA7075-graphene composites tribological data by Li et al [41].…”

Section: Impact Of Sliding Distancementioning

confidence: 90%

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

Prasanth

Jeevanandam²,

Selvaraju³

et al. 2023

Journal of Nanomaterials

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In this research, the friction and wear of AA7075 nanocomposites reinforced with graphene and graphite were studied. Graphene’s inclusion dramatically enhanced the material’s mechanical characteristics, friction, and wear resistance. AA7075 is strengthened with less graphene, and AA7075, reinforced with more graphite, exhibits similar wear and friction behavior. Wear rate and coefficient of friction predictions for AA7075-graphene nanocomposites were made using five machine learning (ML) regression models. ML simulations reveal that the wear and friction of AA7075-graphene composites are most sensitive to the proportion of graphene presence, the loadings, and the hardness.

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Section: Impact Of Sliding Distancementioning

confidence: 90%

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

Prasanth

Jeevanandam²,

Selvaraju³

et al. 2023

Journal of Nanomaterials

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“…As Gaussian process regression (GPR) is a powerful method that provides credible intervals for the predicted states, it has recently been used for probabilistic predictions [28,14,23,20,2,4,37,19,43,21,13,44,3,7,10,22]. A Gaussian process is completely defined by its mean and covariance function, which we also refer to as the Gaussian process model (GPM).…”

Section: Introductionmentioning

confidence: 99%

On Integrating Prior Knowledge into Gaussian Processes for Prognostic Health Monitoring

Pfingstl,

Zimmermann

2022

Preprint

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Gaussian process regression is a powerful method for predicting states based on given data. It has been successfully applied for probabilistic predictions of structural systems to quantify, for example, the crack growth in mechanical structures. Typically, predefined mean and covariance functions are employed to construct the Gaussian process model. Then, the model is updated using current data during operation while prior information based on previous data is ignored. However, predefined mean and covariance functions without prior information reduce the potential of Gaussian processes.This paper proposes a method to improve the predictive capabilities of Gaussian processes. We integrate prior knowledge by deriving the mean and covariance functions from previous data. More specifically, we first approximate previous data by a weighted sum of basis functions and then derive the mean and covariance functions directly from the estimated weight coefficients. Basis functions may be either estimated or derived from problem-specific governing equations to incorporate physical information.The applicability and effectiveness of this approach are demonstrated for fatigue crack growth, laser degradation, and milling machine wear data. We show that well-chosen mean and covariance functions, like those based on previous data, significantly increase look-ahead time and accuracy. Using physical basis functions further improves accuracy. In addition, computation effort for training is significantly reduced.

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“…Over the last decade, many researchers have frequently been using GPR for nonlinear regression, see Reference, 20–32 as the model provides not only the predicted function value itself but also its credible interval. A GP is fully defined by its mean and covariance functions, m y , θ ( x ) and k y , θ ( x , x ′ ) , with…”

Section: Introductionmentioning

confidence: 99%

Warped Gaussian processes for predicting the degradation of aerospace structures

Pfingstl

Braun

Nasrollahi

et al. 2022

Structural Health Monitoring

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Gaussian processes (GPs) can be used to predict future states of a system with credible intervals when considering multiple previous trajectories for training. For example, predicting the degradation of mechanical structures is one application in which they have shown their usefulness. In modeling the system output as a GP, the output is presumed to be normally distributed—assuming the predictions to be defined from negative to positive infinity. However, this assumption does not hold in many applications as, for example, crack lengths and damage indices can only assume positive values. Moreover, several degradation trajectories for training are rare in real-world applications, and the current state of a monitored system, which is used to update the prediction, can often be not directly measured. This paper presents an approach that utilizes warped GPs for treating data that is not normally distributed while considering multiple degradation trajectories for training. The approach is successfully applied to two different crack propagation examples: first, an analytically computed pre-cracked infinite plate, and second, two equally manufactured aluminum structures that resemble a lower section of a wing. For the investigated aerospace structures, we use finite element (FE) simulations to generate multiple degradation trajectories for training. To estimate their hidden degradation states, we infer the current crack length from strain measurements by using Bayesian inference. The results show that the approach of warped GPs provides more accurate predictions than using standard ones for non-normally distributed data, as is the case for crack growth problems. The approach enables quick training of warped GPs while considering multiple training trajectories. Additionally, the crack lengths estimated from strain measurements agree well with the visually inspected ones. Ultimately, the presented approach enables estimating the current and future degradation states with credible intervals that can be used to improve maintenance scheduling.

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An online‐offline prognosis model for fatigue life prediction under biaxial cyclic loading with overloads

Cited by 14 publications

References 26 publications

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

Study of Friction and Wear Behavior of Graphene-Reinforced AA7075 Nanocomposites by Machine Learning

On Integrating Prior Knowledge into Gaussian Processes for Prognostic Health Monitoring

Warped Gaussian processes for predicting the degradation of aerospace structures

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