Dynamic neural modeling of fatigue crack growth process in ductile alloys

Xie, Linsen; Yang, Ying; Zhou, Zhenghua; Zheng, Jinchuan; Tao, Mengqiu; Man, Zhihong

doi:10.1016/j.ins.2016.05.010

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…Fathi and Aghakouchak 17 used FNNs and RBFNNs to predict weld magnification factors. Kang and Song 140 used an FNN, and Xie et al 141 used dynamic NNs to determine the crack opening load. Haque and Sudhakar 142 developed an FNN model to predict the fracture toughness from microstructure.…”

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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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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“…This concept realises an enhancement layer linking the input layer to the output layer -consistent with the original concept of the RVFLN. Note that recently developed RVFLNs in the literature mostly neglect the direct connection because they are designed with a zero-order output node [8], [11], [14], [15], [17], [41], [50], [51], [63], [66], [67] . The direct connection expands the output node to a higher degree of freedom, which aims to improve the local mapping aptitude of the output node.…”

Section: IImentioning

confidence: 99%

Parsimonious random vector functional link network for data streams

Pratama

Angelov

Lughofer

et al. 2018

Information Sciences

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the theory of random vector functional link network (RVFLN) has provided a breakthrough in the design of neural networks (NNs) since it conveys solid theoretical justification of randomized learning. Existing works in RVFLN are hardly scalable for data stream analytics because they are inherent to the issue of complexity as a result of the absence of structural learning scenarios. A novel class of RVLFN, namely parsimonious random vector functional link network (pRVFLN), is proposed in this paper. pRVFLN adopts a fully flexible and adaptive working principle where its network structure can be configured from scratch and automatically generated in accordance with nonlinearity and time-varying property of system being modelled. pRVFLN is equipped with complexity reduction scenarios where inconsequential hidden nodes can be pruned and input features can be dynamically selected. pRVFLN puts into perspective an online active learning mechanism which expedites the training process and relieves operator's labelling efforts. In addition, pRVFLN introduces a non-parametric type of hidden node, developed using an interval-valued data cloud. The hidden node completely reflects the real data distribution and is not constrained by a specific shape of the cluster. All learning procedures of pRVFLN follow a strictly single-pass learning mode, which is applicable for online time-critical applications. The advantage of pRVFLN was verified through numerous simulations with real-world data streams. It was benchmarked with recently published algorithms where it demonstrated comparable and even higher predictive accuracies while imposing the lowest complexities. Furthermore, the robustness of pRVFLN was investigated and a new conclusion is made to the scope of random parameters where it plays vital role to the success of randomized learning. I. IntroductionFor decades, research in artificial neural networks has mainly investigated the best way to determine network free parameters, which reduces error as close as possible to zero [18]. Various approaches [55], [56] were proposed, but a large volume of work is based on a first or second-order derivative approach in respect to the loss function [19], [36]. Due to the rapid technological progress in data storage, capture, and transmission [34], the machine learning community has encountered an information explosion, which calls for scalable data analytics. Significant growth of the problem space has led to a scalability issue for conventional machine learning approaches, which require iterating entire batches of data over multiple epochs. This phenomenon results in a strong demand for a simple, fast machine learning algorithm to be well-suited for deployment in numerous data-rich applications [58]. This provides a strong case for research in the area of randomized neural networks (RNNs) [10], [13], [52], which was very popular in late 80's and early 90's. RNNs offer an algorithmic framework, which allows them to generate most of the network parameters randomly while still retaining reaso...

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“…With regards to airframe components, although there might be many reasons for failure, in terms of prognosis, the two most important forms are fatigue and corrosion Findlay and Harrison (2002); Bhaumik et al (2008). Discussions considering crack propagation issue can be found in DuQuesnay et al (2003); Nagaraja et al (2007); Xie et al (2016); Li et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Ensemble of Hybrid Neural Networks to Compensate for Epistemic Uncertainties: A Case Study in System Prognosis

Dourado

Viana

2021

Preprint

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In this contribution, a case study considering an unexpected corrosion-fatigue crack propagation issue in an aircraft fleet is used to discuss how to compensate for incomplete knowledge in time dependent responses integration and extrapolation. For the considered application, degradation resulting from mechanical fatigue is well understood and accounted in the damage models. However, the unexpected corrosion effects are not accounted in damage integration, yielding a large discrepancy between predicted and observed crack lengths. To address this epistemic uncertainty in the fleet damage accumulation model, hybrid neural networks cells are formulated; where physics-informed layers address well-understood aspects of the degradation, and data-driven layers are trained to act as correction terms. The considered case study encompasses highly imbalanced data sets with uncertainties acting asynchronously. To improve overall accuracy, ensemble learning techniques are adapted to merge the resulting hybrid neural network cells predictions. Lastly, a heuristic based on optimal ensemble weights is presented to help in the decision-making task of defining safe operation of the fleet. Results show that our proposed approach was capable of compensating for the epistemic uncertainties, and that the proposed heuristic can be used to rank aircraft damage severity, allowing to prioritize aircraft for inspection and/or route reassignment.

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Dynamic neural modeling of fatigue crack growth process in ductile alloys

Cited by 14 publications

References 15 publications

Fatigue modeling using neural networks: A comprehensive review

Fatigue modeling using neural networks: A comprehensive review

Parsimonious random vector functional link network for data streams

Ensemble of Hybrid Neural Networks to Compensate for Epistemic Uncertainties: A Case Study in System Prognosis

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