Automated fatigue damage detection and classification technique for composite structures using Lamb waves and deep autoencoder

Lee, Hyunseong; Lim, Hyung Jin; Skinner, Travis; Chattopadhyay, Aditi; Hall, Asha

doi:10.1016/j.ymssp.2021.108148

Cited by 68 publications

(18 citation statements)

References 33 publications

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“…Novel AI-based methods for the inspection of the Automated Fiber Placement (AFP) process have also been presented by several researchers [139][140][141][142][143] . As part of health monitoring of structures, machine/deep learning models have been used for defect/damage detection [144][145][146][147][148][149][150] , characterization of cracks/delamination [151][152][153] and classification of impact levels 154 . Yu et al 154 demonstrated that probabilistic Bayesian and traditional artificial neural networks can successfully classify the energy levels of different impact events based on the signals obtained from a network of piezoelectric sensors.…”

Section: The Meta-verse Of Composites Manufacturingmentioning

confidence: 99%

Graphene nanoparticles as data generating digital materials in industry 4.0

Ali

Irfan

Khan

et al. 2023

Sci Rep

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One of the potential applications of 2D materials is to enhance multi-functionality of structures and components used in aerospace, automotive, civil and defense industries. These multi-functional attributes include sensing, energy storage, EMI shielding and property enhancement. In this article, we have explored the potential of using graphene and its variants as data generating sensory elements in Industry 4.0. We have presented a complete roadmap to cover three emerging technologies i.e. advance materials, artificial intelligence and block-chain technology. The utility of 2D materials such as graphene nanoparticles is yet to be explored as an interface for digitalization of a modern smart factory i.e. “factory-of-the-future”. In this article, we have explored how 2D material enhanced composites can act as an interface between physical and cyber spaces. An overview of employing graphene-based smart embedded sensors at various stages of composites manufacturing processes and their application in real-time structural health monitoring is presented. The technical challenges associated with interfacing graphene-based sensing networks with digital space are discussed. Additionally, an overview of the integration of associated tools such as artificial intelligence, machine learning and block-chain technology with graphene-based devices and structures is also presented.

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Section: The Meta-verse Of Composites Manufacturingmentioning

confidence: 99%

Graphene nanoparticles as data generating digital materials in industry 4.0

Ali

Irfan

Khan

et al. 2023

Sci Rep

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“…Instead of manual feature extraction for machine learning, various damage detection methods based on deep learning have been developed to automatically feature extraction and damage detection. Lee et al [ 15 ] adopted a deep autoencoder (DAE) to capture hidden representation and effective tracking of signal variations, and the reconstruction error was used to diagnosis fatigue damage in composites structures. Chen et al [ 16 ] and Wu et al [ 17 ] converted the Lamb wave signals into a two-dimensional time-frequency spectrogram with the continuous wavelet transform, then input them into a 2D convolutional neural network (CNN) to classify damage.…”

Section: Introductionmentioning

confidence: 99%

FEM Simulation-Based Adversarial Domain Adaptation for Fatigue Crack Detection Using Lamb Wave

Wang

Liu²,

Zhang

et al. 2023

Sensors

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Lamb wave-based damage detection technology shows great potential for structural integrity assessment. However, conventional damage features based damage detection methods and data-driven intelligent damage detection methods highly rely on expert knowledge and sufficient labeled data for training, for which collecting is usually expensive and time-consuming. Therefore, this paper proposes an automated fatigue crack detection method using Lamb wave based on finite element method (FEM) and adversarial domain adaptation. FEM-simulation was used to obtain simulated response signals under various conditions to solve the problem of the insufficient labeled data in practice. Due to the distribution discrepancy between simulated signals and experimental signals, the detection performance of classifier just trained with simulated signals will drop sharply on the experimental signals. Then, Domain-adversarial neural network (DANN) with maximum mean discrepancy (MMD) was used to achieve discriminative and domain-invariant feature extraction between simulation source domain and experiment target domain, and the unlabeled experimental signals samples will be accurately classified. The proposed method is validated by fatigue tests on center-hole metal specimens. The results show that the proposed method presents superior detection ability compared to other methods and can be used as an effective tool for cross-domain damage detection.

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“…There are number of studies reporting damage classification in composites using machine learning (ML) approaches. [5][6][7] Lee et al 8 proposed unsupervised deep auto encoder (DAE) based damage classification for fatigue damage evolution from matrix cracking to delamination on carbon fiber reinforced polymer (CFRP) composite plate specimens. They use two different thresholds on reconstruction error computed at output of DAE to distinguish between matrix cracking and delamination.…”

Section: Introductionmentioning

confidence: 99%

Classification of damages in honeycomb composite sandwich structure in presence of data loss

Sawant

Banerjee

Sethi

2023

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVII

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In this paper, we propose a technique for classifying different types of damages in honeycomb composite sandwich structures (HCSS) using guided wave-based structural health monitoring (GW-SHM) systems that can work well even when data loss is lost for chunks of time. Although damage classification is important for deciding the course of action for usage, repair, and replacement, we show that there is overlap in the amplitude characteristics between different types of damages. This problem can be further exasperated when data is lost in chunks due to attenuation, physical damage, electromagnetic interference, and hardware faults. Reliable signal models are not always available for interpolation-based data recovery in such cases. First, we simulated the loss of 10% to 50% samples in experimentally collected data and recovered the signals using orthogonal matching pursuit with an error consistently below 0.1%. Next, we extracted ten features from the recovered signals in both time and frequency domains. We eliminated four features based on correlation analysis to improve the classification performance. We tried multiple classifiers to distinguish between healthy structure and four types of damages: lost film adhesive, teflon release film, core crush, and high density core in HCSS, and obtained perfect classification accuracy with random forest classifier and an optimal feature set based on feature importance. Due to smaller size and computational efficiency, these models are best suited for edge implementation and in situ monitoring. Also, models using these statistical features are portable to other structures as they are independent of material properties.

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Automated fatigue damage detection and classification technique for composite structures using Lamb waves and deep autoencoder

Cited by 68 publications

References 33 publications

Graphene nanoparticles as data generating digital materials in industry 4.0

Graphene nanoparticles as data generating digital materials in industry 4.0

FEM Simulation-Based Adversarial Domain Adaptation for Fatigue Crack Detection Using Lamb Wave

Classification of damages in honeycomb composite sandwich structure in presence of data loss

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