Curing monitoring of bonded composite patch at constant temperature with electromechanical impedance and system parameters evaluation approach

Zhu, Jianjian; Wen, Jinshan; Chen, Chunyang; Liu, Xiao; Lan, Zifeng; Wang, Yishou; Qing, Xinlin

doi:10.1088/1361-665x/ac3d72

Cited by 2 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fu et al [19] proposed a cloud-based wireless impedance monitoring system for corrosion detection of small-diameter pipelines which comprises a smart corrosion sensing node and a wireless impedance monitoring system. Zhu et al [20] proposed an approach that combines data expansion, seriesto-image conversion, and an intelligent model to predict the external impact energy of sandwich composite materials. Although EMI has been employed in many structures, there are still few reports on its application research on stifened panels.…”

Section: Introductionmentioning

confidence: 99%

An Electromechanical Impedance‐Based Imaging Algorithm for Damage Identification of Chemical Milling Stiffened Panel

Jiang,

Zhou,

Chen

et al. 2024

Structural Control and Health Monitoring

View full text Add to dashboard Cite

The multiple intersecting stiffeners on the chemical milling stiffened panel (CMSP) limit the application of active health monitoring methods on it. An imaging algorithm based on electromechanical impedance (EMI) and probability-weighting is proposed to achieve quantitative evaluation and localization of the damage on CMSP. The proposed algorithm compensates for the difference in sensor performance with coefficients and there is no need to determine the key parameters of the algorithm through prior experiments. In the paper, the applicability of ultrasonic guided wave (GW) and EMI on CMSP was first studied through the finite element method. Based on EMI and the mean absolute percentage deviation (MAPD), the selected damage indicator (DI), a probability-weighted damage imaging algorithm are proposed and experimentally verified. The results indicate that due to the reflection and attenuation effects of stiffeners on GW, the signal characteristics of damage scattering waves are contaminated, making it difficult to quantitatively characterize the damage from GW signals through DIs. MAPD is positively correlated with the damage degree and has consistency in characterizing the signal of different PZTs under the same working condition. The feasibility and accuracy of the proposed algorithm are verified through experiments which show a strong engineering application capability.

show abstract

Section: Introductionmentioning

confidence: 99%

An Electromechanical Impedance‐Based Imaging Algorithm for Damage Identification of Chemical Milling Stiffened Panel

Jiang,

Zhou,

Chen

et al. 2024

Structural Control and Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Due to the features of cost-effectiveness, simplicity of post-signal processing, and high sensitivity to DoC variation, electromechanical impedance (EMI) has proven its advantage over diverse techniques available for DoC monitoring, such as infrared thermal imaging and optoelectronic measurement [1][2][3][4]. The EMI method has further been advanced with the increased introduction of machine learning (ML) algorithms [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Curing process monitoring of polymeric composites with Gramian angular field and transfer learning-boosted convolutional neural networks

Zhu,

Su,

Wang

et al. 2023

Smart Mater. Struct.

Self Cite

View full text Add to dashboard Cite

Continuous and accurate monitoring of the degree of curing (DoC) is essential for ensuring the structural integrity of fabricated composites during service. Although machine learning (ML) has shown effectiveness in DoC monitoring, its generalization and extendibility are limited when applied to other curing-related scenarios not included in the previous learning process. To break through this bottleneck, we propose a novel DoC monitoring approach that utilizes transfer learning (TL)-boosted convolutional neural networks (CNN) alongside Gramian angular field (GAF)-based imaging processing. The effectiveness of the proposed approach is validated through experiments on metal/polymeric composite co-bonded structures and carbon fiber reinforced polymers (CFRP) using raw sensor data collected through the electromechanical impedance (EMI) and fiber Bragg grating (FBG) measurements. Four indicators, Accuracy, Precision, Recall, and F1-score, are introduced to evaluate the performance of generalization and extendibility of the proposed approach. The indicator scores of the proposed approach exceed 99% and outperform other conventional ML algorithms on the FBG dataset of the target domain, demonstrating the effectiveness of the proposed approach in reusing the pre-trained base model (PBM) on the composite curing monitoring issues.

show abstract

Curing monitoring of bonded composite patch at constant temperature with electromechanical impedance and system parameters evaluation approach

Cited by 2 publications

References 38 publications

An Electromechanical Impedance‐Based Imaging Algorithm for Damage Identification of Chemical Milling Stiffened Panel

An Electromechanical Impedance‐Based Imaging Algorithm for Damage Identification of Chemical Milling Stiffened Panel

Curing process monitoring of polymeric composites with Gramian angular field and transfer learning-boosted convolutional neural networks

Contact Info

Product

Resources

About