Importance Sampling and Feature Fusion Paradigm-Boosted Multi-Modal Convolutional Neural Networks: Deployment in Composite Curing Process Monitored by Electro-Mechanical Impedance

The increasing application of composite materials in various industrial sectors is driven by their lightweight nature, high strength-to-stiffness ratio, and corrosion resistance. Effective monitoring of the curing process is crucial for ensuring quality and performance. Electro-Mechanical Impedance...

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Vydáno v:	IEEE access Ročník 13; s. 1
Hlavní autoři:	Gao, Zeyuan, Zhao, Xin, Li, Meng, Han, Zhibin, Zhu, Jianjian
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.01.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Artificial neural networks Complexity Composite curing Composite materials Convolutional neural networks Corrosion resistance Curing Data models electro-mechanical impedance Electromagnetic interference Feature extraction Heterogeneity Impedance Importance sampling Importance sampling algorithm Mechanical impedance Monitoring Monte Carlo methods multi-modal learning Neural networks Proposals Real time Training
ISSN:	2169-3536, 2169-3536
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Abstract	The increasing application of composite materials in various industrial sectors is driven by their lightweight nature, high strength-to-stiffness ratio, and corrosion resistance. Effective monitoring of the curing process is crucial for ensuring quality and performance. Electro-Mechanical Impedance (EMI) offers promising, non-destructive, real-time monitoring, but the complexity of EMI signals poses challenges. Convolutional Neural Networks (CNNs) have the potential to enhance EMI-based monitoring accuracy. However, training CNNs on multi-modal EMI signals requires addressing data heterogeneity, class imbalance, and computational complexity at present. This study develops the Importance Sampling Algorithm-optimized Multi-Modal CNNs (ISA-MM-CNNs) paradigm for EMI-based evaluation of composite curing processes. By prioritizing informative samples and capturing complementary information from diverse EMI signal modalities, we aim to improve the robustness and efficiency of CNNs in evaluating curing degrees. This study outlines EMI monitoring challenges, details the ISA-MM-CNNs paradigm, and discusses data preprocessing, network architecture, and training optimization. Experimental results demonstrate the superiority of the developed ISA-MM-CNNs and suggest further studies for the curing monitoring of composites.
AbstractList	The increasing application of composite materials in various industrial sectors is driven by their lightweight nature, high strength-to-stiffness ratio, and corrosion resistance. Effective monitoring of the curing process is crucial for ensuring quality and performance. Electro-Mechanical Impedance (EMI) offers promising, non-destructive, real-time monitoring, but the complexity of EMI signals poses challenges. Convolutional Neural Networks (CNNs) have the potential to enhance EMI-based monitoring accuracy. However, training CNNs on multi-modal EMI signals requires addressing data heterogeneity, class imbalance, and computational complexity at present. This study develops the Importance Sampling Algorithm-optimized Multi-Modal CNNs (ISA-MM-CNNs) paradigm for EMI-based evaluation of composite curing processes. By prioritizing informative samples and capturing complementary information from diverse EMI signal modalities, we aim to improve the robustness and efficiency of CNNs in evaluating curing degrees. This study outlines EMI monitoring challenges, details the ISA-MM-CNNs paradigm, and discusses data preprocessing, network architecture, and training optimization. Experimental results demonstrate the superiority of the developed ISA-MM-CNNs and suggest further studies for the curing monitoring of composites.
Author	Gao, Zeyuan Han, Zhibin Zhu, Jianjian Zhao, Xin Li, Meng
Author_xml	– sequence: 1 givenname: Zeyuan surname: Gao fullname: Gao, Zeyuan organization: College of Aviation Engineering, Civil Aviation Flight University of China, Guanghan, P.R. China – sequence: 2 givenname: Xin surname: Zhao fullname: Zhao, Xin organization: College of Aviation Engineering, Civil Aviation Flight University of China, Guanghan, P.R. China – sequence: 3 givenname: Meng surname: Li fullname: Li, Meng organization: College of Aviation Engineering, Civil Aviation Flight University of China, Guanghan, P.R. China – sequence: 4 givenname: Zhibin surname: Han fullname: Han, Zhibin organization: Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, Delft, The Netherlands – sequence: 5 givenname: Jianjian orcidid: 0009-0009-5382-1041 surname: Zhu fullname: Zhu, Jianjian email: zhujj.work@cafuc.edu.cn organization: College of Aviation Engineering, Civil Aviation Flight University of China, Guanghan, P.R. China
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SubjectTerms	Algorithms Artificial neural networks Complexity Composite curing Composite materials Convolutional neural networks Corrosion resistance Curing Data models electro-mechanical impedance Electromagnetic interference Feature extraction Heterogeneity Impedance Importance sampling Importance sampling algorithm Mechanical impedance Monitoring Monte Carlo methods multi-modal learning Neural networks Proposals Real time Training
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Title	Importance Sampling and Feature Fusion Paradigm-Boosted Multi-Modal Convolutional Neural Networks: Deployment in Composite Curing Process Monitored by Electro-Mechanical Impedance
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