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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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10930734/ |
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| author | Xin Zhao Zeyuan Gao Meng Li Zhibin Han Jianjian Zhu |
| author_facet | Xin Zhao Zeyuan Gao Meng Li Zhibin Han Jianjian Zhu |
| author_sort | Xin Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-449e9944b27146dfb930b1bca58a4c27 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-449e9944b27146dfb930b1bca58a4c272025-08-20T03:40:52ZengIEEEIEEE Access2169-35362025-01-0113496304964210.1109/ACCESS.2025.355150810930734Importance Sampling and Feature Fusion Paradigm-Boosted Multi-Modal Convolutional Neural Networks: Deployment in Composite Curing Process Monitored by Electro-Mechanical ImpedanceXin Zhao0Zeyuan Gao1https://orcid.org/0009-0009-5382-1041Meng Li2Zhibin Han3Jianjian Zhu4College of Aviation Engineering, Civil Aviation Flight University of China, Guanghan, ChinaCollege of Aviation Engineering, Civil Aviation Flight University of China, Guanghan, ChinaCollege of Aviation Engineering, Civil Aviation Flight University of China, Guanghan, ChinaFaculty of Aerospace Engineering, Delft University of Technology, Delft, HS, The NetherlandsCollege of Aviation Engineering, Civil Aviation Flight University of China, Guanghan, ChinaThe 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.https://ieeexplore.ieee.org/document/10930734/Composite curingconvolutional neural networkselectro-mechanical impedanceimportance sampling algorithmmulti-modal learning |
| spellingShingle | Xin Zhao Zeyuan Gao Meng Li Zhibin Han Jianjian Zhu Importance Sampling and Feature Fusion Paradigm-Boosted Multi-Modal Convolutional Neural Networks: Deployment in Composite Curing Process Monitored by Electro-Mechanical Impedance IEEE Access Composite curing convolutional neural networks electro-mechanical impedance importance sampling algorithm multi-modal learning |
| title | Importance Sampling and Feature Fusion Paradigm-Boosted Multi-Modal Convolutional Neural Networks: Deployment in Composite Curing Process Monitored by Electro-Mechanical Impedance |
| title_full | Importance Sampling and Feature Fusion Paradigm-Boosted Multi-Modal Convolutional Neural Networks: Deployment in Composite Curing Process Monitored by Electro-Mechanical Impedance |
| title_fullStr | Importance Sampling and Feature Fusion Paradigm-Boosted Multi-Modal Convolutional Neural Networks: Deployment in Composite Curing Process Monitored by Electro-Mechanical Impedance |
| title_full_unstemmed | Importance Sampling and Feature Fusion Paradigm-Boosted Multi-Modal Convolutional Neural Networks: Deployment in Composite Curing Process Monitored by Electro-Mechanical Impedance |
| title_short | Importance Sampling and Feature Fusion Paradigm-Boosted Multi-Modal Convolutional Neural Networks: Deployment in Composite Curing Process Monitored by Electro-Mechanical Impedance |
| title_sort | importance sampling and feature fusion paradigm boosted multi modal convolutional neural networks deployment in composite curing process monitored by electro mechanical impedance |
| topic | Composite curing convolutional neural networks electro-mechanical impedance importance sampling algorithm multi-modal learning |
| url | https://ieeexplore.ieee.org/document/10930734/ |
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