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

Title:
Importance Sampling and Feature Fusion Paradigm-Boosted Multi-Modal Convolutional Neural Networks: Deployment in Composite Curing Process Monitored by Electro-Mechanical Impedance
Authors:
Xin Zhao, Zeyuan Gao, Meng Li, Zhibin Han, Jianjian Zhu
Source:
IEEE Access, Vol 13, Pp 49630-49642 (2025)
Publisher Information:
Institute of Electrical and Electronics Engineers (IEEE), 2025.
Publication Year:
2025
Subject Terms:
Composite curing, convolutional neural networks, electro-mechanical impedance, Electrical engineering. Electronics. Nuclear engineering, multi-modal learning, importance sampling algorithm, TK1-9971
Document Type:
Academic journal Article
ISSN:
2169-3536
DOI:
10.1109/access.2025.3551508
Access URL:
https://doaj.org/article/449e9944b27146dfb930b1bca58a4c27
Rights:
CC BY
Accession Number:
edsair.doi.dedup.....fe5440ce7c4d906c72cb5709b26b98e7
Database:
OpenAIRE

Further Information

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.