Defect Location Analysis of CFRP Plates Based on Morphological Filtering Technique
Carbon fiber reinforced polymers (CFRP) have become an essentials structural material in advanced manufacturing sectors, including new-energy vehicles and precision equipment, owing to their superior strength-to-weight ratio, exceptional fatigue resistance, and lightweight characteristics. However,...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11008626/ |
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| Summary: | Carbon fiber reinforced polymers (CFRP) have become an essentials structural material in advanced manufacturing sectors, including new-energy vehicles and precision equipment, owing to their superior strength-to-weight ratio, exceptional fatigue resistance, and lightweight characteristics. However, CFRP components are prone to developing surface defects, such as microcracks, interlinear delamination, and porosity, during both manufacturing processes and their service life. These millimeter-scale imperfections can reduce structural integrity by up to 60%, potentially leading to catastrophic failures in safety-critical applications. Current nondestructive evaluation methods face challenges in reliably distinguishing genuine defects from surface texture artifacts while maintaining operational efficiency. In this study, we propose a novel defect localization method for CFRP plates using an advanced morphological filtering technique. The proposed approach employs opening and closing operations to effectively separate genuine defects from noise artifacts. A subsequent defec-region decision model was established to accurately identify and localize the damage features. The experimental results demonstrate that the method achieves rapid and precise localization of surface defects on CFRP plates, significantly enhancing detection accuracy and efficiency. This method not only overcomes the limitations of traditional inspection techniques but also provides a robust solution for real-time quality assessment in industrial applications. |
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| ISSN: | 2169-3536 |