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Advanced Thermal Imaging Processing and Deep Learning Integration for Enhanced Defect Detection in Carbon Fiber-Reinforced Polymer Laminates

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Publicat a:Materials vol. 18, no. 7 (2025), p. 1448
Autor principal: Renan Garcia Rosa
Altres autors: Bruno Pereira Barella, Iago Garcia Vargas, Tarpani, José Ricardo, Herrmann, Hans-Georg, Fernandes, Henrique
Publicat:
MDPI AG
Matèries:
Fiber reinforced polymers
Preprocessing
Deep learning
Nondestructive testing
Defects
Image segmentation
Temperature
Carbon fiber reinforced plastics
Neural networks
Process controls
Polynomials
Strength to weight ratio
Thermal imaging
Thermography
Heat
Manufacturing
Laminates
Performance evaluation
Radiation
Composite materials
Preventive maintenance
Carbon fiber reinforcement
Signal to noise ratio
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Resum:Carbon fiber-reinforced polymer (CFRP) laminates are widely used in aerospace, automotive, and infrastructure industries due to their high strength-to-weight ratio. However, defect detection in CFRP remains challenging, particularly in low signal-to-noise ratio (SNR) conditions. Conventional segmentation methods often struggle with noise interference and signal variations, leading to reduced detection accuracy. In this study, we evaluate the impact of thermal image preprocessing on improving defect segmentation in CFRP laminates inspected via pulsed thermography. Polynomial approximations and first- and second-order derivatives were applied to refine thermographic signals, enhancing defect visibility and SNR. The U-Net architecture was used to assess segmentation performance on datasets with and without preprocessing. The results demonstrated that preprocessing significantly improved defect detection, achieving an Intersection over Union (IoU) of 95% and an F1-Score of 99%, outperforming approaches without preprocessing. These findings emphasize the importance of preprocessing in enhancing segmentation accuracy and reliability, highlighting its potential for advancing non-destructive testing techniques across various industries.
ISSN:1996-1944
DOI:10.3390/ma18071448
Font:Materials Science Database