ArticlePolymers2021
Enhanced Defect Detection in Carbon Fiber Reinforced Polymer Composites via Generative Kernel Principal Component Thermography.
Article in Polymers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Recent Trends in Non-Destructive Testing Approaches for Composite Materials: A Review of Successful Implementations.Materials (Basel, Switzerland) · 2025Review
- Advanced Non-Destructive Testing Simulation and Modeling Approaches for Fiber-Reinforced Polymer Pipes: A Review.Materials (Basel, Switzerland) · 2025Review
- Brief Review of Vibrothermography and Optical Thermography for Defect Quantification in CFRP Material.Sensors (Basel, Switzerland) · 2025Review
- Review
- Quality Control of Carbon Look Components via Surface Defect Classification with Deep Neural Networks.Sensors (Basel, Switzerland) · 2023Article
- Enhanced Soft Sensor with Qualified Augmented Samples for Quality Prediction of the Polyethylene Process.Polymers · 2022Article
- Article
- Article
- Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Increasing machine learning methods are being applied to infrared non-destructive assessment for internal defects assessment of composite materials. However, most of them extract only linear features, which is not in accord with the nonlinear characteristics of infrared data. Moreover, limited infrared images tend to restrict the data analysis capabilities of machine learning methods. In this work, a novel generative kernel principal component thermography (GKPCT) method is proposed for defect detection of carbon fiber reinforced polymer (CFRP) composites. Specifically, the spectral normalization generative adversarial network is proposed to augment the thermograms for model construction. Sequentially, the KPCT method is used by feature mapping of all thermogram data using kernel principal component analysis, which allows for differentiation of defects and background in the dimensionality-reduced data. Additionally, a defect-background separation metric is designed to help the performance evaluation of data analysis methods. Experimental results on CFRP demonstrate the feasibility and advantages of the proposed GKPCT method.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.