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Automatic void content assessment of composite laminates using a machine-learning approach
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/pat.5226openalex-snapshot
Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology
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Neural Networks and Deep Learning: A Textbook
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Studies on the failure analysis of composite materials with manufacturing defects
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ImageNet Large Scale Visual Recognition Challenge
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On the optical assessment of the void content in composite materials
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Effects of cure cycle pressure and voids on some mechanical properties of carbon/epoxy laminates
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Effect of void content on the strength of composite laminates
inferredcites →https://doi.org/10.1016/j.acha.2019.06.004openalex-snapshot
Universality of deep convolutional neural networks
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Experimental characterization of voids in high fibre volume fraction composites processed by high injection pressure RTM
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An objective comparison of common vacuum assisted resin infusion processes
inferredcites →https://doi.org/10.1016/j.compositesa.2019.105708openalex-snapshot
Experimental comparative study of the variants of high-temperature vacuum-assisted resin transfer moulding
inferredcites →https://doi.org/10.1016/j.compscitech.2005.01.001openalex-snapshot
Three-dimensional features of void morphology in resin transfer molded composites
inferredcites →https://doi.org/10.1016/j.compscitech.2009.06.012openalex-snapshot
Influence of prepreg conditions on the void occurrence and tensile properties of woven glass fiber-reinforced polyimide composites
inferredcites →https://doi.org/10.1016/j.compscitech.2010.09.009openalex-snapshot
Void formation in composite prepregs – Effect of dissolved moisture
inferredcites →https://doi.org/10.1016/j.compscitech.2011.04.009openalex-snapshot
Compressive failure of UD-CFRP containing void defects: In situ SEM microanalysis
inferredcites →https://doi.org/10.1016/j.compscitech.2013.09.019openalex-snapshot
Measurement of voids in composites by X-ray Computed Tomography
inferredcites →https://doi.org/10.1016/j.compscitech.2014.02.009openalex-snapshot
X-ray microtomography applications for quantitative and qualitative analysis of porosity in woven glass fiber reinforced thermoplastic
inferredcites →https://doi.org/10.1016/j.compstruct.2005.02.001openalex-snapshot
Effects of cure cycles on void content and mechanical properties of composite laminates
inferredcites →https://doi.org/10.1016/j.compstruct.2015.05.057openalex-snapshot
Micro-computed tomography analysis of tubular braided composites
inferredcites →https://doi.org/10.1016/j.compstruct.2018.02.096openalex-snapshot
Quality assessment of porous CFRP specimens using X-ray Computed Tomography data and Artificial Neural Networks
inferredcites →https://doi.org/10.1016/j.ijfatigue.2016.10.004openalex-snapshot
Fatigue behaviour of glass/epoxy laminates in the presence of voids
inferredcites →https://doi.org/10.1016/j.ndteint.2011.11.011openalex-snapshot
Characterisation of voids in fibre reinforced composite materials
inferredcites →https://doi.org/10.1016/j.neucom.2016.11.066openalex-snapshot
Ultrasonic signal classification and imaging system for composite materials via deep convolutional neural networks
inferredcites →https://doi.org/10.1080/03602559.2012.689050openalex-snapshot
Porosity Defect Morphology Effects in Carbon Fiber – Epoxy Composites
inferredcites →https://doi.org/10.1109/access.2019.2896920openalex-snapshot
RIC-Unet: An Improved Neural Network Based on Unet for Nuclei Segmentation in Histology Images
inferredcites →https://doi.org/10.1109/access.2019.2908991openalex-snapshot
NAS-Unet: Neural Architecture Search for Medical Image Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/lgrs.2018.2802944openalex-snapshot
Road Extraction by Deep Residual U-Net
inferredcites →https://doi.org/10.1109/tgrs.2018.2858817openalex-snapshot
Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set
inferredcites →https://doi.org/10.1109/tmi.2018.2845918openalex-snapshot
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
inferredcites →https://doi.org/10.1109/tpami.2016.2572683openalex-snapshot
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.1117/12.2558958openalex-snapshot
Intelligent infrared thermography inspection of subsurface defects
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1177/0021998314541993openalex-snapshot
Effects of voids on quasi-static and tension fatigue behaviour of carbon-fibre composite laminates
inferredcites →https://doi.org/10.1177/0021998317710083openalex-snapshot
Comparison of methods for the characterization of voids in glass fiber composites
inferredcites →https://doi.org/10.1177/0021998318772152openalex-snapshot
Voids in fiber-reinforced polymer composites: A review on their formation, characteristics, and effects on mechanical performance
inferredcites →https://doi.org/10.1177/002199838702100306openalex-snapshot
A Note on the Effects of Voids Upon the Hygral and Mechanical Properties of AS4/3502 Graphite/Epoxy
inferredcites →https://doi.org/10.1177/002199839703100303openalex-snapshot
Effects of Voids on the Mechanical Strength and Ultrasonic Attenuation of Laminated Composites
inferredcites →https://doi.org/10.3390/jcs4040178openalex-snapshot
Automated Identification of Defect Morphology and Spatial Distribution in Woven Composites
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Uni- and Multi Variate Analysis of Short Beam Shear Strength of Composites Containing Voids
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