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Uni- and Multi Variate Analysis of Short Beam Shear Strength of Composites Containing Voids

https://doi.org/10.2139/ssrn.4823281
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inferredcites →https://doi.org/10.1007/s10853-020-05148-7openalex-extracted
Automated segmentation of computed tomography images of fiber-reinforced composites by deep learning
inferredcites →https://doi.org/10.1016/0010-4361(95)96808-jopenalex-extracted
Effects of cure cycle pressure and voids on some mechanical properties of carbon/epoxy laminates
inferredcites →https://doi.org/10.1016/0263-8223(94)90044-2openalex-extracted
Effect of void content on the strength of composite laminates
inferredcites →https://doi.org/10.1016/0266-3538(92)90047-7openalex-extracted
Contact finite element analysis of three- and four-point short-beam bending of unidirectional composites
inferredcites →https://doi.org/10.1016/0266-3538(95)00128-xopenalex-extracted
Reduction in interlaminar shear strength by discrete and distributed voids
inferredcites →https://doi.org/10.1016/j.compositesa.2004.03.007openalex-extracted
The influence of void content on the structural flexural performance of unidirectional glass fibre reinforced polypropylene composites
inferredcites →https://doi.org/10.1016/j.compositesa.2019.04.029openalex-extracted
A parametric study of segmentation thresholds for X-ray CT porosity characterisation in composite materials
inferredcites →https://doi.org/10.1016/j.compositesa.2021.106527openalex-extracted
Phase segmentation of uncured prepreg X-Ray CT micrographs
inferredcites →https://doi.org/10.1016/j.compositesb.2021.109282openalex-extracted
Prediction of two-phase composite microstructure properties through deep learning of reduced dimensional structure-response data
inferredcites →https://doi.org/10.1016/j.compscitech.2011.11.023openalex-extracted
3D damage characterisation and the role of voids in the fatigue of wind turbine blade materials
inferredcites →https://doi.org/10.1016/j.compscitech.2013.10.006openalex-extracted
Effects of defects on interlaminar tensile fatigue behavior of carbon/epoxy composites
inferredcites →https://doi.org/10.1016/j.compstruct.2017.06.037openalex-extracted
Neural network assisted multiscale analysis for the elastic properties prediction of 3D braided composites under uncertainty
inferredcites →https://doi.org/10.1016/j.compstruct.2018.02.096openalex-extracted
Quality assessment of porous CFRP specimens using X-ray Computed Tomography data and Artificial Neural Networks
inferredcites →https://doi.org/10.1016/j.compstruct.2022.115383openalex-extracted
Automatic void content assessment of composite laminates using a machine-learning approach
inferredcites →https://doi.org/10.1016/j.compstruct.2022.115472openalex-extracted
Predicting short beam shear strength reduction in carbon/epoxy laminates containing voids
inferredcites →https://doi.org/10.1016/j.compstruct.2023.117301openalex-extracted
Effects of defects on the transverse mechanical response of unidirectional fibre-reinforced polymers: DEM simulation and deep learning prediction
inferredcites →https://doi.org/10.1016/j.conbuildmat.2022.130099openalex-extracted
Automated image segmentation of 3D printed fibrous composite micro-structures using a neural network
inferredcites →https://doi.org/10.1016/j.ijfatigue.2006.02.033openalex-extracted
The effect of voids on the flexural fatigue performance of unidirectional carbon fibre composites developed for wind turbine applications
inferredcites →https://doi.org/10.1016/j.ijsolstr.2013.12.014openalex-extracted
Nonlinear shear behavior and interlaminar shear strength of unidirectional polymer matrix composites: A numerical study
inferredcites →https://doi.org/10.1016/j.mechmat.2021.104191openalex-extracted
Deep learning prediction of stress fields in additively manufactured metals with intricate defect networks
inferredcites →https://doi.org/10.1016/s0266-3538(01)00157-9openalex-extracted
The influence of porosity on the interlaminar shear strength of carbon/epoxy and carbon/bismaleimide fabric laminates
inferredcites →https://doi.org/10.1016/s0266-3538(03)00097-6openalex-extracted
Physical and mechanical properties of unidirectional plant fibre composites—an evaluation of the influence of porosity
inferredcites →https://doi.org/10.1016/s1003-6326(10)60091-xopenalex-extracted
Influence of voids on interlaminar shear strength of carbon/epoxy fabric laminates
inferredcites →https://doi.org/10.1016/s1005-0302(11)60028-5openalex-extracted
Influence of Voids on the Tensile Performance of Carbon/epoxy Fabric Laminates
inferredcites →https://doi.org/10.1038/s41598-019-50144-wopenalex-extracted
Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning
inferredcites →https://doi.org/10.1106/wl60-vfnn-7weu-4davopenalex-extracted
Ultrasonic and Interlaminar Properties of Highly Porous Composites
inferredcites →https://doi.org/10.1111/j.1553-2712.2004.tb01379.xopenalex-extracted
Advanced Statistics: Linear Regression, Part II: Multiple Linear Regression
inferredcites →https://doi.org/10.1115/1.4047477openalex-extracted
Predicting Flexural Strength of Additively Manufactured Continuous Carbon Fiber-Reinforced Polymer Composites Using Machine Learning
inferredcites →https://doi.org/10.1177/0021998306065289openalex-extracted
Critical Void Content for Thermoset Composite Laminates
inferredcites →https://doi.org/10.1177/0021998314541993openalex-extracted
Effects of voids on quasi-static and tension fatigue behaviour of carbon-fibre composite laminates
inferredcites →https://doi.org/10.1177/0021998318772152openalex-extracted
Voids in fiber-reinforced polymer composites: A review on their formation, characteristics, and effects on mechanical performance
inferredcites →https://doi.org/10.1177/002199839202601006openalex-extracted
Void Effects on the Interlaminar Shear Strength of Unidirectional Graphite-Fiber-Reinforced Composites
inferredcites →https://doi.org/10.1177/002199839703100303openalex-extracted
Effects of Voids on the Mechanical Strength and Ultrasonic Attenuation of Laminated Composites
inferredcites →https://doi.org/10.1177/07316844231154585openalex-extracted
Manufacturing composite laminates with controlled void content through process control
inferredcites →https://doi.org/10.1557/mrc.2019.32openalex-extracted
Machine learning for composite materials
inferredcites →https://doi.org/10.21236/ada258817openalex-extracted
Effects of Porosity on Delamination of Resin-Matrix Composites
inferredcites →https://doi.org/10.3390/jcs4040178openalex-extracted
Automated Identification of Defect Morphology and Spatial Distribution in Woven Composites
inferredcites →https://doi.org/10.3390/jcs8100387openalex-extracted
Predicting Mechanical Properties from Microstructure Images in Fiber-Reinforced Polymers Using Convolutional Neural Networks
inferredcites →https://doi.org/10.4028/www.scientific.net/kem.383.35openalex-extracted
Influence of Porosity on the Interlaminar Shear Strength of Fibre-Metal Laminates
inferredcites →https://doi.org/10.5281/zenodo.15414410openalex-extracted
THE EFFECT OF VOIDS ON THE FLEXURAL FATIGUE PERFORMANCE OF UNIDIRECTIONAL CARBON FIBRE COMPOSITES
inferredcites →https://doi.org/10.1197/j.aem.2003.09.006openalex-snapshot
Advanced Statistics: Linear Regression, Part II: Multiple Linear Regression
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