On the public citation graph
Quality assessment of porous CFRP specimens using X-ray Computed Tomography data and Artificial Neural Networks
0 signed · 95 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/0263-8223(94)90044-2openalex-snapshot
Effect of void content on the strength of composite laminates
inferredcites →https://doi.org/10.1016/j.cam.2014.11.062openalex-snapshot
Levenberg–Marquardt method for solving systems of absolute value equations
inferredcites →https://doi.org/10.1016/j.compositesb.2016.07.019openalex-snapshot
A numerical methodology for simulating the mechanical behavior of CFRP laminates containing pores using X-ray computed tomography data
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.2011.05.002openalex-snapshot
Effect of curing cycle on void distribution and interlaminar shear strength in polymer-matrix composites
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.compstruc.2007.08.004openalex-snapshot
Applying neural networks, genetic algorithms and fuzzy logic for the identification of cracks in shafts by using coupled response measurements
inferredcites →https://doi.org/10.1016/j.compstruct.2017.06.037openalex-snapshot
Neural network assisted multiscale analysis for the elastic properties prediction of 3D braided composites under uncertainty
inferredcites →https://doi.org/10.1016/j.icheatmasstransfer.2016.09.011openalex-snapshot
Experimental investigation and modeling of thermal radiative properties of f-CNTs nanofluid by artificial neural network with Levenberg–Marquardt algorithm
inferredcites →https://doi.org/10.1016/j.ijsolstr.2011.04.021openalex-snapshot
Numerical modeling of carbon/carbon composites with nanotextured matrix and 3D pores of irregular shapes
inferredcites →https://doi.org/10.1016/j.jmst.2016.04.011openalex-snapshot
Effects of Process-Induced Voids on the Properties of Fibre Reinforced Composites
inferredcites →https://doi.org/10.1016/j.mechmat.2007.05.004openalex-snapshot
Nonlinear constitutive models for FRP composites using artificial neural networks
inferredcites →https://doi.org/10.1016/j.msea.2005.06.047openalex-snapshot
Automatic voxel-based generation of 3D microstructural FE models and its application to the damage analysis of composites
inferredcites →https://doi.org/10.1016/j.ndteint.2012.08.011openalex-snapshot
High resolution non-destructive evaluation of defects using artificial neural networks and wavelets
inferredcites →https://doi.org/10.1016/j.ndteint.2016.11.013openalex-snapshot
Nondestructive characterization of fiber orientation in short fiber reinforced polymer composites with X-ray vector radiography
inferredcites →https://doi.org/10.1016/j.tafmec.2017.04.020openalex-snapshot
Multiscale finite element prediction of shear and flexural properties of porous CFRP laminates utilizing X-ray CT data
inferredcites →https://doi.org/10.1016/s0266-3538(03)00106-4openalex-snapshot
Artificial neural networks applied to polymer composites: a review
inferredcites →https://doi.org/10.1016/s0376-0421(01)00015-xopenalex-snapshot
Soft computing in multidisciplinary aerospace design—new directions for research
inferredcites →https://doi.org/10.1109/tvcg.2013.177openalex-snapshot
MObjects--A Novel Method for the Visualization and Interactive Exploration of Defects in Industrial XCT Data
inferredcites →https://doi.org/10.1177/0021998311415442openalex-snapshot
Effects of vacuum pressure, inlet pressure, and mold temperature on the void content, volume fraction of polyester/e-glass fiber composites manufactured with VARTM process
inferredcites →https://doi.org/10.1177/0021998311429618openalex-snapshot
Effects of variation in autoclave pressure, temperature, and vacuum-application time on porosity and mechanical properties of a carbon fiber/epoxy composite
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/10.1177/0021998315602049openalex-snapshot
Evaluation of porosity effects on the mechanical properties of carbon fiber-reinforced plastic unidirectional laminates by X-ray computed tomography and mechanical testing
inferredcites →https://doi.org/10.31274/etd-180810-634openalex-snapshot
Artificial neural network analysis of the mechanical properties of tungsten fiber/bulk metallic glass matrix composites via neutron diffraction and finite element modelling
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4485546openalex-extracted
Artificial Neural Network Approach for Assessing Impact Performance of Natural-Fiber Composites Exposed to Uv Radiation
inferred← citeshttps://doi.org/10.2139/ssrn.4823281openalex-extracted
Uni- and Multi Variate Analysis of Short Beam Shear Strength of Composites Containing Voids
inferred← citeshttps://doi.org/10.1002/pc.29038openalex-snapshot
Multi‐scale elastic properties of 2. 5D woven composites with void defects
inferred← citeshttps://doi.org/10.1002/pc.29488openalex-snapshot
A new strategy for preparing web‐reinforced foam sandwich composites with excellent mechanical properties
inferred← citeshttps://doi.org/10.1002/pc.29501openalex-snapshot
Fast prediction of curing residual stress in polymer‐matrix composites by a deep learning method
inferred← citeshttps://doi.org/10.1007/s00289-025-05638-1openalex-snapshot
Advancements of machine learning techniques in fiber-filled polymer composites: a review
inferred← citeshttps://doi.org/10.1007/s10443-021-09891-1openalex-snapshot
Artificial Neural Network (ANN)-Based Residual Strength Prediction of Carbon Fibre Reinforced Composites (CFRCs) After Impact
inferred← citeshttps://doi.org/10.1007/s10443-023-10167-zopenalex-snapshot
Three-Dimensional Analysis of Porosity in As-Manufactured Glass Fiber/Vinyl Ester Filament Winded Composites Using X-Ray Micro-Computed Tomography
inferred← citeshttps://doi.org/10.1007/s10921-020-00737-7openalex-snapshot
X-ray CT-Based Defect Evaluation of Continuous CFRP Additive Manufacturing
inferred← citeshttps://doi.org/10.1007/s10921-021-00809-2openalex-snapshot
Estimation of Impact Energies in Composites Using an Out-of-Distribution Generalization of Stacked Models Trained with Shearography and Thermography Images
inferred← citeshttps://doi.org/10.1007/s10921-024-01116-2openalex-snapshot
Automated Detection of Delamination Defects in Composite Laminates from Ultrasonic Images Based on Object Detection Networks
inferred← citeshttps://doi.org/10.1007/s11696-025-04353-5openalex-snapshot
Experimental and ANN-based mechanical analysis of chemically modified carbon fiber-reinforced polymer composite
inferred← citeshttps://doi.org/10.1007/s11831-022-09775-yopenalex-snapshot
Fiber Reinforced Composite Manufacturing With the Aid of Artificial Intelligence – A State-of-the-Art Review
inferred← citeshttps://doi.org/10.1007/s12289-020-01544-1openalex-snapshot
Experimental and numerical investigation of the effects of porosity on the in-plane shear properties of CFRPs using the V-notched rail shear test method
inferred← citeshttps://doi.org/10.1007/s40430-025-05697-5openalex-snapshot
Advancements and emerging trends in integrating machine learning and deep learning for SHM in mechanical and civil engineering: a comprehensive review
inferred← citeshttps://doi.org/10.1007/s41062-025-02371-6openalex-snapshot
Predicting axial load capacity of CFRP fire-damaged RC columns through DANN
inferred← citeshttps://doi.org/10.1016/b978-0-443-18644-8.00012-5openalex-snapshot
Predicting the mechanical behavior of carbon fiber-reinforced polymer using machine learning methods: a systematic review
inferred← citeshttps://doi.org/10.1016/j.compositesa.2026.109808openalex-snapshot
Application of machine learning in the micromechanics of composites: a review
inferred← citeshttps://doi.org/10.1016/j.compscitech.2020.108093openalex-snapshot
High resolution X-ray computed tomography: A versatile non-destructive tool to characterize CFRP-based aircraft composite elements
inferred← citeshttps://doi.org/10.1016/j.compscitech.2022.109417openalex-snapshot
Defect imaging in carbon fiber composites by acoustic shearography
inferred← citeshttps://doi.org/10.1016/j.compstruct.2018.10.105openalex-snapshot
Compressive behaviour of a damaged omega stiffened panel: Damage detection and numerical analysis
inferred← citeshttps://doi.org/10.1016/j.compstruct.2019.111383openalex-snapshot
Recurrence quantitative analysis for porosity characterization of CFRP with complex void morphology
inferred← citeshttps://doi.org/10.1016/j.compstruct.2020.112514openalex-snapshot
Machine learning in composites manufacturing: A case study of Automated Fiber Placement inspection
inferred← citeshttps://doi.org/10.1016/j.compstruct.2022.115383openalex-snapshot
Automatic void content assessment of composite laminates using a machine-learning approach
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.105834openalex-snapshot
Data-driven failure prediction of Fiber-Reinforced Polymer composite materials
inferred← citeshttps://doi.org/10.1016/j.heliyon.2023.e23019openalex-snapshot
Crashworthy optimization of skeleton-filled FRP tubes based on back propagation neural network
inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e25288openalex-snapshot
Effects of porosity on ultrasonic attenuation coefficient, shear properties and failure mechanisms of CF/EP laminates
inferred← citeshttps://doi.org/10.1016/j.ijthermalsci.2019.106196openalex-snapshot
Defect depth retrieval method based on nonlinear transformation for pulsed thermographic inspection
inferred← citeshttps://doi.org/10.1016/j.indcrop.2025.122001openalex-snapshot
Predicting the thermal degradation of Agave americana L. biowaste fibers using kinetic analysis and artificial neural networks
inferred← citeshttps://doi.org/10.1016/j.infrared.2026.106673openalex-snapshot
Real-Time defect detection in CFRP with enhanced YOLO via differencing and attention for line laser thermography
inferred← citeshttps://doi.org/10.1016/j.jcomc.2021.100126openalex-snapshot
Design analysis for thermoforming of thermoplastic composites: prediction and machine learning-based optimization
inferred← citeshttps://doi.org/10.1016/j.jcomc.2023.100347openalex-snapshot
A review and framework for modeling methodologies to advance automated fiber placement
inferred← citeshttps://doi.org/10.1016/j.jcomc.2026.100806openalex-snapshot
From microstructure to damage: a deep learning review for fiber-reinforced composite micromechanical response prediction
inferred← citeshttps://doi.org/10.1016/j.jmapro.2025.04.014openalex-snapshot
A review on modeling strategies in understanding the process mechanism of 3D printed continuous fiber-reinforced thermoplastic composites
inferred← citeshttps://doi.org/10.1016/j.matdes.2020.108553openalex-snapshot
The use of X-ray computed tomography for design and process modeling of aerospace composites: A review
inferred← citeshttps://doi.org/10.1016/j.matdes.2026.115698openalex-snapshot
CT and image post-processing for fiber composites: Defect analysis, deep learning, digital volume correlation, and FE simulation – A review
inferred← citeshttps://doi.org/10.1016/j.msea.2025.147792openalex-snapshot
Prediction of the compressive strength and carpet plot for cross-material CFRP laminate based on deep transfer learning
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2021.103021openalex-snapshot
Ultrasonic signal classification and porosity testing for CFRP materials via artificial neural network
inferred← citeshttps://doi.org/10.1016/j.mtcomm.2024.110462openalex-snapshot
Tensile performance prediction of CFRPs with voids using multiscale analysis and neural networks
inferred← citeshttps://doi.org/10.1016/j.ndteint.2024.103194openalex-snapshot
A comparison of X-ray attenuation, differential phase, and dark-field contrast imaging for the detection of porosity in carbon fiber reinforced cyanate ester
inferred← citeshttps://doi.org/10.1016/j.optlaseng.2021.106830openalex-snapshot
Quantitative non-destructive evaluation of CFRP delamination defect using laser induced chirp-pulsed radar photothermal tomography
inferred← citeshttps://doi.org/10.1080/10589759.2024.2324149openalex-snapshot
Non-destructive testing and structural health monitoring technologies for carbon fiber reinforced polymers: a review
inferred← citeshttps://doi.org/10.1080/15440478.2025.2537069openalex-snapshot
Sustainable Renewable Biofuel Production Toward Pyrolysis of Fibers Biowaste Agave Americana L . and Thermodynamics Mechanisms Kinetic Parameters Triplet Assessment
inferred← citeshttps://doi.org/10.1080/19475411.2022.2054878openalex-snapshot
Machine Learning Based Quantitative Damage Monitoring of Composite Structure
inferred← citeshttps://doi.org/10.1109/jsen.2025.3546361openalex-snapshot
Eddy Current Magneto-Optical Imaging Based on Phase Change for CFRP Defects
inferred← citeshttps://doi.org/10.1109/tim.2023.3277996openalex-snapshot
Automatic Detection of CFRP Subsurface Defects via Thermal Signals in Long Pulse and Lock-In Thermography
inferred← citeshttps://doi.org/10.1109/tim.2025.3551920openalex-snapshot
Subsurface Defect Detection in Carbon Fiber-Reinforced Plastics Using Infrared Polarization Imaging
inferred← citeshttps://doi.org/10.1111/jace.70319openalex-snapshot
Explainable prediction framework for high‐temperature residual strength of SiC f /SiC composites driven by XGBoost + SHAP
inferred← citeshttps://doi.org/10.1117/1.oe.58.10.103104openalex-snapshot
Image enhancement method for laser infrared thermography defect detection in aviation composites
inferred← citeshttps://doi.org/10.1134/s2075113324020394openalex-snapshot
Laser–Ultrasonic Study of Local Porosity Distribution in Carbon Fiber Reinforced Plastic Stringer Panels
inferred← citeshttps://doi.org/10.1145/3637543.3654657openalex-snapshot
X-Factor: Deep Learning-based PCB Counterfeit Detection using X-Ray CT Techniques for Hardware Assurance
inferred← citeshttps://doi.org/10.1155/2018/3791543openalex-snapshot
Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests
inferred← citeshttps://doi.org/10.1155/2023/6619208openalex-snapshot
Bayesian Regularization Backpropagation Neural Network for Glass Beams in Lateral–Torsional Buckling
inferred← citeshttps://doi.org/10.1177/00219983211050705openalex-snapshot
A review of applications of CT imaging on fiber reinforced composites
inferred← citeshttps://doi.org/10.14489/td.2023.06.pp.012-019openalex-snapshot
QUANTITATIVE ANALYSIS OF THE DISTRIBUTION OF LOCAL POROSITY IN CARBON FIBER STRUCTURES BY LASER OPTOACOUSTIC METHOD
inferred← citeshttps://doi.org/10.20868/upm.thesis.65451openalex-snapshot
Characterization and real-time process monitoring of thermoplastic composites manufacturing processes
inferred← citeshttps://doi.org/10.2139/ssrn.5352319openalex-snapshot
Evolutionary Pattern of Pore Characteristics in the Manufacturing Process of Carbon/Carbon Composites Based on Xct
inferred← citeshttps://doi.org/10.21611/qirt.2020.054openalex-snapshot
Research on the detection of delamination defect of CFRP using thermal-wave radar tomography (TWRT)
inferred← citeshttps://doi.org/10.23919/ccc64809.2025.11179647openalex-snapshot
Simultaneous Detection of Surface and Subsurface Defects in CFRP Based on Infrared Polarization Imaging
inferred← citeshttps://doi.org/10.28991/cej-03091147openalex-snapshot
A Fuzzy Inference System in Constructional Engineering Projects to Evaluate the Design Codes for RC Buildings
inferred← citeshttps://doi.org/10.3390/applmech2030031openalex-snapshot
Deep Learning Based Uncertainty Analysis in Computational Micromechanics of Composite Materials
inferred← citeshttps://doi.org/10.3390/cryst10090811openalex-snapshot
Prediction of Properties of FRP-Confined Concrete Cylinders Based on Artificial Neural Networks
inferred← citeshttps://doi.org/10.3390/jcs8100387openalex-snapshot
Predicting Mechanical Properties from Microstructure Images in Fiber-Reinforced Polymers Using Convolutional Neural Networks
inferred← citeshttps://doi.org/10.3390/ma17143545openalex-snapshot
Probability Prediction of Curing Process-Induced Deformation for V-Shape Composite Structures Based on FEM Method and Data Mining
inferred← citeshttps://doi.org/10.3390/ma19173752openalex-snapshot
Quantitative Evaluation of Thermomechanical Fatigue Damage in Composite Materials Using X-Ray Computed Tomography and Machine Learning
inferred← citeshttps://doi.org/10.3390/mca27030038openalex-snapshot
Interval-Based Computation of the Uncertainty in the Mechanical Properties and the Failure Analysis of Unidirectional Composite Materials
inferred← citeshttps://doi.org/10.3390/polym14214507openalex-snapshot
Multi-Criteria Evaluation of the Failure of CFRP Laminates for Frames in the Automotive Industry
inferred← citeshttps://doi.org/10.3390/polym16040538openalex-snapshot
Artificial Neural Network Approach for Assessing Mechanical Properties and Impact Performance of Natural-Fiber Composites Exposed to UV Radiation
inferred← citeshttps://doi.org/10.70675/265a59efz29bbz4252za0dez9824b7d56a66openalex-snapshot
Analyse et modélisation des chemins d'efforts et de la dégradation des assemblages de type HYPER joints
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.