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Strategic Layer Reworking Using Hybrid Additive Manufacturing and Deep Learning for Defect-Free Parts

https://doi.org/10.2139/ssrn.4855922
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Tomographic Volumetric Additive Manufacturing of Silicon Oxycarbide Ceramics
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Quantitative and Real‐Time Control of 3D Printing Material Flow Through Deep Learning
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Side-Aware Boundary Localization for More Precise Object Detection
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Application of a hybrid process for high precision manufacture of difficult to machine prismatic parts
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Laser ultrasonic inspection of additive manufactured components
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Vision-based real-time monitoring of extrusion additive manufacturing processes for automatic manufacturing error detection
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Segmentation-based deep-learning approach for surface-defect detection
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State of the Art in Defect Detection Based on Machine Vision
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Factors effecting real-time optical monitoring of fused filament 3D printing
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In situ real time defect detection of 3D printed parts
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Open source computer vision-based layer-wise 3D printing analysis
inferredcites →https://doi.org/10.1016/j.addma.2021.102272openalex-extracted
Characterization of porosity and hollow defects in ceramic objects built by extrusion additive manufacturing
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Automated recognition and correction of warp deformation in extrusion additive manufacturing
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Real-Time Structural Validation for Material Extrusion Additive Manufacturing
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Process planning for additive and subtractive manufacturing technologies
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CenterNet-based defect detection for additive manufacturing
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Hybrid additive and subtractive machine tools – Research and industrial developments
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A review on modern defect detection models using DCNNs – Deep convolutional neural networks
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Additive manufacturing of structural ceramics: a historical perspective
inferredcites →https://doi.org/10.1016/j.mfglet.2019.09.005openalex-extracted
Autonomous in-situ correction of fused deposition modeling printers using computer vision and deep learning
inferredcites →https://doi.org/10.1016/j.ultras.2021.106560openalex-extracted
Non-contact imaging of subsurface defects using a scanning laser source
inferredcites →https://doi.org/10.1016/s0955-2219(96)00163-xopenalex-extracted
Modern approaches for the production of ceramic components
inferredcites →https://doi.org/10.1017/pds.2023.276openalex-extracted
MULTISENSOR FUSION-BASED DIGITAL TWIN IN ADDITIVE MANUFACTURING FOR IN-SITU QUALITY MONITORING AND DEFECT CORRECTION
inferredcites →https://doi.org/10.1023/a:1016102210277openalex-extracted
Stereolithography of structural complex ceramic parts
inferredcites →https://doi.org/10.1038/s41467-022-31985-yopenalex-extracted
Generalisable 3D printing error detection and correction via multi-head neural networks
inferredcites →https://doi.org/10.1108/rpj-01-2019-0025openalex-extracted
Hybrid additive manufacturing of precision engineered ceramic components
inferredcites →https://doi.org/10.1109/access.2019.2905264openalex-extracted
Fault Diagnosis of Delta 3D Printers Using Transfer Support Vector Machine With Attitude Signals
inferredcites →https://doi.org/10.1109/access.2023.3293056openalex-extracted
Toward Accurate Fused Deposition Modeling 3D Printer Fault Detection Using Improved YOLOv8 With Hyperparameter Optimization
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-extracted
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.91openalex-extracted
You Only Look Once: Unified, Real-Time Object Detection
inferredcites →https://doi.org/10.1109/fendt47723.2019.8962808openalex-extracted
Visual Detection of AM Surface Defects Based on Laser Ultrasound Technology
inferredcites →https://doi.org/10.1109/icept50128.2020.9202566openalex-extracted
Defect detection: Defect Classification and Localization for Additive Manufacturing using Deep Learning Method
inferredcites →https://doi.org/10.1111/ijac.12557openalex-extracted
Additive Manufacturing of Dense Ceramic Parts via Direct Ink Writing of Aqueous Alumina Suspensions
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PROCESS ZONE SIZE FAILURE CRITERION AND PROBABILISTIC FRACTURE ASSESSMENT CURVES FOR CERAMICS
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Image-Based Surface Defect Detection Using Deep Learning: A Review
inferredcites →https://doi.org/10.1115/msec2018-6623openalex-extracted
Scalable Detection of Defects in Additively Manufactured PLA Components
inferredcites →https://doi.org/10.1121/10.0008972openalex-extracted
Ultrasound in situ characterization of hybrid additively manufactured Ti6Al4V
inferredcites →https://doi.org/10.14743/apem2018.2.281openalex-extracted
In-process non-destructive ultrasonic testing application during wire plus arc additive manufacturing
inferredcites →https://doi.org/10.3390/ma11030457openalex-extracted
Evaluation of Fracture Strength of Ceramics Containing Small Surface Defects Introduced by Focused Ion Beam
inferredcites →https://doi.org/10.3390/ma13245755openalex-extracted
Using Deep Learning to Detect Defects in Manufacturing: A Comprehensive Survey and Current Challenges
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