FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4283457795> ?p ?o ?g. }

• W4283457795 endingPage "6455" @default.
• W4283457795 startingPage "6455" @default.
• W4283457795 abstract "The non-destructive testing methods offer great benefit in detecting and classifying the weld defects. Among these, infrared (IR) thermography stands out in the inspection, characterization, and analysis of the defects from the camera image sequences, particularly with the recent advent of deep learning. However, in IR, the defect classification becomes a cumbersome task because of the exposure to the inconsistent and unbalanced heat source, which requires additional supervision. In light of this, authors present a fully automated system capable of detecting defective welds according to the electrical resistance properties in the inline mode. The welding process is captured by an IR camera that generates a video sequence. A set of features extracted by such video feeds supervised machine learning and deep learning algorithms in order to build an industrial diagnostic framework for weld defect detection. The experimental study validates the aptitude of a customized convolutional neural network architecture to classify the malfunctioning weld joints with mean accuracy of 99% and median f1 score of 73% across five-fold cross validation on our locally acquired real world dataset. The outcome encourages the integration of thermographic-based quality control frameworks in all applications where fast and accurate recognition and safety assurance are crucial industrial requirements across the production line." @default.
• W4283457795 created "2022-06-26" @default.
• W4283457795 creator A5004471741 @default.
• W4283457795 creator A5017765291 @default.
• W4283457795 creator A5023534711 @default.
• W4283457795 creator A5042966118 @default.
• W4283457795 creator A5050013557 @default.
• W4283457795 creator A5058629014 @default.
• W4283457795 creator A5058740837 @default.
• W4283457795 creator A5058770707 @default.
• W4283457795 creator A5080788119 @default.
• W4283457795 date "2022-06-25" @default.
• W4283457795 modified "2023-10-01" @default.
• W4283457795 title "Inline Defective Laser Weld Identification by Processing Thermal Image Sequences with Machine and Deep Learning Techniques" @default.
• W4283457795 cites W1965796793 @default.
• W4283457795 cites W1967320885 @default.
• W4283457795 cites W1981404976 @default.
• W4283457795 cites W2003140759 @default.
• W4283457795 cites W2075394955 @default.
• W4283457795 cites W2083647296 @default.
• W4283457795 cites W2087347434 @default.
• W4283457795 cites W2176751782 @default.
• W4283457795 cites W2330783888 @default.
• W4283457795 cites W2793605784 @default.
• W4283457795 cites W2883792546 @default.
• W4283457795 cites W2911923560 @default.
• W4283457795 cites W2926004311 @default.
• W4283457795 cites W2971659906 @default.
• W4283457795 cites W2973272210 @default.
• W4283457795 cites W3018717613 @default.
• W4283457795 cites W3025156940 @default.
• W4283457795 cites W3041194416 @default.
• W4283457795 cites W3049460138 @default.
• W4283457795 cites W3083625639 @default.
• W4283457795 cites W3089524077 @default.
• W4283457795 cites W3097663992 @default.
• W4283457795 cites W3121719033 @default.
• W4283457795 cites W3122768396 @default.
• W4283457795 cites W3131678530 @default.
• W4283457795 cites W3135097192 @default.
• W4283457795 cites W3162403620 @default.
• W4283457795 cites W3196797957 @default.
• W4283457795 cites W3210998748 @default.
• W4283457795 cites W3214896938 @default.
• W4283457795 cites W4236137412 @default.
• W4283457795 doi "https://doi.org/10.3390/app12136455" @default.
• W4283457795 hasPublicationYear "2022" @default.
• W4283457795 type Work @default.
• W4283457795 citedByCount "10" @default.
• W4283457795 countsByYear W42834577952022 @default.
• W4283457795 countsByYear W42834577952023 @default.
• W4283457795 crossrefType "journal-article" @default.
• W4283457795 hasAuthorship W4283457795A5004471741 @default.
• W4283457795 hasAuthorship W4283457795A5017765291 @default.
• W4283457795 hasAuthorship W4283457795A5023534711 @default.
• W4283457795 hasAuthorship W4283457795A5042966118 @default.
• W4283457795 hasAuthorship W4283457795A5050013557 @default.
• W4283457795 hasAuthorship W4283457795A5058629014 @default.
• W4283457795 hasAuthorship W4283457795A5058740837 @default.
• W4283457795 hasAuthorship W4283457795A5058770707 @default.
• W4283457795 hasAuthorship W4283457795A5080788119 @default.
• W4283457795 hasBestOaLocation W42834577951 @default.
• W4283457795 hasConcept C108583219 @default.
• W4283457795 hasConcept C111919701 @default.
• W4283457795 hasConcept C119857082 @default.
• W4283457795 hasConcept C120665830 @default.
• W4283457795 hasConcept C121332964 @default.
• W4283457795 hasConcept C126838900 @default.
• W4283457795 hasConcept C127413603 @default.
• W4283457795 hasConcept C153180895 @default.
• W4283457795 hasConcept C154945302 @default.
• W4283457795 hasConcept C158355884 @default.
• W4283457795 hasConcept C168820333 @default.
• W4283457795 hasConcept C19474535 @default.
• W4283457795 hasConcept C2779222261 @default.
• W4283457795 hasConcept C31972630 @default.
• W4283457795 hasConcept C41008148 @default.
• W4283457795 hasConcept C50644808 @default.
• W4283457795 hasConcept C56529433 @default.
• W4283457795 hasConcept C71924100 @default.
• W4283457795 hasConcept C78519656 @default.
• W4283457795 hasConcept C81363708 @default.
• W4283457795 hasConcept C98045186 @default.
• W4283457795 hasConceptScore W4283457795C108583219 @default.
• W4283457795 hasConceptScore W4283457795C111919701 @default.
• W4283457795 hasConceptScore W4283457795C119857082 @default.
• W4283457795 hasConceptScore W4283457795C120665830 @default.
• W4283457795 hasConceptScore W4283457795C121332964 @default.
• W4283457795 hasConceptScore W4283457795C126838900 @default.
• W4283457795 hasConceptScore W4283457795C127413603 @default.
• W4283457795 hasConceptScore W4283457795C153180895 @default.
• W4283457795 hasConceptScore W4283457795C154945302 @default.
• W4283457795 hasConceptScore W4283457795C158355884 @default.
• W4283457795 hasConceptScore W4283457795C168820333 @default.
• W4283457795 hasConceptScore W4283457795C19474535 @default.
• W4283457795 hasConceptScore W4283457795C2779222261 @default.
• W4283457795 hasConceptScore W4283457795C31972630 @default.
• W4283457795 hasConceptScore W4283457795C41008148 @default.

• next