Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285172975> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W4285172975 endingPage "856" @default.
- W4285172975 startingPage "851" @default.
- W4285172975 abstract "Laser surface engineering of cutting tools is used to improve the performance of cutting processes via altering the material interaction between the tool surface and workpiece. Laser processing applied to cemented carbide cutting tools can induce various thermal and mechanical surface defects including porosity, splatter, cracks, balling, spherical pores, voids, and dissociation. Those defects could be detrimental to the integrity of the tool, therefore parametric optimization is crucial to limit and control possible post-processing defects. This study aimed to identify and classify surface defects in post laser processed carbides to better understand the relationship between parameters and resultant surface integrity. A region convolutional neural network (R-CNN) was trained for identification and classification of these surface defects using scanning electron microscopy images (SEM) as inputs. The R-CNN provided a quantitative analysis of each defect with an average accuracy of 91%. Using the data from the R-CNN matched with the laser parameters, a back propagation neural network (BPNN) was trained to act as a predictive network. The network predicts the number and proportion of defects when the tool grain size, roughness and laser parameters are entered. The accuracy of this predictive network was 96.6%. The effect of individual laser parameters on the surface integrity is estimated by this method, enabling the optimization of laser processing in cutting tools. For the first time this can be used to predict tool performance based on tool’s surface integrity." @default.
- W4285172975 created "2022-07-14" @default.
- W4285172975 creator A5043023424 @default.
- W4285172975 creator A5084186245 @default.
- W4285172975 date "2022-01-01" @default.
- W4285172975 modified "2023-10-18" @default.
- W4285172975 title "Surface defect detection and prediction in carbide cutting tools treated by lasers" @default.
- W4285172975 cites W2078204413 @default.
- W4285172975 cites W2799667353 @default.
- W4285172975 cites W2921196744 @default.
- W4285172975 cites W2940740179 @default.
- W4285172975 cites W2976818521 @default.
- W4285172975 cites W2995643826 @default.
- W4285172975 cites W3005497291 @default.
- W4285172975 cites W3037565425 @default.
- W4285172975 cites W3084976099 @default.
- W4285172975 cites W3094005054 @default.
- W4285172975 cites W3155559117 @default.
- W4285172975 cites W3163574299 @default.
- W4285172975 cites W3184690940 @default.
- W4285172975 doi "https://doi.org/10.1016/j.procir.2022.05.198" @default.
- W4285172975 hasPublicationYear "2022" @default.
- W4285172975 type Work @default.
- W4285172975 citedByCount "0" @default.
- W4285172975 crossrefType "journal-article" @default.
- W4285172975 hasAuthorship W4285172975A5043023424 @default.
- W4285172975 hasAuthorship W4285172975A5084186245 @default.
- W4285172975 hasBestOaLocation W42851729751 @default.
- W4285172975 hasConcept C107365816 @default.
- W4285172975 hasConcept C120665830 @default.
- W4285172975 hasConcept C121332964 @default.
- W4285172975 hasConcept C141349535 @default.
- W4285172975 hasConcept C154945302 @default.
- W4285172975 hasConcept C159985019 @default.
- W4285172975 hasConcept C191897082 @default.
- W4285172975 hasConcept C192562407 @default.
- W4285172975 hasConcept C2776363543 @default.
- W4285172975 hasConcept C41008148 @default.
- W4285172975 hasConcept C50644808 @default.
- W4285172975 hasConcept C520434653 @default.
- W4285172975 hasConcept C523214423 @default.
- W4285172975 hasConcept C5335593 @default.
- W4285172975 hasConcept C71039073 @default.
- W4285172975 hasConcept C81363708 @default.
- W4285172975 hasConceptScore W4285172975C107365816 @default.
- W4285172975 hasConceptScore W4285172975C120665830 @default.
- W4285172975 hasConceptScore W4285172975C121332964 @default.
- W4285172975 hasConceptScore W4285172975C141349535 @default.
- W4285172975 hasConceptScore W4285172975C154945302 @default.
- W4285172975 hasConceptScore W4285172975C159985019 @default.
- W4285172975 hasConceptScore W4285172975C191897082 @default.
- W4285172975 hasConceptScore W4285172975C192562407 @default.
- W4285172975 hasConceptScore W4285172975C2776363543 @default.
- W4285172975 hasConceptScore W4285172975C41008148 @default.
- W4285172975 hasConceptScore W4285172975C50644808 @default.
- W4285172975 hasConceptScore W4285172975C520434653 @default.
- W4285172975 hasConceptScore W4285172975C523214423 @default.
- W4285172975 hasConceptScore W4285172975C5335593 @default.
- W4285172975 hasConceptScore W4285172975C71039073 @default.
- W4285172975 hasConceptScore W4285172975C81363708 @default.
- W4285172975 hasLocation W42851729751 @default.
- W4285172975 hasLocation W42851729752 @default.
- W4285172975 hasOpenAccess W4285172975 @default.
- W4285172975 hasPrimaryLocation W42851729751 @default.
- W4285172975 hasRelatedWork W1553274644 @default.
- W4285172975 hasRelatedWork W1982963433 @default.
- W4285172975 hasRelatedWork W2035069777 @default.
- W4285172975 hasRelatedWork W2039943527 @default.
- W4285172975 hasRelatedWork W2138519773 @default.
- W4285172975 hasRelatedWork W2364121375 @default.
- W4285172975 hasRelatedWork W2584504420 @default.
- W4285172975 hasRelatedWork W4285238427 @default.
- W4285172975 hasRelatedWork W4379383666 @default.
- W4285172975 hasRelatedWork W2795950837 @default.
- W4285172975 hasVolume "108" @default.
- W4285172975 isParatext "false" @default.
- W4285172975 isRetracted "false" @default.
- W4285172975 workType "article" @default.