FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2015454961> ?p ?o ?g. }

• W2015454961 endingPage "11656" @default.
• W2015454961 startingPage "11651" @default.
• W2015454961 abstract "In this paper, the regression analysis (RA) and artificial neural network (ANN) are presented for the prediction of tool–chip interface temperature depends on cutting parameters in machining. The RA and ANN model for prediction tool–chip interface temperature are developed and mathematical equations derived for tool–chip interface temperature prediction are obtained. The tool–chip interface temperature results obtained from mathematical equations with RA and ANN model and the experimental results available in the literature obtained by using AISI 1117 steel work piece with embedded K type thermocouple into the uncoated cutting tool (Korkut, Boy, Karacan, & Şeker, 2007) are compared. The coefficient of determination (R2) both training and testing data for temperature prediction in the ANN model are determined as 0.999791289 and 0.997889303 whereas; R2 for both training and testing data in the RA model are computed as 0.999063 and 0.999427, respectively. The correlation obtained by the training ANN model are better than the one obtained by training RA model. The training ANN model with the Levenberg–Marquardt (LM) algorithm provides more accurate prediction and is quite useful in the calculation of tool–chip interface temperature when compared with the trained RA method in machining. On the other hand, prediction values obtained the testing RA model is slightly better performance than the testing ANN model. The results show that the tool–chip interface temperature equation derived from RA and ANN model can be used for prediction." @default.
• W2015454961 created "2016-06-24" @default.
• W2015454961 creator A5020400365 @default.
• W2015454961 creator A5048653844 @default.
• W2015454961 creator A5070242746 @default.
• W2015454961 date "2011-09-01" @default.
• W2015454961 modified "2023-09-29" @default.
• W2015454961 title "Application of regression and artificial neural network analysis in modelling of tool–chip interface temperature in machining" @default.
• W2015454961 cites W1965980998 @default.
• W2015454961 cites W1966772443 @default.
• W2015454961 cites W1970079504 @default.
• W2015454961 cites W1971327724 @default.
• W2015454961 cites W1978618432 @default.
• W2015454961 cites W1978652841 @default.
• W2015454961 cites W1992156898 @default.
• W2015454961 cites W1992735017 @default.
• W2015454961 cites W2006544565 @default.
• W2015454961 cites W2010286846 @default.
• W2015454961 cites W2030438867 @default.
• W2015454961 cites W2049121352 @default.
• W2015454961 cites W2050921036 @default.
• W2015454961 cites W2051808716 @default.
• W2015454961 cites W2053494525 @default.
• W2015454961 cites W2054583885 @default.
• W2015454961 cites W2063830586 @default.
• W2015454961 cites W2068837256 @default.
• W2015454961 cites W2071340990 @default.
• W2015454961 cites W2071455760 @default.
• W2015454961 cites W2077658359 @default.
• W2015454961 cites W2080103499 @default.
• W2015454961 cites W2087070363 @default.
• W2015454961 cites W2101800469 @default.
• W2015454961 cites W2107077827 @default.
• W2015454961 cites W2136459762 @default.
• W2015454961 cites W2155482699 @default.
• W2015454961 cites W2162807413 @default.
• W2015454961 doi "https://doi.org/10.1016/j.eswa.2011.03.044" @default.
• W2015454961 hasPublicationYear "2011" @default.
• W2015454961 type Work @default.
• W2015454961 sameAs 2015454961 @default.
• W2015454961 citedByCount "64" @default.
• W2015454961 countsByYear W20154549612012 @default.
• W2015454961 countsByYear W20154549612013 @default.
• W2015454961 countsByYear W20154549612014 @default.
• W2015454961 countsByYear W20154549612015 @default.
• W2015454961 countsByYear W20154549612016 @default.
• W2015454961 countsByYear W20154549612017 @default.
• W2015454961 countsByYear W20154549612018 @default.
• W2015454961 countsByYear W20154549612019 @default.
• W2015454961 countsByYear W20154549612020 @default.
• W2015454961 countsByYear W20154549612021 @default.
• W2015454961 countsByYear W20154549612022 @default.
• W2015454961 countsByYear W20154549612023 @default.
• W2015454961 crossrefType "journal-article" @default.
• W2015454961 hasAuthorship W2015454961A5020400365 @default.
• W2015454961 hasAuthorship W2015454961A5048653844 @default.
• W2015454961 hasAuthorship W2015454961A5070242746 @default.
• W2015454961 hasConcept C105795698 @default.
• W2015454961 hasConcept C113843644 @default.
• W2015454961 hasConcept C119857082 @default.
• W2015454961 hasConcept C127413603 @default.
• W2015454961 hasConcept C128990827 @default.
• W2015454961 hasConcept C129307140 @default.
• W2015454961 hasConcept C152877465 @default.
• W2015454961 hasConcept C154945302 @default.
• W2015454961 hasConcept C157915830 @default.
• W2015454961 hasConcept C159985019 @default.
• W2015454961 hasConcept C165005293 @default.
• W2015454961 hasConcept C168068576 @default.
• W2015454961 hasConcept C173608175 @default.
• W2015454961 hasConcept C192562407 @default.
• W2015454961 hasConcept C2780092901 @default.
• W2015454961 hasConcept C33923547 @default.
• W2015454961 hasConcept C41008148 @default.
• W2015454961 hasConcept C48921125 @default.
• W2015454961 hasConcept C50644808 @default.
• W2015454961 hasConcept C523214423 @default.
• W2015454961 hasConcept C76155785 @default.
• W2015454961 hasConcept C78519656 @default.
• W2015454961 hasConcept C83546350 @default.
• W2015454961 hasConceptScore W2015454961C105795698 @default.
• W2015454961 hasConceptScore W2015454961C113843644 @default.
• W2015454961 hasConceptScore W2015454961C119857082 @default.
• W2015454961 hasConceptScore W2015454961C127413603 @default.
• W2015454961 hasConceptScore W2015454961C128990827 @default.
• W2015454961 hasConceptScore W2015454961C129307140 @default.
• W2015454961 hasConceptScore W2015454961C152877465 @default.
• W2015454961 hasConceptScore W2015454961C154945302 @default.
• W2015454961 hasConceptScore W2015454961C157915830 @default.
• W2015454961 hasConceptScore W2015454961C159985019 @default.
• W2015454961 hasConceptScore W2015454961C165005293 @default.
• W2015454961 hasConceptScore W2015454961C168068576 @default.
• W2015454961 hasConceptScore W2015454961C173608175 @default.
• W2015454961 hasConceptScore W2015454961C192562407 @default.
• W2015454961 hasConceptScore W2015454961C2780092901 @default.
• W2015454961 hasConceptScore W2015454961C33923547 @default.
• W2015454961 hasConceptScore W2015454961C41008148 @default.
• W2015454961 hasConceptScore W2015454961C48921125 @default.

• next