FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2794268718> ?p ?o ?g. }

• W2794268718 endingPage "4777" @default.
• W2794268718 startingPage "4766" @default.
• W2794268718 abstract "Abstract This study presents an approach for predicting the surface roughness during hard turning of AISI 52100 steel using regression analysis (RA) and artificial neural network (ANN). The surface roughness data required to generate and evaluate RA and ANN models have been obtained by conducting real time experiments according to Taguchi L27 (3^13) orthogonal array. In the development of RA and ANN models, machining parameters of cutting speed, feed and depth of cut were considered as model variables. The analysis of variance (ANOVA), Anderson–Darling test and normal probability plots were used to check the validity of RA model and determine the significant parameter affecting the surface roughness. Based on mean sum squared error (MSE) and absolute average error in prediction (AE p ) of training and testing data, an optimum feed forward neural network based on back propagation was developed with 3 input neurons, 2 hidden layers, 6 neurons in each hidden layer and 1 output neuron (3-6-6-1 architecture). The MATLAB toolbox has been utilized for training and testing of neural network model. The surface roughness values predicted from RA and ANN models were compared with experimental results. The predicted results using RA and ANN model indicate a good agreement between the predicted values and experimental results. The predictive model approach presented in this study is quite satisfactory in predicting surface roughness results during hard turning of AISI 52100 steel. Relatively, the prediction capability of ANN model was seen to be better than the RA model presented in this work and is expected to be useful in reducing time consuming and expensive experimental runs. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of 7th International Conference of Materials Processing and Characterization." @default.
• W2794268718 created "2018-03-29" @default.
• W2794268718 creator A5008976923 @default.
• W2794268718 creator A5024837164 @default.
• W2794268718 creator A5086094111 @default.
• W2794268718 date "2018-01-01" @default.
• W2794268718 modified "2023-10-09" @default.
• W2794268718 title "Application Of Regression And Artificial Neural Network Analysis In Modelling Of Surface Roughness In Hard Turning Of AISI 52100 Steel" @default.
• W2794268718 cites W1964066444 @default.
• W2794268718 cites W1969759403 @default.
• W2794268718 cites W1970206294 @default.
• W2794268718 cites W1971033426 @default.
• W2794268718 cites W1978618432 @default.
• W2794268718 cites W1992894697 @default.
• W2794268718 cites W1999531737 @default.
• W2794268718 cites W2005585791 @default.
• W2794268718 cites W2018288936 @default.
• W2794268718 cites W2030089400 @default.
• W2794268718 cites W2054326553 @default.
• W2794268718 cites W2076140947 @default.
• W2794268718 cites W2088347837 @default.
• W2794268718 cites W2093584482 @default.
• W2794268718 cites W2111939944 @default.
• W2794268718 cites W2135828483 @default.
• W2794268718 cites W2159589542 @default.
• W2794268718 cites W2162807413 @default.
• W2794268718 cites W2297920821 @default.
• W2794268718 cites W4210483269 @default.
• W2794268718 cites W4239984742 @default.
• W2794268718 cites W795948579 @default.
• W2794268718 doi "https://doi.org/10.1016/j.matpr.2017.12.050" @default.
• W2794268718 hasPublicationYear "2018" @default.
• W2794268718 type Work @default.
• W2794268718 sameAs 2794268718 @default.
• W2794268718 citedByCount "37" @default.
• W2794268718 countsByYear W27942687182019 @default.
• W2794268718 countsByYear W27942687182020 @default.
• W2794268718 countsByYear W27942687182021 @default.
• W2794268718 countsByYear W27942687182022 @default.
• W2794268718 countsByYear W27942687182023 @default.
• W2794268718 crossrefType "journal-article" @default.
• W2794268718 hasAuthorship W2794268718A5008976923 @default.
• W2794268718 hasAuthorship W2794268718A5024837164 @default.
• W2794268718 hasAuthorship W2794268718A5086094111 @default.
• W2794268718 hasConcept C105795698 @default.
• W2794268718 hasConcept C107365816 @default.
• W2794268718 hasConcept C119857082 @default.
• W2794268718 hasConcept C152877465 @default.
• W2794268718 hasConcept C154945302 @default.
• W2794268718 hasConcept C159985019 @default.
• W2794268718 hasConcept C191897082 @default.
• W2794268718 hasConcept C192562407 @default.
• W2794268718 hasConcept C2524010 @default.
• W2794268718 hasConcept C2776799497 @default.
• W2794268718 hasConcept C33923547 @default.
• W2794268718 hasConcept C41008148 @default.
• W2794268718 hasConcept C50644808 @default.
• W2794268718 hasConcept C71039073 @default.
• W2794268718 hasConcept C83546350 @default.
• W2794268718 hasConceptScore W2794268718C105795698 @default.
• W2794268718 hasConceptScore W2794268718C107365816 @default.
• W2794268718 hasConceptScore W2794268718C119857082 @default.
• W2794268718 hasConceptScore W2794268718C152877465 @default.
• W2794268718 hasConceptScore W2794268718C154945302 @default.
• W2794268718 hasConceptScore W2794268718C159985019 @default.
• W2794268718 hasConceptScore W2794268718C191897082 @default.
• W2794268718 hasConceptScore W2794268718C192562407 @default.
• W2794268718 hasConceptScore W2794268718C2524010 @default.
• W2794268718 hasConceptScore W2794268718C2776799497 @default.
• W2794268718 hasConceptScore W2794268718C33923547 @default.
• W2794268718 hasConceptScore W2794268718C41008148 @default.
• W2794268718 hasConceptScore W2794268718C50644808 @default.
• W2794268718 hasConceptScore W2794268718C71039073 @default.
• W2794268718 hasConceptScore W2794268718C83546350 @default.
• W2794268718 hasIssue "2" @default.
• W2794268718 hasLocation W27942687181 @default.
• W2794268718 hasOpenAccess W2794268718 @default.
• W2794268718 hasPrimaryLocation W27942687181 @default.
• W2794268718 hasRelatedWork W1995485687 @default.
• W2794268718 hasRelatedWork W2003085719 @default.
• W2794268718 hasRelatedWork W2008768820 @default.
• W2794268718 hasRelatedWork W2081951157 @default.
• W2794268718 hasRelatedWork W2124346094 @default.
• W2794268718 hasRelatedWork W2354420192 @default.
• W2794268718 hasRelatedWork W2545697222 @default.
• W2794268718 hasRelatedWork W2617105262 @default.
• W2794268718 hasRelatedWork W2945409216 @default.
• W2794268718 hasRelatedWork W4311687354 @default.
• W2794268718 hasVolume "5" @default.
• W2794268718 isParatext "false" @default.
• W2794268718 isRetracted "false" @default.
• W2794268718 magId "2794268718" @default.
• W2794268718 workType "article" @default.