Matches in SemOpenAlex for { <https://semopenalex.org/work/W2068415097> ?p ?o ?g. }
Showing items 1 to 77 of
77
with 100 items per page.
- W2068415097 endingPage "838" @default.
- W2068415097 startingPage "833" @default.
- W2068415097 abstract "In this study, we report on the rubber compounds in a model passenger tire selected for modeling their cure curves at different temperatures using Adaptive Neuro-Fuzzy Inference Systems (ANFIS). Optimum cure time of the rubber compounds are predicted using ANFIS model. Equivalent cure concept (ECC), that is traditionally used in rubber and tire industries and Artificial Neural Networks (ANN) were also used to predict optimum cure time of the same rubber compounds, in our previous study. The results of three techniques, i.e. ANFIS, ANN and ECC were compared in view of prediction error criteria. The effects of fuzzy membership functions and the number of rules were investigated for ANFIS model. The best ANFIS architecture that provides minimum percentage error was determined for a selected compound. Then, the best ANFIS architecture was also used for predicting optimum cure time of the other 10 rubber compounds. The performances of ANFIS, ANN and ECC were compared by means of prediction errors. For overall evaluation of the compounds, while average percentage error calculated by ANFIS was found as 3.89% that of was found as 4.23% for ANN model and as 7.15% for ECC. These results show that ANFIS could be used as a more powerful technique than traditional ECC and ANN to predict optimum cure time of rubber compounds." @default.
- W2068415097 created "2016-06-24" @default.
- W2068415097 creator A5007447644 @default.
- W2068415097 creator A5041808692 @default.
- W2068415097 creator A5054987515 @default.
- W2068415097 date "2012-03-01" @default.
- W2068415097 modified "2023-10-02" @default.
- W2068415097 title "Predicting optimum cure time of rubber compounds by means of ANFIS" @default.
- W2068415097 cites W1967904753 @default.
- W2068415097 cites W1997823516 @default.
- W2068415097 cites W2019207321 @default.
- W2068415097 cites W2051265726 @default.
- W2068415097 cites W2079572060 @default.
- W2068415097 cites W2085008259 @default.
- W2068415097 cites W2106260996 @default.
- W2068415097 cites W2112875492 @default.
- W2068415097 cites W2116308743 @default.
- W2068415097 cites W2119815578 @default.
- W2068415097 cites W2190809137 @default.
- W2068415097 cites W2255744579 @default.
- W2068415097 cites W2278508445 @default.
- W2068415097 cites W2282880532 @default.
- W2068415097 cites W2283641953 @default.
- W2068415097 cites W2323858726 @default.
- W2068415097 doi "https://doi.org/10.1016/j.matdes.2011.03.062" @default.
- W2068415097 hasPublicationYear "2012" @default.
- W2068415097 type Work @default.
- W2068415097 sameAs 2068415097 @default.
- W2068415097 citedByCount "31" @default.
- W2068415097 countsByYear W20684150972012 @default.
- W2068415097 countsByYear W20684150972013 @default.
- W2068415097 countsByYear W20684150972014 @default.
- W2068415097 countsByYear W20684150972015 @default.
- W2068415097 countsByYear W20684150972016 @default.
- W2068415097 countsByYear W20684150972018 @default.
- W2068415097 countsByYear W20684150972019 @default.
- W2068415097 countsByYear W20684150972020 @default.
- W2068415097 countsByYear W20684150972022 @default.
- W2068415097 countsByYear W20684150972023 @default.
- W2068415097 crossrefType "journal-article" @default.
- W2068415097 hasAuthorship W2068415097A5007447644 @default.
- W2068415097 hasAuthorship W2068415097A5041808692 @default.
- W2068415097 hasAuthorship W2068415097A5054987515 @default.
- W2068415097 hasConcept C126348684 @default.
- W2068415097 hasConcept C127413603 @default.
- W2068415097 hasConcept C159985019 @default.
- W2068415097 hasConcept C176933379 @default.
- W2068415097 hasConcept C192562407 @default.
- W2068415097 hasConcept C39432304 @default.
- W2068415097 hasConcept C77595967 @default.
- W2068415097 hasConceptScore W2068415097C126348684 @default.
- W2068415097 hasConceptScore W2068415097C127413603 @default.
- W2068415097 hasConceptScore W2068415097C159985019 @default.
- W2068415097 hasConceptScore W2068415097C176933379 @default.
- W2068415097 hasConceptScore W2068415097C192562407 @default.
- W2068415097 hasConceptScore W2068415097C39432304 @default.
- W2068415097 hasConceptScore W2068415097C77595967 @default.
- W2068415097 hasLocation W20684150971 @default.
- W2068415097 hasOpenAccess W2068415097 @default.
- W2068415097 hasPrimaryLocation W20684150971 @default.
- W2068415097 hasRelatedWork W1994103032 @default.
- W2068415097 hasRelatedWork W2051270029 @default.
- W2068415097 hasRelatedWork W2065582168 @default.
- W2068415097 hasRelatedWork W2082293200 @default.
- W2068415097 hasRelatedWork W2137307547 @default.
- W2068415097 hasRelatedWork W2380293314 @default.
- W2068415097 hasRelatedWork W2899084033 @default.
- W2068415097 hasRelatedWork W2929659624 @default.
- W2068415097 hasRelatedWork W2943188944 @default.
- W2068415097 hasRelatedWork W4285802202 @default.
- W2068415097 hasVolume "35" @default.
- W2068415097 isParatext "false" @default.
- W2068415097 isRetracted "false" @default.
- W2068415097 magId "2068415097" @default.
- W2068415097 workType "article" @default.