FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387403257> ?p ?o ?g. }

• W4387403257 endingPage "01238" @default.
• W4387403257 startingPage "01238" @default.
• W4387403257 abstract "In the welding processes, parametric optimization is crucial, and intelligent prediction makes use of data availability to cut the cost of experimental operations. This article proposes adopting the adaptive neuro-fuzzy inference system (ANFIS) model for predicting ultimate tensile strength in TIG-MIG hybrid welding. Experiments are designed and optimized according to Taguchi’s principles. Proposed neural network models are developed using experimental data. Three input process parameters ( MIG voltage, TIG current and gas flow rate) were designed in an L9 orthogonal array at three levels each. The maximum tensile obtained was 868.3 MPa. The signal-to-noise ratio shows that the optimum parameter setting that maximizes the tensile strength corresponds to MIG Voltage (V) = 25, TIG Current (A) =180, and Gas flow rate =19 L/mm. The analysis of variance shows that the gas flow rate had the most influence on the ultimate tensile strength with a 42.35% contribution, followed by the MIG voltage with 31.67%, and TIG current with 18.13% contribution. The developed ANFIS model is 99.9 % accurate at the training (MAPE training = 0.1670) and 96.3% accurate at the testing (MAPE training = 0.1670) for predicting the ultimate tensile strength. The R 2 -values of the models at training and testing were closer to unity depicts a good fit between the experimental and predicted values of the response. The lower RMSE values (RMSE training =1.8963, RMSE training = 4.8194) indicates the lower deviation of the experiment values of ultimate tensile strength from the predicted values. These results imply that ANFIS models can reduce experimental costs and hurdles associated with the trial and error approach to get the appropriate welding parameters. Therefore experimental designs for other plate thicknesses and similar processes could be built and predicted without actual experimentation." @default.
• W4387403257 created "2023-10-07" @default.
• W4387403257 creator A5007199173 @default.
• W4387403257 creator A5019406052 @default.
• W4387403257 creator A5022143224 @default.
• W4387403257 creator A5074104836 @default.
• W4387403257 creator A5077506090 @default.
• W4387403257 date "2023-01-01" @default.
• W4387403257 modified "2023-10-07" @default.
• W4387403257 title "Optimization and Prediction of TIG-MIG hybrid Joint Strength using Adaptive Neuro-Fuzzy Inference System (ANFIS) Model" @default.
• W4387403257 cites W1989962050 @default.
• W4387403257 cites W2019207321 @default.
• W4387403257 cites W2047632198 @default.
• W4387403257 cites W2048047008 @default.
• W4387403257 cites W2150258537 @default.
• W4387403257 cites W2198869952 @default.
• W4387403257 cites W2337859202 @default.
• W4387403257 cites W2565311775 @default.
• W4387403257 cites W2571100953 @default.
• W4387403257 cites W2597129536 @default.
• W4387403257 cites W2604737429 @default.
• W4387403257 cites W2737125494 @default.
• W4387403257 cites W2739652057 @default.
• W4387403257 cites W2762656229 @default.
• W4387403257 cites W2883980089 @default.
• W4387403257 cites W2904166845 @default.
• W4387403257 cites W2907114940 @default.
• W4387403257 cites W2919856226 @default.
• W4387403257 cites W3132362632 @default.
• W4387403257 cites W3185294164 @default.
• W4387403257 cites W3191079058 @default.
• W4387403257 cites W3197629355 @default.
• W4387403257 cites W4205296749 @default.
• W4387403257 cites W4206666190 @default.
• W4387403257 cites W4206822733 @default.
• W4387403257 cites W4211102922 @default.
• W4387403257 cites W4214650499 @default.
• W4387403257 cites W4254673537 @default.
• W4387403257 cites W4291278449 @default.
• W4387403257 cites W4292641120 @default.
• W4387403257 doi "https://doi.org/10.1051/e3sconf/202343001238" @default.
• W4387403257 hasPublicationYear "2023" @default.
• W4387403257 type Work @default.
• W4387403257 citedByCount "0" @default.
• W4387403257 crossrefType "journal-article" @default.
• W4387403257 hasAuthorship W4387403257A5007199173 @default.
• W4387403257 hasAuthorship W4387403257A5019406052 @default.
• W4387403257 hasAuthorship W4387403257A5022143224 @default.
• W4387403257 hasAuthorship W4387403257A5074104836 @default.
• W4387403257 hasAuthorship W4387403257A5077506090 @default.
• W4387403257 hasBestOaLocation W43874032571 @default.
• W4387403257 hasConcept C105795698 @default.
• W4387403257 hasConcept C112950240 @default.
• W4387403257 hasConcept C139945424 @default.
• W4387403257 hasConcept C150217764 @default.
• W4387403257 hasConcept C154945302 @default.
• W4387403257 hasConcept C159985019 @default.
• W4387403257 hasConcept C186108316 @default.
• W4387403257 hasConcept C192562407 @default.
• W4387403257 hasConcept C19474535 @default.
• W4387403257 hasConcept C195975749 @default.
• W4387403257 hasConcept C20480867 @default.
• W4387403257 hasConcept C33923547 @default.
• W4387403257 hasConcept C3450827 @default.
• W4387403257 hasConcept C41008148 @default.
• W4387403257 hasConcept C50644808 @default.
• W4387403257 hasConcept C58166 @default.
• W4387403257 hasConcept C83469408 @default.
• W4387403257 hasConceptScore W4387403257C105795698 @default.
• W4387403257 hasConceptScore W4387403257C112950240 @default.
• W4387403257 hasConceptScore W4387403257C139945424 @default.
• W4387403257 hasConceptScore W4387403257C150217764 @default.
• W4387403257 hasConceptScore W4387403257C154945302 @default.
• W4387403257 hasConceptScore W4387403257C159985019 @default.
• W4387403257 hasConceptScore W4387403257C186108316 @default.
• W4387403257 hasConceptScore W4387403257C192562407 @default.
• W4387403257 hasConceptScore W4387403257C19474535 @default.
• W4387403257 hasConceptScore W4387403257C195975749 @default.
• W4387403257 hasConceptScore W4387403257C20480867 @default.
• W4387403257 hasConceptScore W4387403257C33923547 @default.
• W4387403257 hasConceptScore W4387403257C3450827 @default.
• W4387403257 hasConceptScore W4387403257C41008148 @default.
• W4387403257 hasConceptScore W4387403257C50644808 @default.
• W4387403257 hasConceptScore W4387403257C58166 @default.
• W4387403257 hasConceptScore W4387403257C83469408 @default.
• W4387403257 hasLocation W43874032571 @default.
• W4387403257 hasOpenAccess W4387403257 @default.
• W4387403257 hasPrimaryLocation W43874032571 @default.
• W4387403257 hasRelatedWork W2039947585 @default.
• W4387403257 hasRelatedWork W2765961949 @default.
• W4387403257 hasRelatedWork W2768005043 @default.
• W4387403257 hasRelatedWork W3119865579 @default.
• W4387403257 hasRelatedWork W3178576217 @default.
• W4387403257 hasRelatedWork W4244255161 @default.
• W4387403257 hasRelatedWork W4285102093 @default.
• W4387403257 hasRelatedWork W4285104253 @default.
• W4387403257 hasRelatedWork W4318676890 @default.
• W4387403257 hasRelatedWork W4385195237 @default.

• next