FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3133678093> ?p ?o ?g. }

• W3133678093 endingPage "203741" @default.
• W3133678093 startingPage "203741" @default.
• W3133678093 abstract "The aim of the present study is to develop a neural network model for the prediction of slurry erosion ( SE ) of heavy-duty pump impeller steels and casing material. The heavy-duty pump impeller has a wide range of applications like slurry transportation system and coal conveying system of thermal power stations, and various other industries like mining, chemical and marine industry. In the present study, the slurry erosion performance of three different pump impeller steels namely 18Cr-8Ni, 16Cr-10Ni-2Mo and super duplex 24Cr-6Ni-3Mo-N, and one pump casing material namely Grey cast iron was tested. The SE experiments were carried out on a laboratory scale pot tester using the sand as an erodent. A data set was produced to develop a prediction-based neural network (NN) model and 70% of this data set was used as input to NN model. The learning of NN model was based on an artificial neural network (ANN) and used to build a prediction model that can predict the results while the input data was supplied to it. Firstly, the data were divided into training, validation, and testing. The NN model was trained on 70% of the original dataset and validated by another 15% data. A total of 30 training epochs were performed for training, testing, and validation of the model. The validation of the model indicates that the build NN model was the best fit and comprises no over-fitting and under-fitting issues. In the end, the prepared NN model was given the remaining 15% data for testing. It outputs the corresponding values for each input. These predicted values are then compared with the actual ground truth to check the robustness of the designed model. The various measures used for evaluating the model were R 2 (coefficient of determination), Root mean square error, etc. Results show that 18Cr-8Ni steel exhibits the best SE performance followed by 16Cr-10Ni-2Mo, 24Cr-6Ni-3Mo-N, and Grey cast iron. • The 18Cr-8Ni steel showed the best performance followed by 16Cr-10Ni-2Mo, 24Cr-6Ni-3Mo-N, and Grey cast iron. • Maximum slurry erosion in for steels occurs at impact angle of 30° and 60° for grey cast iron. • The grey cast iron shows a pure brittle erosion behavior whereas stainless steels showed the semi-brittle behavior. • The error percentage lies between 0 and 6%, which establishes the efficiency of the ANN model. • MAE and RMSE readings show that the ANN model prediction error was the least compared to MLR, PLSR and PCR." @default.
• W3133678093 created "2021-03-15" @default.
• W3133678093 creator A5042730658 @default.
• W3133678093 creator A5074120314 @default.
• W3133678093 date "2021-07-01" @default.
• W3133678093 modified "2023-10-14" @default.
• W3133678093 title "Neural network prediction of slurry erosion of heavy-duty pump impeller/casing materials 18Cr-8Ni, 16Cr-10Ni-2Mo, super duplex 24Cr-6Ni-3Mo-N, and grey cast iron" @default.
• W3133678093 cites W1963925930 @default.
• W3133678093 cites W1967377424 @default.
• W3133678093 cites W1974028264 @default.
• W3133678093 cites W1977148732 @default.
• W3133678093 cites W1979386309 @default.
• W3133678093 cites W1981145928 @default.
• W3133678093 cites W1986601155 @default.
• W3133678093 cites W1987524576 @default.
• W3133678093 cites W1988789637 @default.
• W3133678093 cites W1997883170 @default.
• W3133678093 cites W2001618104 @default.
• W3133678093 cites W2011899901 @default.
• W3133678093 cites W2023800360 @default.
• W3133678093 cites W2034526600 @default.
• W3133678093 cites W2038571690 @default.
• W3133678093 cites W2038790820 @default.
• W3133678093 cites W2039240409 @default.
• W3133678093 cites W2054775594 @default.
• W3133678093 cites W2055633858 @default.
• W3133678093 cites W2058933500 @default.
• W3133678093 cites W2062590958 @default.
• W3133678093 cites W2069486778 @default.
• W3133678093 cites W2071221236 @default.
• W3133678093 cites W2071841240 @default.
• W3133678093 cites W2084256918 @default.
• W3133678093 cites W2088749583 @default.
• W3133678093 cites W2114872704 @default.
• W3133678093 cites W2135402833 @default.
• W3133678093 cites W2155482699 @default.
• W3133678093 cites W2165947143 @default.
• W3133678093 cites W2216125166 @default.
• W3133678093 cites W2256578114 @default.
• W3133678093 cites W2278391380 @default.
• W3133678093 cites W2294490746 @default.
• W3133678093 cites W2313114507 @default.
• W3133678093 cites W2523729404 @default.
• W3133678093 cites W2766058853 @default.
• W3133678093 cites W2811125213 @default.
• W3133678093 cites W2887465192 @default.
• W3133678093 cites W2888531716 @default.
• W3133678093 cites W2890166665 @default.
• W3133678093 cites W2913856398 @default.
• W3133678093 cites W2933297688 @default.
• W3133678093 cites W2954120103 @default.
• W3133678093 cites W2961765410 @default.
• W3133678093 cites W2966705853 @default.
• W3133678093 cites W2979448324 @default.
• W3133678093 cites W3049233373 @default.
• W3133678093 cites W632511877 @default.
• W3133678093 cites W1995214680 @default.
• W3133678093 doi "https://doi.org/10.1016/j.wear.2021.203741" @default.
• W3133678093 hasPublicationYear "2021" @default.
• W3133678093 type Work @default.
• W3133678093 sameAs 3133678093 @default.
• W3133678093 citedByCount "14" @default.
• W3133678093 countsByYear W31336780932021 @default.
• W3133678093 countsByYear W31336780932022 @default.
• W3133678093 countsByYear W31336780932023 @default.
• W3133678093 crossrefType "journal-article" @default.
• W3133678093 hasAuthorship W3133678093A5042730658 @default.
• W3133678093 hasAuthorship W3133678093A5074120314 @default.
• W3133678093 hasConcept C127413603 @default.
• W3133678093 hasConcept C153005164 @default.
• W3133678093 hasConcept C154945302 @default.
• W3133678093 hasConcept C159985019 @default.
• W3133678093 hasConcept C192562407 @default.
• W3133678093 hasConcept C30399818 @default.
• W3133678093 hasConcept C41008148 @default.
• W3133678093 hasConcept C50644808 @default.
• W3133678093 hasConcept C78519656 @default.
• W3133678093 hasConcept C94293008 @default.
• W3133678093 hasConceptScore W3133678093C127413603 @default.
• W3133678093 hasConceptScore W3133678093C153005164 @default.
• W3133678093 hasConceptScore W3133678093C154945302 @default.
• W3133678093 hasConceptScore W3133678093C159985019 @default.
• W3133678093 hasConceptScore W3133678093C192562407 @default.
• W3133678093 hasConceptScore W3133678093C30399818 @default.
• W3133678093 hasConceptScore W3133678093C41008148 @default.
• W3133678093 hasConceptScore W3133678093C50644808 @default.
• W3133678093 hasConceptScore W3133678093C78519656 @default.
• W3133678093 hasConceptScore W3133678093C94293008 @default.
• W3133678093 hasLocation W31336780931 @default.
• W3133678093 hasOpenAccess W3133678093 @default.
• W3133678093 hasPrimaryLocation W31336780931 @default.
• W3133678093 hasRelatedWork W2125579824 @default.
• W3133678093 hasRelatedWork W2202971461 @default.
• W3133678093 hasRelatedWork W233139833 @default.
• W3133678093 hasRelatedWork W2359791618 @default.
• W3133678093 hasRelatedWork W2369060265 @default.
• W3133678093 hasRelatedWork W2894766629 @default.
• W3133678093 hasRelatedWork W2906905361 @default.

• next