FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3187635228> ?p ?o ?g. }

• W3187635228 endingPage "76" @default.
• W3187635228 startingPage "51" @default.
• W3187635228 abstract "Machine learning (ML) can offer many advantages in predicting material properties over traditional materials development methods based solely on limited experimental investigations or physical-based simulations with the capability to reduce development cost, risk, and time. However, so far, limited efforts have been made to predict alloy oxidation kinetics and spallation behavior via ML due to the lack of consistently measured and sufficient experimental data and the inherent complexity in oxidation behavior of multicomponent high-temperature alloys. A previous study reported the ability of ML to predict oxidation kinetics of NiCr-based alloys as a function of alloy composition and operating conditions. In the current work, the performance of a ML model in predicting rate constants and spallation probability was evaluated in light of the roles of the data distribution of the experimental dataset (data analytics), the alloy composition, the exposure environment and the chosen oxidation approach to extracting kinetic values from the measured mass changes (but using either a simple parabolic law or a statistical cyclic oxidation model). Potential strategies to improve the predictions and enhance the extrapolative capability of the previously trained model will be discussed." @default.
• W3187635228 created "2021-08-16" @default.
• W3187635228 creator A5004269532 @default.
• W3187635228 creator A5007884412 @default.
• W3187635228 creator A5016836139 @default.
• W3187635228 creator A5052190429 @default.
• W3187635228 creator A5052206279 @default.
• W3187635228 creator A5054600006 @default.
• W3187635228 creator A5089386027 @default.
• W3187635228 date "2021-08-06" @default.
• W3187635228 modified "2023-10-18" @default.
• W3187635228 title "Lessons Learned in Employing Data Analytics to Predict Oxidation Kinetics and Spallation Behavior of High-Temperature NiCr-Based Alloys" @default.
• W3187635228 cites W1652159234 @default.
• W3187635228 cites W1905819920 @default.
• W3187635228 cites W1964297552 @default.
• W3187635228 cites W1977009436 @default.
• W3187635228 cites W1989065519 @default.
• W3187635228 cites W1990657605 @default.
• W3187635228 cites W1991972069 @default.
• W3187635228 cites W1994295937 @default.
• W3187635228 cites W1994616231 @default.
• W3187635228 cites W2006334489 @default.
• W3187635228 cites W2010509357 @default.
• W3187635228 cites W2015033554 @default.
• W3187635228 cites W2019974415 @default.
• W3187635228 cites W2021282700 @default.
• W3187635228 cites W2021664209 @default.
• W3187635228 cites W2029747513 @default.
• W3187635228 cites W2033904036 @default.
• W3187635228 cites W2035063817 @default.
• W3187635228 cites W2036757093 @default.
• W3187635228 cites W2036978641 @default.
• W3187635228 cites W2041585306 @default.
• W3187635228 cites W2047701249 @default.
• W3187635228 cites W2052943778 @default.
• W3187635228 cites W2055395520 @default.
• W3187635228 cites W2064946723 @default.
• W3187635228 cites W2070735525 @default.
• W3187635228 cites W2074912852 @default.
• W3187635228 cites W2094040832 @default.
• W3187635228 cites W2094804201 @default.
• W3187635228 cites W2138763184 @default.
• W3187635228 cites W2143307659 @default.
• W3187635228 cites W2148800931 @default.
• W3187635228 cites W2165059547 @default.
• W3187635228 cites W2537621949 @default.
• W3187635228 cites W2604452321 @default.
• W3187635228 cites W2622267228 @default.
• W3187635228 cites W2739554556 @default.
• W3187635228 cites W2745893024 @default.
• W3187635228 cites W2791709340 @default.
• W3187635228 cites W2898198372 @default.
• W3187635228 cites W2899025860 @default.
• W3187635228 cites W2921873493 @default.
• W3187635228 cites W2922127369 @default.
• W3187635228 cites W2969389191 @default.
• W3187635228 cites W2980496444 @default.
• W3187635228 cites W2984492522 @default.
• W3187635228 cites W2992115806 @default.
• W3187635228 cites W3038129978 @default.
• W3187635228 cites W3119986036 @default.
• W3187635228 cites W340769489 @default.
• W3187635228 cites W623620752 @default.
• W3187635228 doi "https://doi.org/10.1007/s11085-021-10076-1" @default.
• W3187635228 hasPublicationYear "2021" @default.
• W3187635228 type Work @default.
• W3187635228 sameAs 3187635228 @default.
• W3187635228 citedByCount "3" @default.
• W3187635228 countsByYear W31876352282022 @default.
• W3187635228 countsByYear W31876352282023 @default.
• W3187635228 crossrefType "journal-article" @default.
• W3187635228 hasAuthorship W3187635228A5004269532 @default.
• W3187635228 hasAuthorship W3187635228A5007884412 @default.
• W3187635228 hasAuthorship W3187635228A5016836139 @default.
• W3187635228 hasAuthorship W3187635228A5052190429 @default.
• W3187635228 hasAuthorship W3187635228A5052206279 @default.
• W3187635228 hasAuthorship W3187635228A5054600006 @default.
• W3187635228 hasAuthorship W3187635228A5089386027 @default.
• W3187635228 hasBestOaLocation W31876352282 @default.
• W3187635228 hasConcept C11608111 @default.
• W3187635228 hasConcept C121332964 @default.
• W3187635228 hasConcept C124101348 @default.
• W3187635228 hasConcept C140086010 @default.
• W3187635228 hasConcept C148898269 @default.
• W3187635228 hasConcept C152568617 @default.
• W3187635228 hasConcept C185544564 @default.
• W3187635228 hasConcept C186060115 @default.
• W3187635228 hasConcept C191897082 @default.
• W3187635228 hasConcept C192562407 @default.
• W3187635228 hasConcept C2780026712 @default.
• W3187635228 hasConcept C41008148 @default.
• W3187635228 hasConcept C62520636 @default.
• W3187635228 hasConcept C79158427 @default.
• W3187635228 hasConcept C86803240 @default.
• W3187635228 hasConcept C97355855 @default.
• W3187635228 hasConceptScore W3187635228C11608111 @default.
• W3187635228 hasConceptScore W3187635228C121332964 @default.
• W3187635228 hasConceptScore W3187635228C124101348 @default.

• next