Matches in SemOpenAlex for { <https://semopenalex.org/work/W2891889422> ?p ?o ?g. }
Showing items 1 to 67 of
67
with 100 items per page.
- W2891889422 endingPage "160" @default.
- W2891889422 startingPage "155" @default.
- W2891889422 abstract "Micro-transmissions, consisting of micro-gears with a module <200µm, are used in manifold industrial applications, e.g. the medical industry. Due to the technological limits of their manufacturing processes, micro-gears show large shape deviations compared to their size, which significantly influence their lifetime. Thus, for micro-gears a model has been developed to enable a prognosis of their lifetime based on areal measurements of the gear geometry, finite elements simulations as well as lifetime experiments. To significantly reduce the amount of experiments, existing prior knowledge is additionally used as input to the lifetime model by means of Bayesian statistics. To enable a time-efficient application of the model for industrial series production, in this article the application of a machine learning approach based on artificial neural networks is investigated. The uncertainty of the model is evaluated according to the principles of the Guide to the Expression of Uncertainty in Measurement (GUM)." @default.
- W2891889422 created "2018-09-27" @default.
- W2891889422 creator A5046200607 @default.
- W2891889422 creator A5050002224 @default.
- W2891889422 creator A5064892952 @default.
- W2891889422 creator A5078541712 @default.
- W2891889422 date "2018-01-01" @default.
- W2891889422 modified "2023-09-26" @default.
- W2891889422 title "Meta-Model Based on Artificial Neural Networks for Tooth Root Stress Analysis of Micro-Gears" @default.
- W2891889422 cites W1985579611 @default.
- W2891889422 cites W2016267557 @default.
- W2891889422 cites W2034544282 @default.
- W2891889422 cites W2100485669 @default.
- W2891889422 cites W2107376597 @default.
- W2891889422 cites W2125336244 @default.
- W2891889422 cites W2156909104 @default.
- W2891889422 cites W2622669607 @default.
- W2891889422 cites W775378478 @default.
- W2891889422 cites W94052953 @default.
- W2891889422 doi "https://doi.org/10.1016/j.procir.2018.04.031" @default.
- W2891889422 hasPublicationYear "2018" @default.
- W2891889422 type Work @default.
- W2891889422 sameAs 2891889422 @default.
- W2891889422 citedByCount "9" @default.
- W2891889422 countsByYear W28918894222019 @default.
- W2891889422 countsByYear W28918894222020 @default.
- W2891889422 countsByYear W28918894222021 @default.
- W2891889422 countsByYear W28918894222022 @default.
- W2891889422 countsByYear W28918894222023 @default.
- W2891889422 crossrefType "journal-article" @default.
- W2891889422 hasAuthorship W2891889422A5046200607 @default.
- W2891889422 hasAuthorship W2891889422A5050002224 @default.
- W2891889422 hasAuthorship W2891889422A5064892952 @default.
- W2891889422 hasAuthorship W2891889422A5078541712 @default.
- W2891889422 hasBestOaLocation W28918894221 @default.
- W2891889422 hasConcept C107673813 @default.
- W2891889422 hasConcept C127413603 @default.
- W2891889422 hasConcept C154945302 @default.
- W2891889422 hasConcept C41008148 @default.
- W2891889422 hasConcept C50644808 @default.
- W2891889422 hasConcept C78519656 @default.
- W2891889422 hasConceptScore W2891889422C107673813 @default.
- W2891889422 hasConceptScore W2891889422C127413603 @default.
- W2891889422 hasConceptScore W2891889422C154945302 @default.
- W2891889422 hasConceptScore W2891889422C41008148 @default.
- W2891889422 hasConceptScore W2891889422C50644808 @default.
- W2891889422 hasConceptScore W2891889422C78519656 @default.
- W2891889422 hasLocation W28918894221 @default.
- W2891889422 hasOpenAccess W2891889422 @default.
- W2891889422 hasPrimaryLocation W28918894221 @default.
- W2891889422 hasRelatedWork W2114151954 @default.
- W2891889422 hasRelatedWork W2358913626 @default.
- W2891889422 hasRelatedWork W2359791618 @default.
- W2891889422 hasRelatedWork W2368988737 @default.
- W2891889422 hasRelatedWork W2386387936 @default.
- W2891889422 hasRelatedWork W2394386606 @default.
- W2891889422 hasRelatedWork W2899084033 @default.
- W2891889422 hasRelatedWork W2992140536 @default.
- W2891889422 hasRelatedWork W32248825 @default.
- W2891889422 hasRelatedWork W1941115011 @default.
- W2891889422 hasVolume "75" @default.
- W2891889422 isParatext "false" @default.
- W2891889422 isRetracted "false" @default.
- W2891889422 magId "2891889422" @default.
- W2891889422 workType "article" @default.