Matches in SemOpenAlex for { <https://semopenalex.org/work/W3006584476> ?p ?o ?g. }
- W3006584476 endingPage "1933" @default.
- W3006584476 startingPage "1917" @default.
- W3006584476 abstract "As additive manufacturing (AM) continues to mature, an efficient and effective method to identify parts which are eligible for AM as well as gaining insight on what values it may add to a product is needed. Prior methods are naturally developed and highly experience-dependent, which falls short for its objectiveness and transferability. In this paper, a decision support system (DSS) framework for automatically determining the candidacy of a part or assembly for AM applications is proposed based on machine learning (ML) and carefully selected candidacy criteria. With the goal of supporting efficient candidate screening in the early conceptual design stage, these criteria are further individually decoded to decisive parameters which can be extracted from digital models or resource planning databases. Over 200 existing industrial examples are manually collected and labelled as training data; meanwhile, multiple regression algorithms are tested against each AM potential to find better predictive performance. The proposed DSS framework is implemented as a web application with integrated cloud-based database and ML service, which allows advantages of easy maintenance, upgrade, and retraining of ML models. Two case studies of a hip implant and a throttle pedal are used as demonstrating examples. This preliminary work provides a promising solution for lowering the requirements of non-AM experts to find suitable AM candidates." @default.
- W3006584476 created "2020-02-24" @default.
- W3006584476 creator A5034006964 @default.
- W3006584476 creator A5056875120 @default.
- W3006584476 creator A5070626046 @default.
- W3006584476 creator A5087130062 @default.
- W3006584476 date "2020-02-14" @default.
- W3006584476 modified "2023-09-26" @default.
- W3006584476 title "Towards an automated decision support system for the identification of additive manufacturing part candidates" @default.
- W3006584476 cites W1487611565 @default.
- W3006584476 cites W1903329854 @default.
- W3006584476 cites W1963885879 @default.
- W3006584476 cites W1964027383 @default.
- W3006584476 cites W1969566657 @default.
- W3006584476 cites W1989905233 @default.
- W3006584476 cites W1995105354 @default.
- W3006584476 cites W1995575199 @default.
- W3006584476 cites W1997364989 @default.
- W3006584476 cites W2002645541 @default.
- W3006584476 cites W2022898572 @default.
- W3006584476 cites W2024127739 @default.
- W3006584476 cites W2025613381 @default.
- W3006584476 cites W2034534589 @default.
- W3006584476 cites W2041198307 @default.
- W3006584476 cites W2042830635 @default.
- W3006584476 cites W2052763947 @default.
- W3006584476 cites W2063784058 @default.
- W3006584476 cites W2073208440 @default.
- W3006584476 cites W2119224932 @default.
- W3006584476 cites W2135695572 @default.
- W3006584476 cites W2154435902 @default.
- W3006584476 cites W2176669455 @default.
- W3006584476 cites W2197692950 @default.
- W3006584476 cites W2211412427 @default.
- W3006584476 cites W2269084860 @default.
- W3006584476 cites W2274718518 @default.
- W3006584476 cites W227520449 @default.
- W3006584476 cites W2412419169 @default.
- W3006584476 cites W2414423160 @default.
- W3006584476 cites W2460941942 @default.
- W3006584476 cites W2475009926 @default.
- W3006584476 cites W2548343078 @default.
- W3006584476 cites W2569120233 @default.
- W3006584476 cites W2570740944 @default.
- W3006584476 cites W2586297576 @default.
- W3006584476 cites W2598096074 @default.
- W3006584476 cites W2613270911 @default.
- W3006584476 cites W2745008464 @default.
- W3006584476 cites W2762176217 @default.
- W3006584476 cites W2765199117 @default.
- W3006584476 cites W2782846875 @default.
- W3006584476 cites W2782891637 @default.
- W3006584476 cites W2890973402 @default.
- W3006584476 cites W2895497546 @default.
- W3006584476 cites W2900338134 @default.
- W3006584476 cites W2903257126 @default.
- W3006584476 cites W2903402321 @default.
- W3006584476 cites W2909096226 @default.
- W3006584476 cites W2915627018 @default.
- W3006584476 cites W2921065443 @default.
- W3006584476 cites W2933287546 @default.
- W3006584476 cites W2948371315 @default.
- W3006584476 cites W2959009062 @default.
- W3006584476 cites W2989173527 @default.
- W3006584476 cites W2989368580 @default.
- W3006584476 cites W4214784255 @default.
- W3006584476 cites W4241119137 @default.
- W3006584476 doi "https://doi.org/10.1007/s10845-020-01545-6" @default.
- W3006584476 hasPublicationYear "2020" @default.
- W3006584476 type Work @default.
- W3006584476 sameAs 3006584476 @default.
- W3006584476 citedByCount "35" @default.
- W3006584476 countsByYear W30065844762020 @default.
- W3006584476 countsByYear W30065844762021 @default.
- W3006584476 countsByYear W30065844762022 @default.
- W3006584476 countsByYear W30065844762023 @default.
- W3006584476 crossrefType "journal-article" @default.
- W3006584476 hasAuthorship W3006584476A5034006964 @default.
- W3006584476 hasAuthorship W3006584476A5056875120 @default.
- W3006584476 hasAuthorship W3006584476A5070626046 @default.
- W3006584476 hasAuthorship W3006584476A5087130062 @default.
- W3006584476 hasBestOaLocation W30065844762 @default.
- W3006584476 hasConcept C111919701 @default.
- W3006584476 hasConcept C116834253 @default.
- W3006584476 hasConcept C124101348 @default.
- W3006584476 hasConcept C136264566 @default.
- W3006584476 hasConcept C144133560 @default.
- W3006584476 hasConcept C155202549 @default.
- W3006584476 hasConcept C162324750 @default.
- W3006584476 hasConcept C206345919 @default.
- W3006584476 hasConcept C2778712577 @default.
- W3006584476 hasConcept C2780378061 @default.
- W3006584476 hasConcept C31258907 @default.
- W3006584476 hasConcept C41008148 @default.
- W3006584476 hasConcept C59822182 @default.
- W3006584476 hasConcept C79974875 @default.
- W3006584476 hasConcept C86803240 @default.
- W3006584476 hasConceptScore W3006584476C111919701 @default.