FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2956046114> ?p ?o ?g. }

• W2956046114 endingPage "729" @default.
• W2956046114 startingPage "721" @default.
• W2956046114 abstract "Additive manufacturing (AM), also known as three-dimensional printing, is gaining increasing attention from academia and industry due to the unique advantages it has in comparison with traditional subtractive manufacturing. However, AM processing parameters are difficult to tune, since they can exert a huge impact on the printed microstructure and on the performance of the subsequent products. It is a difficult task to build a process–structure–property–performance (PSPP) relationship for AM using traditional numerical and analytical models. Today, the machine learning (ML) method has been demonstrated to be a valid way to perform complex pattern recognition and regression analysis without an explicit need to construct and solve the underlying physical models. Among ML algorithms, the neural network (NN) is the most widely used model due to the large dataset that is currently available, strong computational power, and sophisticated algorithm architecture. This paper overviews the progress of applying the NN algorithm to several aspects of the AM whole chain, including model design, in situ monitoring, and quality evaluation. Current challenges in applying NNs to AM and potential solutions for these problems are then outlined. Finally, future trends are proposed in order to provide an overall discussion of this interdisciplinary area." @default.
• W2956046114 created "2019-07-12" @default.
• W2956046114 creator A5010555261 @default.
• W2956046114 creator A5014691077 @default.
• W2956046114 creator A5023717595 @default.
• W2956046114 creator A5041289789 @default.
• W2956046114 creator A5052050917 @default.
• W2956046114 date "2019-08-01" @default.
• W2956046114 modified "2023-10-14" @default.
• W2956046114 title "Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives" @default.
• W2956046114 cites W1498436455 @default.
• W2956046114 cites W1600545872 @default.
• W2956046114 cites W179875071 @default.
• W2956046114 cites W1932140642 @default.
• W2956046114 cites W1950162932 @default.
• W2956046114 cites W1977177161 @default.
• W2956046114 cites W2004915807 @default.
• W2956046114 cites W2014579544 @default.
• W2956046114 cites W2016034459 @default.
• W2956046114 cites W2023381719 @default.
• W2956046114 cites W2030362608 @default.
• W2956046114 cites W2043301592 @default.
• W2956046114 cites W2051363668 @default.
• W2956046114 cites W2065657250 @default.
• W2956046114 cites W2082674896 @default.
• W2956046114 cites W2093500299 @default.
• W2956046114 cites W2095576930 @default.
• W2956046114 cites W2099446180 @default.
• W2956046114 cites W2112796928 @default.
• W2956046114 cites W2130522179 @default.
• W2956046114 cites W2138767560 @default.
• W2956046114 cites W2143366758 @default.
• W2956046114 cites W2156740851 @default.
• W2956046114 cites W2210796340 @default.
• W2956046114 cites W2250894080 @default.
• W2956046114 cites W2503161491 @default.
• W2956046114 cites W2524869550 @default.
• W2956046114 cites W2552227324 @default.
• W2956046114 cites W2563395460 @default.
• W2956046114 cites W2587991918 @default.
• W2956046114 cites W2595834889 @default.
• W2956046114 cites W2601051689 @default.
• W2956046114 cites W2622422653 @default.
• W2956046114 cites W2734256217 @default.
• W2956046114 cites W2750470740 @default.
• W2956046114 cites W2765931665 @default.
• W2956046114 cites W2766660291 @default.
• W2956046114 cites W2769833465 @default.
• W2956046114 cites W2771371125 @default.
• W2956046114 cites W2772428090 @default.
• W2956046114 cites W2784327700 @default.
• W2956046114 cites W2794251843 @default.
• W2956046114 cites W2803236799 @default.
• W2956046114 cites W2809775572 @default.
• W2956046114 cites W2897822284 @default.
• W2956046114 cites W2919115771 @default.
• W2956046114 cites W2952027030 @default.
• W2956046114 cites W2963121817 @default.
• W2956046114 cites W2963748441 @default.
• W2956046114 cites W3099859964 @default.
• W2956046114 cites W4210952225 @default.
• W2956046114 cites W4210984920 @default.
• W2956046114 doi "https://doi.org/10.1016/j.eng.2019.04.012" @default.
• W2956046114 hasPublicationYear "2019" @default.
• W2956046114 type Work @default.
• W2956046114 sameAs 2956046114 @default.
• W2956046114 citedByCount "254" @default.
• W2956046114 countsByYear W29560461142019 @default.
• W2956046114 countsByYear W29560461142020 @default.
• W2956046114 countsByYear W29560461142021 @default.
• W2956046114 countsByYear W29560461142022 @default.
• W2956046114 countsByYear W29560461142023 @default.
• W2956046114 crossrefType "journal-article" @default.
• W2956046114 hasAuthorship W2956046114A5010555261 @default.
• W2956046114 hasAuthorship W2956046114A5014691077 @default.
• W2956046114 hasAuthorship W2956046114A5023717595 @default.
• W2956046114 hasAuthorship W2956046114A5041289789 @default.
• W2956046114 hasAuthorship W2956046114A5052050917 @default.
• W2956046114 hasBestOaLocation W29560461141 @default.
• W2956046114 hasConcept C111472728 @default.
• W2956046114 hasConcept C111919701 @default.
• W2956046114 hasConcept C119857082 @default.
• W2956046114 hasConcept C127413603 @default.
• W2956046114 hasConcept C13736549 @default.
• W2956046114 hasConcept C138885662 @default.
• W2956046114 hasConcept C142362112 @default.
• W2956046114 hasConcept C150799807 @default.
• W2956046114 hasConcept C153349607 @default.
• W2956046114 hasConcept C154945302 @default.
• W2956046114 hasConcept C189950617 @default.
• W2956046114 hasConcept C199360897 @default.
• W2956046114 hasConcept C201995342 @default.
• W2956046114 hasConcept C2779530757 @default.
• W2956046114 hasConcept C2780451532 @default.
• W2956046114 hasConcept C2780801425 @default.
• W2956046114 hasConcept C41008148 @default.
• W2956046114 hasConcept C50644808 @default.
• W2956046114 hasConcept C98045186 @default.

• next