Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386072743> ?p ?o ?g. }
- W4386072743 endingPage "4537" @default.
- W4386072743 startingPage "4526" @default.
- W4386072743 abstract "Abstract Aluminum alloys are widely used in each sector of engineering because of their lower density coupled with higher strength compared to many existing alloys of other metals. Due to these unique characteristics, there is acceleration in demand and discovery of new aluminum alloys with targeted properties and compositions. Traditional methods of designing new materials with desired properties, like ‘domain specialists and trial-and-error ' approaches, are laborious and costly. These techniques also lead to the expansion of alloy search area. Also, high demand for recycling of aluminum alloys requires fewer alloy groups. We suggest a machine learning design system to reduce the number of grades in the 6XXX series of aluminum alloys by collecting the features involving chemical composition and tensile properties at T6 tempering state. This work demonstrates the efficiency of grouping the aluminum alloys into a number of clusters by a combined PCA and K-means algorithm. To understand the physics inside the clusters we used an explainable artificial intelligence algorithm and connected the findings with sound metallurgical reasoning. Through machine learning we will narrow down the search space of 6XXX series aluminum alloys to few groups. This work offers a useful method for reducing compositional space of aluminum alloys." @default.
- W4386072743 created "2023-08-23" @default.
- W4386072743 creator A5051513184 @default.
- W4386072743 creator A5084771155 @default.
- W4386072743 creator A5085371643 @default.
- W4386072743 creator A5092675765 @default.
- W4386072743 date "2023-08-22" @default.
- W4386072743 modified "2023-10-12" @default.
- W4386072743 title "Classification of T6 Tempered 6XXX Series Aluminum Alloys Based on Machine Learning Principles" @default.
- W4386072743 cites W1968641316 @default.
- W4386072743 cites W1968647673 @default.
- W4386072743 cites W1972630723 @default.
- W4386072743 cites W1977556410 @default.
- W4386072743 cites W1996119578 @default.
- W4386072743 cites W1997284061 @default.
- W4386072743 cites W2015244027 @default.
- W4386072743 cites W2034477739 @default.
- W4386072743 cites W2054804828 @default.
- W4386072743 cites W2055751730 @default.
- W4386072743 cites W2071558614 @default.
- W4386072743 cites W2094881122 @default.
- W4386072743 cites W2099242680 @default.
- W4386072743 cites W2118043271 @default.
- W4386072743 cites W2123306226 @default.
- W4386072743 cites W2128728535 @default.
- W4386072743 cites W2132055141 @default.
- W4386072743 cites W2217934174 @default.
- W4386072743 cites W2337110853 @default.
- W4386072743 cites W2338205709 @default.
- W4386072743 cites W2347129741 @default.
- W4386072743 cites W2434873530 @default.
- W4386072743 cites W2508122532 @default.
- W4386072743 cites W2568014457 @default.
- W4386072743 cites W2585152223 @default.
- W4386072743 cites W2604192278 @default.
- W4386072743 cites W2619405272 @default.
- W4386072743 cites W2732102225 @default.
- W4386072743 cites W2734691712 @default.
- W4386072743 cites W2755837508 @default.
- W4386072743 cites W2760202681 @default.
- W4386072743 cites W2770192582 @default.
- W4386072743 cites W2779222234 @default.
- W4386072743 cites W2782634521 @default.
- W4386072743 cites W2855289976 @default.
- W4386072743 cites W2888661076 @default.
- W4386072743 cites W2900500090 @default.
- W4386072743 cites W2921873493 @default.
- W4386072743 cites W2923537029 @default.
- W4386072743 cites W2940163937 @default.
- W4386072743 cites W2952061350 @default.
- W4386072743 cites W2970765526 @default.
- W4386072743 cites W2972418846 @default.
- W4386072743 cites W2976720228 @default.
- W4386072743 cites W3098905070 @default.
- W4386072743 cites W3142698583 @default.
- W4386072743 cites W3180309787 @default.
- W4386072743 cites W4248214415 @default.
- W4386072743 cites W4296019797 @default.
- W4386072743 cites W4296284597 @default.
- W4386072743 cites W593381567 @default.
- W4386072743 doi "https://doi.org/10.1007/s11837-023-06025-9" @default.
- W4386072743 hasPublicationYear "2023" @default.
- W4386072743 type Work @default.
- W4386072743 citedByCount "0" @default.
- W4386072743 crossrefType "journal-article" @default.
- W4386072743 hasAuthorship W4386072743A5051513184 @default.
- W4386072743 hasAuthorship W4386072743A5084771155 @default.
- W4386072743 hasAuthorship W4386072743A5085371643 @default.
- W4386072743 hasAuthorship W4386072743A5092675765 @default.
- W4386072743 hasBestOaLocation W43860727431 @default.
- W4386072743 hasConcept C112950240 @default.
- W4386072743 hasConcept C119857082 @default.
- W4386072743 hasConcept C127413603 @default.
- W4386072743 hasConcept C140009343 @default.
- W4386072743 hasConcept C143724316 @default.
- W4386072743 hasConcept C151730666 @default.
- W4386072743 hasConcept C154945302 @default.
- W4386072743 hasConcept C179133402 @default.
- W4386072743 hasConcept C18762648 @default.
- W4386072743 hasConcept C191897082 @default.
- W4386072743 hasConcept C192562407 @default.
- W4386072743 hasConcept C207948433 @default.
- W4386072743 hasConcept C2780026712 @default.
- W4386072743 hasConcept C41008148 @default.
- W4386072743 hasConcept C513153333 @default.
- W4386072743 hasConcept C78519656 @default.
- W4386072743 hasConcept C86803240 @default.
- W4386072743 hasConceptScore W4386072743C112950240 @default.
- W4386072743 hasConceptScore W4386072743C119857082 @default.
- W4386072743 hasConceptScore W4386072743C127413603 @default.
- W4386072743 hasConceptScore W4386072743C140009343 @default.
- W4386072743 hasConceptScore W4386072743C143724316 @default.
- W4386072743 hasConceptScore W4386072743C151730666 @default.
- W4386072743 hasConceptScore W4386072743C154945302 @default.
- W4386072743 hasConceptScore W4386072743C179133402 @default.
- W4386072743 hasConceptScore W4386072743C18762648 @default.
- W4386072743 hasConceptScore W4386072743C191897082 @default.
- W4386072743 hasConceptScore W4386072743C192562407 @default.