{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T18:15:56Z","timestamp":1771524956426,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643682242","type":"print"},{"value":"9781643682259","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,29]]},"abstract":"<jats:p>3D printing technologies define the essence of Additive Manufacturing and make possible the agile production of customized parts from different materials, with lower unit cost and waste generation. Currently, one of the most widespread 3D printer technologies is the Fused Deposition Modeling (FDM) type, which is the object of this paper. The choice of 3D printing equipment depends on the alignment of the purpose of use and technical knowledge to consider certain requirements. Therefore, this choice can be time-consuming and\/or imprecise. In this sense, this work aimed to classify FDM-type 3D printer models by applying the ELECTRE-MOr method, a Multi-criteria Decision Aiding (MCDA) method. As a result, based on a categorization between classes, the FABER 10 alternative was the only one that presented class A performance in all evaluated scenarios, based on criteria defined by the experts consulted in this study.<\/jats:p>","DOI":"10.3233\/faia210240","type":"book-chapter","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T17:46:51Z","timestamp":1635875211000},"source":"Crossref","is-referenced-by-count":32,"title":["Multicriteria Analysis in Additive Manufacturing: An ELECTRE-MOr Based Approach"],"prefix":"10.3233","author":[{"given":"Paula","family":"Drumond","sequence":"first","affiliation":[{"name":"Military Institute of Engineering, Brazil"}]},{"given":"Marcio Pereira","family":"Bas\u00edlio","sequence":"additional","affiliation":[{"name":"Military Police of the State of Rio de Janeiro, Brazil"}]},{"given":"Igor Pinheiro de Ara\u00fajo","family":"Costa","sequence":"additional","affiliation":[{"name":"Federal Fluminense University, Brazil"}]},{"given":"Daniel Augusto de Moura","family":"Pereira","sequence":"additional","affiliation":[{"name":"Federal University of Campina Grande, Brazil"}]},{"given":"Carlos Francisco Sim\u00f5es","family":"Gomes","sequence":"additional","affiliation":[{"name":"Federal Fluminense University, Brazil"}]},{"given":"Marcos","family":"dos Santos","sequence":"additional","affiliation":[{"name":"Military Institute of Engineering, Brazil"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Modern Management based on Big Data II and Machine Learning and Intelligent Systems III"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA210240","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T17:48:45Z","timestamp":1635875325000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA210240"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"ISBN":["9781643682242","9781643682259"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia210240","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,29]]}}}