Mostrar registro simples

dc.contributor.authorFragassa, Cristianopt_BR
dc.contributor.authorBabic, Matejpt_BR
dc.contributor.authorBergmann, Carlos Perezpt_BR
dc.contributor.authorMinak, Giangiacomopt_BR
dc.date.accessioned2019-06-25T02:39:50Zpt_BR
dc.date.issued2019pt_BR
dc.identifier.issn2075-4701pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/196242pt_BR
dc.description.abstractThe ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to o er acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 di erent machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results o er high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofMetals. Basel, Suíça. Vol. 9, no. 5 (May 2019), Art. 557, 21 p.pt_BR
dc.rightsOpen Accessen
dc.subjectMaterial properties predictionen
dc.subjectPropriedades dos materiaispt_BR
dc.subjectAnálise de dadospt_BR
dc.subjectExperimental data analysisen
dc.subjectductile/spheroidal cast iron (SGI)en
dc.subjectFerro fundidopt_BR
dc.subjectcompact graphite cast iron (CGI)en
dc.subjectRedes neurais artificiaispt_BR
dc.subjectMachine Learning (RF)en
dc.subjectPattern recognitionen
dc.subjectRandom Forest (RF)en
dc.subjectArtificial Neural Network (NN)en
dc.subjectk-nearest neighbours (kNN)en
dc.titlePredicting the tensile behaviour of cast alloys by a pattern recognition analysis on experimental datapt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001094369pt_BR
dc.type.originEstrangeiropt_BR


Thumbnail
   

Este item está licenciado na Creative Commons License

Mostrar registro simples