Predicting the tensile behaviour of cast alloys by a pattern recognition analysis on experimental data
dc.contributor.author | Fragassa, Cristiano | pt_BR |
dc.contributor.author | Babic, Matej | pt_BR |
dc.contributor.author | Bergmann, Carlos Perez | pt_BR |
dc.contributor.author | Minak, Giangiacomo | pt_BR |
dc.date.accessioned | 2019-06-25T02:39:50Z | pt_BR |
dc.date.issued | 2019 | pt_BR |
dc.identifier.issn | 2075-4701 | pt_BR |
dc.identifier.uri | http://hdl.handle.net/10183/196242 | pt_BR |
dc.description.abstract | The 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.mimetype | application/pdf | pt_BR |
dc.language.iso | eng | pt_BR |
dc.relation.ispartof | Metals. Basel, Suíça. Vol. 9, no. 5 (May 2019), Art. 557, 21 p. | pt_BR |
dc.rights | Open Access | en |
dc.subject | Material properties prediction | en |
dc.subject | Propriedades dos materiais | pt_BR |
dc.subject | Análise de dados | pt_BR |
dc.subject | Experimental data analysis | en |
dc.subject | ductile/spheroidal cast iron (SGI) | en |
dc.subject | Ferro fundido | pt_BR |
dc.subject | compact graphite cast iron (CGI) | en |
dc.subject | Redes neurais artificiais | pt_BR |
dc.subject | Machine Learning (RF) | en |
dc.subject | Pattern recognition | en |
dc.subject | Random Forest (RF) | en |
dc.subject | Artificial Neural Network (NN) | en |
dc.subject | k-nearest neighbours (kNN) | en |
dc.title | Predicting the tensile behaviour of cast alloys by a pattern recognition analysis on experimental data | pt_BR |
dc.type | Artigo de periódico | pt_BR |
dc.identifier.nrb | 001094369 | pt_BR |
dc.type.origin | Estrangeiro | pt_BR |
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