In this paper an automatic procedure based on a machine learning approach is proposed to classify ductile cast iron specimens according to the American Society for Testing and Materials guidelines. The mechanical properties of a specimen are strongly influenced by the peculiar morphology of their graphite elements and useful characteristics, the features, are extracted from the specimens’ images; these characteristics examine the shape, the distribution and the size of the graphite particle in the specimen, the nodularity and the nodule count. The principal components analysis are used to provide a more efficient representation of these data. Support vector machines are trained to obtain a classification of the data by yielding sequential binary classification steps. Numerical analysis is performed on a significant number of images providing robust results, also in presence of dust, scratches and measurement noise.

Classification of ductile cast iron specimens: A machine learning approach / De Santis, Alberto; Iacoviello, Daniela; Di Cocco, Vittorio; Iacoviello, Francesco. - In: FRATTURA E INTEGRITÀ STRUTTURALE. - ISSN 1971-8993. - STAMPA. - 11:42(2017), pp. 231-238. [10.3221/IGF-ESIS.42.25]

Classification of ductile cast iron specimens: A machine learning approach

De Santis, Alberto;Iacoviello, Daniela
;
2017

Abstract

In this paper an automatic procedure based on a machine learning approach is proposed to classify ductile cast iron specimens according to the American Society for Testing and Materials guidelines. The mechanical properties of a specimen are strongly influenced by the peculiar morphology of their graphite elements and useful characteristics, the features, are extracted from the specimens’ images; these characteristics examine the shape, the distribution and the size of the graphite particle in the specimen, the nodularity and the nodule count. The principal components analysis are used to provide a more efficient representation of these data. Support vector machines are trained to obtain a classification of the data by yielding sequential binary classification steps. Numerical analysis is performed on a significant number of images providing robust results, also in presence of dust, scratches and measurement noise.
2017
Ductile cast irons; Graphite nodules; Machine learning approach; Mechanics of Materials; Mechanical Engineering
01 Pubblicazione su rivista::01a Articolo in rivista
Classification of ductile cast iron specimens: A machine learning approach / De Santis, Alberto; Iacoviello, Daniela; Di Cocco, Vittorio; Iacoviello, Francesco. - In: FRATTURA E INTEGRITÀ STRUTTURALE. - ISSN 1971-8993. - STAMPA. - 11:42(2017), pp. 231-238. [10.3221/IGF-ESIS.42.25]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1015498
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