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Alberto De Santis http://orcid.org/0000-0001-5175-4951 Daniela Iacoviello http://orcid.org/0000-0003-3506-1455 Vittorio Di Cocco Francesco Iacoviello http://orcid.org/0000-0002-9382-6092

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.

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    How to Cite

    De Santis, A., Iacoviello, D., Di Cocco, V. and Iacoviello, F. (2017) “Classification of ductile cast iron specimens: a machine learning approach”, Frattura ed Integrità Strutturale, 11(42), pp. Pages 231–238. doi: 10.3221/IGF-ESIS.42.25.

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