Fracture and Structural Integrity e-ISSN 1971-8993 Open access
Miscellanea Vol. 11, No. 42 (2017)

Classification of ductile cast iron specimens: a machine learning approach

Author and affiliation details
  • Alberto De Santis Corresponding author Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, via Ariosto 25, 00185, Rome, Italy ORCID iD0000-0001-5175-4951 desantis@dis.uniroma1.it
  • Daniela Iacoviello Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, via Ariosto 25, 00185, Rome, Italy ORCID iD0000-0003-3506-1455
  • Vittorio Di Cocco Department of Civil and Mechanical Engineering, Università di Cassino e del Lazio Meridionale, via G. Di Blasio 43, 03043 Cassino (Fr), Italy
  • Francesco Iacoviello Department of Civil and Mechanical Engineering, Università di Cassino e del Lazio Meridionale, via G. Di Blasio 43, 03043 Cassino (Fr), Italy ORCID iD0000-0002-9382-6092
DOI
10.3221/IGF-ESIS.42.25
Pages
Pages 231-238
Published
Licence
CC BY 4.0
Published inVol. 11, No. 42 (2017)Go to the issue

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.

Keywords

How to cite

APA

De Santis, A., Iacoviello, D., Di Cocco, V., & Iacoviello, F. (2017). Classification of ductile cast iron specimens: a machine learning approach. Fracture and Structural Integrity, 11(42), Pages 231–238. https://doi.org/10.3221/IGF-ESIS.42.25

IEEE

A. De Santis, D. Iacoviello, V. Di Cocco, and F. Iacoviello, “Classification of ductile cast iron specimens: a machine learning approach,” Fracture and Structural Integrity, vol. 11, no. 42, pp. Pages 231–238, 2017, doi: 10.3221/IGF-ESIS.42.25.

MLA

De Santis, Alberto, et al. “Classification of ductile cast iron specimens: a machine learning approach.” Fracture and Structural Integrity, vol. 11, no. 42, 2017, pp. Pages 231–238. https://doi.org/10.3221/IGF-ESIS.42.25.

Chicago

De Santis, Alberto, Daniela Iacoviello, Vittorio Di Cocco, and Francesco Iacoviello. 2017. “Classification of ductile cast iron specimens: a machine learning approach.” Fracture and Structural Integrity 11 (42): Pages 231–238. https://doi.org/10.3221/IGF-ESIS.42.25.

Harvard

De Santis, A., Iacoviello, D., Di Cocco, V. and Iacoviello, F. (2017) ‘Classification of ductile cast iron specimens: a machine learning approach’, Fracture and Structural Integrity, 11(42), pp. Pages 231–238. doi: 10.3221/IGF-ESIS.42.25.

Vancouver

De Santis A, Iacoviello D, Di Cocco V, Iacoviello F. Classification of ductile cast iron specimens: a machine learning approach. Fracture and Structural Integrity. 2017;11(42):Pages 231-238. doi:10.3221/IGF-ESIS.42.25

BibTeX

@article{desantis2017, author = {De Santis, Alberto and Iacoviello, Daniela and Di Cocco, Vittorio and Iacoviello, Francesco}, title = {Classification of ductile cast iron specimens: a machine learning approach}, journal = {Fracture and Structural Integrity}, year = {2017}, volume = {11}, number = {42}, pages = {Pages 231--238}, doi = {10.3221/IGF-ESIS.42.25}, url = {https://doi.org/10.3221/IGF-ESIS.42.25}, issn = {1971-8993} }

RIS

TY - JOUR AU - De Santis, Alberto AU - Iacoviello, Daniela AU - Di Cocco, Vittorio AU - Iacoviello, Francesco TI - Classification of ductile cast iron specimens: a machine learning approach JO - Fracture and Structural Integrity T2 - Fracture and Structural Integrity PY - 2017 DA - 2017/09/29 VL - 11 IS - 42 SP - Pages 231 EP - 238 DO - 10.3221/IGF-ESIS.42.25 UR - https://doi.org/10.3221/IGF-ESIS.42.25 SN - 1971-8993 ER -

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