Cast iron is a very common and useful metal alloy, characterized by its high carbon content (>4%) in the allotropic state of graphite. The correct shape and distribution of graphite are essential for ensuring that the material has the right properties. The present investigation examines the metallurgical and mechanical characterization of a spheroidal (nodular) cast iron, an alloy that derives its name and its excellent properties from the presence of graphite as spheroidal nodules. Experimental data are detected and considered from a data mining perspective, with the scope to extract new and little-known information. Specifically, a machine learning toolkit (i.e., Orange Data Mining) is used as a means of permitting supervised learners/classifiers (such as neural networks, k-nearest neighbors, and many others) to understand related metallurgical and mechanical features. An accuracy rate of over 90% can be considered as representative of the method. Finally, interesting considerations emerged regarding the dimensional effect on the variation in the solidification rates, microstructure, and properties.

Fragassa C. (2022). Investigating the Material Properties of Nodular Cast Iron from a Data Mining Perspective. METALS, 12(9), 1-27 [10.3390/met12091493].

Investigating the Material Properties of Nodular Cast Iron from a Data Mining Perspective

Fragassa C.
2022

Abstract

Cast iron is a very common and useful metal alloy, characterized by its high carbon content (>4%) in the allotropic state of graphite. The correct shape and distribution of graphite are essential for ensuring that the material has the right properties. The present investigation examines the metallurgical and mechanical characterization of a spheroidal (nodular) cast iron, an alloy that derives its name and its excellent properties from the presence of graphite as spheroidal nodules. Experimental data are detected and considered from a data mining perspective, with the scope to extract new and little-known information. Specifically, a machine learning toolkit (i.e., Orange Data Mining) is used as a means of permitting supervised learners/classifiers (such as neural networks, k-nearest neighbors, and many others) to understand related metallurgical and mechanical features. An accuracy rate of over 90% can be considered as representative of the method. Finally, interesting considerations emerged regarding the dimensional effect on the variation in the solidification rates, microstructure, and properties.
2022
Fragassa C. (2022). Investigating the Material Properties of Nodular Cast Iron from a Data Mining Perspective. METALS, 12(9), 1-27 [10.3390/met12091493].
Fragassa C.
File in questo prodotto:
File Dimensione Formato  
220_METALS, Dealing with the Material Properties of Cast Iron.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 882.88 kB
Formato Adobe PDF
882.88 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/926138
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 7
social impact