Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

João P. Papa (Autor) Outra; Rodrigo Y. M. Nakamura (Autor) Outra; Victor Hugo C. de Albuquerque (Autor) FEUP; Alexandre X. Falcão (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Expert Systems with Applications

Vol. 40 Nº 16

Páginas: 590-597

ISSN: 0957-4174

Editora: Elsevier

Indexação

ISI Web of Knowledge - 29 Citações

ISI Web of Science

Scopus - 44 Citações

COMPENDEX

INSPEC

Classificação Científica

FOS: Ciências da engenharia e tecnologias

CORDIS: Ciências Tecnológicas

Outras Informações

ID Authenticus: P-002-13Z

DOI: 10.1016/j.eswa.2012.07.062

Abstract (EN): The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8