Quantification of the microstructures of hypoeutectic white cast iron using mathematical morphology and an artificial neural network

被引：26

作者：

De Albuquerque V.H.C. ^{[1
]}

Tavares J.M.R.S. ^{[2
]}

Cortez P.C. ^{[3
]}

机构：

[1] Centro de Ciências Tecnológicas (CCT), Núcleo de Pesquisas Tecnológicas (NPT), Sala NPT/CCT, CEP, 60811-905, Fortaleza, Ceará, Av. Washington Soares

[2] Instituto de Engenharia Mecânica e Gestão Industrial (INEGI), Departamento de Engenharia Mecânica (DEMec), Faculdade de Engenharia da Universidade Do Porto (FEUP), 4200-465 Porto, Rua Dr. Roberto Frias, s/n

[3] Centro de Tecnologia (CT), Departamento de Engenharia de Teleinformática (DETI), Campus do PICI S/N, CEP, 60455-970, Fortaleza, Ceará, Av. Humberto Monte, s/n

来源：

International Journal of Microstructure and Materials Properties | 2010年 / 5卷 / 01期

关键词：

Image processing and analysis; Image quantification; Image segmentation;

D O I：

10.1504/IJMMP.2010.032501

中图分类号：

学科分类号：

摘要：

This paper describes an automatic system for segmentation and quantification of the microstructures of white cast iron. Mathematical morphology algorithms are used to segment the microstructures in the input images, which are later identified and quantified by an artificial neuronal network (ANN). A new computational system was developed because ordinary software could not segment the microstructures of this cast iron correctly, which is composed of cementite, pearlite and ledeburite. For validation purpose, 30 samples were analysed. The microstructures of the material in analysis were adequately segmented and quantified, which did not happen when we used ordinary commercial software. Therefore, the proposed system offers researchers, engineers, specialists and others, a valuable and competent tool for automatic and efficient microstructural analysis from images. Copyright © 2010 Inderscience Enterprises Ltd.

引用

页码：52 / 64

页数：12

共 16 条

[1]

Abdelhay A., Application of artificial neural networks to predict the carbon content and the grain size for carbon steels, Egyptian Journal of Solids, 25, pp. 229-243, (2005)

[2]

Bhadeshia H.K.D.H., Neural Networks and Genetic Algorithms in Materials Science and Engineering, (1994)

[3]

Bhadeshia H.K.D.H., Neural networks in materials science, ISIJ International, 39, pp. 966-979, (1999)

[4]

Biernacki R., Kozlowski J., Myszka D., Perzyk M., Prediction of properties of austempered ductile iron assisted by artificial neural network, Materials Science (Medagotyra), 12, pp. 11-15, (2006)

[5]

Callister W., Materials Science and Engineering: An Introduction, (2006)

[6]

De Albuquerque V.H.C., De Alexandria A.R., Cortez P.C., Tavares J.M.R.S., Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images, NDT & e International, 42, pp. 644-651, (2009)

[7]

De Albuquerque V.H.C., Cortez P.C., Alexandria A.R., Tavares J.M.R.S., A new solution for automatic microstructures analysis from images based on a backpropagation artificial neural network, Nondestructive Testing and Evaluation, 23, pp. 273-283, (2008)

[8]

Gonzalez R.C., Woods R.E., Digital Image Processing, (2008)

[9]

Haykin S., Neural Networks: A Comprehensive Foundation, (1994)

[10]

Kim I., Jeong Y., Lee C., Yarlagadda P., Prediction of welding parameters for pipeline welding using an intelligent system, The International Journal of Advanced Manufacturing Technology, 22, pp. 713-719, (2003)

← 1 2 →