A New Solution for Automatic Microstructures Analysis from Images Based on a Backpropagation Artificial Neural Network

• Type: Article in International Scientific Journal
• Date: 2008

Authors

Victor Hugo C. de Albuquerque (Author) Other; Paulo C. Cortez (Author) Other; Auzuir R. de Alexandria (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Nondestructive Testing and Evaluation

Vol. 23 No. 4

Pages: 273-283

ISSN: 1058-9759

Publisher: Taylor & Francis

Indexing

ISI Web of Science

Scopus

COMPENDEX

Scientific classification

CORDIS: Technological sciences > Engineering

Other information

DOI: 10.1080/10589750802258986

Abstract (EN): This article presents a new solution to segment and quantify the microstructures from images of nodular, grey, and malleable cast irons, based on an artificial neural network. The neural network topology used is the multilayer perception, and the algorithm chosen for its training was the backpropagation. This solution was applied to 60 samples of cast iron images and results were very similar to the ones obtained by visual human tests. This was better than the information obtained from a commercial system that is very popular in this area. In fact, this solution segmented the images of microstructures materials more efficiently. Thus, we can conclude that it is a valid and adequate option for researchers, engineers, specialists, and professionals from materials science field to realise a microstructure analysis from images faster and automatically.

Language: English

Type (Professor's evaluation): Scientific