A neural network classifier for the die casting industry

• Type: Article in International Conference Proceedings Book
• Date: 2000

Authors

João Cardoso (Author) FEUP; Nuno Fidalgo (Author) FEUP; Manuel Matos (Author) FEUP

Conference proceedings International

Title: Proceedings of the 11th Portuguese Conference on Pattern Recognition

Pages: 43-48

11th Portuguese Conference on Pattern Recognition

Porto, Portugal

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Physical sciences > Computer science > Systems design > Neural networks

Other information

Abstract (EN): This article describes an application of neural networks for on-line classification of defective parts on the die casting industry. The productive process is described with special emphasis on online data collection and treatment. Feature selection is a two step process. The first step uses the sequencial forward search strategy with a KNN classifier as the feature quality measure and the second step uses a neural network to asses the first step selected features relevance. The feedforward neural network has one hidden layer and uses the backpropagation learning rule. The neural network topology is determined using the error rate on a validation dataset. Analysis and results from a particular die cast real data are presented.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6