Quality control of a light metal die casting process using artificial neural networks

authored by

Matthias Becker

Abstract

In this work we present an approach that uses a neural net for an online control of the cooling process in light metal die casting industry. Normally the die casting process is controlled manually or semi-manually, and quality control is done well after the cooling process. In our approach we increase the product quality during the production process by monitoring the cooling process with an infra red camera and heating or cooling different parts of the mold. The control is done using a neural net, which has been trained with data from previous casting processes, where the quality has been judged by experts. We conclude that this approach is a feasible way to online monitor and increase product quality in die casting.

Organisation(s)

Human-Computer Interaction Section  

Type

Conference contribution

Pages

113-117

No. of pages

5

Publication date

2009

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Artificial Intelligence, Computational Theory and Mathematics, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

Electronic version(s)

https://doi.org/10.1109/ICCCYB.2009.5393950 (Access: Closed)