Details
Original language | English |
---|---|
Title of host publication | ICCC 2009 |
Subtitle of host publication | IEEE 7th International Conference on Computational Cybernetics |
Pages | 113-117 |
Number of pages | 5 |
Publication status | Published - 2009 |
Event | IEEE 7th International Conference on Computational Cybernetics, ICCC 2009 - Palma de Mallorca, Spain Duration: 26 Nov 2009 → 29 Nov 2009 |
Publication series
Name | ICCC 2009 - IEEE 7th International Conference on Computational Cybernetics |
---|
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.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computational Theory and Mathematics
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
ICCC 2009: IEEE 7th International Conference on Computational Cybernetics. 2009. p. 113-117 5393950 (ICCC 2009 - IEEE 7th International Conference on Computational Cybernetics).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Quality control of a light metal die casting process using artificial neural networks
AU - Becker, Matthias
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77949607299&partnerID=8YFLogxK
U2 - 10.1109/ICCCYB.2009.5393950
DO - 10.1109/ICCCYB.2009.5393950
M3 - Conference contribution
AN - SCOPUS:77949607299
SN - 978-1-4244-5310-8
T3 - ICCC 2009 - IEEE 7th International Conference on Computational Cybernetics
SP - 113
EP - 117
BT - ICCC 2009
T2 - IEEE 7th International Conference on Computational Cybernetics, ICCC 2009
Y2 - 26 November 2009 through 29 November 2009
ER -