Estimation of mechanical properties of steel strip in hot dip galvanising lines
In this paper, the application of data mining and artificial intelligence techniques stemming from other problem areas to the particular case of a galvanised steel manufacturing process, is presented. The main goal is to optimise the quality control of galvanised steel by developing a predictive mod...
| Autores: | , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2004 |
| País: | España |
| Recursos: | Universidad de La Rioja (UR) |
| Repositorio: | RIUR. Repositorio Institucional de la Universidad de La Rioja |
| OAI Identifier: | oai:portal.dialnet.es:doc/5bbc6853b750603269e806cc |
| Acesso em linha: | https://investigacion.unirioja.es/documentos/5bbc6853b750603269e806cc |
| Access Level: | acceso abierto |
| Palavra-chave: | Annealing Artificial Intelligence Data mining Hot dip galvanising line (HDGL) |
| Resumo: | In this paper, the application of data mining and artificial intelligence techniques stemming from other problem areas to the particular case of a galvanised steel manufacturing process, is presented. The main goal is to optimise the quality control of galvanised steel by developing a predictive model of the mechanical properties according to the chemical composition and manufacturing conditions in the annealing furnace. |
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