Determination of hot- and cold-rolling texture of steels: A combined Bayesian Neural
The work reported in the present paper outlines the use of a combined artificial neural network model capable of fast online prediction of textures in low and extralow carbon steels. We approach the problem by a Bayesian framework neural network model that takes into account as input to the model th...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/65653 |
| Acceso en línea: | http://hdl.handle.net/10261/65653 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial neural networks Texture prediction Anisotropy Hot rolling Cold rolling Steel |
| Sumario: | The work reported in the present paper outlines the use of a combined artificial neural network model capable of fast online prediction of textures in low and extralow carbon steels. We approach the problem by a Bayesian framework neural network model that takes into account as input to the model the influence of 23 parameters describing the chemical composition and the thermomechanical processes, such as austenite and ferrite rolling, coiling, cold working and subsequent annealing, involved in the production route of low and extralow carbon steels. The output of the model is in the form of fibre texture data. The predictions of the network provide an excellent match to the experimentally measured data. The results presented in the present paper demonstrate that this model can help in optimising the normal anisotropy rm of steel products |
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