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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/8559

    Título
    Optimizing the operating conditions in a high precision industrial process using soft computing techniques
    Autor
    Villar, José R.
    Corchado, EmilioAutoridad UBU Orcid
    Sedano, Javier
    Curiel Herrera, Leticia ElenaAutoridad UBU Orcid
    Villar, José Ramón
    Publicado en
    Expert Systems. 2012, V. 29, n. 3, p. 276-299
    Editorial
    Blackwell Publishing
    Fecha de publicación
    2012-07
    ISSN
    0266-4720
    DOI
    10.1111/j.1468-0394.2011.00588.x
    Abstract
    This interdisciplinary research is based on the application of unsupervized connectionist architectures in conjunction with modelling systems and on the determining of the optimal operating conditions of a new high precision industrial process known as laser milling. Laser milling is a relatively new micro-manufacturing technique in the production of high-value industrial components. The industrial problem is defined by a data set relayed through standard sensors situated on a laser-milling centre, which is a machine tool for manufacturing high-value micro-moulds, micro-dies and micro-tools. The new three-phase industrial system presented in this study is capable of identifying a model for the laser-milling process based on low-order models. The first two steps are based on the use of unsupervized connectionist models. The first step involves the analysis of the data sets that define each case study to identify if they are informative enough or if the experiments have to be performed again. In the second step, a feature selection phase is performed to determine the main variables to be processed in the third step. In this last step, the results of the study provide a model for a laser-milling procedure based on low-order models, such as black-box, in order to approximate the optimal form of the laser-milling process. The three-step model has been tested with real data obtained for three different materials: aluminium, cooper and hardened steel. These three materials are used in the manufacture of micro-moulds, micro-coolers and micro-dies, high-value tools for the medical and automotive industries among others. As the model inputs are standard data provided by the laser-milling centre, the industrial implementation of the model is immediate. Thus, this study demonstrates how a high precision industrial process can be improved using a combination of artificial intelligence and identification techniques.
    Palabras clave
    Unsupervized learning
    Exploratory projection pursuit
    Modelling systems
    Industrial applications
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/8559
    Versión del editor
    https://doi.org/10.1111/j.1468-0394.2011.00588.x
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    • Artículos GICAP
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