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End milling: A neural approach for defining cutting conditions

Abstract:

The purpose of this paper is to present a new adaptive solution based on a feed forward neural network (FNN) in order to improve the task of selecting cutting conditions for milling operations. From a set of inputs parameters, such as work material, its mechanical properties, and the type of cutting tool, the system suggests feed rate and cutting speed values. The four main issues related to the neural network-based techniques, namely, the selection of a proper topology of the neural network, the input representation, the training method and the output format are discussed. The proposed network was trained using a set of inputs parameters provided by cutting operations manuals and tool manufacturers catalogues. Some tests and results show that adaptative solution proposed yields performance improvements. Finally, future work and potential applications are outlined.

Año de publicación:

2008

Keywords:

    Fuente:

    scopusscopus

    Tipo de documento:

    Conference Object

    Estado:

    Acceso restringido

    Áreas de conocimiento:

    • Ingeniería de manufactura
    • Aprendizaje automático

    Áreas temáticas:

    • Física aplicada
    • Métodos informáticos especiales

    Contribuidores:

    Durán O.Durán O.
    Nibaldo RodríguezNibaldo Rodríguez
    Consalter L.A.Consalter L.A.