IDEAS home Printed from https://ideas.repec.org/a/ids/ijmtma/v36y2022i2-3-4p168-182.html
   My bibliography  Save this article

Optimisation method for NC machining parameters of mechanical mould based on artificial neural network

Author

Listed:
  • Renxing Wen

Abstract

In order to overcome the problems of low production profit and high processing cost existing in traditional methods, an optimisation method for NC machining parameters of mechanical mould based on artificial neural network is proposed. Considering the cutting speed, feed rate, cutting depth, machine power and spindle speed in the process of NC machining of mechanical mould, the maximum profit, minimum processing cost and maximum productivity are taken as the optimisation objectives, and the objective function of NC machining parameters optimisation of mechanical mould is constructed. The NC machining parameters of mechanical mould are taken as the input of parameter optimisation model, and the artificial neural network is used to solve the model. The experimental results show that the proposed method has high production profit, low processing cost, high productivity and good practical application effect.

Suggested Citation

  • Renxing Wen, 2022. "Optimisation method for NC machining parameters of mechanical mould based on artificial neural network," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 36(2/3/4), pages 168-182.
  • Handle: RePEc:ids:ijmtma:v:36:y:2022:i:2/3/4:p:168-182
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=123662
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijmtma:v:36:y:2022:i:2/3/4:p:168-182. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=21 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.