IDEAS home Printed from https://ideas.repec.org/a/ids/injdan/v6y2014i3p261-274.html
   My bibliography  Save this article

Machining parameter optimisation by genetic algorithm and artificial neural network

Author

Listed:
  • Nafis Ahmad
  • Tomohisha Tanaka
  • Yoshio Saito

Abstract

Machining operations are used for creating surfaces by cutting away unwanted materials from workpieces. These operations are highly constrained and non-linear in nature. As a result traditional techniques are not suitable for machining parameter optimisation. Turning and milling are the two most commonly used machining operations where machining time or cost is minimised by optimising cutting parameters. The important constraints are maximum cutting force, machine power, available rotational speed, tool deflection, required surface finish cusp height etc. Here, a genetic algorithm (GA) and artificial neural network (ANN)-based hybrid approach is presented. The proposed approach gives more emphasis on searching optimum cutting parameters near boundaries of feasible and infeasible solution spaces. The optimum solution obtained by this method also does not violate constraints for a specific machining operation. An example of ball end milling operation is presented to explain this technique.

Suggested Citation

  • Nafis Ahmad & Tomohisha Tanaka & Yoshio Saito, 2014. "Machining parameter optimisation by genetic algorithm and artificial neural network," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 6(3), pages 261-274.
    • Handle: RePEc:ids:injdan:v:6:y:2014:i:3:p:261-274
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=63061
    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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jasleen Kaur & Khushdeep Dharni, 2022. "Application and performance of data mining techniques in stock market: A review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 219-241, October.

    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:injdan:v:6:y:2014:i:3:p:261-274. 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=282 .

    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.