IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v29y2018i7d10.1007_s10845-016-1193-2.html
   My bibliography  Save this article

ANN modelling and Elitist teaching learning approach for multi-objective optimization of $$\upmu $$ μ -EDM

Author

Listed:
  • Kalipada Maity

    (National Institute of Technology)

  • Himanshu Mishra

    (National Institute of Technology)

Abstract

Fabrication of micro-holes has been carried out in Inconel 718 using micro electrical discharge machining operation. Artificial neural network modelling has been carried out to predict Material Removal Rate, Overcut effect and Recast Layer thickness. The training, testing and validation data sets were collected by conducting experiments. It is observed that ANN is a powerful prediction tool. It provides agreeable results when experimental and predicted data are compared. Further optimization of the process variables has been carried out using different meta heuristic approaches like Elitist Teaching learning based optimization, Multi-Objective Differential Evolution and Multi-Objective Optimization using an Artificial Bee Colony algorithm. The comparisons are carried out to improve the accuracy of the model on the basis of Pareto front solutions.

Suggested Citation

  • Kalipada Maity & Himanshu Mishra, 2018. "ANN modelling and Elitist teaching learning approach for multi-objective optimization of $$\upmu $$ μ -EDM," Journal of Intelligent Manufacturing, Springer, vol. 29(7), pages 1599-1616, October.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:7:d:10.1007_s10845-016-1193-2
    DOI: 10.1007/s10845-016-1193-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-016-1193-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-016-1193-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Anshuman Kumar Sahu & Siba Sankar Mahapatra, 2021. "Prediction and optimization of performance measures in electrical discharge machining using rapid prototyping tool electrodes," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2125-2145, December.

    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:spr:joinma:v:29:y:2018:i:7:d:10.1007_s10845-016-1193-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.