IDEAS home Printed from https://ideas.repec.org/a/wsi/srlxxx/v29y2022i03ns0218625x22500354.html
   My bibliography  Save this article

Modeling And Multi-Response Optimization Of Abrasive Water Jet Machining Using Ann Coupled With Nsga-Ii

Author

Listed:
  • ABHIMANYU K. CHANDGUDE

    (School of Mechanical Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, Maharashtra-411038, India)

  • SHIVPRAKASH B. BARVE

    (School of Mechanical Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, Maharashtra-411038, India)

Abstract

This paper aims to develop a predictive model and optimize the performance of the abrasive water jet machining (AWJM) during machining of carbon fiber-reinforced plastic (CFRP) epoxy laminates composite through a unique approach of artificial neural network (ANN) linked with the nondominated sorting genetic algorithm-IIÂ (NSGA-II). Initially, 80 AWJM experimental runs were carried out to generate the data set to train and test the ANN model. During the experimentation, the stand-off distance (SOD), water pressure, traverse speed and abrasive mass flow rate (AMFR) were selected as input AWJM variables and the average surface roughness and kerf width were considered as response variables. The established ANN model predicted the response variable with mean square error of 0.0027. Finally, the ANN coupled NSGA-II algorithm was applied to determine the optimum AWJM input parameters combinations based on multiple objectives.

Suggested Citation

  • Abhimanyu K. Chandgude & Shivprakash B. Barve, 2022. "Modeling And Multi-Response Optimization Of Abrasive Water Jet Machining Using Ann Coupled With Nsga-Ii," Surface Review and Letters (SRL), World Scientific Publishing Co. Pte. Ltd., vol. 29(03), pages 1-10, March.
    • Handle: RePEc:wsi:srlxxx:v:29:y:2022:i:03:n:s0218625x22500354
    DOI: 10.1142/S0218625X22500354
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0218625X22500354
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0218625X22500354?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.

    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:wsi:srlxxx:v:29:y:2022:i:03:n:s0218625x22500354. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/srl/srl.shtml .

    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.