IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v28y2017i8d10.1007_s10845-015-1050-8.html
   My bibliography  Save this article

Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm)

Author

Listed:
  • Kumar Abhishek

    (National Institute of Technology)

  • V. Rakesh Kumar

    (National Institute of Technology)

  • Saurav Datta

    (National Institute of Technology)

  • Siba Sankar Mahapatra

    (National Institute of Technology)

Abstract

The present paper focuses on machining (turning) aspects of CFRP (epoxy) composites by using single point HSS cutting tool. The optimal setting i.e. the most favourable combination of process parameters (such as spindle speed, feed rate, depth of cut and fibre orientation angle) has been derived in view of multiple and conflicting requirements of machining performance yields viz. material removal rate, surface roughness, SR $$(\hbox {R}_{\mathrm{a}})$$ ( R a ) (of the turned product) and cutting force. This study initially derives mathematical models (objective functions) by using statistics of nonlinear regression for correlating various process parameters with respect to the output responses. In the next phase, the study utilizes a recently developed advanced optimization algorithm teaching–learning based optimization (TLBO) in order to determine the optimal machining condition for achieving satisfactory machining performances. Application potential of TLBO algorithm has been compared to that of genetic algorithm (GA). It has been observed that exploration of TLBO appears more fruitful in contrast to GA in the context of this case experimental research focused on machining of CFRP composites.

Suggested Citation

  • Kumar Abhishek & V. Rakesh Kumar & Saurav Datta & Siba Sankar Mahapatra, 2017. "Parametric appraisal and optimization in machining of CFRP composites by using TLBO (teaching–learning based optimization algorithm)," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1769-1785, December.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:8:d:10.1007_s10845-015-1050-8
    DOI: 10.1007/s10845-015-1050-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-015-1050-8
    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/s10845-015-1050-8?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. Elango Natarajan & Varadaraju Kaviarasan & Wei Hong Lim & Sew Sun Tiang & S. Parasuraman & Sangeetha Elango, 2020. "Non-dominated sorting modified teaching–learning-based optimization for multi-objective machining of polytetrafluoroethylene (PTFE)," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 911-935, April.
    2. Huifeng Su & Renzhuang Li & Ming Yang, 2021. "An experimental study of modified physical performance test of low-temperature epoxy grouting material for grouting joints with tenon and mortise," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 667-677, March.
    3. Pauline Ong & Chon Haow Chong & Mohammad Zulafif Rahim & Woon Kiow Lee & Chee Kiong Sia & Muhammad Ariff Haikal Ahmad, 2020. "Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 227-247, January.
    4. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.

    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:28:y:2017:i:8:d:10.1007_s10845-015-1050-8. 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.