IDEAS home Printed from https://ideas.repec.org/a/igg/jamc00/v7y2016i1p65-78.html
   My bibliography  Save this article

Error Optimization of Machine Vision based Tool Movement using a Hybrid CLONALG and PSO Algorithm

Author

Listed:
  • Prasant Kumar Mahapatra

    (Central Scientific Instruments Organization, Chandigarh, India)

  • Anu Garg

    (Central Scientific Instruments Organization, Chandigarh, India)

  • Amod Kumar

    (Central Scientific Instruments Organization, Chandigarh, India)

Abstract

A machine vision system with single monochrome CCD camera and backlight was developed for tool positioning and verification. While evaluating the performance of the developed vision system, the experiments showed that the output of machine vision system was not comparable to the output of sensors embedded in motion stages. Inherent factors like Imaging setup, camera calibration, environmental effects etc. are responsible for the error. These errors must be minimized to achieve maximum efficiency of developed vision system. In this paper, a novel hybrid algorithm is proposed to optimize the tool position error. The proposed algorithm comprises of CLONALG (one of the techniques of Artificial Immune System) and Particle Swarm Optimization (PSO) (a global optimization algorithm). Hybrid algorithm is tested on tool movement ranging from 0.020 mm to 7 mm. Performance of proposed algorithm is evaluated and also compared with CLONALG and PSO algorithms individually.

Suggested Citation

  • Prasant Kumar Mahapatra & Anu Garg & Amod Kumar, 2016. "Error Optimization of Machine Vision based Tool Movement using a Hybrid CLONALG and PSO Algorithm," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 7(1), pages 65-78, January.
    • Handle: RePEc:igg:jamc00:v:7:y:2016:i:1:p:65-78
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJAMC.2016010104
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:igg:jamc00:v:7:y:2016:i:1:p:65-78. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.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.