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Reseach Article

Particle Swarm Optimization (PSO) based Tool Position Error Optimization

by Prasant Kumar Mahapatra, Spardha, Inderdeep Kaur Aulakh, Amod Kumar, Swapna Devi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 72 - Number 23
Year of Publication: 2013
Authors: Prasant Kumar Mahapatra, Spardha, Inderdeep Kaur Aulakh, Amod Kumar, Swapna Devi
10.5120/12683-9461

Prasant Kumar Mahapatra, Spardha, Inderdeep Kaur Aulakh, Amod Kumar, Swapna Devi . Particle Swarm Optimization (PSO) based Tool Position Error Optimization. International Journal of Computer Applications. 72, 23 ( June 2013), 25-32. DOI=10.5120/12683-9461

@article{ 10.5120/12683-9461,
author = { Prasant Kumar Mahapatra, Spardha, Inderdeep Kaur Aulakh, Amod Kumar, Swapna Devi },
title = { Particle Swarm Optimization (PSO) based Tool Position Error Optimization },
journal = { International Journal of Computer Applications },
issue_date = { June 2013 },
volume = { 72 },
number = { 23 },
month = { June },
year = { 2013 },
issn = { 0975-8887 },
pages = { 25-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume72/number23/12683-9461/ },
doi = { 10.5120/12683-9461 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:38:42.935685+05:30
%A Prasant Kumar Mahapatra
%A Spardha
%A Inderdeep Kaur Aulakh
%A Amod Kumar
%A Swapna Devi
%T Particle Swarm Optimization (PSO) based Tool Position Error Optimization
%J International Journal of Computer Applications
%@ 0975-8887
%V 72
%N 23
%P 25-32
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

High-precision tool positioning is one of the fundamental requirements for the industry now-a-days. Earlier, tool positioning and its verification were done using sensors etc. In this paper, an algorithm has been proposed to increase the tool positioning accuracy by analyzing the information obtained using CCD camera. The images of lathe tool are used for carrying out the experiments. Firstly, the images of lathe tool, before and after movement, are captured. From these images, the distance traversed by the tool is calculated which is the observed distance. Tool positioning can be achieved accurately if the errors arising out of target (distance expected to be traversed by the tool) and observed position of the tool are optimized. This paper addresses positional errors and presents an error optimization method using arithmetic measures such as mean, median and Particle Swarm Optimization (PSO) based nature-inspired technique. Finally, the results of the two arithmetic measures are compared with the results of PSO which shows the capability of PSO to converge towards the optimal solution.

References
  1. Kennedy J. and Eberhart R. 1995. Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, University of Western Australia, Perth, Western Australia (27 Nov. -1 Dec. ), vol. 4,1942-1948.
  2. Xiaohong R. , Weidong X. , Yong S. and Yinggao Y. 2011. Real-time thermal error compensation on machine tools using improved BP neural network. Proceedings of the International Conference on Electric Information and Control Engineering, Wuhan, China (April 15-17), 630-632.
  3. Zhitian W. , Yuanxin W. , Xiaoping H. and Meiping W. 2013. Calibration of Three-Axis Magnetometer Using Stretching Particle Swarm Optimization Algorithm. IEEE Transactions on Instrumentation and Measurement 62, 281-292.
  4. Sahoo N. C. , Ganguly S. and Das D. 2012. Multi-objective planning of electrical distribution systems incorporating sectionalizing switches and tie-lines using particle swarm optimization. Swarm and Evolutionary Computation 3, 15-32.
  5. Man To W. , Xiangjian H. and Wei-Chang Y. 2011. Image clustering using Particle Swarm Optimization. Proceedings of the IEEE Congress on Evolutionary Computation, New Orleans, USA (June 5-8), 262-268.
  6. Jurkovic J. , Korosec M. and Kopac J. 2005. New approach in tool wear measuring technique using CCD vision system. International Journal of Machine Tools and Manufacture 45, 1023-1030.
  7. Nanda S. J. 2009 Artificial immune systems: principle, algorithms and applications. Master Thesis. Rourkela, National Institute of Technology.
  8. Yadav R. and Mandal D. 2011. Optimization of Artificial Neural Network for Speaker Recognition using Particle Swarm Optimization. International Journal of Soft Computing and Engineering 1,80-84.
  9. Gong C. , Yuan J. and Ni J. 2000. Nongeometric error identification and compensation for robotic system by inverse calibration. International Journal of Machine Tools and Manufacture 40, 2119-2137.
  10. Al?c? G. , Jagielski R. , Ahmet ?ekercio?lu Y. and Shirinzadeh B. 2006. Prediction of geometric errors of robot manipulators with Particle Swarm Optimisation method. Robotics and Autonomous Systems 54, 956-966.
  11. Schutte J. F. (2005), The Particle Swarm Optimization Algorithm [PowerPoint slides]. Retrieved from https://www. google. co. in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDcQFjAA&url=https%3A%2F%2Fbitbucket. org%2F12er%2Fpso%2Fsrc%2Fe1371b5bba75%2Fdoc%2Fliterature%2FSlides%2FPSO_introduction. pdf&ei=tfiBUdmkAdHHrQeE14GgCA&usg=AFQjCNGcyiS37y_uhkL1bURIn3n502PvsA&sig2=C_2H0Bq3Q8dyOb2zpidAUw&bvm=bv. 45960087,d. bmk
  12. Toofani A. 2012. Solving Routing Problem using Particle Swarm Optimization. International Journal of Computer Applications 52, 16-18.
  13. Huanglin Z. , Yong S. and Haiyan Z. 2009. Thermal Error Compensation on Machine Tools Using Rough Set Artificial Neural Networks. Proceedings of the WRI World Congress on Computer Science and Information Engineering, Los Angeles, USA (31 Mar. - 2 April), 51-55.
  14. Navalertporn T. and Afzulpurkar N. V. 2011. Optimization of tile manufacturing process using particle swarm optimization. Swarm and Evolutionary Computation 1, 97-109.
  15. Gonzalez R. C. , Woods R. E. , and Eddins S. L. 2011. Digital Image Processing using MATLAB, 2nd ed. , Tata McGraw-Hill Education Private Ltd, India, pp. 511-516.
  16. Shih F. Y. and Wu Y. -T. 2004. Fast Euclidean distance transformation in two scans using a 3 × 3 neighborhood. Computer Vision and Image Understanding 93, 195-205.
  17. Optics and Optical Instruments Catalog, 70th Anniversary Edition, Edmund Optics Singapore Pvt. Ltd. , 18 Woodlands, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Tool positioning Error Optimization Particle Swarm Optimization Image Processing