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Comparison of Artificial Immune System and Particle Swarm Optimization Techniques for Error Optimization of Machine Vision Based Tool Movements

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Abstract

In conventional tool positioning technique, sensors embedded in the motion stages provide the accurate tool position information. In this paper, a machine vision based system and image processing technique for motion measurement of lathe tool from two-dimensional sequential images captured using charge coupled device camera having a resolution of 250 microns has been described. An algorithm was developed to calculate the observed distance travelled by the tool from the captured images. As expected, error was observed in the value of the distance traversed by the tool calculated from these images. Optimization of errors due to machine vision system, calibration, environmental factors, etc. in lathe tool movement was carried out using two soft computing techniques, namely, artificial immune system (AIS) and particle swarm optimization (PSO). The results show better capability of AIS over PSO.

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Acknowledgments

This work is supported by the Council of Scientific & Industrial Research (CSIR, India), New Delhi under the Network programme in collaboration with CSIR-CMERI, Durgapur. Authors are thankful to Director, CSIR-CSIO for his guidance during investigation.

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Authors and Affiliations

  1. Biomedical Instrumentation Division (V-2), CSIR-Central Scientific Instruments Organisation, Sector-30, Chandigarh, 160030, India

    Prasant Kumar Mahapatra, Spardha Sethi & Amod Kumar

  2. Department of Computer Science & Engineering, Chandigarh University, Gharuan, 160055, Punjab, India

    Spardha Sethi

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  1. Prasant Kumar Mahapatra
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Correspondence to Prasant Kumar Mahapatra.

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Mahapatra, P.K., Sethi, S. & Kumar, A. Comparison of Artificial Immune System and Particle Swarm Optimization Techniques for Error Optimization of Machine Vision Based Tool Movements. J. Inst. Eng. India Ser. C 96, 363–372 (2015). https://doi.org/10.1007/s40032-015-0181-1

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