CLASSIFICATION OF COLOR CODED RESISTOR BASED ON POWER RATING

Main Article Content

Shubhangi Satish Katti
Nitin Madhukar Kulkarni

Abstract

This paper describes the application of Machine Vision for classification of used color coded resistors into four different categories viz. 1/4 watt,1/2 watt,1 watt and 2watt. Physical dimension of the color coded resistor has been considered as an important feature that provides the power handling capability of that specific resistor. The blob measurement method has been employed for differentiating the resistors based on their power rating. This method could be further used for classification of lead broken resistors based on the material used for fabrication, which would be useful for recovery of the material during recycling process as well as dimension based classification of all types of Resistors

Downloads

Download data is not yet available.

Article Details

Section
Articles
Author Biographies

Shubhangi Satish Katti, Fergusson College

Electronic Science Associate Professor

Nitin Madhukar Kulkarni, Fergusson College,Pune

Department of Electronics Science Associate Professor

References

Biswajit Debnath, Priyankar Roychowdhury,, Rayan Kundu.†Electronic Components (EC) Reuse and Recycling – A New Approach towards WEEE Managementâ€, International Conference on Solid Waste Management, 5IconSWM 2015 * Procedia Environmental Sciences 35 ( 2016 ) 656 – 668 1878-0296 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).doi: 10.1016/j.proenv.2016.07.060 Available online at www.sciencedirect.com

Yung-Sheng Chen; Jeng-Yau Wang; 18 July 2016 Computer vision on color-band resistor and its cost-effective diffuse light source design

J. of Electronic Imaging, 25(6), 061409 (2016). doi:10.1117/1.JEI.25.6.061409

J. M. R. Sanches and M. S. Piedade, “An Automatic Visual Inspection System of the Quality of Painting of Metal Film Mini Resistors,†presented at International Conference on Signal Processing Applications and Technology (ICSPAT'97), 1997.

M. Moganti, F. Ercal, C. H. Dagli, and S. Tsunekawa, “Automatic PCB Inspection Algorithms: A Survey,†Computer Vision and Image Understanding, vol. 63, pp. 287-313, 1996.

Griese.H.,:Potter,H,: Reichl H.â€Quality Assured automated Dissassembly of Electronic Components for Reuseâ€Electronics and Environment,2002 IEEE international Symposium. Pages 299-305.ISSN:1095-2020,ISBN:0-7803-7214-X

H. Li and J. C. Lin, “Using Fuzzy Logic to Detect Dimple Defects of Polished Wafer Surfaces,†IEEE Transactions on Industry Applications, vol. 30, pp. 1530-1543, 1994.

A. Mital, M. Govindaraju, and B. Subramani, “A Comparison between Manual and Hybrid Methods in Parts Inspection,†Integrated Manufacturing Systems, vol. 9, pp. 344-349, 1998.

E. R. Davies, “Automated Visual Inspection,†in Machine Vision, vol. Chapter 19, 2nd Edition ed: Academic Press, 1998, pp. 471-502.

Pryor, Gallagher Donovan; Goldstein, Alex; Tannenbaum, Allen Circuits Assembly;Aug2004, Vol. 15 Issue 8, p36

Keyence Machine Vision Application Giude www.keyence.com 2005

Machine Vision and Its Application Using IEEE 1394 Camera and Vision Assistant 7.1Jainy Sachdeva*, Indra

Gupta** XXXII NATIONAL SYSTEMS CONFERENCE, NSC 2008, December 17-19, 2008