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Recognition of features of parts subjected to motion using ARTMAP incorporated in a flexible vibratory bowl feeder system

Published online by Cambridge University Press:  10 February 2006

S.K. SIM ,
PATRICK S.K. CHUA ,
M.L. TAY  and
YUN GAO
S.K. SIM
Affiliation:
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
PATRICK S.K. CHUA
Affiliation:
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
M.L. TAY
Affiliation:
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
YUN GAO
Affiliation:
Sybase (Singapore) Pte. Ltd., Singapore
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Abstract

The recognition and identification of parts are important processes in modern manufacturing systems. Although machine vision systems have played an important role in these tasks, there are still challenges in performing these tasks in which parts may be in motion and subjected to noise. Using a flexible vibratory bowl feeder system as a test bed to simulate motion of parts subjected to noise, scanned signatures of part features are acquired using fiber optic sensors and a data acquisition system. Because neural networks have been shown to exhibit good pattern recognition capability, ARTMAP, a neural network that learns patterns under supervision, was incorporated into the feeder system. The pattern recognition capability of the feeder system is dependent on a set of parameters that characterized ARTMAP, the sampling rate of the data acquisition system, and the mean speed of the vibrating parts. The parameters that characterized ARTMAP are the size of an input vector, the vigilance, threshold value of the nonlinear noise suppression function, and the learning rate. Through extensive training and testing of the ARTMAP within the feeder system, it was shown that high success rates of recognition of parts features in motion under noisy conditions can be obtained provided these parameters of ARTMAP are appropriately selected.

Keywords

ART2ARTMAPProgrammable Vibratory Bowl Feeder
Type
Research Article
Information
AI EDAM , Volume 20 , Issue 1 , February 2006 , pp. 19 - 34
Copyright
2006 Cambridge University Press

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