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.