Dimensional and angular measurements using least squares and neural networks

Abstract

In this study a machine vision approach is developed for dimensional and angular measurements of manufactured components comprising straight line segments. We aim at the measurements of distance between two parallel lines and angle between two intersecting lines using both least mean square (LMS) and artificial neural network (ANN) techniques. LMS models estimate the line parameters based on the sum of squared perpendicular distances, rather than the vertical distances, between the observed data points and the line. A set of 23 gauge blocks of varying sizes is used to evaluate the performance of the LMS line estimators. Experimental results show that the measurement errors of the LMS models are affected by the line length and orientation of digital images. ANN techniques are, therefore, used to adjust the measurement errors resulting from the LMS models. Two back-propagation neural networks are developed, one for measuring the distance between two parallel lines, and the other for measuring the angle between two intersecting lines. Experimental results show that the ANNs are very effective for correcting the measurement errors regardless of line lengths and orientations of digital images. A 90% improvement in measurement accuracy for the ANN compared to the LMS was achieved. By using the ANNs, the measurement accuracy and flexibility in manufacturing applications can be significantly improved.