Automated inspection of machine parts

Abstract

Describes a CAD-model-based machine vision system for dimensional inspection of machined parts, with emphasis on the theory behind the system. The original contributions of the work are: the use of precise definitions of geometric tolerances suitable for use in image processing, the development of measurement algorithms corresponding directly to these definitions; the derivation of the uncertainties in the measurement tasks; and the use of this uncertainty information in the decision-making process. Experimental results have verified the uncertainty derivations statistically and proved that the error probabilities obtained by propagating uncertainties are lower than those obtainable without uncertainty propagation.<>