Development of an optimal measuring device selection system using neural networks

An optimal measuring device selection system using neural networks is proposed. There are two major steps. First, the measuring information such as curvature, normal, type of surface, edge and facet approximation is extracted from the computer-aided design model. Second, the best suitable measuring device is determined using the neural network system based on the knowledge of the measuring parameters and measuring resources. An example of machine selection is implemented to evaluate the performance of the system.