Surface classification using artificial neural networks

Quality control tools in manufacturing industry would be significantly enhanced by the development of methods for the consistent and reliable classification of three-dimensionally imaged machined surfaces. Such tools would boost the capability of manufacturers to carry out inter- and intra-surface differentiation during the manufacturing phase of component life-cycles. This Paper presents an approach to such a classification based on artificial neural networks (ANN). ANN techniques are increasingly used to resolve demanding problems across the spectrum of engineering disciplines. They are particularly suited for handling classification problems, especially those dealing with noisy data and highly non-linear relationships. Furthermore, once trained, their operation gives them a distinct speed advantage over other technologies. In this Paper, the authors use adaptive resonance theory (ART2a) and back propagation (BP) neural networks to classify (differentiate between) a number of machined surfaces. The authors compare the results with those obtained from conventional methods to determine the effectiveness of the proposed technique.