Surface analysis of cast aluminum by means of artificial vision and AI-based techniques

Resumen

An architecture for surface analysis of continuous cast aluminum strip is described. The data volume to be processed has forced up the development of a high-parallel architecture for high-speed image processing. An especially suitable lighting system has been developed for defect enhancing in metallic surfaces. A special effort has been put in the design of the defect detection algorithm to reach two main objectives: robustness and low processing time. These goals have been achieved combining a local analysis together with data interpretation based on syntactical analysis that has allowed us to avoid morphological analysis. Defect classification is accomplished by means of rule-based systems along with data-based classifiers. The use of clustering techniques is discussed to perform partitions in R n by SOM, divergency methods to reduce the feature vector applied to the data-based classifiers. The combination of techniques inside a hybrid system leads to near 100% classification success.