Comparison of different intelligent methods for process and quality monitoring

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Abstract

We compare three computer-aided systems used for on-line process and quality monitoring in metal processing industries. In a running manufacturing process measurement data are taken, from which significant quality statements are extracted. For this we apply on one hand an artificial neural network, which learns to classify the data adequately by using given exemplary process states; on the other hand we designed a fuzzy logic system that carries out the same task knowledge-based. Furthermore we present investigations of fuzzy clustering techniques to obtain information about the process. Moreover, topology optimization by evolutionary algorithms is considered to obtain optimal structures of the multilayer perceptron used. The quality features extracted are then passed on to the next hierarchical level, where they are processed within the framework of an integrated manufacturing and quality control system.

Keywords

comparison
neural networks
rule-based fuzzy systems
fuzzy clustering
topology optimization

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