Neural network-based quality controllers for manufacturing systems

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Abstract
This paper demonstrates that neural networks can be used effectively for quality control of non-linear static time-variant processes where the process physics and mechanistic models are not well understood. The emphasis of the paper is on models for both identification and real-time process parameter design of manufacturing systems. Both multi-layer feed-forward perceptron networks and radial basis function networks have been used to monitor the process performance characteristics. An iterative inversion based approach for optimizing the process controllable parameters in the presence of noise variables is discussed. Simulation results reveal that the identification and parameter design schemes suggested are effective.

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