Use of neural networks for quality assurance in concrete product manufacturing

Application of artificial neural networks (KNN) in quality assurance in the manufacturing of concrete products is discussed. KNN system are able to detect the characteristics of frequency spectrum deviations, and to classify the individual deviation types to optimize the frequency spectrum and to ensure product quality. Artificial neural networks has ability to learn on their own, to adapt themselves to the problem, and applied where no clear solution algorithm can be used. KNN application include the generalization and classification of data, speech recognition, abstraction of functions, and process control. KNN learning process could be coupled with the standard production cycle of the block machine in order to design a fully self-learning, adaptive system. The block height can be used as additional product quality criterion to be processed by the KNN.