Application of neural-based algorithms as statistical tools for quality control of manufacturing processes

The use of neural networks began to be applied because the traditional control charts used for monitoring manufacturing process, in some cases, did not provide the possibility of correctly and quickly signalling the existing causes of variation. In today's manufacturing environment, neural networks present increasing usefulness for implementing the automation of statistical process control. This chapter targets issues on the use of neural networks for quality control of manufacturing processes, concerning the way of operation of each network model, the network's architecture and the results provided. Applications of neural networks for pattern recognition and for detection of mean and/or variance shifts in process are discussed. Comparisons between the performances of the neural approach and those of traditional control charts are also presented. Results prove that the neural network model is a useful alternative to the existing control schemes.