Control of complex manufacturing processes: A comparison of statistical process control methods with a radial basis function neural network

Manufacturing processes are increasingly subject to tighter control and more frequent monitoring, in many cases using real time data collection systems. It is now recognized that complex interactions of auto- and cross-correlation exist in data observations from process industries, batch processes, and the traditional parts industry. New control models that capture both multivariate and time series effects are needed to effectively monitor manufacturing processes. In this research, we investigate the ability of radial basis function neural networks to monitor and control complex manufacturing processes that exhibit both auto- and cross-correlation. We demonstrate that the radial basis function network is superior to three control models recently proposed for complex manufacturing processes: multivariate Shewhart, multivariate EWMA, and a feed forward neural network with logistic units trained by back-propagation (often called a back-propagation neural network).