QUALITY MONITORING OF CONTINUOUS-FLOW PROCESSES

In this research, existing quality control techniques for monitoring continuous flow processes are evaluated. Autocorrelation is a common characteristic of continuous flow process data, and the effect of the autocorrelated data is modelled as an autoregressive time series model of order one or two. The process is simulated on the computer for various process parameters, and the effectiveness of a given statistical process control technique for detecting known process disturbances is evaluated by determining the average run length. Due to the limitations of existing statistical process control techniques, a recursive Kalman filter is proposed as an alternative for eliminating the autocorrelation from the process data. The modelled manufacturing process, the computer simulation results, and the recursive Kalman filter are summarized in this paper.