MIMO EWMA-CUSUM condition-based Statistical Process Control in Manufacturing Processes

Abstract

To meet the challenges of the big data age, an urgent requirement from diverse manufacturing industries is to develop a systematic time-variant methodology to make good use of the condition parameters to benefit more from the monitoring point of view. With condition-based Statistical Process Control (SPC), we develop a time-variant Exponentially Weighted Moving Average-Cumulative Sum (EWMA-CUSUM) anomaly detection mechanism which can monitor real-time multi-condition parameters, as well as multi-output quality characteristics simultaneously and efficiently. This technique enables the process user to conduct the visualization in real-time, thus, affording the representation of the information from huge volume of data. In order to demonstrate the implementation for the monitoring of a real manufacturing process, the Wire Electrochemical Tuning (WECT) process is adopted as a practical application. The proposed mechanism is superior to the conventional univariate charting mechanism by 18.75% in terms of detection accuracy and it has great potential to be employed in a large area of factorial applications.