Interpreting statistical process control (SPC) charts using machine learning and expert system techniques

Statistical process control (SPC) charts are one of several tools used in quality control. The SPC quality control tool has been under-utilized due to the lack of experienced personnel able to identify and interpret patterns within the control charts. The Special Projects Office of the Center for Supportability and Technology Insertion (CSTI) has developed a hybrid machine-learning and expert-system software tool which automates the process of constructing and interpreting control charts. The software tool draws control charts, identifies various chart patterns, advises what each pattern means, and suggests possible corrective actions. The application is easily modifiable for process specific applications through simple modifications to the knowledge base portion using any word processing software. The authors discuss control charts, software functionality, software design, machine learning, and the expert system.<<ETX>>