Fuzzy modeling within the statistical process control framework

This paper links the well-known technique of statistical process control (SPC) monitoring to the concept of rule-based fuzzy modeling. A family of if ... then rules with fuzzy predicates describes the set of steady state input-output relationships when the process variations are due to process noise (common causes). The ability of the SPC method to on-line diagnose a change in the distribution of the process variables is used to identify a new operating point of the systems, and consequently the initiation of a new potential rule. The model is applied as a decision support tool to help identify the optimal changes of the inputs associated with the special causes and to minimize the time for their elimination. A case study on automotive paint process optimization that is based on this concept is presented.