IntelliSPC: a hybrid intelligent tool for on-line economical statistical process control
Statistical process control (SPC) has become one of the most commonly used tools, for maintaining an acceptable and stable level of quality characteristics in today's manufacturing. With the movement towards a computer integrated manufacturing (CIM) environment, computer based algorithms need to be developed to implement the various SPC tasks automatically. This paper presents a hybrid intelligent tool (IntelliSPC) in which a neural network based control chart pattern recognition system, an expert system based control chart alarm interpretation system and a quality cost simulation system were integrated for on-line SPC. IntelliSPC was designed to provide the quality practitioners with the status of the process (in-control or out-of-control), the plausible causes for the out-of-control situation and cost-effective actions against the out-of-control situation. This tool was intended to be implemented in a scenario where sample data are being collected on-line by automated inspection devices and monitored by control charts. An implementation example is provided to demonstrate how the proposed hybrid system could be usefully applied in a real-world automated production line. This work confirms the potential synergies of hybrid artificial intelligence (AI) techniques in a complex problem solving procedure, such as an automated SPC scheme.
statistical process control neural network control charts pattern recognition expert system quality cost stimulation