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SPC provides statistical techniques and graphical displays control charts to enable the quality of conformance to be monitored and special causes of process variability to be eliminated(Montgomery D.C. 2005). Combining with computer technology and the automatic control technology, SPC has realized the product quality on-line control. With online SPC system, a large amount of manufacturing process quality data can be collected and stored in SPC database. These data can be visited via the operation in SPC database, and many kinds of quality reports can be generated for specific using. However, this technology has not been all well applied in enterprises(Venkatesan G., 2003). In practical manufacturing process, after the operators getting the analyzed results from SPC system, they need to make decision to eliminate the fluctuation in manufacturing process. But it is largely dependent on the personal knowledge and experience level of the operator on-site. Under this specific situation, the decision results are uncertain, and it may also lead to the quality problems can’t be solved in time. So, it is very important to find appropriate methods to support the operators’ decision making, especially the techniques which can facilitate the operators make right decision in time. Considering the existing limitations discussed above, this research is prompted by the need to have a technique that will enable the intelligent acquisition of Failure Mode and Effects Analysis (FMEA) knowledge for the decision making in the quality control process. The study specifically looks at the intelligent knowledge searching and reasoning in FMEA repository, which can facilitate the operators decision making. In this chapter, FMEA knowledge sharing in the manufacturing process quality control will be discussed after the brief review of FMEA, and an application system named IQCS has been developed to manage the FMEA knowledge for the manufacturing process quality continual improvement.

The Application Research of Intelligent Quality Control Based on Failure Mode and Effects Analysis Knowledge Management