Abstract: In the process of quality control, certain interrelations exist between product faults and the inspection and selection of the related parts based on statistics theory. The data mining model is established in order to discover these interrelated rules, it includes quality forecast algorithm and quality association- association rule algorithm. The goal is to find rules in which believability is larger than the presetting value and to forecast the situation of the product quality in the future. The data mining tool is developed and the data warehouse and data market for generating the product quality information are constructed, and the discovering flows of predicting quality and finding interrelated rules based on data mining and grey theory are also designed. Finally, two examples of forecasting the situation of selection and inspection of electronic parts and finding classification of the fault modes are presented, and the algorithms’ availability and efficiency are verified.

Key words: Data mining, Association rule, Computer aided quality control(CAQC), Grey theory, Quality information database