Statistical data reduction lor manufacturing testing

Abstract

This paper deals with statistical reduction of the large volume of data that are generated by modern computerized testing in manufacturing environment. Two statistical algorithms have been developed to reduce dynamically, in real time, the amount of manufacturing testing, while maintaining the quality level, thus reducing the volume of data generated. In algorithm 1, homogeneous clusters of tests are formed based on statistical correlation coefficients, and then a subset of tests from each cluster are executed. In algorithm 2, the correlation coefficients arc used to derive statistical prediction equations, which are then used to decide if certain test are to be performed on an item. A method and algorithm for statistically discarding manufacturing test data (associated with an item) is given in algorithm 3. Algorithm 4, deals with computerized stratification and sample selection of manufacturing test data. An method for computerized sampling based on test values, for retaining a subset of the data, is provided in the algorithm 5.