Data mining for discovering defect associations and patterns to improve product quality in printed circuit board assembly

To achieve operational excellence, companies must satisfy the customers' quality expectations while reducing the number of encountered defects and reworks. This can be achieved only if companies produce the product right at the first time. In this study, defects, which are encountered while producing printed circuit board cards are investigated via data mining rather than conventional methods. Both associations and sequential patterns of defects were analyzed to find out a common root cause that can trigger different kind of defects. Two different sets of defect data are investigated in this study. Apriori algorithm was selected for association rules, while Sequential Pattern Discovery using Equivalence Classes – SPADE algorithm was considered for sequential pattern mining. Defects are analyzed in two ways for association mining; card-based and year-based. On the other hand, in sequential pattern mining, associated defects relationship over time were investigated. During the investigations, there were huge number of rule sets to be analyzed and these rule sets are not easy to analyze or have a meaningful conclusion. Thus, meta rules approach was utilized to come up with reliable results. One goal of this thesis is to standardize this analysis method for different companies at different application areas. To achieve this goal, two companies' data were used to standardize the method and compare the results. R software is used to create an interface for visualization of data, which extends the standardization procedure. Within this thesis, exemplifications for organizing data, implementation methods for analysis, a procedure for generating meaningful rule sets as well as visualization tools for comparison of results is provided.