Development of a soldering quality classifier system using a hybrid data mining approach

Soldering failures lead to considerable manufacturing costs in the electronics assembly industry. Soldering problems can be caused by improper parameter settings during paste stencil printing, component placement, the solder reflow process or combinations thereof in surface mount assembly (SMA). Data mining has emerged as one of the most dynamic fields in processing large manufacturing databases and process knowledge extraction. In this study, the integration of a probabilistic network of the SMA line and a hybrid data mining approach is employed to identify soldering defect patterns, classify soldering quality, and predict new instances according to significant process inputs. The hybrid data mining approach uses a two-stage clustering method that utilizes the self-organizing map (SOM) to derive the preliminary number of clusters and their centroids from the statistical process control (SPC) database, followed by the use of K-means to precisely classify instances into definite classes of soldering quality. The See5 induction system is then applied to induce the decision tree and ruleset to elucidate associations among the defect patterns, process parameters, and assembly yield. Finally, visual C++ programming codes are implemented for both production rule retrieval and graphical user interface establishment. The effectiveness of the proposed classifier is illustrated through a real-world application to resolve practical manufacturing problems.