Development of an integrated reflow soldering control system using incremental hybrid process knowledge

In this paper, we present an industrial application of intelligent knowledge-based reflow soldering control system to economically determine process parameters and assess the outputs for surface mount assembly. The system performance of surface mounting is significantly affected by three major interconnected process segments (including solder paste application, component placement, and solder reflow) and in combination. Many surface mount manufacturers have suffered both quality and productivity losses due to the lacks of effective line setup procedures, rapid process troubleshooting, and appropriate corrective action. This proposed system develops an integrated process control scheme, the framework of hybrid process knowledge extraction through neuro-fuzzy data training, and a knowledge-based system with a GUI man–machine interface for predicting and controlling the given system performance. The efficiency and effectiveness of the proposed system are illustrated using DPMO measurement and productivity analysis.