Abstract
With the demand for various types of electronic products tend to be light, thin, short and small. The precision of parts and the density between the parts of the printed circuit board will increase. In order to control product cost effectively and enhance production quality and efficiency, the manufacturers must find the advanced manufacturing technology to improve surface mount technology. Most surface mount technology problems are correlated to solder paste printing (Pan et al. 1999, Huang 2010, Tsai 2008), and solder paste volume is an important quality measure of solder paste printing process. Therefore, controlling the solder paste volume is a key production technologies to obtain high–yield rate and maintain production effectiveness. Controlling the solder paste volume is an operating window problem, plenty of parameters will affect the output; if we only rely on engineer’s experiences to determine the values, the defect rate may increase owing to the unstable manufacturing process. This study proposes a systematic procedure for parameter optimization of the solder paste printing process. First, historical data and engineering knowledge are used to determine the significant factors. Second, the ideal squeegee pressure and optimal parameter settings are determined by using Taguchi methods. Also, we propose utilizing back-propagation neural network and desirability function and integrating genetic algorithm, particle swarm optimization, and simulate anneal to obtain the optimal parameter combination. A real case was implemented and analyzed to demonstrate the proposed approach’s effectiveness. The results show that our proposed method can reduce the waste of solder paste volume and substantially increase the operation window width in order to improve product quality.