Process optimization of SnCuNi soldering material using artificial parametric design

The European Union has implemented the directive restriction of hazardous substances (RoHS) prohibiting the uses of tin-lead solder. SAC305 (Sn96.5/Ag3.0/Cu0.5) has come into widespread use as a candidate soldering material in the electronics manufacturing industry. Nevertheless, the price of silver has increased dramatically in recent years. This study evaluates the feasibility of replacing the commonly used SAC305 with low cost SnCuNi (Sn99.25/Cu0.7/Ni0.05/Ge; SCN) solder alloy in wave soldering for high layer count printed circuit board. However, the melting temperature of SCN alloy is 227, 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }\text{ C}$$\end{document} higher than SAC305. The objective of this research is to investigate manufacturing issues and propose an optimal process. Process parameters such as soldering temperature and dwell time are determined to achieve the desired quality levels. Multiple quality characteristics, namely assembly yield and solder joint pull strength, are considered. Thus, this study compares two approaches, integration of principal component analysis/grey relational analysis and artificial neural networks (ANN) combined with genetic algorithms (GA), to resolve the problems of multiple quality characteristics. The results of verification test shows that samples prepared with the process scenario suggested by the ANN combined with GA are superior. The process scenario with maximum desirability value is 268.64 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{\circ }\text{ C}$$\end{document} soldering temperature and 7.42 s dwell time, indicating the recommended manufacturing process.