A neural network-based prediction model for fine pitch stencil-printing quality in surface mount assembly
The soldering problems in surface mount assembly can represent considerable production cost increases and yield loss. About 60% of the soldering defect problems can be attributed to the solder paste stencil printing process. This paper proposes to solve a solder-paste stencil-printing quality problem by a neural network approach. Employment of a neuro-computing approach allows multiple inputs to the generation of multiple outputs. In this study, the inputs are composed of eight important factors in modeling the nonlinear behavior of the stencil-printing process for predicting deposited paste volumes. A 38-3 fractional factorial experimental design is conducted to efficiently collect structured data used for neural network training and testing. The results show that the proposed neural-network model is effective in solving a practical application.
neural network stencil-printing fractional factorial experimental design surface mount technology