The solder paste printing (SPP) is a critical procedure in a surface mount technology (SMT) based assembly line, which is one of the major attributes to the defect of the printed circuit boards (PCBs). The quality of SPP is influenced by multiple factors, such as the squeegee speed, pressure, the stencil separation speed, cleaning frequency, and cleaning profile. During printing, the printer environment is dynamically varying due to the physical change of solder paste, which can result in a dynamic variation of the relationships between the printing results and the influential factors. To reduce the printing defects, it is critical to understand such dynamic relationships. This research focuses on determining the printing performance during printing by implementing a wavelet filtering-based temporal recurrent neural network. To reduce the noise factor in the solder paste inspection (SPI) data, this research applies a three-dimensional dual-tree complex wavelet transformation for low-pass noise filtering and signal reconstruction. A recurrent neural network is utilized to model the performance prediction with low noise interference. Both printing sequence and process setting information are considered in the proposed recurrent network model. The proposed approach is validated using practical dataset and compared with other commonly used data mining approaches. The results show that the proposed wavelet-based multi-dimensional temporal recurrent neural network can effectively predict the printing process performance and can be a high potential approach in reducing the defects and controlling cleaning frequency. The proposed model is expected to advance the current research in the application of smart manufacturing in surface mount technology.

锡膏印刷（SPP）是基于表面贴装技术（SMT）的装配线中的关键步骤，这是造成印刷电路板（PCB）缺陷的主要属性之一。SPP的质量受多个因素的影响，例如刮板速度，压力，模板分离速度，清洗频率和清洗轮廓。在打印过程中，打印机环境由于焊膏的物理变化而动态变化，这可能导致打印结果与影响因素之间关系的动态变化。为了减少打印缺陷，了解这种动态关系至关重要。这项研究的重点是通过实现基于小波滤波的时间递归神经网络来确定打印过程中的打印性能。为了降低焊膏检查（SPI）数据中的噪声因子，本研究将三维双树复小波变换应用于低通噪声滤波和信号重建。利用递归神经网络对具有低噪声干扰的性能预测进行建模。在建议的循环网络模型中考虑了打印顺序和过程设置信息。所提出的方法已使用实际数据集进行了验证，并与其他常用数据挖掘方法进行了比较。结果表明，所提出的基于小波的多维时域递归神经网络可以有效地预测印刷过程的性能，是减少缺陷和控制清洗频率的高潜力方法。