This research aims to develop a dynamic prediction model to assist real-time decision making of a stencil printing process by maintaining high prediction accuracy of the printing process. In a Surface Mount Technology (SMT) assembly line, the stencil printing process (SPP) accounts for more than 50% of the defectiveness of printed circuit boards (PCBs). During the printing process, environmental changes such as humidity or wear of blades may induce the PCBs printing results to deviate from the target volume. Thus, real-time adjustment of the printer settings (e.g., printing parameters, clean cycles, etc.) based on prediction of printing volumes is critical to maintain a high printing performance. However, research has been limited in real time SPP control, which is partially due to the difficulties in predicting the paste volumes with high accuracy and time efficiency. To tackle the challenges, this research proposes novel online learning models for real-time SPP status prediction. The prediction model is implemented by selecting advanced online learning models to estimate the printing volumes in averages and standard deviations (SDs) considering different pad sizes with different clean ages, directions, printer parameters, etc. The model performances are evaluated in Root Mean Square Error (RMS E),R2, etc. From comparison, the Support Vector Regressor (SVR) shows outstanding prediction performance withR2values of 92% and 81% for volume averages and SDs. This research emphasizes the potential of using online learning as a preliminary process for effective real-scenario SMT assembly dynamic control.
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