Real-Time Stencil Printing Optimization Using a Hybrid Multi-Layer Online Sequential Extreme Learning and Evolutionary Search Approach

This article aims to develop a dynamic optimization model performing real-time control of a stencil printing process (SPP) by maintaining the optimal printer parameter settings. In a surface mount technology (SMT) assembly line, stencil printing is a major process that affects the yield of printed circuit boards (PCBs). During printing, environmental changes may induce the PCB’s printing results to deviate from initial optimal outcomes. To consistently improve the system performance, a real-time adaptation of the printer settings is an effective and cost-efficient approach. This research proposes a hybrid online optimization model by using online learning to predict real-time SPP volumes and an evolutionary search (ES) technique to determine the optimal settings. The prediction model investigates the printing volumes’ transfer efficiency (TE) in averages and standard deviations (SDs) with relevant features. From the model selection of the online-based learning, the multi-layer online sequential extreme learning machine (MOSELM) shows outstanding prediction performance with
$R^{2}$
values of 97% for volume averages and 81% for SDs. From the real implicational results, the system achieves a
$C_{pk}=2.8$
, outperforming other advanced models. The proposed framework exhibits a good balance between accuracy and retraining efficiency, promising effective SMT assembly dynamic control.