Toward Industry 4.0 in Surface Mount Technology: Smart Manufacturing in Stencil Printing Operations

Abstract

As the foremost point of contact in surface mount technology (SMT) operations, the stencil/solder paste printer (SPP) is a major contributor to assembly defects in the printed circuit board (PCB) fabrication process. Initial printing defects lead to reductions in throughput, quality and reliability of the entire downstream system. Because of the high variation in SMT processes, a predictive, real-time machine learning (ML) algorithm to optimize printer settings based on the estimation of printing volumes is a promising solution to improving production performance and process capability. Building upon existing literature, this research discusses the implementation of a lab-trained ML model to predict optimal printing parameters and reduce changeover time within SPP processes through the training and testing of large solder paste inspection (SPI) datasets. To sustain growing market demand for smart electronics manufacturing (SEM), successful development and validation of a dynamic prediction system for optimizing printing parameters provides significant potential for improvements in first pass yield rates and system throughput.