Toward Industry 4.0 in Surface Mount Technology: Smart Manufacturing in Stencil Printing Operations
Main Article Content
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.
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He, J., Cen, Y., Li, Y., Alelaumi, S. M., & Won, D. (2021). A novel placement method for mini-scale passive components in surface mount technology. The International Journal of Advanced Manufacturing Technology, 115(5), 1475-1485.
Khader, N., & Yoon, S. W., 2018. Stencil printing process optimization to control solder paste volume transfer efficiency. IEEE Trans. Compon. Packag. Manuf. Technol. 8(9), 1686-1694.
Kocsi, B., Matonya, M.M., Pusztai, L.P., Budai, I., 2020. Real-Time Decision-Support System for High-Mix Low-Volume Production Scheduling in Industry 4.0. Processes 2020, 8, 912.
Lee, Y. T., Kumaraguru, S., Jain, S., Robinson, S., Helu, M., Hatim, Q. Y., Rachuri, S., Dornfeld, D., Saldana, C. J., & Kumara, S. (2017). A Classification Scheme for Smart Manufacturing Systems' Performance Metrics. Smart and sustainable manufacturing systems, 1(1), 52–74. https://doi.org/10.1520/SSMS20160012
Lu, H., Wang, H., Yoon, S. W., Won, D., 2019. Real-time stencil printing optimization using a hybrid multi-layer online sequential extreme learning and evolutionary search approach, IEEE Trans. Compon. Packag. Manuf. Technol. 9 (12) (2019) 2490–2498.
Park, S., Lu, H., Wang, H., Yoon, S. W., & Won, D., 2019. Dynamic predictive modeling of solder paste volume with real time memory update in a stencil printing process. Procedia Manufacturing 38 (2019) 108-116.PCBA, F., 2019. PCB manufacturing solder paste printing common defects and solutions. Retrieved from https://www.jbpcba.com/Article/pcbmanufacturingso.html
Wang, H., Lu, H., Alelaumi, S., & Yoon, S. W., 2021. A wavelet based multi-dimensional temporal recurrent neural network for stencil printing performance prediction. Robotics and Computer-Integrated Manufacturing 71 (2021) 102129.
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D., 2018. Deep Learning for Smart Manufacturing: Methods and Applications. Journal of Manufacturing Systems 48 144-156
Yi, R. (2021, July 25). The history of Surface Mount Technology (SMT). RCY. Retrieved from https://www.szrcypcb.com/the-history-of-surface-mount-technology-smt/.