A Dual-Tree Complex Wavelet Transform Simulation Model for Improved Noise Modeling and Prediction of Real-Time Stencil-Printing Process

被引:1
作者
Gupta, Rahul [1 ]
Cao, Nieqing [2 ]
Won Yoon, Sang [1 ]
Jin, Yu [1 ]
Won, Daehan [1 ]
机构
[1] SUNY Binghamton, Dept Syst Sci & Ind Engn, Binghamton, NY 13902 USA
[2] Xian Jiaotong Liverpool Univ, Sch Intelligent Mfg Ecosyst, Suzhou 215123, Peoples R China
来源
IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY | 2024年 / 14卷 / 10期
关键词
Printing; Noise; Predictive models; Accuracy; Manufacturing; Wavelet transforms; Solid modeling; Signal processing; simulation; smart manufacturing; stencil-printing process (SPP); surface mount technology (SMT); wavelet;
D O I
10.1109/TCPMT.2024.3449047
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This article presents a dynamic simulation model for the stencil-printing process (SPP) in surface mount technology (SMT) assembly lines, focusing on accurately replicating the real-time stencil printing while allowing adjustments to printer settings. The model offers a time and cost-effective alternative to the experiments and a reliable testing environment for researchers and technologists investigating advanced algorithms and strategic methodologies in SMT printing. SPP is influenced by various controllable factors, such as printer parameters. However, an additional challenge arises from uncontrollable environmental noise that affects the printing quality, leading to uneven solder paste application and machine precision that brings randomness to the results. Recognizing the need to mitigate the effects of this environmental noise and enhance the accuracy of the simulator, the proposed simulation model incorporates a dual-tree complex wavelet transform (DTCWT) algorithm. DTCWT used in this model addresses the challenge of environmental noise affecting the printing quality, showcasing an enhanced capability in noise reduction and signal clarity. The noise from the SPP data is modeled and extracted from the DTCWT model and introduced into the simulation model to improve the prediction accuracy. The simulation model demonstrated an improvement of 36% in Volume AVG and 62% in Volume STD accuracy on root-mean-squared error (RMSE), marking a significant advancement over the statistical simulator.
引用
收藏
页码:1872 / 1880
页数:9
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