A Knowledge-Based System for Stencil Printing Process Planning and Control

錫膏印刷製程規劃與控制知識庫系統之發展

10.29977/JCIIE.200711.0008

蔡聰男(Tsung-Nan Tsai)

錫膏鋼板印刷 ； 表面黏著技術 ； 印刷電路板 ； 模糊類神經 ； 模糊品質損失函數 ； stencil printing ； surface mount technology ； printed circuit board ； neuro-fuzzy ； fuzzy quality loss function

工業工程學刊

24卷6期（2007 / 11 / 01）

513 - 521

英文

表面黏著技術(Surface mount technology, SMT)為電子工業最重要發展之一。SMT生產製程主要包含三個製造程序：錫膏印刷(Stencil printing process, SPP)、零件黏貼(Component placement)、及迴焊作業(Solder reflow)。SPP 具有高度作業複雜度與多品質特性，因而平均約60%之焊接缺點乃源自於SPP 控制之不當。本研究針對SPP發展出一套知識庫系統用以協助工程師從事製程規劃與焊接品質改善之日常作業。本研究中首先收集一個複合製程資料集，其結合實驗設計(3(上標8-3)=243)與統計品管歷史記錄，接著運用模糊分群演算法將此複合資料集進行資料群組與處理以移除衝突、不一致、多餘之資料點、及保證資料可靠度，接著運用模糊品質損失函數(Fuzzy quality loss function)將已分群資料集依據印刷品質績效損失程度進行資料之輸出項轉換，繼而應用模糊類神經(Neuro-fuzzy)資料學習過程以將SPP建模並擷取製程知識以建構知識庫。最後經由客製化程式撰寫與開發出一套具有圖形化介面之知識庫系統以協助工程師從事SPP輸出項預測與評估整體印刷績效。經由實務資料驗證顯示，本系統已於實務上協助提昇案例工廠之焊接品質水準與生產系統生產力。

Surface mount technology (SMT) is one of the most important developments in electronic industry. A surface mount assembly (SMA) has three consecutive manufacturing steps: solder paste stencil printing, component placement, and solder reflow. Stencil printing process (SPP) involves highly operation complexity and has multiple quality characteristics, and averagely accounts for 60% of soldering defects in SMA. This work presents a knowledgebased system for SPP planning and control to upgrade soldering quality level and system performance. A hybrid data set contains a 3(superscript 8-3) experimental design and statistical process control (SPC) records was collected firstly, and followed by data processing for removing conflicted, inconsistent, and redundant samples through a fuzzycluster algorithm. The output columns of the clustered data were then transformed using fuzzy quality loss function (FQLF) with respect to the dissatisfaction of printing performance. The neuro-fuzzy technique was adapted to model and learn SPP knowledge from the transformed data set into a SPP knowledge base. Finally, a GUI man-machine interface was developed to help engineers in predicting responses and evaluating the overall SPP performance. The empirical evaluations of soldering quality and productivity demonstrate the effectiveness and efficiency of this proposed system.

1. Arafeh, L.,H. Singh,S. K. Putatunda(1999).A neuron fuzzy approach to material processing.IEEE Transactions on System, Man, and Cybernetics,29,362-370.
2. Bao, X.,N. C. Lee,R. B. Raj,K. P. Rangan,A. Maria(1997).Engineering solder paste performance through controlled stress rheology analysis.Soldering and Surface Mount Technology,10,26-35.
3. Bolloju, N.(1999).Decision model formulation of subjective classification problem-solving knowledge using neuro-fuzzy classifier and its effectiveness.International Journal of Approximate Reasoning,21,197-213.
4. Chi, S.-C,L. C. Hsu(2001).A fuzzy Taguchi experimental method for problems with multi-attribute quality characteristics and its application on plasma ARC welding.Journal of the Chinese of Industrial Engineers,18,97-110.
5. Coma, O.,C. Mascle,M. Balazinski(2004).Application of a fuzzy decision support system in a design for assembly methodology.International Journal of Computer Integrated Manufacturing,17,83-94.
6. Franklin M. F.(1984).Constructing tables of minimum aberration pn−m designs.Technometrics,26,225-232.
7. He, D.,N. N. Ekere,M. A. Currie(1998).The behavior of solder pastes in stencil printing with vibrating squeegee.IEEE Transactions on Component, Packaging, and Manufacturing Technology,21,317-324.
8. Hsieh, K. L.(1999).Ph.D. thesis, National Chiao-Tung University, Taiwan.Taiwan:National Chiao-Tung University.
9. Inform Software Corporation(2004).fuzzy TECH user`s manual (Version 5.5).
10. Itoh, M.(1999).Technical Report.Tokyo, Japan:KOKI Company Limited.
11. Jang, J. S.(1993).ANFIS: adaptive network-based fuzzy inference systems.IEEE Transactions on System, Man, and Cybernetics,22,665-685.
12. Kosko, B.(1992).Neural Networks and Fuzzy Systems.New Jersey:Prentice-Hall.
13. Liu, Y.,L. Zuo,T. Cheng(2000).A neural network based fuzzy learning controller and its experimental application to milling.International Journal of Computer Integrated Manufacturing,13,461-466.
14. Lotfi, A.,A. C. Tsoi(1996).Learning fuzzy inference systems using an adaptive membership function scheme.IEEE Transactions on System, Man, and Cybernetics,26,326-332.
15. Markstein, H. W.(1997).Controlling the variables in stencil printing.Electronic Packaging and Production,37,48-56.
16. Morris J. R.,T. Wojcik(1991).Stencil printing of solder paste for fine-pitch surface mount assembly.IEEE Transactions on Components, Hybrids, and Manufacturing Technology,14,560-566.
17. Pan, J.,G. L. Tonkay,R. H. Storer,R. M. Sallade,D. J. Leandri(2004).Critical variables of solder paste stencil printing for micro-BGA and fine pitch QFP.IEEE Transactions on Packaging and Manufacturing Technology,27,125-132.
18. Perona, M.(1998).Manufacturing conformity assessment through Taguchi`s quality loss function.International Journal of Quality & Reliability Management,15,931-946.
19. Sarvar, F.,P. P. Conway(1998).Effective modeling of the reflow soldering process: Use of a modeling tool for product and process design.IEEE Transactions on Component, Packaging and Manufacturing Technology,21,165-171.
20. Zadeh, L.(1965).Fuzzy sets.Information Control,8,338-353.
21. Zimmermann, H. J.(1996).Fuzzy Set Theory and Its Applications.Massachusetts:Kluwer Academic.