Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/87099
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dc.titleNeural network modeling with confidence bounds: A case study on the solder paste deposition process
dc.contributor.authorHo, S.L.
dc.contributor.authorXie, M.
dc.contributor.authorTang, L.C.
dc.contributor.authorXu, K.
dc.contributor.authorGoh, T.N.
dc.date.accessioned2014-10-07T10:24:14Z
dc.date.available2014-10-07T10:24:14Z
dc.date.issued2001-10
dc.identifier.citationHo, S.L., Xie, M., Tang, L.C., Xu, K., Goh, T.N. (2001-10). Neural network modeling with confidence bounds: A case study on the solder paste deposition process. IEEE Transactions on Electronics Packaging Manufacturing 24 (4) : 323-332. ScholarBank@NUS Repository.
dc.identifier.issn1521334X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/87099
dc.description.abstractThe formation of reliable solder joints in electronic assemblies is a critical issue in surface mount manufacturing. Stringent control is placed in the solder paste deposition process to minimize soldering defects and achieve high assembly yield. Time series process modeling of the solder paste quality characteristics using neural networks (NN) is a promising approach that complements traditional control charting schemes deployed on-line. In this paper, we present the study of building a multilayer feedforward neural network for monitoring the solder paste deposition process performance. Modeling via neural networks provides not only useful insights in the process dynamics, it also allows forecasts of future process behavior to be made. Data measurements collected on ball grid array (BGA) and quad flat pack (QFP) packages are used to illustrate the NN technique and the forecast accuracies of the models are summarized. Furthermore, in order to quantify the errors associated with the forecasted point estimates, asymptotically valid prediction intervals are computed using nonlinear regression. Simulation results showed that the prediction intervals constructed give asonably satisfactory coverage percentages as compared to the nominal confidence levels. Process control using NN with confidence bounds provides more quality information on the performance of the deposition process for better decision making and continuous improvement.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/6104.980042
dc.sourceScopus
dc.subjectModeling and forecasting
dc.subjectNeural networks
dc.subjectNonlinear regression
dc.subjectPrediction intervals
dc.subjectProcess control
dc.subjectSolder paste deposition
dc.typeArticle
dc.contributor.departmentINDUSTRIAL & SYSTEMS ENGINEERING
dc.description.sourcetitleIEEE Transactions on Electronics Packaging Manufacturing
dc.description.volume24
dc.description.issue4
dc.description.page323-332
dc.description.codenITEPF
dc.identifier.isiut000173531700014
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