Conference Paper

Process control in a high-noise environment using a limited number of measurements

Sch. of Electr. & Comput Eng., Georgia Inst. of Technol., Atlanta, GA, USA
DOI: 10.1109/ACC.2003.1239084 Conference: American Control Conference, 2003. Proceedings of the 2003, Volume: 1
Source: IEEE Xplore

ABSTRACT In this paper, we develop a hybrid control algorithm that produces control values for processes where only a limited number of function evaluations are available for the control law generation. This situation arises, for example, in stencil printing processes in printed circuit board manufacturing, where the cost associated with multiple function evaluations is prohibitive: The proposed control algorithm is given by a modified version of a constrained conjugated-gradient method, transitioned into a windowed-smoothed block-form of the least-squares affine estimator.

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    ABSTRACT: Ph.D. Magnus Egerstedt
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    ABSTRACT: This paper presents a neural network model for the stencil printing process (SPP) in surface-mount technology (SMT) manufacturing of printed circuit boards (PCBs). A practical model description that decomposes the overall steady-state process in independently modeled subspaces is provided. The neural network model can be updated in real-time procuring a method to control the process by dynamically searching the optimal set point of the control variables. The optimization is performed by minimizing the weighted mean squared error with respect to the desired solder brick height or volume; furthermore, in the case when multiple solutions exist, the set point that yields the lowest variance is used. The process simulator is mainly suitable for offline testing and debugging of more complex closed-loop control algorithms for the SPP optimization providing a common and realistic framework for algorithm performance evaluation. An important consideration in this paper is based on the fact that the estimation of the sampled moments of the probability distributions is made using a statistically significant number of data samples from each board, for each component type, for each printing direction, and for each pad orientation.
    IEEE Transactions on Electronics Packaging Manufacturing 02/2008; · 1.16 Impact Factor
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    ABSTRACT: The distillation of massive quantities of solder paste inspection data into relevant quality information allows rapid understanding of the low production yield in PCB assembly. The statistical diagnosis method proposed in this paper provides more meaningful insights into the defect mechanisms than traditional yield analysis methods, which can identify the assignable causes of defects and their effects on yield by integrating MLPCA and logistic regression model. This offers a systematic representation on the impacts of process condition changes to the variation of solder paste profile. The probabilistic latent variable model allows ML estimation to determine the latent space by iteratively maximizing the likelihood function. In contrast to standard PCA, this approach is also efficient for multivariate process analysis when some sample data are missing. The clustering algorithm over the projected regression coefficients onto the latent space is relatively easy to implement with affordable computational effort. Experimental study demonstrates that the statistical interpretation of solder defect distributions can be enhanced by intuitive pattern visualization for process fault identification and variation reduction.
    01/2009; , ISBN: 978-3-902613-53-0

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