Abstract
In this study, the approach of using neural networks is implemented for demonstrating its effectiveness in the real-time application of microelectroform on the different geometry forms. Three back-propagation neural networks are established via the training process with the numerical database to predict the distributions of Sh/Shmax, ACf/Cfmax and I/Imax. Comparisons of the predictions with the test target vectors indicate that the averaged root-meansquared errors from three back-propagation neural networks are well within 4.15 agent technology. Then, to fabricate the microstructure of higher surface accurate, higher hardness, lower residual stress and can be duplicated perfectly. Nevertheless, the instant knowledge of micro-electrforming characteristics is practically needed for many industrial agents technology applications.
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References
Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Addison-Wesley, New York (1991)
Demuth, H., Beale, M.: Neural Network Toolbox User’s Guidance. The Math Works Inc., Natick, Massachusetts (1993)
Yang, H., Kang, S.W.: Improvement of Thickness Uniformity in Nickel Electroforming for the LIGA Process. J. Machine Tools and Manufacture 40, 1065–1072 (2000)
Zeller, R.L., Lsndau, U.: Electrochemically Active Surface Area. Voltammetric Charge Correlation for Ruthenium and Iridium Dioxide Electrodes. J. Electrochem. 138(4), 489–494 (2001)
Chan, K.C., Tan, H.J.: Numerical Analysis of an Inside-out Tube Inversion Process. J. of Materials Processing Technology 66, 130–136 (1997)
Lee, S.L., Lee, Y.F., Chang, M.H., Lin, J.C.: Pulse Plating Effects During Ni-W Electrode position. Corrosion Prevention & Control, 71-76 (1999)
Yin, K.M., Jan, S.L., Lee, C.C.: Current Pulse with Reverse Plating of Nickel-Iron Alloys in a Sulphate Bath. Surface and Coatings Technology 88, 219–225 (1996)
Andricacos, P.C., Tabib, J., Romankiw, L.T.: Stripping Voltammeter of Nickel-Iron Film Electrodeposited on Platinum Using a Rotating Ring-Disk Electrode. J. Electrochem. 135(5), 1172–1174 (1988)
Yamasaki, T., Schlobmacher, P., Ehrlich, K., Ogino, Y.: Formation of Amorphous Electrodeposited Ni-W Alloys and Their Nanocrystallization. Nanostructured Materials 10(3), 375–388 (1998)
Gould, R.D., Lopez, M.G.: Electrical Conductivity and Dynamics of Electroforming in Al-SiOx-Al Thin Film Sandwich Structure. Thin Solid Films 433, 315–320 (2003)
Hessami, S., Tobias, C.W.: A Mathematical Model for Anomalous Co deposition of Nickel-Iron on a Rotating Disk Electrode. J. Electrochem. 136, 3611–3616 (1989)
Pesco, A.M., Cheh, H.Y.: The Current Distribution within Plated Through-Holes: II. The Effect of Periodic Electrolysis. J. Electrochem. 136(2), 408–414 (1989)
Kondo, K., Fukui, K., Uno, K., Shinohara, K.: Shape Evolution of Electrodeposited Copper Bumps. J. Electrochem. 143(6), 1880–1886 (1996)
Georgiadou, M.: Modeling Current Density Distribution in Electrochemical Systems. Electrochemical Acta, 48 (2003) 4089-4095.
Duchanoy, C., Lapicque, F.: Current Distributions on Patterned Electrodes in Laminar Flow. Chemical Engineering Science 55, 1115–1126 (2000)
Beck, U., Smith, D.T., Reiners, G., Dapkunas, S.J.: Mechanical Properties of SiO2 and Si3N4 Coatings: A BAM/NIST Co-Operative Project. Thin Solid Films 332, 164–171 (1998)
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Shiah, SW., Chang, PY., Heh, TY., Lin, PH. (2008). Real-Time Neural Networks Application of Micro-Electroforming for Different Geometry Forms. In: Nguyen, N.T., Jo, G.S., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2008. Lecture Notes in Computer Science(), vol 4953. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78582-8_3
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DOI: https://doi.org/10.1007/978-3-540-78582-8_3
Publisher Name: Springer, Berlin, Heidelberg
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