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Real-Time Neural Networks Application of Micro-Electroforming for Different Geometry Forms

  • Sheau-Wen Shiah
  • Pai-Yu Chang
  • Tzeng-Yuan Heh
  • Po-Hung Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4953)

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.

Keywords

Neural Networks Real-Time Micro-Electroforming 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sheau-Wen Shiah
    • 1
  • Pai-Yu Chang
    • 2
  • Tzeng-Yuan Heh
    • 1
  • Po-Hung Lin
    • 1
  1. 1.Chung Cheng Institute of TechnologyNational Defense UniversityTahsiTaiwan 335. R.O.C.
  2. 2.Fortune Institute of TechnologyTaiwan 842. R.O.C.

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