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RBF Neural Networks-Based Software Sensor for Aluminum Powder Granularity Distribution Measurement

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Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

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Abstract

For aluminum powder nitrogen atomizing process, it is important but difficult to establish the aluminum powder granularity distribution using real-time measurements of exiting online sensors. In this paper, a novel software sensor model based on RBF Neural Networks is presented to estimate the granularity distribution of aluminum powder by means of measurements of melted aluminum level and temperature, atomizing nitrogen temperature and pressure, and recycle nitrogen temperature and pressure combined with granularity statistics distribution of the aluminum powder. The software sensor model can be obtained by training the RBFNN offline or online iteratively. An error analysis is carried out to illustrate effectiveness of the proposed software sensor model of aluminum powder granularity distribution.

Supported by State Science & Technology Pursuing Project (2001BA204B01) of China and Foundation for University Key Teacher by the Ministry of Education of China.

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Author information

Authors and Affiliations

  1. Institute of Advanced Control Technology, Dalian University of Technology, Dalian, China, 116024

    Yonghui Zhang, Cheng Shao & Qinghui Wu

Authors
  1. Yonghui Zhang
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  2. Cheng Shao
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  3. Qinghui Wu
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Zhang, Y., Shao, C., Wu, Q. (2004). RBF Neural Networks-Based Software Sensor for Aluminum Powder Granularity Distribution Measurement. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_137

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_137

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

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