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Prediction of Surface Texture Characteristics in Turning of FRPs Using ANN

  • Conference paper
Book cover Engineering Applications of Neural Networks (EANN 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 383))

Abstract

The objective of the present study is to develop an artificial neural network (ANN) in order to predict surface texture characteristics for the turning performance of a fiber reinforced polymer (FRP) composite. Full factorial design of experiments was designed and conducted. The process parameters considered in the experiments were cutting speed and feed rate, whilst the depth of cut has been held constant. The corresponding surface texture parameters that have been studied are the Ra and Rt. A feed forward back propagation neural network was fitted on the experimental results. It was found that accurate predictions of performance can be achieved through the feed forward back propagation (FFBP) neural network developed for the surface texture parameters.

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Author information

Authors and Affiliations

  1. Dept. of Mechanical Engineering, Technological Education Institution of Western Macedonia, Greece

    Stefanos Karagiannis

  2. Dept. of Mechanical Engineering, Technological Education Institution of Larissa, Greece

    Vassilis Iakovakis & John Kechagias

  3. Dept. of Mechanical Engineering Educators, School of Pedagogical & Technological Education (ASPETE), GR-14121, Greece

    Nikos Fountas & Nikolaos Vaxevanidis

Authors
  1. Stefanos Karagiannis
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  2. Vassilis Iakovakis
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  3. John Kechagias
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  4. Nikos Fountas
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  5. Nikolaos Vaxevanidis
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Editor information

Editors and Affiliations

  1. Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200, Orestiada, Hellas

    Lazaros Iliadis

  2. Frederick University of Cyprus, Cyprus

    Harris Papadopoulos

  3. Faculty of Engineering and Computing, Coventry University, UK

    Chrisina Jayne

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© 2013 Springer-Verlag Berlin Heidelberg

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Karagiannis, S., Iakovakis, V., Kechagias, J., Fountas, N., Vaxevanidis, N. (2013). Prediction of Surface Texture Characteristics in Turning of FRPs Using ANN. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_15

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  • DOI: https://doi.org/10.1007/978-3-642-41013-0_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41012-3

  • Online ISBN: 978-3-642-41013-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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