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Neural Network Model Identification Studies to Predict Residual Stress of a Steel Plate Based on a Non-destructive Barkhausen Noise Measurement

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Machine Learning in Industry

Part of the book series: Management and Industrial Engineering ((MINEN))

Abstract

In this study, a systematic comparative study is made between selected ways for identification of a neural network model for the prediction of the residual stress of steel based on the non-destructive Barkhausen noise measurement. The compared approaches are the deterministic forward selection with and without filter and a stochastic genetic algorithm-based approach. All the algorithms make use of the extreme learning machine as a model basis. The main objective is to propose a systematic procedure for identifying a prediction model for the considered system. The results of this study show that the algorithmic approach might be considered necessary not only to reduce the effort for model selection but also to select models with high prediction performance. It was also found that the genetic algorithm proposed earlier by the authors is applicable for selecting a well-generalizing model to the system, but the performance of the deterministic selection techniques is also comparable to the genetic algorithm.

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Author information

Authors and Affiliations

  1. Process Metallurgy Research Unit, University of Oulu, P.O. Box 4300, 90014, Oulu, FI, Finland

    Tero Vuolio

  2. Environmental and Chemical Engineering, University of Oulu, P.O. Box 4300, 90014, Oulu, FI, Finland

    Olli Pesonen

  3. Department of Process and Environmental Engineering, University of Oulu, P.O. Box 4300, 90014, Oulu, FI, Finland

    Aki Sorsa

  4. Materials Science and Environmental Engineering, University of Tampere, P.O. Box 589, 33014, Tampere, Finland

    Suvi Santa-aho

Authors
  1. Tero Vuolio
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  2. Olli Pesonen
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  3. Aki Sorsa
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  4. Suvi Santa-aho
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Corresponding author

Correspondence to Tero Vuolio .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

    Shubhabrata Datta

  2. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    J. Paulo Davim

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Vuolio, T., Pesonen, O., Sorsa, A., Santa-aho, S. (2022). Neural Network Model Identification Studies to Predict Residual Stress of a Steel Plate Based on a Non-destructive Barkhausen Noise Measurement. In: Datta, S., Davim, J.P. (eds) Machine Learning in Industry. Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-75847-9_2

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  • DOI: https://doi.org/10.1007/978-3-030-75847-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75846-2

  • Online ISBN: 978-3-030-75847-9

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