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Application of Artificial Neural Network to Predict the Effects of Severe Shot Peening on Properties of Low Carbon Steel

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Machining, Joining and Modifications of Advanced Materials

Part of the book series: Advanced Structured Materials ((STRUCTMAT,volume 61))

Abstract

Mechanical failures in most cases originate from the exterior layers of the components. It is considerably effective to apply methods and treatments capable to improve the mechanical properties on component’s surface. Surface nanocrystallization produced by severe plastic deformation (SPD) processes such as severe shot peening (SSP) is increasingly considered in the recent years. However, artificial intelligence systems such as artificial neural network (ANN) as an efficient approach instead of costly and time consuming experiments is widely employed to predict and optimize the science and engineering problems in the last decade. In the present study the application of ANN in predicting of SSP effective parameters has been investigated and evaluated. The Back propagation (BP) error algorithm is used to network’s training. In order to train the ANN, experimental tests on AISI 1017 mild steel specimens were conducted and the data was gathered. Testing of the ANN is carried out using experimental data not used during training. Almen intensity, residual stress, crystallite size, full width at half maximum (FWHM) and hardness were modeled. Different networks with different inputs are developed for modeling of each mentioned parameters. The Almen intensity, hardness, crystallite size, FWHM and residual stress have least mean error respectively for the accomplished modeling. The comparison of obtained results of ANN’s response and experimental values indicates that the networks are tuned well and the ANN can be used to predict the SSP effective parameters and it can be an alternative way for calculating of parameters of this process.

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Acknowledgment

The first author would like to thank Prof. G.H. Farrahi, Chairman of Mechanical Engineering Department of Sharif University of Technology for his help and guidance.

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Authors and Affiliations

  1. Mechanical Engineering Department, Sharif University of Technology—International Campus, Amirkabir Square, 7941776655, Kish Island, Iran

    Erfan Maleki

  2. School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran

    Gholam Hossein Farrahi & Khalil Sherafatnia

  3. Materials Life Estimation and Improvement Laboratory, Sharif University of Technology, Tehran, Iran

    Erfan Maleki, Gholam Hossein Farrahi & Khalil Sherafatnia

Authors
  1. Erfan Maleki
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  2. Gholam Hossein Farrahi
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  3. Khalil Sherafatnia
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Correspondence to Erfan Maleki .

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Editors and Affiliations

  1. Griffith School of Engineering, Griffith University, Southport, Queensland, Australia

    Andreas Öchsner

  2. Institut für Mechanik, Otto-von-Guericke-Universität, Magdeburg, Germany

    Holm Altenbach

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Maleki, E., Farrahi, G.H., Sherafatnia, K. (2016). Application of Artificial Neural Network to Predict the Effects of Severe Shot Peening on Properties of Low Carbon Steel. In: Öchsner, A., Altenbach, H. (eds) Machining, Joining and Modifications of Advanced Materials . Advanced Structured Materials, vol 61. Springer, Singapore. https://doi.org/10.1007/978-981-10-1082-8_5

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  • DOI: https://doi.org/10.1007/978-981-10-1082-8_5

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  • Online ISBN: 978-981-10-1082-8

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