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Fatigue limit prediction and analysis of nano-structured AISI 304 steel by severe shot peening via ANN

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Abstract

AISI 304 stainless steel is very widely used for industrial applications due to its good integrated performance and corrosion resistance. However, shot peening (SP) is known as one of the effectual surface treatments processes to provide superior properties in metallic materials. In the present study, a comprehensive study on SP of AISI 304 steel including 42 different SP treatments with a wide range of Almen intensities of 14–36 A and various coverage of 100–2000% was carried out. Varieties of experiments were accomplished for the investigation of the microstructure, grain size, surface topography, hardness and residual stresses as well as axial fatigue behavior. After experimental investigations, artificial neural networks modeling was carried out for parametric analysis and optimization. The results indicated that, treated specimens with higher severity had more desirable properties and performances.

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Authors and Affiliations

  1. Mechanical Engineering Department, Sharif University of Technology-International Campus, Kish Island, Iran

    Erfan Maleki

  2. Mechanical Engineering Department, Politecnico di Milano, Milan, Italy

    Erfan Maleki

  3. Mechanical Engineering Department, Karabuk University, Karabuk, Turkey

    Okan Unal

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  1. Erfan Maleki
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Appendix

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Table 4 Related information of the different SP treatments on steel AISI 304 and the correspondence measured properties with respect to the training and testing data set for developing the ANN
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4

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Maleki, E., Unal, O. Fatigue limit prediction and analysis of nano-structured AISI 304 steel by severe shot peening via ANN. Engineering with Computers 37, 2663–2678 (2021). https://doi.org/10.1007/s00366-020-00964-6

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