Fracture and Structural Integrity e-ISSN 1971-8993 Open access
SI: Russian mechanics contributions for Structural Integrity Vol. 18, No. 70 (2024)

Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy

Author and affiliation details
  • Mikhail Verezhak Institute of Continuous Media Mechanics of the Ural Branch of Russian Academy of Science (ICMM UB RAS), Russia ORCID iD0000-0003-2278-9439
  • Aleksei Vshivkov Institute of Continuous Media Mechanics of the Ural Branch of Russian Academy of Science (ICMM UB RAS), Russia ORCID iD0000-0002-7667-455X
  • Elena Gachegova Institute of Continuous Media Mechanics of the Ural Branch of Russian Academy of Science (ICMM UB RAS), Russia
  • Maria Bartolomei Institute of Continuous Media Mechanics of the Ural Branch of Russian Academy of Science (ICMM UB RAS), Russia ORCID iD0009-0003-3193-7605
  • Alexander Mayer Chelyabinsk State University (CSU), Russia ORCID iD0000-0002-8765-6373
  • Sathya Swaroop Vellore Institute of Technology, India
DOI
10.3221/IGF-ESIS.70.07
Pages
121-132
Published
Licence
CC BY 4.0
Published inVol. 18, No. 70 (2024)Go to the issue

Abstract

The paper is devoted to the development of the method of laser shock peening (LSP) of metals. To optimize the mode of LSP for Ti-6Al-4V specimens a deep learning model for predicting residual stresses by laser shock peening was developed. A numerical-experimental method was used to carry out the model training, in which an experimental study of the effect of different processing mode on the depth and distribution of residual stresses was carried out. The Johnson-Cook model was used as the governing relationship for modeling the dynamic deformation process. At the second stage, the problem of static equilibrium of a body with a plastically deformed area was numerically solved to determine residual stresses. The results of research on determination of the optimal configuration of the deep learning model showed that when using sinusoidal activation function of the neural network with 4 hidden layers and the number of neurons 10, the best level of accuracy in solving the problem is achieved. The obtained model allows us to optimally determine the LSP mode according to the given limitations of values and depth of residual stresses.

Keywords

Article history

  1. ReceivedUntil the next stage: 16 days
  2. RevisedUntil the next stage: 0 days
  3. AcceptedUntil the next stage: 13 days
  4. Published

How to cite

APA

Verezhak, M., Vshivkov, A., Gachegova, E., Bartolomei, M., Mayer, A., & Swaroop, S. (2024). Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy. Fracture and Structural Integrity, 18(70), 121–132. https://doi.org/10.3221/IGF-ESIS.70.07

IEEE

M. Verezhak, A. Vshivkov, E. Gachegova, M. Bartolomei, A. Mayer, and S. Swaroop, “Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy,” Fracture and Structural Integrity, vol. 18, no. 70, pp. 121–132, 2024, doi: 10.3221/IGF-ESIS.70.07.

MLA

Verezhak, Mikhail, et al. “Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy.” Fracture and Structural Integrity, vol. 18, no. 70, 2024, pp. 121–132. https://doi.org/10.3221/IGF-ESIS.70.07.

Chicago

Verezhak, Mikhail, Aleksei Vshivkov, Elena Gachegova, Maria Bartolomei, Alexander Mayer, and Sathya Swaroop. 2024. “Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy.” Fracture and Structural Integrity 18 (70): 121–132. https://doi.org/10.3221/IGF-ESIS.70.07.

Harvard

Verezhak, M., Vshivkov, A., Gachegova, E., Bartolomei, M., Mayer, A. and Swaroop, S. (2024) ‘Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy’, Fracture and Structural Integrity, 18(70), pp. 121–132. doi: 10.3221/IGF-ESIS.70.07.

Vancouver

Verezhak M, Vshivkov A, Gachegova E, Bartolomei M, Mayer A, Swaroop S. Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy. Fracture and Structural Integrity. 2024;18(70):121-132. doi:10.3221/IGF-ESIS.70.07

BibTeX

@article{verezhak2024, author = {Verezhak, Mikhail and Vshivkov, Aleksei and Gachegova, Elena and Bartolomei, Maria and Mayer, Alexander and Swaroop, Sathya}, title = {Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy}, journal = {Fracture and Structural Integrity}, year = {2024}, volume = {18}, number = {70}, pages = {121--132}, doi = {10.3221/IGF-ESIS.70.07}, url = {https://doi.org/10.3221/IGF-ESIS.70.07}, issn = {1971-8993} }

RIS

TY - JOUR AU - Verezhak, Mikhail AU - Vshivkov, Aleksei AU - Gachegova, Elena AU - Bartolomei, Maria AU - Mayer, Alexander AU - Swaroop, Sathya TI - Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy JO - Fracture and Structural Integrity T2 - Fracture and Structural Integrity PY - 2024 DA - 2024/08/14 VL - 18 IS - 70 SP - 121 EP - 132 DO - 10.3221/IGF-ESIS.70.07 UR - https://doi.org/10.3221/IGF-ESIS.70.07 SN - 1971-8993 ER -

Licence

Creative Commons Attribution 4.0 International

May be shared and adapted, provided appropriate credit is given. Copyright © 2024 Mikhail Verezhak, Aleksei Vshivkov, Elena Gachegova, Maria Bartolomei, Alexander Mayer, Sathya Swaroop.

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Authors are allowed to retain both the copyright and the publishing rights of their articles without restrictions.  

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