Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy
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 mo...
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| Format: | Article |
| Language: | English |
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Gruppo Italiano Frattura
2024-08-01
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| Series: | Fracture and Structural Integrity |
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| Online Access: | https://fracturae.com/index.php/fis/article/view/5090 |
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| author | Mikhail Verezhak Aleksei Vshivkov Elena Gachegova Maria Bartolomei Alexander Mayer Sathya Swaroop |
| author_facet | Mikhail Verezhak Aleksei Vshivkov Elena Gachegova Maria Bartolomei Alexander Mayer Sathya Swaroop |
| author_sort | Mikhail Verezhak |
| collection | DOAJ |
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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.
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| format | Article |
| id | doaj-art-928feaedad194793b71dd7f665131bfe |
| institution | Kabale University |
| issn | 1971-8993 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Fracture and Structural Integrity |
| spelling | doaj-art-928feaedad194793b71dd7f665131bfe2025-02-03T09:55:20ZengGruppo Italiano FratturaFracture and Structural Integrity1971-89932024-08-01187010.3221/IGF-ESIS.70.07Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloyMikhail Verezhak0https://orcid.org/0000-0003-2278-9439Aleksei Vshivkov1https://orcid.org/0000-0002-7667-455XElena Gachegova2Maria Bartolomei3https://orcid.org/0009-0003-3193-7605Alexander Mayer4https://orcid.org/0000-0002-8765-6373Sathya Swaroop5Institute of Continuous Media Mechanics of the Ural Branch of Russian Academy of Science (ICMM UB RAS), RussiaInstitute of Continuous Media Mechanics of the Ural Branch of Russian Academy of Science (ICMM UB RAS), RussiaInstitute of Continuous Media Mechanics of the Ural Branch of Russian Academy of Science (ICMM UB RAS), RussiaInstitute of Continuous Media Mechanics of the Ural Branch of Russian Academy of Science (ICMM UB RAS), RussiaChelyabinsk State University (CSU), RussiaVellore Institute of Technology, India 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. https://fracturae.com/index.php/fis/article/view/5090Laser shock peeningDeep learningNumerical simulationTitanium alloyResidual stress |
| spellingShingle | Mikhail Verezhak Aleksei Vshivkov Elena Gachegova Maria Bartolomei Alexander Mayer Sathya Swaroop Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy Fracture and Structural Integrity Laser shock peening Deep learning Numerical simulation Titanium alloy Residual stress |
| title | Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy |
| title_full | Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy |
| title_fullStr | Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy |
| title_full_unstemmed | Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy |
| title_short | Application of deep learning for technological parameter optimization of laser shock peening of Ti-6Al-4V alloy |
| title_sort | application of deep learning for technological parameter optimization of laser shock peening of ti 6al 4v alloy |
| topic | Laser shock peening Deep learning Numerical simulation Titanium alloy Residual stress |
| url | https://fracturae.com/index.php/fis/article/view/5090 |
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