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Comprehensive optimization of shot peening intensity using a hybrid model with AI-based techniques via Almen tests

  • Kadir Kaan Karaveli

    Kadir Kaan Karaveli graduated with a B.Sc. in Materials Engineering from Yıldırım Beyazıt University in 2016. He earned his M.Sc. in Advanced Materials and Nanotechnology from Abdullah Gül University in 2018 and is currently pursuing a PhD in Materials Science and Mechanical Engineering at the same university. Professionally, he works as an MRB Structural Design Engineer at Turkish Aerospace Industries. Karaveli has extensive expertise in metallic and composite structures, ballistic systems, and structural repair methodologies, with multiple publications in these areas.

     EMAIL logo     und Burak Bal

    Burak Bal received his PhD degree in Department of Mechanical Engineering from Koc University, in 2015. He is currently Professor in the Department of Mechanical Engineering, Abdullah Gül University in Türkiye. His research interest lies in the broad area of multi-scale experimental and computational mechanics of materials under extreme environments.
Veröffentlicht/Copyright: 3. Juni 2025
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Materials Testing
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Abstract

Shot peening is a crucial surface treatment technique that significantly improves the mechanical properties of metallic components, particularly their fatigue resistance and ability to withstand corrosion cracking. This study aims to optimize the shot peening process for aviation applications by evaluating and comparing various mathematical modeling and optimization techniques. Seven mathematical models were analyzed using a neuro-regression method (NRM), among which the second-order trigonometric non-linear (SOTN) model exhibited the highest reliability, achieving R2 values of 0.93 and 0.90 for training and testing datasets, respectively. To improve the model’s robustness, four optimization algorithms – differential evolution (DE), simulated annealing (SA), Nelder–Mead (NM), and random search (RS) – were applied to the SOTN model. Although each technique offered valuable insights, performance fluctuations across different intensity ranges necessitated the development of a hybrid optimization model that combines the strengths of all four methods. The hybrid model achieved a mean error of approximately 2.69 %, outperforming individual approaches and demonstrating strong potential for reliable shot peening optimization across a wide range of target intensities. These findings provide a comprehensive methodology for AI-based optimization of surface treatment processes in engineering applications.

Keywords: Almen tests; modeling; neuro-regression; process optimization; surface treatment
Corresponding author: Kadir Kaan Karaveli, Mechanical Engineering Department, Abdullah Gul University, Kayseri, 38080, Türkiye; and Turkish Aerospace Industries, Ankara, 06980, Türkiye, E-mail:

Funding source: Turkish Aerospace Industries

Award Identifier / Grant number: 2021-TUSAS-BAP-01

About the authors

Kadir Kaan Karaveli

Kadir Kaan Karaveli graduated with a B.Sc. in Materials Engineering from Yıldırım Beyazıt University in 2016. He earned his M.Sc. in Advanced Materials and Nanotechnology from Abdullah Gül University in 2018 and is currently pursuing a PhD in Materials Science and Mechanical Engineering at the same university. Professionally, he works as an MRB Structural Design Engineer at Turkish Aerospace Industries. Karaveli has extensive expertise in metallic and composite structures, ballistic systems, and structural repair methodologies, with multiple publications in these areas.

Burak Bal

Burak Bal received his PhD degree in Department of Mechanical Engineering from Koc University, in 2015. He is currently Professor in the Department of Mechanical Engineering, Abdullah Gül University in Türkiye. His research interest lies in the broad area of multi-scale experimental and computational mechanics of materials under extreme environments.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors states no conflict of interest.

  6. Research funding: This research was funded by Türk Havacılık ve Uzay Sanayii (Turkish Aerospace Industries), Award Number: 2021-TUSAS-BAP-01.

  7. Data availability: Not applicable.

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Published Online: 2025-06-03
Published in Print: 2025-07-28

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Auxetic behavior of Ti6Al4V lattice structures manufactured by laser powder bed fusion
  3. Hardness, shear strength, and microstructure of friction stir lap welded copper/aluminum using various parameters
  4. Influence of oscillating fiber laser welding process parameters on the fatigue response and mechanical performance of butt-jointed TWIP980 steels
  5. Fatigue analysis of TIG welded joints of dissimilar aluminum alloys
  6. Corrosion of self-piercing riveting joint in a Cl− and HSO3− environment
  7. Effect of annealing for stress relief on the surface integrity of gray cast iron with lamellar graphite after cavitation erosion
  8. Effects of boride coating on wear behaviour of biomedical grade Ti–45Nb alloy
  9. Effect of space holder agent on microstructural and mechanical properties of commercially pure titanium
  10. Impact and biaxial tensile-after impact behaviors of inter-ply hybrid carbon-glass/epoxy composite plates
  11. Improvement of the mechanical and wear properties of Al6061 alloys with quartz and SiC hybrid reinforcements by powder metallurgy
  12. Surface strain-based calculation of principal directional strains in multi-material carbon fiber laminates
  13. Tribological and mechanical behaviour of Al 359 composites reinforced with B4Cp, SiCp and flyash
  14. Effect of Al-based coatings deposited by EASC on exfoliation corrosion susceptibility of EN AW 7020-T6
  15. Green synthesis of CuO nanoparticles using Curcuma longa L. extract: composite dielectric and mechanical properties
  16. Comprehensive optimization of shot peening intensity using a hybrid model with AI-based techniques via Almen tests
https://doi.org/10.1515/mt-2024-0375
Schlagwörter für diesen Artikel
Almen tests; modeling; neuro-regression; process optimization; surface treatment

Artikel in diesem Heft

  1. Frontmatter
  2. Auxetic behavior of Ti6Al4V lattice structures manufactured by laser powder bed fusion
  3. Hardness, shear strength, and microstructure of friction stir lap welded copper/aluminum using various parameters
  4. Influence of oscillating fiber laser welding process parameters on the fatigue response and mechanical performance of butt-jointed TWIP980 steels
  5. Fatigue analysis of TIG welded joints of dissimilar aluminum alloys
  6. Corrosion of self-piercing riveting joint in a Cl− and HSO3− environment
  7. Effect of annealing for stress relief on the surface integrity of gray cast iron with lamellar graphite after cavitation erosion
  8. Effects of boride coating on wear behaviour of biomedical grade Ti–45Nb alloy
  9. Effect of space holder agent on microstructural and mechanical properties of commercially pure titanium
  10. Impact and biaxial tensile-after impact behaviors of inter-ply hybrid carbon-glass/epoxy composite plates
  11. Improvement of the mechanical and wear properties of Al6061 alloys with quartz and SiC hybrid reinforcements by powder metallurgy
  12. Surface strain-based calculation of principal directional strains in multi-material carbon fiber laminates
  13. Tribological and mechanical behaviour of Al 359 composites reinforced with B4Cp, SiCp and flyash
  14. Effect of Al-based coatings deposited by EASC on exfoliation corrosion susceptibility of EN AW 7020-T6
  15. Green synthesis of CuO nanoparticles using Curcuma longa L. extract: composite dielectric and mechanical properties
  16. Comprehensive optimization of shot peening intensity using a hybrid model with AI-based techniques via Almen tests
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