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Mechanical behavior of composite pipe structures under compressive force and its prediction using different machine learning algorithms

  • Ilyas Bozkurt

    Ilyas Bozkurt received his BSc degree in the Department of Mechanical Engineering, Aksaray University, Aksaray, Turkey in 2012. He received MSc degree in the Department of Mechanical Engineering, Inonu University, Malatya, Turkey in 2017. He received his PhD degree in the Department of Mechanical Engineering, Firat University, Elazig, Turkey in 2022. He is currently a postdoctoral researcher in the Mus Alparslan University. His research interests include impact, solid mechanics, finite element method, fiber-reinforced composites, and failure analysis.

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Published/Copyright: December 4, 2024
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Materials Testing
From the journal Materials Testing Volume 67 Issue 1

Abstract

Thanks to machine learning algorithms, the performance of composites with high energy absorption capacity can be predicted with high accuracy rates with a small number of data. The aim of this study is to experimentally and numerically determine the crushing performances of glass/epoxy composite pipe structures under compressive force and to predict their compression behavior with the help of different machine learning algorithms. In the study, the crushing performances of composite pipes (peak force (PF), peak force displacement (PFD), mean crushing force (MCF), specific energy absorption (SEA), and total inner energy (TIE)) were determined for different specimen thicknesses, specimen lengths, mesh sizes, numbers of integration points, diameters (D), and compression directions (axial and radial). Additionally, the maximum strength values of composite pipes under force were estimated with the help of Linear Regression (LR), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN) machine learning algorithms. The data taken from the ANN algorithm were found to be more reliable in estimating the PF and TIE values, with an accuracy rate of 92 %. When determining the MCF value, it was found that the data obtained from the LR algorithm was more reliable than other algorithms, with an accuracy rate of 80 %.

Keywords: composite pipes; compression test; machine learning algorithms; progressive damage analysis; finite element method
Corresponding author: Ilyas Bozkurt, Mechanical Engineering, 162324 Mus Alparslan University , Mus, 49250, Türkiye, E-mail:

About the author

Ilyas Bozkurt

Ilyas Bozkurt received his BSc degree in the Department of Mechanical Engineering, Aksaray University, Aksaray, Turkey in 2012. He received MSc degree in the Department of Mechanical Engineering, Inonu University, Malatya, Turkey in 2017. He received his PhD degree in the Department of Mechanical Engineering, Firat University, Elazig, Turkey in 2022. He is currently a postdoctoral researcher in the Mus Alparslan University. His research interests include impact, solid mechanics, finite element method, fiber-reinforced composites, and failure analysis.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The author has 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 author state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Published Online: 2024-12-04
Published in Print: 2025-01-29

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. The effect of MWCNT concentration on the electrical resistance change characteristic of glass/fiber epoxy composites under low cycle fatigue loading
  3. Effect of TMAB and ZrC concentration on mechanical and morphological properties of Ni–B/ZrC composite electrodeposition
  4. Influence of pulse duration and frequency of laser surface texturing on the surface roughness and microstructure of CoCr28Mo alloy for biomedical applications
  5. Comparison of Ni-based SiC and B4C reinforcements on a TIG-coated AISI 1040 steel
  6. Fatigue life of friction stir spot welds between Z91 magnesium alloy and ENAW7075-T651 aluminum alloy
  7. Effect of wire feed speed and arc length on weld bead geometry in synergistic controlled pulsed MIG/MAG welding
  8. Structural and morphological behavior of Al-based hybrid composites reinforced by SiC and WS2 inorganic material
  9. Impact behavior of natural material-based sandwich composites
  10. Effects of different production methods and hybridization on mechanical characteristics of basalt, flax, and jute fiber-reinforced composites
  11. Effect of heat treatment on interface characteristics and mechanical properties of explosive welded Cu/Ti composites
  12. Enhanced mechanical properties of Sr-modified Al–Mg–Si alloy by thermo-mechanical treatment
  13. Mechanical properties of laser welded similar and dissimilar steel joints of TBF1050 and DP1000 steel sheets
  14. Mechanical behavior of composite pipe structures under compressive force and its prediction using different machine learning algorithms
  15. Advanced structural design of engineering components utilizing an artificial neural network and GNDO algorithm
  16. Energy efficiency in materials testing by reactive power – part 1: power recirculating method in wear testing
https://doi.org/10.1515/mt-2024-0192
Keywords for this article
composite pipes; compression test; machine learning algorithms; progressive damage analysis; finite element method

Articles in the same Issue

  1. Frontmatter
  2. The effect of MWCNT concentration on the electrical resistance change characteristic of glass/fiber epoxy composites under low cycle fatigue loading
  3. Effect of TMAB and ZrC concentration on mechanical and morphological properties of Ni–B/ZrC composite electrodeposition
  4. Influence of pulse duration and frequency of laser surface texturing on the surface roughness and microstructure of CoCr28Mo alloy for biomedical applications
  5. Comparison of Ni-based SiC and B4C reinforcements on a TIG-coated AISI 1040 steel
  6. Fatigue life of friction stir spot welds between Z91 magnesium alloy and ENAW7075-T651 aluminum alloy
  7. Effect of wire feed speed and arc length on weld bead geometry in synergistic controlled pulsed MIG/MAG welding
  8. Structural and morphological behavior of Al-based hybrid composites reinforced by SiC and WS2 inorganic material
  9. Impact behavior of natural material-based sandwich composites
  10. Effects of different production methods and hybridization on mechanical characteristics of basalt, flax, and jute fiber-reinforced composites
  11. Effect of heat treatment on interface characteristics and mechanical properties of explosive welded Cu/Ti composites
  12. Enhanced mechanical properties of Sr-modified Al–Mg–Si alloy by thermo-mechanical treatment
  13. Mechanical properties of laser welded similar and dissimilar steel joints of TBF1050 and DP1000 steel sheets
  14. Mechanical behavior of composite pipe structures under compressive force and its prediction using different machine learning algorithms
  15. Advanced structural design of engineering components utilizing an artificial neural network and GNDO algorithm
  16. Energy efficiency in materials testing by reactive power – part 1: power recirculating method in wear testing
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