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Performance evaluation of artificial neural networks for identification of failure modes in composite plates

  • Serkan Balli

    Dr. Serkan Balli was born in 1979. He is Associate Professor in the Information Systems Engineering Department at Muğla Sıtkı Kocman University, Turkey. He received his MSc degree from the Statistics and Computer Science, Muğla University, Turkey in 2005 and his PhD degree in the Department of Computer Engineering, Ege University, Turkey in 2010. His research interests include fuzzy logic, expert systems, intelligent systems and decision support systems.

     EMAIL logo     and Faruk Sen

    Dr. Faruk Sen was born in 1977. He is a Professor of Energy Systems Engineering, Technology Faculty, Muğla Sıtkı Koçman University. He is a mechanical engineer. He obtained his PhD degree from Dokuz Eylul University, Izmir, Turkey, 2007. His research interests include coating materials, modeling, failure analyses, joints, composite materials and finite element methods.
Published/Copyright: June 30, 2021
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Materials Testing
From the journal Materials Testing Volume 63 Issue 6

Abstract

The aim of this work is to identify failure modes of double pinned sandwich composite plates by using artificial neural networks learning algorithms and then analyze their accuracies for identification. Mechanically pinned specimens with two serial pins/bolts for sandwich composite plates were used for recognition of failure modes which were obtained in previous experimental studies. In addition, the empirical data of the preceding work was determined with various geometric parameters for various applied preload moments. In this study, these geometric parameters and fastened/bolted joint forms were used for training by artificial neural networks. Consequently, ten different backpropagation training algorithms of artificial neural network were applied for classification by using one hundred data values containing three geometrical parameters. According to obtained results, it was seen that the Levenberg-Marquardt backpropagation training algorithm was the most successful algorithm with 93 % accuracy rate and it was appropriate for modeling of this problem. Additionally, performances of all backpropagation training algorithms were discussed taking into account accuracy and error ratios.

Keywords: Artificial neural networks; classification; bolted joint; pinned joint; fastened joint; Sandwich composite
Associate Prof. Dr. Serkan Balli Department of Information Systems Engineering Technology Faculty, Muğla Sıtkı Koçman University 48000 Kötekli-Muğla, Turkey

About the authors

Dr. Serkan Balli

Dr. Serkan Balli was born in 1979. He is Associate Professor in the Information Systems Engineering Department at Muğla Sıtkı Kocman University, Turkey. He received his MSc degree from the Statistics and Computer Science, Muğla University, Turkey in 2005 and his PhD degree in the Department of Computer Engineering, Ege University, Turkey in 2010. His research interests include fuzzy logic, expert systems, intelligent systems and decision support systems.

Dr. Faruk Sen

Dr. Faruk Sen was born in 1977. He is a Professor of Energy Systems Engineering, Technology Faculty, Muğla Sıtkı Koçman University. He is a mechanical engineer. He obtained his PhD degree from Dokuz Eylul University, Izmir, Turkey, 2007. His research interests include coating materials, modeling, failure analyses, joints, composite materials and finite element methods.

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Published Online: 2021-06-30
Published in Print: 2021-06-30

© 2021 Walter de Gruyter GmbH, Berlin/Boston, Germany

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  2. Component-oriented testing and simulation
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  4. Mechanical testing
  5. Innovative bond strength testing of tin-based alloys for sliding bearings on steel supports
  6. Corrosion testing
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https://doi.org/10.1515/mt-2020-0094
Keywords for this article
Artificial neural networks; classification; bolted joint; pinned joint; fastened joint; Sandwich composite

Articles in the same Issue

  1. Frontmatter
  2. Component-oriented testing and simulation
  3. Influence of various procedures for the determination of flow curves on the predictive accuracy of numerical simulations for mechanical joining processes
  4. Mechanical testing
  5. Innovative bond strength testing of tin-based alloys for sliding bearings on steel supports
  6. Corrosion testing
  7. Corrosion behavior of a new 25Cr-3Ni-7Mn-0.66 N duplex stainless steel in artificial seawater
  8. Mechanical testing
  9. Ballistic performance of powder metal Al5Cu-B4C composite as monolithic and laminated armor
  10. Component-oriented testing and simulation
  11. Performance assessments of the material for the traction motor cores of an electric racing kart
  12. Materials testing for welding and additive manufacturing applications
  13. Microstructure and strain rate-dependent deformation behavior of PBF-EB Ti6Al4V lattice structures
  14. Materialography/Analysis of physical properties
  15. Structural, electronic and thermodynamic investigation of Ag2GdSi, Ag2GdSn and Ag2Gd Pb Heusler alloys: First-principles calculations
  16. Component-oriented testing and simulation
  17. Application of an inventive problem solving approach for developing new generation vehicle crash boxes
  18. Hybrid Taguchi-Lévy flight distribution optimization algorithm for solving real-world design optimization problems
  19. Optimization of constrained mechanical design problems using the equilibrium optimization algorithm
  20. Component-Oriented teasting and simulation
  21. A novel hybrid water wave optimization algorithm for solving complex constrained engineering problems
  22. Mechanical Testing
  23. Performance evaluation of artificial neural networks for identification of failure modes in composite plates
  24. Production-oriented testing
  25. Optimization of electrohydraulic forming process parameters using the response surface methodology
  26. Production-oriented teating
  27. Experimental and numerical investigation of the thrust force and temperature generation during a drilling process
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