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Identification of the tip mass parameters in a beam-tip mass system using response surface methodology

  • Hakan Gökdağ

    Hakan Gökdağ has been Professor of Mechanical Engineering at Bursa Technical University since 2019. He received his MSc in 2005 on applications of linear theory of vibrations and his PhD in 2010 on wavelet transform based structural damage detection. From 2009 to 2010, he was a visitor researcher at Mechanical Engineering, Imperial College London. His research interests include applications of mechanical vibrations, experimental modal analysis, structural damage detection, wavelet transform and its applications, applied mathematics and signal processing for sound and vibration applications.

         and Hilal Doğanay Katı

    Hilal Doğanay Katı received her Bachelor degree from the Department of Mathematics and Mechanical Engineering, Ataturk University in Erzurum, Turkey. She received her Master degree from the Department of Mechanical Engineering, University College London (UCL) in UK and her PhD at Bursa Technical University. She is currently Assistant Professor Dr. at Bursa Technical University, Mechanical Engineering Department. Her research interests are based on mechanical vibrations, mathematical modelling and modal analysis.

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Published/Copyright: February 9, 2024
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Materials Testing
From the journal Materials Testing Volume 66 Issue 8

Abstract

In this study, a response surface based approach is introduced to determine the physical parameters of the tip mass of a beam – tip mass system, such as mass, mass moment of inertia and coordinates of the centre of gravity with respect to the beam end point. To this end, first, a difference function was formulated based on the differences between the peak frequencies and peak amplitudes of the experimental and analytical frequency response functions. Later, observation points were established in the design space using orthogonal arrays, and a response surface was developed using the difference function values at these points. Next, the tip mass parameters were determined by minimizing the response surface with genetic algorithm and particle swarm optimization as well as fmincon, a gradient-based solver of the Matlab program. For comparison purposes, those parameters were obtained by also direct minimization of the difference function with the same algorithms. It was concluded that the tip mass parameters were successfully determined within reasonable error limits by the response surface method with less computational burden. Finally, the effect of design space width on the response surface quality is demonstrated numerically.

Keywords: response surface; optimization; Euler–Bernoulli beam with tip mass; genetic algorithm; particle swarm optimization
Corresponding author: Hilal Doğanay Katı, Department of Mechanical Engineering, Bursa Technical University – Mimar Sinan Campus, Yıldırım, Bursa, Türkiye, E-mail:

About the authors

Hakan Gökdağ

Hakan Gökdağ has been Professor of Mechanical Engineering at Bursa Technical University since 2019. He received his MSc in 2005 on applications of linear theory of vibrations and his PhD in 2010 on wavelet transform based structural damage detection. From 2009 to 2010, he was a visitor researcher at Mechanical Engineering, Imperial College London. His research interests include applications of mechanical vibrations, experimental modal analysis, structural damage detection, wavelet transform and its applications, applied mathematics and signal processing for sound and vibration applications.

Hilal Doğanay Katı

Hilal Doğanay Katı received her Bachelor degree from the Department of Mathematics and Mechanical Engineering, Ataturk University in Erzurum, Turkey. She received her Master degree from the Department of Mechanical Engineering, University College London (UCL) in UK and her PhD at Bursa Technical University. She is currently Assistant Professor Dr. at Bursa Technical University, Mechanical Engineering Department. Her research interests are based on mechanical vibrations, mathematical modelling and modal analysis.

  1. Research ethics: Not applicable.

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

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: Data will be made on request.

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Published Online: 2024-02-09
Published in Print: 2024-08-27

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/mt-2023-0330
Keywords for this article
response surface; optimization; Euler–Bernoulli beam with tip mass; genetic algorithm; particle swarm optimization

Articles in the same Issue

  1. Frontmatter
  2. An improved white shark optimizer algorithm used to optimize the structural parameters of the oil pad in the hydrostatic bearing
  3. Microstructure and oxidation of a Ni–Al based intermetallic coating formation on a Monel-400 alloy
  4. Friction, wear, and hardness properties of hybrid vehicle brake pads and effects on brake disc roughness
  5. Development of sinter linings for high-speed trains
  6. Wear resistance optimized by heat treatment of an in-situ TiC strengthened AlCoCrFeNi laser cladding coating
  7. Mechanical analysis of hybrid structured aircraft wing ribs with different geometric gaps
  8. Thermo-mechanical characteristics of spent coffee grounds reinforced bio-composites
  9. Compositional zoning and evolution of symplectite coronas in jadeitite
  10. Effect of niobium addition on the microstructure and wear properties of mechanical alloyed Cu–Al–Ni shape memory alloy
  11. Optimization of electric vehicle design problems using improved electric eel foraging optimization algorithm
  12. Effects of infill pattern and compression axis on the compressive strength of the 3D-printed cubic samples
  13. Microstructure and tribological properties of aluminum matrix composites reinforced with ZnO–hBN nanocomposite particles
  14. Marathon runner algorithm: theory and application in mathematical, mechanical and structural optimization problems
  15. Parameter identification of Yoshida–Uemori combined hardening model by using a variable step size firefly algorithm
  16. Identification of the tip mass parameters in a beam-tip mass system using response surface methodology
  17. Production and characterization of waste walnut shell powder that can be used as a sustainable eco-friendly reinforcement in biocomposites
  18. Anticorrosion performance of a zinc-rich cycloaliphatic epoxy resin coating containing CeO2 nanoparticle
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