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Crushing performance of an additively manufactured bio-inspired hybrid energy absorption profile

  • Cihan Savaş

    Cihan Savaş received his MSc degree in Mechanical Engineering from TOBB University of Economics and Technology in 2023. He works at TR Motor Power Systems, and his main fields of interests include stress analysis, design and optimization of aero-engine and aircraft structural components.

     ORCID logo     , Murat Altın

    Murat Altın serves as an associate professor in the Department of Automotive Engineering at Gazi University, located in Ankara, Türkiye. He earned his PhD from Gazi University in 2017 and has since been actively engaged in both teaching and research endeavors. His research interests encompass materials deformation, structural impact/crashworthiness, metal foams, and finite element analysis.

     ORCID logo     , Mehmet Ali Güler

    Mehmet Ali Güler is a full professor in the Department of Mechanical Engineering at the American University of the Middle East (AUM), Kuwait since 2018. Prior to AUM, he was a full professor at TOBB University of Economics and Technology, Türkiye since 2015. He received his MSc and PhD degrees in mechanical engineering from Lehigh University, Bethlehem, PA, in 1996, and 2001, respectively. Guler worked as senior structural and thermal engineer at Cd-adapco (now SIEMENS), Melville NY, USA (2000–2005) and as a chief engineer at Temsa Global (2005–2006), Adana, Türkiye. He was a Fulbright scholar at The University of Arizona, Tucson, AZ between 2013 and 2014. Guler has authored more than 90 peer-reviewed journal articles and 20 conference papers on contact and fracture mechanics, peridynamics, crashworthiness and finite element analysis.

     ORCID logo     and Erdem Acar

    Dr. Erdem Acar is a professor in the Mechanical Engineering Department at TOBB University of Economics and Technology, located in Ankara, Turkey. His research interests include design optimization, design of automobile and aircraft structures (in particular composite structures), finite element analysis, ballistic simulations, and uncertainty analysis. He is an associate fellow of the American Institute of Aeronautics and Astronautics (AIAA) and he has been serving as a Review Editor for the Journal of Structural and Multidisciplinary Optimization, Springer, since 2017.

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Published/Copyright: August 30, 2024
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Materials Testing
From the journal Materials Testing Volume 66 Issue 11

Abstract

Bio-inspired structures have applications in various industries, including automotive, defense, aerospace, and biomedical industries, owing to their combination of high-strength and lightweight properties. To enhance their energy absorption performance, a novel design was developed by integrating a spiral component, inspired by the cross section of the date palm tree trunk (Latin: Phoenix dactylifera), into an empty aluminum tube. The energy absorption performance of a bio-inspired hybrid energy-absorbing profile (BIHEAP) was experimentally and numerically investigated. To ensure the reliability of the numerical studies, finite element models were generated using ANSYS LS-DYNA and subsequently validated through axial crushing tests. Design optimization studies were carried out using surrogate-based models, such as the response surface model and Kriging surrogate models, to increase the energy absorption performance of the BIHEAP, which has three different design variables (spiral revolution, wall thickness, and number of spiral tubes). The initial design of the BIHEAP exhibited a specific energy absorption capacity (SEA) and crush force efficiency (CFE) that surpassed those of the empty aluminum tube by 17.2 % and 4.6 %, respectively. The optimized BIHEAP design demonstrated SEA and CFE values that were 21.4 % and 32 % greater than those of the empty aluminum tube, respectively. When the initial and optimized BIHEAP design were compared, it was found that SEA and CFE was increased by 3.5 % and 26.1 %, respectively.

Keywords: bio-inspired design; additive manufacturing; finite element analysis; energy absorption; crush efficiency
Corresponding author: Erdem Acar, Department of Mechanical Engineering, TOBB University of Economics and Technology, Sogutozu Cad. No:43, Cankaya, Ankara, 06560, Türkiye, E-mail:

Funding source: Türkiye Bilimsel ve Teknolojik Araştırma Kurumu

Award Identifier / Grant number: 222M364

About the authors

Cihan Savaş

Cihan Savaş received his MSc degree in Mechanical Engineering from TOBB University of Economics and Technology in 2023. He works at TR Motor Power Systems, and his main fields of interests include stress analysis, design and optimization of aero-engine and aircraft structural components.

Murat Altın

Murat Altın serves as an associate professor in the Department of Automotive Engineering at Gazi University, located in Ankara, Türkiye. He earned his PhD from Gazi University in 2017 and has since been actively engaged in both teaching and research endeavors. His research interests encompass materials deformation, structural impact/crashworthiness, metal foams, and finite element analysis.

Mehmet Ali Güler

Mehmet Ali Güler is a full professor in the Department of Mechanical Engineering at the American University of the Middle East (AUM), Kuwait since 2018. Prior to AUM, he was a full professor at TOBB University of Economics and Technology, Türkiye since 2015. He received his MSc and PhD degrees in mechanical engineering from Lehigh University, Bethlehem, PA, in 1996, and 2001, respectively. Guler worked as senior structural and thermal engineer at Cd-adapco (now SIEMENS), Melville NY, USA (2000–2005) and as a chief engineer at Temsa Global (2005–2006), Adana, Türkiye. He was a Fulbright scholar at The University of Arizona, Tucson, AZ between 2013 and 2014. Guler has authored more than 90 peer-reviewed journal articles and 20 conference papers on contact and fracture mechanics, peridynamics, crashworthiness and finite element analysis.

Erdem Acar

Dr. Erdem Acar is a professor in the Mechanical Engineering Department at TOBB University of Economics and Technology, located in Ankara, Turkey. His research interests include design optimization, design of automobile and aircraft structures (in particular composite structures), finite element analysis, ballistic simulations, and uncertainty analysis. He is an associate fellow of the American Institute of Aeronautics and Astronautics (AIAA) and he has been serving as a Review Editor for the Journal of Structural and Multidisciplinary Optimization, Springer, since 2017.

Acknowledgments

The authors acknowledge the The Scientific and Technological Research Council of Turkey (TÜBİTAK) award number 222M364 and TOBB University of Economics and Technology, Graduate School of Engineering and Science for providing funding and scholarship for the first author.

  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: Not applicable.

Appendix A: Brief description of the LHS design of experiment

In this study, LHS was used to identify 30 training points between the lower and upper bounds. In the Latin hypercube sampling (LHS) method, the entire range of values for each variable is subdivided into m segments, ensuring equal probability within each segment. This results in the partitioning of the entire design space surrounding n variables into mn cells, each characterized by an equal probability distribution. For instance, in the case of two variables and six segments, the design space is intricately divided into 36 cells, as shown in Figure 19. Design points were selected by generating random samples, with each assigned a cell number indicating its segment for each variable. The final design points, indicated as the marked cells in Figure 19, were obtained by ensuring each new sample differs in segment number from all the previous samples for each variable.

Figure 19: LHS for two design variables with six design points.
Figure 19:

LHS for two design variables with six design points.

Appendix B: Polynomial response surface (PRS) methodology

The prevalent selection among polynomial response surface (PRS) models is the use of a second-order model and specifically a second-degree algebraic polynomial function, as detailed in Equation (11) [55].

(11) f ˆ = b 0 + ∑ i = 1 L b i x i + ∑ i = 1 L b i i x i 2 + ∑ i = 1 L − 1 ∑ j = i + 1 L b i j x i x j

where f ˆ represents an approximation of the actual response function f using the response surface. The input vector x consists of L variables, and the coefficients b 0, b i , b ii , and b ij are determined using the least-squares technique.

Appendix C: Kriging (KR)

The basic approach of Kriging is centered around estimating the response in a specific form as shown in Equation (12).

(12) f ( x ) = p ( x ) + Z ( x )

with f: Response function of interest, p: Known polynomial that globally approximates the response and Z(x): Stochastic component responsible for generating the deviations.

The Kriging model effectively interpolates the sampled response data with a stochastic component possessing a mean value of zero and covariance, as stated in Equation (13).

(13) C O V [ Z ( x i ) , Z ( x j ) ] = σ 2 R [ R ( x i , x j ) ] )

with R: NxN correlation matrix, N: Number of training points, and R(xi,xj): Correlation function that characterizes the correlation between the two training points x i and x j.

Typically, the correlation function is selected as Gaussian. Thus, the relationship is defined as shown in Equation (14).

(14) R ( θ ) = ∏ k = 1 L e − θ k d k 2

with L: Number of variables, and d k = e k i − e k j : Distance between the kth components of the two training points x i and x j and θ k: Unknown parameters to be determined.

Once the correlation function is selected, the prediction of the response function, denoted as f, is given by Equation (15):

(15) f ˆ ( x ) = β ˆ + r T ( x ) R − 1 ( f − β ˆ p )

with r T (x): Correlation vector of length N between the prediction point x and the N sampling points, f: Responses at the N points and p: L-vector of ones, particularly when p(x) is treated as a constant. T

he vector r and scalar β ˆ can be expressed as shown in Equation (16).

(16) r T ( x ) = [ R ( x , x 1 ) , R ( x , x 2 ) , … , R ( x , x N ) T ] , β ˆ = ( p T R ‐ 1 p ) ‐ 1 p T R ‐ 1 f

The variance of the output model (which is distinct from the variance of the sampled output) can be estimated using Equation (17):

(17) σ ˆ 2 = ( f − β ˆ p ) T R − 1 ( f − β ˆ p ) N

The estimation of unknown parameters θ k involves solving the constrained maximization problem presented in Equation (18) [56].

(18) Max   Φ ( Θ ) = − [ N I n ( σ ˆ 2 ) + I n | R | ] 2

(19) s . t .   Θ > 0

with: Vector of unknown parameters θ , and σ ˆ and R: Functions of Θ. T

he MATLAB Kriging toolbox developed by Lophaven et al. [57] was used in this study.

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Published Online: 2024-08-30
Published in Print: 2024-11-26

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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  4. Effect of deformation on the mechanical property of reduced activation ferritic/martensitic steel refined by closed-dual equal channel angular pressing
  5. Effect of post-oxidation times in the nitrocarburizing process on the wear behavior of an AISI 4140 steel
  6. Production of (B4C+FeTi) reinforced and Fe based composites by mechanical alloying
  7. Tensile strength of friction stir additive manufactured laminated AA 6061/TiC/GS composites
  8. Microstructure evolution of AlSi10Mg alloy in RAP process
  9. Wear behaviour of titanium diboride and zirconium carbide reinforced LM13 hybrid composite for automotive applications
  10. Influence of post heat treatment on tribological and microstructural properties of plasma wire arc additive manufactured maraging steels
  11. Artificial neural network infused quasi oppositional learning partial reinforcement algorithm for structural design optimization of vehicle suspension components
  12. Optimization of vehicle conceptual design problems using an enhanced hunger games search algorithm
  13. Optimization of vehicle crashworthiness problems using recent twelve metaheuristic algorithms
  14. Development of zeolite 5A-incorporated polyvinyl alcohol membrane for desalination by pervaporation
  15. Characterization of bauxite residue filled sisal/glass fiber reinforced hybrid composites for structural applications
  16. Microstructure and mechanical properties of Al2O3/AZ61 Mg alloy surface composite developed using friction stir processing and groove reinforcement filling processes
  17. Method for the design and evaluation of binary sensitivity tests
https://doi.org/10.1515/mt-2024-0253
Keywords for this article
bio-inspired design; additive manufacturing; finite element analysis; energy absorption; crush efficiency

Articles in the same Issue

  1. Frontmatter
  2. Crushing performance of an additively manufactured bio-inspired hybrid energy absorption profile
  3. Implant bone screw characteristics of a printed PLA-based material
  4. Effect of deformation on the mechanical property of reduced activation ferritic/martensitic steel refined by closed-dual equal channel angular pressing
  5. Effect of post-oxidation times in the nitrocarburizing process on the wear behavior of an AISI 4140 steel
  6. Production of (B4C+FeTi) reinforced and Fe based composites by mechanical alloying
  7. Tensile strength of friction stir additive manufactured laminated AA 6061/TiC/GS composites
  8. Microstructure evolution of AlSi10Mg alloy in RAP process
  9. Wear behaviour of titanium diboride and zirconium carbide reinforced LM13 hybrid composite for automotive applications
  10. Influence of post heat treatment on tribological and microstructural properties of plasma wire arc additive manufactured maraging steels
  11. Artificial neural network infused quasi oppositional learning partial reinforcement algorithm for structural design optimization of vehicle suspension components
  12. Optimization of vehicle conceptual design problems using an enhanced hunger games search algorithm
  13. Optimization of vehicle crashworthiness problems using recent twelve metaheuristic algorithms
  14. Development of zeolite 5A-incorporated polyvinyl alcohol membrane for desalination by pervaporation
  15. Characterization of bauxite residue filled sisal/glass fiber reinforced hybrid composites for structural applications
  16. Microstructure and mechanical properties of Al2O3/AZ61 Mg alloy surface composite developed using friction stir processing and groove reinforcement filling processes
  17. Method for the design and evaluation of binary sensitivity tests
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