Crashworthiness Optimization of Closed Cell–Sandwiched Aluminum Foam Crash Box Using FE and ANN Modeling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F25%3A10259578" target="_blank" >RIV/61989100:27230/25:10259578 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/eng2.70353" target="_blank" >https://onlinelibrary.wiley.com/doi/full/10.1002/eng2.70353</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/eng2.70353" target="_blank" >10.1002/eng2.70353</a>
Alternative languages
Result language
angličtina
Original language name
Crashworthiness Optimization of Closed Cell–Sandwiched Aluminum Foam Crash Box Using FE and ANN Modeling
Original language description
The crashworthiness optimization of closed-cell aluminum foam-filled sandwiched crash boxes is a critical aspect of vehicle occupant safety, aimed at enhancing the energy absorption capability of these structures during collisions. This study is focused on enhancing the crash box energy absorption capacity by using closed-cell-sandwiched aluminum foam characterized by lightweight and high-energy absorption properties. The design of the experiment (DOE) is used to determine the minimum number of runs by considering cell size, void fraction, and density as input parameters and energy absorption as output parameters. The finite element analysis (FEA) is conducted using ABAQUS with tetrahedral element type under impact loading conditions by considering good mesh quality, well-defined boundary conditions, and material models. An artificial neural network (ANN) integrated with a genetic algorithm (GA) is used to predict and optimize the maximum possible energy absorption capacity. After analysis, the maximum energy absorption of 255 J is identified from 27 runs, achieved with a combination of cell size, porosity, and density of (10, 15, and 2.6). To optimize energy absorption and determine optimal parameters, results from Abaqus are input into the ANN model. The ANN generates a fitting function with a high R value (0.989) and minimum error (1.34). The fitness function is then exported to the GA optimization tool, refining it to achieve an optimized energy absorption of 256.69 J. The optimal parameters identified through this process are cell size 10, porosity 0.162, and density 2.6. From the results obtained, we can conclude that the use of integrated computational methodologies can enhance crashworthiness optimization of complex foam geometries to provide a high-performance energy-absorbing crash box. © 2025 Elsevier B.V., All rights reserved.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20301 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Engineering Reports
ISSN
2577-8196
e-ISSN
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Volume of the periodical
7
Issue of the periodical within the volume
9
Country of publishing house
US - UNITED STATES
Number of pages
21
Pages from-to
"not paged"
UT code for WoS article
001585376600043
EID of the result in the Scopus database
2-s2.0-105016382580