Shape Optimization of an Automotive Rubber Bumper Using a Surrogate Model-Based Parameter Tuning of a Genetic Algorithm

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dc.contributor.advisorHuri, Dávid
dc.contributor.authorAl Aqrabawi, Omar Mohammad Ali
dc.contributor.departmentDE--Műszaki Kar
dc.date.accessioned2026-02-02T16:39:03Z
dc.date.available2026-02-02T16:39:03Z
dc.date.created2025-11-23
dc.description.abstractThis thesis focuses on the shape optimization of an automotive rubber bumper using a computational method based on genetic algorithms. A canonical Genetic Algorithm was implemented in MATLAB and tuned through benchmark tests and hyperparameter analysis. To improve accuracy and reduce computational cost, the GA was hybridized with the Nelder–Mead algorithm and supported by a surrogate model created using Support Vector Regression. The surrogate model approximated the objective function derived from finite element simulations of the rubber bumper, modeled in ANSYS with a Mooney–Rivlin hyperelastic material. A semi-automatic MATLAB–ANSYS interface was developed due to software limitations, enabling real FEA-based objective evaluations. The hybrid method successfully identified the optimal geometric parameters with high accuracy. The results show that the developed optimization framework is effective for nonlinear, simulation-driven engineering design problems.
dc.description.courseGépészmérnöki
dc.description.degreeBSc/BA
dc.format.extent40
dc.identifier.urihttps://hdl.handle.net/2437/403940
dc.language.isoen
dc.rights.infoHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectOptimization
dc.subjectGenetic Algorithm
dc.subjectRubber Bumper
dc.subject.dspaceMűszaki tudományok::Gépészet
dc.titleShape Optimization of an Automotive Rubber Bumper Using a Surrogate Model-Based Parameter Tuning of a Genetic Algorithm
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