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Universitas Hasanuddin
Research output:Contribution to journalArticlepeer-review

Optimization of Two-Side Door Beams Under Bending Load

Djamaluddin F.

International Review of Mechanical Engineering

Q2
Published: 2023Citations: 1

Abstract

The automobile industry is focused on optimizing the structure of vehicles to enhance crashworthiness and energy absorption capabilities. This optimization involves utilizing highly nonlinear computational analysis and design techniques that consider various materials and structural characteristics. The objective of this research has been to propose a crashworthiness design for the circular side door beam that connected to side door structures. In order to address the complex issue of crashworthiness design optimization, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been employed. Additionally, the Radial Basic Function (RBF) algorithm has been used to create surrogate models, which has improved the effectiveness of optimization process. The analysis has focused on two side door beam by adjusting their cross-sectional shape and material properties in order to enhance their energy-absorbing performance. In order to determine the maximum energy absorbed, the optimization problem has been formulated by considering the maximum peak load as a constraint, as well as beam cross-section, thickness, and density as variables for achieving the maximum specific energy absorption. The Finite Element Analysis (FEA) has been also utilized in order to achieve the optimal design and conduct a simulation analysis of two side door beam in an automobile.

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10.15866/ireme.v17i5.23277

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CrashworthinessSciences
Finite element methodSciences
Beam (structure)Sciences
Side impactSciences
Structural engineeringSciences
BendingSciences
SortingSciences
Constraint (computer-aided design)Sciences
Optimal designSciences
Genetic algorithmSciences
Surrogate modelSciences
Process (computing)Sciences
Computer scienceSciences
EngineeringSciences
Mechanical engineeringSciences
AlgorithmSciences
Operating systemSciences
Machine learningSciences