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<p>Thin-walled tubular structures are extensively employed as energy-absorbing elements in the automotive sector, owing to their lightweight characteristics and exceptional energy absorption capabilities. These components are engineered to dissipate energy during impact events, thereby mitigating the forces transmitted to vehicle occupants. In this research, crash boxes featuring diverse cellular configurations were developed using computer-aided design (CAD) tools. To explore the effect of bending angles on the crash performance of multi-cell thin-walled structures, designs were fabricated with no bending, as well as at angles of 45°, 90°, and 135°. The crashworthiness of the designed crash boxes was evaluated through quasi-static analyses performed at a constant velocity using Radioss, a nonlinear open-source finite element (FE) solver, integrated into a computer-aided engineering (CAE) platform. The FE analysis outcomes demonstrated that both the specific energy absorption (SEA) and energy absorption (EA) metrics of the multi-cell thin-walled structures diminished as the bending angles increased. This suggests that bending angles play a crucial role in determining the deformation behavior of the pipes during crashes. Additionally, an increase in the number of cells in the multi-cell thin-walled structures led to a reduction of approximately 25% in SEA and 23% in EA, highlighting the impact of cellular complexity on energy absorption efficiency. CA1 and CB1 crash boxes, which are the most efficient in terms of energy absorption performance of the crash boxes, were optimized using a multi-objective genetic algorithm (MOGA), an artificial intelligence-based optimization technique. As a result of the optimization, the EA value of the CA1 crash box increased by approximately 9.67% and the CB2 crash box by 2.3%, demonstrating that AI-based optimization methods such as MOGA can effectively improve the efficiency of crash boxes. As a result of the study, it was observed that EA values increased due to the increase in the number of cells in the crash boxes and it was determined that CB1 crash boxes had optimum energy absorption.</p>

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Investigation of energy absorption performances of twisted multi-cell crash boxes optimized with artificial ıntelligence methods

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Mehmet Kopar,
  • Medeni Sömer

摘要

Thin-walled tubular structures are extensively employed as energy-absorbing elements in the automotive sector, owing to their lightweight characteristics and exceptional energy absorption capabilities. These components are engineered to dissipate energy during impact events, thereby mitigating the forces transmitted to vehicle occupants. In this research, crash boxes featuring diverse cellular configurations were developed using computer-aided design (CAD) tools. To explore the effect of bending angles on the crash performance of multi-cell thin-walled structures, designs were fabricated with no bending, as well as at angles of 45°, 90°, and 135°. The crashworthiness of the designed crash boxes was evaluated through quasi-static analyses performed at a constant velocity using Radioss, a nonlinear open-source finite element (FE) solver, integrated into a computer-aided engineering (CAE) platform. The FE analysis outcomes demonstrated that both the specific energy absorption (SEA) and energy absorption (EA) metrics of the multi-cell thin-walled structures diminished as the bending angles increased. This suggests that bending angles play a crucial role in determining the deformation behavior of the pipes during crashes. Additionally, an increase in the number of cells in the multi-cell thin-walled structures led to a reduction of approximately 25% in SEA and 23% in EA, highlighting the impact of cellular complexity on energy absorption efficiency. CA1 and CB1 crash boxes, which are the most efficient in terms of energy absorption performance of the crash boxes, were optimized using a multi-objective genetic algorithm (MOGA), an artificial intelligence-based optimization technique. As a result of the optimization, the EA value of the CA1 crash box increased by approximately 9.67% and the CB2 crash box by 2.3%, demonstrating that AI-based optimization methods such as MOGA can effectively improve the efficiency of crash boxes. As a result of the study, it was observed that EA values increased due to the increase in the number of cells in the crash boxes and it was determined that CB1 crash boxes had optimum energy absorption.