A Study on the Finite Element Analysis for the Optimized Design of Bumper Towing Cap for Vehicles

Gyuho Shim･Teawon Kim (SECO ECOPLASTIC)

The bumper towing cap for automobiles is a part that is mounted on the bumper of the cap type as well. It prevents exposure to the hole where the towing screw is mounted. It also aims to maintain the design. When the toeing cap is designed robustly, it is difficult for the user to remove it. Conversely, when designed loosely, it can be removed while driving. Therefore, in this study, a study on finite element analysis for the optimization design of the toeing cap was conducted.

Integrated CAE framework from manufacturing processes to performance prediction

Hiroyoshi Horibe･Ichiro Tanaka･Tayuki Fujii･Kazumasa Usui･Satoshi Yamamoto (Honda R&D)

Simultaneous consideration and acceleration of vehicle body structure and manufacturing methods, which are changing due to electrification, are required. A mechanism was developed to accurately transfer physical property changes necessary for performance prediction between manufacturing processes and reflect them in the performance model. Changes occurring in pressing, welding, and drying processes were generated and transferred, and their impact on performance prediction results was verified using test pieces, reporting examples of effectiveness.

Relationship between Structural Complexity, Objective Function, and Smoothness in Topology Optimization

Akihito Takeda (SCSK Minori Solutions)･Shinichi Maruyama･Kazuhiro Izui･Shinji Nishiwaki (Kyoto University)

Topology optimization provides a high degree of design freedom among structural optimization methods. This study investigates how varying computational parameters affect the relationships between optimal shape complexity, objective function value, and smoothness, represented by surface area. Results indicate that increased structural complexity improves objective function values, while higher smoothness is associated with simpler shapes.

Developing of an Geometry-aware AI model for Predicting Stress Distribution in Aluminum Wheels under Impact

Changgon Kim (Hyundai Motor)

This study proposes a geometry-aware Graph Neural Network (GNN) model for predicting stress distributions under dynamic impact on aluminum wheels. The model incorporates a weighted loss function and an edge reconstruction technique to improve accuracy in stress concentration regions. Using high-fidelity 3D finite element data from 85 wheel designs, we applied edge augmentation to enhance generalization with limited samples. The GNN model achieved a MAPE of 7.0% in critical regions, significantly reducing computation time compared to conventional FE analysis. This results demonstrate the feasibility of using GNNs for early-stage structural performance prediction in automotive development.

Optimal Undercover Design : From Drag-Centric to Weight and TCO Integrated

WOOKHYUN HAN･Hyun Gon Jung･Min Kyul Yun･Min Ho Kim･Hee Je Eom･Kwang Chan Ko (Hyundai Motor)

The vehicle undercover is designed to reduce aerodynamic drag, ultimately enhancing fuel efficiency and decreasing client ownership costs, which is crucial for competitive advantage. Focusing solely on aerodynamic improvements may contradict the goal of fuel efficiency improvement or minimizing client costs. This study proposes an optimal undercover design by considering aerodynamic performance, weight, and development costs. The study results indicate that effective undercover design requires simultaneous consideration of aerodynamic effects and weight implications, and that the savings in total cost of ownership (TCO) due to improved fuel efficiency must exceed the development costs.

Relationship between Void Distribution Function of Soft Polyurethane Foam and Mechanical Property (Compression Force - Displacement Characteristic)(Second Report)
-Methodology Validation and Compression Force - Displacement Characteristics Studies Using Void Distribution Function-

MINORU INOUE (Mazda / Kagawa Univ. / Hiroshima Univ.)･KEISUKE SUZUKI (Kagawa University)･HIROYUKI ITO (TOYO SEAT Co.Ltd.)

In the first report, CAE methodology to evaluate mechanical properties (compression force – displacement characteristics, hereafter F-S characteristics) of the soft urethane foams using void distribution function was presented. In this second report, verifications and validations for the developed CAE methodology described in the first report were done and then the studies on F-S characteristics and the ability to control F-S characteristics of the soft urethane foams by means of the developed CAE methodology are investigated and presented.