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Technical Paper

Combining FEM-Optimization and Durability Analysis to Reach Lower Levels of Component Weight

2004-09-27
2004-32-0085
Utilizing the Finite Element Method (FEM), two evolving CAE methods have recently reached a high level of efficiency and accuracy to optimize the properties of components in respect of stiffness, stress level or fatigue resistance. The first type of CAE-methods is a family of optimization methods known as (parameter-free) shape and topology optimization. Very remarkable and useful results can be generated with these methods to reach considerable stiffness increases or stress and weight reductions. The second CAE-method is fatigue life prediction which gives reasonable accurate outputs for component life if input data like stress history and material properties are well known. State of the art algorithms and software can handle a combination of complex load histories, detailed material description and large FEM-data to give reliable results in short time.
Technical Paper

Lightweight Design Enabled by Innovative CAE Based Development Method Using Topology Optimization

2024-04-09
2024-01-2454
Carbon neutrality has become a significant target. One essential parameter regarding energy consumption and emissions is the mass of vehicles. Lightweight design improves the result of vehicle life cycle assessment (LCA), increases efficiency, and can be a step towards sustainability and CO2 neutrality. Weight reduction through structural optimization is a challenging task. Typical design development procedures have to be overcome. Instead of just a facelift or the creation of a derivative of the predecessor design, completely alternative design creation methods have to be applied. Automated structural optimization is one tool for exploring completely new design approaches. Different methods are available and weight reduction is the focus of topology optimization. This paper describes a fatigue life homogenization method that enables the weight reduction of vehicle parts. The applied CAE process combines fatigue life prediction and topology optimization.
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