Application of Deep Learning in Topological Optimization for Structure of Commercial Vehicle Front Axle
Promoting energy efficiency and emission reduction is crucial in the automotive industry,and lightweight design is a key strategy.Commercial vehicles benefit significantly from the lightweight designof the front axle,employedas a critical suspension component.This paper focuses on deep learning for topological optimization to achieve lightweight design for front axles.The approach involves establishing lightweight design objectives and performing a feasibility analysis using topological optimization through the Optistruct platform.A Res-CB-Unet convolutional neural network,based on the Unet architecture,is then constructed.The network is trained using input parameters derived from static analysis results of the initial front axle structure,considering factors such as load and dimensional variations.A corresponding dataset of labeled topological configurations for the front axle is generated.The results of this study demonstrate that the proposed method rapidly and effectively predicts the front axle's topological configuration,potentially outperforming traditional methods,thus significantly enhancing computational efficiency.This approach provides valuable technical support and a practical pathway for efficient lightweight designof typical automotive structures.
front axlelightweight designdeep learningtopological optimizationRes-CB-Unet network
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