基于机器学习的汽车设计智能拟合方法

doi:10.13229/j.cnki.jdxbgxb.20241285

Abstract

Abstract:

In order to improve the work efficiency of automobile enterprises， provide the design framework for designers， and effectively improve the accuracy and objectivity of the design， a method of using machine learning was proposed to fit the preliminary work of vehicle design and the later personalized cover parts design. This method is mainly based on diffusion models and text inversion technology. Firstly， the user profile was classified and the model was trained. Secondly， a high-degree fitting with the help of the existing corresponding user portrait of the molding car with high market feedback was performed. By adjusting user profile data and other information， pre defined vehicle renderings， car coverage kits， etc. can be generated. Compared with existing processes， the proposed method can highly fit the pre design work and provide designers with more intuitive and objective pre definition of vehicle models. Therefore， this method has broad application prospects in fields such as automotive design， market research， styling strategy formulation， creative draft generation， and panel design.

Key words: vehicle engineering, vehicle design, user portrait, diffusion models, artificial intelligence, text inversion technology

CLC Number:

TP18

Wei LAN,Zheng ZHOU,Guan-yu WANG,Wei WANG,Miao-miao ZHANG. Intelligent fitting method for vehicle design based on machine learning[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(9): 2858-2863.

Figures/Tables 4

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Intelligent fitting method for vehicle design based on machine learning

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