文献类型：期刊文献

英文题名：Deep Analogical Generative Design and Evaluation: Integration of Stable Diffusion and LoRA

作者：Chen, Yumiao[1];Ruan, Huanhuan[2]

机构：[1]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200000, Peoples R China;[2]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200000, Peoples R China

年份：2025

卷号：147

期号：5

外文期刊名：JOURNAL OF MECHANICAL DESIGN

收录：;EI(收录号：20251418157452);WOS:【SCI-EXPANDED(收录号:WOS:001460622400003)】；

基金：National Natural Science Foundation of China(No. 51905175).Shanghai Soft Science Key Project (No. 24692109400).Research Project of Humanities and Social Sciences of the Ministry of Education (No. 24YJA760013).Xie Youbai Design Science Research Foundation (No. XYB-DS-202301).

语种：英文

外文关键词：analogical reasoning; Stable Diffusion; automobile styling; intelligent generation; eye tracking methodology

摘要：The rapid evolution of generative design through artificial intelligence has opened new avenues for innovative product styling. Integrating this efficient generative technology with established professional theories presents a novel challenge in contemporary international design research. In response to this challenge, this article introduces a pioneering and collaborative approach for the swift generation of automobile styling designs. The primary objective is to investigate an intelligent generation method that incorporates analogical reasoning and Stable Diffusion to support industrial designers in innovating product styling. This study scrutinizes traditional analogical reasoning design alongside the intelligent analogical reasoning design proposed herein, elucidating the distinctions through multidimensional comparisons using illustrative examples. The proposed methodological framework encompasses several key steps. Initially, a dataset comprising branded automobile images is meticulously constructed. Subsequently, an exclusive style model is trained leveraging Stable Diffusion techniques, coupled with advanced computer graphics and machine learning methodologies. Following this, design requirements are inputted, facilitating intelligent analogical reasoning design across multiple spatial dimensions to yield diverse and innovative automobile styling solutions. Finally, eye-tracking experiments are conducted to quantitatively compare the traditional analogical reasoning design approach with the Stable Diffusion-based analogical reasoning design method. The results substantiate that the latter effectively generates innovative and diversified automobile design solutions. This research contributes to enhancing the quality of automobile styling design, optimizing the design efficiency of enterprises, and catalyzing innovation in the automobile styling design process.