融合大模型微调的工业产品可控生成设计研究

Research on Controllable Generation Design of Industrial Products Based on Fine-Tuning of Large Models

摘要: 针对传统工业设计迭代低效，以及现有AIGC图像模型(如U-Net架构)在处理严苛机械几何约束时易发生透视失真的技术痛点，本文以工具车为验证载体，提出一种高精度、强约束的工业产品可控生成设计方法。研究确立具备全局空间感知能力的Flux大模型为基座，通过构建融合单图多视角重构技术的领域数据集，并应用LoRA微调技术进行深度微调，成功内化了品牌家族化特征。为突破单一文本提示的控制局限，本文进一步构建了融合显式几何结构锁定与隐式视觉风格迁移的双向多模态控制矩阵。实验表明，该生成范式在高保真地维持产品三维几何结构的前提下，实现了广域的材质与美学泛化，为装备制造企业的数字化研发与敏捷创新提供了切实可行的系统工程方案。

Abstract: In response to the inefficiency of traditional industrial design iterations and the technical challenges of existing AIGC image models (such as the U-Net architecture) when dealing with strict mechanical geometric constraints, which often lead to perspective distortion, this paper uses a tool vehicle as a verification platform and proposes a high-precision and strongly constrained controllable generation design method for industrial products. The study establishes the Flux large model with global spatial perception capabilities as the base. By constructing a domain dataset that integrates single-image multi-view reconstruction technology and applying LoRA fine-tuning technology for deep fine-tuning, the brand family characteristics have been successfully internalized. To break through the control limitations of a single text prompt, this paper further constructs a bidirectional multimodal control matrix that integrates explicit geometric structure locking and implicit visual style transfer. Experiments show that this generation paradigm can maintain the three-dimensional geometric structure of the product with high fidelity, and achieve wide-area material and aesthetic generalization, providing a practical and feasible system engineering solution for the digital R&D and agile innovation of equipment manufacturing enterprises.