结构感知生成设计中三维形状性能评价的设计表示

IF 1.8 Q3 ENGINEERING, MANUFACTURING
Design Science Pub Date : 2023-01-01 DOI:10.1017/dsj.2023.25
Xingang Li, Charles Xie, Zhenghui Sha
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引用次数: 0

摘要

数据驱动生成设计(DDGD)方法利用深度神经网络基于现有数据创建新的设计。结构感知的DDGD方法可以处理复杂的几何形状,并自动将单独的组件组装成系统,在促进创造性设计方面显示出前景。然而,确定适当的矢量化设计表示(VDR)来评估由结构感知DDGD模型生成的3D形状,在很大程度上仍未被探索。为此,我们对代理模型在利用vdr预测三维形状工程性能方面的性能进行了比较分析,这些模型来自两种来源:编码结构和几何信息的结构感知DDGD模型的训练潜在空间和仅编码几何信息的嵌入方法。我们进行了两个案例研究:一个是考虑阻力系数的3D汽车模型,另一个是考虑阻力和升力系数的3D飞机模型。我们的研究结果表明,使用潜在向量作为vdr会显著降低代理模型的预测结果。此外,在嵌入方法中增加vdr的维数不一定能改善预测,特别是当vdr包含更多与工程性能无关的信息时。因此,在选择用于代理建模的vdr时,必须谨慎使用从训练结构感知的DDGD模型中获得的潜在向量,尽管一旦训练完成,它们更容易获得。应注意与工程性能相关的底层物理。本文为结构感知DDGD不同类型的vdr对代理建模的有效性提供了经验证据,从而为人工智能生成设计构建更好的代理模型提供了便利。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design representation for performance evaluation of 3D shapes in structure-aware generative design
Abstract Data-driven generative design (DDGD) methods utilize deep neural networks to create novel designs based on existing data. The structure-aware DDGD method can handle complex geometries and automate the assembly of separate components into systems, showing promise in facilitating creative designs. However, determining the appropriate vectorized design representation (VDR) to evaluate 3D shapes generated from the structure-aware DDGD model remains largely unexplored. To that end, we conducted a comparative analysis of surrogate models’ performance in predicting the engineering performance of 3D shapes using VDRs from two sources: the trained latent space of structure-aware DDGD models encoding structural and geometric information and an embedding method encoding only geometric information. We conducted two case studies: one involving 3D car models focusing on drag coefficients and the other involving 3D aircraft models considering both drag and lift coefficients. Our results demonstrate that using latent vectors as VDRs can significantly deteriorate surrogate models’ predictions. Moreover, increasing the dimensionality of the VDRs in the embedding method may not necessarily improve the prediction, especially when the VDRs contain more information irrelevant to the engineering performance. Therefore, when selecting VDRs for surrogate modeling, the latent vectors obtained from training structure-aware DDGD models must be used with caution, although they are more accessible once training is complete. The underlying physics associated with the engineering performance should be paid attention. This paper provides empirical evidence for the effectiveness of different types of VDRs of structure-aware DDGD for surrogate modeling, thus facilitating the construction of better surrogate models for AI-generated designs.
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来源期刊
Design Science
Design Science ENGINEERING, MANUFACTURING-
CiteScore
4.80
自引率
12.50%
发文量
19
审稿时长
22 weeks
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