Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding
Part-Aware Product Design Agent Using Deep Generative Network and Local Linear Embedding
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DOI：
10.24251/hicss.2021.640
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发表时间：
2021
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影响因子：
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通讯作者：
Xingang Li;Charles Xie;Zhenghui Sha
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文献类型：
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作者：
Xingang Li;Charles Xie;Zhenghui Sha
在这项研究中，我们提出了一种数据驱动的生成式设计方法，可以增强人类的创造力，在产品形状设计的目标，提高系统性能。该方法由两个模块组成：1）3D网格生成设计模块，可以使用变分自动编码器（VAE）生成零件感知的3D对象，以及2）低保真度评估模块，可以基于局部线性嵌入（LLE）快速评估3D对象的工程性能。这种方法有两个独特的特点。首先，它生成可以更好地捕捉表面细节的3D网格（例如，平滑度和曲率）给定各个部分的互连和约束（即，部件感知），而不是生成整体3D形状。其次，基于LLE的求解器可以评估生成的3D形状的工程性能，以实现实时评估。我们的方法被应用到汽车设计，以减少空气阻力的最佳空气动力学性能。
In this study, we present a data-driven generative design approach that can augment human creativity in product shape design with the objective of improving system performance. The approach consists of two modules: 1) a 3D mesh generative design module that can generate part-aware 3D objects using variational auto-encoder (VAE), and 2) a low-fidelity evaluation module that can rapidly assess the engineering performance of 3D objects based on locally linear embedding (LLE). This approach has two unique features. First, it generates 3D meshes that can better capture surface details (e.g., smoothness and curvature) given individual parts’ interconnection and constraints (i.e., part-aware), as opposed to generating holistic 3D shapes. Second, the LLE-based solver can assess the engineering performance of the generated 3D shapes to realize real-time evaluation. Our approach is applied to car design to reduce air drag for optimal aerodynamic performance.