Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes
Evolving a Psycho-Physical Distance Metric for Generative Design Exploration of Diverse Shapes
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DOI：
10.1115/1.4043678
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发表时间：
2019-11-01
影响因子：
3.3
通讯作者：
Suzuki, Hiromasa
中科院分区：
文献类型：
--
作者：
Khan, Shahroz;Gunpinar, Erkan;Suzuki, Hiromasa
本文提出了一种创成式设计方法，该方法在设计空间探索阶段考虑用户的心理因素，从而创造出独特的设计方案。将用户对设计的感知判断提取为心理-物理距离度量，然后将其集成到设计探索步骤中，以生成用于参数化计算机辅助设计(CAD)形状的设计备选方案。为此，首先通过定义几何参数并确定这些参数的范围来对CAD模型进行参数化。利用最近提出的基于欧氏距离的抽样教-学优化算法(S-TLBO)生成CAD模型的初始设计方案，可以在设计空间中抽样N个填充空间的设计方案。然后将相似的设计聚类，并进行用户研究以捕捉受试者对聚类对之间的差异的知觉反应。此外，还引入了最远点排序技术，以使用户研究中的受试者正在比较的簇中的设计数量相等。然后，进行了非线性回归分析，以心理-物理距离度量的形式在受试者的知觉反应和几何参数之间建立了数学关联。最后，利用所获得的心理-物理距离度量来探索CAD模型的不同设计方案。另一项用户研究旨在比较使用欧几里得和建议的心理-物理距离度量时设计之间的多样性。根据用户研究，使用后一种指标生成的设计更加独特。
In this paper, a generative design approach is proposed that involves the users' psychological aspect in the design space exploration stage to create distinct design alternatives. Users' perceptual judgment about designs is extracted as a psycho-physical distance metric, which is then integrated into the design exploration step to generate design alternatives for the parametric computer-aided design (CAD) shapes. To do this, a CAD model is first parametrized by defining geometric parameters and determining ranges of these parameters. Initial design alternatives for the CAD model are generated using Euclidean distance-based sampling teaching-learning-based optimization (S-TLBO), which is recently proposed and can sample N space-filling design alternatives in the design space. Similar designs are then clustered, and a user study is conducted to capture the subjects' perceptual response for the dissimilarities between the cluster pairs. In addition, a furthest-point-sorting technique is introduced to equalize the number of designs in the clusters, which are being compared by the subjects in the user study. Afterward, nonlinear regression analyses are carried out to construct a mathematical correlation between the subjects' perceptual response and geometric parameters in the form of a psycho-physical distance metric. Finally, a psycho-physical distance metric obtained is utilized to explore distinct design alternatives for the CAD model. Another user study is designed to compare the diversification between the designs when the Euclidean and the suggested psycho-physical distance metrics are utilized. According to the user study, designs generated with the latter metric are more distinct.