Bionic Design Model for Co-creative Product Innovation Based on Deep Generative and BID

被引:7
|
作者
Deng, ZhengGen [1 ]
Lv, Jian [1 ]
Liu, Xiang [1 ]
Hou, YuKang [1 ]
机构
[1] Guizhou Univ, Minist Educ, Key Lab Adv Mfg Technol, Guiyang 550025, Guizhou, Peoples R China
关键词
Bionic design; Deep generative; Product design; Image generation; StyleGAN; BIOMIMICRY; NOVELTY;
D O I
10.1007/s44196-023-00187-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Bio-inspired design (BID) is an abstract process, if we can visualize the process of fusing abstract biological inspiration with figurative product shapes, and combine it with artificial intelligence technology to express the designer's creativity, it will greatly improve the efficiency and accuracy of product shape bionic design. To address this problem, we combine BID with deep generative (DG) model to build a co-creative deep generative bio-inspired design (DGBID) model. Firstly, the designers used perceptual engineering and eye-movement experiments to select the bionic creature that best fits the bionic product and the suitable bionic product and bionic image, respectively. Then, the images are embedded into the potential space of StyleGAN, and the potential relationship between the two is visualized using StyleGAN's image morphing technique, which generates a new bionic fusion scheme. Finally, the contour lines of the solution are extracted as a reference, the designer is involved in the optimization of the scheme as a sketch, and the hand-drawn sketch is transformed into a real product solution using style migration techniques. The entire bionic design experiment process is a co-creative approach with artificial intelligence technology as the lead and designer participation. The feasibility of the method is verified using the side view of a car as a bionic product. The results show that the integration of bionic technology with deep generative model technology can accelerate the innovation and development of bionic products and provide designers with design references and rapid-generation tools.
引用
收藏
页数:15
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