人工智能图像生成促进感性工程设计过程

S. Ishihara, Rueikai Kuo, K. Ishihara
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引用次数: 0

摘要

感性工学的方法通过调查用户潜在的感性,然后将其反映在产品和服务的开发和持续的阐述中,从而帮助设计过程(Nagamachi, 1991,2012)。通过感性工学在产品开发中的应用,我们了解到有几个共同的困难。1. 评估样品缺乏多样性:市场上的产品在设计上存在局限性。2. 每个人都被鼓励参与感性工程的设计过程:非设计师的参与对成功的产品有很大的贡献。3.设计师很少,太忙了:设计师应该减少工作量,腾出时间进行更创新的思考。在本研究中,为了缓解这些问题,我们将AI图像生成器(人工智能的最新发展)应用于感性工程设计过程。牛奶盒感性研究的结果(Ishihara et al., 1996)是本研究的起点。首先,我们研究了StableDiffusion (Rombach et al. 2021),这是一个图像生成人工智能系统。StableDiffusion (SD)似乎缺乏牛奶盒的形状知识。然后我们用“超级网络”框架对其形状进行增量学习。创造创意的一种常见方法是借鉴周边地区的创意。红色的牛奶盒相当多。另一方面,我们的啤酒罐感性研究(Ishihara, 1998)表明,红色在Premium, Gorgeous, Affected和Showy的感性之间有很强的关系。然后,我们试着涂上一层红色。人工智能生成的“红花牛奶盒”设计精美。我们1996年的研究表明,蓝色是牛奶盒的首选颜色。蓝色的抽象形状经常用于它;然后人们把感性分为“简单的”、“适当的”和“单调的”。在这次尝试中,我们寻求一种“现代”和“精致”的蓝色和抽象的设计。人工智能生成的设计成功地将现代触感融入了蓝色设计。此外,我们还尝试了更多新颖的“彩绘现代牛奶盒”的创意。牛奶盒有不同的颜色,并已含蓄或明确地为“少年”和“温柔”的感性。这次试验也增加了现代感,获得了新颖的设计。最后,对“杰克逊·波洛克画的牛奶盒”进行了考察。结果反映了他在发明“水滴画”之前的20世纪40年代的抽象绘画。在其对感性工程学的长期追求中,KE方法论显示了其创新和解决问题的设计的激励作用。这项研究挑战了使用尖端人工智能技术来推动更多创新设计的挑战。随着人工智能技术的扩展,推动创新创造的进一步方法与人类互动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI image generation boosts Kansei engineering design process
Methods of Kansei engineering help the design process by surveying users’ latent Kansei, then reflecting it on product and service development and continuous elaborations of them (Nagamachi, 1991, 2012). Through our applications of Kansei engineering to product development, we have learned that there are several common difficulties. 1. Evaluation samples’ lack of variety: products in the market have limitations in design. 2. Everyone is encouraged to participate in the design process with Kansei engineering: non-designers participation greatly contributes to successful products. 3. Designers are few and too busy: designers’ efforts should be reduced and make time to think more innovatively. In this study, to ease these problems, we have applied AI image generators, a recent development of artificial intelligence, to the Kansei engineering design process.Milkcarton Kansei studies result (Ishihara et al., 1996) is the starting point of this study. First, we examine StableDiffusion (Rombach et al. 2021), an image generation artificial intelligence system. StableDiffusion (SD) seems to lack milkcarton’s shape knowledge. Then we made incremental learning of its shape with the “Hypernetworks” framework. A common method to make innovative ideas is borrowing ideas from neighboring areas. Milkcarton in red is quite a few. On the other hand, our Beer can Kansei study (Ishihara, 1998) shows that Red color has strong relation between Kansei of Premium, Gorgeous, Affected, and Showy. Then, we try to apply a red color. The AI-generated “Red flower milkcarton” is nicely designed. Our 1996 study showed that blue has the preferred color for milkcarton. Blue abstract shapes are too often used for it; then people have Kansei as “simple”, “proper” and “monotonous”. In this attempt, we seek a “modern” and “refined” touch to blue and abstract design. The AI-generated design successfully incorporated modern touch to blue-based design. Also, we have tried more novel ideas of “Colorful painting modern milkcarton”. Milkcartons have different colors and have been implicitly or explicitly intended for “Juvenile” and “Tender” Kansei. This trial also adds modern touch, and novel designs are obtained. Finally, “Jackson Pollock painting milkcarton” was examined. The result reflects his abstract painting in the 1940s, before his invention of “drop painting”.In its long-year quest for Kansei engineering, KE methodology shows its stimulating role of innovative and problem-solving design. This study challenged the use of cutting-edge AI technology to boost more innovative designs. Along with AI technology extends, further methods for boosting innovative creation interacting with humans.
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