AI image generation boosts Kansei engineering design process

Open Access
Conference Proceedings
Authors: Shigekazu IshiharaRueikai KuoKeiko Ishihara

Abstract: 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.

Keywords: Kansei Engineering, Artificial Intelligence, Image generation

DOI: 10.54941/ahfe1002988

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