Abstract
Due to the continuous release of new products, manufacturers must design products constantly to meet the diversified and differentiated needs of customers. In order to avoid the displacement by market competitors, enterprises and manufacturers must study the multiple combinations of product shapes to design products for meeting user’s needs. At the same time, users’ consumption levels and aesthetic concepts are constantly improved, consumer demand has become more personalized and diversified, and then the development of manufacturing and information industries has made people experience material results while paying more attention to their emotional needs. As an evolutionary optimization algorithm, the individual fitness values of the interactive genetic algorithm are directly obtained from the user’s own preferences and the user could give a higher fitness degree to his favorite design individual, or directly selects his satisfied individuals as the next generation of individuals in the evolutionary process. Hence, the IGA method is used to effectively design innovative productions based on users’ emotional demand. However, the inaccurate judgment and identify of the user’s demand for product image style will increase the complexity of the design, and resulting in increased user fatigue. To respond to the challenge, this study propose a combination method of interactive genetic algorithm and fuzzy kano model (FKM) research methods, in which FKM is used to more accurately excavate the product image style that satisfies the user’s perceptual needs, thus guiding the direction of product modeling evolution, and achieving user demand-driven production evolution design. Finally, through the application of the electric bicycle case to prove the practicability and effectiveness of the method. In addition, the proposed method has increased the user satisfaction in the NPD. This method is also applicable to the styling aesthetics study for other industrial products.
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Wang, T., Zhou, M. (2020). New Production Development and Research Based on Interactive Evolution Design and Emotional Need. In: Harris, D., Li, WC. (eds) Engineering Psychology and Cognitive Ergonomics. Mental Workload, Human Physiology, and Human Energy. HCII 2020. Lecture Notes in Computer Science(), vol 12186. Springer, Cham. https://doi.org/10.1007/978-3-030-49044-7_19
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