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Open AccessArticle

Product Innovation Design Based on Deep Learning and Kansei Engineering

by Huafeng Quan 1, Shaobo Li 1,* and Jianjun Hu 1,2,*
1
School of Mechanical Engineering, Guizhou University, Guiyang 550025, China
2
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2018, 8(12), 2397; https://doi.org/10.3390/app8122397
Received: 17 October 2018 / Revised: 12 November 2018 / Accepted: 20 November 2018 / Published: 26 November 2018
(This article belongs to the Special Issue Machine Learning and Compressed Sensing in Image Reconstruction)
Creative product design is becoming critical to the success of many enterprises. However, the conventional product innovation process is hindered by two major challenges: the difficulty to capture users’ preferences and the lack of intuitive approaches to visually inspire the designer, which is especially true in fashion design and form design of many other types of products. In this paper, we propose to combine Kansei engineering and the deep learning for product innovation (KENPI) framework, which can transfer color, pattern, etc. of a style image in real time to a product’s shape automatically. To capture user preferences, we combine Kansei engineering with back-propagation neural networks to establish a mapping model between product properties and styles. To address the inspiration issue in product innovation, the convolutional neural network-based neural style transfer is adopted to reconstruct and merge color and pattern features of the style image, which are then migrated to the target product. The generated new product image can not only preserve the shape of the target product but also have the features of the style image. The Kansei analysis shows that the semantics of the new product have been enhanced on the basis of the target product, which means that the new product design can better meet the needs of users. Finally, implementation of this proposed method is demonstrated in detail through a case study of female coat design. View Full-Text
Keywords: deep learning; KENPI; Kansei engineering; neural style transfer; product innovative design deep learning; KENPI; Kansei engineering; neural style transfer; product innovative design
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Quan, H.; Li, S.; Hu, J. Product Innovation Design Based on Deep Learning and Kansei Engineering. Appl. Sci. 2018, 8, 2397.

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