Product innovation concept generation based on deep learning and Kansei engineering

Industrial designers often present their initial concepts as design sketches. Rapid creation of new product conceptual images that meet users' affective preferences remains challenging in real design environments. However, few published works in affective design directly assist industrial designers in creating product conceptual images. Thus, we propose a product concept generation approach framework based on deep learning and Kansei engineering (PCGA-DLKE) to assist industrial designers. Our work focuses on dataset collection, pre-processing, affective preferences recognition, conceptual image generation model and product style transfer networks. To mark users' affective preferences, we established an affective recognition model by Kansei engineering and deep convolutional neural networks. To address the product conceptual image generation problem, we proposed a product design GAN model (PD-GAN), generating product conceptual images with affective preferences. An improved fast neural style transfer network was successfully trained to meet users' style preferences. This study aims to assist industrial designers in finding innovative concepts with affective preference. The Kansei evaluation shows that the innovation of the new product concept has been enhanced, indicating that the approach can better assist industrial designers in creating designs that meet users' emotional needs. Hand drill design and bicycle helmet design are taken as a case study.

Automated summary

This study proposes a product concept generation approach framework based on deep learning and Kansei engineering to assist industrial designers in creating product conceptual images that meet users' affective preferences. The focus is on dataset collection, pre-processing, affective preferences recognition, conceptual image generation model, and product style transfer networks. Through Kansei engineering and deep learning, an affective recognition model is successfully established, and an improved fast neural style transfer network is proposed to meet the users' design style preferences.