Application of Generative Adversarial Network for Designer Assistance System

Bo Liu, You Hsun Wu, Yang Cheng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution


With the continual innovation of technology and product design methods, consumer needs change, and there is a gradual shift in product development from a function-oriented to consumer-oriented approach. The transformation of consumer perceptions into product design elements has become the focus of designers. This study develops an automated design generation system for product forms, which assists product designers to rapidly incorporate the consumer perceptions on the product form into the design process. It combines the Kansei engineering theory with the deep convolutional generative adversarial network (DCGAN), using the side-view contouring of cars as a case study. A questionnaire is given to the participants for selecting the top six subjective terms perceived by consumers in car styling. Further, 1006 car side views are scored on the six Kansei characteristics using a semantic differential scale. The results are clustered and analysed using fuzzy c-means. Subsequently, the clusters are applied as training datasets for the DCGAN, which generates antagonistic data to support the design process. The results of this study show that the data generated through the proposed method can facilitate designers in adjusting the product shape and realising an efficiently product shape design that matches the consumer's desired imagery.

Original languageEnglish
Title of host publicationIntelligent Sustainable Systems - Selected Papers of WorldS4 2021
EditorsAtulya K. Nagar, Dharm Singh Jat, Gabriela Marín-Raventós, Durgesh Kumar Mishra
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages9
ISBN (Print)9789811663086
Publication statusPublished - 2022
Event5th World Conference on Smart Trends in Systems Security and Sustainability, WS4 2021 - Virtual Online
Duration: 2021 Jul 292021 Jul 30

Publication series

NameLecture Notes in Networks and Systems
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389


Conference5th World Conference on Smart Trends in Systems Security and Sustainability, WS4 2021
CityVirtual Online

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications


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