Consumer-oriented optimal eco-product form design

Chen Fu Chen, Chung Hsing Yeh, Yang-Cheng Lin

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

2 Citations (Scopus)

Abstract

This paper presents a neural network (NN) approach for determining the design combination of product form elements that match a given eco-product value (EPV) and product image. A morphological analysis is used to extract form elements from these sample office chairs. The experimental study identifies 7 office chair design elements and 27 representative office chairs as experimental samples for developing NN models. A best-performing NN model is chosen to examine the complex relationship between 7 design elements and 6 product images as well as 15 EPV attributes which are identified and categorized into aesthetic, functional, and environmental dimensions. With the NN model, an office chair design database is built consisting of 960 different combinations of design elements, together with their associated EPV and product image values. The application of the database provides product designers with the best combination of product form elements for illustrating the aesthetic, functional, and environmental-friendly attributes as well as particular design concept represented by product image word pairs to an office chair design, thus facilitating the eco-product form deign process.

Original languageEnglish
Title of host publication3CA 2010 - 2010 International Symposium on Computer, Communication, Control and Automation
Pages602-605
Number of pages4
DOIs
Publication statusPublished - 2010 Sep 3
Event2010 International Symposium on Computer, Communication, Control and Automation, 3CA 2010 - Tainan, Taiwan
Duration: 2010 May 52010 May 7

Publication series

Name3CA 2010 - 2010 International Symposium on Computer, Communication, Control and Automation
Volume2

Other

Other2010 International Symposium on Computer, Communication, Control and Automation, 3CA 2010
CountryTaiwan
CityTainan
Period10-05-0510-05-07

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Control and Systems Engineering

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