A Hybrid Approach to Determining the Best Combination on Product Form Design

2005 ◽  
pp. 235-241 ◽  
Author(s):  
Yang-Cheng Lin ◽  
Hsin-Hsi Lai ◽  
Chung-Hsing Yeh ◽  
Chen-Hui Hung
Keyword(s):  
Hybrid Approach ◽  
Product Form ◽  
Form Design ◽  
Product Form Design

Study on the Application of Kansei Engineering in Product Form Design

2013 ◽  
Vol 274 ◽  
pp. 513-516 ◽  
Author(s):  
Yu Qing Xu ◽  
Kun Chen ◽  
Hai Bin Qin ◽  
Zhao Yang Wang
Keyword(s):  
Product Form ◽  
Kansei Engineering ◽  
Consumer Group ◽  
Main Technique ◽  
Form Design ◽  
Main Factors ◽  
Product Form Design

Design mode of product form is constructed combining Kansei Engineering (KE) with Ergonomics. KE is used as main technique to transform consumers' feelings and images of shape, size, material, operability of product into design form futures. The main factors and problems of Kansei experiment and Kansei analysis are also identified.Ergonomics acts as the assisting technique for meeting physiological criteria of consumer group. An implement of a product form design was conducted, and then the feasibility of proposed design mode based on KE is discussed which provides related products’ reference to designers.

A neural network based approach for product form design

2002 ◽  
Vol 23 (1) ◽  
pp. 67-84 ◽  
Author(s):  
Shih-Wen Hsiao ◽  
H.C Huang
Keyword(s):  
Neural Network ◽  
Product Form ◽  
Form Design ◽  
Product Form Design

Modeling Product Form Preference Using Gestalt Principles, Semantic Space, and Kansei

2012 ◽  
Author(s):  
José E. Lugo ◽  
Stephen M. Batill ◽  
Laura Carlson
Keyword(s):  
Semantic Space ◽  
Product Form ◽  
Sources Of Information ◽  
Gestalt Principles ◽  
Design Concepts ◽  
Design Variables ◽  
Form Design ◽  
Product Form Design ◽  
Different Sources ◽  
Product Preference

Engineers describe design concepts using design variables. Users develop their visual judgment of products by mentally grouping design variables according to Gestalt principles, extracting meaning using semantic dimensions and attaching attributes to the products, as reflected in Kansei methodology. The goal of this study was to assess how these different sources of information and representations of product form (design variables, Gestalt variables, Kansei attributes, and semantic dimensions) could combine to best predict product preference for both designers and users. Sixteen wheel rim designs were created using four design variables that were also combined into higher-order Gestalt variables. Sixty-four participants viewed each rim, and rated it according to semantic dimensions and Kansei attributes, and provided an overall “like” rating. The most reliable prediction of product preference were developed using Gestalt variables in combination with the meaning and emotion the users attached to the product. Finally, implications for designers are discussed.

Computer-Aided Form Generation for Product Design

2010 ◽  
Vol 97-101 ◽  
pp. 3785-3788
Author(s):  
Hung Cheng Tsai ◽  
Tien Li Chen ◽  
Hung Jung Tsai ◽  
Fei Kung Hung
Keyword(s):  
Computer Aided Design ◽  
Product Form ◽  
Knowledge Based ◽  
Blending Method ◽  
Computer Aided ◽  
Design Activities ◽  
Form Design ◽  
Product Form Design ◽  
Aided Design ◽  
Product Forms

The product form design activities involve a high degree of uncertainty and complexity and are therefore not easily formulated, coded and regularized. Consequently, very few of the computer-aided design approaches presented in the literature can support the conceptual form design tasks typically performed at the preliminary stages of a product’s development cycle. To enable designers to perform their design activities more objectively and efficiently, this paper combines the principles of fuzzy set theory, the shape-blending method and genetic algorithms to generate a knowledge-based approach for product form design based upon a database describing the relationships between different product forms and their corresponding perceptual image evaluations.

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