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Extension-Based Clustering Method: An Approach to Support Adaptable Design of the Product

[+] Author Affiliations
Yanwei Zhao, Huijun Tang, Nan Su, Wanliang Wang

Zhejiang University of Technology, Hangzhou, China

Paper No. MSEC2007-31205, pp. 841-849; 9 pages
  • ASME 2007 International Manufacturing Science and Engineering Conference
  • ASME 2007 International Manufacturing Science and Engineering Conference
  • Atlanta, Georgia, USA, October 15–18, 2007
  • Conference Sponsors: Manufacturing Division
  • ISBN: 0-7918-4290-8 | eISBN: 0-7918-3809-9
  • Copyright © 2007 by ASME


Design for product adaptability is one of the techniques used to provide customers with products that exactly meet their requirements. Clustering methods have been used extensively in the study of product adaptability design. Of the clustering methods, the fuzzy clustering method is the most widely in the design field. The three main kinds of fuzzy clustering methods are the transitive closure method, the dynamic direct method and the maximum tree method. The dynamic direct clustering method has been found to produce design solutions with the lowest cost. In this paper, a new approach for obtaining adaptable product designs using the clustering method is proposed. The method consists of three steps. Firstly, the extension distance formula is used to determine the distance between two products in a product database. The product design space and the distances between individuals are used as grouping criteria in this step. Secondly, the minimal distance between products is used to obtain the clustering index. Thirdly, the threshold value is used to divide the products in the database into groups. Customer demands and the results obtained from the adaptable function (based on the extension distance formula) are used to evaluate the fitness of the groups and their corresponding products. The product with the largest adaptable function value to demand ratio is selected product. In order to the show the advantage of using the extension-clustering method, both the extension-clustering method and the dynamic direct method are presented and compared. The comparison indicates that the extension-clustering method leads to quicker evaluations of design alternatives and results that more closely match customers’ demands. An example of the adaptable design of circular saws tools is used to demonstrate that with the extension-clustering design method a high variety of intelligent configurations can be obtained with significant rapidity.

Copyright © 2007 by ASME
Topics: Design



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