TY - GEN
T1 - Data mining and fuzzy clustering to support product family design
AU - Moon, Seung Ki
AU - Kumara, Soundar R.T.
AU - Simpson, Timothy W.
PY - 2006
Y1 - 2006
N2 - In mass customization, data mining can be used to extract valid, previously unknown, and easily interpretable information from large product databases in order to improve and optimize engineering design and manufacturing process decisions. A product family is a group of related products based on a product platform, facilitating mass customization by providing a variety of products for different market segments cost-effectively. In this paper, we propose a method for identifying a platform along with variant and unique modules in a product family using data mining techniques. Association rule mining is applied to develop rules related to design knowledge based on product function, which can be clustered by their similarity based on functional features. Fuzzy c-means clustering is used to determine initial clusters that represent modules. The clustering result identifies the platform and its modules by a platform level membership function and classification. We apply the proposed method to determine a new platform using a case study involving a power tool family.
AB - In mass customization, data mining can be used to extract valid, previously unknown, and easily interpretable information from large product databases in order to improve and optimize engineering design and manufacturing process decisions. A product family is a group of related products based on a product platform, facilitating mass customization by providing a variety of products for different market segments cost-effectively. In this paper, we propose a method for identifying a platform along with variant and unique modules in a product family using data mining techniques. Association rule mining is applied to develop rules related to design knowledge based on product function, which can be clustered by their similarity based on functional features. Fuzzy c-means clustering is used to determine initial clusters that represent modules. The clustering result identifies the platform and its modules by a platform level membership function and classification. We apply the proposed method to determine a new platform using a case study involving a power tool family.
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U2 - 10.1115/detc2006-99287
DO - 10.1115/detc2006-99287
M3 - Conference contribution
AN - SCOPUS:33751344622
SN - 079183784X
SN - 9780791837849
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - Proceedings of 2006 ASME International Design Engineering Technical Conferences and Computers and Information In Engineering Conference, DETC2006
PB - American Society of Mechanical Engineers (ASME)
T2 - 2006 ASME International Design Engineering Technical Conferences and Computers and Information In Engineering Conference, DETC2006
Y2 - 10 September 2006 through 13 September 2006
ER -