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Hierarchical fuzzy neural network‐based serviceability evaluation

Hierarchical fuzzy neural network‐based serviceability evaluation The ability to economically and effectively service products provides an avenue for extending the product's useful life. Quantifying approaches are needed to assist in the serviceability evaluation. In this study, the serviceability characteristics of a product are categorized into three groups: disassembly, reassembly, and handling. Often, many of the serviceability characteristics of a product cannot be defined completely or properly using crisp design data. Moreover, the serviceability analysis is an imprecise science characterized by ill-structured and subjective evaluation criteria. A formal methodology for representing and processing the design information of an artifact using a hierarchical fuzzy neural network (FNN) model is presented. First, three FNNs are used to compute the disassembly, reassembly, and handling indices. Second, the output of the three FNNs is fed into a separate FNN to compute the serviceability index. The designer can use the proposed model to rank alternate designs by computing the serviceability indices of each candidate design. The working of the proposed model is demonstrated by using two designs for a steam iron. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Agile Management Systems Emerald Publishing

Hierarchical fuzzy neural network‐based serviceability evaluation

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Publisher
Emerald Publishing
Copyright
Copyright © 2000 MCB UP Ltd. All rights reserved.
ISSN
1465-4652
DOI
10.1108/14654650010337140
Publisher site
See Article on Publisher Site

Abstract

The ability to economically and effectively service products provides an avenue for extending the product's useful life. Quantifying approaches are needed to assist in the serviceability evaluation. In this study, the serviceability characteristics of a product are categorized into three groups: disassembly, reassembly, and handling. Often, many of the serviceability characteristics of a product cannot be defined completely or properly using crisp design data. Moreover, the serviceability analysis is an imprecise science characterized by ill-structured and subjective evaluation criteria. A formal methodology for representing and processing the design information of an artifact using a hierarchical fuzzy neural network (FNN) model is presented. First, three FNNs are used to compute the disassembly, reassembly, and handling indices. Second, the output of the three FNNs is fed into a separate FNN to compute the serviceability index. The designer can use the proposed model to rank alternate designs by computing the serviceability indices of each candidate design. The working of the proposed model is demonstrated by using two designs for a steam iron.

Journal

International Journal of Agile Management SystemsEmerald Publishing

Published: Aug 1, 2000

Keywords: Neural networks; Design; Agility

References