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A Methodology on Estimating the Product Life Cycle Cost using Artificial Neural Networks in the Conceptual Design Phase  

서광규 (상명대학교 산업정보시스템공학과)
박지형 (한국과학기술연구원 CAD/CAM연구센터)
Publication Information
Journal of the Korean Society for Precision Engineering / v.21, no.9, 2004 , pp. 85-94 More about this Journal
Abstract
As over 70% of the total life cycle cost (LCC) of a product is committed at the early design stage, designers are in an important position to substantially reduce the LCC of the products they design by giving due to life cycle implications of their design decisions. During early design stages, there may be competing concepts with dramatic differences. In addition, the detailed information is scarce and decisions must be made quickly. Thus, both the overhead in developing parametric LCC models fur a wide range of concepts, and the lack of detailed information make the application of traditional LCC models impractical. A different approach is needed, because a traditional LCC method is to be incorporated in the very early design stages. This paper explores an approximate method for providing the preliminary LCC, Learning algorithms trained to use the known characteristics of existing products might allow the LCC of new products to be approximated quickly during the conceptual design phase without the overhead of defining new LCC models. Artificial neural networks are trained to generalize product attributes and LCC data from pre-existing LCC studies. Then the product designers query the trained artificial model with new high-level product attribute data to quickly obtain an LCC for a new product concept. Foundations fur the learning LCC approach are established, and then an application is provided.
Keywords
Life Cycle Cost; Product attribute; Artificial neural networks;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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