Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/144995
Title: Eco-modular product architecture identification and assessment for product recovery
Authors: Kim, Samyeon
Moon, Seung Ki
Keywords: Engineering::Mechanical engineering
Issue Date: 2019
Source: Kim, S., & Moon, S. K. (2019). Eco-modular product architecture identification and assessment for product recovery. Journal of Intelligent Manufacturing, 30(1), 383-403. doi:10.1007/s10845-016-1253-7
Journal: Journal of Intelligent Manufacturing
Abstract: In order to improve the efficiency of disassembly and product recovery of an abandoned product at the end-of-life stage, it is essential to develop modular product architecture by considering manufacturing and recovering processes in early product design stage. In this paper, a novel concept of a design methodology is introduced to develop eco-modular product architecture and assess the modularity of the architecture from the viewpoint of product recovery. Eco-modular product architecture contributes to enhancing product recovery processes by recycling and reusing modules without full disassembly at component or material levels. It leads to less consumption of natural resources and less landfill damage to the environment. Three sustainable modular drivers, namely, interface complexity, material similarity, and lifespan similarity, are introduced to reconstruct the modular architecture of commercial products into the eco-modular architecture. Alternatives of modular architectures are identified by Markov Cluster Algorithm based on these sustainable modular drivers and physical interconnections of the components of product architecture. To select the eco-modular architecture from these alternatives, we propose modularity assessment metrics to identify independent interactions between modules and the degrees of similarity within each module. To demonstrate the effectiveness of the proposed methodology, a case study is performed with a coffee maker.
URI: https://hdl.handle.net/10356/144995
ISSN: 0956-5515
DOI: 10.1007/s10845-016-1253-7
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2016 Springer Science+Business Media New York. This is a post-peer-review, pre-copyedit version of an article published in Journal of Intelligent Manufacturing. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10845-016-1253-7
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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