Research Papers: Design Theory and Methodology

Design for Upgradability Algorithm: Configuring Durable Products for Competitive Reutilization

[+] Author and Article Information
Ke Xing

School of Advanced Manufacturing and Mechanical Engineering, University of South Australia, South Australia 5095, Australiake.xing@unisa.edu.au

Martin Belusko

Institute of Sustainable Systems and Technologies, University of South Australia, South Australia 5095, Australiamartin.belusko@unisa.edu.au

J. Mech. Des 130(11), 111102 (Oct 03, 2008) (14 pages) doi:10.1115/1.2976446 History: Received March 04, 2007; Revised July 24, 2008; Published October 03, 2008

Product upgrade, achieved through the improvement of the functionality of reused or remanufactured products, is often accepted as an effective way to attain a competitive reutilization. Design for upgradability (DFU) is a tool that primarily focuses on enhancing a product’s functional as well as physical fitness for ease of upgrade. This paper presents the development of a novel approach and its implementation algorithm for a systematic design of product upgradability. The framework of this approach consists of two major phases––modeling and optimization. Fuzzy logic is used as a tool to facilitate the modeling of a product’s upgradability based on its technical characteristics and the reutilization mode. In the optimization phase, a new DFU optimization program is developed by using genetic algorithm techniques. The objective of a product’s DFU optimization is defined so as to configure/redesign a product for the maximal level of upgradability with minimal associated costs and violations of engineering, economic, and environmental constraints. A case study on a solar heating system is presented to demonstrate the application of the proposed DFU algorithm and its effectiveness in generating optimal configurations for the system, which are reflected as significant improvements in the system’s upgradability, cost efficiency, and overall functionality.

Copyright © 2008 by American Society of Mechanical Engineers
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Figure 1

Correspondence between DFU and GPD tasks in conceptual design

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Figure 2

An example of matrix chromosome representation for a DFU solution

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Figure 3

Case example: a solar heating system (33)

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Figure 4

Constraints on component clustering and module formations

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Figure 5

Chromosome representation of a DFU solution

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Figure 6

Best chromosome and decoded solution for scenarios 3 and 4



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