Disassembly sequence structure graphs: An optimal approach for multiple-target selective disassembly sequence planning
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- 作者：Shana Smith ; ssmith@ntu.edu.tw ; [Author Vitae] ; Greg Smith ; [Author Vitae] ; Wei-Han Chen ; [Author Vitae]
- 关键词：DSSG ; Optimal ; Multiple target ; Selective disassembly sequence planning ; Disassembly sequence structure graph
- 刊名：Advanced Engineering Informatics
- 出版年：2012
- 出版时间：April, 2012
- 年：2012
- 卷：26
- 期：2
- 页码：306-316
- 全文大小：586 K
文摘
Modern green products must be easy to disassemble. Specific target components must be accessed and removed for repair, reuse, recycling, or remanufacturing. Prior studies describe various methods for removing selective targets from a product. However, solution quality, model complexity, and searching time have not been considered thoroughly. The goal of this study is to improve solution quality, minimize model complexity, and reduce searching time. To achieve the goal, this study introduces a new ¡®disassembly sequence structure graph?(DSSG) model for multiple-target selective disassembly sequence planning, an approach for creating DSSGs, and methods for searching DSSGs. The DSSG model contains a minimum set of parts that must be removed to remove selected targets, with an order and direction for removing each part. The approach uses expert rules to choose parts, part order, and part disassembly directions, based upon physical constraints. The searching methods use rules to remove all parts, in order, from the DSSG. The DSSG approach is an optimal approach. The approach creates a high quality minimum-size model, in minimum time. The approach finds high quality, practical, realistic, physically feasible solutions, in minimum time. The solutions are optimized for number of removed parts, part order, part disassembly directions, and reorientations. The solutions remove parts in practical order. The solutions remove parts in realistic directions. The solutions consider contact, motion, and fastener constraints. The study also presents eight new design rules. The study results can be used to improve the product design process, increase product life-cycle quality, and reduce product environmental impact.