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A computer-based product classification and component detection for demanufacturing processes

delete2011-09-01
delete6
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OA
AI
F
Funbi Emmanuel Simolowo *
A
Ali Mousavi
P
P. O. Adjapong
DOI:10.1080/0951192X.2011.579169delete
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摘要

摘要

En 中文
The aim of this paper is to propose a novel computer-based product classification, component detection and tracking for demanufacturing and disassembly process. This is achieved by introducing a series of automated and sequential product scanning, component identification, image analysis and sorting - leading to the development of a bill of material (BOM). The produced BOM can then be associated with the relevant disassembly/demanufacture proviso. The proposed integrated image sorting and product classification (ISPC) approach can be considered as a step forward in automation of demanufacturing activities. The ISPC model proposed in this paper utilises and builds on the state-of-the-art technology and current body of research in computer-integrated demanufacturing and remanufacturing (CIDR). An appraisal of the latest research material and the factors that inhibit CIDR methods in practice are presented. A novel solution for the integration of imaging and material identification techniques to overcome some of the existing shortcomings of automated recycling processes is proposed in this paper. The proposed product scanning and component detection ISPC software consists of four distinct models: the repertory database, the search engine, the product-attributes updater and the image sorting and classification algorithm. The software framework that integrates the four components is presented in this paper. Finally, an overall assessment of applying ISPC at various stages of CIDR processes concludes the article.
Keyword:
computer-integrated demanufacturing
disassembly processes
image sorting
product classification
software development
recycling technologies
sustainable manufacture

期刊

I
International Journal of Computer Integrated Manufacturing
IF:
4
论文数:
2.3K
被引数:
3.4K

机构

B
brunel university
学者数:
5.8K
论文数: 7.1K
被引数: 9
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