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contributor authorVincent Wang, Xi
contributor authorLopez N, Brenda N.
contributor authorIjomah, Winifred
contributor authorWang, Lihui
contributor authorLi, Jinhui
date accessioned2017-05-09T01:20:42Z
date available2017-05-09T01:20:42Z
date issued2015
identifier issn1087-1357
identifier othermanu_137_06_061010.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/158763
description abstractWaste electrical and electronic equipment (WEEE) is both valuable and harmful since it contains a large number of profitable and hazardous materials and elements at the same time. At component level, many parts of the discarded equipment are still functional and recoverable. Thus, it is necessary to develop a distributed and intelligent system to support WEEE component recovery and recycling. In recent years, the Cloud concept has gained increasing popularity since it provides a serviceoriented architecture (SOA) that integrates various resources over the network. Cloud manufacturing systems are proposed worldwide to support operational manufacturing processes. In this research, Cloud manufacturing is further extended to the WEEE recovery and recycling context. The Cloud services are applied in WEEE recovery and recycling processes by tracking and management services. These services include all the stakeholders from the beginning to the end of life of the electric and electronic equipment. A Cloudbased WEEE recovery system is developed to provide modularized recovery services on the Cloud. A data management system is developed as well, which maintains the knowledge throughout the product lifecycle. A product tracking mechanism is also proposed with the help of the Quick Respond code method.
publisherThe American Society of Mechanical Engineers (ASME)
titleA Smart Cloud Based System for the WEEE Recovery/Recycling
typeJournal Paper
journal volume137
journal issue6
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4030304
journal fristpage61010
journal lastpage61010
identifier eissn1528-8935
treeJournal of Manufacturing Science and Engineering:;2015:;volume( 137 ):;issue: 006
contenttypeFulltext


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