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contributor authorA. Khan
contributor authorD. Ceglarek
date accessioned2017-05-09T00:02:58Z
date available2017-05-09T00:02:58Z
date copyrightFebruary, 2000
date issued2000
identifier issn1087-1357
identifier otherJMSEFK-27355#215_1.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/124028
description abstractSensing for the system-wide diagnosis of dimensional faults in multi-fixture sheet metal assembly presents significant issues of complexity due to the number of levels of assembly and the number of possible faults at each level. The traditional allocation of sensing at a single measurement station is no longer sufficient to guarantee adequate fault diagnostic information for the increased parts and levels of a complex assembly system architecture. This creates a need for an efficient distribution of limited sensing resources to multiple measurement locations in assembly. The proposed methodology achieves adequate diagnostic performance by configuring sensing to provide an optimally distinctive signature for each fault in assembly. A multi-level, two-step, hierarchical optimization procedure using problem decomposition, based on assembly structure data derived directly from CAD files, is used to obtain such a novel, distributed sensor configuration. Diagnosability performance is quantified in the form of a defined index, which serves the dual purpose of guiding the optimization and establishing the diagnostic worth of any candidate sensor distribution. Examples, using a multi-fixture layout, are presented to illustrate the methodology. [S1087-1357(00)70801-X]
publisherThe American Society of Mechanical Engineers (ASME)
titleSensor Optimization for Fault Diagnosis in Multi-Fixture Assembly Systems With Distributed Sensing
typeJournal Paper
journal volume122
journal issue1
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.538917
journal fristpage215
journal lastpage226
identifier eissn1528-8935
keywordsSensors
keywordsManufacturing
keywordsJigs and fixtures AND Optimization
treeJournal of Manufacturing Science and Engineering:;2000:;volume( 122 ):;issue: 001
contenttypeFulltext


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