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Special Section Articles

Dynamic Modeling of Manufacturing Equipment Capability Using Condition Information in Cloud Manufacturing

[+] Author and Article Information
Wenjun Xu

School of Information Engineering,
Wuhan University of Technology,
Wuhan 430070, China
Key Laboratory of Fiber Optic Sensing
Technology and Information Processing,
Ministry of Education,
122 Luoshi Road,
Wuhan 430070, China
e-mail: xuwenjun@whut.edu.cn

Jiajia Yu

School of Mechanical
and Electronic Engineering,
Wuhan University of Technology,
Wuhan 430070, China
Key Laboratory of Fiber Optic Sensing
Technology and Information Processing,
Ministry of Education,
Wuhan 430070, China
e-mail: yujj1991@126.com

Zude Zhou

School of Information Engineering,
Wuhan University of Technology,
Wuhan 430070, China
School of Mechanical
and Electronic Engineering,
Wuhan University of Technology,
Wuhan 430070, China
e-mail: zudezhou@whut.edu.cn

Yongquan Xie

School of Information Engineering,
Wuhan University of Technology,
Wuhan 430070, China
Key Laboratory of Fiber Optic Sensing
Technology and Information Processing,
Ministry of Education,
122 Luoshi Road,
Wuhan 430070, China
e-mail: xyqwhut0728@163.com

Duc Truong Pham

School of Mechanical Engineering,
University of Birmingham,
Edgbaston,
Birmingham B15 2TT, UK
e-mail: d.t.pham@bham.ac.uk

Chunqian Ji

School of Mechanical Engineering,
University of Birmingham,
Edgbaston,
Birmingham B15 2TT, UK
e-mail: c.ji@bham.ac.uk

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received October 15, 2014; final manuscript received March 7, 2015; published online July 8, 2015. Assoc. Editor: Xun Xu.

J. Manuf. Sci. Eng 137(4), 040907 (Aug 01, 2015) (14 pages) Paper No: MANU-14-1536; doi: 10.1115/1.4030079 History: Received October 15, 2014; Revised March 07, 2015; Online July 08, 2015

There is a growing need of knowledge description of manufacturing equipment and their capabilities for users, in order to efficiently obtain the on-demand services of manufacturing equipment in cloud manufacturing, and the understanding of the manufacturing capability of equipment is the most important basis for optimizing the cloud service management. During the manufacturing processes, a number of uncertain incidents may occur, which could degrade the manufacturing system performance or even paralyze the production line. Hence, all aspects about the equipment should be reflected within the knowledge description, and the static and dynamic information are both included in the knowledge model of manufacturing equipment. Unification and dynamics are the most important characteristics of the framework of knowledge description. The primary work of this study is fourfold. First, three fundamental ontologies are built, namely, basic information ontology, functional ontology, and manufacturing process ontology. Second, the correlation between the equipment ontology and the fundamental ontology that forms the unified description framework is determined. Third, the mapping relationship between the real-time condition data and the model of manufacturing equipment capability ontology is established. On the basis of the mapping relationship, the knowledge structure of the manufacturing equipment capability ontology is able to update in real-time. Finally, a prototype system is developed to validate the feasibility of the proposed dynamic modeling method. The system implementation demonstrates that the proposed knowledge description framework and method are capable of reflecting the current conditions and the dynamic capability of manufacturing equipment.

Copyright © 2015 by ASME
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Figures

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Fig. 1

Dynamic modeling of manufacturing equipment and capability

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Fig. 2

Model of manufacturing equipment

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Fig. 3

Ontology of basic information

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Fig. 4

Ontology of manufacturing function

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Fig. 5

Ontology of manufacturing process

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Fig. 6

Classification of manufacturing equipment

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Fig. 7

Ontology of manufacturing equipment

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Fig. 8

Dynamic model of manufacturing equipment ontology

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Fig. 9

Mapping relationship

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Fig. 10

Function flow chart

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Fig. 11

Initial ontology of X6332Z

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Fig. 12

Manufacturing process of X6332Z before production

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Fig. 13

X6332Z task condition

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Fig. 14

owl description of load rate

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Fig. 15

X6332Z machining condition

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Fig. 16

owl description of equipment ability index

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Fig. 17

X6332Z operation condition

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Fig. 18

FFT analysis of vibration signal

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Fig. 19

owl description of failure message

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Fig. 20

Manufacturing process of X6332Z under production

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