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Special Section Articles

Scalability Planning for Cloud-Based Manufacturing Systems

[+] Author and Article Information
Dazhong Wu

The G.W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: dwu42@gatech.edu

David W. Rosen

The G.W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: david.rosen@me.gatech.edu

Dirk Schaefer

The G.W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: dirk.schaefer@me.gatech.edu

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received October 8, 2014; final manuscript received March 25, 2015; published online July 8, 2015. Assoc. Editor: Lihui Wang.

J. Manuf. Sci. Eng 137(4), 040911 (Aug 01, 2015) (13 pages) Paper No: MANU-14-1511; doi: 10.1115/1.4030266 History: Received October 08, 2014; Revised March 25, 2015; Online July 08, 2015

Cloud-based manufacturing (CBM) has recently been proposed as an emerging manufacturing paradigm that may potentially change the way manufacturing services are provided and accessed. In the context of CBM, companies may opt to crowdsource part of their manufacturing tasks that are beyond their existing in-house manufacturing capacity to third-party CBM service providers by renting their manufacturing equipment instead of purchasing additional machines. To plan manufacturing scalability for CBM systems, it is crucial to identify potential manufacturing bottlenecks where the entire manufacturing system capacity is limited. Because of the complexity of manufacturing resource sharing behaviors, it is challenging to model and analyze the material flow of CBM systems in which sequential, concurrent, conflicting, cyclic, and mutually exclusive manufacturing processes typically occur. To address and further study this issue, we develop a stochastic Petri nets (SPNs) model to formally represent a CBM system, model and analyze the uncertainties in the complex material flow of the CBM system, evaluate manufacturing performance, and plan manufacturing scalability. We validate this approach by means of a delivery drone example that is used to demonstrate how manufacturers can indeed achieve rapid and cost-effective manufacturing scalability in practice by combining in-house manufacturing and crowdsourcing in a CBM setting.

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Figures

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Fig. 1

Cloud-based cyber-physical manufacturing systems

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Fig. 4

Material flow of the existing manufacturing system

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Fig. 5

(a) A snapshot of 3D printing service communities in the world [11]. (b) A snapshot of the 3D printer community in New York. (c) Search for a printer in 3D Hubs.

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Fig. 6

Sub-SPNs for modeling material flow in building the propeller and motor

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Fig. 7

SPN for modeling the entire manufacturing system

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Fig. 8

System capacity versus run length

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Fig. 9

Actual capacity versus required capacity for the original manufacturing system

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Fig. 10

New SPN model for the new manufacturing system

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Fig. 11

Actual capacity versus required capacity for the new CBM system

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