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Research Papers

Optimizing Energy Consumption in a Decentralized Manufacturing System

[+] Author and Article Information
Rebecca Ilsen

Institute for Manufacturing Technology and
Production Systems,
University of Kaiserslautern,
Kaiserslautern D-67663, Germany
e-mail: publications.fbk@mv.uni-kl.de

Hermann Meissner

Institute for Manufacturing Technology and
Production Systems,
University of Kaiserslautern,
Kaiserslautern D-67663, Germany
e-mail: hermann.meissner@mv.uni-kl.de

Jan C. Aurich

Institute for Manufacturing Technology and
Production Systems,
University of Kaiserslautern,
Kaiserslautern D-67663, Germany
e-mail: jan.aurich@mv.uni-kl.de

Contributed by the Manufacturing Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received September 29, 2015; final manuscript received August 10, 2016; published online February 16, 2017. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 17(2), 021006 (Feb 16, 2017) (7 pages) Paper No: JCISE-15-1311; doi: 10.1115/1.4034585 History: Received September 29, 2015; Revised August 10, 2016

The deployment of modern information and communication technologies (ICT) within manufacturing systems leads to the creation of so-called cyber-physical production systems that consist of intelligent interconnected production facilities. One of the expected features of cyber-physical production systems is found to be the capability of self-organization and decentralized process planning in manufacturing. The functionality as well as the benefit of such self-organization concepts is yet to be proved. In this paper, the implementation of a virtual test field for the simulation of manufacturing systems based on a multi-agent system modeling concept is presented and used to evaluate a concept of decentralized process planning. Thereby, special focus is laid on the impact on energy consumption. The simulation results show the potential for energy reduction in manufacturing by a decentralized process-planning concept and yields hints for further development of such concepts.

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Figures

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Fig. 1

Four different types of agents are implemented to represent the products, product orders, and machines

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Fig. 2

Representation of the interaction protocol between the order agents (OA) and machine agents (MCA and MPA)

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Fig. 3

Cumulative energy consumption of all the machines in the simulated production system (M1) in the different cases of weighting factor variation (S1–S6)

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Fig. 4

Cumulative machine times according to the different cases of weighting factor variation (S1–S6) and M1

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Fig. 6

Cumulative energy consumption of the manufacturing system running with different machines (cases M1–M5) and different machine selection criteria (S1 in dark gray and S5 in light gray)

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Fig. 7

Cumulative machine times in the different scenarios of machines operating (cases M1–M5) and for different machine selection criteria (S1 in dark gray and S5 in light gray)

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Fig. 5

For each machine (case M1), the type of products produced is shown and how long it takes to produce them with variation of the machine tool selection criteria (S1 to S6): (a) machine type 1 dry and wet, (b) machine type 2 dry and wet, and (c) machine type 3

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