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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Kagermann, H. , Wahlster, W. , and Helbig, J. , 2013, “ Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Final Report of the Industrie 4.0 Working Group,” Acatech—National Academy of Science and Engineering, Munich, Germany.
Broy, M. , 2013, “ Engineering Cyber-Physical Systems: Challenges and Foundations,” Complex Systems Design and Management, M. Aiguier , Y. Caseau , D. Krob , and A. Rauzy , eds., Springer, Berlin, Heidelberg, pp. 1–13.
Li, Y. , Chang, Q. , Jin, X. , and Ni, J. , 2015, “ Stochastic Energy Opportunity Windows in Advanced Manufacturing Systems,” ASME Paper No. MSEC2015-9257.
Leitão, P. , 2009, “ Agent-Based Distributed Manufacturing Control: A State-of-the-Art Survey,” Eng. Appl. Artif. Intell., 22(7), pp. 979–991. [CrossRef]
Warnecke, H.-J. , 1993, The Fractal Company, Springer, Berlin.
Brennan, R. W. , Christensen, J. H. , Gruver, W. A. , Kotak, D. B. , Norrie, D. H. , and van Leeuwen, E. H. , 2005, “ Holonic Manufacturing Systems: A Technical Overview,” The Industrial Information Technology Handbook (Industrial Electronics Series), Richard Zurawski , ed., CRC Press LLC, Boca Raton, FL, pp. 160-1–160-15.
Okino, N. , 1993, “ Bionic Manufacturing System,” Flexible Manufacturing Systems, J. Peklenik , ed., CIRP and Univerza v Ljubljani, Faculty of Mechanical Engineering, Ljubljana, Slovenia and Paris, pp. 73–95.
Monostori, L. , Váncza, J. , and Kumara, S. , 2006, “ Agent-Based Systems for Manufacturing,” Ann. CIRP, 55(2), pp. 697–720. [CrossRef]
van Brussel, H. , Wyns, J. , Valckenaers, P. , Bongaerts, L. , and Peeters, P. , 1998, “ Reference Architecture for Holonic Manufacturing Systems: PROSA,” Comput. Ind., 37(3), pp. 255–274. [CrossRef]
Leitão, P. , and Restivo, F. , 2006, “ Adacor: A Holonic Architecture for Agile and Adaptive Manufacturing Control,” Comput. Ind., 57(2), pp. 121–130. [CrossRef]
Chemnitz, M. , Krüger, J. , Patzlaff, M. , and Tuguldur, E.-O. , 2010, “ SOPRO—Advancements in the Self-Organising Production,” IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Sept. 13–16.
Zühlke, D. , 2009, “ Smartfactory—A Vision Becomes Reality,” IFAC Proc., 42(4), pp. 31–39.
Helu, M. , and Hedberg, T., Jr. , 2015, “ Enabling Smart Manufacturing Research and Development Using a Product Lifecycle Test Bed,” 43rd Proceedings of the North American Manufacturing Research Institution of SME, Charlotte, NC, June 8–12, pp. 86–97.
Ilsen, R. , Meissner, H. , and Aurich, J. C. , 2015, “ Virtual Test Field for Sustainability Assessment of Cybertronic Production Systems,” ASME Paper No. MSEC2015-9232.
Thiede, S. , and Herrmann, C. , 2011, “ Energy Flow Simulation for Manufacturing Systems,” Advances in Sustainable Manufacturing, G. Seliger , M. M. Khraisheh , and I. Jawahir , eds., Springer, Berlin Heidelberg, pp. 275–280.
Thiede, S. , 2012, Energy Efficiency in Manufacturing Systems, Springer, Berlin.
Weinert, N. , Rohrmus, D. , and Dudeck, S. , 2012, “ Energy-Aware Production Planning Based on EnergyBlocks in a Siemens AG Generator Plant,” Sustainable Manufacturing, G. Seliger , ed., Springer, Berlin, pp. 211–216.
Verl, A. , Westkämper, E. , Abele, E. , Dietmair, A. , Schlechtendahl, J. , Friedrich, J. , Haag, H. , and Schrems, S. , 2011, “ Architecture for Multilevel Monitoring and Control of Energy Consumption,” Glocalized Solutions for Sustainability in Manufacturing, J. Hesselbach and C. Herrmann , eds., Springer, Berlin, pp. 347–352.
Diaz, N. , and Dornfeld, D. , 2012, “ Cost and Energy Consumption Optimization of Product Manufacture in a Flexible Manufacturing System,” Leveraging Technology for a Sustainable World, D. A. Dornfeld and B. S. Linke , eds., Springer, Berlin Heidelberg, pp. 411–416.
Weiss, G. , ed., 2013, Multiagent Systems (Intelligent Robotics and Autonomous Agents), 2nd ed., The MIT Press, Cambridge, MA.
Diaz, N. , Redelsheimer, E. , and Dornfeld, D. , 2011, “ Energy Consumption Characterization and Reduction Strategies for Milling Machine Tool Use,” Glocalized Solutions for Sustainability in Manufacturing, J. Hesselbach and C. Herrmann , eds., Springer, Berlin Heidelberg, pp. 263–267.


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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. 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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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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