%0 Conference Paper %1 GalaskeStrangAnderl15WCECS %A Galaske, Nadia %A Strang, Daniel %A Anderl, Reiner %B Proceedings of the World Congress on Engineering and Computer Science (WCECS 2015), San Francisco, USA, Volume II %C Hong Kong %D 2015 %E Ao, S. I. %E Douglas, Craig %E Grundfest, W. S. %E Burgstone, Jon %I Newswood %K 01624 paper embedded ai engineering factory adaptive process optimize zzz.cps zzz.sfit zzz.i40 zzz.sfit.tp11 %P 1035--1040 %T Process Deviations in Cyber-Physical Production Systems %U http://www.iaeng.org/publication/WCECS2015/WCECS2015_pp1035-1040.pdf %V 2220 %X Global markets lead to the development of new production techniques, to guarantee competitiveness especially in high-wage countries. In this context, the German federal government supports the research and development of production systems based on cyber-physical systems (Industrie 4.0). In cyber-physical production systems all participants of the production process possess individual information about themselves and are equipped with actors, sensors, and a communication interface. Machines, workers, resources and components can interact and autonomously develop and execute process relevant decisions. Cyber-physical production systems are characterized as highly flexible systems that enable adaptive production processes. The processes for each individual component of the same product can vary in the order of the process steps and the performing manufacturing stations. For each product, a standard process and alternative processes are defined. If a deviation in the standard process occurs during the production of an individual component, the standard process is not feasible anymore. Through the interaction of process participants, the system can autonomously define an appropriate reaction and execute it by using actors of the participants and the intralogistics. Regardless of the deviation, the production of the individual component can proceed in cyber-physical production systems. In this paper, process deviations and appropriate reactions of cyber-physical production systems are analyzed, described in models, and simulated, to illustrate the benefits of cyberphysical production systems and to develop a process deviation management system for actual, physical production systems based on cyber-physical systems. %@ 978-988-14047-2-5

@inproceedings{GalaskeStrangAnderl15WCECS, abstract = {Global markets lead to the development of new production techniques, to guarantee competitiveness especially in high-wage countries. In this context, the German federal government supports the research and development of production systems based on cyber-physical systems (Industrie 4.0). In cyber-physical production systems all participants of the production process possess individual information about themselves and are equipped with actors, sensors, and a communication interface. Machines, workers, resources and components can interact and autonomously develop and execute process relevant decisions. Cyber-physical production systems are characterized as highly flexible systems that enable adaptive production processes. The processes for each individual component of the same product can vary in the order of the process steps and the performing manufacturing stations. For each product, a standard process and alternative processes are defined. If a deviation in the standard process occurs during the production of an individual component, the standard process is not feasible anymore. Through the interaction of process participants, the system can autonomously define an appropriate reaction and execute it by using actors of the participants and the intralogistics. Regardless of the deviation, the production of the individual component can proceed in cyber-physical production systems. In this paper, process deviations and appropriate reactions of cyber-physical production systems are analyzed, described in models, and simulated, to illustrate the benefits of cyberphysical production systems and to develop a process deviation management system for actual, physical production systems based on cyber-physical systems.}, added-at = {2016-04-06T14:54:32.000+0200}, address = {Hong Kong}, author = {Galaske, Nadia and Strang, Daniel and Anderl, Reiner}, biburl = {https://www.bibsonomy.org/bibtex/2c5d2980073319fbbd214740f04a2f738/flint63}, booktitle = {Proceedings of the World Congress on Engineering and Computer Science (WCECS 2015), San Francisco, USA, Volume II}, editor = {Ao, S. I. and Douglas, Craig and Grundfest, W. S. and Burgstone, Jon}, file = {IAENG:2015/GalaskeStrangAnderl15WCECS.pdf:PDF}, groups = {public}, interhash = {a60a7b4aabf059faf78bacdca061935c}, intrahash = {c5d2980073319fbbd214740f04a2f738}, isbn = {978-988-14047-2-5}, issn = {2078-0958}, keywords = {01624 paper embedded ai engineering factory adaptive process optimize zzz.cps zzz.sfit zzz.i40 zzz.sfit.tp11}, organization = {International Association of Engineers (IAENG)}, pages = {1035--1040}, publisher = {Newswood}, series = {Lecture Notes in Engineering and Computer Science}, timestamp = {2017-07-13T18:11:12.000+0200}, title = {Process Deviations in Cyber-Physical Production Systems}, url = {http://www.iaeng.org/publication/WCECS2015/WCECS2015_pp1035-1040.pdf}, username = {flint63}, volume = 2220, year = 2015 }