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         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/2c9324e84efe8aab59d8f55f1b95a8d99/isw-bibliothek",         
         "tags" : [
            "DigitalTwin","MachineLearning","grk2198","isw","myown"
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         "intraHash" : "c9324e84efe8aab59d8f55f1b95a8d99",
         "interHash" : "359b8b3bc1b319e37551a5aff6196ff4",
         "label" : "Reinforcement Learning of Material Flow Control Logic Using Hardware-in-the-Loop Simulation",
         "user" : "isw-bibliothek",
         "description" : "",
         "date" : "2019-05-15 01:21:27",
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         "pub-type": "inproceedings",
         "booktitle": "2018 First International Conference on Artificial Intelligence for Industries (AI4I)",
         "year": "2018", 
         "url": "https://ieeexplore.ieee.org/document/8665712/", 
         
         "author": [ 
            "Florian Jaensch","Akos Csiszar","Annika Kienzlen","Alexander Verl"
         ],
         "authors": [
         	
            	{"first" : "Florian",	"last" : "Jaensch"},
            	{"first" : "Akos",	"last" : "Csiszar"},
            	{"first" : "Annika",	"last" : "Kienzlen"},
            	{"first" : "Alexander",	"last" : "Verl"}
         ],
         "pages": "77-80","abstract": "In this paper the concept of reinforcement learning agent is presented, which can deduce the correct control policy of a plant by acting in its digital twin (the HiL simulation). This way the agent substitutes a real control system. By using reinforcement learning methods, a proof of concept application is presented for a simplistic material flow system, with the same type of access to the digital twin which a PLC controller-hardware would have. With the presented approach the agent is able to find the correct control policy.",
         "doi" : "10.1109/AI4I.2018.8665712",
         
         "bibtexKey": "8665712"

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            "DigitalTwin","DigitaleFabrik","MachineLearning","grk2198","isw","myown"
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         "intraHash" : "08e7136aaad4731582ec69d690483ae6",
         "interHash" : "5b083907fa1783d06c168d70a4adf590",
         "label" : "Digital Twins of Manufacturing Systems as a Base for Machine Learning",
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         "date" : "2019-01-17 22:17:56",
         "changeDate" : "2020-01-20 14:27:38",
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         "booktitle": "2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)",
         "year": "2018", 
         "url": "https://ieeexplore.ieee.org/document/8600844/", 
         
         "author": [ 
            "Florian Jaensch","Akos Csiszar","Christian Scheifele","Alexander Verl"
         ],
         "authors": [
         	
            	{"first" : "Florian",	"last" : "Jaensch"},
            	{"first" : "Akos",	"last" : "Csiszar"},
            	{"first" : "Christian",	"last" : "Scheifele"},
            	{"first" : "Alexander",	"last" : "Verl"}
         ],
         "pages": "1-6","abstract": "In the engineering phase of modern manufacturing systems, simulation-based methods and tools have been established to face the increasing demands on time-efficiency and profitability. In the application of these simulation solutions, model-based digital twins are created, as multi-domain simulation models to describe the behavior of the manufacturing system. During the production process, a data-driven digital twin arises in the context of industry 4.0 based on an increasing networking and new cloud technologies. Recent developments in machine learning of fer new possibilities in conjunction with the digital twin. These range from data-based learning of models to learning control logic of complex systems. This paper proposes a combined model-based and data-driven concept of a digital twin. It shows how to use machine learning in connection with these models, in order to archive faster development times of manufacturing systems.",
         "doi" : "10.1109/M2VIP.2018.8600844",
         
         "bibtexKey": "8600844"

      }
	  
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