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    <completedDate>2022-02-24</completedDate>
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    <title language="eng">Standardized framework for evaluating centralized and decentralized control systems in modular assembly systems</title>
    <parentTitle language="eng">2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)</parentTitle>
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      <first_name>Sebastian</first_name>
      <last_name>Mayer</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Arnet</last_name>
    </author>
    <author>
      <first_name>Dennis</first_name>
      <last_name>Gankin</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>virtual commissioning</value>
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      <language>eng</language>
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      <value>cyber-physical production system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>digital manufacturing</value>
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      <value>scheduling</value>
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      <language>eng</language>
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      <value>production control</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-agent system</value>
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    <subject>
      <language>eng</language>
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      <value>modular assembly system</value>
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    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
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  <doc>
    <id>1470</id>
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    <language>eng</language>
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    <title language="eng">Adaptive production control with negotiating agents in modular assembly systems</title>
    <parentTitle language="eng">2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)</parentTitle>
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    <author>
      <first_name>Sebastian</first_name>
      <last_name>Mayer</last_name>
    </author>
    <author>
      <first_name>Dennis</first_name>
      <last_name>Gankin</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Arnet</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-agent system</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>negotiation-based coordination</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>intelligent manufacturing</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>production scheduling</value>
    </subject>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>production control</value>
    </subject>
    <subject>
      <language>eng</language>
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      <value>modular assembly system</value>
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    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
  </doc>
  <doc>
    <id>1309</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2335</pageFirst>
    <pageLast>2351</pageLast>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>32</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
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    <completedDate>2022-02-18</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Modular production control using deep reinforcement learning: proximal policy optimization</title>
    <abstract language="eng">EU regulations on CO2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent’s action, state, and reward design.</abstract>
    <parentTitle language="eng">Journal of Intelligent Manufacturing</parentTitle>
    <identifier type="issn">1572-8145</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13092</identifier>
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    <author>
      <first_name>Sebastian</first_name>
      <last_name>Mayer</last_name>
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    <author>
      <first_name>Tobias</first_name>
      <last_name>Classen</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>modular production</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>production control</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>production scheduling</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep reinforcement learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>proximal policy optimization</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automotive industry</value>
    </subject>
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    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1309/Mayer2021_ModularProductionControlUsingD.pdf</file>
  </doc>
  <doc>
    <id>6028</id>
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    <publishedYear>2021</publishedYear>
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    <language>eng</language>
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    <completedDate>2025-06-26</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Modular Production Control with Multi-Agent Deep Q-Learning</title>
    <parentTitle language="eng">Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</parentTitle>
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    <author>
      <first_name>Dennis</first_name>
      <last_name>Gankin</last_name>
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    <author>
      <first_name>Sebastian</first_name>
      <last_name>Mayer</last_name>
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    <author>
      <first_name>Jonas</first_name>
      <last_name>Zinn</last_name>
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    <author>
      <first_name>Birgit</first_name>
      <last_name>Vogel-Heuser</last_name>
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      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
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    <id>2922</id>
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    <publishedYear>2022</publishedYear>
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    <language>eng</language>
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    <publisherName>Springer</publisherName>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-10-24</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Damage Detection of Coated Milling Tools Using Images Captured by Cylindrical Shaped Enclosure Measurement Setup</title>
    <parentTitle language="eng">Advances in Computational Collective Intelligence. 14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28–30, 2022, Proceedings</parentTitle>
    <identifier type="isbn">978-3-031-16209-1</identifier>
    <identifier type="isbn">978-3-031-16210-7</identifier>
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    <author>
      <first_name>Mühenad</first_name>
      <last_name>Bilal</last_name>
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    <editor>
      <first_name>Costin</first_name>
      <last_name>Bădică</last_name>
    </editor>
    <author>
      <first_name>Christian</first_name>
      <last_name>Mayer</last_name>
    </author>
    <editor>
      <first_name>Jan</first_name>
      <last_name>Treur</last_name>
    </editor>
    <author>
      <first_name>Sunil</first_name>
      <last_name>Kancharana</last_name>
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    <editor>
      <first_name>Djamal</first_name>
      <last_name>Benslimane</last_name>
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    <author>
      <first_name>Markus</first_name>
      <last_name>Bregulla</last_name>
    </author>
    <editor>
      <first_name>Bogumiła</first_name>
      <last_name>Hnatkowska</last_name>
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    <author>
      <first_name>Rafal</first_name>
      <last_name>Cupek</last_name>
    </author>
    <editor>
      <first_name>Marek</first_name>
      <last_name>Krótkiewicz</last_name>
    </editor>
    <author>
      <first_name>Adam</first_name>
      <last_name>Ziębiński</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>quality inspection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>damage detection</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>image processing</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>tool regrinding</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>measurement setup</value>
    </subject>
    <collection role="institutes" number="19310">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="persons" number="23276">Bregulla, Markus</collection>
  </doc>
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    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <publisherName>IEEE</publisherName>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Adaptive Production Control in a Modular Assembly System – Towards an Agent-based Approach</title>
    <parentTitle language="eng">Proceedings, 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)</parentTitle>
    <identifier type="isbn">978-1-7281-2927-3</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/INDIN41052.2019.8972152</enrichment>
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    <enrichment key="THI_conferenceName">2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki (Finland), 22.-25.07.2019</enrichment>
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    <author>
      <first_name>Sebastian</first_name>
      <last_name>Mayer</last_name>
    </author>
    <author>
      <first_name>Nikolas</first_name>
      <last_name>Höhme</last_name>
    </author>
    <author>
      <first_name>Dennis</first_name>
      <last_name>Gankin</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>intelligent manufacturing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>scheduling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>production control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-agent system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>modular assembly system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated guided vehicles</value>
    </subject>
    <collection role="institutes" number="15984">Fakultät Elektrotechnik und Informatik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
  </doc>
  <doc>
    <id>1473</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>293</pageFirst>
    <pageLast>300</pageLast>
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    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
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    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Adaptive production control in a modular assembly system based on partial look-ahead scheduling</title>
    <parentTitle language="eng">Proceedings, 2019 IEEE International Conference on Mechatronics (ICM)</parentTitle>
    <identifier type="isbn">978-1-5386-6959-4</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ICMECH.2019.8722904</enrichment>
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    <enrichment key="THI_conferenceName">2019 IEEE International Conference on Mechatronics (ICM), Ilmenau (Germany), 18.-20.03.2019</enrichment>
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    <author>
      <first_name>Sebastian</first_name>
      <last_name>Mayer</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>scheduling</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>production control</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>production planning</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>modular assembly system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>automated guided vehicles</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cyber-physical systems</value>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>intelligent manufacturing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>genetic algorithm</value>
    </subject>
    <collection role="institutes" number="15984">Fakultät Elektrotechnik und Informatik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
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    <completedYear/>
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    <language>eng</language>
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    <volume>vol. 5: VISAPP</volume>
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    <publisherName>SciTePress</publisherName>
    <publisherPlace>Setúbal</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">High Resolution Mask R-CNN-based Damage Detection on Titanium Nitride Coated Milling Tools for Condition Monitoring by using a New Illumination Technique</title>
    <abstract language="eng">The implementation of intelligent software in the manufacturing industry is a technology of growing importance and has highlighted the need for improvement in automatization, production, inspection, and quality assurance. An automated inspection system based on deep learning methods can help to enhance inspection and provide a consistent overview of the production line. Camera-based imaging systems are among the most widely used tools, replacing manual industrial quality control tasks. Moreover, an automatized damage detection system on milling tools can be employed in quality control during the coating process and to simplify measuring tool life. Deep Convolutional Neural Networks (DCNNs) are state-of-the-art methods used to extract visual features and classify objects. Hence, there is great interest in applying DCNN in damage detection and classification. However, training a DCNN model on Titanium-Nitride coated (TiN) milling tools is extremely challenging. Due to the coating, the optical properties such as reflection and light scattering on the milling tool surface make image capturing for computer vision tasks quite challenging. In addition to the reflection and scattering, the helical-shaped surface of the cutting tools creates shadows, preventing the neural network from efficient training and damage detection. Here, in the context of applying an automatized deep learning-based method to detect damages on coated milling tools for quality control, the light has been shed on a novel illumination technique that allows capturing high-quality images which makes efficient damage detection for condition monitoring and quality control reliable. The method is outlined along with results obtained in training a ResNet 50 and ResNet 101 model reaching an overall accuracy of 83% from a dataset containing bounding box annotated damages. For instance and semantic segmentation, the state-of-the-art framework Mask R-CNN is employed.</abstract>
    <parentTitle language="eng">Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications</parentTitle>
    <identifier type="issn">2184-4321</identifier>
    <identifier type="isbn">978-989-758-555-5</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27498</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.5220/0010781800003124</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_conferenceName">17th International Conference on Computer Vision Theory and Applications (VISAPP), online, 06.-08.02.2022,</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Mühenad</first_name>
      <last_name>Bilal</last_name>
    </author>
    <editor>
      <first_name>Giovanni Maria</first_name>
      <last_name>Farinella</last_name>
    </editor>
    <author>
      <first_name>Sunil</first_name>
      <last_name>Kancharana</last_name>
    </author>
    <editor>
      <first_name>Petia</first_name>
      <last_name>Radeva</last_name>
    </editor>
    <author>
      <first_name>Christian</first_name>
      <last_name>Mayer</last_name>
    </author>
    <editor>
      <first_name>Kadi</first_name>
      <last_name>Bouatouch</last_name>
    </editor>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Pfaller</last_name>
    </author>
    <author>
      <first_name>Leonid</first_name>
      <last_name>Koval</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Bregulla</last_name>
    </author>
    <author>
      <first_name>Rafal</first_name>
      <last_name>Cupek</last_name>
    </author>
    <author>
      <first_name>Adam</first_name>
      <last_name>Ziębiński</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Predictive Maintenance</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Damage Detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Illumination Source</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mask R-CNN</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19310">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="persons" number="23276">Bregulla, Markus</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2749/107818.pdf</file>
  </doc>
</export-example>
