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    <id>5831</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
    <pageNumber>340-347</pageNumber>
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    <title language="eng">Energy Prediction for CNC Machines Using G-Code Evaluation, Machine Learning and a Real-World Training Part</title>
    <parentTitle language="eng">2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)</parentTitle>
    <identifier type="doi">10.1109/ICMRE64970.2025.10976308</identifier>
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    <author>Anna-Maria Schmitt</author>
    <author>Eddi Miller</author>
    <author>Andreas Schiffler</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
  </doc>
  <doc>
    <id>5832</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
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    <title language="eng">Deep Reinforcement Learning for Adaptive Job Shop Scheduling in Robotic Cells: Handling Disruptions Effectively</title>
    <parentTitle language="eng">2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)</parentTitle>
    <identifier type="url">10.1109/ICMRE64970.2025.10976238</identifier>
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    <author>Eddi Miller</author>
    <author>Anna-Maria Schmitt</author>
    <author>Tobias Kaupp</author>
    <author>Andreas Schiffler</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
  </doc>
  <doc>
    <id>3004</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
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    <language>eng</language>
    <pageFirst>383</pageFirst>
    <pageLast>390</pageLast>
    <pageNumber>8</pageNumber>
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    <publisherName>Springer</publisherName>
    <publisherPlace>Cham</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
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    <title language="eng">Cascaded Scheduling for Highly Autonomous Production Cells with AGVs</title>
    <abstract language="eng">Highly autonomous production cells are a crucial part of manufacturing systems in industry 4.0 and can contribute to a sustainable value-adding process. To realize a high degree of autonomy in production cells with an industrial robot and a machine tool, an experimental approach was carried out to deal with numerous challenges on various automation levels. One crucial aspect is the scheduling problem of tasks for each resource (machine tool, tools, robot, AGV) depending on various data needed for a job-shop scheduling algorithm. The findings show that the necessary data has to be derived from different automation levels in a company: horizontally from ERP to shop-floor, vertically from the order handling department to the maintenance department. Utilizing that data, the contribution provides a cascaded scheduling approach for machine tool jobs as well as CNC and robot tasks for highly autonomous production cells supplied by AGVs.</abstract>
    <parentTitle language="eng">Manufacturing Driving Circular Economy: Proceedings of the 18th Global Conference on Sustainable Manufacturing, October 5-7, 2022, Berlin ; Lecture Notes in Mechanical Engineering</parentTitle>
    <identifier type="doi">https://doi.org/10.1007/978-3-031-28839-5_43</identifier>
    <identifier type="isbn">978-3-031-28838-8</identifier>
    <identifier type="isbn">978-3-031-28839-5</identifier>
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    <author>Eddi Miller</author>
    <author>Tobias Kaupp</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
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