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    <title language="eng">A Peak Shaving Approach in Manufacturing Combining Machine Learning and Job Shop Scheduling</title>
    <abstract language="eng">Computerized Numerical Control (CNC) plays an important role in highly autonomous manufacturing systems with multiple machine tools. The necessary Numerical Control (NC) programs to manufacture the parts are mostly written in standardized G-code. An a priori evaluation of the energy demand of CNC-based machine processes opens up the possibility of scheduling multiple jobs according to balanced energy consumption over a production period. Due to this, we present a combined Machine Learning (ML) and Job-Shop-Scheduling (JSS) approach to evaluate G-code for a CNC-milling process with respect to the energy demand of each G-command. The ML model training data are derived by the Latin hypercube sampling (LHS) method facing the main G-code operations G00, G01, and G02. The resulting energy demand for each job enhances a JSS algorithm to smooth the energy demand for multiple jobs, as peak power consumption needs to be avoided due to its expense.</abstract>
    <parentTitle language="eng">Lecture Notes in Mechanical Engineering</parentTitle>
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    <title language="eng">Advances in machine learning detecting changeover processes in cyber physical production systems</title>
    <abstract language="eng">The performance indicator, Overall Equipment Effectiveness (OEE), is one of the most important ones for production control, as it merges information of equipment usage, process yield, and product quality. The determination of the OEE is oftentimes not transparent in companies, due to the heterogeneous data sources and manual interference. Furthermore, there is a difference in present guidelines to calculate the OEE. Due to a big amount of sensor data in Cyber Physical Production Systems, Machine Learning methods can be used in order to detect several elements of the OEE by a trained model. Changeover time is one crucial aspect influencing the OEE, as it adds no value to the product. Furthermore, changeover processes are fulfilled manually and vary from worker to worker. They always have their own procedure to conduct a changeover of a machine for a new product or production lot. Hence, the changeover time as well as the process itself vary. Thus, a new Machine Learning based concept for identification and characterization of machine set-up actions is presented. Here, the issue to be dealt with is the necessity of human and machine interaction to fulfill the entire machine set-up process. Because of this, the paper shows the use case in a real production scenario of a small to medium size company (SME), the derived data set, promising Machine Learning algorithms, as well as the results of the implemented Machine Learning model to classify machine set-up actions.</abstract>
    <parentTitle language="eng">Journal of Manufacturing and Materials Processing</parentTitle>
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    <author>Bastian Engelmann</author>
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    <title language="eng">Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems</title>
    <parentTitle language="eng">Sensors</parentTitle>
    <enrichment key="opus.import.data">@articleneuber2022evaluation, title=Evaluation of the Influence of Machine Tools on the Accuracy of Indoor Positioning Systems, author=Neuber, Till and Schmitt, Anna-Maria and Engelmann, Bastian and Schmitt, Jan, journal=Sensors, volume=22, number=24, pages=10015, year=2022, publisher=MDPI</enrichment>
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    <author>Till Neuber</author>
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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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    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
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    <publishedYear>2024</publishedYear>
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    <title language="eng">Implications from Legacy Device Environments on the Conceptional Design of Machine Learning Models in Manufacturing</title>
    <parentTitle language="eng">Journal of Manufacturing and Materials Processing</parentTitle>
    <identifier type="url">https://doi.org/10.3390/jmmp8010015</identifier>
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    <author>Bastian Engelmann</author>
    <author>Anna-Maria Schmitt</author>
    <author>Lukas Theilacker</author>
    <author>Jan Schmitt</author>
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    <file>https://opus4.kobv.de/opus4-fhws/files/5454/Engelmann_Implications_Legacy_Device.pdf</file>
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    <publishedYear>2024</publishedYear>
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    <publisherName>Taylor &amp; Francis</publisherName>
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    <title language="eng">Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data</title>
    <parentTitle language="eng">Applied Artificial Intelligence</parentTitle>
    <identifier type="url">https://doi.org/10.1080/08839514.2024.2381317</identifier>
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    <author>Bastian Engelmann</author>
    <author>Anna-Maria Schmitt</author>
    <author>Moritz Heusinger</author>
    <author>Vladyslav Borysenko</author>
    <author>Niklas Niedner</author>
    <author>Jan Schmitt</author>
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    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/5708/Engelmann_Changeover_Events_Manufacturing.pdf</file>
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    <id>5609</id>
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    <publishedYear>2024</publishedYear>
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    <title language="eng">G-code evaluation in CNC milling to predict energy consumption through Machine Learning</title>
    <abstract language="eng">Computerized Numeric Control (CNC) plays an essential role in highly autonomous manufacturing systems for interlinked process chains for machine tools. NC-programs are mostly written in standardized G-code. Evaluating CNC-controlled manufacturing processes before their real application is advantageous due to resource efficiency. One dimension is the estimation of the energy demand of a part manufactured by an NC-program, e.g. to discover optimization potentials. In this context, this paper presents a Machine Learning (ML) approach to assess G-code for CNC-milling processes from the perspective of the energy demand of basic G-commands. We propose Latin Hypercube Sampling as an efficient method of Design of Experiments to train the ML model with minimum experimental effort to avoid costly setup and implementation time of the model training and deployment.</abstract>
    <parentTitle language="eng">Advances in Industrial and Manufacturing Engineering</parentTitle>
    <identifier type="url">https://doi.org/10.1016/j.aime.2024.100140</identifier>
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    <licence>Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International</licence>
    <author>Anna-Maria Schmitt</author>
    <author>Eddi Miller</author>
    <author>Bastian Engelmann</author>
    <author>Rafael Batres</author>
    <author>Jan Schmitt</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CNC machine tools</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>G-code</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Energy consumption</value>
    </subject>
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    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
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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>
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    <title language="eng">Classification of Production Process Phases with Multivariate Time Series Techniques</title>
    <parentTitle language="deu">2024 22nd International Conference on Research and Education in Mechatronics (REM)</parentTitle>
    <identifier type="doi">10.1109/REM63063.2024.10735481</identifier>
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    <author>Anna-Maria Schmitt</author>
    <author>Anna Antonov</author>
    <author>Jan Schmitt</author>
    <author>Bastian Engelmann</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>
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    <publishedYear>2024</publishedYear>
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    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
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    <volume>15</volume>
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    <publisherName>Frontiers</publisherName>
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    <belongsToBibliography>1</belongsToBibliography>
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    <title language="eng">Self-employed and stressed out? The impact of stress and stress management on entrepreneurs’ mental health and performance</title>
    <parentTitle language="eng">Frontiers in Psychology</parentTitle>
    <identifier type="doi">10.3389/fpsyg.2024.1365489</identifier>
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    <author>Sophia Kiefl</author>
    <author>Sophie Fischer</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
    <file>https://opus4.kobv.de/opus4-fhws/files/5604/Fischer_Self-employed_stressed_out.pdf</file>
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    <language>eng</language>
    <pageFirst>613</pageFirst>
    <pageLast>618</pageLast>
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    <edition/>
    <issue/>
    <volume>98</volume>
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    <publisherName>Elsevir</publisherName>
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    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2021-09-20</publishedDate>
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    <title language="eng">Sustainable Aspects of a Metal Printing Process Chain with Laser Powder Bed Fusion (LPBF)</title>
    <abstract language="eng">Production companies are getting more and more aware of the relevancy of energy costs and the environmental impact of their manufactured products. Hence, the knowledge about the energy intensity of new process technologies as metal printing becomes increasingly crucial. Therefore, data about the energy intensity of entire process chains allow a detailed assessment of the life cycle costs and environmental impact of metal printed parts. As metal printing with Laser Powder Bed Fusion (LPBF) is applied from rapid prototyping to serial manufacturing processes more and more, sustainability data are useful to support a valid scale-up scenario and energetic improvements of the 3D-printing machinery as well as peripheral aggregates used in the process chain. The contribution aims to increase the transparency of the LPBF process chain in terms of its energy consumption. Therefore a generalized model to assess sustainability aspects of metal printed parts is derived. For this purpose, the LPBF process chain with the essential pre-, main- and post-processes is evaluated regarding its energy intensity. Here, the sub-processes, for example wet and dry cleaning of the printer, sieving of the metal powder or sand-blasting of the part are analyzed as well as the main printing process. Based on the derived experimental data from an installed, industry-like process chain, a model is created, which tends to generalize the experimental findings to evaluate other metal printed parts and process chain variants in terms of their energy intensity.</abstract>
    <parentTitle language="eng">Procedia CIRP</parentTitle>
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    <author>Dennis Ochs</author>
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    <author>Andreas Schiffler</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fang">Fakultät für angewandte Natur- und Geisteswissenschaften</collection>
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    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="ddc" number="671">Metallverarbeitung und Rohprodukte aus Metall</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
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