<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>5708</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Taylor &amp; Francis</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <enrichment key="opus.source">publish</enrichment>
    <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>
    <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>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/5708/Engelmann_Changeover_Events_Manufacturing.pdf</file>
  </doc>
  <doc>
    <id>5609</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>2024</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <enrichment key="opus.source">publish</enrichment>
    <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>
    <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>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/5609/Schmitt_G-code_evaluation_CNC_milling.pdf</file>
  </doc>
  <doc>
    <id>5455</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>2024</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Assessing the Influence of Sensor-Induced Noise on Machine-Learning-Based Changeover Detection in CNC Machines</title>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="url">https://doi.org/10.3390/s24020330</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <author>Vinai George Biju</author>
    <author>Anna-Maria Schmitt</author>
    <author>Bastian Engelmann</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>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/5455/Engelmann_Assessing_Sensor-induced_noise.pdf</file>
  </doc>
  <doc>
    <id>5454</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>2024</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <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>
    <enrichment key="opus.source">publish</enrichment>
    <author>Bastian Engelmann</author>
    <author>Anna-Maria Schmitt</author>
    <author>Lukas Theilacker</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>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/5454/Engelmann_Implications_Legacy_Device.pdf</file>
  </doc>
  <doc>
    <id>2231</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>110</pageFirst>
    <pageLast>127</pageLast>
    <pageNumber>18</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>36</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-02-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards gestured-based technologies for human-centred smart factories</title>
    <abstract language="eng">Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, − monitoring or – control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator – ErgoTakt – derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers’ ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown.</abstract>
    <parentTitle language="eng">International Journal of Computer Integrated Manufacturing</parentTitle>
    <identifier type="url">https://doi.org/10.1080/0951192X.2022.2121424</identifier>
    <identifier type="issn">1362-3052</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <licence>Creative Commons - CC BY-ND - Namensnennung - Keine Bearbeitungen 4.0 International</licence>
    <author>Jan Schmitt</author>
    <author>Bastian Engelmann</author>
    <author>Vito Modesto Manghisi</author>
    <author>Markus Wilhelm</author>
    <author>Antonello Uva</author>
    <author>Michele Fiorentino</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>gesture-based monitoring</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>gesture-based control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>manufacturing</value>
    </subject>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="Regensburger_Klassifikation" number="ZG - ZS">Technik</collection>
    <collection role="ddc" number="670">Industrielle Fertigung</collection>
    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/2231/Schmitt_Gesture-based_technologies.pdf</file>
  </doc>
  <doc>
    <id>2003</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>143</pageFirst>
    <pageLast>146</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>5</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2022-03-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Internationalisierung in Pandemiezeiten, technische Möglichkeiten, Lehr- und Forschungskonzepte mal anders gedacht</title>
    <abstract language="deu">Eines der zentralen strategischen Ziele unserer Hochschule ist die Internationalisierung, sowie der »internationalisation@home«. Als die weltweite Corona-Pandemie die Präsenzlehre und -forschung ebenso wie den internationalen Austausch von Studierenden und Forschenden zu Beginn 2020 quasi zum Erliegen brachte wurden die Rufe nach digitalen Angeboten im internationalen Bereich schnell laut. Vor diesem Hintergrund reagierte der »Deutsche Akademische Auslandsdienst (DAAD)« mit der kurzfristig ins Leben gerufenen Förderlinie »International Virtual Academic Collaboration« (IVAC), um internationale Hochschulkooperationen und weltweite Mobilität unter digitalen Vorzeichen strategisch zu gestalten und auszubauen [1].</abstract>
    <parentTitle language="deu">FHWS Science Journal</parentTitle>
    <identifier type="url">https://nbn-resolving.org/urn:nbn:de:bvb:863-opus-19389</identifier>
    <identifier type="issn">2196-6095</identifier>
    <identifier type="urn">urn:nbn:de:bvb:863-opus-20035</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Eddi Miller</author>
    <author>Christine Barthelme</author>
    <author>Andreas Schiffler</author>
    <author>Bastian Engelmann</author>
    <author>Jan Schmitt</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>internationalisierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>covid</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>corona</value>
    </subject>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/2003/SJ21.2_internationalisierung_pandemie.pdf</file>
  </doc>
  <doc>
    <id>2001</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>135</pageFirst>
    <pageLast>142</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>5</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2022-03-22</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Optimierung von Prozessen und Werkzeugmaschinen durch Bereitstellung, Analyse und Soll-Ist-Vergleich von Produktionsdaten</title>
    <abstract language="deu">Mit einem Umsatz von 103 Milliarden Euro ist die Metallindustrie eine der größten deutschen Industriebranchen. Diese ist von volatilen Marktbedingungen und hohem Wettbewerb geprägt [1][2]. Kleine und mittlere produzierende Unternehmen (sogenannte KMU) sehen zunehmend gravierende Probleme bei der Einhaltung von Lieferterminen bedingt durch hohe Durchlaufzeiten in der Produktion [3]. Neben kaufmännischen Planungssystemen zur Erstellung von Produktionsplänen nutzen Unternehmen als Planungsgrundlage weiterhin Excel mit 31 % und manuelle Prozesse mit 10 % [4]. Gleiches gilt für Produktwechselvorgänge auf Maschinen (Rüsten). Aufgrund dieser Aspekte ist es notwendig, die Rentabilität der KMU in der Metallindustrie zu steigern. Das wird durch effiziente Produktionsplanung und -steuerung, sowie der daraus resultierenden hohen Reaktionsfähigkeit und Flexibilität realisiert. Daher ist die Produktionsplanung auf die Markt- und Kundenanforderungen und die Anlageneffektivität auf ein hohes und stabiles Niveau auszurichten [5]. Hier bietet die Erfassung von Echtzeitdaten eine adäquate Reaktion auf die genannten Anforderungen. Ebenfalls liefert sie großes Potenzial für die Produktionsplanung und -steuerung, um die Disposition und Koordination von Arbeitsaufträgen zu optimieren. Weiterhin werden Störgrößen oder unvorhergesehene Planungsabweichungen reduziert [4][6]. Zusätzlich ist eine erhöhte Transparenz und Verbesserung menschlicher Entscheidungsprozesse notwendig. Dies kann durch datengetriebene Methoden unterstützt und sichergestellt werden [7]. Ein Ansatz zur Optimierung des Produktionsergebnisses kann durch die Erhöhung der Anlagenproduktivität selbst realisiert werden. Dazu muss die Verfügbarkeit der Anlagen durch Lokalisierung und Reduzierung von Verlusten erhöht werden. Die Umrüstungsprozesse tragen stark negativ zur Verfügbarkeit einer Produktion bei. Eine Steigerung der Gesamtanlageneffektivität (overall equipment effectiveness oder kurz OEE) in einer Fertigungsumgebung ist jedoch möglich durch eine intelligente Nutzung von Sensordaten mit Techniken wie z. B. Machine Learning (ML).</abstract>
    <parentTitle language="deu">FHWS Science Journal</parentTitle>
    <identifier type="url">https://nbn-resolving.org/urn:nbn:de:bvb:863-opus-19389</identifier>
    <identifier type="issn">2196-6095</identifier>
    <identifier type="urn">urn:nbn:de:bvb:863-opus-20012</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <licence>Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International</licence>
    <author>Jan Hofmann</author>
    <author>Lars Eberhardt</author>
    <author>Moritz Heusinger</author>
    <author>Alexander Dobhan</author>
    <author>Bastian Engelmann</author>
    <author>Frank-Michael Schleif</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>produktionsdaten</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>metall</value>
    </subject>
    <collection role="Regensburger_Klassifikation" number="Q">Wirtschaftswissenschaften</collection>
    <collection role="ddc" number="338">Produktion</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/2001/SJ21.2_optimierung_von_prozessen.pdf</file>
  </doc>
  <doc>
    <id>1981</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>5</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2021-09-27</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Towards Material-Batch-Aware Tool Condition Monitoring</title>
    <abstract language="eng">In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework.</abstract>
    <parentTitle language="eng">Journal of Manufacturing and Materials Processing</parentTitle>
    <identifier type="url">https://doi.org/10.3390/jmmp5040103</identifier>
    <identifier type="issn">2504-4494</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Bastian Engelmann</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>manufacturing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>material</value>
    </subject>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="Regensburger_Klassifikation" number="ZG - ZS">Technik</collection>
    <collection role="ddc" number="670">Industrielle Fertigung</collection>
    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
    <thesisPublisher>Hochschule für Angewandte Wissenschaften Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/1981/Engelmann_Material-batch-aware_monitoring.pdf</file>
  </doc>
</export-example>
