Echtzeit-Kompensation thermischer Fehler von Maschinen mittels OPC UA/Virtual Climatization for Machine Tools – Real-Time Thermal Error Compensation in Machine Tools via OPC UA

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Cover of Volume: wt Werkstattstechnik online Volume 115 (2025), Edition 11-12
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wt Werkstattstechnik online

Volume 115 (2025), Edition 11-12

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Publisher
VDI fachmedien, Düsseldorf
Copyright year
2025
ISSN-Online
1436-4980
ISSN-Print
1436-4980

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Open Access Full access

Volume 115 (2025), Edition 11-12

Echtzeit-Kompensation thermischer Fehler von Maschinen mittels OPC UA/Virtual Climatization for Machine Tools – Real-Time Thermal Error Compensation in Machine Tools via OPC UA

Authors:
ISSN-Print
1436-4980
ISSN-Online
1436-4980
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Strict real-time requirements in path interpolation for machine tools pose challenges for error compensation systems: Position-dependent correction values must be reliably available every 2 ms. Detailed temperature models are too computationally intensive for this purpose. Therefore, the authors conceptualize, develop, and test a look-up table-based system for correcting thermal and static errors. This system encodes the error behavior in geometry error tables, which are regularly updated via OPC UA. As a result, the cycle-time requirements are significantly reduced.

Bibliography

  1. [1] Bossut, M.; Diem, C.; Ivanov, D. et al.: Globale Krisen bewältigen: Mit Daten zu resilienteren Lieferketten. Wirtschaftsdienst 105 (2025) 3, S. 205–211, doi.org/10.2478/wd-2025–0054 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  2. [2] Jung, S.: Globale Lieferketten – Zwischen Kosteneinsparungen & Resilienz, Handelsblatt Research Institute. Stand: 2022. Internet: research.handelsblatt.com/wp-content/uploads/2024/07/Playbook_HRI_ServiceNow_Lieferketten.pdf. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  3. [3] Everstream Analytics (Hrsg.): Risiken im Jahr 2025: Geopolitik und kritische Mineralienbeschaffung. Stand: 2025. Internet: www.everstream.ai/de/articles/risiken-im-jahr-2025-geopolitik-und-kritische-mineralienbeschaffung/. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  4. [4] ifok GmbH; Bundesministerium für Umwelt, Klimaschutz, Naturschutz und nukleare Sicherheit (BMUKN) (Hrsg.): Auf dem Weg zur Kreislaufwirtschaft. Die Entwicklung der gesetzlichen Rahmenbedingungen. Stand: Internet: www.kreislaufwirtschaft-deutschland.de/kreislaufwirtschaft/regulierung. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  5. [5] Wörner, J.-D.; Schmidt, Christoph M. (Hrsg.): Sicherheit, Resilienz und Nachhaltigkeit. acatech IMPULS. Stand: 2022. Internet: www.acatech.de/publikation/sicherheit-resilienz-und-nachhaltigkeit/download-pdf?lang=de. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  6. [6] Marrenbach, D.; Mack, J.: Resilienz in Wertschöpfungsnetzwerken. wt Werkstattstechnik online 115 (2025) 06, S. 381–388. DOI: https://doi.org/10.37544/1436–4980–2025–06–15 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  7. [7] Bauer, J.: Industrielle Ökologie. Theoretische Annäherung an ein Konzept nachhaltiger Produktionsweisen. Dissertation, Universität Stuttgart, 2008 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  8. [8] Ulrich, H.: Management. Schriftenreihe Unternehmung und Unternehmungsführung, Band 13, Bern: P. Haupt 1984 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  9. [9] Ellen MacArthur Foundation (eds.): Growth within: A circular economy vision for a competitive Europe. Stand: 2015. Internet: www.ellenmacarthurfoundation.org/growth-within-a-circular-economy-vision-for-a-competitive-europe. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  10. [10] Buß, D.; Gebauer, H.; Glawar, R. et al.: Resiliente Wertschöpfung in der produzierenden Industrie – innovativ, erfolgreich, krisenfest. Whitepaper RESYST. Stand: 2021. Internet: www.fraunhofer.de/s/ePaper/Whitepaper/RESYST/index.html#0. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  11. [11] Tauer, R.; Aechtner, J.: Modell Deutschland Circular Economy: Eine umfassende Circular Economy für Deutschland 2045 zum Schutz von Klima und Biodiversität. WWF Deutschland. Stand: Juni 2023. Internet:www.wwf.de/fileadmin/fm-wwf/Publikationen-PDF/Unternehmen/WWF-Modell-Deutschland-Circular-Economy-Broschuere.pdf. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  12. [12] Conrad, R.; Hoeborn, G.; Neudert, P. K. et al.: Seizing the Potentials of Ecosystems. Whitepaper FIR e. V. an der RWTH Aachen. Stand: 2022. Internet: https://epub.fir.de/files/1290/fir-whitepaper-seizing-the-potentials-of-ecosystems_j36sU3OFzw.pdf . Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  13. [13] Stich, V.; Hoeborn, G.; Spindler, D. M.: Wettbewerbsvorteil der Zukunft – Ecosystem Design. In: Voß, P. H. (Hrsg.): Die Neuerfindung der Logistik: wie sich die Logistikindustrie für das Zeitalter der Volatilität rüstet. Wiesbaden: Springer Gabler 2023, S. 115–129 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  14. [14] Klapper, L.; Spindler, D.; Hoeborn, G. et al.: Circular Ecosystem Development: A Process Framework and Practical Application. Proceedings of NBM 2025. Stand: 2025. Internet: epub.fir.de/files/3862/fir_Klapper_et_al_Circular_Ecosystem_Development_Framework_Preprint_2025.pdf. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  15. [15] Pietrulla, F.: Circular ecosystems: A review. Cleaner and Circular Bioeconomy 3 (2022), #100031, doi.org/10.1016/j.clcb.2022.100031 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  16. [16] Daheim, C.; Jöster-Morisse, C.; Störmer, E. et al.: Kreislaufwirtschaft in Deutschland und der EU: Positionen und Perspektiven. Gütersloh: Bertelsmann-Stiftung, 2024, doi.org/10.11586/2025003 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  17. [17] Hummler, A.; Lindner, R.; Posch, D. et al.: Deutschlands zirkuläre Zukunft: Wie Missionen die Transformation zur Circular Economy beschleunigen. Stand: 2023. Internet: https://www.bertelsmann-stiftung.de/fileadmin/files/PicturePark/2024–02/BST_Focus_Paper_Deutschlands_zirkulaere_Zukunft_ID2007.pdf. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  18. [18] Bundesministerium für Wirtschaft und Klimaschutz BMWK (Hrsg.): Resilienz im Kontext von Industrie 4.0. Whitepaper der Plattform Industrie 4.0, Fraunhofer-Institut für Produktionstechnik und Automatisierung IPA. Stand: 2022. Internet: www.plattform-i40.de/IP/Redaktion/DE/Downloads/Publikation/Resilienz.pdf?__blob=publicationFile&v=1. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  19. [19] Wissenschaftliche Gesellschaft für Produktionstechnik WGP (Hrsg.): Resiliente Fabriken in bewegten Zeiten. Stand: 09.01.2025. Internet: wgp.de/de/resiliente-fabriken-in-bewegten-zeiten/. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  20. [20] Fromhold-Eisebith, M.: Circular Economy trifft urban-regionale Resilienz – Synergien für eine nachhaltig-anpassungsfähige Stadtentwicklung. Standort 47 (2023) 1, S. 33–39 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  21. [21] Liao, A.: Warranty Chain Management. Digitalization and Sustainability. Singapore: Springer Nature 2022, doi.org/10.1007/978–981–19–2104–9 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  22. [22] Alivojvodic, V.; Kokalj, F.: Redefining Waste: R-Strategies and Metrics as a Framework for Driving Progress of Circular Economy Performance. In: Mitrovic, N.; Mladenovic, G.; Mitrovic, A. (eds.): New Trends in Engineering Research 2024. Cham: Springer Nature 2024, pp. 75–91 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  23. [23] Attajer, A.; Chaabane, S.; Darmoul, S. et al.: Evaluation of Operational Resilience in Cyber-Physical Production Systems: literature review. IFAC-PapersOnLine 55 (2022) 10, pp. 2264–2269, doi.org/10.1016/j.ifacol.2022.10.045 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  24. [24] Da Silva, S. B. G.; Barros, M. V.; Radicchi, J. Â. Z. et al.: Opportunities and challenges to increase circularity in the product’s use phase. Sustainable Futures 8 (2024), #100297, doi.org/10.1016/j.sftr.2024.100297 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  25. [25] Recycling Magazin: Remanufacturing: Besser als neu. Stand: 19.05.2023. Internet: www.recyclingmagazin.de/2023/05/19/ remanufacturing-besser-als-neu/. Zugriff am 18.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  26. [26] Wachter, C.; Beckschulte, S.; Hinrichs, M. P. et al.: Strategies for Resilient Manufacturing: A Systematic Literature Review of Failure Management in Production. Procedia CIRP 130 (2024), pp. 1393–1402, doi.org/10.1016/j.procir.2024.10.257 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  27. [27] Rincón-Guio, C.; Sarache, W.: Mapping the Multifaceted Landscape of Resilience in Manufacturing Strategy: A Systematic Literature Review and Future Research Avenues. Journal of Industrial Integration and Management 09 (2024) 2, pp. 157–193, doi.org/10.1142/S2424862224500064 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  28. [28] Faruquee, M.; Paulraj, A.; Irawan, C. A.: A typology of supply chain resilience: recognising the multi-capability nature of proactive and reactive contexts. Production Planning & Control 35 (2024) 12, S. 1503–1523, doi.org/10.1080/09537287.2023.2202151 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  29. [29] Rincón-Guio, C.; Rico, A.; Triana-Garcia, R. et al.: Strengthening Resilience in the Manufacturing Sector: A Capabilities-Based Approach. In: Ivanov, V.; Silva, F. J. G.; Trojanowska, J. et al. (eds.): Advances in Design, Simulation and Manufacturing VIII. Proceedings of the 8th International Conference on Design, Simulation, Manufacturing: The Innovation Exchange, DSMIE-2025, Porto, Portugal, doi.org/10.1007/978–3–031–95211–1_17 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  30. [30] Ali, M.; Nazir, S.; Junaid, M.: Blockchain Driven Supply Chain Management and Supply Chain Resilience: Role of Intellectual Capital. In: Mubarik, M. S.; Shahbaz, M. (eds.): Blockchain Driven Supply Chain Management. A Multi-dimensional Perspective. Singapore: Springer Nature 2023, pp. 239–254., doi.org/10.1007/978–981–99–0699–4_14 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  31. [31] Brandon‐Jones, E.; Squire, B.; Autry, C. W. et al.: A Contingent Resource‐Based Perspective of Supply Chain Resilience and Robustness. Journal of Supply Chain Management 50 (2014) 3, S. 55–73, doi.org/10.1111/jscm.12050 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  32. [32] Ghobakhloo, M.; Iranmanesh, M.; Foroughi, B. et al.: Industry 4.0 digital transformation and opportunities for supply chain resilience: a comprehensive review and a strategic roadmap. Production Planning & Control 36 (2025) 1, pp. 61–91, doi.org/10.1080/09537287.2023.2252376 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  33. [33] Bentz, D.; Doan, A.; Meldt, L. et al.: Resilienz in der industriellen Produktion. Eine Aufnahme der Ist-Situation. Darmstadt: TU Darmstadt, 2025, doi.org/10.26083/tuprints-00029006 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  34. [34] Ferhat, S.; Oger, R.; Ballot, E. et al.: Building a collaborative manufacturing system’s network resilience through an adaptability potential analysis. European Journal of Innovation Management (2024), doi.org/10.1108/EJIM-12–2023–1144 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  35. [35] Steinmeyer, M.; Metternich, J.: Resilienz aus der Wertstromperspektive. ZWF – Zeitschrift für wirtschaftlichen Fabrikbetrieb 118 (2023) 9, S. 605–609, doi.org/10.1515/zwf-2023–1123 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  36. [36] Caillaud, E.; Goepp, V.; Berrah, L.: Towards “transformative” resilience for the sustainability of Industry 4.0. IFAC-PapersOnLine 58 (2024) 19, pp. 391–396, doi.org/10.1016/j.ifacol.2024.09.243 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  37. [37] Hackl, J.: Wirkmodell der Eigenschaften modularer Produktstrukturen. Dissertation, Technische Universität Hamburg, 2021 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  38. [38] Mayer, A.; Thoemmes, F.; Rose, N. et al.: Theory and Analysis of Total, Direct, and Indirect Causal Effects. Multivariate behavioral research 49 (2014) 5, pp. 425–442, doi.org/10.1080/00273171.2014.931797 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  39. [39] Hitchcock, C.: Causal Modelling. In: Beebee, H.; Hitchcock, C.; Menzies, P. (eds.): The Oxford Handbook of Causation. Oxford: Oxford University Press 2009, pp. 299–314 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  40. [40] Kleinberg, S.: Going From Models to Action. In: Illari, P.; Russo, F. (eds.): The Routledge Handbook of Causality and Causal Methods. New York: Routledge 2024, pp. 506–517 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-6
  41. [1] Gaubinger, K.: VUCA-Welt als zentrale Herausforderung für den Mittelstand. In: Gaubinger, K. (Hrsg.): Hybrides Innovationsmanagement für den Mittelstand in einer VUCA-Welt. Berlin, Heidelberg: Springer-Verlag 2021, S. 1–27 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  42. [2] Herbane, B.: Rethinking organizational resilience and strategic renewal in SMEs. Entrepreneurship & Regional Development 31 (2019) 5–6, pp. 476–495 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  43. [3] Schuh, G.; Patzwald, M.; Krebs, L. et al.: Resilienz im strategischen Management produzierender Unternehmen. Konzeptpapier. Stand: 2022. Internet: www.ipt.fraunhofer.de/content/dam/ipt/de/documents/whitepaper/fraunhofer-ipt-publikation-konzeptpapier-resilienz-im-strategischen-management.pdf. Zugriff am 28.10.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  44. [4] Meschnig, G.: Volatilität nutzen – flexible Strategien, agile Organisationen. Controlling & Management 56 (2012) 3, S. 172–173 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  45. [5] Kohl, H.; Buß, D.; Gebauer, H. et al.: White Paper »RESYST«. Resiliente Wertschöpfung in der produzierenden Industrie – innovativ, erfolgreich, krisenfest. Stand: 2021. Internet: publica.fraunhofer.de/bitstreams/57596bf7–1c31–4311-b708–5eee7650d06f/download. Zugriff am 28.10.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  46. [6] Potter, C.; Waterfall, G.: Information Security Breaches Survey. Technical Report. Internet: www.pwc.co.uk/assets/pdf/olpapp/uk-information-security-breaches-survey-technical-report.pdf. Zugriff am 28.10.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  47. [7] Bundesverband Metall: Struktur- und Konjunkturdaten zum Metallhandwerk. Internet: www.metallhandwerk.de/struktur-und-konjunkturdaten-zum-metallhandwerk/. Zugriff am 29.10.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  48. [8] Unkrig, E. R.: Resilienz im Unternehmen – den Faktor Mensch fördern. Handlungsempfehlungen und praktische Umsetzung. Heidelberg: Springer Gabler 2021 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  49. [9] Schmitt, R.; Bauernhansl, T.; Bergs, T. et al.: WGP-Standpunkt Resilienz. WGP-Positionspapier zur Resilienz in Produktionssystemen. Internet: wgp.de/wp-content/uploads/2025/01/Positionspapier-Resilienz_final.pdf. Zugriff am 28.10.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  50. [10] Hémond, Y.; Robert, B.: Preparedness: the state of the art and future prospects. Disaster Prevention and Management: An International Journal 21 (2012) 4, pp. 404–417 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  51. [11] Henne, I.: How end-to-end data collaboration optimises value creation in supply chains. Journal of Supply Chain Management, Logistics and Procurement 6 (2023) 1, p. 14–25 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  52. [12] Kübler, P.; Schneider, M.; Groß, E. et al.: IIoT Platforms as a central database for digital production. Stand: 2022. Internet: publica.fraunhofer.de/items/968b3fbd-2a35–45ba-a033-e0ce4b162a5b/request-a-copy?bitstream=fdca5e49–3705–4875–9ae7-c821108c6519. Zugriff am 28.10.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  53. [13] Jelschow, V.; Schneider, M.; Groß, E. et al.: Auswahl von IIoT-Plattformen in der Produktion. Strategische Fragestellungen für die Anforderungsanalyse. wt Werkstattstechnik online 112 (2022) 5, S. 336–341. Düsseldorf: VDI Fachmedien, doi.org/10.37544/1436–4980–2022–05–66 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  54. [14] Groß, E.; Schneider, M.; Schel, D. et al.: Erfolgreich mit IIoT-Plattformen. Von der Auswahl bis zur Einführung: IIoT-Plattformen in der Produktion. Webinar. Stand: 2022, Internet: www.ipa.fraunhofer.de/de/veranstaltungen-messen/veranstaltungen/2022/iiot-plattform.html?mtm_campaign=22–10–13-sm-iiot. Zugriff am 29.10.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  55. [15] Liang, L.; Han, Z.; Xie, J. et al.: S&T Innovation Platform Sharing Service Contract Mechanism to Achieve Supply Chain Resilience. Sustainability 14 (2022) 21, #14124 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  56. [16] Bullinger-Hoffmann, A.; Rieprich, M.; Ramm, S. et al.: Resiliente Wertschöpfungsnetzwerke in der Gesundheitswirtschaft. Studie zum Umgang mit krisenbedingten Störungen in Zeiten von Industrie 4.0. Chemnitz: Technische Universität Chemnitz 2022 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  57. [17] Schneider, M.; Groß, E.; Gramberg, T. et al.: IIoT-Plattformen und digitale Services für den Mittelstand. So funktioniert plattformbasierte Wertschöpfung in der Produktion. Webinar. Stand: 2023, Internet: www.ipa.fraunhofer.de/de/veranstaltungen-messen/veranstaltungen/2023/iiot_fuer_mittelstand.html. Zugriff am 29.10.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  58. [18] Gross, E.; Schneider, M.; Birenbaum, C.: Resiplat– Resilienzsteigerung in metallbe- und -verarbeitenden Unternehmen durch ein vernetztes Plattformökosystem. Internet: www.ipa.fraunhofer.de/de/referenzprojekte/resiplat.html. Zugriff am 28.10.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  59. [19] Liberati, A.; Altman, D. G.; Tetzlaff, J. et al.: The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Journal of clinical epidemiology 62 (2009) 10, e1–34 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  60. [20] Mayring, P.; Fenzl, T.: Qualitative Inhaltsanalyse. In: Baur. N.; Blasius, J. (Hrsg.): Handbuch Methoden der empirischen Sozialforschung. Wiesbaden: Springer VS 2022, S. 691–706 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  61. [21] Benfer, M.; Verhaelen, B.; Peukert, S. et al.: Resilience Measures in Global Production Networks. A Literature Review and Conceptual Framework. Die Unternehmung 75 (2021) 4, S. 491–520, www.jstor.org/stable/27284470 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  62. [22] Shishodia, A.; Sharma, R.; Rajesh, R. et al.: Supply chain resilience: A review, conceptual framework and future research. The International Journal of Logistics Management 34 (2023) 4, pp. 879–908 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  63. [23] Christopher, M.; Peck, H.: Building the Resilient Supply Chain. The International Journal of Logistics Management 15 (2004) 2, pp. 1–14 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  64. [24] Tang, C. S.: Perspectives in supply chain risk management. International Journal of Production Economics 103 (2006) 2, pp. 451–488 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-15
  65. [1] Cascio, J.: Facing the Age of Chaos. Internet: medium.com/@cascio/facing-the-age-of-chaos-b00687b1f51d. Zugriff am 10.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  66. [2] Maryna, S.; Kateryna, Z.: Strategies for Resilience in a Dynamic World: from VUCA to BANI. Proceedings of the 10th SocraticLectures, Ljubljana, Slovenia: Faculty of Health Sciences, University of Ljubljana, 2024, pp. 185–189, doi.org/10.55295/PSL.2024.I23 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  67. [3] Henchey, N.: Making Sense of Future Studies. Alternatives: Perspectives on Society, Technology and Environment 7 (1978) 2, pp. 24–27 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  68. [4] Fritz J.; Dieckmann, I.: Erfolgsfaktor Trends: Strategisch reagieren und langfristig profitieren – Qualitätsmanagement als Schlüssel für Innovation. VDI-Z Integrierte Produktion 166 (2025) 01–02, pp. 70–74 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  69. [5] Conway, M.: Scenario Planning: An Innovative Approach to Strategy Development. Foresight Futures. Melbourne: Swinburne University of Technology 2004 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  70. [6] Groß B.; Mandir, E. Zukünfte gestalten: Spekulation, Kritik, Innovation. Mainz: Hermann Schmidt Verlag 2023 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  71. [7] Voros, J.: A generic foresight process framework. Foresight 5 (2003) 3, pp. 10–21, doi.org/10.1108/14636680310698379 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  72. [8] Mintzberg, H.: The Rise and Fall of Strategic Planning. Harvard Business Review (1994) reprint #94107 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  73. [9] Hancock T.; Bezold, C.: Possible Futures, Preferable Futures. The Healthcare Forum Journal 37 (1994) 2, pp. 23–29 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  74. [10] Taleb, N. N. The Black Swan. London: Penguin Books 2010 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  75. [11] Polak, F. The Image of the Future. Amsterdam: Elsevier Scientific Publishing 1973 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  76. [12] Horton, A.: A simple guide to successful foresight. Foresight: The Journal of Futures Studies, Strategic Thinking and Policy 1 (1999) 1, pp. 5–9, doi.org/10.1108/14636689910802052 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  77. [13] Puglisi, M.: The study of the futures: an overview of futures studies methodologies. In: Camarda, D.; Grassini, L. (eds.): Interdependency between agriculture and urbanization: Conflicts on sustainable use of soil and water. Options Méditerranéennes: Série A. Séminaires Méditerranéens, 44, CIHEAM, (2001), pp. 439–463. Internet: om.ciheam.org/om/pdf/a44/02001611.pdf. Zugriff am 10.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  78. [14] Ketonen-Oksi S.; Vigren, M.: Methods to Imagine Transformative Futures: An Integrative Literature Review. Futures 157(2024) #103341, pp. 1–13, doi.org/10.1016/j.futures.2024.103341 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  79. [15] Wilkinson, A.: Scenario Practices: In Search of Theory. Journal of Futures Studies 13, (2009) 3, pp. 107–114 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  80. [16] Hines, A.; Benoit, H.; Leong, L. et al.: Mapping archetype scenarios across the three horizons. Futures 162 (2024), pp. 1–21, # 103418, doi.org/10.1016/j.futures.2024.103418 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  81. [17] Sharpe B.; Hodgson, T.: Intelligent Infrastructure Futures Technology Forward Look. Department of Trade and Industry – Foresight Programme of the Office of Science and Technology. Stand: 2006. Internet: www.decisionintegrity.co.uk/DIL%20Infrastructure%20Technology%20Forward%20Look%20-%20Sharpe&Hodgson.pdf. Zugriff am 10.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  82. [18] Ariza-Álvarez, A.; Soria-Lara, J. A.: Participatory mapping in exploratory scenario planning: Necessity or luxury? Futures 160 (2024) #103398, doi.org/10.1016/j.futures.2024.103398 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  83. [19] Inayatullah, S.: Macrohistory and Futures Studies. Futures 30 (1998) 5, pp. 381–394, doi.org/10.1016/S0016–3287(98)00043–3 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  84. [20] Sosa, R.: Accretion Theory of Ideation: Evaluation Regimes for Ideation Stages. Design Science 5 (2019) 1, pp. 1–33, doi.org/10.1017/dsj.2019.22 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  85. [21] Gonçalves M.; Cash, P.: The Life Cycle of Creative Ideas: A Study of the Ideation Process. Design Studies 72, (2020) #100988, pp. 1–33, doi.org/10.1016/j.destud.2020.100988 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  86. [22] Hiltunen, E.: The future sign and its three dimensions. Futures 40 (2008) 3, pp. 247–260, doi.org/10.1016/j.futures.2007.08.021 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  87. [23] Curry A.; Hodgson, A.: Seeing in Multiple Horizons: Connecting Futures to Strategy. Journal of Futures Studies 17 (2012) 1, pp. 1–20 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  88. [24] Choo, C. W. Information Management for the Intelligent Organization: The Art of Scanning the Environment. Medford, NJ: American Society for Information Systems by Information Today 1998 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  89. [25] Slaughter, R. A.: Mapping the Future: Creating a Structural Overview of the Next 20 Years. Journal of Futures Studies 1, (1996) 1, pp. 5–27 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  90. [26] Roland Berger Institute: Trend Compendium 2050: Six Megatrends That Will Shape the World. Stand: 2023. Internet: www.rolandberger.com/de/Insights/Global-Topics/Trend-Compendium/. Zugriff am 10.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  91. [27] McKinsey & Company: Technology Trends Outlook 2025. Stand: 2025. Internet: www.mckinsey.com/business-functions/mckinsey-digital/our-insights/the-top-trends-in-tech. Zugriff am 10.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  92. [28] Dufva, M.; Rekola, S.: Megatrends 2023: Five Trends Reshaping Our Future.” Sitra – The Finnish Innovation Fund. Stand: 2023. Internet: www.sitra.fi/en/publications/megatrends-2023/. Zugriff am 10.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  93. [29] Interaction Design Foundation: What is How Might We? (HMW). Stand:2025. Internet: www.interaction-design.org/literature/topics/how-might-we. Zugriff am 10.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  94. [30] Auernhammer J.; Roth, B.: The Origin and Evolution of Stanford University’s Design Thinking: From Product Design to Design Thinking in Innovation Management. Journal of Product Innovation Management 38 (2021) 6, pp. 623–644, doi.org/10.1111/jpim.12594 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-26
  95. [1] Glöckner, H.: System Dynamics Modeling of Reactive Failure Management in Production Systems. Aachen: Apprimus 2020 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  96. [2] Lee, J.; Siahpour, S.; Jia, X. et al.: Introduction to resilient manufacturing systems. Manufacturing Letters 32 (2022) 32, S. 24–27. Internet: https://doi.org/10.1016/j.mfglet.2022.02.002. Zugriff am 05.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  97. [3] Wachter, C.; Beckschulte, S.; Padrón Hinrichs, M. et al.: Strategies for Resilient Manufacturing: A Systematic Literature Review of Failure Management in Production. Procedia CIRP 130 (2024), S. 1393–1402. Internet: https://doi.org/10.1016/j.procir.2024.10.257. Zugriff am 05.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  98. [4] Beckschulte, S.; Padrón Hinrichs, M.; Pirrone, L. et al.: Manufacturing Data Analytics Study 2023 - Empirical Industry Study. Aachen: Apprimus 2023 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  99. [5] Soares Ito, A.; Ylipää, T.; Gullander, P. et al.: Prioritisation of root cause analysis in production disturbance management. International Journal of Quality & Reliability Management 39 (2022) 5, S. 1133–1150. Internet: https://doi.org/10.1108/IJQRM-12-2020-0402. Zugriff am 05.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  100. [6] Gao, Y.; Xiong, Y.; Gao, X. et al.: Retrieval-Augmented Generation for Large Language Models: A Survey (2024). Internet: https://arxiv.org/pdf/2312.10997v5. Zugriff am 05.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  101. [7] Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature 521 (2015) 7553, pp. 452–459. https://doi.org/10.1038/nature14541. Zugriff am 05.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  102. [8] Wang, C.; Liu, X.; Yue, Y. et al.: Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity (2023). Internet: https://arxiv.org/pdf/2310.07521v3. Zugriff am 03.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  103. [9] Jiang, M.; Lin, B. Y.; Wang, S. et al.: Knowledge-augmented Methods for Natural Language Processing. Singapore: Springer Nature Singapore; Imprint Springer 2024 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  104. [10] Barnett, S.; Kurniawan, S.; Thudumu, S. et al.: Seven Failure Points When Engineering a Retrieval Augmented Generation System. Proceedings of the IEEE/ACM 3rd International Conference on AI Engineering (2024), S. 194–199. Internet: https://doi.org/10.1145/3644815.3644945. Zugriff am 05.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  105. [11] ISO/IEC 25012, Ausgabe 2008 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  106. [12] ISO 8000-8. Data quality - Part 8: Information and data quality: Concepts and measuring. Ausgabe 2015 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  107. [13] Günther, L. C.; Colangelo, E.; Wiendahl, H.-H. et al.: Data quality assessment for improved decision-making: a methodology for small and medium-sized enterprises. Procedia Manufacturing 29 (2019), S. 583–591. Internet: https://doi.org/10.1016/j.promfg.2019.02.114. Zugriff am 05.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  108. [14] Aamodt, A.; Nygård, M.: Different roles and mutual dependencies of data, information, and knowledge — An AI perspective on their integration. 1995 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  109. [15] Ackoff, R. L.: From data to wisdom. In: Journal of Applied Systems Analysis, 16 (1989), S. 3–9 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  110. [16] North, K.: Wissensorientierte Unternehmensführung, 7. Aufl., Wiesbaden: Springer Gabler (2021) Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  111. [17] Wang, B.; Wei, C.; Liu, Z. et al.: Resilience of Large Language Models for Noisy Instructions (2024). Internet: https://arxiv.org/pdf/2404.09754. Zugriff am 03.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  112. [18] Zhang, X.; Yang, C.; Yan, H. et al.: Does Correction Remain A Problem For Large Language Models? (2023). Internet: https://arxiv.org/pdf/2308.01776. Zugriff am 03.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  113. [19] Chen, J.; Lin, H.; Han, X. et al.: Benchmarking Large Language Models in Retrieval-Augmented Generation (2023). Internet: https://arxiv.org/pdf/2309.01431v2. Zugriff am 03.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  114. [20] Kamm, S.; Jazdi, N.; Weyrich, M.: Knowledge Discovery in Heterogeneous and Unstructured Data of Industry 4.0 Systems: Challenges and Approaches. Procedia CIRP 104 (2021), S. 975–980. Internet: https://10.1016/j.procir.2021.11.164. Zugriff am 03.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  115. [21] Batini, C.; Cappiello, C.; Francalanci, C. et al.: Methodologies for data quality assessment and improvement. ACM Computing Surveys 41 (2009) 3, S. 1–52. Internet: https://doi.org/10.1145/1541880.1541883. Zugriff am 05.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  116. [22] Bodenbenner, M.; Montavon, B.; Schmitt, R. H.: FAIR sensor services - Towards sustainable sensor data management. Measurement: Sensors 18 (2021), S. 100206. Internet: https://doi.org/10.1016/j. measen.2021.100206. Zugriff am 05.12.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-35
  117. [1] Baldassarre, B.: Circular economy for resource security in the European Union (EU): Case study, research framework, and future directions. Ecological Economics 227 (2025), pp. 1–16, doi.org/10.1016/j.ecolecon.2024.108345 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  118. [2] Sutherland, A. B.; Conde, Á.; Novak, M. et al.: The Circularity Gap Report 2025. Stand: 2025. Internet: global.circularity-gap.world/. Zugriff am 11.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  119. [3] Lörsch, K.: Aufwerten statt wegwerfen. Wertsteigernde Kreislaufwirtschaft als Schlüssel zur Nachhaltigkeit. FIR-Flash 4/2023. Internet: epub.fir.de/frontdoor/deliver/index/docId/3202/file/fir_loersch_Kreislaufwirtschaft_Newsletter_2023.pdf. Zugriff am 12.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  120. [4] Meyer, A.: Qualität von Dienstleistungen: Entwurf eines praxisorientierten Qualitätsmodells. Marketing – ZFP Journal of Research and Management 9 (1987) 3, S. 187–195 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  121. [5] Corsten, H.; Gössinger, R.: Dienstleistungsmanagement. Berlin: De Gruyter Oldenbourg Verlag 2015 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  122. [6] Engelhardt, W.H.; Kleinaltenkamp, M.; Reckenfelderbäumer, M.: Leistungsbündel als Absatzobjekte. Schmalenbach Journal of Business Research 45 (1993) 5, S. 395–426 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  123. [7] Schuh, G.; Gudergan, G.; Grefrath, C.: Geschäftsmodelle für industrielle Dienstleistungen. In: Schuh, G.; Gudergan, G.; Kampker, A. (Hrsg.): Management industrieller Dienstleistungen. Reihe Handbuch Produktion und Management. Band 8. Heidelberg: Springer Vieweg Verlag 2016, S. 65–104 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  124. [8] Kropik, M.: Produktionsleitsysteme für die Automobilindustrie. Digitalisierung des Shop-Floors in der Automobilproduktion. Heidelberg: Springer Vieweg Verlag 2021, S. 319–342 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  125. [9] Jing, Z.; Li, L.; Lyu, Y. et al.: Autonomous Services: The Evolution of Services Through Intelligent Vehicles. IEEE Transactions on Intelligent Vehicles 8 (2023) 11, S. 4468–4473, doi.org/10.1109/TIV.2023.3332877 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  126. [10] Canty, R. B.; Koscher, B. A.; McDonald, M. A. et al.: Integrating autonomy into automated research platforms. Digital Discovery 2 (2023) 5, pp. 1259–1268, doi.org/10.1039/D3DD00135K Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  127. [11] Arnold, P. V.: Reliability Case Study at Aluminum Plant. Century Aluminum is making a break from its reactive past. Stand: 12.09.2025. Internet: www.reliableplant.com/Read/13578/reliability. Zugriff am 11.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  128. [12] Frederiksen, R. D.; Bocewicz, G.; Radzki, G. et al.: Cost-Effectiveness of Predictive Maintenance for Offshore Wind Farms: A Case Study. Energies 17 (2024) 13, #3147, doi.org/10.3390/en17133147 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  129. [13] Schaeffler Technologies:OPTIME C1 und FAG OPTIME C4. Mehr als nur automatisch – intelligente Schmierstoffgeber. Internet: medias.schaeffler.de/de/lubricate/lubricators/smart-lubrication. Zugriff am 12.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  130. [14] Second Hands: Homepage. Stand: 2025. Internet: secondhands.eu/. Zugriff am: 11.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  131. [15] Maximator Hydrogen: Wasserstoff-Verdichtung ohne Zwischenspeicherung. Stand: 2025. Internet: www.maximator-hydrogen.de/newsroom/wasserstoff-verdichtung-ohne-zwischenspeicherung. Zugriff am: 11.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  132. [16] Geissdoerfer, M.; Pieroni, M. P.; Pigosso, D. C. et al.: Circular business models: A review. Journal of Cleaner Production 277 (2020), #123741, doi.org/10.1016/j.jclepro.2020.123741 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  133. [17] Boos, W.: {Vortragsfolien] Upgrade Circular Economy für nachhaltige Wettbewerbsfähigkeit. Vortrag bei den TuWAs-Netzwerktagen, 14.05.2025. FIR e. V. an der RWTH Aachen, Aachen 2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-43
  134. [1] Garrel, J. von; Jahn, C.: Design Framework for the Implementation of AI-based (Service) Business Models for Small and Medium-sized Manufacturing Enterprises. Journal of the Knowledge Economy 14 (2023) 3, pp. 3551–3569 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  135. [2] Gassmann, O.; Frankenberger, K.; Csik, M.: Geschäftsmodelle entwickeln. 55 innovative Konzepte mit dem St. Galler Business Model Navigator. München: Hanser 2017 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  136. [3] Kohtamäki, M.; Parida, V.; Oghazi, P. et al.: Digital servitization business models in ecosystems: A theory of the firm. Journal of Business Research 104 (2019), pp. 380–392 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  137. [4] Osterwalder, A.: The business model ontology a proposition in a design science approach. Dissertation, Universität Lausanne, 2004 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  138. [5] Paiola, M.; Gebauer, H.: Internet of things technologies, digital servitization and business model innovation in BtoB manufacturing firms. Industrial Marketing Management 89 (2020), pp. 245–264 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  139. [6] Garrel, J. von; Jahn, C.: Servitization durch KI, Wie die Transformation von Geschäftsmodellen in produzierenden KMU. Zfo – Zeitschrift Führung und Organisation (2025) 5, S. 286–289 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  140. [7] Baines, T.; Lightfoot, H.; Smart, P. et al.: Servitization of manufacture. Journal of Manufacturing Technology Management 24 (2013) 4, pp. 637–646 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  141. [8] Qvist-Sørensen, P.: Applying IIoT and AI – Opportunities, Requirements and Challenges for Industrial Machine and Equipment Manufacturers to Expand Their Services. Central European Business Review 9 (2020) 2, pp. 46–77 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  142. [9] Fliess, S.; Lexutt, E.: How to be successful with servitization – Guidelines for research and management. Industrial Marketing Management 78 (2019), pp. 58–75 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  143. [10] Brax, S. A.; Visintin, F.: Meta-model of servitization: The integrative profiling approach. Industrial Marketing Management 60 (2017), pp. 17–32 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  144. [11] Boll-Westermann, S.; Faisst, W.: Neue Geschäftsmodelle mit Künstlicher Intelligenz: Zielbilder, Fallbeispiele und Gestaltungsoptionen. Stand: 2019. Internet: d-nb.info/1229019480/34. Zugriff am 29.10.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  145. [12] Metelskaia, I.; Ignatyeva, O.; Denef, S. et al.: A business model template for AI solutions. ICIST ‘18: 2018 International Conference on Intelligent Science and Technology, London, United Kingdom, 2018, pp. 35–41, doi.org/10.1145/3233740.3233750 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-49
  146. [1] Emonts, D.; Sanders, M. P.; Dahlem, P. et al.: Virtuelle Klimatisierung/Virtual Climatization. wt Werkstattstechnik online 111 (2021) 11-12, S. 887–892 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  147. [2] Dahlem, P.; Emonts, D.; Sanders, M. P. et al.: A review on enabling technologies for resilient and traceable on-machine measurements. Journal of Machine Engineering 20 (2020) 2, S. 5–17 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  148. [3] Gao, W.; Ibaraki, S.; Donmez, M. A. et al.: Machine tool calibration: Measurement, modeling, and compensation of machine tool errors. International Journal of Machine Tools and Manufacture (2023), S. 104017 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  149. [4] Schmitt, R. H.; Peterek, M.; Morse, E. et al.: Advances in Large-Scale Metrology – Review and future trends. CIRP Annals 65 (2016) 2, S. 643–665 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  150. [5] Mayr, J.; Jedrzejewski, J.; Uhlmann, E. et al.: Thermal issues in machine tools. CIRP Annals 61 (2012) 2, S. 771–791 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  151. [6] Wang, Y.; Cao, Y.; Qu, X. et al.: A review of the application of machine learning techniques in thermal error compensation for CNC machine tools. Measurement 243 (2025), S. 116341 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  152. [7] Pelegrino, D.; Santos, R.; Coelho, R.: Experimental evaluation of energy consumption in machine tools: a case study for a two-spindle turning center. Berlin/Heidelberg: Springer Berlin Heidelberg 2019 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  153. [8] Khan, K.; Su, C.-W.; Zhu, M. N.: Examining the behaviour of energy prices to COVID-19 uncertainty: A quantile on quantile approach. Energy (Oxford, England) 239 (2022), p. 122430 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  154. [9] Enescu, A.-G.; Szeles, M. R.: Discussing energy volatility and policy in the aftermath of the Russia–Ukraine conflict. Frontiers in Environmental Science 11 (2023) Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  155. [10] Cannon, A. J.: Twelve months at 1.5 °C signals earlier than expected breach of Paris Agreement threshold. Nature Climate Change 15 (2025) 3, S. 266–269 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  156. [11] Bevacqua, E.; Schleussner, C.-F.; Zscheischler, J.: A year above 1.5 °C signals that Earth is most probably within the 20-year period that will reach the Paris Agreement limit. Nature Climate Change 15 (2025) 3, S. 262–265 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  157. [12] Wang, H.; Jiao, S.; Bu, K. et al.: Digital transformation and manufacturing companies’ ESG responsibility performance. Finance Research Letters 58 (2023), S. 104370 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  158. [13] Shokrani, A.; Arrazola, P. J.; Biermann, D. et al.: Sustainable machining: Recent technological advances. CIRP Annals 73 (2024) 2, S. 483–508 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  159. [14] Abubakr, M.; Abbas, A. T.; Tomaz, I. et al.: Sustainable and Smart Manufacturing: An Integrated Approach. Sustainability 12 (2020) 6, S. 2280 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  160. [15] Blaser, P.: Adaptive Learning Control for Thermal Error Compensation, ETH Zurich, 2020 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  161. [16] Brecher, C.; Wissmann, A.: Optimierung des thermischen Verhaltens von Fräsmaschinen. Zeitschrift für wirtschaftlichen Fabrikbetrieb 104 (2009) 6, S. 437–441 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  162. [17] Dahlem, P.: Hybrid modeling of transient volumetric machine tool errors for virtual climatization, RWTH Aachen University, 2023 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  163. [18] McKeown, P. A.; Loxham, J.: Some aspects of the design of high precision measuring machines. CIRP Annals 22 (1973) 1, S. 139 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  164. [19] Schwenke, H.; Franke, M.; Hannaford, J. et al.: Error mapping of CMMs and machine tools by a single tracking interferometer. CIRP Annals 54 (2005) 1, S. 475–478 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  165. [20] Busch, K.; Kunzmann, H.; Waldele, F.: Numerical Error-Correction of a Coordinate Measuring Machine, 1984, S. 278–282 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  166. [21] Baum, C.; Brecher, C.; Klatte, M. et al.: Thermally induced volumetric error compensation by means of integral deformation sensors. Procedia CIRP 72 (2018), S. 1148–1153 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  167. [22] Montavon, B.; Dahlem, P.; Schmitt, R. H.: Effektive Analyse von Werkzeugmaschinenkalibrierdaten*/Effective analysis of machine tool calibration data. VoluSoft as a showcase for user-friendly visualization and processing of machine tool calibration data. wt Werkstattstechnik online 108 (2018) 11-12, S. 755–759 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  168. [23] Siemens AG: Funktionsbeschreibung VCS „Volumetric Compensation System“. NCK-Dokumentation, 2017 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  169. [24] Dr. Johannes Heidenhain GmbH: Optionen und Zubehör für TNC-Steuerungen, 2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  170. [25] Fanuc GE CNC Europe S.A.: Functions for 3-dimensional Error Compensation, 2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  171. [26] Cao, L.; Park, C.-H.; Chung, S.-C.: Real-time thermal error prediction and compensation of ball screw feed systems via model order reduction and hybrid boundary condition update. Precision Engineering 77 (2022), S. 227–240 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  172. [27] Narendra Reddy, T.; Shanmugaraj, V.; Vinod, P. et al.: Real-time Thermal Error Compensation Strategy for Precision Machine tools. Materials Today: Proceedings 22 (2020), S. 2386–2396 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  173. [28] Guevel, F.; Viprey, F.; Euzenat, C. et al.: Development and performance evaluation of real-time geometric error compensation through position feedback modification in 5-axis machining. The International Journal of Advanced Manufacturing Technology 137 (2025) 11-12, S. 5565–5584 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  174. [29] Liu, Q.; Guo, H.; Ma, Y. et al.: Real-time error compensation of a 5-axis machining robot using externally mounted encoder systems. The International Journal of Advanced Manufacturing Technology 120 (2022) 3-4, S. 2793–2802 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  175. [30] Chen, T.-C.; Chang, C.-J.; Hung, J.-P. et al.: Real-Time Compensation for Thermal Errors of the Milling Machine. Applied Sciences 6 (2016) 4, S. 101 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  176. [31] Esmaeili, S. M.; Mayer, J. R. R.: CNC table based compensation of inter-axis and linear axis scale gain errors for a five-axis machine tool from symbolic variational kinematics. CIRP Annals 70 (2021) 1, S. 439–442 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  177. [32] Thiem, X.; Großmann, K.; Mühl, A.: Modular Control Integrated Correction of Thermoelastic Errors of Machine Tools Based on the Thermoelastic Functional Chain. Advanced Materials Research 1018 (2014), S. 411–418 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  178. [33] Li, J.; Mei, B.; Shuai, C. et al.: A volumetric positioning error compensation method for five-axis machine tools. The International Journal of Advanced Manufacturing Technology 103 (2019) 9-12, S. 3979–3989 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  179. [34] Thiem, X.; Kauschinger, B.; Mühl, A. et al.: Challenges in the Development of a Generalized Approach for the Structure Model Based Correction. Applied Mechanics and Materials 794 (2015), S. 387–394 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  180. [35] Sanders, M. P.; Bodenbenner, M.; Dahlem, P. et al.: Laser Tracker-Based on-the-Fly Machine Tool Calibration without Real-Time Synchronization. Journal of Manufacturing and Materials Processing 7 (2023) 2, S. 60 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  181. [36] Slamani, M.; Mayer, J. R. R.; Cloutier, G. M.: Modeling and experimental validation of machine tool motion errors using degree optimized polynomial including motion hysteresis. Experimental Techniques 35 (2011) 1, S. 37–44 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  182. [37] Sanders, M. P.: Volumetric Error Model for Online Machine Tool Compensation. Dissertation, RWTH Aachen University, 2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  183. [38] Sanders, M. P.; Bodenbenner, M.; Wolfschläger, D. et al.: Machine tool thermal error experiment, 2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  184. [39] Siemens AG: Sinumerik 840D sl - CNC Commissioning: NC, PLC, Drive Commissioning Manual, 2019 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-54
  185. [1] Breznik, Matic ; Buchmeister, Borut ; Vujica Herzog, Nataša: Evaluation of the EAWS Ergonomic Analysis on the Assembly Line: Xsens vs. Manual Expert Method— A Case Study. In: Sensors Bd. 25, MDPI AG (2025), Nr. 15, S. 4564 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  186. [2] Brolin, A.; Thorvald, P. ; Case, K.: Experimental study of cognitive aspects affecting human performance in manual assembly. In: Production & Manufacturing Research Bd. 5, Informa UK Limited (2017), Nr. 1, S. 141–163 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  187. [3] Biondi, F. N. ; Cacanindin, A. ; Douglas, C. ; Cort, J.: Overloaded and at Work: Investigating the Effect of Cognitive Workload on Assembly Task Performance. In: Human Factors: The Journal of the Human Factors and Ergonomics Society Bd. 63, SAGE Publications (2020), Nr. 5, S. 813–820 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  188. [4] Berlin, C.; Bergman Wollter, M.; Chafi, Maral Babapour ; Falck, A.-C. ; Örtengren, R.: A Systemic Overview of Factors Affecting the Cognitive Performance of Industrial Manual Assembly Workers. In: Lecture Notes in Networks and Systems: Springer International Publishing, 2021. ISBN 9783030746070, S. 371–381 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  189. [5] Zhou, L.; Zhang, L.; Konz, N.: Computer Vision Techniques in Manufacturing. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems Bd. 53, Institute of Electrical and Electronics Engineers (IEEE) (2023), Nr. 1, S. 105–117 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  190. [6] Qamar, R.; Zardari, B. A.: Application of Computer Vision in Manufacturing. In: Machine Vision and Industrial Robotics in Manufacturing: CRC Press, 2024, S. 36–56 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  191. [7] Zhou, Longfei ; Zhang, Lin ; Konz, Nicholas: Computer Vision Techniques in Manufacturing. In: IEEE Transactions on Systems, Man, and Cybernetics: Systems Bd. 53, Institute of Electrical and Electronics Engineers (IEEE) (2023), Nr. 1, S. 105–117 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  192. [8] Ettalibi, Abdelfatah ; Elouadi, Abdelmajid ; Mansour, Abdeljebar: AI and Computer Vision-based Real-time Quality Control: A Review of Industrial Applications. In: Procedia Computer Science Bd. 231, Elsevier BV (2024), S. 212–220 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  193. [9] Raisul Islam, Md ; Zakir Hossain Zamil, Md ; Eshmam Rayed, Md ; Mohsin Kabir, Md ; Mridha, M. F. ; Nishimura, Satoshi ; Shin, Jungpil: Deep Learning and Computer Vision Techniques for Enhanced Quality Control in Manufacturing Processes. In: IEEE Access Bd. 12, Institute of Electrical and Electronics Engineers (IEEE) (2024), S. 121449–121479 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  194. [10] Pathak, Ajeet Ram ; Pandey, Manjusha ; Rautaray, Siddharth: Application of Deep Learning for Object Detection. In: Procedia Computer Science Bd. 132, Elsevier BV (2018), S. 1706–1717 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  195. [11] Jiang, P.; Ergu, D. ; Liu, F. ; Cai, Y.; Ma, B.: A Review of Yolo Algorithm Developments. In: Procedia Computer Science Bd. 199, Elsevier BV (2022), S. 1066–1073 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  196. [12] Redmon, J. ; Divvala, S. ; Girshick, R.; Farhadi, A.: You Only Look Once: Unified, Real-Time Object Detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) : IEEE, 2016, S. 779–788 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  197. [13] Aboyomi, Dalmar Dakari ; Daniel, Cleo: A Comparative Analysis of Modern Object Detection Algorithms: YOLO vs. SSD vs. Faster R-CNN. In: ITEJ (Information Technology Engineering Journals) Bd. 8, IAIN Syekh Nurjati Cirebon (2023), Nr. 2, S. 96–106 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  198. [14] Kang, S.; Hu, Z.; Liu, L.; Zhang, K.; Cao, Z.: Object Detection YOLO Algorithms and Their Industrial Applications: Overview and Comparative Analysis. In: Electronics Bd. 14, MDPI AG (2025), Nr. 6, S. 1104 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  199. [15] Claeys, A.; Hoedt, S.; Landeghem, H. Van; Cottyn, J.: Generic Model for Managing Context-Aware Assembly Instructions. In: IFAC-PapersOnLine Bd. 49, Elsevier BV (2016), Nr. 12, S. 1181–1186 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  200. [16] Josifovska, K.; Yigitbas, E.; Engels, G.: A Digital Twin-Based Multi-modal UI Adaptation Framework for Assistance Systems in Industry 4.0. In: International Conference on Human-Computer Interaction, Springer, Cham (2019), S. 398-409 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  201. [17] Bao, J.; Guo, D.; Li, J.-, Zhang, J.: The modelling and operations for the digital twin in the context of manufacturing. In: Enterprise Information Systems Bd. 13, Informa UK Limited (2018), Nr. 4, S. 534–556 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  202. [18] Claeys, A. ; Hoedt, S. ; Schamp, M. ; Landeghem, H. Van ; Cottyn, J.: Ontological Model for Managing Context-aware Assembly Instructions. In: IFAC-PapersOnLine Bd. 51, Elsevier BV (2018), Nr. 11, S. 176–181 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  203. [19] Nuy, L.; Rotering, J.; Rachner, J.; Kiesel, R.; Schmitt, R. H.: Conception of a data model for a digital twin for context-specific work instructions. In: Procedia CIRP Bd. 118, Elsevier BV (2023), S. 312–317 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  204. [20] Cramer, S.; Hoffmann, M.; Schlegel, P.; Kemmerling, M.; Schmitt, R. H.: Towards a flexible process-independent meta-model for production data. In: Procedia CIRP Bd. 99, Elsevier BV (2021), S. 586–591 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  205. [21] Liang, J.; Pelzer, L.; Müller, K. ; Cramer, S.; Greb, C.; Hopmann, C. ; Schmitt, R. H.: Towards predictive quality in production by applying a flexible process-independent meta-model. In: Procedia CIRP Bd. 104, Elsevier BV (2021), S. 1251–1256 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-63
  206. [1] Webb, C.; Ip, S.; Bathula, N. V. et al.: Current Status and Future Perspectives on MRNA Drug Manufacturing. Molecular pharmaceutics 19 (2022) 4, pp. 1047–1058 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  207. [2] Hou, X.; Zaks, T.; Langer, R. et al.: Lipid nanoparticles for mRNA delivery. Nature reviews. Materials 6 (2021) 12, pp. 1078–1094 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  208. [3] Chaudhary, N.; Weissman, D.; Whitehead, K. A.: mRNA vaccines for infectious diseases: principles, delivery and clinical translation. Nature reviews. Drug discovery 20 (2021) 11, pp. 817–838 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  209. [4] Vargason, A. M.; Anselmo, A. C.; Mitragotri, S.: The evolution of commercial drug delivery technologies. Nature Biomedical Engineering 5 (2021) 9, pp. 951–967 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  210. [5] Blache, U.; Popp, G.; Dünkel, A. et al.: Potential solutions for manufacture of CAR T cells in cancer immunotherapy. Nature communications 13 (2022) 1, #5225 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  211. [6] Hajj, K. A.; Whitehead, K. A.: Tools for translation: non-viral materials for therapeutic mRNA delivery. Nature reviews. Materials 2 (2017) 10 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  212. [7] vfa. Die forschenden Pharma-Unternehmen: mRNA-Impfstoffe für Schutzimpfungen. Stand: 06.09.2025. Internet: www.vfa.de/mrna-schutzimpfungen. Zugriff am 17.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  213. [8] Fortune Business Insights: MRNA-Impfstoffe Marktgröße, Aktien- und Industrieanalyse nach Typ, nach Indikation, nach Distributionskanal und Regionalvorhersagungen, 2025–2032. Internet: www.fortunebusinessinsights.com/de/mrna-impfstoffmarkt-113546. Zugriff am 17.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  214. [9] Schmidt, A.; Helgers, H.; Vetter, F. L. et al.: Process Automation and Control Strategy by Quality-by-Design in Total Continuous mRNAManufacturing Platforms. Processes 10 (2022) 9, #1783 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  215. [10] Schmidt, A.; Helgers, H.; Vetter, F. L. et al.: Digital Twin of mRNA-Based SARS-COVID-19 Vaccine Manufacturing towards Autonomous Operation for Improvements in Speed, Scale, Robustness, Flexibility and Real-Time Release Testing. Processes 9 (2021) 5, #748 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  216. [11] Mitchell, M. J.; Billingsley, M. M.; Haley, R. M. et al.: Engineering precision nanoparticles for drug delivery. Nature reviews. Drug discovery 20 (2021) 2, pp. 101–124 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  217. [12] Roces, C. B.; Lou, G.; Jain, N. et al.: Manufacturing Considerations for the Development of Lipid Nanoparticles Using Microfluidics. Pharmaceutics 12 (2020) 11, doi.org/10.3390/pharmaceutics12111095 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  218. [13] Ripoll, M.; Martin, E.; Enot, M. et al.: Optimal self-assembly of lipid nanoparticles (LNP) in a ring micromixer. Scientific reports 12 (2022) 1, #9483 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  219. [14] McKenzie, R. E.; Minnell, J. J.; Ganley, M. et al.: mRNA Synthesis and Encapsulation in Ionizable Lipid Nanoparticles. Current protocols 3 (2023) 9, 1–47, doi.org/10.1002/cpz1.898 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  220. [15] Hengelbrock, A.; Schmidt, A.; Helgers, H. et al.: Scalable mRNA Machine for Regulatory Approval of Variable Scale between 1000 Clinical Doses to 10 Million Manufacturing Scale Doses. Processes 11 (2023) 3, #745 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  221. [16] Grieves, M.: Digital Twin: Manufacturing Excellence through Virtual Factory Replication. Whitepaper. Stand: 2015. Internet: www.researchgate.net/publication/275211047_Digital_Twin_Manufacturing_Excellence_through_Virtual_Factory_Replication. Zugriff am 17.11.2025: Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  222. [17] Destro, F.; Barolo, M.: A review on the modernization of pharmaceutical development and manufacturing – Trends, perspectives, and the role of mathematical modeling. International journal of pharmaceutics 620 (2022), #121715 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  223. [18] Tao, F.; Cheng, J.; Qi, Q. et al.: Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology 94 (2018) 9–12, pp. 3563–3576 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  224. [19] Mansurali, A.; Praveen Kumar, T.; Ramakrishnan, S.: Digital Twins in Green Manufacturing: Enhancing Sustainability and Efficiency. In: Sharma, R.; Rana, G.; Agarwal, S. (Edit.): Green innovations for industrial development and business sustainability. Models and implementation strategies. Boca Raton, FL: CRC Press 2024, pp. 153–165 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  225. [20] Hankel, M.; Rexroth, B.: Industrie 4.0: Das Referenzarchitekturmodell Industrie 4.0 (RAMI 4.0), Plattform Industrie 4.0. Internet: www.plattform-i40.de/IP/Redaktion/DE/Downloads/Publikation/zvei-faktenblatt-rami.pdf?__blob=publicationFile&v=4. Zugriff am 24.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  226. [21] Bader, S. R.; Maleshkova, M.: The Semantic Asset Administration Shell. In: Acosta Deibe, M.; Cudré-Mauroux, P.; Maleshkova, M. et al. (Edit.): Semantic systems. The power of AI and knowledge graphs : Proceedings of 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, 2019, pp. 159–174 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  227. [22] Perno, M.; Hvam, L.; Haug, A.: Implementation of digital twins in the process industry: A systematic literature review of enablers and barriers. Computers in Industry 134 (2022), #103558 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  228. [23] Errandonea, I.; Beltrán, S.; Arrizabalaga, S.: Digital Twin for maintenance: A literature review. Computers in Industry 123 (2020), #103316 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  229. [24] ISPE (ed.): ISPE Baseline® Guide: Volume 8 – Pharma 4.0™. Stand: 2023. Internet: guidance-docs.ispe.org/doi/book/10.1002/9781946964724. Zugriff am 24.11.2025. Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  230. [25] Mathews, J.; Hort, S.: COPE. Empower your lab with COPE. Stand: 2024. Internet: cope-software.de/. Zugriff am 17.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  231. [26] Fraunhofer IESE: Eclipse BaSyx™. Internet: basyx.org/. Stand: 2025. Zugriff am 17.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  232. [27] Bleul, R.: Nanomedizin. Nanopartikelbasierte Plattform für Gesundheitslösungen der Zukunft. Stand: 2025. Internet: www.imm.fraunhofer.de/de/geschaeftsbereiche/geschaeftsbereich-chemie/nanomedizin.html. Zugriff am 17.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  233. [28] Höbel, P.: Inline/Online Dynamic Light Scattering. Internet: www.imm.fraunhofer.de/content/dam/imm/de/documents/PDFs-neu2018/E-und-C/IMM-Inline-Online_Dynamic_Light_Scattering.pdf. Zugriff am 17.11.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-71
  234. [1] Falck, A.-C.; Örtengren, R.; Rosenqvist, M. et al.: Criteria for Assessment of Basic Manual Assembly Complexity. Procedia CIRP 44 (2016), S. 424–428. DOI: https://doi.org/10.1016/j.procir.2016.02.152 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  235. [2] Schuh, G.; Dölle, C.: Sustainable Innovation. Nachhaltig Werte schaffen. 2. Auflage. Berlin, Heidelberg: Springer Verlag 2021 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  236. [3] Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz (Hrsg.): Nationale Kreislaufwirtschaftsstrategie. Stand: September 2024. Internet: https://www.bundesumweltministerium.de/fileadmin/Daten_BMU/Download_PDF/Abfallwirtschaft/nationale_kreislaufwirtschaftsstrategie_bf.pdf. Zugriff am: 08.09.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  237. [4] Schuh, G.; Brandenburg, U.; Cuber, S.: Aufgaben. In: Schuh, G.; Stich, V. (Hrsg.): Produktionsplanung und -steuerung; Bd 1: Grundlagen der PPS. Berlin, Heidelberg: Springer Verlag 2012, S. 29–81 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  238. [5] Schmidt, C.; Meier, C.; Kompa, S.: Informationssysteme für das Produktionsmanagement. In: Schuh, G.; Schmidt, C. (Hrsg.): Produktionsmanagement. Reihe Handbuch Produktion und Management; Bd. 5. Berlin: Springer Vieweg 2014, S. 281–378 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  239. [6] Kurbel, K.: ERP und SCM. Enterprise Resource Planning und Supply Chain Management in der Industrie. 9., überarb. u. erw. Auflage. Berlin [u. a.]: De Gruyter Verlag 2021 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  240. [7] Hanschke, I.: Strategisches Management der IT-Landschaft. Ein praktischer Leitfaden für das Enterprise Architecture Management. 4., aktualis. u. erw. Auflage. München: Hanser 2023 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  241. [8] Schönsleben, P.: Integrales Informationsmanagement. Informationssysteme für Geschäftsprozesse – Management, Modellierung, Lebenszyklus und Technologie. 2., vollst. überarb. u. erw. Auflage. Berlin [u. a.]: Springer Verlag 2001 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  242. [9] Frohm, J.; Lindström, V.; Stahre, J. et al.: Levels of Automation in Manufacturing. Ergonomia – International Journal of Ergonomics and Human Factors 30(2008)3, pp. 1–28 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  243. [10] Horvitz, E.; Mulligan, D.: Machine learning: Trends, perspectives, and prospects. Science (New York, N.Y.) 349(2015)6245, pp. 253–255 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  244. [11] Vaswani, A.; Shazeer, N.; Parmar, N. et al.: Attention is All you Need. In: NIPS‘17: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach (CA)/USA, 2017, pp. 6000–6010 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  245. [12] GRI (Hrsg.): Global Sustainability Standards Board: Konsolidierte GRI-Standards. Stand: 2021. Internet: https://www.triple-innova.de/fileadmin/user_upload/GRI-Standards_Konsolidierte-Fassung_2021_231018.pdf&ved=2ahUKEwizkMDJpdOPAxUwSfEDHZK4GZEQFnoECB0QAQ&usg=AOvVaw2nyUs4FCsK-EsRF-mwFALP. Zugriff am: 08.09.2025. Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  246. [13] Potting, J.; Hekkert, M.; Worrell, E. et al.: Circular Economy: Measuring innovation in the product chain. Policy Report. Stand: Januar 2017. Internet: https://www.pbl.nl/downloads/pbl-2016-circular-economy-measuring-innovation-in-product-chains-2544pdf&ved=2ahUKEwi1n7_HptOPAxXDRfEDHbsZJ6kQFnoECBkQAQ&usg=AOvVaw1z-VcYxkzpjekysibpikHo. Zugriff am: 08.09.2025 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-78
  247. [1] Wiendahl, H.-P.: Fertigungsregelung. Logistische Beherrschung von Fertigungsabläufen auf Basis des Trichtermodells. München, Wien: Carl Hanser Verlag, 1997 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  248. [2] Xiong, H.; Shi, S.; Ren, D.; Hu, J.: A survery of job shop scheduling problem: The types and models. Computers & Operations Research 142 (2022) 105731 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  249. [3] Zhang, X.; Zhu, G.-Y.: A literature review of reinforcement learning methods applied to job-shop scheduling problems. Computers & Operations Research 175 (2025) 106929 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  250. [4] Land, A. H.; Doig, A. G.: An automatic method of solving discrete programming problems. Econometrica 28 (1960) 3, S. 497–520 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  251. [5] IBM Corp.: IBM ILOG CPLEX Optimization Studio documentation. Firmenschrift. 2024. Internet: https://www.ibm.com/docs/en/icos/22.1.2?announcement=ilog-cplex-optimization-studio-221. Zugriff: 2025-10-02 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  252. [6] Gurobi Optimization: Gurobi Optimizer Reference Manual. Firmenschrift. 2024. Internet: https://docs.gurobi.com/projects/optimizer/en/current/index.html. Zugriff: 2025-10-02 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  253. [7] Perron, L.; Didier, F.: Cp-sat v9.10. Google. 2024. Internet: https://developers.google.com/optimization/cp/cp_solver/. Zugriff: 2025-10-02 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  254. [8] Lödding, H.: Verfahren der Fertigungssteuerung. Berlin, Heidelberg: Springer, 2016 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  255. [9] Xie, J.; Gao, L.; Peng, K.; Li, X.; Li, H.: Review on flexible job shop scheduling. IET Collaborative Intelligent Manufacturing 1 (2019) 3, S. 67–77 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  256. [10] Chaudry, I. A.; Khan, A. A.: A research survey: review of flexible job shop scheduling techniques. International Transactions in Operational Research 23 (2016) 3, S. 551–591 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  257. [11] Türkyilmaz, A.; Senvar, Ö.; Ünal, I.; Bulkan, S.: A research survey: heuristic approaches for solving multi objective flexible job shop problems. Journal of Intelligent Manufacturing 31 (2020), S. 1949–1983 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  258. [12] Wang, R.; Wang, G.; Sun, J.; Deng, F.; Chen, J.: Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning (2024). Internet: https://arxiv.org/abs/2305.05119. Zugriff: 2025-10-02 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  259. [13] Mnih, V.; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; Riedmiller, M.: Playing Atari with Deep Reinforcement Learning (2013). Internet: https://arxiv.org/abs/1312.5602. Zugriff: 2025-10-02 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  260. [14] Sutton, R. S.; Barto, A. G.: Reinforcement Learning. Massachusetts: MIT Press, 2018 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  261. [15] Lin, L.-J.: Reinforcement Learning for Robots using Neural Networks. Dissertation. Carnegie Mellon University. 1993 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  262. [16] Saidi-Mehrabad, M.; Fattahi, P.: Flexible job shop scheduling with tabu search algorithms. The International Journal of Advanced Manufacturing Technology 32 (2007) 5, S. 563 - 570 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  263. [17] Rabiee, M.; Zandieh, M.; Ramezani, P.: Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches. International Journal of Production Research 50 (2012) 24, S. 7327–7342 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  264. [18] Liu, H.; Abraham, A.; Wang, Z.: A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems. Fundamenta Informaticae 95 (2009) 4, S. 465–489 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  265. [19] Kacem, I.; Hammadi, S.; Borne, P.: Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation 60 (2002) 3 – 5, S. 245–276 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  266. [20] Liang, E.; Liaw, R.; Moritz, P.; Nishihara, R.; Fox, R.; Goldberg, K.; Gonzalez, J.; Jordan, M.; Stoica, I.: RLlib: Abstractions for Distributed Reinforcement Learning (2018). Internet: https:// https://arxiv.org/pdf/1712.09381. Zugriff: 2025-10-02 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  267. [21] Liu, C.; Chen, K.; Wang, H.; Yang, B.; Leng, J.: Job shop scheduling by Deep Dual-Q Network with Prioritized Experience Replay for resilient production control in flexible manufacturing system. Computers & Operations Research 183 (2025) 107190 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90
  268. [22] Belmamoune, M. A.; Ghomri, L.; Yahouni, Z.: Solving a Job Shop Scheduling Problem Using Q-Learning Algorithm. In: SOHOMA 2022 12th international workshop on service oriented, holonic and multi-agent manufacturing systems for industry of the future, Bukarest, Rumänien, September 2022, S. 196–209 Open Google Scholar doi.org/10.37544/1436-4980-2025-11-12-90

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