Strukturdynamisches Verhalten der Hauptspindel im Betrieb/Structural-dynamic behavior of the main spindle during machining – Determining the main spindle’s structural-dynamic properties based on operating conditions
Table of contents
Bibliographic information

wt Werkstattstechnik online
Volume 115 (2025), Issue 07-08
- Authors:
- | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
- Publisher
- VDI fachmedien, Düsseldorf
- Copyright Year
- 2025
- ISSN-Online
- 1436-4980
- ISSN-Print
- 1436-4980
Chapter information
Volume 115 (2025), Issue 07-08
Strukturdynamisches Verhalten der Hauptspindel im Betrieb/Structural-dynamic behavior of the main spindle during machining – Determining the main spindle’s structural-dynamic properties based on operating conditions
- ISSN-Print
- 1436-4980
- ISSN-Online
- 1436-4980
- Preview:
As a key component of a milling machine, the main spindle decisively determines the accuracy and performance limits of the milling process. During machining, the spindle is simultaneously subjected to static and dynamic loads. Variations in process parameters and operating conditions influence its structural-dynamic properties—both static and dynamic. Continuous monitoring of these properties enables the early detection and avoidance of machining operations that could potentially damage the spindle.
Bibliography
No match found. Try another term.
- [1] United Nations: Paris Agreement. 12.12.2015 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [2] Bundesministerium für Wirtschaft und Klimaschutz: Klimaschutz in Zahlen. Aktuelle Emissionstrends und Klimaschutzmaßnahmen in Deutschland, Berlin, Heidelberg, 2022 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [3] Umweltbundesamt: Finale Daten für 2023. Klimaschädliche Emissionen sanken um zehn Prozent. 15.01.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [4] Heimes, H.; Kampker, A.; Kehrer, M. et al.: Strategien zur Erreichung der Emissionsziele im Nutzfahrzeugsektor, 2023 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [5] O‘Hayre, R.; Cha, S.-W.; Colella, W. et al.: Fuel Cell Fundamentals. Wiley 2016 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [6] El-Shafie, M.: Hydrogen production by water electrolysis technologies: A review. Results in Engineering 20 (2023), S. 101426 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [7] Heimes, H. H.; Kampker, A.; Kehrer, M. et al.: Brennstoffzellensysteme. In: Kampker, A.; Heimes, H. H. (Hrsg.): Elektromobilität. Berlin, Heidelberg: Springer Berlin Heidelberg 2024, S. 153–164 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [8] Verein Deutscher Ingenieure: Entwicklung technischer Produkte und Systeme. VDI 2221. November 2019 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [9] Tjarks, G.: PEM-Elektrolyse-Systeme zur Anwendung in Power-to-Gas Anlagen. Dissertation, Forschungszentrum Jülich, 2017 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [10] Zhang, J.; Zhang, H.; Wu, J. et al.: Techniques for PEM Fuel Cell Testing and Diagnosis. In: Pem Fuel Cell Testing and Diagnosis. Elsevier 2013, S. 81–119 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [11] Wagner, N.: Einsatz der Impedanzspektroskopie in der Brennstoffzellenforschung. tm - Technisches Messen 78 (2011) 1, S. 30–35 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [12] WU, J.; YUAN, X.; WANG, H. et al.: Diagnostic tools in PEM fuel cell research: Part I Electrochemical techniques. International Journal of Hydrogen Energy 33 (2008) 6, S. 1735–1746 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [13] Kampker, A.; Heimes, H.; Kehrer, M. et al.: Produktion von Elektrolyseursystemen, Frankfurt am Main, 2023 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [14] Oh, S.; Lim, D.; Han, Y. et al.: Development of Chemical and Mechanical Acceleration Stress Test Method for PEMFC Polymer Membranes. 2023 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [15] Mohrdieck, C.; Venturi, M.; Breitrück, K.: Mobile Anwendungen. In: Töpler, J.; Lehmann, J. (Hrsg.): Wasserstoff und Brennstoffzelle. Berlin, Heidelberg: Springer Berlin Heidelberg 2017, S. 59–113 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [16] Lu, G.; Yan, Q.; Li, S. et al.: Design and performance study of a kilowatt-class PEMFC stack with metal fiber felts as flow fields. International Journal of Electrochemical Science 19 (2024) 11, S. 100839 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [17] Jaramillo Rodríguez, N. D.; Luxa, A.; Jürgensen, L.: Adaptation and Application of a Polarisation Curve Test Protocol for a Commercial Pem Electrolyser on Cell and Stack Level. Acta Mechanica et Automatica 17 (2023) 3, S. 395–404 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [18] Gul, E.; Baldinelli, G.; Farooqui, A. et al.: AEM-electrolyzer based hydrogen integrated renewable energy system optimisation model for distributed communities. Energy Conversion and Management 285 (2023), S. 117025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [19] Holst, M.; Aschbrenner, S.; Smolinka, T. et al.: Cost forecast for low temperature electrolysis. Technology driven bottom-up prognosis for PEM and alkaline water electrolysis systems. A cost analysis study on behalf of Clean Air Task Force, 2021 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [20] Bernat, R.; Milewski, J.; Dybinski, O. et al.: Review of AEM Electrolysis Research from the Perspective of Developing a Reliable Model. Energies 17 (2024) 20, S. 5030 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [21] Klell, M.; Eichlseder, H.; Trattner, A.: Erzeugung. In: Klell, M.; Eichlseder, H.; Trattner, A. (Hrsg.): Wasserstoff in der Fahrzeugtechnik. Wiesbaden: Springer Fachmedien Wiesbaden 2018, S. 71–108 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [22] Bender, B.; Feldhusen, J.; Krause, D. et al.: Grundlagen technischer Systeme und des methodischen Vorgehens. In: Bender, B.; Göhlich, D. (Hrsg.): Dubbel Taschenbuch für den Maschinenbau 2: Anwendungen. Berlin, Heidelberg: Springer Berlin Heidelberg 2020, S. 3–63 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [23] Gericke, K.; Bender, B.; Pahl, G. et al.: Der Produktentwicklungsprozess. In: Bender, B.; Gericke, K. (Hrsg.): Pahl/Beitz Konstruktionslehre. Berlin, Heidelberg: Springer Berlin Heidelberg 2021, S. 57–93 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [24] Europäische Kommission: Richtlinie 2014/34/EU. Richtlinie 2014/34/EU. 29.3.2014 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [25] Deutsches Institut für Normung: 22734. Wasserstofferzeuger auf Grundlage der Elektrolyse von Wasser. Oktober 2024 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [26] Wartzack, S.: Auswahl- und Bewertungsmethoden. In: Bender, B.; Gericke, K. (Hrsg.): Pahl/Beitz Konstruktionslehre. Berlin, Heidelberg: Springer Berlin Heidelberg 2021, S. 307–334 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [27] Verein Deutscher Ingenieure: VDI 2225. Konstruktionsmethodik. Beuth Verlag 1998 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-6
- [1] Statista: Preisentwicklung für Lithiumcarbonat in den Jahren 2012 bis 2024. Stand: 2025. Internet: de.statista.com/statistik/daten/studie/1323981/umfrage/preisentwicklung-fuer-lithiumcarbonat/. Zugriff am 24.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [2] Sawicki, M.; Shaw, L. L.: Advances and challenges of sodium ion batteries as post lithium ion batteries. RSC Advances 65 (2015) 5, pp. 53129–53154 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [3] Wang, Y.; Ou, R.; Yang, J. et al.: The safety aspect of sodium ion batteries for practical applications. Journal of Energy Chemistry 95 (2024), pp. 407–427 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [4] Feng, X.; Ouyang, M.; Liu, X. et al.: Thermal runaway mechanism of lithium ion battery for electric vehicles: A review. Energy Storage Materials 10 (2018), pp. 246–267 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [5] Hu, P.; Aifantis, K. E.: Sodium‐Ion Batteries. In: Kumar, R.; Aifantis, K.; Hu, P. (Hrsg.): Rechargeable Ion Batteries. Hoboken, New Jersey/ USA: Wiley 2023, pp. 269–298 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [6] Deb, D.; Sai Gautam, G.: Critical overview of polyanionic frameworks as positive electrodes for Na-ion batteries. Journal of Materials Research 37 (2022) 19, pp. 3169–3196 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [7] Yu, Y.: Sodium-ion batteries. Energy storage materials and technologies. Weinheim: Wiley-VCH 2022 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [8] Xie, M.; Wu, F.; Huang, Y.: Sodium-Ion Batteries. Berlin: De Gruyter 2022 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [9] Moon, H.; Innocenti, A.; Liu, H. et al.: Bio-Waste-Derived Hard Carbon Anodes Through a Sustainable and Cost-Effective Synthesis Process for Sodium-Ion Batteries. ChemSusChem 16 (2023) 1, e202201713 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [10] Velumani, D.; Bansal, A.: Thermal Behavior of Lithium- and Sodium-Ion Batteries: A Review on Heat Generation, Battery Degradation, Thermal Runway – Perspective and Future Directions. Energy & Fuels 36 (2022) 23, pp. 14000–14029 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [11] Liu, K.; Liu, Y.; Lin, D. et al.: Materials for lithium-ion battery safety. Science advances 4 (2018) 6, eaas9820 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [12] Yang, C.; Xin, S.; Mai, L. et al.: Materials Design for High‐Safety Sodium‐Ion Battery. Advanced Energy Materials 11 (2021) 2, doi.org/10.1002/aenm.202000974 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [13] Wang, Y.; Feng, X.; Huang, W. et al.: Challenges and Opportunities to Mitigate the Catastrophic Thermal Runaway of High‐Energy Batteries. Advanced Energy Materials 13 (2023) 15, doi.org/10.1002/aenm.202203841 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [14] Bates, A. M.; Preger, Y.; Torres-Castro, L. et al.: Are solid-state batteries safer than lithium-ion batteries? Joule 6 (2022) 4, pp. 742–755 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [15] Wu, X.; Song, K.; Zhang, X. et al.: Safety Issues in Lithium Ion Batteries: Materials and Cell Design. Frontiers in Energy Research 7 (2019), doi.org/10.3389/fenrg.2019.00065 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [16] Mei, W.; Cheng, Z.; Wang, L. et al.: Thermal hazard comparison and assessment of Li-ion battery and Na-ion battery. Journal of Energy Chemistry 102 (2025), pp. 18–26 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [17] Zakharchenko, T. K.; Nikiforov, D. I.; Serdyukov, G. D. et al.: Thermal Runaway of Na‐Ion Batteries with Na 3 V 2 O 2 (PO 4 ) 2 F Cathodes. Batteries & Supercaps 8 (2025) 2, doi.org/10.1002/batt.202400386 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [18] Yue, Y.; Jia, Z.; Li, Y. et al.: Thermal runaway hazards comparison between sodium-ion and lithium-ion batteries using accelerating rate calorimetry. Process Safety and Environmental Protection 189 (2024), pp. 61–70 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [19] Robinson, J. B.; Finegan, D. P.; Heenan, T. M. M. et al.: Microstructural Analysis of the Effects of Thermal Runaway on Li-Ion and Na-Ion Battery Electrodes. Journal of Electrochemical Energy Conversion and Storage 15 (2018) 1, doi.org/10.1115/1.4038518 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [20] Li, Z.; Cheng, Z.; Yu, Y. et al.: Thermal runaway comparison and assessment between sodium-ion and lithium-ion batteries. Process Safety and Environmental Protection 193 (2025), pp. 842–855 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [21] Abbott, K. C.; Buston, J. E.; Gill, J. et al.: Comprehensive gas analysis of a 21700 Li(Ni0.8Co0.1Mn0.1O2) cell using mass spectrometry. Journal of Power Sources 539 (2022), #231585 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [22] Schöberl, J.; Ank, M.; Schreiber, M. et al.: Thermal runaway propagation in automotive lithium-ion batteries with NMC-811 and LFP cathodes: Safety requirements and impact on system integration. eTransportation 19 (2024), # 100305, doi.org/10.1016/j.etran.2023.100305 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [23] Ohneseit, S.; Finster, P.; Floras, C. et al.: Thermal and Mechanical Safety Assessment of Type 21700 Lithium-Ion Batteries with NMC, NCA and LFP Cathodes–Investigation of Cell Abuse by Means of Accelerating Rate Calorimetry (ARC). Batteries 9 (2023) 5, #237 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-19
- [1] König, A.; Nicoletti, L.; Schröder, D. et al.: An Overview of Parameter and Cost for Battery Electric Vehicles. World Electric Vehicle Journal 12 (2021) 1, S. 21 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [2] Suttakul, P.; Wongsapai, W.; Fongsamootr, T. et al.: Total cost of ownership of internal combustion engine and electric vehicles: A real-world comparison for the case of Thailand. Energy Reports 8 (2022), S. 545–553 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [3] Paarmann, S.; Schreiber, M.; Chahbaz, A. et al.: Short‐Term Tests, Long‐Term Predictions – Accelerating Ageing Characterisation of Lithium‐Ion Batteries. Batteries & Supercaps 7 (2024) 11 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [4] Menye, J. S.; Camara, M.-B.; Dakyo, B.: Lithium Battery Degradation and Failure Mechanisms: A State-of-the-Art Review. Energies 18 (2025) 2, S. 342 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [5] Clerici, D.; Martelli, S.; Mocera, F. et al.: Mechanical characterization of lithium-ion batteries with different chemistries and formats. Journal of Energy Storage 84 (2024), S. 110899 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [6] Popp, H.; Koller, M.; Jahn, M. et al.: Mechanical methods for state determination of Lithium-Ion secondary batteries: A review. Journal of Energy Storage 32 (2020), S. 101859 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [7] Ponomareva, A.: Battery Management System (BMS): Effective Ways to Measure State-of-Charge and State-of-Health. Medium (2021) Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [8] Hu, X.; Feng, F.; Liu, K. et al.: State estimation for advanced battery management: Key challenges and future trends. Renewable and Sustainable Energy Reviews 114 (2019), S. 109334 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [9] S, V.; Che, H. S.; Selvaraj, J. et al.: State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges. Applied Energy 369 (2024), S. 123542 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [10] Han, X.; Lu, L.; Zheng, Y. et al.: A review on the key issues of the lithium ion battery degradation among the whole life cycle. eTransportation 1 (2019), S. 100005 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [11] Zhuo, M.; Offer, G.; Marinescu, M.: Degradation model of high-nickel positive electrodes: Effects of loss of active material and cyclable lithium on capacity fade. Journal of Power Sources 556 (2023), S. 232461 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [12] Birkl, C. R.; Roberts, M. R.; McTurk, E. et al.: Degradation diagnostics for lithium ion cells. Journal of Power Sources 341 (2017), S. 373–386 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [13] Lin, X.; Khosravinia, K.; Hu, X. et al.: Lithium Plating Mechanism, Detection, and Mitigation in Lithium-Ion Batteries. Progress in Energy and Combustion Science 87 (2021), S. 100953 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [14] Adenusi, H.; Chass, G. A.; Passerini, S. et al.: Lithium Batteries and the Solid Electrolyte Interphase (SEI)—Progress and Outlook. Advanced Energy Materials 13 (2023) 10 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [15] Su, L.; Xu, Y.; Dong, Z.: State‐of‐health estimation of lithium‐ion batteries: A comprehensive literature review from cell to pack levels. Energy Conversion and Economics 5 (2024) 4, S. 224–242 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [16] Gervillié-Mouravieff, C.; Bao, W.; Steingart, D. A. et al.: Non-destructive characterization techniques for battery performance and life-cycle assessment. Nature Reviews Electrical Engineering 1 (2024) 8, S. 547–558 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [17] Orcioni, S.; Buccolini, L.; Ricci, A. et al.: Lithium-ion Battery Electrothermal Model, Parameter Estimation, and Simulation Environment. Energies 10 (2017) 3, S. 375 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [18] Pozzato, G.; Allam, A.; Pulvirenti, L. et al.: Analysis and key findings from real-world electric vehicle field data. Joule 7 (2023) 9, S. 2035–2053 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [19] Yuan, Q.; Hao, W.; Su, H. et al.: Investigation on Range Anxiety and Safety Buffer of Battery Electric Vehicle Drivers. Journal of Advanced Transportation 2018 (2018), S. 1–11 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [20] Zhao, J.; Feng, X.; Tran, M.-K. et al.: Battery safety: Fault diagnosis from laboratory to real world. Journal of Power Sources 598 (2024), S. 234111 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [21] Wang, S.; Ren, D.; Xu, C. et al.: Lithium plating induced volume expansion overshoot of lithium-ion batteries: Experimental analysis and modeling. Journal of Power Sources 593 (2024), S. 233946 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [22] Louli, A. J.; Ellis, L. D.; Dahn, J. R.: Operando Pressure Measurements Reveal Solid Electrolyte Interphase Growth to Rank Li-Ion Cell Performance. Joule 3 (2019) 3, S. 745–761 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [23] Proff, H.; Bowman, K.; Robinson, R. et al.: 2024 Global Automotive Consumer Study. Key Findings: Global Focus Countries (2024), S. 1–26 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [24] Deutsche Automobil Treuhand GmbH: DAT Report 025 | Kurzbericht. 2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [25] Sieg, J.; Schmid, A. U.; Rau, L. et al.: Fast-charging capability of lithium-ion cells: Influence of electrode aging and electrolyte consumption. Applied Energy 305 (2022), S. 117747 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [26] Aviloo GmbH: Erstmalige Messung der Batteriedegradation in Abhängigkeit zum Schnellladeanteil. 29.03.2023 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [27] Hackmann, M.; Knörzer, H.; Peuffer, J. et al.: Battery aging in practice: Analysis of over 7,000 vehicles provide deep insights into battery life and vehicle residual value (2024) Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-26
- [1] IEEE Transportation Electrification Conference and Expo (ITEC). 2012 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [2] Roca-Puigròs, M.; Marmy, C.; Wäger, P. et al.: Modeling the transition toward a zero emission car fleet: Integrating electrification, shared mobility, and automation. Transportation Research Part D: Transport and Environment 115 (2023), S. 103576 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [3] Tober, W.: Praxisbericht Elektromobilität und Verbrennungsmotor. Analyse elektrifizierter Pkw-Antriebskonzepte. Wiesbaden: Springer 2016 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [4] Philipp Keller; Tim Wicke: Verkaufszahlen von Elektroautos: Vorübergehende Flaute oder anhaltende Trendumkehr? Stand: 24.10.2024. Internet: https://www.isi.fraunhofer.de/de/blog/themen/batterie-update/elektroautos-verkaufszahlen-hybrid-flaute-deutschland-europa.html. Zugriff am 29.03.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [5] Palmer, C.: The Drive for Electric Motor Innovation. Engineering 8 (2022), S. 9–11 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [6] Haas, A.; Hackmann, W.: Process for Manufacturing Stator Wave Windings for Electric Traction Motors. MTZ worldwide 83 (2022) 5, S. 62–65 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [7] o.V.: New Energy Vehicle Hairpin Motor With Huge Market Growth Space and Policy Advantages. Stand: 09.12.2022. Internet: https://en.cnhonest.com/news/234.html. Zugriff am 09.12.2022 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [8] Global Hairpin Stator Market Growth 2022-2028, Intelligence Market Report, London, UK, 2022 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [9] Hans-Martin Fischer: Spannungsklassen in der Elektromobilität. Frankfurt am Main: ZVEI 2013 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [10] El Hadraoui, H.; Zegrari, M.; Chebak, A. et al.: A Multi-Criteria Analysis and Trends of Electric Motors for Electric Vehicles. World Electric Vehicle Journal 13 (2022) 4, S. 65 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [11] Binder, A.: Elektrische Maschinen und Antriebe. Grundlagen, Betriebsverhalten. Berlin, Heidelberg: Springer 2017 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [12] Gundabattini, E.; Kuppan, R.; Solomon, D. G. et al.: A review on methods of finding losses and cooling methods to increase efficiency of electric machines. Ain Shams Engineering Journal 12 (2021) 1, S. 497–505 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [13] Bailoni, M.; Nategh, S.; Gaussens, B. et al.: A Study on Insulation Components of High Voltage Electrical Machines Used in Electric Vehicles. IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society, Brussels, Belgium, 2022, S. 1–6 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [14] Sihvo, V.; Nerg, J.; Pyrhonen, J.: Insulation System and Thermal Design of a Hermetically Sealed Turbo-Generator Operating in a Small-Power CHP Plant. 2007 International Conference on Clean Electrical Power, Capri, Italy, 2007, S. 45–50 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [15] Mancinelli, P.; Stagnitta, S.; Cavallini, A.: Qualification of Hairpin Motors Insulation for Automotive Applications. IEEE Transactions on Industry Applications 53 (2017) 3, S. 3110–3118 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [16] Kampker, I. A.; Dorn, B.; Brans, F. et al.: Stator design for flexible manufacturing in hairpin technology, Newcastle, UK, 2022, pp. 6–11 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [17] Fleischer, J.; Hausmann, L.; Wirth, F.: Production-oriented design of electric traction drives with hairpin winding, Enschede, Niederlande, 2021, pp. 169–174 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [18] Born, H. C.; Schaffrath, M.; Tege, D. et al.: Analysis of the Influence of Various Bending Parameters on the Resulting Electrical Properties of Bent Hairpins. 2023 13th International Electric Drives Production Conference (EDPC), Regensburg, Germany, 2023, S. 1–8 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [19] Hanisch, L. V.; Dietrich, T.-H.; Henke, M.: Analysis of Partial Discharges and Failure Mechanism in Electrical Machines with Hairpin Winding. 2021 IEEE 13th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Dallas, TX, USA, 2021, S. 1–7 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [20] Madonna, V.; Giangrande, P.; Zhao, W. et al.: Insulation Capacitance as Diagnostic Marker for Thermally Aged, Low Voltage Electrical Machines. 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), Harbin, China, 2019, S. 1–5 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [21] Mancinelli, P.; Stagnitta, S.; Cavallini, A.: Lifetime analysis of an automotive electrical motor with hairpin wound stator. 2016 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP), Toronto, ON, Canada, 2016, S. 877–880 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [22] He, C.; Tenbohlen, S.; Beltle, M.: Ageing Analysis of Hairpin Windings in Inverter-Fed Motor Under PWM Voltage. Energies 18 (2025) 6, S. 1376 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-34
- [1] Brecher, C.; Weck, M.: Werkzeugmaschinen, Fertigungssysteme. Berlin: Springer Vieweg 2017 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [2] Ersoy, M.; Gies, S.: Fahrwerkhandbuch. Wiesbaden: Springer Fachmedien 2017 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [3] Runge, W.; Gaedke, A.; Heger, M. et al.: Elektrisch lenken. ATZextra 15 (2010) 2, S. 68–75 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [4] Robert Bosch GmbH: Elektrolenkung mit achsparalleler Servoeinheit. Internet: https://www.bosch-mobility.com/de/loesungen/lenkung/elektrolenkung-mit-achsparalleler-servoeinheit/. Zugriff am 17.05.2023 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [5] DMGMori: DMX 60 U. Hochleistungs- und präzises Universal-Fräszentrum für Werkstücke bis zu D630 x 450 mm und 300 kg. Internet: https://de.dmgmori.com/produkte/maschinen/fraesen/5-achs-fraesen/dmx-u/dmx-60-u. Zugriff am 08.05.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [6] August Steinmeyer GmbH & Co.KG: Schmierung von KGT. Stand: 10.04.2025. Internet: https://www.steinmeyer.com/de/technik/schmierung-und-abstreifer/. Zugriff am 10.04.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [7] Fleischer, J.; Schopp, M.; Broos, A. et al.: Datenbasis für lastabhängige Prozesseingriffe*. wt Werkstattstechnik online 97 (2007) 7-8, S. 491–497 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [8] Forstmann, J.: Kugelgewindetriebe im Einsatz an Kunststoffspritzgießmaschinen. , Disseration Universität Duisburg, Essen, 2010 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [9] Spohrer, A.: Steigerung der Ressourceneffizienz und Verfügbarkeit von Kugelgewindetrieben durch adaptive Schmierung. Dissertation Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [10] Klein, W. H.: Zustandsüberwachung von Rollen-Profilschienenführungen und Kugelgewindetrieben. Aachen: Apprimus-Verl. 2011 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [11] Münzing, T.: Auslegung von Kugelgewindetrieben bei oszillierenden Bewegungen und dynamischer Belastung. Dissertation, Universität Stuttgart Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [12] Shannon, C. E.: Communication in the Presence of Noise. Proceedings of the IRE 37 (1949) 1, S. 10–21 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [13] VDI: Richtlinie VDI 3832. Körperschallmessungen zur Zustandsbeurteilung von Wälzlagern in Maschinen und Anlagen (2013) Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [14] Helwig, N. J.: Zustandsbewertung industrieller Prozesse mittels multivariater Sensordatenanalyse am Beispiel hydraulischer und elektromechanischer Antriebssysteme, 2018 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-45
- [1] Altintas, Y.; Brecher, C.; Weck, M. et al.: Virtual Machine Tool. CIRP Annals 54 (2005) 2, S. 115–138 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [2] Quintana, G.; Ciurana, J.: Chatter in machining processes: A review. International Journal of Machine Tools and Manufacture 51 (2011) 5, S. 363–376 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [3] Navarro-Devia, J.; Chen, Y.; Dao, D. et al.: Chatter detection in milling processes—a review on signal processing and condition classification. The International Journal of Advanced Manufacturing Technology 125 (2023) 9-10, S. 3943–3980 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [4] Brecher, C.; Chavan, P.; Epple, A.: Investigations on the limitations of rapid experimental determination of stability boundaries during milling. Mechanics & Industry 18 (2017) 6, S. 608 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [5] Ji, Y.; Wang, X.; Liu, Z. et al.: Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation. Journal of Sound and Vibration 433 (2018), S. 138–159 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [6] Chen, Y.; Li, H.; Hou, L. et al.: Feature extraction using dominant frequency bands and time-frequency image analysis for chatter detection in milling. Precision Engineering 56 (2019), S. 235–245 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [7] Tran, M. Q.; Liu, M. K.: Chatter Identification in End Milling Process Based on Cutting Force Signal Processing. IOP Conference Series: Materials Science and Engineering 654 (2019) 1, S. 12001 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [8] Schmitz, T.; Smith, S.: Machining Dynamics. Cham: Springer International Publishing 2019 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [9] Liu, X.; Wang, Z.; Li, M. et al.: Feature extraction of milling chatter based on optimized variational mode decomposition and multi-scale permutation entropy. The International Journal of Advanced Manufacturing Technology 114 (2021) 9-10, S. 2849–2862 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [10] Tran, M.; Liu, M.; Tran, Q.: Analysis of Milling Chatter Vibration Based on Force Signal in Time Domain. In: Sattler, K.-U.; Nguyen, D. C.; Vu, N. P. et al. (Hrsg.): Advances in Engineering Research and Application. Cham: Springer International Publishing 2021, S. 192–199 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [11] Zhao, Y.; Adjallah, K.; Sava, A. et al.: MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling. ISA transactions 113 (2021), pp. 39–51 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [12] Chen, Y.; Li, H.; Hou, L. et al.: Chatter detection for milling using novel p-leader multifractal features. Journal of Intelligent Manufacturing 33 (2022) 1, S. 121–135 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [13] Hauptfleischová, B.; Novotný, L.; Falta, J. et al.: In-Process Chatter Detection in Milling: Comparison of the Robustness of Selected Entropy Methods. Journal of Manufacturing and Materials Processing 6 (2022) 5, S. 125 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [14] Perrelli, M.; Cosco, F.; Gagliardi, F. et al.: In-Process Chatter Detection Using Signal Analysis in Frequency and Time-Frequency Domain. Machines 10 (2022) 1, S. 24 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [15] Wang, Y.; Zhang, M.; Tang, X. et al.: A kMap optimized VMD-SVM model for milling chatter detection with an industrial robot. Journal of Intelligent Manufacturing 33 (2022) 5, S. 1483–1502 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [16] Xu, X.; Zhou, T.; Wan, L. et al.: Detection of modulated chatter using moving average difference spectrum analysis. Journal of Sound and Vibration 517 (2022), S. 116568 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [17] Zheng, Q.; Chen, G.; Jiao, A.: Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM. The International Journal of Advanced Manufacturing Technology 120 (2022) 1-2, S. 1237–1251 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [18] Unver, H.; Sener, B.: A novel transfer learning framework for chatter detection using convolutional neural networks. Journal of Intelligent Manufacturing 34 (2023) 3, S. 1105–1124 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [19] Zhang, P.; Gao, D.; Hong, D. et al.: Improving generalisation and accuracy of on-line milling chatter detection via a novel hybrid deep convolutional neural network. Mechanical Systems and Signal Processing 193 (2023), S. 110241 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [20] Brecher, C.; Chavan, P.; Epple, A.: Efficient determination of stability lobe diagrams by in-process varying of spindle speed and cutting depth. Advances in Manufacturing 6 (2018) 3, S. 272–279 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [21] Garrett, S. L.: The Simple Harmonic Oscillator. In: Garrett, S. L. (Hrsg.): Understanding Acoustics. Cham: Springer International Publishing 2020, S. 59–131 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [22] Brecher, C.; Chavan, P.; Epple, A.: Investigations on the limitations of rapid experimental determination of stability boundaries during milling. Mechanics & Industry 18 (2017) 6, S. 608 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [23] Brecher, C.; Klimaschka, R.; Neus, S.: Effiziente Ermittlung dynamischer Prozessgrenzen/Efficient determination of dynamic process limits. wt Werkstattstechnik online 114 (2024) 05, S. 220–229 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [24] Reinhart, G.; Milberg, J.; Trucks, V.: Rechnergestützte Beurteilung von Getriebestrukturen in Werkzeugmaschinen. Berlin, Heidelberg: Springer Berlin Heidelberg 1996 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [25] Simonyan, K.; Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [26] Brecher, C.; Weck, M.: Werkzeugmaschinen, Fertigungssysteme. Berlin, Heidelberg: Springer Vieweg 2017 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [27] Bosetti, P.; Leonesio, M.; Parenti, P.: On Development of an Optimal Control System for Real-time Process Optimization on Milling Machine Tools. Procedia CIRP 12 (2013), S. 31–36 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-53
- [1] Abele, E.; Altintas, Y.; Brecher, C.: Machine tool spindle units. CIRP Annals 59 (2010) 2, pp. 781–802 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [2] Eckel, H.-M.: Kinematische Analyse von Spindellagern unter statischen und dynamischen Kräften. Aachen: Apprimus Verlag 2019 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [3] Möhring, H.-C.; Wiederkehr, P.; Erkorkmaz, K. et al.: Self-optimizing machining systems. CIRP Annals 69 (2020) 2, pp. 740–763 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [4] Brecher, C.; Eckel, H.-M.; Motschke, T. et al.: Estimation of the virtual workpiece quality by the use of a spindle-integrated process force measurement. CIRP Annals 68 (2019) 1, pp. 381–384 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [5] Brecher, C.; Eckel, H.-M.; Fey, M. et al.: Verfahren zur Erfassung einer axialen Verlängerung einer rotierenden Welle relativ zu einem Gehäuse. Patent. Internet: depatisnet.dpma.de/DepatisNet/depatisnet?action=bibdat&docid=WO002021098978A1. Zugriff am 28.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [6] Chavan, P. S.: Substructuring methods for efficient prediction of spindle-holder-tool assembly dynamics. Dissertation, RWTH Aachen University, 2023 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [7] Brecher, C.; Weck, M.: Werkzeugmaschinen Fertigungssysteme 2. Heidelberg: Springer-Verlag 2017 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [8] Schmitz, T.; Betters, E.; Budak, E. et al.: Review and status of tool tip frequency response function prediction using receptance coupling. Precision Engineering 79 (2023), pp. 60–77 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [9] Falker, J.: Analyse des Betriebsverhaltens von Hochgeschwindigkeits-Wälzlagern unter radialen Lasten. Dissertation, RWTH Aachen University, 2019 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [10] Cao, Y.; Altintas, Y.: A General Method for the Modeling of Spindle-Bearing Systems. Journal of Mechanical Design 126 (2004) 6, pp. 1089–1104 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [11] Mesys AG: MESYS Shaft Calculation. User Manual. Internet: www.mesys.ch/manual/mesys_shaft_calculation.html. Zugriff am 28.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [12] Lunze, J.: Regelungstechnik 2. Mehrgrößensysteme, Digitale Regelung. Berlin, Heidelberg: Springer-Verlag 2016 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [13] Polóni, T.; Kolmanovsky, I.; Rohaľ-Ilkiv, B.: Simple Input Disturbance Observer-Based Control: Case Studies. Journal of Dynamic Systems, Measurement, and Control 140 (2018) 1, doi.org/10.1115/1.4037298 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-63
- [1] Schuh, G.; Zeller, V.; Stich, V.: Digitalisierungs- und Informationsmanagement. Berlin: Springer 2022 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [2] Brüggemann, H.; Bremer, P.: Grundlagen Qualitätsmanagement. Von den Werkzeugen über Methoden zum TQM. Wiesbaden: Springer Vieweg 2020 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [3] Verein Deutscher Ingenieure e.V.: VDI/VDE 2206 - Entwicklung mechatronischer und cyber-physischer Systeme. Berlin: Beuth Verlag GmbH 2021 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [4] Mayer-Bachmann, R.: Integratives Anforderungsmanagement. Konzept und Anforderungsmodell am Beispiel der Fahrzeugentwicklung. Universitätsverlag Karlsruhe 2007 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [5] Bender, B.; Gericke, K.: Pahl/Beitz Konstruktionslehre. Berlin: Springer 2021 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [6] Mistler, M.: Entwicklung eines Vorgehenskonzeptes zum modellbasierten agilen Anforderungsmanagement (Requirements Engineering und Requirements Management) für Organisationen – REMOt, 2021 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [7] Schäuffele, J.; Zurawka, T.: Automotive Software Engineering. Wiesbaden: Springer Fachmedien 2016 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [8] Ackermann, M.: Mobility-as-a-Service. The Convergence of Automotive and Mobility Industries. Cham: Springer International Publishing; Imprint Springer 2021 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [9] Eigner, M.: System Lifecycle Management. Berlin: Springer 2021 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [10] Pischinger, S.; Seiffert, U.: Vieweg Handbuch Kraftfahrzeugtechnik. Wiesbaden: Springer Fachmedien 2021 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [11] Schlüter, N.: Generic Systems Engineering. Ein methodischer Ansatz zur Komplexitätsbewältigung. Berlin: Springer Vieweg 2023 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [12] Löckel, K.: Systems Engineering - Ganzheitlicher Ansatz mit großem Potenzial. In: ATZproduktion 2018, S. 18–23 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [13] Haberfellner, R.; Weck, O. L. de; Fricke, E. et al.: Systems Engineering. Fundamentals and Applications. Cham: Birkhäuser 2019 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [14] Hellenbrand, D.: Transdisziplinäre Planung und Synchronisation mechatronischer Produktentwicklungsprozesse. München: Dr. Hut 2013 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [15] Cadet, M.; Schulte, T.; Dickopf, T. et al.: Modellbasierte Entwicklung cybertronischer Systeme in der frühen Phase des Entwicklungsprozesses – Ein Vergleich mit der klassischen Produktentwicklung. In: Stuttgarter Symposium für Produktentwicklung 2017, S. 1–10 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [16] Gerhardt, F.: Framework zur Einführung des Systems Engineering auf Basis von PLM/ALM Lösungen in Enterprise Architekturen. In: ProduktDaten Journal 2016, S. 48–52 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [17] Deuter, A.; Otte, A.; Ebert, M. et al.: Developing the Requirements of a PLM/ALM Integration: An Industrial Case Study. In: 4th International Conference on System-Integrated Intelligence 2018, S. 107–113 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-70
- [1] Tschirner, C.: Rahmenwerk zur Integration des modellbasierten Systems Engineering in die Produktentstehung mechatronischer Systeme. Dissertation, Universität Paderborn, 2016 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [2] Fresemann, C.; Stark, R.; Sauer, C. et al.: Lücken und Herausforderungen bei der praktischen Umsetzung des Model-Based Systems Engineering. Konstruktion (2019) 06, S. 81–83, doi. org/10.37544/0720-5953-2019-06-81 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [3] Thommes, K.; Iseke, A.; Schneider, M.: Digitales und prädiktives Kompetenzmanagement. Heidelberg: Springer Vieweg 2024 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [4] Incose: Incose Systems Engineering Handbook. Hoboken, New Jersey/ USA: Wiley 2023 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [5] Verein Deutscher Ingenieure (VDI): VDI/VDE 2206 Entwicklung mechatronischer und cyber-physischer Systeme. Düsseldorf: VDI-Verlag 2021 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [6] Schlüter, N.: Generic Systems Engineering. Heidelberg: Springer Vieweg 2023 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [7] Albers, A.; Dumitrescu, R.; Gausemeier, J. et al. (Hrsg.): Strategie Advanced Systems Engineering – Leitinitiative zur Zukunft des Engineering und Innovationsstandorts Deutschland. Stand: 2022. Internet: www.advanced-systems-engineering.de/wp-content/uploads/ASE_Strategie.pdf. Zugriff am 08.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [8] Rusch, F. R.; Willems, W.; Demke, N. et al.: Linking product development’s and society’s view on sustainability to enhance the contextual derivation and validation of requirements. IFAC-PapersOnLine 58 (2024) 19, pp. 1198–1203 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [9] Schneider, B.; Schüle, S.; Kürümlüoglu, M. et al..: Advanced Systems Engineering unternehmensindividuell konfigurieren und einführen – Advanced Systems Engineering Assessment. WT Werkstattstechnik 114 (2024) 6, S. 259–267. Internet: www.werkstattstechnik.de. Düsseldorf: VDI Fachmedien Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [10] Douglass, B. P.: Agile Systems Engineering. Amsterdam: Elsevier Inc. 2016 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [11] Rusch, F. R.; Demke, N.; Willems, W. et al.: Context-based Derivation of Holistic Sustainability Requirements in the Early Phase of Product Development. Procedia CIRP 122 (2024), pp. 306–311 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [12] Baughey, K.: Functional and Logical Structures: A Systems Engineering Approach. SAE Technical Paper (2011) 2011–01–0517, doi.org/10.4271/2011–01–0517 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [13] Hoffmann, H.-P.: Systems Engineering Best Practices with the Rational Solution for Systems and Software Engineering. Somers NY: Deskbook Release 4.1 2013 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [14] Péraire, C.; Edwards, M.; Fernandes, A. et al.: The IBM Rational Unified Process for System z. Stand: 2007. Internet: www.redbooks.ibm.com/redbooks/pdfs/sg247362.pdf. Zugriff am 08.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [15] Voirin, J.-L.: Model-based System and Architecture Engineering with the Arcadia Method. London: ISTE Press – Elsevier 2017 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [16] Salehi, V.; Florian, G.; Taha, J.: Implementation of Systems Modeling Language (SysML) in Consideration of the CONSENS Approach. DS 92: Proceedings of the DESIGN 2018 15th International Design Conference, 2018, pp. 2987–2998 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [17] Pearce, P.; Hause, M.: ISO-15288: OOSEM and Model-Based Submarine Design. Stand: 2012. Internet: www.omg.org/sysml/Pearce_Hause_ISO-15288_OOSEM_and_Model-Based_Submarine_Design_SETE_APCOSE_20121.pdf. Zugriff am 08.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [18] London, B.: A Model-Based Systems Engineering Framework for Concept Development. Cambridge, MA: Massachusetts Institute of Technology 2012 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [19] Robinson, K.; Tramoundans, D.; Harvey, D. et al.: Demonstrating Model-Based Systems Engineering. Systems Engineering / Test & Evaluation Conference, Adelaide, 2010. Internet: www.shoalgroup.com/wp-content/uploads/2017/06/Robinson-et-al-2010-Demonstrating-Model-Based-Systems-Engineering-for-Specifying-Complex-Capability-SETE-2010.pdf. Zugriff am 08.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [20] Estefan, J.: Survey of Model-Based Systems Engineering (MBSE) Methodologies. Incose MBSE Initiative, Pasadena, CA, 2008. Internet: https://www.omg.org/sysml/MBSE_Methodology_Survey_RevB.pdf. Zugriff am 08.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [21] Incose: Incose Systems Engineering Competency Framework. Incose Technical Product Reference: Incose-TP-2018–002–01.0. Stand: 2018. Internet: www.incose.org/docs/default-source/professional-development-portal/isecf.pdf?sfvrsn=dad06bc7_4.Zugriff am 08.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [22] Presland, I.; Whitcomb, C.; Zipes, L. (eds).: ECF Annex D: Incose Systems Engineering Competency Framework. Systems Engineering Competency Assessment Guide. Hoboken, New Jersey/ USA: John Wiley & Sons Inc. 2023 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [23] Sheard, S.: Twelve Systems Engineering Roles. Systems Engineering Practices & Tools 6 (1996) 1, pp. 478–485 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [24] Sheard, S.: The Value of Twelve Systems Engineering Roles. Systems Engineering Practices & Tools 6 (1996) 1, pp. 894–902 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [25] Gräßler, I.; Oleff, C.; Hentze, J.: Role Model for Systems Engineering Application. Proceedings of the Design Society: International Conference on Engineering Design, 2019, pp. 1265–1274, doi.org/10.1017/dsi.2019.132 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [26] Gräßler, I.; Thiele, H.; Grewe, B. et al.: Responsibility Assignment in Systems Engineering. Proceedings of the Design Society 2 (2022) 1, pp. 1875–1884, doi.org/10.1017/pds.2022.190 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [27] Hage, H.: Rahmenwerk zur automatisierten Verifikation eines systemorientierten Produktentstehungsprozesses in der Automobilentwicklung. Dissertation, Helmut-Schmidt-Universität Hamburg, 2024 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-77
- [1] Königs, M.; Brecher, C.: Process-parallel virtual quality evaluation for metal cutting in series production. Procedia Manufacturing (2018) 26:1087–1093. doi:10.1016/j.promfg.2018.07.145 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [2] Fertig, A.; Weigold, M.; Chen, Y.: Machine Learning based quality prediction for milling processes using internal machine tool data. Advances in Industrial and Manufacturing Engineering 4:100074. (2022) doi:10.1016/j.aime.2022.100074 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [3] Wellmann, F.: Datengetriebene, kontextadaptive Produktivitätssteigerung von NC-Zerspanprozessen, 1. Aufl. Apprimus Wissenschaftsverlag, Aachen (2019) Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [4] Bilgili, D.; Kecibas, G.; Besirova, C.; Chehrehzad, M.R.; Burun, G.; Pehlivan, T.; Uresin, U.; Emekli, E.; Lazoglu, I.: Tool flank wear prediction using high-frequency machine data from industrial edge device. Procedia CIRP 118:483–488. (2023) doi:10.1016/j.procir.2023.06.083 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [5] Butler, Q.; Ziada, Y.; Stephenson, D.; Andrew Gadsden, S.: Condition Monitoring of Machine Tool Feed Drives: A Review. J. Manuf. Sci. Eng 144 (10) (2022). doi:10.1115/1.4054516 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [6] Yamada Y, Kakinuma Y (2016) Sensorless cutting force estimation for full-closed controlled ball-screw-driven stage. Int J Adv Manuf Technol 87(9-12):3337–3348. doi:10.1007/s00170-016-8710-5 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [7] Aslan, D.; Altintas, Y.: Prediction of Cutting Forces in Five-Axis Milling Using Feed Drive Current Measurements. IEEE/ASME Trans. Mechatron. 23 (2018) 2, S. 833–844. doi:10.1109/TMECH.2018.2804859 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [8] Saraie, H.; Sakahira, M.; Ibaraki, S.; Matsubara, A.; Kakino, Y.; Fujishima, M.: 326 Monitoring and Adaptive Control of Cutting Forces Based on Spindle Motor and Servo Motor Currents in Machining Centers. LEM21 2003 (0) S. 555–560. doi:10.1299/jsmelem.2003.555 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [9] Mostaghimi, H.; Park, CI.; Kang, G.; Park, S.S.; Lee, D. Y.: Reconstruction of cutting forces through fusion of accelerometer and spindle current signals. Journal of Manufacturing Processes 68:990–1003, (2021) doi:10.1016/j.jmapro.2021.06.007 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [10] Fey, M.; Epple, A.; Kehne, S.; Brecher, C.: Verfahren zur Bestimmung der Achslast auf Linear- und Rundachsen G01L 1/04, (2016) Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [11] Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Computation 9(8):1735–1780. doi:10.1162/neco.1997.9.8.1735 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [12] Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, Puneet Agarwal: Long short term memory networks for anomaly detection in time series. Proceedings 2015 (89), S. 89–94 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [13] Denkena, B.; Bergmann, B.; Stoppel, D.: (Hrsg) Reconstruction of Process Forces in a Five-Axis Milling Center with a LSTM Neural Network in Comparison to a Model-Based Approach. Springer, Cham, 2023 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [14] Li, B.; Liu, T.; Liao, J.; Feng, C.; Yao, L.; Zhang, J.: Non-invasive milling force monitoring through spindle vibration with LSTM and DNN in CNC machine tools. Measurement 210:112554. doi:10.1016/j.measurement.2023.112554, 2023) Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [15] Aswani, A.; Noam, S.; Parmar, N.; Uszkoreit, J.; Jones, L.; Aidan, N.; Kaiser, G. L.; Polosukhin, I.: Attention is all you need. Advances in neural information processing systems, 2017 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [16] Li, Shiyang, Xiaoyong Jin, Yao Xuan, Xiyou Zhou, Wenhu Chen, Yu-Xiang Wang, and Xifeng Yan: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Advances in neural information processing systems 32:1–11 (2019) Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [17] Zerveas, G.; Jayaraman, S.; Patel, D.; Bhamidipaty, A.; Eickhoff, C.: A Transformer-based Framework for Multivariate Time Series Representation Learning (2020) Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [18] Li, Z.; Rao, Z.; Pan, L.; Wang, P.; Xu, Z.: Ti-MAE: Self-Supervised Masked Time Series Autoencoders (2023) Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [19] Cheng, M.; Liu, Q.; Liu, Z.; Zhang, H.; Zhang, R.; Chen, E.: TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders (2023) Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-88
- [1] Hönig, H.; Lorenz, B.: Low-Cost-Digitalisierung in der Produktion. Erste Schritte zur Vernetzung von Produktionsteilnehmern in KMU. Zeitschrift für wirtschaftlichen Fabrikbetrieb 112 (2017) 12, S. 895–898 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [2] Lanza, G., Rühl, J.; Peters, S.: Bewertung von Stückzahl und Variantenflexibilität in der Produktion. Produktionskosten und Risiken abhängig von Varianten und Stückzahlen. Zeitschrift für wirtschaftlichen Fabrikbetrieb 104 (2009) 11, S. 1039–1044 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [3] Westkämper, E., Bullinger; H.-J., Horváth, P.; Zahn, E.: Montageplanung – effizient und marktgerecht. Berlin: Springer-Verlag 2013 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [4] McCausland, L.: Engaging a Deskless Workforce. A white paper for communications professionals. Stand: 2020. Internet: www.shsmd.org/system/files/media/file/2020/04/Whitepaper%20-%20Engaging%20a%20Deskless%20Workforce%20%20-%20Lexi%20McCausland.pdf. Zugriff am 01.08.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [5] Quinyx: State of the Frontline Workforce 2024. Stand: 2024. Internet: www.quinyx.com/hubfs/Quinyx-State-of-the-Frontline-Workforce-Studie-2024.pdf. Zugriff am 31.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [6] Henke, J.: Eine Methodik zur Steigerung der Wertschöpfung in der manuellen Montage komplexer Systeme. Dissertation, Universität Stuttgart, 2015 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [7] Institut für Mittelstandsforschung IfM Bonn: KMU-Definition des IfM Bonn. Stand: 2025. Internet: www.ifm-bonn.org/definitionen-/kmu-definition-des-ifm-bonn. Zugriff am 31.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [8] Operations1: Homepage. Stand: 2025. Internet: operations1.com/de. Zugriff am 31.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [9] Testify: Homepage. Stand: 2025. Internet: www.testify.io/. Zugriff am 31.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [10] VKS: VKS. Werkerassistenzsystem. Stand: 2025. vksapp.com/de/werkerassistenzsystem, Zugriff am 31.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [11] SightProc GmbH: Werkerassistenzsystem. Digitale Werkerassistenz für Ihre Fertigungsprozesse. Effizienz und Qualität neu definiert. Internet: www.sightproc.de/. Zugriff am 31.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [12] Lindner, D.; Leyh, C.: Organizations in Transformation: Agility as Consequence or Prerequisite of Digitization? In: Abramowicz, W.; Paschke, A. (eds): Business Information Systems. BIS 2018. Lecture Notes in Business Information Processing 320. Cham: Springer, doi.org/10.1007/978–3–319–93931–5_7 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [13] Lindner, D.; Leyh, C.: Digitalisierung von KMU – Fragestellungen, Handlungsempfehlungen sowie Implikationen für IT-Organisation und IT-Servicemanagement. HMD Praxis der Wirtschaftsinformatik 56 (2019) 2, S. 402–418 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [14] Oberländer, M.; Bipp, T.: Do digital competencies and social support boost work engagement during the COVID-19 pandemic? Computers in human behavior 130 (2022), #107172 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [15] Prümper, J., Hartmannsgruber, K.; Frese, M.: KFZA – Kurzfragebogen zur Arbeitsanalyse. Zeitschrift für Arbeits-und Organisationspsychologie (1995) 39, S. 125–131 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [16] Schaufeli, W., Salanova, M., Gonzalez-Romá, V. et al.: The measurement of engagement and burnout: A confirmative analytic approach. Journal of Happiness Studies 3 (2002) 1, pp. 71–92 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [17] Hanebeck, J., Käppler, M., Lorenz, M. et al.: Entwicklung einer adaptiven Mensch-Maschine-Schnittstelle zur Unterstützung in der manuellen Montage. Smarte Technologien und Augmented Reality in der Arbeitswelt (2023), S. 19–21, doi.org/10.21934/baua:bericht20231027 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [18] Vue.js Organization: Vue.js. The Progressive JavaScript Framework. Stand: 2025. Internet: vuejs.org/. Zugriff am 31.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [19] Bootstrap: Bootstrap. Build fast, responsive sites with Bootstrap, 2025. Internet: getbootstrap.com/. Zugriff am 31.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [20] Prümper, J.; Anft, M.: ISONORM 9241/10. Beurteilung von Software auf Grundlage der Internationalen Ergonomie-Norm ISO 9241/10. Stand: 1993. Internet:www.torstenstapelkamp.de/wp-content/uploads/2017/08/Fragebogen_ISONORM_9241_10.pdf. Zugriff am 01.08.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [21] Guest, G.; Bunce, A.; Johnson, L.: How Many Interviews Are Enough? Field Methods 18 (2006) 1, pp. 59–82 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-94
- [1] Bang, K.; Markeset, T.: Impact of globalization on model of competition and companies’ competitive situation. In: Frick, J.; Laugen, B. T. (eds.): Advances in Production Management Systems. Value Networks: Innovation, Technologies, and Management. APMS 2011. IFIP AICT 384. Heidelberg. Springer-Verlag 2012, pp. 276–286 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [2] Pallant, J.; Sands, S; Karpen, I.: Product customization: a profile of consumer demand. Journal of Retailing and Consumer Services 54 (2020), #102030 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [3] Xiang, J.; Kong, D.; Zhang, F.: Labor cost, robots, and product quality. China Economic Review Volume 90, (2025), #102373 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [4] Sun, Q.; Gao, D.; Chen, X.; Sun, J.; Huang, N.; Gao, F.: A collaborative robotic screw assembly system using 6-PUS parallel mechanism. ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering (2023) pp. 179–186, doi.org/10.1115/DETC2023–114479 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [5] Liau, Y.; Ryu, K.: Task allocation in human-robot collaboration (HRC) based on task characteristics and agent capability for mold assembly. Procedia Manufacturing 51 (2020), pp. 179–186 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [6] Kötter, D.; Wiedon, G.; Meierkord, D. et al.: Development of an augmented reality user interface for collaborative robotics in quality inspection for manufacturing. 5th International Conference on Control and Robotics (ICCR), 2023, pp. 107–112 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [7] Ögren, P.; Sprague, C.: Behavior Trees in Robot Control Systems. Annual Review of Control, Robotics, and Autonomous Systems (2022), pp. 81–107, doi.org/10.48550/arXiv.2203.13083 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [8] Colledanchise, M.; Ögren, P.: Behavior Trees in Robotics and AI: An Introduction. CRC Press, doi.org/10.48550/arXiv.1709.00084 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [9] Liau, Y.; Ryu, K.: Status Recognition Using Pre-Trained YOLOv5 for Sustainable Human-Robot Collaboration (HRC) System in Mold Assembly. Sustainability 21 (2021) 13, #12044 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [10] Fukuda K. et al. Assembly Motion Recognition Framework Using Only Images. IEEE/SICE International Symposium on System Integration (SII) (2020), pp. 1242–1247 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [11] Mujahid, A. et al.: Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model. Applied Sciences 11 (2021) 9, #4164 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [12] Kaczmarek, W. et al.: Industrial Robot Control by Means of Gestures and Voice Commands in Off-Line and On-Line Mode. Sensors 20 (2020) 21, #6358 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [13] Zhao, S. et al.: Assembly state detection based on deep learning and object matching. IEEE 18th International Conference on Automation Science and Engineering (CASE) (2022), pp. 1695–1700 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [14] Neto, P. et al.: Gesture-based human-robot interaction for human assistance in manufacturing. The International Journal on Advanced Manufacturing Technology 101 (2019) 1–4, pp. 119–135 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [15] Intel RealSense: D435i camera. Product description. Date: 2024. Internet: www.intelrealsense.com/depth-camera-d435i/. Accessed: 22.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [16] Polycam Learn: Homepage. Date: 2024. Internet: poly.cam/. Accessed: 22.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [17] Blender: Software description. Date: 2024. Internet: www.blender.org/about/. Accessed: 22.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [18] NVIDIA Isaac Sim. Framework description. Date: 2024. Internet: developer.nvidia.com/isaac/sim . Accessed: 22.07.2025 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102
- [19] Trinh, M. et al.: Safe and Flexible Planning of Collaborative Assembly Processes Using Behavior Trees and Computer Vision. Intelligent Human Systems Integration (IHSI) 69 (2023), pp. 869–879 Open Google Scholar DOI: 10.37544/1436-4980-2025-07-08-102