[1]
M. Paech, Advanced semi-automatic straightening technology, Wire Journal International (2008) 74–79.
Google Scholar
[2]
K. Richter, F. Reuther, R. Müller, D. Landgrebe, Investigating the Influence of Bending Parameters on the Springback Behavior of Ultra-High Strength Spring Strips, MSF (2018) 125–133.
DOI: 10.4028/www.scientific.net/MSF.918.125
Google Scholar
[3]
M. Grüber, Konzepte zur Steuerung des Richtwalzprozesses bei variierenden Richtguteigenschaften, PhD thesis, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, 2019. ISBN: 978-3-95886-307-1.
DOI: 10.15420/ecr.2007.0.1.51
Google Scholar
[4]
A. Amor, M. Rachik, H. Sfar, Combination of Finite-Element and Semi-Analytical Models for Sheet Metal Leveling Simulation, KEM (2011) 182–189.
DOI: 10.4028/www.scientific.net/KEM.473.182
Google Scholar
[5]
M. Gräler, R. Springer, C. Henke, A. Trächtler, W. Homberg, Assisted setup of forming processes: compensation of initial stochastic disturbances, Procedia Manufacturing 25 (2018) 358–364.
DOI: 10.1016/j.promfg.2018.06.104
Google Scholar
[6]
L. Steinweder, A.J. Kainz, K. Krimpelstaetter, K. Zeman, Numerical Simulation of Tension Losses and Reaction Forces in Tension Levellers, Steel Research Intl., Special Edition: 10th ICTP (2011) 343–348.
Google Scholar
[7]
M. Liewald, T. Bergs, P. Groche, B.-A. Behrens, D. Briesenick, M. Müller, P. Niemietz, C. Kubik, F. Müller, Perspectives on data-driven models and its potentials in metal forming and blanking technologies, Production Engineering Research and Development 16 (2022) 607–625.
DOI: 10.1007/s11740-022-01115-0
Google Scholar
[8]
H. Peters, A. Mazur, A. Trächtler, B. Hammer, Integration of a digital twin for data-driven modeling of punch-bending processes using the asset administration shell, in: P. Carlone, L. Filice, D. Umbrello (Eds.), Material Forming: ESAFORM 2025, Materials Research Forum, 2025, p.1538–1547.
DOI: 10.21741/9781644903599-166
Google Scholar
[9]
M. Jacoby, T. Usländer, Digital Twin and Internet of Things - Current Standards Landscape, Applied Sciences 10 (2020) 6519.
DOI: 10.3390/app10186519
Google Scholar
[10]
B. Boss, S. Malakuti, S.-W. Lin, T. Usländer, E. Clauer, M. Hoffmeister, L. Stojanovic, Digital Twin and Asset Administration Shell Conepts and Application in the Industrial Internet and Industrie 4.0: An Industrial Internet Consortium and Plattform Industrie 4.0 Whitepaper (2020). https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/Digital-Twin-and-Asset-Administration-Shell-Concepts.pdf.
DOI: 10.1515/auto-2021-0074
Google Scholar
[11]
T. Miny, M. Thies, L. Lukic, S. Käbisch, K. Oladipupo, C. Diedrich, T. Kleinert, Overview and Comparison of Asset Information Model Standards, IEEE Access 11 (2023) 99189–99221.
DOI: 10.1109/ACCESS.2023.3312286
Google Scholar
[12]
S. Kamm, S.S. Veekati, T. Müller, N. Jazdi, M. Weyrich, A survey on machine learning based analysis of heterogeneous data in industrial automation, Computers in Industry 149 (2023).
DOI: 10.1016/j.compind.2023.103930
Google Scholar
[13]
The Structure of the Administration Shell: Trilateral Perspectives from France, Italy and Germany (2018). https://www.plattform-i40.de/IP/Redaktion/EN/Downloads/Publikation/hm-2018-trilaterale-coop.html.
Google Scholar
[14]
M. Volkmann, A. Wagner, J. Hermann, M. Ruskowski, Asset Administration Shells and GAIA-X Enabled Shared Production Scenario, in: F.J.G. Silva, L.P. Ferreira, J.C. Sá, M.T. Pereira, C.M.A. Pinto (Eds.), Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems: Volume 2, Springer Nature Switzerland, Cham, 2024, p.187–199.
DOI: 10.1007/978-3-031-38165-2_23
Google Scholar
[15]
A. Löcklin, H. Vietz, D. White, T. Ruppert, N. Jazdi, M. Weyrich, Data administration shell for data-science-driven development, Procedia CIRP 100 (2021) 115–120.
DOI: 10.1016/j.procir.2021.05.019
Google Scholar
[16]
M. Pourjafarian, C. Plociennik, M.H. Rimaz, P. Stein, M. Vogelgesang, C. Li, S. Knetsch, S. Bergweiler, M. Ruskowski, A Multi-Stakeholder Digital Product Passport Based on the Asset Administration Shell, in: 2023 IEEE 28th International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2023.
DOI: 10.1109/ETFA54631.2023.10275715
Google Scholar
[17]
M.A. Inigo, A. Porto, B. Kremer, A. Perez, F. Larrinaga, J. Cuenca, Towards an Asset Administration Shell scenario: a use case for interoperability and standardization in Industry 4.0, in: P. Varga, D. Zuckerman (Eds.), NOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium, IEEE, 2020.
DOI: 10.1109/NOMS47738.2020.9110410
Google Scholar
[18]
X. Ye, J. Jiang, C. Lee, N. Kim, M. Yu, S.H. Hong, Toward the Plug-and-Produce Capability for Industry 4.0: An Asset Administration Shell Approach, IEEE Industrial Electronics Magazine 14 (2020) 146–157.
DOI: 10.1109/MIE.2020.3010492
Google Scholar
[19]
A. Wagner, M. Volkmann, J. Hermann, M. Ruskowski, Machining of Individualized Milled Parts in a Skill-Based Production Environment, in: F.J.G. Silva, A.B. Pereira, R.D.S.G. Campilho (Eds.), Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems: Volume 1, Springer Nature Switzerland, Cham, 2024, p.283–292.
DOI: 10.1007/978-3-031-38241-3_32
Google Scholar
[20]
Z. Muller-Zhang, T. Kuhn, A Digital Twin-based Approach Performing Integrated Process Planning and Scheduling for Service-based Production, in: F. Gao (Ed.), 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2022.
DOI: 10.1109/ETFA52439.2022.9921643
Google Scholar
[21]
A. Alexopoulos, G. Kalogeras, K. Koutras, A. Kalogeras, Why Asset Administration Shells: A Survey on Uses and Challenges, IEEE Access 13 (2025) 126582–126609.
DOI: 10.1109/ACCESS.2025.3589931
Google Scholar
[22]
S. Wurm, V. Lohrmann, M. Wieczorek, P. Blanke, C. Fimmers, O. Petrovic, W. Herfs, Service-based tool lifecycle analysis based on AAS, Procedia CIRP 130 (2024) 1562–1568.
DOI: 10.1016/j.procir.2024.10.283
Google Scholar
[23]
F. Kaya, E. Şanlı, Ö. Albayrak, P. Ünal, P. Kirci, Asset Administration Shell Tool Comparison: A Case Study with Real Digital Twins Used in Petrochemical Industry, Sensors (Basel) 25 (2025).
DOI: 10.3390/s25071978
Google Scholar
[24]
A. Mazur, H. Peters, A. Artelt, L. Koller, C. Hartmann, A. Trächtler, B. Hammer, Studying the Generalization Behavior of Surrogate Models for Punch-Bending by Generating Plausible Counterfactuals, in: W. Senn, M. Sanguineti, A. Saudargiene, I.V. Tetko, A.E.P. Villa, V. Jirsa, Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025, Springer Nature Switzerland, Cham, 2025, p.192–203.
DOI: 10.1007/978-3-032-04555-3_16
Google Scholar
[25]
H. Wang, abqpy 2025: Type hints for Abaqus/Python scripting, 2025. https://github.com/haiiliin/abqpy (accessed 16 January 2026).
Google Scholar
[26]
Industrial Digital Twin Association, IDTA 02008-1-1 Time Series Data (2023). https://github.com/admin-shell-io/submodel-templates/blob/main/published/Time%20Series%20Data/1/1/IDTA%2002008-1-1_Submodel_TimeSeriesData.pdf.
DOI: 10.62628/idta.02006-3-0
Google Scholar