Publikationen

Auflistung aller Veröffentlichungen im Rahmen des DFG SPP 2422

Publikationen nach Teilprojekten

Teilprojekt 1: Datengetriebene Prozessmodellierung in der Stanz-Biege-Technologie

  1. Koller, T. Ladner, and M. Althoff, “Set-Based Training for Neural Network Verification,” Aug. 2025, [Online]. Available: https://doi.org/10.48550/arXiv.2401.14961.

  2. Wendl, L. Koller, T. Ladner, and M. Althoff, “Training Verifiably Robust Agents Using Set-Based Reinforcement Learning.” [Online]. Available: https://doi.org/10.48550/arXiv.2408.09112.

  3. L. Koller, C. Hartmann, L. Martinitz, W. Volk, and M. Althoff, “Training robust neural networks for uncertainty prediction in stamping technology,” At-Automatisierungstechnik, vol. 73, no. 3, pp. 198–209, Mar. 2025, Available: https://doi.org/10.1515/auto-2024-0132.

  4. L. Koller, T. Ladner, and M. Althoff, “Out of the Shadows: Exploring a Latent Space for Neural Network Verification,” May 2025, [Online]. Available: https://doi.org/10.48550/arXiv.2505.17854.

  5. L. Martinitz, L. Koller, W. Volk, M. Althoff, and C. Hartmann, “An image-based, unified feature extraction framework for experimental and synthetic data for data-driven modelling in stamping and bending,” Journal of Physics: Conference Series, vol. 3104, no. 1, p. 012050, Sep. 2025. Available: https://doi.org/10.1088/1742-6596/3104/1/012050.
 
Teilprojekt 2: Ableitung von Wirkzusammenhängen zur Wirkflächenauslegung auf Basis einer datengetriebenen Prozessmodellierung für das Feinschneiden

  1. A. Holzapfel, A. Felipe Posada-Moreno, and S. Trimpe, “Concept extraction for time series with ECLAD,” pp. 78–80, Available: https://doi.org/10.11576/dataninja-1178.

  2. Kazempour, C. Zelenka, A. Kara, A. Lohrer, and P. Kroger, “Do good scores imply good embeddings? On the necessity of inspecting manifold learning evaluation results by multiple criteria,” in IEEE International Conference on Data Mining Workshops, ICDMW, IEEE Computer Society, 2024, pp. 317–324. Available: https://doi.org/10.1109/ICDMW65004.2024.00047.

  3. F. Schweinshaupt, T. Stoel, M. Müller, T. Herrig, and T. Bergs, “Thermomechanical modeling of the shearing process during fine blanking of quenched and tempered steel,” in Procedia Structural Integrity, Elsevier B.V., 2024, pp. 214–223, Available: https://doi.org/10.1016/j.prostr.2024.06.028.
     
  4. A. Holzapfel, A. F. Posada-Moreno, and S. Trimpe, “Concept Extraction for Time Series with ECLAD-ts,” Apr. 2025, Available: http://arxiv.org/abs/2504.05024.
Teilprojekt 3: Optimierung des Wirkflächendesigns schnelllaufender Folgeverbundwerkzeuge unter Nutzung maschineller Lernalgorithmen
  1. W. Stammer, A. Wüst, D. Steinmann, and K. Kersting, “Neural Concept Binder,” Jun. 2024, Available:
    https://doi.org/10.48550/arXiv.2406.09949.
     

  2. Wüst et al., “Pix2Code: Learning to Compose Neural Visual Concepts as Programs.” Available: https://dl.acm.org/doi/10.5555/3702676.3702855.

  3. J. Moske, M. Schumann, A. Wüst, K. Kersting, and P. Groche, “Simulation Driven Modeling of Strip Misalignment: Enhancing Process Insight and Failure Prediction in Sheet Metal Forming,” Journal of Physics: Conference Series, vol. 3104, no. 1, p. 012058, Sep. 2025, Available: https://doi.org/10.1088/1742-6596/3104/1/012058.

  4. D. Steinmann, W. Stammer, A. Wüst, and K. Kersting, “Object Centric Concept Bottlenecks,” Advances in Neural Information Processing Systems, 38 arXiv, Oct. 2025, [Online]. Available: https://doi.org/10.48550/arXiv.2505.24492.

  5. A. Wüst et al., “Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad?,” In Forty-second International Conference on Machine Learning, Jul. 2025, [Online]. Available: https://doi.org/10.48550/arXiv.2410.19546.
Teilprojekt 4: Datengetriebene Modellierung von mehrstufigen Stanzbiegeprozessen

  1. A. Mazur, I. Roberts, D. Leins, A. Schulz, and B. Hammer, Visualizing and Improving 3D Mesh Segmentation with DeepView. [Online]. Available: https://doi.org/10.14428/esann/2024.ES2024-135.

  2. H. Peters, A. Mazur, A. K. Pandey, A. Trächtler, B. Hammer, and W. Homberg, “Development of a digital twin for data-driven modeling of punch-bending processes using a graphical modeling notation,” At-Automatisierungstechnik, vol. 73, no. 3, pp. 173–184, Mar. 2025, Available: https://doi.org/10.1515/auto-2024-0112.

  3. H. Peters et al., “Novel approach for data-driven modelling of multi-stage straightening and bending processes,” in Materials Research Proceedings, Association of American Publishers, 2024, pp. 2289–2298. Available: https://doi.org/10.21741/9781644903131-252.

  4. H. Peters, A. Mazur, A. Trächtler, and B. Hammer, “Integration of a digital twin for data-driven modeling of punch-bending processes using the asset administration shell,” in Materials Research Proceedings, Association of American Publishers, 2025, pp. 1538–1547, Available: https://doi.org/10.21741/9781644903599-166.

  5. A. Mazur, H. Peters, A. Artelt, L. Koller, C. Hartmann, A. Trächtler and B. Hammer, “Studying the Generalization Behavior of Surrogate Models for Punch-Bending by Generating Plausible Counterfactuals,” Lecture notes in computer science, pp. 192–203, Sep. 2025, Available: https://doi.org/10.1007/978-3-032-04555-3_16.
Teilprojekt 5: Prozessdatengetriebene Modellierung zur Robustifizierung von Scherschneid-Kragenzieh-Prozessen mittels effektiver Werkzeugwirkflächengestaltung unter Berücksichtigung der Kantenrisssensitivität

  1. M. Riemer, K. Silbermann, V. Kräusel, D. Langhammer, and A. Koschmider, “Shear cutting: Model-based prediction of material parameters based on synthetic process force signals,” in Materials Research Proceedings, Association of American Publishers, 2024, pp. 1334–1342. Available: https://doi.org/10.21741/9781644903131-148.

  2. J. Korenek, M. Riemer, V. Kräusel, D. Langhammer, and A. Koschmider, “Experimental study on automation and data-driven modeling in the punching-collar forming process,” At-Automatisierungstechnik, vol. 73, no. 3, pp. 185–197, Mar. 2025: Available: https://doi.org/10.1515/auto-2024-0104.
Teilprojekt 6: Entwicklung eines datengetriebenen Modells zur Bewertung und Verbesserung der Prozessrobustheit bei der Wirkflächenauslegung von Tiefziehwerkzeugen
  1. C. Heinzel, S. Thiery, and N. ben Khalifa, “Study on the effects of tool design and process parameters on the robustness of deep drawing,” in Materials Research Proceedings, Association of American Publishers, 2024, pp. 1488–1497. Available: https://doi.org/10.21741/9781644903131-165.

  2. C. Heinzel, L. Wollschlaeger, B.-M. Nurmatov, J. Heger, and N. Ben Khalifa, “Optimizing dataset design for data-driven models of the deep drawing process using active transfer learning,” Journal of Physics: Conference Series, vol. 3104, no. 1, p. 012064, Sep. 2025. Available: https://doi.org/10.1088/1742-6596/3104/1/012064.

  3. L. Wollschlaeger, C. Heinzel, S. Thiery, M. Z. el Abdine, N. ben Khalifa, and J. Heger, “Increased Reliability of Draw-In Prediction in a Single Stage Deep-Drawing Operation via Transfer Learning,” in Procedia CIRP, Elsevier B.V., 2024, pp. 270–275. Available: https://doi.org/10.1016/j.procir.2024.10.086.
Teilprojekt 7: Einrichtassistenzsystem für Transferpressen auf KI-Basis

  1. M. Nagel et al., “Robust inline reconstruction of geometric features in sheet metal forming based on laser profile sensors,” At-Automatisierungstechnik, vol. 73, no. 3, pp. 210–219, Mar. 2025, Available: https://doi.org/10.1515/auto-2024-0133.

  2. R. Krimm, L. Hinz, M. Nagel, and J. Scheu, “Setup assistent for transfer presses,” WT Werkstattstechnik, vol. 113, no. 10. VDI Fachmedien GmBH & Co. KG, pp. 401–406, 2023. Available: https://doi.org/10.37544/1436-4980-2023-10-23.

  3. R. Krimm, S. Hübner, D. Schmiele, L. Hinz, M. Nagel, and J.-U. Gellermann, “Method planning for deep drawing in the production of workpieces with variable shapes,” MATEC Web of Conferences, vol. 408, p. 01005, 2025. Available: https://doi.org/10.1051/matecconf/202540801005.
Teilprojekt 8: Datenbasierte Werkzeugeinarbeitung in der Blechumformung

  1. M. Hohmann, A. Yiming, L. Penter, S. Ihlenfeldt, and O. Niggemann, “An AI approach for predicting the active surface of deep drawing tools in try-out,” at - Automatisierungstechnik, vol. 73, no. 4, pp. 251–260, Apr. 2025, Available: https://doi.org/10.1515/auto-2024-0130.

  2. M. Hohmann, S. Eilermann, W. Großmann, and O. Niggemann, “Design Automation: A Conditional VAE Approach to 3D Object Generation Under Conditions,” in IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Institute of Electrical and Electronics Engineers Inc., 2024. Available: https://doi.org/10.1109/ETFA61755.2024.10710828.

Teilprojekt 9: Robuste Wirkflächenauslegung für mehrstufige Blechumformprozesse auf Basis einer daten- und berechnungsbasierten Ersatzmodellierung der Bauteilrückfederung

  1. S. Baum, P. Heinzelmann, M. Liewald, and M. Weyrich, “Enhancing FEA sheet metal forming simulations through 3D geometric data-driven surrogate modeling,” at - Automatisierungstechnik, vol. 73, no. 4, pp. 261–270, Apr. 2025, Available: https://doi.org/10.1515/auto-2024-0116.

  2. Heinzelmann, S. Baum, K. R. Riedmüller, M. Liewald, and M. Weyrich, “A Comprehensive Benchmark Dataset for Sheet Metal Forming: Advancing Machine Learning and Surrogate Modelling in Pro-cess Simulations,” MATEC Web of Conferences, vol. 408, p. 01090, 2025, Available: https://doi.org/10.1051/matecconf/202540801090.
Teilprojekt 10: Methode zur Auslegung von Formwerkzeugen zum Rotationszugbiegen von Bogen-in-Bogen-Geometrien

  1. A. Yazdani, J. Knoche, B. Engel, and K. van Laerhoven, “Tube geometry prediction in rotary draw bending process using Random Forest regression,” at - Automatisierungstechnik, vol. 73, no. 4, pp. 223–231, Apr. 2025, Available: https://doi.org/10.1515/auto-2024-0131. 

  2. Z. Oz, J. Knoche, A. Yazdani, B. Engel, and K. van Laerhoven, “TubeBEND: A Real-World Dataset for Geometry Prediction in Rotary Draw Bending.” [Online] Zenodo, Juli 30, 2025, Available: https://doi.org/10.5281/zenodo.16614082.
Teilprojekt 11: Datengestützte Bestimmung und Prognose des Wirkflächenzustandes und –eingriffs bei Fließprozessen vom Band

  1. B. Vogel-Heuser et al., “DSL4DPiFS – a graphical notation to model data pipeline deployment in forming systems,” at - Automatisierungstechnik, vol. 73, no. 4, pp. 232–250, Apr. 2025, Available: https://doi.org/10.1515/auto-2024-0114.

  2. K. Lou, “Numerical and experimental investigation on full backward extrusion process in forming of pins from DC04 coil,” 2025, pp. 159–167. Available: https://doi.org/10.21741/9781644903551-20.

  3. B. Vogel-Heuser, A. Fay, and M. Liewald, “Data-driven process modeling in metal forming,” at - Automatisierungstechnik, vol. 73, no. 4, pp. 221–222, Apr. 2025, Available: https://doi.org/10.1515/auto-2025-2001.

  4. A. Vicaria, B. Vogel-Heuser, M. Krüger, M. Merklein, and M. Lechner, “A Comparative Investigation Introducing Regularization Techniques in Linear Regression Models for Quality Prediction in Forming Technology,” in IEEE International Conference on Industrial Engineering and Engineering Management, IEEE Computer Society, 2024, pp. 1230–1235. Available: https://doi.org/10.1109/IEEM62345.2024.10857210.

  5. B. Vogel-Heuser et al., “A lightweight sensor ontology for supporting sensor selection, deployment, and data processing in forming processes,” Production Engineering, Dec. 2024, Available: https://doi.org/10.1007/s11740-024-01290-2.

  6. M. Vogel, K. Luo, B. Vogel-Heuser, and M. Merklein, “Analysis of friction properties of DC04 using pin extrusion test with modified parameters,” in Materials Research Proceedings, Association of American Publishers, 2025, pp. 1096–1105, Available: https://doi.org/10.21741/9781644903599-119.
Teilprojekt 12: Transparente KI-gestützte Prozessmodellierung im Gelenkschmieden

  1. C. Glaubitz, M. Rothgänger, H. Monke, E. Ortlieb, J. Peddinghaus, and K. Brunotte, “Grundlagen und Potenziale für Data-Mining-Anwendungen in der In-line-Messung beim Gesenkschmieden,” at - Automatisierungstechnik, vol. 73, no. 4, pp. 271–280, Apr. 2025, Available: https://doi.org/10.1515/auto-2024-0138.

  2. C. Glaubitz, J. Raible, H. Monke, M. Rothgänger, J. Peddinghaus and K. Brunotte, "KI-gestützte Prozessoptimierung in der Massivumformung" Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 120, no. s1, 2025, pp. 257-262, Available: https://doi.org/10.1515/zwf-2024-0124.

  3. E. Ortlieb et al., “Data acquisition in industrial forging conditions,” WT Werkstattstechnik, vol. 113, no. 10, pp. 419–424, 2023, Available: https://doi.org/10.37544/1436-4980-2023-10-41.

  4. H. Monke, B. Sae-Chew, B. Fresz, and M. F. Huber, “From Confusion to Clarity: ProtoScore - A Framework for Evaluating Prototype-Based XAI,” Association for Computing Machinery (ACM), Jun. 2025, pp. 2215–2231, Available: https://doi.org/10.1145/3715275.3732151.
     
  5. C. Glaubitz, M. Rothgänger, E. Ortlieb, J. Peddinghaus, and K. Brunotte, “Laser triangulation for quality monitoring in automated series forging processes: A method for evaluating the component quality feature ‘flash,’” in Materials Research Proceedings, Association of American Publishers, 2025, pp. 917–926, Available: https://doi.org/10.21741/9781644903599-98.
Teilprojekt 13: DatProForge-Datengetriebene Prozessmodellierung von Gesenkschmiedeprozessen zur Erhöhung der Produktivität mittels adaptiver Werkzeugkonstruktionsmethodik

  1. B. Vogel-Heuser et al., “DSL4DPiFS – a graphical notation to model data pipeline deployment in forming systems,” at - Automatisierungstechnik, vol. 73, no. 4, pp. 232–250, Apr. 2025, Available: https://doi.org/10.1515/auto-2024-0114. 

  2. Y. Jiang, A. Alimov, M. Knaack, S. Härtel, and M. Gardill, “Data-driven approaches for predicting underfill in hot bulk forging processes,” in Materials Research Proceedings, Association of American Publishers, 2025, pp. 1962–1971. Available: https://doi.org/10.21741/9781644903599-211.
     
  3. A. Alimov, Y. Jiang, M. Gardill, and S. Härtel, “Integrating stochastic effects and uncertainties into inverse analysis of hot bulk forging processes through automated API-driven finite element simulations and machine learning,” in Materials Research Proceedings, Association of American Publishers, 2025, pp. 1528–1537, Available: https://doi.org/10.21741/9781644903599-165.
     
  4. A. Alimov, S. Härtel, M. Gardill, M. Knaack, and J. Buhl, “Acquisition of ram tilting and frame stretching with radar sensors during hot forging,” WT Werkstattstechnik, vol. 113, no. 10. VDI Fachmedien GmBH & Co. KG, pp. 425–431, 2023, Available: https://doi.org/10.37544/1436-4980-2023-10-47.
Weitere Veröffentlichungen
  1. M. Liewald, B. Vogel-Heuser, T. Bergs, M. Huber, and P. Kröger, “Advancing data-driven process modeling in metal forming,” At-Automatisierungstechnik, vol. 73, no. 3, pp. 162–172, Mar. 2025, Available: https://doi.org/10.1515/auto-2024-0118.
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