Publikationen und veröffentlichte Forschungsdatensätze

Auflistung aller Veröffentlichungen im Rahmen des DFG SPP 2422

Veröffentlichungen der Arbeitskreise

  1. [Preprint] M. Schumann, A. Wüst, F. Divo et al., “Structured Representation of Simulation and Annotation Data for Machine Learning in Forming Technologies,” Jan. 13, 2026. Available: https://doi.org/10.21203/rs.3.rs-8561321/v1.

  2. [Preprint] D. Gelbich, P. Niemietz, J. Moske, M. Schumann et al., "Towards hybrid and explainable data-driven models for forming processes: addressing the gap between simulation, process data, and model validation," Jan. 13, 2026. Available: https://doi.org/10.21203/rs.3.rs-8589495/v1

Veröffentlichte Forschungsdatensätze

  1. M. Buchner: "Stanz-Biege-Prozess zur Herstellung von Busbars", Jan. 2026, [Online]. Available: https://github.com/Maximilianbuchner/Stamping-Bending-Dataset-TUM.git.

  1. J. Korenek, M. Riemer, V. Kräusel, D. L. Hahn, D. Langhammer and A. Koschmider: „Experimental study on automation and data-driven modeling in the punching-collar forming process [Dataset]“, Zenodo, Dezember 2026, [Online]. Available: https://doi.org/10.5281/zenodo.17866837.

  2. J. Korenek, M. Riemer, D. L. Hahn, V. Kräusel , F. Fonger, P. T. Michalski and A. Koschmider: „Data4Collar: Numerical Simulation Dataset for the Multi-Stage Shear Cutting and Collar Forming Process [Dataset]”, Zenodo, January 2026, [Online]. Available: https://doi.org/10.5281/zenodo.18400421.

  1. M. Nagel, D. Schmiele, N. Ayaz, L. Hinz, R. Krimm, B.-A. Behrens: "Multi-stage transfer press setup [Dataset]", LUIS, Januar 2026, [Online]. Available: https://doi.org/10.25835/r9yt5ut7.

  1. S. Baum, P. Heinzelmann: „Deep Drawing and Cutting Simulations (DDACS) Dataset”, DaRUS, Aug. 2025, [Online]. Available: https://doi.org/10.18419/DARUS-4801.

  1. Z. Oz, J. Knoche, A. Yazdani, B. Engel, K. Van Laerhoven: "TubeBEND: A Real-World Dataset for Geometry Prediction in Rotary Draw Bending [Data set]", Juli 2025, [Online]. Available: https://doi.org/10.5281/zenodo.16614082.

  1. M. Krüger, B. Vogel-Heuser, J. Höfgen, A. Harms, and M. Merklein, “Managing datasets from sheet metal forming systems using the self-describing file format HDF5,” Production Engineering, vol. 20, no. 2, May 2026, Available: https://gitlab.lrz.de/TUMWAIS/public/spp_2422_teilprojekt-11/-/tree/master/.

  1. C. Glaubitz, E. Ortlieb: "SPP Datensatz TP12", 2026, [Online]. Available: https://data.uni-hannover.de/dataset/153132a6-5ae3-4cd9-8291-80471ac77a72.

  1. Y. Jiang, A. Alimov, S. Härtel, and M. Gardill: "DFG SPP 2422 DatProForge (Subproject 13): Dataset Collection – FEM Simulation Part 1“. Zenodo, October 2025, [Online]. Available: https://doi.org/10.5281/zenodo.17492666.

Wissenschaftliche Publikationen

  1. L. 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. M. 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.

  1. A. Holzapfel, A. Felipe Posada-Moreno, and S. Trimpe, “Concept extraction for time series with ECLAD,” in dataninja.nrw sAIOnARA 2024 Conference : Shaping Trustworthy AI: Opportunities, Innovation & Achievements for Reliable Approaches, pp. 78–80, Available: https://doi.org/10.11576/dataninja-1178.

  2. D. Kazempour, C. Zelenka, A. Kara, A. Lohrer, and P. Kröger, “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.

  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. A. 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.

  6. J. Moske, M. Schumann, and P. Groche, “Vergleich direkter und indirekter Kraftsensorkonzepte in Folgeverbundwerkzeugen,” Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 120, no. 10, pp. 687–694, 2025. Available: https://doi.org/10.1515/zwf-2025-1117.

  7. [PrePrint] M. Schumann, J. Moske, A. Wüst, and P. Groche, “Camera-based feature extraction and uncertainty analysis in deep drawing in progressive dies.” Oct. 30, 2025. Available: https://doi.org/10.21203/rs.3.rs-7970217/v1.

  8. [Preprint] M. Kraus, D. Steinmann, A. Wüst, A. Kokozinski, and K. Kersting, “Right on Time: Revising Time Series Models by Constraining Their Explanations,” in Machine Learning and Knowledge Discovery in Databases. Research Track, Ed., Cham: Springer Nature Switzerland, 2026, pp. 490–507. Available: https://doi.org/10.1007/978-3-032-06109-6_28.

  9. A. Holzapfel, A., Posada Moreno, A. F., & Trimpe, S. (2025, July). Concept Extraction for Time Series with ECLAD-ts. In World Conference on Explainable Artificial Intelligence, pp. 90-112. Cham: Springer Nature Switzerland, Available: https://doi.org/10.1007/978-3-032-08317-3_5.

  10. [Preprint] M. Schumann, J. Moske, A. Wüst, K. Kersting, and P. Groche, “Requirements for numeric models as sources of synthetic data for predicting real-world data sets.” Nov. 21, 2025. Available: https://doi.org/10.21203/rs.3.rs-8164519/v1.

  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.

  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.

  3. [Preprint] D. L. Hahn, J. Korenek, M. Riemer, V. Kräusel, M. Dix and A. Koschmider, "Automated non-destructive characterization for the cutting surfaces of punched holes using a laser profiler", Jan. 21, 2026. Available: https://doi.org/10.21203/rs.3.rs-8609328/v1.

  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.

  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.

  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.

  3. M. Hohmann, A. Yiming, L. Penter, S. Ihlenfeldt, and O. Niggemann, “A Data-Driven Approach for Automating the Design Process of Deep Drawing Tools,” Journal of Physics: Conference Series, vol. 3104, no. 1, p. 012061, Sep. 2025. Available: https://doi.org/10.1088/1742-6596/3104/1/012061.

  4. C. Coelho, M. Hohmann, D. Fernández, L. Penter, S. Ihlenfeldt, and O. Niggemann, "Data-Driven Temperature Modelling of Machine Tools by Neural Networks: A Benchmark", Sep. 2025, Available: https://doi.org/10.48550/arXiv.2510.03261.

  5. C. Coelho, D. Fernández, M. Hohmann, L. Penter, S. Ihlenfeldt, and O. Niggemann, "An Open Dataset for Temperature Modelling in Machine Tools", Sep. 2025, Available: https://doi.org/10.48550/arXiv.2509.16222.

  1. S. Baum, P. Heinzelmann, M. Liewald, M. Weyrich: "Addressing Discrepancies between Synthetic and Real-World Data of Deep-Drawing Manufacturing Systems in AI Applications," The 18th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2024. Available: https://doi.org/10.1016/j.procir.2026.01.125.

  2. M. Zhang, F. Ji, A. Vicaria, P. Heinzelmann et al.: "DSL4DPiFS – a graphical notation to model data pipeline deployment in forming systems," at – Automatisierungstechnik, Sonderheft: SPP 2422. Available: https://doi.org/10.1515/auto-2024-0114.

  3. 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.

  4. P. 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.

  5. P. Heinzelmann, S. Baum, K. Riedmüller, M. Liewald, M. Weyrich: "A Data-Driven Surrogate Model for Predicting Springback in Deep Drawing Processes, 13th International Conference and Workshop on Numerical Simulation of 3D Sheet Metal Forming Processes," NUMISHEET 2025. Available: https://doi.org/10.1088/1742-6596/3104/1/012060.

  6. D. Gelbich, M. Schumann, J. Moske, P. Niemietz, P. Heinzelmann, et al.: "Towards hybrid and explainable data-driven models for forming processes: addressing the gap between simulation, process data, and model validation," Journal of Intelligent Manufacturing. Available: https://doi.org/10.21203/rs.3.rs-8589495/.

  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.

  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.

  7. M. Krüger, B. Vogel-Heuser, J. Höfgen, A. Harms, and M. Merklein, “Managing datasets from sheet metal forming systems using the self-describing file format HDF5,” Production Engineering, vol. 20, no. 2, May 2026, Available: https://doi.org/10.1007/s11740-025-01413-3.

  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.

  6. C. Glaubitz, M. Rothgänger, R. Reder, J. Peddinghaus, and K. Brunotte, “Multisensorisches Monitoring in der Warmmassivumformung/Multisensor process monitoring in hot bulk forming - Sound and vibration in a big-data context,” wt Werkstattstechnik online, vol. 115, no. 10, pp. 750–758, 2025, Available: https://doi.org/10.37544/1436-4980-2025-10-54.

  7. H. Monke, B. Fresz, M. Bernreuther, Y. Chen, and M. F. Huber, “Efficiently Transforming Neural Networks into Decision Trees: A Path to Ground Truth Explanations with RENTT,” arXiv, Nov. 2025, [Online]. Available: https://doi.org/10.48550/arXiv.2511.09299.

  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.

  5. Y. Jiang et al., “A 120 GHz Industrial Radar Sensor Network for Condition Monitoring of a Forging Process,” IEEE Sensors Journal, 2025, Available: https://doi.org/10.1109/JSEN.2025.3636060.

  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.

  1. M. Liewald: "Datengetriebene Prozessmodellierung in der Umformtechnik", Kick-Off SPP 2422, Stuttgart, Juni 2023.

  2. M. Liewald: "Datengetriebene Prozessmodellierung in der Umformtechnik", 24. Umformtechnisches Kolloquium Hannover, Hannover, März 2024.

  3. M. Liewald: "Datengetriebene Prozessmodellierung in der Umformtechnik", Industriekolloquium SPP 2422, Stuttgart, März 2025.

  4. M. Liewald: "Keynote: Research Perspectives on Data-Driven Modelling in Metal Forming Processes", Forming Technology Forum 2025, Enschede, September 2025.

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