@inproceedings{MathiasAkmalAsifetal.2025, author = {Mathias, Selvine George and Akmal, Muhammad Uzair and Asif, Saara and Knollmeyer, Simon and Koval, Leonid and Grossmann, Daniel}, title = {Handling Anonymized Non-numerical Features in Data Using Transformations for Regression Models}, booktitle = {Artificial Intelligence Applications and Innovations: 21st IFIP WG 12.5 International Conference, AIAI 2025, Limassol, Cyprus, June 26-29, 2025, Proceedings, Part III}, editor = {Maglogiannis, Ilias and Iliadis, Lazaros and Andreou, Andreas and Papaleonidas, Antonios}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-96231-8}, doi = {https://doi.org/10.1007/978-3-031-96231-8_8}, pages = {99 -- 111}, year = {2025}, language = {en} } @article{VogtMuellerKampaetal.2021, author = {Vogt, Anna and Mueller, Ralph and Kampa, Thomas and Stark, Rainer and Großmann, Daniel}, title = {Concept and architecture for information exchange between digital twins of the product (CPS) and the production system (CPPS)}, journal = {Procedia CIRP}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2212-8271}, doi = {https://doi.org/10.1016/j.procir.2021.11.217}, pages = {1292 -- 1297}, year = {2021}, abstract = {The Digital Twin concept and CPS- and IIoT-based approaches are increasingly important topics concerning future Industry 4.0 architectures. They offer high potential for dynamical aspects in intelligent production planning and control as well as part traceability and documentation. Standardized information exchange is an upcoming requirement among the whole supply chain. This paper presents a concept for a Digital Twin architecture based on motor production in the automotive industry. The key aspect is an information exchange structure for Digital Twins of products and production systems that are combined using principles of Dynamic Aggregation.}, language = {en} } @article{MayrKnollmeyerJeknicetal.2026, author = {Mayr, Thomas and Knollmeyer, Simon and Jeknic, Isidora and Fink, Leon and Hensel, Ralph and Huber, Marco F. and Großmann, Daniel}, title = {Enhancing Process Planning in the Automotive Industry: Extracting Procedural Knowledge from Assembly Operation Descriptions using Large Language Models}, volume = {2026}, journal = {Procedia CIRP}, number = {138}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2212-8271}, doi = {https://doi.org/10.1016/j.procir.2026.01.137}, pages = {797 -- 802}, year = {2026}, abstract = {The non-standardized, human-written assembly descriptions have been a long-standing obstacle in the automotive assembly industry, making it difficult to use these data in the planning stage. The paper presents a standardization method based on Large Language Models (LLMs), focusing on consistency and clarity. This approach does not only streamline the planning processes but also facilitates the benchmarking of productivity and process quality across different individual parts and assembly lines, thus enabling comprehensive efficiency analysis and ultimately leading to more informed decision-making. Our method was evaluated at a German OEM in the automotive sector, demonstrating its practical applicability and effectiveness.}, language = {en} } @inproceedings{AkmalMathiasAsifetal.2025, author = {Akmal, Muhammad Uzair and Mathias, Selvine George and Asif, Saara and Koval, Leonid and Knollmeyer, Simon and Großmann, Daniel}, title = {Humanizing AI: A Human-Centered Architecture to Developing Trustworthy Intelligent Systems}, booktitle = {CS \& IT Conference Proceedings}, publisher = {AIRCC}, address = {[s. l.]}, isbn = {978-1-923107-57-1}, url = {https://www.csitcp.com/abstract/15/158csit03}, pages = {27 -- 44}, year = {2025}, language = {en} }