Team and publications

  1. Kempten University of Applied Sciences
  2. Research
  3. Research institutes
  4. Institute for Data-optimised Manufacturing (IDF)
  5. Team and publications

Our Team - Innovation driven by collaboration

Gruppenbild auf einer Dachterasse

Behind every successful research project lies a strong team. Our research centre pools more than 20 dedicated experts from the fields of machining technology, manufacturing engineering and data science. Together, we work on cutting-edge solutions to support companies in our region with data-driven approaches and to boost their competitiveness.

Our close collaboration with regional and international companies enables us to develop innovative methods, optimise processes and create sustainable added value using state-of-the-art technology. Our interdisciplinary expertise and direct interaction with industry generate practical solutions that help companies work more efficiently, precisely and economically.

We are more than a research team – we are your partner for digital transformation in manufacturing.

Publications

2026

 

  • R. Seliger, U.Göhner: "AI-Driven Defect Detection and Adaptive Control for Winding Process Optimization", Submitted to Systems in Industrial Robotics, Automation and IoT - EUROCAST 2026: 20th International Conference.
  • D. Ulrich, R. Seliger, F. Schirmeier: "End-to-End Bill of Process Generation Using Multi-Modal Transformers", Submitted to Systems in Industrial Robotics, Automation and IoT - EUROCAST 2026: 20th International Conference.
  • C. Buhl, P. Schulenburg, U. Göhner: "Powder Bed Defect Detection in L-PBF Using YOLO RT-DETR", Submitted to "Green AI" and SW-Tools for Sustainable Energy and Materials Consumption - EUROCAST 2026: 20th International Conference.
  • T. Jäkel, F. Schirmeier: "Automated Tool Wear Detection for Industry 4.0: Image Segmentation of Photometric Stereo Data", Submitted to Computer Aided Systems Theory - EUROCAST 2026: 20th International Conference.
  • M. Götz, M. Grieshammer, P. Kellner, F. Schirmeier: "Self-Supervised CAD-Face Localization in Technical Drawings", Submitted to DEXA 2026, International Workshop on Optimisation of Industrial Production with AI Algorithms.

 

2025

  • M. Grieshammer, P. Kellner, M. Götz, A. Schwarz, D. Ulrich, F. Schirmeier: "KI als Brücke zwischen technischer Zeichnung und CAD-Modell“, in maschinenbau / Ausgabe 4/2025, Springer Fachmedien Wiesbaden. Zum Artikel
  • S. Unsin, B. Müller, F. Schirmeier, T. Jäkel: "Towards Real-time Tool Wear Detection on Edge Devices: A Lightweight Dimensionality Reduction Approach for Spindle Integrated Cutting Force Sensor Data", DEXA 2025, International Workshop on Optimisation of Industrial Production with AI Algorithm. https://doi.org/10.1007/978-3-032-02003-1_10
  • R. Seliger, M. Micheler, S. Gül-Ficici, U. Göhner: "A Vision-Guided Approach to Pick-and-Place Robotics: From Assembly Drawings to Industrial Assembly Automation", DEXA 2025, International Workshop on Optimisation of Industrial Production with AI Algorithm. https://doi.org/10.1007/978-3-032-02003-1_9
  • F. Lischka, A. Schwarz, D. Wiesner, C. Wald, F. Schirmeier, U. Göhner: "Prediction of CNC Manufacturing Time Under Real-World Conditions Using Graph Convolutional Networks", DEXA 2025, International Workshop on Optimisation of Industrial Production with AI Algorithm. https://doi.org/10.1007/978-3-032-02003-1_8
  • C. Buhl, F. Waheed, U. Göhner: "Deep learning-based defect detection in laser powder bed fusion", DEXA 2025, International Workshop on Optimisation of Industrial Production with AI Algorithm. https://doi.org/10.1007/978-3-032-02003-1_7
  • G. Schäfer, R. Seliger, J. Rehrl, S. Huber, S. Hirlaender: "Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control", DEXA 2025, International Workshop on Optimisation of Industrial Production with AI Algorithm. https://doi.org/10.1007/978-3-032-02003-1_6
  • T. Jäkel, F. Schirmeier: "Deep Photometric Stereo for Tool Wear Inspection", DEXA 2025, International Workshop on Optimisation of Industrial Production with AI Algorithm. https://doi.org/10.1007/978-3-032-02003-1_5
  • Wald, C., Jung, T. and Schirmeier, F. 3D Convolutional Neural Network to Predict the Energy Consumption of Milling Processes. In Proceedings of the 14th International Conference on Data Science, Technology and Applications - Volume 1: DATA. https://doi.org/10.5220/0013457000003967 
  • T. Jäkel, S. Unsin, B. Müller, F. Schirmeier: "Cost-Effective Surface Quality Measurement and Advanced Data Analysis for Reamed Bores", Journal of Manufacturing and Materials Processing. https://doi.org/10.3390/jmmp9030099
  • F. Lischka, M. Götz, F. Schirmeier: "KPI-Berechnung mit homomorpher Verschlüsselung“, In maschinenbau / Ausgabe 1/2025, Springer Fachmedien Wiesbaden. Zum Artikel

 

2024

  • D. Wiesner, F. Schirmeier: "Performance and computation time gains caused by sampling rate reduction in time series deep anomaly detection", In Computer Aided Systems Theory - EUROCAST 2024: 19th International. https://doi.org/10.1007/978-3-031-83885-9_17
  • R. Seliger, M. Micheler, S. Gül-Ficici, U. Göhner: "Accelerating Manual Pick-and-Place Operations with AR-Projected CAD Plans and AI-Assisted Object Recognition", In Computer Aided Systems Theory - EUROCAST 2024: 19th International. https://doi.org/10.1007/978-3-031-82957-4_20
  • M. Spiegel, S. Guel-Ficici, U. Göhner: “Machine learning using a hybrid quantum classical algorithm with Amplitude Data Encoding“, In Computer Aided Systems Theory - EUROCAST 2024: 19th International. https://doi.org/10.1007/978-3-031-82957-4_16
  • M. Götz, M. Rost, D. Wilkner, and F. Schirmeier: "Unsupervised Segmentation of CNC Milling Sensor Data into Comparable Cutting Conditions“, Database and Expert Systems Applications, 35th International Conference, DEXA 2024, Naples, Italy, 2024. https://doi.org/10.1007/978-3-031-68312-1_12
  • S. Unsin, C. Dorer, B. Müller, T. Jung, A. Limmer, F. Schirmeier, "Schritt für Schritt zur Zerspanungs-KI", In maschinenbau / Ausgabe 4/2024, Springer Fachmedien Wiesbaden.
  • R. Seliger, S. Gül-Ficici, U. Göhner: "From Paper to Pixels: A Multi-modal Approach to Understand and Digitize Assembly Drawings for Automated Systems", In B. Moser et al. *Database and Expert Systems Applications - DEXA 2024 Workshops*. https://doi.org/10.1007/978-3-031-68302-2_7

 

2023

  • A. Limmer, M. Götz, F. Schirmeier: "Produktionsdaten in der Zerspanung sicher austauschen“, In maschinenbau / Ausgabe 4/2023, Springer Fachmedien Wiesbaden.
  • S. Würtz, K. Bogenberger, U. Göhner, “Big Data and Discrete Optimization for Electric Urban Bus Operations”, Transportation Research Record, vol. 2677, no. 3, pp. 389–401, 2023. https://doi.org/10.1177/03611981221115427

 

2022

  • T. R. Chhetri, S. Aghaei, A. Fensel, U. Göhner, S. Gül-Ficici, and J. Martinez-Gil, “Optimising Manufacturing Process with Bayesian Structure Learning and Knowledge Graphs”, In Computer Aided Systems Theory ‐ EUROCAST 2022, 2022, pp. 594–602. https://doi.org/10.1007/978-3-031-25312-6_70