M. Schürmann, S. Varshneya, L. Bachert, J. Platz, M. Wagner, M. Kloft, J.C. Aurich: A deep learning approach to modeling spatial powder stream distribution in directed energy deposition. Production Engineering 20/2 (2026): 60. 10.1007/s11740-026-01438-2

M. Klar, P. Ruediger, M. Schuermann, G.T. Gören, M. Glatt, B. Ravani, J.C. Aurich: Explainable generative design in manufacturing for reinforcement learning based factory layout planning. Journal of Manufacturing Systems 72 (2024): S. 74-92. 10.1016/j.jmsy.2023.11.012

M. Wagner, F. Frideres, M. Schürmann, M. Klar, J.C. Aurich: Investigating the Influence of Prompt Design in the Generation of Failure Mode and Effects Analysis Using Large Language Models. In: Srihari, K., Khasawneh, M.T., Yoon, S., Won, D. (eds) Flexible Automation and Intelligent Manufacturing: The Future of Automation and Manufacturing: Intelligence, Agility, and Sustainability. FAIM 2025. Lecture Notes in Mechanical Engineering. Springer, Cham (2026): S.58-65. 1007/978-3-032-07675-5_6 

M. Klar, M. Werrel, M. Schürmann, J.C. Aurich: Development of a Product-related automated Life Cycle Assessment method. Procedia CIRP 135 (2025): S. 564-569. 10.1016/j.procir.2025.01.064

M. Schürmann, S. Varshneya, M. Klar, S. Ghansiyal, M. Kloft, J.C. Aurich: A framework for in-situ process control in metal additive manufacturing using anomaly-driven reinforcement learning. Procedia CIRP 134 (2025): S. 211-216. 10.1016/j.procir.2025.03.050

J. Gayer, M. Schürmann, M. Hussong, M. Klar, J.C. Aurich: Automated fixture designs using reinforcement learning. Procedia CIRP 134 (2025): S. 133-138. 10.1016/j.procir.2025.03.021