Von Juli 2019 bis November 2023 Oberingenieur Produktionssysteme.

Von Mai 2017 bis Novmber 2023 Wissenschaftlicher Mitarbeiter am FBK.

P. Ruediger-Flore, M. Glatt, M. Hussong, J.C. Aurich: CAD-based data augmentation and transfer learning empowers part classification in manufacturing. The International Journal of Advanced Manufacturing Technology 125 (2023): S. 5605–5618. 10.1007/s00170-023-10973-6 

M. Hussong, M. Glatt, J. C. Aurich: Deep Transfer Learning in der Arbeitsplanung - Konzept zur Anwendung von Deep Transfer Learning am Beispiel der Fertigungsvorgangsermittlung. WT Werkstatttechnik online 113/6 (2023): S.224-228. 10.37544/1436-4980-2023-06-16

M.M. Müller, S. Ghansiyal, M. Huber, B. Kirsch, M. Glatt, J.C. Aurich: Ein Konzept zur Entwicklung eines wrtschaftlicheren PBF-LB - Steigerung der Wirtschaftlichkeit bei der additiven Fertigung. wt Werkstattstechnik online 113/6 (2023): S.237-241. 0.37544/1436–4980–2023–06–29

P. Ruediger-Flore, M. Klar, M. Hussong, J. Mertes, L. Yi, M. Glatt, P. Kölsch, J. C. Aurich: Neural Radiance Fields in der Fabrikplanung - Untersuchung von Neural Radiance Fields zur Modellrekonstruktion in der Fabrikplanung. WT Werkstattstechnik  113/6 (2023) S.219-223 10.37544/1436-4980-2023-06-11

M. Klar, M. Glatt, J.C. Aurich: Performance comparison of reinforcement learning and metaheuristics for factory layout planning. CIRP Journal of Manufacturing Science and Technology 45 (2023): S. 10-25 10.1016/j.cirpj.2023.05.008 

P. Schworm, X. Wu, M. Klar, J. Gayer, M. Glatt, J.C. Aurich: Resilience optimization in manufacturing systems using Quantum Annealing. Manufacturing Letters 36 (2023): S. 13–17.

J. Mertes, D. Lindenschmitt, M. Amirrezai, N. Tashakor, M. Glatt, C. Schellenberger, S.M. Shah, A. Karnoub, C. Hobelsberger, L. Yi, S. Götz, J.C. Aurich, H.D. Schotten: Evaluation of 5G-capable framework for highly mobile, scalable human-machine interfaces in cyber-physical production systems. Journal of Manufacturing Systems 64 (2022): S. 578-593.

P. Schworm, X. Wu, M. Glatt, J.C. Aurich: Solving flexible job shop scheduling problems in manufacturing with Quantum Annealing. Production Engineering - Production Management (2022): DOI10.1007/s11740-022-01145-8

L. Yi, P. Langlotz, M. Hussong, M. Glatt, F. J. P. Sousa, J. C. Aurich: An integrated energy management system using double deep Q-learning and energy storage equipment to reduce energy cost in manufacturing under real-time pricing condition: A case study of scale-model factory. CIRP Journal of Manufacturing Science and Technology 38 (2022): S. 844-860.

M. Klar, P. Schworm, X. Wu, M. Glatt, J.C. Aurich: Quantum Annealing based factory layout planning. Journal of Manufacturing Letters 32 (2022): S. 1-4.

P. Langlotz, M. Klar, M. Glatt, J.C. Aurich: Automatisierte Wertstrommethode unter Nutzung von Reinforcement Learning. ZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb 117/6 (2022): S. 395-399.

P. M. Simon, D. Müller, M. Klar, K. Gutzeit, H. Teich, R. Eisseler, P. Kölsch, B. Kirsch, M. Glatt, H.-C. Möhring, J. C. Aurich: Transfer Learning in der Zerspanung: Grundlage für Prozessverbesserungen und innovative Geschäftsmodelle. ZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb 117/1-2 (2022): S. 79-83.

P. Schworm, M. Klar, M. Glatt, J.C. Aurich: Konzept zur Lösung von industriellen Reihenfolgeplanungsproblemen - Mittels Quanten-Annealing im Kontext von Industrie 4.0. ZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb 116/11 (2021): S. 766-770.

J. Mertes, M. Klar, D. Lindenschmitt, M. Glatt, H. D. Schotten, J. C. Aurich: Adaptive Informationsdarstellung in der industriellen Produktion: Konzeption eines 5G-fähigen AR-Systems. ZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb 116/11 (2021): S.757-761.

M. Hussong, M. Glatt, P. Rüdiger-Flore, S. Varshneya, P. Liznerski, M. Kloft, J. C. Aurich: Deep Learning zur Unterstützung der Arbeitsplanung: Ein Konzept zur Ermittlung von Vorgangsfolgen durch künstliche neuronale Netze. ZWF - Zeitschrift für wirtschaftlichen Fabrikbetrieb 116/10 (2021): S. 648-651.

P. Ruediger-Flore, M. Glatt; J. C. Aurich: Herausforderungen für die Nutzbarkeit von Maschinellem Lernen bei hohem Variantenreichtum und kleinen Serien - Implikationen am Beispiel der Nutzfahrzeugproduktion. Zeitschrift für wirtschaftlichen Fabrikbetrieb 116/7-8 (2021): S. 1-6.

M. Klar, M. Glatt, J.C. Aurich: An implementation of a reinforcement learning based algorithm for factory layout planning. Journal of Manufacturing Letters 30 (2021): S. 1-4.

M. Glatt, C. Sinnwell, L. Yi, S. Donohoe, B. Ravani, J.C. Aurich: Modeling and implementation of a digital twin of material flows based on physics simulation. Journal of Manufacturing Systems 58/2 (2021): S. 231-245.

L. Yi, S. Ehmsen, M. Glatt, J.C. Aurich: Modeling and software implementation of manufacturing costs in additive manufacturing. CIRP Journal of Manufacturing Science and Technology 33 (2021): S. 380-388.

M. Glatt, P. Kölsch, C. Siedler, P. Langlotz, S. Ehmsen, J.C. Aurich: Edge-based Digital Twin to trace and ensure sustainability in cross-company production networks. Procedia CIRP 98 (2021): S. 276-281.

L. Yi, M. Glatt, P. Sridhar, K. de Payrebrune, B.S. Linke, B. Ravani, J.C. Aurich: An eco-design for additive manufacturing framework based on energy performance assessment. Additive Manufacturing 33 (2020): S. 101120.

L. Yi, M. Glatt, T.-Y. Thomas Kuo, A. Ji, B. Ravani, J.C. Aurich: A method for energy modeling and simulation implementation of machine tools of selective laser melting. Journal of Cleaner Production 263 (2020): S. 121282.

M. Glatt, P. Kölsch, N. Krenkel, P. Langlotz, C. Siedler, L. Yi, J.C. Aurich: Rahmenwerk zur Einordnung Digitaler Zwillinge in Produktionssystemen. ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 115/6 (2020): S. 429-433.

F. J. P. Sousa, R. Hall, A. Souza, P. Langlotz, M. Glatt, J. C. Aurich: Fusion of physical principles and data-driven based models: An Industry 4.0 perspective for improving the polishing process of stoneware tiles. Production Engineering-Research and Development (2020).

H. Hotz, M. Glatt, B. Kirsch, J.C. Aurich: Quality protection of technical products – Unique identification with a hidden fingerprint in smart materials. Materials Letters: X 8C (2020): 100056.

M. Glatt, H. Hotz, P. Kölsch, A. Mukherjee, B. Kirsch, J.C. Aurich: Predicting the martensite content of metastable austenitic steels after cryogenic turning using machine learning. The International Journal of Advanced Manufacturing Technology (2020): DOI:10.1007/s00170-020-06160-6.

M. Glatt, P. Kölsch, N. Krenkel, P. Langlotz, C. Siedler, L. Yi, J. C. Aurich: Rahmenwerk zur Einordnung Digitaler Zwillinge in Produktionssystemen. ZWF – Zeitschrift für wirtschaftlichen Fabrikbetrieb 115/6 (2020): S. 429-433.

P. Langlotz, M. Glatt, F. J. P. Sousa, J. C. Aurich: Modelle als Grundlage für den Digitalen Zwilling – Fusion von physikalischen und datengetriebenen Modellen. ZWF – Zeitschrift für wirtschaftlichen Fabrikbetrieb 115/5 (2020): S. 340-343.

M.F. Glatt, L. Yi, G. Mert, B.S. Linke, J.C. Aurich: Technical Product-Service Systems: Analysis and reduction of the Cumulative Energy Demand. Journal of Cleaner Production 206 (2019): S. 727-740.

M. Glatt, C. Sinnwell, S. Basten, J.C. Aurich, G. Andreis-Hofmann, A. Quinten: Optimierungspotentiale bei der Fertigung von Werkstoffproben - Handlungsempfehlungen zur wirtschaftlicheren Gestaltung einer Probenwerkstatt. VDI-Z Integrierte Produktion 160/10 (2018): S. 39-41.

M. Glatt, J.C. Aurich: Physiksimulation cyber-physischer Produktionssysteme - Planung und Steuerung cyber-physischer Produktionssysteme durch physikalische Simulation. wt Werkstattstechnik online 108/4 (2018): S. 217-220.

M. Hussong, S. Varshneya, P. Rüdiger-Flore, M. Glatt, M. Kloft, J.C. Aurich: A process planning system using deep artificial neural networks for the prediction of operation sequences. Procedia CIRP 120 - Proceedings of the 56th CIRP International Conference on Manufacturing Systems (2023): S. 135-140. 10.1016/j.procir.2023.08.025

P. Ruediger-Flore, M. Klar, M. Hussong, A. Mukherjee, M. Glatt, J.C. Aurich:Comparing Binary Classification and Autoencoders for Vision-Based Anomaly Detection in Material Flow. Procedia CIRP 121 - Proceedings of the 11th CIRP Global Web Conference (2024): S. 138-143. 10.1016/j.procir.2023.09.241

M. Klar, P. Rüdiger, M. Scheidt, M. Hussong, M. Glatt , B. Ravani, Jan C. Aurich: Development of a Machine Learning Model that represents the characteristics of a Manufacturing Systems. Procedia CIRP 122 - Proceedings of the 31st CIRP Conference on Life Cycle Engineering (2024): S. 175-180. 10.1016/j.procir.2024.01.026

J. Mertes, M. Glatt, C. Schellenberger, P. M. Simon, L. Yi, H. D. Schotten, J. C. Aurich: Implementation and Evaluation of 5G-enabled sensors for Machine Tools. Procedia CIRP 120 - Proceedings of the 56th CIRP Conference on Manufacturing Systems (2023): S. 45-50. 10.1016/j.procir.2023.08.009

M. Glatt, P. Kölsch, M. Wagner, J. Mertes, J. C. Aurich: Framework for synergetic integration of heterogenous Digital Twins in Manufacturing Systems. Procedia CIRP 120 - Proceedings of the 56th CIRP Conference on Manufacturing Systems  (2023): S. 798-803. 10.1016/j.procir.2023.09.078

M.M. Müller, S. Ghansiyal, B. Kirsch, M. Glatt, J.C. Aurich: Investigation of the Process Windows of PBF-LB/Ti6Al4V for Variable Laser Spot Diameters. Proceedings of the 23nd Machining Innovations Conference for Aerospace Industry (2023): S. 50-56 10.2139/ssrn.4657776

S. Ghansiyal, L. Yi, P. M. Simon, M. Klar, M. M. Müller, M. Glatt, J. C. Aurich: Anomaly detection towards zero defect manufacturing using generative adversarial networks. Procedia CIRP 120 - Proceedings of the 56th 56th CIRP Conference on Manufacturing Systems (2023): S. 1457-1462. 10.1016/j.procir.2023.09.193

S. Ehmsen, L. Yi, M. Glatt, J.C. Aurich: Evaluating the environmental impact of high-speed laser directed energy deposition: A life cycle assessment. 56th CIRP Conference on Manufacturing Systems (2023)

J. Mertes, M. Glatt, L. Yi, M. Klar, B. Ravani ,J.C. Aurich: Modeling and Implementation of a 5G-Enabled Digital Twin of a Machine Tool Based on Physics Simulation. Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. IRTG 2023. Springer (2023): S.90–110. 10.1007/978-3-031-35779-4_6 

M. Klar, J. Mertes, M. Glatt, B. Ravani, J. C. Aurich: A Holistic Framework for Factory Planning Using Reinforcement Learning. Proceedings of the 3rd Conference on Physical Modeling for Virtual Manufacturing Systems and Processes. IRTG 2023. Springer (2023). S. 129–148. 10.1007/978-3-031-35779-4_8 

S. Ghansiyal, L. Yi,  J. Steiner-Stark, M.M. Müller, B. Kirsch, M. Glatt, J.C. Aurich: A conceptual framework for layerwise energy prediction in laser-based powder bed fusion process using machine learning. Procedia CIRP 116 - Proceedings of the 30th CIRP Confrence on Life Cycle Engineering (2023): S. 7-12. 10.1016/j.procir.2023.02.002

S. Ehmsen, M. Glatt, J.C. Aurich: Influence of process parameters on the power consumption of high-speed laser directed energy deposition. Procedia CIRP 116 - 30th CIRP Life Cycle Engineering Conference (2023): S. 89-94. DOI: 10.1016/j.procir.2023.02.016.

A. Mukherjee, M. Glatt, W. Mustafa, M. Kloft, J. C. Aurich: Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience. Procedia CIRP 115 - Proceedings of the 10th CIRP Global Web Conference (2022): S. 83-88.

J. Mertes, M. Glatt, C. Schellenberger, M. Klar, H.D. Schotten, J.C. Aurich: Development of a 5G-enabled Digital Twin of a Machine Tool. Procedia CIRP 107 (2022): S. 173-178.

C.Siedler, J.Mertes, Li Yi, M.Glatt, C.Schellenberger, H.D.Schotten, J.C.Aurich: 5G as an enabler for cloud-based machine tool control. Procedia CIRP 104 - Proceedings of the 54th CIRP Conference on Manufacturing Systems (2021):S.235–240

L. Yi, K. Gutzeit, S. Ehmsen, P. Kölsch, M. Glatt, J.C. Aurich: Exploration of the potential of polymer 4D printing: Experiments on the printing quality and the impact of the temperature and geometry on the shape-changing effect. Procedia CIRP 103 - Proceedings of the 9th CIRP  Global Web Conference: S. 103-108.

M. Glatt, S. Greco, L. Yi, B. Kirsch, J.C. Aurich: Development and implementation of a system for the automated removal of parts produced by Fused Deposition Modeling. Procedia CIRP 103 – Proceedings of the 9th CIRP Global Web Conference (2021): S. 109-114.

M. Glatt, P. Kölsch, C. Siedler, P. Langlotz, S. Ehmsen, J. C. Aurich: Edge-based Digital Twin to trace and ensure sustainability in cross-company production networks. Procedia CIRP 98 – Proceedings of the 28th CIRP Conference on Life Cycle Engineering (2021): S. 276-281.

L. Yi, S. Ehmsen, M. Glatt, J.C. Aurich: A case study on the part optimization using eco-design for additive manufacturing based on energy performance assessment. Procedia CIRP 96 (2021): S. 91-96.

R. Oberlé, S. Schorr, L. Yi, M. Glatt, D. Bähre, J.C. Aurich: A Use Case to Implement Machine Learning for Life Time Prediction of Manufacturing Tools. Procedia CIRP 93/1 (2020): S. 1484-1489.

M. Glatt, D. Kull, B. Ravani, J.C. Aurich: Validation of a physics engine for the simulation of material flows in cyber-physical production systems. Procedia CIRP 81 - Proceedings of the 52nd CIRP Conference on Manufacturing Systems (2019): S. 494-499.

M. Glatt, J.C. Aurich: Physical modeling of material flows in cyber-physical production systems. Procedia Manufacturing 28 - 7th International conference on Changeable, Agile, Reconfigurable and Virtual Production (2019): S. 10-17.

M. Glatt, G. Kasakow, J.C. Aurich: Combining physical simulation and discrete-event material flow simulation. Procedia CIRP 72 - Proceedings of the 51st CIRP Conference on Manufacturing Systems (2018): S. 420-425.