| Machine learning and deep learning based predictive quality in manufacturing: a systematic review H Tercan, T Meisen Journal of Intelligent Manufacturing 33 (7), 1879-1905, 2022 | 147 | 2022 |
| Transfer-learning: Bridging the gap between real and simulation data for machine learning in injection molding H Tercan, A Guajardo, J Heinisch, T Thiele, C Hopmann, T Meisen Procedia Cirp 72, 185-190, 2018 | 123 | 2018 |
| Motion planning for industrial robots using reinforcement learning R Meyes, H Tercan, S Roggendorf, T Thiele, C Büscher, M Obdenbusch, ... Procedia CIRP 63, 107-112, 2017 | 91 | 2017 |
| Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer H Tercan, P Deibert, T Meisen Journal of Intelligent Manufacturing 33 (1), 283-292, 2022 | 53 | 2022 |
| Industrial transfer learning: Boosting machine learning in production H Tercan, A Guajardo, T Meisen 2019 IEEE 17th international conference on industrial informatics (INDIN) 1 …, 2019 | 45 | 2019 |
| Improving the laser cutting process design by machine learning techniques H Tercan, TA Khawli, U Eppelt, C Büscher, T Meisen, S Jeschke Production Engineering 11, 195-203, 2017 | 37 | 2017 |
| Interdisciplinary data driven production process analysis for the internet of production R Meyes, H Tercan, T Thiele, A Krämer, J Heinisch, M Liebenberg, G Hirt, ... Procedia Manufacturing 26, 1065-1076, 2018 | 28 | 2018 |
| Insights and example use cases on industrial transfer learning B Maschler, H Vietz, H Tercan, C Bitter, T Meisen, M Weyrich Procedia CIRP 107, 511-516, 2022 | 19 | 2022 |
| Combined learning processes for injection moulding based on simulation and experimental data C Hopmann, S Jeschke, T Meisen, T Thiele, H Tercan, M Liebenberg, ... AIP conference proceedings 2139 (1), 2019 | 19 | 2019 |
| Use of classification techniques to design laser cutting processes H Tercan, T Al Khawli, U Eppelt, C Büscher, T Meisen, S Jeschke Procedia CIRP 52, 292-297, 2016 | 15 | 2016 |
| Verifying the availability of cloud applications M Siebenhaar, O Wenge, R Hans, H Tercan, R Steinmetz International Conference on Cloud Computing and Services Science 2, 489-494, 2013 | 11 | 2013 |
| Dynamic storage location assignment in warehouses using deep reinforcement learning C Waubert de Puiseau, DT Nanfack, H Tercan, J Löbbert-Plattfaut, ... Technologies 10 (6), 129, 2022 | 9 | 2022 |
| Injection molding setup by means of machine learning based on simulation and experimental data C Hopmann, J Heinisch, H Tercan ANTEC 2018 Conference and Tradeshow, Orlando, Florida, USA, 2018 | 9 | 2018 |
| Evaluating a Session-based Recommender System using Prod2vec in a Commercial Application. H Tercan, C Bitter, T Bodnar, P Meisen, T Meisen ICEIS (1), 610-617, 2021 | 6 | 2021 |
| Deep learning based visual quality inspection for industrial assembly line production using normalizing flows RF Maack, H Tercan, T Meisen 2022 IEEE 20th International Conference on Industrial Informatics (INDIN …, 2022 | 5 | 2022 |
| schlably: A Python framework for deep reinforcement learning based scheduling experiments CW de Puiseau, J Peters, C Dörpelkus, H Tercan, T Meisen SoftwareX 22, 101383, 2023 | 4 | 2023 |
| A Filter is Better Than None: Improving Deep Learning-Based Product Recommendation Models by Using a User Preference Filter MA Gomes, H Tercan, T Bodnar, P Meisen, T Meisen 2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th …, 2021 | 4* | 2021 |
| Towards a deep learning-based online quality prediction system for welding processes Y Hahn, R Maack, G Buchholz, M Purrio, M Angerhausen, H Tercan, ... Procedia CIRP 120, 1047-1052, 2023 | 3 | 2023 |
| Advanced data enrichment and data analysis in manufacturing industry by an example of laser drilling process Y Wang, H Tercan, T Thiele, T Meisen, S Jeschke, W Schulz 2017 ITU Kaleidoscope: Challenges for a Data-Driven Society (ITU K), 1-5, 2017 | 3 | 2017 |
| Curriculum Learning in Job Shop Scheduling using Reinforcement Learning CW de Puiseau, H Tercan, T Meisen arXiv preprint arXiv:2305.10192, 2023 | 2 | 2023 |
