| Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology S Studer, TB Bui, C Drescher, A Hanuschkin, L Winkler, S Peters, ... Machine Learning & Knowledge Extraction, 392-413, 2021 | 309 | 2021 |
| Measuring global production effectiveness G Lanza, J Stoll, N Stricker, S Peters, C Lorenz Procedia CIRP 7, 31-36, 2013 | 70 | 2013 |
| Digitalization of automotive industry–scenarios for future manufacturing S Peters, JH Chun, G Lanza Manufacturing Review, 2016 | 57 | 2016 |
| Machine learning-based analysis of in-cylinder flow fields to predict combustion engine performance A Hanuschkin, S Schober, J Bode, J Schorr, B Böhm, C Krüger, S Peters International Journal of Engine Research, 2019 | 43 | 2019 |
| A readiness level model for new manufacturing technologies S Peters Production Engineering 9 (5-6), 647-654, 2015 | 40 | 2015 |
| Integrated capacity planning over highly volatile horizons G Lanza, S Peters CIRP annals 61 (1), 395-398, 2012 | 39 | 2012 |
| Adjusting the factory planning process when using immature technologies R Kopf, L Schlesinger, S Peters, G Lanza Procedia CIRP 41, 1011-1016, 2016 | 36 | 2016 |
| Automotive manufacturing technologies–an international viewpoint S Peters, G Lanza, N Jun, J Xiaoning, Y Pei-Yun, M Colledani Manufacturing Review 1 (10), 1-12, 2014 | 32 | 2014 |
| Investigation of cycle-to-cycle variations in a spark-ignition engine based on a machine learning analysis of the early flame kernel A Hanuschkin, S Zündorf, M Schmidt, C Welch, J Schorr, S Peters, ... Proceedings of the Combustion Institute 38 (4), 5751-5759, 2021 | 29 | 2021 |
| Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review A Kuznietsov, B Gyevnar, C Wang, S Peters, SV Albrecht arXiv preprint arXiv:2402.10086, 2024 | 24 | 2024 |
| Ad-hoc rescheduling and innovative business models for shock-robust production systems G Lanza, N Stricker, S Peters Procedia CIRP 7, 121-126, 2013 | 24 | 2013 |
| Deep feature learning of in-cylinder flow fields to analyze cycle-to-cycle variations in an SI engine D Dreher, M Schmidt, C Welch, S Ourza, S Zündorf, J Maucher, S Peters, ... International Journal of Engine Research, 2020 | 23 | 2020 |
| Dynamic optimization of manufacturing systems in automotive industries G Lanza, S Peters, HG Herrmann CIRP Journal of Manufacturing Science and Technology 5 (4), 235-240, 2012 | 21 | 2012 |
| Assessment of flexible quantities and product variants in production G Lanza, K Peter, J Rühl, S Peters CIRP Journal of Manufacturing Science and Technology 3 (4), 279-284, 2010 | 13 | 2010 |
| AUTOtech. agil: Architecture and Technologies for Orchestrating Automotive Agility R van Kempen, B Lampe, M Leuffen, L Wirtz, F Thomsen, G Bilkei-Gorzo, ... Universitätsbibliothek der RWTH Aachen, 2023 | 11 | 2023 |
| Customer-driven planning and control of global production networks-balancing standardisation and regionalisation T Arndt, J Hochdoerffer, E Moser, S Peters, G Lanza Proceedings of the 18th Cambridge International Manufacturing Symposium 11 …, 2014 | 10 | 2014 |
| Bewertung von Stückzahl-und Variantenflexibilität in der Produktion G Lanza, J Rühl, S Peters ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb 104 (11), 1039-1044, 2009 | 10 | 2009 |
| Markoffsche Entscheidungsprozesse zur Kapazitäts-und Investitionsplanung von Produktionssystemen S Peters Shaker, 2013 | 9 | 2013 |
| The Inadequacy of Discrete Scenarios in Assessing Deep Neural Networks TK Mori, X Liang, L Elster, S Peters IEEE Access 10, 118236-118242, 2022 | 8 | 2022 |
| Optimal investment policies in premature manufacturing technologies S Peters International Journal of Production Research 53 (13), 3963-3974, 2015 | 8 | 2015 |