| Energy efficient machine tools B Denkena, E Abele, C Brecher, MA Dittrich, S Kara, M Mori CIRP Annals 69 (2), 646-667, 2020 | 101 | 2020 |
| Exergy analysis of incremental sheet forming MA Dittrich, TG Gutowski, J Cao, JT Roth, ZC Xia, V Kiridena, F Ren, ... Production Engineering 6, 169-177, 2012 | 81 | 2012 |
| Machine learning approach for optimization of automated fiber placement processes J Brüning, B Denkena, MA Dittrich, T Hocke Procedia CIRP 66, 74-78, 2017 | 67 | 2017 |
| Shifting value stream patterns along the product lifecycle with digital twins B Schleich, MA Dittrich, T Clausmeyer, R Damgrave, JA Erkoyuncu, ... Procedia CIRP 86, 3-11, 2019 | 59 | 2019 |
| Electrical energy and material efficiency analysis of machining, additive and hybrid manufacturing A Wippermann, TG Gutowski, B Denkena, MA Dittrich, Y Wessarges Journal of Cleaner Production 251, 119731, 2020 | 56 | 2020 |
| Cooperative multi-agent system for production control using reinforcement learning MA Dittrich, S Fohlmeister CIRP Annals 69 (1), 389-392, 2020 | 53 | 2020 |
| Self-optimizing tool path generation for 5-axis machining processes MA Dittrich, F Uhlich, B Denkena CIRP journal of manufacturing science and technology 24, 49-54, 2019 | 47 | 2019 |
| Inverse determination of constitutive equations and cutting force modelling for complex tools using Oxley's predictive machining theory B Denkena, T Grove, MA Dittrich, D Niederwestberg, M Lahres Procedia Cirp 31, 405-410, 2015 | 38 | 2015 |
| Automated production data feedback for adaptive work planning and production control B Denkena, MA Dittrich, S Wilmsmeier Procedia Manufacturing 28, 18-23, 2019 | 32 | 2019 |
| Towards dry machining of titanium-based alloys: A new approach using an oxygen-free environment HJ Maier, S Herbst, B Denkena, MA Dittrich, F Schaper, S Worpenberg, ... Metals 10 (9), 1161, 2020 | 26 | 2020 |
| Simulation based planning of machining processes with industrial robots J Brüning, B Denkena, MA Dittrich, HS Park Procedia Manufacturing 6, 17-24, 2016 | 26 | 2016 |
| Augmenting milling process data for shape error prediction B Denkena, MA Dittrich, F Uhlich Procedia CIRP 57, 487-491, 2016 | 23 | 2016 |
| Investigations on a standardized process chain and support structure related rework procedures of SLM manufactured components B Denkena, MA Dittrich, S Henning, P Lindecke Procedia Manufacturing 18, 50-57, 2018 | 22 | 2018 |
| Self-optimizing cutting process using learning process models B Denkena, MA Dittrich, F Uhlich Procedia Technology 26, 221-226, 2016 | 21 | 2016 |
| A deep q-learning-based optimization of the inventory control in a linear process chain MA Dittrich, S Fohlmeister Production Engineering 15 (1), 35-43, 2021 | 19 | 2021 |
| Statistical approaches for semi-supervised anomaly detection in machining B Denkena, MA Dittrich, H Noske, M Witt Production Engineering 14, 385-393, 2020 | 19 | 2020 |
| Methodology for integrative production planning in highly dynamic environments B Denkena, MA Dittrich, S Jacob Production Engineering 13, 317-324, 2019 | 19 | 2019 |
| Energy efficiency in machining of aircraft components B Denkena, MA Dittrich, S Jacob Procedia Cirp 48, 479-482, 2016 | 19 | 2016 |
| Data-based ensemble approach for semi-supervised anomaly detection in machine tool condition monitoring B Denkena, MA Dittrich, H Noske, D Stoppel, D Lange CIRP Journal of Manufacturing Science and Technology 35, 795-802, 2021 | 17 | 2021 |
| Improving technological machining simulation by tailored workpiece models and kinematics V Böß, B Denkena, B Breidenstein, MA Dittrich, HN Nguyen Procedia CIRP 82, 224-230, 2019 | 16 | 2019 |
