| Learning factory modules for smart factories in industrie 4.0 C Prinz, F Morlock, S Freith, N Kreggenfeld, D Kreimeier, B Kuhlenkötter Procedia CiRp 54, 113-118, 2016 | 281 | 2016 |
| Systematic review on machine learning (ML) methods for manufacturing processes–Identifying artificial intelligence (AI) methods for field application S Fahle, C Prinz, B Kuhlenkötter Procedia CIRP 93, 413-418, 2020 | 256 | 2020 |
| Lean meets Industrie 4.0–a practical approach to interlink the method world and cyber-physical world C Prinz, N Kreggenfeld, B Kuhlenkötter Procedia Manufacturing 23, 21-26, 2018 | 146 | 2018 |
| New perspectives for generating smart PSS solutions–life cycle, methodologies and transformation B Kuhlenkötter, U Wilkens, B Bender, M Abramovici, T Süße, J Göbel, ... Procedia Cirp 64, 217-222, 2017 | 127 | 2017 |
| A local process model for simulation of robotic belt grinding X Ren, M Cabaravdic, X Zhang, B Kuhlenkötter International Journal of Machine Tools and Manufacture 47 (6), 962-970, 2007 | 124 | 2007 |
| An efficient method for solving the Signorini problem in the simulation of free-form surfaces produced by belt grinding X Zhang, B Kuhlenkötter, K Kneupner International Journal of Machine Tools and Manufacture 45 (6), 641-648, 2005 | 109 | 2005 |
| Concept for an evolutionary maturity based Industrie 4.0 migration model L Stefan, W Thom, L Dominik, K Dieter, K Bernd Procedia Cirp 72, 404-409, 2018 | 103 | 2018 |
| Key challenges of digital business ecosystem development and how to cope with them K Lenkenhoff, U Wilkens, M Zheng, T Süße, B Kuhlenkötter, X Ming Procedia Cirp 73, 167-172, 2018 | 99 | 2018 |
| Automatic classification of defects on the product surface in grinding and polishing X Zhang, C Krewet, B Kuhlenkötter International Journal of Machine Tools and Manufacture 46 (1), 59-69, 2006 | 85 | 2006 |
| Handbuch Mensch-Roboter-Kollaboration R Müller, J Franke, D Henrich, B Kuhlenkötter, A Raatz, A Verl Carl Hanser Verlag GmbH Co KG, 2023 | 83 | 2023 |
| Learning factories’ trainings as an enabler of proactive workers’ participation regarding Industrie 4.0 M Reuter, H Oberc, M Wannöffel, D Kreimeier, J Klippert, P Pawlicki, ... Procedia Manufacturing 9, 354-360, 2017 | 83 | 2017 |
| Real-time simulation and visualization of robotic belt grinding processes X Ren, B Kuhlenkötter The International Journal of Advanced Manufacturing Technology 35, 1090-1099, 2008 | 83 | 2008 |
| Simulation and verification of belt grinding with industrial robots X Ren, B Kuhlenkötter, H Müller International journal of machine tools and manufacture 46 (7-8), 708-716, 2006 | 81 | 2006 |
| Teaching methods-time measurement (MTM) for workplace design in learning factories F Morlock, N Kreggenfeld, L Louw, D Kreimeier, B Kuhlenkötter Procedia Manufacturing 9, 369-375, 2017 | 71 | 2017 |
| Implementation of a learning environment for an Industrie 4.0 assistance system to improve the overall equipment effectiveness C Prinz, D Kreimeier, B Kuhlenkötter Procedia manufacturing 9, 159-166, 2017 | 67 | 2017 |
| A non-quadratic constitutive model under non-associated flow rule of sheet metals with anisotropic hardening: modeling and experimental validation J Min, JE Carsley, J Lin, Y Wen, B Kuhlenkötter International Journal of Mechanical Sciences 119, 343-359, 2016 | 62 | 2016 |
| Human‐robot collaboration–new applications in industrial robotics C Thomas, B Matthias, B Kuhlenkötter International conference on competitive manufacturing, 293-299, 2016 | 59 | 2016 |
| Dynamic performance of industrial robot in corner path with CNC controller K Wu, C Krewet, B Kuhlenkötter Robotics and Computer-Integrated Manufacturing 54, 156-161, 2018 | 57 | 2018 |
| Cyber physical systems for life cycle continuous technical documentation of manufacturing facilities A Barthelmey, D Störkle, B Kuhlenkötter, J Deuse Procedia Cirp 17, 207-211, 2014 | 48 | 2014 |
| Application and analysis of force control strategies to deburring and grinding F Domroes, C Krewet, B Kuhlenkoetter Modern Mechanical Engineering 3 (02), 11-18, 2013 | 48 | 2013 |
