TY - JOUR A1 - Lodes, Lukas A1 - Hass, Erik A1 - Dachtler, Kristina A1 - Schiendorfer, Alexander T1 - SmartManPy – Open Source Synthetic Manufacturing Data JF - Procedia Computer Science N2 - Data Science projects in manufacturing often suffer from difficult access to the necessary high-quality data, such as sensor readings or product quality records. In addition, strict rules due to data protection and intellectual property concerns hinder broad use of the provided data for innovative research, especially in the academic context. To enable the research and evaluation of approaches for a variety of Industry 4.0 use cases, we developed SmartManPy, an extension to the ManPy discrete event simulation environment. We extended the scope of the project to meet our needs and are now able to simulate production lines and the resulting data in just a few lines of code. SmartManPy (Smart Manufacturing Python) includes complex synthetic feature generation, quality control and failures based on recorded features, and data drift simulation. We additionally added a basic environment to train AI agents using reinforcement learning. Our code is released to the public on GitHub. UR - https://doi.org/10.1016/j.procs.2025.01.245 Y1 - 2025 UR - https://doi.org/10.1016/j.procs.2025.01.245 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-57650 SN - 1877-0509 VL - 2025 IS - 253 SP - 1830 EP - 1839 PB - Elsevier CY - Amsterdam ER -