| Zugehörige Institution(en) am KIT | Institut für Produktionstechnik (WBK) |
| Publikationstyp | Forschungsdaten |
| Publikationsdatum | 01.10.2025 |
| Erstellungsdatum | 26.09.2025 |
| Identifikator | DOI: 10.35097/vnnu3n9z7ndsnhfd KITopen-ID: 1000185172 |
| Lizenz | Creative Commons Namensnennung 4.0 International |
| Schlagwörter | Machine Tool, Process Monitoring, Anomaly Detection, Machine Learning, Milling, CNC |
| Liesmich | This dataset contains process data from milling operations performed on a three-axis milling machine (CMX 600V, DMG Mori) and is complementary to DOI:10.35097/hvvwn1kfwf7qt48z. The data was collected in a controlled laboratory setting using industrially relevant components, tools, and machining strategies to reflect the diversity and variability of practical milling scenarios. Process signals from the machine controller were recorded using a Siemens Industrial Edge device at a sampling rate of 500 Hz. Additional high-frequency force and acceleration data were measured using a force platform (Kistler Type Z 3393) and a spindle-mounted acceleration sensor (PCB Type 356A33). The analog signals were amplified via a charge amplifier (Kistler Type 5015A1000 K) and acquired using a Data Translation DT9836 data acquisition card at 10 kHz. Raw data from the force and acceleration sensors are provided as MATLAB timetable files (.mat). The process signals from the Siemens Industrial Edge are stored in JSON format (.json). In addition, each experiment includes a consolidated MATLAB file in which all available signals (Edge and sensor data) are temporally aligned and stored as a single timetable object. To achieve this, the 500 Hz Edge signals were interpolated using the PCHIP method (Piecewise Cubic Hermite Interpolating Polynomial) to match the 10 kHz resolution of the sensor data. This unified file enables direct signal comparison across all sources and simplifies further analysis. In total, 54 milling experiments are included. NC programs, labeled pictures, and detailed documentation are provided to support benchmarking applications. Documents:
Experimental Setup:
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| Art der Forschungsdaten | Dataset |
| Nachgewiesen in | OpenAlex |
| Relationen in KITopen | |
| Globale Ziele für nachhaltige Entwicklung |