Zugehörige Institution(en) am KIT | Institut für Produktionstechnik (WBK) |
Publikationstyp | Forschungsdaten |
Publikationsdatum | 19.10.2022 |
Erstellungsdatum | 01.04.2022 - 01.09.2022 |
Identifikator | DOI: 10.5445/IR/1000151546 KITopen-ID: 1000151546 |
Lizenz | Creative Commons Namensnennung 4.0 International |
Schlagwörter | Anomaly Detection; Transfer Learning; Machine Learning; Production Science |
Liesmich | The dataset consists of seven folders. Each folder represents one milling run. In each milling run the depth of cut was set to 3 mm. A folder contains a maximum of three json files. The number of files depends on the time needed for each run which is a function of milling tool diameter and feed rate. Files in each folder were numerated in sequence. For example, folder “run1” contains the files “run1_1” and “run1_2” with the last number indicating the order in which the files were generated. The frequency of recording datapoints was set to 500 Hz. folder name | number of json files | tool diameter | tool breakage | tool type Each json file consists of a header and a payload. The header lists all parameters that were recorded such as position, motor torque and motor current of each of a maximum of five axes of a milling machine. However, the machine used in our experiments is a 3-axis machining center which leaves the payload of 2 possible additional axes to be empty. In the payload the sequential data for each parameter can be found. A list of recorded signals can be found in Table 2. Table 2. recorded signals during milling Signal index in payload | Signal name | Signal Address |Type
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Art der Forschungsdaten | Dataset |