https://fordatis.fraunhofer.de/handle/fordatis/35 Sun, 13 Sep 2026 04:49:12 GMT 2026-09-13T04:49:12Z https://fordatis.fraunhofer.de/handle/fordatis/426 Titel: Datensatz zur KI-gestützten Klassifizierung von defekten Sägeblättern an einer Kappsäge mit Mikrofon-, Accelerometer- und Ultraschalldaten Datenautorinnen und Datenautoren: Jongmanns, Marcel Zusammenfassung: A miter saw was equipped with a sensor node consisting of a wideband MEMS microphone, an accelerometer and an ultrasound transducer. Data has been collected for 4 different saw blades: two different sharp blades, one blunt and one demolished. There is reference data measured when the saw it off and when the saw is actively rotating, but not inside wood. The sawing data consists of dataset with narrow and wide wooden laths.; Eine Kappsäge wurde mit einem Sensorknoten aus Mikrofon, Accelerometer und Ultraschallsender ausgestattet. Es wurde Daten für 4 unterschiedliche Sägeblätter gesammelt: 2 neue, scharfe Blätter, 1 stumpfes und 1 durch einen Hammer demoliertes. Es wurden Referenzdaten für den Fall, dass die Säge aus ist und für den Fall, dass sich die Säge in der Luft dreht. Weiter wurden Schnitte an dünnen und breiten Holzlatten durchgeführt. Fri, 01 Nov 2024 00:00:00 GMT https://fordatis.fraunhofer.de/handle/fordatis/426 2024-11-01T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/348 Titel: Dataset for AI-assisted detection of the wear level of a cutting tool on a CNC mill Datenautorinnen und Datenautoren: Jongmanns, Marcel; Städter, Philipp; Meisel, Tenia Zusammenfassung: A METROM CNC mill was equipped with a microphone and an accelerometer to determine whether the used milling tool was in a good state or blunt. The measurement data was collected over several measurement series. The used tool was replaced at the beginning of each series with a new, similar tool. The working material was always steel. Machine parameters such as feed rate and rotational speed of the tool has been varied between different series. The machine was operated over a defined time frame while data was collected. After that, the machine was stopped, and the operator classified the quality of cut. The quality is directly related to the condition of the tool. The higher the wear, the worse the quality. Using this approach, we build a dataset consisting of different measurement series to show the degradation of a CNC cutter using vibration and acoustic measurements.; A METROM CNC mill was equipped with a microphone and an accelerometer to determine whether the used milling tool was in a good state or blunt. The measurement data was collected over several measurement series. The used tool was replaced at the beginning of each series with a new, similar tool. The working material was always steel. Machine parameters such as feed rate and rotational speed of the tool has been varied between different series. The machine was operated over a defined time frame while data was collected. After that, the machine was stopped, and the operator classified the quality of cut. The quality is directly related to the condition of the tool. The higher the wear, the worse the quality. Using this approach, we build a dataset consisting of different measurement series to show the degradation of a CNC cutter using vibration and acoustic measurements. Sun, 01 Jan 2023 00:00:00 GMT https://fordatis.fraunhofer.de/handle/fordatis/348 2023-01-01T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/347 Titel: Data set for AI a ssisted detection of the belt tension on a conveyer belt for condition monitoring Datenautorinnen und Datenautoren: Jongmanns, Marcel; Devi, Sharda Zusammenfassung: A measurement setup based on an accelerometer, MEMS gyroscope and magnetometer was used to gather data on a conveyor belt to determine the tension of the belt. 100,000 data points are split into 10 measurement series with 10,000 data points each. Each data point consists of 9 time series of the 3 sensors with 3 axis each. Sun, 01 Jan 2023 00:00:00 GMT https://fordatis.fraunhofer.de/handle/fordatis/347 2023-01-01T00:00:00Z