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dc.contributor.authorJongmanns, Marcel-
dc.contributor.authorStädter, Philipp-
dc.contributor.authorMeisel, Tenia-
dc.date.accessioned2023-10-11T13:07:40Z-
dc.date.available2023-10-11T13:07:40Z-
dc.date.issued2023-
dc.identifier.urihttps://fordatis.fraunhofer.de/handle/fordatis/348-
dc.identifier.urihttp://dx.doi.org/10.24406/fordatis/290-
dc.description.abstractA 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.de
dc.description.abstractA 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.en
dc.description.sponsorshipThe data is part of the iCampus – Fortune project funded by the Federal Ministry of Education and Research of Germany (BMBF), grant number 16ES1128K.en
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/en
dc.subjectcondition monitoringen
dc.subjectpredictive maintenanceen
dc.subjectCNC millen
dc.subjectZustandsüberwachungde
dc.subjectMaschinendatende
dc.subjectmachine learningen
dc.subjecttool sharpnessen
dc.subject.ddcDDC::600 Technik, Medizin, angewandte Wissenschaften::620 Ingenieurwissenschaftenen
dc.titleDataset for AI-assisted detection of the wear level of a cutting tool on a CNC millen
dc.typeTabular Dataen
dc.contributor.funderBundesministerium für Bildung und Forschung BMBF (Deutschland)en
dc.description.technicalinformationA METROM CNC mill was equipped with an accelerometer and microphone. The CNC mill is fully encased and uses a pentapod geometry to move the spindle with the tool. The accelerometer is an Analog ADXL1005, and the wideband MEMS microphone is a Knowles SPU0410LR5H. The data was sampled by a modified version of the Fraunhofer IPMS CMUT evaluation kit. The sampling rate of the analog inputs was set to 1.953.125Hz. The accelerometer was mounted at a stationary part of the spindle to measure vibrations caused by the tool. It was fixed using adhesives as well as a cable tie. The microphone was mounted using a beam to be near the tool. It was aimed towards the tool and protected by a fabric against loose chips emitted by the tool. While the flying chips could not damage the sensors, they could very well interfere with the measured sounds. During the operation of the machine, no lubricant or additional cooling was used. The recorded time series are provided as HDF5 file and have been further processed using Matlab or Python in different publications.de
dc.description.technicalinformationA METROM CNC mill was equipped with an accelerometer and microphone. The CNC mill is fully encased and uses a pentapod geometry to move the spindle with the tool. The accelerometer is an Analog ADXL1005, and the wideband MEMS microphone is a Knowles SPU0410LR5H. The data was sampled by a modified version of the Fraunhofer IPMS CMUT evaluation kit. The sampling rate of the analog inputs was set to 1.953.125Hz. The accelerometer was mounted at a stationary part of the spindle to measure vibrations caused by the tool. It was fixed using adhesives as well as a cable tie. The microphone was mounted using a beam to be near the tool. It was aimed towards the tool and protected by a fabric against loose chips emitted by the tool. While the flying chips could not damage the sensors, they could very well interfere with the measured sounds. During the operation of the machine, no lubricant or additional cooling was used. The recorded time series are provided as HDF5 file and have been further processed using Matlab or Python in different publications.en
dc.relation.issupplementto10.1109/JSEN.2023.3273458-
dc.relation.issupplementto10.3390/s23094461-
fordatis.groupMikroelektroniken
fordatis.instituteIPMS Fraunhofer-Institut für Photonische Mikrosystemeen
fordatis.rawdatatrueen
fordatis.sponsorship.projectid16ES1128Ken
fordatis.sponsorship.projectnameiCampus Arbeitspaket 6: Multi-Sensor Condition Monitoringen
fordatis.sponsorship.projectacronymForTuneen
fordatis.date.start2020-11-
fordatis.date.end2021-05-
Appears in Collections:Fraunhofer-Institut für Photonische Mikrosysteme IPMS

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20230926-JON-Documentation_Fortune1_CNC_mill_data.pdfDocumentation of the measurement setup414,61 kBAdobe PDFDownload/Open
20230926-JON-Documentation_Fortune1_CNC_mill_data.xlsxOverview of the machine parameters33,83 kBMicrosoft Excel XMLDownload/Open
images_steel.zipImages of the steel measurements: tools, chips, cuts1,44 GBZIPDownload/Open
images_parameter2.zipImages of parameter variation 2: tools, chips, cuts1,68 GBZIPDownload/Open
parameter2.hdf511,07 GBUnknownDownload/Open
parameter3.hdf53,18 GBUnknownDownload/Open
parameter4.hdf54,4 GBUnknownDownload/Open
steel.hdf519,25 GBUnknownDownload/Open
parameter5.hdf54,14 GBUnknownDownload/Open
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