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Straka, Michal and Kroft, Luboš, 2026, AE data – drilling Ti-6Al-4V (raw data, FFT filtering, TSfresh features), DSpace at University of West Bohemia, http://hdl.handle.net/20.500.14592/108
dc.contributor.authorStraka, Michal
dc.contributor.authorKroft, Luboš
dc.date.accessioned2026-06-23T09:08:58Z
dc.date.available2026-06-23T09:08:58Z
dc.date.issued2026
dc.descriptionThis dataset contains raw AE signals from drilling titanium alloy Ti-6Al-4V (18 experiments, 180 holes, drill D = 2.5 mm), FFT-filtered data (band 90–400 kHz), and features extracted using the TSfresh library for machine learning. The dataset supports research on tool wear prediction and process condition classification.
dc.description.sponsorshipEH23_021/0009165 Vývoj digitálních dvojčat konstrukčních komponent s podporou on-line monitoringu jejich provozního zatěžování a simulacemi v laboratorních podmínkách
dc.identifier.urihttp://hdl.handle.net/20.500.14592/108
dc.language.isoen
dc.publisherZápadočeská univerzita v Plzni
dc.rightsCreative Commons - Attribution 4.0 International (CC BY 4.0)
dc.rightsDataset bude dostupný od 31.12.2027.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectakustická emise
dc.subjectvrtání
dc.subjectTi-6Al-4V
dc.subjectopotřebení nástroje
dc.subjectstrojové učení
dc.subjectTSfresh
dc.subjectFFT
dc.titleAE data – drilling Ti-6Al-4V (raw data, FFT filtering, TSfresh features)
dc.typedataset
local.files.count1
local.files.size1420313073
local.has.filesyes
local.subject.translatedacoustic emission
local.subject.translateddrilling
local.subject.translatedTi-6Al-4V
local.subject.translatedtool wear
local.subject.translatedmachine learning
local.subject.translatedTSfresh
local.subject.translatedFFT

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