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Chip segmentation frequency based strategy for tool condition monitoring during turning of Ti-6Al-4V

González, Germán; Schwär, Daniel ORCID iD icon; Segebade, Eric; Heizmann, Michael; Zanger, Frederik


Tool condition monitoring in machining reduces downtimes, maximizes productivity rate and improves the quality of the end-product. However, it still poses a challenge due to the complex non-stationary character of the tool wear and the several uncertainties coming from the machining processes. Recent studies provide new strategies for indirect tool monitoring. Unfortunately due to the unbalance between big amounts of data, low accuracy and high complexity they are not feasible in an industrial environment. The present research work proposes a strategy for tool condition monitoring during turning of Ti-6Al-4V using acoustic emission signals and the chip segmentation frequency as measurement variable. Three different approaches for wear estimation using different AE-data processing methods are presented. Through their combination, a strategy for qualitative and quantitative tool wear monitoring is proposed.

Verlagsausgabe §
DOI: 10.5445/IR/1000142094
Veröffentlicht am 19.01.2022
DOI: 10.1016/j.procir.2021.09.047
Zitationen: 3
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Industrielle Informationstechnik (IIIT)
Institut für Produktionstechnik (WBK)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000142094
Erschienen in 18th CIRP Conference on Modeling of Machining Operations (CMMO), Ljubljana, Slovenia, June 15-17, 2021. Ed.: E. Govekar
Veranstaltung 18th CIRP Conference on Modeling of Machining Operations (CMMO 2021), Ljubljana, Slowenien, 15.06.2021 – 17.06.2021
Verlag Elsevier
Seiten 276–280
Serie Procedia CIRP ; 102
Schlagwörter Condition monitoring; wear; acoustic emission; segmentation frequency
Nachgewiesen in Dimensions
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