Acoustic emission—A promising and challenging technique for process monitoring in sheet metal forming

Research output: Contribution to journalArticleResearchpeer review

Authors

  • Bernd Arno Behrens
  • Sven Hübner
  • Kai Wölki
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Details

Original languageEnglish
Pages (from-to)281-288
Number of pages8
JournalJournal of manufacturing processes
Volume29
Publication statusPublished - 18 Sept 2017

Abstract

Acoustic emission (AE) is a non-destructive testing method with a high potential for quality and process monitoring in sheet metal forming. In this paper the possibilities and limitations of this testing method with regard to sheet metal forming are presented. For this purpose several rectangle cups were manufactured from the low carbon (DC06) as well as the dual-phase steel (HCT600X) by deep drawing. The analysis of the measured acoustic emissions was focused on two objectives. First, a correlation between the cracking and the acquired resulting acoustic emissions were created. The second objective of the analysis was to verify the suitability of the AE-technique for process monitoring. The results have shown that acoustic emissions arised during the deep drawing process enable not only the detection of crackings, but also different process deviations such as lacking lubrication and wrong material. But it has to be considered that the feasibility of the acoustic emission technique as a process monitoring tool depends from the material ductility which limits the fields of application.

Keywords

    Acoustic emission, Deep drawing, Process monitoring

ASJC Scopus subject areas

Cite this

Acoustic emission—A promising and challenging technique for process monitoring in sheet metal forming. / Behrens, Bernd Arno; Hübner, Sven; Wölki, Kai.
In: Journal of manufacturing processes, Vol. 29, 18.09.2017, p. 281-288.

Research output: Contribution to journalArticleResearchpeer review

Behrens BA, Hübner S, Wölki K. Acoustic emission—A promising and challenging technique for process monitoring in sheet metal forming. Journal of manufacturing processes. 2017 Sept 18;29:281-288. doi: 10.1016/j.jmapro.2017.08.002
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AU - Wölki, Kai

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