Computational methods for the detection of wear and damage to milling tools br

Research output: Contribution to journalArticleResearchpeer-review

Authors

  • Thomas Klünsner
  • Manfred Mücke
  • Lukas Hanna
  • Tamara Teppernegg
  • Martin Treichler
  • Patrick Peissl
  • Christoph Czettl

Organisational units

External Organisational units

  • Materials Center Leoben Forschungs GmbH
  • Ceratizit Austria GmbH
  • Tool Consulting and Management GmbH
  • Ro-Ra Aviation Systems

Abstract

The current paper presents a new computational approach to detect wear and damage to milling tools' cutting edges. The proposed approach is independent from exact information on tool-workpiece interaction conditions and only requires that they remain constant for compared milling operations. Additionally, the approach was thoroughly tested on time-series data obtained from an industrial-scale milling process, instrumented by commercially available instrumentation equipment, during which 18 identical parts were milled. The time-series data contains the bending moments in the x and y directions as well as the torque and tension acting on the milling tool. Some measures used are systematic in nature, based on shape, rotation and work needed for milling, whereas others are statistical in nature, describing the change in the distribution of the data. All of the measures proposed in the current work are relative and mutually invariant, meaning they address different information content of the data independently. A comparison of the mentioned measures with the real-world damage evolution of the milling tool's cutting edges for multiple produced parts yielded consistent results and suggests a high potential for practical tool damage detection in industrial production.

Details

Original languageEnglish
Pages (from-to)78-87
Number of pages10
JournalJournal of manufacturing processes
Volume82.2022
Issue numberOctober
Early online date2 Aug 2022
DOIs
Publication statusPublished - Oct 2022