Comparison and analysis of audible sound energy emissions during single point machining of HSTS with PVD TiCN cutter insert across full tool life

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

In precision engineering, tool wear affects the dimensional accuracy and surface finish of machined components. Currently, errors associated with tool wear remain uncompensated for and are usually only detected at the end of the machine cycle, by which time the product may be scrap. If real-time, accurate monitoring were available, machine parameters could be adjusted to compensate for tool wear thereby minimising waste. Experienced machinists in the corresponding author's organisation, a precision engineering CNC machining manufacturing organisation, have been able to detect a poorly performing cutting operation through the sound emitted from the machining centre during the various phases of the cutting cycle and, although not a precise science, appear capable of informally differentiating between a good process and a degraded one. In this article experimental work was undertaken on a single point machining operation whereby the sound energy emissions from the machine were logged and analysed for the full life of the tools. The experiments demonstrated consistent acoustic signatures, which are specific to the tool in a known good cutting state, and distinct, but also consistent sound energy signatures, in a known bad cutting state. The experimental measurements replicated the audible range of human hearing and sought to determine what encouraged experienced machinists to declare a machining process to be in a state of degradation. The experimentation was undertaken at Schivo Precision, Waterford, Ireland.

Original languageEnglish
Pages (from-to)53-62
Number of pages10
JournalWear
Volume313
Issue number1-2
DOIs
Publication statusPublished - 15 May 2014

Keywords

  • Acoustic emissions
  • Computer numerical control (CNC) machining
  • Cutting tool degradation
  • Cutting tool performance
  • Process monitoring
  • Sound energy process monitoring

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