Experimental validation of chip production rate as a tool wear identification in micro-endmilling

Jue Hyun Lee, Angela Sodemann, Anuj K. Bajaj

Research output: Contribution to journalArticle

Abstract

Lack of tool wear data prevents the analytical tool wear models in micro-endmilling process proposed in the past from being properly evaluated. And the methods for tool wear data collection of a micro-endmill are scarce, due to the difficulty and tedium involved in repeatedly measuring tool wear during a cutting process. Although there are many aspects to micro-endmill wear, including flank wear, crater wear, and built-up edge, the cutting edge radius wear is a particularly important measure of wear in micro-milling owing to the influence of the minimum chip thickness effect on cutting forces and stability of the cutting process. This research proposes a more efficient and labor-saving method for tool wear data collection in which the cutting edge radius of a micro-endmill is determined by monitoring the rate of chip production during a cutting process. And micro-endmilling experiments are conducted to validate our proposed tool wear data collection method. It is found that the chip production rate decreases gradually as the uncut chip thickness becomes smaller. The number of chips tends to have a larger drop when the feedrate crosses the critical feedrate which corresponds to the minimum chip thickness.

Original languageEnglish (US)
JournalInternational Journal of Advanced Manufacturing Technology
DOIs
StatePublished - Jan 1 2019

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Wear of materials
Personnel
Monitoring

Keywords

  • Chip production rate
  • Micro-endmilling
  • Minimum chip thickness
  • Tool wear
  • Uncut chip thickness

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

Cite this

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title = "Experimental validation of chip production rate as a tool wear identification in micro-endmilling",
abstract = "Lack of tool wear data prevents the analytical tool wear models in micro-endmilling process proposed in the past from being properly evaluated. And the methods for tool wear data collection of a micro-endmill are scarce, due to the difficulty and tedium involved in repeatedly measuring tool wear during a cutting process. Although there are many aspects to micro-endmill wear, including flank wear, crater wear, and built-up edge, the cutting edge radius wear is a particularly important measure of wear in micro-milling owing to the influence of the minimum chip thickness effect on cutting forces and stability of the cutting process. This research proposes a more efficient and labor-saving method for tool wear data collection in which the cutting edge radius of a micro-endmill is determined by monitoring the rate of chip production during a cutting process. And micro-endmilling experiments are conducted to validate our proposed tool wear data collection method. It is found that the chip production rate decreases gradually as the uncut chip thickness becomes smaller. The number of chips tends to have a larger drop when the feedrate crosses the critical feedrate which corresponds to the minimum chip thickness.",
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