Detecting tool wear in face milling with different workpiece materials

D. W. Cho, W. C. Choi, H. Y. Lee

Research output: Contribution to journalArticle

1 Citation (Scopus)


This paper proposes a neural network for the decision-making system for monitoring tool wear while working materials such as A16061, SB41, SM45C. The raw cutting forces signals are filtered and processed with adaptive AR modeling. The AR parameters and cutting conditions are used as input to the neural network along with the frequency band energy. The experimental results show that each neural network trained for each specified material can recognize tool wear with a more than 85% detection rate. When the normalized tensile strength of each material is used as additional input to the unified neural network, the network still has a success rate higher than 80%.

Original languageEnglish
Pages (from-to)559-564
Number of pages6
JournalKey Engineering Materials
Issue number187 PART 1
Publication statusPublished - 2000


  • Face Milling
  • Neural Network
  • Tool Wear
  • Workpiece Material

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'Detecting tool wear in face milling with different workpiece materials'. Together they form a unique fingerprint.

  • Cite this