Detecting tool wear in face milling with different workpiece materials

D. W. Cho, Woo Chun Choi, H. Y. Lee

Research output: Contribution to journalArticle

Abstract

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
JournalKey Engineering Materials
Volume183
Publication statusPublished - 2000 Jan 1

Fingerprint

Wear of materials
Neural networks
Frequency bands
Strength of materials
Tensile strength
Decision making
Monitoring

ASJC Scopus subject areas

  • Chemical Engineering (miscellaneous)
  • Ceramics and Composites

Cite this

Detecting tool wear in face milling with different workpiece materials. / Cho, D. W.; Choi, Woo Chun; Lee, H. Y.

In: Key Engineering Materials, Vol. 183, 01.01.2000.

Research output: Contribution to journalArticle

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