Real-time tool wear identification using sensor integration with neural network

Nouri Levy, Mengchu Zhou, Yu Quan

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations

Abstract

Real-time identification of tool wear in shop floor environment is essential for optimization of machining processes and implementation of automated manufacturing systems. In order to realize the real-time tool wear condition monitoring for different cutting conditions, a sensor integration strategy which combines the information from multiple sensors and machining parameters is proposed. Experiment results show that under different conditions, a higher rate of tool wear identification can be achieved by using sensor integration model with neural network. Also, the neural network is a very effective method of sensor integration for on-line monitoring of tool abnormalities.

Original languageEnglish (US)
Pages (from-to)1050-1051
Number of pages2
JournalProceedings of the IEEE Conference on Decision and Control
Volume2
StatePublished - Dec 1 1994
EventProceedings of the 33rd IEEE Conference on Decision and Control. Part 1 (of 4) - Lake Buena Vista, FL, USA
Duration: Dec 14 1994Dec 16 1994

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

Fingerprint Dive into the research topics of 'Real-time tool wear identification using sensor integration with neural network'. Together they form a unique fingerprint.

Cite this