On-line robust identification of tool-wear via multi-sensor neural-network fusion

Yu Quan, Meng Chu Zhou, Zhenbi Luo

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Real-time identification and monitoring of tool-wear in shop-floor environments is essential for the optimization of machining processes and the implementation of automated manufacturing systems. This paper analyzes the signals from an acoustic emission sensor and a power sensor during machining processes, and extracts a set of feature parameters that characterize the tool-wear conditions. In order to realize real-time and robust tool-wear monitoring for different cutting conditions, a sensor-integration strategy that combines the information obtained from multiple sensors (acoustic emission sensor and power sensor) with machining parameters is proposed. A neural network based on an improved backpropagation algorithm is developed, and a prototype scheme for the real-time identification of tool-wear is implemented. Experiments under different conditions have proved that a higher rate of tool-wear identification can be achieved by using the sensor integration model with a neural network. The results also indicate that neural networks provide a very effective method of implementing sensor integration for the on-line monitoring of tool abnormalities.

Original languageEnglish (US)
Pages (from-to)717-722
Number of pages6
JournalEngineering Applications of Artificial Intelligence
Volume11
Issue number6
DOIs
StatePublished - Dec 1998

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Keywords

  • Automated manufacturing
  • Machining processes
  • Neural networks
  • Sensor fusion
  • Tool-wear identification

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