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 language | English (US) |
|---|---|
| Pages (from-to) | 1050-1051 |
| Number of pages | 2 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 2 |
| State | Published - 1994 |
| Event | Proceedings of the 33rd IEEE Conference on Decision and Control. Part 1 (of 4) - Lake Buena Vista, FL, USA Duration: Dec 14 1994 → Dec 16 1994 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Modeling and Simulation
- Control and Optimization