신경회로망을 이용한 밀링 작업에서의 공구 감시 = Neural Network Based Tool Condition Monitoring in Face Milling Operations

다국어 초록 (Multilingual Abstract)

An on-line tool wear detection system for face milling operations is developed, and experimentally evaluated. The system employs multiple sensors(AE sensor and dynamometer), and the signals from these sensors are processed using multichannel autoregressive (AR) series model. Decision on the state of cutting tool was made by neural network (multilayered perceptron) using the AR coefficients as input pattern. To learn the necessary input/output mapping for tool wear detection, the parameters of the network are adjusted according to the back propagation (BP) method during off-line training. The results of experimental evaluation show that the system works well over a wide range of cutting conditions (feed rate, depth of cut), and the ability of the system to detect tool wear is improved due to the generalization and self-organizing properties of the neural network.

목차 (Table of Contents)

• 1. Introduction
• 2. Sensor Signals from Milling Operations
• 3. Signal Processing
• 4. Feature Selection
• 5. Multilayered Perceptron
• 6. Experiment
• 7. Experimental Results
• 8. Conclusions