الملخص الإنجليزي
Abstract :
In the industrial sector, tool health monitoring has taken on significant importance due to
its ability to save labor costs, time, and waste. The approach used in this research uses spectrograms
of airborne acoustic emission data and a convolutional neural network variation called the Residual
Network to monitor the tool health of an end-milling machine. The dataset was created using three
different types of cutting tools: new, moderately used, and worn out. For various cut depths, the
acoustic emission signals generated by these tools were recorded. The cuts ranged from 1 mm to
3 mm in depth. In the experiment, two distinct kinds of wood—hardwood (Pine) and softwood
(Himalayan Spruce)—were employed. For each example, 28 samples totaling 10 s were captured.
The trained model’s prediction accuracy was evaluated using 710 samples, and the results showed an
overall classification accuracy of 99.7%. The model’s total testing accuracy was 100% for classifying
hardwood and 99.5% for classifying softwood.
Keywords: acoustic emission; spectrograms; convolutional neural network; signal processing; feature
extraction; tool health monitoring
