Intelligent wireless tool wear monitoring system based on chucked tool condition monitoring ring and deep learning

Abstract

The widespread application of tool condition monitoring technology in practical manufacturing processes cannot be separated from the development of wireless monitoring technology. However, most existing toolholder-type wireless monitoring technologies alter the original structure, which may result in reduced stiffness, significant cost increases, or diminished spindle compatibility. To address this issue, this study proposes an Intelligent Wireless Tool Condition Monitoring (IWTCM) system composed of an independently developed monitoring ring and a deep-learning model.The developed monitoring ring acquisition module acquires tool shank vibration signals with a power consumption of only 0.458 W. The monitoring ring housing design, based on a chuck-type structure, can clamp onto toolholders with diameters ranging from 40 to 80 mm. Reliability tests demonstrate that the proposed monitoring ring output is highly comparable to the output of commercial vibration-signal sensors. Additionally, the monitoring ring has been verified for dynamic balancing. The tool wear condition recognition model built based on the Convolutional Neural Networks − Long Short Term Memory (CNN-LSTM) classical deep learning algorithm uses vibration data collected from the monitoring ring as input and recognition accuracy can reach 100 % in the test set, which verifies the excellent performance of the proposed IWTCM system. This study further developed a tool condition monitoring software that bridges the gap in such software. Based on the principle of multi-threading, the monitoring software realizes serial communication, data saving, data visualization, and tool wear condition recognition.