A Study on Tool Breakage Detection Technology Based on Current Sensing and Non-Contact Signal Analysis

Abstract

Highlights: What are the main findings? A non-contact tool breakage monitoring method based on spindle current sensing signals is proposed. The accuracy and computational efficiency of ANN, DNN, and CNN models are verified and compared. What is the implication of the main finding? Applicable to machining environments where vibration sensors cannot be installed. Enables the real-time monitoring of tool status without hardware modification, facilitating predictive maintenance. Tool breakage in CNC machining often leads to reduced productivity and increased maintenance costs. This study proposes a non-contact tool breakage detection method using spindle current signals captured by an SCT013 current sensor. The sensor easily attaches to the motor line without any hardware modification and provides real-time current signals for frequency domain analysis. Fast Fourier Transform (FFT) is employed to extract spectral features, particularly focusing on high-frequency energy spikes at the moment of breakage. A total of 20 experiments were conducted, and consistent spectral anomalies were observed. Additionally, deep learning models including ANN, DNN, and CNN were compared for automated detection performance. The results indicate that the proposed system can reliably detect tool breakage by identifying frequency domain anomalies that emerge within 1–3 s after the actual event, based on processed current signals. While the inference time of deep learning models ranges from 15 to 58 s, the detection mechanism captures the breakage characteristics early in the signal, enabling timely tool condition evaluation.