In the context of single-machine tool processing, it is still possible to maintain normal production through manual monitoring and worker experience. However, for advanced systems such as Flexible Manufacturing Systems (FMS), Computer-Integrated Manufacturing Systems (CIMS), and unmanned chemical plants, real-time online monitoring and control of tool wear and damage have become essential. Timely detection of tool degradation and damage is a critical factor in enhancing the automation level of the production process, ensuring product quality, and preventing damage to the machine, tools, and workpieces.
There are various methods for monitoring tool wear and damage, which can be broadly categorized into direct and indirect measurement techniques. Direct methods include optical, contact resistance, and radioactive approaches, while indirect methods involve cutting force or power measurement, tool and workpiece monitoring, temperature analysis, vibration studies, acoustic emission (AE) detection, and motor current or power monitoring.
Each of these existing methods has its own advantages and limitations. In this study, we focus on combining acoustic emission (AE) and motor current signals as monitoring parameters. The AE signal is advantageous because it avoids the low-frequency noise that dominates during startup, offering a higher signal-to-noise ratio in the region of interest. It also provides fast response and high sensitivity, although it may be susceptible to interference under heavy loads. On the other hand, the motor current signal is easier to extract, applicable to all machining processes, and does not interfere with normal cutting, but it has slower response times and lower sensitivity at light loads.
By integrating AE and motor current signals, we can leverage their respective strengths, compensate for their weaknesses, expand the monitoring range, and improve both the accuracy and success rate of detecting tool wear and breakage. During the cutting process, tool wear or fracture causes changes in cutting force, which in turn affects the motor's output torque and results in variations in the motor current. This method allows for real-time, indirect monitoring of tool conditions.
Acoustic emission occurs when strain energy is released as an elastic wave due to material deformation or fracture under external or internal forces. AE signals typically have a low amplitude and wide frequency range. Analysis shows that AE signals from normal cutting are mainly caused by plastic deformation of the workpiece, with most of the energy concentrated below 100 kHz. However, when the tool wears or breaks, the AE signal in the 100–300 kHz frequency range significantly increases. Therefore, a band-pass filter should be used to monitor this specific frequency range for effective tool condition monitoring.
The principle of using AE and motor current signals to detect tool wear and damage involves different strategies depending on the load level. In the light load zone, the AE envelope signal is monitored using a threshold method. In the medium load zone, both AE and motor current signals are combined to improve the discrimination success rate. If the AE signal exceeds its threshold and the motor current also exceeds its threshold within a delay time (ds), the tool is considered worn or broken. Otherwise, no alarm is triggered, and the system continues monitoring. This "AND" logic approach takes advantage of the real-time and sensitive nature of AE signals while considering the hysteresis and strong anti-interference capability of the motor current signal.
In the high-load zone, the motor current signal becomes the primary monitoring parameter, with the AE signal serving as a supplementary check. A grid voltage monitoring line is included to eliminate the impact of power supply fluctuations, thereby improving the system’s robustness. Additionally, an automatic subtraction of the initial cutting current is implemented to enhance the sensitivity of current signal monitoring by focusing on the variation in current rather than the absolute value. The block diagram of the monitoring system illustrates these key components and their interconnections.
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