Using acoustic emission and motor current detection technology to monitor tool breakage
Abstract A tool breakage monitoring system with unique characteristics was developed by using acoustic emission (AE) and motor current multi-feature parameter fusion detection method. The software and hardware structure of the system was introduced, and a mathematical model for parameter detection was established. The feasibility of the system for online monitoring of tool breakage was proved by experiments.
Keywords Online monitoring of tool breakage Acoustic emission Motor
current Real-time monitoring of cutting tool breakage is an important means to realize the automation and unmanned production process, ensure product quality, improve production efficiency, and reduce equipment failures. In the research on tool breakage monitoring, scholars from various countries have proposed many schemes, such as monitoring the surface roughness and size of the processed parts, the changes in cutting force and vibration during the processing process, to determine the breakage and wear of the tool. Many of the research methods are still in the laboratory stage, and a few methods such as monitoring motor current and spindle torque have begun to be used in actual production. This paper proposes a dual-parameter comprehensive method of using AE signals and monitoring spindle motor current to determine the damage state of the cutting tool. This method is less affected by cutting conditions and has the characteristics of high monitoring accuracy, strong system anti-interference ability, intuitive signal display, high sensitivity, real-time online detection and easy use.
1 Tool breakage monitoring principle
This system uses acoustic emission (AE) and motor torque detection technology to monitor tool breakage. Acoustic emission refers to the phenomenon of elastic waves generated by the release of stored energy when solid materials break. The AE signal is detected by the sensor to obtain the corresponding electrical signal, which is divided into two types: burst type and continuous type. The former is generated by tiny cracks and cracks in solid materials, and the latter is manifested by the material during plastic deformation. Tool breakage detection takes burst AE signals as the object, uses AE signals to detect the position and intensity of the emission source, understands the changes in the internal structure of the object being measured, and realizes the monitoring of the cutting tool status. In the cutting process, due to the influence of metal deformation, extrusion, friction, etc. and changes in the cutting environment, the AE signal characteristics directly derived from the cutting point are complex and the spectrum is rich, which deepens the difficulty of AE signal identification and measurement when the tool is broken. Extracting AE signals of tool breakage from these signals is the key to online monitoring. The device shown in Figure 1 is used to extract the AE signal characteristics of the cutting tool. The AE sensor is installed on the tool holder. The AE signal detected by the sensor is amplified by the preamplifier and the main amplifier. It is collected and stored by a waveform recorder with a sampling frequency of MHz, and sent to a digital oscilloscope for time domain characteristic analysis. The results are plotted by a plotter. Figure 2 shows the frequency distribution of the AE signal in the cutting process. The figure shows that the frequency of mechanical noise and machine tool idling noise in the cutting process is in the range of <100kHz, while the frequency of the AE signal when the tool is broken is in the range of 300-500kHz, and the peak value is large. Therefore, to correctly extract the AE signal of tool breakage, the AE sensor with a suitable bandwidth and the frequency range of the bandpass filter should be selected. At the same time, a multi-parameter real domain frequency domain comprehensive processing method should be adopted to effectively suppress various environmental noises and improve the monitoring accuracy.
Monitoring of motor current. During the cutting process, tool breakage changes, resulting in changes in cutting force. Changes in cutting force cause changes in spindle torque, which in turn cause changes in motor current. Monitoring the size of the motor current can indirectly determine the state of the tool. The difference between the current value at a certain moment and the current value during normal cutting is used as the characteristic signal of tool damage I = |I1-I0|ΣK threshold (1)
where: I1 is the measured value of the current at a certain moment;
I0 is the measured value of the current at normal cutting or the beginning of cutting.
When the cutting conditions, tool materials, workpiece materials, etc. change, the I1 value changes, and the I0 value also changes accordingly, but the difference between the two does not change much, that is, the change in the I value mainly depends on the damage state of the tool, and has little to do with the impact of changes in cutting conditions. However, in actual production, large grid voltage fluctuations and the start and stop of surrounding equipment will affect the I1 value. Therefore, the fluctuation of the grid voltage should also be monitored and its influence on the motor current should be removed. At this time, the discriminant formula for monitoring tool damage using motor current should be I = |I1-I0|-F|U1-U0|ΣK threshold (2)
where: U1 is the measured value of the voltage at a certain moment;
U0 is the voltage value at the beginning of cutting;
F is the ratio of the current change caused by the grid voltage fluctuation.
In order to help engineering supervision and testing practitioners continuously improve their professional and technical level, China Engineering Testing Network (http://www.cngcjc.com) provides free testing papers and testing standards.
2. The composition and characteristics of the monitoring system
. The tool breakage monitoring system is shown in Figure 3. In the figure, the motor current processing module is used to process the voltage signal output by the Hall sensor indicating the motor current size. This signal is a time domain amplitude signal. After a certain operation, the I and U values of formula (2) are obtained, and then after A/D conversion, it is processed by a microcomputer. The structure of the AE signal processing module is shown in Figure 4. The experimental results shown in Figure 2 show that the frequency range of the AE signal of cutting tool breakage is between 300 and 500kHz. In order to effectively amplify the mV level signal output by the AE sensor, a wide-band and high-gain two-stage amplifier circuit is required. In order to correctly extract the useful AE signal, it is connected to a 100-600kHz bandpass filter. After detection processing, it is sent to the computer at the same time as the amplitude signal output by the detector 2. The two signals are compared to obtain the amplitude and frequency distribution of the AE signal of tool breakage. The computer processes and analyzes the signals of the AE sensor and the Hall sensor to determine the damage state of the tool.
Working principle of the monitoring system. When the power is turned on, the computer starts working. After initializing each input and output port, the motor current signal and the AE sensor signal are collected respectively, and compared with the set threshold value and other processing to identify whether the tool is damaged. If it is damaged, an alarm signal is issued.
3 Experiments
The tool damage monitoring experiment was carried out on the CA6140 lathe. The workpiece is a 45# steel bar with a diameter of 50mm, and the turning tool is a carbide blade. The cutting speed is 400r/min and the feed rate is 0.2mm/r. In order to cause the turning tool to break, a 2mm drill bit is first embedded in the workpiece. The experiment was carried out 30 times. When the tool is confirmed to be damaged, the detection rate of the monitoring system is shown in Table 1.
4 Conclusion
This paper takes turning as an example to introduce the tool damage monitoring system we developed. Experiments and applications have shown that it can not only sensitively and accurately detect tool breakage under various conditions, but is also suitable for online detection of tool breakage and wear in similar processing processes such as boring, milling, and drilling, and has a high system detection rate. This system has the following characteristics:
(1) The multi-parameter comprehensive analysis and judgment method of monitoring motor current and AE signals can effectively improve the success rate of tool breakage detection.
(2) The external setting method of the monitoring threshold makes the monitoring system suitable for various processing environments and conditions, and has high anti-interference ability and working reliability.
(3) The hardware and software of the monitoring system adopt a modular structure, which is easy to modify and maintain, increases flexibility and versatility, and is suitable for online monitoring.
Reference address:Monitoring tool breakage using acoustic emission and motor current detection technology
Abstract A tool breakage monitoring system with unique characteristics was developed by using acoustic emission (AE) and motor current multi-feature parameter fusion detection method. The software and hardware structure of the system was introduced, and a mathematical model for parameter detection was established. The feasibility of the system for online monitoring of tool breakage was proved by experiments.
Keywords Online monitoring of tool breakage Acoustic emission Motor
current Real-time monitoring of cutting tool breakage is an important means to realize the automation and unmanned production process, ensure product quality, improve production efficiency, and reduce equipment failures. In the research on tool breakage monitoring, scholars from various countries have proposed many schemes, such as monitoring the surface roughness and size of the processed parts, the changes in cutting force and vibration during the processing process, to determine the breakage and wear of the tool. Many of the research methods are still in the laboratory stage, and a few methods such as monitoring motor current and spindle torque have begun to be used in actual production. This paper proposes a dual-parameter comprehensive method of using AE signals and monitoring spindle motor current to determine the damage state of the cutting tool. This method is less affected by cutting conditions and has the characteristics of high monitoring accuracy, strong system anti-interference ability, intuitive signal display, high sensitivity, real-time online detection and easy use.
1 Tool breakage monitoring principle
This system uses acoustic emission (AE) and motor torque detection technology to monitor tool breakage. Acoustic emission refers to the phenomenon of elastic waves generated by the release of stored energy when solid materials break. The AE signal is detected by the sensor to obtain the corresponding electrical signal, which is divided into two types: burst type and continuous type. The former is generated by tiny cracks and cracks in solid materials, and the latter is manifested by the material during plastic deformation. Tool breakage detection takes burst AE signals as the object, uses AE signals to detect the position and intensity of the emission source, understands the changes in the internal structure of the object being measured, and realizes the monitoring of the cutting tool status. In the cutting process, due to the influence of metal deformation, extrusion, friction, etc. and changes in the cutting environment, the AE signal characteristics directly derived from the cutting point are complex and the spectrum is rich, which deepens the difficulty of AE signal identification and measurement when the tool is broken. Extracting AE signals of tool breakage from these signals is the key to online monitoring. The device shown in Figure 1 is used to extract the AE signal characteristics of the cutting tool. The AE sensor is installed on the tool holder. The AE signal detected by the sensor is amplified by the preamplifier and the main amplifier. It is collected and stored by a waveform recorder with a sampling frequency of MHz, and sent to a digital oscilloscope for time domain characteristic analysis. The results are plotted by a plotter. Figure 2 shows the frequency distribution of the AE signal in the cutting process. The figure shows that the frequency of mechanical noise and machine tool idling noise in the cutting process is in the range of <100kHz, while the frequency of the AE signal when the tool is broken is in the range of 300-500kHz, and the peak value is large. Therefore, to correctly extract the AE signal of tool breakage, the AE sensor with a suitable bandwidth and the frequency range of the bandpass filter should be selected. At the same time, a multi-parameter real domain frequency domain comprehensive processing method should be adopted to effectively suppress various environmental noises and improve the monitoring accuracy.
Monitoring of motor current. During the cutting process, tool breakage changes, resulting in changes in cutting force. Changes in cutting force cause changes in spindle torque, which in turn cause changes in motor current. Monitoring the size of the motor current can indirectly determine the state of the tool. The difference between the current value at a certain moment and the current value during normal cutting is used as the characteristic signal of tool damage I = |I1-I0|ΣK threshold (1)
where: I1 is the measured value of the current at a certain moment;
I0 is the measured value of the current at normal cutting or the beginning of cutting.
When the cutting conditions, tool materials, workpiece materials, etc. change, the I1 value changes, and the I0 value also changes accordingly, but the difference between the two does not change much, that is, the change in the I value mainly depends on the damage state of the tool, and has little to do with the impact of changes in cutting conditions. However, in actual production, large grid voltage fluctuations and the start and stop of surrounding equipment will affect the I1 value. Therefore, the fluctuation of the grid voltage should also be monitored and its influence on the motor current should be removed. At this time, the discriminant formula for monitoring tool damage using motor current should be I = |I1-I0|-F|U1-U0|ΣK threshold (2)
where: U1 is the measured value of the voltage at a certain moment;
U0 is the voltage value at the beginning of cutting;
F is the ratio of the current change caused by the grid voltage fluctuation.
In order to help engineering supervision and testing practitioners continuously improve their professional and technical level, China Engineering Testing Network (http://www.cngcjc.com) provides free testing papers and testing standards.
2. The composition and characteristics of the monitoring system
. The tool breakage monitoring system is shown in Figure 3. In the figure, the motor current processing module is used to process the voltage signal output by the Hall sensor indicating the motor current size. This signal is a time domain amplitude signal. After a certain operation, the I and U values of formula (2) are obtained, and then after A/D conversion, it is processed by a microcomputer. The structure of the AE signal processing module is shown in Figure 4. The experimental results shown in Figure 2 show that the frequency range of the AE signal of cutting tool breakage is between 300 and 500kHz. In order to effectively amplify the mV level signal output by the AE sensor, a wide-band and high-gain two-stage amplifier circuit is required. In order to correctly extract the useful AE signal, it is connected to a 100-600kHz bandpass filter. After detection processing, it is sent to the computer at the same time as the amplitude signal output by the detector 2. The two signals are compared to obtain the amplitude and frequency distribution of the AE signal of tool breakage. The computer processes and analyzes the signals of the AE sensor and the Hall sensor to determine the damage state of the tool.
Working principle of the monitoring system. When the power is turned on, the computer starts working. After initializing each input and output port, the motor current signal and the AE sensor signal are collected respectively, and compared with the set threshold value and other processing to identify whether the tool is damaged. If it is damaged, an alarm signal is issued.
3 Experiments
The tool damage monitoring experiment was carried out on the CA6140 lathe. The workpiece is a 45# steel bar with a diameter of 50mm, and the turning tool is a carbide blade. The cutting speed is 400r/min and the feed rate is 0.2mm/r. In order to cause the turning tool to break, a 2mm drill bit is first embedded in the workpiece. The experiment was carried out 30 times. When the tool is confirmed to be damaged, the detection rate of the monitoring system is shown in Table 1.
4 Conclusion
This paper takes turning as an example to introduce the tool damage monitoring system we developed. Experiments and applications have shown that it can not only sensitively and accurately detect tool breakage under various conditions, but is also suitable for online detection of tool breakage and wear in similar processing processes such as boring, milling, and drilling, and has a high system detection rate. This system has the following characteristics:
(1) The multi-parameter comprehensive analysis and judgment method of monitoring motor current and AE signals can effectively improve the success rate of tool breakage detection.
(2) The external setting method of the monitoring threshold makes the monitoring system suitable for various processing environments and conditions, and has high anti-interference ability and working reliability.
(3) The hardware and software of the monitoring system adopt a modular structure, which is easy to modify and maintain, increases flexibility and versatility, and is suitable for online monitoring.
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