Monitoring and analysis of drilling condition of t

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Monitoring and analysis of deep hole drilling condition

deep hole drilling is carried out in a closed state, and the cutting condition of the tool cannot be observed directly. At present, we can only judge whether the drilling process is normal by listening to the sound, looking at the chips, observing the pressure gauge, touching the drill pipe vibration and other appearance phenomena based on experience. In addition to normal wear and tear, due to the large length diameter ratio of the drilled hole and the poor rigidity of the process system, the drill bit is easy to vibrate, and the chips are easy to cause blockage, so the deep hole drilling is often subject to random damage in general. Because the deep hole drilling process is very unstable, there is a great need for a deep hole drilling monitoring system to monitor the status and relevant information in the drilling process in real time, and process the signal in real time to identify the abnormal status in the processing process, and take measures such as changing the drilling amount or changing the tool in time to eliminate the abnormal status, and display and alarm when necessary, so as to avoid accidents and huge economic losses, Thus, the tool durability can be greatly improved to ensure the normal operation of deep hole drilling

due to the complex working conditions of deep hole machining, there is still a lack of in-depth understanding of the physical sources that cause these state changes. At present, there is no relatively perfect method and system to monitor the deep hole drilling process. The traditional single factor monitoring and single factor model analysis methods have great limitations. The influence of random factors is too large to correctly reflect the real situation of the system

this paper mainly studies the monitoring scheme and signal analysis of deep hole drilling process. A multi-sensor deep hole drilling force and drilling vibration signal acquisition device is designed. Multi information fusion technology is used to analyze and process a variety of signals collected in the process of deep hole drilling, and accurate judgment samples are obtained to provide criteria for the monitoring and control of the drilling process

monitoring scheme and device

cutting force contains a lot of information in the cutting process. When almost all cutting faults occur, the cutting force signal changes accordingly. However, different cutting methods show different characteristic signals. Relying on this single criterion to judge tool wear or damage will cause some false positives. Therefore, according to the characteristics of deep hole drilling system, a multi-sensor force and vibration measuring device for deep hole drilling is designed in this study, which has the advantages of strong versatility, fast dynamic response and convenient installation and use

Figure 1 Schematic diagram of multi-sensor force and vibration measuring device

the principle of multi-sensor force and vibration measuring device is shown in Figure 1: the sensor device is equipped with strain sensors and accelerometers, which are connected to the tailstock of deep hole drilling machine; The sensing device is universal and applicable to all drill pipes with different diameters (i.e. applicable to all holes with different diameters); The hoop is a clamp fastened on the drill pipe. Its function is to transmit the axial force and torque borne by the drill pipe to the sensing device. The hoop is not universal and should be matched with the drill pipe. The deep hole drilling force and vibration signals collected by the sensor device are transmitted to the computer through the data collector for analysis and processing, and the correct monitoring results are obtained, which can be used for real-time control of the drilling process

experimental study

this experimental study collected two working condition signals, normal and failure, in the process of deep hole machining. Using various bits with different wear degrees and changing the cutting parameters, we can obtain the signals of different experimental States and the degree of influence on the signals. The data collector uses four channels to collect force, torque, vertical and horizontal vibration signals respectively

data acquisition under normal drilling state

change law of tool state signal during initial wear and normal wear of tools

change rule of tool state signal by changing rotating speed

change rule of tool state signal by changing feed rate

data acquisition under simulated fault state

the change law of tool state signal when the tool is rapidly worn and damaged

change rule of tool state signal when chip is blocked

the change rule of tool state signal when the guide block breaks

signal analysis

because the measurement signal contains not only useful information reflecting the working state of the object, but also a large number of useless background noise, the useful information reflecting the working state is often submerged in useless background noise, which is generally difficult to find and extract directly. After analyzing the time domain waveform of the four signals of force, torque, vertical vibration and horizontal vibration, it is found that the difference between the original signals in the time domain is not obvious, and it can not be used for bit monitoring. Therefore, the original signal should be processed by detecting the number of turns of wire rod

when analyzing the four signals in the frequency domain, it is found that the force and torque signals have no obvious spectral peaks in the frequency domain, and there is no obvious change law. Only when the tool is damaged, there is a characteristic frequency. Therefore, time series analysis is applied to the force and torque signals. The AR model is used to estimate its parameters, and the residual equation of the time series model of the force is obtained

the vibration signal changes obviously in the frequency domain signal. After the spectrum analysis of the horizontal and vertical vibration signals, see Figure 2

Figure 2 power spectrum analysis of low-frequency vibration signal

from the above analysis, the following conclusions can be drawn:

the characteristic of the change of the power spectrum density of vibration signal in the low-frequency spectrum is that with the increase of tool wear, the amplitude of the main peak increases rapidly, and then tends to be flat, while the frequency position of the main peak moves from high frequency to low frequency. When the tool is rapidly worn and nearly damaged, the frequency of the main peak in the horizontal direction is concentrated at about 400Hz, and many new spectral peaks appear; The frequency of the main peak in the vertical direction is concentrated at about 210hz, and many new spectral peaks also appear. This change reflects the generation and development of tool wear, and excites the harmonic components of the tool workpiece machine tool processing system through the cutting force, resulting in the change of the vibration modal parameters of the system. The appearance of multi spectral peaks disperses the signal energy. Therefore, the amplitude of spectral peaks increases slowly, while the cutting damping caused by the deterioration of the conditions in the contact area between the tool and the workpiece increases, which reduces the frequency position of the main peak of vertical vibration

the variation law of the high-frequency power spectrum is basically the same as that of the low-frequency band, but the law is not prominent in the low-frequency band. The frequency of the main peak amplitude appearing in the high-frequency region is very small. In terms of the mechanical property experiments of metals, nonmetals, composites and products, there are few spectral peaks, because the forced vibration source of the cutting system is generally within 1000Hz. Therefore, the high frequency spectrum can effectively isolate or weaken the harmonic components of the processing system

pattern recognition

the success of state classification depends largely on feature analysis and the selection of feature quantities. The dynamic system of deep hole machining is a random process, which is difficult to be analyzed by a certain deterministic time function. The purpose of feature analysis is to transform the original signal into a characteristic quantity and find out its relationship with the working condition. There are many such characteristic quantities, but the separability is different in order to reflect the working condition law, sensitivity and aggregation type in the mode space. It is necessary to select the characteristic quantity with good regularity and strong sensitivity as the mode vector on the basis of feature analysis to make it have better separability

after analyzing the characteristics of vibration, axial force and torque signals in time domain and frequency domain respectively, based on the time series model, power spectrum and autocorrelation spectrum, the characteristic spectrum peak of horizontal vibration and the time series model of force are selected. The change of resistance is transformed into the residual variance of voltage or current change through Wheatstone bridge as the mode vector, and the weighted vector w= (-45401310090300) t is obtained by perceptron algorithm. Therefore, the classification function is obtained:

through the above classification function, we determine the machining state of the tool, as shown in the test sample in Table 1. After many tests, the correct rate is more than 90%


the experiment shows that the designed multi-sensor force and vibration measuring device for deep hole drilling is simple to install, reliable to operate, and the combined signals are complementary, which improves the accuracy of monitoring, and is suitable for monitoring the state of drilling tools in the process of deep hole drilling

the vibration signal has significant characteristics reflecting the tool wear state in the frequency domain. Under different drilling conditions and quantities. Its power spectrum changes differently. But the change trend is consistent

choose the amplitude of the main peak of horizontal vibration and the residual variance of the force as the eigenvector. Form a pattern vector. It can comprehensively reflect the running state of deep hole machining system. (end)

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