CN105058165A - Tool abrasion loss monitoring system based on vibration signals - Google Patents

Abstract

The invention discloses a tool wear amount monitoring system based on vibration signals, which includes a CNC milling machine, a milling cutter, a workpiece, a vibration sensor, a filter amplifier, an intelligent signal acquisition and processing instrument, a PC equipped with data processing and analysis software, and a vibration sensor. The quantity is two, one is installed at one end of the workbench, and the other is installed at the side of the spindle of the milling cutter. The workpiece is placed on the workbench of the CNC machine tool. After the vibration signal is processed by the filter amplifier and the intelligent signal acquisition and processing instrument, it is transmitted to the PC installed with the data processing and analysis software, and is analyzed and saved by the data processing and analysis software. The invention establishes a certain relationship between the amount of wear of a tool when machining titanium alloy and the characteristic value of its vibration signal, thereby achieving the purpose of on-line monitoring of tool wear and playing an important role in improving the tool processing efficiency of large-scale numerically controlled machine tools.

Description

Tool Wear Monitoring System Based on Vibration Signal

technical field

The invention relates to tool wear detection, in particular to a tool wear amount monitoring system based on vibration signals.

Background technique

In the machining process, tool wear is an inevitable phenomenon. In recent years, the wide application of high-performance machine tools has not only greatly improved the processing quality and efficiency, but also promoted automated production. While bringing convenience, this requires the CNC machine tool to be equipped with a tool monitoring system to monitor tool failures such as tool wear and tool breakage, judge the working status of the machine tool tool, and improve the work efficiency of the CNC machine tool. Machine tool failures caused by tool wear failures account for a large proportion of all machine tool failures, and the downtime caused by tool failures exceeds 20% of the total downtime of machine tools. During machine tool processing, the tool is under different processing conditions, and the status of the tool is also in a process of constant change. If the tool monitoring system is not equipped, the machine tool is still working when the tool fails, which may cause the entire processing process to be interrupted and the workpiece to be scrapped. In the worst case, the entire system may cease to function. This increases time cost and production cost. The data shows that the downtime of CNC machine tools equipped with tool monitoring system is greatly reduced, which is 1/4 of the failure time of ordinary CNC machine tools, the production efficiency is increased by 10-60%, and the utilization rate of machine tools is increased by 50%.

Therefore, exploring the mechanism of tool wear can greatly improve the machining efficiency of machine tools, significantly reduce machining costs, and create huge economic benefits. However, the tool wear mechanism is very complicated, and the tool wear monitoring in the process of machine tool processing in my country is only in the research and development stage. The research of Kennamtal Company in the United States shows that once the CNC machine tool is equipped with an online tool monitoring system, it can save 30% of the processing cost. Therefore, it is of great research value to develop an intelligent tool condition monitoring system for tool damage failures, to ensure safe production, not to produce waste products, and to protect CNC machine tools.

The methods of tool wear monitoring commonly used at home and abroad are divided into direct measurement method and indirect measurement method.

Among them, the direct measurement method is to directly measure the size of tool wear and damage. The specific methods are as follows: discharge current measurement method, optical fiber measurement method, microstructure coating method, resistance measurement method, ray measurement method and computer image processing method, etc.; through these monitoring methods , can grasp the situation of tool wear to a certain extent, but there are still many deficiencies. The direct measurement method has two main disadvantages:

First, downtime detection is required, which takes a long time, which takes up a lot of production time;

Second, transient damage that occurs suddenly during processing cannot be detected;

The direct method has certain limitations, which limit the promotion and application of this method.

The indirect method is to measure the tool wear state by measuring the signal closely related to the tool wear and damage state and the related physical quantities in the processing process. Among them, the current measurement method and the acoustic emission detection method are more popular. It can also monitor the cutting force, torque, and workpiece. Geometric dimensions, workpiece surface quality, chip shape, noise or vibration, etc. to reflect the wear state of the tool. Among them, in the indirect measurement method, the converted signals that can be monitored include mechanical, pneumatic, electromagnetic, optical, acoustic and other physical quantities.

In the past few decades, a lot of research work has been done on the automatic monitoring of tool wear and damage in China, and corresponding breakthroughs have been made. For example, Yang Shuzi of Huazhong University of Science and Technology and others believed that with the intensification of tool wear, the main peak frequency on the power spectrum image moved to the low frequency end; Dr. Jiang Chengyu of Nanjing University of Aeronautics and Astronautics proposed a frequency band energy method for online turning tool wear detection.

In foreign countries, tool online monitoring has also developed greatly. For example, I.Tansel predicts tool breakage by predicting the average value of cutting force. He uses the previous 9 averages to predict the next average and compares with the actual value. Tool breakage is predicted by comparing the predicted value with the selected threshold value. Ertunc, H.M He used fuzzy operation to input the measured force signal and energy signal into a single fuzzy algorithm, and then sent the output to the fuzzy center algorithm to judge the state of the tool during the cutting process.

However, their research is limited to the case of ordinary workpieces processed by ordinary tools, and no corresponding conclusions have been given for processing difficult-to-machine materials. Compared with ordinary materials, difficult-to-machine materials have higher hardness and more difficult milling, and special tools must be selected for processing, so their processing characteristics cannot be simply copied.

Contents of the invention

In order to solve the above problems, the present invention provides a tool wear amount monitoring system based on vibration signals.

In order to achieve the above object, the technical scheme that the present invention takes is:

Tool wear monitoring system based on vibration signal, including CNC milling machine, milling cutter, workpiece, vibration sensor, filter amplifier, intelligent signal acquisition and processing instrument and PC with data processing and analysis software installed, the number of vibration sensors is two, one It is installed on one end of the workbench, and the other is installed on the spindle side of the milling cutter. The workpiece is placed on the workbench of the CNC machine tool. The vibration sensor is connected to the intelligent signal acquisition and processing instrument through the filter amplifier, and the vibration signal collected by the vibration sensor is passed through the filter amplifier. After processing with the intelligent signal acquisition and processing instrument, it is transmitted to a PC equipped with data processing and analysis software, and is analyzed and saved by the data processing and analysis software.

Wherein, the CNC milling machine adopts XH714 CNC machining center.

Wherein, the vibration signal sensor is an acceleration sensor, the end of which has strong magnetism, and can be well adsorbed on the measured object.

Among them, the data processing and analysis software uses the following functions: Crest kurtosis coefficient function, Rms square root function and Kurtosis standard deviation function.

The present invention has following benefit effect:

Establish a certain relationship between the wear amount of the tool when machining titanium alloy and its vibration signal characteristic value, so as to achieve the purpose of online monitoring of tool wear, which plays an important role in improving the machining efficiency of large-scale CNC machine tools; Through the analysis and research of the data, a clear conclusion has been obtained, which enriches the subject research in the field of tool online monitoring; according to the experimental results and data analysis of this experimental research, the following conclusions are obtained: After the Crest kurtosis coefficient The tool vibration acceleration signal transformed by the function and the Rms square root function has a positive correlation with the tool wear amount; the tool vibration acceleration signal transformed by the Kurtosis standard deviation function has a negative correlation with the tool wear amount.

Description of drawings

FIG. 1 is a schematic structural diagram of a tool wear monitoring system based on vibration signals according to an embodiment of the present invention.

Fig. 2 is the wear length of tool A according to the embodiment of the present invention.

Fig. 3 is the wear width of tool A in the embodiment of the present invention.

Fig. 4 is the wear length of tool B in the embodiment of the present invention.

Fig. 5 is the wear width of tool B in the embodiment of the present invention.

Fig. 6 is the working table Crest of tool A in the embodiment of the present invention.

Fig. 7 is tool A workbench Rms in the embodiment of the present invention

Fig. 8 is the working table Kurtosis of tool A in the embodiment of the present invention.

Fig. 9 is the spindle Crest of tool A in the embodiment of the present invention.

Fig. 10 is the tool A spindle Rms in the embodiment of the present invention.

Fig. 11 is the Kurtosis of the tool A spindle in the embodiment of the present invention.

Fig. 12 is the spindle Crest of tool B in the embodiment of the present invention.

Fig. 13 is the tool B spindle Rms in the embodiment of the present invention.

Fig. 14 is the Kurtosis of the tool B spindle in the embodiment of the present invention.

Fig. 15 is the tool B workbench Crest in the embodiment of the present invention

Fig. 16 is the table Rms of the tool B in the embodiment of the present invention.

Fig. 17 is Kuetosis of tool B workbench in the embodiment of the present invention.

Detailed ways

In order to make the objects and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

Example

In this embodiment, the model that the equipment is manufactured by Nanjing Second Machine Tool Works is the XH714 CNC machining center; the used cutters include a cutter (cutter A) that is a model CG10SEKT1204AFFN-HL and a cutter (cutter B) that a model is SEHT1204AFTNJA5030; The material is α-β titanium alloy TC4 (Ti-6Al-4V), and its size is 80×200×200 (mm). The chemical composition (weight percent) and mechanical properties of titanium alloy TC4 are shown in Table 1 and Table 2, respectively.

Table 1 Chemical composition of titanium alloy TC4

element Ti AL V Fe C N h o other Proportion main component 6.4 3.7 0.24 0.085 0.023 0.011 0.17 <0.10

Table 2 Mechanical properties of titanium alloy TC4

3RUSB handheld digital microscope is used; the vibration signal acquisition and processing device used is the DHDAS software platform independently developed by Donghua Testing. This software platform mainly includes vibration signal acquisition device and processing software and vibration sensor.

The vibration signal acquisition device and processing software include the underlying driver program, communication protocol, etc., integrating data acquisition, basic analysis, order analysis, on-site dynamic balance, impact waveform detection, embodiment modal analysis, acoustic analysis and other engineering applications and analysis , using a modular management mechanism, the use of more simple and convenient. It can automatically identify system parameters, fully program-controlled instrument range, filter and sampling parameter settings, complete real-time signal acquisition, analysis and processing, and realize virtual instrument functions, multi-functional modular management and "one-key setting" operation. The two vibration signal sensors used in conjunction with the DHDAS software platform are acceleration sensors. In this embodiment, the vibration data collected by the DHDAS software platform are stored in txt text, and the MATLAB software is called to analyze the data.

There are 4 cutting parameters used, namely the spindle speed n (rpm), the feed rate v (mm/min), the cutting depth dp (mm), and the cutting width de (mm). According to the processing performance of CNC machining center XH714, the selection range of the four cutting parameters determined is shown in Table 3.

Table 3 Selection range of cutting parameters

parameter Spindle speed Feed rate depth of cut Cutting width scope 700-1300 80-120 0.1-0.5 20-60

According to the range of its cutting parameters, this embodiment adopts the orthogonal embodiment method. Considering the number of times in the examples, each cutting parameter is set to 3 different values, see Table 5 for details. Since the size of the titanium alloy to be processed and the requirements of the processing technology should be considered in the actual embodiment, the cutting width needs to be set as a constant value. In this embodiment, the cutting width is set to be 40mm, which is half of the workpiece processing surface size (80mm). The grouping parameters obtained in this embodiment are shown in Table 6.

Table 5 Cutting parameters of CNC machining center

Table 6 CNC machining center group cutting parameters

As shown in Figure 1, each device is connected. Specifically, one vibration sensor 4 is installed on one end of the workbench 3, and the other is installed on the main shaft side of the milling cutter 2. The workpiece 3 is placed on the workbench of the CNC machine tool 1. The vibration sensor 4, the filter amplifier 5, the intelligent signal acquisition and processing instrument 7, and the PC 6 installed with data processing and analysis software are sequentially connected through data lines.

Each process needs to be stopped 10 times, and the wear state of 5 blades is photographed with a microscope each time. When shooting, the 3RUSB handheld digital microscope is fixed on the measuring height gauge, and the distance between the lens and the measuring blade is adjusted. When the object distance is fixed, rotate the roller with your fingers until you see the clearest image, and then calibrate it for the measurement of the wear amount of the blade in the later stage, and record the vibration signal of each segment at the same time.

The processing surface of the titanium alloy to be processed is 80×200 (mm), and a stop is performed every time a complete plane is processed, that is, a stop is performed for 2 strokes (because the cutting width is 40mm).

The data of tool A wear are shown in Table 7 and Table 8. Accordingly, the curve of the wear amount of tool A is drawn, as shown in Fig. 2 and Fig. 3 .

Table 7 Wear length of tool A in each stage (mm)

stage 1 2 3 4 5 6 7 8 9 10 No. 1 blade 1.082 1.252 1.541 1.627 1.680 1.755 1.803 1.901 1.972 2.232 No. 2 blade 0.926 1.238 1.305 1.414 1.700 1.873 1.968 2.11 2.186 2.617 No. 3 blade 0.783 1.062 1.129 1.327 1.394 1.576 1.801 1.882 2.082 2.273 No. 4 blade 0.643 0.729 0.836 0.927 1.115 1.209 1.268 1.446 1.678 1.853 No. 5 blade 1.052 0.582 0.981 1.094 1.196 1.353 1.892 1.95 2.104 2.323

Table 8 Wear width of tool A in each stage (mm)

stage 1 2 3 4 5 6 7 8 9 10 No. 1 blade 0.850 1.010 1.224 1.252 1.274 1.328 1.377 1.432 1.488 1.762 No. 2 blade 0.780 0.940 1.019 1.064 1.171 1.300 1.333 1.444 1.626 2.019 No. 3 blade 0.623 0.815 0.850 0.855 0.966 1.084 1.172 1.212 1.304 1.392 No. 4 blade 0.486 0.640 0.691 0.710 0.823 0.842 0.876 0.944 0.995 1.100 No. 5 blade 0.839 0.488 0.689 0.694 0.828 1.011 1.095 1.112 1.203 1.343

It can be seen from Fig. 2 that after cutting, the wear length of blade 2 is the largest, the wear amount of blade 5 has the fastest changing trend, and the wear amount of blade 3 increases slowly, which is similar to a straight line. The large difference in the second time is due to the error of installation measurement. The wear amount of blade 4 is the smallest, which is caused by the fact that the five blades installed on the milling cutter are not on the same plane, the wear amount of blade 1 increases rapidly at the beginning and end, and the growth rate in the middle is almost flat.

It can be seen from Figure 3 that the change trend of blade wear width is basically consistent with the change trend of tool wear length.

The data of tool B wear are shown in Table 9 and Table 10. Accordingly, draw the curve of the wear amount of tool B, as shown in Figure 4 and Figure 5.

Table 9 Wear length of tool B in each stage (mm)

stage 1 2 3 4 5 6 7 8 9 10 No. 1 blade 0.994 1.016 1.178 1.482 1.732 2.021 2.235 2.675 2.892 3.067 No. 2 blade 1.022 1.092 1.202 1.628 1.754 2.002 3.315 3.419 3.789 3.965 No. 3 blade 1.133 1.284 1.358 1.627 2.334 2.919 3.307 3.594 3.749 4.120 No. 4 blade 0.968 1.076 1.278 1.656 1.851 2.791 2.970 3.257 3.689 4.062 No. 5 blade 0.920 1.030 1.240 1.556 1.880 1.941 2.183 2.791 3.205 3.823

Table 10 Tool B wear width in each stage (mm)

stage 1 2 3 4 5 6 7 8 9 10 No. 1 blade 0.719 0.86 0.89 1.066 1.084 1.307 1.481 1.574 1.773 1.938 No. 2 blade 0.808 0.852 0.912 1.134 1.224 1.385 2.079 2.101 2.433 2.603 No. 3 blade 0.842 0.869 0.900 1.009 1.516 1.913 2.221 2.366 2.504 2.726 No. 4 blade 0.780 0.818 0.850 0.936 1.071 1.856 1.968 2.126 2.364 2.466 No. 5 blade 0.770 0.818 1.004 1.092 1.294 1.338 1.468 1.789 2.120 2.464

It can be seen from Fig. 4 that the wear length of the No. 3 blade is the largest and has a large change trend; the change trend of the No. 1 blade is relatively flat, similar to a straight line, and the wear amount is the smallest; The blade did not have a small wear amount of tool A. The quality of this blade has a lot to do with human error. The wear amount of No. 5 blade changed relatively flat in the early stage, and increased sharply in the later stage.

It can be seen from Figure 5 that the change trend of the blade wear width is basically consistent with the length.

Save the time-acceleration vibration signal in text format, and use Matlab software to transform it through Crest kurtosis coefficient function, Rms square root function and Kurtosis standard deviation function respectively, and the obtained data are presented in a line graph.

The data obtained by transforming the vibration signal of tool A through three functions are shown in Table 11.

According to the calculated data in Table 11, the curves of the characteristic value of the vibration signal of tool A are drawn, as shown in Fig. 6 to Fig. 11 .

From the six graphs in Fig. 6 to Fig. 11, it can be seen that with the wear of each stage, the characteristic value of the vibration signal of tool A does not have a clear trend. Through the analysis to embodiment design and embodiment process, draw the reason that causes this result to have the following points:

1. Tool A itself is not suitable for processing titanium alloys, and is in an abnormal wear state during processing in this embodiment.

2. Sparks appeared on tool A during processing in the embodiment, which interfered with the collection of vibration signals and distorted the data.

3. Human error exists in the embodiment.

Table 11 Eigenvalues of tool A vibration signal

The data obtained by transforming the vibration signal of tool B through three functions are shown in Table 12.

According to the data calculated in Table 12, the curves of the characteristic value of the vibration signal of tool B are drawn, as shown in Fig. 12 to Fig. 17 .

From the six graphs in Fig. 12 to Fig. 17, it can be seen that with the wear of each stage, the characteristic value of the vibration signal of tool B has a relatively obvious trend.

The vibration acceleration signal collected at the spindle position is transformed by the Crest kurtosis coefficient function and the Rms square root function, showing a positive correlation with the wear stage; after being transformed by the Kurtosis standard deviation function, it shows a negative correlation with the wear stage.

Table 12 Eigenvalues of tool B vibration signal

The eigenvalues of the vibration acceleration signals collected at the position of the workbench have no obvious trend. Through the analysis to embodiment design and embodiment process, draw the reason that causes this result to have the following points:

1. The difference in the vibration signal itself at the worktable and the spindle leads to different results.

2. The titanium alloy workpiece to be processed is installed on the workbench, and the natural frequencies of the two are different, which leads to the disorder of the vibration signal.

3. Human error exists in the embodiment.

Regardless of tool A or tool B, with the milling of titanium alloy workpieces, the wear amount shows a significant increase, which is basically consistent with the typical curve of tool wear.

When processing the vibration acceleration signal, it is found that the vibration acceleration signal of the tool B spindle position is transformed by the Crest kurtosis coefficient function and the Rms square root function, showing a positive correlation with the wear stage; after being transformed by the Kurtosis standard deviation function, it presents a positive correlation with Negative correlation in wear phase. Considering that the wear amount of tool B is also positively correlated with the wear stage, the relationship between the vibration acceleration signal characteristics and the wear amount can be obtained, as shown in Table 13.

Table 13 The relationship between the characteristic value of the vibration acceleration signal of tool B and the amount of wear

The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications should also be It is regarded as the protection scope of the present invention.

Claims (4)

1. based on the tool abrasion monitoring system of vibration signal, it is characterized in that, comprise CNC milling machine (1), milling cutter (2), workpiece (3), vibrating sensor (4), filter amplifier (5), intelligent signal collection processing instrument (7) and be provided with the PC (6) of Data Management Analysis software, the quantity of vibrating sensor (4) is two, the one end being arranged on workbench (3), another is arranged on the main shaft side of milling cutter (2), workpiece (3) is placed on the workbench of Digit Control Machine Tool (1), vibrating sensor (4) is connected with intelligent signal collection processing instrument (7) by filter amplifier (5), after the process of the vibration signal collected of vibrating sensor (4) amplifier (5) and intelligent signal collection processing instrument (7) after filtering, be sent to the PC (6) being provided with Data Management Analysis software, by Data Management Analysis software analysis, preserve.

2. the tool abrasion monitoring system based on vibration signal according to claim 1, is characterized in that, described CNC milling machine (1) adopts XH714 numerical control machining center.

3. the tool abrasion monitoring system based on vibration signal according to claim 1, is characterized in that, described vibration signal sensor (4) is acceleration transducer, and there is strong magnetic its end, can be good at being adsorbed on measured object.

4. the tool abrasion monitoring system based on vibration signal according to claim 1, is characterized in that, Data Management Analysis software adopts with minor function: Crest coefficient of kurtosis function, Rms square root function and Kurtosis standard deviation function.