JPS61187654A - Apparatus for detecting abrasion of tool - Google Patents

Abstract

PURPOSE:To attain to smooth cutting work, by detecting the abrasion of a tool on the basis of the AE signal obtained at the time of cutting processing of a machine tool regardless of the kind of generated cut trash. CONSTITUTION:The work 3 fixed to the base 2 on the table 1 of a ball disc is subjected to cutting processing by a drill 4. An AE sensor 5 is provided on the table 1 and the output of the sensor 5 is applied to an A/D converter 11 through a preamplifier 6, BPF7, an amplifier 8, a full-wave rectifier 9 and LPF10. A contact sensor 12 for detecting the contact of the drill 4 with the work 3 by measuring the impedance between the drill 4 and the table 1 is provided to the drill 4. The digital data converted by the A/D converter 11 and the detection signal of the sensor 12 are applied to a microcomputer MC13 and a numerical value control apparatus 14 for controlling the ball disc is connected to MC13 which, in turn, detects the abrasion of the drill 4 on the basis of these input signals through an operational processing means to display the same on a display device 15.

Description

[Detailed Description of the Invention] [Field of the Invention] The present invention relates to a tool wear detection device that automatically detects tool breakage in a machine tool by using acoustic emission (hereinafter referred to as AE) generated during cutting. .

[Summary of the invention]

In the tool wear detection device according to the present invention, the average amplitude and maximum probability density of the AE multiplied signal generated during cutting with a machine tool increase as the tool wears and deforms, and as the tool wear progresses, the average value sharply increases. Tool wear is detected based on this increase. In this way, a tool wear signal can be obtained before the tool breaks, and it becomes possible to take appropriate measures such as replacing the tool.

[Background of the invention]

To detect the degree of wear on tools in conventional machine tools, such as drill presses and lathes, workers either observe the tip of the tool with a microscope after the cutting process is complete, or interrupt the cutting process and inspect the tip of the tool with a tanchi sensor. I was trying to do a contact test. However, with the recent progress in factory automation, there is a strong demand for automatic detection of such tool wear.

As a method for detecting tool wear in machine tools, the present applicant has already used an AE sensor that detects the AE multiplied signal generated during cutting in Japanese Patent Application No. 59-6747, etc., and based on the average level of the output. We are proposing a device to detect tool wear (unpublished). However, in machine tools, various outputs are generated depending on the chips produced by the machined object (hereinafter referred to as the workpiece), so depending on the type of cutting, the reliability of tool wear detection may decrease. Ta.

[Purpose of the invention]

The present invention has been made in view of the above-mentioned problems with machine tools, and is designed to detect tool wear based on the AE multiplied signal obtained during cutting with the machine tool, regardless of the type of chips generated. The purpose of the present invention is to provide a tool wear detection device with improved performance.

[Structure and effects of the invention]

The present invention includes an AE sensor that is installed near a tool of a machine tool and converts the AE multiplied signal into an electric signal during machining, a sampling means that samples the AE multiplied signal amplitude data for a predetermined time every cutting by the machine tool, and a sampling means an average value calculation means for calculating the average value of the sampled amplitudes; an average value difference calculation means for calculating the difference between the average value of the AE multiplied signal amplitude obtained after cutting and the immediately preceding average value;
maximum density detection means for detecting the maximum density of the probability density function of the amplitude values sampled by the sampling means at each predetermined time; and comparison for comparing the average value of the mean value difference calculation means and the maximum density calculation means with a predetermined threshold level. The present invention is characterized by comprising a means for detecting tool wear based on the output of the comparing means.

According to the present invention having such characteristics, cutting and
It is possible to detect with high accuracy that the wear associated with tool use has reached a limit value without interrupting cutting during machining. Therefore, when tool wear is detected, it is possible to take appropriate measures such as stopping the subsequent cutting process or replacing the tool. Furthermore, if tool wear is detected, the possibility that the tool will suddenly break during cutting will be reduced, making it possible to proceed with the cutting work smoothly.

[Explanation of Examples]

(Overall Configuration of Embodiment) FIG. 1 is a block diagram showing the overall configuration of an embodiment of a tool wear detection device according to the present invention. The tool wear detection device of this embodiment is applied to a drilling machine, and a vice 2 is mounted on a table 1 of the drilling machine, and a workpiece 3 is fixed by the vice 2 above the vice 2. Then, the workpiece 3 is cut by lowering the drill 4 of the drilling machine in the axial direction. Here, an AE sensor 5 for detecting the AE multiplied signal is provided on the table l of the drilling machine. The AE sensor 5 is a wideband AE sensor that detects an AE multiplied signal during cutting, and its output is given to a preamplifier 6. The preamplifier 6 amplifies the AE multiplied signal of the AE sensor 5 to a predetermined level, and provides the amplified output to a bandpass filter. The band pass filter 7 is used to generate an AE multiplied signal during cutting, for example, at a frequency of 100.
The AE multiplied signal of the frequency from KHz to IMHz is transmitted to the next stage amplifier 8. Amplifier 8 amplifies the output of the bandpass filter to a predetermined level and transmits the output to full-wave rectifier 9. The output of the full-wave rectifier 9 is given to a low-pass filter 10. The low-pass filter IO obtains an envelope signal of the AE-multiplied signal by cutting off the AE-multiplied signal having a frequency of 2 K Ilz or more, and its output is given to the A/D converter 11 . The full-wave rectifier 9 and the low-pass filter 1° detect the AE multiplied signal or a signal having the amplitude thereof. The A/D converter 11 converts the envelope signal into a digital signal at every predetermined clock cycle. On the other hand, the drill 4 is provided with a contact sensor 12 that detects when the tip of the drill 4 comes into contact with the workpiece 3 by measuring the impedance between the drill 4 and the table 1 . Then, the digital data converted by the A/D converter 11 and the detection signal of the contact sensor 12 are given to the microcomputer 13. Also connected to the microcomputer 13 is a numerical control device 14 that controls the drilling machine. Based on these input signals, the microcomputer 13 detects the wear of the tool, in this case the drill 4, by arithmetic processing procedures to be described later, and provides the detected wear to the display 15.

(RAM memory map) Figure 2 shows random access memory (hereinafter referred to as RAM) 16 which is a storage means inside the microcomputer 13.
This is a memory map showing the memory contents of . As shown in this figure, the RAM 16 contains an initial flag that is set when the tool first cuts, a delay time timer Td that determines the delay time from the start of tool cutting until sampling the AE signal, and a sampling timer that determines the sampling time of the AE signal. Ts is provided. Further, the RAM 16 stores a predetermined number of A
Sample data areas X(0), X(1), X(n) holding E times signal sample data,
Probability density tables A(0), A(1), A(k) indicating the probability densities of these sample data are provided. Each area of the probability density table holds the number of data that takes the value of each sample data. The RAM 16 also stores a sample data pointer i used when signal processing sample data, and a maximum density function that stores a sample data number corresponding to sample data X (i) with the highest probability of occurrence as a result of arithmetic processing described later. A field of value registers m is provided. Furthermore, the AE multiplied signal average value A
Each region of E M, successive average value difference ΔA E (V), and A E (m) indicating the maximum density function are provided, and a threshold level θth region for detecting the limit value of the average value. is provided.

(Operation of this embodiment) Next, the operation of this embodiment will be described with reference to waveform diagrams and flowcharts. When the operation starts, first, in step 20, an initial flag in the RAM 16 is set. Then, proceeding to step 21, the drill 4 is detected by the contact sensor 12.
Waits for a signal when the tip of the workpiece 3 comes into contact with the workpiece 3. Instead of the signal from the contact sensor 12, a signal at the time of contact may be received from the numerical control device 14. Now, when the drill 4 comes into contact with the workpiece 3, the process proceeds to step 22, where the delay time timer Td is operated and the delay time timer Td waits for time amplification (step 23).

After contact, the drill 4 is lowered in the axial direction to form an opening in the workpiece 3. The AE multiplied signal generated at that time is applied to the AE sensor 5 via the work 3 and the table 1. The signal is amplified by this AE multiplied signal preamplifier 6, passed through a bandpass filter 7, and amplified to a predetermined level by an amplifier 8, and then provided to a full-wave rectifier 9. Then, it is rectified by a full-wave rectifier 9 and filtered by a low-pass filter 10 to obtain an AE multiplied signal envelope signal, which is then sent to the A/D converter 1.
1 is converted into a digital signal at a predetermined period.

The delay time timer Td is set so that the A/D conversion value is not processed for a delay time Td after the drill 4 contacts the workpiece 3 until the position shown in FIG. At this delay time Td, the AE multiplication signal is at a low level as shown in Fig. 5(a).And after the delay time Td elapses, the drill 4 is at a low level as shown in Fig. 4(b). Therefore, as shown in FIG. 5 (al), a large level AE multiplied signal is obtained at the sampling time Ts. Therefore, when the delay time timer Td times up, step 2 is performed.
Proceeding to step 4, the operation of the sampling timer Ts is started.

Thereafter, the process proceeds to step 25, where the A/D converted value provided by the A/D converter 11 is fetched and stored in RAMI-6. The process then proceeds to step 26 to check whether the sampling timer Ts times up. In this way, the loop of steps 25 and 26 is repeated until the sampling timer Ts times up.
AM16 sample data area X (0) to X (n)
are stored sequentially. Sampling timer Ts is shown in Figure 5)
The sampling time for calculating the spectrum of the AE-multiplied signal envelope route signal is determined as shown in FIG. 2, and it is assumed that, for example, 2000 points of A/D conversion are performed every 0.5 mS for 1 second. If the sampling timer TS times up in step 26, the process proceeds to step 27, where the taking in of the A/D converted value provided by the A/D converter 11 is prohibited. Next, the process proceeds to routine 28, where the average value AEfV) is calculated by dividing the total value of the A/D conversion data obtained during sampling by the number of data.

Then, the process proceeds to routine 29 to obtain the maximum value AE ((2)) of the AE multiplied signal probability density function as described later.Then, the process proceeds to step 30 to check whether the first time flag is set.If this flag is set, In step 31, the first time flag is cleared and the process returns to step 21. If the first time flag is not set, the process proceeds to step 32 to calculate the average value difference ΔA E M of the AE multiplied signals during the previous cutting.

Now, the maximum density ππ(m) increases according to the number of cuttings, as shown in an example in FIG. And already, the patent application was made in 1988-11
As shown in No. 0564, when arc-shaped chips or coil-shaped chips are obtained, the average value of the AE multiplied signal amplitude A E M and the maximum density A E (m) are almost at the same level. . Also, if spiral-shaped chips are generated, A
The average value A E M of the E-fold signal amplitude is a large value, but
Maximum density AE (- shows almost no change. Figure 7 shows AE
FIG. 3 is a diagram showing the average value A EM of the double signal amplitude, which fluctuates depending on the type of chips generated each time the cutting is performed. On the other hand, the signal level of the maximum density AE (ITI) varies from tool to tool. Therefore, in order to reduce these effects, as shown in FIG.
Find the average value difference ΔA E (Vl) between successive cutting times, and calculate the weighted average of this signal and AE ((2) 1;'
(v, m) is the wear function.

k1ΔAEM+2AE(m)=F (v, m) Here, kl and k2 are fixed constants, and their sum is 1. FIG. 9 is a graph showing an example of a change in the wear function F(v, m) with respect to the number of cuttings. As shown in this figure, it is known that as the wear increases, the wear function F(v, m) increases significantly. Therefore, tool wear can be detected by setting a threshold value at a predetermined level of this wear function.

Therefore, in step 33 it is checked whether the wear function F(v,m) exceeds the threshold level θth. If the wear function F (v+m) does not exceed this level, the process returns to step 21 and repeats the same process, and if it exceeds this level, the process proceeds to step 34 to output a tool wear detection signal. This wear detection signal makes it possible to display wear on the display 15 and perform appropriate processing such as replacing the tool as necessary.

(Maximum Density Calculation Process) Next, the maximum density calculation process of routine 29 will be described. FIG. 10 is a flowchart showing this operation. When the operation starts, first in step 40, probability density data area A (XfPl) Cp=
o −n ) and clear the sample data pointer i,
Proceeding to step 41, the sample data pointer i and the last value n of the sample data are compared.

Initially, the sample data pointer i is 0, so the process advances to step 42 to read the first sample data X (0), and then the process advances to step 43 to the probability data area corresponding to the value of the read sample data A(X
(0)) is incremented. The process then proceeds to step 44 where the value of the sample data pointer i is incremented, and the process returns to step 41. In this way, by repeating the loop from step 41 to step 44 until the sample data pointer i reaches the last value n of the sample data, all sample data values are distributed into probability data areas according to their sizes, and A (X Complete the probability density function table of (i)). and step 4
Step 5 clears the sample data pointer i and maximum value register m, and step 46 compares the sample data pointer i with the final value n. If these values are not equal, step 47 is reached. In step 47, since the first sample data pointer i is O, it is checked whether the probability density data A(X(ml) exceeds A(X(01)). In step 48, the value of the sample data pointer i at that time is stored in the maximum value register m, and in step 49, the sample data pointer i is incremented.As in the initial operation, in step 47, A(X(m
)) does not exceed the probability value A (X(1)) of the sample data, increment i in step 49 without going through step 7 and step 46.
Return to By repeating the loop of test steps 46 to 49, all probability density data A (0) to A (
The maximum value A(Xfml) is found for g)), and at the same time, the sample data number i corresponding to this maximum value is held in the maximum value register m. The maximum value A (X ((b)) thus obtained is written into a predetermined area of the RAM 16 as the maximum density AE ((2) (step 50), and this process is terminated.

In this example, only tool wear detection is performed, but AE
It is also possible to combine this with tool breakage detection processing based on the multiplied signal output.

Furthermore, although this embodiment describes a wear detection device that is used in a drilling machine using a numerical control device, the present invention is applicable to other machine tools such as various machine tools such as lathes and milling machines,
Furthermore, it can also be applied to large-scale machining centers.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing the overall configuration of an embodiment of the tool wear detection device according to the present invention, and FIG. 2 is a memory map showing the storage contents of the RAM 16 in the microcomputer.
FIG. 3 is a flowchart showing the operation of the tool wear detection device of this embodiment, and FIGS. 4(a) and 4(b) show the drill 4.
5th sectional view showing successive machining states of workpiece 3 by
The figure is a waveform diagram showing the AE signal waveform obtained by one cutting and the envelope signal obtained from the low-pass filter 10, and Fig. 6 is the maximum density A of the AE multiplied signal with respect to the number of cuttings of the tool.
E (ml, Fig. 7 shows the average value of the AE multiplied signal AE M with respect to the number of cuttings, Fig. 8 shows the average value difference ΔA E M between the successive average values, and Fig. 9 shows the wear function F (v, m). A graph showing the change in the number of cuttings, Figure 10 shows the change in A for each cutting.
3 is a flowchart showing a process for detecting the maximum value of the E-fold signal. 1-・-Table 2-・-Vise 3-・-・-
Work 4・----1-Drill 5-・-・AE
Sensor 6--------Preamplifier 7-・-・
−・−Band pass filter 8−・・・・−・−Amplifier
9--Full wave rectifier 10--Low pass filter 11--A/D converter 12--
tli sensor 13---Microcomputer 14--Numerical controller 15--
--1 Display 16---------RA M Patent applicant Tateishi Electric Co., Ltd. agent Patent attorney Yoshiki Okamoto (1 person) Figure 1 1--Table 3-111・-Wharf 4-----・Do1Jru5----・AEt''7Figure 2Figure 4(a) Figure 4(b)Figure 51 D ・King S1 11 Power ÷ --- Figure 3 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10

Claims (2)

[Claims] (1) an AE sensor that is installed near a tool of a machine tool and converts an AE signal during machining into an electrical signal; a sampling means that samples amplitude data of the AE signal for a predetermined time every time the machine tool cuts; and the sampling means an average value calculation means for calculating the average value of the amplitudes sampled by the sampling; and an average value difference calculation means for calculating the difference between the average value of the amplitude of the AE signal obtained after cutting and the immediately preceding average value; maximum density detection means for detecting the maximum density of the probability density function of the amplitude values sampled by the means at each predetermined time; and a comparison for comparing the average value of the mean value difference calculation means and the maximum density calculation means with a predetermined threshold level. A tool wear detection device comprising: means for detecting tool wear based on the output of the comparison means. (2) The tool wear detection device according to claim 1, wherein the comparison means compares a weighted average of the average value difference and maximum density with a predetermined threshold level.