JPS63212447A - Monitoring device in cutting machine - Google Patents

Abstract

PURPOSE:To enable effective monitoring with lesser malfunction, in a monitoring device for a cutting machine such as a press die, by performing distinction of idle cutting or not by sensing signals from an AE sensor, and sensing if there is any abnormality on the basis of the result of distinction. CONSTITUTION:A work 10 is cut by a tool 14. Because the cutting machine is equipped with an AE sensor 20, AE generated by operation of the cutting machine will be sensed thereby. The sensing signal of this AE sensor 20 is input into band pass filters 22, 32, 44, and here is taken out an idle-solid distinguishing signal of the frequency band, which suits to distinction of idle or solid cutting. This signal is input into an idle-solid distinguishing circuit 26 to undergo comparison with the distinctive value stored previously. The sensing signal of the AE sensor 20 is input into the band pass filter 32, and here is taken out a signal for distinction of any abnormality followed by comparison with the comparative value. The signal for sensing of an abnormality at the time of idle cutting is compared with different comparative value from at the time of solid cutting, and an abnormality is sensed out.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a monitoring device for a cutting machine, and particularly to an improvement in abnormality identification.

[Prior Art] Cutting machines such as press dies are being operated continuously and unmanned for long periods of time due to the expansion of NC from cutting to tool changing, setup, and the like. and,
In such continuous unmanned operation, accurate abnormality detection is extremely important to ensure safety. Conventionally, the following methods have been used to detect such an abnormality.

■Detecting breakage of tools, etc. using an AE sensor.

For example, it is shown in Japanese Patent Publication No. 61-284675.

■To perform signal processing on the values detected by the vibration sensor through multiple band-pass filters to determine chatter. For example, it is shown in Japanese Patent Laid-Open No. 58-108420.

Furthermore, the use of an AE sensor to determine whether actual cutting or idle cutting is performed is disclosed in, for example, Japanese Patent Application Laid-Open No. 159354/1982 and Japanese Patent Application Laid-Open No. 173462/1982.

[Problems to be Solved by the Invention] In such conventional devices, one abnormality detection device is provided to detect one abnormality. Each abnormality detection device always performed detection independently. For this reason, it was not possible to detect abnormalities according to the operating state, and accurate monitoring was not possible.

The present invention was made to solve these problems, and an object of the present invention is to provide a monitoring device for a cutting machine that can always accurately detect an abnormality using an AE sensor.

[Means for Solving the Problems] A monitoring device for a cutting machine of the present invention is a monitoring device for a cutting machine that cuts a material to be cut using a tool, and includes an AE sensor attached to the cutting machine; This A
A band-pass filter that extracts a signal for identifying the actual cutting in a predetermined frequency band suitable for distinguishing between empty cutting and actual cutting from the detection value obtained by the E sensor, and An empty/actual identification circuit that compares with a pre-stored identification value and distinguishes between empty cutting and actual cutting based on the size of the identification value, and abnormality detection in a frequency band suitable for abnormality detection from the detected value obtained by the AE sensor. and an abnormality identification circuit that compares the signal for abnormality detection with different comparison values depending on whether during idle cutting or actual cutting, and identifies abnormalities based on the magnitude of the comparison values. It is characterized by

[Operation] The monitoring device in such a cutting machine operates as follows.

The material to be cut is cut by a tool. Here, since an AE sensor is attached to the cutting machine, the AE generated by the operation of the cutting machine is used for other purposes. The detection signal of this AE sensor is input to a band pass filter, where a signal for identification of actual and empty cutting in a frequency band suitable for identifying empty cutting and actual cutting is extracted. This signal for identifying reality and reality is input to a reality and reality identification circuit, where it is compared with previously stored identification values. Then, based on the size, it is determined whether the cut is idle cutting or actual cutting. Further, the detection signal of the AE sensor is input to a bandpass filter that extracts a signal in a frequency band suitable for abnormality detection, and a signal for abnormality identification is extracted here. This abnormality detection signal is then compared with a comparison value. Here, this comparison is changed depending on whether the time is idle cutting or actual cutting. That is, a plurality of comparison values for abnormality detection are stored, and the abnormality detection signal is compared with different comparison values during idle cutting and during actual cutting. Anomalies are detected by this comparison.

[Example] Next, an example of the present invention will be described based on the drawings.

FIG. 1 shows an example of the overall configuration of a system having a monitoring device for a cutting machine according to the present invention. A workpiece 10 is placed and fixed on a table 12. The tool 14 is connected to a main shaft of a main shaft motor via a main shaft head 18 supported by a machine frame 16. AE sensor 2
0 is attached to the table 12. And this A
The detection signal from the E-sensor 20 is input to the bandpass filter 22 of the real/real identification line A. This band pass filter 22 receives a signal a1 in a frequency band of 50 to 80 kHz.
It allows the signal to pass through and cuts the low-band signal. This signal al with a frequency of 50 to 80 kHz is sent to the processing circuit 2.
4 will be done manually. This processing circuit 24 consists of an amplifier, a double-wave detection circuit, a peak hold circuit, an averaging circuit, a pulsing circuit, etc., and converts an AC waveform signal into a DC waveform signal a2 that is easy to process in the next process. be.

This DC waveform signal a2 is inputted to the tip identification circuit 26. The sharp point identification circuit 26 also receives an input of the real/real identification setting value from the identification value setting circuit 28 .

Then, the sharp edge identification circuit 26 compares the AE output value and the actual/real identification setting value.

Then, the signal a3 as a result of this comparison is sent to the controller 30.
is input. The detection signal from the AE sensor 20 is also input to the bandpass filter 32 of the abnormality detection line B. This bandpass filter 32 is a filter that transmits only the signal b1 in the frequency band of 10 to 40 kHz.

The signal b1 that has passed through the bandpass filter 32 is input to the processing circuit 34. This processing circuit 34 has the same configuration and similar functions as the processing circuit 24 described above. The signal b2 obtained by this processing circuit 34 is input to an abnormality identification circuit 36. The abnormality identification circuit 36 uses the signal b2 and the comparison value setting circuit 3.
This is to compare the comparison value from 8.

The resulting signal b3 is then input to the controller 30. Further, the comparison result of the abnormality identification circuit 36 is also input to the diagnostic circuit 40. This diagnostic circuit 40 determines whether it is abnormal or normal based on the comparison result signal b3, and sends a signal to the warning circuit 42 if it is abnormal. The warning circuit 42 displays an abnormality when receiving an abnormality signal. Here, the signal X from the controller 30 is also input to the abnormality identification circuit 36. This corresponds to the signal a3 obtained by the sharp edge identification circuit 26, and the abnormality identification circuit 36 is controlled to perform the comparison only during idle cutting.

The detection signal of the AE sensor 20 is also input to the bandpass filter 44 of the feed rate control line C.

This bandpass filter 44 is a filter that only transmits signals of 50 to 80 kHz. The filtered signal CI is processed by the processing circuit 46, and then the signal C2
The signal is input to the abnormality identification circuit 48 as follows.

The upper and lower limit values from the upper and lower limit setting circuit 50 are also input to the abnormality identification circuit 48, and the upper and lower limits are compared with the signal c3. Then, by comparing with these upper and lower limits,
A feed rate control signal c3 is input to the controller 30.

Note that the bandpass filter 44 and the processing circuit 46 have exactly the same functions as the bandpass filter 22 and the processing circuit 24. Therefore, the bandpass filter 44 and the processing circuit 46 may be omitted, and the signal a2 from the processing circuit 24 may be input to the comparison circuit 48.

Furthermore, there are a plurality of abnormality detection lines each including a bandpass filter, a processing circuit, an abnormality identification circuit, etc. into which the detection signal from the AE sensor 20 is input, and necessary abnormality detection is similarly performed at the necessary time.

At this time, each bandpass filter is set to an appropriate transmission frequency band.

The controller 30 then supplies a necessary control signal y to the control panel 52 according to the input signal. Control panel 52
supplies an operation signal to the corresponding operation end according to this signal y.

Next, the operation of such an embodiment will be explained. When cutting is performed using a cutting machine, an NC program is first read. Then, a predetermined cutting process is performed based on this NC program.

In other words, in order to perform cutting, first the material to be cut 10
is set on the table 12. This is usually done automatically by an automatic pallet changer (APC) according to a program. In other words, the APC transports the workpiece 10,
Set (sit) on table 12. Additionally, tools are automatically replaced according to the program. In this exchange, the attachment to the spindle is usually replaced by an automatic attachment changer (AAC), and then the tool 14 is replaced by an automatic tool changer (ATC).
is exchanged. After the cutting conditions are set, the cutting material 10 is cut by the rotating tool 14.

The relative movement of the tool 14 and the table 12 is performed by controlling a feed mechanism (not shown) according to locus information included in the program. Normally, the table 12 moves in one direction (X direction), and the spindle head 18 moves in one horizontal direction and vertical direction (Y, Z directions). When the machining of one workpiece 10 is thus completed, the workpiece 10 is replaced by the APC. In addition, if necessary, trial cutting is performed on a test piece of a certain shape and material, and the wear of the tool is inspected depending on the cutting condition.

Then, at each stage of these cutting processes, the AE sensor 2
Monitoring using 0 is performed. Here, AE sensor 2
The AE detection signal based on 0 will be explained. AE sensor 2
The AE detected in step 2 is an abbreviation for acoustic emission, which is "a phenomenon in which energy released as a solid deforms or breaks becomes an acoustic pulse and propagates." Here, the causes of AE generation in cutting machines include the following. Before machining, friction of bearings etc. in APC, collision between workpiece 10 and table 12 when workpiece 10 is seated on table 12, AAT, AT
There may be collisions between parts during C operation. The main causes of AE when the workpiece is cut by a tool include cracks in the workpiece 10, plastic deformation of the workpiece 10 on the shear surface, friction between the workpiece 10 and the tool 14, and fracture of chips. , a collision between the chips and the workpiece 10 or the tool 14 , an elastic wave caused by a collision between the tool 14 and the workpiece 10 , and chatter between the tool 14 and the workpiece 10 . Furthermore, when the tool 14 is damaged, when the tool 14 is chipped, when there is friction between the workpiece 10 and the table 12 due to abnormal seating of the workpiece 10,
A special AE is generated when an abnormality occurs, such as when bearings are damaged during movement of the cheaply 12, tools 14, etc. AE generated due to such causes is detected by the AE sensor 20.

The detection signal obtained by the AE sensor 20 repeats up and down depending on the vibration, and the amplitude represents the magnitude of the vibration. FIG. 2 ASB shows an example of the results of frequency analysis of the detection signal when cutting a cast iron workpiece. Here, FIG. 2A shows the actual cutting, and FIG. 2B shows the dry cutting. In this way, the AE detection signal during actual cutting has 5
There is a peak in the frequency band of 0 to 80 kHz, and there is a peak in the frequency band of 50 kHz or less during idle cutting. From this, the AE generated by cutting has a frequency of 5
It can be seen that the frequency range is from 0 to 80 kHz. In addition, peaks during dry cutting are caused by table feed, etc., and AE caused by such causes has a frequency of 10 to 10.
It can be seen that the frequency is in the 40kHz band.

First, a case will be described in which idle cutting and actual cutting are discriminated based on the detection signal obtained by the AE sensor 20. The detection signal generated by cutting is an AC waveform signal that intermittently repeats maximum amplitude depending on the frequency with which the tool collides with the workpiece.

For example, if a two-blade tool is rotated at 1100 rpm, the AC waveform will have a peak of 2000 rotations per minute. The detection signal obtained by such an AE sensor 20 is input to a bandpass filter 22, where a 50k
Signals below Hz and above 80kHz are cut,
Only the ~80kHz signal is extracted. This frequency band is determined by investigating the frequency of AE depending on the material of the material to be cut through frequency analysis. The frequency band of 50 to 80 kHz in this example is a general value when the material to be cut is made of cast iron, and for example, in the case of steel material, a range of several kHz to 20 kHz is selected. Note that the upper and lower limits of the band-pass filter 22 may also be included in the NC program and automatically set depending on the material of the material to be cut. The signal al of the specific frequency thus obtained is input to the processing circuit 26, where the following processing is performed. In other words, after being amplified by an amplifier,
Both waves are detected. This detected signal is input to a peak hold circuit, where the peak values are sequentially held. This peak value is subjected to averaging processing in an averaging circuit, and is then manually inputted to the apex identification circuit 26 as a DC waveform signal a2. Note that the signal a2 may be pulsed by a pulse generator.

FIG. 3 shows an example of the signal a2 which is the output value of the processing circuit 24 when the flat plate-shaped workpiece 10 is cut. As is clear from FIG. 3, the signal a2 increases when the tool begins to contact the workpiece 10 and when the tool leaves the workpiece 10. In other words, this signal becomes maximum at the start and end of cutting. Therefore, it can be seen that the response to changes between idle cutting and actual cutting is very good, and it is very suitable for this discrimination.

Further, from FIG. 3, it can be seen that the signal a2 corresponds to the magnitude of the load during actual cutting.

Accordingly, a detailed experiment was conducted regarding this, and it was found that the second signal a2 of the cutting load increases as the depth of cut increases, and as the feed rate increases, the signal a2 also increases.

From this, it can be seen that the signal a2 has a positive correlation with the depth of cut and the feed rate. This is thought to be because the magnitude and frequency of AE occurrence increases as the depth of cut and feed rate increase, and it is clear that the signal a2 can be used for feed rate control if appropriate signal processing is performed.

In addition to the signal a2 obtained in this way, the identification value is input to the identification circuit 26 from the identification value setting circuit 28. Here, this identification value is set in advance depending on the type of tool, and is also stored in a memory built into the identification value setting circuit 28, for example, a nonvolatile memory such as a bubble memory, and is recalled at any time.

In the initial state, the reality/truth discrimination circuit 26 compares the signal a2 with a discrimination value that is equal to or higher than a predetermined noise level. Normally, idle cutting is performed at first, so the signal a2 is smaller than the identification value, and it is identified that idle cutting is being performed. Then, the signal a3 of this identification result is input to the controller 30.

The detection signal of the AE sensor 20 is also input to a bandpass filter 32. This bandpass filter 32 has a 10kHz
Since it cuts signals below z and above 40kHz, 1
A 0-40kHz signal b1 is output. This frequency band is determined based on the results of frequency analysis of AE when the bearing is damaged. In the case of a normal bearing, there is a peak in this frequency band, and when an abnormality occurs, a very large AE is generated in this band.

The output signal bl of the bandpass filter 32 is transmitted to the processing circuit 3.
4, and after being subjected to the same processing as in the processing circuit 26, it is manually inputted to the abnormality identification circuit 36 as a signal b2. The abnormality identification circuit 36 compares the input signal b2 of the specific frequency with the comparison value input from the comparison value setting circuit 38.

This comparison value is set to a value that is sufficiently larger than the noise level. Here, this abnormality identification circuit 36 includes a controller 3.
A signal X for cusp identification is supplied from 0 to 0. Comparison of the signal b2 and the comparison value is performed only during idle cutting. This is because, during actual cutting, a very large AE is generated as a result of the cutting, and the signal b2 that is manually input to the abnormality identification circuit 36 also includes the signal associated with this cutting. During actual cutting, the bearing cannot extract a signal corresponding to damage, and the bearing cannot detect damage. Since comparison is performed only during dry cutting, damage to the bearing can be detected accurately.

The signal b3 as a result of this comparison is supplied to the controller 30 and the diagnostic circuit 38, and if the signal b2 is larger than the comparison value, an abnormality signal is supplied to the warning circuit, and the warning circuit 42 displays an abnormality. Further, a signal from the controller 30 is sent to the control panel 52, and processing is stopped if necessary.

In the processing machine of this embodiment, the tool 14 and the workpiece 1
The speed of relative movement of zero, that is, the feed speed is also controlled. That is, when sharp cutting is identified, the controller 30 controls the feed rate using the control panel 56. For example, when the idle/actual identification circuit 26 identifies that idle cutting is being performed, the controller 30 calls the corresponding feed rate coefficient k. For example, a value of 2.0, which is greater than or equal to 1.0, is adopted as the feed rate coefficient in the case of idle cutting. This feed speed coefficient is multiplied by the feed command value included in the NC program. In other words, 2 of the feed command value
Movement is performed at twice the speed.

Furthermore, the bandpass filter 4 of the feed rate control line C
4. Using the signal c2 processed by the processing circuit 46, feed rate control during actual cutting is performed.

That is, when it is detected by the signal X that actual cutting has started, the comparison circuit 48 compares the upper limit value and lower limit value from the upper and lower limit setting circuit 50 with the signal c2.

If the signal c2 is larger than the upper limit value, the cutting load is high, so the controller 30 outputs a value of 1.0 or less as the feed rate coefficient.

Further, when the signal C2 is smaller than the lower limit value, the cutting load is small, so the controller 30 outputs a value of 160 or more as the feed rate coefficient.

Changes to this feed rate coefficient are made in proportion to the difference from the set value.
1. You may determine the amount of increase or decrease from 0, but
Normally, the value of k is gradually changed until the signal C2 falls within the upper and lower limits. Alternatively, the excess amount may be divided into a plurality of ranks, the feed rate coefficient k may be stored in advance in correspondence with each rank, and the corresponding feed rate coefficient k may be selected. In this way, during actual cutting, the feed rate is controlled using the signal C2, and cutting is always performed with the optimum load.

Note that the feed rate coefficient is normally changed within the range of O15 to 2.0, and no further changes are made. This is to ensure that major changes do not adversely affect other elements of the cutting process (wear and breakage of cutting tools, precision and roughness of the cutting surface). For example, if the feed rate is too high and the load suddenly increases, damage to the tool may occur due to control delay. In such a case, if the maximum speed is set in accordance with the strength of the tool and the control delay time, damage to the tool can be prevented.

Further, in the cutting machine of this embodiment, other abnormalities are also detected using the detected values of the AE sensor 20. Bandpass filter 32, processing circuit 34, abnormality identification circuit 36
A plurality of abnormality detection lines having the same configuration as the comparison value setting circuit 38, the diagnostic circuit 40, and the warning circuit 42 are provided. The frequency bands extracted by the bandpass filters are different between these abnormality detection lines. Through frequency analysis of the detected values of the AE sensor under various conditions,
For example, the AE frequency band of the signal corresponding to the following abnormality was found.

■Tool chipping...50 to 100kHz ■Tool chipping...100 to 200kHz ■Tool wear...10 to 120kHz These frequency bands are set in each bandpass filter, and by comparison with the corresponding setting values. Anomaly detection is performed. Note that these abnormalities are detected during actual cutting.

In the above embodiment, there is one AE sensor 20, but two or more may be provided, or they may be installed at other locations. For example, an AE sensor 20 is attached to the spindle head 18. When installed here, it is possible to effectively detect deterioration of the spindle bearing, gear cracks, and scoring during idle cutting. In addition, to detect tool breakage, tool chipping, tool wear, etc., AE is applied to both the spindle head and table.
Install the sensor and calculate the sum of detected values during actual cutting.

More effective monitoring can be achieved by performing the same processing as in the above embodiment. Furthermore, interference between the workpiece, tool, and spindle head can also be detected.

Further, when the feed rate controller identifies actual cutting, it is preferable to count the actual cutting time with a timer (not shown). This time is then accumulated for each tool. Since the service life of each tool is known, when the actual cutting time reaches this service time, the ATC (Auto Tool Cha
tool change is automatically performed by the tool.

Further, when necessary, trial cutting of a test piece made of a material having a predetermined shape may be performed, and wear of the tool may be detected by the AE.

[Effects of the Invention] As described above, according to the monitoring device for a cutting machine of the present invention, idle cutting and actual cutting are discriminated based on the detection signal of the AE sensor, and based on this discrimination result, for example, table feed is controlled during idle cutting. Since abnormality detection such as the following is performed, effective monitoring with fewer malfunctions can be performed.

[Brief explanation of the drawing]

Fig. 1 is an overall configuration diagram showing an overview of a system of an example of a monitoring device for a cutting machine according to the present invention, Fig. 2 is a waveform diagram showing the frequency characteristics of the detected value of the AE sensor, and Fig. 3 is a material to be cut. FIG. 3 is a waveform diagram showing an example of an AE sensor output value during cutting. 10... Material to be cut 12... Table 14... Tool 20... AE sensor 22... Bandpass filter 30... Controller.

Claims (1)

[Scope of Claim] A monitoring device for a cutting machine that cuts a workpiece with a tool, comprising: an AE sensor attached to the cutting machine;
A band-pass filter extracts a signal for identifying the actual cutting in a predetermined frequency band suitable for distinguishing between empty cutting and actual cutting from the detection value obtained by the E sensor, and a The signal is compared with a pre-stored discrimination value, and the difference is used to distinguish between idle cutting and actual cutting based on the size of the signal.The detection value obtained by the above-mentioned AE sensor is used to select a frequency band suitable for abnormality detection. A bandpass filter that extracts a signal for abnormality detection, and an abnormality identification circuit that compares the signal for abnormality detection with different comparison values depending on whether it is during idle cutting or actual cutting, and identifies an abnormality based on the magnitude of the comparison value. A monitoring device for a cutting machine, comprising: