JP2022052946A - Plastic working abnormality detector and plastic working abnormality detection method - Google Patents

Abstract

To detect with high accuracy the interaction of a tool, a workpiece and cutting debris on the surface of the workpiece at plastic working time.SOLUTION: A plastic working abnormality detector of an embodiment comprises: a contactless acoustic emission sensor arranged at a position a prescribed distance apart from a plastic working object, for detecting acoustic emissions generated when processing the plastic working object by a plastic working device and having propagated through the air; an analysis unit for performing a time-frequency analysis on the detection signal of the contactless acoustic emission sensor; and a diagnosis unit for detecting, on the basis of the analysis result of the analysis unit, that working abnormality has occurred when a frequency component of a prescribed threshold or greater exists in a prescribed frequency band.SELECTED DRAWING: Figure 1

Description

An embodiment of the present invention relates to a plastic working abnormality detecting device and a plastic working abnormality detecting method.

Conventionally, in a plastic working apparatus, a contact type AE sensor for detecting acoustic emission (AE), which is an elastic wave phenomenon that occurs when a minute fracture occurs in a work piece to be plastically worked when plastic working is performed, is provided. There was something. Then, the frequency analysis of the output signal of the contact type AE sensor for each unit time is performed in time series, and the power for the frequency is calculated in time series for each unit time.

Japanese Patent No. 1959360 Japanese Unexamined Patent Publication No. 2005-305541 Japanese Unexamined Patent Publication No. 2000-271674 Japanese Patent Publication No. 2019-512331

However, in the prior art, in order to capture the phenomenon that the elastic energy stored inside is released as an elastic wave when the work piece to be plastically deformed or broken, the contact type AE sensor is a sound wave of about several tens of kHz to 1 MHz. Was adopted to detect.
Therefore, it was not possible to detect with high sensitivity the interaction between the tool, the work material, and unnecessary metal scraps generated during the work on the surface of the work material during plastic working.

INDUSTRIAL APPLICABILITY The present invention has been made in view of the above, and it is possible to detect with high sensitivity the interaction between a tool, a work material and unnecessary metal scraps generated during machining on the surface of a work material during plastic working. It is an object of the present invention to provide a plastic working abnormality detecting device and a plastic working abnormality detecting method.

The plastic working abnormality detection device of the embodiment is arranged at a position separated from the plastic working object by a predetermined distance, and detects acoustic emissions generated when the plastic working object is machined by the plastic working device and propagated in the atmosphere. Based on the analysis results of the non-contact acoustic emission sensor, the analysis unit that performs time-frequency analysis of the detection signal of the non-contact acoustic emission sensor, and the analysis unit, there are frequency components above a predetermined threshold in a predetermined frequency band. In this case, a diagnostic unit for detecting the occurrence of a processing abnormality is provided.

FIG. 1 is a schematic block diagram of a plastic working abnormality detection system. FIG. 2 is an explanatory diagram of an example of a time-series waveform during press working of a semiconductor lead frame. FIG. 3 is a diagram showing the signal at the time of press working acquired by the contact type AE sensor as a time series AE signal intensity and a time-frequency map. FIG. 4 is a diagram showing a signal during ultra-precision machining acquired by a laser microphone as a time-frequency map. FIG. 5 is an explanatory diagram in the case where one step is performed for each of the plan-shaped workpieces at different positions.

FIG. 1 is a schematic block diagram of a plastic working abnormality detection system.
The plastic processing abnormality detection system 10 includes a contact type AE sensor 11, a preamplifier 12 that amplifies the voltage of the output signal of the contact type AE sensor 11, a main amplifier 13 that amplifies the power of the output signal of the preamplifier 12, and an acceleration sensor 14. , A charge converter 15, a laser microphone 16 that collects sound using the principle of a fabric perot interferometer, an interference signal analysis unit 17 that analyzes the output signal of the laser microphone 16, and work of a cutting device, a press device, and the like. Data analysis of high-speed signals based on the laser sensor 18 that detects the operation timing of the mechanical MT, the output signal of the main amplifier, the output signal of the charge converter 15, the output signal of the interference signal analysis unit 17, and the output signal of the laser sensor 18 ( Refer to the high-speed signal data analyzer (analysis unit) 19 that performs real-time frequency analysis) and extracts the feature amount, and the machining condition database 20A and the threshold database 20B based on the feature amount extracted by the high-speed signal data analyzer 19. A diagnostic system that generates and outputs a control command for the machine tool MT, controls the machine machine MT, and detects that a machining error has occurred when a frequency component above a predetermined threshold is present in a predetermined frequency band. (Diagnosis unit) 20 and.

In the following description, a press machine tool will be described as an example of the machine tool MT.
In a press machine tool, the speed of vertical movement of the movable die (punch) is given as the number of rotations (times / minute).
More specifically, in a machine tool MT configured as a press machine tool, high-speed machining of several hundred revolutions / minute is performed, to give an example of punching of lead frame parts used for semiconductors. In addition to the process of punching out such a material, a forming process such as a bending process or a deep drawing process may be performed.

Therefore, it is desirable to provide a trigger sensor or the like for measuring the movable die as a reference for the time axis of the machining operation. Therefore, in the present embodiment, the laser sensor 18 for detecting the operation timing is provided.

The contact type AE sensor 11 captures phenomena such as material breakage, punch breakage, and material biting abnormality (residue rising), and outputs a detection signal to the preamplifier 12.
The preamplifier 12 amplifies the voltage of the detection signal output by the contact type AE sensor 11 and outputs the voltage amplification signal to the main amplifier 13.
The main amplifier 13 amplifies the power of the voltage amplification signal and outputs it to the high-speed signal data analyzer 19.

The acceleration sensor 14 can be used when an abnormality that can be detected by the above-mentioned contact type AE sensor occurs in a wider range (captures a large amount of energy), and outputs a detection signal to the charge converter 15. More specifically, the acceleration sensor 14 is configured as a charge output type acceleration sensor, and outputs a detection signal proportional to the applied acceleration to the charge converter.
It is amplified by the charge converter 15 and output to the high-speed signal data analyzer 19.

The laser microphone 16 captures a phenomenon in which sufficient detection sensitivity cannot be obtained by the contact-type AE sensor 11 and the acceleration sensor 14 such as insufficient lubrication (galling / seizure) on the material surface as sound propagating in the atmosphere, and captures the detection signal. Output to the interference signal analysis unit 17.
The interference signal analysis unit 17 analyzes the output signal of the laser microphone 16 and outputs it to the high-speed signal data analyzer 19.

The high-speed signal data analysis device 19 performs data analysis (real-time frequency analysis) of the high-speed signal based on the output signal of the interference signal analysis unit 17 and the output signal of the laser sensor 18 to extract the feature amount.

In this case, the high-speed signal data analysis device 19 has an A / D conversion device that performs analog / digital conversion, and has a frequency filter function, a real-time frequency analysis function, and the like.

The frequency band of the contact type AE sensor 11 described above is about several tens of kHz-1 MHz.
The frequency band of the acceleration sensor 14 is about 0 to 20 kHz.
Further, the frequency band of the laser microphone 16 is about 10 Hz-1 MHz.

On the other hand, the frequency of the AE related to the fracture phenomenon of the metal material is said to be about several tens of kHz-1 MHz, and it is known that the frequency and the amplitude intensity of the AE differ depending on the phenomenon.
Further, in the case of cutting, in the stable cutting state, there is a peak at a low frequency of 0.25-0.3 MHz due to plastic deformation of the work material. In addition, crack propagation phenomena such as tool breakage spread to the high frequency side.

In addition, the elastic wear that occurs after long-term use of the tool appears with relatively small amplitudes between 0.25-1 MHz.
Further, in strong adhesive wear in which the slip friction surface is in contact with and fused to be seized, a large amplitude is generated at a high frequency of 1 to 1.5 MHz.

In this way, the frequency of AE is much higher than the frequency of mechanical vibration and noise in the surrounding environment = 0 to 1 kHz, and if appropriate filtering is provided for the original signal, information related to tool wear and fracture can be obtained. be able to.

The laser microphone 16 of the present embodiment can detect sounds of different frequencies (up to about 1 MHz) generated according to a processing / destruction phenomenon of a metal material. Generally, AE parameters (maximum amplitude, energy, RMS amplitude, AE count value, etc.) at time intervals on the order of milliseconds are used, but detailed frequency information is lost.

Therefore, the high-speed signal data analysis device 19 of the present embodiment is provided with a frequency filter for the original signal and a real-time frequency analysis function as described above, and the reason for the occurrence of the phenomenon can be estimated.

The diagnostic system 20 includes a processing condition database (DB) 20A for referring to processing conditions according to a product and a threshold database (DB) 20B for referring to a threshold value for determination.
Then, the diagnostic system 20 detects that a processing abnormality has occurred when a frequency component equal to or higher than a predetermined threshold value is present in a predetermined frequency band, and performs corresponding control.
In addition to or in addition to the processing condition database 20A and the threshold database, it may be provided with a function of inferring a state from a model in which data collected in real time is learned.

Further, it is desirable to appropriately select the control command to the machine tool MT when an abnormality (defect) is detected by the diagnostic system 20 according to the quality standard, processing speed, etc. of the target process.

Therefore, for example, when a signal related to a surface abnormality is detected in the final finishing of cutting, if the tool rotation is stopped immediately after that, the surface texture may be further adversely affected, so that the processing is completed once. A control command is output to perform a procedure such as confirming the relevant part later.

Further, when it is desired to minimize the damage to the die in press working, a control command may be output so as to stop the machining operation immediately after detecting an abnormal sound.

Further, regarding the diagnostic system 20 that gives such a control command, if high speed of response is important, it is desirable to incorporate it in the same hardware as the high-speed signal data analyzer 19, but for medium- to long-term data. When judging from the trend, it is also possible to construct a server or the like on the network as a diagnostic system 20 and adopt a configuration in which a control command is output.

Here, an arrangement example of each of the above sensors will be described.
The signal of the contact type AE sensor 11 catches material breakage well in both the removal process by cutting and the punching process by pressing, but there is a problem that it is difficult to isolate and capture only the signal caused by the abnormality of the surface and appearance of the object to be processed. ..

Further, since the AE attenuates in a complicated mode according to the structure of the propagating solid, it is desirable to install the sensor as close to the processing point as possible.
However, depending on the structure of the machine, it may not be possible to obtain sufficient signal strength due to the influence of the contact state at the interface between the parts, and it may take a lot of time to adjust the individual setup.
On the other hand, the laser microphone 16 can be expected to have a uniform attenuation rate with the distance between the processing point and the gap between the sensors as a guide, so that high versatility and detection reproducibility can be ensured.

Hereinafter, a more specific description will be given.
As an example, as the machine tool MT, a case where a progressive press device having a movable upper die and a fixed lower die and the machining process is divided into a plurality of machining steps is used as an example.
This progressive press device is used, for example, for punching a semiconductor lead frame.

In this case, the contact type AE sensor 11 and the acceleration sensor 14 are fixed to the lower mold at a position closer to the first half process, for example.
Further, the laser microphone 16 is located at a position that can be detected in both the first half process and the second half process, and is located upstream of the workpiece (in the case of the above example, the semiconductor lead frame) in the transport direction at a predetermined distance from the mold. It is provided at a separated position.
Further, the laser sensor 18 for detecting the operation timing is fixed to, for example, a movable upper mold.

In addition, the placement position of the sensor and the frequency to be adopted differ depending on the processing method.
Hereinafter, it will be described in detail.
First, the case of cutting will be described.
In cutting, the cutting edge is sharp immediately after replacing with a new tool, but wear progresses according to the usage time, and cutting resistance (rotational torque required for material removal) also increases. Such changes over time can be grasped by monitoring signals of known torque sensors, acceleration sensors, etc. at a relatively low cost.
However, when cutting with a very small depth of cut, such as final finishing by cutting, the wear of the cutting tool is not predominant, and there are problems such as poor chip discharge and deterioration of surface properties due to biting, so AE detection is possible. is necessary.
However, unlike the contact-type AE sensor 11 described above, the solid-borne AE sensor cannot be fixed to a rotating cutting tool, and the above signal can be detected with high sensitivity when fixed to a work material. difficult.
Therefore, it is desirable to install the laser microphone 16 at a distance of about 100 mm from the cutting tool (a range in which the attenuation of sound propagating in the atmosphere is sufficiently small) and use it for monitoring the state of surface texture.

As an example, it is conceivable to monitor the time change of the total amount of energy in the frequency band of 150 to 500 kHz with respect to the original signal. In particular, in precision machining over a long period of time, it was difficult to verify the presence or absence of abnormalities over the entire surface. It is possible to limit the inspection lead time and shorten the inspection lead time.

Next, the case of press working will be described.
In punching press working, sensing regarding the interaction between the die and the punching material is applied from the same viewpoint as cutting. On the other hand, in deep drawing press working, seizure on the surface of the material to be processed is directly linked to quality, so we also pay attention to the high frequency band (for example, the frequency band of 500-1 MHz) caused by phenomena such as adhesion wear. Is desirable.

As an example, it is conceivable to monitor the time change of the frequency band energy of 500-1 MHz with respect to the original signal.
In this way, it is desirable to use the frequency band of interest as appropriate according to the phenomenon to be detected.

The above arrangement example and the frequency to be detected are examples, and can be arbitrarily set as long as the desired signal can be detected at a position and frequency.

FIG. 2 is an explanatory diagram of an example of a time-series waveform during semiconductor lead frame processing.
In the example of FIG. 2, a signal corresponding to the output of the laser sensor 18 is input to the first input channel ch1 of the high-speed signal data analyzer 19.

Further, a signal corresponding to the output of the acceleration sensor 14 is input to the first input channel ch2 of the high-speed signal data analyzer 19 via the charge converter 15.
Further, a signal corresponding to the output of the contact type AE sensor 11 is input to the first input channel ch3 of the high-speed signal data analyzer 19 via the preamplifier 12 and the main amplifier 13.

As shown by the broken line circle in FIG. 2A, the RMS amplitude, which is the output of the interference signal analysis unit 17 corresponding to the output of the contact type AE sensor 11, is a shock wave-like material when the punch / workpiece comes into contact with each other. It captures the signal of breakage.
Then, the diagnostic system 20 can determine normality / abnormality for each press shot by paying attention to, for example, the time-series waveform at the time of material breakage and the frequency analysis result.

FIG. 3 is a diagram showing signals at the time of punching press working acquired by a contact type AE sensor as a time-series AE signal intensity and a time-frequency map.

In FIG. 3, the upper figure shows a time-series signal and a time-frequency analysis signal of the AE signal strength at the time of normal cutting acquired by the contact type AE sensor 11.
In addition, the lower figure shows the time-series signal and time frequency of the AE signal strength acquired by the contact type AE sensor 11 when the scrap material is intentionally inserted and abnormal biting occurs near the surface of the material during cutting. The analysis signal is shown.

In the normal state, one shock wave appears at a certain time, but in the abnormal state, both the AE signal strength and the time frequency analysis signal tend to be different.
Therefore, it is considered effective to monitor the amplitude intensity of the time series, the band energy of a specific frequency (the sum of the amplitudes of the frequency bands that correlate with the abnormal phenomenon), and the like for a certain time range.
As described above, it can be seen that the solid-borne AE signal detected by the contact-type AE sensor 11 can capture the moment of material breakage and detect an abnormality in foreign matter intervention.

FIG. 4 is a diagram showing a signal during ultra-precision machining acquired by a laser microphone as a time-frequency map.
As shown in FIG. 4, the sound in the high frequency band propagates in the atmosphere and reaches the laser microphone 16 only when an abnormality such as biting occurs in the vicinity of the surface during cutting.

That is, as shown by the broken line ellipse in FIG. 4, a signal having an intensity in the band of 150 to 500 kHz is obtained as the high frequency band.
Therefore, when a signal is detected in this frequency band (150-500 kHz), it can be easily detected that abnormal cutting has occurred based on the output signal of the laser microphone 16.

FIG. 5 is an explanatory diagram in the case where one step is performed for each of the plan-shaped workpieces at different positions.
In FIG. 5, a flat work material having a length of 100 mm is cut three times at different positions (see FIGS. 5 (A) to 5 (C)), and the fourth cut is made. The amount is set to zero and no cutting is performed (see FIG. 5 (D)).

It can be seen that the interaction between the tool, the work material, and the chips on the surface of the work material is detected with high sensitivity, unlike the piezoelectric AE sensor (installed on the work material) that directly detects the material breakage due to cutting.
Another feature is that the spike signal does not have periodicity.

From the above, even when applied to press processing, by extracting a laser microphone signal in a certain time range based on the rotation trigger signal and monitoring the time-series amplitude intensity, band energy of a specific frequency, etc., only the piezoelectric AE sensor can be used. Compared with the case of using the above, it is possible to more accurately estimate the state related to the surface texture and appearance quality of the object to be processed.

Next, the effect of the embodiment will be described.
The signal of the contact type AE sensor 11 catches material breakage well in both the removal process by cutting and the punching process by pressing, but there is a problem that it is difficult to isolate and capture only the signal caused by the abnormality of the surface and appearance of the object to be processed. ..

Further, since the AE attenuates in a complicated mode according to the structure of the propagating solid, it is desirable to install the sensor as close to the processing point as possible.

However, depending on the structure of the machine, it may not be possible to obtain sufficient signal strength due to the influence of the contact state at the interface between the parts, and it may take a lot of time to adjust the individual setup.
On the other hand, the laser microphone 16 can be expected to have a uniform attenuation rate with the distance between the processing point and the gap between the sensors as a guide, so that high versatility and detection reproducibility can be ensured.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and variations thereof are included in the scope and gist of the invention, and are also included in the scope of the invention described in the claims and the equivalent scope thereof.

10 Plastic machining abnormality detection system 11 Contact type AE sensor 12 Preamplifier 13 Main amplifier 14 Acceleration sensor 15 Charge converter 16 Laser microphone 17 Interference signal analysis unit 18 Laser sensor 19 High-speed signal data analyzer 20 Diagnostic system 20A Machining condition database 20B Threshold database MT Machine Tools

Claims (4)

A non-contact acoustic emission sensor that is placed at a predetermined distance from the work piece and detects acoustic emissions that are generated when the work piece is machined and propagated in the atmosphere.
An analysis unit that performs time-frequency analysis of the detection signal of the non-contact acoustic emission sensor, and
Based on the analysis results of the analysis unit, a diagnostic unit that detects that a processing abnormality has occurred when a frequency component equal to or higher than a predetermined threshold value is present in a predetermined frequency band, and a diagnostic unit.
A plastic working abnormality detection device equipped with. The non-contact acoustic emission sensor is configured as a laser microphone using the Fabry-Perot interferometer principle.
The plastic working abnormality detection device according to claim 1. The predetermined frequency band is 150 kHz to 500 kHz.
The plastic working abnormality detection device according to claim 1 or 2. It is equipped with a non-contact acoustic emission sensor that is placed at a position separated from the work piece by a predetermined distance and detects the acoustic emission generated when the work piece is machined by a plastic working device and propagated in the atmosphere. It is a plastic working abnormality detection method executed by a plastic working abnormality detection device.
The process of performing time-frequency analysis of the detection signal of the non-contact acoustic emission sensor and
Based on the result of the time-frequency analysis, it is determined whether or not a frequency component having a predetermined threshold value or more is present in a predetermined frequency band, and if a frequency component having a predetermined threshold value or more is present, a processing abnormality has occurred. The process of detecting that and
A method for detecting plastic working abnormalities.

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