JP2020181380A - Abnormality sign detection system and method - Google Patents

Abstract

To provide technology for detecting an abnormality sign in machine tools with high sensitivity.SOLUTION: An abnormality sign detection system is a system for detecting an abnormality sign in a machine tool 1 having a rotating tool 3, and comprises: an AE sensor 2 installed in the machine tool 1 or a work 4; a signal processing circuit obtaining an AE signal from the AE sensor 2; an AE event detection circuit detecting AE events from the AE signals; an AE event rate calculation circuit calculating an AE event rate that is the number of AE events per unit time by using the AE events; a determination circuit determining whether a plurality of index values arranged in a time series have reached a first state in which they are continuous for the first consecutive number of times or more within a first range, and if so, detecting as an abnormality sign; and an output control circuit performing, when the abnormality sign is detected, output control including an alert output indicating the abnormality sign or processing operation stop.SELECTED DRAWING: Figure 1

Description

The present invention relates to a technique for detecting an abnormality sign in a tool of a machine tool, for example, by using an information processing technique.

A machine equipped with a rotating mechanism, for example, a machine tool equipped with a rotating tool such as a drill, performs processing such as cutting or grinding on a work (in other words, an object, a work material, etc.) by rotating the tool. During machining, abnormalities such as damage to tools or workpieces may occur. It is preferable that the machine tool or the information processing system including the machine tool can detect the abnormality in advance as a sign of abnormality.

As an abnormality sign detection system of the prior art example, there is a technique of determining an abnormality or an abnormality sign using a parameter value such as a voltage of an AE signal using an AE (acoustic emission) sensor. As a general method, there is known a method of determining an abnormality or the like when the parameter value exceeds a threshold value in correspondence with the phenomenon that the parameter value of the signal increases exponentially at the time of abnormality.

As an example of the prior art related to the above-mentioned abnormality sign detection, International Publication No. 2009/096551 (Patent Document 1) can be mentioned. Patent Document 1 describes that the bearing diagnostic system includes a sensor attached to the bearing, a monitoring and diagnostic device connected to the sensor, and the like, and the following. This diagnostic system divides one rotation time of the rotating shaft supported by the bearing or the intermittent operation time of one rotation by intermittent operation into multiple sections, compares the abnormality judgment reference level with the measurement data of each section, and compares the sections. The presence or absence of abnormality is determined for each.

International Publication No. 2009/096551

In the determination method in the abnormality sign detection system of the prior art example, for example, a threshold value is set for the amplitude of the AE signal, and when the amplitude exceeds the threshold value, it is determined as an abnormality or an abnormality sign. In such a method, the amplitude of the AE signal needs to be large, and it is difficult to detect when only a minute signal is generated in the case of low load machining, for example. Further, even if such a method can be detected, it is often too late at the time of detection, and it is difficult to prevent an abnormality such as damage to the tool. An object of the present invention is to provide a technique capable of detecting an abnormality sign in a machine tool with high sensitivity.

A typical embodiment of the present invention has the following configurations. The abnormality sign detection system of one embodiment is an abnormality sign detection system that detects an abnormality sign in a machine tool having a rotating tool, from the AE sensor installed on the machine tool or the workpiece and the AE sensor. An AE event that calculates the AE event rate, which is the number of AE events per unit time, using the signal processing circuit that acquires the AE signal, the AE event detection circuit that detects the AE event from the AE signal, and the AE event. It is determined whether or not the rate calculation circuit and the plurality of index values arranged in the time series have reached the first state in which they are continuous for the first consecutive number of times or more within the first range, and the first state is reached. If this is the case, the system includes a determination circuit that detects the abnormality sign, and an output control circuit that controls the output including the alert output indicating the abnormality sign or the stop of the machining operation when the abnormality sign is detected.

According to a typical embodiment of the present invention, a sign of abnormality in a machine tool can be detected with high sensitivity.

It is a figure which shows the structure of the abnormality sign detection system of embodiment of this invention. It is a figure which shows the configuration example of a tool, a work, a table, an AE sensor and the like in embodiment. It is a figure which shows the configuration example which installs the AE sensor in the work in embodiment. It is a figure which shows the configuration example of the AE sensor in embodiment. It is a figure which shows the propagation path of an AE signal in an embodiment. It is a figure which shows the structural example of the detection circuit of an AE signal in embodiment. It is a figure which shows the processing flow of the abnormality sign detection function in embodiment. It is a figure which shows the example of the AE wave in embodiment. It is a figure which shows another example of an AE wave in embodiment. It is a figure which shows the 1st method of AE event detection in embodiment. It is a figure which shows the 2nd method of AE event detection in embodiment. It is a figure which shows the calculation of the AE event rate in embodiment. It is a figure which shows the example of the AE wave corresponding to the change of the state of a tool in an embodiment. It is a figure which shows the 1st example of the AE wave and the AE event rate in embodiment. It is a figure which shows the 2nd example of the AE wave and the AE event rate in embodiment. It is a figure which shows the 3rd example of the AE wave and the AE event rate in embodiment. It is a figure which shows the 1st example of the abnormality sign determination in embodiment. It is a figure which shows the 2nd example of the abnormality sign determination in embodiment. It is a figure which shows the 3rd example of the abnormality sign determination in embodiment. It is a figure which shows the transition example of the AE amplitude and AE event rate in a long-term in embodiment. It is a figure which shows the example of the AE wave at the time of processing and the time of not processing in the embodiment. It is a figure which shows the example of the GUI screen in embodiment. It is a figure which shows the example of the state of the cutting edge of a tool in embodiment.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In principle, the same parts are designated by the same reference numerals in all the drawings for explaining the embodiments, and the repeated description will be omitted.

[Comparison example]
As a comparative example with respect to the embodiment, the abnormality sign determination method of the prior art example compares the parameter values such as the voltage and current of the signal from the sensor such as the AE sensor with the preset threshold value to compare the abnormality or abnormality sign. To judge. For example, when the peak amplitude exceeds the threshold value, it is determined to be abnormal. In the case of high-load machining, a large value of voltage or current can be obtained, and the determination can be made from that value. Examples of high-load machining include roughing and semi-finishing. However, in the case of low load machining, since the obtained voltage and current values are small, it is difficult to judge from those values, and high-sensitivity detection cannot be realized. In the case of low-load machining, for example, the machining state is not easily reflected in the current of the spindle, so it is difficult to make a determination using the current. In the case of low load machining, the signal change is often small before the occurrence of an abnormality. Further, when an expensive measuring instrument is used, it may be possible to detect an abnormality or a sign of abnormality from the values of current and voltage, but it is difficult to introduce the expensive measuring instrument to the site. Further, even in this case, since the method of determining the time when the parameter value increases and exceeds the threshold value as an abnormality is common, the detection cannot be performed until immediately before the occurrence of the tool abnormality. If the detection is slow in this way, in most cases, an abnormality such as a broken tool will occur. That is, it is difficult to detect a sign of abnormality and prevent the tool abnormality before the tool abnormality occurs.

(Embodiment)
An abnormality sign detection system and a method according to an embodiment of the present invention will be described with reference to FIGS. 1 to 23. The abnormality sign detection system of the embodiment is a system that detects an abnormality sign for a tool or the like of a machine tool. The machine tool is equipped with an AE sensor or the like, which is an element necessary for detection. It should be noted that a part of the abnormality sign detection function may or may not be implemented in the machine tool. The abnormality sign detection method of the embodiment is a method having steps executed in the abnormality sign detection system of the embodiment.

The abnormality sign detection system uses an AE sensor to monitor the abnormality sign detection related to an abnormality of a tool or the like when machining a workpiece by a machine tool. The AE sensor can detect even a minute signal in the case of low load machining. The abnormality sign detection system detects an abnormality sign with high accuracy by making a determination by a predetermined method using the signal of the AE sensor even when only a minute signal can be obtained in the case of low load machining. The predetermined method is a method of determining using the AE event rate (AE event rate). The abnormality sign detection system determines the abnormality sign from the state of the AE event rate in chronological order. The abnormality sign detection system determines that the value of the AE event rate is an abnormality sign when it is in a state of continuously appearing within a predetermined range. When an abnormality sign is detected, the abnormality sign detection system performs output control such as alarm output and operation stop. As a result, it is possible to take measures such as stopping the operation before an abnormality such as a breakage occurs in the tool, in other words, before the degree of the abnormality increases.

[Abnormal sign detection system]
FIG. 1 shows the configuration of the abnormality sign detection system of the embodiment. The abnormality sign detection system is mainly composed of the machine tool 1. The machine tool 1 includes a housing 11, a control device 10, a preamplifier 70, a signal processing circuit 71, an oscilloscope 72, and the like. The user operates the machine tool 1. The user may be a worker who performs processing work, an administrator who monitors an abnormality, or the like.

The control device 10 is a device that performs main control including machining control of the machine tool 1, and is equipped with an abnormality sign detection function. A control device connected separately from the machine tool 1 may be provided, and an abnormality sign detection function may be mounted on the control device. The main body of the machine tool 1 other than the control device 10 includes a housing 11, a spindle stage 31, a spindle 32, a tool 3, an AE sensor 2 (AE sensor unit 20), a table 6, a vise 5, a liquid feed pipe 7, and the like. .. Each part such as the spindle stage 31 and the table 6 is connected to the housing 11. The spindle stage 31 drives the movement of the spindle 32. The spindle 32 includes a holder for holding the tool 3 and drives the rotation of the tool 3. The spindle 32 includes a rotating shaft 33 connected to the tool 3.

The table 6 has a mechanism capable of moving and rotating in the directions of the three orthogonal axes (X, Y, Z) shown in the drawing. A vise 5 is provided on the table 6. The vise 5 fixes the work 4. The work 4 is an object or material to be machined, such as cutting with a tool 3.

The liquid feed pipe 7 sends the cutting fluid 8 as a liquid for machining to a portion where the work 4 is machined by the tool 3, such as cutting. The main shaft 32 may be provided with a liquid feeding pipe 7.

The AE sensor 2 detects an AE wave generated during processing. The AE sensor 2 is installed at any position on the machine tool 1. The location is a location where the AE wave from the tool 3 or the like propagates, and is not particularly limited, and a position where the AE wave can be detected as easily as possible may be selected. In the installation example 9A in FIG. 1, the AE sensor 2 is installed so as to be fixed to the spindle 32. In this example, two AE sensors 2 are fixed to the spindle 32. The AE sensor 2 may be installed in the form of an AE sensor unit 20 as in the example of FIG. 3 described later.

In FIG. 1, another installation example of the AE sensor 2 is also shown. Installation example 9B is an example in which the AE sensor 2 is installed on the work 5. Installation example 9C is an example in which the AE sensor 2 is installed on the vise 5. Installation example 9D is an example in which the AE sensor 2 is installed on the table 6. Installation example 9E is an example in which the AE sensor 2 is installed in a hollow position via a support device. This position is a position near a processing portion or a portion to which the cutting fluid 8 is supplied. Installation example 9F shows an example in which the AE sensor 2 is installed so as to be embedded in the table 6.

The preamplifier 70, the signal processing circuit 71, and the oscilloscope 72 are elements for acquiring the AE signal, and correspond to the configuration example of FIG. 6 described later. The AE sensor 2 or the AE sensor unit 20 is connected to the preamplifier 70 via a signal cable. The preamplifier 70 is connected to the signal processing circuit 71 via a signal cable, and the signal processing circuit 71 is connected to the oscilloscope 72 via a signal cable. The oscilloscope 72 is connected to the connection interface unit 103 of the control device 10 via a signal cable. In the subsequent stage from the signal processing circuit 71, data transmission may be performed not only by wired communication using a signal cable but also by wireless communication.

Each part of the machine tool 1 is connected to the control device 10 via a signal cable or the like, and the drive is controlled by the control device 10. The control device 10 controls the drive of the table 6, the spindle 32, the tool 3, the liquid feed pipe 7, and the like when the work 4 is machined. The control device 10 acquires and inputs an AE signal from the AE sensor 2 at the time of machining, and performs control including detection of an abnormality sign related to the state of the tool 3 or the like.

The control device 10 includes a processor 101, a memory 102, a connection interface unit 103, an input device 104, a display device 105, a speaker 106, a lamp 107, and the like, and these are connected to each other via a bus or the like. The processor 101 is composed of a CPU, a ROM, a RAM, and the like, and constitutes a controller of the machine tool 1. The processor 101 realizes the abnormality sign detection function by executing the process according to the control program 111.

The memory 102 is composed of a non-volatile storage device or the like, and stores various data and information handled by the processor 101. Control information 121, setting information 122, and the like are stored in the memory 102. The control information 121 corresponds to NC data, and is data or information for NC control of machining of the work 4. The setting information 122 includes system setting information and user setting information related to the abnormality sign detection function.

The connection interface unit 103 is an interface portion for connecting an input device 104 or the like, a communication interface device (not shown), or the like. The input device 104 is a device such as an operation panel, a button, or a keyboard that accepts a user's input operation. The display device 105 is a device that displays various information to the user through a graphical user interface (GUI) on a display screen. The speaker 106 is a voice output device that outputs voices such as guides and alarms. The lamp 107 is a device such as an LED that emits light in response to a guide, an alarm, or the like. The control device 10 may be built in the main body of the machine tool 1 or may be externally connected. The control device 10 may be composed of a computer such as a PC or a server. Another computer, storage device, communication device, or the like may be connected to the control device 10.

[Tables, tools, and machining]
FIG. 2 shows a configuration example of a table 6, a vise 5, a work 4, a tool 3, and the like of the machine tool 1, and an installation example 9B of the AE sensor 2. Installation example 9B is an example in which the AE sensor unit 20 is installed as the AE sensor 2 on the side surface of the work 4. A signal cable 220 comes out from the AE sensor unit 20 and is connected to the preamplifier 70. Since noise is easily picked up between the AE sensor 2 and the preamplifier 70, it is preferable to use a signal cable 220 having a short cable length and low noise.

The work 4 is, for example, SUS304 stainless steel and has a rectangular parallelepiped shape. For example, the upper surface of the work 4 is a machined surface WS. The tool 3 comes into contact with the machined surface WS to perform machining such as cutting. An example of the tool 3 is a drill. An example of machining is hole machining in which a hole having a small diameter is formed on the machined surface WS by cutting a drill which is a tool 3. This hole drilling is, in particular, a plurality of hole drilling in which a plurality of holes are continuously formed at a plurality of parallel positions.

The side surface of the work 4 is fixed by the vise 5. The position, orientation, and other states of the work 4 can be changed by moving and rotating the three axes of the table 6. The position, orientation, and other states of the tool 3 can be changed by driving the spindle 32. The tool 3 protrudes below the main shaft 32 in the vertical direction (Z direction). The tip of the tool 3 is a small-diameter drill.

As a machining example, milling using a drill, which is a tool 3, is handled. When machine tool 1 drills holes by milling, for example, by moving the table 6 in the X and Y directions, a predetermined position (that is, a hole forming target position) on the machining surface WS of the work 4 is the tip of the tool 3. Make sure it comes to the bottom. The machine tool 1 moves the tool 3 downward in the Z direction while rotating the tool 3. As a result, the machine tool 1 digs a hole while applying the tool 3 to the machining surface WS. After forming the hole, the machine tool 1 moves the tool 3 upward in the Z direction. The machine tool 1 moves the spindle stage 31 or the table 6 for fixing the work 4 in the X and Y directions for machining the next hole. In the case of horizontal hole drilling, the spindle stage 31, the spindle 32, and the tool 3 are located at positions rotated 90 degrees from the positions of FIGS. 1 and 2, and the machine tool 1 Xs the tool 3 and the work 4. , Y, Z direction.

The tool 3 is, for example, a small-diameter drill made of high-speed steel. The tool 3 has a spiral groove and a blade at the tip as shown. As an example of the dimensions of the tool 3, the diameter (blade diameter) on the tip side is 1 mm, the shank diameter is 3 mm, the total length is 60 mm, the protrusion (the length protruding from the holder) is 30 mm, and the groove length is 12 mm.

As a machining example, the machining conditions in the case of hole drilling by cutting are as follows. The hole diameter is 1 mm and the hole depth is 1 mm. The rotation speed of the tool 3 is, for example, 4800 rpm, which is 12.5 milliseconds when converted into a rotation period (= 1 rotation time). The feed amount is 0.02 mm / rev. The contact time in the machining time of one hole is 0.6 seconds. The number of holes in multi-hole machining is several hundred to one thousand and several hundred. Further, there are cases where the cutting fluid 8 is supplied using the liquid feed pipe 7 during processing (so-called wet type) and cases where the cutting fluid 8 is not supplied (so-called dry type), and both are applicable. When the material of the tool 3 is high-speed steel, a load is applied, so that the tool 3 is hard to break. On the other hand, when the tool 3 is made of a cemented carbide material, it is likely to break when a large load is applied.

[Work and AE sensor]
FIG. 3 shows a configuration example in which the AE sensor unit 20 including the AE sensor 2 is installed on one side surface of the work 4 corresponding to the installation example 9B. The outline of the installation location in the case of the installation example 9C on the vise 5 is also shown. The outer cover of the AE sensor unit 20 is shown in FIG. This cover is fixed to one side surface of the work 4. The AE sensor 2 (FIG. 4 described later) is housed in this cover. The signal cable 220 comes out from a part of the hole of this cover. This cover has waterproof and oil-proof properties. In another embodiment, a type of AE sensor 2 having waterproof and oil-proof properties may be applied. In that case, the mechanism such as the cover can be simplified. An acoustic coupler (FIGS. 4 and 5) described later is provided at a fixed portion of the AE sensor 2 on the side surface of the work 4. This acoustic coupler is, for example, grease, wax or an adhesive.

[AE sensor (1)]
FIG. 4 shows a cross-sectional structure of the AE sensor 2 in an installed state with respect to the installation surface 401 as a configuration example. There are various types of known AE sensors, all of which are applicable, but in the embodiment, the following types of AE sensors 2 are applied. This type is a broadband type and a single-ended type. The wideband type includes a flat band as a frequency characteristic, and has a wide band of, for example, 100 kHz to 1 MHz. The AE sensor 2 includes a piezoelectric element 21, a receiving plate 22, a damper 23, a shield case 25, a lid 26, a connector 27, a signal cable 28, and the like. In this type, resonance is suppressed by providing a damper 23 on the upper side of the piezoelectric element 21. The sensitivity of the AE sensor 2 is, for example, 40 to 55 dB. The standard of sensitivity is 0 dB = 1 V / m / s. The receiving plate 22, particularly the receiving surface thereof, is fixed to the installation surface 401 via an acoustic coupler such as grease 402. For fixing, various means such as adhesion and screwing can be applied. The signal cable 28 from the piezoelectric element 21 is connected to the connector 27, and is connected to the external signal cable 220 through the connector 27.

The installation surface 401 is the surface of the spindle 32 in the installation example 9A of FIG. 1, and the surface of the work 4 in the installation example 9B of FIG. The AE wave from the installation surface 401 propagates to the receiving plate 22 via the acoustic coupler. It is desirable to use an acoustic coupler such as grease 402 to bring air into close contact with the boundary surface between the members so that air does not intervene in the propagation path. The piezoelectric element 21 converts the AE wave propagating on the receiving plate 22 into an electric signal (that is, an AE signal) by the piezoelectric effect, and outputs the AE signal from the signal cable 28. Piezoelectric ceramics such as PZT (lead zirconate titanate) are used in the piezoelectric element 21 so that minute strain can be detected. The wideband type AE sensor 2 is suitable for observing phenomena having various characteristics having different frequencies. The AE sensor 2 as shown in FIG. 4 is further housed in the cover as shown in FIG. 3 to be configured as the AE sensor unit 20. The cover of the AE sensor unit 20 has a structure for fixing to the installation surface 401.

[AE sensor (2)]
The AE sensor 2 installed on the spindle 32 in the installation example 9A of FIG. 1 and the AE sensor 2 installed on the work 4 in the installation example 9B of FIG. 3 are generated and propagated from the tool 3 and the work 4 during machining. The AE wave is detected as an AE signal. The AE wave reflects a state such as a force received by the tool 3 from the work 4 as the tool 3 rotates. The AE wave includes frequencies from, for example, 100 kHz to 1 MHz. As the sampling frequency of the AE signal in the AE sensor 2, a frequency of 2 MHz or more, for example, 4 MHz is used. The control device 10 acquires the AE signal from the AE sensor 2 through the preamplifier 70, the signal processing circuit 71, and the oscilloscope 72. The control device 10 acquires various information based on the date and time, the ID, and the control information 111 together with the AE signal. The ID (identification information) includes the ID of the machine tool 1, the tool 3, the work 4, the AE sensor 2, and the like.

In addition to the AE sensor 2, the machine tool 1 may be provided with other types of sensors. Examples of other types of sensors include voltmeters, current meters, force sensors, acceleration sensors, displacement sensors, gyro sensors, ultrasonic sensors, strain gauges, laser Doppler vibration meters, temperature sensors, noise meters, cameras, MEMS sensors, etc. Can be mentioned. Sound level meters measure environmental sounds. The AE sensor 2 may be installed so as to be embedded inside a table 6 or the like. It is preferable that the AE sensor 2 is installed at a position where it is difficult to detect environmental sounds. The machine tool 1 may be provided with a sound absorbing material for absorbing operating sounds and the like.

[AE sensor (3)]
In the installation example 9A of FIG. 1, two AE sensors 2 are installed with respect to the spindle 32. When the AE sensor 2 is installed on the spindle 32, basically, a sufficient effect can be obtained even with one AE sensor 2. Further, when two AE sensors 2 are installed, the following effects can be obtained. As the two AE sensors 2, for example, those having different frequency bands are used. For example, when the material of the work 4 (particularly the speed of sound and the density) changes, the frequency of the AE wave also changes. Therefore, by detecting using a plurality of AE sensors 2 having different frequency bands, it is possible to deal with materials having a wide range of acoustic characteristics.

Further, when the table 6 is installed as in the installation example 9D, for example, two or more AE sensors 2 having the same frequency band may be installed. In this case, the position of the AE generation source can be calculated from the arrival time difference of the AE wave by using two or more AE signals. The sound velocity of the AE wave and the position of the AE sensor at that time are known information. The number of AE sensors 2 required for one-dimensional position positioning is two. By installing two or more AE sensors 2 on the same plane of the table 6, the position of the AE source can be determined. That is, it is possible to specify in detail the position where the abnormality of the tool 3 or the like has occurred. When the position is determined, the AE sensor 2 is preferably installed on the same plane as the upper surface (or the lower surface or the inside of the table 6) of the table 6 as in the installation example 9D, rather than the side surface of the table 6. When three or more identical AE sensors 2 are used, two-dimensional position positioning is possible.

[AE wave propagation path]
FIG. 5 shows the propagation path of the AE wave in various installation examples. (A) shows a propagation path corresponding to the installation example 9A. The sound source in which the AE wave is generated is a place where the tool 3 such as a drill and the work 4 come into contact with each other. The AE wave generated by machining propagates from the tool 3 to the spindle 32. The AE wave propagates from the spindle 32 to the AE sensor 2 through an acoustic coupler 501 such as grease. (B) shows a propagation path corresponding to the installation example 9B. The AE wave generated during machining propagates from the tool 3 to the work 4. The AE wave propagates from the work 4 to the AE sensor 2 through the acoustic coupler 501. (C) shows the propagation path corresponding to the installation example 9C. The AE wave generated by machining propagates from the tool 3 to the work 4, and propagates from the work 4 to the vise 5. The AE wave propagates from the vise 5 to the AE sensor 2 through the acoustic coupler 501. (D) shows the propagation path corresponding to the installation example 9D. This propagation path includes the two types of paths shown. The AE wave generated during machining propagates from the tool 3 to the work 4, propagates from the work 4 to the table 6, and propagates from the table 6 to the AE sensor 2 through the acoustic coupler 501. Further, the AE wave generated by machining propagates from the tool 3 to the work 4, propagates from the work 4 to the vise 5, propagates from the vise 4 to the table 6, and propagates from the table 6 to the AE sensor 2 through the acoustic coupler 501. To do.

(E) shows a propagation path corresponding to the installation example 9E. This propagation path includes the three types of paths shown. In the first path, the AE wave generated by machining propagates from the tool 3 to the AE sensor 2 via the cutting fluid 8. The cutting fluid 8 functions as an acoustic coupler. In the second path, the AE wave propagates from the work 4 to the AE sensor 2 via the cutting fluid 8. In the third path, the AE wave propagates from the tool 3 to the work 4 and propagates from the work 4 to the AE sensor 2 via the cutting fluid 8. AE waves can be propagated through the cutting fluid 8 which is a liquid. AE waves are well transmitted at the interface between solids. At the interface between solid and liquid, AE waves are slightly reflected but transmitted. At the interface between solid and gas, AE waves are almost totally reflected. Therefore, it is preferable that the propagation path does not involve air.

[Circuit configuration example]
FIG. 6 shows a configuration example of a circuit or the like for detecting an AE signal from the AE sensor 2 and detecting an abnormality sign or the like. The AE signal from the AE sensor 2 is amplified by the preamplifier 70 and then input to the signal processing circuit 71 (may be referred to as a first signal processing circuit for explanation). The signal processing circuit 71 is a portion that performs the first signal processing of the AE signal. The signal processing circuit 71 may be a circuit connected to the connection interface unit 103 inside the control device 10, or may be an independent device connected to the outside of the control device 10. The signal processing circuit 71 includes a BPF (bandpass filter) 81, a main amplifier 82, an envelope detection circuit 83, and an AE event detection circuit 84.

The BPF 81 passes an AE signal in a predetermined frequency band. The BPF 81 removes unnecessary signals in the low frequency region, which are vibration components. The main amplifier 82 further amplifies the AE signal. In this example, the preamplifier 70 and the main amplifier 82 have a two-stage amplification configuration, but a one-stage amplification configuration may be used. In the case of machining where the signal level of the sound source is high, only the preamplifier 70 may be used and the gain of the main amplifier 82 may be set to 0 dB. Alternatively, only the preamplifier 70 may be used and the main amplifier 82 may not be connected. The envelope detection circuit 83 performs the envelope detection described later from the AE signal. The AE event detection circuit 84 detects an AE event (AE event) from the AE signal by the method described later. The BPF 81 may be provided in front of the preamplifier 70. Further, the BPF 81 may be provided after the main amplifier 82. Further, a full-wave rectifier circuit or the like may be further provided after the main amplifier 82.

An oscilloscope 72 is connected to the subsequent stage of the AE event detection circuit 84 of the signal processing circuit 71. The oscilloscope 72 is a waveform measuring instrument, and includes an ADC (analog-to-digital conversion circuit) 85, a display 86, and a recording unit 87. The oscilloscope 72 and the signal processing circuit 71 may be configured as an integrated device. The ADC 85 converts an AE signal, which is an analog signal, into a digital signal by sampling at a predetermined frequency. The display 86 is a waveform display, and displays the state (amplitude, AE event, etc.) of the AE wave of the AE signal on the display screen. The recording unit 87 records the AE signal data as a log and enables output.

A signal processing circuit (may be referred to as a second signal processing circuit for explanation) provided in the control device 10 is connected to the subsequent stage of the ADC 85 of the oscilloscope 72. The second signal processing circuit may be a circuit connected to the connection interface unit 103, or may be realized by software program processing by the processor 101. The second signal processing circuit includes an AE event rate calculation circuit 91, an abnormality sign determination circuit 92, an output control circuit 93, a threshold value setting circuit 94, a threshold value setting circuit 95, and the like. The control device 10 includes a machining control function 90 as an existing function. The machining control function 90 is a function that generates control information 121 for machining and controls the drive of the tool 3, the table 6, and the like based on the control information 121. The control information 121 also includes the tool information 123.

The AE event rate calculation circuit 91 inputs the AE signal, which is a digital signal from the ADC 85, and acquires the tool information 123 of the control information 121. The tool information 123 includes information such as the rotation speed or rotation cycle of the tool 3. The AE event rate calculation circuit 91 calculates the AE event rate as the index value ED from the input information. The AE event rate is the number of AE events per unit time.

The abnormality sign determination circuit 92 inputs the information of the AE event rate (index value ED) from the AE event rate calculation circuit 91, determines the abnormality sign related to the state of the machine tool 1 by using the threshold value, and determines the determination result information. Is output. The abnormality sign determination circuit 92 compares a plurality of index value EDs arranged in a time series with a threshold value to determine the presence or absence of an abnormality sign. The threshold value setting circuit 94 dynamically or statically sets the threshold value for determining the abnormality sign with respect to the abnormality sign determination circuit 92. The threshold value, which is a set value, includes the number of consecutive times N and the first range H1 described later.

The threshold value setting circuit 95 sets a threshold value for AE event detection (for example, a two-level voltage threshold value described later) for the AE event detection circuit 84.

The output control circuit 93 performs predetermined output control according to the determination result information from the abnormality sign determination circuit 92. Output control includes alert output 93A, operation stop control 93B, graph display 93C, signal waveform display 93D, and the like. The alert output 93A is an alert that conveys an abnormality sign to the user, for example, an alert voice output from the speaker 106, an alert light emission of the lamp 107, an alert information display on the display screen of the display device 105, and the like. The alert output 93A can also be an alert output of a plurality of stages (in other words, a plurality of levels regarding the degree of abnormality) described later. The operation stop control 93B is a control for immediately stopping the machining operation. The graph display 93C is a display of a graph of the AE event rate, an abnormality sign determination result, and the like on a display screen of the display device 105 or the like. The signal waveform display 93D is a display of an AE wave graph or the like on a display screen of the display device 105 or the like. From each of the above outputs, the user can recognize a sign of abnormality before it becomes an abnormality such as damage to the tool 3 of the machine tool 1, and can deal with it.

As a modification of the configuration of FIG. 6, the envelope detection circuit 83 and the AE event detection circuit 84 of the first signal processing circuit 71 may be provided in the second signal processing circuit of the control device 10. Various modifications are possible, such as adding or deleting circuit parts, separating or merging circuit parts, and so on.

[AE measurement conditions]
The AE measurement conditions in the embodiment are as follows. As for the amplifier gain, the gain of the preamplifier 70 was set to 20 dB, and the gain of the main amplifier 82 was set to 0 dB. The BPF 81 was subjected to high-pass filter processing for the purpose of removing noise of vibration components, and the sampling frequency was measured at 4 MHz. The cutoff frequency of the HPF (high-pass filter) of the BPF 81 was set to 20 kHz in order to measure a signal of 20 kHz or more, which is a general ultrasonic region. Of the BPF81, the LPF (low-pass filter) was THRU (Through; no filtering). Since the AE frequency band with respect to the plasticity of the metal material is generally 100 kHz to 1 MHz, the cutoff frequency of the HPF is set to about 100 kHz in order to specially detect the AE signal accompanying the plastic deformation of the metal material. You may.

[Processing flow]
FIG. 7 shows a processing flow of the abnormality sign detection function in the abnormality sign detection system and method of the embodiment. FIG. 7 has steps S1 to S8, which will be described below in the order of steps. Step S1 is the setting of the machine tool 1 and the work 4, and the start of machining. The setting includes installing the AE sensor 2 in advance at a predetermined position of the machine tool 1 as shown in FIG. The setting includes setting of initial setting values by the threshold value setting circuit 94 and the threshold value setting circuit 95. The user inputs a machining start instruction to the machine tool 1. The control device 10 generates control information 121 and starts machining control and abnormality sign detection control.

In step S2, the control device 10 acquires and inputs the AE signal and related information from the AE sensor 2 through the signal processing circuit 71 and the like. In step S3, the AE event detection circuit 84 of the signal processing circuit 71 of FIG. 6 detects an AE event from the AE signal (FIG. 10 or 11 described later). In step S4, the AE event rate calculation circuit 91 of FIG. 6 calculates the AE event rate as the index value ED from the AE signal (including the AE event information).

In step S5, the threshold value setting circuit 94 of the control device 10 sets, for example, a dynamic threshold value as the threshold value in the abnormality sign determination circuit 92. This threshold is the number of consecutive times N, the first range H1, and the like. The threshold value setting circuit 94 determines the dynamic threshold value using the information from the AE event rate calculation circuit 91.

In step S6, the abnormality sign determination circuit 92 arranges the index value ED of the AE event rate obtained in step S4 in chronological order, and determines the abnormality sign at each time point using the threshold value set in step S5. The abnormality sign determination is a determination as to whether or not a plurality of index value EDs are in a continuous state (referred to as a first state) within the first range H1 and in a continuous number of times N or more in a time series. If the determination result in step S6 is that there is an abnormality sign (YES), the process proceeds to step S7, and if there is no abnormality sign (NO), the process proceeds to step S8.

In step S7, the output control circuit 93 performs output control (the above-mentioned alarm output 93A and operation stop control 93B) according to the abnormality sign determination result (first state) in step S6.

In step S8, the control device 10 confirms whether or not the machining is completed, for example, whether or not all the machining of a plurality of holes is completed. If not, the control device 10 returns to step S2 and repeats the same procedure. End the flow.

[Tool rotation speed and machining time]
The control device 10 refers to information such as the rotation speed of the tool 3 from the tool information 123 in the control information 121, and grasps the rotation cycle (= 1 rotation time) of the tool 3. The rotation speed of the tool 3 is, for example, the rotation speed per minute ([rpm]). The rotation speed is, for example, 4800 rpm (80 Hz in frequency). The rotation cycle can be calculated from the number of rotations (or rotation speed, etc.). For example, from 4800 rpm, the rotation period is 12.5 ms. The AE event rate calculation circuit 91 sets the rotation period of the tool 3 as a unit time TU (FIG. 12 described later), and counts the number of AE events for each unit time TU to obtain the AE event rate.

In the machining example of the embodiment, abnormality sign detection is performed for a long time in the machining of a plurality of holes. The machining time for one hole is, for example, 1 second. For example, when machining a thousand holes, the machining time requires at least 1,000 seconds.

[AE waves and abnormalities]
AE is a phenomenon in which elastic energy stored inside when a material is deformed or broken is emitted as sound waves (called AE waves). The AE wave is an ultrasonic wave. The AE wave is generated by the occurrence of deformation or the like before the material is destroyed. A method for non-destructively evaluating deformation of a material using AE waves is called an AE method. In the AE method, an AE sensor is used. The AE sensor detects elastic waves emitted from the microscopic fracture of the material. Since the AE signal is weak, an amplifier is used. The AE method can detect signs of abnormality such as destruction by observing the behavior of AE waves in real time.

FIG. 8 shows a sudden AE wave as an example of the AE wave. (A) shows a set of sudden AE waves. A part of (A) enlarged in time is shown in (B). (B) shows a plurality of sudden AE waves. (C) shows a part of (B) enlarged in time. (C) shows two sudden AE waves. One sudden AE wave corresponds to one AE event. Such a sudden AE wave may be generated before an abnormality occurs in the tool 3 or the work 4. Such a sudden AE wave will be described later, but for example, chips generated by cutting cannot be completely discharged to the outside of the hole and remain between the hole and the drill, or chips adhere to the tip of the tool 3. , It is thought that it is generated by the influence of the chips.

An abnormal state is a state different from the defined normal state. Examples of abnormalities include damage and breakage in the tool 3, the work 4, the spindle 32, and the like. In the case of a tool 3 such as a small-diameter drill, breakage may occur as an abnormality. Other abnormalities include cracks and chipped cutting edges. The abnormality that is the target of abnormality sign detection is a general term that includes such damage or breakage of the tool 3. In the case of breakage of the tool 3, the damage to the work 4 is also large, and the processing up to that point is wasted. Further, in the case of breakage, the load on the machine tool 1 is large, which causes a machine failure. In particular, in the case of finish processing which is a low load processing, it is often the final process, and it is difficult to repair in the subsequent process. The user wants to prevent the tool 3 from breaking as much as possible.

The abnormality sign detection system of the embodiment detects such a sudden AE wave as an AE event, calculates the AE event rate from a plurality of AE events, and determines the abnormality sign from the state of the AE event rate. In particular, the abnormality sign detection system detects a phenomenon in which the index value ED of the AE event rate continuously occurs within a certain range as an abnormality sign. As a result, it is possible to deal with the problem before the tool 3 is broken.

FIG. 9 shows a variation of the sudden AE wave. (A) shows one sudden AE wave with simple attenuation. The AE event detection circuit 84 counts such a sudden AE wave as one AE event. (B) shows a sudden AE wave having a complicated shape that appears to have a plurality of peaks at the rising edge. The AE event detection circuit 84 also counts such a sudden AE wave as one AE event. (C) shows a sudden AE wave having a complicated shape that appears to have a plurality of peaks in attenuation. The AE event detection circuit 84 also counts such a sudden AE wave as one AE event.

As in the example above, there can be AE waves of various shapes. It is difficult for the determination method in the prior art example to detect an abnormality sign with high accuracy for such AE waves having various shapes. On the other hand, since the abnormality sign detection system of the embodiment is a determination method for determining the phenomenon of the AE event rate, it is possible to detect the abnormality sign with high accuracy even for such AE waves having various shapes.

[AE event detection]
10 and 11 show an example of an AE event detection method. The AE event detection circuit 84 detects an AE event using the method of FIG. 10 or 11. Which method to use can be set in advance. The AE event detection method is not limited to this example, and other methods may be applied.

FIG. 10 shows a method of determining and detecting an AE event using a one-level voltage threshold value V1 as a threshold value. The vertical axis of the AE wave of the AE signal in FIG. 10 is the voltage value. (A) shows the peak amplitude of the voltage value of the AE wave. The AE event detection circuit 84 compares the amplitude of the AE wave with the voltage threshold value V1, and measures the time point at which the amplitude becomes the voltage threshold value V1 or more as the AE count as shown in (c). (B) is the duration of the AE event associated with the AE count of (c). Time point t1 is the start time of the AE event, and time point t2 is the end time of the AE event. (D) indicates the rise time of the AE event, and is the time from the time point t1 to the time point of the peak amplitude of (a). The AE event detection circuit 84 counts and detects the duration as in (b) as one AE event based on the AE count in (c).

FIG. 11 shows a method of determining and detecting an AE event using two levels of high and low voltage thresholds {voltage thresholds V _H , _VL }. (A) shows an AE wave of an AE signal which is an analog signal. The vertical axis shows the amplitude of the voltage. (B) shows a waveform (envelope waveform) after performing full-wave rectification and envelope detection by the envelope detection circuit 83 of FIG. 6 from the AE signal of (A). AE event detection circuit 84, for this waveform, two-level voltage threshold _V H, as compared with the _{V L} to detect and determine the AE event. The envelope is a line connecting the points of the peak amplitude of the voltage. The waveform of (B) is shown after full-wave rectification is performed from the waveform of (A). Full-wave rectification is rectification that converts a negative voltage part into a positive voltage.

The AE event detection circuit 84 detects as one AE event the period from when the voltage value of the waveform of (B) exceeds the voltage threshold voltage V _H on the high side to when it becomes equal to or less than the voltage threshold voltage _VL on the low side. (C) shows an AE event count pulse, in other words, an AE event detection signal based on the waveform of (B), and the portion detected as a 1AE event is in the pulse-on state.

[AE event rate calculation]
FIG. 12 shows the calculation of the AE event rate. FIG. 12 shows an example of an AE signal with the horizontal axis as time. The AE event rate calculation circuit 91 counts the number of AE events for each unit time TU on the time series of the AE signal, and sets the AE event rate, which is the number of AE events per unit time, as the index value ED. FIG. 12 shows the number of AE events for each unit time TU.

The example of the AE signal in (A) shows a part of the processing of a certain hole. The example of the AE signal in (B) shows a part of the machining of another one hole using the same tool 3. In the example of (A), sudden AE waves are sporadically generated. In the unit times TU1 to TU8, the index value ED, which is the AE event rate, is {1,1,1,1,0,0,1,1} in order.

At the time of (B), the tool 3 has already experienced the machining of a large number of holes as compared with the case of (A), and the deterioration is progressing. Therefore, in the example of (B), a plurality of sudden side AE waves are continuously generated. In the unit times TU1 to TU8, the index value ED, which is the AE event rate, is {7,5,6,5,5,5,6,5} in order.

The AE event rate calculation circuit 91 appropriately counts the AE waves arranged over the section of the unit time TU at any of the unit time TUs.

[AE wave according to changes in the state of tools, etc.]
FIG. 13 shows an example of the transition of the AE wave according to the change in the state of the tool 3 and the like. (A) shows an AE wave when the tool 3 is in a normal state, and a sudden AE wave (corresponding AE event) exceeding the threshold value is not detected. (B) is a case within the normal range, and some sudden AE waves appear sporadically. (C) corresponds to the case of an abnormal sign, and a plurality of sudden AE waves appear continuously. In other words, a plurality of sudden AE waves are observed as continuous AE waves. That is, in this state, a plurality of AE events appear in succession. The AE event rate can be calculated from such a continuous AE wave. When the AE event rate is in a continuous state within a predetermined range, it is determined as an abnormality sign. (D) shows a continuous type AE wave corresponding to the case of an abnormality immediately before the occurrence of a tool abnormality. (E) shows the case of an abnormality when a tool abnormality occurs. The abnormality sign detection system of the embodiment detects the abnormality sign at the time point (C).

[Transition of AE event rate]
FIG. 14 shows an example of the time-series transition of the amplitude of the AE signal and the corresponding AE event rate. FIG. 14 shows the transition in the time for machining a certain hole (referred to as the machining time TP), (A) shows the amplitude of the AE signal, and (B) shows the index value ED which is the AE event rate. The example of FIG. 14 shows a case corresponding to a normal range or an early sign of abnormality. In (A), the unit of amplitude is [mV]. The processing time TP is, for example, 1 second. In (B), one point indicates the AE event rate when the unit time TU is one rotation time of the tool. In the first normal period, the index value ED is 0. In the normal case, 0 is continuous as the index value ED, and even if a value other than 0 appears, it does not appear continuously and returns to 0. After that, for example, in period 141, 1 and 2 appear as index values ED. If a value other than 0 appears continuously as the index value ED, there is a possibility of abnormality.

FIG. 15 is an example of another processing time TP, and similarly shows a case corresponding to an abnormality sign. In the example of FIG. 15, the index value ED is larger than that of FIG. For example, in period 151, index values ED = 2, 3 and 4 appear.

FIG. 16 is an example of another processing time TP, and similarly shows a case corresponding to an abnormality sign. In the example of FIG. 16, a case immediately before an abnormality such as damage to the tool 3 occurs is shown. In the example of FIG. 16, the index value ED is larger than that of FIG. For example, in period 161 the index values ED = 5, 6 and 7 appear. At time point 162, an abnormality such as damage to the tool 3 has occurred. In the period after the time point 162, the index value ED has decreased to a smaller value than before.

As shown in the examples of FIGS. 14, 15, and 16, a phenomenon in which a plurality of AE event rates are continuously saturated within a certain range can be observed in a time series, and this phenomenon can be associated with an abnormality sign. it can. In this phenomenon, the plurality of AE event rates are distributed within a certain range with some variation.

[Example of abnormality sign judgment (1)]
FIG. 17 shows an example of abnormality sign determination using the AE event rate. FIG. 17 shows a partial enlargement corresponding to period 141 in FIG. As an example of setting the threshold value, an example of setting the number of consecutive times N and the first range H1 is shown. For example, the number of consecutive times N is 5. The first range H1 has an integer of 0 or more. The first range H1 has a reference value D0 = 1 and a variation range of ± 1. The reference value D0 is the reference of the first range H1. The variation range indicates the variation allowed in the index value ED. However, in this example, the first range H1 has a condition that it is an integer of 1 or more without including 0. That is, the first range H1 is set as a range {1,2} in which the upper limit value DH is 2 and the lower limit value DL is 1.

The abnormality sign determination circuit 92 of the control device 10 is set in the first state in which a plurality of index values ED are values within the first range H1 and are in a continuous state for the number of consecutive times N or more in the time series. Judged as an abnormal sign. In the illustrated example, the period 171 and the period 172 are detected as an abnormality sign because 1 or 2 is consecutive as the index value ED five times.

The following may be used as a modification of the above determination. A condition that allows 0 as the index value ED in the first range H1 (in other words, in the values that are consecutive multiple times in the first range H1, some values are 1 or more, and some values include 0. (Condition that it is good). For example, the first range H1 is set as a range {0, 1, 2} in which the upper limit value DH is 2 and the lower limit value DL is 0 under the condition that the reference value D0 = 1 and the variation range is ± 1, 0. Will be done. In this case, for example, in the period 173, since the values in the first range H1 are continuous five times, it can be detected as an abnormality sign.

FIG. 18 shows another determination example with another AE signal. FIG. 18 shows a partial enlargement corresponding to period 151 in FIG. As a threshold setting example, the number of consecutive times N is set to 10. The first range H1 has a reference value D0 = 2, a variation range of ± 2, and an integer of 1 or more. That is, the first range H1 has an upper limit value DH = 4 and a lower limit value DL = 1, and is set as a range {1, 2, 3, 4}. With this setting, the abnormality sign detection circuit 92 similarly performs the abnormality sign determination. For example, the period 181 is detected as an abnormality sign because the value in the first range H1 appears 10 times in succession.

The reference value D0 = 2 is set dynamically, for example. For example, it is assumed that the reference value D0 = 1 is initially set. Next, when the index value ED = 2 appears to some extent, the reference value D0 = 2 is updated accordingly. After that, the reference value D0 is updated according to the value of the index value ED that appears to some extent.

By using the above determination, the abnormality sign detection system can detect an abnormality sign and take measures such as stopping the operation before the tool 3 or the like becomes abnormal.

FIG. 19 shows another determination example with another AE signal. FIG. 19 shows a partial enlargement corresponding to period 161 of FIG. As a threshold setting example, the number of consecutive times N is set to 5. The first range H1 has a reference value D0 = 6, a variation range of ± 1, and an integer of 1 or more. That is, the first range H1 has an upper limit value DH = 7 and a lower limit value DL = 5, and is set as a range {5, 6, 7}. The reference value D0 = 6 is dynamically set. With this setting, the abnormality sign detection circuit 92 similarly performs the abnormality sign determination. For example, the period 191 is detected as an abnormality sign because the values in the first range H1 appear five times in a row.

As another threshold setting example, a condition may be added that the number of consecutive times N needs to be continuous with the same value within the first range H1. For example, the same value and the number of consecutive times N = 5 or more. In the case of this setting, for example, in the period 192, the index value ED = 6 continues 5 times, so that it is detected as an abnormality sign. The same determination can be made even in the case where the variation range is not provided.

[Example of abnormality sign judgment (2)]
FIG. 20 shows an example of changes in the amplitude of the AE wave and the AE event rate over a long period of time, and an example of two-step abnormality sign determination. (A) shows the transition of the amplitude of the AE signal. (B) shows the transition of the AE event rate corresponding to (A) in chronological order. Since the time axis is long, the AE event rates of the plurality of unit time TUs in (B) are compressed in time and shown in the figure. For example, at time point 201, damage or breakage has occurred as a tool abnormality. In the case of the prior art example in which the abnormality sign is determined using the amplitude as in (A), the amplitude becomes large in the period 202 immediately before the tool abnormality at the time point 201, so that the detection is possible. However, even if an abnormality sign can be detected at that time, it cannot be dealt with, the degree of destruction is large, and it is too late. In the prior art example, since the amplitude is small in the period before the period 202, the abnormality sign cannot be detected.

On the other hand, in the case of the determination using the AE event rate in the abnormality sign detection system of the embodiment as in (B), the abnormality sign can be detected long before the time point 201 of the tool abnormality. For example, by performing the determination with the first threshold value setting, the abnormality sign detection of the first stage can be performed at any time within the period 203, and the output control of the first stage can be realized correspondingly. The first threshold setting is, for example, the number of consecutive times N = 10, and the first range H1 is {1, 2, 3}. The output control of the first stage is, for example, the first alarm output. The first alarm is an alarm that informs that the life of the tool 3 is near.

Further, for example, by performing the determination of the second stage by setting the second threshold value, the abnormality sign of the second stage can be detected at any time within the period 204, and the output control of the second stage can be performed accordingly. realizable. The second threshold setting is, for example, the number of consecutive times N = 10, and the first range H1 is {1, 2, 3, 4, 5}. The output control of the second stage is, for example, a second alarm output or an operation stop control. The second alarm is an alarm indicating that the degree of abnormality is greater than that of the first alarm. Similarly, it is possible to make a determination in three or more stages. The period 204 can be, for example, a time several seconds to several tens of seconds before the time point 201 of the tool abnormality, and there is a time margin. The period 203 can be an earlier time.

[AE waveform during processing and non-processing]
FIG. 21 shows an example of AE waveforms in time series during processing and non-processing. The processing time TP is the processing time of one hole, and the non-processing time TN is the time in the non-processing state between the processing time TP. In the non-machining time TN, since the tool 3 is not in contact with the work 4, a sudden AE wave or the like does not occur. The control device 10 can grasp the machining time TP and the non-machining time TN from the waveform of such an AE signal, and can be used for machining control. Based on the grasp, the control device 10 may execute the abnormality sign determination only at the processing time TP, for example.

[Unit time setting]
The reason for setting the unit time TU in the AE event rate as one rotation time (corresponding rotation period) of the tool 3 is supplemented. As shown in FIG. 12 and the like described above, the control device 10 sets the unit time TU as one rotation time, and calculates the AE event rate for each unit time TU in the time series. As a result, as a phenomenon suitable for detecting an abnormality sign, a phenomenon of continuous or saturated AE event rate as in the above-mentioned examples of FIGS. 14 to 19 appears.

One of the reasons why the sudden AE wave (corresponding AE event) shown in FIG. 8 appears in the AE wave during machining is that chips adhere to the tool 3 during machining and the holes of the tool 3 and the work 4 are formed. There is a case where processing is performed while biting the chips between and. Chips are clogged in the groove and blade at the tip of the tool 3. In such a case, a breaking phenomenon occurs for the tool 3 and the like. It is considered that a sudden AE wave (corresponding AE event) is generated based on this fracture phenomenon. Based on the above consideration, the adhesion and detachment of chips can be estimated from the transition of the appearance density of AE events per rotation time of the tool 3. Further, it is considered that there is an upper limit to the chips that can adhere to the tool 3. Therefore, it is considered that there is an upper limit to the frequency of the fracture phenomenon (corresponding AE event rate) that occurs in one rotation time due to the adhesion of chips. From this, it is considered that the continuous and saturated phenomena related to the AE event rate as shown in FIG. 20 and the like appear.

FIG. 23 shows an example of the state of the tool corresponding to the case where a sudden AE wave is generated. As a case where a sudden AE wave is generated, it is conceivable that chips generated by cutting adhere to the cutting edge of a drill, which is a tool, and chips are accumulated in a groove. The state of FIG. 23 is the state of the cutting edge of the drill observed when an abnormality sign is detected and the operation is stopped in the system of the embodiment. This state is an abnormal state in which chips are stuck to the cutting edge. When processing was continued in this state, it was confirmed that a sudden AE wave was generated. In such a state, chips are likely to be clogged, and if machining is continued, the tool is likely to be broken or broken.

Depending on how the unit time TU is set, the above-mentioned phenomenon of continuity or saturation of the AE event rate does not always appear, and in that case, suitable abnormality sign detection cannot be performed. That is, it is necessary to select and set a suitable time for the unit time TU. For example, when the unit time TU is set sufficiently smaller than the one rotation time of the tool 3, the values of the AE event rates are not continuous. Further, when the unit time TU is set sufficiently larger than one rotation time of the tool 3 (for example, 1 second), the above phenomenon does not appear and the phenomenon increases rapidly. In this case, it becomes similar to the determination method of the prior art example, and suitable detection cannot be performed.

Further, when an appropriate unit time TU is not set when dividing the time by the unit time TU, the machining time TP and the non-machining time TN (particularly the time when the tool 3 is not in contact with the work 4) as shown in FIG. 21 are used. It will divide the time without distinguishing. Therefore, for example, when the unit time TU is too long, the ratio of the processing time TP within the divided time becomes short, and the accuracy of abnormality sign detection deteriorates.

[Threshold setting]
The threshold value for detecting an AE event and the threshold value for determining an abnormality sign may be either a static set value or a dynamic set value. The number of consecutive times N and the first range H1 may be set according to the type, material, dimension, rotation speed of the tool 3, the type, material, and dimension of the work 4, the type and load of machining, and the like. As an example, the number of consecutive times N can be set to 3, 5, 10, 15, and the like, but is not limited to this.

The following is an example of a method for dynamically setting a threshold value for determining an abnormality sign using the AE event rate. The control device 10 measures past data (for example, some unit time TU) from the input AE signal, and once determines and sets a threshold value from the measured data. For example, the threshold value setting circuit 94 determines and sets the first range H1 and the number of consecutive times N. The abnormality sign determination circuit 92 uses the threshold value to compare with the latest index value ED and determines the abnormality sign. After a certain amount of time has passed, the control device 10 similarly updates the threshold value from the past data.

Other methods include the following. The threshold value setting circuit 94 of the control device 10 counts the number of appearances for each value of the index value ED that appears from the AE signal. In the example of FIG. 18, the index value ED = 2 first appears for the first time, and it is continuous three times. The threshold value setting circuit 94 counts the number of consecutive times for the index value ED = 2. When the index value ED = 2 appears a certain number of times, the threshold value setting circuit 94 updates the reference value D0 of the first range H1 to 2. After that, the index value ED = 3 appears for the first time, and it continues three times in a row. Similarly, the threshold value setting circuit 94 counts the number of consecutive times for the index value ED = 3, and updates the reference value D0 to 3 when the index value ED = 3 appears a certain number of times. For example, the threshold value setting circuit 94 may set the value of the index value ED having the largest number of appearances as the reference value D0. In this way, the first range H1 can be changed dynamically. Further, the threshold value setting circuit 94 may dynamically set the first range H1 according to the variation range of the appearance of each value of the index value ED.

In the abnormality sign detection system of the embodiment, when the determination method is based on the dynamic threshold setting, the abnormality sign detection can be performed without depending on the specific threshold setting. In the case of this determination method, it is possible to suitably detect processing conditions such as the work 4 of an unknown material.

[GUI screen]
The control device 10 displays information such as the AE wave of the AE signal, the AE event, the AE event rate, and the threshold value for determination on the display screen of the display device 10 or the oscilloscope 72 through the GUI, and the user confirms and sets the information. Is possible. Information including graphs such as FIGS. 12, 14 to 16, 17 to 19, and 20 can be displayed on the screen.

FIG. 22 shows an example of a GUI screen. In this screen example, the continuous number N and the first range H1 can be confirmed and set as the threshold value for determining the abnormality sign based on the user's operation. Different settings can be made for each tool 3. As a modification, the user may be able to set the unit time TU.

[Effects, etc.]
As described above, according to the abnormality sign detection system of the embodiment, the abnormality sign of the tool 3 or the like in the machine tool 1 can be detected with high sensitivity. According to the embodiment, it is possible to realize highly versatile abnormality sign detection that can correspond to various processing conditions. According to the method of determining an abnormality sign using the AE event rate in the embodiment, as compared with the method of determining using the voltage, current, duration, cutting force, acceleration, etc. of the AE wave in the prior art example, Even in the case of a minute signal, it is possible to detect an abnormality sign with high accuracy. According to the embodiment, it is possible to detect and deal with the breakage or loss of the tool 3 before it occurs. Even if the worker performing the processing work is not an expert, the worker can check the result of abnormality sign detection and perform the work efficiently while preventing breakage or loss. According to the embodiment, since each tool 3 can be used until just before the end of its life, it is possible to improve processing efficiency and reduce costs. According to the embodiment, it is possible to detect the tool 3 even at a stage where the degree of abnormality is small before the tool 3 breaks. Therefore, it is possible to prevent the broken portion of the tool 3 from being buried in the work 4 and becoming irreparable, and there is a high possibility that the work 4 can be repaired at the stage of minor damage.

In the prior art example, when setting a threshold value for determination using amplitude, it is necessary to set an appropriate threshold value that differs depending on the material, shape, sensor position, etc. of the tool, work, etc., but the appropriate threshold value Difficult to set. On the other hand, according to the embodiment, it is easy to set the threshold value for determination, and it can be applied to processing conditions such as unknown materials.

Further, conventionally, in the case of processing using a workpiece of an unknown material, for example, processing is performed on a trial basis under non-optimal processing conditions. According to the embodiment, even in that case, the abnormality sign detection function can be used to deal with it. That is, according to the embodiment, machining can be started under the first non-optimal machining conditions, an abnormality sign can be detected at an early stage, the machining operation can be stopped with an alert, and then the user can set the machining conditions to be more appropriate. Processing can be performed by modifying the conditions.

The abnormality sign detection function and method in the embodiment are not limited to the above-mentioned examples of the tool 3 and the machining conditions, but are similarly applicable to various machine tools 1, the tool 3, the work 4, and the machining conditions. It can also be applied to reamer processing, grinding processing, turning processing, milling processing, etc. as processing. It can also be applied to a turning insert or the like as a tool 3. As another example of the tool 3, a high-speed steel drill was used to perform machining using a drill having a diameter in the range of 0.5 mm to 6.0 mm. In addition, processing was performed by each of a dry method and a wet method. In these cases, it was confirmed that the abnormality sign can be detected based on the same phenomenon of continuous or saturated AE event rates.

The abnormality sign detection system of the embodiment grasps the characteristics of the abnormality under the condition of a certain tool 3 or the work 4 from the result of observing and recording the AE event rate or the like when machining is performed. be able to. For example, it can be seen that the appearance of AE events is not periodic and that AE event rates in a range of 1 or more occur continuously. In addition, it can be seen that the AE event rate does not rise sharply, and that the AE event rate has an upper limit (in other words, saturation). The abnormality sign detection system may set or update a threshold value for determining an abnormality sign or the like for processing under the same conditions based on the grasp of such characteristics. As an application, the abnormality sign detection system of the embodiment may perform abnormality sign determination by using machine learning using data of the index value ED of the AE event rate as an input. Machine learning updates a model that includes a threshold for determining anomalous signs.

The following is also possible as a modification of the abnormality sign detection system of the embodiment. The method for determining an abnormality sign using the AE event rate may be as follows. In the modified example, the control device 10 does not perform the continuous determination using the continuous number N described above, but makes a determination using the reference value D0 or the first range H1 as the threshold value. When the index value ED of the AE signal reaches the reference value D0 or falls within the first range H1, the control device 10 detects it as an abnormality sign. Although the present invention has been specifically described above based on the embodiments, the present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the gist.

1 ... Machine tool, 2 ... AE sensor, 3 ... Tool, 4 ... Work, 5 ... Vise, 6 ... Table, 7 ... Liquid pipe, 8 ... Cutting fluid, 10 ... Control device, 32 ... Spindle.

Claims (12)

An abnormality sign detection system that detects anomaly signs in a machine tool that has a rotating tool.
With the AE sensor installed on the machine tool or workpiece,
A signal processing circuit that acquires the AE signal from the AE sensor, and
An AE event detection circuit that detects an AE event from the AE signal,
An AE event rate calculation circuit that calculates the AE event rate, which is the number of AE events per unit time, using the AE event.
It is determined whether or not the plurality of AE event rates arranged in the time series have reached the first state in which they are continuous for the first consecutive number of times or more within the first range, and if the first state is reached. The judgment circuit that detects as an abnormality sign and
When the abnormality sign is detected, an output control circuit that performs an alert output indicating the abnormality sign or an output control including processing operation stop, and an output control circuit.
An abnormality sign detection system equipped with. In the abnormality sign detection system according to claim 1,
The first range has an integer of 0 or more, and is a range in which a predetermined variation with respect to a reference value is allowed.
Abnormality sign detection system. In the abnormality sign detection system according to claim 1,
A threshold setting circuit for dynamically setting the first range based on the AE signal is provided.
Abnormality sign detection system. In the abnormality sign detection system according to claim 1,
The AE event rate calculation circuit grasps the rotation cycle of the tool based on the control information of the machine tool, and sets the rotation cycle to the unit time.
Abnormality sign detection system. In the abnormality sign detection system according to claim 1,
The AE event detection circuit uses a one-level voltage threshold value to count the time when the peak amplitude of the voltage of the AE signal becomes equal to or higher than the voltage threshold value, and detects the period during which the count continues as one AE event.
Abnormality sign detection system. In the abnormality sign detection system according to claim 1,
In the AE event detection circuit, the voltage amplitude in the waveform after performing saturation line detection from the AE signal using the first voltage threshold and the second voltage threshold, which are two-level voltage thresholds, is the first. The period from when the voltage threshold is exceeded until when the voltage decreases below the second voltage threshold is detected as a 1AE event.
Abnormality sign detection system. In the abnormality sign detection system according to claim 1,
The determination circuit uses a plurality of threshold values in a plurality of stages including a first threshold value in the first stage and a second threshold value in the second stage as the threshold value including the first range and the first consecutive number of times, and the index is used. Judging about the first state of the value,
The output control circuit performs different output control according to the stage in which the abnormality sign is determined.
Abnormality sign detection system. In the abnormality sign detection system according to claim 1,
The AE sensor is installed on the spindle to which the tool is connected.
Abnormality sign detection system. In the abnormality sign detection system according to claim 1,
The AE sensor is installed on the work.
Abnormality sign detection system. In the abnormality sign detection system according to claim 1,
The AE sensor is installed on a vise or table to which the work is fixed.
Abnormality sign detection system. In the abnormality sign detection system according to claim 1,
A time-series graph of the index value and a threshold value for the determination are displayed on the display screen.
Abnormality sign detection system. It is an abnormality sign detection method in an abnormality sign detection system that detects an abnormality sign in a machine tool having a rotating tool.
The step of acquiring the AE signal from the AE sensor installed on the machine tool or the work, and
The step of detecting an AE event from the AE signal and
Using the AE event, the step of calculating the AE event rate, which is the number of AE events per unit time, and
It is determined whether or not the plurality of AE event rates arranged in the time series have reached the first state in which they are continuous for the first consecutive number of times or more within the first range, and if the first state is reached. The step to detect as an abnormality sign and
When the abnormality sign is detected, a step of performing output control including an alert output indicating the abnormality sign or a processing operation stop, and
An abnormality sign detection method having.