JP2008180558A - Damage state detection method, damage state detection program and damage state detector - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a damage state detection method of a mold or the like capable of keeping and enhancing the processing precision of a processing target and the quality of the processing target by precisely detecting the damage state of the mold or the like by suppressing the effect due to an amplitude signal caused by the operation or the like of a processing machine. <P>SOLUTION: An AE sensor 200 for detecting acoustic emission is installed in the die 102 of a forging machine 100 for subjecting a work WK to forging processing and the forging machine 100 is subjected to empty striking operation to detect an AE detection signal. An effective range for extracting AE from the AE detection signal detected on the basis of the detected AE detection signal at the time of forging processing of the work WK is set to a computer apparatus 401. When the work WK is subjected to the forging processing, the amplitude signal in the effective range set from the detected AE detection signal is extracted and the fractal dimension (m) of AE is calculated on the basis of the extracted amplitude signal to judge the life of a die 102. Further, when the die 102 reaches life, the operation of the forging machine 100 is stopped. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention is used in a processing machine that plastically processes or cuts a workpiece, and detects a damaged state of a die used for plastic working or a cutting tool used for cutting, and a damaged state detection. The present invention relates to a program and a damage state detection apparatus.

In general, in a processing apparatus that performs plastic processing or cutting on a workpiece such as metal, a mold (for example, a die or a punch) for performing plastic processing or cutting is performed during processing of the workpiece. For example, the cutting tool (drill or bite) may be damaged such as cracks, chips or cracks, and the processing accuracy of the workpiece may be reduced. In order to prevent a decrease in processing accuracy due to damage to a mold or the like during processing of such a workpiece, for example, acoustic emission (hereinafter simply referred to as “AE”) as shown in Patent Documents 1 to 3 below. ) Using a method or apparatus for detecting or predicting breakage of a mold or the like. Here, AE is an elastic wave that is generated in accordance with the occurrence or progress of deformation or destruction of a material constituting a mold or the like.
Japanese Patent Application Laid-Open No. 09-251007 JP 2001-179363 A JP 2005-125480 A

  Specifically, Patent Document 1 discloses a method / apparatus for evaluating deterioration of a mold for pressing a workpiece based on a change in AE generated from the workpiece when the workpiece is pressed. It is disclosed. Further, Patent Document 2 discloses a method / apparatus for detecting breakage of a mold based on a difference in each AE between a case where a mold for processing a workpiece is damaged and a case where no damage is present. Has been. Further, in Patent Document 3, there is a method for performing processing while predicting the life of a cutting tool using a change in fractal dimension of AE generated from a cutting tool for cutting a workpiece and a cutting resistance of the cutting tool. It is disclosed.

  However, in the case of detecting AE while performing plastic processing or cutting on the workpiece, the amplitude generated due to plastic processing / cutting or operation of the processing machine other than AE in the detection signal. A signal (elastic wave) is included. In particular, in plastic working such as forging, rolling, and pressing, large vibrations and noises are generated due to the impact and friction between the workpiece and the mold, so that many amplitudes other than AE are included in the detection signal. A signal is included. In addition, noise caused by plastic processing / cutting or operation of a processing machine is also superimposed on the detected AE. For this reason, there is a problem in that the accuracy of detection or prediction of damage to a mold or the like performed based on such a detection signal is lowered, and the machining accuracy and quality of the workpiece are lowered.

  The present invention has been made to address the above problems, and its purpose is to suppress the influence of the amplitude signal and noise generated due to plastic processing or cutting of a workpiece, operation of the processing machine, and the like with high accuracy. By detecting the state of damage to the mold, etc., it is possible to maintain and improve the machining accuracy and the quality of the work piece, such as a die state detection method, a damage state detection program, and a damage state. It is to provide a detection device.

  In order to achieve the above object, a feature of the present invention is to detect the state of breakage of a die for plastic working or a cutting tool for cutting used in a processing machine for plastic working or cutting a workpiece. An AE detection step of detecting an acoustic emission of the mold or the cutting tool by using an AE sensor for detecting acoustic emission, and obtaining an amplitude signal corresponding to the acoustic emission, An amplitude signal extraction step for extracting a predetermined amplitude signal from the amplitude signals acquired in the AE detection step, and a fractal dimension calculation for calculating a fractal dimension based on the predetermined amplitude signal extracted in the amplitude signal extraction step Step and the fractal dimension calculated in the fractal dimension calculation step. There are certain to include a damaged state detection step for detecting the state of damage of the mold or the cutting tool. In this case, the predetermined amplitude signal may be defined by an amplitude value.

  According to the feature of the present invention configured as described above, a predetermined signal is extracted from an amplitude signal including AE generated in a die used for plastic working of a workpiece or a cutting tool used for cutting. Then, the fractal dimension of the AE is calculated using the extracted amplitude signal, and the state of breakage of the mold or the like is detected based on the calculated fractal dimension. Therefore, if the AE amplitude signal generated in a mold or the like used for processing the workpiece is known, the fractal dimension is accurately calculated by extracting the AE amplitude signal from the amplitude signal detected by the AE sensor. can do. AE is an elastic wave generated according to the destruction of the object, and the fractal dimension is an index that can evaluate the degree of the destruction. Therefore, the operator can accurately detect the state of breakage of the mold or the like based on the value of the fractal dimension. As a result, the processing accuracy for the workpiece and the quality of the workpiece can be maintained and improved.

  Another feature of the present invention is that in the damage state detection method, an AE sensor is used to detect vibration caused by the operation of a processing machine that does not perform plastic processing or cutting on the workpiece. A non-machined AE detecting step for obtaining a non-machined amplitude signal corresponding to the vibration, and an amplitude signal determining step for determining the predetermined amplitude signal based on the non-machined amplitude signal obtained in the non-machined AE detecting step; It is to include. According to this, even if the amplitude signal of AE generated in a mold or the like used for processing a workpiece is unknown, it is possible to detect a noise component peculiar to the mold and the processing machine in which the mold is used. . Then, a predetermined amplitude value to be extracted may be determined so as to exclude the detected noise component. This also makes it possible to detect the state of breakage of the mold or the like with high accuracy, and to maintain and improve the processing accuracy for the workpiece and the quality of the workpiece.

  According to another aspect of the present invention, in the damage state detection method, the damage state detection step includes a life determination step of determining the life of the die or the cutting tool using the fractal dimension calculated in the fractal dimension calculation step. There is to include. According to this, it is possible to know the usage limit of a mold before damaging a mold or the like used for processing a workpiece. Thereby, it can prevent processing a to-be-processed object with the damaged metal mold | die. This also makes it possible to maintain and improve the machining accuracy for the workpiece and the quality of the workpiece.

  According to another aspect of the present invention, in the damage state detection method, the damage state detection step includes a life prediction step of predicting the life of the die or the cutting tool using the fractal dimension calculated in the fractal dimension calculation step. There is to include. According to this, it is possible to predict the remaining amount (period, number of times of processing, etc.) that can be used by the mold or the like used for processing the workpiece. Thereby, it is possible to prevent the workpiece from being processed with a damaged mold or the like, and it is possible to maintain and improve the processing accuracy for the workpiece and the quality of the workpiece.

  According to another aspect of the present invention, in the damage state detection method, the breakage state detection step includes a breakage detection step of detecting breakage of the die or the cutting tool using the fractal dimension calculated in the fractal dimension calculation step. There is to include. According to this, when a mold or the like used for processing the workpiece is damaged during the processing, it is possible to quickly recognize the damage of the mold or the like. As a result, it is possible to prevent the workpiece from being continuously processed with a damaged mold or the like, and it is possible to prevent the mass production of defective products.

  Another feature of the present invention is that the damaged state detection method further includes a fractal dimension output step of outputting a signal based on the fractal dimension calculated in the fractal dimension calculation step to the processing machine. According to this, the operation of the processing machine can be controlled based on the calculated fractal dimension. Thereby, for example, when a die or the like used for processing the workpiece is damaged during processing, appropriate processing such as quickly stopping the processing machine can be automatically performed.

  Further, the present invention can be implemented not only as a method invention but also as an apparatus invention for realizing the method. In this case, in the breakage state detection device, the AE sensor used in the AE detection means may be attached to a support portion that supports a mold or a cutting tool, for example. In this case, it is difficult to attach the AE sensor to the mold itself due to the size of the mold, the wide space of the AE sensor, the usage environment of the mold such as the displacement of the mold, etc. However, it is possible to detect acoustic emission from a mold or the like.

  The present invention can also be implemented as an invention of a program to be executed by a computer device in addition to the invention of the device.

  Hereinafter, an embodiment of a damage state detection method according to the present invention will be described with reference to the drawings. FIG. 1 is a block diagram schematically showing a configuration of a mold breakage state detection system to which a breakage state detection method according to the present invention is applied. The die breakage state detection system includes a forging machine 100, an AE sensor 200, an AE device 300, and a personal computer (hereinafter referred to as "personal computer") 400.

  The forging machine 100 is a plastic working device that includes a punch 101 and a die 102 and forges a workpiece WK made of carbon steel. The punch 101 is a die that plastically deforms the workpiece WK by colliding with the workpiece WK, and is configured by forming a super hard material (for example, SKD61) in a substantially convex shape. The punch 101 reciprocates in the horizontal direction toward the die 102 by a feed mechanism (not shown) of the forging machine 100. The die 102 is a mold that plastically deforms the work WK while supporting the work WK with which the punch 101 collides, and is configured by forming a super hard material (for example, SDK 61) in a substantially cylindrical shape. The die 102 is fixed in the horizontal direction on the side surface of the back plate 103 provided in the vertical direction. The back plate 103 is a plate-like member that supports the die 102 and receives an impact caused by forging the work WK.

  Further, the forging machine 100 is provided with a control device 104 for controlling various operations for forging the workpiece WK. The control device 104 includes an operation panel 104a for receiving an input operation by an operator, and controls various operations for forging the workpiece WK according to the input operation from the operator. Further, a personal computer 400 is connected to the control device 104, and the forging process of the workpiece WK is stopped according to a stop control signal from the personal computer 400. The forging machine 100 is a forging machine that continuously forges the workpiece WK (100 times / min). Therefore, the forging machine 100 is provided with a workpiece supply / discharge machine (not shown) for continuously supplying the workpiece WK to the forging machine 100 and removing the forged workpiece WK from the forging machine 100 continuously. Has been.

  An AE sensor 200 is attached to the outer periphery of the die 102 in the forging machine 100. The AE sensor 200 is a piezoelectric element that detects acoustic emission (hereinafter simply referred to as “AE”) generated from the die 102 and outputs an electrical signal corresponding to the detected AE as an AE detection signal. That is, the AE sensor 200 detects AE caused by deformation or breakage that occurs in the die 102 due to the forging process of the workpiece WK. In this case, since AE is an elastic wave, the AE detection signal output from the AE sensor 200 is an amplitude signal corresponding to the detected AE.

  The AE device 300 includes a preamplifier 301, a band pass filter 302, a main amplifier 303, and a detection circuit 304. The preamplifier 301 amplifies the AE detection signal output from the AE sensor 200 and outputs the amplified AE detection signal to the bandpass filter 302. The band pass filter 302 is a band limiting filter for removing noise superimposed on the AE detection signal output from the AE sensor 200. Specifically, the low frequency component and the high frequency component in the AE detection signal output from the AE sensor 200 are cut by a predetermined threshold value. The AE detection signal output from the AE sensor 200 at the time of forging the workpiece WK is superimposed with a low frequency component (20 KHz or less) due to the impact received by the die 102 or vibration caused by the operation of the forging machine 100, and various A high frequency component (MHz region) generated in the control circuit is superimposed. Therefore, the band pass filter 302 removes noise caused by these mechanical factors and noise caused by electrical factors from the AE detection signal output from the AE sensor 200. The band pass filter 302 is provided with an operator (not shown) for adjusting the range of noise to be removed according to the voltage value.

  The main amplifier 303 amplifies the AE detection signal output from the bandpass filter 302 and outputs the amplified AE detection signal to the detection circuit 304. That is, the AE detection signal from which noise has been removed by the band pass filter 302, in other words, the AE detection signal having a high ratio of AE is amplified. In the present embodiment, the total gain of the preamplifier 301 and the main amplifier 303 is 60 dB. The detection circuit 304 is a circuit for extracting a positive signal from the AE detection signal output from the main amplifier 303.

  The personal computer 400 includes a computer device 401, an input device 402, and a display device 403. The computer device 401 is an arithmetic device that includes a CPU, a ROM, a RAM, a hard disk, and the like, and performs various types of calculation (information) processing according to a program (not shown) recorded in the ROM or the hard disk. Further, the computer device 401 executes the programs shown in FIGS. 2 and 4 according to the instructions of the worker, thereby inputting the AE detection signal output from the AE device 300 and detecting the state of breakage of the die 102. . The input device 402 includes a keyboard and a mouse, and inputs an instruction from an operator to the computer device 401. The display device 403 includes a liquid crystal display, and displays the operating state of the computer device 401 and the processing result by the computer device 401.

  Next, the operation of the mold breakage state detection system configured as described above will be described. First, the worker turns on each power switch (not shown) in the forging machine 100, the AE device 300, and the personal computer 400, and enters a standby state waiting for input of a command from the worker. Next, the worker sets an effective range of AE. The effective range of AE is a range of an AE detection signal used for detection of a broken state of a die, specifically, a die 102, among AE detection signals output from the AE device 300. The effective range of this AE is defined by the amplitude value of the AE detection signal.

  First, the operator operates an operator (not shown) of the bandpass filter 302 of the AE device 300 to set the noise component removal range to 0, that is, do not remove the noise component from the AE detection signal output from the AE sensor 200. Set to state. Next, the operator operates the operation panel 104a of the control device 104 in a state where the workpiece WK is not supplied to the forging machine 100, and starts the forging operation of the forging machine 100. In other words, the forging machine 100 is driven by a hammering operation. Next, the operator operates the input device 402 of the personal computer 400 to instruct the computer device 401 to execute the effective range defining program shown in FIG. In response to this instruction, the computer apparatus 401 starts execution of the effective range defining program in step S100, and starts input of the AE detection signal in step S102. In this case, the AE sensor 200 attached to the die 102 of the forging machine 100 detects an elastic wave caused by the operation of the forging machine 100 and outputs it to the AE device 300.

  The AE device 300 amplifies the AE detection signal output from the AE sensor 200 as it is and outputs a positive signal to the computer device 401. Therefore, the computer device 401 continuously inputs the AE detection signal that is continuously output as the forging machine 100 is driven by the hammering operation. Next, in step S <b> 104, the computer device 401 causes the display device 403 to display a change over time in the magnitude (amplitude) of the input AE detection signal. An example of the change over time of the magnitude of the AE detection signal displayed on the display device 403 is shown in FIG. In FIG. 3, the vertical axis represents voltage (V) and the horizontal axis represents time (S). The change with time of the magnitude of the AE detection signal naturally does not include AE generated in the die 102 by forging the workpiece WK. In other words, all the AE detection signals detected by the AE sensor are signals that are generated due to the punching operation of the forging machine 100.

  Next, the computer apparatus 401 sets the effective range of AE in step S106. Specifically, the computer device 401 stores the effective range of the AE input by the operator in a storage device such as a built-in RAM. The operator determines an appropriate AE effective range based on the change over time of the magnitude of the AE detection signal displayed on the display device 403. Specifically, as shown in FIG. 3, an amplitude of 1.0 V or more appears at a time interval of approximately 0.6 seconds in the change with time of the magnitude of the AE detection signal. This coincides with the timing at which the punch 101 forges the workpiece WK. That is, the above-described amplitude of 1.0 V or more at substantially equal intervals is an amplitude generated due to the operation of the punch 101. Further, the amplitude that appears continuously in the vicinity of 0 V is an amplitude that is generated due to the operation of the forging machine 100. Therefore, in this embodiment, the operator sets an AE detection signal having an amplitude in the range of 0.2 to 1.0 V as the effective range of AE.

  Then, the computer device 401 ends the execution of the effective range defining program in step S108. Next, the operator operates an operator (not shown) of the band-pass filter 302 of the AE device 300 to set the noise component removal range to 0.2 V or less. Thereby, the signal of the frequency band whose voltage value is 0.2 V or less is cut as a noise component. The noise component removal range and the lower limit value of the effective range of the AE may be different values. Next, the operator causes the forging machine 100 to start forging the work WK. Specifically, the supply of the work WK by the work supply / discharge machine is started, and the operation panel 104a of the forging machine 100 is operated to instruct the start of the forging process of the work WK. As a result, the forging machine 100 starts forging the workpiece WK at a rate of 100 pieces per minute.

  Next, the operator operates the input device 402 of the personal computer 400 to instruct the computer device 401 to execute the damage state detection program shown in FIG. In response to this instruction, the computer apparatus 401 starts execution of the damaged state detection program in step S200, and starts input of the AE detection signal in step S202. In this case, the AE sensor 200 attached to the die 102 of the forging machine 100 detects an elastic wave generated due to the forging process of the work WK by the forging machine 100 and outputs it to the AE device 300.

  The AE device 300 removes a noise component from the AE detection signal output from the AE sensor 200, amplifies the electrical signal from which the noise component is removed, and outputs a positive signal to the computer device 401. Accordingly, the computer device 401 continuously inputs AE detection signals that are continuously output as the workpiece WK is forged. Next, in step S204, the computer apparatus 401 extracts an AE detection signal having an amplitude range (0.2 to 1 V) set as the effective range of the AE from the input electrical signal. As a result, among the elastic waves generated due to the operation of the forging machine 100 (including the operation of the punch 101), the elastic waves having an amplitude outside the amplitude range (0.2 to 1 V) set as the effective range of the AE are removed. Is done. That is, an elastic wave having an amplitude included in the amplitude range (0.2 to 1 V) set as the effective range of AE includes components (noise components) other than AE. However, most of the noise components other than the AE included in the AE detection signal can be removed, and the noise components other than the AE included in the extracted AE detection signal are set so as not to affect the detection of the damaged state. be able to.

Next, the computer device 401 detects the maximum amplitude value A (V) in the extracted AE detection signal in step S206, and calculates the frequency distribution of the detected maximum amplitude value A (V) in step S208. To do. In step S210, the computer apparatus 401 calculates the fractal dimension m using the frequency distribution of the calculated maximum amplitude value A (V). In this case, if the maximum amplitude value is A (V) and the frequency is f (A), the relationship of Equation 1 shown below holds. In the following formula 1, c is a constant set for each measurement condition.
f (A) = c · A ^−m Expression 1

Therefore, in step S210, the computer apparatus 401 calculates the fractal dimension m using the above Equation 1, and stores the calculated fractal dimension m. The calculation of the fractal dimension m in step S210 is repeatedly executed 20 times. Thereby, 20 fractal dimensions m (1) to (20) which are continuous in time series are calculated and stored. Next, in step S212, the computer apparatus 401 calculates the average value m _Av of the 20 fractal dimensions m. This is to suppress the influence of individual variations in the values of the 20 fractal dimensions m. In step S214, the computer apparatus 401 determines whether the die 102 has reached the end of its life (usage limit). Specifically, it is determined whether or not the calculated average value m _Av of the fractal dimension m is less than 2.

This is based on the well-known fact that the fractal dimension m when the material is deformed tends to be a value of 2 or more and the fractal dimension m when the material is broken tends to approach 1. It is. In addition, the property of this fractal dimension m was also confirmed by experiments by the present inventors. FIG. 5 is a graph showing the relationship between the number N of forging processes of the workpiece WK and the average value m _Av of the fractal dimension m by the inventors' experiment. In FIG. 5, the average value m _Av of the fractal dimension m tends to decrease after the number N = 2650 of forging processes of the workpiece WK. Further, after the number of times N = 2750 of forging of the workpiece WK, the average value m _Av of the fractal dimension m is 2 or less. Then, the number N of forging processes of the workpiece WK reached 2800, and the average value m _Av of the fractal dimension m became the minimum (1.8), and the die 102 was broken.

Therefore, when the average value m _Av of the fractal dimension m is 2 or more, the computer apparatus 401 determines that the die 102 has not reached the end of its life and determines “No” and proceeds to step S216. On the other hand, if the average value m _Av of the fractal dimension m is less than 2, it is determined that the die 102 has reached the end of its service life, and the process proceeds to step S222.

If it is determined that the die 102 has not reached the end of its life, the computer device 401 causes the display device 403 to display that the die 102 has not reached its end of life in step S216. In step S218, the computer apparatus 401 erases the stored fractal dimensions m (1) to (20) and the average value m _Av of the fractal dimensions m, and refreshes the memory.

  Next, in step S220, the computer apparatus 401 determines whether or not to end this damage state detection program. Specifically, the execution of the breakage state detection program is performed according to whether or not an instruction to interrupt the execution of the program is input from the operator and whether or not there is an AE detection signal output from the AE device 300 and input to the computer device 401. It is determined whether or not to end. That is, when an instruction to interrupt the execution of the program is input from the operator, or when the AE detection signal is no longer input to the computer device 401, the computer device 401 determines that the execution of the damage state detection program is terminated as “Yes And proceeds to step S226, where the execution of the damaged state detection program is terminated. On the other hand, when the instruction for interrupting the execution of the program is not input from the operator, or when the AE detection signal is input to the computer apparatus 401, the computer apparatus 401 determines “No” as continuing the execution of the damage state detection program. And the process returns to step S210. That is, the detection of the damaged state of the die 102 is continued.

  On the other hand, when it is determined that the die 102 has reached the end of its life, the computer device 401 outputs a stop control signal for stopping the operation to the control device 104 of the forging machine 100 in step S222. In S224, the display unit 403 displays that the die 102 has reached the end of its life. As a result, the forging machine 100 immediately stops the forging operation of the workpiece WK. Further, the operator can recognize that the die 102 has reached the end of its life by checking the display on the display device 403. In this case, the operator replaces the die 102 of the forging machine 100 with a new one. In step S226, the computer apparatus 401 ends the execution of the damaged state detection program.

  When all the forging processes of the workpiece WK are completed, the operator operates the operation panel 104a of the forging machine 100 to stop the operation of the forging machine 100, and operates the input device 402 of the personal computer 400 to operate the broken state detection program. Suspend execution. Thereby, the operation of the forging machine 100 is stopped, the execution of the breakage state detection program by the computer device 401 is ended, and the work for forging work WK is ended.

  As can be understood from the above description of operation, according to the above embodiment, noise components caused by the machining operation of the forging machine 100 are detected from the AE detection signals including the AE generated in the die 102 used for the forging process of the workpiece WK. Eliminate and calculate the fractal dimension. In other words, the fractal dimension m is calculated based on the AE detection signal from which noise components of the die 102 and the forging machine 100 are removed, and the breakage state of the die 102 is detected. For this reason, it is possible to detect the state of breakage of the die 102 with high accuracy. As a result, the machining accuracy for the workpiece WK and the quality of the workpiece can be maintained and improved.

Further, according to the embodiment, the life of the die 102 is determined based on the calculated average value m _Av of the fractal dimension m. According to this, the use limit of the die 102 can be known before the breakage of the die 102 used for the forging process of the workpiece WK. Thereby, it is possible to prevent the workpiece WK from being processed by the damaged die 102. This also makes it possible to maintain and improve the machining accuracy for the workpiece WK and the quality of the workpiece.

  According to the above embodiment, when it is determined that the die 102 has reached the end of its life, the computer device 401 outputs a stop control signal to the control device 104 of the forging machine 100. And the forging machine 100 stops an operation | movement according to this stop control signal. For this reason, even if the die 102 is not damaged, the use limit has been reached. In other words, it is possible to interrupt the continuation of the processing by the die 102 in which a sign of damage appears. Thereby, it is possible to prevent the workpiece WK from being forged with the damaged die 102.

  Furthermore, in carrying out the present invention, the present invention is not limited to the above embodiment, and various modifications can be made without departing from the object of the present invention.

In the above embodiment, the computer device 401 is configured to determine whether or not the die 102 has reached the end of its life based on the average value m _Av of the fractal dimension m. However, the present invention is not limited to this. For example, the average value m _Av of the fractal dimension m may be simply displayed on the display device 403 so that the operator can determine the life of the die 102. Further, a determination other than whether or not the die 102 has reached the end of its life may be performed using the average value m _Av of the fractal dimension m. For example, the use limit such as the remaining time in which the die 102 can be used and the remaining number of machining operations may be predicted using the average value m _Av of the fractal dimension m. Further, the breakage of the die 102 may be detected using the average value m _Av of the fractal dimension m. According to the relationship between the number N of forging processes of the workpiece WK and the average value m _Av of the fractal dimension m shown in FIG. 5, the die 102 was broken when the average value m _Av of the fractal dimension m was 1.8. Therefore, the average value m _Av of the fractal dimension m is monitored, and when the average value m _Av of the fractal dimension m reaches 1.8, it is determined that the die 102 has been destroyed. .

Moreover, in the said embodiment, when the die | dye 102 reached the lifetime, the computer apparatus 401 was comprised so that a stop control signal might be output with respect to the control apparatus 104 of the forging machine 100, However, It is limited to this. is not. For example, the computer device 401 may be configured to output a signal representing the calculated average value m _Av of the fractal dimension m to the control device 104 of the forging machine 100. In this case, the control device 104 of the forging machine 100 may be configured to determine whether to continue or stop the forging operation according to the average value m _Av of the input fractal dimension m.

Further, it is not always necessary to connect the computer device 401 and the forging machine 100 to automatically control the operation of the forging machine 100. That is, the computer apparatus 401, information based on the average value m _Av of the calculated fractal dimension m (e.g., the lifetime based on the average value m _Av value and fractal dimension m of the average value m _Av of fractal dimension m (Determination information) may be simply displayed on the display device 403. In this case, the worker only has to operate the forging machine 100 according to the display on the display device 403. In the case of automatically controlling the forging machine 100 by the average value m _Av of fractal dimension m, not just to simply stop the operation of forging machine 100 appropriately according to the value of the average value m _Av of fractal dimension m The processing conditions and the processing environment may be changed.

  In the above-described embodiment, the forging machine 100 is operated in a rolling manner prior to the forging process of the workpiece WK in order to set the amplitude signal extracted from the AE detection signal. However, when the amplitude signal extracted from the AE detection signal is known or can be predicted, it is not always necessary to perform the stroke driving operation as in the above embodiment.

Moreover, in the said embodiment, in order to determine the lifetime of the die | dye 102, although the average value _mAv of 20 fractal dimensions m (1)-(20) was calculated, it is not limited to this. The life of the die 102 may be determined using the calculated value of one fractal dimension m, or the life of the die 102 based on the rate of change, amount of change, transition, etc. of the continuous fractal dimension m. Or the life of the die 102 may be determined based on various statistics such as deviation of fractal dimension m, skewness or kurtosis. In addition to these determination results, the processing state (pressure, displacement, number of processing, etc.) in the forging machine 100 is sensed, and the life of the die 102 is determined in consideration of these processing states. Good.

  Moreover, in the said embodiment, although the lifetime of the die | dye 102 was determined, you may comprise so that the lifetime of the punch 101 may be determined instead of or in addition to this. Moreover, in the said embodiment, although this invention was applied to the horizontal forging machine 100, you may apply this invention to another kind of forging machine. For example, the present invention may be applied to a multistage forging machine (part former) having a plurality of punches and dies. In this case, an AE sensor may be attached to each punch or die to detect a breakage state, or an AE sensor may be attached to a support portion to which each punch or die is commonly attached (supported). You may make it detect and the state of damage by attaching. For example, an AE sensor may be attached to a support portion (a back plate 103 in the above-described embodiment) that supports a plurality of dies that are attached to detect breakage of each die.

  As described above, when it is difficult to attach the AE sensor to the mold itself by attaching the AE sensor to the support portion that supports the mold (for example, the size of the mold, the size of the installation space of the AE sensor, the mold, The acoustic emission from the mold can be detected. In other words, if an AE sensor is attached to an object to which acoustic emission (elastic wave) generated from the mold is transmitted, it is not always necessary to attach the AE sensor to the mold itself. This also applies to the case where it is difficult to attach the AE sensor to the cutting tool itself.

  Moreover, in the said embodiment, although this invention was applied to the forging machine 100, it is not limited to this. That is, the present invention relates to a mold used for other plastic processing (for example, rolling, press processing) on a workpiece, or a cutting tool (for example, a drill or a bite) for performing cutting on a workpiece. It can be widely applied.

1 is a block diagram schematically showing an overall configuration of a mold breakage state detection system according to an embodiment of the present invention. It is a flowchart which shows the process of the effective range prescription program performed by the computer apparatus shown in FIG. It is a graph which shows the time-dependent change of the magnitude | size (amplitude) of the AE detection signal output from an AE sensor at the time of the color hammering operation of the forging machine shown in FIG. It is a flowchart which shows the process of the damage state detection program performed by the computer apparatus shown in FIG. It is the graph which showed the relationship between the frequency | count of the forging process of the workpiece | work WK of the forging machine shown in FIG. 1, and the average value of a fractal dimension.

Explanation of symbols

WK ... Workpiece, 100 ... Forging machine, 101 ... Punch, 102 ... Die, 103 ... Back plate, 104 ... Control device, 300 ... AE device, 301 ... Preamplifier, 302 ... Bandpass filter, 303 ... Main amplifier, 400 ... PC 401: Computer device 402: Input device 403: Display device

Claims (22)

In a breakage state detection method for detecting a breakage state of a die for plastic working or a cutting tool for cutting work used in a processing machine for plastic working or cutting a workpiece,
An AE detection step of detecting an acoustic emission of the mold or the cutting tool using an AE sensor for detecting acoustic emission, and obtaining an amplitude signal corresponding to the acoustic emission;
An amplitude signal extraction step of extracting a predetermined amplitude signal from the amplitude signals acquired in the AE detection step;
A fractal dimension calculation step for calculating a fractal dimension based on the predetermined amplitude signal extracted in the amplitude signal extraction step;
A breakage state detection method comprising: a breakage state detection step of detecting a breakage state of the mold or the cutting tool using the fractal dimension calculated in the fractal dimension calculation step. The damage state detection method according to claim 1, further comprising:
The AE sensor is used to detect vibration caused by the operation of the processing machine that does not perform the plastic processing or the cutting process on the workpiece, and obtain a non-machining amplitude signal corresponding to the vibration. A non-working AE detection step;
A breakage state detection method comprising: an amplitude signal determination step for determining the predetermined amplitude signal based on the non-working amplitude signal acquired in the non-working AE detection step. In the damage state detection method according to claim 1 or claim 2,
The predetermined amplitude signal is a damage state detection method defined by an amplitude value. In the breakage state detection method according to any one of claims 1 to 3,
The damaged state detecting step includes
A damage state detection method including a life determination step of determining a life of the mold or the cutting tool using the fractal dimension calculated in the fractal dimension calculation step. In the damage state detection method according to any one of claims 1 to 4,
The damaged state detecting step includes
A damage state detection method including a life prediction step of predicting a life of the die or the cutting tool using the fractal dimension calculated in the fractal dimension calculation step. In the damage state detection method according to any one of claims 1 to 5,
The damaged state detecting step includes
A breakage state detection method including a breakage detection step of detecting breakage of the mold or the cutting tool using the fractal dimension calculated in the fractal dimension calculation step. The damage state detection method according to any one of claims 1 to 6, further comprising:
A damage state detection method including a fractal dimension output step of outputting a signal based on the fractal dimension calculated in the fractal dimension calculation step to the processing machine. Provided with a display device for displaying information on detection of the state of breakage of the mold for plastic working or the cutting tool for cutting used in a processing machine for plastic working or cutting of a workpiece In a damage state detection program to be realized using a computer,
In the computer,
Using an AE sensor for detecting acoustic emission, an amplitude signal corresponding to the acoustic emission of the mold or the cutting tool is acquired,
A predetermined amplitude signal is extracted from the acquired amplitude signals;
Calculating a fractal dimension based on the extracted predetermined amplitude signal;
A damage state detection program characterized in that information based on the calculated fractal dimension is displayed on the display device. In the damage state detection program according to claim 8,
In addition to the computer,
Using the AE sensor, a non-working amplitude signal corresponding to vibration caused by the operation of the processing machine that does not perform the plastic working or the cutting work on the workpiece is acquired,
A damaged state detection program for determining the predetermined amplitude signal based on the acquired non-processed amplitude signal. In the damage state detection program according to claim 8 or 9,
The predetermined amplitude signal is a damage state detection program defined by an amplitude value. In the damage state detection program according to any one of claims 8 to 10,
In addition to the computer,
A breakage state detection program for determining the life of the mold or the cutting tool using the calculated fractal dimension. In the breakage state detection program according to any one of claims 8 to 11,
In addition to the computer,
A damage state detection program for predicting the life of the die or the cutting tool by using the calculated fractal dimension. In the damage state detection program according to any one of claims 8 to 12,
In addition to the computer,
A breakage state detection program for detecting breakage of the mold or the cutting tool using the calculated fractal dimension. In the damage state detection program according to any one of claims 8 to 13,
In addition to the computer,
A damage state detection program for causing the processing machine to output a signal based on the calculated fractal dimension. In a breakage state detection device for detecting a breakage state of a die for plastic working or a cutting tool for cutting work used in a processing machine for plastic working or cutting a workpiece,
AE detection means for detecting acoustic emission of the mold or the cutting tool using an AE sensor for detecting acoustic emission and outputting an amplitude signal corresponding to the acoustic emission;
Amplitude signal extraction means for extracting a predetermined amplitude signal from the amplitude signals output from the AE detection means;
Fractal dimension calculating means for calculating a fractal dimension based on the predetermined amplitude signal extracted by the amplitude signal extracting means;
A damage state detection apparatus comprising: display means for displaying information based on the fractal dimension calculated by the fractal dimension calculation means. The damage state detection apparatus according to claim 15, further comprising:
The AE sensor is used to detect vibration caused by the operation of the processing machine that does not perform the plastic processing or the cutting process on the workpiece, and obtain a non-machining amplitude signal corresponding to the vibration. Non-processed AE detection means;
A damage state detection apparatus comprising: an amplitude signal determination unit that determines the predetermined amplitude signal based on a non-processed amplitude signal acquired by the non-processed AE detection unit. In the damage state detection apparatus according to claim 15 or 16,
The predetermined amplitude signal is a damage state detection device defined by an amplitude value. The damage state detection device according to any one of claims 15 to 17, further comprising:
A breakage state detection device comprising life determination means for determining the life of the die or the cutting tool using the fractal dimension calculated by the fractal dimension calculation means. The damage state detection device according to any one of claims 15 to 18, further comprising:
A damage state detection apparatus comprising life prediction means for predicting the life of the die or the cutting tool using the fractal dimension calculated by the fractal dimension calculation means. The damage state detection device according to any one of claims 15 to 19, further comprising:
A breakage state detection device comprising breakage detection means for detecting breakage of the mold or the cutting tool using the fractal dimension calculated by the fractal dimension calculation means. The damage state detection device according to any one of claims 15 to 20, further comprising:
A breakage state detection device comprising fractal dimension output means for outputting a signal based on the fractal dimension calculated by the fractal dimension calculation means to the processing machine. In the breakage state detection device according to any one of claims 15 to 21,
The AE sensor used in the AE detection means is a breakage state detection device attached to a support portion that supports the mold or the cutting tool.

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