KR102496503B1 - Intelligent forging machine and its accuracy diagnosis method - Google Patents

Abstract

The present invention can accurately determine the abnormality and lifespan of a mold capable of cold forging a workpiece such as a bolt, nut, or various pins in a multi-step through a series of steps using the abnormal sound signal of the mold and the temperature-compensated pressure value. It relates to an intelligent forging system and a method for precise diagnosis thereof, comprising: a punch block in which at least one punch is installed; a die block in which at least one mold is installed to correspond to the punch; a pressure sensor installed on at least one of the mold, the mold assembly in which the mold is installed, the punch block, and combinations thereof to sense a load applied from the punch; When measuring the load of the pressure sensor, an AE sensor (acoustic emission sensor); and a control unit that compares the mold sound signal measured by the AE sensor with a pre-input normal range when measuring the load of the pressure sensor, and outputs an abnormal signal when it is out of the range.

Description

Intelligent forging machine and its accuracy diagnosis method}

The present invention relates to an intelligent forging system and a precise diagnosis method thereof, and more particularly, to process workpieces such as bolts, nuts, and various pins through a series of steps using an abnormal sound signal of a mold and a temperature-compensated pressure value. It relates to an intelligent forging system that can accurately determine the abnormality and lifespan of a mold that can be formed by cold forging and a precise diagnosis method thereof.

In general, forging is one of the metal processing methods for forming a material into a desired shape by applying a compressive load, and a conventional forging machine used for this purpose includes a die block equipped with a die or mold for supporting a workpiece such as a material to be processed, and A punch block to which a tool such as a punch is mounted is provided, and the punch is reciprocally moved toward the die block using a hydraulic or electric motor to mold the workpiece into a shape corresponding to the shape of the mold. Among these forging machines, a multi-stage forging machine is one in which a plurality of molds are mounted on a die block, respectively, and the molds vary in size little by little according to the order of arrangement, and the mold corresponding to the molding to be finally obtained is placed last. A transfer for transferring the workpiece is installed, and the workpiece is sequentially transferred to each die by the transfer, and is formed step by step by the molds and finally formed into a desired shape by the last mold.

However, in the conventional multi-stage forging machine, a plurality of molds are mounted on a die block, and fatigue failure may occur through destruction by sand burning and propagation of cracks in tool steel, carbide, etc. used as a mold material during a long forming process. And, these phenomena can cause damage to the mold.

Mold damage due to seizure and fatigue failure can have a fatal adverse effect on subsequent processes due to the nature of multi-stage forging even if it occurs in any one of a plurality of molds, especially fixtures such as nuts, bolts, or fixing pins. If a defect occurs in the field, it may be fatal to the safety of vehicles, buildings, structures, etc., which are fixed with these defective fixtures.

The present invention is to solve various problems, including the above problems, by mounting pressure sensors and temperature sensors on each mold assembly, based on the pressure value corrected for the abnormal sound signal and temperature value of the mold, each In addition to being able to more accurately detect mold abnormalities in real time, the life of the mold can be detected by considering abnormal sound signals or changes in the coincidence rate between the calibrated pressure value and the reference value, and the deviations can be accumulated to make the workpiece in multiple stages. An intelligent forging system that can fundamentally prevent the occurrence of defective products by detecting whether or not the deformation energy received during the forging process is exceeded, and can detect even abnormalities in the blocks by detecting the signal difference of the pressure sensor between the die block and the punch block. It is an object of the present invention to provide a precise diagnosis method thereof. However, these tasks are illustrative, and the scope of the present invention is not limited thereby.

Intelligent forging system according to the spirit of the present invention for solving the above problems, punch block in which at least one punch is installed; a die block in which at least one mold is installed to correspond to the punch; a pressure sensor installed on at least one of the mold, the mold assembly in which the mold is installed, the punch block, and combinations thereof to sense a load applied from the punch; When measuring the load of the pressure sensor, an AE sensor (acoustic emission sensor); and a control unit that compares the mold sound signal measured by the AE sensor with a pre-input normal range when measuring the load of the pressure sensor, and outputs an abnormal signal when it is out of the range.

In addition, according to the present invention, an external acoustic sensor installed at a distance greater than a predetermined distance from the AE sensor to compensate for the mold acoustic signal measured by the AE sensor from external acoustic sound; may further include.

In addition, according to the present invention, a temperature sensor installed in the vicinity of the pressure sensor to compensate for the temperature deviation of the measured pressure signal measured by the pressure sensor and measuring the temperature when the pressure sensor measures the pressure; further includes , The control unit corrects the measured pressure signal applied from the pressure sensor with a temperature compensating pressure signal in consideration of the measured temperature signal measured by the temperature sensor, compares the temperature compensating pressure signal with a pre-input normal range, If it deviates, an abnormal signal can be output.

In addition, according to the present invention, the control unit stores information about an expected reference waveform of a normal expected correction signal, an expected upper limit waveform that is allowed to be high from the expected reference waveform, and an expected lower limit waveform that is allowed to be low from the expected reference waveform a predicted correction waveform storage unit; A mold abnormality sound determination unit that compares the mold sound signal measured by the AE sensor with a compensation sound signal obtained by attenuating the external sound signal measured by the external sound sensor with a pre-input normal range and outputs an abnormal signal when it is out of range ; a temperature compensating pressure signal calculator calculating the temperature compensating pressure signal obtained by offsetting the temperature value of the measured temperature signal measured by the temperature sensor from the pressure value of the measured pressure signal applied from the pressure sensor; a compensating pressure determination unit determining whether the temperature compensation pressure signal falls within a normal range between the expected upper limit waveform and the expected lower limit waveform; a normal determination signal output unit outputting a normal determination signal when the temperature compensation pressure signal falls within a normal range between the expected upper limit waveform and the expected lower limit waveform; and an abnormal determination signal output unit configured to output an abnormal determination signal when the temperature compensation pressure signal is out of a normal range between the expected upper limit waveform and the expected lower limit waveform.

In addition, according to the present invention, the control unit predicts the life of the mold based on the upper limit value of N times stress of the temperature compensation pressure signal using the S-N curve of the mold, or the temperature compensation pressure signal and the expected criterion It may further include a mold life predicting unit that calculates a coincidence rate or degree of agreement of waveforms and predicts a life span of the mold by measuring a change in the agreement rate or a change in the degree of agreement.

In addition, according to the present invention, the workpiece is forged first in the first mold, then transferred to the second mold, or first forged in the first mold, then secondarily forged in the second mold, The punch block is a cold multi-stage forging type punch block in which a plurality of punches are installed in a row so that it can be transferred to a tertiary or higher multi-stage mold, and the pressure sensor is a mold in which at least one of the molds is installed. It is installed on any one or more of the assemblies, any one or more of the punch blocks, and any one or more of combinations thereof, and the AE sensor, at least any one or more of the molds in which the pressure sensor is installed, the mold It is installed on any one or more of the installed mold assemblies, any one or more of the punch blocks, and any one or more of combinations thereof, and the die block is a cold die in which a plurality of the molds are installed side by side so as to correspond to the punches. It may be a multi-stage forged mold block.

In addition, according to the present invention, the AE sensor, the first AE sensor installed in the primary mold or the primary mold assembly; and a second AE sensor installed in the secondary mold or the secondary mold assembly.

Further, according to the present invention, the pressure sensor may include: a first pressure sensor installed in the primary mold or the primary mold assembly; and a second pressure sensor installed on the second mold or the second mold assembly, wherein the temperature sensor includes: a first temperature sensor installed on the first mold or the first mold assembly; and a second temperature sensor installed in the secondary mold or the secondary mold assembly, wherein the control unit generates the temperature compensation pressure signal calculated using the first pressure sensor and the first temperature sensor and the Calculate a deviation rate or degree of deviation of the expected reference waveform, and calculate a deviation rate or degree of deviation between the temperature compensation pressure signal calculated using the second pressure sensor and the second temperature sensor and the expected reference waveform, a deviation accumulator that calculates an accumulated sum of the deviation rates or the deviation degrees; an accumulation compensation pressure determining unit determining whether the cumulative sum is within a normal range; a normal accumulation signal output unit outputting a normal accumulation determination signal when the accumulated sum falls within the normal range; and an abnormal accumulation signal output unit configured to output an abnormal accumulation determination signal when the cumulative sum exceeds the normal range.

In addition, according to the present invention, the mold assembly includes a carbide tip having a cavity into which a workpiece is inserted to correspond to the punch, a supporter supporting a lower surface of the carbide tip, and an outer diameter of the carbide tip and the supporter. A mold comprising a bushing surrounding a face; and a first vertical groove, the upper surface of which is in contact with the lower surface of the supporter and the lower surface of the bushing so that the pressure of the mold is transmitted, and is formed on one side in a direction perpendicular to the pressure direction of the mold, a second groove, and a second groove. and a spacer in which three grooves are formed, wherein the pressure sensor is a piezo (piezoelectric) sensor that is inserted into the first vertical groove of the spacer and converts the pressure transmitted to the spacer into an electrical form, and the temperature The sensor is a contact or non-contact temperature sensor inserted into the second groove of the spacer and converting thermal energy transmitted to the spacer into an electrical form, and the AE sensor is inserted into the third groove of the spacer, , It may be a microphone sensor that converts the acoustic energy transmitted to the spacer into an electrical form.

In addition, according to the present invention, the punch-side pressure sensor installed in the punch or the punch assembly in which the punch is installed; further comprising, the control unit, the pressure signal applied from the punch-side pressure sensor and the pre-input normal range If it is out of this range by comparison, an abnormal signal may be output.

On the other hand, a precision diagnosis method of an intelligent forging system according to the spirit of the present invention for solving the above problems is a punch block in which at least one punch is installed, a die block in which at least one mold is installed to correspond to the punch, the punch A pressure sensor installed on at least one of the mold, the mold assembly in which the mold is installed, the punch block, and combinations thereof to sense the load applied from the pressure sensor, generated in the mold when the load of the pressure sensor is measured An AE sensor (acoustic emission sensor) installed on at least one of the mold, the mold assembly in which the mold is installed, the punch block, and combinations thereof to measure the abnormal sound, and the mold measured by the AE sensor An external acoustic sensor installed at a predetermined distance or more from the AE sensor to compensate for the acoustic signal from external sound, installed near the pressure sensor to compensate for the temperature deviation of the measured pressure signal measured by the pressure sensor, When measuring the pressure of the pressure sensor, the temperature sensor for measuring the temperature and the load of the pressure sensor are measured, the mold sound signal measured by the AE sensor is compared with a pre-input normal range, and an abnormal signal is output when it is out of the range. In consideration of the measured temperature signal measured by the temperature sensor, the measured pressure signal applied from the pressure sensor is corrected with a temperature compensating pressure signal, and the temperature compensating pressure signal is compared with a pre-input normal range, and an abnormal signal is output when it is out of the normal range. In the precision diagnosis method of an intelligent forging system comprising a control unit, for the expected reference waveform of the normal expected correction signal, the expected upper limit waveform that is allowed to be high from the expected reference waveform, and the expected lower limit waveform that is allowed to be low from the expected reference waveform a predicted correction waveform storing step of storing information; a temperature compensation pressure signal calculating step of calculating the temperature compensation pressure signal obtained by offsetting the temperature value of the measured temperature signal measured by the temperature sensor from the pressure value of the measured pressure signal applied from the pressure sensor; a compensating pressure determination step of determining whether the temperature compensation pressure signal falls within a normal range between the expected upper limit waveform and the expected lower limit waveform; Mold abnormal sound determination step of comparing the mold sound signal measured by the AE sensor with a compensation sound signal obtained by attenuating the external sound signal measured by the external sound sensor with a pre-input normal range and outputting an abnormal signal when it is out of range ; a normal determination signal output step of outputting a normal determination signal when the temperature compensation pressure signal falls within a normal range between the expected upper limit waveform and the expected lower limit waveform; and an abnormal determination signal output step of outputting an abnormal determination signal when the temperature compensation pressure signal is out of a normal range between the expected upper limit waveform and the expected lower limit waveform.

In addition, according to the present invention, the life of the mold is predicted based on the upper limit of N times stress of the temperature compensation pressure signal using the S-N curve of the mold, or the coincidence rate between the temperature compensation pressure signal and the expected reference waveform, or The method may further include a mold life prediction step of calculating a match and predicting a life of the mold by measuring a change in the match rate or a change in the match.

In addition, according to the present invention, the AE sensor, the first AE sensor installed in the primary mold or the primary mold assembly; and a second AE sensor installed in the secondary mold or the secondary mold assembly.

Further, according to the present invention, the pressure sensor may include: a first pressure sensor installed in the primary mold or the primary mold assembly; and a second pressure sensor installed on the second mold or the second mold assembly, wherein the temperature sensor includes: a first temperature sensor installed on the first mold or the first mold assembly; and a second temperature sensor installed in the secondary mold or the secondary mold assembly, wherein the first pressure sensor and the temperature compensation pressure signal calculated using the first temperature sensor are calculated by measuring the expected reference waveform. Calculate a deviation rate or degree of deviation, calculate a deviation rate or degree of deviation between the temperature compensation pressure signal calculated using the second pressure sensor and the second temperature sensor and the expected reference waveform, and calculate these deviation rates or a deviation accumulation step of calculating a cumulative sum of the deviation degrees; an accumulated compensation pressure determination step of determining whether the cumulative sum is within a normal range; a normal accumulation signal outputting step of outputting a normal accumulation determination signal when the cumulative sum falls within the normal range; and an abnormal accumulation signal outputting step of outputting an abnormal accumulation determination signal when the cumulative sum exceeds the normal range.

According to some embodiments of the present invention made as described above, each mold assembly is equipped with pressure sensors, AE sensors, and temperature sensors, and each based on a pressure value corrected for an abnormal sound signal and a temperature value. In addition to being able to more accurately detect mold abnormalities in real time, the mold life span can be detected by considering the change in the matching rate between the corrected pressure value and the reference value, and by accumulating deviations, the workpiece receives By detecting whether or not the deformation energy is exceeded, it is possible to fundamentally prevent the occurrence of defective products, and it has an effect of detecting even abnormality of the blocks by detecting the signal difference of the pressure sensor between the die block and the punch block. Of course, the scope of the present invention is not limited by these effects.

1 is an external perspective view of an intelligent forging system according to some embodiments of the present invention.
Figure 2 is a cross-sectional view of the intelligent forging system of Figure 1;
Figure 3 is an enlarged cross-sectional view showing an enlarged die block of the intelligent forging system of FIG.
Figure 4 is a perspective view showing a mold assembly of the intelligent forging system of Figure 3;
5 is a partially cut perspective view showing a mold assembly of the intelligent forging system of FIG. 4;
Figure 6 is a block diagram showing a control unit of the intelligent forging system of FIG.
Figure 7 is a flow chart showing a precision diagnosis method of the intelligent forging system according to some embodiments of the present invention.
8 is a graph showing pressure values measured over time in the temperature compensation pressure signal calculation step of the precision diagnosis method of an intelligent forging system according to some embodiments of the present invention.
9 is a graph showing pressure correction values obtained by offsetting temperature values from pressure values measured over time in the temperature compensation pressure signal calculation step of the precision diagnosis method of the intelligent forging system of FIG. 7 .
10 is a graph for predicting the life of the mold based on the upper limit of N times stress of the temperature compensation pressure signal using the SN curve of the mold in the mold life prediction step of the precision diagnosis method of the intelligent forging system of FIG. 7 .
11 is a captured screen for predicting the life of the mold in real time based on the sensing load in the mold life prediction step of the precision diagnosis method of the intelligent forging system of FIG. 7 .
12 and 13 are photographs showing a phenomenon of damage to a mold due to seizure and fatigue failure, such as a carbide tip generated in a mold of a conventional forging machine.
14 to 18 are diagrams showing examples of numerical analysis diagrams and load graphs step by step during forming of an intelligent forging system according to some embodiments of the present invention.

Hereinafter, several preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

The embodiments of the present invention are provided to more completely explain the present invention to those skilled in the art, and the following examples may be modified in many different forms, and the scope of the present invention is as follows It is not limited to the examples. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the spirit of the invention to those skilled in the art. In addition, the thickness or size of each layer in the drawings is exaggerated for convenience and clarity of explanation.

Throughout the specification, when referring to an element, such as a film, region, or substrate, being located “on,” “connected to,” “stacked on,” or “coupled to” another element, reference is made to that one element. It can be interpreted that an element directly contacts “on,” “connected to,” “stacked on,” or “coupled to” another component, or that another component interposed therebetween may exist. On the other hand, when an element is said to be located "directly on," "directly connected to," or "directly coupled to," another element, it is interpreted that there are no intervening elements. do. Like symbols refer to like elements. As used herein, the term "and/or" includes any one and all combinations of one or more of the listed items.

In this specification, terms such as first and second are used to describe various members, components, regions, layers and/or portions, but these members, components, regions, layers and/or portions are limited by these terms. It is self-evident that no These terms are only used to distinguish one element, component, region, layer or section from another region, layer or section. Thus, a first member, component, region, layer or section described in detail below may refer to a second member, component, region, layer or section without departing from the teachings of the present invention.

Also, relative terms such as "over" or "above" and "bottom" or "below" may be used herein to describe the relationship of some elements to other elements as illustrated in the figures. Relative terms can be understood as intended to include other orientations of the element in addition to the orientation depicted in the figures. For example, if an element is turned over in the figures, elements that are depicted as being on the face of the other elements will have orientation on the face of the bottom of the other elements. Thus, the term "top" as an example may include both "bottom" and "top" directions, depending on the particular orientation of the figure. If the element is oriented in another direction (rotated 90 degrees relative to the other direction), relative statements used herein may be interpreted accordingly.

Terms used in this specification are used to describe specific embodiments and are not intended to limit the present invention. As used herein, the singular form may include the plural form unless the context clearly indicates otherwise. Also, when used herein, "comprise" and/or "comprising" specifies the presence of the recited shapes, numbers, steps, operations, elements, elements, and/or groups thereof. and does not exclude the presence or addition of one or more other shapes, numbers, operations, elements, elements and/or groups.

Hereinafter, embodiments of the present invention will be described with reference to drawings schematically showing ideal embodiments of the present invention. In the drawings, variations of the depicted shape may be expected, depending on, for example, manufacturing techniques and/or tolerances. Therefore, embodiments of the inventive concept should not be construed as being limited to the specific shape of the region shown in this specification, but should include, for example, a change in shape caused by manufacturing.

Figure 1 is an external perspective view of an intelligent forging system 100 according to some embodiments of the present invention, Figure 2 is a cross-sectional view of the intelligent forging system 100 of FIG.

First, as shown in FIGS. 1 and 2, the intelligent forging system 100 according to some embodiments of the present invention includes a punch block 10 in which at least one punch P is installed, and the punch ( A die block 20 in which at least one mold M is installed to correspond to P), and the mold M or a mold in which the mold M is installed so that the load applied from the punch P can be sensed. A pressure sensor (S1) installed in the assembly 30 and installed near the pressure sensor (S1) to compensate for the temperature deviation of the temperature compensation pressure signal measured by the pressure sensor (S1), the pressure sensor ( A mold assembly in which at least the mold and the mold are installed so that the temperature sensor S2 for measuring the temperature when the pressure of S1) is measured, and the abnormal sound generated in the mold when the load is measured by the pressure sensor S1 is measured , AE sensor (S3) (acoustic emission sensor) installed on any one or more of the punch block and combinations thereof, and the mold sound signal measured by the AE sensor (S3) to be compensated for from external sound When measuring the load of the external acoustic sensor (S4) and the pressure sensor installed at a distance of more than a certain distance from the AE sensor (S3), the mold sound signal measured by the AE sensor is compared with the pre-input normal range, and if it is out of range, it is abnormal A signal is output, and the pressure signal applied from the pressure sensor (S1) is corrected with a temperature compensation pressure signal in consideration of the measured temperature signal measured by the temperature sensor (S2), and the temperature compensation pressure signal and the previously input normal pressure signal are corrected. A controller 40 that compares the range and outputs an abnormal signal when out of range may be included.

Here, for example, the intelligent forging system 100 according to some embodiments of the present invention is a kind of equipment capable of cold forging a workpiece such as a bolt, nut, or various pins in a multi-step through a series of steps. can

However, the present invention is not necessarily limited to multi-stage cold forging, and can be applied to all forging equipment having the punch block 10 and the die block 20 described above.

More specifically, for example, the workpiece 1 is first forged in the primary mold M1, then transferred to the secondary mold M2, and secondarily forged in the secondary mold M2. , The punch block 10 is a cold multi-stage forging type punch block in which a plurality of the punches P1, P2, and P3 are installed in a row so that they can be transferred to the tertiary mold M3, and the die block 20 ) may be a cold multi-stage forging mold block in which a plurality of the molds M1, M2, and M3 are installed side by side to correspond to the punches P1, P2, and P3.

Here, for example, the pressure sensor S1 includes at least one of the molds, one or more of the mold assemblies in which the mold is installed, one or more of the punch blocks, and one or more of combinations thereof. , and the AE sensor (S3) includes at least one of the molds in which the pressure sensor (S1) is installed, one or more of the mold assemblies in which the mold is installed, one or more of the punch blocks, and Any one or more of these combinations may be installed.

In addition, the temperature sensor S2 also includes at least one of the molds in which the pressure sensor S1 is installed, one or more of the mold assemblies in which the mold is installed, one or more of the punch blocks, and Any one or more of the combinations may be installed.

More specifically, for example, the pressure sensor (S1), the temperature sensor (S2), and the AE sensor (S3) are installed only in, for example, a three-stage mold that can be relatively severely damaged among multi-stage molds, or After that, it can be installed in various numbers and in various positions, such as being installed in a specific mold.

However, the technical idea of the present invention can be applied to all forging equipment with two or more stages, and although three stages are illustrated in the drawing, it can be applied to all forging equipment with two stages or three stages or more.

In addition, for example, as shown in Figures 1 and 2, the pressure sensor (S1) of the intelligent forging system 100 according to some embodiments of the present invention, the primary mold (M1) or the primary A first pressure sensor (S11) installed in the mold assembly 30, a second pressure sensor (S12) installed in the secondary mold (M2) or the secondary mold assembly 30, and the tertiary mold (M3) ) or a third pressure sensor (S13) installed in the tertiary mold assembly 30.

In addition, for example, as shown in Figures 1 and 2, the temperature sensor (S2) of the intelligent forging system 100 according to some embodiments of the present invention, the primary mold or the primary mold assembly A first temperature sensor (S21) installed, a second temperature sensor (S22) installed in the second mold or the second mold assembly, and a third temperature sensor (installed in the tertiary mold or the tertiary mold assembly) S23) may be included.

In addition, for example, as shown in Figures 1 and 2, the AE sensor (S3) of the intelligent forging system 100 according to some embodiments of the present invention, the primary mold or the primary mold assembly A first AE sensor (S31) installed, a second AE sensor (S32) installed in the second mold or the second mold assembly, and a third AE sensor (installed in the tertiary mold or the tertiary mold assembly) S33) may be included.

However, the technical idea of the present invention can be applied to all equipment installed with two or more sensors, and although three sensors are illustrated in the drawing, it can be applied to all forged equipment with two or more than three sensors installed. .

Figure 3 is an enlarged cross-sectional view showing the die block 20 of the intelligent forging system 100 of Figure 2, Figure 4 is a perspective view showing the mold assembly 30 of the intelligent forging system 100 of Figure 3, 5 is a partially cut perspective view showing the mold assembly 30 of the intelligent forging system 100 of FIG. 4 .

More specifically, for example, as shown in FIGS. 1 to 5, the mold assembly 30 has a carbide tip 31 in which a cavity into which the workpiece 1 is inserted is formed to correspond to the punch. And, a primary mold (M1) including a supporter 32 supporting the lower surface of the carbide tip 31 and a bushing 33 surrounding the outer diameter surface of the carbide tip 31 and the supporter 32, and The upper surface is in contact with the lower surface of the supporter 32 of the primary mold M1 and the lower surface of the bushing 33 so that the pressure of the primary mold M1 is transmitted, and the primary mold M1 is placed on one side. It may include a spacer 34 having a first vertical groove G1 formed in a direction perpendicular to the pressure direction of the pressure, a second groove G2 and a third groove G3 formed therein.

Here, a through hole through which the eject pin 35 of FIGS. 2 and 3 can pass is formed in the primary mold M1, the supporter 32, and the spacer 34, and the workpiece after primary molding is formed. (1) may be discharged to the outside of the primary mold (M1).

In addition, for example, the first pressure sensor S11 is inserted into the first vertical groove G1 of the spacer 34 and converts the pressure transmitted to the spacer 34 into a current form. Piezo) element can be applied.

In addition, for example, the first temperature sensor S21 is inserted into the second groove G2 of the spacer 34 and converts the thermal energy transmitted to the spacer 34 into an electrical form. It may be a non-contact temperature sensor.

In addition, for example, the AE sensor (S3) is inserted into the third groove (G3) of the spacer (34), a microphone sensor that converts the acoustic energy transmitted from the spacer (34) into an electrical form can

Therefore, when the punch (P) molds the workpiece (1), the molding load is transmitted to the primary mold (M1), which is transmitted to the spacer (34) via the supporter (32), At this time, the AE sensor (S3) can be applied to the control unit 40 by converting abnormal acoustic energy such as breaking sound or breaking sound transmitted to the spacer 34 into an electrical form, and the external sound The sensor S4 converts external acoustic energy into an electrical form and applies it to the control unit 40 so as to compensate for the mold acoustic signal measured by the AE sensor from external acoustic sound, and at the same time, the spacer 34 The first pressure sensor S11 converts the load applied to the electrical signal into an electric signal, that is, the current may be applied to the control unit 40 .

At this time, the first temperature sensor (S11) converts the temperature value representing the ambient temperature at the time of measuring the pressure value of the first pressure sensor (S11) into an electrical signal, that is, a current, to the control unit 40 at the same time can be authorized.

Therefore, when measuring the load of the pressure sensor, the control unit 40 compares the mold sound signal measured by the AE sensor S3 with a pre-input normal range, and outputs an abnormal signal when it is out of this range, and the pressure The temperature compensation pressure signal obtained by canceling the temperature value of the measured temperature signal measured by the temperature sensor S2 from the pressure value of the temperature compensation pressure signal applied from the sensor S1 may be calculated.

Figure 6 is a block diagram showing the control unit 40 of the intelligent forging system 100 of FIG.

As shown in Figures 1 to 6, the control unit 40 of the intelligent forging system 100 according to some embodiments of the present invention, the expected reference waveform of the normal expected correction pressure signal, and the high from the expected reference waveform An expected waveform storage unit 41 for storing information about an expected upper limit waveform that is allowed and an expected lower limit waveform that is allowed to be lower than the expected reference waveform, and the mold sound signal measured by the AE sensor measured by the external acoustic sensor A mold abnormality sound determining unit 42 that compares the compensation sound signal obtained by attenuating the external sound signal with a pre-input normal range and outputs an abnormal signal when the range is out of range, and the temperature compensation pressure applied from the pressure sensor S1 A temperature compensation pressure signal calculator 43 for calculating the temperature compensation pressure signal obtained by offsetting the temperature value of the measured temperature signal measured by the temperature sensor S2 from the pressure value of the signal, and the temperature compensation pressure signal Compensation pressure judging unit 44 for discriminating whether the temperature compensation pressure signal belongs to the normal range between the expected upper limit waveform and the expected lower limit waveform; A normal determination signal output unit 45 for outputting a signal and an abnormal determination signal output unit 46 for outputting an abnormal determination signal when the temperature compensation pressure signal is out of the normal range between the expected upper limit waveform and the expected lower limit waveform can include

Here, the compensation pressure determining unit 44 may be performed instead by an artificial intelligence unit using a deep learning method in addition to discriminating using the expected waveform storage unit 41.

Therefore, the control unit 40 of the present invention provides information on an expected reference waveform of a normal expected correction pressure signal, an expected upper limit waveform that is allowed to be high from the expected reference waveform, and an expected lower limit waveform that is allowed to be low from the expected reference waveform. Stored, the mold sound signal measured by the AE sensor (S3) is compared with the compensation sound signal obtained by attenuating the external sound signal measured by the external sound sensor (S4) with a pre-input normal range, and if it is out of range, it is abnormal. A signal is output, and the temperature compensation pressure signal is calculated by canceling the temperature value of the measured temperature signal measured by the temperature sensor (S2) from the pressure value of the temperature compensation pressure signal applied from the pressure sensor (S1). , determining whether the temperature compensation pressure signal belongs to a normal range between the expected upper limit waveform and the expected lower limit waveform, and determining whether the temperature compensation pressure signal is normal if it belongs to a normal range between the expected upper limit waveform and the expected lower limit waveform A series of steps of outputting a signal or outputting an abnormal determination signal when the temperature compensation pressure signal is out of a normal range between the expected upper limit waveform and the expected lower limit waveform may be performed.

Therefore, the plurality of pressure sensors (S11) (S12) (S13) and the temperature sensors (S21) (S22) (S23) and the AE sensors (S31) (S32) in each of the mold assembly 30 ) (S33) and an external acoustic sensor (S4) are installed to detect abnormalities of each of the molds (M1, M2, and M3) in real time based on a reference acoustic value or a temperature-corrected pressure value, and these molds (M1) (M2) (M3), when an error occurs in one or more molds, it is promptly notified to the user or operator, so that if foreign matter adheres to the mold or damage to the mold occurs, it is prevented from adversely affecting the subsequent process. You can follow up immediately.

In addition, for example, as shown in FIG. 6, the control unit 40 predicts the life of the mold based on the upper limit value of N times stress of the temperature compensation pressure signal using the S-N curve of the mold, or the temperature It may further include a mold life predicting unit 46 that calculates a coincidence rate or degree of agreement between the compensation pressure signal and the expected reference waveform, and predicts the life of the mold by measuring a change in the agreement rate or a change in the degree of agreement.

Therefore, for example, according to the S-N curve of the mold, regardless of the temperature, the life of the mold can be predicted very accurately based on the upper limit of N times stress with the temperature compensation pressure signal, which is almost similar to the load applied to the actual mold.

In addition, for example, if the agreement rate between the actual measured value and the expected standard value was 99% or more in the first month, but if the agreement rate decreased by 1% to 98% after one month, if the failure rate decreased by 1% every month to 80%, then after 10 months Since it can be seen that there is a high possibility of failure, it is possible to respond by predicting the lifespan of the mold in advance, such as preparing the mold in advance after 10 months.

Therefore, it is possible to predict the individual lifespan of each of the molds M1, M2, and M3 in advance in consideration of the change in the matching rate between the measured pressure and the reference value, so that follow-up measures can be performed.

In addition, as shown in FIGS. 1 to 6 , the controller 40 determines the temperature compensation pressure signal calculated using the first pressure sensor S11 and the first temperature sensor S21 and the expected temperature. A deviation rate or degree of deviation of the reference waveform is calculated, and a deviation rate or degree of deviation between the temperature compensation pressure signal calculated using the second pressure sensor (S12) and the second temperature sensor (S22) and the expected reference waveform Calculating a deviation rate or a deviation degree between the temperature compensation pressure signal calculated using the third pressure sensor S13 and the third temperature sensor S23 and the expected reference waveform, and calculating these deviation rates a deviation accumulation unit 48 that calculates a cumulative sum of the variances or the deviation degrees; an accumulation compensation pressure determination unit 49 that determines whether or not the cumulative sum is within a normal range; It may further include a normal accumulation signal output unit 50 outputting a normal accumulation determination signal and an abnormal accumulation signal output unit 51 outputting an abnormal accumulation determination signal when the accumulated sum exceeds the normal range.

Therefore, the control unit 40 of the present invention, the temperature compensation pressure signal measured by the first pressure sensor (S11), the second pressure sensor (S12), and the third pressure sensor (S13) and the expected standard Each deviation rate or deviation degree of the waveform is calculated, a cumulative sum value of the deviation rates or the deviation degree is calculated, and it is determined whether the cumulative sum value is within a normal range. If the accumulated sum value is within the normal range, it is normal. A series of steps of outputting a cumulative determination signal or outputting an abnormal cumulative determination signal when the cumulative sum exceeds the normal range may be performed.

Therefore, it is possible to fundamentally prevent the occurrence of defective products by accumulating the deviation between the actual value and the abnormal value generated in each mold and detecting whether or not the deformation energy received by the workpiece in the multi-stage forging process is exceeded.

If the total amount of deformation energy that the workpiece 1 receives during the multi-stage forging process exceeds the normal value, the workpiece 1 cannot overcome this and is cracked or damaged internally or externally, or is easily damaged by various impact loads, etc. of defective products may occur. The present invention can prevent defects in molds as well as such workpieces in advance.

On the other hand, as shown in Figures 1 and 2, the intelligent forging system 100 according to some embodiments of the present invention, the punch (P) or the punch side installed in the punch assembly in which the punch (P) is installed A pressure sensor S5 may be further included.

Accordingly, the control unit 40 compares the pressure signal applied from the punch-side pressure sensor S5 with a pre-input normal range, and outputs an abnormal signal when it is out of range.

In addition, the control unit 40 can detect even abnormality of the blocks by detecting the signal difference of the pressure sensors S1 and S5 between the die block 20 and the punch block 10.

Therefore, for example, if the signal difference between the pressure sensors S1 and S5 between the die block 20 and the punch block 10 is out of the normal value, an abnormal phenomenon has occurred in one of the two or the pressure has been applied unevenly. It can be judged that it has been done, and the user or operator can take follow-up measures.

Here, although not shown, it is also possible to calculate the temperature-compensated pressure value by installing a temperature sensor near the punch-side pressure sensor S5.

Figure 7 is a flow chart showing a precision diagnosis method of the intelligent forging system 100 according to some embodiments of the present invention.

As shown in Figures 1 to 7, the precision diagnosis method of the intelligent forging system 100 according to some embodiments of the present invention, in the diagnosis process of the first mold (M1), expected normal expected correction signal An expected waveform storing step (S110) of storing information about a reference waveform, an expected upper limit waveform that is allowed to be high from the expected reference waveform, and an expected lower limit waveform that is allowed to be low from the expected reference waveform (S110), and the first AE sensor (S31) Mold abnormal sound determination step of comparing the mold sound signal measured in the mold sound signal attenuated the external sound signal measured by the external sound sensor S4 with a pre-input normal range and outputting an abnormal signal when it is out of the range (S120) and the temperature compensation pressure signal obtained by canceling the temperature value of the measured temperature signal measured by the first temperature sensor (S21) from the pressure value of the measured pressure signal applied from the first pressure sensor (S11). A temperature compensation pressure signal calculation step (130) to calculate, a measured pressure determination step (S140) for determining whether or not the temperature compensation pressure signal belongs to a normal range between the expected upper limit waveform and the expected lower limit waveform, and A normal determination signal output step (S160) of outputting a normal determination signal if the temperature compensation pressure signal falls within the normal range between the expected upper limit waveform and the expected lower limit waveform (S150), and the temperature compensation pressure signal If is out of the normal range between the expected upper limit waveform and the expected lower limit waveform, an abnormal determination signal output step (S170) of outputting an abnormal determination signal and the temperature compensation using the SN curve of the first mold (M1) Based on the upper limit of N times stress of the pressure signal, the life of the primary mold M1 is predicted, or the coincidence rate or degree of agreement between the temperature compensation pressure signal and the expected reference waveform is calculated, and the change in the agreement rate or the To predict the life of the first mold (M1) by measuring the change in coincidence may include a mold life prediction step (S180).

In addition, for example, in the process of diagnosing the second mold M2, an expected reference waveform of a normal expected correction signal, an expected upper limit waveform that is allowed to be high from the expected reference waveform, and an expected lower limit waveform that is allowed to be low from the expected reference waveform An expected waveform storing step (S210) of storing information about the external acoustic signal measured by the external acoustic sensor (S4) in the mold acoustic signal measured by the second AE sensor (S32) and a compensation acoustic signal attenuated A mold abnormal sound determination step (S220) of comparing a pre-input normal range and outputting an abnormal signal when it is out of the normal range, and the second temperature at the pressure value for the measured pressure signal applied from the second pressure sensor (S12) A temperature compensation pressure signal calculation step (230) of calculating the temperature compensation pressure signal obtained by offsetting the temperature value of the measured temperature signal measured by the sensor (S22), and the temperature compensation pressure signal is the expected upper limit waveform and the expected lower limit waveform The measured pressure determination step (S240) of determining whether or not it belongs to the normal range between (S240) and whether it is within the normal range (S250) determines whether the temperature compensation pressure signal belongs to the normal range between the expected upper limit waveform and the expected lower limit waveform. a normal determination signal output step (S260) of outputting a normal determination signal; and an abnormal determination signal output step of outputting an abnormal determination signal when the temperature compensation pressure signal is out of the normal range between the expected upper limit waveform and the expected lower limit waveform. (S270) and the S-N curve of the second mold M2 is used to predict the life of the second mold M2 based on the upper limit value of N times stress of the temperature compensation pressure signal, or to predict the lifespan of the second mold M2 at the temperature A mold life prediction step (S280) of calculating the coincidence rate or the degree of agreement between the compensation pressure signal and the expected reference waveform, and predicting the life of the second mold M2 by measuring the change in the agreement rate or the change in the degree of agreement (S280) can do.

In addition, for example, in the process of diagnosing the third mold M3, the expected reference waveform of the normal expected correction signal, the expected upper limit waveform that is allowed to be high from the expected reference waveform, and the expected lower limit waveform that is allowed to be low from the expected reference waveform An expected waveform storing step (S310) of storing information about, and a compensation sound signal obtained by attenuating the external sound signal measured by the external sound sensor (S4) from the mold sound signal measured by the third AE sensor (S33) A mold abnormal sound determination step (S320) of comparing a pre-input normal range and outputting an abnormal signal when it is out of the normal range, and the third temperature at the pressure value for the measured pressure signal applied from the third pressure sensor (S13) A temperature compensation pressure signal calculation step (330) of calculating the temperature compensation pressure signal obtained by offsetting the temperature value of the measured temperature signal measured by the sensor (S23), and the expected upper limit waveform and the expected lower limit waveform The measured pressure determining step (S340) of determining whether or not it belongs to the normal range between (S340) and whether it is within the normal range (S350) determines whether the temperature compensation pressure signal belongs to the normal range between the expected upper limit waveform and the expected lower limit waveform. a normal determination signal output step (S360) of outputting a normal determination signal; and an abnormal determination signal output step of outputting an abnormal determination signal when the temperature compensation pressure signal is out of the normal range between the expected upper limit waveform and the expected lower limit waveform. (S370) and using the S-N curve of the tertiary mold M3, the life of the tertiary mold M3 is predicted based on the upper limit value of N times stress of the temperature compensation pressure signal, or the temperature A mold life prediction step (S380) of calculating the coincidence rate or the degree of agreement between the compensation pressure signal and the expected reference waveform, and predicting the life of the tertiary mold M3 by measuring the change in the agreement rate or the change in the degree of agreement (S380) can do.

Then, the precision diagnosis method of the intelligent forging system 100 according to some embodiments of the present invention, the temperature compensation pressure signal calculated using the first pressure sensor (S11) and the first temperature sensor (S21) and a deviation rate or degree of deviation of the expected reference waveform, and a deviation rate between the temperature compensation pressure signal calculated using the second pressure sensor S12 and the second temperature sensor S22 and the expected reference waveform. Alternatively, a deviation degree is calculated, and a deviation rate or deviation degree between the temperature compensation pressure signal calculated using the third pressure sensor (S13) and the third temperature sensor (S23) and the expected reference waveform is calculated, and these A deviation accumulation step (S410) of calculating the cumulative sum of the deviation rates or the deviation degrees, an accumulation compensation pressure determining step (S420) of determining whether the cumulative sum is within a normal range, and the cumulative sum is within a normal range. A normal accumulation signal output step of determining whether or not the cumulative sum belongs to the normal range (S430) and outputting a normal accumulation determination signal if the cumulative sum falls within the normal range (S440) and outputs an abnormal accumulation determination signal if the cumulative sum is out of the normal range It may include an abnormal accumulation signal output step (S450).

Here, the deviation rates or the deviation degrees can be summed up by the number of sensors, and by comparing the theoretical sum value and the actual sum value in the middle or final step, it can be determined step by step whether the accumulated amount of strain energy falls within the normal range. .

For example, if the workpiece 1 is within the normal range in the 1st and 2nd order, but the amount of deformation is little by little, it can be determined that no defective product will be generated even if the amount of deformation slightly exceeds the normal range in the 3rd order. , If the workpiece 1 is within the normal range in the first, second, and third stages, but excess energy is accumulated at each stage, and the total sum of the final deformation energy exceeds the normal range, a defective product occurs. It can be predicted that there is a high probability of

Figure 8 is a graph showing the pressure value measured over time in the temperature compensation pressure signal calculation step (S130) of the precision diagnosis method of the intelligent forging system according to some embodiments of the present invention.

As shown in FIG. 8, the pressure value (blue) measured over time in the temperature compensation pressure signal calculation step (S130) has an irregular wave shape, but as a whole, it rapidly increases at first and then gradually becomes gentle as time passes. It can be seen that there is

9 is a graph showing a pressure correction value obtained by offsetting the temperature value from the pressure value measured over time in the temperature compensation pressure signal calculation step (S130) of the precision diagnosis method of the intelligent forging system of FIG. 7.

Subsequently, as shown in the upper graph of FIG. 9, the temperature value (red) measured over time in the temperature compensation pressure signal calculation step (S130) is similar to the pressure value as a whole, and at first rapidly increases. It can be seen that there is a characteristic that the DA becomes gentler as time goes by.

Therefore, as shown in the lower graph of FIG. 9, after compensating by offsetting the temperature value (red) of FIG. 9 from the pressure value (blue) of FIG. It can be seen that it fluctuates in the form of a wave.

Therefore, by offsetting the temperature value from the measured pressure value, a very accurate actual load value can be measured regardless of the temperature, and this can be monitored in real time, and furthermore, a very accurate actual life span can be predicted based on this.

10 is a graph for predicting the life of the mold based on the upper limit of N times stress of the temperature compensation pressure signal using the S-N curve of the mold in the mold life prediction step of the precision diagnosis method of the intelligent forging system of FIG. 7. It is a graph.

That is, as shown in FIG. 10, even if the S-N curve of the existing mold is used, the life span can be accurately predicted based on the upper limit of N times actually applied stress based on the temperature compensation pressure signal.

11 is a captured screen for predicting the life of the mold in real time based on the sensing load in the mold life prediction step (S180) of the precision diagnosis method of the intelligent forging system of FIG. 10.

Therefore, as shown in FIG. 11, since the current amount of mold usage can be predicted in real time based on the temperature compensation pressure signal, and the mold mounting date can be predicted based on this, product defects and production stoppage due to mold damage can be prevented in advance.

12 and 13 are photographs showing a phenomenon of damage to a mold due to seizure and fatigue failure, such as a carbide tip generated in a mold of a conventional forging machine.

As shown in FIGS. 12 and 13, in the conventional multi-stage forging machine, it can be confirmed that foreign substances are burned in the mold and the carbide tip is damaged or separated.

14 to 18 are diagrams showing examples of numerical analysis diagrams and load graphs step by step during forming of an intelligent forging system according to some embodiments of the present invention.

As shown in FIGS. 14 to 18, the maximum expected loads and actual pressure measurement graphs of the loads acting on each mold are different through a total of 5 steps from STEP1 to STEP5, and numerical analysis according to the shape of each step As described above, since the pressure distributions are all different, theoretical normal graphs can be extracted through repetitive experiments and numerical analysis, and the present invention measures the actual pressure values outside the normal range of these normal graphs or their cumulative sum in real time. It is possible to immediately check the abnormal state of each mold or work piece by detecting it with

Here, the peak value of each load graph may vary depending on the shape of an actual mold or specifications of a workpiece, and numerical analysis may also vary depending on a numerical analysis method or numerical value.

Although the present invention has been described with reference to the embodiments shown in the drawings, this is merely exemplary, and those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims.

1: Workpiece
10: punch block
P: Punch
P1: 1st punch
P2: 2nd punch
P3: 3rd punch
20: die block
M: mold
M1: 1st mold
M2: 2nd mold
M3: 3rd mold
30: mold assembly
31: carbide tip
32: Supporter
33: bushing
34: spacer
35: eject pin
G1: first vertical groove
G2: second groove
G3: third groove
S1: pressure sensor
S11: first pressure sensor
S12: second pressure sensor
S13: 3rd pressure sensor
S2: pressure sensor
S21: first pressure sensor
S22: second pressure sensor
S23: Third pressure sensor
S3: AE sensor
S31: First AE sensor
S32: Second AE sensor
S33: Third AE sensor
S4: external acoustic sensor
S5: Punch side pressure sensor
40: control unit
41: expected waveform storage unit
42: mold abnormal sound determination unit;
43: temperature compensation pressure signal output unit
44: compensation pressure determining unit
45: normal determination signal output unit
46: abnormal determination signal output unit
47: mold life prediction unit
48: deviation accumulation unit
49: cumulative compensation pressure determining unit
50: normal cumulative signal output unit
51: abnormal cumulative signal output unit
100: intelligent forging system

Claims (14)

A punch block in which at least one punch is installed;
a die block in which at least one mold is installed to correspond to the punch;
a pressure sensor installed on at least one of the mold, the mold assembly in which the mold is installed, the punch block, and combinations thereof to sense a load applied from the punch;
When measuring the load of the pressure sensor, an AE sensor (acoustic emission sensor);
When measuring the load of the pressure sensor, a control unit that compares the mold sound signal measured by the AE sensor with a pre-input normal range and outputs an abnormal signal when it is out of the normal range;
an external acoustic sensor installed at a predetermined distance or more from the AE sensor to compensate for the mold acoustic signal measured by the AE sensor from external sound; and
A temperature sensor installed near the pressure sensor to compensate for the temperature deviation of the measured pressure signal measured by the pressure sensor and measuring the temperature when the pressure sensor measures the pressure; includes,
The control unit,
In consideration of the measured temperature signal measured by the temperature sensor, the measured pressure signal applied from the pressure sensor is corrected with a temperature compensating pressure signal, the temperature compensating pressure signal is compared with a pre-input normal range, and an abnormal signal is generated when it is out of the normal range. output, intelligent forging system. delete delete According to claim 1,
The control unit,
an expected correction waveform storage unit that stores information about an expected reference waveform of a normal expected correction signal, an expected upper limit waveform that is allowed to be high from the expected reference waveform, and an expected lower limit waveform that is allowed to be low from the expected reference waveform;
A mold abnormality sound determination unit that compares the mold sound signal measured by the AE sensor with a compensation sound signal obtained by attenuating the external sound signal measured by the external sound sensor with a pre-input normal range and outputs an abnormal signal when it is out of range ;
a temperature compensating pressure signal calculator calculating the temperature compensating pressure signal obtained by offsetting the temperature value of the measured temperature signal measured by the temperature sensor from the pressure value of the measured pressure signal applied from the pressure sensor;
a compensating pressure determination unit determining whether the temperature compensation pressure signal falls within a normal range between the expected upper limit waveform and the expected lower limit waveform;
a normal determination signal output unit outputting a normal determination signal when the temperature compensation pressure signal falls within a normal range between the expected upper limit waveform and the expected lower limit waveform; and
an abnormal determination signal output unit outputting an abnormal determination signal when the temperature compensation pressure signal is out of a normal range between the expected upper limit waveform and the expected lower limit waveform;
Including, intelligent forging system. According to claim 4,
The control unit,
Using the SN curve of the mold, the life of the mold is predicted based on the upper limit of N times stress of the temperature compensation pressure signal, or the coincidence rate or the degree of agreement between the temperature compensation pressure signal and the expected reference waveform is calculated, and the agreement rate a mold life prediction unit for predicting the life of the mold by measuring a change in or a change in the degree of matching;
Further comprising, intelligent forging system. According to claim 4,
The work piece is first forged in the first mold and then transferred to the second mold, or first forged in the first mold, second forged in the second mold, and then transferred to a third or higher multi-stage mold. The punch block is a cold multi-stage forged punch block in which a plurality of the punches are installed in a row so that it can be,
The pressure sensor is installed on at least one of the molds, one or more of the mold assemblies in which the mold is installed, one or more of the punch blocks, and any one or more of combinations thereof,
The AE sensor is installed on at least one of the molds in which the pressure sensor is installed, in one or more of the mold assemblies in which the mold is installed, in one or more of the punch blocks, and in any one or more of combinations thereof become,
The die block is a cold multi-stage forging mold block in which a plurality of the molds are installed side by side so as to correspond to the punches, intelligent forging system. According to claim 6,
The AE sensor,
a first AE sensor installed in the primary mold or the primary mold assembly; and
a second AE sensor installed in the secondary mold or the secondary mold assembly;
Including, intelligent forging system. According to claim 7,
The pressure sensor,
a first pressure sensor installed in the primary mold or the primary mold assembly; and
Including; a second pressure sensor installed in the secondary mold or the secondary mold assembly,
The temperature sensor,
a first temperature sensor installed in the primary mold or the primary mold assembly; and
A second temperature sensor installed in the secondary mold or the secondary mold assembly; includes,
The control unit,
Calculate a deviation rate or degree of deviation between the temperature compensation pressure signal calculated using the first pressure sensor and the first temperature sensor and the expected reference waveform, and using the second pressure sensor and the second temperature sensor a deviation accumulator that calculates a deviation rate or degree of deviation between the calculated temperature compensation pressure signal and the expected reference waveform, and calculates a cumulative sum of the deviation rates or the degree of deviation;
an accumulation compensation pressure determining unit determining whether the cumulative sum is within a normal range;
a normal accumulation signal output unit outputting a normal accumulation determination signal when the accumulated sum falls within the normal range; and
an abnormal accumulation signal output unit outputting an abnormal accumulation determination signal when the cumulative sum exceeds the normal range;
Further comprising, intelligent forging system. According to claim 8,
The mold assembly,
A mold including a carbide tip having a cavity into which a workpiece is inserted to correspond to the punch, a supporter supporting a lower surface of the carbide tip, and a bushing surrounding the carbide tip and an outer diameter surface of the supporter; and
An upper surface of the mold is in contact with the lower surface of the supporter and the lower surface of the bushing so that the pressure of the mold is transmitted, and a first vertical groove portion formed on one side in a direction perpendicular to the pressure direction of the mold, a second groove portion, and a third A spacer in which a groove is formed; includes,
The pressure sensor is a piezo (piezoelectric) sensor inserted into the first vertical groove of the spacer and converting the pressure transmitted to the spacer into an electrical form,
The temperature sensor is a contact or non-contact temperature sensor that is inserted into the second groove of the spacer and converts thermal energy transferred to the spacer into an electrical form,
The AE sensor is a microphone sensor that is inserted into the third groove of the spacer and converts the acoustic energy transmitted to the spacer into an electrical form, the intelligent forging system. According to claim 1,
a punch side pressure sensor installed in the punch or the punch assembly in which the punch is installed;
Including more,
The control unit,
Intelligent forging system that compares the pressure signal applied from the punch-side pressure sensor with a pre-input normal range and outputs an abnormal signal when out of this range. A punch block in which at least one punch is installed, a die block in which at least one mold is installed to correspond to the punch, a mold assembly in which at least the mold and the mold are installed to sense a load applied from the punch, and the punch block And a pressure sensor installed on any one or more of the combinations thereof, and when measuring the load of the pressure sensor, at least the mold, the mold assembly in which the mold is installed, the punch block, and AE sensor (acoustic emission sensor) installed on any one or more of these combinations, and an external sound installed at a certain distance or more from the AE sensor to compensate for the mold sound signal measured by the AE sensor from external sound A sensor, a temperature sensor installed near the pressure sensor to compensate for the temperature deviation of the measured pressure signal measured by the pressure sensor, and measuring the temperature when measuring the pressure of the pressure sensor, and when measuring the load of the pressure sensor, Compares the mold sound signal measured by the AE sensor with a pre-input normal range, outputs an abnormal signal when it is out of the range, and compensates the measured pressure signal applied from the pressure sensor in consideration of the measured temperature signal measured by the temperature sensor for temperature compensation In the precision diagnosis method of an intelligent forging system comprising a control unit that corrects with a pressure signal, compares the temperature compensation pressure signal with a pre-input normal range, and outputs an abnormal signal when out of it,
An expected correction waveform storing step of storing information about an expected reference waveform of a normal expected correction signal, an expected upper limit waveform that is allowed to be high from the expected reference waveform, and an expected lower limit waveform that is allowed to be low from the expected reference waveform;
a temperature compensation pressure signal calculating step of calculating the temperature compensation pressure signal obtained by offsetting the temperature value of the measured temperature signal measured by the temperature sensor from the pressure value of the measured pressure signal applied from the pressure sensor;
a compensating pressure determination step of determining whether the temperature compensation pressure signal falls within a normal range between the expected upper limit waveform and the expected lower limit waveform;
Mold abnormal sound determination step of comparing the mold sound signal measured by the AE sensor with a compensation sound signal obtained by attenuating the external sound signal measured by the external sound sensor with a pre-input normal range and outputting an abnormal signal when it is out of range ;
a normal determination signal output step of outputting a normal determination signal when the temperature compensation pressure signal falls within a normal range between the expected upper limit waveform and the expected lower limit waveform; and
an abnormal determination signal output step of outputting an abnormal determination signal when the temperature compensation pressure signal is out of a normal range between the expected upper limit waveform and the expected lower limit waveform;
Containing, the precision diagnosis method of the intelligent forging system. According to claim 11,
Using the SN curve of the mold, the life of the mold is predicted based on the upper limit of N times stress of the temperature compensation pressure signal, or the coincidence rate or the degree of agreement between the temperature compensation pressure signal and the expected reference waveform is calculated, and the agreement rate A mold life prediction step of predicting the life of the mold by measuring a change in or a change in the degree of coincidence;
Further comprising a precision diagnosis method of the intelligent forging system. According to claim 11,
The AE sensor,
A first AE sensor installed in the primary mold or the primary mold assembly; and
A second AE sensor installed in the secondary mold or the secondary mold assembly;
Containing, the precision diagnosis method of the intelligent forging system. According to claim 11,
The pressure sensor,
A first pressure sensor installed in the primary mold or the primary mold assembly; and
Including; a second pressure sensor installed in the secondary mold or the secondary mold assembly,
The temperature sensor,
a first temperature sensor installed in the primary mold or the primary mold assembly; and
A second temperature sensor installed in the secondary mold or the secondary mold assembly; includes,
Calculate a deviation rate or degree of deviation between the temperature compensation pressure signal calculated using the first pressure sensor and the first temperature sensor and the expected reference waveform, and using the second pressure sensor and the second temperature sensor a deviation accumulation step of calculating a deviation rate or a degree of deviation between the calculated temperature compensation pressure signal and the expected reference waveform, and calculating a cumulative sum of the deviation rates or the deviation degrees;
an accumulation compensation pressure determination step of determining whether the cumulative sum is within a normal range;
a normal accumulation signal outputting step of outputting a normal accumulation determination signal when the cumulative sum falls within the normal range; and
an abnormal accumulation signal outputting step of outputting an abnormal accumulation determination signal when the cumulative sum exceeds the normal range;
Further comprising, precision diagnosis method of the intelligent forging system.

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