CN114558972A - Intelligent forging system and diagnosis method thereof - Google Patents

Abstract

The intelligent forging system according to the inventive concept may include: a punch block provided with at least one punch; a die block provided with at least one die to correspond to the punch; a pressure sensor provided on the die or a die assembly provided with the die so as to be able to sense a load applied by the punch; and a control unit for comparing the measured pressure signal applied by the pressure sensor with a previously inputted normal range and outputting an abnormal signal if the measured pressure signal is out of the range.

Description

Intelligent forging system and diagnosis method thereof

Technical Field

The present invention relates to an intelligent forging system and a diagnosis method thereof, and more particularly, to an intelligent forging system and a precision diagnosis method thereof, which can perform multi-step rapid-cooling forging of a workpiece such as a bolt, a nut, or various pins through a series of steps using an abnormal sound signal of a die and a temperature-corrected pressure value, and can accurately judge abnormality and a life of the die.

Background

Forging is one of metal working methods for forming a material into a target shape by applying a compressive load, and for this purpose, a forging machine is generally used which has a die block to which a die or a die for supporting a workpiece such as a material to be worked is attached, a press block to which a tool such as a punch is attached, and the like, and has a structure in which the punch is reciprocated toward the die block by a hydraulic pressure or a motor to form the workpiece in a shape corresponding to the shape of the die. The multi-stage forging machine of the forging machine is provided with a plurality of dies which are slightly different in specification according to the arrangement order and are arranged at the end of each die block corresponding to a final formed product, and the die blocks are provided with a transfer device for transferring a processed product, so that the processed product can be sequentially transferred to the dies and formed by the dies in stages by the transfer device, and finally formed into a target form based on the final die.

Disclosure of Invention

[ problem ] to provide a method for producing a semiconductor device

However, the conventional multistage forging machine is configured such that a plurality of dies are attached to a die block, and a fatigue fracture phenomenon, which damages the dies, may be caused by a tool steel used as a die material during a long-term forming process, a hard material, or the like, which may cause fracture due to adhesion, propagation of fracture, or the like.

Even if the phenomenon of the die damage due to the adhesion and the fatigue failure occurs in any one of the plurality of dies, the subsequent process is badly affected by the characteristics of the multistage forging, and particularly, if a failure occurs in a fastener such as a bolt, a nut, or a fixing pin, the safety of a vehicle, a building, a structure, or the like fixed by the defective fastener is deadly.

The present invention has been made to solve the above-mentioned problems, and it is an object of the present invention to provide an intelligent forging system and a precise diagnosis method thereof, in which a pressure sensor and a temperature sensor are mounted on each die assembly, and an abnormality of each die can be sensed in real time and more accurately based on an abnormal acoustic signal of the die and a pressure value of a corrected temperature value, and a life of the die can be sensed by a change in a rate of coincidence between the abnormal acoustic signal or the corrected pressure value and a reference value, and an excessive deformation energy received by a workpiece in a multistage forging process can be sensed by accumulating deviations, thereby preventing a defective product from a source, and even sensing an abnormality of a punch block by sensing a signal difference of the pressure sensor between the die block and the punch block. However, the technical problems are merely exemplary, and the scope of the present invention is not limited thereto.

[ technical solution ] A

The intelligent forging system of the present inventive concept to solve the above technical problems may include: a punch block provided with at least one punch; a die block provided with at least one die to correspond to the punch; a pressure sensor provided on the die or a die assembly provided with the die so as to be able to sense a load applied by the punch; and a control unit for comparing the measured pressure signal applied by the pressure sensor with a previously inputted normal range and outputting an abnormal signal if the measured pressure signal is out of the range.

In the intelligent forging system, in order to enable the workpiece to be transferred to the second die after the first forging and forming in the first die and to the third die after the second forging and forming in the second die, the press block may be a cold multi-stage forging press block in which a plurality of the punches are provided in a row, and the die block may be a cold multi-stage forging die block in which a plurality of the dies are provided in parallel so as to correspond to the punches, and the pressure sensor may include: a first pressure sensor provided in the first mold or the first mold assembly; a second pressure sensor provided in the second mold or the second mold assembly; and a third pressure sensor provided in the third mold or the third mold assembly, wherein the control unit determines whether the mold is abnormal or predicts a life of the mold, using a measurement pressure signal of the pressure sensor.

In the intelligent forging system, the control unit calculates a deviation rate or a deviation degree of the measured pressure signal measured by the first pressure sensor from the expected reference waveform, calculates a deviation rate or a deviation degree of the measured pressure signal measured by the second pressure sensor from the expected reference waveform, calculates a deviation rate or a deviation degree of the measured pressure signal measured by the third pressure sensor from the expected reference waveform, and calculates an accumulated total value of the deviation rate or the deviation degree thereof, and determines whether the accumulated total value is within a normal range, outputs a normal accumulation determination signal if the accumulated total value is within the normal range, and outputs an abnormal accumulation determination signal if the measured total value is outside the normal range.

In the smart forging system, the mold assembly may include: a die including a cemented carbide insert having a cavity formed therein for inserting a workpiece to correspond to the punch; a support for supporting the underside of the carbide insert; and a sleeve surrounding the carbide tip and the outer diameter surface of the support; and a spacer having an upper surface contacting a lower surface of the support of the mold and a lower surface of the sleeve in order to transmit the pressure of the mold, and having a first vertical groove unit formed at one side thereof in a direction perpendicular to the pressure direction of the mold, wherein the pressure sensor is to be inserted into the first vertical groove unit of the spacer of the mold assembly.

The intelligent forging system may further include a temperature sensor disposed adjacent to the pressure sensor and configured to measure a temperature of the pressure sensor when the pressure sensor measures pressure to compensate for a temperature deviation of a measured pressure signal measured by the pressure sensor.

In the intelligent forging system, the control unit corrects the measured pressure signal applied from the pressure sensor with a temperature correction pressure signal based on the measured temperature signal measured by the temperature sensor, compares the temperature correction pressure signal with a normal range input in advance, and outputs an abnormal signal if the range is exceeded.

In the intelligent forging system, the control unit may include a die life prediction unit that predicts the life of the die using an S-N bending curve of the die material with reference to an upper limit value of N-times stress of the temperature correction pressure value, or calculates a rate of coincidence or a degree of coincidence of the temperature compensation pressure signal with the expected reference waveform, and predicts the life of the die by measuring a change in the rate of coincidence or a change in the degree of coincidence.

The intelligent forging system may further include an AE sensor (acoustic emission sensor) provided at least at the die or the die assembly of the die so as to measure an abnormal sound generated in the die when the load of the pressure sensor is measured.

In the intelligent forging system, the control unit compares the die acoustic signal measured by the AE sensor with a previously input normal range when the load of the pressure sensor is measured, and outputs an abnormal signal if the range is exceeded.

The smart forging system may further include an external acoustic sensor, and further include an external acoustic sensor disposed at a distance from the AE sensor to compensate the die acoustic signal measured by the AE sensor by an external sound.

In the intelligent forging system, the spacer is further formed with a second vertical groove portion, and further includes a temperature sensor disposed near the pressure sensor and measuring a temperature of the pressure sensor when the pressure sensor measures pressure to compensate for a temperature deviation of a measured pressure signal measured by the pressure sensor, the temperature sensor may be a contact or non-contact temperature sensor inserted into the second groove portion of the spacer and converting thermal energy transferred to the spacer into an electrical energy form.

In the smart forging system, the spacer may be further formed with a third vertical groove portion, and further include an AE sensor provided at least at the die or a die assembly provided with the die to measure abnormal sound generated in the die when measuring a load of the pressure sensor, and the AE sensor may be a microphone sensor inserted into the third groove portion of the spacer and converting sound energy transmitted to the spacer into an electric energy form.

The intelligent forging system may further include a punch side pressure sensor provided at the punch or a punch assembly provided with the punch, and the control unit may determine whether there is an abnormality in the punch or the punch assembly based on a measured pressure signal of the punch side pressure sensor.

In the intelligent forging system, the control unit may accumulate pressure signal data measured by at least the first pressure sensor, the second pressure sensor, and the third pressure sensor when forming a workpiece, and may determine whether or not the first mold, the second mold, and the third mold are abnormal or predict the life of the mold based on the accumulated data by a machine learning method using artificial intelligence.

[ PROBLEMS ] the present invention

According to some embodiments of the present invention configured as described above, the pressure sensor is mounted on the mold or the mold assembly, so that the mold abnormality can be sensed in real time, and the life of the mold can be sensed based on the change in the matching rate of the measured pressure with the reference value. Further, the measurement pressure can be corrected by adding a temperature sensor and an AE sensor, thereby increasing the sensing efficiency. Of course, the scope of the present invention is not limited to these effects.

Drawings

FIG. 1 is an external perspective view of an intelligent forging system according to some embodiments of the invention.

Fig. 2 is a cross-sectional view of the intelligent forging system of fig. 1.

FIG. 3 is an enlarged cross-sectional view of a die block of the intelligent forging system of FIG. 2.

Fig. 4 is a perspective view of a die assembly of the intelligent forging system of fig. 3.

Fig. 5 is a partial perspective cross-sectional view of a die assembly of the intelligent forging system of fig. 4.

FIG. 6 is a block diagram of a control unit of the intelligent forging system of FIG. 2.

FIG. 7 is a flow diagram of a method for precision diagnostics of an intelligent forging system in accordance with some embodiments of the invention.

Fig. 8 is a graph of pressure values measured according to time in the temperature compensated pressure signal calculation step of the precision diagnosis method of the intelligent forging system according to some embodiments of the present invention.

Fig. 9 is a graph of a pressure correction value for offsetting a temperature value from a pressure value measured according to time in the temperature compensation pressure signal calculation step of the precision diagnosis method of the intelligent forging system of fig. 7.

Fig. 10 is a graph for predicting the life of the die using the S-N bending curve of the die material based on the upper limit value of the stress N times of the temperature correction pressure value in the die life predicting step of the precision diagnosis method of the intelligent forging system of fig. 7.

Fig. 11 is a graph of an example of a measured pressure signal sensed by a die side pressure sensor of the intelligent forging system of fig. 1.

Fig. 12 to 14 are diagrams for comparing the detection states of the measured pressure signals in the case where the pressure sensor is provided in the "punch portion" (left graph) and the case where the pressure sensor is provided in the "die portion" (right graph) in the present invention when a failure occurs in the "phenomenon" photograph.

FIG. 15 is a cross-sectional view of an intelligent forging system according to another embodiment of the invention.

FIG. 16 is a cross-sectional view of an intelligent forging system according to yet another embodiment of the invention.

[ description of reference numerals ]

1: workpiece 10: punching block

P: punch P1: first punch

P2: second punch P3: third punch

20: a die block M: die set

M1: first mold M2: second mold

M3: third mold 30: mold assembly

31: cemented carbide tip 32: support object

33: the sleeve 34: spacer member

35: ejector pin G1: first vertical slot unit

G2: second tank unit G3: third tank unit

S1: pressure sensor S11: first pressure sensor

S12: second pressure sensor S13: third pressure sensor

S2: temperature sensor S21: first temperature sensor

S22: second temperature sensor S23: third temperature 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: the control unit 41: expected waveform memory cell

42: mold abnormal sound determination unit 43: temperature compensated pressure signal calculation unit

44: compensation pressure determination unit 45: normal discrimination signal output unit

46: abnormal determination signal output unit 47: mold life prediction unit

48: deviation accumulation unit 49: accumulated compensation pressure discrimination unit

50: normal accumulation signal output unit 51: abnormal accumulation signal output unit

100: intelligent forging system

Detailed Description

Hereinafter, 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 fully explain to those skilled in the art that the embodiments described below can be modified into various forms without limiting the scope of the present invention to these embodiments. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. In addition, the thickness or size of each layer in the drawings will be exaggerated for convenience of description and accuracy of description.

The terminology used in the description is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification, the singular forms "a", "an", and "the" may include the plural forms unless the context clearly dictates otherwise. Furthermore, as used herein, the terms "comprises" and/or "comprising" are intended to specify the presence of stated shapes, integers, steps, acts, components, elements, and/or combinations thereof, but do not preclude the presence or addition of one or more other shapes, integers, steps, acts, components, elements, and/or groups thereof.

Embodiments of the present invention will be described below with reference to the accompanying drawings, which schematically show preferred embodiments of the invention. In the figures, the shown shape variations can be predicted, for example, from manufacturing techniques and/or tolerances (tolerance). Therefore, the embodiments of the present invention should not be construed as being limited to the specific shapes within the range shown in the present specification, and for example, should include shape changes caused by a manufacturing process.

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

First, as shown in fig. 1 and 2, an intelligent forging system 100 according to some embodiments of the present invention may include: a punch block 10 provided with at least one punch P; a die block 20 provided with at least one or more dies M to correspond to the punch P; a pressure sensor S1 provided on the die M or the die assembly 30 provided with the die M so as to be able to sense a load applied by the punch M; and a control unit 40 for comparing the measured pressure signal applied from the pressure sensor S1 with a previously inputted normal range and outputting an abnormal signal if the measured pressure signal is out of the normal range.

In the intelligent forging system 100, the control unit 40 basically measures the load applied to the die M or the die assembly 30 based on the measurement value of the pressure sensor S1, and continuously monitors the load (measurement pressure signal) to determine whether the die M is abnormal, thereby predicting the life of the die M. This will be explained in detail later.

In addition, in the modification of this embodiment, the smart forging system 100 may further include an additional sensor in addition to the pressure sensor S1, and the determination of whether there is an abnormality in the die M and the life prediction capability may be further improved by the pressure sensor S1 and the measurement value of the additional sensor.

For example, in some embodiments, the smart forging system 100 further includes a temperature sensor S2 disposed proximate the pressure sensor S1 and measuring a temperature at which the pressure sensor S1 measures pressure to compensate for temperature deviations of the measured pressure signal measured by the pressure sensor S1. The control unit 40 corrects the temperature compensated pressure signal inputted from the pressure sensor S1 with a temperature corrected pressure signal based on the measured temperature signal measured by the temperature sensor S2, compares the temperature corrected pressure signal with a normal range inputted in advance, and outputs an abnormal signal if the range is out.

As another example, in some embodiments, the smart forging system 100 may further include an AE sensor S3(acoustic emission sensor) disposed at least one of the die M, the die assembly 30 in which the die M is disposed, the press block 10, and a combination thereof, so as to measure an abnormal sound generated in the die M when a load of the pressure sensor S1 is measured. Optionally, the intelligent forging system 100 may further include an external sound sensor S4 disposed at a distance from the AE sensor S3 to compensate the die sound signal measured by the AE sensor S3 by external sound. When the load of the pressure sensor S1 is measured, the control unit 40 compares the die acoustic signal measured by the AE sensor S3 with a previously input normal range, and outputs an abnormal signal if the load exceeds the normal range.

As yet another example, in some embodiments, smart forging system 100 may include temperature sensor S2 and AE sensor S3 in addition to pressure sensor S1. When the load of the pressure sensor S1 is measured, the control unit 40 compares the die acoustic signal measured by the AE sensor S3 with a previously input normal range, and outputs an abnormal signal if the load exceeds the normal range. Then, based on the measured temperature signal measured by the temperature sensor S2, the temperature compensation pressure signal inputted from the pressure sensor S1 is corrected by a temperature correction pressure signal, and the temperature correction pressure signal is compared with a normal range inputted in advance, and if the temperature correction pressure signal is out of the range, an abnormal signal is outputted.

In the foregoing embodiment, the intelligent forging system 100 may be a device that performs multi-stage cold forging of a workpiece such as a bolt, a nut, or various pins through a series of steps. However, the present invention is not limited to the multistage cold forging, and is applicable to all forging apparatuses having the press block 10 and the die block 20.

More specifically, for example, in order to transfer the workpiece 1 to the second die M2 after the first forging and forming in the first die M1 and to transfer the workpiece to the third die M3 after the second forging and forming in the second die M2, the press block 10 may be a cold multi-stage forging press block in which a plurality of the punches P1, P2 and P3 are provided in a row, and the die block 20 may be a cold multi-stage forging die block in which a plurality of the dies M1, M2 and M3 are provided in parallel to the punches P1, P2 and P3. Hereinafter, the mold assembly 30 provided with the first mold M1 is referred to as a first mold assembly 30, the mold assembly 30 provided with the second mold M2 is referred to as a second mold assembly 30, and the mold assembly 30 provided with the third mold M3 is referred to as a third mold assembly 30.

For example, the pressure sensor S1 may be disposed at least one of the first to third molds M1, M2 and M3 or at least one of the first to third mold assemblies 30, and the AE sensor S3 may be disposed at least one of the first to third molds M1, M2 and M3 or at least one of the first to third mold assemblies 30 at which the pressure sensor S1 is disposed. The temperature sensor S2 may be provided in at least one of the first to third molds M1, M2, and M3 provided with the pressure sensor S1, or in at least one of the first to third mold assemblies 30.

However, the technical idea of the present invention is applicable to all forging apparatuses of two or more stages, and although three stages are illustrated in the drawings, the technical idea of the present invention is also applicable to forging apparatuses of two or three or more stages.

Further, for example, as shown in fig. 1 and 2, the pressure sensor S1 of the intelligent forging system 100 according to some embodiments of the present invention may include a first pressure sensor S11 provided at the first die M1 or the first die assembly 30, a second pressure sensor S12 provided at the second die M2 or the second die assembly 30, and a third pressure sensor S13 provided at the third die M3 or the third die assembly 30.

Further, for example, as shown in fig. 1 and 2, the temperature sensor S2 of the smart forging system 100 according to some embodiments of the present invention may include a first temperature sensor S21 provided at the first die M1 or the first die assembly 30, a second temperature sensor S22 provided at the second die M2 or the second die assembly 30, and a third temperature sensor S23 provided at the third die or the third die assembly 30.

Further, for example, as shown in fig. 1 and 2, the AE sensor S3 of the smart forging system 100 according to some embodiments of the present invention may include a first AE sensor S31 provided at the first die M1 or the first die assembly 30, a second AE sensor S32 provided at the second die M2 or the second die assembly 30, and a third AE sensor S33 provided at the third die M3 or the third die assembly 30.

However, the technical idea of the present invention is applicable to all apparatuses provided with at least two sensors, and the case where three sensors are provided is illustrated in the drawings as an example, but is also applicable to a forging apparatus provided with two or more sensors.

Fig. 3 is an enlarged sectional view of the die block 20 of the smart forging system 100 of fig. 2, enlarged and illustrated, fig. 4 is a perspective view of the die assembly 30 of the smart forging system 100 of fig. 3, and fig. 5 is a partial perspective sectional view of the die assembly 30 of the smart forging system 100 of fig. 4.

More specifically, for example, as shown in fig. 1 to 5, the mold assembly 30 may include: a die M including a cemented carbide insert 31 having a cavity formed therein for inserting the workpiece 1 to correspond to the punch, and a spacer 34; a support 32 for supporting the lower surface of the cemented carbide tip 31; and a sleeve 33 surrounding the cemented carbide tip 31 and the outer diameter surface of the support 32. The spacer 34 is formed with a first vertical groove unit G1, a second groove unit G2, and a third groove unit G3 on one side, which are perpendicular to the pressing direction of the mold M, so that the upper surface of the spacer contacts the lower surface of the support 32 and the lower surface of the sleeve 33 of the mold M in order to transmit the pressing force of the mold M.

In the mold M1, the support 32, and the spacer 34, through holes through which the ejector pins 35 of fig. 2 and 3 can pass are formed, so that the workpiece 1 can be discharged to the outside of the first mold M1 after the first molding.

Also, for example, the pressure sensor S1 for being inserted in the first vertical groove unit G1 of the spacer 34 may employ a piezoelectric (Piezo) element that converts the pressure transmitted to the spacer 34 into a current form.

Also, for example, the temperature sensor S2 for insertion into the second groove unit G2 of the spacer 34 may be a contact or non-contact temperature sensor that converts thermal energy transferred to the spacer 34 into an electrical energy form.

Further, for example, the AE sensor S3 used for the third groove cell G3 inserted in the spacer 34 may be a microphone sensor that converts sound energy transmitted to the spacer 34 into an electric energy form.

Therefore, when the punch P forms the workpiece 1, a forming load is transmitted to the first die M1 and transmitted to the spacer 34 through the support 32, and at this time, the AE sensor S3 converts abnormal sound energy such as a breaking sound or a crushing sound transmitted to the spacer 34 into an electric energy form and applies the electric energy form to the control unit 40, and in order to compensate the die sound signal measured by the AE sensor S3 by an external sound, the external sound sensor S4 converts external sound energy into an electric energy form and applies the electric energy form to the control unit 40, and the first pressure sensor S11 converts a load acting on the spacer 34 into an electric signal, that is, into an electric current and applies the electric current to the control unit 40.

At this time, the first temperature sensor S21 may convert a temperature value, which indicates an ambient temperature when the pressure value of the first pressure sensor S31 is measured, into an electrical signal, i.e., into an electric current, and simultaneously apply it to the control unit 40.

Therefore, when the load of the pressure sensor is measured, the control unit 40 may compare the die acoustic signal measured by the AE sensor S3 with a previously input normal range, output an abnormal signal if the load exceeds the normal range, and calculate the temperature correction pressure signal that cancels the temperature value of the measured temperature signal measured by the temperature sensor S2 based on the pressure value of the temperature compensation pressure signal applied by the pressure sensor S1.

When the pressure sensor S1, the temperature sensor S2, and/or the AE sensor S3 are directly provided on the mold M, the load measurement may be easy, but the installation of the sensors may not be easy. For example, when a groove is formed in the mold M, the mold M may be broken, and when the groove is attached to the mold M, the sensor may be damaged when the mold M is subjected to a load. However, as described above, when the pressure sensor S1, the temperature sensor S2, and/or the AE sensor S3 are provided in the mold assembly 30, for example, in the spacer 34, there is an advantage that the load applied to the mold M can be measured almost constantly and the mold M does not need to be deformed.

Fig. 6 is a block diagram of control unit 40 of intelligent forging system 100 of fig. 2.

As shown in fig. 1 to 6, the control unit 40 of the intelligent forging system 100 according to some embodiments of the present invention may include: an expected waveform storage unit 41 for storing information of an expected reference waveform for a normal expected correction pressure signal, an expected upper limit waveform that is allowed to be higher than the expected reference waveform, and an expected lower limit waveform that is allowed to be lower than the expected reference waveform; a die abnormal sound discrimination unit 42 that compares a compensation sound signal obtained by attenuating the external sound signal measured by the external sound sensor from the die sound signal measured by the AE sensor with a previously input normal range, and outputs an abnormal signal if the compensation sound signal is out of the range; a temperature-compensated pressure signal calculation unit 43 that calculates the temperature-corrected pressure signal that cancels the temperature value of the measured temperature signal measured by the temperature sensor S2, from among the pressure values applied from the pressure sensor S1 for the temperature-compensated pressure signal; a compensation pressure judging unit 44 that judges 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 45 that outputs a normal determination signal when it is determined that 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 46 that outputs an abnormal determination signal when it is determined that the temperature compensated pressure signal is out of a normal range between the expected upper limit waveform and the expected lower limit waveform.

The compensation pressure determination unit 44 may determine normal and abnormal or predicted lives by using the expected waveform storage unit 41, or may determine normal and abnormal or predicted lives of data such as load, sound, temperature, etc. by using an artificial intelligence unit that repeatedly learns and guides specific data using a deep learning method, and predict lives by calculating loads of respective portions of the mold using a finite element method, and may improve installation conditions, design conditions, etc. in the field by using feedback.

For example, the design of the forging process and the manufacture of the tool of the forging process can be realized by determining the appropriateness of the initial forging stroke setting using the sensing value, diagnosing the state of the process or the equipment by analyzing the change in the sensing value during the progress of the process, analyzing the influence on the process due to the temperature rise of the equipment by the heat generated during the long-term continuous process, and considering the data of the change in the initial production or during the process.

Thus, the control unit 40 of the invention can perform the following steps: storing information for an expected reference waveform for a normal expected corrected pressure signal, an expected upper limit waveform allowed to be higher than the expected reference waveform, and an expected lower limit waveform allowed to be lower than the expected reference waveform; comparing a compensation acoustic signal attenuating the external acoustic signal measured by the external acoustic sensor S4 from the mold acoustic signal measured by the AE sensor S3 with a previously inputted normal range, and outputting an abnormal signal if the range is exceeded; calculating the temperature correction pressure signal which cancels the temperature value of the measured temperature signal measured by the temperature sensor S2 from the pressure value applied by the pressure sensor S1 for the temperature compensation pressure signal, and judging whether the temperature compensation pressure signal belongs to the normal range between the expected upper limit waveform and the expected lower limit waveform; and outputting a normal judgment signal when judging that the temperature compensation pressure signal belongs to a normal range between the expected upper limit waveform and the expected lower limit waveform, or outputting an abnormal judgment signal when judging that the temperature compensation pressure signal exceeds the normal range between the expected upper limit waveform and the expected lower limit waveform.

Therefore, by installing the plurality of pressure sensors S11, S12, and S13, the temperature sensors S21, S22, and S23, the AE sensors S31, S32, and S33, and the external acoustic sensor S4 on each of the mold assemblies 30, abnormality of each of the molds M1, M2, and M3 is sensed in real time based on a standard acoustic value or a temperature-corrected pressure value, and if abnormality occurs in at least one of the molds M1, M2, and M3, a user or an operator is promptly notified, so that if an impurity sticking phenomenon of the mold or a breakage phenomenon of the mold occurs, a follow-up measure can be taken immediately before a follow-up process is affected.

Further, for example, as shown in fig. 6, the control unit 40 may further include a die life prediction unit 47 predicting the life of the die with reference to an upper limit value of N times of stress of the temperature correction pressure value using an S-N bending curve of the die material or calculating a rate of coincidence or a degree of coincidence of the temperature compensation pressure signal with the expected reference waveform, and predicting the life of the die by measuring a change in the rate of coincidence or a change in the degree of coincidence.

Therefore, for example, the life of the mold can be predicted with high accuracy based on the upper limit value of the N-th order stress by using the temperature correction pressure value in which the load applied to the actual mold is almost similar regardless of the temperature, based on the S-N curve of the mold material.

In addition, for example, if the rate of coincidence of the actual measurement value with the expected reference value is 99% or more on the first day but the rate of coincidence decreases from 1% to 98% after the first day and the rate of coincidence at the time of failure decreases from 1% to 80% per day, the failure occurrence rate can be predicted to be high after the next 20 days, and therefore, after 20 days, appropriate measures such as preparation of a mold are taken in advance by predicting the life of the mold.

Therefore, the individual lives of the molds M1, M2, and M3 can be predicted in advance by considering the change in the matching rate between the measurement pressure and the reference value, and follow-up measures can be taken.

In addition, as shown in fig. 1 to 6, the control unit 40 may further include: a deviation accumulation unit 48 that calculates a deviation rate or a deviation degree of the temperature-compensated pressure signal from the expected reference waveform calculated by the first pressure sensor S11 and the first temperature sensor S21, calculates a deviation rate or a deviation degree of the temperature-compensated pressure signal from the expected reference waveform calculated by the second pressure sensor S12 and the second temperature sensor S22, calculates a deviation rate or a deviation degree of the temperature-compensated pressure signal from the expected reference waveform calculated by the third pressure sensor S13 and the third temperature sensor S23, and calculates an accumulated total value of the deviation rates or the deviation degrees; an accumulated compensation pressure determination unit 49 that determines whether the accumulated total value is within a normal range; a normal accumulation signal output unit 50 that outputs a normal accumulation determination signal when it is determined that the accumulation total value belongs to the normal range; and an abnormal accumulation signal output unit 51 that outputs an abnormal accumulation determination signal when it is determined that the measurement total value is out of the normal range.

Therefore, the control unit 40 of the present invention may perform a series of steps of: the deviation ratio or deviation degree of the temperature compensation pressure signal measured by the first pressure sensor S11, the second pressure sensor S12, and the third pressure sensor S13 is calculated, respectively, and the deviation ratio or the cumulative total value of the deviation degrees is calculated, whether the cumulative total value is within a normal range is determined, and a normal cumulative determination signal is output if the cumulative total value belongs to the normal range, or an abnormal cumulative determination signal is output if the measured total value exceeds the normal range.

Therefore, by accumulating the deviation between the actual value and the ideal value generated in each die, whether the processed object bears excessive deformation energy in the multi-stage forging process is sensed, so that defective products can be prevented from the source.

If the total amount of deformation energy received by the workpiece 1 in the multistage forging process exceeds a normal value, the workpiece 1 cannot receive a defective product which may cause internal and external cracks or damages or may be easily damaged by various impacts. The invention can prevent the defect of the mould and even the processed object.

In addition, as shown in fig. 1 and 2, the intelligent forging system 100 according to some embodiments of the present invention may further include a punch side pressure sensor S5 provided on the punch P or a punch assembly of the punch P. Although only one punch side pressure sensor S5 is illustrated in fig. 1 and 2, the number of punch side pressure sensors S5 may be variously changed in the modification of the embodiment, and the punch side pressure sensors S5 may be provided in the number of punches P or the punch assembly so as to correspond to each punch P, for example.

Therefore, the control unit 40 may compare the temperature compensation pressure signal applied from the punch side pressure sensor S5 with a previously input normal range, and output an abnormal signal if the range is exceeded. The punch side pressure sensor S5 is mainly used to determine whether or not there is an abnormality in the punch P or the punch assembly. Thus, the punch side pressure sensor S5 determines whether or not the punch P or the punch assembly is abnormal, and the die side pressure sensor S1 provided in the die M or the die assembly 30 determines whether or not the die M or the die assembly 30 is abnormal.

In some embodiments, the control unit 40 may even sense an abnormality of the die block by sensing a difference in signals of the pressure sensors S1, S5 between the die block 20 and the punch block 10.

Therefore, for example, if the signal difference between the pressure sensors S1, S5 of the die block 20 and the punch block 10 exceeds the normal value, it is determined that an abnormal phenomenon or an uneven pressure is generated, and the user or operator can take follow-up measures.

However, although not shown, a temperature sensor may be provided in the vicinity of the punch side pressure sensor S5, whereby a pressure value for temperature compensation can be calculated.

In addition, in the foregoing embodiment, at least one of the temperature sensor S2, the AE sensor S3, the external acoustic sensor S4, and a combination thereof may be omitted in the smart forging system 100.

For example, as shown in FIG. 15, if all of the temperature sensor S2, the AE sensor S3, and the acoustic sensor S4 are omitted, the control unit 40 may determine whether the forging system 100 is normal or not by the information of the pressure sensor S1. For example, the control unit 40 can determine whether the measured pressure signal belongs to a normal range between an expected upper limit waveform and an expected lower limit waveform even without temperature compensation, output a normal discrimination signal if the measured pressure signal belongs to the normal range between the expected upper limit waveform and the expected lower limit waveform, and output an abnormal discrimination signal if the measured pressure signal exceeds the normal range between the expected upper limit waveform and the expected lower limit waveform.

Further, if the forging system 100 is a multistage forging die, the control unit 40 calculates a deviation ratio or a deviation degree of the measured pressure signal measured by the second pressure sensor S12 from the expected reference waveform, calculates a deviation ratio or a deviation degree of the measured pressure signal measured by the third pressure sensor S13 from the expected reference waveform, by calculating a deviation ratio or a deviation degree of the measured pressure signal measured by the first pressure sensor S11 from the expected reference waveform, and calculates the accumulated total value of the deviation ratio or the deviation degree thereof, and judges whether the accumulated total value is within a normal range, if the cumulative total value falls within the normal range, a normal cumulative determination signal is output, and if the measured total value exceeds the normal range, an abnormal cumulative determination signal is output.

As another example, as shown in fig. 16, if there are no AE sensor S3 and no sound sensor S4, the control unit 40 calculates the temperature correction pressure signal applied from the pressure sensor S1 for the pressure value of the measured pressure signal to cancel the temperature value of the measured temperature signal measured by the temperature sensor S2, and determines whether the temperature compensation pressure signal belongs to the normal range between the expected upper limit waveform and the expected lower limit waveform, outputs a normal discrimination signal if the temperature compensation pressure signal belongs to the normal range between the expected upper limit waveform and the expected lower limit waveform, and outputs an abnormal discrimination signal if the temperature compensation pressure signal exceeds the normal range between the expected upper limit waveform and the expected lower limit waveform.

Further, if the forging system 100 is a multi-stage forging die, the control unit 40 calculates a deviation rate or a deviation degree of the temperature compensation pressure signal from the expected reference waveform calculated using the first pressure sensor S11 and the first temperature sensor S21, calculates a deviation rate or a deviation degree of the temperature compensation pressure signal from the expected reference waveform calculated using the second pressure sensor S12 and the second temperature sensor S22, and calculates an accumulated total value of the deviation rate or the deviation degree thereof, and determines whether the accumulated total value is within a normal range, outputs a normal accumulation determination signal if the accumulated total value belongs to the normal range, and outputs an abnormal accumulation determination signal if the measured total value exceeds the normal range.

In the above-described embodiment, if the forging system 100 is used, the control unit 40 in the multi-stage forging die can monitor the states of the first to third dies M1, M2, and M3 in real time and individually using the sensors provided in the respective stages, further, continuously accumulate the measurement data, and determine whether the first to third dies M1, M2, and M3 are abnormal or predict the lives of the first to third dies M1, M2, and M3 using the accumulated data. The presence or absence of abnormality or the prediction of the life of the first to third molds M1, M2, and M3 may be determined by comparison with a reference value, by estimation using an S-N curve, or by a mechanical learning method using artificial intelligence, as described above. For example, in fig. 15, the accumulated data is data of the measured pressure signals of the first to third pressure sensors S11, S12, and S13, in fig. 16, the accumulated data is data of the measured pressure signals of the first to third pressure sensors S11, S12, and S13 and the measured temperature signals of the first to third temperature sensors S21, S22, and S23, and in fig. 2, the accumulated data may be data of the measured pressure signals of the first to third pressure sensors S11, S12, and S13, the measured temperature signals of the first to third temperature sensors S21, S22, and S23, and the measured sound wave signals of the first to third AE sensors S31, S32, and S33.

FIG. 7 is a flow chart of a method for fine diagnosis of the intelligent forging system 100 in accordance with some embodiments of the present invention.

As shown in fig. 1 to 7, the method for precisely diagnosing an intelligent forging system 100 according to some embodiments of the present invention may include, during the diagnosis of the first die M1: an expected waveform storing step S110 of storing information of an expected reference waveform of a normal expected correction signal, an expected upper limit waveform allowed to be higher than the expected reference waveform, and an expected lower limit waveform allowed to be lower than the expected reference waveform; a mold abnormal sound discriminating step S120 of comparing a compensation sound signal attenuating the external sound signal measured by the external sound sensor S4 from the mold sound signal measured by the first AE sensor S31 with a previously inputted normal range and outputting an abnormal signal if the compensation sound signal is out of the range; a temperature compensated pressure signal calculation step 130 of calculating the temperature corrected pressure signal in which the temperature value of the measured temperature signal measured by the first temperature sensor S21 is offset from the pressure value applied from the first pressure sensor S11 for the temperature compensated pressure signal; a measured pressure judging step S140 of judging whether the temperature compensation pressure signal falls within a normal range between the expected upper limit waveform and the expected lower limit waveform; judging whether the normal range is included S150; a normal discrimination signal output step S160 of outputting a normal discrimination signal if the temperature compensated pressure signal belongs to a normal range between the expected upper limit waveform and the expected lower limit waveform; an abnormal discriminating signal outputting step S170 of outputting an abnormal discriminating signal if the temperature compensated pressure signal exceeds a normal range between the expected upper limit waveform and the expected lower limit waveform; and a die life predicting step S180 of predicting the life of the first die M1 based on the upper limit value of the stress of the temperature correction pressure value for N times by using the S-N curve of the first die M1, or calculating a matching rate or a matching degree of the temperature correction pressure signal with the expected reference waveform, and measuring a change in the matching rate or the change in the matching degree to predict the life of the first die M1.

Further, for example, in the diagnosis process of the second mold M2, it may include: an expected waveform storing step S210 of storing information of an expected reference waveform of a normal expected correction signal, an expected upper limit waveform allowed to be higher than the expected reference waveform, and an expected lower limit waveform allowed to be lower than the expected reference waveform; a die abnormal sound discrimination step S220 of comparing a compensation sound signal, which attenuates the external sound signal measured by the external sound sensor S4 from the die sound signal measured by the second AE sensor S32, with a previously inputted normal range, and outputting an abnormal signal if the compensation sound signal is out of the normal range; a temperature compensated pressure signal calculation step 230 of calculating the temperature corrected pressure signal in which the pressure value applied from the second pressure sensor S12 for the temperature compensated pressure signal cancels the temperature value of the measured temperature signal measured by the second temperature sensor S22; a measured pressure judging step S240 of judging whether the temperature compensation pressure signal falls within a normal range between the expected upper limit waveform and the expected lower limit waveform; judging whether the normal range is included S250; a normal discrimination signal output step S260 of outputting a normal discrimination signal if the temperature compensated pressure signal belongs to a normal range between the expected upper limit waveform and the expected lower limit waveform; an abnormal discrimination signal output step S270 of outputting an abnormal discrimination signal if the temperature compensated pressure signal exceeds a normal range between the expected upper limit waveform and the expected lower limit waveform; and a die life predicting step S280 of predicting the life of the second die M2 based on the upper limit value of the stress of the temperature correction pressure value for N times by using the S-N curve of the second die M2, or calculating a matching rate or a matching degree of the temperature correction pressure signal with the expected reference waveform, and measuring a change in the matching rate or the change in the matching degree to predict the life of the second die M2.

Further, for example, in the diagnosis process of the third mold M3, it may include: an expected waveform storing step S310 of storing information of an expected reference waveform of a normal expected correction signal, an expected upper limit waveform allowed to be higher than the expected reference waveform, and an expected lower limit waveform allowed to be lower than the expected reference waveform; a die abnormal sound discrimination step S320 of comparing a compensation sound signal, which attenuates the external sound signal measured by the external sound sensor S4 from the die sound signal measured by the third AE sensor S33, with a previously input normal range, and outputting an abnormal signal if the compensation sound signal is out of the normal range; a temperature compensation pressure signal calculation step 330 of calculating the temperature correction pressure signal in which the temperature value of the measured temperature signal measured by the third temperature sensor S23 is cancelled out of the pressure values applied from the third pressure sensor S13 for the temperature compensation pressure signal; a measured pressure judging step S340 of judging whether the temperature compensation pressure signal falls within a normal range between the expected upper limit waveform and the expected lower limit waveform; judging whether the normal range is included or not S350; a normal discrimination signal output step S360 of outputting a normal discrimination signal if the temperature compensation pressure signal belongs to a normal range between the expected upper limit waveform and the expected lower limit waveform; an abnormal discrimination signal output step S370 of outputting an abnormal discrimination signal if the temperature compensated pressure signal exceeds a normal range between the expected upper limit waveform and the expected lower limit waveform; and a mold life predicting step S380 of predicting the life of the third mold M3 based on the upper limit value of the stress of the temperature-corrected pressure value for N times by using the S-N curve of the third mold M3, or calculating a matching rate or a matching degree of the temperature-corrected pressure signal and the expected reference waveform, measuring a change in the matching rate or a change in the matching degree, and predicting the life of the third mold M3.

Next, a method for a fine diagnosis of an intelligent forging system 100 according to some embodiments of the present invention may include: a deviation accumulation step S410 of calculating a deviation rate or a deviation degree of the temperature-compensated pressure signal from the expected reference waveform calculated by the first pressure sensor S11 and the first temperature sensor S21, calculating a deviation rate or a deviation degree of the temperature-compensated pressure signal from the expected reference waveform calculated by the second pressure sensor S12 and the second temperature sensor S22, calculating a deviation rate or a deviation degree of the temperature-compensated pressure signal from the expected reference waveform calculated by the third pressure sensor S13 and the third temperature sensor S23, and calculating an accumulated total value of these deviation rates or deviation degrees; an accumulated compensation pressure determination step S420 of determining whether the accumulated total value is within a normal range; a normal accumulation signal output step S440 of judging whether the accumulated total value is in a normal range S430, and outputting a normal accumulation judgment signal if the accumulated total value is in the normal range; and an abnormal accumulation signal output step S450 of outputting an abnormal accumulation determination signal if the measured total value exceeds the normal range.

The deviation ratio or the deviation degree may be summed up by the number of sensors, and whether the accumulated amount of deformation energy falls within a normal range may be judged in sub-steps by comparing a theoretical total value and an actual total value in an intermediate step or a final step.

For example, if the workpiece 1 falls within the normal range in the first and second times and the deformation amount is slightly insufficient, it can be determined that no defective product has occurred in the third time even if the deformation amount is slightly out of the normal range, whereas if the workpiece 1 falls within the normal range in the first, second, and third times, but the total of the final deformation energies exceeds the total normal range due to the accumulation of the excess energy in each step, it can be predicted that the probability of the occurrence of a defective product is high.

In addition, as described above, at least any one of the temperature sensor S2, the AE sensor S3, the external acoustic sensor S4, and a combination thereof may be omitted, and the expected waveform storing step may be a storing step of information on an expected reference waveform for a normal expected pressure signal, an expected upper limit waveform allowed to be higher than the expected reference waveform, and an expected lower limit waveform allowed to be lower than the expected reference waveform.

Fig. 8 is a graph of the pressure values measured according to time in the temperature compensated pressure signal calculation step S130 of the precision diagnosis method of the intelligent forging system according to some embodiments of the present invention.

As is apparent from fig. 8, since the pressure sensor may have temperature dependency, the pressure value (blue) measured with time in the temperature compensated pressure signal calculation step S130 has an irregular waveform shape, and has a characteristic that the pressure value gradually increases with time after an initial rapid increase as a whole.

Fig. 9 is a graph of a pressure correction value for offsetting a temperature value from a pressure value measured according to time in the temperature compensation pressure signal calculation step S130 of the precision diagnosis method of the intelligent forging system of fig. 7.

As is apparent from the upper graph of fig. 9, the temperature value (red) measured with time in the temperature compensation pressure signal calculation step S130 is similar to the pressure value as a whole, and has a characteristic that the temperature value increases gradually with time after the initial rapid increase.

Therefore, as shown in the lower graph of fig. 9, as can be seen from the trend graph in which the temperature value (red) of fig. 9 is compensated for by canceling out the pressure value (blue) of fig. 8, the temperature value changes in a fluctuating manner within a certain range, that is, between the upper limit line and the lower limit line.

Therefore, by offsetting the temperature value from these measured pressure values, the actual load value can be measured very accurately regardless of the temperature, and can be monitored in real time, so that the actual life can be predicted very accurately based thereon.

Fig. 10 is a graph for predicting the life of the die using the S-N bending curve of the die material with reference to the upper limit value of the N-th stress of the temperature correction pressure value in the die life predicting step of the precision diagnosis method of the intelligent forging system of fig. 7.

That is, as shown in fig. 10, even when the S-N curve of the conventional mold material is used, the life can be accurately predicted with reference to the temperature-corrected pressure value and with reference to the upper limit value of the stress actually applied N times.

Therefore, the current usage amount of the mold can be predicted in real time and very accurately by using the temperature correction pressure value as a reference, and the mold installation date can be expected based on the prediction, so that the situations of poor products and production interruption caused by mold breakage can be prevented in advance.

However, since the intelligent forging system according to some embodiments of the present invention includes, for example, 5 steps in total, the maximum expected load of the load acting on each die is different from the actual pressure measurement curve, and numerical interpretation is performed according to the shape of each step, and since the pressure distribution is different from each other, a theoretically normal curve can be extracted by the repeated experiment and numerical interpretation.

The peak value of each load graph may be different depending on the actual mold form, the specification of the workpiece, and the like, and the numerical interpretation may be different depending on the numerical interpretation method, the numerical value, and the like.

Fig. 11 is a graph of an example of a measured pressure signal sensed by the die side pressure sensor S1 of the intelligent forging system 100 of fig. 1.

For example, as shown in fig. 11, the measured pressure signal (WW) sensed by each of the mold side pressure sensors S1 has a pattern that gradually increases with time until it reaches a peak value and then gradually decreases.

However, the pattern may have various forms depending on the type of the mold, the use environment, the specification of the workpiece, and the like, and fig. 11 illustrates the overall tendency for the sake of explanation.

As shown in FIG. 11, the control unit 40 of the intelligent forging system 100 according to some embodiments of the present invention may perform a series of steps including: information of an expected reference waveform W1 storing a normal expected pressure signal (an expected correction pressure signal), an expected upper limit waveform W2 allowed to be higher than the expected reference waveform (an expected correction reference waveform), and an expected lower limit waveform W3 allowed to be lower than the expected reference waveform, determines whether the measured pressure signal WW belongs to a normal range between the expected upper limit waveform W2 and the expected lower limit waveform W3, outputs a normal discrimination signal if the measured pressure signal WW belongs to a normal range a between the expected upper limit waveform W2 and the expected lower limit waveform W3, or outputs an abnormal discrimination signal if the measured pressure signal WW exceeds the normal range a between the expected upper limit waveform W2 and the expected lower limit waveform W3.

Fig. 12 to 14 are diagrams for comparing the detection states of the measured pressure signals in the case where the pressure sensor is provided in the "punch part (punch or punch assembly)" (left-side graph) and the case where the pressure sensor is provided in the "die part (die or die assembly)" (right-side graph) of the present invention when a failure occurs in the "phenomenon" photograph.

For example, as is clear from the "phenomenon" photograph of fig. 12, when the external appearance defect (left dotted line) actually occurs due to the surface groove of the material or the die, as shown in the left graph, when the pressure sensor is provided at the "punch part", since the measured pressure signal (the center line of the M shape as a whole) falls within the normal range indicated by the wide area around the measured pressure signal, the defect is erroneously determined as "undetected", whereas in the present invention, since the pressure sensor S1 is provided at the "die part", the measured pressure signal (the center line of the M shape as a whole) exceeds the normal range indicated by the wide area around the measured pressure signal at the middle peak portion (the right dotted line), and the defect is accurately determined as "detected".

Further, for example, as is clear from the "phenomenon" photograph of fig. 13, as an example of a shape defect in which a product is actually forged while being obliquely inserted into a die in a process, as shown in the left side graph, when a pressure sensor is provided in a "punch portion", since a measurement pressure signal (the center line of the M-shape as a whole) falls within a normal range indicated by a wide region in the periphery thereof, a defect phenomenon is erroneously determined as "undetected", whereas in the present invention, since the pressure sensor S1 is provided in the "die portion", the measurement pressure signal (the center line of the M-shape as a whole) exceeds the normal range indicated by the wide region in the periphery thereof at a middle peak portion (a right side dotted line portion), and a defect phenomenon is accurately determined as "detected".

Further, for example, as shown in the photograph of "phenomenon", in fig. 14, as another example of a shape defect (left-side dotted line portion) in which a product obliquely enters a die and is forged in an actual process, as shown in the left-side graph, when the pressure sensor is provided in the "punch portion", since the measured pressure signal (the center line of the M-shape as a whole) falls within the normal range of the periphery thereof indicated by the wide area, the defect phenomenon is erroneously determined as "undetected", whereas, in the present invention, since the pressure sensor S1 is provided in the "die portion", the measured pressure signal (the center line of the M-shape as a whole) exceeds the normal range of the periphery thereof indicated by the wide area, and the defect phenomenon is accurately determined as "detected".

Therefore, the present invention is different from a general press in which a forging process of one product is finished by 1 cycle, a plurality of dies and punch sets are inserted corresponding to each other, and a multistage forging process is performed by performing a continuous process on 1 product. Therefore, the installation position of the sensor is different from that of the stamping die, each die can be monitored, so that the abnormity of each die can be judged in real time, and the accuracy can be reduced due to weakening or overlapping of signals generated by each section in the existing condition that the pressure sensor is only installed on the stamping block.

According to the present invention, in the multistage forging process, the pressure sensor, the temperature sensor, and the AE sensor are respectively provided on the spacer, the housing, the wedge, and the like, which respectively correspond to the individual dies, so that the accuracy and the sensitivity of the sensors can be improved, the cost can be saved, and the field operation can be smoothly performed. In addition, although illustrated, an optical sensor, a magnetic sensor, or the like may be further provided.

While the invention has been described with reference to the embodiments shown in the drawings, which are intended to be illustrative only, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof. Therefore, the true technical scope of the present invention should be determined according to the technical idea of the appended claims.

Claims (14)

1. An intelligent forging system, comprising:

a punch block provided with at least one punch;

a die block provided with at least one or more dies to correspond to the punch;

a pressure sensor provided on the die or a die assembly provided with the die so as to be able to sense a load applied by the punch; and

and a control unit for comparing the measured pressure signal applied by the pressure sensor with a previously inputted normal range and outputting an abnormal signal if the measured pressure signal is out of the range.

2. The intelligent forging system according to claim 1, wherein the press block is a cold multi-stage forging press block in which a plurality of punches are arranged in a row, the press block is a cold multi-stage forging mold block in which a plurality of the punches are arranged side by side so that the plurality of the dies correspond to the punches, so that the work can be transferred to the second die after the first forging and the second forging in the first die and the work can be transferred to the third die,

the pressure sensor includes:

a first pressure sensor provided in the first mold or the first mold assembly;

a second pressure sensor provided in the second mold or the second mold assembly; and

a third pressure sensor provided at the third mold or the third mold assembly,

the control unit determines whether the mold is abnormal or predicts the life of the mold by using a measured pressure signal of the pressure sensor.

3. The intelligent forging system according to claim 2, wherein the control unit calculates a rate of deviation or a degree of deviation of the measured pressure signal measured by the first pressure sensor from the expected reference waveform, calculates a rate of deviation or a degree of deviation of the measured pressure signal measured by the second pressure sensor from the expected reference waveform, calculates a rate of deviation or a degree of deviation of the measured pressure signal measured by the third pressure sensor from the expected reference waveform, and calculates the cumulative total value of the deviation ratios or the deviation degrees thereof, and judges whether the cumulative total value is within a normal range, and outputting a normal accumulation determination signal if the accumulated total value is within the normal range, and outputting an abnormal accumulation determination signal if the measured total value is out of the normal range.

4. The intelligent forging system of claim 1, wherein the die assembly comprises:

a die including a cemented carbide insert having a cavity formed therein for inserting a workpiece to correspond to the punch; a support for supporting the underside of the carbide insert; and a sleeve surrounding the carbide tip and the outer diameter surface of the support; and

a spacer having an upper surface contacting a lower surface of the supporter of the mold and a lower surface of the sleeve in order to transmit the pressure of the mold, and having a first vertical groove unit formed at one side thereof in a direction perpendicular to the pressure direction of the mold,

the pressure sensor is to be inserted into a first vertical groove unit of the spacer of the mold assembly.

5. The intelligent forging system of claim 1, further comprising a temperature sensor disposed near the pressure sensor for measuring a temperature of the pressure sensor at the time of measuring the pressure to compensate for a temperature deviation of a measured pressure signal measured by the pressure sensor.

6. The intelligent forging system as recited in claim 5, wherein the control unit corrects the measured pressure signal applied from the pressure sensor with a temperature correction pressure signal based on the measured temperature signal measured by the temperature sensor, compares the temperature correction pressure signal with a normal range input in advance, and outputs an abnormal signal if the range is exceeded.

7. The intelligent forging system as recited in claim 5, wherein the control unit includes a die life prediction unit that predicts a life of a die based on an upper limit value of N-times stress of the temperature correction pressure value using an S-N bending curve of a die material, or calculates a rate of coincidence or a degree of coincidence of the temperature compensation pressure signal with the expected reference waveform, and predicts the life of the die by measuring a change in the rate of coincidence or a change in the degree of coincidence.

8. The intelligent forging system as recited in claim 1, further comprising an AE sensor provided at least at the die or a die assembly provided with the die so as to measure an abnormal sound generated in the die when a load of the pressure sensor is measured.

9. The intelligent forging system as recited in claim 8, wherein the control unit compares a die acoustic signal measured by the AE sensor with a previously input normal range when the load of the pressure sensor is measured, and outputs an abnormal signal if the range is exceeded.

10. The intelligent forging system of claim 8, further comprising an external acoustic sensor disposed at a distance from the AE sensor to compensate the die acoustic signal measured by the AE sensor by external sound.

11. The intelligent forging system as set forth in claim 4, wherein the spacer is further formed with a second vertical groove portion, further comprising a temperature sensor disposed near the pressure sensor and measuring a temperature when the pressure sensor measures the pressure to compensate for a temperature deviation of a measured pressure signal measured by the pressure sensor, the temperature sensor being a contact or non-contact temperature sensor inserted into the second groove unit of the spacer and converting the thermal energy transferred to the spacer into an electric energy form.

12. The intelligent forging system according to claim 4, wherein the spacer is further formed with a third vertical groove portion, further comprising an AE sensor provided at least at the die or a die assembly provided with the die to be able to measure an abnormal sound generated in the die when a load of the pressure sensor is measured, the AE sensor being a microphone sensor inserted in the third groove unit of the spacer and converting an acoustic energy transmitted to the spacer into an electric energy form.

13. The intelligent forging system of claim 1, further comprising a punch side pressure sensor provided on the punch or a punch assembly provided with the punch,

the control unit determines whether or not there is an abnormality in the punch or the punch assembly based on a measured pressure signal from the punch-side pressure sensor.

14. The intelligent forging system according to claim 2, wherein the control unit accumulates pressure signal data measured by at least the first pressure sensor, the second pressure sensor, and the third pressure sensor when forming the workpiece, and determines whether or not there is an abnormality in the first mold, the second mold, and the third mold, or predicts a life of the mold, based on the accumulated data and by a machine learning method using artificial intelligence.

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