JP2005003388A - System for estimating occurrence of crack in mold - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a system for estimating the occurrence of a crack in a mold of an injection molding machine by certainly detecting the occurrence sign of the crack in the mold. <P>SOLUTION: This estimate system 10 for the occurrence of a crack in the mold is equipped with an injection molding machine 12 having a fixed mold platen (mold) 14 and a movable mold platen (mold) 15 both of which are arranged so as to be opposed to each other and constituted so as to mold a work by combining both plates 14 and 15, the AE sensor 11 arranged on the fixed mold platen 14 and a control part 30 for measuring the sensor signal from the AE sensor 11 by ring down counting analyzing processing to detect the occurrence sign of the crack in the molding part 13 of the fixed mold platen 14 and/or movable mold platen 15 of the injection molding machine 12. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a mold crack occurrence prediction system that enables prediction of mold crack occurrence in an injection molding machine.
[0002]
[Prior art]
In general, an injection molding machine is operated by repeating a series of operations of closing a mold, injecting a resin material into the mold, opening the mold after pressurization, and taking out the molded product. Do.
[0003]
For example, in an injection molding machine that molds a connector used in a wire harness or the like routed in a vehicle such as an automobile, the inside of the connector has a complicated shape having a plurality of projections and depressions. It has a complicated shape corresponding to the connector.
[0004]
In order to create a complicated shape in such a molded part of a mold, hundreds of metal pins called so-called pieces having, for example, a square bar shape having a side of, for example, 2 mm to 3 mm and a length of, for example, 50 mm are combined. Is formed.
[0005]
By the way, the fact that the metal piece of the die piece due to stress concentration causes breakage is presumed by the beach mark of the broken cross section (that is, a shell-like wavy pattern generated in the cross section at the time of metal fatigue failure). However, it is not easy to grasp the indirect factor of the external force that generates this stress concentration.
[0006]
Therefore, it has been proposed to use an acoustic emission (ie, Acoustic Emission (hereinafter referred to as AE)) measurement method as a nondestructive inspection method that is a piece breakage detection technique. This AE measurement method is defined as “using a phenomenon in which the strain energy stored in the material is released as an elastic wave when the material is deformed or a crack (ie, crack) is generated”. Has been.
[0007]
In other words, the AE measurement method means that, for example, elastic waves caused by the occurrence and development of cracks, transformations and deformations in materials, peeling and cracking of corrosion products, mechanical wear, friction, leakage, etc. are in solids. The phenomenon is observed when it appears as vibration on the outer surface of the solid, and this elastic wave is detected by a transducer placed on the surface of the solid, that is, an AE sensor. This is a method for evaluating the destruction process of a solid by performing signal processing of signals.
[0008]
As an example of an inspection apparatus using such an AE measurement method, an inspection apparatus 70 shown in FIG. 10 is known (see, for example, Patent Document 1). The inspection device 70 is provided with a quenching coil around the workpiece WP supported by the chucks 71 and 72, and an acoustic wave is detected in the upper chuck 72 via a coupling material (not shown) capable of propagating AE. An AE sensor 74 composed of a piezoelectric element as means 73 is embedded vertically with its detection part facing the work WP. Further, on the side surface of the upper chuck 72, a pseudo AE sensor 75 made of a piezoelectric element is provided horizontally with the detection unit facing the detection unit of the AE sensor 74.
[0009]
The AE sensor 74 is connected to the amplifying means 76. In the amplifying means 76, a preamplifier 77 is connected to the AE sensor 74, and a main amplifier 78 is connected to the preamplifier 77. Further, a level measuring circuit 79 including a comparison circuit is connected to the main amplifier 78, and the level measuring circuit 79 is connected to a preamplifier 77 in order to vary the amplification degree.
[0010]
The amplifying means 76 is connected to a crack occurrence determining means 81 having a microcomputer 80. In the crack occurrence determination means 81, a low pass filter 82 is connected to the main amplifier 78, a sample hold circuit 83 is connected to the low pass filter 82, and an A / D converter 84 is connected to the sample hold circuit 83. Yes. A setting unit 85 is connected to the sample hold circuit 83 and the A / D converter 84. A memory 86 is connected to the A / D converter 84, and a gate circuit 87 and a periodicity detection unit 88 are connected to the memory 86.
[0011]
A start signal generator 89 is connected to the gate circuit 87. The periodicity detection unit 88 includes a delay circuit and a product-sum circuit, and an output signal can be input to the memory 86 again. Further, a microcomputer 80 is connected to the periodicity detection unit 88. The microcomputer 80 includes a determination unit 90 to which an output signal from the periodicity detection unit 88 is input, and a section setting unit 91 and a reference setting unit 92 that can send the output signal to the determination unit 90. The signal can be output as a result output to an external monitor (not shown). A pulse generation circuit 93 is connected to the pseudo AE sensor 75. The pseudo AE sensor 75 and the pulse generation circuit 93 constitute reference signal generation means 94.
[0012]
[Patent Document 1]
JP 08-005614 A (page 2-5, FIG. 2)
[0013]
[Problems to be solved by the invention]
The crack detection method using the AE measurement method has already been established as a method for finding a crack in a product (work), a method for finding a crack in a mold, or the like as taught in Patent Document 1. However, these are methods that use amplitude data as an analysis method for the AE signal. If the AE signal at the time of abnormality does not increase significantly compared to the AE signal at the time of steady operation of machines, molding machines, etc., cracks are detected. It is a difficult method to do. That is, the crack cannot be detected if the amplitude data of the AE signal at the time of the crack is in a minute state.
[0014]
In the case of an injection molding machine, an AE signal with an excessive amplitude is output from the AE sensor when the mold is opened and closed, and at the same timing, a crack may occur in the molded part in the mold or the piece may break. Found by the inventors. That is, even when the AE signal indicating the amplitude due to the crack of the piece is generated when opening and closing the mold, it is smaller than the amplitude value of the AE signal generated by the vibration due to the mold opening and closing operation. It was found that these analysis methods are difficult to identify and cannot detect cracks.
[0015]
If a piece is cracked or broken, all products after the occurrence of this abnormality will be defective, which will deteriorate the product yield and affect the delivery date of the product. The impact on the management) is enormous.
[0016]
As a countermeasure, the worker inspects the finished products one by one to check whether there are any abnormalities, and if a defective product is found, stop the injection molding machine and replace the mold. The method of exchanging is taken. Therefore, the abnormal phenomenon is surely caught at the time of breakage of the piece, and more preferably, the abnormal phenomenon is surely caught before the piece is cracked (in other words, the sign that the piece is cracked is surely caught). The development of a technology that can predict the occurrence of cracks has been desired.
[0017]
The present invention has been made in view of the above circumstances, and an object of the present invention is to make it possible to reliably detect the occurrence of cracks in a mold of an injection molding machine and to predict the occurrence of cracks in the mold. It is to provide a mold crack generation prediction system.
[0018]
[Means for Solving the Problems]
In order to achieve the above-mentioned object, the mold crack occurrence prediction system according to the present invention is as described in claim 1,
An injection molding machine having molds arranged opposite to each other and molding a workpiece by combining the molds;
An acoustic emission sensor disposed on the mold;
A control unit for detecting a sign of crack occurrence in the mold by measuring a sensor signal from the acoustic emission sensor by a ringdown count analysis process;
It is characterized by having.
[0019]
According to the first aspect of the present invention, the control for measuring the sensor signal from the acoustic emission sensor disposed on the mold of the injection molding machine by the ring-down count analysis process to detect the sign of the occurrence of cracks in the mold. Since the mold crack generation prediction system is equipped with the part, the number of peaks of the sensor signal waveform measured by the ring-down count analysis process is managed so that the metal fatigue tendency of the mold can be grasped and the crack of the mold can be generated. The occurrence of cracks can be predicted by catching abnormal phenomena before. Therefore, it is possible to easily manage the maintenance schedule and improve productivity.
[0020]
Further, as described in claim 2, in the mold crack occurrence prediction system according to claim 1,
The control unit performs the ring-down count analysis process by detecting only the sensor signal in a predetermined period before and after the mold is opened and closed.
[0021]
According to the second aspect of the present invention, the control unit performs the ring-down count analysis process using only the sensor signal in a predetermined period before and after the mold is opened and closed. Measure only sensor signals during a predetermined period before and after opening the mold and during a predetermined period before and after closing the mold, and avoid waveform data of sensor signals that do not need to be generated in each cycle of injection molding machine operation. be able to. Therefore, since the total amount of data can be reduced, the waveforms of many necessary sensor signals can be measured over a long period of time.
[0022]
The present invention has been briefly described above. Further, details of the present invention will be further clarified by reading embodiments of the invention described below with reference to the accompanying drawings.
[0023]
DETAILED DESCRIPTION OF THE INVENTION
DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, preferred embodiments of the invention will be described in detail with reference to the accompanying drawings.
FIG. 1 is an external perspective view showing an embodiment of a mold crack occurrence prediction system according to the present invention, FIG. 2 is a sectional view when a mold used in the injection molding machine shown in FIG. 1 is closed, and FIG. 4 is a block configuration diagram of the control unit shown in FIG. 1, FIG. 4 is a timing chart for explaining the timing of taking in the sensor signal (AE signal) by the control unit shown in FIG. 1 and FIG. 3, and FIG. FIG. 6 is a diagram for explaining a counting method based on a ring-down count analysis method used in the crack occurrence prediction system, and FIG. 6 is a diagram showing an example of networking using a plurality of mold crack occurrence prediction systems in FIG. 7, 8 and 9 are operation flowcharts of the control unit operating according to the processing program.
[0024]
As shown in FIG. 1, a mold crack occurrence prediction system 10 according to an embodiment of the present invention includes a substrate 16, a fixed mold plate (mold) 14 and a movable mold plate (mold) that are arranged to face each other. ) 15, and an injection molding machine 12 for forming a workpiece by combining the fixed mold plate 14 and the movable mold plate 15, and acoustic emission (hereinafter referred to as AE) disposed on the fixed mold plate 14. ) The sensor 11 and the sensor signal from the AE sensor 11 (in other words, the AE signal) are measured by ring-down count (or also referred to as oscillation count) analysis processing, and the fixed mold of the injection molding machine 12 is measured. And a control unit 30 that detects signs of cracks occurring in the molded part 13 of the plate 14 and / or the movable mold plate 15, and is a system that enables prediction of cracks in the molded part 13 .
[0025]
The AE sensor 11 is fixed to the fixed mold plate 14 by a magnet base 11a via a magnet base (magnetic detachable fixing machine) 11a. The AE sensor 11 is a detection element that detects an elastic wave generated when the fixed mold plate 14 and the movable mold plate 15 are opened and closed.
[0026]
The molding part 13 is detachably fixed to the inner surface of the fixed mold plate 14 and the inner surface of the movable mold plate 15, respectively. For example, a connector used for a wire harness routed in a vehicle such as an automobile is used. Used for molding. Since the molding part 13 has a complicated shape with a plurality of irregularities inside the connector to be molded, it has a complicated shape corresponding to the connector. In addition, in order to create a complicated shape, the molding portion 13 is formed by combining several hundred pieces (not shown) having, for example, a square bar shape having a side of, for example, 2 mm to 3 mm and a length of, for example, 50 mm. .
[0027]
The fixed mold plate 14 and the movable mold plate 15 are connected via four guide rods 17 fixed to the movable mold plate 15, and these guide rods 17 are connected to the fixed mold plate 14 and the fixed mold plate. The stationary mold plate 14 and the movable mold plate 15 are opened and closed by sliding in the substrate 16 that fixes the plate 14.
[0028]
When the fixed mold plate 14 and the movable mold plate 15 are closed, the injection molding machine 12 injects a resin material into the molding unit 13 and pressurizes, and then the fixed mold plate 14 and the movable mold plate 15. And a series of operations (i.e., one-cycle operation) of taking out the molded product and taking out the molded product are repeated.
[0029]
As shown in FIG. 2, one molding portion 13 disposed on the fixed mold plate 14 side includes a pot plunger 18, a pot 19 disposed outside the pot plunger 18, a cavity 20, and the cavity 20. The other molding part 13 which has the spool bush 21 arrange | positioned in the center part and is arrange | positioned at the movable mold plate 15 side has the plunger 22 with which the ejector pin 23 was penetrated. Then, as shown in FIG. 2, in a state where the fixed mold plate 14 and the movable mold plate 15 are closed, a work material 45 is injected by injecting a resin material into a space a formed between the pair of molding portions 13. Is formed.
[0030]
As shown in FIG. 3, the control unit 30 includes a preamplifier (preamplifier unit) 31, an LPF / HPF (LPF / HPF unit) 32, a variable gain amplifier (variable gain amplifier unit) 33, an AE monitor (AE monitor). Section) 34, an EVENT comparator (EVENT comparator section) 35, a discrete level setting section (discrete level setting) 36, a gate signal (gate signal generating section) 37, and a CPU 38.
[0031]
The preamplifier 31 amplifies the sensor signal (10 μV to several tens of mV) received from the AE sensor 11 with a fixed gain (10 dB).
[0032]
The LPF / HPF 32 is a 100 kHz to 1 MHz band-pass filter composed of a high-pass filter HPF and a low-pass filter LPF for separating the sensor signal amplified by the preamplifier 31 and a signal such as noise.
[0033]
The variable gain amplifier 33 corrects detection level differences due to errors between the levels of the AE sensors 11, the preamplifier 31, the LPF / HPF 32, and the like when a plurality of mold crack occurrence prediction systems 10 are provided (see FIG. 6).
[0034]
The AE monitor 34 is an image display device such as an oscilloscope for visually confirming the raw waveform of the AE sensor 11.
[0035]
The EVENT comparator 35 converts the sensor signal into an EVENT pulse signal that can be counted from an analog signal. In this way, the sensor signal and the noise signal are separated by converting to the EVENT pulse signal.
[0036]
The discretion level setting unit 36 generates a reference voltage for setting a threshold value in the EVENT comparator 35 and supplies it to the EVENT comparator 35.
[0037]
The gate signal 37 is a gate pulse signal that is a timing position for counting and counting the EVENT pulse signal. The gate signal 37 is obtained from a relay signal given from a relay (not shown) of the opening / closing sensor of the injection molding machine 12 when the movable contact on the armature side of the relay contacts or separates from the fixed contact. This is a pulse signal from which a signal generated by a chattering operation in which the stationary contact is unnecessarily continuously separated is removed.
[0038]
The CPU 38 uses the gate pulse signal from the gate signal 37 and measures the time to calculate the optimum timing of the sensor signal. This measurement timing is measured every time following the operation of the injection molding machine 12. The CPU 38 counts the EVENT pulse signal according to the gate pulse signal and the measurement time determined in advance for the molding unit 13 (that is, the fixed mold plate 14 and the movable mold plate 15). The counted value is transferred to the database personal computer 42 (see FIG. 6) through the network.
[0039]
The control unit 30 includes a predetermined period before and after the mold opening during which the pieces in the fixed mold plate (mold) 14 and the movable mold plate (mold) 15 (that is, the molding unit 13) can contact each other. Waveform data of a sensor signal generated during a predetermined period before and after the mold is closed is collected and counted based on a ring down count analysis method (that is, measured by a ring down count analysis process).
[0040]
As shown in FIG. 4, the timing at which the control unit 30 takes in the AE signal (sensor signal) is a direct crack of a piece in one cycle of the injection molding machine 12 (one cycle is 15 to 30 seconds, for example). An AE signal for only Z seconds when the molding unit 13 opens and closes is taken in. The control unit 30 captures the AE waveform (that is, the waveform of the AE signal) at an important point that occurs once or twice within each period, and measures the ring-down count of the AE waveform.
[0041]
Specifically, the take-in timing by the control unit 30 detects opening and closing of the fixed mold plate (mold) 14 and the movable mold plate (mold) 15 of the injection molding machine 12 (that is, opening and closing of the molding unit 13). A relay signal output from an open / close sensor (not shown) is used.
[0042]
In the mold “open”, the AE signal is set to be taken V seconds after the mold “closed” one cycle before, and the time from the relay signal (ON) at the mold “open” to X seconds is measured. The control unit 30 collects waveform data of the AE signal for Z (that is, W + X) seconds.
[0043]
On the other hand, when the mold is “closed”, the AE signal is set to be taken in Y seconds after the relay signal (ON) when the mold is “open”, and from the relay signal (OFF) when the mold is “closed”. The control unit 30 collects waveform data of the AE signal for a total of Z (that is, W + X) seconds up to X seconds.
[0044]
As shown in FIG. 5, the control unit 30 counts the number C of the peaks of the AE signal waveform exceeding the threshold D set by the discrete level setting unit 36 in order to count the number of occurrences and the occurrence frequency of the AE signals. Perform ring-down count analysis processing to count all. That is, according to the mold crack occurrence prediction system 10, by managing the number C of peaks of the AE signal waveform measured by the ring-down count analysis process, the tendency of metal fatigue of the piece of the formed part 13 is grasped, and the occurrence of the crack of the piece The occurrence of cracks can be predicted by catching abnormal phenomena before. Therefore, it is possible to easily manage the maintenance schedule and improve productivity.
[0045]
Further, as described above, in the mold crack occurrence prediction system 10, several seconds such as Z seconds before and after opening of the molding portion 13 and Z seconds before and after closing of the molding portion 13 in which the pieces of the molding portion 13 can contact each other can occur. Only the AE waveform is measured. Thereby, AE waveform data which does not need to be generated in each cycle of operation of the injection molding machine 12 can be avoided and collected. Accordingly, the total amount of data can be reduced (in other words, the memory area of the CPU 38 of the control unit 30 can be effectively used), so that many necessary AE waveforms can be measured over a long period of time. .
[0046]
As shown in FIG. 6, when the above-described mold crack occurrence prediction system 10 is applied to the entire factory, a large number of injection molding machines (referred to as “units” in FIG. 6) 12a, 12b, In order to monitor all 12c... 12n, an AE sensor 11 is attached to each injection molding machine 12a, 12b, 12c... 12n, and further, the control unit 30 described above is analyzed by a ring down count analysis method. Is a compact measuring terminal compatible with a personal computer for a TCP / IP network that is configured to be integrated with only the functions necessary for the network and to be able to capture measurement data into the personal computer 42 through the hubs 41a and 41b and a communication cable (for example, a LAN cable). 30a, 30b, 30c ... 30n as injection molding machines 12a, 12b, 12c - are respectively attached to 12n.
[0047]
Therefore, since the measurement terminals 30a, 30b, 30c... 30n are linked (connected) on the network, the measurement data from the measurement terminals 30a, 30b, 30c. Since the data is directly taken into 42, continuous data collection is possible. In addition, measurement data relating to a large number of collected injection molding machines 12a, 12b, 12c,. The degree of freedom of using measurement data can be increased.
[0048]
Furthermore, when changing the model number of the molds of the injection molding machines 12a, 12b, 12c,... 12n, adjustment / setting of the mold opening / closing data collection timing time occurs, and these can also be performed collectively on the personal computer 42 side. (That is, the measurement terminals 30a, 30b, 30c,... 30n and the personal computer 42 are capable of bidirectional communication on the network), so that they are attached to the injection molding machines 12a, 12b, 12c,. Further, the setting work performed by visiting the measuring terminals 30a, 30b, 30c,..., 30n can be omitted, and the workability can be improved. Also, when adding an injection molding machine to be newly measured, it is possible to cope with this by simply extending the communication cable forming the network, and wiring work can be easily performed.
[0049]
Each of the measurement terminals 30a, 30b, 30c,... 30n is a small and lightweight case (for example, size: 130 mm × 100 mm × 30 mm, weight: about 100 g), preamplifier, band filter, gain adjustment, waveform shaping, network I / By accommodating F, etc. in one piece and shielding the whole, it is also easy to make it extremely difficult to be affected by external noise (disturbance). Thereby, it is possible to improve the measurement accuracy, and since it is small and light, the installation work can be easily performed.
[0050]
Further, since the AE sensor 11 can be easily attached / detached by the magnet base 11a, the AE sensor 11 can be attached and removed at the time of mold replacement in each of the injection molding machines 12a, 12b, 12c. Replacement can also be performed easily.
[0051]
Furthermore, various alarming means for predicting the occurrence of an abnormal phenomenon (that is, the occurrence of a crack) from the personal computer 42, which is a data collection device, can be systematized. For example, alarm notification at the site is performed with a rotating lamp. Or, a system may be constructed such as ringing a portable pager as necessary, or reporting using a mobile phone.
[0052]
Next, an operation flow of the control unit 30 in the mold crack occurrence prediction system 10 will be described with reference to flowcharts shown in FIGS. 7 and 8 are main routines, and FIG. 9 is an opening / closing operation of the fixed mold plate (mold) 14 and the movable mold plate (mold) 15 (in other words, an opening / closing operation of the molding portion 13). Is a subroutine that governs
[0053]
At the same time as the power is turned on, the operation of the control unit 30 can be started in accordance with the processing program recorded in the CPU 38 of the control unit 30 until the molding unit 13 of the injection molding machine 12 starts an opening operation or a closing operation. It will be in a standby state during. At this time, since the opening / closing timing of the molding unit 13 is not determined in the subroutine, the number of pulses is not counted.
[0054]
While the injection molding machine 12 is operating, the time average of 5 cycles of the opening / closing operation of the molding unit 13 is taken, and if the average value is in a normal state within 30 seconds / 1 cycle, the opening / closing timing is determined, and the mold The opening time is set and the mold closing time is set, and both time data are taken into the personal computer (PC) 42 for database (that is, the operation flow corresponding to this operation is step 131 → step 132 → Step 133 → Step 134 → Step 135 → Step 136 → Step 137 → Step 138 → Step 138 → Step 139 → Step 140 → Step 141 → Step 142). At this time, the database personal computer (PC) 42 displays numerically the mold opening time and the mold closing time (ie, step 142). If it is not within 30 seconds / 1 cycle in step 136, the process proceeds to step 143 and the measurement is terminated.
[0055]
Pulses corresponding to the number of peaks of the waveform of the sensor signal (AE signal) exceeding the threshold only in Z seconds before and after the molding unit 13 is opened due to the setting of the mold opening time (ie, step 119). The number is counted and taken into the database personal computer (PC) 42 (that is, the operation flow corresponding to this operation is as follows: Step 101 → Step 102 → Step 103 → Step 104 → Step 105 → Step 106 → Step 107 → Step 108 → Step 109 → Step 115 → Step 116 → Step 117 → Step 118). At this time, the raw waveform of the AE sensor 11 corrected by the variable gain amplifier 33 is displayed on the AE monitor 34 (ie, step 113). The discretion level setting unit 36 provides a reference voltage (ie, step 114).
[0056]
In the subsequent routine, due to the mold closing time being set (ie, step 120), the peak of the waveform of the sensor signal (AE signal) exceeding the threshold only during Z seconds before and after the molding portion 13 is closed. The number of pulses corresponding to the number is counted and taken into the personal computer (PC) 42 for database (that is, the operation flow corresponding to this operation is as follows: Step 101 → Step 102 → Step 103 → Step 104 → Step 105 → Step 106 → Step 107 → Step 108 → Step 109 → Step 110 → Step 111 → Step 112 → Step 121).
[0057]
The present invention is not limited to the above-described embodiments, and modifications, improvements, etc. can be made as appropriate. In addition, the material, shape, dimension, form, number, numerical value, arrangement location, and the like of each component in the above-described embodiment are arbitrary and are not limited as long as the present invention can be achieved.
[0058]
【The invention's effect】
As described above, according to the present invention, the sensor signal from the acoustic emission sensor disposed on the mold of the injection molding machine is measured by the ring-down count analysis process to detect signs of occurrence of cracks in the mold. Since the mold crack occurrence prediction system is equipped with a control unit that controls the number of peaks of the sensor signal waveform measured by the ring-down count analysis process, the metal fatigue tendency of the mold is grasped, and the crack of the mold The occurrence of a crack can be predicted by capturing an abnormal phenomenon before the occurrence of the crack. Therefore, it is possible to easily manage the maintenance schedule and improve productivity.
[0059]
Further, according to the present invention, since the control unit performs the ring-down count analysis process using only the sensor signal in a predetermined period before and after the opening and closing of the mold, when the mold is opened, the molds can contact each other. By measuring only sensor signals during a predetermined period before and after the mold and a predetermined period before and after the mold is closed, waveform data of sensor signals that do not need to be generated in each cycle of the injection molding machine can be collected. . Therefore, since the total amount of data can be reduced, the waveforms of many necessary sensor signals can be measured over a long period of time.
[Brief description of the drawings]
FIG. 1 is an external perspective view showing an embodiment of a mold crack occurrence prediction system according to the present invention.
FIG. 2 is a cross-sectional view of a mold used in the injection molding machine shown in FIG. 1 when closed.
FIG. 3 is a block configuration diagram of a control unit shown in FIG. 1;
FIG. 4 is a timing chart for explaining the sensor signal (AE signal) capture timing by the control unit shown in FIGS. 1 and 3;
FIG. 5 is a diagram for explaining a counting method based on a ring down count analysis method used in the mold crack occurrence prediction system of FIG. 1;
6 is a diagram showing an example of networking using a plurality of mold crack occurrence prediction systems of FIG. 1 in a factory.
FIG. 7 is an operation flowchart of a control unit that operates according to a processing program.
FIG. 8 is an operation flowchart of a control unit that operates according to a processing program.
FIG. 9 is an operation flowchart of a control unit that operates according to a processing program.
10 is a schematic view of an inspection apparatus disclosed in Patent Document 1. FIG.
[Explanation of symbols]
10: Mold crack occurrence prediction system
11: AE sensor
13: Molding part (part of mold)
14: Fixed mold plate (mold)
15: Movable mold plate (mold)
12: Injection molding machine
12a, 12b, 12c ... 12n: Injection molding machine (unit)
30: Control unit
30a, 30b, 30c ... 30n: Measurement terminal (control unit)
42: Personal computer
45: Work

Claims (2)

An injection molding machine having molds arranged opposite to each other and molding a workpiece by combining the molds;
An acoustic emission sensor disposed on the mold;
A control unit for detecting a sign of crack occurrence in the mold by measuring a sensor signal from the acoustic emission sensor by a ringdown count analysis process;
A mold crack occurrence prediction system characterized by comprising: 2. The mold crack occurrence prediction system according to claim 1, wherein the control unit performs the ring-down count analysis process by detecting only the sensor signal in a predetermined period before and after the mold is opened and closed.

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