Method and device for diagnosing plasticization state of injection molding machine

Abstract

To realize a practical, convenient, and easy-to-use plasticization state diagnostic device, which performs accurate and reliable quality diagnosis and trouble determination for the plasticization state.SOLUTION: A plurality of AE sensors 6r, 6f for detecting AE waves We are disposed at a plurality of different positions Xr, Xf in a front-to-rear direction Fs on a heating cylinder 2, and position data Dx relating to a generation position of the AE waves We is obtained based on arrival times tr, tf of the AE waves We obtained from each AE sensor 6r, 6f. The number of times that the AE wave signals Sfe... relating to the AE waves We generated during a predetermined sampling period Tp and for each predetermined position data Dx exceed a predetermined threshold L is obtained as attack number data Da, and the quantity of AE wave components Sp that exceed the predetermined threshold L is obtained as event count data De, and an occurrence pattern Ps of the attack number data Da and the event count data De for the position data Dx... is at least graphically displayed.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a method and apparatus for diagnosing the plasticizing state of an injection molding machine, which are suitable for use when plasticizing a molding material supplied into a heating cylinder by the rotation of a screw.

Background Art

[0002] Generally, an injection device of an injection molding machine has a function of plasticizing a molding material (such as pellets) supplied into the heating cylinder from a material supply part provided at the rear part of the heating cylinder heated by a heating part by the rotation of a screw. Therefore, accurately grasping the plasticizing state of the molding material in the heating cylinder is important from the viewpoint of avoiding the occurrence of molding defects and ensuring high molding quality, and various monitoring devices for grasping the plasticizing state have also been proposed.

[0003] Conventionally, as this type of monitoring device, there is already known a material monitoring device for an injection molding machine described in Patent Document 1 proposed by the present applicant. The material monitoring device described in Patent Document 1 aims to quickly investigate the cause of a poor melting state and take countermeasures, and to avoid plasticizing defects in advance and realize an ideal plasticizing process. Specifically, it senses an acoustic emission wave generated when the molding material supplied into the heating cylinder from a material supply part provided at the rear part of the heating cylinder heated by a plurality of heating parts including a post-heating part for heating the rear part of the heating cylinder is deformed or sheared inside the heating cylinder by the rotation of the screw, and converts it into an electrical signal. It is configured to include an acoustic emission wave sensing sensor, an acoustic emission detection part for detecting quantitative acoustic emission data related to the deformation or shear of the molding material from the electrical signal, and a material response processing functional part for performing a predetermined material response process based on the use of this acoustic emission data.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, the material monitoring device described in Patent Document 1 mentioned above also had the following problems to be solved.

[0006] That is, by disposing it at the rear of the heating cylinder, an acoustic emission wave sensor (AE sensor) that senses the acoustic emission wave generated when the molding material is deformed or sheared inside the heating cylinder and converts it into an electrical signal is used. Although it has the merit of being able to detect quantitative acoustic emission data related to the deformation or shearing of the molding material from this electrical signal, since it is a quantitative and local detection, for example, from the perspective of obtaining more specific and practical information such as where in the heating cylinder it is generated or what caused the sound, there were certain difficulties that could not necessarily be said to be sufficient.

[0007] Therefore, from the perspective of ensuring plasticization information from a planar (three-dimensional) and more qualitative perspective with respect to the heating cylinder, and realizing a more practical, convenient, and user-friendly monitoring device (plasticization state diagnosis device), there was room for further improvement.

[0008] An object of the present invention is to provide a method and device for diagnosing the plasticization state of an injection molding machine that solves the problems existing in such background art.

Means for Solving the Problems

[0009] In order to solve the above-described problems, the method for diagnosing the plasticization state of the injection molding machine M according to the present invention diagnoses the plasticization state of the molding material R supplied into the heating cylinder 2 from the material supply unit 4 provided at the rear of the heating cylinder 2 heated by the heating unit 3f... when plasticizing the molding material R by the rotation of the screw 5. When diagnosing, a plurality of AE sensors 6r, 6f for detecting AE waves We are arranged at a plurality of positions Xr, Xf in different front-rear directions Fs in the heating cylinder 2, and position data Dx related to the generation position of the AE wave We is obtained based on the arrival times tr, tf of the AE waves We obtained from each AE sensor 6r, 6f. At the same time, for each predetermined sampling period Tp and each predetermined position data Dx, the number of occurrences of the AE wave signal Sfe... related to the AE wave We that exceeds a preset threshold value L is obtained as the attack number data Da, and the quantity of the AE wave component Sp that exceeds the preset threshold value L is obtained as the event count data De. The generation pattern Ps of the attack number data Da and the event count data De with respect to the position data Dx... is at least graphically displayed.

[0010] In addition, in order to solve the above-described problems, the plasticization state diagnosis device 1 of the injection molding machine M according to the present invention is a plasticization state diagnosis device 1 of the injection molding machine M that diagnoses the plasticization state of the molding material R supplied into the heating cylinder 2 from the material supply unit 4 provided at the rear of the heating cylinder 2 heated by the heating unit 3f... when plasticizing the molding material R by the rotation of the screw 5. The plasticization state diagnosis device 1 includes a plurality of AE sensors 6r, 6f that detect AE waves We disposed at a plurality of different positions Xr, Xf in the front-rear direction Fs of the heating cylinder 2, a position calculation processing unit Ex that obtains position data Dx related to the generation position of the AE wave We based on the arrival times tr, tf of the AE waves We obtained from each AE sensor 6r, 6f, an attack number calculation processing unit Ea that obtains the number of occurrences of the AE wave signals Sre, Sfe related to the AE waves We generated every predetermined sampling period Tp and every predetermined position data Dx... as attack number data Da... that exceed a preset threshold value L, and an event count calculation processing unit Ee that obtains the quantity of AE wave components Sp... that exceed the preset threshold value L as event count data De..., and a graphic display unit 8 that at least graphically displays the generation pattern Ps of the attack number data Da... and the event count data De... with respect to the position data Dx....

[0011] On the one hand, according to a preferred embodiment of the present invention, when implementing the plasticization state diagnosis method, each threshold value L for detecting the attack number data Da and the event count data De can be set to be the same or different. Further, the detection signals obtained from the AE sensors 6r and 6f can be subjected to noise removal processing with the mixed signals other than those generated by the plasticization process by the heating cylinder 2 as noise components Np..., and the detection signals obtained from the AE sensors 6r and 6f can be subjected to filtering processing to pass only the set specific frequency Bs or the set specific frequency band. On the other hand, when configuring the plasticization state diagnosis device 1, the graphic display unit 8 can display the generation patterns Ps of the attack number data Da... and the event count data De... with respect to the position data Dx... in a graph format. Further, the AE sensors 6r and 6f include a first AE sensor 6r and a second AE sensor 6f, the first AE sensor 6r can be disposed on the rear side of the heating cylinder 2, and the second AE sensor 6f can be disposed on the front side of the heating cylinder 2. Also, the AE sensors 6r and 6f can be attached to the heating cylinder 2 independently.

Effect of the Invention

[0012] According to the plasticization state diagnosis method and device 1 of the injection molding machine M according to the present invention as described above, the following remarkable effects are achieved.

[0013] (1) AE sensors 6r and 6f are respectively disposed at a plurality of different positions Xr and Xf in the front-rear direction Fs of the heating cylinder 2, and based on each AE sensor 6r and 6f, position data Dx, attack number data Da, and event count data De are obtained, and the generation patterns Ps of the attack number data Da... and the event count data De... with respect to the position data Dx... are at least graphically displayed. Therefore, information from a three-dimensional (stereoscopic) perspective and even a qualitative perspective on the heating cylinder 2, such as where the AE wave We is generated in the heating cylinder 2 and what caused the AE wave We, can be accurately obtained. As a result, it becomes possible to realize a more practical, convenient, and user-friendly plasticization state diagnosis device 1, and accurate and reliable quality diagnosis and trouble determination for the plasticization state can be performed.

[0014] (2) In a preferred embodiment, when implementing the plasticization state diagnosis method, since each threshold value L for detecting the attack number data Da and the event count data De can be set to be the same or different, it becomes possible to set flexible and optimal threshold values L corresponding to the attack number data Da and the event count data De respectively, and accurate attack number data Da and event count data De for the AE wave We can be obtained.

[0015] (3) In a preferred embodiment, when implementing the plasticization state diagnosis method, if noise removal processing is performed on the detection signals obtained from the AE sensors 6r, 6f with respect to the mixed signals other than those generated by the plasticization process by the heating cylinder 2 as noise components Np..., ambient environmental noise, that is, useless noise components Np... (disturbances) other than the AE wave signals Sre, Sfe accompanying the plasticization of the molding material R can be excluded, so that more accurate AE wave signals Sre, Sfe can be obtained.

[0016] (4) In a preferred embodiment, when implementing the plasticization state diagnosis method, if filtering processing is performed on the detection signals obtained from the AE sensors 6r, 6f to allow only the set specific frequency Bs or the set specific frequency band to pass through, the AE wave signals Sre, Sfe obtained along with the plasticization of the molding material R, specifically, the AE wave We when the molding material R breaks or the AE wave We when the molding material R collides with metal can be specified and detected, so that the plasticization state and the trouble occurrence state can be diagnosed more accurately.

[0017] (5) In a preferred embodiment, when configuring the graphic display unit 8, if the occurrence pattern Ps of the attack number data Da... and the event count data De... with respect to the position data Dx... is displayed in a graph format, graphic display can be enabled by a combination of general-purpose graphs such as bar graphs and line graphs, so that the plasticization state and the trouble occurrence state can be easily and quickly grasped from the generated occurrence pattern Ps.

[0018] (6) In a preferred embodiment, when arranging the AE sensors 6r and 6f, by configuring them with the first AE sensor 6r and the second AE sensor 6f, if the first AE sensor 6r is arranged on the rear side of the heating cylinder 2 and the second AE sensor 6f is arranged on the front side of the heating cylinder 2, a series of states from the solid state such as pellets to the molten state after plasticization in the vicinity of the material supply unit 4 can be set as the detection target. Therefore, the plasticization state in the heating cylinder 2 can be grasped in a sufficient and optimal state.

[0019] (7) In a preferred embodiment, when arranging the AE sensors 6r and 6f, if they are attached to the heating cylinder 2 individually, they can be arranged without interfering with other temperature sensors or other functions, etc. Since the installation position can be selected flexibly, the degree of freedom in arrangement can be increased, the complexity around the heating cylinder 2 can be avoided, and the space occupancy efficiency can be improved.

Brief Description of the Drawings

[0020]

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Embodiments for Carrying Out the Invention

[0021] Next, preferred embodiments according to the present invention will be given and described in detail with reference to the drawings.

[0022] First, the overall schematic configuration of an injection molding machine M equipped with a plasticization state diagnosis device 1 according to the present embodiment will be described with reference to FIG. 1.

[0023] The injection molding machine M includes an injection device Mi shown in FIG. 1 and a mold clamping device (not shown). The injection device Mi is installed on the upper surface of a movable platen 21 that can move in the front-rear direction Fs by a nozzle touch drive unit. 22 is a front support disk portion fixed to the front upper surface of the movable platen 21, and 23 is a rear support disk portion fixed to the rear upper surface of the movable platen 21. Between the front support disk portion 22 and the rear support disk portion 23, four guide shafts 24... are installed, and a slide unit 25 is slidably mounted on the guide shafts 24...

[0024] Further, by attaching the rear end of the heating cylinder 2 to the front surface of the front support plate portion 22, the front side of the heating cylinder 2 is made to project forward. This heating cylinder 2 is provided with an injection nozzle 2n at the front end and a hopper 2h at the upper end of the rear portion. The lower end supply port of this hopper 2h communicates with the inside of the heating cylinder 2 via a vertical material supply path 26, and the material supply portion 4 is constituted by this material supply path 26 and the hopper 2h. Further, on the outer peripheral surface of the heating cylinder 2 including the injection nozzle 2n, a plurality of heating portions 3f, 3m, 3r using a band heater for heating the heating cylinder 2 are sequentially attached from the front side to the rear side in the front-rear direction Fs. Each heating portion 3f... is attached independently, and a plurality of temperature sensors 9... for detecting the heating temperature of each heating zone are attached, and feedback control of the heating temperature is performed by the molding machine controller 41.

[0025] A screw 5 is inserted into the heating cylinder 2, and the rear end side of this screw 5 is made to reach the rear of the front support plate portion 22 through an opening provided in the front support plate portion 22. Further, a driven pulley 27f is rotatably attached to the front surface portion of the slide unit 25, and the rear end of the screw 5 is coupled to the central position of the driven pulley 27f. Further, a servo motor 28 for screw rotation is fixed to the upper surface portion of the slide unit 25. A drive pulley 27d is fixed to the rotation shaft of this servo motor 28, and a timing belt 27b is bridged between this drive pulley 27d and the driven pulley 27f to constitute a rotation transmission mechanism. A rotary encoder 30 for detecting the rotation (rotation speed) of the servo motor 28 is attached to this servo motor 28.

[0026] On the other hand, a servo motor 31 for screw forward and backward movement is fixed to the rear surface of the rear support plate portion 23. Further, the rear end of a screw portion 32s constituting a ball screw mechanism 32 is rotatably attached to the front surface of the rear support plate portion 23, and the rotation shaft of the servo motor 31 is coupled to the rear end of the screw portion 32s. A rotary encoder 33 for detecting the rotation (rotation speed) of the servo motor 31 is attached to this servo motor 31. Further, the front end of a nut portion 32n constituting the ball screw mechanism 32 is fixed to the rear surface portion of the slide unit 25, and the front end side of the screw portion 32s is screwed into the nut portion 32n.

[0027] On the other hand, 41 is a molding machine controller that controls the overall control of the injection molding machine M. It has hardware such as a CPU and a memory, and has a computer function with a processing program (software) for executing various control processes including various arithmetic processes and sequence control. Therefore, the output side ports of the molding machine controller 41 are connected to the above-described servo motors 28, 31 and each heating section 3f..., and the input side ports of the molding machine controller 41 are connected to the above-described rotary encoders 30, 33, and further to temperature sensors 9... attached corresponding to each heating section 3f.... The above is the basic configuration of the injection device Mi.

[0028] Next, the configuration of the plasticization state diagnosis device 1 according to the present embodiment will be specifically described with reference to FIGS. 1-5 and 9.

[0029] As shown in FIG. 3 (FIGS. 1-2), the plasticization state diagnosis device 1 includes a plurality of AE sensors (acoustic emission wave sensing sensors) 6r... using piezoelectric ceramic elements or the like that detect AE (acoustic emission) waves We disposed at a plurality of different positions Xr... in the front-rear direction Fs in the heating cylinder 2.

[0030] For example, it includes two AE sensors 6r..., namely, a first AE sensor 6r and a second AE sensor 6f disposed at two positions Xr and Xf. In this case, as shown in FIG. 3, the first AE sensor 6r is disposed at a position Xr near the rear end of the rear heating section 3r on the rear side of the heating cylinder 2, and the second AE sensor 6f is disposed at a position Xf near the front in the middle heating section 3m on the front side of the heating cylinder 2.

[0031] In this way, when disposing the AE sensors 6r and 6f, if the first AE sensor 6r is disposed on the rear side of the heating cylinder 2 and the second AE sensor 6f is disposed on the front side of the heating cylinder 2, a series of states from the solid state of pellets or the like in the vicinity of the material supply section 4 to the molten state after plasticization can be set as the detection target, so that the plasticization state in the heating cylinder 2 can be grasped sufficiently and optimally.

[0032] FIG. 5 shows an AE sensor unit 6u that houses a sensor body 6rm in an AE sensor 6r (the same applies to the AE sensor 6f). This AE sensor unit 6u includes a sensor holder 52 configured in a cylindrical shape. Inside the lower side of this sensor holder 52, a cylindrical sensor body 6rm is disposed, and connection leads 6rc of this sensor body 6rm are led out to the outside from the peripheral surface portion of the sensor holder 52. Further, a sensing surface 6rms that is the lower end surface of the sensor body 6rm faces the outside from the lower end of the sensor holder 52. On the other hand, a plunger 53 is disposed inside the upper side of the sensor holder 52 so as to be displaceable in the axial direction, and a coil spring 54 that biases this plunger 53 downward (toward the sensor body 6rm side) is attached. Thereby, the sensor body 6rm is fixed by the pressing of the tip of the plunger 53, and stable measurement can be performed. An alumina sheet 54 is interposed between the tip of the plunger 53 and the upper end surface of the sensor body 6rm, and an alumina sheet 55 is attached to the lower end surface of the sensor body 6rm. Thereby, reduction of electrical noise is achieved.

[0033] Also, the attachment of the AE sensor 6r (the same applies to the AE sensor 6f) to the heating cylinder 2 can be performed as shown in FIGS. 4 and 6. In this case, as shown in FIG. 4, mounting screw holes 56 are formed in a radial direction of the heating cylinder 2 at a predetermined position in the empty space on the peripheral surface of the heating cylinder 2, and a waveguide rod 57 is screwed and fixed to these mounting screw holes 56. Then, the tip of the waveguide rod 57, that is, the inner end surface 57i is made to coincide with the inner wall surface of the heating cylinder 2, and the lower end surface of the above-described AE sensor unit 6u is attached and fixed to the rear end surface of the waveguide rod 57. At this time, the above-described alumina sheet 55 is interposed between the lower end surface of the sensor body 6rm and the outer end surface of the waveguide rod 57.

[0034] In this way, when attached to the heating cylinder 2 alone, it can be arranged without interfering with other temperature sensors or other functions, etc., so that the arrangement position can be selected flexibly. Therefore, the degree of freedom in arrangement can be increased, the complexity around the heating cylinder 2 can be avoided, and the space occupancy efficiency can be increased.

[0035] Then, as shown in FIG. 2, the first AE sensor 6r and the second AE sensor 6f are each connected to the sensor port of the controller main body 42 in the molding machine controller 41 via preamplifiers 43r and 43f, and each of the above-described temperature sensors 9... is also connected to the sensor port of the controller main body 42. The controller main body 42 incorporates hardware such as a CPU and functions as the main part of a computer system that performs various arithmetic processes and various control processes.

[0036] Note that, although the case of using the molding machine controller 41 is illustrated, a separate high-speed processing computer may be prepared, and necessary analysis processing may be performed using a separate processing system by providing the outputs of the first AE sensor 6r and the second AE sensor 6f to the high-speed processing computer.

[0037] The internal memory 42m includes a program area 42mp for storing various processing programs (software) for executing various arithmetic processes and various control processes (sequence control), and a data area 42md capable of storing various data (databases). In particular, the program area 42mp includes a diagnostic processing program for realizing the plasticization state diagnosis method according to the present embodiment.

[0038] As a result, the molding machine controller 41 including the internal memory 42m and the controller main body 42 functions as the main part of the plasticization state diagnosis device 1, that is, as a data processing unit 7 that processes the AE wave signals Sre and Sfe obtained from each of the AE sensors 6r and 6f, as shown in FIG. 2. That is, based on the arrival times tr and tf of the AE wave We obtained from each of the AE sensors 6r and 6f by a diagnosis processing program, a position calculation processing unit Ex that obtains position data Dx related to the generation position of the AE wave We, a predetermined sampling period Tp, and a predetermined position data Dx... The attack number calculation processing unit Ea that obtains the number of occurrences of the AE wave signals Sre and Sfe related to the AE wave We generated each time as the attack number data Da... exceeding a preset threshold value L, and the quantity of the AE wave component Sp... exceeding the preset threshold value L as the event count data De... And functions as a data processing unit 7 having an event count calculation processing unit Ee.

[0039] Furthermore, a display 42d is attached to the controller main body 42. The display 42d includes a display main body 42dm and a touch panel 42dt attached to the display main body 42dm. Therefore, various setting operations and selection operations can be performed on the touch panel 42dt. This display 42d constitutes a graphic display unit 8 that at least graphically displays the generation pattern Ps of the attack number data Da... and the event count data De... with respect to the position data Dx....

[0040] FIG. 9 shows an example of the display screen 61 of the display 42d including the graphic display unit 8. This display screen 61 has a screen configuration in which a setting unit 8s is displayed on the upper side of the screen and the graphic display unit 8 is displayed on the lower side of the screen.

[0041] In this case, the setting unit 8s includes a threshold setting unit 62 for setting a threshold value L, an aggregation interval setting unit 63 for setting an aggregation interval, a data display unit 64 for displaying various data values and the like, a switching key 65, and the like. The threshold setting unit 62 can set each threshold value L for each AE wave signal Sre, Sfe obtained from the AE sensors 6r, 6f (see FIG. 8). At this time, each threshold value L may be set to be the same or different. Thus, since each threshold value L can be set to be the same or different, it is possible to detect the AE wave We with high flexibility and reliability corresponding to different installation positions of the AE sensors 6r, 6f.

[0042] Also, the threshold setting unit 62 can set each threshold value L for detecting the attack count data Da and the event count data De. At the time of setting, it can be switched by the switching key 65. Also in this case, each threshold value L may be set to be the same or different. Thus, since each threshold value L can be set to be the same or different, it is possible to set a flexible and optimal threshold value L corresponding to the attack count data Da and the event count data De respectively, and to obtain accurate attack count data Da and event count data De for the AE wave We.

[0043] Furthermore, the aggregation interval setting unit 63 can set the sampling period Tp, for example, by "0" to "10" (10 [seconds]). Also, on the data display unit 64, set values and calculation values such as the band-pass frequency (frequency band) during filtering processing, the event count value, and the attack count value are displayed, and various display functions and various setting functions can be switched by using the switching key 65.

[0044] On the one hand, the graphic display unit 8 has a function of at least graphically displaying the occurrence pattern Ps of the attack number data Da and the event count data De with respect to the position data Dx. The illustrated graphic display unit 8 can be displayed in a graph format by marking the position data Dx in the front-rear direction Fs of the heating cylinder 2 on the horizontal axis and marking the event count data De and the attack number data Da on the vertical axis. Specifically, the attack number data Da is displayed by a bar graph Ga..., and the event count data De is displayed by a line graph Gs....

[0045] In this way, when configuring the graphic display unit 8, if the occurrence pattern Ps of the attack number data Da... and the event count data De... with respect to the position data Dx... is displayed in a graph format, graphic display can be enabled by combining general-purpose graphs such as bar graphs and line graphs. Therefore, the plasticized state and the trouble occurrence state can be easily and quickly grasped from the generated occurrence pattern Ps.

[0046] Note that the methods for obtaining the illustrated position data Dx, attack number data Da, and event count data De will be specifically described together with the processing procedure of the plasticized state diagnosis method described later.

[0047] On the other hand, the output port of the controller main body 42 is connected to the above-described screw rotation servo motor 28 via a motor driver (servo amplifier) 45. The rotation speed (rotation rate) of the screw rotation servo motor 28 is detected by a rotary encoder 30, and the detected rotation speed signal is applied to the motor driver 45 and the controller main body 42. Further, each band heater in the above-described heating units 3f, 3m, 3r is connected to the output port of the controller main body 42 via a heater driver 46.

[0048] Next, the operation (function) of the plasticized state diagnosis device 1 according to the present embodiment including the operation of the injection device Mi in the injection molding machine M, that is, the plasticized state diagnosis method will be described with reference to FIGS. 1 to 5 and FIGS. 7 to 13 according to the flowchart shown in FIG. 6.

[0049] First, various setting processes necessary for implementing the plasticization state diagnosis method are performed using the setting unit 8s on the display screen 61 of the display 42d shown in FIG. 9 (step S1).

[0050] Specifically, using the threshold setting unit 62, a threshold L for the AE wave signal Sre obtained from the first AE sensor 6r displayed on CH1 is set, and a threshold L for the AE wave signal Sfe obtained from the second AE sensor 6f displayed on CH2 is set. Each threshold L... may be the same or different. It is also possible to set the threshold L for only one of CH1 and CH2. In the case of FIG. 8 illustrated, it is set around 0.2 [V].

[0051] Also, the sampling period Ts is set by the aggregation interval setting unit 63. In the illustrated case, it is set to about 10 [seconds]. Further, by selecting and displaying the setting screen with the switching key 65, various setting processes of setting values such as the band-pass frequency of the band-pass filter (for example, 20 - 80 [kHz] (plastic fracture), etc.) and the ON / OFF of the noise cancellation waveform (for example, the registered waveform measured in advance) can be performed.

[0052] When the setting process is completed, the operation of the injection molding machine M, that is, the molding process, is started (step S2). The molding machine controller 41 monitors the molding process during the operation of the injection molding machine M. When the plasticization process starts, the controller main body 42 performs the process of taking in the detection signals based on the AE waves We obtained from the respective AE sensors 6r, 6f (steps S3, S4). In this case, the detection signals obtained from the respective AE sensors 6r, 6f are amplified by the preamplifiers 43r, 43f, respectively, and then applied to the controller main body 42 (step S5).

[0053] In the controller main body 42, noise removal processing is performed on the amplified detection signal (step S6). The noise removal processing removes the noise component Np that has no relevance to the plasticized state in the heating cylinder 2. In this case, the preset noise frequency components may be removed, or the operating noise of the injection molding machine M and the like may be measured in advance and registered as the noise component Np..., and the noise component cancellation processing may be performed. Note that, if necessary, the type related to the noise component Np can be selected or switched between ON and OFF.

[0054] In this way, if noise removal processing is performed on the detection signal obtained from the AE sensors 6r and 6f to remove the mixed signals other than those generated by the plasticizing process by the heating cylinder 2 as the noise component Np..., ambient environmental noise, that is, useless noise components Np... (disturbances) other than the AE wave signals Sre and Sfe accompanying the plasticization of the molding material R can be excluded, and more accurate AE wave signals Sre and Sfe can be obtained.

[0055] The detection signal on which the noise removal processing has been completed is subjected to filtering processing that allows only the set band-pass frequency Bs to pass through (step S7). Thereby, the detection signals related to the specific frequency Bs such as the frequency band (20 - 80 [kHz]) at which the molding material R breaks and the frequency band (200 - 350 [kHz]) at which metals collide can be extracted. In the illustrated case, the band-pass frequency Bs is set, but it may be set as a band-pass frequency band having a predetermined bandwidth.

[0056] In this way, if filtering processing that allows only the set specific frequency Bs or the set specific frequency band to pass through is performed on the detection signal obtained from the AE sensors 6r and 6f, the AE wave signals Sre and Sfe obtained accompanying the plasticization of the molding material R, specifically, the AE wave We when the molding material R breaks and the AE wave We when the molding material R collides with metal can be specified and detected, so that the plasticized state and the trouble occurrence state can be diagnosed more accurately.

[0057] Figures 7 and 8 show the signal waveforms of the AE wave signals Sre and Sfe on which noise removal processing and filtering processing have been performed. This signal waveform can be displayed on a separate screen by switching the above-described switching key 65 (Steps S8 and S9).

[0058] Further, since the detection signal for which the filtering processing has been completed functions as the AE wave signals Sre and Sfe, arithmetic processing related to diagnostic data processing is performed by the data processing unit 7 shown in FIG. 2 (Step S10). In this case, it is performed over the sampling period Ts set by the aggregation interval setting unit 63, for example, 10 [seconds] in the illustrated case.

[0059] Now, assume that during the sampling period Ts, a relatively large-amplitude AE wave We exceeding the threshold value L as shown in FIG. 8 occurs. In this case, first, attack number arithmetic processing is performed by the attack number arithmetic processing unit Ea (Step S11). That is, the magnitude of the amplitude of the AE wave signal Sre (Sfe) is determined by the threshold value L, and when it exceeds the threshold value L, it is determined that an attack has occurred, resulting in attack number data Da of "1" time.

[0060] Next, event count arithmetic processing is performed by the event count arithmetic processing unit Ee to count the quantity of waveform components Sp... exceeding the threshold value L and obtain event count data De (Step S12). Then, the quantity obtained by the counting becomes the event count data De.

[0061] Furthermore, based on the obtained AE wave signals Sre and Sfe, the position calculation processing unit Ex performs calculation processing of the position data Dx (step S13). In the position calculation processing unit Ex, the position data Dx related to the generation position of the AE wave We is obtained based on the arrival times tr and tf of the AE wave We obtained from each AE sensor 6r and 6f. That is, as shown in FIG. 7, since a time difference Δt based on the delay occurs between the AE wave signals Sre and Sfe obtained from each AE sensor 6r and 6f, the generation position of the AE wave We is obtained as the position data Dx based on the installation positions Xr and Xf of each AE sensor 6r and 6f, the time difference Δt, and the speed of sound. Note that the longitudinal frequency of iron is used for the speed of sound.

[0062] Through these series of data processes, the attack number data Da, the event count data De, and the position data Dx are associated with each other, and thus temporary registration is performed in the associated state. The above is the data process when one attack occurs. After that, during the sampling period Ts, when the next attack occurs, the attack number data Da, the event count data De, and the position data Dx are obtained by the same processing procedure (steps S14, S10...). These series of data processes are performed until the set sampling period Ts ends.

[0063] When the sampling period Ts ends, aggregation processing of the associated attack number data Da..., event count data De..., position data Dx... during the sampling period Ts is performed (step S15). Further, the aggregated attack number data Da..., event count data De..., position data Dx... are graphically displayed by the graphic display unit 8 as shown in FIG. 9 (step S16).

[0064] The exemplary graphic display is presented in a graph format. On the horizontal axis, the positions in the front-back direction Fs of the heating cylinder 2 are set at sequential position ranges, for example, at intervals of 20 [mm]. When position data Dx exists within this position range, the aggregated value of the attack number data Da... corresponding to the position data Dx is displayed by a bar graph Ga, and the aggregated value of the event count data De... is displayed by a line graph Gs.

[0065] As a result, the generation pattern Ps by the bar graph Ga and the line graph Gs will be displayed. With respect to the display of this generation pattern Ps, a predetermined diagnostic process including the quality of the plasticized state or the like can be performed by an operator or AI (artificial intelligence) (step S17).

[0066] Figures 10 - 13 show examples of various generation patterns Ps. In this case, Figures 10 and 11 show the generation pattern Ps when "GPPS (general-purpose polystyrene) resin" is used for the molding material R and the band-pass frequency Bs is set to 50 - 60 [kHz]. Figure 10 shows the case where the heating temperature is set to "low temperature 170 [°C]", while Figure 11 shows the case where the heating temperature is set to "appropriate temperature 200 [°C]".

[0067] The set 50 - 60 [kHz] for the band-pass frequency Bs is the frequency at which plastic destruction is assumed. As is clear from the generation patterns Ps shown in Figures 10 and 11, in Figure 11, the attack number data Da and the event count data De temporarily increase at the initial stage of plasticization, indicating a normal plasticized state. In contrast, in Figure 10, it can be confirmed that the occurrence numbers of the attack number data Da and the event count data De increase, and moreover, it is in a state of insufficient plasticization, such as occurring in a wide range in the front-back direction Fs of the heating cylinder 2, particularly near the front end of the heating cylinder 2.

[0068] Also, in the case of Fig. 12, the basic conditions are the same as those in the case of Fig. 11, but the generation pattern Ps when the heating temperature is set to "high temperature 240 [°C]" is shown. As is clear from Fig. 12, it can be confirmed that it is in a plasticized state with not much difference compared to the case of "appropriate temperature 200 [°C]" shown in Fig. 11.

[0069] Furthermore, in the case of Fig. 13, the basic conditions are the same as those in the case of Fig. 11, but the case when the band-pass frequency Bs is set to 220 [kHz] is shown. The frequency range of 200 - 350 [kHz] of the set frequency is the frequency at which generation due to metal contact is assumed. As is clear from Fig. 13, although the unmelted molding material R contacts the screw 5 at the rear of the heating cylinder 2 and attack number data Da and event count data De are generated, it can be confirmed that almost no generation occurs thereafter, so it can be confirmed that it is normally in a plasticized state.

[0070] In this way, it can be confirmed that the generation pattern Ps graphically displayed by the graphic display unit 8 reflects the plasticized state as a pattern. Therefore, diagnosis processing such as determining the quality of the plasticized state or the occurrence of troubles based on the generation of abnormal sounds can be performed by an operator or AI, and furthermore, by accumulating and learning the generation pattern Ps... as big data, the diagnosis accuracy can be further improved.

[0071] This diagnosis result can be displayed on the display screen of the display 42d as a diagnosis result of the quality of the plasticized state, or an abnormal occurrence result, etc., or output as a control command for necessary stop control and operation control (step S18).

[0072] Therefore, according to the plasticizing state diagnosis device 1 (plasticizing state diagnosis method) of the injection molding machine M according to the present embodiment, as a basic configuration, a plurality of AE sensors 6r, 6f that detect AE waves We arranged at a plurality of different positions Xr, Xf in the front-rear direction Fs in the heating cylinder 2, and a position calculation processing unit Ex that obtains position data Dx related to the generation position of the AE wave We based on the arrival times tr, tf of the AE waves We obtained from each AE sensor 6r, 6f, a predetermined sampling period Tp, and a predetermined position data Dx... An attack number calculation processing unit Ea that obtains the number of occurrences of AE wave signals Sre, Sfe related to the AE wave We generated every... as attack number data Da... that exceeds a preset threshold value L, and an event count calculation processing unit Ee that obtains the quantity of AE wave components Sp... that exceed a preset threshold value L as event count data De..., and a graphic display unit 8 that at least graphically displays the generation pattern Ps of the attack number data Da... and the event count data De... with respect to the position data Dx..., it is possible to accurately obtain information from a planar (three-dimensional) perspective and further a qualitative perspective on the heating cylinder 2, such as where the AE wave We is generated in the heating cylinder 2 and what causes the AE wave We. As a result, it becomes possible to realize a more practical, convenient, and user-friendly plasticizing state diagnosis device 1, and it is possible to perform an accurate and highly reliable pass / fail diagnosis and trouble determination on the plasticizing state.

[0073] As described above, the preferred embodiment has been described in detail. However, the present invention is not limited to such an embodiment, and can be arbitrarily changed, added, or deleted within the scope not departing from the gist of the present invention in terms of detailed configuration, shape, quantity, etc.

[0074] For example, although the AE sensors 6r and 6f are shown as being disposed at two locations, the rear side and the front side of the heating cylinder 2, generally, the AE sensors 6r... can be disposed at two or more different positions respectively. Also, the rear side and the front side of the heating cylinder 2 where the AE sensors 6r and 6f are disposed indicate a relative positional relationship, not an absolute position. Therefore, the position on the front side means that it exists on the front side with respect to the position on the rear side, and does not mean that it exists at the front part of the heating cylinder 2 as an absolute position, and it may exist in the middle part or the rear part of the heating cylinder 2. On the other hand, although it is desirable to perform noise removal processing and filtering processing, it is not an essential component. Further, for the graphic display unit 8, it is desirable to display the generation pattern Ps of the attack number data Da... and the event count data De... with respect to the position data Dx... in a graph format, but as long as it can be graphically displayed and the generation pattern Ps can be grasped, it can be implemented in various graphic formats other than the graph format.

Industrial Applicability

[0075] The plasticization state diagnosis method and apparatus according to the present invention can be used in various injection molding machines that plasticize a molding material supplied into a heating cylinder by the rotation of a screw.

Explanation of Reference Numerals

[0076] 1: Plasticization state diagnosis apparatus, 2: Heating cylinder, 3f...: Heating part, 4: Material supply part, 5: Screw, 6r: AE sensor (first AE sensor), 6f: AE sensor (second AE sensor), 7: Data processing part, 8: Graphic display part, 9: Temperature sensor, M: Injection molding machine, R: Molding material, Fs: Front-rear direction, Xr: Position of heating cylinder, Xf: Position of heating cylinder, We: AE wave, tr: Arrival time, tf: Arrival time, Sre: AE wave signal, Sfe: AE wave signal, L: Threshold value, Ps: Generation pattern, Ex: Position calculation processing part, Ea: Attack number calculation processing part, Ee: Event count calculation processing part

Claims

1. A method for diagnosing the plasticization state of an injection molding machine that diagnoses the plasticization state of a molding material supplied from a material supply unit provided at the rear of a heating cylinder heated by a heating unit into the heating cylinder by the rotation of a screw. A plurality of AE sensors for detecting AE waves are arranged at a plurality of different positions in the front-rear direction of the heating cylinder, and position data related to the generation position of the AE wave is obtained based on the arrival time of the AE wave obtained from each AE sensor. At the same time, for each predetermined sampling period and the predetermined position data, the number of occurrences of the AE wave signal related to the AE wave that exceeds a preset threshold is obtained as attack number data, and the quantity of the AE wave component that exceeds the preset threshold is obtained as event count data. A method for diagnosing the plasticization state of an injection molding machine, characterized in that at least a generation pattern of the attack number data and the event count data with respect to the position data is graphically displayed.

2. The method for diagnosing the plasticization state of an injection molding machine according to claim 1, wherein each threshold for detecting the attack number data and the event count data is set to be the same or different.

3. The method for diagnosing the plasticization state of an injection molding machine according to claim 1, wherein the detection signal obtained from the AE sensor performs noise removal processing on the mixed signals other than those generated during the plasticization process by the heating cylinder as noise components.

4. The method for diagnosing the plasticization state of an injection molding machine according to claim 1, wherein the detection signal obtained from the AE sensor performs filtering processing to pass only a set specific frequency or a set specific frequency band.

5. A plasticization state diagnosis device for an injection molding machine that diagnoses the plasticization state of a molding material supplied into a heating cylinder from a material supply unit provided at the rear of the heating cylinder heated by a heating unit when the molding material is plasticized by the rotation of a screw. The device includes a plurality of AE sensors disposed at different positions in the front-rear direction of the heating cylinder for detecting AE waves, a position calculation processing unit for obtaining position data related to the generation position of the AE waves based on the arrival times of the AE waves obtained from each AE sensor, an attack number calculation processing unit for obtaining, as attack number data, the number of occurrences of the AE wave signals related to the AE waves that occur during a predetermined sampling period and for each predetermined position data and that exceed a preset threshold value, and an event count calculation processing unit for obtaining, as event count data, the quantity of the AE wave components that exceed the preset threshold value. The device also includes a data processing unit having these components, and a graphic display unit for at least graphically displaying the generation patterns of the attack number data and the event count data with respect to the position data. The plasticization state diagnosis device for an injection molding machine is characterized by this configuration.

6. The plasticization state diagnosis device for an injection molding machine according to claim 5, wherein the graphic display unit displays the generation patterns of the attack number data and the event count data with respect to the position data in a graph format.

7. The plasticization state diagnosis device for an injection molding machine according to claim 5, wherein the AE sensor includes a first AE sensor and a second AE sensor, the first AE sensor is disposed on the rear side of the heating cylinder, and the second AE sensor is disposed on the front side of the heating cylinder.

8. The plasticization state diagnosis device for an injection molding machine according to claim 6, wherein the AE sensor is attached to the heating cylinder individually.

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