Management device for learned model and injection molding machine

Abstract

The invention relates to a management device of a learned model and an injection molding machine, which improves safety. A learning completion model according to one embodiment includes an input layer, one or more intermediate layers connected to the input layer, and an output layer connected to the intermediate layers, and causes a computer to function as follows: machine learning based on first data and positive solution information is performed; the first data indicates a result detected during the production of a product by an injection molding machine or a result measured after the production of the product, and the positive solution information divides a quantity related to the production of the product into two or more sections and indicates, for each of the sections, whether or not a value included in the section is a positive solution. When second data indicating a result detected during production or a result measured after production is input from the input layer, the output layer outputs a degree to which a value included in each of the sections is a positive solution.

Description

Technical Field

[0001] This application claims priority based on Japanese Patent Application No. 2023-181176 filed on October 20, 2023. The entire contents of the Japanese Patent Application are incorporated herein by reference.

[0002] The present invention relates to a management device for a learning completed model and an injection molding machine. Background Art

[0003] In the past, in order to accurately produce a product using an injection molding machine, it was necessary to accurately set the product. In order to set the product, the quality of the product produced with the setting was confirmed, and the setting was repeatedly readjusted to derive the accurate setting. Therefore, the setting required time and effort from the technician.

[0004] In recent years, with the improvement of computer processing power, there is a trend of development of artificial intelligence. For example, Patent Document 1 proposes a technology for estimating quality through machine learning.

[0005] Patent Document 1: Japanese Patent Application Publication No. 2021-191619

[0006] However, in the technology described in Patent Document 1, the quality is estimated from the detection data detected during molding using a learned model, but it is difficult to understand how reliable the estimated quality is. Summary of the invention

[0007] One embodiment of the present invention provides a technology capable of dividing a quantity related to production of a product into two or more sections and outputting the degree of a correct answer for each section, thereby enabling setting and control in consideration of reliability.

[0008] A learned model involved in one embodiment of the present invention has an input layer, one or more intermediate layers connected to the input layer, and an output layer connected to the intermediate layer, so that the computer functions in the following manner: performing machine learning based on first data and correct answer information, the first data representing a result detected during production of a product by an injection molding machine or a result measured after production of the product, the correct answer information dividing a quantity related to production of the product into two or more intervals and indicating for each interval whether a value contained in the interval is a correct answer, and when second data representing a result detected during production or a result measured after production is input from the input layer, the output layer outputs the degree to which the value contained in each interval is a correct answer.

[0009] Effects of the Invention

[0010] According to one aspect of the present invention, safety is improved by taking reliability into consideration. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 This is a diagram showing a state of the injection molding machine according to one embodiment when mold opening is completed.

[0012] Figure 2 It is a diagram showing a state of the injection molding machine according to one embodiment when the mold is clamped.

[0013] Figure 3 This is a schematic diagram showing the cooperation related to the learned model by the learning device and the control device of the injection molding machine according to the first embodiment.

[0014] Figure 4 This is a diagram showing an example of the functional configuration of the learning device and the control device according to the first embodiment.

[0015] Figure 5 This is a diagram illustrating various parameters for each shot number used in the evaluation acquisition unit and the correct answer information calculation unit according to the first embodiment.

[0016] Figure 6 It is a diagram showing an example of a method for calculating information related to a correct answer of a mold clamping force setting value by the correct answer information calculation unit according to the first embodiment.

[0017] Figure 7 This is a diagram illustrating information related to a correct answer generated by the correct answer information calculation unit according to the first embodiment.

[0018] Figure 8 This is a diagram illustrating a configuration of a learned model used in the learning device and the control device according to the first embodiment.

[0019] Fig. 9 This is a diagram illustrating information based on a row vector output from the inference unit according to the first embodiment.

[0020] Fig.10 This is a diagram showing a first example of a graph showing the degree of certainty for each interval output from the inference unit according to the first embodiment.

[0021] Fig.11 This is a diagram showing a second example of a graph showing the degree of certainty for each interval output from the inference unit according to the first embodiment.

[0022] Fig.12 Schematic diagram showing inference performed by a learned model mounted on a control device according to a modification.

[0023] Fig.13It is a diagram illustrating the configuration of a learning device, a group management device, and an injection molding machine according to a second embodiment.

[0024] Explanation of symbols

[0025] 10-injection molding machine, 700-control device, 701-CPU, 702-storage medium, 711-communication control unit, 712-update unit, 713-injection molding processing unit, 714-setting unit, 715-inference unit, 716-display control unit, 721-learned model storage unit, 1300-learning device, 1301-CPU, 1302-storage medium, 1303-communication interface, 1311-acquisition unit, 1312-evaluation acquisition unit, 1313-correct answer information calculation unit, 1314-training data generation unit, 1315-learning unit, 1316-communication control unit, 1321-training data storage unit, 1322-learned model storage unit, 2300-group management device. DETAILED DESCRIPTION

[0026] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. Furthermore, the embodiments described below are not intended to limit the embodiments of the invention but are intended to be illustrative, and all features and combinations thereof described in the embodiments are not necessarily the essence of the invention. In addition, in each of the accompanying drawings, the same or corresponding structures are sometimes marked with the same or corresponding symbols, and the description is omitted.

[0027] Figure 1 It is a diagram showing a state of the injection molding machine according to the first embodiment when mold opening is completed. Figure 2 1 is a diagram showing the state of the injection molding machine involved in the first embodiment when the mold is clamped. In this specification, the X-axis direction, the Y-axis direction and the Z-axis direction are directions perpendicular to each other. The X-axis direction and the Y-axis direction represent horizontal directions, and the Z-axis direction represents a vertical direction. When the mold clamping device 100 is horizontal, the X-axis direction is the mold opening and closing direction, and the Y-axis direction is the width direction of the injection molding machine 10. The negative side of the Y-axis direction is called the operating side, and the positive side of the Y-axis direction is called the opposite side of the operating side.

[0028] like Figure 1-2As shown, the injection molding machine 10 includes: a mold clamping device 100 for opening and closing a mold device 800; an ejection device 200 for ejecting a molded product molded by the mold device 800; an injection device 300 for injecting a molding material into the mold device 800; a moving device 400 for moving the injection device 300 forward and backward relative to the mold device 800; a control device 700 for controlling each component of the injection molding machine 10; and a frame 900 for supporting each component of the injection molding machine 10. The frame 900 includes a mold clamping device frame 910 for supporting the mold clamping device 100 and an injection device frame 920 for supporting the injection device 300. The mold clamping device frame 910 and the injection device frame 920 are respectively installed on the base plate 2 via leveling casters 930. The control device 700 is arranged in the internal space of the injection device frame 920. The components of the injection molding machine 10 are described below.

[0029] (Mold clamping device)

[0030] In the description of the mold clamping device 100, the moving direction of the movable platen 120 when the mold is closed (for example, the positive direction of the X axis) is set to the front, and the moving direction of the movable platen 120 when the mold is opened (for example, the negative direction of the X axis) is set to the rear.

[0031] The mold clamping device 100 performs mold closing, pressurization, mold clamping, depressurization, and mold opening of the mold device 800. The mold device 800 includes a fixed mold 810 and a movable mold 820. The mold clamping device 100 is, for example, horizontal, and the mold opening and closing direction is the horizontal direction. The mold clamping device 100 has a fixed platen 110 on which the fixed mold 810 is mounted, a movable platen 120 on which the movable mold 820 is mounted, and a moving mechanism 102 that moves the movable platen 120 relative to the fixed platen 110 in the mold opening and closing direction.

[0032] The fixed platen 110 is fixed to the mold clamping device frame 910. The fixed mold 810 is attached to the surface of the fixed platen 110 that faces the movable platen 120.

[0033] The movable platen 120 is arranged to be movable in the mold opening and closing direction relative to the mold clamping device frame 910. A guide 101 for guiding the movable platen 120 is laid on the mold clamping device frame 910. The movable mold 820 is attached to the surface of the movable platen 120 facing the fixed platen 110.

[0034] The moving mechanism 102 performs mold closing, pressurization, mold clamping, depressurization, and mold opening of the mold device 800 by moving the movable platen 120 forward and backward relative to the fixed platen 110. The moving mechanism 102 includes a toggle seat 130 disposed with a gap between the fixed platen 110, a connecting rod 140 connecting the fixed platen 110 and the toggle seat 130, a toggle mechanism 150 that moves the movable platen 120 relative to the toggle seat 130 in the mold opening and closing direction, a mold clamping motor 160 that operates the toggle mechanism 150, a motion conversion mechanism 170 that converts the rotational motion of the mold clamping motor 160 into a linear motion, and a mold thickness adjustment mechanism 180 that adjusts the gap between the fixed platen 110 and the toggle seat 130.

[0035] The toggle seat 130 is arranged with a gap between the fixed platen 110 and is placed on the mold clamping device frame 910 so as to be movable in the mold opening and closing direction. In addition, the toggle seat 130 can be arranged so as to be movable along a guide member laid on the mold clamping device frame 910. The guide member of the toggle seat 130 can be common to the guide member 101 of the movable platen 120.

[0036] In addition, in the present embodiment, the fixed pressure plate 110 is fixed relative to the mold clamping device frame 910, and the elbow seat 130 is configured to be freely movable relative to the mold clamping device frame 910 in the mold opening and closing direction, but the elbow seat 130 may also be fixed relative to the mold clamping device frame 910, and the fixed pressure plate 110 may be configured to be freely movable relative to the mold clamping device frame 910 in the mold opening and closing direction.

[0037] The connecting rod 140 connects the fixed platen 110 and the toggle seat 130 at a distance L in the mold opening and closing direction. A plurality of connecting rods 140 (e.g., four) may be used. The plurality of connecting rods 140 are arranged parallel to the mold opening and closing direction and extend according to the mold clamping force. A connecting rod strain detector 141 for detecting the strain of the connecting rod 140 may be provided on at least one of the connecting rods 140. The connecting rod strain detector 141 sends a signal indicating its detection result to the control device 700. The detection result of the connecting rod strain detector 141 is used for detecting the mold clamping force, etc.

[0038] In addition, in this embodiment, the tie rod strain detector 141 is used as the mold clamping force detector for detecting the mold clamping force, but the present invention is not limited to this. The mold clamping force detector is not limited to the strain type, and can also be a piezoelectric type, a capacitive type, a hydraulic type, an electromagnetic type, etc., and its installation position is not limited to the tie rod 140.

[0039] The toggle mechanism 150 is disposed between the movable platen 120 and the toggle seat 130, and moves the movable platen 120 in the mold opening and closing direction relative to the toggle seat 130. The toggle mechanism 150 includes a crosshead 151 that moves in the mold opening and closing direction and a pair of link groups that are bent and extended by the movement of the crosshead 151. The pair of link groups each includes a first link 152 and a second link 153 that are connected by a pin or the like so as to be able to bend and extend freely. The first link 152 is mounted by a pin or the like so as to be able to swing freely relative to the movable platen 120. The second link 153 is mounted by a pin or the like so as to be able to swing freely relative to the toggle seat 130. The second link 153 is mounted to the crosshead 151 via a third link 154. When the crosshead 151 is moved forward and backward relative to the toggle seat 130, the first link 152 and the second link 153 are bent and extended so that the movable platen 120 is moved forward and backward relative to the toggle seat 130.

[0040] In addition, the structure of the toggle mechanism 150 is not limited to Figure 1 and Figure 2 For example, in Figure 1 and Figure 2 In the embodiment, the number of nodes of each link group is 5, but it may be 4, or one end of the third link 154 may be connected to the node between the first link 152 and the second link 153.

[0041] The mold clamping motor 160 is mounted on the toggle base 130 and operates the toggle mechanism 150. The mold clamping motor 160 advances and retreats the crosshead 151 relative to the toggle base 130, thereby bending and extending the first link 152 and the second link 153, thereby advancing and retreating the movable platen 120 relative to the toggle base 130. The mold clamping motor 160 is directly connected to the motion conversion mechanism 170, but may be connected to the motion conversion mechanism 170 via a belt, a pulley, or the like.

[0042] The motion conversion mechanism 170 converts the rotational motion of the mold clamping motor 160 into the linear motion of the cross head 151. The motion conversion mechanism 170 includes a screw shaft and a screw nut screwed with the screw shaft. Balls or rollers may be interposed between the screw shaft and the screw nut.

[0043] The mold clamping device 100 performs a mold closing process, a pressure increasing process, a mold clamping process, a pressure releasing process, a mold opening process, and the like under the control of the control device 700 .

[0044] In the mold closing process, the mold clamping motor 160 is driven to move the crosshead 151 to the mold closing end position at a set moving speed, and the movable platen 120 is moved forward to make the movable mold 820 contact the fixed mold 810. For example, the position and moving speed of the crosshead 151 are detected using a mold clamping motor encoder 161. The mold clamping motor encoder 161 detects the rotation of the mold clamping motor 160 and sends a signal indicating the detection result to the control device 700.

[0045] In addition, the crosshead position detector for detecting the position of the crosshead 151 and the crosshead moving speed detector for detecting the moving speed of the crosshead 151 are not limited to the mold clamping motor encoder 161, and a conventional detector can be used. In addition, the movable platen position detector for detecting the position of the movable platen 120 and the movable platen moving speed detector for detecting the moving speed of the movable platen 120 are not limited to the mold clamping motor encoder 161, and a conventional detector can be used.

[0046] In the pressure increasing step, the mold clamping motor 160 is further driven to further advance the cross head 151 from the mold closing end position to the mold clamping position, thereby generating a mold clamping force.

[0047] In the mold closing process, the mold closing motor 160 is driven to maintain the position of the cross head 151 at the mold closing position. In the mold closing process, the mold closing force generated in the pressure increasing process is maintained. In the mold closing process, a cavity space 801 is formed between the movable mold 820 and the fixed mold 810 (refer to Figure 2 ), the injection device 300 fills the cavity space 801 with liquid molding material. The filled molding material is solidified, thereby obtaining a molded product.

[0048] The number of cavity spaces 801 may be one or more. In the latter case, multiple molded products can be obtained at the same time. An insert may be arranged in a part of the cavity space 801, and a molding material may be filled in another part of the cavity space 801. A molded product in which the insert and the molding material are integrated can be obtained.

[0049] In the depressurization process, the mold clamping motor 160 is driven to retract the crosshead 151 from the mold clamping position to the mold opening start position, so that the movable platen 120 is retracted to reduce the mold clamping force. The mold opening start position and the mold closing end position may be the same position.

[0050] In the mold opening process, the mold clamping motor 160 is driven to move the crosshead 151 backward from the mold opening start position to the mold opening completion position at a set moving speed, and the movable platen 120 is moved backward to separate the movable mold 820 from the fixed mold 810. Then, the ejector device 200 ejects the molded product from the movable mold 820.

[0051] The setting conditions in the mold closing process, the pressure increasing process and the mold clamping process are uniformly set as a series of setting conditions. For example, the moving speed, position (including the mold closing start position, the moving speed switching position, the mold closing end position and the mold clamping position) and the mold clamping force of the crosshead 151 in the mold closing process and the pressure increasing process are uniformly set as a series of setting conditions. The mold closing start position, the moving speed switching position, the mold closing end position and the mold clamping position are arranged in sequence from the back to the front, and indicate the starting point and end point of the interval for setting the moving speed. The moving speed is set for each interval. The moving speed switching position can be one or more. The moving speed switching position can also be not set. Only one of the mold clamping position and the mold clamping force can be set.

[0052] The setting conditions in the pressure release process and the mold opening process are also set in the same way. For example, the moving speed and position (mold opening start position, moving speed switching position and mold opening completion position) of the crosshead 151 in the pressure release process and the mold opening process are uniformly set as a series of setting conditions. The mold opening start position, the moving speed switching position and the mold opening completion position are arranged in sequence from the front to the back, and represent the starting point and end point of the interval for setting the moving speed. The moving speed is set for each interval. The moving speed switching position can be one or more. The moving speed switching position can also be not set. The mold opening start position and the mold closing end position can be the same position. In addition, the mold opening completion position and the mold closing start position can be the same position.

[0053] In addition, the moving speed and position of the movable platen 120 may be set instead of the moving speed and position of the crosshead 151. Furthermore, the mold clamping force may be set instead of the position of the crosshead (eg, mold clamping position) and the position of the movable platen.

[0054] However, the toggle mechanism 150 amplifies the driving force of the mold clamping motor 160 and transmits it to the movable platen 120. The amplification ratio is also called the toggle magnification. The toggle magnification changes according to the angle θ (hereinafter also referred to as "link angle θ") formed by the first link 152 and the second link 153. The link angle θ is obtained from the position of the crosshead 151. When the link angle θ is 180°, the toggle magnification becomes the maximum.

[0055] When the thickness of the mold device 800 changes due to replacement of the mold device 800, temperature change of the mold device 800, etc., mold thickness adjustment is performed to obtain a predetermined mold clamping force when the mold is clamped. In the mold thickness adjustment, for example, the interval L between the fixed platen 110 and the toggle seat 130 is adjusted so that the link angle θ of the toggle mechanism 150 becomes a predetermined angle at the time when the movable mold 820 contacts the fixed mold 810.

[0056] The mold clamping device 100 has a mold thickness adjustment mechanism 180. The mold thickness adjustment mechanism 180 adjusts the interval L between the fixed platen 110 and the toggle seat 130, thereby adjusting the mold thickness. In addition, the mold thickness adjustment is performed, for example, during the period from the end of the molding cycle to the start of the next molding cycle. The mold thickness adjustment mechanism 180 has, for example: a screw shaft 181 formed at the rear end of the connecting rod 140; a screw nut 182 that is held in the toggle seat 130 so as to be rotatable and unable to move forward and backward; and a mold thickness adjustment motor 183 that rotates the screw nut 182 that is threaded with the screw shaft 181.

[0057] A lead screw shaft 181 and a lead screw nut 182 are provided for each connecting rod 140. The rotational driving force of the mold thickness adjustment motor 183 can be transmitted to the plurality of lead screw nuts 182 via the rotational driving force transmission unit 185. The plurality of lead screw nuts 182 can be rotated synchronously. In addition, by changing the transmission path of the rotational driving force transmission unit 185, the plurality of lead screw nuts 182 can also be rotated individually.

[0058] The rotational driving force transmission part 185 is composed of, for example, gears. In this case, a driven gear is formed on the outer periphery of each lead screw nut 182, a driving gear is mounted on the output shaft of the mold thickness adjustment motor 183, and an intermediate gear meshing with a plurality of driven gears and the driving gear is held rotatably at the center of the toggle seat 130. In addition, the rotational driving force transmission part 185 may be composed of a belt, a pulley, etc. instead of a gear.

[0059] The operation of the mold thickness adjustment mechanism 180 is controlled by the control device 700. The control device 700 drives the mold thickness adjustment motor 183 to rotate the lead screw nut 182. As a result, the position of the toggle seat 130 relative to the connecting rod 140 is adjusted, and the interval L between the fixed platen 110 and the toggle seat 130 is adjusted. In addition, a plurality of mold thickness adjustment mechanisms may be used in combination.

[0060] The mold thickness adjustment motor encoder 184 is used to detect the interval L. The mold thickness adjustment motor encoder 184 detects the rotation amount and rotation direction of the mold thickness adjustment motor 183, and sends a signal indicating the detection result to the control device 700. The detection result of the mold thickness adjustment motor encoder 184 is used to monitor and control the position and interval L of the toggle seat 130. In addition, the toggle seat position detector for detecting the position of the toggle seat 130 and the interval detector for detecting the interval L are not limited to the mold thickness adjustment motor encoder 184, and a conventional detector can be used.

[0061] The mold clamping device 100 may include a mold temperature regulator for regulating the temperature of the mold device 800. The mold device 800 includes a flow path of a temperature regulating medium therein. The mold temperature regulator regulates the temperature of the temperature regulating medium supplied to the flow path of the mold device 800, thereby regulating the temperature of the mold device 800.

[0062] Furthermore, the mold clamping device 100 of the present embodiment is a horizontal type in which the mold opening and closing direction is the horizontal direction, but may be a vertical type in which the mold opening and closing direction is the vertical direction.

[0063] The mold clamping device 100 of this embodiment includes the mold clamping motor 160 as a driving source, but may include a hydraulic cylinder instead of the mold clamping motor 160. Furthermore, the mold clamping device 100 may include a mold opening and closing linear motor or a mold clamping electromagnet.

[0064] (Ejector device)

[0065] In the description of the ejection device 200, similar to the description of the mold clamping device 100, etc., the moving direction of the movable platen 120 when the mold is closed (for example, the positive direction of the X-axis) is set to the front, and the moving direction of the movable platen 120 when the mold is opened (for example, the negative direction of the X-axis) is set to the rear for description.

[0066] The ejector device 200 is mounted on the movable platen 120 and moves forward and backward together with the movable platen 120. The ejector device 200 includes an ejector rod 210 for ejecting the molded product from the mold device 800 and a drive mechanism 220 for moving the ejector rod 210 along the moving direction of the movable platen 120 (X-axis direction).

[0067] The ejector rod 210 is arranged to be able to move forward and backward in the through hole of the movable platen 120. The front end of the ejector rod 210 contacts the ejector plate 826 of the movable mold 820. The front end of the ejector rod 210 may be connected to the ejector plate 826 or may not be connected thereto.

[0068] The driving mechanism 220 includes, for example, an ejector motor and a motion conversion mechanism that converts the rotational motion of the ejector motor into the linear motion of the ejector rod 210. The motion conversion mechanism includes a screw shaft and a screw nut screwed with the screw shaft. Balls or rollers may be interposed between the screw shaft and the screw nut.

[0069] The ejector device 200 performs an ejection process under the control of the control device 700. In the ejection process, the ejector rod 210 is moved forward from the standby position to the ejection position at a set moving speed, and the ejector plate 826 is moved forward to eject the molded product. Then, the ejector motor is driven to move the ejector rod 210 backward at a set moving speed, and the ejector plate 826 is moved backward to the original standby position.

[0070] For example, an ejector motor encoder is used to detect the position and moving speed of the ejector rod 210. The ejector motor encoder detects the rotation of the ejector motor and sends a signal indicating the detection result to the control device 700. In addition, the ejector rod position detector for detecting the position of the ejector rod 210 and the ejector rod moving speed detector for detecting the moving speed of the ejector rod 210 are not limited to the ejector motor encoder, and a conventional detector can be used.

[0071] (Injection device)

[0072] In the description of the injection device 300, unlike the description of the mold clamping device 100 and the description of the ejection device 200, the moving direction of the screw 330 during filling (for example, the negative direction of the X-axis) is set to the front, and the moving direction of the screw 330 during metering (for example, the positive direction of the X-axis) is set to the rear for description.

[0073] The injection device 300 is provided on a sliding base 301, and the sliding base 301 is configured to be able to move forward and backward freely relative to the injection device frame 920. The injection device 300 is configured to be able to move forward and backward freely relative to the mold device 800. The injection device 300 contacts the mold device 800, and fills the molding material measured in the cylinder 310 into the cavity space 801 in the mold device 800. The injection device 300 includes, for example, a cylinder 310 for heating the molding material, a nozzle 320 provided at the front end of the cylinder 310, a screw 330 configured to be able to move forward and backward freely and to rotate freely in the cylinder 310, a metering motor 340 for rotating the screw 330, an injection motor 350 for moving the screw 330 forward and backward, and a load detector 360 for detecting the load transmitted between the injection motor 350 and the screw 330.

[0074] The cylinder 310 heats the molding material supplied from the supply port 311. The molding material includes, for example, resin. The molding material is formed into, for example, granules and supplied to the supply port 311 in a solid state. The supply port 311 is formed at the rear of the cylinder 310. A cooler 312 such as a water-cooled cylinder is provided on the periphery of the rear of the cylinder 310. A heater 313 such as a belt heater and a temperature detector 314 are provided on the periphery of the cylinder 310 in front of the cooler 312.

[0075] The cylinder 310 is divided into a plurality of regions along the axial direction (e.g., the X-axis direction) of the cylinder 310. Heaters 313 and temperature detectors 314 are respectively provided in the plurality of regions. Set temperatures are respectively set for the plurality of regions, and the control device 700 controls the heater 313 so that the detected temperature of the temperature detector 314 becomes the set temperature.

[0076] The nozzle 320 is provided at the front end of the cylinder 310 and presses the mold device 800. A heater 313 and a temperature detector 314 are provided on the outer periphery of the nozzle 320. The control device 700 controls the heater 313 so that the detected temperature of the nozzle 320 becomes a set temperature.

[0077] The screw 330 is configured to be rotatable and to be able to move forward and backward freely in the cylinder 310. When the screw 330 is rotated, the molding material is transported forward along the spiral groove of the screw 330. While being transported forward, the molding material is gradually melted by the heat from the cylinder 310. As the liquid molding material is transported forward to the front of the screw 330 and accumulated in the front part of the cylinder 310, the screw 330 is retracted. Then, when the screw 330 is advanced, the liquid molding material accumulated in front of the screw 330 is injected from the nozzle 320 and filled in the mold device 800.

[0078] The check ring 331 is installed at the front of the screw 330 so as to be able to move forward and backward. The check ring 331 acts as a check valve to prevent the molding material from flowing backward from the front to the rear of the screw 330 when the screw 330 is pushed forward.

[0079] When the screw 330 is advanced, the check ring 331 is pushed backward by the pressure of the molding material in front of the screw 330, and moves back relative to the screw 330 to a closed position that blocks the flow path of the molding material (see FIG. Figure 2 ). Thus, the molding material accumulated in front of the screw 330 is prevented from flowing backward.

[0080] On the other hand, when the screw 330 is rotated, the check ring 331 is pushed forward by the pressure of the molding material conveyed to the front along the spiral groove of the screw 330, and moves forward relative to the screw 330 to an open position (refer to Figure 1 ). As a result, the molding material is transported to the front of the screw 330.

[0081] The check ring 331 may be a co-rotating type that rotates together with the screw 330 or a non-co-rotating type that does not rotate together with the screw 330 .

[0082] In addition, the injection device 300 may include a driving source that moves the check ring 331 forward and backward relative to the screw 330 between the open position and the closed position.

[0083] The metering motor 340 rotates the screw 330. The driving source for rotating the screw 330 is not limited to the metering motor 340, and may be, for example, a hydraulic pump or the like.

[0084] The injection motor 350 moves the screw 330 forward and backward. A motion conversion mechanism that converts the rotational motion of the injection motor 350 into the linear motion of the screw 330 is provided between the injection motor 350 and the screw 330. The motion conversion mechanism, for example, includes a screw shaft and a screw nut screwed with the screw shaft. Balls, rollers, etc. may be provided between the screw shaft and the screw nut. The driving source that moves the screw 330 forward and backward is not limited to the injection motor 350, and may be, for example, a hydraulic cylinder.

[0085] The load detector 360 detects the load transmitted between the injection motor 350 and the screw 330. The detected load is converted into pressure by the control device 700. The load detector 360 is provided in the transmission path of the load between the injection motor 350 and the screw 330, and detects the load acting on the load detector 360.

[0086] The load detector 360 sends a signal of the detected load to the control device 700. The load detected by the load detector 360 is converted into a pressure acting between the screw 330 and the molding material, and is used to control and monitor the pressure on the screw 330 from the molding material, the back pressure on the screw 330, and the pressure acting on the molding material from the screw 330.

[0087] In addition, the pressure detector for detecting the pressure of the molding material is not limited to the load detector 360 , and a conventional detector can be used. For example, a nozzle pressure sensor or a mold internal pressure sensor can be used. The nozzle pressure sensor is provided at the nozzle 320 .

[0088] The injection device 300 performs a metering process, a filling process, a pressure-maintaining process, etc. under the control of the control device 700. The filling process and the pressure-maintaining process can be collectively referred to as an injection process.

[0089] In the metering process, the metering motor 340 is driven to rotate the screw 330 at a set speed, and the molding material is transported to the front along the spiral groove of the screw 330. As a result, the molding material is gradually melted. As the liquid molding material is transported to the front of the screw 330 and accumulated in the front part of the cylinder 310, the screw 330 is moved backward. For example, the metering motor encoder 341 is used to detect the rotation speed of the screw 330. The metering motor encoder 341 detects the rotation of the metering motor 340 and sends a signal indicating the detection result to the control device 700. In addition, the screw speed detector that detects the rotation speed of the screw 330 is not limited to the metering motor encoder 341, and a conventional detector can be used.

[0090] In order to limit the rapid retreat of the screw 330 during the metering process, the injection motor 350 may be driven to apply a set back pressure to the screw 330. For example, the back pressure on the screw 330 may be detected using a load detector 360. When the screw 330 retreats to the metering end position and a predetermined amount of molding material is accumulated in front of the screw 330, the metering process is completed.

[0091] The position and speed of the screw 330 in the metering process are uniformly set as a series of setting conditions. For example, the metering start position, the speed switching position and the metering end position are set. These positions are arranged in sequence from the front to the back, and represent the starting point and the end point of the interval for setting the speed. The speed is set for each interval. The speed switching position can be one or more. The speed switching position can also be not set. In addition, the back pressure can be set for each interval.

[0092] In the filling process, the injection motor 350 is driven to make the screw 330 move forward at a set moving speed, and the liquid molding material accumulated in front of the screw 330 is filled into the cavity space 801 in the mold device 800. For example, the injection motor encoder 351 is used to detect the position and moving speed of the screw 330. The injection motor encoder 351 detects the rotation of the injection motor 350 and sends a signal indicating its detection result to the control device 700. If the position of the screw 330 reaches the set position, the switch from the filling process to the pressure holding process (the so-called V / P switch) is performed. The position where the V / P switch is performed is also referred to as the V / P switching position. The set moving speed of the screw 330 can be changed according to the position of the screw 330, time, etc.

[0093] The position and moving speed of the screw 330 in the filling process are uniformly set as a series of setting conditions. For example, the filling start position (also called the "injection start position"), the moving speed switching position and the V / P switching position are set. These positions are arranged in sequence from the back to the front, and represent the starting point and the end point of the interval for setting the moving speed. The moving speed is set for each interval. There can be one or more moving speed switching positions. The moving speed switching position can also be not set.

[0094] The upper limit value of the pressure of the screw 330 is set for each interval of the moving speed of the screw 330. The pressure of the screw 330 is detected by the load detector 360. When the pressure of the screw 330 is below the set pressure, the screw 330 moves forward at the set moving speed. On the other hand, when the pressure of the screw 330 exceeds the set pressure, the screw 330 moves forward at a moving speed slower than the set moving speed for the purpose of protecting the mold so that the pressure of the screw 330 becomes below the set pressure.

[0095] In addition, in the filling process, after the position of the screw 330 reaches the V / P switching position, the screw 330 can be paused at the V / P switching position and then the V / P switching can be performed. Instead of stopping the screw 330 before the V / P switching is to be performed, the screw 330 can be moved forward or backward at a slow speed. In addition, the screw position detector for detecting the position of the screw 330 and the screw moving speed detector for detecting the moving speed of the screw 330 are not limited to the injection motor encoder 351, and a conventional detector can be used.

[0096] In the pressure holding process, the injection motor 350 is driven to push the screw 330 forward, and the pressure of the molding material at the front end of the screw 330 (hereinafter also referred to as "holding pressure") is maintained at the set pressure, and the molding material remaining in the cylinder 310 is pushed toward the mold device 800. The insufficient amount of molding material in the mold device 800 due to cooling shrinkage can be supplemented. For example, the holding pressure is detected using a load detector 360. The setting value of the holding pressure can be changed according to the elapsed time since the start of the pressure holding process. The holding pressure and the holding time of the holding pressure in multiple pressure holding processes can be set separately, or they can be set uniformly as a series of setting conditions.

[0097] In the pressure holding process, the molding material in the cavity space 801 in the mold device 800 is gradually cooled, and at the end of the pressure holding process, the entrance of the cavity space 801 is blocked by the solidified molding material. This state is called gate sealing, which prevents the backflow of the molding material from the cavity space 801. After the pressure holding process, the cooling process begins. In the cooling process, the molding material in the cavity space 801 is solidified. In order to shorten the molding cycle time, a metering process can be performed in the cooling process.

[0098] In addition, the injection device 300 of this embodiment is a coaxial screw type, but it can also be a pre-plastic type. The injection device of the pre-plastic type supplies the molding material melted in the plasticizing cylinder to the injection cylinder, and injects the molding material into the mold device from the injection cylinder. In the plasticizing cylinder, the screw is configured to be able to rotate freely and cannot move forward or backward, or the screw is configured to be able to rotate freely and move forward and backward freely. On the other hand, in the injection cylinder, the plunger is configured to move forward and backward freely.

[0099] Furthermore, the injection device 300 of the present embodiment is a horizontal type in which the axial direction of the cylinder 310 is in the horizontal direction, but it may be a vertical type in which the axial direction of the cylinder 310 is in the up-down direction. The mold clamping device combined with the vertical injection device 300 may be a vertical type or a horizontal type. Similarly, the mold clamping device combined with the horizontal injection device 300 may be a horizontal type or a vertical type.

[0100] (Mobile device)

[0101] In the description of the moving device 400, similar to the description of the injection device 300, the moving direction of the screw 330 during filling (e.g., the negative direction of the X axis) is set to the front, and the moving direction of the screw 330 during metering (e.g., the positive direction of the X axis) is set to the rear.

[0102] The moving device 400 moves the injection device 300 forward and backward relative to the mold device 800. The moving device 400 also presses the nozzle 320 relative to the mold device 800 to generate nozzle contact pressure. The moving device 400 includes a hydraulic pump 410, a motor 420 as a driving source, and a hydraulic cylinder 430 as a hydraulic actuator.

[0103] The hydraulic pump 410 has a first port 411 and a second port 412. The hydraulic pump 410 is a bidirectionally rotatable pump, and generates hydraulic pressure by sucking working fluid (e.g., oil) from one of the first port 411 and the second port 412 and discharging it from the other port by switching the rotation direction of the motor 420. Alternatively, the hydraulic pump 410 can also suck working fluid from a tank and discharge the working fluid from one of the first port 411 and the second port 412.

[0104] The motor 420 operates the hydraulic pump 410. The motor 420 drives the hydraulic pump 410 with a rotation direction and torque corresponding to a control signal from the control device 700. The motor 420 may be an electric motor or an electric servomotor.

[0105] The hydraulic cylinder 430 includes a cylinder body 431, a piston 432, and a piston rod 433. The cylinder body 431 is fixed to the injection device 300. The piston 432 divides the interior of the cylinder body 431 into a front chamber 435 as a first chamber and a rear chamber 436 as a second chamber. The piston rod 433 is fixed to the fixed platen 110.

[0106] The front chamber 435 of the hydraulic cylinder 430 is connected to the first port 411 of the hydraulic pump 410 via the first flow path 401. The working fluid discharged from the first port 411 is supplied to the front chamber 435 via the first flow path 401, thereby pushing the injection device 300 forward. The injection device 300 moves forward and the nozzle 320 is pressed against the fixed mold 810. The front chamber 435 functions as a pressure chamber that generates the nozzle contact pressure of the nozzle 320 by the pressure of the working fluid supplied from the hydraulic pump 410.

[0107] On the other hand, the rear chamber 436 of the hydraulic cylinder 430 is connected to the second port 412 of the hydraulic pump 410 via the second flow path 402. The hydraulic fluid discharged from the second port 412 is supplied to the rear chamber 436 of the hydraulic cylinder 430 via the second flow path 402, thereby pushing the injection device 300 backward. The injection device 300 retreats and the nozzle 320 separates from the fixed mold 810.

[0108] In this embodiment, the moving device 400 includes the hydraulic cylinder 430 , but the present invention is not limited thereto. For example, instead of the hydraulic cylinder 430 , an electric motor and a motion conversion mechanism that converts the rotational motion of the electric motor into the linear motion of the injection device 300 may be used.

[0109] (Control device)

[0110] The control device 700 is constituted by a computer, for example. Figure 1-2 As shown, the control device 700 has a CPU (Central Processing Unit) 701, a storage medium such as a memory 702, an input interface 703, an output interface 704, and a communication interface 705. The control device 700 performs various controls by causing the CPU 701 to execute a program stored in the storage medium 702. In addition, the control device 700 receives signals from the outside through the input interface 703 and sends signals to the outside through the output interface 704.

[0111] The control device 700 repeatedly produces molded products by repeatedly performing metering steps, mold closing steps, pressure increasing steps, mold clamping steps, filling steps, pressure holding steps, cooling steps, pressure releasing steps, mold opening steps, and ejection steps. A series of actions for obtaining molded products, such as the actions from the metering step to the next metering step, are also referred to as "injection" or "molding cycle". In addition, the time required for one injection is also referred to as "molding cycle time" or "cycle time".

[0112] A molding cycle includes, for example, a metering process, a mold closing process, a pressure increasing process, a mold closing process, a filling process, a pressure holding process, a cooling process, a pressure releasing process, a mold opening process, and an ejection process in sequence. The order here is the order in which each process starts. The filling process, the pressure holding process, and the cooling process are performed during the mold closing process. The start of the mold closing process may also coincide with the start of the filling process. The end of the pressure releasing process coincides with the start of the mold opening process.

[0113] In addition, multiple processes can be performed simultaneously for the purpose of shortening the molding cycle time. For example, the metering process can be performed during the cooling process of the previous molding cycle, or during the mold closing process. In this case, the mold closing process can be performed at the beginning of the molding cycle. In addition, the filling process can be started during the mold closing process. In addition, the ejection process can be started during the mold opening process. When an on-off valve is set to open and close the flow path of the nozzle 320, the mold opening process can be started during the metering process. Because even if the mold opening process is started during the metering process, as long as the on-off valve closes the flow path of the nozzle 320, the molding material will not leak from the nozzle 320.

[0114] In addition, a primary molding cycle may include processes other than the metering process, the mold closing process, the pressure increasing process, the mold clamping process, the filling process, the pressure holding process, the cooling process, the pressure releasing process, the mold opening process, and the ejection process.

[0115] For example, after the pressure holding process is completed and before the metering process begins, a pre-metering suction process may be performed to retract the screw 330 to a preset metering start position. The pressure of the molding material accumulated in front of the screw 330 can be reduced before the metering process begins, and the screw 330 can be prevented from retreating sharply when the metering process begins.

[0116] Furthermore, after the metering process is completed and before the filling process begins, a post-metering backsucking process may be performed in which the screw 330 is retracted to a preset filling start position (also referred to as an "injection start position"). The pressure of the molding material accumulated in front of the screw 330 can be reduced before the filling process begins, and leakage of the molding material from the nozzle 320 before the filling process begins can be prevented.

[0117] The control device 700 is connected to an operation device 750 that receives input operations from a user and a display device 760 that displays a screen. The operation device 750 and the display device 760 are composed of, for example, a touch panel 770 and can be integrated. The touch panel 770 as the display device 760 displays a screen under the control of the control device 700. Information such as the settings of the injection molding machine 10 and the current state of the injection molding machine 10 can be displayed on the screen of the touch panel 770. The touch panel 770 can receive operations in the displayed screen area. In addition, operation parts such as buttons and input fields for receiving input operations from the user can be displayed in the screen area of ​​the touch panel 770. The touch panel 770 as the operation device 750 detects the input operation of the user on the screen and outputs a signal corresponding to the input operation to the control device 700. Thus, for example, the user can confirm the information displayed on the screen while operating the operation part set on the screen to set the injection molding machine 10 (including the input of the set value). In addition, the user operates the operation part set on the screen, thereby enabling the injection molding machine 10 corresponding to the operation part to operate. In addition, the operation of the injection molding machine 10 may be, for example, the operation (including stopping) of the mold clamping device 100, the ejection device 200, the injection device 300, the moving device 400, etc. Furthermore, the operation of the injection molding machine 10 may be the switching of the screen displayed on the touch panel 770 as the display device 760, etc.

[0118] In addition, the operating device 750 and the display device 760 of this embodiment are described as being integrated into the touch panel 770, but they may be provided independently. In addition, a plurality of operating devices 750 may be provided. The operating device 750 and the display device 760 are arranged on the operating side (negative direction of the Y axis) of the mold clamping device 100 (more specifically, the fixed platen 110).

[0119] (First embodiment)

[0120] When molding with an injection molding machine, there is a desire to use a learned model for setting. In this case, machine learning is required to generate a learned model.

[0121] In the learning stage of machine learning, it is also possible to consider using an injection molding machine for learning processing. However, there is a problem that the processing device of the injection molding machine requires a higher processing power. Therefore, it is possible to consider using an information processing device different from the injection molding machine for learning processing.

[0122] Therefore, in this embodiment, an example of executing the learning phase and the reasoning phase in different devices is described. Specifically, the learning model produced in the learning phase of the learning device is mounted on the injection molding machine. In addition, this embodiment is used as an example to describe an example of executing the learning phase and the reasoning phase in different devices, but it is not limited to the method of executing the learning phase and the reasoning phase in different devices, and the learning phase and the reasoning phase can also be performed in the same device.

[0123] Figure 3 This is a schematic diagram showing the cooperation between the learning device and the control device of the injection molding machine according to the present embodiment and the learned model.

[0124] exist Figure 3 In the illustrated example, a test injection molding machine 1350 , a learning device 1300 , and a control device 700 for the injection molding machine 10 are illustrated.

[0125] exist Figure 3 In the example shown, it can be considered that the test injection molding machine 1350 and the learning device 1300 are owned by, for example, the manufacturer of the injection molding machine 10, but it is not limited to the method of being owned by the manufacturer of the injection molding machine. For example, they can be owned by the shipping destination of the injection molding machine, or they can also be owned by the service provider who generates the learned model. Figure 3 The injection molding machine 10 and the control device 700 shown may also show the status before shipment, for example.

[0126] The test injection molding machine 1350 is an injection molding machine for machine learning. The structure of the test injection molding machine 1350 is the same as that of the injection molding machine 10, so the description thereof is omitted.

[0127] The test injection molding machine 1350 involved in this embodiment produces a molded product (an example of a product) according to the settings based on the user. In addition, the test injection molding machine 1350 outputs the waveform data measured during the injection molding to the learning device 1300. In this embodiment, data that represents the detection results of the sensors provided in the test injection molding machine 1350 or the injection molding machine 10 in a time series is referred to as waveform data.

[0128] The learning device 1300 generates training data based on the input waveform data, and stores the training data in the training data storage unit 1321 .

[0129] In order to apply artificial intelligence, it is necessary to perform a learning (training) phase and an inference (inference) phase.

[0130] The learning device 1300 executes the learning phase. Therefore, the learning unit 1315 of the learning device 1300 causes the neural network to read the training data stored in the training data storage unit 1321, and generates a network with adjusted synaptic weights and biases as a learned model LM.

[0131] For example, the learning device 1300 reads a large amount of training data and performs machine learning based on back propagation using a neural network, thereby generating a learned model LM.

[0132] The learning device 1300 may be, for example, a local server installed in a factory or a cloud server. Furthermore, the learning device 1300 may be a fixed terminal device or a portable terminal device (mobile terminal) installed in a factory or the like. For example, a desktop PC (Personal Computer) may be included in the fixed terminal device. Furthermore, for example, a smart phone, a tablet terminal, a portable PC, etc. may be included in the mobile terminal device.

[0133] The control device 700 of the injection molding machine 10 executes the inference phase. The control device 700 inputs data into the learned model LM and causes the learned model LM to perform inference. Furthermore, the control device 700 sets predetermined items of molding conditions based on the output results from the learned model LM.

[0134] In the present embodiment, the structure of the learned model LM generated by the learning unit 1315 of the learning device 1300 and the learned model LM used by the inference unit 715 of the control device 700 can be made universal. In addition, the learning device 1300 can also transmit the weights and deviations of the learned model LM to the control device 700. The control device 700 updates the learned model LM based on the received weights and deviations, thereby making it consistent with the learned model LM of the learning unit 1315 of the learning device 1300. Thus, the learning unit 1315 of the learning device 1300 and the inference unit 715 of the control device 700 of the injection molding machine 10 can be installed in different languages. For example, the learning unit 1315 of the learning device 1300 can also be installed using Python (a high-level computer programming language). On the other hand, the inference unit 715 of the control device 700 can also be installed using C++, for example.

[0135] Furthermore, the learning device 1300 and the control device 700 can have different CPUs, OSs, development languages, etc. The control device 700 can use a CPU with a lower processing speed than the learning device 1300. This can achieve cost reduction.

[0136] Furthermore, the structural information of the weights and biases transmitted only needs to include the values ​​of each weight, the values ​​of each bias and parameter structure information. For example, it can also be an object that describes the values ​​of each weight and each bias according to a data structure and through a prescribed method (such as JSON (JavaScript Object Notation: data exchange format), XML (Extensible Markup Language: Extensible Markup Language), ONNX (Open Neural Network Exchange: Open Neural Network Exchange), etc.).

[0137] Thus, the learning device 1300 can transmit information indicating the structure of the weights and the biases to the control device 700 of the injection molding machine 10 .

[0138] In addition, the present embodiment is not limited to the method of transmitting information indicating the structure of weights and biases from the learning device 1300 to the injection molding machine 10, and the learning model LM itself may be transmitted from the learning device 1300 to the control device 700 of the injection molding machine 10. In this case, the CPU, OS, development language, etc. may be made common between the control device 700 and the learning device 1300.

[0139] Thus, the control device 700 can perform inference using the learned model LM generated in the learning phase of the learning device 1300 .

[0140] Figure 4 1 is a diagram showing an example of the functional configuration of the learning device 1300 and the control device 700 according to the present embodiment. Figure 4 As shown, the learning device 1300 and the control device 700 are connected via a communication line NW.

[0141] The functions of the learning device 1300 are implemented by any hardware or any combination of hardware and software. Figure 4 As shown, the learning device 1300 includes a CPU 1301 , a storage medium 1302 , and a communication interface 1303 .

[0142] The storage medium 1302 stores various installed programs, and stores files and data required for various processes, etc. The storage medium 1302 includes, for example, a HDD (Hard Disc Drive), an SSD (Solid State Drive), a flash memory, and the like.

[0143] The storage medium 1302 according to this embodiment includes a training data storage unit 1321 and a learned model storage unit 1322 .

[0144] The training data storage unit 1321 stores training data used for learning the learned model LM. The structure of the training data will be described later.

[0145] The learned model storage unit 1322 stores the learned model LM.

[0146] The communication interface 1303 is used as an interface for connecting to an external device so as to enable communication. Thus, the learning device 1300 can communicate with an external device such as the injection molding machine 10 through the communication interface 1303. In addition, the communication interface 1303 may have multiple communication interfaces depending on the communication method between the connected device and the like.

[0147] The CPU 1301 of the learning device 1300 executes a program stored in the storage medium 1302. Thus, the CPU 1301 includes an acquisition unit 1311, an evaluation acquisition unit 1312, a correct answer information calculation unit 1313, a training data generation unit 1314, a learning unit 1315, and a communication control unit 1316 as functional units.

[0148] The learning device 1300 involved in this embodiment generates a learning model LM through the above-mentioned structure. The learning model LM is, for example, when waveform data representing the detection results detected by the sensor provided in the injection molding machine 10 during the injection molding of the injection molding machine 10 is input in a time series, the range of items that can be set as items related to the molding conditions of the injection molding machine 10 is divided into two or more intervals, and the confidence of each of the intervals is output. The confidence indicates the degree to which the value included in the interval is a correct answer as the value set for the item. Therefore, by referring to the output information, the value to be set for the item can be inferred.

[0149] First, the configuration of the learning device 1300 for generating training data used to generate the learned model LM will be described.

[0150] Each time injection molding is performed by setting a setting value for an item to be set, the acquisition unit 1311 acquires waveform data (an example of first data) indicating a detection result detected by a sensor provided in the test injection molding machine 1350 from the test injection molding machine 1350 .

[0151] In this embodiment, the case where the item to be set is a clamping force setting value is described. Moreover, each time the clamping force setting value is set for molding a molded product, the acquisition unit 1311 acquires waveform data (an example of aggregate data) representing the clamping force detected by the clamping force detector from the beginning to the end of the molding cycle from the test injection molding machine 1350. In addition, this embodiment does not limit the acquired waveform data to waveform data representing the clamping force from the beginning to the end of the molding cycle, as long as it is waveform data representing the clamping force for each injection. For example, the acquired waveform data may also be waveform data representing a part or the entire molding cycle, or waveform data similar to the previous and next injections.

[0152] The learning device 1300 according to the present embodiment generates training data for outputting information related to the setting of the mold clamping force setting value. Next, an example of a method for generating training data is described. In addition, the method for generating training data is not limited to the generation method shown below, and various methods can be considered.

[0153] Every time a mold clamping force setting value is set and injection molding is performed based on the mold clamping force setting value, the learning device 1300 according to the present embodiment generates training data for evaluating a molded product based on injection molding.

[0154] Specifically, each time the clamping force setting value is set and injection molding is performed based on the clamping force setting value, the evaluation acquisition unit 1312 acquires evaluation information indicating the evaluation of the molded product. For example, the evaluation acquisition unit 1312 acquires a value indicating the measurement result measured by a sensor provided in the injection molding machine 10 (hereinafter referred to as a measurement value) as the evaluation information. The evaluation of the molded product involved in this embodiment is based on whether burrs are generated in the molded product. That is, the evaluation acquisition unit 1312 involved in this embodiment acquires a measurement value that is an indicator of whether burrs are generated in the molded product.

[0155] The measured value may be, for example, the difference in mold clamping force generated during the molding cycle (detected by the mold clamping force detector).

[0156] Figure 5 13 is a diagram illustrating various parameters for each (shot material) number used in the evaluation acquisition unit 1312 and the correct answer information calculation unit 1313 according to the present embodiment. Figure 5In the example shown, the clamping force setting value, clamping force detection value, measured value, clamping force recommended value, and clamping force adjustment value are shown in correspondence with each (shot) number. The clamping force detection value refers to, for example, the highest value (peak value) among the clamping force detection values ​​detected during the period from the beginning to the end of the molding cycle. Figure 5 In the example shown, the mold clamping force setting value is made different for each shot, and molding conditions other than the mold clamping force setting value are made fixed.

[0157] The evaluation acquisition unit 1312 acquires the measured value each time the mold clamping force setting value is set and the injection molding is performed, in other words, according to each (shot) number. The measured value involved in this embodiment is the difference in mold clamping force. Specifically, the measured value is a value obtained by subtracting the detected value of the mold clamping force detected at the beginning of filling from the detected value of the mold clamping force detected at the moment before the mold device 800 is opened.

[0158] In this embodiment, the acquired difference in mold clamping force (in other words, the increased mold clamping force) is regarded as the amount by which the mold device 800 is opened due to the molding material entering the parting line surface. Therefore, the evaluation acquisition unit 1312 calculates the difference in mold clamping force and acquires the calculated difference in mold clamping force as a measurement value corresponding to the burr generated in the molded product.

[0159] In addition, the present embodiment does not limit the measured value corresponding to the burr generated in the molded product to the value obtained by subtracting the detected value of the mold clamping force from the detected value of the mold clamping force. For example, the result of measuring the amount of burr on the molded product taken out of the molding machine may be used.

[0160] In addition, in this embodiment, the example in which the evaluation acquisition unit 1312 acquires the measurement value (indicating the evaluation of the presence or absence of burrs) each time the mold clamping force setting value is set and injection molding is performed is described, but the method is not limited to acquiring the measurement value each time the mold clamping force setting value is set and injection molding is performed. Figure 5 As shown, the evaluation acquisition unit 1312 may acquire the difference in mold clamping force as a measurement value (indicating the evaluation of the presence or absence of burrs) every time the mold clamping force detection value is detected during injection molding.

[0161] The correct answer information calculation unit 1313 calculates, as information indicating a correct answer, a mold clamping force setting value estimated to result in no burr on the molded product, based on the measurement value (an example of evaluation information) acquired for each mold clamping force setting value or mold clamping force detection value.

[0162] Figure 6 This is a diagram showing an example of a method for calculating information related to a correct answer of a mold clamping force setting value by the correct answer information calculation unit 1313 according to the present embodiment. Figure 6In the example shown, the correct information calculation unit 1313 approximates the measured value of each mold clamping force using two straight lines, line 1601 and line 1602. As an approximation method for the two straight lines, for example, the least square method is used. Then, the correct information calculation unit 1313 calculates the intersection of the two straight lines as the recommended mold clamping force value. Figure 6 In the example shown, the correct answer information calculation unit 1313 calculates the mold clamping force 380 [kN], which is the intersection of the line 1601 and the line 1602, as the recommended mold clamping force value. Figure 6 This is an example of a calculation method, and the method is not limited to this calculation method. For example, the correct answer information calculation unit 1313 may perform curve approximation instead of two straight line approximation, and calculate the change point of the curve as the recommended mold clamping force value.

[0163] exist Figure 5 In the example shown, the recommended mold clamping force value, 380 [kN], and the mold clamping force adjustment value for adjusting the mold clamping force detection value are shown for each number. Figure 5 The clamping force adjustment value shown is a case where the value is used to adjust the clamping force detection value, but it may also be a value for adjusting the current clamping force setting value. In this embodiment, the clamping force adjustment value is used as information related to the correct answer.

[0164] In the present embodiment, the correct answer information calculation unit 1313 divides the range of the mold clamping force setting value that can be set by the injection molding machine 10 into units of predetermined intervals (e.g., 100 [kN]). Furthermore, the correct answer information calculation unit 1313 generates information related to the correct answer indicating whether the mold clamping force adjustment value is included in each interval obtained by dividing the range that can be set as the mold clamping force setting value.

[0165] Figure 7 2 is a diagram illustrating information related to the correct answer generated by the correct answer information calculation unit 1313 according to the present embodiment. Figure 7 The example shown shows whether each interval "100" includes a mold clamping force adjustment value in the range of "-1000" to "1000" of the mold clamping force setting value that can be set in the injection molding machine 10. Figure 7 In the example shown, "1" indicates that the clamping force adjustment value is included, and "0" indicates that the clamping force adjustment value is not included. Figure 7 In the example shown, each interval is a half-open interval, "[" indicates a closed interval, in other words, indicates a case larger than the closed interval, and ")" indicates an open interval, in other words, indicates a case smaller than the closed interval.

[0166] like Figure 5As shown, in (injection) number "1", the clamping force setting value is "1000", the clamping force adjustment value is "990", the clamping force recommended value is "380", and the clamping force adjustment value is "-610".

[0167] Therefore, in Figure 7 The example shown shows that in (shot) number "1", the interval [-700, -600) is "1", in other words, the clamping force adjustment value is included in this interval. The other intervals are "0".

[0168] Moreover, the correct answer information calculation unit 1313 generates a row vector indicating whether the mold clamping force adjustment value is included in each interval as information related to the correct answer. The row vector corresponding to the (shot) number "1" is, for example, [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]. This row vector is a row with "1" or "0" set for each 100 (as a half-open interval) in the range of "1000" to "-1000". "1" indicates that the mold clamping force adjustment value is included, and "0" indicates that the mold clamping force adjustment value is not included.

[0169] The correct answer information calculation unit 1313 also generates row vectors for numbers "2" to "11". The generation method is the same as that for the above-mentioned number "1", so the description is omitted.

[0170] Return to Figure 4 The training data generation unit 1314 generates training data by combining the waveform data of each (shot material) number acquired by the acquisition unit 1311 and the row vector of each (shot material) number generated by the correct information calculation unit 1313. Furthermore, the training data generation unit 1314 stores the generated training data in the training data storage unit 1321.

[0171] This embodiment illustrates a method for generating training data, and training data may be generated by methods other than the above method. In addition, in this embodiment, an example is described in which the correct answer information calculation unit 1313 automatically generates information related to the correct answer based on the evaluation information, but it is not limited to the method of automatically generating information based on the evaluation information. For example, the correct answer information calculation unit 1313 may also generate information related to the correct answer based on information input by the user via the operating device. In addition, after calculating the information related to the correct answer, the correct answer information calculation unit 1313 may receive confirmation of the information related to the correct answer based on the user's naked eyes, and modify the information.

[0172] Furthermore, the method is not limited to the method in which the learning device 1300 performs all the steps until the generation of the learning model LM is completed automatically. For example, after the learning device 1300 generates a plurality of training data, the user may confirm the contents of each of the plurality of training data.

[0173] In this embodiment, a plurality of training data may be referred to as a training data set. The training data set is a collection of training data based on a combination of waveform data as input and a row vector indicating whether each section includes a mold clamping force adjustment value as output.

[0174] Then, after the user completes confirmation and correction of the contents included in the training data set, the learning device 1300 can generate a learned model LM based on the training data set.

[0175] (Generation of learning completed model, etc.)

[0176] The learning unit 1315 generates a learned model LM by performing machine learning based on the training data group stored in the training data storage unit 1321. The learned model LM is generated by applying supervised learning to a basic learning model.

[0177] Specifically, the learning unit 1315 generates a learned model LM by performing machine learning based on waveform data and row vectors contained in the training data group, wherein the waveform data represents the result detected by the mold clamping force detector for each value set as the mold clamping force setting value or each value detected as the mold clamping force detection value, and the row vector represents whether each interval obtained by dividing the range that can be set as the mold clamping force setting value contains a mold clamping force adjustment value.

[0178] The learned model LM is such that, when waveform data (an example of the second data) representing the timing of the clamping force detected during molding by the injection molding machine 10 or the test injection molding machine 1350 is input from the input layer, a row vector representing the degree of confidence is output from the output layer for each interval obtained by dividing the settable range of the clamping force, and the degree of confidence indicates that the clamping force adjustment value is included in the interval.

[0179] Furthermore, the learned model LM may be updated by additionally learning a new training data set to the existing learned model LM.

[0180] As machine learning for generating the learned model LM, for example, a neural network may be applied. As another example, machine learning using a deep neural network (DNN: Deep Neural Network), i.e., deep learning (Deep Learning), may also be applied. As deep learning, for example, convolutional neural networks, RNN (Recurrent Neural Networks: Recurrent Neural Networks) and LSTM (Long Short Term Memory: Long Short Term Memory) may also be applied.

[0181] Figure 8 This is a diagram illustrating the configuration of a learned model LM used in the learning device 1300 and the control device 700 according to the first embodiment.

[0182] Figure 8 The learned model LM shown is composed of an input layer 1911, one or more intermediate layers 1912 connected to the input layer, and an output layer 1913 connected to the intermediate layer. The input layer has, for example, 2200 nodes, the intermediate layer has, for example, 200 nodes, and the output layer has 20 nodes.

[0183] When input data 1901 corresponding to 2200 nodes is input, the learned model LM outputs output data 1902 corresponding to 20 nodes.

[0184] The input data 1901 is, for example, waveform data that time-series represents actual values ​​that change during injection molding. The input data 1901 may also time-series represent changes in the mold clamping force, which is the detection result of a mold clamping force detector during injection molding.

[0185] The output data 1902 outputs, for example, a row vector for adjusting a specified item of the injection molding machine 10. The output data 1902 may also be, for example, a row vector for adjusting a mold clamping force setting value set when the injection molding machine 10 performs injection molding. Specifically, the output data 1902 is a row vector representing the degree of confidence for each interval obtained by dividing the range that can be set as the mold clamping force setting value. The confidence indicates the degree to which the mold clamping force adjustment value contained in the interval is a correct answer as the value of the adjusted mold clamping force setting value. The confidence of this embodiment is a numerical value in the range of 0 to 1. If the confidence is "1", it means that the probability that the mold clamping force adjustment value is contained in the interval is 100%, and if the confidence is "0", it means that the probability that the mold clamping force adjustment value is contained in the interval is 0%.

[0186] Thus, the learned model LM outputs a row vector indicating the degree of certainty for each section of the range of settable mold clamping force setting values ​​when the waveform data of the mold clamping force detected by the mold clamping force detector is input.

[0187] When the learning unit 1315 performs learning using the training data, preprocessing may be performed on the waveform data included in the training data. In addition, preprocessing may be performed during inference by the inference unit 715 described later.

[0188] In this embodiment, for example, preprocessing may be performed by dividing the clamping force setting value or the clamping force detection value used in the waveform data by the rated clamping force so that the clamping force setting value or the clamping force detection value converges within the range of 1.0.

[0189] As a modification different from the present embodiment, the correct information calculated by the correct information calculation unit 1313 may be preprocessed by label smoothing or the like. Label smoothing is a method of allocating the probability value subtracted from the correct label to the incorrect label for a hard correct label consisting of only "0" or "1" to mitigate the difference between the correct label and the incorrect label. Therefore, in the modification, the correct information is not "0" or "1", but is represented by a decimal value between "0" and "1". In the case of label smoothing, for example, the row vector [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] becomes the row vector [0.01,0.01,0.01,0.81,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01,0.01]. The other processing is the same as in the implementation mode, so the processing is omitted. In this variant, by applying this preprocessing, over-learning can be suppressed and the accuracy can be improved.

[0190] In the test injection molding machine 1350 according to this embodiment, various mold devices are installed and various molding conditions are set. In addition, in the learning device 1300, a training data set is generated based on various inspection results of molded products produced by the test injection molding machine 1350.

[0191] In this way, the learning device 1300 prepares a training data set based on various molding conditions set in the test injection molding machine 1350 and various mold devices set in the test injection molding machine 1350. The learning device 1300 performs machine learning using the training data set to generate a learned model LM. As a result, regardless of the molding conditions and the type of the mold device 800, reasoning using the learned model LM can be performed.

[0192] Therefore, in the control device 700 of the injection molding machine 10, regardless of the installed mold device 800 and the set molding conditions, the mold clamping force setting value can be changed using the learned model LM.

[0193] exist Figure 8 In the example shown, in the input layer, the variables corresponding to each node are defined as the input variable vector x 0～2199 In the middle layer, the variables corresponding to each node are defined as the intermediate variable vector z 0～199 In the output layer, the variable corresponding to the node is defined as the output variable vector y 0～19 .

[0194] At this time, calculate the intermediate variable vector z of the middle layer 0 The formula is calculated by the following formula (1). In formula (1), the weight vector w 0～199,0～2199 is used to output the intermediate variable vector z from each node of the input layer 0～199 The weight vector of each value (200×2200). In addition, the intermediate variable vector z 1～199 It can also be calculated by using the same formula, so the description is omitted. Figure 8 The deviation b is omitted 0～199 , but it is set as the bias used in the neural network. The activation function φ can be considered to use the ReLU function, for example, but other functions can also be used.

[0195] z 0 =φ(w 0,0～2199 ·x 0～2199 +b 0 )……(1)

[0196] Calculate the output variable vector y of the output layer 0 The formula is calculated by the following formula (2). In formula (2), the weight vector w' 0～199 is a weight vector used to output values ​​from each node in the middle layer to the output variable y. In addition, the output variable vector y 1～19 It can also be calculated by using the same formula, so the description is omitted. Figure 8The bias b' is omitted in , but it is set as the bias used in the neural network. The activation function φ' can be considered to use the ReLU function, for example, but other functions can also be used.

[0197] y 0 =φ'(w' 0,0～199 ·z 0～199 +b')……(2)

[0198] Furthermore, when learning or reasoning is performed using the learned model LM, the cross entropy error used in the multi-classification problem is used. Thus, each element of the row vector output from the learned model LM can be outputted not as 0 or 1 but as a numerical value between 0 and 1 (hereinafter referred to as confidence).

[0199] In this embodiment, the output variable vector y is composed of 20 elements. 0～19 The number of elements included in the row vector is not limited to 20, but is determined by the number of intervals obtained by dividing the range to be adjusted. The number of intervals can be 2 or more.

[0200] Furthermore, in the present embodiment, an example is described in which the settable range is determined based on the rated clamping force that can be set in the injection molding machine 10, but the method is not limited to determining the settable range based on the rating of the injection molding machine 10. In the settable range, a range limited by a predetermined method may be used.

[0201] Furthermore, the items to be set in this embodiment are not qualitative data (categorical data, nominal scale) but quantitative data (continuous scale). Furthermore, quantitative data includes both discrete values ​​such as integers and continuous values ​​such as real numbers.

[0202] That is, in this embodiment, the set value of the item to be set can be calculated even when the regression model is used, but it is difficult to recognize how confident the learned model is about the set value calculated when the regression model is used. Therefore, it is difficult for the user to guess whether there is no problem in setting the set value.

[0203] In contrast, the learned model LM according to the present embodiment outputs the degree of certainty for each section, so the control device 700 or the user can estimate whether the adjustment value is correct for each section.

[0204] The learning unit 1315 stores the generated learned model LM in the learned model storage unit 1322 .

[0205] Back to Figure 4The communication control unit 1316 uses the communication I / F 1303 to send and receive information with external devices such as the injection molding machine 10. For example, the communication control unit 1316 may send the learned model LM stored in the learned model storage unit 1322 to the control device 700 of the injection molding machine 10. In addition, the communication control unit 1316 may extract a structure representing parameters (e.g., weights and biases) set in each layer constituting the learned model LM stored in the learned model storage unit 1322, and send the structure to the control device 700.

[0206] exist Figure 4 Components of the control device 700 of the injection molding machine 10 are shown as functional blocks. Figure 4 The functional blocks shown in the figure are conceptual functional blocks and do not have to be physically constructed as shown in the figure. All or part of each functional block can be functionally or physically dispersed / integrated in any unit to form a structure. All or any part of each processing function performed in each functional block is implemented by a program executed by CPU701. Alternatively, each functional block can also be implemented as hardware based on wired logic. Figure 4 As shown, the CPU 701 of the control device 700 includes a communication control unit 711, an update unit 712, an injection molding processing unit 713, a setting unit 714, and a display control unit 716. In addition, the control device 700 includes a learned model storage unit 721 in the storage medium 702.

[0207] The learned model storage unit 721 stores the learned model LM. The structure of each layer of the learned model LM is the same as that of the learned model LM stored in the learned model storage unit 1322 of the learning device 1300.

[0208] The communication control unit 711 uses the communication I / F 705 to send and receive information to and from an external device such as the learning device 1300. For example, the communication control unit 711 may receive the learned model LM from the learning device 1300. In addition, the communication control unit 711 may receive information representing a structure of parameters (e.g., weights and biases) set in each layer constituting the learned model LM.

[0209] In addition, in this embodiment, as a method of obtaining the learned model LM or the information representing the structure, an example of receiving from the learning device 1300 is described, but the method of obtaining the learned model LM or the information representing the structure is not limited. For example, the learned model LM or the information representing the structure may also be obtained via an external storage medium.

[0210] The updating unit 712 updates the learned model LM stored in the learned model storage unit 721 using the received learned model LM or information indicating the structure. Thus, the updating unit 712 can make the learned model LM stored in the learned model storage unit 721 consistent with the learned model LM of the learning device 1300 .

[0211] In the case of updating by information representing a structure, the updating unit 712 may also be configured as a program for updating the learned model LM, which structure represents parameters (e.g., weights and biases) set in each layer constituting the learned model LM. The updating unit 712 may also determine whether the number of layers defined between the learned model LM stored in the learned model storage unit 721 and the received information representing the structure and the number of nodes per layer are consistent. Furthermore, the updating unit 712 updates the learned model LM when it is determined to be consistent. When the updating unit 712 determines to be inconsistent, it may not update the learned model LM but output an alarm screen with a different structure to the display device 760, etc.

[0212] The injection molding processing unit 713 executes processing for producing a molded product by the injection molding machine 10. For example, when producing a molded product, the injection molding processing unit 713 may perform injection molding after setting each item constituting the molding conditions.

[0213] The setting unit 714 includes an inference unit 715 , and sets the mold clamping force setting value based on information acquired from the inference unit 715 .

[0214] The inference unit 715, for example, inputs waveform data (an example of the second data) representing the result detected by the clamping force detector during the production of the molded product by the injection molding processing unit 713 into the input layer of the learned model LM, and receives from the output layer of the learned model LM a row vector representing the degree of certainty for each interval obtained by dividing the range that can be set as the clamping force setting value.

[0215] Furthermore, the inference unit 715 may pre-process the waveform data before inputting the waveform data to the input layer.

[0216] Then, the setting unit 714 sets the mold clamping force setting value based on the row vector received from the inference unit 715 .

[0217] Fig. 9 is a diagram illustrating information based on a row vector output from the inference unit 715 according to this embodiment. Fig. 9 In the example shown, for the range from "1000" to "-1000", the interval label, the center value of the interval, the certainty, and the cumulative sum of the certainty are shown in association with each (half-open interval) "100".

[0218] The confidence can be interpreted as the probability that the clamping force adjustment value is included in the interval. Fig. 9 In the example shown, the half-open interval [-200, -100) with a confidence level of "0.21" can be interpreted as having the highest probability of containing the clamping force adjustment value.

[0219] Then, the setting unit 714 calculates the mold clamping force adjustment value based on the row vector output from the inference unit 715 , and sets the mold clamping force setting value based on the calculated mold clamping force adjustment value.

[0220] For example, the setting unit 714 determines the interval label "[-200, -100)" with the highest confidence based on the confidence of each interval. Furthermore, when the center value is used, the setting unit 714 determines "-150" as the mold clamping force adjustment value, and sets it using the mold clamping force adjustment value and the current mold clamping force setting value or the detected mold clamping force detection value. In addition, the present embodiment is not limited to the method of determining the center value as the mold clamping force adjustment value. For example, the setting unit 714 may also determine the upper limit value or the lower limit value of the interval as the mold clamping force adjustment value. Subsequent mold clamping force adjustment values ​​are also not limited to the center value.

[0221] Furthermore, the present embodiment is not limited to the method of determining the mold clamping force adjustment value based on the interval with the highest certainty, and the mold clamping force adjustment value may be determined based on the cumulative sum of the certainties.

[0222] For example, the threshold of the cumulative sum of confidence may be determined to be 0.7. In this case, the setting unit 714 determines the interval label "[-100,0)" in which the cumulative sum of confidence of each interval is greater than 0.7. Furthermore, when the center value is used, the setting unit 714 determines "-50" as the mold clamping force adjustment value, and uses the mold clamping force adjustment value and the current mold clamping force setting value or the detected mold clamping force detection value for setting. In this way, by increasing the threshold of the cumulative sum, the mold clamping force setting value becomes larger. When the mold clamping force setting value becomes larger, it contributes to the safety side with respect to the generation of burrs, and thus the generation of burrs can be suppressed.

[0223] In this embodiment, the threshold value of the cumulative sum can also be set to be adjustable. The user or maintenance personnel can adjust the accuracy of the clamping force adjustment value according to the condition of the injection molding machine 10 (including the mold device 800). Therefore, by adjusting the threshold value of the cumulative sum by the user or maintenance personnel, it is possible to suppress the overload of the mold device 800 caused by the inference error of the clamping force adjustment value. Or it is possible to suppress the generation of burrs.

[0224] Furthermore, in this embodiment, when the setting unit 714 determines the clamping force adjustment value based on the cumulative sum of the confidence levels, the clamping force adjustment value at which the cumulative sum of the confidence levels just reaches the threshold may be obtained by linear interpolation instead of the center value of the interval.

[0225] exist Fig. 9 In the row data shown, when linear interpolation is performed on the mold clamping force adjustment value that just reaches 50% of the threshold value, the setting unit 714 performs calculation according to the following formula (3).

[0226] -250+{(0.50-0.48) / (0.69-0.48)}×{-150-(-250)}=-240.48[kN]……(3)

[0227] Furthermore, the setting unit 714 determines the mold clamping force adjustment value by the above method, but is not limited to the method of setting it based on the mold clamping force adjustment value. For example, the mold clamping force adjustment value may be determined based on the operation of the user who visually recognizes the information by displaying information output from the learned model LM to the user.

[0228] The display control unit 716 displays information on the display device 760. For example, the display control unit 716 may display image information indicating the degree of certainty (an example of the degree of correct answer) for each section.

[0229] Fig.10 715 according to the present embodiment. Fig.10 The image information of the chart shown is displayed on the display device 760. The user can refer to Fig.10 The graph shown can identify the probability (certainty) of each interval being used as the mold clamping force adjustment value, and the user can adjust the mold clamping force adjustment value based on the certainty.

[0230] For example, the user may refer to the image information and input the mold clamping force adjustment value via the operating device 750. Furthermore, the user may refer to the image information to set the threshold of the cumulative sum of confidence. Furthermore, the user may refer to the image information to select a method for setting the mold clamping force adjustment value. For example, the user may select to set the mold clamping force adjustment value for the interval with the highest confidence or to set the mold clamping force adjustment value for the interval with the cumulative sum of confidence above a predetermined threshold.

[0231] The setting unit 714 involved in this embodiment can also switch whether to set the clamping force setting value (an example of a specified item) according to the confidence level of each interval. Specifically, the setting unit 714 can also switch the setting method of the clamping force setting value according to the kurtosis representing the confidence level of each interval. The kurtosis is an indicator of the sharpness of the distribution of the confidence level of each interval included in the row vector. The calculation method of the kurtosis can be any method regardless of whether it is a known method.

[0232] Fig.11 2 is a diagram showing a second example of a graph showing the degree of certainty for each interval output from the inference unit 715 according to the present embodiment. Fig.11 In the example shown, the confidence level for each interval and the cumulative value 2101 of the confidence level are shown. Fig.11 In the example shown, Fig.10 Compared with the example shown in the figure, the confidence is not dispersed in multiple intervals, and the situation with high kurtosis can be grasped.

[0233] exist Fig.11 In the example shown, the confidence level is greater than 0.8 in the half-open interval "[-100,0)". Fig.11 The example shown indicates that the probability that the clamping force adjustment value is included in "[–100,0)" is very high.

[0234] Therefore, the setting unit 714 may determine the interval label with the highest confidence level when the kurtosis is higher than the first reference, determine the mold clamping force adjustment value based on the interval label, and set the value based on the mold clamping force adjustment value and the current mold clamping force setting value or the detected mold clamping force detection value. In addition, instead of using the kurtosis as the judgment standard, whether the highest confidence level is above a specified value may be used for judgment. The same applies to the following.

[0235] Furthermore, the setting unit 714 may determine an interval label in which the cumulative sum of the confidence levels of each interval is greater than a specified value when the kurtosis is lower than the first benchmark and higher than the second benchmark, and determine the mold clamping force adjustment value based on the interval label, and set the value based on the mold clamping force adjustment value and the current mold clamping force setting value or the detected mold clamping force detection value.

[0236] Furthermore, when the kurtosis is lower than the second reference, the setting unit 714 may determine that the mold clamping force adjustment value should not be performed based on the row vector because the values ​​are too dispersed, and terminate the setting process of the mold clamping force adjustment value.

[0237] In this embodiment, the case where waveform data is input into the learned model LM is described. However, this embodiment is not limited to the case where waveform data is input into the learned model LM, and after the molded product is injection molded, aggregate data representing the measurement results of the molded product may be input. The aggregate data may include, for example, the measurement results of the amount of burrs at a plurality of positions preset on the molded product after injection molding.

[0238] In this embodiment, the clamping force adjustment value can be set in consideration of the confidence level of each interval, and the clamping force setting value can be set using the clamping force adjustment value. In this embodiment, the quality accuracy of the molded product produced after the setting can be improved in the injection molding machine 10. In addition, the clamping force setting value can be set automatically, thereby reducing the operation burden of the user and the processing burden of the control device 700.

[0239] In the present embodiment, an example of setting the clamping force setting value is described. However, the present embodiment does not limit the setting object to the clamping force setting value. As the setting object, any setting for injection molding, especially quantitative data that needs to be adjusted according to the conditions and environment for injection molding, can be used. The setting object can be a detection value detected by a sensor set on the injection molding machine 10, or it can be the time required for a specified process of the injection molding machine 10. For example, it can also be applied to the holding time, VP switching position, holding pressure setting, filling speed setting, filling pressure, and back pressure setting. Moreover, as setting information related to metering, it can also be applied to metering speed, metering delay, and metering end position. Moreover, as setting information related to clamping, in addition to the clamping force setting value, it can also be applied to boost timing, mold opening and closing speed, and mold opening position. Moreover, as setting information related to the ejection device 200, the ejection position, ejection pressure, ejection speed, and ejection compression timing can also be applied. Moreover, as settings related to the temperature in the injection molding machine 10, the cylinder temperature setting, nozzle temperature setting, water cooling temperature, and mold temperature can also be applied. Furthermore, the present invention can also be applied to the size of the mold device 800, the characteristics of the molding material, and the like.

[0240] In this embodiment, the data obtained from the sensor of the injection molding machine 10 or the external measuring device (including the camera device) is pre-processed and then input into the learning model LM, thereby achieving the setting of the specified items in an end-to-end manner. Therefore, the processing can be simplified compared with the past. In the above embodiment, the processing burden can be reduced compared with the past, and there is no need to use a high-performance CPU, etc., so that cost reduction can be achieved.

[0241] Furthermore, in the above-described embodiment and modified example, when the adjustment value is set based on the cumulative sum of the confidences, even if the learned model LM is used, the adjustment value can be changed simply by adjusting the threshold value. Therefore, the setting corresponding to the condition of the injection molding machine 10 (including the mold device 800) can be easily changed.

[0242] (Variation Example)

[0243] In the above embodiment, an example of the learned model LM outputting a row vector representing the confidence of each interval obtained by dividing the range that can be set for the item to be set is described. However, the above embodiment does not limit the information output by the learned model LM to information related to the item to be set. For example, the learned model LM may also output a row vector representing the confidence of the evaluation included in each interval obtained by dividing the range of the evaluation of the molded product. In this modified example, the case of estimating the weight of the molded product as the evaluation of the molded product is described.

[0244] Fig.12 Schematic diagram showing inference performed by the learned model LM mounted in the control device 700 according to the present modification.

[0245] The learned model LM involved in this variant example is generated by machine learning based on waveform data and row vectors contained in the training data group, wherein the waveform data represents the results detected by the mold force detector, and the row vector represents whether the weight of the molded product produced when the waveform data is detected is included in each interval obtained by dividing the range that can be detected as the weight of the injection molding machine 10.

[0246] Furthermore, when the waveform data 2201 is input, the learned model LM of the control device 700 outputs a row vector 2202 indicating the degree of certainty for each interval in which the weight of the molded product is divided into units of 1 g.

[0247] The waveform data 2201 may be data indicating the time series of one or more of the position of the screw 330 , the holding pressure detection value, and the mold clamping force detection value, for example.

[0248] exist Fig.12 In the row vector 2202 shown in , if the confidence is 0.79 in the half-open interval [31.0, 32.0), it becomes the highest value. Therefore, the control device 700 can also determine the weight of the molded product based on the half-open interval [31.0, 32.0). For example, the control device 700 can also determine the center value 31.5 of the half-open interval [31.0, 32.0) as the weight of the molded product.

[0249] In the above embodiment, the case where the elements constituting the row vector output from the learned model LM are the confidence (0 to 1) of each interval is described. However, the present embodiment does not limit the elements constituting the row vector output from the learned model LM to the confidence (0 to 1) of each interval, as long as it is information indicating whether the interval is a correct answer.

[0250] In addition, the present modification example does not limit the evaluation of the molded product to the weight, and for example, the evaluation may be the size of the molded product, as long as it is quantitative data related to the quality characteristics of the molded product.

[0251] (Second embodiment)

[0252] In the above embodiment, an example is described in which the control device 700 of the injection molding machine 10 performs settings using the learned model as the management device of the injection molding machine 10. However, the above embodiment is not limited to the method in which the control device 700 of the injection molding machine 10 performs settings using the learned model. Therefore, in the second embodiment, an example is described in which the group management device 2300 that controls the injection molding machine 10 performs settings using the learned model LM.

[0253] Fig.13 2 is a diagram illustrating the configuration of a learning device 1300, a group management device 2300, and an injection molding machine 10 according to the present embodiment. Fig.13 As shown, the group management device 2300 manages, for example, eight injection molding machines 10. The number of injection molding machines to be managed is an example, and any number may be used.

[0254] In the present embodiment, the setting using the learned model LM by the control device 700 shown in the above-described embodiment is applied to a group management device 2300 having a group management function for a plurality of injection molding machines 10 .

[0255] The learning device 1300 according to the present embodiment has the same configuration as that of the first embodiment. The learning device 1300 transmits information on the learned model LM to the group management device 2300 via the communication line NW.

[0256] The communication line NW is, for example, an Internet communication line. When the learning device 1300 and the group management device 2300 communicate with each other, it is preferable to connect them via a VPN (Virtual Private Network). By connecting via a VPN, the security of communication can be improved.

[0257] The group management device (an example of a management device) 2300 is a device that manages multiple injection molding machines 10 from the perspective of productivity, and is connected to each injection molding machine 10. Like the above-mentioned control device 700, it receives molding conditions and detection results obtained from various sensors, and assists in the management and planning of production conditions.

[0258] The group management device 2300 may be realized by a personal computer, for example. However, generally, the group management device 2300 does not have a function of controlling the injection molding operation of each injection molding machine 10, but if the function of the personal computer is expanded, it can also have a control function.

[0259] Similar to the control device 700 , the group management device 2300 includes a storage device (not shown), and the learned model LM is stored in the storage device.

[0260] The group management device 2300 includes a CPU (not shown) similarly to the control device 700, and by causing the CPU to execute a program stored in a storage device, a communication control unit 711, an update unit 712, an injection molding processing unit 713, a setting unit 714 including an inference unit 715, and a display control unit 716 are implemented similarly to the control device 700. The processing executed by each structure is the same as that in the above-mentioned embodiment, and therefore the description thereof is omitted.

[0261] The group management device 2300 can be connected to the learning device 1300 via a communication line NW.

[0262] The group management device 2300 receives information on the learned model LM from the learning device 1300. The group management device 2300 updates the learned model LM based on the received information.

[0263] As an aid to the management and planning of production conditions, for example, in the group management device 2300, an adjustment value may be calculated for a predetermined item based on the waveform data shown in the received detection result, and a request to set the adjustment value may be sent to each injection molding machine 10. In addition, in the group management device 2300, the display control unit 716 may display image information indicating the confidence level of each interval. Furthermore, the user may also set each injection molding machine 10 with reference to the displayed image information.

[0264] <Function>

[0265] In the above-mentioned embodiment and modification, when waveform data is input to the input layer of the learned model LM, a column vector is output from the output layer, and the column vector represents the confidence of each interval, and the confidence represents the degree to which the value contained in the interval is a correct answer. Therefore, by referring to the confidence, it is possible to identify to what extent the value contained in the interval can be trusted. For example, the control device 700 or the user can grasp whether to perform settings based on the values ​​contained in the interval by referring to the confidence. Therefore, it is possible to improve the safety when performing settings, processing or numerical calculations based on values.

[0266] In the above-mentioned embodiment and variant, the quantitative data is divided into each interval, and the confidence in the interval is output. Therefore, in the learned model, the classification problem processed for outputting the confidence can be returned to the quantitative data. Therefore, the quantitative data can be used for the evaluation of molded products, the setting, control or numerical calculation related to injection molding.

[0267] The above is an explanation of the implementation of the learning model and the management device of the injection molding machine involved in the present invention, but the present invention is not limited to the above implementation, etc. Various changes, corrections, replacements, additions, deletions and combinations can be made within the scope of the technical solution. Of course, these also fall within the technical scope of the present invention.

Claims

1. A learned model comprising an input layer, one or more intermediate layers connected to the input layer, and an output layer connected to the intermediate layer, wherein: The learned model is used to enable the computer to function in the following manner: performing machine learning based on first data and correct answer information, wherein the first data represents a result detected during production of a product by an injection molding machine or a result measured after the product is produced, and the correct answer information divides a quantity related to production of the product into two or more intervals and indicates, for each interval, whether a value included in the interval is a correct answer, When second data indicating a result detected during production or a result measured after production is input from the input layer, the output layer outputs, for each of the intervals, the degree to which the value included in the interval is a correct answer.

2. The learning completion model according to claim 1, wherein: The correct answer information classifies a range that can be set as a predetermined item into two or more intervals, and indicates, for each interval, whether a value included in the interval as a value set for the predetermined item is a correct answer. The output layer outputs, for each of the intervals, a degree of certainty indicating the degree to which a value included in the interval is a correct answer as a value set for the predetermined item.

3. The learning completion model according to claim 1, wherein: The correct answer information classifies a range that can be set as the evaluation of the product into two or more intervals, and indicates, for each interval, whether a value included in the interval is a correct answer as the evaluation of the product. The output layer outputs, for each of the intervals, a degree of certainty indicating the degree to which a value included in the interval is evaluated as a correct answer for the product.

4. A management device for an injection molding machine, comprising: A learning-completed model having an input layer, one or more intermediate layers connected to the input layer, and an output layer connected to the intermediate layer, and performing machine learning based on first data and correct answer information, wherein the first data represents a result detected during production of a product by an injection molding machine or a result measured after production of the product, and the correct answer information divides a quantity related to production of the product into two or more intervals and indicates, for each interval, whether a value included in the interval is a correct answer; and The inference unit acquires, from the output layer, the degree to which the value included in each of the intervals is a correct answer, when second data indicating a result detected during production or a result measured after production is input from the input layer.

5. The management device for an injection molding machine according to claim 4, further comprising: The display control unit displays, for each of the intervals, image information indicating the extent to which a value included in the interval is a correct answer.

6. The management device for an injection molding machine according to claim 4, wherein: The correct answer information classifies a range that can be set as a predetermined item into two or more intervals, and indicates, for each interval, whether a value included in the interval as a value set for the predetermined item is a correct answer. The management device for the injection molding machine further includes a setting unit that sets the predetermined item based on the degree of certainty output from the output layer for each of the intervals indicating that the value included in the interval as the value set for the predetermined item is a correct answer.

7. The management device for an injection molding machine according to claim 6, wherein: The setting unit sets the value included in the interval to the predetermined item based on the cumulative value of the degree of certainty of each of the intervals.

8. The management device for an injection molding machine according to claim 6, wherein: The setting unit switches whether to set the predetermined item according to the degree of certainty of each of the intervals.

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