Molding condition adjustment method, computer program, molding condition adjustment device, and injection molding machine

Abstract

To provide a molding condition adjustment method that can determine the molding condition items to be adjusted, the adjustment range, and the adjustment increment width, and automatically perform condition setting.SOLUTION: In a molding condition adjustment method for adjusting the molding conditions of an injection molding machine, mold information indicating mold characteristics, resin information indicating resin characteristics, or defect information indicating defect phenomena in a molded product is acquired, the molding condition items to be adjusted, the adjustment range, and the adjustment increment width are determined based on the acquired mold information, resin information, or defect information, and molding control is executed while changing the molding conditions based on the determined molding condition items to be adjusted, the adjustment range, and the adjustment increment width.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a molding condition adjustment method, a computer program, a molding condition adjustment device, and an injection molding machine. [Background technology]

[0002] When a new mold is installed in an injection molding machine and injection molding is performed for the first time, the molding conditions must first be determined by adjusting the settings of various molding parameters. These molding parameters include, for example, injection cylinder temperature, injection pressure, injection speed, holding pressure switch position, and screw back position. Adjustments to these molding parameters are made based on the operator's experience, and repeated trial and error is required to obtain the appropriate molding conditions.

[0003] Patent Document 1 discloses a molding data collection method for obtaining the most suitable molding conditions. The molding data collection method according to Patent Document 1 first determines rough molding conditions. Next, for each molding condition item selected sequentially from the rough molding conditions, the molding condition value is sequentially increased or decreased by a predetermined increment within a predetermined allowable limit range to create a plurality of molding conditions. Next, molding data is collected for each molding condition while automatically performing continuous molding operations for a set number of times or for a set time under the created plurality of molding conditions. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 09-216265 Summary of the Invention [Problem to be solved by the invention]

[0005] However, no method is disclosed for automatically adjusting molding conditions by taking into consideration the characteristics of the mold, the properties of the resin, or the state of the molded product.

[0006] An object of the present disclosure is to provide a molding condition adjustment method, a computer program, a molding condition adjustment device, and an injection molding machine that can determine the molding condition items to be adjusted, the adjustment range, and the adjustment increment width, and automatically perform condition setting. [Means for solving the problem]

[0007] The molding condition adjustment method according to this embodiment is a molding condition adjustment method for adjusting the molding conditions of an injection molding machine, which acquires mold information indicating the characteristics of the mold, resin information indicating the properties of the resin, or defect information indicating defects in the molded product, and determines the molding condition items to be adjusted, the adjustment range, and the adjustment increment size based on the acquired mold information, resin information, or defect information, and performs molding control while changing the molding conditions based on the determined molding condition items to be adjusted, the adjustment range, and the adjustment increment size.

[0008] The computer program (program product) according to this embodiment is a computer program for causing a computer to execute a process for adjusting molding conditions of an injection molding machine, and acquires mold information indicating the characteristics of the mold, resin information indicating the properties of the resin, or defect information indicating a defective phenomenon in the molded product, and determines the molding condition items to be adjusted, the adjustment range, and the adjustment increment width based on the acquired mold information, resin information, or defect information, and causes the computer to execute a process for executing molding control while changing the molding conditions based on the determined molding condition items to be adjusted, the adjustment range, and the adjustment increment width.

[0009] The molding condition adjustment device according to this embodiment is a molding condition adjustment device that adjusts the molding conditions of an injection molding machine, and includes an acquisition unit that acquires mold information indicating the characteristics of the mold, resin information indicating the properties of the resin, or defect information indicating defective phenomena in the molded product; a determination unit that determines the molding condition items to be adjusted, the adjustment range, and the adjustment increment width based on the acquired mold information, resin information, or defect information; and a control unit that executes molding control while changing the molding conditions based on the determined molding condition items to be adjusted, the adjustment range, and the adjustment increment width.

[0010] The injection molding machine according to this aspect includes the molding condition adjusting device. [Effects of the Invention]

[0011] According to the present invention, molding condition items to be adjusted, the adjustment range and adjustment step width can be determined, and the conditions can be automatically set. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a schematic diagram illustrating an example of the configuration of an injection molding machine according to a first embodiment. [Figure 2] 1 is a block diagram showing an example of the configuration of a molding condition adjusting device according to a first embodiment. [Figure 3] FIG. 2 is a conceptual diagram showing a condition assignment learning model according to the first embodiment. [Figure 4] 3 is a flowchart showing a processing procedure of a processor according to the first embodiment. [Figure 5] FIG. 3 is a schematic diagram showing an example of a molding condition adjustment screen according to the first embodiment. [Figure 6] FIG. 10 is a schematic diagram illustrating an example of the configuration of an injection molding machine according to a second embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing a condition assignment learning model according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] Specific examples of a molding condition adjustment method, a computer program, a molding condition adjustment device, and an injection molding machine according to embodiments of the present invention will be described below with reference to the drawings. At least some of the embodiments described below may be combined in any manner. Note that the present invention is not limited to these examples, but is defined by the claims, and all modifications within the meaning and scope equivalent to the claims are intended to be included.

[0014] 1 is a schematic diagram showing an example of the configuration of an injection molding machine 1 according to embodiment 1. The injection molding machine 1 according to embodiment 1 includes a mold clamping device 2 that clamps a mold 21, an injection device 3 that plasticizes and injects molding material, and a control device 4. The control device 4 functions as a molding condition adjustment device according to embodiment 1.

[0015] The mold clamping device 2 includes a fixed platen 22 fixed on a bed 20, a mold clamping housing 23 provided slidably on the bed 20, and a movable platen 24 that similarly slides on the bed 20. The fixed platen 22 and the mold clamping housing 23 are connected by a plurality of, for example, four tie bars 25, 25, .... The movable platen 24 is configured to be slidable between the fixed platen 22 and the mold clamping housing 23. A mold clamping mechanism 26 is provided between the mold clamping housing 23 and the movable platen 24.

[0016] The mold clamping mechanism 26 is configured by, for example, a toggle mechanism. The mold clamping mechanism 26 may also be configured by a direct pressure type mold clamping mechanism, that is, a mold clamping cylinder. The fixed platen 22 and the movable platen 24 are provided with a fixed mold 21a and a movable mold 21b, respectively, and when the mold clamping mechanism 26 is driven, the mold 21 is opened and closed.

[0017] The injection device 3 is provided on a base 30. The injection device 3 includes a heating cylinder 31 having a nozzle 31a at its tip, and a screw 32 disposed within the heating cylinder 31 so as to be rotatable in both the circumferential and axial directions. The screw 32 is driven in both the rotational and axial directions by a drive mechanism 33. The drive mechanism 33 is composed of a rotary motor that drives the screw 32 in the rotational direction, a motor that drives the screw 32 in the axial direction, and the like. Note that the drive mechanism 33 shown in FIG. 1 is covered with a cover, and therefore the internal configuration is not shown.

[0018] A hopper 34 into which molding material is poured is provided near the rear end of the heating cylinder 31. The injection molding machine 1 also includes a nozzle touch device 35 that moves the injection unit 3 in the front-to-rear direction (the left-to-right direction in FIG. 1). When the nozzle touch device 35 is driven, the injection unit 3 moves forward and the nozzle 31a of the heating cylinder 31 touches the contact portion of the fixed platen 22.

[0019] 2 is a block diagram showing an example of the configuration of the control device 4 according to the first embodiment. The control device 4 is a computer that controls the operations of the mold clamping device 2 and the injection device 3, and includes, as its hardware configuration, a processor (control unit) 41, a storage unit 42, a control signal output unit 43, and an operation panel 40. The control device 4 is a device that adjusts the molding conditions of the injection molding machine 1. The control device 4 may be a server device connected to a network. The control device 4 may also be configured to perform distributed processing by a plurality of computers, or may be realized by a plurality of virtual machines provided in a single server, or may be realized by using a cloud server.

[0020] The processor 41 includes an arithmetic circuit such as a central processing unit (CPU), a multi-core CPU, a graphics processing unit (GPU), a general-purpose computing on graphics processing units (GPGPU), a tensor processing unit (TPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a neural processing unit (NPU), an internal storage device such as a read-only memory (ROM) or a random-access memory (RAM), an I / O terminal, a timer, etc. The processor 41 executes a computer program (program product) 42a stored in a storage unit 42 (described later) to implement the molding condition adjusting method according to the first embodiment. Each functional unit of the control device 4 may be realized by software, or part or all of the functional units may be realized by hardware.

[0021] The storage unit 42 is a non-volatile memory such as a hard disk, an EEPROM (Electrically Erasable Programmable ROM), or a flash memory. The storage unit 42 stores a computer program 5 for causing a computer to execute a molding condition adjustment process. The storage unit 42 also stores a condition setting learning model 6 for setting molding conditions. The configuration of the condition setting learning model 6 will be described in detail later. The storage unit 42 also includes an adjustment history DB7 that stores the history of molding conditions set in the process of setting conditions using the molding condition adjustment method. The adjustment history DB7 stores, for example, molding condition items, setting values ​​for those items, and date and time information when those setting values ​​were set, in association with each other.

[0022] The computer program 5 according to the first embodiment may be recorded on a recording medium 50 in a computer-readable manner. The storage unit 42 stores the computer program 5 read from the recording medium 50 by a reading device. The recording medium 50 is a semiconductor memory such as a flash memory. The recording medium 50 may also be an optical disc such as a CD (Compact Disc)-ROM, a DVD (Digital Versatile Disc)-ROM, or a BD (Blu-ray (registered trademark) Disc). Furthermore, the recording medium 50 may also be a magnetic disc such as a flexible disk or a hard disk, a magneto-optical disc, or the like.

[0023] Furthermore, the computer program 5 according to the first embodiment may be downloaded from an external server connected to a communication network and stored in the storage unit .

[0024] The control signal output unit 43 outputs a control signal to the injection molding machine 1 for controlling the operation of the injection molding machine 1 in accordance with the control of the processor 41 based on the molding conditions.

[0025] The operation panel 40 is an interface for setting molding conditions and the like for the injection molding machine 1 and for operating the injection molding machine 1. The operation panel 40 includes a display panel 40a, an operation unit 40b, and a communication unit 40c.

[0026] The display panel 40a is a display device such as an LCD panel or an organic EL display panel, and under the control of the processor 41, displays a reception screen for accepting settings of molding conditions for the injection molding machine 1, the status of the injection molding machine 1, and the implementation status of the molding condition adjustment method according to this embodiment 1.

[0027] The operation unit 40b is an input device for inputting and adjusting molding conditions for the injection molding machine 1, and includes operation buttons, a touch panel, etc. The operation unit 40b provides the processor 41 with data indicating the received molding conditions.

[0028] The communication unit 40c is a communication circuit that communicates with an external device via a wired or wireless connection to acquire information. For example, the communication unit 40c can communicate with an external storage medium such as a USB to acquire data. The communication unit 40c can acquire data from an external computer via wired communication via a LAN cable or wireless communication. The communication unit 40c can acquire molding conditions that are initially set in the injection molding machine 1, mold image data (mold information), and the like from the external storage medium or external computer.

[0029] The injection molding machine 1 is set with the following molding conditions: Injection start position The set values ​​that determine the molding conditions, such as the resin temperature in the mold, nozzle temperature, cylinder temperature (heater temperature), hopper temperature, mold clamping force, injection speed, injection acceleration, injection peak pressure (injection pressure), and injection stroke, are set.

[0030] In addition, the injection molding machine 1 is set with set values ​​that determine molding conditions such as cylinder tip resin pressure, backflow prevention ring seating state, dwell pressure, dwell pressure switching speed, dwell pressure switching position, dwell pressure completion position, cushion position, metering back pressure, and metering torque.

[0031] Furthermore, set values ​​that define molding conditions such as the metering completion position, screw retraction speed, cycle time, mold closing time, injection time, pressure holding time, metering time, and mold opening time are set in the injection molding machine 1. Further molding conditions such as cooling time, screw rotation speed, mold opening / closing speed, ejection speed, and number of ejections are set in the injection molding machine 1. Then, once these set values ​​have been set, the injection molding machine 1 operates in accordance with these set values.

[0032] 3 is a conceptual diagram showing the configuration of the condition-finding learning model 6 according to embodiment 1. The condition-finding learning model 6 is a trained machine learning model that, when mold image data, resin information, and defect information are input, outputs information related to the degree of necessity for adjustment of multiple items that are molding conditions, the adjustment range, and the adjustment step size.

[0033] The mold image data is image data obtained by capturing images of runners through which the molten raw resin flows, flow paths such as gates, and cavities or cores corresponding to the shape of the molded product.

[0034] The resin information includes data indicating the melting point or viscosity of the raw resin fed into the injection molding machine 1. The defect information includes data indicating the presence or absence of burrs, sink marks, flow marks, cracks, weld lines, voids, silver streaks, or shorts in the molded product.

[0035] The parameter setting is performed by repeatedly taking test shots while changing the setting value of a specific item by a specific increment. The adjustment range includes an initial value that is set when parameter setting is performed and an end value. The adjustment increment is the amount by which the setting value is changed each time a test shot is performed.

[0036] The conditioning learning model 6 includes a first input layer 61a and a first intermediate layer 62a to which mold image data is input, a second input layer 61b and a second intermediate layer 62b to which resin information and defect information are input, and an output layer 63.

[0037] The first input layer 61a and the first hidden layer 62a have the same configuration as the input layer and hidden layer of a convolutional neural network (CNN). The first input layer 61a has a plurality of nodes to which pixel values ​​of each pixel constituting the mold image are input. The first hidden layer 62a has a configuration in which convolution layers that convolute the pixel values ​​of each pixel of the mold image data input to the first input layer 61a and pooling layers that map the pixel values ​​convolved in the convolution layers are alternately connected. The first hidden layer 62a extracts feature values ​​of the mold image while compressing pixel information of the mold image, and outputs the extracted feature values ​​of the mold image data to the second hidden layer 62b.

[0038] The second input layer 61b has a node to which resin information is input and a node to which defect information is input. The second intermediate layer 62b has multiple intermediate layers each having multiple nodes, and the input-side intermediate layer nodes are connected to the nodes of the second input layer 61b and the output-side nodes of the first intermediate layer 62a. The second intermediate layer 62b receives input of mold image data features, resin information, and defect information, and extracts information related to adjusting molding conditions.

[0039] The output layer 63 has multiple nodes corresponding to multiple items related to molding conditions. The node corresponding to the first item includes a node that outputs the degree of necessity for adjustment of the setting value related to that item, a node that outputs the adjustment range of that setting value, and a node that outputs the adjustment increment width. Similar nodes are provided for other items. The degree of necessity for adjustment is a probability indicating whether or not adjustment of the setting value related to that item is necessary, and is, for example, a numerical value between 0 and 1. Note that the configuration of the output layer 63 is not particularly limited as long as it can obtain information indicating the molding condition item to be adjusted, the adjustment range, and the adjustment increment width.

[0040] The conditioning learning model 6 can be generated by machine learning using training data including mold image data, resin information, and defect information, as well as molding condition items to be adjusted in such conditioning, adjustment ranges, and adjustment increments. For example, the processor 41 performs machine learning on the conditioning learning model 6 by optimizing the weight coefficients of the conditioning learning model 6 using an error backpropagation method, an error gradient descent method, or the like using the training data.

[0041] 4 is a flowchart showing the processing procedure of the processor 41 according to the first embodiment. The processor 41 accepts initial settings of molding conditions via the operation panel 40 and sets setting values ​​for various items related to the accepted molding conditions (step S111). The processor 41 then controls the operation of the injection molding machine 1 according to the set molding conditions (step S112). The injection molding performed in step S112 is a test shot for obtaining information necessary to carry out the molding condition adjustment method according to the first embodiment. Specifically, the operator observes the state of the molded product obtained by executing the test shot and checks for any defects in the molded product.

[0042] Next, the processor 41 determines whether or not an automatic condition setting mode using the molding condition adjustment method according to the present embodiment 1 has been selected (step S113). The operator can use the operation panel 40 to select whether or not to set molding conditions using the automatic condition setting mode according to the present embodiment 1. The processor 41 stores whether or not to use the automatic condition setting mode set by the operator.

[0043] When it is determined that the automatic condition setting mode is not selected (step S113: NO), the processor 41 executes a manual adjustment process of the molding conditions (step S114) and ends the process. Specifically, the processor 41 accepts the adjustment contents of the molding conditions by the operator on the operation panel 40 and sets the adjusted molding conditions.

[0044] When it is determined that the automatic condition setting mode is selected (step S113: YES), the processor 41 acquires mold information via the operation panel 40 (step S115). The mold information is, for example, mold image data obtained by capturing an image of the mold 21.

[0045] Next, processor 41 acquires resin information including data indicating the melting point or viscosity of the raw resin via operation panel 40 (step S116). If the resin information is included in the molding conditions, the resin information may be acquired from the molding conditions initially set in step S111. Alternatively, a table may be provided that associates the type of resin with the resin information, and processor 41 may be configured to extract the resin information from the table using the information indicating the type of resin input via operation panel 40 as a key.

[0046] Next, the processor 41 acquires defect information for the molded product via the operation panel 40 (step S117). Specifically, the operator checks the condition of the molded product obtained by the test shot in step S112, and inputs defect information indicating the presence or absence of burrs, sink marks, flow marks, cracks, weld lines, voids, silver streaks, or short circuits in the molded product via the operation panel 40.

[0047] The processor 41 that executes the processes of steps S115 to S117 functions as an acquisition unit that acquires mold information, resin information, and defect phenomena.

[0048] Next, the processor 41 inputs the mold information, resin information, and defect information acquired in the processes of steps S115 to S117 into the condition assignment learning model 6. The processor 41 determines the molding condition item to be adjusted based on the degree of necessity of adjustment of each of the multiple items output from the condition assignment learning model 6 (step S118). For example, the processor 41 determines the item with the greatest degree of necessity of adjustment as the item to be adjusted. Note that the processor 41 may also determine the item with a degree of necessity of adjustment equal to or greater than a predetermined threshold as the item to be adjusted. The processor 41 may determine multiple types of items as the item to be adjusted, but for simplicity of explanation in the present embodiment 1, it determines one item as the item to be adjusted.

[0049] Processor 41 also refers to data indicating the adjustment range output from the node corresponding to the item determined in step S118, and determines the adjustment range of the set value (step S119). Processor 41 also refers to data indicating the step size output from the node corresponding to the item determined in step S118, and determines the adjustment step size of the set value (step S120).

[0050] The processor 41 that executes the processes of steps S117 to S119 functions as a determination unit that determines the molding condition items to be adjusted, the adjustment range, and the adjustment step size based on the acquired mold information, resin information, or defect information.

[0051] Processor 41 adjusts the setting values ​​of the setting items determined in step S118 using the adjustment range and step size determined in steps S119 and S120, and stores the adjusted values ​​as adjustment history in storage unit 42 (step S121). Specifically, processor 41 executes a process of sequentially changing the setting values ​​of the setting items requiring adjustment from the initial value to the end value of the adjustment range by the step size determined in step S120. Processor 41 controls the operation of injection molding machine 1 in accordance with the adjusted molding conditions (step S122).

[0052] After completing the process of step S122, processor 41 determines whether the setting value has reached the end value (step S123). If it is determined that the setting value of the item to be adjusted has not reached the end value (step S123: NO), processor 41 returns the process to step S121 and repeats the adjustment process.

[0053] If it is determined that the setting value of the item to be adjusted has reached the end value (step S123: YES), defect information for the molded product molded using the optimal value is acquired (step S124), and it is determined whether or not there are still defects (step S125). The defect information is sufficient as long as it includes at least data indicating whether the molded product is good or bad. The operator checks the condition of the multiple molded products molded in the processes of steps S121 to S123, and inputs defect information for the best molded product into operation panel 40. Processor 41 determines whether or not there are still defects after the automatic adjustment based on the defect information via operation panel 40.

[0054] The processor 41 may be configured to acquire molded product image data obtained by capturing an image of the molded product and determine whether the molded product is good or bad based on the acquired molded product image data. The quality of the molded product may be determined based on rules or using a learning model previously obtained by machine learning.

[0055] If it is determined in step S125 that there is a defect (step S125: YES), processor 41 returns the process to step S117 and continues the process of adjusting the molded product. If it is determined that there is no defect (step S125: NO), processor 41 ends the molding condition adjustment process.

[0056] 5 is a schematic diagram showing an example of the molding condition adjustment screen 8 according to embodiment 1. When performing the molding condition adjustment method, the processor 41 displays the molding condition adjustment screen 8. The molding condition adjustment screen 8 includes a setting value adjustment section 81 and a condition setting mode display section 82.

[0057] The set value adjustment unit 81 includes a set value display unit 81a, a set value input unit 81b, and an adjustment history display unit 81c. The processor 41 displays one item related to molding conditions and the current set value of that item on the set value display unit 81a. The set value input unit 81b includes soft keys such as a numeric keypad, a "CRL" key, and an "Enter" key. When the operator operates the set value input unit 81b and inputs a set value, the processor 41 stores the input set value as the set value of the item displayed on the set value display unit 81a.

[0058] Meanwhile, the processor 41 reads out the history of setting values, ie, the molding condition items and setting values ​​from the adjustment history DB 7, and displays the read-out items and setting values ​​on the adjustment history display unit 81c. When the operator operates the pull-down button, the processor 41 displays a list of previously set items and setting values. The processor 41 may be configured to display a list of multiple items and setting values ​​related to previously set molding conditions.

[0059] The condition extraction mode display section 82 includes an automatic condition extraction mode selection section 82a, an initial value display section 82b, an end value display section 82c, and an increment display section 82d. The automatic condition extraction mode selection section 82a is, for example, a checkbox, and the operator can select whether or not to use the automatic condition extraction mode by operating the automatic condition extraction mode selection section 82a. The processor 41 accepts operation of the automatic condition extraction mode selection section 82a and stores whether or not to use the automatic condition extraction mode.

[0060] When the use of the automatic condition setting mode is selected, processor 41 displays the items determined in step S118 in setting value display section 81a. Processor 41 also displays the initial value and end value of the adjustment range determined in step S119 in initial value display section 82b and end value display section 82c. Similarly, processor 41 displays the step size determined in step S120 in step size display section 82d.

[0061] When the processor 41 controls injection molding while changing the set values ​​through the processes of steps S121 to S123, the processor 41 displays the changed set values ​​on the set value display unit 81a each time.

[0062] According to the molding condition adjustment screen 8, the operator can instruct the implementation of the molding condition adjustment method according to the first embodiment by operating the condition setting mode display section 82. By checking the display of the condition setting mode display section 82, the operator can know what adjustment range and in what increments the set values ​​are being changed. By checking the display of the set value display section 81a, the operator can know the molding condition items and set values ​​that are currently being automatically adjusted.

[0063] According to the injection molding machine 1 of embodiment 1 configured as described above, it is possible to determine the molding condition items to be adjusted, the adjustment range, and the adjustment increment width based on the mold image data, resin information, and defect information, and automatically execute the condition setting.

[0064] Specifically, the processor 41 can automatically set conditions such as the injection speed, injection pressure, heater temperature, dwell pressure, dwell switching position, injection start position, resin temperature in the mold, nozzle temperature, hopper temperature, mold clamping force, injection acceleration, injection stroke, resin pressure at the tip of the cylinder, backflow prevention ring seating state, dwell switching speed, dwell completion position, cushion position, metering back pressure, metering torque, metering completion position, screw retraction speed, cycle time, mold closing time, injection time, dwell time, metering time, mold opening time, cooling time, screw rotation speed, mold opening / closing speed, ejection speed, or number of ejections, based on mold image data, resin information indicating the melting point and viscosity of the raw resin, and defect information indicating the presence or absence of burrs, sink marks, flow marks, cracks, weld lines, voids, silver streaks, or shorts in the molded product.

[0065] Furthermore, the processor 41 and the adjustment history display unit 81c can display the history of the items and setting values ​​set in the automatic condition setting mode.

[0066] In the first embodiment, the condition setting learning model 6 using a neural network has been described, but the condition setting information may be determined using other known machine learning models such as an SVM (Support Vector Machine) or a Bayesian network.

[0067] Furthermore, in the first embodiment, an example has been described in which conditions are determined using all of the mold information, resin information, and defect information, but conditions may also be determined using any one of the mold information, resin information, and defect information.

[0068] Furthermore, in the first embodiment, the condition assignment learning model 6 learned by supervised learning has been described, but the condition assignment information may be determined using a model learned by reinforcement learning.

[0069] The control device 4 that uses reinforcement learning is configured as follows. The processor 41 of the control device 4 has, as functional units, an observation unit, a reward calculation unit, and an agent. Note that each functional unit of the control device 4 may be realized by software, or some or all of them may be realized by hardware.

[0070] The observation unit acquires mold information, resin information, and defect information, and outputs the acquired information as observation data to the agent and the reward calculation unit.

[0071] The reward calculation unit calculates reward data based on the acquired observation data, particularly defect information, and outputs the calculated reward data to the agent. For example, if the degree of defect of the molded product is high, the reward value will be small or negative. Also, the reward value will be larger the fewer times it takes to obtain a defect-free molded product.

[0072] The agent may be, for example, a reinforcement learning model with a deep neural network such as DQN, A3C, or D4PG, or a model-based reinforcement learning model such as PlaNet or SLAC.

[0073] In the case of a reinforcement learning model with a deep neural network, the agent is equipped with a DQN (Deep Q-Network) and determines an action a according to a state s of the injection molding machine 1 indicated by observation data. The state s includes mold information, resin information, and defect information.

[0074] DQN is a neural network model that outputs the value of each of multiple actions a when a state s indicated by observed data is input. The multiple actions a are the items to be adjusted, the adjustment range, and the adjustment step size. High-value actions a represent appropriate conditioning conditions. The agent selects a high-value action a, and the selected action a causes the injection molding machine 1 to transition to another state. After the state transition, the agent receives the reward calculated by the reward calculation unit, and is trained to maximize profits, i.e., the cumulative reward.

[0075] More specifically, DQN has an input layer, a hidden layer, and an output layer. The input layer has a plurality of nodes to which a state s, i.e., observed data, is input. The output layer has a plurality of nodes that correspond to a plurality of actions a, respectively, and output the value Q(s, a) of the action a in the input state s.

[0076] Based on the state s, action a, and the reward r obtained from that action, the value Q expressed by the following formula (1) is used as training data, and various weighting coefficients that characterize the DQN can be adjusted to enable reinforcement learning of the agent's DQN. Q(s,a)←Q(s,a)+α(r+γmaxQ(snext,anext)-Q(s,a))...(1) however, s:Status a:Action α: learning coefficient r:Reward γ: discount rate maxQ(snext,anext): The maximum Q value for the next possible action

[0077] (Embodiment 2) The injection molding machine 1 according to the second embodiment differs from the first embodiment in that it automatically acquires defect information on molded products and automatically adjusts molding conditions. Since the other configurations of the injection molding machine 1 are the same as those of the injection molding machine 1 according to the first embodiment, the same parts are given the same reference numerals and detailed explanations are omitted.

[0078] 6 is a schematic diagram illustrating an example of the configuration of an injection molding machine 1 according to embodiment 2. The injection molding machine 1 according to embodiment 2 includes an imaging device 9 that captures an image of a molded product. The imaging device 9 captures an image of the molded product and outputs molded product image data obtained by the image capture to the control device 4.

[0079] 7 is a conceptual diagram showing a condition assignment learning model 6 according to embodiment 2. The condition assignment learning model 6 according to embodiment 2 includes a first input layer 61a and a first intermediate layer 62a, a second input layer 61b and a second intermediate layer 62b, an output layer 63, and a third input layer 61c and a third intermediate layer 62c to which molded product image data is input.

[0080] The third input layer 61c and the third intermediate layer 62c have a configuration similar to that of the input layer and intermediate layer of a convolutional neural network, for example. The third input layer 61c has a plurality of nodes to which the pixel values ​​of each pixel constituting the molded product image are input. The third intermediate layer 62c has a configuration in which a convolutional layer that convolves the pixel values ​​of each pixel of the molded product image data input to the third input layer 61c and a pooling layer that maps the pixel values ​​convolved in the convolutional layer are alternately connected. The third intermediate layer 62c extracts feature values ​​of the molded product image while compressing the pixel information of the molded product image, and outputs the extracted feature values ​​of the molded product image data to the second intermediate layer 62b.

[0081] The second intermediate layer 62b includes multiple intermediate layers each having multiple nodes, and the input-side intermediate layer nodes are connected to the nodes of the second input layer 61b, the output-side nodes of the first intermediate layer 62a, and the output-side nodes of the third intermediate layer 62c. The second intermediate layer 62b receives input of mold image data features, resin information, and molded product image data features, and extracts information related to adjustments to molding conditions. The features of the molded product image data are information equivalent to defect information. The configuration of the output layer 63 is the same as in embodiment 1.

[0082] In the process of step S117, the processor 41 according to the second embodiment acquires molded product image data from the imaging device 9. Then, in the process of step S118, the processor 41 inputs the acquired mold image data, resin information, and molded product image data into the condition setting learning model 6. The processor 41 determines the molding condition items to be adjusted based on the degree of adjustment necessity of each of the multiple items output from the condition setting learning model 6. The following process is the same as in the first embodiment.

[0083] However, the processing of steps S124 and S125 can be performed using the condition setting learning model 6. Specifically, the processor 41 acquires molded product image data obtained by imaging the molded product molded in the processing of step S122. Then, the processor 41 inputs the mold image data, resin information, and molded product image data into the condition setting learning model 6. If the degree of adjustment necessity output from the condition setting learning model 6 is equal to or greater than a predetermined value, the processor 41 determines that there is a defect in step S125, and if the degree of adjustment necessity is less than the predetermined value, the processor 41 determines that there is no defect in step S125.

[0084] According to the injection molding machine 1 of the second embodiment configured as above, molding conditions can be automatically determined simply by inputting mold image data and resin information. [Explanation of symbols]

[0085] 1 injection molding machine 2 Mold clamping device 3 Injection device 4. Control device 5. Computer Programs 50 Recording Media 6. Conditional Learning Model 7 Operation history DB

Claims

1. A molding condition adjusting method for adjusting molding conditions of an injection molding machine, comprising: Obtaining mold information including image data relating to the mold cavity and indicating the characteristics of the mold, resin information including data indicating the melting point or viscosity of the raw material resin and indicating the properties of the raw material resin, and defect information including data indicating the presence or absence of burrs, sink marks, flow marks, cracks, weld lines, voids, silver streaks or shorts in the molded product and indicating defective phenomena in the molded product under the current molding conditions, When the mold information, resin information, and defect information are input, the acquired mold information, resin information, and defect information are input into a learning model that outputs information related to the necessity of adjusting a plurality of items that are molding conditions, the adjustment range, and the adjustment step width, thereby determining the molding condition items to be adjusted, the adjustment range, and the adjustment step width; Molding control is performed while automatically changing molding conditions based on the determined molding condition items to be adjusted, adjustment range, and adjustment step width. How to adjust molding conditions.

2. The molding condition items include injection speed, injection pressure, heater temperature, dwell pressure, dwell switching position, injection start position, resin temperature in the mold, nozzle temperature, hopper temperature, mold clamping force, injection acceleration, injection stroke, cylinder tip resin pressure, backflow prevention ring seating state, dwell switching speed, dwell completion position, cushion position, metering back pressure, metering torque, metering completion position, screw retreat speed, cycle time, mold closing time, injection time, dwell time, metering time, mold opening time, cooling time, screw rotation speed, mold opening / closing speed, ejection speed, or number of ejections. The molding condition adjusting method according to claim 1.

3. Display the history of changes to molding conditions The molding condition adjusting method according to claim 1 or 2.

4. A computer program for causing a computer to execute a process for adjusting molding conditions of an injection molding machine, Obtaining mold information including image data relating to the mold cavity and indicating the characteristics of the mold, resin information including data indicating the melting point or viscosity of the raw material resin and indicating the properties of the raw material resin, and defect information including data indicating the presence or absence of burrs, sink marks, flow marks, cracks, weld lines, voids, silver streaks or shorts in the molded product and indicating defective phenomena in the molded product under the current molding conditions, When the mold information, resin information, and defect information are input, the acquired mold information, resin information, and defect information are input into a learning model that outputs information related to the necessity of adjusting a plurality of items that are molding conditions, the adjustment range, and the adjustment step width, thereby determining the molding condition items to be adjusted, the adjustment range, and the adjustment step width; Molding control is performed while automatically changing molding conditions based on the determined molding condition items to be adjusted, adjustment range, and adjustment step width. A computer program for causing the computer to execute a process.

5. A molding condition adjusting device that adjusts molding conditions of an injection molding machine, an acquisition unit that acquires mold information including image data related to the mold cavity and indicating the characteristics of the mold, resin information including data indicating the melting point or viscosity of the raw material resin and indicating the properties of the raw material resin, and defect information including data indicating the presence or absence of burrs, sink marks, flow marks, cracks, weld lines, voids, silver streaks or shorts in the molded product and indicating defects in the molded product under the current molding conditions; a determination unit that determines molding condition items to be adjusted, adjustment ranges and adjustment increments by inputting the acquired mold information, resin information and defect information into a learning model that outputs information related to the necessity of adjusting a plurality of molding condition items, the adjustment ranges and adjustment increments when the mold information, resin information and defect information are input; a control unit that automatically changes molding conditions and executes molding control based on the determined molding condition items to be adjusted, adjustment ranges, and adjustment step widths; A molding condition adjusting device comprising:

6. An injection molding machine equipped with the molding condition adjustment device described in claim 5.

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