COMPUTER PROGRAM, INFORMATION PROCESSING METHOD, INFORMATION PROCESSING APPARATUS, AND INJECTION MOLDING SYSTEM

Abstract

To provide an information processing device capable of maintaining required production quantity and quality, or reducing demand power or power consumption, and capable of optimizing production efficiency or power consumption.SOLUTION: An information processing device for adjusting each setting value of a plurality of injection molding machines having motors comprises: a state observation unit for observing at least either one of respective setting values of the plurality of injection molding machines and quantity and quality of molded products manufactured by the plurality of injection molding machines, and a state relating to power consumed by the plurality of injection molding machines; a determination unit for determining an adjustment amount of the setting value containing at least one of acceleration, acceleration time, maximum speed and constant-speed operation time of the motors included in the plurality of injection molding machines based on state information acquired by observing by the state observation unit; and a transmission part for transmitting the determined adjustment amount of the setting value to the injection molding machines.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a computer program, an information processing method, an information processing device, and an injection molding system. [Background technology]

[0002] Patent Document 1 discloses an injection molding scheduling system that automatically plans a molding sequence within a lot from basic data related to ordered products requested by a customer, based on a preregistered manufacturing process file.

[0003] Patent Document 2 discloses a schedule adjustment method for adjusting the injection process schedules of multiple injection molding machines connected to a network so as not to exceed the maximum allowable power. In the schedule adjustment method, one injection molding machine connected to the network broadcasts its own injection process schedule to the other injection molding machines. The other injection molding machines receive the broadcasted injection process schedule and determine their own injection process schedules based on the received injection process schedule so as to fall within the maximum allowable power.

[0004] Patent Document 3 discloses an online AI management system that stores molding condition programs for when various molding defects occur in a workstation connected to multiple injection molding machines, and supports the setting of molding conditions for each injection molding machine.

[0005] Patent Document 4 discloses a state determination device that uses a machine learning device to determine an abnormality in an injection molding machine from the operating state of injection molding.

[0006] Patent document 5 discloses a power management system that measures the power consumption of multiple injection molding machines, determines whether or not power reduction measures are necessary based on the maximum allowable power amount, and instructs the injection molding machines to implement power reduction measures if necessary. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Publication No. 3-262622 [Patent Document 2] JP 2011-121283 A [Patent Document 3] Japanese Patent Application Publication No. 2-143825 [Patent Document 4] JP 2018-167424 A [Patent Document 5] JP 2010-240915 A Summary of the Invention [Problem to be solved by the invention]

[0008] However, no technology is disclosed for adjusting the settings of the acceleration, acceleration time, and the like of the motor of an injection molding machine while taking into consideration the production quantity and quality or power.

[0009] An object of the present invention is to provide a computer program, an information processing method, an information processing device, and an injection molding system that can maintain the required production quantity and quality, or reduce demand power or power consumption, and optimize production efficiency or power consumption. [Means for solving the problem]

[0010] The computer program of this embodiment is a computer program for causing a computer to execute a process of adjusting the setting values ​​of each of a plurality of injection molding machines having a motor, and causes the computer to execute a process of acquiring status information obtained by observing at least one of the setting values ​​of each of the plurality of injection molding machines, the quantity and quality of molded products produced by the plurality of injection molding machines, and a status related to the power consumed by the plurality of injection molding machines, determining an adjustment amount for the setting values ​​including at least one of the acceleration, acceleration time, maximum speed, and constant speed operating time of the motors of the plurality of injection molding machines based on the acquired status information, and transmitting the determined adjustment amount for the setting values ​​to the injection molding machines.

[0011] The information processing method according to the present embodiment is an information processing method for adjusting the setting values ​​of each of a plurality of injection molding machines having a motor, and obtains status information obtained by observing at least one of the setting values ​​of each of the plurality of injection molding machines, the quantity and quality of molded products produced by the plurality of injection molding machines, and a status related to the power consumed by the plurality of injection molding machines, and determines an adjustment amount for the setting value including at least one of the acceleration, acceleration time, maximum speed, and constant speed operating time of the motors of the plurality of injection molding machines based on the obtained status information, and transmits the determined adjustment amount for the setting value to the injection molding machine.

[0012] The information processing device of the present embodiment is an information processing device that adjusts the setting values ​​of each of multiple injection molding machines having motors, and is equipped with a status observation unit that observes at least one of the setting values ​​of each of the multiple injection molding machines, the quantity and quality of molded products produced by the multiple injection molding machines, and a status related to the power consumed by the multiple injection molding machines, a determination unit that determines an adjustment amount of the setting value including at least one of the acceleration, acceleration time, maximum speed, and constant speed operating time of the motors of the multiple injection molding machines based on the status information observed by the status observation unit, and a transmission unit that transmits the adjustment amount of the setting value determined by the determination unit to the injection molding machine.

[0013] The injection molding system of this embodiment comprises a plurality of injection molding machines each having a motor and the information processing device, wherein the information processing device transmits adjustment amounts of the setting values ​​of the plurality of injection molding machines to the injection molding machines, and the injection molding machines receive the adjustment amounts transmitted from the information processing device and adjust the setting values ​​based on the received adjustment amounts. Effect of the Invention

[0014] According to the above, it is possible to maintain a required production quantity and quality, or to reduce demand power or power consumption, thereby optimizing production efficiency or power consumption. [Brief description of the drawings]

[0015] [Figure 1] 1 is a block diagram showing an example of the configuration of an injection molding system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a block diagram showing a detailed configuration of an injection molding machine that constitutes the injection molding system. [Diagram 3] FIG. 2 is a block diagram showing a detailed configuration of a measurement unit constituting the injection molding system. [Figure 4] FIG. 2 is an explanatory diagram conceptually illustrating the function of the injection molding system. [Diagram 5] FIG. 2 is a block diagram conceptually showing the configuration of a learning model. [Figure 6] FIG. 2 is a conceptual diagram showing the power consumption of each process in a molding cycle in chronological order. [Figure 7] 6 is a graph showing the operation and power consumption of an electric motor before adjustment of a setting value. [Figure 8] 11 is a graph showing the operation and power consumption of the electric motor after the setting value is adjusted. [Figure 9] 13 is a flowchart showing a processing procedure of the information processing device. [Figure 10] 13 is a flowchart showing a processing procedure of the information processing device. [Figure 11] 13 is a flowchart showing a procedure for a remuneration calculation process. [Figure 12] FIG. 1 is a conceptual diagram showing an example of an improvement in the operation end timing of two injection molding machines manufacturing different molded products. [Figure 13] FIG. 13 is a schematic diagram showing an example of a monitor screen showing an adjustment status of a setting value. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] Specific examples of a computer program, an information processing method, an information processing device, and an injection molding system according to embodiments of the present invention will be described below with reference to the drawings. At least some of the embodiments and modifications described below may be combined as desired. Note that the present invention is not limited to these examples, but is indicated by the claims, and is intended to include all modifications within the meaning and scope equivalent to the claims.

[0017] FIG. 1 is a block diagram showing an example of the configuration of an injection molding system according to this embodiment, FIG. 2 is a block diagram showing a detailed configuration of an injection molding machine 2 constituting the injection molding system, and FIG. 3 is a block diagram showing a detailed configuration of a measurement unit 4 constituting the injection molding system. The molding device system according to this embodiment includes an information processing device 1, a plurality of injection molding machines 2 installed in a factory, a factory power meter 3, and a measurement unit 4. The plurality of injection molding machines 2, the factory power meter 3, and the measurement unit 4 are connected to the information processing device 1 via a wired or wireless communication line, and the information processing device 1 transmits and receives various information between each injection molding machine 2, the factory power meter 3, and the measurement unit 4, and optimizes molding conditions. The information processing device 1 may be configured to transmit and receive information with the measurement unit 4 via the control device 22 of the injection molding machine 2. In particular, the injection molding system according to this embodiment can reduce demand power and power usage while maintaining the required production quantity and quality by adjusting the set values ​​such as acceleration, acceleration time, maximum speed, and constant speed operation time of the motor of the injection molding machine 2, and the heater temperature, thereby making it possible to optimize production efficiency and power usage.

[0018] The information processing device 1 is a computer, and includes a processing unit 11, a storage unit 12, and a communication unit 13 as hardware configurations. The processing unit 11 includes 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), a neural processing unit (NPU), and other arithmetic circuits, a read only memory (ROM), a random access memory (RAM), and other internal storage devices, an I / O terminal, and the like. The processing unit 11 executes a computer program P stored in the storage unit 12 described later, thereby implementing the information processing method according to the present embodiment, and functions as a state observation unit 11a, a determination unit 11b, a transmission unit 11c, a reward calculation unit 11d, and a reinforcement learning unit 11e (see FIG. 4). Details of each functional unit will be described later. Each functional unit of the information processing device 1 may be realized by software, or a part or all of the functional units may be realized by hardware. The information processing device 1 may be a server device connected to a network (not shown). The information processing device 1 may be configured with multiple computers for distributed processing, may be realized by multiple virtual machines provided in one server, or may be realized by using a cloud server.

[0019] The storage unit 12 is a non-volatile memory such as a hard disk, an EEPROM (Electrically Erasable Programmable ROM), or a flash memory. The storage unit 12 stores a computer program P for causing a computer to execute the information processing method according to the present embodiment. The storage unit 12 also stores a learning model 10 that performs reinforcement learning for adjusting the setting values ​​of the injection molding machine 2. The learning model 10 will be described in detail later. The computer program P according to the present embodiment may be recorded in a computer-readable manner on a recording medium 14. The storage unit 12 stores the computer program P read from the recording medium 14 by a reading device (not shown). The recording medium 14 is a semiconductor memory such as a flash memory. The recording medium 14 may also be an optical disk such as a CD (Compact Disc)-ROM, a DVD (Digital Versatile Disc)-ROM, or a BD (Blu-ray (registered trademark) Disc). The recording medium 14 may also be a magnetic disk such as a flexible disk or a hard disk, a magnetic optical disk, or the like. The computer program P according to the present embodiment may also be downloaded from an external server (not shown) connected to a communication network (not shown) and stored in the storage unit 12.

[0020] The communication unit 13 is a communication circuit that transmits and receives information according to a predetermined communication protocol such as Ethernet (registered trademark). The communication unit 13 is connected to the multiple injection molding machines 2, the factory power measuring devices 3, and the measurement unit 4 via a wired or wireless communication network, and the processing unit 11 can transmit and receive various information between the injection molding machines 2, the factory power measuring devices 3, and the measurement unit 4 via the communication unit 13. The communication unit 13 may be configured to transmit and receive information to and from the measurement unit 4 via the control device 22.

[0021] The factory power meter 3 measures the power consumed by the multiple injection molding machines 2, the information processing device 1, and various other electrical devices in the factory, and transmits information indicating the total power consumption consumed in the factory to the information processing device 1. The total power consumption is information that is the basis for calculating the demand power (demand value) in the factory. The demand power is a value measured as the average power consumption for a specified demand time (30 minutes). The power fee consists of a basic fee and a power fee determined by the amount of power actually used, and the demand power is used as the basis for calculating this basic fee. Specifically, the largest demand power in a month is the maximum demand value for that month, and the basic fee is determined, for example, by the maximum demand value for the most recent 12 months. In order to reduce the demand value that affects the basic fee, it is not sufficient to only monitor the power consumption of the injection molding machines 2, so the factory power meter 3 measures the total power consumption in the entire factory.

[0022] The injection molding machine 2 includes an injection device body (not shown) and a mold clamping device disposed in front of the injection device body. The injection device body includes a heating cylinder, a heater 20 for heating the heating cylinder, a hopper for feeding a resin material into the heating cylinder, a screw provided in the heating cylinder so as to be drivable in the rotational direction and the axial direction, an injection electric motor 21a for driving the screw in the axial direction, and a metering and plasticizing electric motor 21b for driving the screw in the rotational direction. The mold clamping device includes a toggle mechanism for opening and closing the mold and clamping the mold so that the mold does not open when the molten resin injected from the injection device body is filled into the mold, a mold opening and closing electric motor 21c for driving the toggle mechanism, and an ejection electric motor 21d for ejecting the molded product from the mold. Hereinafter, the injection electric motor 21a, the metering and plasticizing electric motor 21b, the mold opening / closing electric motor 21c, and the ejection electric motor 21d will be collectively referred to as the electric motor 21.

[0023] The injection molding machine 2 also includes a control device 22 that controls the operation of the injection device main body. The control device 22 accepts operations by an operator and controls the operation of the injection molding machine 2. The operator can input set values ​​that determine the molding conditions of the injection molding machine 2 by operating an operation panel of the control device 22. The set values ​​that are molding conditions include, for example, in-mold resin temperature, cylinder temperature, mold clamping force, injection speed, injection acceleration, injection peak pressure, injection stroke, cylinder tip resin pressure, backflow prevention ring seating state, holding pressure switching pressure, holding pressure switching speed, holding pressure switching position, holding pressure completion position, cushion position, metering back pressure, metering torque, metering completion position, screw retreat speed, cycle time, intermediate time (waiting time between molding cycles), mold closing time, injection time, holding pressure time, metering time, mold opening time, etc. In particular, the set values ​​to be adjusted according to this embodiment include, for example, the acceleration, acceleration time, maximum speed, and constant speed operation time of the electric motor 21a for injection, the electric motor 21b for metering and plasticization, the electric motor 21c for mold opening and closing, and the electric motor 21d for ejection. Note that the acceleration and acceleration time include negative acceleration and acceleration time, that is, the deceleration and deceleration time of each electric motor 21. The set values ​​to be adjusted according to this embodiment also include the heater temperature. Furthermore, the intermediate time, which is the waiting time between molding cycles, the driving carrier frequency of the semiconductor that drives the electric motor 21, and the downtime of the electric motor 21 may be settings to be adjusted.

[0024] Furthermore, the control device 22 receives an allowable adjustment range of the set value according to the information processing method of this embodiment. The allowable adjustment range is information indicating the upper and lower limits of the adjustment amount of the set value that can be automatically adjusted by the information processing device 1. The operator can input the allowable adjustment range of the set value by operating the operation panel of the control device 22. The control device 22 transmits to the information processing device 1 information indicating basic information of the molded product, the set value of the injection molding machine 2, the adjustment allowable range of the set value, the power consumption in the injection molding machine 2, the temperature of the heating cylinder, information indicating the quantity and quality of the molded product manufactured per unit time, and the identifier of the injection molding machine 2. The information processing device 1 receives information transmitted from the control device 22 as described later, calculates an adjustment amount of the set value of each injection molding machine 2 that reduces the peak power, the demand power, and the average power consumption while maintaining the required production quantity and quality based on the received information, and transmits the calculated adjustment amount to the control device 22 of each injection molding machine 2. The control device 22 receives the adjustment amount transmitted from the information processing device 1, and adjusts the set values ​​of the intermediate time, the heater temperature, the acceleration of each electric motor 21, the acceleration time, the maximum speed and the constant speed operation time, the pause time, the semiconductor drive carrier frequency, etc. based on the received adjustment amount. The control device 22 executes reception of the adjustment amount and adjustment control in real time.

[0025] The measurement unit 4 is a measuring device that measures physical quantities related to the state of the injection unit body and the molded product. The physical quantities include temperature, position, speed, acceleration, current, voltage, pressure, time, image data, torque, force, distortion, power consumption, etc. In particular, the measurement unit 4 according to this embodiment includes a molding machine power measurement unit 4a, a temperature measurement unit 4b, a quantity measurement unit 4c, a quality measurement unit 4d, etc.

[0026] The molding machine power measurement unit 4a measures the power consumption of the injection molding machine 2 and outputs the information obtained by the measurement to the control device 22. The temperature measurement unit 4b measures the temperature of the heating cylinder and transmits the information obtained by the measurement to the information processing device 1. The quantity measurement unit 4c measures the quantity molded by the injection molding machine 2 per unit time and transmits the information obtained by the measurement to the information processing device 1. The quantity measurement unit 4c includes, for example, an imaging device, an optical sensor, etc., measures the quantity of molded products, and transmits the information obtained by the measurement to the information processing device 1. The quality measurement unit 4d specifies the quality of the molded products and transmits the information obtained by the measurement to the information processing device 1. The quality measurement unit 4d includes, for example, an imaging device, an optical measuring instrument such as a laser displacement sensor, a weight scale, a strength measuring instrument, etc. The quality measuring unit 4d can identify quality abnormalities related to burrs, sink marks, and other deformations of the molded product, for example, based on a camera image taken of the molded product and the amount of deformation of the molded product obtained by a laser displacement sensor. The quality measuring unit 4d can also identify the quality of the molded product based on optical measurement values ​​such as the chromaticity and brightness of the molded product obtained by an optical measuring instrument, the weight of the molded product measured by a weight scale, the strength of the molded product measured by a strength measuring instrument, and the like. The molding machine power measuring unit 4a, the temperature measuring unit 4b, the quantity measuring unit 4c, or the quality measuring unit 4d may output information obtained by measurement to the control device 22, and transmit the information to the information processing device 1 via the control device 22. The measuring unit 4 may be built into or mounted on the injection molding machine 2.

[0027] FIG. 4 is an explanatory diagram conceptually showing the functions of the injection molding system. Each functional unit and learning model 10 of the information processing device 1 will be described. The state observation unit 11a acquires information transmitted from the information processing device 1 and the factory power meter 3. Specifically, the state observation unit 11a acquires a plurality of set values ​​of the injection molding machine 2 transmitted from the information processing device 1, the adjustment allowable range of each set value, the power consumption in the injection molding machine 2, the temperature of the heating cylinder, information indicating the quantity and quality of molded products manufactured per unit time, and the identifier of the injection molding machine 2. In addition, the state observation unit 11a acquires information indicating the total power consumption of the factory transmitted from the factory power meter 3. Furthermore, the state observation unit 11a acquires target conditions. The target conditions include, for example, target quantity conditions indicating the target quantity of molded products to be manufactured within a predetermined period, molded product quality conditions required for the molded products, target demand power in the factory, target peak power, and target average power. The target conditions may be acquired from the injection molding machine 2, or may be acquired from another device that manages the operations of a plurality of injection molding machines 2, or may be input to the information processing device 1. The state observation unit 11a provides various information acquired from the injection molding machine 2, the factory power measuring device 3, etc. as state information to the learning model 10 and the reward calculation unit 11d.

[0028] 5 is a block diagram conceptually showing the configuration of the learning model 10. The learning model 10 is, for example, a DQN (Deep Q Network). The learning model 10 has an input layer 10a to which state information provided from a state observation unit 11a is input, an intermediate layer 10b that extracts features of the state information, and an output layer 10c that outputs value information related to the adjustment amount of the setting value of each of the multiple injection molding machines 2. The value information is, for example, a value when an action is taken to adjust a certain setting value by a specific adjustment amount in a certain state, that is, a state action value (Q value).

[0029] The input layer 10a has a plurality of nodes to which target conditions, setting values ​​of the multiple injection molding machines 2, power information, quantity of molded products, quality of the molded products, heater temperature, etc. are input, and passes the input information to the intermediate layer 10b. The target conditions are, for example, the target quantity of molded products to be manufactured in a factory within a specified period, the quality of the molded products required for the molded products, the target demand power in the factory, the target peak power, the target average power, etc. The power information includes the total power consumption in the factory and the power consumption of each of the multiple injection molding machines 2. The quantity and quality of molded products and heater temperature input to the input layer 10a are the quantity and quality of molded products manufactured by each of the multiple injection molding machines 2, and the heater temperature of each of the multiple injection molding machines 2.

[0030] The intermediate layer 10b compresses the state information input to the input layer 10a and extracts the feature quantities of the current state of the injection molding system. For convenience of drawing, the intermediate layer 10b is illustrated as one block, but the intermediate layer 10b is made up of multiple layers, and each layer has multiple nodes.

[0031] The output layer 10c has a plurality of nodes corresponding to the adjustment amounts of the set values ​​in the plurality of injection molding machines 2, and each node outputs value information of the corresponding adjustment amount. There are a plurality of set values ​​set in the plurality of injection molding machines 2, and a plurality of adjustment amounts for each set value. In the example shown in FIG. 5, the node corresponding to the adjustment amount +ΔA for the set value A of the first injection molding machine 2 outputs value information for adjusting the set value A by the adjustment amount +ΔA. Similarly, the node corresponding to the adjustment amount -ΔB for the set value B outputs value information for adjusting the set value B by the adjustment amount -ΔB. Similarly, value information is output in the same manner for the plurality of set values ​​of the second injection molding machine 2. The set values ​​for which value information is output from the learning model 10 include the acceleration, acceleration time, maximum speed and constant speed operation time, and heater temperature of the injection electric motor 21a, the metering and plasticization electric motor 21b, the mold opening and closing electric motor 21c, and the ejection electric motor 21d of the plurality of injection molding machines 2. Furthermore, the set values ​​for which value information is output from the learning model 10 may include intermediate time, the driving carrier frequency of the semiconductor that drives the electric motor 21, and the downtime of the electric motor 21.

[0032] The determination unit 11 shown in FIG. 4 determines the adjustment amount of the set value of each of the multiple injection molding machines 2 based on the value information output from the learning model 10. Specifically, among the adjustment amounts of the set values ​​of each of the multiple injection molding machines 2, the determination unit 11b determines the set value and adjustment amount corresponding to the node that outputs the value information with the largest Q value as the set value and adjustment amount to be adjusted. However, the determination unit 11b determines the adjustment amount so that the adjustment amount of the set value is within the adjustment allowable range. Note that the determination unit 11b may be configured to determine one set value and adjustment amount with the largest Q value, or to determine a predetermined number of set values ​​and adjustment amounts with the largest Q value, or to determine the set value and adjustment amount in the order of priority from the set value of the injection molding machine 2 with the largest production quantity and power consumption, or to determine one set value and adjustment amount for each injection molding machine 2, or to determine one set value and adjustment amount for each electric motor 21 of the multiple injection molding machines 2. The transmitting unit 11c transmits the adjustment amount determined by the determining unit 11b, that is, the adjustment amount of the setting value of each of the multiple injection molding machines 2, to the control device 22 of each injection molding machine 2.

[0033] The remuneration calculation unit 11d calculates the remuneration based on the state information provided by the state observation unit 11a. If the quantity and quality of the molded products manufactured by the injection molding machine 2 satisfy the target quantity condition and the target quality condition, the remuneration is added, and if the target quantity condition and the target quality condition are not satisfied, the remuneration calculation unit 11d adds the remuneration when the demand power is smaller than the target demand power, and subtracts the remuneration when the demand power is larger than the target demand power. Furthermore, the remuneration calculation unit 11d adds the remuneration when the peak power or the average power consumed by the multiple injection molding machines 2 is smaller than the target peak power and the target average power, and subtracts the remuneration when the peak power or the average power is larger than the target peak power and the target average power.

[0034] The reinforcement learning unit 11e trains the learning model 10 by machine learning using the state-action value Q calculated before the adjustment of the set value and the TD error obtained by the reward after the adjustment of the set value, based on the reward output from the reward calculation unit 11d. The value update formula is expressed by the following formula (1).

[0035] Q(st,at)←Q(st,at)+α(R+γmaxQ(st+1,at+1)-Q(st,at))…(1) however, st: state at time t at: Action at time t (adjustment of set value) Q(st,at): State-action value α: learning coefficient R: Immediate reward γ: time discount rate maxQ(st+1,at+1): State action value when selecting the action with the maximum value for the next step

[0036] The reinforcement learning unit 11e performs machine learning on the learning model 10 by optimizing the weighting coefficients of the learning model 10 using the backpropagation method, the error gradient descent method, or the like so that the TD error, i.e., the second term on the right-hand side of the above equation (1) (TD error), approaches zero.

[0037] Although Q-learning has been described as a machine learning method, other value iteration methods such as SARSA may be used. Furthermore, policy gradient methods may be used as machine learning methods. Furthermore, although DQN has been described as the learning model 10, a table that associates state information with action values ​​may be used.

[0038] Fig. 6 is a conceptual diagram showing the power consumption of each process in a molding cycle over time. The horizontal axis indicates time, and the vertical axis indicates power consumption. The injection molding process mainly includes (1) a mold closing process, (2) an injection process, (3) a mold opening process, (4) a measurement (and plasticization) process, and (5) an ejection process, and steps (1) to (5) are performed continuously to constitute an injection molding cycle. Fig. 6 shows the change in power consumption of each process over time in two cycles.

[0039] (1) The mold closing process is a process in which the mold is closed by the mold clamping device, and the mold opening and closing electric motor 21c is driven. (2) The injection process is a process in which the measured and plasticized resin is injected into a mold, and is driven by the injection electric motor 21a. (3) The mold opening process is a process for opening the mold to remove the molded product that has been cooled inside the mold, and is driven by the mold opening / closing electric motor 21c. (4) The metering step is a step performed in parallel with the mold opening step to measure and plasticize the resin to be injected in the next injection molding cycle, and is driven by the electric motor 21b for metering and plasticization. (5) The ejection process is a process in which, after the mold is opened, the molded product is removed from the mold by an ejection pin, and is driven by the ejection electric motor 21d.

[0040] Fig. 7 is a graph showing the power consumption of electric motor 21 before the set value is adjusted, and Fig. 8 is a graph showing the power consumption of electric motor 21 after the set value is adjusted. The horizontal axis shows time, and the vertical axis shows power consumption. As shown in Figs. 7 and 8, the power consumption of electric motor 21 can be changed by the motor's acceleration, acceleration time, maximum speed, and constant speed operation time.

[0041] 9 and 10 are flowcharts showing the processing procedure of the information processing device 1. During the operation of the multiple injection molding machines 2, the information processing device 1 adjusts the setting values ​​of each injection molding machine 2 in real time by executing the following processes. The information processing device 1 acquires target conditions (step S11). The information processing device 1 acquires information indicating the setting values ​​and the adjustment allowable range from the injection molding machines 2 (step S12). The information processing device 1 acquires information indicating the power consumption of each injection molding machine 2, information indicating the quantity and quality of molded products, and information indicating the heater temperature from the multiple injection molding machines 2 (step S13). The information processing device 1 acquires information indicating the total power consumption from the factory power meter 3 (step S14).

[0042] The processing unit 11 inputs the acquired information on the target conditions, the setting value of each injection molding machine 2, power consumption, the quantity and quality of the molded products, the heater temperature, and the total power consumption of the factory into the learning model 10, thereby calculating the value of the adjustment amount of the setting value of each injection molding machine 2 (step S15). The processing unit 11 determines the setting value and adjustment amount to be adjusted based on the calculated value of each adjustment amount and the adjustment allowable range (step S16). The processing unit 11 determines the adjustment amount of the setting value, whose adjustment amount is within the adjustment allowable range and has a large value, as the adjustment object. Next, the processing unit 11 transmits the determined adjustment amount to one or more injection molding machines 2 (step S17). The control device 22 of the injection molding machine 2 receives the adjustment amount transmitted from the information processing device 1, changes the setting value based on the received adjustment amount, and controls the molding process of the injection molding machine 2 using the changed setting value.

[0043] Next, after adjusting the set values, the processing unit 11 of the information processing device 1 acquires information indicating the power consumption of each injection molding machine 2, information indicating the quantity and quality of molded products, and information indicating the heater temperature from the multiple injection molding machines 2 (step S18). The information processing device 1 acquires information indicating the total power consumption from the factory power meter 3 (step S19). Then, the processing unit 11 calculates the remuneration based on the acquired various state information (step S20).

[0044] 11 is a flowchart showing the procedure of the reward calculation process. For the sake of explanation, the following will be explained by dividing the steps into steps for each element for adding or subtracting the reward, but it goes without saying that the reward may be calculated in one step using a function or a table.

[0045] The processing unit 11 adds or subtracts a reward according to the actually measured demand power of the factory and the peak power and average power based on the power consumption of each injection molding machine 2 relative to the target power (step S51). Specifically, the processing unit 11 adds the reward when the measured demand power is equal to or less than the target demand power, and subtracts the reward when the measured demand power exceeds the target demand power. Similarly, the processing unit 11 adds the reward when the peak power and average power are equal to or less than the target peak power and target average power, and subtracts the reward when the target peak power and target average power are exceeded.

[0046] Furthermore, the processing unit 11 adds or subtracts the reward according to the quantity of molded products relative to the target quantity condition (step S52). Specifically, if the quantity of molded products manufactured by the injection molding machine 2 satisfies the target quantity condition, the processing unit 11 adds or subtracts the reward if the quantity of molded products does not satisfy the target quantity condition.

[0047] Furthermore, the processing unit 11 adds or subtracts the reward depending on the quality of the molded product relative to the target quality condition (step S53). Specifically, if the quality of the molded product manufactured by the injection molding machine 2 satisfies the target quality condition, the processing unit 11 adds or subtracts the reward if the quality of the molded product does not satisfy the target quality condition.

[0048] Furthermore, the processing unit 11 adds or subtracts the reward according to the measured heater temperature relative to the target heater temperature (step S54). Specifically, when the actually measured heater temperature of the injection molding machine 2 deviates from the target heater temperature, the reward is subtracted.

[0049] Returning to FIG. 9, after completing the calculation of the reward, the processing unit 11 performs reinforcement learning on the learning model 10 based on the calculated reward so as to reduce, for example, the TD error (step S21).

[0050] Next, the processing unit 11 determines whether or not a predetermined processing end condition is satisfied (step S22). If it is determined that the processing end condition is satisfied (step S22: YES), the processing unit 11 ends the processing. If it is determined that the processing end condition is not satisfied (step S22: YES), the processing unit 11 returns the processing to step S13.

[0051] By the above process, the setting values ​​of each injection molding machine 2 are adjusted in real time during the molding process in multiple injection molding machines 2, optimizing the molding conditions. Specifically, it is possible to reduce the demand power, peak power, and average power while maintaining the required production volume and quality, thereby optimizing the production efficiency and power usage.

[0052] Fig. 12 is a conceptual diagram showing an example of an improvement in the operation end timing of two injection molding machines 2 that manufacture different molded products. In the example shown in Fig. 12, two injection molding machines 2 are operating, with the first injection molding machine 2 operating for three cycles and the second injection molding machine 2 operating for five cycles in a given operating time. The two injection molding machines 2 manufacture different types of molded products. The upper diagram in FIG. 12 shows the molding cycle before the adjustment of the set values, and the lower diagram shows the molding cycle before the adjustment of the set values. When the acceleration, acceleration time, intermediate time, etc. of each electric motor 21 are adjusted so that the demand power, peak power, and average power are reduced while maintaining the required production quantity and quality, the cycle time of each injection molding machine 2 is naturally lengthened. Then, when it is required that the first and second injection molding machines 2 manufacture the required quantity of molded products at a predetermined operation end time, the set values ​​of the electric motor 21 are adjusted so that the operation end times of the first and second injection molding machines 2 are aligned. In this way, the production of the required quantity of molded products by the first injection molding machine 2 and the production of the required quantity of molded products by the second injection molding machine 2 end at the same time, which contributes to reducing excess inventory.

[0053] The information processing device 1 may be configured to display the adjustment status of the set values ​​of the multiple electric motors 21 of the multiple injection molding machines 2 on a display device (not shown).

[0054] 13 is a schematic diagram showing an example of a monitor screen showing the adjustment status of the set values. The monitor screen has an operation status display section 91 showing the operation status of the multiple injection molding machines 2, and an adjustment amount display section 92 showing the adjustment amount of the set value in each injection molding machine 2. The operating status display unit 91 displays the operating status before the adjustment of the set values ​​with a solid line, and displays the operating status after the adjustment of the set values ​​with a dashed line. The operating status is displayed, for example, by the cycle times and intermediate times of the multiple injection molding machines 2 as the lengths of the arrow images and the intervals between the arrow images. The adjustment amount display unit 92 displays the set values ​​and adjustment amounts of the acceleration, acceleration time, maximum speed, and constant speed operating time of each of the electric motors 21a for injection, 21b for metering and plasticization, 21c for mold opening and closing, and 21d for ejection in each injection molding machine 2.

[0055] Furthermore, the control device 22 of the injection molding machine 2 may be configured to display the set values ​​and adjustment amounts of the machine itself.

[0056] According to the injection molding system of this embodiment configured as described above, by adjusting the settings of the electric motors 21 of the multiple injection molding machines 2 with respect to the acceleration, acceleration time, maximum speed or constant speed operation time, heater temperature, intermediate time, etc., it is possible to reduce the demand power and power usage while maintaining the required production quantity and quality, thereby optimizing the production efficiency and power usage.

[0057] Specifically, by adjusting detailed settings such as the acceleration, acceleration time, maximum speed and constant speed operation time, heater temperature, etc. of the injection electric motor 21a, the metering and plasticizing electric motor 21b, the mold opening and closing electric motor 21c, and the ejection electric motor 21d of the injection molding machine 2, it is possible to optimize production efficiency and power consumption.

[0058] In addition, the end points of the molding cycles of the injection molding machines 2 are adjusted to coincide at the end of the operating time, which also makes it possible to reduce excess inventory.

[0059] Furthermore, the information processing device 1 can perform reinforcement learning of a method for adjusting the setting values ​​and adjust in real time the setting values ​​of the injection molding machines 2. In particular, by using DQN, it is possible to appropriately adjust a plurality of setting values ​​in a plurality of injection molding machines 2.

[0060] Furthermore, since the information processing device 1 is configured to adjust the setting values ​​within the adjustment tolerance range of each setting value set by the operator, it is possible to optimize production efficiency and power consumption through reinforcement learning while ensuring stable operation of the injection molding machine 2.

[0061] In the present embodiment, an example in which the injection molding system automatically adjusts the set values ​​has been described, but the final adjustment operation of the set values ​​may be performed by an operator. For example, the control device 22 of the injection molding machine 2 may be configured to display the adjustment amount transmitted from the information processing device 1 to the injection molding machine 2, accept the adjustment operation and decision of the set values ​​from the operator, and change the set values ​​according to the accepted adjustment amount. In addition, some of the set values ​​may be manually adjusted by the operator. Furthermore, although an example in which the demand power and the power usage are reduced while maintaining the required production quantity and quality has been described, the production efficiency or the power usage may be optimized based on at least one of the production quantity and quality and the power consumption. [Explanation of symbols]

[0062] 1. Information processing device 2 Injection molding machine 3 Factory power measuring instruments 10 Learning Model 10a Input layer 10b Middle layer 10c output layer 11 Processing section 11a Status observation section 11b Decision section 11c Transmitter 11d Reward Calculation Department 11e Reinforcement Learning Department 12 Storage section 13. Communications Department 14 Recording media 20 Heater 21 Electric motor 21a Injection electric motor 21b Metering and plasticizing electric motors 21c Electric motor for opening and closing the mold 21d Ejection electric motor 22 Control device 4 Measuring part 4a Molding machine power measurement section 4b Temperature measurement section 4c Quantity measuring part 4d quality measurement department 9 Monitor screen 91 Operation status display section 92 Adjustment amount display section P Computer Program

Claims

1. A computer program for causing a computer to execute a process of adjusting setting values ​​of a plurality of injection molding machines each having an electric motor for injection, an electric motor for metering and plasticization, an electric motor for opening and closing a mold, and an electric motor for ejecting a molded product, the computer program comprising: Obtaining status information obtained by observing the setting values ​​of each of the multiple injection molding machines, the quantity and quality of molded products manufactured by the multiple injection molding machines, and the status related to the power consumed by the multiple injection molding machines; determining adjustment amounts of set values ​​including acceleration, acceleration time, maximum speed, and constant speed operation time of each electric motor of the plurality of injection molding machines based on the acquired status information; The determined adjustment amount of the set value is transmitted to the injection molding machine. A computer program for causing the computer to execute a process.

2. When the state information is input, the acquired state information is input to a learning model that outputs value information related to an adjustment amount of a setting value, thereby outputting value information; The amount of adjustment of the set value is determined based on the value information output from the learning model. The computer program product according to claim 1, for causing a computer to carry out a process.

3. Calculating a reward for the adjustment of the setting value based on the state information obtained after transmitting the determined adjustment amount of the setting value; The learning model is subjected to reinforcement learning based on the calculated reward. A computer program product according to claim 2, for causing a computer to execute a process.

4. If the quantity of the molded products meets a predetermined target quantity condition, the reward is added, and if the quantity of the molded products does not meet the target quantity condition, the reward is subtracted.

4. A computer program product according to claim 3, for causing a computer to carry out a process.

5. If the quality of the molded product meets the specified quality conditions, the reward is added, and if the quality of the molded product does not meet the quality conditions, the reward is subtracted.

5. A computer program product according to claim 3 or 4, for causing a computer to execute a process.

6. The lower the demand power of a facility having a plurality of the injection molding machines, or the lower the peak power or average power consumed by the plurality of the injection molding machines, the higher the reward. A computer program product according to any one of claims 3 to 5, for causing a computer to execute a process.

7. the injection molding machine includes a heater; The amount of adjustment of the set values ​​including the acceleration, acceleration time, maximum speed, constant speed operation time and the heater temperature of each electric motor of the injection molding machine is determined. A computer program product according to any one of claims 1 to 6, for causing a computer to execute a process.

8. acquiring information indicating an adjustment allowable range of a setting value of the injection molding machine; The adjustment amount of the set value is determined within the obtained adjustment allowable range. A computer program product according to any one of claims 1 to 7, for causing a computer to carry out a process.

9. 1. An information processing method for adjusting setting values ​​of a plurality of injection molding machines each having an electric motor for injection, an electric motor for metering and plasticization, an electric motor for opening and closing a mold, and an electric motor for ejecting a molded product, comprising: Obtaining status information obtained by observing the setting values ​​of each of the multiple injection molding machines, the quantity and quality of molded products manufactured by the multiple injection molding machines, and the status related to the power consumed by the multiple injection molding machines; determining adjustment amounts of set values ​​including acceleration, acceleration time, maximum speed, and constant speed operation time of each electric motor of the plurality of injection molding machines based on the acquired status information; The determined adjustment amount of the set value is transmitted to the injection molding machine. Information processing methods.

10. An information processing device for adjusting setting values ​​of a plurality of injection molding machines each having an electric motor for injection, an electric motor for metering and plasticization, an electric motor for opening and closing a mold, and an electric motor for ejecting a molded product, comprising: a status observation unit that observes the setting values ​​of each of the multiple injection molding machines, the quantity and quality of molded products manufactured by the multiple injection molding machines, and a status related to power consumed by the multiple injection molding machines; a determination unit that determines adjustment amounts of set values ​​including acceleration, acceleration time, maximum speed, and constant speed operation time of each electric motor of the plurality of injection molding machines based on state information observed by the state observation unit; a transmission unit that transmits the adjustment amount of the set value determined by the determination unit to the injection molding machine; An information processing device comprising:

11. a plurality of injection molding machines each having an electric motor for injection, an electric motor for metering and plasticization, an electric motor for opening and closing a mold, and an electric motor for ejecting a molded product; The information processing device according to claim 10 . Equipped with The information processing device transmits adjustment amounts of setting values ​​of the plurality of injection molding machines to the injection molding machines, and the injection molding machines receive the adjustment amounts transmitted from the information processing device and adjust the setting values ​​based on the received adjustment amounts. Injection molding system.

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