Information processor, information processing method, and information processing program

Abstract

To accurately predict the state of a mold used in manufacture of a resin molding.SOLUTION: An information processor (2) includes: a data acquisition part (201) for acquiring data for prediction including at least a first variable indicating a difference between a set value when a resin molding is manufactured using a mold and a measured value corresponding to the set value, and a second variable indicating secular change of the measured value; and a state prediction part (202) for predicting the state of the mold using a state prediction model (211) of the mold.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] Methods for predicting the quality of plastic molded products produced by injection molding machines have been proposed. For example, the invention described in Patent Document 1 performs a shell mesh flow analysis based on the shape data of the plastic molded product and extracts multiple nodes along the primary weld formed at the meeting area of ​​two resin flows. The invention described in Patent Document 1 then estimates the position of the flow front of the weld interface formed on the cross section of the plastic molded product in the thickness direction at the completion of molding, and estimates the line connecting the flow fronts of the weld interface corresponding to the multiple nodes as the secondary weld. This makes it possible to visualize the positional relationship between the primary and secondary welds, enabling quality prediction of the plastic molded product. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2023-173918 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, biodegradable plastics, which are decomposed by living organisms, have been attracting attention. However, biodegradable plastics have a lower decomposition temperature than general resins. Therefore, when molding biodegradable plastics, mold contamination due to thermal decomposition products is more likely to occur than when molding general resins. Although this is not limited to injection molding, depending on the state of mold contamination, there is a risk of adversely affecting the quality of resin molded products. Therefore, it is necessary to take the state of mold contamination into account when predicting the quality of resin molded products, but Patent Document 1 does not disclose this point, leaving room for improvement.

[0005] An object of one aspect of the present invention is to accurately predict the state of a mold used in manufacturing a resin molded product. [Means for solving the problem]

[0006] In order to solve the above problem, an information processing device according to one embodiment of the present invention includes a data acquisition unit that acquires prediction data including at least a first variable that indicates the difference between a set value when a resin molded product is manufactured using a target mold and an actual measured value corresponding to the set value, and a second variable that indicates the change in the actual measured value over time, and a condition prediction unit that predicts the condition of the target mold from the prediction data using a condition prediction model of the mold used to manufacture the resin molded product, in which the first variable and the second variable are at least explanatory variables.

[0007] In order to solve the above problem, an information processing method according to one embodiment of the present invention includes a data acquisition step of acquiring prediction data including at least a first variable indicating the difference between a set value when a resin molded product is manufactured using a target mold and an actual measured value corresponding to the set value, and a second variable indicating the change in the actual measured value over time, and a state prediction step of predicting the state of the target mold from the prediction data using a state prediction model of the mold used to manufacture the resin molded product, the first variable and the second variable being at least explanatory variables.

[0008] The information processing device according to each aspect of the present invention may be realized by a computer. In this case, the information processing program that causes the computer to operate as each part (software element) of the information processing device to realize the information processing device on the computer, and the computer-readable recording medium on which the program is recorded, also fall within the scope of the present invention. [Effects of the Invention]

[0009] According to one aspect of the present invention, it is possible to accurately predict the state of a mold used in manufacturing a resin molded product. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a block diagram showing an example of a configuration of a main part of an information processing device according to a first embodiment of the present invention. [Figure 2] 1 is a diagram illustrating an overview of an information processing system according to a first embodiment of the present invention. [Figure 3] FIG. 1 is a diagram illustrating an example of the configuration of an injection molding machine and its flow paths. [Figure 4] FIG. 2 is a schematic diagram showing an example of the flow of data such as manufacturing data during the manufacture of a resin molded product. [Figure 5] 4 is a flowchart showing an example of processing executed by the information processing device according to the first embodiment of the present invention. [Figure 6] FIG. 10 is a block diagram showing an example of a configuration of a main part of an information processing device according to a second embodiment of the present invention. [Figure 7] FIG. 2 is a schematic diagram showing an example of the flow of data such as manufacturing data during the manufacture of a resin molded product. [Figure 8] 10 is a flowchart showing an example of processing executed by an information processing device according to a second embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0011] [Embodiment 1] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In the description of the drawings, the same parts are designated by the same reference numerals and the description thereof will be omitted.

[0012] (Outline of information processing system) An overview of the information processing system 5 according to the first embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an overview of the information processing system 5. Fig. 2 also shows an overview of an injection molding machine. The information processing system 5 is a system that can be used to support injection molding, and includes information processing devices 1 and 2. Below, the injection molding to be supported will be described, followed by a description of the information processing device 1 and the information processing device 2.

[0013] Injection molding uses an injection molding machine that includes an injection unit, a mold, and a mold clamping unit. In injection molding, resin pellets are first fed from a hopper into a cylinder, and the resin in the cylinder is heated by a heater to form molten resin. Next, a screw is advanced by a motor, and the molten resin is injected into the mold. The injected molten resin is cooled and solidified inside the mold. After the molten resin has solidified, the crosshead is moved to open the mold along the tie bars, and the molded resin product is removed from the mold by an ejector mechanism.

[0014] The removed resin molded product is inspected for quality issues before being used as a finished product. On the other hand, resin molded products that do not meet quality standards are deemed defective. Figure 2 shows an example in which a spoon is used as a resin molded product, and its quality is determined using a visual inspection machine. The information processing system 5 can support any injection molding process. While the configuration of the injection molding machine used and the resin molded product to be injection-molded are not particularly limited, the information processing system 5 is particularly suitable for molding resins with low decomposition temperatures. For example, the information processing system 5 can be used effectively for molding biodegradable plastics. Specifically, the resin may be a poly(3-hydroxyalkanoate)-based resin or a polyhydroxyalkanoate resin such as poly(3-hydroxybutyrate-co-3-hydroxyhexanoate). Polyhydroxyalkanoate resins are biodegradable aliphatic polyesters.

[0015] The information processing device 1 generates a state prediction model by machine learning using training data, and the information processing device 2 predicts the state of the mold from the prediction data using the state prediction model.

[0016] (Configuration examples of information processing device 1 and information processing device 2) 1 is a block diagram showing an example of the main configuration of the information processing device 1 and the information processing device 2. Below, each configuration of the information processing device 1 and the information processing device 2 will be described in order based on FIG.

[0017] 1, the information processing device 1 includes a control unit 10 that controls each unit of the information processing device 1, and a storage unit 11 that stores various data used by the information processing device 1. The information processing device 1 also includes a communication unit 12 that enables the information processing device 1 to communicate with other devices, an input unit 13 that accepts various data input to the information processing device 1, and an output unit 14 that enables the information processing device 1 to output various data. The control unit 10 also includes a training data acquisition unit 101 and a learning unit 102. A training dataset 111 is stored in the storage unit 11.

[0018] The information processing device 1 may be configured as a general-purpose computer. When the information processing device 1 is configured as a general-purpose computer, the control unit 10 may be configured to include a processor, a read-only memory (ROM), a random access memory (RAM), etc. The processor can read programs from the ROM and execute various programs using the RAM as a working area. The storage unit 11 may be configured as a hard disk drive (HDD), a solid state drive (SSD), etc.

[0019] The input unit 13 is typically a keyboard or a mouse, but is not limited to these and may be any configuration that allows various data to be input. For example, the input unit 13 may be a microphone, a touch panel, etc. The output unit 14 is typically a display, but is not limited to these and may be any configuration that allows various data to be output. For example, the output unit 14 may be a speaker, a warning light, etc.

[0020] The training dataset 111 stored in the storage unit 11 will be described. The training dataset 111 is a dataset including multiple pieces of training data (labeled data used in machine learning). Machine learning is a technique for learning features included in input data and generating a "model" that predicts results corresponding to newly input data. A state prediction model 211, which will be described later, is a trained model generated by machine learning using the training dataset 111.

[0021] As will be described in detail later, the condition prediction model 211 predicts the condition of a mold used to manufacture a resin molded product using manufacturing data, a first variable, and a second variable as input data. The predicted "mold condition" may be expressed, for example, as an evaluation value that evaluates the degree of mold contamination. As described above, when a biodegradable plastic with a low decomposition temperature is used for a resin molded product, mold contamination due to thermal decomposition products is likely to occur. Therefore, predicting the degree of mold contamination can be advantageously used, for example, when predicting the quality of a resin molded product using the prediction results. The first variable indicates the difference between a setting value (SV) when a resin molded product is manufactured using the mold and an actual measured value (PV) corresponding to the setting value. The second variable indicates changes in the actual measured value over time. The first and second variables will be described in detail later. Manufacturing data is data acquired during the manufacture of a resin molded product and is data that is associated with the condition of the mold. For example, setting values ​​indicating manufacturing conditions and actual measured values ​​measured during manufacturing are examples of manufacturing data. Furthermore, an index value calculated using such data may also be considered to be “manufacturing data.” In this case, the first variable and the second variable can also be considered to be manufacturing data.

[0022] The "mold condition" to be predicted is not limited to the degree of mold contamination. The "mold condition" to be predicted may also be whether or not the mold needs to be cleaned, the number of injections remaining until cleaning is required, or the next time the mold will be cleaned. In this case, the "mold condition" may be expressed as an index value indicating whether or not the mold needs to be cleaned. In the following, as an example, unless otherwise specified, the condition prediction model 211 will be described as predicting the "degree of mold contamination" as the mold condition.

[0023] The training data set 111 includes a plurality of pieces of training data used for machine learning of the state prediction model 211. The training data included in the training data set 111 is obtained by associating input data input to the state prediction model 211 with values ​​to be output by the state prediction model 211 as correct answer data. This input data can also be referred to as explanatory variables, and the correct answer data as objective variables.

[0024] The condition prediction model 211 is a model that predicts the degree of contamination of a mold from the manufacturing data, first variable, and second variable of a resin molded product. Therefore, the training data included in the training data set 111 is data in which the degree of contamination of the mold when the manufacturing data, first variable, and second variable are applied to the "input data" that is the manufacturing data, first variable, and second variable of the resin molded product is associated as "correct answer data."

[0025] Examples of the "input data" including the manufacturing data, first variables, and second variables are described below. The manufacturing data are various actual values ​​measured during the manufacturing of a resin molded product, such as the temperature of the resin being kneaded inside the screw, the temperature around the heaters installed in various parts of the cylinder, the injection pressure at the injection port, and screw position data. These actual values ​​are measured by temperature sensors, pressure sensors, position sensors, etc. installed in the injection molding machine.

[0026] As described above, the first variable indicates the difference between a set value when a resin molded product is manufactured using a mold and the corresponding measured value. The set value, when associated with the measured value, is a value for setting the resin temperature, heater temperature, injection pressure, screw position, etc., and such set value is input, for example, by an operator. While not limited to injection molding, a discrepancy between a set value and its corresponding measured value can occur due to factors such as resin characteristics and environmental conditions such as temperature and humidity. Using the discrepancy between the set value and its corresponding measured value as input data enables highly accurate predictions that take into account resin characteristics, environmental conditions, etc.

[0027] As described above, the second variable indicates the change in the actual measurement value over time. The change in the actual measurement value over time may be represented, for example, by the difference between the actual measurement value measured a predetermined time ago and the actual measurement value measured at the current time. The predetermined time may be set appropriately depending on the pattern of the change in the actual measurement value over time. For example, if the actual measurement value fluctuates over approximately 10 minutes, the predetermined time may be set to 10 minutes. By using training data including such a second variable indicating the change in the actual measurement value over time, it is possible to learn the changes in various actual measurement values ​​over time. The actual measurement value at a certain point in time is affected by the previous actual measurement value, that is, the actual measurement value has time dependency. Therefore, by using the change in the actual measurement value over time as input data, it is possible to perform highly accurate predictions that take into account the time dependency of the actual measurement value.

[0028] Next, an example of "correct answer data" will be described. Correct answer data is data that indicates the objective variable of the condition prediction model 211, and the degree of mold contamination is the correct answer data. More specifically, when certain "input data" is input to the condition prediction model 211, the degree of mold contamination that the condition prediction model 211 should output is the "correct answer data" that corresponds to that "input data."

[0029] The training data is generated by associating the above-described input data with the correct answer data, and is stored as a training data set 111 in the storage unit 11. The process of generating the training data set 111 may be performed by the information processing device 1 or another device.

[0030] When generating the state prediction model 211, the training data acquisition unit 101 refers to the storage unit 11 and acquires the training data set 111. The training data acquisition unit 101 outputs the acquired training data set 111 to the learning unit 102.

[0031] The learning unit 102 generates a condition prediction model 211 for predicting the degree of mold contamination through machine learning using the training data set 111 acquired from the training data acquisition unit 101. The condition prediction model 211 is a prediction model that uses the manufacturing data of the resin molded product, a first variable, and a second variable as explanatory variables and the degree of mold contamination as a target variable. The prediction algorithm is not particularly limited. For example, the condition prediction model 211 may be a neural network model, a multiple regression model, a random forest model, or the like.

[0032] The state prediction model 211 generated by the learning unit 102 is transmitted to the information processing device 2 and stored in the storage unit 21 of the information processing device 2. The state prediction model 211 generated by the learning unit 102 may be stored in the storage unit 11.

[0033] On the other hand, the information processing device 2 includes a control unit 20 that controls each unit of the information processing device 2, and a storage unit 21 that stores various data used by the information processing device 2. The information processing device 2 also includes a communication unit 22 that enables the information processing device 2 to communicate with other devices, an input unit 23 that accepts various data input to the information processing device 2, and an output unit 24 that enables the information processing device 2 to output various data. The control unit 20 also includes a data acquisition unit 201, a state prediction unit 202, and an output control unit 203. The storage unit 21 stores a state prediction model 211.

[0034] The data acquisition unit 201 acquires input data to be input to the state prediction model 211. For example, if the input data to the state prediction model 211 are actual measurement values ​​such as resin temperature and injection pressure, which are manufacturing data of a resin molded product, a first variable, and a second variable, the data acquisition unit 201 acquires these data. Regarding the manufacturing data, for example, the data acquisition unit 201 may acquire these actual measurement values ​​from various sensors provided in the injection molding machine. Regarding the first variable, for example, the data acquisition unit 201 may calculate the difference between a set value input by an operator and an actual measurement value corresponding to the set value, and acquire the difference as the first variable. Regarding the second variable, for example, the data acquisition unit 201 may calculate the difference between an actual measurement value measured a predetermined time ago and an actual measurement value measured at the current time, which are stored in the storage unit 21, and acquire the difference as the second variable. The data acquisition unit 201 outputs the acquired manufacturing data, first variable, and second variable to the state prediction unit 202. The manufacturing data, the first variable, and the second variable acquired by the data acquisition unit 201 are data for predicting the degree of contamination of the mold. Therefore, these data acquired by the data acquisition unit 201 can also be collectively referred to as prediction data.

[0035] When the information processing device 2 is configured with a PLC (Programmable Logic Controller), the data acquisition unit 201 may have a function of automatically acquiring actual measured values ​​from various sensors provided in the injection molding machine. The data acquisition unit 201 may also have a function of acquiring data set by the PLC as setting values. In other words, even when the information processing device 2 is configured with a PLC, the data acquisition unit 201 can acquire the manufacturing data of the resin molded product, the first variable, and the second variable by having such a function.

[0036] The condition prediction unit 202 predicts the condition of the mold (degree of contamination of the mold) from the manufacturing data of the resin molded product, the first variable, and the second variable. Specifically, the condition prediction unit 202 predicts the degree of contamination of the mold from the manufacturing data of the resin molded product, the first variable, and the second variable acquired by the data acquisition unit 201, using a condition prediction model 211 generated by learning the relationship between the manufacturing data of the resin molded product, the first variable, and the second variable and the degree of contamination of the mold. The degree of contamination of the mold predicted by the condition prediction unit 202 is output to the output control unit 203.

[0037] The output control unit 203 causes the output unit 24 to output the degree of mold contamination predicted by the state prediction unit 202. The output mode is arbitrary, and for example, the output control unit 203 may output the predicted degree of mold contamination in at least one of the following modes: display output, audio output, and print output. Furthermore, the device that outputs the predicted degree of mold contamination may be a device external to the information processing device 2. Such an output allows the operator to accurately grasp the predicted degree of mold contamination and to make plans necessary for production management, such as when to clean the mold.

[0038] As described above, the information processing device 2 includes a data acquisition unit 201 that acquires prediction data including at least a first variable that indicates the difference between a set value when a resin molded product is manufactured using the target mold and an actual measurement value corresponding to the set value, and a second variable that indicates the change in the actual measurement value over time, and a state prediction unit 202 that predicts the state of the target mold from the prediction data using a state prediction model 211 of the mold used in manufacturing the resin molded product, which has at least the first variable and the second variable as explanatory variables.

[0039] (Regarding manufacturing data for resin molded products) In addition to the resin temperature and injection pressure described above, the manufacturing data for resin molded products may also include the difference between the decomposition temperature at which the molten resin thermally decomposes during injection molding and the temperature of the flow path that guides the molten resin to the mold, the time the molten resin remains in the flow path, and other information. Here, data that can be included in the manufacturing data for resin molded products will be described with reference to FIG. 3. FIG. 3 is a diagram showing an example of the configuration of an injection molding machine and its flow paths. Note that the values ​​described below are both set values ​​that can be set by an operator and values ​​that change over time. Therefore, the values ​​described below can be used as manufacturing data for resin molded products, first variables, and second variables.

[0040] A1 in Figure 3 shows an injection molding machine similar to that in Figure 2. As shown in Figure 3, the cylinder is provided with heaters h1 to h3 that heat the resin inside. In addition, a heater h4 is also provided in the flow path that guides the molten resin injected from the injection port e at the end of the cylinder to the cavity c in the mold. This flow path can also be called a hot runner. Note that the rear part of the cylinder (near the injection port e) can also be considered part of this flow path.

[0041] The temperature of the flow path can be measured by installing a temperature sensor near heater h4. Therefore, using the measurement value of the temperature sensor, it is possible to calculate the difference between the decomposition temperature at which the molten resin thermally decomposes and the temperature of the flow path that leads the molten resin to the mold. Furthermore, since the measurement value of the temperature sensor changes significantly depending on whether or not the molten resin is flowing in the flow path, the measurement value of the temperature sensor can also be used to determine the time that the molten resin remains in the flow path.

[0042] The above-mentioned flow paths may also be branched. A2 in Figure 3 shows an example of a branched flow path configuration. The flow paths include a first-stage flow path r1, a second-stage flow path r2, and a third-stage flow path r3. The molten resin injected from the cylinder first flows into the first-stage flow path r1. Flow path r1 branches into two at its end, each of which leads to the second-stage flow path r2. This results in two flow paths r2. Each of the two flow paths r2 then branches into four, each of which leads to the third-stage flow path r3. This results in eight flow paths r3. These eight flow paths r3 are connected to cavities c1 to c8, respectively. Using a mold with such branched flow paths allows for the simultaneous production of eight molded products in a single injection molding run. In a hot runner, each of the branched flow paths is controlled by a heater. To eliminate unevenness between cavities, the lengths of each flow path are generally designed to be equal.

[0043] When a mold includes multiple cavities in this manner, parameters related to multiple flow paths connected to each of the multiple cavities may be included in the manufacturing data for the resin molded product. In this case, the condition prediction unit 202 may predict the degree of contamination for each of the multiple cavities. This makes it possible to manage the need for mold cleaning on a cavity-by-cavity basis. To predict the degree of contamination for each of the multiple cavities, a model for predicting the degree of contamination for each of the multiple cavities may be generated. In other words, a condition prediction model 211 may be prepared for each cavity.

[0044] Furthermore, for example, by providing a temperature sensor in each of the flow paths connected to each cavity, it is possible to measure the temperature of the molten resin flowing through each path, thereby making it possible to calculate, for each cavity, the difference between the decomposition temperature of the molten resin and the temperature of the path that guides the molten resin to the mold, as well as the residence time of the molten resin in the path.

[0045] (Data flow during manufacturing) FIG. 4 is a schematic diagram showing an example of the flow of data such as manufacturing data during the manufacture of a resin molded product.

[0046] When a resin molded product is being manufactured, the data acquisition unit 201 acquires a plurality of manufacturing data 50, 51, 52,... such as resin temperature and injection pressure, a first variable 60, and a second variable 61. The manufacturing data 50, 51, 52,..., the first variable 60, and the second variable 61 acquired by the data acquisition unit 201 are input to a state prediction model 211.

[0047] The state prediction model 211 predicts the degree of contamination of the mold based on the input manufacturing data 50, 51, 52, ..., the first variable 60, and the second variable 61. In FIG. 4, the degree of contamination of the mold is shown as "mold state 70."

[0048] (Processing flow) Next, the flow of processing executed by the information processing device 2 will be described with reference to Fig. 5. Fig. 5 is a flowchart showing an example of processing executed by the information processing device 2.

[0049] In step S101, the data acquisition unit 201 acquires prediction data. The prediction data includes manufacturing data of the resin molded product, a first variable, and a second variable. The data acquisition unit 201 may acquire data measured by various sensors installed in an injection molding machine as the manufacturing data of the resin molded product. The data acquisition unit 201 may also calculate a difference between a set value input by an operator and an actual measurement value corresponding to the set value, and acquire the difference as the first variable. The data acquisition unit 201 may also calculate a difference between an actual measurement value measured a predetermined time ago and an actual measurement value measured at the current time, which are stored in the storage unit 21, and acquire the difference as the second variable. The prediction data is used as input data for the state prediction model 211 (see FIG. 4).

[0050] The process proceeds to step S102, where the state prediction unit 202 inputs the prediction data acquired in the process of step S101 into the state prediction model 211 to predict the degree of contamination of the mold. As an example, the state prediction model 211 outputs an evaluation value that evaluates the degree of contamination of the mold.

[0051] The process proceeds to step S103, where the output control unit 203 outputs the degree of contamination of the mold predicted in the process of step S102 to the output unit 24. This allows the operator to grasp the accurately predicted degree of contamination of the mold, and allows the operator to make plans necessary for production management, such as when to clean the mold.

[0052] As described above, the information processing method of embodiment 1 includes a data acquisition step (S101) of acquiring prediction data including at least a first variable indicating the difference between a set value when a resin molded product is manufactured using the target mold and an actual measurement value corresponding to the set value, and a second variable indicating the change in the actual measurement value over time, and a state prediction step (S102) of predicting the state of the target mold from the prediction data using a state prediction model 211 of the mold used to manufacture the resin molded product, which has at least the first variable and the second variable as explanatory variables.

[0053] (Action and effect) As described above, according to the first embodiment, the following advantageous effects can be obtained.

[0054] In embodiment 1, a state prediction model 211 of a mold used in manufacturing a resin molded product, which has at least a first variable and a second variable as explanatory variables, is used to predict the state of the target mold from prediction data.

[0055] The first variable indicates the difference between a set value when a resin molded product is manufactured using a mold and an actual measured value corresponding to that set value. Therefore, the above configuration of predicting the state of the mold using the first variable makes it possible to make highly accurate predictions that take into account the characteristics of the resin, environmental conditions, etc.

[0056] The actual measurement value at a certain point in time is affected by the previous actual measurement value, that is, the actual measurement value is time-dependent. Therefore, the above configuration for predicting the state of the mold using the second variable that indicates the change in the actual measurement value over time makes it possible to perform highly accurate predictions that take into account the time-dependence of the actual measurement value.

[0057] The inventors have performed the comparison described below. As shown in Fig. 4, the comparison was performed between a case in which manufacturing data 50, 51, 52,..., a first variable 60, and a second variable 61 were used as input data (hereinafter referred to as an example), and a case in which only manufacturing data 50, 51, 52,... were used as input data (hereinafter referred to as a comparative example). That is, in the comparative example, the first variable 60 and the second variable 61 were not included in the input data of the state prediction model 211.

[0058] The inventors evaluated the performance of the state prediction model 211 for each of the above-described examples and comparative examples using RMSE (Root Mean Square Error). RMSE is a type of index for evaluating the performance of a machine learning model, and indicates the root mean square error between a predicted value predicted by the machine learning model and a correct value. The smaller the RMSE value, the smaller the error and the better the performance of the machine learning model.

[0059] According to calculations by the inventors, the RMSE was 0.7 in the comparative example, whereas the RMSE was <0.7 in the example. From this result, it can be said that by using the first variable and the second variable as input data for the state prediction model 211, the performance of the state prediction model 211 is improved, and it becomes possible to predict the state of the mold with high accuracy.

[0060] The resin molded product may also be a molded product containing a biodegradable resin. Generally, biodegradable resins tend to have large viscosity variations in raw material pellets, and when molding using such raw material pellets, the viscosity variations can easily cause mold contamination. In this regard, the above-mentioned configuration allows the mold condition to be accurately predicted, and therefore the mold can be cleaned or manufacturing conditions can be adjusted as needed based on the prediction results, thereby enabling efficient molding of biodegradable resins.

[0061] The biodegradable resin may also be a polyhydroxyalkanoate-based resin. Because polyhydroxyalkanoate-based resins have excellent degradability, they can solve the environmental problems caused by discarded plastics. Particularly preferably, the polyhydroxyalkanoate-based resin is a poly(3-hydroxyalkanoate)-based resin (hereinafter sometimes referred to as a P3HA-based resin). More specifically, the P3HA-based resin preferably contains 3-hydroxybutyrate (3HB) units. The P3HA-based resin containing 3HB units is preferably selected from the group consisting of poly(3-hydroxybutyrate) (P3HB), poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (P3HB3HV), poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (P3HB3HH), poly(3-hydroxybutyrate-co-3-hydroxyvalerate-co-3-hydroxyhexanoate) (P3HB3HV3HH), poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (P3HB4HB), poly(3-hydroxybutyrate-co-3-hydroxyoctanoate), and poly(3-hydroxybutyrate-co-3-hydroxydecanoate). The P3HA-based resin may contain only one type, or two or more types.

[0062] [Modification of the first embodiment] In the above-described first embodiment, it has been explained that the explanatory variables of the state prediction model 211 include at least either the first variable or the second variable, but other explanatory variables may be further included in the explanatory variables of the state prediction model 211. Specifically, the explanatory variables of the state prediction model 211 may further include at least one of a variable indicating the storage conditions of the raw material resin of the resin molded product and a variable indicating the storage period.

[0063] Examples of the "storage conditions of the raw resin" include environmental conditions such as temperature and humidity in the place where the raw resin is stored, and seasonal conditions indicating the season in which the raw resin is stored. Also, the "storage period of the raw resin" indicates the length of time the raw resin is stored.

[0064] The information processing device 2 may predict the state of the mold by taking into consideration at least one of the storage conditions and storage period of the raw material resin. This is particularly effective when the storage conditions and storage period of the raw material resin have a significant impact on the state of the mold.

[0065] [Embodiment 2] FIG. 6 is a block diagram showing an example of a main configuration of an information processing device 3 according to the second embodiment of the present invention.

[0066] 6, the information processing device 3 includes a control unit 30 that controls each unit of the information processing device 3, and a storage unit 31 that stores various data used by the information processing device 3. The information processing device 3 also includes a communication unit 32 that enables the information processing device 3 to communicate with other devices, an input unit 33 that accepts various data input to the information processing device 3, and an output unit 34 that enables the information processing device 3 to output various data. The control unit 30 also includes a data acquisition unit 301, a state prediction unit 302, a quality prediction unit 303, and an output control unit 304. The storage unit 31 stores a state prediction model 311 and a quality prediction model 312.

[0067] The functions of the data acquisition unit 301, state prediction unit 302, output control unit 304, communication unit 32, input unit 33, and output unit 34 included in the information processing device 3 are similar to the functions of the data acquisition unit 201, state prediction unit 202, output control unit 203, communication unit 22, input unit 23, and output unit 24 included in the information processing device 2 according to embodiment 1, and therefore descriptions thereof will be omitted where appropriate. In addition, the state prediction model 311 stored in the storage unit 31 is similar to the state prediction model 211 stored in the storage unit 21 according to embodiment 1, and therefore descriptions thereof will be omitted where appropriate.

[0068] The quality prediction model 312 stored in the storage unit 31 receives as input the predicted value (evaluation value for evaluating the degree of contamination of the mold) predicted by the condition prediction model 311 and the manufacturing data of the resin molded product, and predicts the quality of the resin molded product manufactured when the manufacturing data of the resin molded product is applied to the degree of contamination of the mold. In this way, the explanatory variables of the quality prediction model 312 include the degree of contamination of the mold.

[0069] Like the state prediction model 311, the quality prediction model 312 is also a model generated by machine learning. However, when generating the quality prediction model 312, training data (data in which input data is associated with correct answer data) used for machine learning is different from the training data used for machine learning of the state prediction model 311. Specifically, when generating the quality prediction model 312, a training dataset different from the training dataset 111 according to the first embodiment is used. Hereinafter, the training dataset different from the training dataset 111 according to the first embodiment will be referred to as training dataset A. The training data included in the training dataset A will be described below.

[0070] The training data included in the training dataset A is a correspondence between "input data (which can also be referred to as explanatory variables)" and "ground truth data (which can also be referred to as objective variables)." This "input data" includes various data related to the quality to be predicted. Specifically, the "input data" includes the degree of mold contamination output from the condition prediction model 311. In addition to this, the "input data" also includes manufacturing data for resin molded products.

[0071] The "correct answer data" in the training data included in the training dataset A may be data indicating the quality of the resin molded product. The "quality of the resin molded product" can be expressed as an evaluation value for evaluating the quality of the resin molded product, such as the defect rate, strength, or dimensions.

[0072] A data set generated by associating such input data with the correct answer data becomes training data for generating the quality prediction model 312. The process of generating the training data set A may be performed by the information processing device 1 according to the first embodiment or by another device.

[0073] The data acquisition unit 301 acquires input data to be input to the condition prediction model 311. The condition prediction unit 302 predicts the degree of contamination of the mold from the input data acquired by the data acquisition unit 301, i.e., the manufacturing data of the resin molded product, the first variable, and the second variable. The degree of contamination of the mold predicted by the condition prediction unit 302 is output to the quality prediction unit 303.

[0074] The quality prediction unit 303 predicts the quality of the resin molded product using the degree of contamination of the mold predicted by the condition prediction unit 302. Specifically, the quality prediction unit 303 predicts the quality of the resin molded product by inputting the degree of contamination of the mold predicted by the condition prediction unit 302 and other data (manufacturing data of the resin molded product) that are explanatory variables of the quality prediction model 312 into a quality prediction model 312. The quality of the resin molded product predicted by the quality prediction unit 303 is output to an output control unit 304.

[0075] The output control unit 304 causes the output unit 34 to output the quality of the resin molded product predicted by the quality prediction unit 303. The output may be in any form, and for example, the output control unit 304 may cause the predicted quality of the resin molded product to be output in at least one form of display output, audio output, or print output. Furthermore, the device that outputs the predicted quality of the resin molded product may be a device external to the information processing device 3. Such an output allows the operator to grasp the accurately predicted quality of the resin molded product.

[0076] As described above, the information processing device 3 includes a quality prediction unit 303 that predicts the quality of a resin molded product manufactured with a target mold from the prediction result of the condition prediction unit 302 using a quality prediction model 312 for a resin molded product that includes explanatory variables that indicate the condition of the target mold.

[0077] (Data flow during manufacturing) Fig. 7 is a schematic diagram showing an example of the flow of each piece of data, such as manufacturing data, during the manufacture of a resin molded product. In the flow of Fig. 7, a flow for predicting the quality of the resin molded product is added to the flow of Fig. 4. The description of the flow described in Fig. 4 will be omitted where appropriate.

[0078] 7, the degree of contamination of the mold predicted by the condition prediction model 311 and the manufacturing data 50, 51, 52, ... acquired by the data acquisition unit 301 are input to the quality prediction model 312. Note that the input data to the quality prediction model 312 may be data related to the quality of the resin molded product, and does not necessarily have to be the same manufacturing data as the input data to the condition prediction model 311.

[0079] The quality prediction model 312 predicts the quality 80 of the resin molded product based on the input degree of contamination of the mold and the manufacturing data 50, 51, 52, .... The prediction results are output as evaluation values ​​for evaluating the quality of the resin molded product, such as the defect rate, strength, dimensions, etc.

[0080] (Processing flow) Next, the flow of processing executed by the information processing device 3 will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of processing executed by the information processing device 3. Note that the processing shown in steps S201, S202, and S204 is similar to the processing shown in steps S101, S102, and S103 shown in Fig. 5, and therefore description thereof will be omitted where appropriate.

[0081] In step S201, the data acquisition unit 301 acquires prediction data. The process proceeds to step S202, where the condition prediction unit 302 inputs the prediction data acquired in the process of step S201 into the condition prediction model 311 to predict the degree of contamination of the mold.

[0082] The process proceeds to step S203, where the quality prediction unit 303 predicts the quality of the resin molded product by inputting the prediction data (excluding the first variable and the second variable) acquired in the process of step S201 and the degree of mold contamination predicted in the process of step S202 into the quality prediction model 312. The quality prediction model 312 outputs evaluation values ​​for evaluating the quality of the resin molded product, such as the defect rate, strength, dimensions, etc., for example.

[0083] The process proceeds to step S204, where the output control unit 304 outputs the quality of the resin molded product predicted in the process of step S203 to the output unit 34. This allows the operator to grasp the quality of the resin molded product that has been predicted with high accuracy.

[0084] (Action and effect) As described above, according to the second embodiment, the following advantageous effects can be obtained.

[0085] In embodiment 2, the state of the target mold is predicted using a state prediction model 311 that predicts the state of the mold used to manufacture the resin molded product from the manufacturing data of the resin molded product, the first variable, and the second variable, and the quality of the resin molded product manufactured with the target mold is predicted from the predicted state of the mold using a quality prediction model 312 for the resin molded product that includes explanatory variables that indicate the state of the mold.

[0086] As described above, in the second embodiment, a two-stage prediction process is adopted in which the output from the condition prediction model 311 is input to the quality prediction model 312. According to the two-stage prediction process, quality is predicted in two stages using two models, thereby improving the accuracy of the quality prediction. More specifically, because the quality prediction model 312 includes explanatory variables that indicate the mold condition predicted by the condition prediction model 311, a so-called "cascade effect" occurs, improving the accuracy of the quality prediction of the resin molded product.

[0087] Furthermore, by configuring the condition prediction model 311 to predict the condition (degree of contamination) of the mold, which is prone to change over time, it is possible to maintain prediction accuracy by updating the condition prediction model 311 relatively frequently, while reducing the update frequency of the quality prediction model 312. This enables model management with increased flexibility and adaptability.

[0088] Furthermore, in machine learning, while a large number of variables are required to generate a predictive model that performs quality prediction as in embodiment 2, adopting a two-stage prediction process reduces the number of variables required for each model, contributing to a reduction in the amount of required data. Furthermore, generating models with fewer variables also contributes to improved flexibility and adaptability in model management.

[0089] Furthermore, in hierarchical processing in which the output from the state prediction model 311 is input to the quality prediction model 312, by selecting or designing appropriate features for each layer, it becomes possible to automatically tune these models, thereby reducing the maintenance of the models.

[0090] Furthermore, the prediction results of the quality prediction model 312 may be used to optimize the manufacturing conditions (e.g., various temperatures and pressures) of the resin molded product. A well-known optimization method may be used for the optimization calculation. Well-known optimization methods include grid search, random search, genetic algorithm, and Bayesian optimization. The optimized manufacturing conditions may be output to the output unit 34. This allows the operator to understand the optimized manufacturing conditions, making it possible to manufacture products with the desired quality. Furthermore, when a PLC is used to control the injection molding machine, the optimized manufacturing conditions are automatically applied, making it possible to automatically manufacture products with the desired quality.

[0091] Furthermore, the operator can also simulate the production of resin molded products by inputting various manufacturing conditions and having the state prediction unit 302 and the quality prediction unit 303 make predictions. In this way, the information processing device 3 can also be used as an auxiliary tool for determining manufacturing conditions for resin molded products.

[0092] Other Embodiments The execution entity of each process described in each of the above-mentioned embodiments is arbitrary and is not limited to the above-mentioned examples. In other words, functions similar to those of information processing devices 1 to 3 can be realized by a plurality of information processing devices that can communicate with each other. For example, the processes shown in FIGS. 5 and 8 may be shared and executed by a plurality of information devices. Furthermore, an information processing device that combines the functions of information processing devices 1 and 2, or an information processing device that combines the functions of information processing devices 1 and 3, is also included in the scope of the present invention.

[0093] Furthermore, although the above-described embodiments have been described with reference to injection molding as a method for processing resin, the present invention is not limited to this and can be applied to various methods for processing resin, such as extrusion molding, vacuum molding, and pressure molding.

[0094] [Software implementation example] The functions of the information processing devices 1 to 3 (hereinafter simply referred to as "devices") can be realized by a program (information processing program) for causing a computer to function as the device, and a program for causing a computer to function as each control block of the device (particularly each part included in control unit 10, control unit 20, and control unit 30).

[0095] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The computer executes the program to realize each function described in each embodiment.

[0096] The program may be stored non-transitory on one or more computer-readable storage media. The storage media may or may not be included in the device. In the latter case, the program may be supplied to the device via any wired or wireless transmission medium.

[0097] In addition, some or all of the functions of each control block can be realized by a logic circuit. For example, an integrated circuit in which a logic circuit that functions as each control block is formed is also included in the scope of the present invention. In addition, the functions of each control block can also be realized by, for example, a quantum computer.

[0098] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of symbols]

[0099] 1, 2, 3 Information processing equipment 202, 302 State prediction unit 303 Quality Prediction Department 211, 311 State prediction model 312 Quality Prediction Model 50~52 Manufacturing data 60 First variable 61 Second variable

Claims

1. a data acquisition unit that acquires prediction data including at least a first variable that indicates the difference between a set value when a resin molded product is manufactured using a target mold and an actual measurement value corresponding to the set value, and a second variable that indicates a change over time in the actual measurement value; a state prediction unit that predicts a state of the target mold from the prediction data using a state prediction model of a mold used in manufacturing the resin molded product, the state prediction model including at least the first variable and the second variable as explanatory variables; Information processing device.

2. the explanatory variables of the state prediction model include at least one of a variable indicating a storage condition of a raw material resin of the resin molded product and a variable indicating a storage period; The data acquisition unit further acquires at least one of the variable indicating a storage condition and the variable indicating a storage period. The information processing device according to claim 1 .

3. a quality prediction unit that predicts the quality of a resin molded product manufactured with the target mold from a prediction result of the state prediction unit using a quality prediction model of the resin molded product including explanatory variables that indicate the state of the target mold; 3. The information processing device according to claim 1.

4. The resin molded article is a molded article containing a biodegradable resin.

3. The information processing device according to claim 1.

5. The biodegradable resin is a polyhydroxyalkanoate-based resin. The information processing device according to claim 4 .

6. An information processing method executed by one or more information processing devices, a data acquisition step of acquiring prediction data including at least a first variable indicating the difference between a set value when a resin molded product is manufactured using the target mold and an actual measurement value corresponding to the set value, and a second variable indicating a change over time in the actual measurement value; a state prediction step of predicting a state of the target mold from the prediction data using a state prediction model of a mold used in manufacturing the resin molded product, the state prediction model including at least the first variable and the second variable as explanatory variables, Information processing methods.

7. 2. An information processing program for causing a computer to function as the information processing device according to claim 1, the information processing program causing a computer to function as the data acquisition unit and the state prediction unit.

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