Learning data generation method, learned model generation method, characteristic value estimation method, learning data generation program, learned model generation program, and characteristic value estimation program

Abstract

Provided are a learning data generation method, a learned model generation method, a characteristic value estimation method, a learning data generation program, a learned model generation program, and a characteristic value estimation program capable of calculating strain that affects warpage in a fiber-reinforced resin molded article in a short time.SOLUTION: A learning data generation step of reading, from a storage unit, a plurality of data sets each including a material condition, a molding condition, and a structure condition of a fiber-reinforced plastic molded article, and a characteristic value of the fiber-reinforced plastic molded article manufactured under the material condition, the molding condition, and the structure condition, and generating learning data using the plurality of data sets, An item belonging to the structure condition includes the principal curing shrinkage strain.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a training data generation method, a trained model generation method, a characteristic value estimation method, a training data generation program, a trained model generation program, and a characteristic value estimation program. [Background technology]

[0002] Fiber-reinforced plastic (FRP) molded products are often made by laminating prepregs, which are sheets of reinforcing fibers aligned in one direction and impregnated with resin, and then molding them by applying pressure and heat. Prepregs are particularly susceptible to shrinkage in the direction perpendicular to the reinforcing fibers, and this shrinkage can cause warping in molded products. Predicting warpage in molded products is often a challenge. Conventionally, warpage has been predicted by simulating warpage based on the combination of materials, etc. (See, for example, Non-Patent Documents 1 to 3.) In this case, the strain that affects warpage in the molded product is predicted, and the warpage is calculated from the prediction results. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] OG Kravchenko et al. / Composites: Part A 99 (2017) 186-197 [Non-patent document 2] Panasonic Technical Journal Vol.55 No.4 Jan.2010 [Non-patent document 3] DENSO Technical Review Vol.4 No.1 1999 Summary of the Invention [Problem to be solved by the invention]

[0004] However, simulation-based predictions take time, and there is a need for technology that can perform calculations in a short amount of time.

[0005] The present invention has been made in consideration of the above, and aims to provide a training data generation method, a trained model generation method, a characteristic value estimation method, a training data generation program, a trained model generation program, and a characteristic value estimation program that can calculate strain that affects warpage in fiber-reinforced plastic molded products in a short period of time. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the objective, the training data generation method of the present invention is a training data generation method in which a computer generates training data for estimating characteristic values ​​related to warpage of a fiber-reinforced plastic molded product, and includes a training data generation step of reading from a storage unit a plurality of data sets each including the material conditions, molding conditions, and structural conditions of the fiber-reinforced plastic molded product, as well as the characteristic values ​​of the fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions, and generating training data using the plurality of data sets, wherein the items belonging to the structural conditions include primary cure shrinkage strain.

[0007] In the learning data generation method of the present invention, in the above invention, the items belonging to the structural conditions include information between layers of the fiber-reinforced plastic molded product, and the information between layers includes two or more of the presence or absence of interlayer reinforcement, geometric information of the interlayer reinforcement, and geometric information of the interlayer resin.

[0008] In the learning data generating method according to the present invention, in the above invention, the principal cure shrinkage strain is calculated from resin cure shrinkage measurement data.

[0009] In the learning data generation method according to the present invention, in the above invention, the characteristic values ​​included in the data set include values ​​measured on a fiber-reinforced plastic molded product having an asymmetric laminate structure.

[0010] In the learning data generating method according to the present invention, in the above invention, the characteristic values ​​include one or more of the amount of deformation, the coefficient of linear expansion, the principal hardening shrinkage strain, and the strain due to stress relaxation.

[0011] The trained model generation method of the present invention is a trained model generation method in which a computer generates a trained model for estimating characteristic values ​​of a fiber-reinforced plastic molded product, and includes: a training data generation step of reading from a storage unit a plurality of data sets each including the material conditions, molding conditions, and structural conditions of the fiber-reinforced plastic molded product, as well as the characteristic values ​​of the fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions, and generating training data using the plurality of data sets; and a trained model generation step of generating a trained model by learning the training data using the material conditions, molding conditions, and structural conditions of the fiber-reinforced plastic molded product as explanatory variables and the characteristic values ​​of the fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions as objective variables, wherein the items belonging to the structural conditions include principal curing shrinkage strain.

[0012] The method for estimating characteristic values ​​of a fiber-reinforced plastic molded product according to the present invention is a method for estimating characteristic values ​​of a fiber-reinforced plastic molded product by a computer, and includes a calculation step of reading from a storage unit a trained model trained using training data generated based on a plurality of data sets each including material conditions, molding conditions, and structural conditions, as well as characteristic values ​​of the fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions, and calculating the characteristic values ​​of the fiber-reinforced plastic molded product to be estimated based on the trained model, wherein the items belonging to the structural conditions include principal curing shrinkage strain.

[0013] The training data generation program of the present invention is a training data generation program that causes a computer to generate training data for estimating characteristic values ​​of a fiber-reinforced plastic molded product, and causes the computer to execute a training data generation step of reading from a storage unit a plurality of data sets each including the material conditions, molding conditions, and structural conditions of the fiber-reinforced plastic molded product, as well as the characteristic values ​​of the fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions, and generating training data using the plurality of data sets, wherein the items belonging to the structural conditions include primary curing shrinkage strain.

[0014] The trained model generation program of the present invention is a trained model generation program in which a computer generates a trained model for estimating characteristic values ​​of a fiber-reinforced plastic molded product, and causes the computer to execute the following steps: a training data generation step of reading from a storage unit a plurality of data sets each including the material conditions, molding conditions, and structural conditions of the fiber-reinforced plastic molded product, as well as the characteristic values ​​of the fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions, and generating training data using the plurality of data sets; and a trained model generation step of generating a trained model by learning the training data using the material conditions, molding conditions, and structural conditions of the fiber-reinforced plastic molded product as explanatory variables and the characteristic values ​​of the fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions as objective variables, and the items belonging to the structural conditions include principal curing shrinkage strain.

[0015] The characteristic value estimation program for fiber-reinforced plastic molded products according to the present invention is a characteristic value estimation program for fiber-reinforced plastic molded products that causes a computer to estimate the characteristic values ​​of a fiber-reinforced plastic molded product, and causes the computer to execute the following calculation steps: read out from a storage unit a trained model that has been trained using training data generated based on a plurality of data sets each including material conditions, molding conditions, and structural conditions, as well as the characteristic values ​​of the fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions; and calculate, based on the trained model, the characteristic values ​​of the fiber-reinforced plastic molded product to be estimated; and the items belonging to the structural conditions include principal curing shrinkage strain. [Effects of the Invention]

[0016] According to the present invention, it is possible to calculate the strain that affects warpage in a fiber-reinforced plastic molded product in a short time. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a diagram showing a schematic configuration of a characteristic value estimation system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing the configuration of a learning device included in a characteristic value estimation system according to an embodiment of the present invention. [Figure 3] Figure 3 shows a list of the contents of the dataset. [Figure 4] FIG. 4 is a block diagram showing the configuration of an estimation device included in a characteristic value estimation system according to an embodiment of the present invention. [Figure 5] FIG. 5 is a diagram for explaining the flow of the estimation process performed by the characteristic value estimation system according to an embodiment of the present invention. [Figure 6] FIG. 6 is a flowchart showing an outline of the learning process performed by the learning device according to an embodiment of the present invention. [Figure 7] FIG. 7 is a flowchart illustrating the estimation process performed by the estimation device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, an embodiment of a characteristic value estimation system according to the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to this embodiment. Furthermore, the individual embodiments of the present invention are not independent and can be implemented in combination with each other as appropriate.

[0019] (Embodiment) 1A to 1C are diagrams showing a schematic configuration of a characteristic value estimation system according to an embodiment of the present invention. The characteristic value estimation system 1 shown in these figures includes a learning device 2 that creates learning data and generates a trained model trained using the created learning data, an estimation device 3 that estimates a characteristic value of an estimation target using the trained model generated by the learning device 2, a display device 4 that displays information including the estimation results of the estimation device 3, and an input device 5.

[0020] The characteristic values ​​estimated by the estimation device 3 are characteristic values ​​of a fiber reinforced plastic molded product made using fiber reinforced plastics (FRP), and are parameters related to warpage of the fiber reinforced plastic molded product. Fiber-reinforced plastic molded products are made by laminating prepregs, which are sheets of reinforcing fibers aligned in one direction and impregnated with resin, in multiple directions, and then molding them by applying pressure and heat.

[0021] Examples of reinforcing fibers include glass fibers, carbon fibers, metal fibers, aromatic polyamide fibers, polyaramid fibers, alumina fibers, silicon carbide fibers, boron fibers, and basalt fibers. These may be used alone or in combination of two or more types. These reinforcing fibers may be surface-treated. Examples of surface treatments include metal deposition treatment, treatment with a coupling agent, treatment with a sizing agent, and treatment with an additive. In this specification, when reinforcing fibers have been surface-treated, the term "reinforcing fibers" includes fibers in a surface-treated state. These reinforcing fibers also include conductive reinforcing fibers. Carbon fibers are preferably used as reinforcing fibers because of their low specific gravity, high strength, and high elastic modulus. Commercially available carbon fibers include "TORAYCA (registered trademark)" T800G-24K, "TORAYCA (registered trademark)" T800S-24K, "TORAYCA (registered trademark)" T700G-24K, "TORAYCA (registered trademark)" T700S-24K, "TORAYCA (registered trademark)" T300-3K, and "TORAYCA (registered trademark)" T1100G-24K (all manufactured by Toray Industries, Inc.).

[0022] The form and arrangement of the reinforcing fibers can be appropriately selected from those in which the reinforcing fibers are arranged in one direction, a laminate of reinforcing fibers arranged in one direction, or a woven form. Fiber-reinforced plastic molded products can have a symmetrical laminate structure in which the fiber direction is symmetrical with respect to the central plane of the prepreg laminate, or an asymmetrical laminate structure in which the fiber direction is asymmetric with respect to the central plane. Here, the central plane corresponds to, for example, the plane between the third and fourth layers, or the face of the third layer facing the fourth layer, in a six-layer structure. The asymmetric laminate structure has a larger amount of warpage than the symmetric laminate structure.

[0023] As the resin, a thermoplastic resin, a thermosetting resin, a photocurable resin, or the like can be used.

[0024] Thermoplastic resins can be polymers with a carbon-carbon bond, amide bond, imide bond, ester bond, ether bond, carbonate bond, urethane bond, urea bond, thioether bond, sulfone bond, imidazole bond, or carbonyl bond in the main chain. Specific examples include polyacrylate, polyolefin, polyamide (PA), aramid, polyester, polycarbonate (PC), polyphenylene sulfide (PPS), polybenzimidazole (PBI), polyimide (PI), polyetherimide (PEI), polysulfone (PSU), polyethersulfone (PES), polyetherketone (PEK), polyetheretherketone (PEEK), polyetherketoneketone (PEKK), polyaryletherketone (PAEK), and polyamideimide (PAI). These can be polymers, or oligomers or monomers can be used for low viscosity and low-temperature application. Of course, depending on the purpose, these may be copolymerized, or various types may be mixed and used as a polymer blend / alloy.

[0025] Examples of thermosetting resins include epoxy resins, maleimide resins, polyimide resins, acetylene-terminated resins, vinyl-terminated resins, allyl-terminated resins, nadic acid-terminated resins, and cyanate ester-terminated resins. These resins can generally be used in combination with a curing agent or a curing catalyst. These thermosetting resins can also be used in combination as appropriate.

[0026] It is also suitable to use a mixture of a thermosetting resin and a thermoplastic resin. A mixture of a thermosetting resin and a thermoplastic resin provides better results than using a thermosetting resin alone. This is because thermosetting resins generally have the disadvantage of being brittle but can be molded at low pressures in an autoclave, while thermoplastic resins generally have the advantage of being tough but are difficult to mold at low pressures in an autoclave. These contradictory properties make it possible to balance physical properties and moldability by using a mixture of these resins. When using a mixture, it is preferable that the thermosetting resin be contained in an amount of more than 50 mass% from the viewpoint of mechanical properties.

[0027] The learning device 2 is electrically connected to the estimation device 3. The learning device 2 selectively extracts learning data, and generates and outputs a trained model by learning using the extracted learning data. FIG. 2 is a block diagram showing the configuration of a learning device provided in a characteristic value estimation system according to an embodiment of the present invention. The learning device 2 has an extraction unit 21, a learning data generation unit 22, a learning unit 23, a control unit 24, and a storage unit 25. In this embodiment, the learning data generation device is configured by at least the extraction unit 21, the learning data generation unit 22, and the storage unit 25.

[0028] The extraction unit 21 extracts a data set from the data sets stored in the storage unit 25 based on the input conditions, and outputs the extracted data set to the learning data generation unit 22 as learning data.

[0029] The data set and extraction of the data set will now be described. The data set has a level including conditions and characteristic values ​​related to the fiber reinforced plastic molded product.

[0030] The extraction unit 21 reads out a plurality of data sets based on the fiber-reinforced plastic molded product to be estimated input via the input device 5, and extracts them according to the conditions. In this embodiment, the extraction unit 21 reads out a plurality of data sets that satisfy the material conditions, molding conditions, and structural conditions that are preset for the fiber-reinforced plastic molded product to be estimated input via the input device 5.

[0031] Fig. 3 is a diagram showing a list of the contents of the data set. Fig. 3 shows an example of a data set read by the extraction unit 21 that satisfies the molding conditions and structural conditions that are preset for the input fiber-reinforced plastic molded product. Fig. 3 shows an example of material conditions set as material conditions 1, 2, 3, 4, ..., molding conditions set as molding conditions 1, 2, 3, 4, ..., structural conditions set as structural conditions 1, 2, 3, 4, ..., and level 1, level 2, ..., level N-1, and level N, to which their characteristic values ​​are respectively associated.

[0032] Here, the material conditions include fiber type, resin type, compounding ratio, interlayer reinforcement type, CF density, resin density, interlayer reinforcement density, CF elastic modulus, resin elastic modulus, etc. As material conditions in the data set, any of the above types is assigned as material condition 1, 2, 3, 4, ..., and a value is set for each level.

[0033] The molding conditions include post-cure conditions, temperature rise temperature (cpm), temperature fall temperature (cpm), molding temperature (first stage), molding temperature (second stage), molding temperature (post-cure), molding pressure (MPa), post-cure pressure (MPa), molding equipment, and a molding profile including temperature and pressure conditions. Note that molding pressure is, for example, the difference from atmospheric pressure. As molding conditions in the data set, any of the above various types are assigned as molding conditions 1, 2, 3, 4, ..., and the corresponding values ​​are set for each level.

[0034] Structural conditions include the primary cure shrinkage strain, FAW (basis weight), number of plies (number of layers), laminate configuration, plate thickness, interlayer information, geometric information on fiber arrangement, and non-destructive testing data. Interlayer information includes the presence or absence of interlayer reinforcement, geometric information on the interlayer reinforcement, and geometric information on the interlayer resin. Geometric information on the interlayer reinforcement includes the shape of the interlayer reinforcement and the average diameter of the interlayer reinforcement. Geometric information on the interlayer resin includes the thickness of the interlayer resin layer, the fiber volume content of the fiber-reinforced resin molded product, the fiber volume content within the layer, and the degree of fiber dispersion. Non-destructive testing data includes the void fraction. As structural conditions in the dataset, one of the above types is assigned as structural condition 1, 2, 3, 4, etc., and a value is set for each level.

[0035] Here, the strain ε that affects warpage is expressed as in the following formula (1) using the linear expansion coefficient α, the hardening shrinkage strain β, and the temperature difference ΔT that is the difference between high temperature (heating temperature) and room temperature. ε=αΔT+β (1) Using the above formula (1), the linear expansion coefficient α and hardening shrinkage strain β can be calculated by substituting the value of the strain ε related to the warp that occurs when several different temperature differences ΔT are applied.

[0036] The hardening shrinkage strain β is expressed as the sum of the main hardening shrinkage strain β' and the strain due to stress relaxation γ, as shown in the following formula (2): where T is temperature, t is time, and E is the elastic modulus. β=β´+γ (2) γ=f(T,t,E) Although the strain γ due to stress relaxation is difficult to calculate, it can be estimated by obtaining the principal hardening shrinkage strain β'. The hardening shrinkage strain β is a parameter that changes in a complex manner depending on the molding conditions, and the number of explanatory variables used for prediction is large and complex. In this case, by decomposing the hardening shrinkage strain β into the principal hardening strain β' and the strain due to stress relaxation γ and incorporating them into the structural conditions, the prediction model for the characteristic value is simplified and the prediction accuracy of the characteristic value (here, strain) is improved.

[0037] The principal cure shrinkage strain β' can be measured according to Japanese Industrial Standard JIS K 6941. Specifically, a minute amount of sample (a few milliliters or less) is used, cured according to the curing conditions, and the change in the resin film thickness is continuously measured, and the shrinkage rate is calculated from the rate of decrease in the film thickness.

[0038] In this embodiment, the extraction unit 21 extracts at least the principal cure shrinkage strain as the structural condition. Furthermore, it is preferable that the items belonging to the structural condition include interlayer information as the extracted structural condition. In this case, the interlayer information includes two or more of the following: the presence or absence of interlayer reinforcement, geometric information of the interlayer reinforcement, and geometric information of the interlayer resin. Furthermore, the extracted structural condition may further include geometric information of fiber arrangement. The geometric information of fiber arrangement includes at least one of the fiber volume content of the fiber-reinforced plastic molded product, the fiber volume content within the layer, and the degree of fiber distribution. The adoption of geometric information of fiber arrangement can improve the prediction accuracy of characteristic values ​​related to deformation, such as elasticity.

[0039] The characteristic values ​​are parameters related to warpage, such as the amount of deformation, the coefficient of linear expansion, the main hardening shrinkage strain (β'), or the strain due to stress relaxation γ.

[0040] The learning data generation unit 22 generates learning data using the data set extracted by the extraction unit 21.

[0041] The learning unit 23 performs learning using the learning data generated by the learning data generation unit 22 to generate a trained model. The trained model is a neural network consisting of an input layer, an intermediate layer, and an output layer, with each layer having one or more nodes. The trained model is generated by training. Information such as network parameters in the trained model is stored in the storage unit 25. The network parameters include information regarding the weights and biases between layers of the neural network.

[0042] The learning performed by the learning unit 23 can employ a known learning method. For example, when generating a trained model by learning using regularization, multiple candidate values ​​for the hyperparameters of a regression model are provided, and learning is performed for each of the provided candidate values ​​for the hyperparameters. Then, for the model obtained by learning using each candidate value, prediction errors are calculated by cross-validation or holdout validation using the learning data, and the regression model that provides the smallest prediction error is selected. The selected regression model is output as the trained model. Note that the hyperparameters referred to here include, for example, the number of layers in a neural network and the regularization coefficient.

[0043] The control unit 24 controls the overall operation of the learning device 2.

[0044] The storage unit 25 stores various programs for operating the learning device 2 and data including various parameters necessary for the operation of the learning device 2. The various programs include a training data generation program that generates training data for generating a trained model, and a trained model generation program that generates a trained model by learning using the training data. The storage unit 25 also has a dataset storage unit 251 that stores multiple datasets that make up the training data. In this embodiment, multiple levels that associate the material conditions, structural conditions, and molding conditions for manufacturing a fiber-reinforced plastic molded product with their characteristic values ​​are stored as datasets.

[0045] The storage unit 25 is configured using a ROM (Read Only Memory) in which various programs etc. are pre-installed, a RAM (Random Access Memory) for storing calculation parameters and data for each process, an HDD (Hard Disk Drive), an SSD (Solid State Drive), etc.

[0046] The various programs can also be widely distributed by recording them on computer-readable recording media such as HDDs, flash memories, CD-ROMs, DVD-ROMs, Blu-ray (registered trademark), etc. The communication network referred to here is configured using, for example, an existing public line network, a LAN (Local Area Network), a WAN (Wide Area Network), etc., and may be wired or wireless.

[0047] The learning device 2 having the above functional configuration is a computer configured using one or more pieces of hardware such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), and an FPGA (Field Programmable Gate Array).

[0048] The estimation device 3 is electrically connected to the learning device 2 and the display device 4. The estimation device 3 outputs an estimation result including characteristic values ​​estimated using the conditions of the fiber-reinforced plastic molded product to be estimated and the trained model acquired from the learning device 2. FIG. 4 is a block diagram showing the configuration of the estimation device provided in the characteristic value estimation system according to an embodiment of the present invention. The estimation device 3 includes a calculation unit 31, a control unit 32, and a storage unit 33.

[0049] The calculation unit 31 calculates the characteristic values ​​of the fiber reinforced plastic molded product that are estimated using each condition of the estimation target acquired from the input device 5 and the trained model acquired from the learning device 2.

[0050] 5 is a diagram illustrating the flow of estimation processing performed by a characteristic value estimation system according to an embodiment of the present invention. The calculation unit 31 acquires a trained model 100 trained using the training data IP extracted by the extraction unit 21. The calculation unit 31 uses this trained model 100 to output an estimated characteristic value OP under the conditions of the estimation target.

[0051] The control unit 32 comprehensively controls the operation of the estimation device 3. The control unit 32 has a display control unit 321 that causes the display device 4 to display the calculation result (estimation result) of the calculation unit 31. The display control unit 321 may cause the display device 4 to display information on the estimation target, such as each condition, in addition to the estimation result.

[0052] The storage unit 33 stores various programs for operating the estimation device 3 and data including various parameters necessary for the operation of the estimation device 3. The various programs include a characteristic value estimation program executed using a trained model. The storage unit 33 is configured using a ROM in which the various programs are pre-installed, and a RAM, HDD, SSD, etc. that store calculation parameters and data for each process.

[0053] The various programs can be recorded on computer-readable recording media such as HDDs, flash memories, CD-ROMs, DVD-ROMs, and Blu-ray (registered trademark) and widely distributed. The estimation device 3 can also acquire the various programs via a communication network. The communication network here is configured using, for example, an existing public line network, LAN, WAN, etc., and may be wired or wireless.

[0054] The estimation device 3 having the above-described functional configuration is a computer configured using one or more pieces of hardware such as a CPU, a GPU, an ASIC, and an FPGA.

[0055] The display device 4 is a display made of liquid crystal or organic EL (Electro Luminescence) or the like, and is electrically connected to the estimation device 3. The display device 4 acquires and displays display data output from the estimation device 3 under the control of the display control unit 321. The display device 4 may also have an audio output function such as a speaker.

[0056] The input device 5 receives input of various information including information such as settings related to the process of estimating characteristic values, and outputs the received information to the learning device 2 and the estimation device 3. The input device 5 is configured using user interfaces such as a keyboard, a mouse, a microphone, and a touch panel.

[0057] Next, each process of the learning device 2 and the estimation device 3 will be described with reference to Fig. 6 and Fig. 7. Fig. 6 is a flowchart showing an outline of the learning process performed by the learning device according to this embodiment.

[0058] First, the learning device 2 acquires estimation conditions via the input device 5 (step S11). The estimation conditions input here are, for example, a fiber-reinforced plastic molded product to be estimated and its characteristic values.

[0059] The learning device 2 generates learning data based on the estimation conditions input via the input device 5 (step S12). The extraction unit 21 reads out, based on the input estimation conditions, from a plurality of data sets stored in the storage unit 25, a plurality of data sets that satisfy the respective conditions (material conditions, molding conditions, and structural conditions) previously set for the fiber-reinforced plastic molded product to be estimated. At this time, the structural conditions include at least the principal cure shrinkage strain β'. The learning data generation unit 22 generates learning data using the data set extracted by the extraction unit 21. Based on the extracted data set, the learning data generation unit 22 generates learning data in which the material conditions, molding conditions, and structural conditions are explanatory variables and the characteristic values ​​are objective variables.

[0060] Then, the learning unit 23 generates a trained model by learning using the training data (step S13).

[0061] The estimation device 3 estimates the characteristic values ​​of the fiber-reinforced plastic molded product to be estimated. Fig. 7 is a flowchart for explaining the estimation process performed by the estimation device according to this embodiment. The calculation unit 31 calculates the characteristic values ​​of the fiber-reinforced plastic molded product to be estimated using the trained model generated by the learning device 2.

[0062] The calculation unit 31 acquires each condition of the fiber reinforced plastic molded product to be estimated via the input device 5 (step S21).

[0063] The calculation unit 31 calculates the characteristic values ​​using the trained model generated in step S13 (step S22). This estimates the characteristic values ​​of the fiber-reinforced plastic molded product to be estimated. At this time, if calculation processing or conversion processing is required using the values ​​obtained by the trained model, the calculation unit 31 performs various processing using these values. For example, if the trained model outputs a curing shrinkage strain β, the calculation unit 31 uses this curing shrinkage strain β to calculate the strain ε that affects warpage.

[0064] Next, the display control unit 321 outputs the estimation result by the calculation unit 31 to the display device 4, and performs display control to display the estimation result on the display device 4 (step S23). The display device 4 displays, for example, the estimated characteristic values ​​together with each condition of the fiber-reinforced plastic molded product that is the estimation target.

[0065] In the embodiment described above, the training data generator generates training data using a plurality of data sets including the material conditions, molding conditions, and structural conditions of the fiber-reinforced plastic molded product, as well as the characteristic values ​​of the fiber-reinforced plastic molded product manufactured under the material conditions, molding conditions, and structural conditions. The training data generator includes the principal cure shrinkage strain β' as an item belonging to the structural conditions. According to this embodiment, it is possible to quickly calculate the strain that affects warpage in a fiber-reinforced plastic molded product that has learned explanatory variables including the principal cure shrinkage strain β'.

[0066] (Other embodiments) Although the embodiments of the present invention have been described above, the present invention should not be limited to the above-described embodiments. For example, the estimation device may have the function of a learning unit. In this case, the estimation device not only calculates the objective variable of the estimation target but also sequentially updates the trained model.

[0067] Furthermore, in the above-described embodiment, an example has been described in which a data set is extracted by the extraction unit 21, but if there is no need for extraction processing, for example, if the data set stored in the storage unit 25 is used as is, a configuration may be adopted in which extraction processing by the extraction unit 21 is not performed (a configuration without the extraction unit 21). In this case, as long as the principal hardening shrinkage strain β' is included as a structural condition, the selection of the data set is not limited to the aspect of the above-described embodiment.

[0068] Furthermore, while the above-described embodiment has been described with respect to a fiber-reinforced resin molded product, the present invention can also be applied to a curable resin molded product formed using a curable resin that is cured by heat or light. Materials in this case include curing-related materials that harden the resin, and additives that do not cause a curing reaction or polymerization reaction. Examples of curable resin compositions include thermosetting resin compositions and photocurable resin compositions. Examples of thermosetting resin compositions include epoxy resin compositions, phenolic resin compositions, and polyurethanes. Examples of photocurable resin compositions include ultraviolet-curable resin compositions that are cured by ultraviolet light.

[0069] Furthermore, the machine learning is not limited to the above-mentioned deep learning, and for example, a support vector machine, a decision tree, a random forest, or a gradient boosting tree may be used. [Explanation of symbols]

[0070] 1. Characteristic value estimation system 2 Learning device 3 Estimation device 4 Display device 5 Input Devices 21 Extraction part 22 Learning data generation unit 23 Learning Department 24, 32 Control section 25, 33 Storage section 31 Calculation section 251 Dataset Storage Unit 321 Display control unit

Claims

1. A learning data generation method in which a computer generates learning data for estimating a characteristic value related to warpage of a fiber-reinforced plastic molded product, a learning data generation step of reading from a storage unit a plurality of data sets each including material conditions, molding conditions, and structural conditions of a fiber-reinforced plastic molded product, as well as characteristic values ​​of the fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions, and generating learning data using the plurality of data sets; Including, The items belonging to the structural conditions include the principal hardening shrinkage strain, Method for generating training data.

2. The items belonging to the structural conditions include information between layers of the fiber-reinforced plastic molded product, The interlayer information includes two or more of: presence or absence of an interlayer reinforcing material; geometric information of the interlayer reinforcing material; and geometric information of an interlayer resin. The learning data generation method according to claim 1 .

3. The main curing shrinkage strain is calculated from resin curing shrinkage measurement data. The learning data generation method according to claim 1 .

4. The property values ​​included in the data set include values ​​measured in a fiber-reinforced resin molded product having an asymmetric laminate structure. The learning data generation method according to claim 1 .

5. The characteristic value includes at least one of a deformation amount, a linear expansion coefficient, a main curing shrinkage strain, and a strain due to stress relaxation. The learning data generation method according to claim 2 .

6. A trained model generation method in which a computer generates a trained model for estimating characteristic values ​​of a fiber-reinforced plastic molded product, a learning data generation step of reading from a storage unit a plurality of data sets each including material conditions, molding conditions, and structural conditions of a fiber reinforced plastic molded product, as well as characteristic values ​​of the fiber reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions, and generating learning data using the plurality of data sets; A trained model generation step of generating a trained model by learning in the training data using material conditions, molding conditions, and structural conditions of a fiber reinforced plastic molded product as explanatory variables and characteristic values ​​of a fiber reinforced plastic molded product manufactured under the material conditions, molding conditions, and structural conditions as objective variables; Including, The items belonging to the structural conditions include the principal hardening shrinkage strain, How to generate a trained model.

7. A method for estimating a characteristic value of a fiber reinforced plastic molded product, in which a computer estimates a characteristic value of a fiber reinforced plastic molded product, A calculation step of reading from the storage unit a trained model trained using training data generated based on a plurality of data sets each including material conditions, molding conditions, and structural conditions, as well as characteristic values ​​of a fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions, and calculating characteristic values ​​of the fiber-reinforced plastic molded product to be estimated based on the trained model; Including, The items belonging to the structural conditions include the principal hardening shrinkage strain, A method for estimating the characteristic values ​​of fiber-reinforced plastic molded products.

8. A learning data generation program that causes a computer to generate learning data for estimating characteristic values ​​of a fiber-reinforced plastic molded product, a learning data generation step of reading from a storage unit a plurality of data sets each including material conditions, molding conditions, and structural conditions of a fiber-reinforced plastic molded product, as well as characteristic values ​​of the fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions, and generating learning data using the plurality of data sets; causing the computer to execute The items belonging to the structural conditions include the principal hardening shrinkage strain, A program for generating training data.

9. A trained model generation program in which a computer generates a trained model for estimating characteristic values ​​of a fiber-reinforced plastic molded product, a learning data generation step of reading from a storage unit a plurality of data sets each including material conditions, molding conditions, and structural conditions of a fiber reinforced plastic molded product, as well as characteristic values ​​of the fiber reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions, and generating learning data using the plurality of data sets; A trained model generation step of generating a trained model by learning in the training data using material conditions, molding conditions, and structural conditions of a fiber reinforced plastic molded product as explanatory variables and characteristic values ​​of a fiber reinforced plastic molded product manufactured under the material conditions, molding conditions, and structural conditions as objective variables; causing the computer to execute The items belonging to the structural conditions include the principal hardening shrinkage strain, A trained model generator.

10. A characteristic value estimation program for a fiber reinforced plastic molded product that causes a computer to estimate a characteristic value of a fiber reinforced plastic molded product, A calculation step of reading from the storage unit a trained model trained using training data generated based on a plurality of data sets each including material conditions, molding conditions, and structural conditions, as well as characteristic values ​​of a fiber-reinforced plastic molded product produced under the material conditions, molding conditions, and structural conditions, and calculating characteristic values ​​of the fiber-reinforced plastic molded product to be estimated based on the trained model; causing the computer to execute The items belonging to the structural conditions include the principal hardening shrinkage strain, A program for estimating the characteristic values ​​of fiber-reinforced plastic molded products.

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