Predictor, prediction method, design method and prediction program

Abstract

To make it easy to grasp the degree of influence on a phase fraction as a material composition is changed.SOLUTION: A predictor has a storage part which stores a learned model learned by using learning data in which material composition of a material to be learned is associated with phase fraction at each temperature of the material to be learned within a prescribed temperature section. By inputting to the learned model the material composition of the material to be predicted, a value predicted about a pre-designated phase is partially differentiated by using the material composition of the material to be predicted at each temperature when the phase fraction is predicted at each temperature of the material to be predicted within the prescribed temperature section. In a heat map where each component of the material to be predicted is disposed on a first axis and each temperature within the prescribed temperature section is disposed on a second axis, respective areas identified on the basis of the respective components and the respective temperatures are displayed in a display mode according to a value obtained by the partial differentiation.SELECTED DRAWING: Figure 10

Description

[Technical field]

[0001] The present disclosure relates to a prediction device, a prediction method, and a prediction program. [Background technology]

[0002] In designing materials such as alloys, it is important to calculate the phase fraction in a thermodynamic equilibrium state over a specified temperature range, and surrogate models that predict the phase fraction based on material composition information (in the case of alloys, the weight percentage of added elements) are being developed.

[0003] Here, the phase fraction in a given temperature range correlates with the material properties. Therefore, when designing materials using the above model, it is important for the designer to know in advance how to change the material composition to approach the desired phase fraction (in the case of an alloy, which phase in which temperature range can be affected by increasing or decreasing the weight percentage of which added element) in order to perform efficient material design. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2020 / 090617 Summary of the Invention [Problem to be solved by the invention]

[0005] In contrast, for example, by increasing or decreasing the weight percent of all additive elements in sequence, predicting the phase fraction within a specified temperature range, and comparing it with the phase fraction before the change, it is possible to grasp in advance the degree of influence of the weight percent of each additive element on the phase fraction. However, with such a method, in the case of an alloy or the like having a large number of additive elements, the designer's workload increases and it is difficult for the designer to grasp the degree of influence on the phase fraction.

[0006] The present disclosure aims to make it easier to understand the degree of effect on phase fractions due to changes in material composition. [Means for solving the problem]

[0007] A prediction device according to a first aspect of the present disclosure, a trained model trained using training data in which a material composition of a training target material is associated with a phase fraction of the training target material at each temperature within a predetermined temperature interval, the trained model having a memory unit configured to predict a phase fraction at an (i+1)th temperature using phase fractions predicted by the trained model for temperatures up to an i-th temperature (i is an integer equal to or greater than 1) within the predetermined temperature interval; When a phase fraction of the material to be predicted at each temperature within the predetermined temperature interval is predicted by inputting a material composition of the material to be predicted into the trained model, a predicted value for a pre-specified phase at each temperature is partially differentiated using the material composition of the material to be predicted; In a heat map in which each component of the material to be predicted is placed on a first axis and each temperature within the specified temperature range is placed on a second axis, each area identified based on each component and each temperature is displayed in a display manner corresponding to the value obtained by performing the partial differentiation.

[0008] A second aspect of the present disclosure is a prediction device according to the first aspect, The trained model is applied with an architecture capable of processing time series data, which is data for each specified time interval, and the trained model predicts the phase fraction for each specified temperature interval based on the material composition of the material to be predicted.

[0009] A third aspect of the present disclosure is a prediction device according to the second aspect, The trained model is any one of RNN, Bidirectional RNN, Seq2Seq, Seq2Seq with Attention mechanism, GRU, LSTM, or Transformer.

[0010] A fourth aspect of the present disclosure is the prediction device according to the third aspect, The trained model is an encoder unit that outputs feature quantities by inputting a material composition of a material to be predicted; The decoder unit predicts the phase fraction at the (i+1)th temperature by inputting the output feature amount and the predicted phase fractions at the temperatures up to the i-th temperature.

[0011] A fifth aspect of the present disclosure is a prediction device according to any one of the first to fourth aspects, The phase fraction is a phase fraction in a thermodynamic equilibrium state.

[0012] A sixth aspect of the present disclosure is a prediction device according to any one of the first to fifth aspects, When predicting the phase fraction at the (i+1)th temperature, the predicted value for the pre-specified phase is partially differentiated using a material composition of the material to be predicted; In the heat map, each area specified based on each of the components and the (i+1)th temperature is displayed in a display mode according to the value obtained by performing the partial differentiation.

[0013] A prediction method according to a seventh aspect of the present disclosure includes: A prediction method in which a computer executes a trained model trained using training data in which a material composition of a training target material is associated with a phase fraction at each temperature of the training target material within a predetermined temperature interval, the trained model being configured to predict a phase fraction at an (i+1)th temperature using phase fractions predicted by the trained model for temperatures up to an (i)th temperature (i is an integer equal to or greater than 1) within the predetermined temperature interval, The computer, a step of inputting a material composition of a material to be predicted into the trained model to predict a phase fraction at each temperature of the material to be predicted within the predetermined temperature interval, partially differentiating a predicted value for a pre-specified phase at each temperature using the material composition of the material to be predicted; In a heat map in which each component of the material to be predicted is placed on a first axis and each temperature within the specified temperature range is placed on a second axis, each area identified based on each component and each temperature is displayed in a display manner corresponding to the value obtained by performing the partial differentiation.

[0014] A prediction program according to an eighth aspect of the present disclosure, A prediction program for causing a computer to execute a trained model trained using training data in which a material composition of a training target material is associated with a phase fraction at each temperature of the training target material within a predetermined temperature interval, the trained model being configured to predict a phase fraction at an (i+1)th temperature using phase fractions predicted by the trained model for temperatures up to an (i)th temperature (i is an integer equal to or greater than 1) within the predetermined temperature interval, The computer includes: a step of inputting a material composition of a material to be predicted into the trained model to predict a phase fraction at each temperature of the material to be predicted within the predetermined temperature interval, partially differentiating a predicted value for a pre-specified phase at each temperature using the material composition of the material to be predicted; and displaying each region specified based on each component and each temperature in a heat map in which each component of the material to be predicted is placed on a first axis and each temperature within the specified temperature range is placed on a second axis in a display manner corresponding to the value obtained by performing the partial differentiation. Effect of the Invention

[0015] According to the present disclosure, it is possible to easily grasp the degree of effect on the phase fraction due to a change in material composition. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a diagram illustrating an example of a system configuration of a prediction system and an example of a functional configuration of a learning device and a prediction device. [Diagram 2]FIG. 2 is a diagram illustrating an example of a hardware configuration of the learning device and the prediction device. [Diagram 3] FIG. 3 is a diagram showing a configuration of learning data and specific examples of input data and correct answer data. [Figure 4] FIG. 4 is a diagram illustrating an example of a functional configuration of the learning data generating unit. [Diagram 5] FIG. 5 is a diagram illustrating an example of a functional configuration of the learning unit. [Figure 6] FIG. 6 is a first diagram illustrating an example of the operation of the learning unit. [Figure 7] FIG. 7 is a second diagram illustrating an example of the operation of the learning unit. [Figure 8] FIG. 8 is a diagram illustrating an example of a method for calculating a loss by the loss function calculation unit. [Figure 9] FIG. 9 is a flowchart showing the flow of the learning process. [Figure 10] FIG. 10 is a diagram illustrating an example of a functional configuration of the prediction unit. [Figure 11A] FIG. 11A is a first diagram illustrating an example of the operation of the prediction unit. [Figure 11B] FIG. 11B is a second diagram illustrating an example of the operation of the prediction unit. [Figure 11C] FIG. 11C is a third diagram illustrating an example of the operation of the prediction unit. [Figure 12] FIG. 12 is a flowchart showing the flow of the prediction process. [Figure 13] FIG. 13 is a diagram showing an example of the predicted phase fraction. [Figure 14] FIG. 14 is a diagram illustrating an example of a heat map. [Figure 15A] FIG. 15A is a first diagram illustrating an example of a verification result. [Figure 15B] FIG. 15B is a second diagram showing an example of the verification result. [Figure 15C] FIG. 15C is a third diagram illustrating an example of the verification result. [Figure 15D] FIG. 15D is a fourth diagram showing an example of the verification result. [Figure 15E]FIG. 15E is a fifth diagram showing an example of the verification result. [Figure 15F] FIG. 15F is a sixth diagram showing an example of the verification result. [Figure 15G] FIG. 15G is a seventh diagram showing an example of the verification result. [Figure 15H] FIG. 15H is an eighth diagram showing an example of the verification result. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, each embodiment will be described with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are denoted by the same reference numerals, and redundant description will be omitted.

[0018] [First embodiment] <System configuration of prediction system, and functional configuration of learning device and prediction device> First, a system configuration of a prediction system including a prediction device according to the first embodiment and functional configurations of a learning device and a prediction device included in the prediction system will be described. In this embodiment, the prediction system is a system that predicts phase fractions (phase fractions in a thermodynamic equilibrium state, hereinafter simply referred to as "phase fractions") at each temperature within a predetermined temperature interval based on material composition information.

[0019] 1 is a diagram illustrating an example of a system configuration of a prediction system and an example of a functional configuration of a learning device and a prediction device. As illustrated in FIG. 1, the prediction system 100 includes a learning device 110 and a prediction device 120.

[0020] A learning program is installed in the learning device 110, and the learning device 110 functions as a learning data generating unit 111 and a learning unit 112 by executing the program.

[0021] The learning data generation unit 111 generates learning data for learning a prediction model (surrogate model) and stores it in the learning data storage unit 113. In this embodiment, the learning data storage unit 113 stores the following as learning data for learning the prediction model: -Multiple material composition information with different combinations, The phase fraction at each temperature within a given temperature interval for each material having material composition information; are stored in association with each other.

[0022] The learning unit 112 reads out the learning data from the learning data storage unit 113, and learns the prediction model using the read out learning data. The learning unit 112 learns the prediction model so that the output data when the material composition information stored in the learning data is input to the prediction model approaches the phase fraction at each temperature within a predetermined temperature interval that is stored in association with the learning data. In this way, the learning unit 112 generates a trained prediction model. The learning unit 112 also stores the generated trained prediction model in a storage unit of the prediction unit 122 of the prediction device 120.

[0023] In this embodiment, the learning unit 112 uses, as a prediction model, an architecture capable of processing time-series data, which is data at predetermined time intervals (architecture generally used for natural language processing, etc.). Seq2Seq with attention mechanism, or Transformer, According to this prediction model, by inputting the material composition information stored in the learning data, it is possible to sequentially output output data corresponding to the phase fraction at each temperature within a predetermined temperature interval.

[0024] Here, "sequentially outputting output data corresponding to the phase fraction at each temperature" refers to, for example, the prediction model outputting output data corresponding to the phase fraction at the (i+1)th temperature using the correct answer data stored in the learning data for the i-th temperatures, where i is an integer equal to or greater than 1.

[0025] In this way, the learning unit 112 has a configuration for sequentially outputting output data corresponding to the phase fraction at each temperature (a configuration for processing the output data corresponding to the phase fraction at each temperature as time-series data), and thus the learning unit 112 can perform learning that reflects the output data corresponding to the phase fraction in adjacent temperature regions.

[0026] A prediction program is installed in the prediction device 120, and the prediction device 120 functions as a material composition input unit 121, a prediction unit 122, and a display unit 123 by executing the program.

[0027] When material composition information of the material to be predicted is input, the material composition input unit 121 accepts it and notifies the prediction unit 122 of it.

[0028] The prediction unit 122 stores in the storage unit the trained prediction model trained by the learning unit 112. The prediction unit 122 also reads out the trained prediction model from the storage unit and inputs the material composition information notified by the material composition input unit 121 to the trained prediction model, thereby sequentially predicting the phase fraction at each temperature within a predetermined temperature interval. In other words, the prediction unit 122 sequentially predicts the phase fraction within a predetermined temperature interval for each predetermined temperature interval.

[0029] Here, "sequentially predicting the phase fraction at specified temperature intervals" refers to, for example, the trained prediction model predicting the phase fraction at the (i+1)th temperature using the phase fractions predicted by the trained prediction model for temperatures up to the i-th temperature (i is an integer greater than or equal to 1).

[0030] Thus, in this embodiment, the prediction unit 122 has a configuration for sequentially predicting the phase fraction at each temperature (i.e., a configuration for processing the phase fraction at each temperature as time-series data by a recurrent network). This allows the prediction unit 122 to perform prediction reflecting prediction data of the phase fraction in adjacent temperature ranges. As a result, it is possible to suppress a decrease in prediction accuracy of the phase fraction compared to, for example, a case where a multilayer neural network is applied (where the prediction result of the phase fraction in adjacent temperature ranges is not reflected). In other words, the prediction unit 122 can improve prediction accuracy when predicting the phase fraction over a predetermined temperature range based on the material composition.

[0031] Furthermore, when the prediction unit 122 predicts the phase fraction within a predetermined temperature range in sequence for each predetermined temperature interval, the prediction unit 122 performs partial differentiation of the predicted value for a pre-specified phase at each temperature using the material composition of the material to be predicted. As a result, the prediction unit 122 can calculate the degree to which the pre-specified phase changes (amount of change) at each temperature when the material composition is changed by a small amount.

[0032] In addition, the prediction unit 122 The horizontal axis (first axis) represents each component of the material to be predicted. -The vertical axis (second axis) represents each temperature within a given temperature range, In each of the arranged heat maps, each area specified based on each component and each temperature is colored according to the value obtained by the partial differentiation.

[0033] The display unit 123 displays the phase fraction at each temperature within a predetermined temperature interval predicted by the prediction unit 122. The display unit 123 displays the phase fraction at each temperature within a predetermined temperature interval predicted by the prediction unit 122 in a different color for each phase. However, the phase fraction at each temperature within a predetermined temperature interval predicted by the prediction unit 122 may be displayed using different line types for each phase, such as a solid line, a dotted line, or a dashed dotted line.

[0034] Furthermore, the display unit 123 displays the heat map generated by the prediction unit 122. This allows the material designer to easily understand, based on the displayed heat map, how to change the material composition to approach a desired phase fraction for each temperature within the displayed predetermined temperature range.

[0035] In this way, according to the prediction device 120 of the first embodiment, when a material designer predicts a phase fraction based on material composition information, the designer can easily grasp, based on the heat map, the degree of effect on the phase fraction due to a change in the material composition.

[0036] <Hardware configuration of learning device and prediction device> Next, a description will be given of the hardware configurations of the learning device 110 and the prediction device 120. Since the learning device 110 and the prediction device 120 have similar hardware configurations, the hardware configurations of the learning device 110 and the prediction device 120 will be collectively described here with reference to FIG.

[0037] Fig. 2 is a diagram showing an example of the hardware configuration of the learning device and the prediction device. As shown in Fig. 2, the learning device 110 and the prediction device 120 each include a processor 201, a memory 202, an auxiliary storage device 203, an I / F (Interface) device 204, a communication device 205, and a drive device 206. The hardware components of the learning device 110 and the prediction device 120 are connected to each other via a bus 207.

[0038] The processor 201 has various arithmetic devices such as a central processing unit (CPU), a graphics processing unit (GPU), etc. The processor 201 reads various programs (for example, a learning program, a prediction program, etc.) onto the memory 202 and executes them.

[0039] The memory 202 has a main storage device such as a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The processor 201 and the memory 202 form a so-called computer, and the processor 201 executes various programs read onto the memory 202, causing the computer to realize the various functions described above.

[0040] The auxiliary storage device 203 stores various programs and various data used when the various programs are executed by the processor 201. For example, the learning data storage unit 113 is realized in the auxiliary storage device 203. Alternatively, a storage unit that stores a trained prediction model is realized in the auxiliary storage device 203.

[0041] The I / F device 204 is a connection device that connects to an operation device 211, which is an example of a user interface device, and a display device 212. The communication device 205 is a communication device for communicating with an external device (not shown) via a network.

[0042] The drive device 206 is a device for setting the recording medium 213. The recording medium 213 here includes media that record information optically, electrically, or magnetically, such as a CD-ROM, a flexible disk, a magneto-optical disk, etc. The recording medium 213 may also include semiconductor memories that record information electrically, such as ROM, flash memory, etc.

[0043] The various programs to be installed in the auxiliary storage device 203 are installed, for example, by setting the distributed recording medium 213 in the drive device 206 and reading out the various programs recorded in the recording medium 213 by the drive device 206. Alternatively, the various programs to be installed in the auxiliary storage device 203 may be installed by being downloaded from a network via the communication device 205.

[0044] <Learning data configuration, input data, and correct answer data> Next, a description will be given of a configuration of learning data and specific examples of input data and correct answer data included in the learning data. Fig. 3 is a diagram showing a configuration of learning data and specific examples of input data and correct answer data.

[0045] As shown in FIG. 3, the learning data 300 includes, as information items, "input data" and "correct answer data."

[0046] The "input data" stores different combinations of material composition information, such as "material composition information 1", "material composition information 2", ... In Fig. 3, reference numeral 301 indicates a specific example of "material composition information 1", and shows an example of the weight percentage of each component (each added element Si, Fe, Cu, Mn, Mg, Cr, Zn, Ti) that constitutes "6013", which is the name of a known aluminum alloy standard.

[0047] Similarly, reference numeral 302 indicates a specific example of "Material Composition Information 2", and represents the weight percentage of each component (each added element Si, Fe, Cu, Mn, Mg, Cr, Zn, Ti) that constitutes "6060", which is the designation of a known aluminum alloy standard.

[0048] The "correct data" stores "phase fraction 1", "phase fraction 2", ..., etc., which are phase fractions at each temperature in a predetermined temperature range (100°C to 800°C in the example of Fig. 3), associated with "material composition information 1", "material composition information 2", ..., etc. In Fig. 3, reference numeral 311 indicates a specific example of "phase fraction 1", and reference numeral 312 indicates a specific example of "phase fraction 2", and in both cases, the horizontal axis indicates temperature and the vertical axis indicates phase fraction.

[0049] Here, reference numerals 311 and 312 are results obtained by operating a high-cost simulator with high prediction accuracy for a long period of time, and in this embodiment, these results are used as correct answer data.

[0050] The simulator here refers to software that calculates the phase fraction at each temperature within a specified temperature interval by thermodynamic equilibrium calculation, and includes software such as CaTCalc (registered trademark) and MatCalc (registered trademark). Alternatively, the simulator may include software such as Termosuite (registered trademark), FactStage (registered trademark), and Pandat (registered trademark). Alternatively, the simulator may include software such as MALT2 (registered trademark), Thermo-Calc (registered trademark), and OpenCalphad (registered trademark).

[0051] <Details of each part of the learning device> Next, each unit of the learning device 110 (learning data generating unit 111, learning unit 112) will be described in detail.

[0052] (1) Functional configuration of the learning data generation unit First, a description will be given of the functional configuration of the learning data generation unit 111. Fig. 4 is a diagram showing an example of the functional configuration of the learning data generation unit.

[0053] As shown in FIG. 4, the learning data generation unit 111 includes an element information input unit 401 , a combination determination unit 402 , a simulation unit 403 , and a storage control unit 404 .

[0054] The element information input unit 401 receives input of, for example, the type of additive element to be added when generating a specific alloy. The element information input unit 401 also receives input of the upper and lower limits of the amount of additive (wt %) for each additive element.

[0055] The combination determination unit 402 randomly selects the amount of each additive element added under the constraints of the upper and lower limits for each added element that has been input, thereby determining multiple combinations of the amounts of each added element added. The combinations of the amounts of each added element added may be created at a predetermined interval between the upper and lower limits. The combination determination unit 402 also notifies the simulation unit 403 of material composition information indicated by the multiple combinations determined.

[0056] The simulation unit 403 calculates the phase fraction at each temperature within a predetermined temperature range by operating the above-mentioned simulator for each of the multiple pieces of material composition information notified by the combination determination unit 402. The simulation unit 403 also notifies the storage control unit 404 of the multiple pieces of material composition information and the corresponding phase fraction at each temperature within the predetermined temperature range.

[0057] The storage control unit 404 generates learning data in which the multiple pieces of material composition information notified by the simulation unit 403 are used as “input data” and the phase fractions at each temperature within the corresponding specified temperature range are used as “correct data,” and stores the data in the learning data storage unit 113.

[0058] (2) Functional configuration of the learning unit Next, a description will be given of the functional configuration of the learning unit 112. Fig. 5 is a diagram showing an example of the functional configuration of the learning unit.

[0059] As shown in FIG. 5, the learning unit 112 includes a prediction model (surrogate model) and a loss function calculation unit 503.

[0060] As described above, the prediction model applies Seq2Seq or Transformer with an Attention mechanism, and includes an encoder unit 501 and a decoder unit 502.

[0061] The encoder unit 501 receives “material composition information 1”, “material composition information 2”, . . . stored in the “input data” of the learning data 300, and outputs feature amounts.

[0062] The decoder unit 502 sequentially outputs output data by receiving the feature values ​​output by the encoder unit 501. Specifically, the decoder unit 502 reads out "phase fraction 1", "phase fraction 2", etc. stored in the "correct answer data" of the learning data 300. The decoder unit 502 then uses the phase fractions at temperatures up to the i-th temperature (i.e., the correct answer data at temperatures up to the i-th temperature) to output output data at the i+1-th temperature. In this way, the decoder unit 502 can sequentially output output data corresponding to the phase fractions at each temperature within a predetermined temperature interval based on the feature values ​​output by the encoder unit 501 by receiving the "material composition information 1" and the "correct answer data" of "phase fraction 1". Similarly, the decoder unit 502 can sequentially output output data corresponding to the phase fractions at each temperature within a predetermined temperature interval based on the feature values ​​output by the encoder unit 501 by receiving the "material composition information 2" and the "correct answer data" of "phase fraction 2".

[0063] It is assumed that a softmax function is used in the output layer of the decoder unit 502. As a result, the multiple values ​​(i.e., values ​​indicating the ratio of each phase) included in the output data of the decoder unit 502 are in the range of 0 to 1, and the sum of the multiple values ​​(i.e., the sum of the values ​​indicating the ratio of each phase) is "1".

[0064] In this way, by using a softmax function in the output layer of the decoder unit 502, output data corresponding to the phase fraction at each temperature within a specified temperature interval can be output sequentially without performing additional calculations.

[0065] The loss function calculation unit 503 reads out “phase fraction 1”, “phase fraction 2”, ... etc. stored in the “correct data” of the learning data 300, and compares it with the output data at the (i+1)th temperature output by the decoder unit 502.

[0066] In addition, the loss function calculation unit 503 calculates multiple types of losses during comparison, and calculates an integrated loss by weighting and adding the calculated multiple types of losses. Furthermore, the loss function calculation unit 503 updates model parameters of the encoder unit 501 and the decoder unit 502 based on the calculated integrated loss. In this way, a trained prediction model (including a trained encoder unit and a trained decoder unit) is generated.

[0067] (3) Learning unit operation example 1 Next, an operation example of the learning unit 112 (mainly an operation example of the prediction model among the prediction model and the loss function calculation unit) will be described. Fig. 6 is a first diagram showing an operation example of the learning unit.

[0068] 6(a) shows a state in which the "input data" of the learning data 300 is read out and input to the encoder unit 501, and the feature is output from the encoder unit 501. FIG. 6(a) also shows a state in which the decoder unit 502 outputs output data when a start signal is input to the decoder unit 502. In the example of FIG. 6(a), the output data output by the decoder unit 502 corresponds to the phase fraction at 800°C. The output data output by the decoder unit 502 (here, the output data corresponding to the phase fraction at 800°C) is notified to the loss function calculation unit 503. The start signal is a signal for starting the operation of the decoder unit 502, and in this embodiment, an arbitrary value (e.g., "000000") different from the output data output from the decoder unit 502 is input to the decoder unit 502 as the start signal.

[0069] FIG. 6(b) shows a state in which the feature quantity output from the encoder unit 501 is input to the decoder unit 502, and the phase fraction at 800° C. (correct data at 800° C.) is input to the decoder unit 502, causing the decoder unit 502 to output output data. In the example of FIG. 6(b), the output data output by the decoder unit 502 corresponds to the phase fraction at 790° C. The output data output from the decoder unit 502 (here, the output data corresponding to the phase fraction at 790° C.) is notified to the loss function calculation unit 503.

[0070] Fig. 6(c) shows a state in which the feature quantity output from the encoder unit 501 is input to the decoder unit 502, and the phase fraction up to 790°C (correct data up to 790°C) is input to the decoder unit 502, causing the decoder unit 502 to output output data. However, due to space limitations, Fig. 6(c) shows only the correct data at 790°C (in reality, the correct data at 800°C and the correct data at 790°C are weighted and input to the decoder unit 502).

[0071] 6(c), the output data output by the decoder unit 502 corresponds to the phase fraction at 780° C. The output data output by the decoder unit 502 (here, the output data corresponding to the phase fraction at 780° C.) is notified to the loss function calculation unit 503.

[0072] Fig. 6(d) shows a state in which the feature quantity output from the encoder unit 501 is input to the decoder unit 502, and the phase fraction up to 120°C (correct data up to 120°C) is input to the decoder unit 502, so that the decoder unit 502 outputs output data. However, due to space limitations, Fig. 6(d) shows only the correct data at 120°C (in reality, each correct data from 800°C to 120°C is weighted and input to the decoder unit 502).

[0073] 6(d), the output data output by the decoder unit 502 corresponds to the phase fraction at 110° C. The output data output by the decoder unit 502 (here, the output data corresponding to the phase fraction at 110° C.) is notified to the loss function calculation unit 503.

[0074] Fig. 6(e) shows a state in which the feature quantity output from the encoder unit 501 is input to the decoder unit 502, and the phase fraction up to 110°C (correct data up to 110°C) is input to the decoder unit 502, so that the decoder unit 502 outputs output data. However, due to space limitations, Fig. 6(e) shows only the correct data at 110°C (in reality, each correct data from 800°C to 110°C is weighted and input to the decoder unit 502).

[0075] 6(e), the output data output by the decoder unit 502 corresponds to the phase fraction at 100° C. The output data output by the decoder unit 502 (here, the output data corresponding to the phase fraction at 100° C.) is notified to the loss function calculation unit 503.

[0076] (4) Learning unit operation example 2 Next, an operation example of the learning unit 112 (mainly an operation example of the loss function calculation unit 503 among the prediction model and loss function calculation units) will be described. FIG. 7 is a second diagram showing an operation example of the learning unit. As shown in FIG. 7, the loss function calculation unit 503 reads out "correct answer data" from the learning data 300. The example in FIG. 7 shows the phase fraction at each temperature within a predetermined temperature interval included in "phase fraction 1", Phase fraction at 800℃ (correct data), · Phase fraction at 790°C (correct data), · Phase fraction at 100°C (correct data), The figure shows the state in which the above information is read out.

[0077] In the example of FIG. 7, the decoder unit 502 sequentially outputs output data based on the feature quantity output from the encoder unit 501 in response to the input of “material composition information 1.” As a result, the loss function calculation unit 503 receives the following: Phase fraction at 800℃ (output data), Phase fraction at 790°C (output data), Phase fraction at 100℃ (output data) This shows how the

[0078] 7 shows how the loss function calculation unit 503 calculates the integrated loss by comparing the phase fraction (correct data) at each temperature from 100° C. to 800° C. with the phase fraction (output data) at each temperature from 100° C. to 800° C. The example of FIG. 7 shows how the loss function calculation unit 503 updates the model parameters of the encoder unit 501 and the decoder unit 502 based on the calculated integrated loss. The functional configuration of the loss function calculation unit 503 for calculating the integrated loss will be described in detail below.

[0079] (5) Details of the functional configuration of the loss function calculation unit 8 is a diagram showing an example of a method for calculating losses by the loss function calculation unit. As shown in FIG. 8, the loss function calculation unit 503 has the following functions for calculating multiple types of losses: A phase fraction error calculation unit 801 at the formation / disappearance temperature; ·Phase fraction error calculation unit 802, A phase fraction error (logarithmic value) calculation unit 803, A phase fraction error (difference value) calculation unit 804, Cross entropy error calculation unit 805, A weighting addition unit 806, has.

[0080] The phase fraction error calculation unit 801 at generation / disappearance temperature compares the phase fraction (correct data) at each temperature from 100°C to 800°C with the phase fraction (output data) at each temperature from 100°C to 800°C for the temperatures at which the curves of each phase fraction become 0 among the correct answer data of the learning data. As a result, the phase fraction error calculation unit 801 at generation / disappearance temperature adds up the errors between the phase fraction identified based on the output data and the phase fraction identified based on the learning data at the temperatures at which each phase identified based on the learning data is generated or disappeared, for all phases. Furthermore, the phase fraction error calculation unit 801 at generation / disappearance temperature outputs the addition result (first addition result).

[0081] The phase fraction error calculation unit 802 compares the phase fraction (correct data) at each temperature between 100° C. and 800° C. with the phase fraction (output data) at each temperature between 100° C. and 800° C. As a result, the phase fraction error calculation unit 802 adds up the phase fraction errors at each temperature for a predetermined temperature interval, and outputs the addition result (second addition result).

[0082] The phase fraction error (logarithmic value) calculation unit 803 compares the phase fraction (correct data) at each temperature from 100° C. to 800° C. with the phase fraction (output data) at each temperature from 100° C. to 800° C. As a result, the phase fraction error (logarithmic value) calculation unit 803 adds up the errors in the logarithmic values ​​of the phase fractions at each temperature within a predetermined temperature interval for the predetermined temperature interval, and outputs the addition result (third addition result).

[0083] The phase fraction error (difference value) calculation unit 804 compares the phase fraction (correct data) at each temperature from 100° C. to 800° C. with the phase fraction (output data) at each temperature from 100° C. to 800° C. As a result, the phase fraction error (difference value) calculation unit 804 adds up the errors in the phase fraction difference values ​​between adjacent temperatures for a predetermined temperature interval, and outputs the addition result (fourth addition result).

[0084] Cross entropy error calculation unit 805 compares the phase fraction (correct data) at each temperature from 100° C. to 800° C. with the phase fraction (output data) at each temperature from 100° C. to 800° C. As a result, cross entropy error calculation unit 805 adds up the errors in the phase fraction ratios at each temperature within a predetermined temperature interval for the predetermined temperature interval, and outputs the addition result (fifth addition result).

[0085] The weighted addition unit 806 calculates an integrated loss by weighting and adding the addition results (the first addition result to the fifth addition result) output from the phase fraction error calculation unit 801 to the cross entropy error calculation unit 805 at the generation / disappearance temperatures.

[0086] In this way, by calculating a plurality of types of losses and performing weighted addition, the loss function calculation unit 503 can process the loss between the output data and the correct answer data in a multifaceted manner. As a result, the learning unit 112 can appropriately update the model parameters when learning the prediction model.

[0087] <Learning process flow> Next, a description will be given of the flow of the learning process performed by the learning device 110. Fig. 9 is a flowchart showing the flow of the learning process.

[0088] In step S901, the learning data generating unit 111 receives input of element information, such as the type of additive element to be added when generating a specific alloy, and the upper and lower limits of the amount (wt %) of each additive element.

[0089] In step S902, the learning data generation unit 111 randomly selects the amount of each additive element added subject to upper and lower limit constraints, thereby determining multiple combinations of the amount of each additive element added (wt %).

[0090] In step S903, the learning data generating unit 111 operates a simulator for each of the material composition information indicated by the determined combinations, and calculates the phase fraction at each temperature within a predetermined temperature interval.

[0091] In step S 904 , the learning data generation unit 111 generates learning data and stores it in the learning data storage unit 113 .

[0092] In step S 905 , the learning unit 112 inputs a start signal to the decoder unit 502 .

[0093] In step S906, the learning unit 112 reads out the "input data" of the learning data, and inputs it to the prediction model.

[0094] In step S907, the learning unit 112 sequentially inputs the phase fractions of the “correct data” of the learning data at the i-th temperature (i.e., the correct data at the i-th temperature) and sequentially outputs the (i+1)-th output data.

[0095] In step S908, the learning unit 112 determines whether or not all output data corresponding to the phase fraction at each temperature within the predetermined temperature range has been output. If it is determined in step S908 that there is a temperature for which output data corresponding to the phase fraction has not been output (NO in step S908), the process returns to step S907.

[0096] On the other hand, if it is determined in step S908 that all the output data corresponding to the phase fraction at each temperature within the predetermined temperature range has been output (YES in step S908), the process proceeds to step S909.

[0097] In step S909, the learning unit 112 reads out the "correct data" from the learning data, and compares it with the output data.

[0098] In step S910, the learning unit 112 calculates a plurality of types of losses, and calculates an integrated loss by weighting and adding the calculated plurality of types of losses. The learning unit 112 also updates model parameters of the encoder unit 501 and the decoder unit 502 based on the calculated integrated loss.

[0099] In step S911, the learning unit 112 determines whether or not to continue the learning process. If it is determined in step S911 that the learning process is to be continued (YES in step S911), the process returns to step S906, and the same process is performed by reading the next "input data" of the learning data.

[0100] On the other hand, if it is determined in step S911 that the learning process is to be ended (NO in step S911), the process proceeds to step S912.

[0101] In step S912, the learning unit 112 stores the trained encoder unit and the trained decoder unit in the storage unit of the prediction device 120 as a trained prediction model.

[0102] <Details of each part of the prediction device> Next, each unit of the prediction device 120 (here, the prediction unit 122) will be described in detail.

[0103] (1) Functional configuration of the prediction unit First, a description will be given of the functional configuration of the prediction unit 122. Fig. 10 is a diagram showing an example of the functional configuration of the prediction unit.

[0104] 10, the prediction unit 122 includes a trained encoder unit 1001 and a trained decoder unit 1002 that have been trained by the training unit 112. The trained encoder unit 1001 and the trained decoder unit 1002 form a trained prediction model.

[0105] The trained encoder unit 1001 calculates features upon input of material composition information (material composition information of the material to be predicted) notified by the material composition input unit 121, and outputs the calculated features to the trained decoder unit 1002.

[0106] The trained decoder unit 1002 receives the feature values ​​output from the trained encoder unit 1001 and sequentially outputs predicted data. Specifically, the trained decoder unit 1002 uses the predicted data at temperatures up to the i-th temperature to output predicted data at the (i+1)th temperature. This enables the trained decoder unit 1002 to predict the phase fraction at each temperature within a predetermined temperature interval based on the feature values ​​output from the trained encoder unit 1001 by receiving the material composition information of the material to be predicted.

[0107] 10, the prediction unit 122 further includes a phase designation unit 1003 and a sensitivity characteristic calculation unit 1004. The phase designation unit 1003 and the sensitivity characteristic calculation unit 1004 form an input sensitivity analysis unit.

[0108] The phase designation unit 1003 accepts input of a phase designated by a material designer. The phase designated by the material designer refers to a phase that the material designer wants to know in advance how much the phase will change at each temperature when the material composition is changed. The phase designation unit 1003 notifies the sensitivity characteristic calculation unit 1004 of the phase that has been input.

[0109] When the sensitivity characteristic calculation unit 1004 outputs the predicted data at the (i+1)th temperature, it partially differentiates the predicted value for the phase notified by the phase designation unit 1003 using the material composition information notified by the material composition input unit 121.

[0110] Specifically, the sensitivity characteristic calculation unit 1004 Partial differential values ​​in each layer calculated by processing in each layer in the trained encoder unit 1001 from when material composition information is input until when features are output, Partial differential values ​​in each layer calculated by processing in each layer in the trained decoder unit 1002 during the time from when a feature is input until a predicted value for a specified phase at the (i+1)th temperature is output; and By using this, "a value predicted for a specified phase at the (i+1)th temperature is partially differentiated using material composition information" is derived.

[0111] In addition, the sensitivity characteristic calculation unit 1004 notifies the display unit 123 (not shown in FIG. 10) of “the value obtained by partially differentiating the value at the (i+1)th temperature predicted for the specified phase using the material composition information.”

[0112] In addition, in the heat map, the display unit 123 colors the area specified based on each component (each added element) of the material to be predicted and the (i+1)th temperature according to the value obtained by partially differentiating. In this way, the display unit 123 can generate and display a heat map for a predetermined temperature interval by performing these processes over a predetermined temperature interval.

[0113] (2) Example of operation of the prediction unit Next, an operation example of the prediction unit 122 will be described. Fig. 11A to Fig. 11C are first to third diagrams showing an operation example of the prediction unit. Among these, Fig. 11A (a) shows a state in which material composition information of a material to be predicted is input to the trained encoder unit 1001, and a feature is output from the trained encoder unit 1001. Fig. 11A (a) also shows a state in which the feature output from the trained encoder unit 1001 is input to the trained decoder unit 1002, and a start signal is input to the trained decoder unit 1002, and predicted data (phase fraction at 800°C) is output.

[0114] Moreover, (a) of FIG. 11A shows the derivation of "a value (partial differential value) obtained by partially differentiating the predicted value for the specified phase at 800°C using the material composition information of the material to be predicted" when the predicted data (phase fraction at 800°C) is output.

[0115] 11A(b) shows a state in which the feature output from the trained encoder unit 1001 is input to the trained decoder unit 1002, and the predicted data (phase fraction at 800° C.) is input to the trained decoder unit 1002. As a result, the trained decoder unit 1002 outputs the predicted data (phase fraction at 790° C.).

[0116] Moreover, (b) of FIG. 11A shows how, when the prediction data (phase fraction at 790°C) is output, the "value (partial differential value) obtained by partially differentiating the predicted value for the specified phase at 790°C using the material composition information of the material to be predicted" is derived.

[0117] FIG. 11B(c) shows a state in which the feature output from the trained encoder unit 1001 is input to the trained decoder unit 1002, and the predicted data (phase fraction up to 790° C.) is input to the trained decoder unit 1002. Note that, due to space limitations, FIG. 11B(c) shows only the phase fraction at 790° C. (In reality, the phase fraction at 800° C. and the phase fraction at 790° C. are weighted and input to the trained decoder unit 1002). As a result, the trained decoder unit 1002 outputs the predicted data (phase fraction at 780° C.).

[0118] Also, (c) of FIG. 11B shows how, when the prediction data (phase fraction at 780°C) is output, the "value (partial differential value) obtained by partially differentiating the predicted value for the specified phase at 780°C using the material composition information of the material to be predicted" is derived.

[0119] FIG. 11B(d) shows a state in which the feature output from the trained encoder unit 1001 is input to the trained decoder unit 1002, and the predicted data (phase fraction up to 120° C.) is input to the trained decoder unit 1002. Note that, due to space limitations, FIG. 11B(d) shows only the phase fraction at 120° C. (in reality, each phase fraction from 800° C. to 120° C. is weighted and input to the trained decoder unit 1002). As a result, the trained decoder unit 1002 outputs the predicted data (phase fraction at 110° C.).

[0120] Also, (d) of FIG. 11B shows how, when the prediction data (phase fraction at 110°C) is output, the "value (partial differential value) obtained by partially differentiating the predicted value for the specified phase at 110°C using the material composition information of the material to be predicted" is derived.

[0121] FIG. 11C (e) shows a state in which the feature output from the trained encoder unit 1001 is input to the trained decoder unit 1002, and the predicted data (phase fraction up to 110° C.) is input to the trained decoder unit 1002. Note that in FIG. 11C (e), due to space limitations, only the phase fraction at 110° C. is shown (in reality, each phase fraction from 800° C. to 110° C. is weighted and input to the trained decoder unit 1002). As a result, the trained decoder unit 1002 outputs the predicted data (phase fraction at 100° C.). Note that the example in FIG. 11C (e) also shows the predicted data (phase fraction at 100° C.) output from the trained decoder unit 1002 as a result of inputting the feature and the predicted data (phase fraction up to 110° C.).

[0122] Moreover, (e) of FIG. 11C shows the derivation of the "value (partial differential value) obtained by partially differentiating the predicted value for the specified phase at 100°C using the material composition information of the material to be predicted" when the predicted data (phase fraction at 100°C) is output.

[0123] <Prediction process flow> Next, a description will be given of the flow of prediction processing by the prediction device 120. Fig. 12 is a flowchart showing the flow of the prediction processing.

[0124] In step S1201, the material composition input unit 121 receives an input of material composition information of a material to be predicted.

[0125] In step S1202, the phase designation unit 1003 accepts the designation of a phase.

[0126] In step S1203, the prediction unit 122 receives a start signal and inputs the input material composition information into the trained prediction model, thereby predicting the phase fraction at each temperature within a predetermined temperature interval.

[0127] In step S1204, the sensitivity characteristic calculation unit 1004 derives partial differential values ​​obtained by partially differentiating the predicted values ​​for the specified phase at each temperature within a predetermined temperature interval using the material composition information.

[0128] In step S1205, the display unit 123 displays the predicted phase fractions at each temperature and also displays a heat map for the specified phase.

[0129] <An example of predicted phase fraction> Next, a description will be given of a specific example of the phase fraction at each temperature within a predetermined temperature interval predicted by the prediction unit 122. Fig. 13 is a diagram showing an example of the predicted phase fraction, and shows prediction data when the weight percentage (here, the weight percentage of the minimum composition) of each additive element constituting "6013", which is the name of a known aluminum alloy standard, is input as material composition information of the material to be predicted.

[0130] In this embodiment, when evaluating the prediction accuracy, the weight percentage (here, the weight percentage of the minimum composition) of each additive element constituting "6013", which is the name of a known aluminum alloy standard, was used as input, and the output when the above-mentioned simulator was operated was used as the correct answer data. In addition, the mean squared logarithmic error (MSLE) loss was used to calculate the loss from the correct answer data. In the case of FIG. 13, the MSLE loss between the predicted data and the correct answer data was 2.45×10 -5 It became.

[0131] In this way, when predicting the phase fraction over a predetermined temperature range based on material composition information, - Using a recurrent network, the phase fraction at each temperature is processed as time series data, - Using multiple types of loss functions to handle losses in a multifaceted way; As a result, the prediction device 120 according to this embodiment can improve prediction accuracy.

[0132] <An example of a heat map> Next, a specific example of a heat map displayed by the display unit 123 will be described. FIG. 14 is a diagram showing an example of a heat map, in which the designer 15 The heat map is shown for the case where "(Mn,Fe)3Si2" is specified.

[0133] As shown in Fig. 14, in a heat map 1400, the horizontal axis indicates each component (each added element) of the material to be predicted. In the case of Fig. 14, the horizontal axis is arranged with the respective added elements constituting "6013", which is the name of the known aluminum alloy standard, Si, Fe, Cu, Mn, Mg, Cr, Zn, and Ti, as each component (each added element) of the material to be predicted.

[0134] 14, in the heat map 1400, the vertical axis indicates each temperature within a predetermined temperature range of 100 to 800° C. In addition, in FIG. 14, reference numeral 1410 indicates an example of coloring according to the partial differential value derived by the prediction unit 122. In the example of FIG. 14, a lighter color indicates a greater degree of influence on the specified phase (a larger partial differential value), and a darker color indicates a smaller degree of influence on the specified phase (a smaller partial differential value).

[0135] According to the example in Figure 14, the material design designer: When the weight percentage of the added element "Fe" is increased, the phase becomes "Al" at around 580℃. 15 (Mn,Fe)3Si2" increases significantly. By increasing the weight percentage of the added element "Mn", the phase "Al" 15 (Mn,Fe)3Si2" always increases, Even if the weight percentage of the added element "Si" is increased, the phase "Al 15 Little effect on (Mn,Fe)3Si2" In this way, it is easy to understand which temperature range of a specified phase can be affected by increasing or decreasing the weight percentage of which added element.

[0136] In this way, by configuring the system so that the degree of influence on the phase fraction due to the change in material composition can be grasped in advance, material designers can perform efficient material design. Note that the above configuration allows processing to be performed in parallel with the output of prediction data (prediction of phase fraction). Therefore, the calculation load of the prediction device 120 can be significantly reduced compared to a method in which, for example, the phase fraction within a specified temperature range is predicted while the weight percentage of all additive elements is increased or decreased in sequence, and then compared with the phase fraction before the change.

[0137] <Heat map verification> Next, the results of a verification performed in the following manner regarding the calculation accuracy of the heat map 1400 shown in FIG. 14 will be described. - Prediction target material: Aluminum alloy (alloy standard "6013"), -Additional elements that change the weight percentage: Si, Fe, Cu, Mn, Mg, Cr, Zn, Ti How to change the weight percentage: Increase the weight percentage by 0.05 from the minimum composition weight percentage (Si=0.6, Fe=0.01, Cu=0.6, Mn=0.2, Mg=0.8, Cr=0.01, Zn=0.01, Ti=0.01, Zr=0, B=0, Bi=0, Pb=0, Al=97.76), -Prescribed temperature range: 100℃~800℃, Specified phase: Al 15 (Mn,Fe)3Si2, -Calculation method of data (difference value) used for verification: Al at each temperature before and after the change 15 The value indicating the proportion of (Mn,Fe)3Si2 is calculated using a simulator (Thermo-Calc (registered trademark)), and the difference value before and after the change is calculated.

[0138] 15A to 15H are first to eighth diagrams showing an example of the verification results. Among them, Fig. 15A shows the calculated difference before and after the change in the case where the weight % of Si is increased by a predetermined weight % (=0.05) from the weight % of the minimum composition (=0.6), and the weight % of Al is decreased by a predetermined weight % (=0.05). (a) Al at each temperature before and after the change 15 The case where the difference value of the value indicating the ratio of (Mn,Fe)3Si2 is calculated using the above partial differential value, (b) Al at each temperature before and after the change 15 The difference between the values ​​indicating the ratio of (Mn,Fe)3Si2 was calculated using a simulator (Thermo-Calc (registered trademark)). respectively (however, the vertical axis range in (b) is narrower).

[0139] Similarly, FIG. 15B shows the calculated difference before and after the change when the weight percent of Fe is increased by a predetermined weight percent (=0.05) from the weight percent of the minimum composition (=0.01), and the weight percent of Al is decreased by a predetermined weight percent (=0.05). (a) Al at each temperature before and after the change 15 The case where the difference value of the value indicating the ratio of (Mn,Fe)3Si2 is calculated using the above partial differential value, (b) Al at each temperature before and after the change 15 The difference between the values ​​indicating the ratio of (Mn,Fe)3Si2 was calculated using a simulator (Thermo-Calc (registered trademark)). respectively (however, the vertical axis range in (b) is narrower).

[0140] Comparing the graph transition from 100°C to 800°C shown in Fig. 15B(a) with the graph transition from 100°C to 800°C shown in Fig. 15B(b), it can be said that the trends are generally consistent. For example, the trend of the difference value increasing around 580°C is consistent.

[0141] Similarly, FIG. 15C shows the calculated difference before and after the change when the weight percent of Cu is increased by a predetermined weight percent (=0.05) from the minimum composition weight percent (=0.6) and the weight percent of Al is decreased by a predetermined weight percent (=0.05). (a) Al at each temperature before and after the change 15 The case where the difference value of the value indicating the ratio of (Mn,Fe)3Si2 is calculated using the above partial differential value, (b) Al at each temperature before and after the change 15 The difference between the values ​​indicating the ratio of (Mn,Fe)3Si2 was calculated using a simulator (Thermo-Calc (registered trademark)). respectively (however, the vertical axis range in (b) is narrower).

[0142] Similarly, FIG. 15D shows the calculated difference before and after the change when the weight percent of Mn is increased by a predetermined weight percent (=0.05) from the weight percent of the minimum composition (=0.2), and the weight percent of Al is decreased by a predetermined weight percent (=0.05). (a) Al at each temperature before and after the change 15 The case where the difference value of the value indicating the ratio of (Mn,Fe)3Si2 is calculated using the above partial differential value, (b) Al at each temperature before and after the change 15 The difference between the values ​​indicating the ratio of (Mn,Fe)3Si2 was calculated using a simulator (Thermo-Calc (registered trademark)). respectively (however, the vertical axis range in (b) is narrower).

[0143] Comparing the transition of the graph from 100°C to 800°C shown in FIG. 15D(a) with the transition of the graph from 100°C to 800°C shown in FIG. 15D(b), it can be said that the trends are generally the same. For example, in the range from 100°C to 580°C, 15 The trend is consistent, with the value showing the proportion of (Mn,Fe)3Si2 increasing.

[0144] Similarly, FIG. 15E (a) shows the calculated difference before and after the change when the weight percentage of Mg is increased by a predetermined weight percentage (=0.05) from the minimum composition weight percentage (=0.8) and the weight percentage of Al is decreased by a predetermined weight percentage (=0.05). (a) Al at each temperature before and after the change 15 The case where the difference value of the value indicating the ratio of (Mn,Fe)3Si2 is calculated using the above partial differential value, (b) Al at each temperature before and after the change 15A case in which the partial differential value of the value indicating the ratio of (Mn,Fe)3Si2 was calculated using a simulator (Thermo-Calc (registered trademark)), respectively (however, the vertical axis range in (b) is narrower).

[0145] Similarly, FIG. 15F shows the calculated difference before and after the change when the weight percent of Cr is increased by a predetermined weight percent (=0.05) from the weight percent of the minimum composition (=0.01) and the weight percent of Al is decreased by a predetermined weight percent (=0.05). (a) Al at each temperature before and after the change 15 The case where the difference value of the value indicating the ratio of (Mn,Fe)3Si2 is calculated using the above partial differential value, (b) Al at each temperature before and after the change 15 The difference between the values ​​indicating the ratio of (Mn,Fe)3Si2 was calculated using a simulator (Thermo-Calc (registered trademark)). respectively (however, the vertical axis range in (b) is narrower).

[0146] Similarly, FIG. 15G shows the calculated difference before and after the change when the weight percentage of Zn is increased by a predetermined weight percentage (=0.05) from the minimum composition weight percentage (=0.01) and the weight percentage of Al is decreased by a predetermined weight percentage (=0.05). (a) Al at each temperature before and after the change 15 The case where the difference value of the value indicating the ratio of (Mn,Fe)3Si2 is calculated using the above partial differential value, (b) Al at each temperature before and after the change 15 The difference between the values ​​indicating the ratio of (Mn,Fe)3Si2 was calculated using a simulator (Thermo-Calc (registered trademark)). respectively (however, the vertical axis range in (b) is narrower).

[0147] Similarly, FIG. 15H shows the calculated difference before and after the change when the weight percent of Ti is increased by a predetermined weight percent (=0.05) from the minimum composition weight percent (=0.01) and the weight percent of Al is decreased by a predetermined weight percent (=0.05). (a) Al at each temperature before and after the change 15 The case where the difference value of the value indicating the ratio of (Mn,Fe)3Si2 is calculated using the above partial differential value, (b) Al at each temperature before and after the change 15 The difference between the values ​​indicating the ratio of (Mn,Fe)3Si2 was calculated using a simulator (Thermo-Calc (registered trademark)). respectively (however, the vertical axis range in (b) is narrower).

[0148] As described above, it can be said that the heat map 1400 shown in FIG. 14 reproduces the tendency for the additional elements (for example, Fe, Mn) that show a large difference before and after the change.

[0149] <Summary> As is clear from the above description, the prediction device 120 according to the first embodiment: The device has a memory unit that stores a trained prediction model trained using training data in which the material composition of the training target material is associated with the phase fraction of the training target material at each temperature within a specified temperature range. By inputting the material composition of the material to be predicted into the trained prediction model, the phase fraction of the material to be predicted at each temperature within a specified temperature interval is predicted, and the predicted value for a pre-specified phase at each temperature is partially differentiated using the material composition of the material to be predicted. In a heat map in which each component of the material to be predicted is placed on the first axis and each temperature within a specified temperature range is placed on the second axis, each area identified based on each component and each temperature is displayed in a color scheme according to the value obtained by partial differentiation.

[0150] As a result, according to the first embodiment, it is possible to easily grasp the degree of influence on the phase fraction caused by a change in the material composition.

[0151] [Second embodiment] In the above first embodiment, the color scheme of the heat map is the color scheme shown by reference numeral 1410 in Fig. 14, but the color scheme may be other than the color scheme shown by reference numeral 1410. Furthermore, the heat map may be expressed in a display form other than the color scheme.

[0152] In the above first embodiment, the case has been described where the calculated partial differential values ​​are expressed using a heat map so that the designer can easily understand which temperature interval phase fraction can be affected by increasing or decreasing the weight percentage of which additive element. However, the method of expressing the calculated partial differential values ​​is not limited to a heat map. For example, in a case where it is desired to understand which temperature in a predetermined temperature interval has a large degree of influence, a graph showing the transition of the degree of influence due to the difference in temperature may be displayed instead of the heat map.

[0153] In the above first embodiment, the alloy composition showing the weight percentage of the additive elements (other metal elements or nonmetal elements) relative to the metal elements contained in the alloy is described as a specific example of the material composition, but the material composition is not limited to the alloy composition. For example, it may be a chemical composition showing the weight percentage of each chemical component contained in a substance other than the alloy.

[0154] In the above first embodiment, either Seq2Seq with an Attention mechanism or Transformer is applied as the trained prediction model. However, the trained prediction model is not limited to these, and other architectures may be applied as long as they are architectures capable of processing time-series data, which is data for each predetermined time interval. Specifically, RNN (Recurrent Neural Network), Bidirectional RNN, Seq2Seq, etc. may be applied. Alternatively, GRU (Gated Recurrent Unit), LSTM (Long Short Term Memory), etc. may be applied.

[0155] Furthermore, in the first embodiment, the learning device 110 and the prediction device 120 are configured as separate entities, but the learning device 110 and the prediction device 120 may be configured as an integrated entity.

[0156] The present invention is not limited to the configurations shown in the above embodiments, combinations with other elements, etc. These points can be changed without departing from the spirit of the present invention, and can be appropriately determined according to the application form.

[0157] This application claims priority based on Japanese Patent Application No. 2022-211219, filed on December 28, 2022, the entire contents of which are incorporated herein by reference. [Explanation of symbols]

[0158] 100: Prediction system 110: Learning device 111: Learning data generation unit 112: Learning Department 120: Prediction device 121: Material composition input section 122: Prediction Department 123: Display section 300: Training data 401: Element information input section 402: Combination determination unit 403: Simulation Department 404: Storage control unit 501: Encoder section 502: Decoder section 503: Loss function calculation unit 801: Phase fraction error calculation section at formation / disappearance temperature 802: Phase fraction error calculation section 803: Phase fraction error (logarithmic value) calculation section 804: Phase fraction error (difference value) calculation unit 805: Cross entropy error calculation unit 806: Weighting addition unit 1001: Trained encoder 1002: Trained decoder part 1003: Phase specification section 1004: Sensitivity characteristic calculation unit 1400: Heatmap

Claims

1. a trained model trained using training data in which a material composition of a training target material is associated with a phase fraction of the training target material at each temperature within a predetermined temperature interval, the trained model having a memory unit configured to predict a phase fraction at an (i+1)th temperature using phase fractions predicted by the trained model for temperatures up to an i-th temperature (i is an integer equal to or greater than 1) within the predetermined temperature interval; When a phase fraction of the material to be predicted at each temperature within the predetermined temperature interval is predicted by inputting a material composition of the material to be predicted into the trained model, a predicted value for a pre-specified phase at each temperature is partially differentiated using the material composition of the material to be predicted; A prediction device which displays, in a heat map in which each component of the material to be predicted is placed on a first axis and each temperature within the specified temperature range is placed on a second axis, each area specified based on each component and each temperature is displayed in a display manner corresponding to the value obtained by performing the partial differentiation.

2. The prediction device of claim 1, wherein the trained model is applied with an architecture capable of processing time series data, which is data for each predetermined time interval, and the trained model predicts phase fraction for each predetermined temperature interval based on the material composition of the material to be predicted.

3. The prediction device according to claim 2 , wherein the trained model is any one of an RNN, a Bidirectional RNN, Seq2Seq, a Seq2Seq with an Attention mechanism, a GRU, a LSTM, or a Transformer.

4. The trained model is an encoder unit that outputs feature quantities by inputting a material composition of a material to be predicted; a decoder unit which receives the output feature amount and the predicted phase fractions at the first to the i-th temperatures and predicts the phase fraction at the i+1-th temperature; The prediction device according to claim 3 , further comprising:

5. The prediction device according to claim 1 , wherein the phase fraction is a phase fraction in a thermodynamic equilibrium state.

6. When predicting the phase fraction at the (i+1)th temperature, the predicted value for the pre-specified phase is partially differentiated using a material composition of the material to be predicted; The prediction device according to claim 1 , wherein in the heat map, each area identified based on each component and the i+1th temperature is displayed in a display mode corresponding to a value obtained by performing the partial differentiation.

7. A prediction method in which a computer executes a trained model trained using training data in which a material composition of a training target material is associated with a phase fraction at each temperature of the training target material within a predetermined temperature interval, the trained model being configured to predict a phase fraction at an (i+1)th temperature using phase fractions predicted by the trained model for temperatures up to an (i)th temperature (i is an integer equal to or greater than 1) within the predetermined temperature interval, The computer, a step of inputting a material composition of a material to be predicted into the trained model to predict a phase fraction at each temperature of the material to be predicted within the predetermined temperature interval, partially differentiating a predicted value for a pre-specified phase at each temperature using the material composition of the material to be predicted; a step of displaying, in a heat map in which each component of the material to be predicted is arranged on a first axis and each temperature within the predetermined temperature range is arranged on a second axis, each region specified based on each component and each temperature in a display mode corresponding to a value obtained by performing the partial differentiation; A forecasting method to perform.

8. A prediction program for causing a computer to execute a trained model trained using training data in which a material composition of a training target material is associated with a phase fraction at each temperature of the training target material within a predetermined temperature interval, the trained model being configured to predict a phase fraction at an (i+1)th temperature using phase fractions predicted by the trained model for temperatures up to an (i)th temperature (i is an integer equal to or greater than 1) within the predetermined temperature interval, The computer includes: a step of inputting a material composition of a material to be predicted into the trained model to predict a phase fraction at each temperature of the material to be predicted within the predetermined temperature interval, partially differentiating a predicted value for a pre-specified phase at each temperature using the material composition of the material to be predicted; a step of displaying, in a heat map in which each component of the material to be predicted is arranged on a first axis and each temperature within the predetermined temperature range is arranged on a second axis, each region specified based on each component and each temperature in a display mode corresponding to a value obtained by performing the partial differentiation; A prediction program for executing the above.

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