Patent ID: 12190032

EMBODIMENTS FOR CARRYING OUT THE INVENTION

Hereinafter, an embodiment will be described with reference to the attached drawings. To make the description easier to understand, the same constitutional elements are denoted by the same reference numerals as much as possible in the respective drawings, and the repetitive explanation will be omitted.

With reference toFIG.1toFIG.9, the configuration of a material design device1according to an embodiment will be described.FIG.1is a block diagram showing the schematic configuration of the material design device1according to an embodiment. The material design device1is a device for designing a material to be designed including a material composed of a plurality of compositions or a material to be produced by combining a plurality of production conditions. In this embodiment, the description will be made by exemplifying an aluminum alloy working as one example of the material to be designed.

As shown inFIG.1, the material design device1is provided with a forward problem analysis unit10, a reverse problem analysis unit20, and a GUI (Graphical User Interface)30. The forward problem analysis unit10outputs material properties satisfying the desired design condition of the material designer by using a learned model13. The reverse problem analysis unit20outputs a design condition satisfying the required properties desired by the material designer by using a design condition-material property table14generated based on the output result of the forward problem analysis unit10. The GUI30is a user interface having a function of displaying the output result of the forward problem analysis unit10and that of the reverse problem analysis unit20to present it to the material designer or a function of accepting adjustment operations of the output result by the material designer.

The forward problem analysis unit10is provided with a design condition setting unit11, a comprehensive prediction point generation unit12, a learned model13, and a design condition-material property table14.

The design condition setting unit11is configured to set a specified range of the design condition of an aluminum alloy which is a material to be designed. The design condition setting unit11can set the specified range of the design condition by, for example, displaying an input screen of design conditions on the GUI30to prompt the material designer to input a specified range.

FIG.2andFIG.3are diagrams showing examples of input screens11A and11B of the design condition setting unit11.FIG.2shows an example of the input screen11A of the items relating to the composition of a raw material among the design conditions.FIG.3shows an example of the input screen11B of the items relating to the production condition among the design conditions. In the input screens11A and11B, the maximum value and the minimum value of each item (in the item relating to a heat treatment, a temperature (° C.) and an execution time (h)) can be input. Note that the input screens11A and11B may be displayed collectively on one screen.

The items of the composition of the raw material include, for example, elements, such as, e.g., Si, Fe, Cu, Mn, Mg, Cr, Ni, Zn, Ti, V, Pb, Sn, Bi, B, P, Zr, and Sr as an additive in percentage by mass (wt %). Note that the percentage by mass of Al is represented by 100%—(the sum of the percentage by mass of the above-described elements).

As the items of the production condition, the items related to a heat treatment include, for example, the temperature (° C.) and the execution time (h) of each processing, such as, e.g., annealing, a solution heat treatment, an artificial aging treatment, a natural aging treatment, a hot working treatment, a cold working treatment, and a stabilizing treatment. The items related to processing conditions include, for example, a processing rate, an extrusion rate, a reduction of area, and a product shape.

The comprehensive prediction point generation unit12generates a plurality of comprehensive prediction points within the specified range of the design condition set by the design condition setting unit11. For example, in a case where a percentage by mass of Si in the composition item and a range of execution time of annealing in the production condition item are specified, first, a plurality of numerical values is calculated within a specified range of the percentage by mass of Si and within the specified range of the annealing execution time in random or predetermined intervals, respectively, and all combinations of the plurality of numerical values in each item are generated. These combinations are output as comprehensive prediction points.

The learned model13is a model formulated by acquiring the correspondence between the input information including the design condition of the aluminum alloy and the output information including the material property value acquired by machine learning. For example, a supervised learning model, such as, e.g., a neural network and a genetic algorithm, can be applied to the learned model13. In the learned model13, learning data including design conditions and material properties of aluminum alloys is acquired from, for example, a known material database, and machine learning is performed using the learning data.

The items of material properties include tensile strength, 0.2% strength, elongation, a linear expansion coefficient, Young's modulus, a Poisson's ratio, a fatigue property, hardness, and creep properties including creep strength and creep strain, shear strength, specific heat capacity, thermal conductivity, electrical resistivity, density, a solidus line, and a liquidus line.

FIG.4is a diagram showing an example of an output screen31A of the forward problem analysis unit10. The output screen31A is displayed on the GUI30via, for example, the information display unit31. InFIG.4, the output (material properties) of the learned model13are limited to only two output, i.e., “tensile strength” and “0.2% strength”, for simplicity of explanation. In the example ofFIG.4, at the upper portion of the output screen31A, a graph31A1is displayed in which each point of the comprehensive prediction points is plotted on the two-dimensional coordinate system with two output of the learned model, i.e., the “tensile strength” and the “0.2% strength”, as the respective axes, and at the lower portion of the output screen31A, box-and-whisker plots31A2of each material property is displayed.

The design condition-material property table14stores data sets in which the material property values calculated by inputting the comprehensive prediction points generated by the comprehensive prediction point generation unit12to the learned model13are associated with the respective points of the comprehensive prediction points. When performing the calculation of the comprehensive prediction points by the learned model13, the forward problem analysis unit10stores the output in the design condition-material property table14by associating with the comprehensive prediction points (inputs).FIG.5is a diagram showing an example of the configuration of the design condition-material property table14. As shown inFIG.5, the inputs (production conditions, material compositions) and the output (material properties) of a learned model are put together as one data set and recorded on the same row of the design condition-material property table14. Each row of the design condition-material property table14is an individual data set, and each column records numerical values of each item of the inputs and the output of the learned models13.

As described above, the forward problem analysis unit10is configured to automatically generate data sets of design conditions and material properties covering all of the range of multidimensional design conditions by simply performing operations of specifying the range of the multidimensional design conditions by the material designer.

The reverse problem analysis unit20is provided with a required property setting unit21and a design condition extraction unit22. Further, the above-described design condition-material property table14is also included in the reverse problem analysis unit20.

The required property setting unit21sets a specified range of a required property of an aluminum alloy which is a material to be designed. The required property setting unit21can set specified ranges of required properties by, for example, displaying an input screen for required properties on the GUI30to prompt the material designer to input specified ranges.

FIG.6is a diagram showing an example of an input screen21A of the required property setting unit21. The items of required properties are the same as those of the material properties described above. In the example ofFIG.6, the “tensile strength” and the “0.2% strength” are selected as the required properties, the numerical value “a” is input as the minimum value of the “tensile strength”, the numerical value “b” is input as the maximum value, the numerical value “c” is input as the minimum value of the tensile strength of “0.2% required properties”, and the numerical value “d” is input as the minimum value.

The design condition extraction unit22extracts the data sets satisfying the required properties set by the required property setting unit21from the design condition-material property table14.

FIG.7andFIG.8are diagrams showing examples of the output screens31B and31C of the reverse problem analysis unit20. The output screens31B and31C are displayed on the GUI30via, for example, the information display unit31. In the output screen31B shown inFIG.7, each point of the comprehensive prediction points is plotted with a black square on the two-dimensional coordinate system with the “tensile strength” and the “0.2% strength” as each axis. In addition, the points satisfying the required properties (the “tensile strength” is in the range of a to b, and the “0.2% strength” is in the range of c to d) set inFIG.6are plotted with white circles. Further, in the output screen31C shown inFIG.8, box-and-whisker plots31C1of the compositions satisfying the required properties are displayed in the upper part, and the box-and-whisker plots31C2of the compositions satisfying the required properties are displayed in the lower part.

Note that the reverse problem analysis unit20can also output the range of the production condition satisfying the required properties. The reverse problem analysis unit20may include the production condition in the input information to the design condition extraction unit22. In this case, the reverse problem analysis unit20will output the compositions satisfying the inputs of the required properties and the request production condition.

As described above, the reverse problem analysis unit20is configured such that the production conditions (compositions or design conditions) satisfying multidimensional required properties can be simultaneously extracted by simply performing the operation for specifying the ranges of the multidimensional required properties by the material designer. Further, without using a simulation or a learning model for the reverse problem analysis, the design condition-material property table14generated by the forward problem analysis unit10is used. Therefore, the calculation cost can also be significantly reduced.

The GUI30includes an information display unit31.

The information display unit31displays the output of the forward problem analysis unit10or that of the reverse problem analysis unit20. For example, as shown inFIG.7andFIG.8, the range of the required property and that of the design condition relating to the data set extracted by the design condition extraction unit22are displayed.

The GUI30may further include a design condition adjustment unit32.

The design condition adjustment unit32adjusts the range of the design condition of the data set extracted by the design condition extraction unit22. The design condition adjustment unit32can adjust the range of the design condition by, for example, the material designer's input operation of changing the composition range of the output screen31cto be displayed on the GUI30.

Further, the design condition extraction unit22can further narrow down the data sets extracted according to the required properties to data sets satisfying the design condition adjusted by the above-described design condition adjustment unit32.

In this case, as shown by the arrow A inFIG.8, for example, when an operation of lowering the maximum value of the predetermined raw material (cu in the example inFIG.8) on the box-and-whisker plot31C1of the composition, narrowing down of the data sets is performed by the design condition adjustment unit32and the design condition extraction unit22in response to this operation. As a result, the box-and-whisker plot31C2of each required property is updated according to the narrowed down data set. For example, as indicated by the arrow B inFIG.8, the maximum value of the 0.2% strength of the required property is displayed in a decreased manner in response to the decrease in the maximum value of Cu. The material designer can observe the variation of the property according to the adjustment of the composition range on the output screen31C to narrow down to a desired composition range.

The reverse problem analysis unit20outputs the design conditions satisfying the required properties, but these design conditions are only those automatically extracted from the comprehensive prediction points of the design condition-material property table14, and the production constraints, such as, e.g., the difficulty of the actual production, have not been considered. For example, there are various production constraints, such as, e.g., it is difficult to handle and therefore it is actually difficult to produce, it takes longer time to produce, it takes time for the processing, the composition causes nests when casting, it is impossible to mold, and it is possible to produce without considering the cost but impossible to produce by using an ordinary plant facility. In a case where the GUI30has the design condition adjustment unit32, it is possible to narrow down the production conditions satisfying the required properties considering the production constraints based on the material designer's rule of thumb by making it possible for the material designer to adjust the output results of the reverse problem analysis unit20by using the design condition adjustment unit32. That is, it becomes possible to perform the material design in which the prediction by machine learning and the material designer's experience work together.

FIG.9is a block diagram showing a hardware configuration of the material design device1. As shown inFIG.9, the material design device1may be configured as a computer system physically including a CPU (Central Processing Unit)101, a RAM (Random Access Memory)102as main storage devices and a ROM (Read Only Memory)103, an input device104, such as, e.g., a keyboard and a mouse, an output device105, such as, e.g., a display, a communication module106, such as, e.g., a network card, which is a data transmission and reception device, and an auxiliary storage device107, such as, e.g., a hard disc.

Each function of the material design device1shown inFIG.9is realized by reading predetermined computer software (material design program) on hardware, such as, e.g., a CPU101and a RAM102to operate the communication module106, the input device104, and the output device105under the control of the CPU101and to read and write the data in the RAM102and the auxiliary storage device107. That is, by running the material design program of this embodiment on a computer, the material design device1functions as the design condition setting unit11, the comprehensive prediction point generation unit12, the required property setting unit21, the design condition extraction unit22, the information display unit31, and the design condition adjustment unit32inFIG.1. It is also possible to realize a model generation function of generating the learned model13in which the correspondence between the input information including design condition of the material to be designed and the output information including the material property value is acquired by machine learning and a data set generation function of storing a data set in which the material property value calculated by inputting the comprehensive prediction point generated by the comprehensive prediction point generation function to the learned model13is associated with each point of the comprehensive prediction points to the design condition-material property table14. The design condition-material property table14shown inFIG.1can be realized by a part of a storage device (the RAM102, the ROM103, the auxiliary storage device107, or the like) provided in the computer. The GUI30shown inFIG.1can be realized by the output device105or the input device104provided in a computer.

The material design program of this embodiment is stored, for example, in a storage device provided by a computer. The material design program may be configured such that a part or all of the program is transmitted via a transmission medium, such as, e.g., a communication line, and is received and recorded (including “installation”) by a communication module or the like provided in a computer. The material design program may also be configured such that a part or all of the program may be recorded (including “installation”) in a computer from a state in which the program is stored in a portable storage medium, such as, e.g., a CD-ROM, a DVD-ROM, and a flash memory.

With reference toFIG.10andFIG.11, a material design method using the material design device1according to the embodiment will be described.FIG.10is a flowchart of the forward problem analysis processing performed by the forward problem analysis unit10.

Note that before performing the forward problem analysis processing ofFIG.10, the processing (model generation step) of generating the learned model13in which the correspondence between the input information including the design condition of the material to be designed and the output information including the material property value is acquired by machine learning is performed. The model generation step may be performed by the material design device1. Alternatively, it may be configured such that the model generation step is performed by another device and the material design device1acquires the learned model13generated by the above-described another device.

In Step S101, the specified range of the design condition of the aluminum alloy which is a material to be designed is set by the design condition setting unit11(Design Condition Setting Step). The design condition setting unit11displays, for example, the input screens11A and11B shown inFIG.2andFIG.3on the GUI30to prompt the material designer to input specified ranges.

In Step S102, a plurality of comprehensive prediction points is generated by the comprehensive prediction point generation unit12within the specified range of the design condition set in Step S101(Comprehensive Prediction Point Generation Step).

In Step S103to S106, the forward problem analysis unit10stores the data set in which the material property values calculated by inputting the comprehensive prediction points generated in Step S102to the learned model13are associated with each point of the comprehensive prediction points in the design condition-material property table14(Data Set Generation Step).

First, in Step S103, one comprehensive prediction point is selected. In Step S104, a material property value is calculated by inputting the comprehensive prediction point selected in Step S103to the learned model13. In Step S105, the comprehensive prediction point of the input of the learned model13selected in Step S103and the material property value of the output are associated with each other and stored in the design condition-material property table14. One data set is generated by the processing of Step S103to S105.

In Step S106, it is determined whether or not there is an unselected comprehensive prediction point. In a case where there is an unselected comprehensive prediction point (Yes in Step S106), the process returns to Step S103to repeat the generation of a data set. In a case where all comprehensive prediction points have been selected (No in Step S106), the generation of the data set is finished, and the process proceeds to Step S107.

In Step S107, the material property value of each comprehensive prediction point calculated in Step S104is displayed on the GUI30by the information display unit31. The information display unit31displays, for example, the output screen31A exemplified inFIG.4on the GUI30. Upon completion of the process of Step S107, the forward problem analysis processing of the main control flow ends.

FIG.11is a flowchart of reverse problem analysis processing performed by the reverse problem analysis unit20and the design condition adjustment unit32.

In Step S201, the specified ranges of the required properties of the aluminum alloy which is a material to be designed are set by the required property setting unit21(Required Property Setting Step). The required property setting unit21displays, for example, the input screen21A shown inFIG.6on the GUI30to prompt the material designer to input specified ranges.

In Step S202, data sets satisfying the required properties set in Step S201are extracted from the design condition-material property table14by the design condition extraction unit22(Design Condition Extraction Step).

In Step S203, the range of the material compositions satisfying the required properties specified in Step S201and the required properties are displayed on the GUI30by the information display unit31using the data sets extracted in Step S203. The information display unit31displays, for example, the output screens31B and31C exemplified inFIG.7andFIG.8on the GUI30.

In Step S204, whether or not the operation of the composition adjustment has been performed by the material designer is determined in the output screen31C showing the ranges of the material compositions satisfying the required properties by the design condition adjustment unit32. As described with reference to the arrow A inFIG.8, the material designer can perform the operation to change the position of the maximum value or the minimum value of the box-and-whisker plot of the material composition in the output screen31C (Design Condition Adjustment Step). When this operation has been performed (Yes in Step S204), the information of the composition range after the adjustment is output to the design condition extraction unit22by the design condition adjustment unit32, and the process proceeds to Step S205. When there was no operation (No in Step S204), the reverse problem analysis processing of the main control flow is terminated.

In Step S205, since the operation of the composition adjustment has been detected in Step S204, the data satisfying the material compositions after the composition range adjustments is narrowed down by the design condition extraction unit22from the data sets extracted in Step S202(Narrow Down Step).

In Step S206, the output screen31C of the required properties displayed in Step S203is updated by the information display unit31using the data set narrowed down in Step205. Upon completion of the processing in Step S206, the reverse problem analysis processing is terminated.

Effects of this embodiment will be described. As the forward problem analysis unit10, the material design device1of this embodiment is provided with the design condition setting unit11for setting the specified ranges of the design conditions of the material to be designed, the comprehensive prediction point generation unit12for generating a plurality of comprehensive prediction points within the specified range set by the design condition setting unit11, and the design condition-material property table14for storing the data set in which the material property values calculated by inputting the comprehensive prediction points generated by the comprehensive prediction point generation unit12to the learned model13are associated with each point of the comprehensive prediction points. Further, as the reverse problem analysis unit20, the material design device1of this embodiment is provided with the required property setting unit21for setting the specified range of the required properties of the material to be designed and the design condition extraction unit22for extracting the data sets satisfying the required properties set by the required property setting unit21from the design condition-material property table14.

As described above, in this embodiment, during the performance of the forward problem analysis, data sets to be used in the reverse problem analysis are generated and stored in the design condition-material property table14. And at the time of performing the reverse problem analysis, data sets satisfying the required properties are extracted by referring to the design condition-material property table14. In other words, the reverse problem analysis performs only the task of searching for the design condition-material property table14without performing any numerical value calculations, such as, e.g., simulation and model calculation. Therefore, the calculation cost can be greatly reduced, and the optimal solution of the design condition satisfying the desired material properties can be derived in a short time.

Further, in a case of performing a reverse problem analysis by a conventional simulation or in a case of adopting a machine learning system to a reverse problem analysis, in a case where there is a plurality of required properties, calculations are performed to gradually reach the optimal solution while performing the adjustment for each property in turn, and candidate material searches will not be collectively performed to satisfy several types of properties at the same time. In many cases, a plurality of material properties has a trade-off relationship, and the trial and error are repeated until it reaches the optimal solution. Therefore, it takes a long time to acquire the optimal solution of the design conditions satisfying the desired material properties. On the other hand, in this embodiment, by setting a plurality of output (material properties) of the learned model13and generating items of a plurality of material properties in the design condition-material property table14, in the reverse problem analysis, the candidate material searches can be collectively performed to satisfy the plurality of types of material properties. With this, even in the case of setting a plurality of types of required properties, the time required to derive the optimal solution can be greatly reduced as compared with the conventional method.

Further, the data set group stored in the design condition-material property table14is information derived from a large number of comprehensive prediction points automatically generated in the forward problem analysis. Therefore, the increment of each item of the design condition and the material property is sufficiently small, and the resolution is high. Therefore, in the reverse problem analysis, it is possible to perform the prediction of the design condition satisfying the required property with high accuracy.

Preferably, the material design device1is provided with the design condition adjustment unit32for adjusting the range of the design condition of the data set extracted by the design condition extraction unit22. In a case where the material design device1has the design condition adjustment unit32, the design condition extraction unit22further narrows down the data sets satisfying the design conditions adjusted by the design condition adjustment unit32.

In this case, depending on the required properties, the design condition extraction unit22can perform the narrowing down of the design condition mechanically extracted by the design condition-material property table14by considering the production constraints and the like based on the experience of the material designer. With this, it becomes possible to perform the material design in which the prediction by machine learning and the material designer's experiences work together, which in turn can extract design conditions that are easier to perform the production.

Further, the material design device1of this embodiment is provided with the information display unit31for displaying the required properties for the data sets extracted by the design condition extraction unit22and the range of the design condition. Furthermore, in a case where the material design device1is provided with the design condition adjustment unit32, the design condition adjustment unit32adjusts the range of the design condition according to the user's operation of changing the range of the design condition displayed on the information display unit31.

In a case where the material design device1is provided with the design condition adjustment unit32, the adjustment operation of the range of the design condition by the material designer can be performed more intuitively on the GUI30, which can be simplified by reducing the burden of the adjustment operation. Further, the result by the adjustment operation can be reflected immediately on the output screens31B and31C. Therefore, the interactive adjustment operation by the material designer can be performed, which makes it possible to perform the adjustment of the range of the design condition more efficiently.

The embodiment has been described above by referring to specific examples. However, the present disclosure is not limited to these specific examples. Modifications in which these specific examples are appropriately modified by those skilled in the art are also encompassed by the scope of the present disclosure as long as they are provided with the features of the present disclosure. Each element included in each of the specific examples described above and the arrangement, condition, shape, and the like thereof are not limited to those exemplified and can be changed as appropriate. Each element provided in each of the above-described specific examples can be appropriately changed in the combination as long as no technical inconsistency occurs.

In the above-described embodiment, as a material to be designed, an aluminum alloy working material (plastic working material), such as, e.g., a rolled material, an extruded material, a drawn material, and a forged material have been described as an example. However, the present invention is not limited thereto. In the present invention, as a material to be designed, a casting material, such as, e.g., a casting material of an aluminum alloy, may be used.

In the above-described embodiment, an aluminum alloy was exemplified as a material to be designed by the material design device1, but alloys other than an aluminum alloy may be used. Such alloys include a Fe alloy (iron alloy), a Cu alloy (copper alloy), a Ni alloy (nickel alloy), a Co alloy (Cobalt alloy), a Ti alloy (titanium alloy), an Mg alloy (magnesium alloy), a Mn alloy (manganese alloy), and a Zn alloy (zinc alloy). The material to be designed may be an inorganic material in general other than alloys, or may be an organic material in general. In short, the material to be designed includes materials composed of a plurality of compositions, or materials produced by combining a plurality of production conditions/treatments (such as, e.g., temperature, pressure, processing, oxidation treatment, acid treatment, proportion, mixture, and stirring).

Further, in the present invention, as an iron alloy as a material to be designed, an iron alloy working material and a casting iron material (iron alloy casting material) are included. The iron alloy working material includes a steel material and a stainless steel, and the casting iron material includes a cast steel material. In the iron alloy working material, as the material composition of the design condition includes at least one of C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, and W. As the production condition, it includes at least one of a molten metal temperature at the time of material casting, a casting speed, an amount of cooling water, a material heating temperature at the time of hot working, a material heating time at the time of hot working, a working speed, a rolling reduction, a hot working temperature, a cooling rate immediately after working, a natural aging time, a heat treatment temperature, a heat treatment time, and a cooling rate of a heat treatment. The material property value of an iron alloy working material includes at least one of 0.2% strength, tensile strength, elongation, Young's modulus, a linear expansion coefficient, an austenite grain size, a ferrite grain size, an impact property, a fatigue property, an SCC property, and an SSC property.

In a casting iron material, the material composition of the design condition includes at least one of C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, W, Ca, Mg, and Ce. The production condition includes at least one of a molten metal temperature at the time of casting, a casting speed, a solidification rate, a cooling rate after solidification, a heat treatment temperature, a heat treatment time, and a cooling rate of a heat treatment. The material property value of the casting iron material includes at least one of the 0.2% strength, the tensile strength, the elongation, the Young's modulus, the linear expansion coefficient, the impact property, and the fatigue property.

In the present invention, as a copper alloy as a material to be designed, it includes a copper alloy working material and a copper alloy casting material. In the copper alloy working material, the material composition of the design condition includes at least one of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As. The production condition includes at least one of the molten metal temperature at the time of the material casting, the casting speed, the amount of cooling water, the homogenization temperature, the homogenization time, the cooling rate after homogenization, the material heating temperature at the time of hot working, the working speed, the cooling rate immediately after the working, the natural annealing temperature, the artificial aging temperature, the artificial aging time, the hot working temperature, the annealing temperature, and the annealing time. The material property value of a copper alloy working material includes at least one of 0.2% strength, tensile strength, elongation, conductivity, thermal conductivity, Young's modulus, and a linear expansion coefficient.

In the copper alloy casting material, the material composition of the design condition includes at least one of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As. The production condition includes at least one of a molten metal temperature at the time of casting, a solution treatment temperature, a solution treatment time, a natural aging time, an artificial aging temperature, an artificial aging time, an annealing temperature, and an annealing time. The material property value of a copper alloy casting material includes at least one of 0.2% strength, tensile strength, elongation, conductivity, thermal conductivity, Young's modulus, and a linear expansion coefficient.

Further, in the present invention, as the material composition of the design condition in a titanium alloy as a material to be designed, it includes at least one of Al, Sn, V, Mo, Zr, Pd, Si, Cr, Ru, Ta, Co, and Ni. As the production condition, it includes a molten metal temperature at the time of casting, a solution treatment temperature, a solution treatment time, an artificial aging temperature, an artificial aging time, an annealing temperature, and an annealing time. The material property value of a titanium alloy includes at least one of 0.2% strength, tensile strength, elongation, Young's modulus, a linear expansion coefficient, and a fatigue property.

INDUSTRIAL APPLICABILITY

The material design device according to the present invention can be used in designing a material composed of a plurality of compositions or a material to be designed including a material produced by combining a plurality of production conditions.

This application claims Japanese Patent Application No. 2018-204439, filed on Oct. 30, 2018, the disclosure of which is incorporated herein by reference in its entirety.

The terms and expressions used herein are for illustration purposes only and are not used for limited interpretation, do not exclude any equivalents of the features shown and stated herein, and it should be recognized that the present invention allows various modifications within the scope of the present invention as claimed.

DESCRIPTION OF SYMBOLS

1: Material design device11: Design condition setting unit12: Comprehensive prediction point generation unit13: Learned model14: Design condition-material property table21: Required property setting unit22: Design condition extraction unit31: Information display unit32: Design condition adjustment unit