Material manufacturing process search method, material manufacturing process search device, and material manufacturing process search program

Abstract

The present invention efficiently searches for a manufacturing process capable of realizing a material to be designed that satisfies desired mechanical properties. This material manufacturing process search method is a method for searching for a material manufacturing process for designing a material to be designed, and comprises a computer executing: an analysis step of analyzing a relationship between a manufacturing process for the material to be designed and mechanical properties of the material to be designed; an extraction step of extracting a candidate for the manufacturing process for the material to be designed that satisfies a target value of the mechanical properties, on the basis of an analysis result in the analysis step; a calculation step of calculating, using thermodynamic calculation, data indicating a material structure of the material to be designed, the data affecting the mechanical properties; and a determination step of determining the suitability of the candidate for the manufacturing process on the basis of the data indicating the material structure calculated by performing thermodynamic calculation on the candidate for the manufacturing process in the calculation step.

Description

Material manufacturing process search method, material manufacturing process search device, and material manufacturing process search program

[0001] The present disclosure relates to a material manufacturing process searching method, a material manufacturing process searching device, and a material manufacturing process searching program.

[0002] Traditionally, when designing a material consisting of multiple compositions or a material manufactured by combining multiple manufacturing conditions, prototypes have been produced while adjusting the "manufacturing process" including the material composition and manufacturing conditions, and the "mechanical properties" of the prototypes have been measured to search for a manufacturing process that will realize the designed material with the desired mechanical properties.

[0003] In contrast, if the relationship between the manufacturing process and the mechanical properties is analyzed in advance and the results of the analysis are used to narrow down the manufacturing processes that will realize the design target material that satisfies the desired mechanical properties, the search for manufacturing processes can be made more efficient.

[0004] International Publication No. 2022 / 264959

[0005] On the other hand, even if the above analysis results are used, there may be cases where the search for a manufacturing process is insufficient and there are many manufacturing processes that can realize a design target material that satisfies the desired mechanical properties. In such cases, the workload of prototyping and measurement cannot be reduced, and the search for a manufacturing process cannot be made more efficient.

[0006] The present disclosure aims to efficiently explore manufacturing processes that can realize designed materials that satisfy desired mechanical properties.

[0007] A material manufacturing process exploration method according to a first aspect of the present disclosure is a material manufacturing process exploration method for designing a design target material including a material consisting of a plurality of compositions or a material manufactured by a combination of a plurality of manufacturing conditions, wherein a computer executes the following steps: an analysis step for analyzing the relationship between the manufacturing process of the design target material and the mechanical properties of the design target material; an extraction step for extracting candidate manufacturing processes for the design target material that satisfy target values ​​of the mechanical properties based on the analysis results of the analysis step; a calculation step for calculating, by thermodynamic calculation, data indicating the material structure of the design target material, which is data that affects the mechanical properties; and a determination step for determining the suitability of the candidate manufacturing processes based on the data indicating the material structure calculated by performing thermodynamic calculation on the candidate manufacturing processes in the calculation step.

[0008] A second aspect of the present disclosure is the material manufacturing process exploration method according to the first aspect, wherein the design target material is an inorganic material.

[0009] A third aspect of the present disclosure is the material manufacturing process exploration method according to the second aspect, wherein the inorganic material is an alloy material.

[0010] A fourth aspect of the present disclosure is the material manufacturing process exploration method according to the third aspect, wherein the alloy material is a wrought material or a cast material.

[0011] A fifth aspect of the present disclosure is the material manufacturing process exploration method according to the fourth aspect, wherein the wrought material is an aluminum alloy wrought material.

[0012] A sixth aspect of the present disclosure is the material manufacturing process exploration method according to the fifth aspect, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes Si, Mg, Cu, Fe, Zr, and Ti, and the manufacturing conditions include a homogenization temperature in a homogenization treatment, a heating temperature before extrusion in a solution treatment, a thickness of the extrusion cross section, an extrusion pressure, a ram speed, and a cooling water temperature.

[0013] A seventh aspect of the present disclosure is the material manufacturing process exploration method according to the sixth aspect, wherein the mechanical properties include tensile strength, 0.2% yield strength, and elongation.

[0014] An eighth aspect of the present disclosure is the material manufacturing process exploration according to the seventh aspect, wherein the data indicating the material structure is data indicating a metal structure, and the data indicating the metal structure includes a liquidus temperature, a Mg solid solution amount before extrusion, a Si solid solution amount before extrusion, a precipitate size, a precipitate density, and a volume fraction.

[0015] A ninth aspect of the present disclosure is the material manufacturing process exploration method according to the fourth aspect, wherein the casting material is an aluminum alloy casting material.

[0016] A tenth aspect of the present disclosure is the material manufacturing process exploration method according to the ninth aspect, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes Si, Mg, Cu, Fe, Zr, and Ti, and the manufacturing conditions include any one of a molten metal temperature during casting, a solution temperature, a solution time, a natural aging time, an artificial aging temperature, an artificial aging time, an annealing temperature, and an annealing time.

[0017] An eleventh aspect of the present disclosure is the material manufacturing process exploration method according to the tenth aspect, wherein the mechanical properties include any one of tensile strength, 0.2% yield strength, elongation, Young's modulus, linear expansion coefficient, and fatigue property.

[0018] A twelfth aspect of the present disclosure is the material manufacturing process exploration method according to the eleventh aspect, wherein the data indicating the material structure is data indicating a metal structure, and the data indicating the metal structure includes any one of a liquidus temperature, a solid solution amount of Si, Fe, Cu, Mn, Mg, Cr, Ni, Zn, Ti, V, Pb, Sn, Bi, B, P, Zr, and Sr during solution treatment, a precipitate size, a precipitate density, and a volume fraction.

[0019] A thirteenth aspect of the present disclosure is the material manufacturing process exploration method according to the fourth aspect, wherein the wrought material is an iron alloy wrought material.

[0020] A fourteenth aspect of the present disclosure is the material manufacturing process exploration method according to the thirteenth aspect, wherein the manufacturing process includes an alloy composition and manufacturing conditions, and the alloy composition includes C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, and W, and the manufacturing conditions include any of a molten metal temperature during casting of the iron alloy, a casting speed, an amount of cooling water, a heating temperature of the iron alloy during hot working, a heating time of the iron alloy during hot working, a processing speed, a rolling reduction, a hot working temperature, a cooling rate after hot working, a natural aging time, a heat treatment temperature, a heat treatment time, and a cooling rate of the heat treatment.

[0021] A fifteenth aspect of the present disclosure is the material manufacturing process exploration method according to the fourteenth aspect, wherein the mechanical properties include any one of tensile strength, 0.2% yield strength, elongation, Young's modulus, linear expansion coefficient, impact properties, and fatigue properties.

[0022] A sixteenth aspect of the present disclosure is the material manufacturing process exploration method according to the fifteenth aspect, wherein the data indicating the material structure is data indicating a metal structure, and the data indicating the metal structure includes a liquidus temperature, a post-processed solid solution amount of C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, and W, a precipitate size, a precipitate density, and a volume fraction.

[0023] A seventeenth aspect of the present disclosure is the material manufacturing process exploration method according to the fourth aspect, wherein the cast material is a cast iron material.

[0024] An eighteenth aspect of the present disclosure is the material manufacturing process exploration method according to the seventeenth aspect, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, W, Ca, Mg, and Ce, and the manufacturing conditions include any of the molten metal temperature during casting, the casting speed, the solidification speed, the cooling speed after solidification, the heat treatment temperature, the heat treatment time, and the cooling speed of the heat treatment.

[0025] A nineteenth aspect of the present disclosure is the material manufacturing process exploration method according to the eighteenth aspect, wherein the mechanical properties include any one of tensile strength, 0.2% yield strength, elongation, Young's modulus, linear expansion coefficient, impact properties, and fatigue properties.

[0026] A twentieth aspect of the present disclosure is the material manufacturing process exploration method according to the nineteenth aspect, wherein the data indicating the material structure is data indicating a metal structure, and the data indicating the metal structure includes a liquidus temperature, a solid solution amount of C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, W, Ca, Mg, and Ce before heat treatment, a precipitate size, a precipitate density, and a volume fraction.

[0027] A twenty-first aspect of the present disclosure is the material manufacturing process exploration method according to the fourth aspect, wherein the wrought material is a copper alloy wrought material.

[0028] A twenty-second aspect of the present disclosure is the material manufacturing process exploration method according to the twenty-first aspect, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition including Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As, and the manufacturing conditions include any one of a molten metal temperature during copper alloy casting, a casting speed, an amount of cooling water, a homogenization temperature, a homogenization time, a cooling rate after homogenization, a copper alloy heating temperature during hot working, a working speed, a cooling rate after working, a natural aging time, an artificial aging temperature, an artificial aging time, a hot working temperature, an annealing temperature, and an annealing time.

[0029] A twenty-third aspect of the present disclosure is the material manufacturing process exploration method according to the twenty-second aspect, wherein the mechanical properties include any one of 0.2% proof stress, tensile strength, elongation, electrical conductivity, thermal conductivity, Young's modulus, and linear expansion coefficient.

[0030] A 24th aspect of the present disclosure is the material manufacturing process exploration method according to the 23rd aspect, wherein the data indicating the material structure is data indicating a metal structure, and the data indicating the metal structure includes a liquidus temperature, a solid solution amount of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As before processing, a precipitate size, a precipitate density, and a volume fraction.

[0031] A twenty-fifth aspect of the present disclosure is the material manufacturing process exploration method according to the fourth aspect, wherein the casting material is a copper alloy casting material.

[0032] A 26th aspect of the present disclosure is the material manufacturing process exploration method according to the 25th aspect, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As, and the manufacturing conditions include any one of a molten metal temperature during casting, a solution temperature, a solution time, a natural aging time, an artificial aging temperature, an artificial aging time, an annealing temperature, and an annealing time.

[0033] A twenty-seventh aspect of the present disclosure is the material manufacturing process exploration method according to the twenty-sixth aspect, wherein the mechanical properties include any one of 0.2% proof stress, tensile strength, elongation, electrical conductivity, thermal conductivity, Young's modulus, and linear expansion coefficient.

[0034] A 28th aspect of the present disclosure is the material manufacturing process exploration method according to the 27th aspect, wherein the data indicating the material composition is data indicating a metal composition, and the data indicating the metal composition includes any one of a liquidus temperature, a solid solution amount of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As after solution treatment, a precipitate size, a precipitate density, and a volume fraction.

[0035] A twenty-ninth aspect of the present disclosure is the material manufacturing process exploration method according to the third aspect, wherein the inorganic material is a titanium alloy.

[0036] A 30th aspect of the present disclosure is the material manufacturing process exploration method according to the 29th aspect, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes Al, Sn, V, Mo, Zr, Pd, Si, Cr, Ru, Ta, Co, and Ni, and the manufacturing conditions include any one of a molten metal temperature during casting, a solution temperature, a solution time, an artificial aging temperature, an artificial aging time, an annealing temperature, and an annealing time.

[0037] A thirty-first aspect of the present disclosure is the material manufacturing process exploration method according to the thirtieth aspect, wherein the mechanical properties include any one of 0.2% proof stress, tensile strength, elongation, Young's modulus, linear expansion coefficient, and fatigue property.

[0038] A thirty-second aspect of the present disclosure is the material manufacturing process exploration method according to the thirty-first aspect, wherein the data indicating the material structure is data indicating a metal structure, and the data indicating the metal structure includes any one of a liquidus temperature, a solid solution amount of Al, Sn, V, Mo, Zr, Pd, Si, Cr, Ru, Ta, Co, or Ni during solution treatment, a precipitate size, a precipitate density, or a volume fraction.

[0039] A material manufacturing process exploration apparatus according to a thirty-third aspect of the present disclosure is an apparatus for exploring materials manufacturing processes that designs design target materials including materials consisting of multiple compositions or materials manufactured by a combination of multiple manufacturing conditions, and includes: an analysis unit that analyzes the relationship between the manufacturing process of the design target material and the mechanical properties of the design target material; an extraction unit that extracts candidate manufacturing processes for the design target material that satisfy target values ​​of the mechanical properties based on analysis results by the analysis unit; a calculation unit that calculates, by thermodynamic calculation, data indicating the material structure of the design target material, which data affects the mechanical properties; and a determination unit that judges the suitability of the candidate manufacturing processes based on the data indicating the material structure calculated by performing thermodynamic calculations on the candidate manufacturing processes in the calculation unit.

[0040] A material manufacturing process exploration program according to a thirty-fourth aspect of the present disclosure causes a computer of a material manufacturing process exploration device that designs a design target material including a material consisting of a plurality of compositions or a material manufactured by a combination of a plurality of manufacturing conditions to execute the following steps: an analysis step that analyzes the relationship between the manufacturing process of the design target material and the mechanical properties of the design target material; an extraction step that extracts candidate manufacturing processes for the design target material that satisfy target values ​​of the mechanical properties based on analysis results in the analysis step; a calculation step that calculates, by thermodynamic calculation, data indicating the material structure of the design target material, which is data that affects the mechanical properties; and a determination step that determines the suitability of the candidate manufacturing processes based on the data indicating the material structure calculated by performing the thermodynamic calculation in the calculation step.

[0041] According to the present disclosure, it is possible to efficiently search for a manufacturing process that can realize a design target material that satisfies desired mechanical properties.

[0042] FIG. 1 is a diagram showing an example of a manufacturing flow for an aluminum alloy extrusion. FIG. 2 is a diagram showing the relationship between manufacturing processes and mechanical properties in the manufacturing flow for an aluminum alloy extrusion. FIG. 3 is a diagram showing the relationship between manufacturing processes and metallographic structures in the manufacturing flow for an aluminum alloy extrusion. FIG. 4 is a diagram showing an overview of processing in a manufacturing process search device. FIG. 5 is a diagram showing an example of the hardware configuration of a manufacturing process search device. FIG. 6 is a diagram showing an example of the functional configuration of a manufacturing process search device. FIG. 7 is a first diagram showing a specific example of processing by an analysis unit. FIG. 8 is a diagram showing an example of verification of analysis results of multiple regression analysis. FIG. 9 is a diagram showing a specific example of processing by an extraction unit. FIG. 10 is a first diagram showing a specific example of processing by a calculation unit. FIG. 11 is a diagram showing a specific example of processing by a determination unit. FIG. 12 is a flowchart showing the flow of manufacturing process search processing. FIG. 13 is a diagram showing an example of search results from manufacturing process search processing. FIG. 14 is a second diagram showing a specific example of processing by an analysis unit. FIG. 15 is a second diagram showing a specific example of processing by a calculation unit. FIG. 16 is a third diagram showing a specific example of processing by an analysis unit. Fig. 17 is a third diagram showing a specific example of processing by the calculation unit. Fig. 18 is a fourth diagram showing a specific example of processing by the analysis unit. Fig. 19 is a fourth diagram showing a specific example of processing by the calculation unit. Fig. 20 is a fifth diagram showing a specific example of processing by the analysis unit. Fig. 21 is a fifth diagram showing a specific example of processing by the calculation unit. Fig. 22 is a sixth diagram showing a specific example of processing by the analysis unit. Fig. 23 is a sixth diagram showing a specific example of processing by the calculation unit. Fig. 24 is a seventh diagram showing a specific example of processing by the analysis unit. Fig. 25 is a seventh diagram showing a specific example of processing by the calculation unit.

[0043] Hereinafter, each embodiment will be described with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0044] [First embodiment] In the first embodiment, a search process for a manufacturing process when the design target material is an inorganic material will be described. More specifically, in the first embodiment, a search process for a manufacturing process when the design target material is an aluminum alloy wrought material, which is an example of an inorganic material, will be described.

[0045] <Manufacturing flow of aluminum alloy extrusion> First, a manufacturing flow of an aluminum alloy extrusion will be described as a manufacturing flow of an aluminum alloy wrought material to which a manufacturing process searched for by the material manufacturing process search device according to the first embodiment (hereinafter simply referred to as the "manufacturing process search device") is applied. Figure 1 is a diagram showing an example of a manufacturing flow of an aluminum alloy extrusion.

[0046] 1 , the production flow of the aluminum alloy extrusion includes a casting process 111, a homogenization process 112, a billet heating process 113, a hot extrusion process 114, a natural age hardening process 115, an artificial age hardening process 116, a sampling process 117, and a mechanical property measurement process 118. The temperature transitions in each process included in the production flow of the aluminum alloy extrusion are indicated by the reference numeral 120.

[0047] The casting process 111 refers to a process in which aluminum metal is melted at a temperature equal to or higher than the liquidus single phase temperature, and then additive metals are mixed and adjusted, and the resulting mixture is turned into a cylindrical billet using a casting machine.

[0048] The homogenization treatment 112 refers to a treatment in which the billet produced by the casting treatment 111 is heated and held at a temperature below the solidus temperature, and then slowly cooled. The structure of the billet produced by the casting treatment 111 contains non-uniform distribution of solute atoms, compound phases in a non-equilibrium state, strain caused during cooling, and the like, and the homogenization treatment 112 is performed for the purpose of eliminating these. In the first embodiment, the temperature during heating in the homogenization treatment 112 is referred to as the "homogenization temperature." Note that in the first embodiment, the "homogenization temperature" is used as one element of the manufacturing conditions included in the manufacturing process.

[0049] The billet heating treatment 113 refers to a treatment in which the billet that has been subjected to the homogenization treatment 112 is heated and then cut into a predetermined length.

[0050] The hot extrusion process 114 refers to a process for forming an aluminum alloy extrusion profile and a process for rapidly cooling the formed aluminum alloy extrusion profile using cooling water. The aluminum alloy extrusion profile is formed by setting the billet cut in the billet heating process 113 in an extruder and then extruding it through a die mold at high pressure.

[0051] In the first embodiment, the temperature of the cut billet before it is extruded is referred to as the “pre-extrusion heating temperature.” In the first embodiment, the “pre-extrusion heating temperature” is used as one element of the manufacturing conditions included in the manufacturing process.

[0052] In the first embodiment, the thickness of the extruded cross section is referred to as the "extruded cross section thickness," and the pressure when the extruder extrudes from the die is referred to as the "extrusion pressure." In the first embodiment, the "extruded cross section thickness" and the "extrusion pressure" are used as elements of the manufacturing conditions included in the manufacturing process.

[0053] In the first embodiment, the speed of the ram (push rod) when the extruder extrudes the cut billet is referred to as the “ram speed.” In the first embodiment, the “ram speed” is used as one element of the manufacturing conditions included in the manufacturing process.

[0054] In the first embodiment, the temperature of the cooling water used to rapidly cool the aluminum alloy extrusion is referred to as the “cooling water temperature.” In the first embodiment, the “cooling water temperature” is used as one element of the manufacturing conditions included in the manufacturing process.

[0055] The natural age hardening treatment 115 refers to a treatment in which the aluminum alloy extrusion 114, which has been quenched in the hot extrusion treatment 114, is kept at room temperature, thereby precipitating the elements dissolved in the supersaturated state over time. By precipitating the elements dissolved in the supersaturated state, the natural age hardening treatment 115 can harden the aluminum alloy extrusion.

[0056] In the artificial age hardening treatment 116, the aluminum alloy extrusion that has been subjected to the natural age hardening treatment 115 is again subjected to β''-Mg ２ This refers to a process of artificially precipitating elements dissolved in a supersaturated state by heating at a temperature equal to or higher than the Si generation temperature. By artificially precipitating elements dissolved in a supersaturated state, the aluminum alloy extrusion can be further hardened by the artificial age hardening process 116.

[0057] The sampling process 117 refers to a process of cutting out a part of the aluminum alloy extrusion that has been subjected to the artificial age hardening process 116 in order to measure the mechanical properties.

[0058] The mechanical property measurement process 118 refers to a process of measuring the mechanical properties of a portion of the aluminum alloy extrusion cut out in the sampling process 117. In the first embodiment, the mechanical property measurement process 118 measures the "tensile strength", "0.2% yield strength", and "elongation" as the mechanical properties.

[0059] <Relationship between Manufacturing Process and Mechanical Properties in the Manufacturing Flow of Aluminum Alloy Extrusion> Next, the relationship between the manufacturing process and mechanical properties in the manufacturing flow of aluminum alloy extrusion will be described. Figure 2 is a diagram showing the relationship between the manufacturing process and mechanical properties in the manufacturing flow of aluminum alloy extrusion.

[0060] As described above, the mechanical properties of the aluminum alloy extrusion manufactured according to the manufacturing flow for the aluminum alloy extrusion are measured in the mechanical property measurement process 118. The mechanical properties measured at this time, namely, tensile strength, 0.2% proof stress, and elongation, correlate with the manufacturing process, which includes: the alloy composition of the aluminum alloy extrusion; and the manufacturing conditions of the aluminum alloy extrusion.

[0061] Therefore, in the manufacturing process exploration system according to the first embodiment, the manufacturing process, including the alloy composition and manufacturing conditions, is used as an "explanatory variable," and the mechanical properties are used as the "target variable," to analyze the relationship between the manufacturing process and the mechanical properties. As shown in Fig. 2, in the first embodiment, the alloy composition of the aluminum alloy extrusion is set to Si (mass%), Mg (mass%), Cu (mass%), Fe (mass%), Zr (mass%), and Ti (mass%).

[0062] In the first embodiment, the homogenization temperature, pre-extrusion heating temperature, extrusion cross-section thickness, ram speed, and cooling water temperature are used as manufacturing conditions for the aluminum alloy extrusion profile. In Fig. 2, the solution treatment associated with the pre-extrusion heating temperature, extrusion cross-section thickness, ram speed, and cooling water temperature is one of the heat treatment processes for aluminum castings, and refers to a process for dissolving Cu and Mg in the aluminum alloy into the aluminum base material. The billet heating treatment 113 and hot extrusion treatment 114 in the manufacturing flow shown in Fig. 1 correspond to the solution treatment shown in Fig. 2.

[0063] <Relationship between manufacturing processes and metallographic structure in the manufacturing flow of aluminum alloy extrusions> Next, the relationship between manufacturing processes and metallographic structure in the manufacturing flow of aluminum alloy extrusions will be described. Figure 3 is a diagram showing the relationship between manufacturing processes and metallographic structure in the manufacturing flow of aluminum alloy extrusions. As described above, the aluminum alloy extrusions manufactured in accordance with the manufacturing flow of aluminum alloy extrusions are subjected to mechanical property measurement processing 118, and their mechanical properties are measured.

[0064] The mechanical properties of aluminum alloy extrusions, such as tensile strength, 0.2% yield strength, and elongation, are affected by the metallographic structure of the aluminum alloy extrusions. Specifically, the mechanical properties of aluminum alloy extrusions are affected by the following data indicating the metallographic structure of the aluminum alloy extrusions: liquidus temperature, amount of dissolved Mg before extrusion, amount of dissolved Si before extrusion, precipitate size, precipitate density, and volume fraction.

[0065] Therefore, in the manufacturing process searching device according to the first embodiment, the manufacturing process is used as an explanatory variable and the metallographic structure is used as a response variable to analyze the relationship between the manufacturing process and the metallographic structure.

[0066] <Outline of Processing in Manufacturing Process Searching Apparatus> Next, an outline of processing in the manufacturing process searching apparatus according to the first embodiment will be described. Fig. 4 is a diagram showing an outline of processing in the manufacturing process searching apparatus. As shown in Fig. 4, the manufacturing process searching apparatus 400 searches for a manufacturing process through four phases.

[0067] The first phase is the analysis phase. In the analysis phase, the manufacturing process searching apparatus 400 acquires past manufacturing process data and past mechanical property data as learning data. In addition, in the analysis phase, the manufacturing process searching apparatus 400 performs multiple regression analysis using the learning data, with the acquired manufacturing process data as an explanatory variable and the acquired mechanical property data as a target variable.

[0068] The second phase is an extraction phase. In the extraction phase, the manufacturing process searching apparatus 400 acquires target values ​​of mechanical property data. Furthermore, in the extraction phase, the manufacturing process searching apparatus 400 extracts, as candidates for manufacturing process data, manufacturing process data that satisfy the target values ​​of the acquired mechanical property data based on the analysis results of the multiple regression analysis.

[0069] The third phase is a calculation phase, in which the manufacturing process searching device 400 calculates data indicating the metal structure for each of the extracted manufacturing process data candidates through thermodynamic calculation.

[0070] Specifically, the amounts of elements dissolved in solid solution and the phase fractions of precipitates in an aluminum alloy are identified using a calculated equilibrium phase diagram based on the CALPHAD (CALculation of Phase Diagrams, Computer Coupling of Phase Diagrams and Thermochemistry) method.

[0071] When elements dissolve in an aluminum alloy during extrusion and then precipitate by artificial precipitation, the state of the precipitate is predicted as follows: Thermodynamic and kinetic data determined based on the CALPHAD method are used. Nucleation, growth, and coarsening of precipitates are calculated using numerical solutions based on the Langer-Schwartz theory and the Kampmann-Wagner method.

[0072] This makes it possible to predict the state of the precipitates, such as their size, density, and volume fraction.

[0073] In predicting the above-mentioned precipitates, interfacial energy is input for the matrix growth and precipitate growth. The precipitate formation site is assumed to be within the matrix, and the precipitate morphology is assumed to be needle-like or plate-like. By performing simulations under more appropriate conditions, the time-dependent changes in precipitates in aluminum alloys can be predicted appropriately.

[0074] The fourth phase is the judgment phase. In the judgment phase, the manufacturing process searching device 400 judges the suitability of each candidate for manufacturing process data based on the data indicating the calculated metallographic structure. As a result, the manufacturing process searching device 400 can output manufacturing process data in which the mechanical property data satisfies the target values ​​and which realizes an appropriate metallographic structure. In other words, the manufacturing process searching device 400 can narrow down candidates for manufacturing process data.

[0075] The manufacturing process data narrowed down by the manufacturing process search device 400 is used to manufacture a prototype, and the mechanical properties are verified. Then, when the target mechanical properties are obtained, the corresponding manufacturing process data is applied to the manufacturing flow of the aluminum alloy extrusion shown in FIG.

[0076] <Hardware Configuration of Manufacturing Process Searching Apparatus> Next, the hardware configuration of the manufacturing process searching apparatus 400 will be described. Fig. 5 is a diagram showing an example of the hardware configuration of the manufacturing process searching apparatus. As shown in Fig. 5, the manufacturing process searching apparatus 400 includes a processor 410, a memory 420, an auxiliary storage device 430, an input / output device 440, a communication device 450, and a drive device 460. Note that the respective hardware components of the manufacturing process searching apparatus 400 are connected to each other via a bus 470.

[0077] The processor 410 has various computing devices such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit). The processor 410 reads and executes various programs on the memory 420. The various programs referred to here include, for example, a material manufacturing process search program (hereinafter simply referred to as a "manufacturing process search program"), which will be described later.

[0078] The memory 420 has a main storage device such as a read-only memory (ROM) or a random access memory (RAM). The processor 410 and the memory 420 form a so-called computer. The computer realizes the above-mentioned various functions by the processor 410 executing various programs read onto the memory 420.

[0079] The auxiliary storage device 430 stores various programs and various data used when the various programs are executed by the processor 410. For example, a learning data storage unit 650 (described later) is realized in the auxiliary storage device 430.

[0080] The input / output device 440 is a connection device that connects to an operation device 471, which is an example of a user interface device, and a display device 472. The communication device 450 is a communication device for communicating with an external device (not shown) via a network.

[0081] The drive device 460 is a device for setting a recording medium 473. The recording medium 473 here includes media that optically, electrically, or magnetically record information, such as a CD-ROM, a flexible disk, or a magneto-optical disk. The recording medium 473 may also include media such as semiconductor memories that electrically record information, such as ROMs and flash memories.

[0082] The various programs to be installed in the auxiliary storage device 430 are installed, for example, by setting the distributed recording medium 473 in the drive device 460 and reading the various programs recorded on the recording medium 473 by the drive device 460. Alternatively, the various programs to be installed in the auxiliary storage device 430 may be installed by being downloaded from a network via the communication device 450.

[0083] <Functional Configuration of Manufacturing Process Searching Apparatus> Next, the functional configuration of the manufacturing process searching apparatus 400 will be described. Fig. 6 is a diagram showing an example of the functional configuration of the manufacturing process searching apparatus. As described above, a manufacturing process searching program is installed in the manufacturing process searching apparatus 400. By executing the manufacturing process searching program, the manufacturing process searching apparatus 400 functions as an analysis unit 610, an extraction unit 620, a calculation unit 630, and a determination unit 640.

[0084] The analysis unit 610 functions in the analysis phase. The analysis unit 610 reads out the learning data stored in the learning data storage unit 650. The learning data storage unit 650 stores: past manufacturing process data applied to the manufacturing flow shown in Fig. 1; and past mechanical property data, which is mechanical property data of an aluminum alloy extrusion manufactured in accordance with a manufacturing flow to which the corresponding past manufacturing process data was applied.

[0085] The analysis unit 610 performs multiple regression analysis using the manufacturing process data included in the read learning data as explanatory variables and the mechanical property data as objective variables. The analysis unit 610 notifies the extraction unit 620 of the analysis results of the multiple regression analysis.

[0086] The extraction unit 620 functions in the extraction phase. The extraction unit 620 acquires target values ​​of mechanical property data. The extraction unit 620 extracts manufacturing process data that satisfy the target values ​​of the acquired mechanical property data based on the analysis results of the multiple regression analysis.

[0087] Specifically, the extraction unit 620 comprehensively generates combinations of values ​​of each element of the manufacturing process data by changing the value of each element in a predetermined increment. The extraction unit 620 inputs each of the comprehensively generated combinations into the analysis results of the multiple regression analysis, thereby predicting mechanical property data for the corresponding combination. The extraction unit 620 determines whether the predicted mechanical property data satisfies the target value of the mechanical property data. If the extraction unit 620 determines that the target value of the mechanical property data is satisfied, it extracts the manufacturing process data of the corresponding combination as a candidate for manufacturing process data. The extraction unit 620 notifies the calculation unit 630 of the extracted candidate for manufacturing process data.

[0088] The calculation unit 630 functions in the calculation phase. The calculation unit 630 calculates data indicating the metallographic structure of each of the manufacturing process data candidates by performing thermodynamic calculations on each of the manufacturing process data candidates notified by the extraction unit 620. The calculation unit 630 notifies the determination unit 640 of the calculated data indicating the metallographic structure and the manufacturing process data candidates.

[0089] The determination unit 640 functions in the determination phase. For each of the manufacturing process data candidates notified by the calculation unit 630, the determination unit 640 determines whether the data indicating the corresponding metallographic structure satisfies the conditions for realizing the target value of the mechanical property data. If the determination unit 640 determines that the data indicating the corresponding metallographic structure among the manufacturing process data candidates satisfies the conditions for realizing the target value of the mechanical property data, the determination unit 640 outputs the manufacturing process data candidate. On the other hand, if the determination unit 640 determines that the data indicating the corresponding metallographic structure among the manufacturing process data candidates does not satisfy the conditions for realizing the target value of the mechanical property data, the determination unit 640 does not output the manufacturing process data candidate. Therefore, the manufacturing process data candidates output by the determination unit 640 are narrowed down to manufacturing process data that satisfy the target value of the mechanical property data and that realize an appropriate metallographic structure.

[0090] <Specific Example of Processing by Each Unit of the Manufacturing Process Searching Apparatus> Next, a specific example of processing by each unit of the analyzing unit 610, the extracting unit 620, the calculating unit 630, and the determining unit 640 of the manufacturing process searching apparatus 400 will be described.

[0091] (1) Specific Example of Processing by the Analysis Unit First, a specific example of processing by the analysis unit 610 of the manufacturing process searching apparatus 400 will be described. Fig. 7 is a first diagram showing a specific example of processing by the analysis unit.

[0092] As shown in FIG. 7, the learning data 710 that the analysis unit 610 reads from the learning data storage unit 650 includes information items such as "ID," "explanatory variables," and "objective variables."

[0093] In "ID", an identification number for identifying a combination of an explanatory variable and a target variable included in the training data 710 is stored.

[0094] The "explanatory variables" include "alloy composition data," "homogenization temperature," "pre-extrusion heating temperature," "extrusion cross-sectional thickness," "extrusion pressure," "ram speed," and "cooling water temperature." The "alloy composition data" further includes "Si mass %," "Mg mass %," "Cu mass %," "Fe mass %," "Zr mass %," and "Ti mass %," which are alloy composition data included in the past manufacturing process data applied to the manufacturing flow shown in FIG. 1 . Note that the data stored in each information item included in the explanatory variables has already been explained, and therefore will not be explained here.

[0095] The "objective variables" include "tensile strength," "0.2% yield strength," and "elongation," which are mechanical properties of the aluminum alloy extrusion manufactured in accordance with the manufacturing flow in which the above-mentioned past manufacturing process data is applied to the manufacturing flow shown in Figure 1. Note that the data stored in each information item included in the objective variables has already been explained, and therefore will not be explained here.

[0096] As shown in FIG. 7, the analysis unit 610 further includes a data input unit 721 , a multiple regression calculation unit 722 , an analysis result output unit 723 , and a verification unit 724 .

[0097] The data input unit 721 reads out the learning data 710 from the learning data storage unit 650 and inputs the data to the multiple regression calculation unit 722 .

[0098] The multiple regression calculation unit 722 performs multiple regression analysis using the manufacturing process data included in the learning data 710 input by the data input unit 721 as explanatory variables and the mechanical property data as response variables.

[0099] The analysis result output unit 723 notifies the extraction unit 620 and the verification unit 724 of the analysis results of the multiple regression analysis performed by the multiple regression calculation unit 722 .

[0100] The verification unit 724 reads out, as verification data 730, the training data stored in the training data storage unit 650, other than the training data 710 that was read out as the training data 710. The information items contained in the verification data 730 are the same as the information items contained in the training data 710, and therefore will not be described here.

[0101] The verification unit 724 predicts mechanical property data for the corresponding manufacturing process data by inputting the manufacturing process data included in the verification data 730 as explanatory variables into the analysis results of the multiple regression analysis. The verification unit 724 verifies the suitability of the analysis results of the multiple regression analysis by determining whether the error between the predicted mechanical property data and the mechanical property data included in the verification data 730 is equal to or less than a predetermined threshold.

[0102] Specifically, if the verification unit 724 determines that the error between the predicted mechanical property data and the mechanical property data included in the verification data 730 is less than a predetermined threshold, it determines that the analysis results of the multiple regression analysis are appropriate.

[0103] 8A and 8B are diagrams illustrating an example of verification of the analysis results of multiple regression analysis. Fig. 8A is a graph showing the relationship between the predicted tensile strength and the tensile strength included in the verification data 730, which is one of the mechanical property data. In Fig. 8A, the horizontal axis represents the predicted tensile strength, and the vertical axis represents the tensile strength included in the verification data 730. The smaller the error between the predicted tensile strength and the tensile strength included in the verification data 730, the smaller the variation of the plotted points relative to the straight line 801. On the other hand, the larger the error between the predicted tensile strength and the tensile strength included in the verification data 730, the greater the variation of the plotted points relative to the straight line 801.

[0104] As shown in FIG. 8(a), for the tensile strength, the root mean square error (RMSE) was 8.3283 and the coefficient of determination (R ２) was 0.68, and the P value was less than 0.0001. In other words, it was verified that the analysis results of the multiple regression analysis for tensile strength were generally good.

[0105] 8( b) is a graph showing the relationship between the predicted 0.2% proof stress and the 0.2% proof stress included in the verification data 730, which is one of the mechanical property data. In FIG. 8( b), the horizontal axis represents the predicted 0.2% proof stress, and the vertical axis represents the 0.2% proof stress included in the verification data 730. The smaller the error between the predicted 0.2% proof stress and the 0.2% proof stress included in the verification data 730, the smaller the variation of the plotted points from the straight line 802. On the other hand, the larger the error between the predicted 0.2% proof stress and the 0.2% proof stress included in the verification data 730, the greater the variation of the plotted points from the straight line 802.

[0106] As shown in FIG. 8(b), for the 0.2% proof stress, the root mean square error (RMSE) was 8.4107 and the coefficient of determination (R ２ ) was 0.77, and the P value was less than 0.0001. In other words, it was verified that the analysis results of the multiple regression analysis for the 0.2% proof stress were also generally good.

[0107] 8(c) is a graph showing the relationship between the predicted elongation rate, which is one of the mechanical property data, and the elongation rate included in the verification data 730. In FIG. 8(c), the horizontal axis represents the predicted elongation rate, and the vertical axis represents the elongation rate included in the verification data 730. The smaller the error between the predicted elongation rate and the elongation rate included in the verification data 730, the smaller the variation of the plotted points relative to the straight line 803. On the other hand, the larger the error between the predicted elongation rate and the elongation rate included in the verification data 730, the greater the variation of the plotted points relative to the straight line 802.

[0108] As shown in FIG. 8(c), the root mean square error (RMSE) for the growth rate was 2.0491, and the coefficient of determination (R ２) was 0.63, and the P value was less than 0.0001. In other words, it was verified that the results of the multiple regression analysis for the growth rate were generally good.

[0109] (2) Specific Example of Processing by Extraction Unit Next, a specific example of processing by the extraction unit 620 of the manufacturing process search apparatus 400 will be described. Fig. 9 is a diagram showing a specific example of processing by the extraction unit.

[0110] As shown in FIG. 9, the extraction unit 620 includes a data modification unit 921 , a prediction unit 922 , a candidate extraction unit 923 , and a candidate output unit 924 .

[0111] The data modification unit 921 comprehensively generates combinations of values ​​of each element of the manufacturing process data by modifying the value of each element of the manufacturing process data by a predetermined step size. As shown in the modification range data 910 in FIG. 9 , a minimum value, a maximum value, and a step size are set in advance for each element of the manufacturing process data. The data modification unit 921 comprehensively generates various combinations of values ​​of each element of the manufacturing process data by modifying the value of each element within the range between the minimum and maximum values ​​indicated in the modification range data 910 using the step size indicated in the modification range data 910.

[0112] The data modification unit 921 notifies the prediction unit 922 of the combination of values ​​of each element of the comprehensively generated manufacturing process data.

[0113] The prediction unit 922 is set with the analysis results of the multiple regression analysis notified by the analysis unit 610. When the prediction unit 922 is notified of the combination of values ​​of each element of the manufacturing process data by the data modification unit 921, the prediction unit 922 inputs the combination of values ​​of each element of the manufacturing process data into the analysis results of the multiple regression analysis, thereby predicting mechanical property data for the corresponding combination. The prediction unit 922 associates the predicted mechanical property data with the combination of values ​​of each element of the manufacturing process data and notifies the candidate extraction unit 923.

[0114] The candidate extraction unit 923 acquires the target value of the mechanical property data. The candidate extraction unit 923 compares the mechanical property data notified by the prediction unit 922 with the target value of the acquired mechanical property data. The candidate extraction unit 923 extracts, as candidates for manufacturing process data, a combination of values ​​of each element of the manufacturing process data corresponding to the mechanical property data notified by the prediction unit 922 when it is determined that the tensile strength included in the mechanical property data notified by the prediction unit 922 is greater than the tensile strength included in the target value of the acquired mechanical property data, when it is determined that the 0.2% yield strength included in the mechanical property data notified by the prediction unit 922 is greater than the 0.2% yield strength included in the target value of the acquired mechanical property data, or when it is determined that the elongation included in the mechanical property data notified by the prediction unit 922 is greater than the elongation included in the target value of the acquired mechanical property data.

[0115] The candidate extraction unit 923 notifies the candidate output unit 924 of the extracted candidates for manufacturing process data.

[0116] The candidate output unit 924 notifies the calculation unit 630 of the candidates for the manufacturing process data notified by the candidate extraction unit 923 .

[0117] (3) Specific Example of Processing by the Calculation Unit Next, a description will be given of a specific example of processing by the calculation unit 630 of the manufacturing process search apparatus 400. Fig. 10 is a first diagram showing a specific example of processing by the calculation unit.

[0118] The calculation unit 630 is a general-purpose thermodynamic calculation software, and for example, thermodynamic calculation software such as Thermo-Calc is applied.

[0119] The thermodynamic calculation software uses a calculated equilibrium phase diagram based on the CALPHAD method to identify the amount of element solid solution and the phase fraction of precipitates in an aluminum alloy.

[0120] Furthermore, this thermodynamic calculation software predicts the state of precipitates when elements dissolve in an aluminum alloy during extrusion and then precipitates are artificially precipitated, as follows: - Thermodynamic and kinetic data determined based on the CALPAD method are used. - Nucleation, growth, and coarsening of precipitates are calculated using numerical solutions based on the Langer-Schwartz theory and the Kampmann-Wagner method.

[0121] As a result, the thermodynamic calculation software can predict the state of the precipitates, such as the size, density, and volume fraction of the precipitates.

[0122] In predicting the above-mentioned precipitates, interfacial energy is input for the matrix growth and precipitate growth. The precipitate formation site is assumed to be within the matrix, and the precipitate morphology is assumed to be needle-like or plate-like. By performing a simulation using the above-mentioned thermodynamic calculation software under more appropriate conditions, the time-dependent changes in precipitates in an aluminum alloy can be appropriately predicted.

[0123] FIG. 10 is a diagram showing the above functions of the thermodynamic calculation software, in which a liquidus temperature and solid solution amount calculation unit 1001 and a precipitate calculation unit 1002 are used.

[0124] The example in FIG. 10 shows that the liquidus temperature and solute amount calculation unit 1001 is used to calculate the liquidus temperature and solute amount for the candidate manufacturing process data notified by the extraction unit 620.

[0125] Specifically, the liquidus temperature and solid solution amount calculation unit 1001 is used, and by inputting alloy composition data included in candidate manufacturing process data and information on the target phase, the following are output: Mg solid solution amount [mass %], Si solid solution amount [mass %], and liquidus temperature [°C].

[0126] It also shows that the precipitate calculation unit 1002 is used to calculate the precipitate size, precipitate density, and volume fraction for the candidate manufacturing process data notified by the extraction unit 620.

[0127] Specifically, by using the precipitate calculation unit 1002, the alloy composition data included in the candidate manufacturing process data and the matrix of the target phase, β"-Mg ２ By inputting the data of the Si phase, the following can be determined: Precipitates (β"-Mg ２ Si) size (average particle diameter) [nm], ・Precipitates (β"-Mg ２ Si) density (number density) [pieces / m ３ ], ・Precipitate (β"-Mg ２ Si) volume fraction [volume %] is output.

[0128] As a result, the calculation unit 630 can associate the candidate manufacturing process data notified by the extraction unit 620 with data indicating the metal structure, such as the liquidus temperature, the amount of solid solution, the precipitate size, the precipitate density, and the volume fraction, and notify the determination unit 640 of the correspondence.

[0129] (4) Specific Example of Processing by Determining Unit Next, a specific example of processing by the determining unit 640 of the manufacturing process searching apparatus 400 will be described. Fig. 11 is a diagram showing a specific example of processing by the determining unit.

[0130] As shown in FIG. 11, the determination unit 640 includes a calculation result acquisition unit 1101 , a condition acquisition unit 1102 , a suitability determination unit 1103 , and a decision candidate output unit 1104 .

[0131] The calculation result acquisition unit 1101 acquires candidates for manufacturing process data and data indicating the corresponding metallographic structure from the calculation unit 630. The calculation result acquisition unit 1101 also notifies the suitability determination unit 1103 of the acquired candidates for manufacturing process data and data indicating the corresponding metallographic structure.

[0132] The condition acquisition unit 1102 acquires "conditions for data indicating metal structure" for realizing the target value of the mechanical property data. The condition acquisition unit 1102 notifies the suitability determination unit 1103 of the acquired conditions for data indicating metal structure.

[0133] The suitability determination unit 1103 determines whether the data indicating the metallographic structure notified by the calculation result acquisition unit 1101 satisfies the "conditions for data indicating the metallographic structure" notified by the condition acquisition unit 1102. If the suitability determination unit 1103 determines that the data indicating the metallographic structure notified by the calculation result acquisition unit 1101 satisfies the "conditions for data indicating the metallographic structure" notified by the condition acquisition unit 1102, the suitability determination unit 1103 determines that the corresponding candidate for manufacturing process data is appropriate. On the other hand, if the suitability determination unit 1103 determines that the data indicating the metallographic structure notified by the calculation result acquisition unit 1101 does not satisfy the "conditions for data indicating the metallographic structure" notified by the condition acquisition unit 1102, the suitability determination unit 1103 determines that the corresponding candidate for manufacturing process data is inappropriate.

[0134] The suitability determining unit 1103 notifies the determined candidate output unit 1104 of the manufacturing process data candidates that have been determined to be suitable.

[0135] The determined candidate output unit 1104 outputs the candidate manufacturing process data notified by the suitability determination unit 1103 as manufacturing process data that satisfies the target value of the mechanical property data and that realizes an appropriate metal structure.

[0136] <Flow of Manufacturing Process Searching Process> Next, a description will be given of the flow of the manufacturing process searching process performed by the manufacturing process searching device 400. Fig. 12 is a flowchart showing the flow of the manufacturing process searching process.

[0137] In step S1201, the manufacturing process searching apparatus 400 acquires learning data.

[0138] In step S1202, the manufacturing process searching apparatus 400 performs multiple regression analysis using the learning data and outputs the analysis results of the multiple regression analysis.

[0139] In step S1203, the manufacturing process searching apparatus 400 acquires a target value of the mechanical property data.

[0140] In step S1204, the manufacturing process searching apparatus 400 extracts candidates for manufacturing process data in which the mechanical property data predicted based on the analysis results of the multiple regression analysis satisfy the target values ​​of the acquired mechanical property data.

[0141] In step S1205, the manufacturing process searching apparatus 400 inputs "1" to a counter i that counts the extracted candidates for manufacturing process data.

[0142] In step S1206, the manufacturing process searching device 400 calculates data indicating the metal structure by thermodynamic calculation for the i-th manufacturing process data candidate from among the extracted manufacturing process data candidates.

[0143] In step S1207, the manufacturing process searching device 400 determines whether the data indicating the metallographic structure calculated for the i-th manufacturing process data candidate satisfies the "conditions for data indicating the metallographic structure" for achieving the target value of the mechanical property data. In this way, the manufacturing process searching device 400 determines the suitability of the i-th manufacturing process data candidate and narrows down the manufacturing process data candidates.

[0144] In step S1208, the manufacturing process searching apparatus 400 determines whether the suitability of all the manufacturing process data candidates has been determined. If it is determined in step S1208 that there are manufacturing process data candidates for which the suitability has not been determined (NO in step S1208), the process proceeds to step S1209.

[0145] In step S1209, the manufacturing process searching apparatus 400 increments the counter i, and then proceeds to step S1206.

[0146] On the other hand, if it is determined in step S1208 that the suitability of all the candidates for manufacturing process data has been determined (YES in step S1208), the process proceeds to step S1210.

[0147] In step S1210, the manufacturing process search apparatus 400 outputs candidates for manufacturing process data that have been determined to be appropriate, and then ends the manufacturing process search process.

[0148] Next, an example of the manufacturing process search process by the manufacturing process search device 400 will be described. Fig. 13 is a diagram showing an example of the search results by the manufacturing process search process, in which the target values ​​of the mechanical property data are set as follows: Tensile strength: 300 [MPa] or more; 0.2% proof stress: 270 [MPa] or more; Elongation: 9.0 [%] or more; and the "conditions for data indicating metal structure" are set as follows: Liquidus temperature: 560 [°C] or more; Amount of Mg solid solution before extrusion: 0.005 [mass %] or more; Amount of Si solid solution before extrusion: 0.01 [mass %] or more; Precipitates (β"-Mg ２ Si) Size (average particle size): 50 [nm] or more, 300 nm or less, Precipitates (β''-Mg ２ Si) Density (number density): 6.0E+27 [pcs / m ３ ] Above, ・Precipitate (β"-Mg ２ 13 shows an example of a search result when the following conditions are met: Si) volume fraction: 10 [vol %] or more. The example of Fig. 13 shows how 14 types of manufacturing process data are output as manufacturing process data that satisfy the target values ​​of the mechanical property data and that realize an appropriate metal structure.

[0149] <Summary> As is clear from the above description, the manufacturing process searching device 400 according to the first embodiment includes: an analysis unit 610 that analyzes the relationship between manufacturing process data for an aluminum alloy extrusion and mechanical property data for the aluminum alloy extrusion by multiple regression analysis; an extraction unit 620 that extracts manufacturing process data candidates for the aluminum alloy extrusion that satisfy target values ​​for the mechanical property data based on the results of the multiple regression analysis by the analysis unit 610; a calculation unit 630 that calculates, for each of the extracted manufacturing process data candidates, data indicating a metallurgical structure, which is data that affects the mechanical property data, by thermodynamic calculation; and a determination unit 640 that determines whether the data indicating a metallurgical structure calculated by the calculation unit 630 through thermodynamic calculation for the extracted manufacturing process data candidates satisfies the "conditions for data indicating a metallurgical structure" for achieving the target values ​​for the mechanical property data. The determination unit 640 determines the suitability of the corresponding manufacturing process data candidate based on whether the "conditions for data indicating a metallurgical structure" are satisfied.

[0150] In this way, the manufacturing process searching device 400 according to the first embodiment can output manufacturing process data that satisfy the target values ​​of the mechanical property data and that realize an appropriate metal structure. In other words, even if there are many manufacturing processes that can realize an aluminum alloy extrusion profile that meets the desired mechanical properties, it can narrow down the options. As a result, according to the first embodiment, it is possible to efficiently search for a manufacturing process that can realize an aluminum alloy extrusion profile that meets the desired mechanical properties.

[0151] [Second embodiment] In the first embodiment, the mechanical property data includes tensile strength, 0.2% yield strength, and elongation percentage, but the mechanical property data is not limited to these, and other mechanical property data may be used.

[0152] Furthermore, in the examples of the first embodiment described above, the target values ​​of the mechanical property data were set to tensile strength ≧300 MPa or more, 0.2% yield strength ≧270 MPa or more, and elongation ≧9.0% or more, but the target values ​​of the mechanical property data are not limited to these.

[0153] In addition, in the above-described first embodiment, the homogenization temperature, pre-extrusion heating temperature, extrusion cross-sectional wall thickness, ram speed, and cooling water temperature are used as the manufacturing conditions. However, the manufacturing conditions are not limited to these, and other manufacturing conditions may be used.

[0154] Furthermore, in the above first embodiment, the data modification unit 921 has been described as comprehensively generating combinations of values ​​for each element of the manufacturing process data, but the method by which the data modification unit 921 generates combinations of values ​​for each element of the manufacturing process data is not limited to this.

[0155] Furthermore, in the above-described first embodiment, the case where the amount of solid solution, the liquidus temperature, the precipitate size, the precipitate density, and the volume fraction are calculated as data indicating the metallographic structure has been described. However, the data indicating the metallographic structure is not limited to these, and data indicating other metallographic structures may also be calculated.

[0156] In the first embodiment, the "conditions for data showing the metal structure" are defined as follows: Liquidus temperature: 560°C or higher; Amount of dissolved Mg before extrusion: 0.005% by mass or higher; Amount of dissolved Si before extrusion: 0.01% by mass or higher; Precipitates (β"-Mg ２ Si) Size (average particle size): 50 [nm] or more, 300 nm or less, Precipitates (β''-Mg ２ Si) Density (number density): 6.0E+27 [pcs / m ３ ] Above, ・Precipitate (β"-Mg ２ Si) volume fraction: 10 [vol %] or more, but the "conditions for data showing metal structure" are not limited to this.

[0157] [Third Embodiment] In the first embodiment, the design target material is an alloy material, and is a wrought aluminum alloy. However, the design target material is not limited to a wrought aluminum alloy, and may be a cast aluminum alloy. The third embodiment will be described below, focusing on the differences from the first embodiment.

[0158] <Specific Example of Processing by the Analysis Unit of the Manufacturing Process Searching Apparatus> First, a description will be given of a specific example of processing by the analysis unit 610 of the manufacturing process searching apparatus 400. Fig. 14 is a second diagram showing a specific example of processing by the analysis unit.

[0159] As in FIG. 7, as shown in FIG. 14, the learning data 710 read by the analysis unit 610 from the learning data storage unit 650 includes information items such as "ID," "explanatory variables," and "objective variables."

[0160] In "ID", an identification number for identifying a combination of an explanatory variable and a target variable included in the training data 710 is stored.

[0161] In the case of aluminum alloy casting materials, the "explanatory variables" include "alloy composition data," "molten metal temperature" during casting, "solution temperature," "solution time," "natural aging time," "artificial aging temperature," "artificial aging time," "annealing temperature," and "annealing time." The "alloy composition data" includes "Si mass %, "Mg mass %, "Cu mass %, "Fe mass %, "Zr mass %, and "Ti mass %."

[0162] The "objective variables" include the mechanical properties of the aluminum alloy casting material, namely, "tensile strength," "0.2% yield strength," "elongation," "Young's modulus," "linear expansion coefficient," and "fatigue properties."

[0163] 14 have already been described in the first embodiment, and therefore will not be described here. Also, the verification data 730 is similar to the training data 710, and therefore will not be described here.

[0164] <Specific Example of Processing by Calculation Unit of Manufacturing Process Searching Apparatus> Next, a description will be given of a specific example of processing by the calculation unit 630 of the manufacturing process searching apparatus 400. Fig. 15 is a second diagram showing a specific example of processing by the calculation unit.

[0165] As shown in FIG. 15 , in the case of an aluminum alloy casting material, the liquidus temperature and solid solution amount calculation unit 1001 displays the following data: Si solid solution amount at the time of solution treatment [mass %], Fe solid solution amount at the time of solution treatment [mass %], Cu solid solution amount at the time of solution treatment [mass %], Mn solid solution amount at the time of solution treatment [mass %], Mg solid solution amount at the time of solution treatment [mass %], Cr solid solution amount at the time of solution treatment [mass %], Ni solid solution amount at the time of solution treatment [mass %], Zn solid solution amount at the time of solution treatment [mass %], Ti solid solution amount at the time of solution treatment [mass %], V solid solution amount at the time of solution treatment [mass %], Pb solid solution amount at the time of solution treatment [mass %], Sn solid solution amount at the time of solution treatment [mass %], Bi solid solution amount at the time of solution treatment [mass %], - Amount of dissolved B at the time of solution treatment [mass %], - Amount of dissolved P at the time of solution treatment [mass %], - Amount of dissolved Zr at the time of solution treatment [mass %], - Amount of dissolved Sr at the time of solution treatment [mass %], - Liquidus temperature [°C].

[0166] In the case of an aluminum alloy casting material, the precipitate calculation unit 1002 calculates the precipitate size [nm], the precipitate density [pieces / m ３ ], Precipitated object volume fraction [volume %], are output.

[0167] As a result, the calculation unit 630 can associate the following with the candidates for manufacturing process data notified by the extraction unit 620, and with data indicating the metal structure, such as the liquidus temperature, the amount of solid solution of Si, Fe, Cu, Mn, Mg, Cr, Ni, Zn, Ti, V, Pb, Sn, Bi, B, P, Zr, and Sr at the time of solution treatment, the precipitate size, the precipitate density, and the precipitate volume fraction, and notify the determination unit 640 of the associations.

[0168] As described above, according to the third embodiment, even when the design object material is a cast aluminum alloy, it is possible to obtain the same effects as those of the first embodiment.

[0169] [Fourth Embodiment] In the first embodiment, the design target material is an alloy material, and is a wrought aluminum alloy. However, the design target material is not limited to a wrought aluminum alloy, and may be a wrought iron alloy. The fourth embodiment will be described below, focusing on the differences from the first embodiment.

[0170] <Specific Example of Processing by the Analysis Unit of the Manufacturing Process Searching Apparatus> First, a description will be given of a specific example of processing by the analysis unit 610 of the manufacturing process searching apparatus 400. Fig. 16 is a third diagram showing a specific example of processing by the analysis unit.

[0171] As in FIG. 7, as shown in FIG. 16, the learning data 710 read by the analysis unit 610 from the learning data storage unit 650 includes information items such as "ID," "explanatory variables," and "objective variables."

[0172] In "ID", an identification number for identifying a combination of an explanatory variable and a target variable included in the training data 710 is stored.

[0173] In the case of wrought iron alloys, the "explanatory variables" include "alloy composition data," "molten metal temperature" during iron alloy casting, "casting speed," "amount of cooling water," "iron alloy heating temperature" during hot working, "iron alloy heating time" during hot working, "working speed," "rolling reduction," "hot working temperature," "cooling rate after hot working," "natural aging time," "heat treatment temperature," "heat treatment time," and "cooling rate for heat treatment." The "alloy composition data" includes "C mass %, "B mass %, "N mass %, "Si mass %, "P mass %, "S mass %, "Mn mass %, "Al mass %, "Ti mass %, "V mass %, "Cr mass %, "Co mass %, "Ni mass %, "Cu mass %, "Zr mass %, "Nb mass %, "Mo mass %, "W mass %, "

[0174] The "objective variables" include the mechanical properties of the wrought iron alloy: "tensile strength," "0.2% yield strength," "elongation," "Young's modulus," "linear expansion coefficient," "impact properties," and "fatigue properties."

[0175] 16 have already been described in the first embodiment, and therefore will not be described here. Also, the verification data 730 is similar to the training data 710, and therefore will not be described here.

[0176] <Specific Example of Processing by Calculation Unit of Manufacturing Process Searching Apparatus> Next, a description will be given of a specific example of processing by the calculation unit 630 of the manufacturing process searching apparatus 400. Fig. 17 is a third diagram showing a specific example of processing by the calculation unit.

[0177] 17 , in the case of a wrought iron alloy, the liquidus temperature and solid solution amount calculation unit 1001 calculates the amount of C solid solution after working [mass %], the amount of B solid solution after working [mass %], the amount of N solid solution after working [mass %], the amount of Si solid solution after working [mass %], the amount of P solid solution after working [mass %], the amount of S solid solution after working [mass %], the amount of Mn solid solution after working [mass %], the amount of Al solid solution after working [mass %], the amount of Ti solid solution after working [mass %], the amount of V solid solution after working [mass %], the amount of Cr solid solution after working [mass %], the amount of Co solid solution after working [mass %], the amount of Ni solid solution after working [mass %], the amount of Cu solid solution after working [mass %], the amount of Zr solid solution after working [mass %], the amount of Nb solid solution after working [mass %], - Amount of Mo solid solution after processing [mass %], - Amount of W solid solution after processing [mass %], - Liquidus temperature [°C].

[0178] In addition, in the case of a wrought iron alloy, the precipitate calculation unit 1002 calculates the precipitate size [nm], the precipitate density [pieces / m ３ ], Precipitated object volume fraction [volume %], are output.

[0179] As a result, the calculation unit 630 can associate the following with the candidates for manufacturing process data notified by the extraction unit 620, and with data indicating the metal structure, such as the liquidus temperature, the amount of solid solution of C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, and W after processing, the precipitate size, the precipitate density, and the precipitate volume fraction, and notify the determination unit 640 of the associations.

[0180] Thus, according to the fourth embodiment, even when the design target material is a wrought iron alloy, it is possible to obtain the same effects as those of the first embodiment.

[0181] [Fifth Embodiment] In the third embodiment, the design target material is an alloy material, and is an aluminum alloy cast material. However, the design target material is not limited to an aluminum alloy cast material, and may be a cast iron material. The fifth embodiment will be described below, focusing on the differences from the third embodiment.

[0182] <Specific Example of Processing by the Analysis Unit of the Manufacturing Process Searching Apparatus> First, a description will be given of a specific example of processing by the analysis unit 610 of the manufacturing process searching apparatus 400. Fig. 18 is a fourth diagram showing a specific example of processing by the analysis unit.

[0183] As in FIG. 14, as shown in FIG. 18, the learning data 710 read by the analysis unit 610 from the learning data storage unit 650 includes information items such as "ID," "explanatory variables," and "objective variables."

[0184] In "ID", an identification number for identifying a combination of an explanatory variable and a target variable included in the training data 710 is stored.

[0185] In the case of cast iron materials, the "explanatory variables" include "alloy composition data," "molten metal temperature" during pouring, "pouring speed," "solidification rate," "cooling rate after solidification," "heat treatment temperature," "heat treatment time," and "cooling rate for heat treatment." The "alloy composition data" includes "C mass %, "B mass %, "N mass %, "Si mass %, "P mass %, "S mass %, "Mn mass %, "Al mass %, "Ti mass %, "V mass %, "Cr mass %, "Co mass %, "Ni mass %, "Cu mass %, "Zr mass %, "Nb mass %, "Mo mass %, "W mass %, "Ca mass %, "Mg mass %, " and "Ce mass %."

[0186] The "objective variables" include the mechanical properties of cast iron material, namely, "tensile strength," "0.2% yield strength," "elongation," "Young's modulus," "linear expansion coefficient," "impact properties," and "fatigue properties."

[0187] 18 have already been described in the first embodiment, and therefore will not be described here. Also, the verification data 730 is similar to the training data 710, and therefore will not be described here.

[0188] <Specific Example of Processing by Calculation Unit of Manufacturing Process Searching Apparatus> Next, a description will be given of a specific example of processing by the calculation unit 630 of the manufacturing process searching apparatus 400. Fig. 19 is a fourth diagram showing a specific example of processing by the calculation unit.

[0189] 19 , in the case of a cast iron material, the liquidus temperature and solid solution amount calculation unit 1001 calculates the C solid solution amount [mass %] before heat treatment, the B solid solution amount [mass %] before heat treatment, the N solid solution amount [mass %] before heat treatment, the Si solid solution amount [mass %] before heat treatment, the P solid solution amount [mass %] before heat treatment, the S solid solution amount [mass %] before heat treatment, the Mn solid solution amount [mass %] before heat treatment, the Al solid solution amount [mass %] before heat treatment, the Ti solid solution amount [mass %] before heat treatment, the V solid solution amount [mass %] before heat treatment, the Cr solid solution amount [mass %] before heat treatment, the Co solid solution amount [mass %] before heat treatment, the Ni solid solution amount [mass %] before heat treatment, the Cu solid solution amount [mass %] before heat treatment, and the Zr solid solution amount [mass %] before heat treatment. - Amount of Nb solid solution before heat treatment [mass %], - Amount of Mo solid solution before heat treatment [mass %], - Amount of W solid solution before heat treatment [mass %], - Amount of Ca solid solution before heat treatment [mass %], - Amount of Mg solid solution before heat treatment [mass %], - Amount of Ce solid solution before heat treatment [mass %], - Liquidus temperature [°C].

[0190] In the case of cast iron material, the precipitate calculation unit 1002 calculates the precipitate size [nm], the precipitate density [pieces / m ３ ], Precipitated object volume fraction [volume %], are output.

[0191] As a result, the calculation unit 630 can associate the following with the candidates for manufacturing process data notified by the extraction unit 620, and with the data indicating the metal structure, such as the liquidus temperature, the amount of solid solution of C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, W, Ca, Mg, and Ce before heat treatment, the precipitate size, the precipitate density, and the precipitate volume fraction, and notify the determination unit 640 of the associations.

[0192] Thus, according to the fifth embodiment, even when the design object material is cast iron, it is possible to obtain the same effects as those of the first embodiment.

[0193] [Sixth Embodiment] In the first embodiment, the design target material is an alloy material, and is a wrought aluminum alloy. However, the design target material is not limited to a wrought aluminum alloy, and may be a wrought copper alloy. The sixth embodiment will be described below, focusing on the differences from the first embodiment.

[0194] <Specific Example of Processing by the Analysis Unit of the Manufacturing Process Searching Apparatus> First, a description will be given of a specific example of processing by the analysis unit 610 of the manufacturing process searching apparatus 400. Fig. 20 is a fifth diagram showing a specific example of processing by the analysis unit.

[0195] As in FIG. 7, as shown in FIG. 20, the learning data 710 read by the analysis unit 610 from the learning data storage unit 650 includes information items such as "ID," "explanatory variables," and "objective variables."

[0196] In "ID", an identification number for identifying a combination of an explanatory variable and a target variable included in the training data 710 is stored.

[0197] In the case of wrought copper alloys, the "explanatory variables" include "alloy composition data," "molten metal temperature" during copper alloy casting, "casting speed," "amount of cooling water," "homogenization temperature," "homogenization time," "cooling rate after homogenization," "copper alloy heating temperature during hot working," "working speed," "cooling rate after working," "natural aging time," "artificial aging temperature," "artificial aging time," "hot working temperature," "annealing temperature," and "annealing time." "Alloy composition data" includes "Zn mass%", "Pb mass%", "Bi mass%", "Sn mass%", "Fe mass%", "P mass%", "Al mass%", "Hg mass%", "Ni mass%", "Mn mass %", "Se mass %", "Te mass %", "O mass %", "S mass %", "Zr mass %", "Be mass %", "Co mass %", "Ti mass %", and "As mass %" are included.

[0198] The "objective variables" include the mechanical properties of the wrought copper alloy: "0.2% yield strength," "tensile strength," "elongation," "electrical conductivity," "thermal conductivity," "Young's modulus," and "linear expansion coefficient."

[0199] 20 have already been described in the first embodiment, and therefore will not be described here. Also, the verification data 730 is similar to the training data 710, and therefore will not be described here.

[0200] <Specific Example of Processing by Calculation Unit of Manufacturing Process Searching Apparatus> Next, a description will be given of a specific example of processing by the calculation unit 630 of the manufacturing process searching apparatus 400. Fig. 21 is a fifth diagram showing a specific example of processing by the calculation unit.

[0201] As shown in FIG. 21 , in the case of a wrought copper alloy material, the liquidus temperature and solid solution amount calculation unit 1001 calculates the amount of Zn solid solution before working [mass %], the amount of Pb solid solution before working [mass %], the amount of Bi solid solution before working [mass %], the amount of Sn solid solution before working [mass %], the amount of Fe solid solution before working [mass %], the amount of P solid solution before working [mass %], the amount of Al solid solution before working [mass %], the amount of Hg solid solution before working [mass %], the amount of Ni solid solution before working [mass %], the amount of Mn solid solution before working [mass %], the amount of Se solid solution before working [mass %], the amount of Te solid solution before working [mass %], the amount of O solid solution before working [mass %], the amount of S solid solution before working [mass %], and the amount of Zr solid solution before working [mass %]. The following are output: Amount of Be solid solution before processing [mass %], Amount of Co solid solution before processing [mass %], Amount of Ti solid solution before processing [mass %], Amount of As solid solution before processing [mass %], and Liquidus temperature [°C].

[0202] In the case of a copper alloy wrought material, the precipitate calculation unit 1002 calculates the precipitate size [nm], the precipitate density [pieces / m ３ ], Precipitated object volume fraction [volume %], are output.

[0203] As a result, the calculation unit 630 can associate the following with the candidates for manufacturing process data notified by the extraction unit 620, and with data indicating the metal structure, such as the liquidus temperature, the amount of solid solution of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As before processing, the precipitate size, the precipitate density, and the precipitate volume fraction, and notify the determination unit 640 of the associations.

[0204] Thus, according to the sixth embodiment, even when the design target material is a wrought copper alloy, the same effects as those of the first embodiment can be obtained.

[0205] [Seventh Embodiment] In the third embodiment, the design target material is an alloy material, and is an aluminum alloy cast material. However, the design target material is not limited to an aluminum alloy cast material, and may be a copper alloy cast material. The seventh embodiment will be described below, focusing on the differences from the third embodiment.

[0206] <Specific Example of Processing by the Analysis Unit of the Manufacturing Process Searching Apparatus> First, a description will be given of a specific example of processing by the analysis unit 610 of the manufacturing process searching apparatus 400. Fig. 22 is a sixth diagram showing a specific example of processing by the analysis unit.

[0207] As in FIG. 14, as shown in FIG. 22, the learning data 710 read by the analysis unit 610 from the learning data storage unit 650 includes information items such as "ID," "explanatory variables," and "objective variables."

[0208] In "ID", an identification number for identifying a combination of an explanatory variable and a target variable included in the training data 710 is stored.

[0209] In the case of a copper alloy casting material, the "explanatory variables" include "alloy composition data," "molten metal temperature" during copper alloy casting, "solution temperature," "solution time," "natural aging time," "artificial aging temperature," "artificial aging time," "annealing temperature," and "annealing time." The "alloy composition data" includes "Zn mass %, "Pb mass %, "Bi mass %, "Sn mass %, "Fe mass %, "P mass %, "Al mass %, "Hg mass %, "Ni mass %, "Mn mass %, "Se mass %, "Te mass %, "O mass %, "S mass %, "Zr mass %, "Be mass %, "Co mass %, "Ti mass %, "As mass %."

[0210] The "objective variables" include the mechanical properties of the copper alloy casting material, namely, "0.2% yield strength," "tensile strength," "elongation," "electrical conductivity," "thermal conductivity," "Young's modulus," and "linear expansion coefficient."

[0211] The components included in the analysis unit 610 have already been described in the first embodiment, and therefore will not be described here.

[0212] <Specific Example of Processing by Calculation Unit of Manufacturing Process Searching Apparatus> Next, a description will be given of a specific example of processing by the calculation unit 630 of the manufacturing process searching apparatus 400. Fig. 23 is a sixth diagram showing a specific example of processing by the calculation unit.

[0213] As shown in FIG. 23 , in the case of a copper alloy casting material, the liquidus temperature and solid solution amount calculation unit 1001 calculates the amount of Zn solid solution before working [mass %], the amount of Pb solid solution before working [mass %], the amount of Bi solid solution before working [mass %], the amount of Sn solid solution before working [mass %], the amount of Fe solid solution before working [mass %], the amount of P solid solution before working [mass %], the amount of Al solid solution before working [mass %], the amount of Hg solid solution before working [mass %], the amount of Ni solid solution before working [mass %], the amount of Mn solid solution before working [mass %], the amount of Se solid solution before working [mass %], the amount of Te solid solution before working [mass %], the amount of O solid solution before working [mass %], the amount of S solid solution before working [mass %], and the amount of Zr solid solution before working [mass %]. The following are output: Amount of Be solid solution before processing [mass %], Amount of Co solid solution before processing [mass %], Amount of Ti solid solution before processing [mass %], Amount of As solid solution before processing [mass %], and Liquidus temperature [°C].

[0214] In the case of a copper alloy casting material, the precipitate calculation unit 1002 calculates the precipitate size [nm], the precipitate density [pieces / m ３ ], Precipitated object volume fraction [volume %], are output.

[0215] As a result, the calculation unit 630 can associate the following with the candidates for manufacturing process data notified by the extraction unit 620, and with data indicating the metal structure, such as the liquidus temperature, the amount of solid solution of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As before processing, the precipitate size, the precipitate density, and the precipitate volume fraction, and notify the determination unit 640 of the associations.

[0216] Thus, according to the seventh embodiment, even when the design target material is a copper alloy casting, it is possible to obtain the same effects as those of the first embodiment.

[0217] [Eighth Embodiment] In the first embodiment, the design target material is an alloy material, and is an aluminum alloy cast material. However, the design target material is not limited to an aluminum alloy cast material, and may be a titanium alloy. The eighth embodiment will be described below, focusing on the differences from the first embodiment.

[0218] <Specific Example of Processing by the Analysis Unit of the Manufacturing Process Searching Apparatus> First, a description will be given of a specific example of processing by the analysis unit 610 of the manufacturing process searching apparatus 400. Fig. 24 is a seventh diagram showing a specific example of processing by the analysis unit.

[0219] As in FIG. 14, as shown in FIG. 24, the learning data 710 read by the analysis unit 610 from the learning data storage unit 650 includes information items such as "ID," "explanatory variables," and "objective variables."

[0220] In "ID", an identification number for identifying a combination of an explanatory variable and a target variable included in the training data 710 is stored.

[0221] In the case of titanium alloys, "explanatory variables" include "alloy composition data," "molten metal temperature," "solution temperature," "solution time," "artificial aging temperature," "artificial aging time," "annealing temperature," and "annealing time." "Alloy composition data" includes "Al mass %, "Sn mass %, "V mass %, "Mo mass %, "Zr mass %, "Pd mass %, "Si mass %, "Cr mass %, "Ru mass %, "Ta mass %, "Co mass %, and "Ni mass %."

[0222] The "objective variables" include the mechanical properties of titanium alloys, namely, "0.2% yield strength," "tensile strength," "elongation," "Young's modulus," "linear expansion coefficient," and "fatigue properties."

[0223] 24 have already been described in the first embodiment, and therefore will not be described here. Also, the verification data 730 is similar to the training data 710, and therefore will not be described here.

[0224] <Specific Example of Processing by Calculation Unit of Manufacturing Process Searching Apparatus> Next, a description will be given of a specific example of processing by the calculation unit 630 of the manufacturing process searching apparatus 400. Fig. 25 is a seventh diagram showing a specific example of processing by the calculation unit.

[0225] As shown in FIG. 25 , in the case of a titanium alloy, the liquidus temperature and solid solution amount calculation unit 1001 outputs: Al solid solution amount [mass %] at the time of solution treatment, Sn solid solution amount [mass %] at the time of solution treatment, V solid solution amount [mass %] at the time of solution treatment, Mo solid solution amount [mass %] at the time of solution treatment, Zr solid solution amount [mass %] at the time of solution treatment, Pd solid solution amount [mass %] at the time of solution treatment, Si solid solution amount [mass %] at the time of solution treatment, Cr solid solution amount [mass %] at the time of solution treatment, Ru solid solution amount [mass %] at the time of solution treatment, Ta solid solution amount [mass %] at the time of solution treatment, Co solid solution amount [mass %] at the time of solution treatment, Ni solid solution amount [mass %] at the time of solution treatment, and liquidus temperature [°C].

[0226] In the case of a titanium alloy casting material, the precipitate calculation unit 1002 calculates the precipitate size [nm], the precipitate density [pieces / m ３ ], Precipitated object volume fraction [volume %], are output.

[0227] As a result, the calculation unit 630 can associate the following with the candidates for manufacturing process data notified by the extraction unit 620, and with data indicating the metal structure, such as the liquidus temperature, the amount of solid solution of Al, Sn, V, Mo, Zr, Pd, Si, Cr, Ru, Ta, Co, and Ni at the time of solution treatment, the precipitate size, the precipitate density, and the precipitate volume fraction, and notify the determination unit 640 of the associations.

[0228] Thus, according to the eighth embodiment, even when the design target material is a titanium alloy, it is possible to obtain the same effects as those of the first embodiment.

[0229] In the above embodiments, the relationship between the manufacturing process data and the mechanical property data is analyzed by multiple regression analysis, but the method for analyzing the relationship between the manufacturing process data and the mechanical property data is not limited to this. For example, the relationship between the manufacturing process data and the mechanical property data may be analyzed by training a machine learning model using training data.

[0230] Furthermore, in each of the above embodiments, the manufacturing process searching apparatus 400 is configured as an integrated apparatus, but the manufacturing process searching apparatus 400 may be configured as a plurality of apparatuses.

[0231] The present disclosure is not limited to the configurations described in the above embodiments, but may be combined with other elements, etc. These aspects can be changed without departing from the spirit of the present disclosure, and can be appropriately determined depending on the application form.

[0232] This application claims priority based on Japanese Patent Application No. 2023-123470, filed on July 28, 2023, the entire contents of which are incorporated herein by reference.

[0233] 400: Manufacturing process searching device 610: Analysis unit 620: Extraction unit 630: Calculation unit 640: Determination unit 710: Learning data 721: Data input unit 722: Multiple regression calculation unit 723: Analysis result output unit 724: Verification unit 730: Verification data 921: Data modification unit 922: Prediction unit 923: Candidate extraction unit 924: Candidate output unit 1001: Liquidus temperature and solid solution amount calculation unit 1002: Precipitate calculation unit

Claims

1. A material manufacturing process exploration method for designing a design target material including a material consisting of a plurality of compositions or a material manufactured by a combination of a plurality of manufacturing conditions, the material manufacturing process exploration method comprising: a computer executing an analysis step of analyzing the relationship between the manufacturing process of the design target material and the mechanical properties of the design target material; an extraction step of extracting candidate manufacturing processes for the design target material that satisfy target values ​​of the mechanical properties based on the analysis results in the analysis step; a calculation step of calculating, by thermodynamic calculation, data indicating the material structure of the design target material, which is data that affects the mechanical properties; and a determination step of determining the suitability of the candidate manufacturing processes based on the data indicating the material structure calculated by performing thermodynamic calculation on the candidate manufacturing processes in the calculation step.

2. The material manufacturing process exploration method according to claim 1, wherein the material to be designed is an inorganic material.

3. The material manufacturing process exploration method according to claim 2, wherein the inorganic material is an alloy material.

4. The material manufacturing process exploration method according to claim 3, wherein the alloy material is a wrought material or a cast material.

5. The material manufacturing process exploration method according to claim 4, wherein the wrought material is an aluminum alloy wrought material.

6. The material manufacturing process exploration method according to claim 5, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes Si, Mg, Cu, Fe, Zr, and Ti, and the manufacturing conditions include a homogenization temperature in a homogenization treatment, a heating temperature before extrusion in a solution treatment, a wall thickness of an extrusion cross section, an extrusion pressure, a ram speed, and a cooling water temperature.

7. The material manufacturing process exploration method according to claim 6, wherein the mechanical properties include tensile strength, 0.2% yield strength, and elongation.

8. The material manufacturing process exploration method according to claim 7, wherein the data indicating the material structure is data indicating a metal structure, and the data indicating the metal structure includes a liquidus temperature, an amount of Mg solid solution before extrusion, an amount of Si solid solution before extrusion, a precipitate size, a precipitate density, and a volume fraction.

9. The material manufacturing process exploration method according to claim 4, wherein the casting material is an aluminum alloy casting material.

10. The material manufacturing process exploration method according to claim 9, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes Si, Mg, Cu, Fe, Zr, and Ti, and the manufacturing conditions include any one of the molten metal temperature at the time of casting, the solution temperature, the solution time, the natural aging time, the artificial aging temperature, the artificial aging time, the annealing temperature, and the annealing time.

11. The material manufacturing process exploration method according to claim 10, wherein the mechanical properties include any one of tensile strength, 0.2% yield strength, elongation, Young's modulus, linear expansion coefficient, and fatigue properties.

12. The material manufacturing process exploration method according to claim 11, wherein the data indicative of the material structure is data indicative of a metallic structure, and the data indicative of the metallic structure includes any one of the liquidus temperature, the amount of solid solution of Si, Fe, Cu, Mn, Mg, Cr, Ni, Zn, Ti, V, Pb, Sn, Bi, B, P, Zr, and Sr during solution treatment, the precipitate size, the precipitate density, and the volume fraction.

13. The material manufacturing process exploration method according to claim 4, wherein the wrought material is an iron alloy wrought material.

14. The material manufacturing process exploration method according to claim 13, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, and W, and the manufacturing conditions include any of the following: molten metal temperature during pouring of the iron alloy, casting speed, amount of cooling water, iron alloy heating temperature during hot working, iron alloy heating time during hot working, processing speed, rolling reduction, hot working temperature, cooling rate after hot working, natural aging time, heat treatment temperature, heat treatment time, and cooling rate for heat treatment.

15. The material manufacturing process exploration method according to claim 14, wherein the mechanical properties include any one of tensile strength, 0.2% yield strength, elongation, Young's modulus, linear expansion coefficient, impact properties, and fatigue properties.

16. The material manufacturing process exploration method according to claim 15, wherein the data indicative of the material structure is data indicative of a metallic structure, and the data indicative of the metallic structure includes liquidus temperature, solid solution amounts of C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, and W after processing, precipitate sizes, precipitate densities, and volume fractions.

17. The material manufacturing process exploration method according to claim 4, wherein the cast material is a cast iron material.

18. The material manufacturing process exploration method according to claim 17, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, W, Ca, Mg, and Ce, and the manufacturing conditions include any one of the molten metal temperature during casting, the casting speed, the solidification rate, the cooling rate after solidification, the heat treatment temperature, the heat treatment time, and the cooling rate for the heat treatment.

19. The material manufacturing process exploration method according to claim 18, wherein the mechanical properties include any one of tensile strength, 0.2% yield strength, elongation, Young's modulus, linear expansion coefficient, impact properties, and fatigue properties.

20. The material manufacturing process exploration method according to claim 19, wherein the data indicative of the material structure is data indicative of a metallic structure, and the data indicative of the metallic structure includes liquidus temperature, solid solution amounts of C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, W, Ca, Mg, and Ce before heat treatment, precipitate size, precipitate density, and volume fraction.

21. The material manufacturing process exploration method according to claim 4, wherein the wrought material is a copper alloy wrought material.

22. The material manufacturing process exploration method according to claim 21, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As, and the manufacturing conditions include any of the following: molten metal temperature during copper alloy casting, casting speed, amount of cooling water, homogenization temperature, homogenization time, cooling rate after homogenization, copper alloy heating temperature during hot working, processing speed, cooling rate after processing, natural aging time, artificial aging temperature, artificial aging time, hot working temperature, annealing temperature, and annealing time.

23. The material manufacturing process exploration method according to claim 22, wherein the mechanical properties include any one of 0.2% proof stress, tensile strength, elongation, electrical conductivity, thermal conductivity, Young's modulus, and linear expansion coefficient.

24. The material manufacturing process exploration method according to claim 23, wherein the data indicating the material structure is data indicating a metallic structure, and the data indicating the metallic structure includes liquidus temperature, solid solution amounts of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As before processing, precipitate sizes, precipitate densities, and volume fractions.

25. The material manufacturing process exploration method according to claim 4, wherein the casting material is a copper alloy casting material.

26. The material manufacturing process exploration method according to claim 25, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As, and the manufacturing conditions include any one of the molten metal temperature at the time of casting, the solution temperature, the solution time, the natural aging time, the artificial aging temperature, the artificial aging time, the annealing temperature, and the annealing time.

27. The material manufacturing process exploration method according to claim 26, wherein the mechanical properties include any one of 0.2% proof stress, tensile strength, elongation, electrical conductivity, thermal conductivity, Young's modulus, and linear expansion coefficient.

28. The material manufacturing process exploration method according to claim 27, wherein the data indicating the material composition is data indicating a metal composition, and the data indicating the metal composition includes any one of the liquidus temperature, the amount of solid solution of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As after solution treatment, the precipitate size, the precipitate density, and the volume fraction.

29. The material manufacturing process exploration method according to claim 3, wherein the inorganic material is a titanium alloy.

30. The material manufacturing process exploration method according to claim 29, wherein the manufacturing process includes an alloy composition and manufacturing conditions, the alloy composition includes Al, Sn, V, Mo, Zr, Pd, Si, Cr, Ru, Ta, Co, and Ni, and the manufacturing conditions include any one of the molten metal temperature at the time of casting, the solution temperature, the solution time, the artificial aging temperature, the artificial aging time, the annealing temperature, and the annealing time.

31. The material manufacturing process exploration method according to claim 30, wherein the mechanical properties include any one of 0.2% proof stress, tensile strength, elongation, Young's modulus, linear expansion coefficient, and fatigue properties.

32. The material manufacturing process exploration method according to claim 31, wherein the data indicating the material structure is data indicating a metallic structure, and the data indicating the metallic structure includes any one of the liquidus temperature, the amount of solid solution of Al, Sn, V, Mo, Zr, Pd, Si, Cr, Ru, Ta, Co, and Ni during solution treatment, the precipitate size, the precipitate density, and the volume fraction.

33. A material manufacturing process exploration apparatus for designing a design target material including a material consisting of a plurality of compositions or a material manufactured by a combination of a plurality of manufacturing conditions, comprising: an analysis unit for analyzing the relationship between the manufacturing process of the design target material and the mechanical properties of the design target material; an extraction unit for extracting candidate manufacturing processes of the design target material that satisfy target values ​​of the mechanical properties based on the analysis results by the analysis unit; a calculation unit for calculating data indicating the material structure of the design target material by thermodynamic calculation, which data affects the mechanical properties; and a judgment unit for judging the suitability of the candidate manufacturing processes based on the data indicating the material structure calculated by performing thermodynamic calculations on the candidate manufacturing processes in the calculation unit.

34. A material manufacturing process exploration program for causing a computer of a material manufacturing process exploration device that designs a design target material including a material consisting of a plurality of compositions or a material manufactured by a combination of a plurality of manufacturing conditions to execute the following steps: an analysis step of analyzing the relationship between the manufacturing process of the design target material and the mechanical properties of the design target material; an extraction step of extracting candidate manufacturing processes for the design target material that satisfy target values ​​of the mechanical properties based on the analysis results in the analysis step; a calculation step of calculating, by thermodynamic calculation, data indicating the material structure of the design target material, which is data that affects the mechanical properties; and a judgment step of judging the suitability of the candidate manufacturing processes based on the data indicating the material structure calculated by performing thermodynamic calculation in the calculation step.

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