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

Abstract

The efficient search enables a manufacturing process of a design target material satisfying desired mechanical characteristics to be realized. A material manufacturing process search method for designing a material to be designed, the method being executed by a computer: an analysis step for analyzing a relationship between a manufacturing process of the material to be designed and a mechanical property of the material to be designed; an extraction step for extracting candidates for a manufacturing process of the material to be designed, which satisfy a target value of mechanical characteristics, on the basis of the analysis results of the analysis step; a calculation step for calculating, by thermodynamic calculation, data representing the material structure of the design target material as data affecting the mechanical characteristics; and a determination step for determining whether or not the candidates for the manufacturing process are suitable on the basis of the data indicating the material structure calculated by thermodynamically calculating the candidates for the manufacturing process in the calculation step.

Description

Technical Field

[0001] This disclosure relates to a material manufacturing process search method, a material manufacturing process search device, and a material manufacturing process search program. Background Technology

[0002] In the past, when designing materials composed of multiple components or materials manufactured by a combination of multiple manufacturing conditions, a trial manufacturing process was carried out while adjusting the "manufacturing process" which includes material composition and manufacturing conditions, and the "mechanical properties (performance)" of the trial manufactured product (prototype / sample) were measured. In this way, a manufacturing process for the design object material that can achieve the desired mechanical properties was searched.

[0003] In this regard, for example, if the relationship between manufacturing process and mechanical properties can be analyzed in advance, and the analysis results can be used to screen the manufacturing processes of the design object material that can achieve the desired mechanical properties, then the search for manufacturing processes can be carried out efficiently.

[0004] [Cited Documents]

[0005] [Patent Documents]

[0006] [Patent Document 1] International Publication No. 2022 / 264959 Summary of the Invention

[0007] [Technical problem to be solved]

[0008] On the other hand, even when utilizing the above analysis results, there may be situations where sufficient screening is not possible, leading to multiple manufacturing processes for the design material that can achieve the desired mechanical properties. In such cases, the workload of experimental manufacturing and measurement cannot be reduced, making it difficult to efficiently search for manufacturing processes.

[0009] The purpose of this disclosure is to efficiently search for manufacturing processes of materials that can achieve the desired mechanical properties.

[0010] [Technical Solution]

[0011] The material manufacturing process search method of the first aspect of this disclosure is a method for searching material manufacturing processes for designing a material object, including materials composed of multiple components or materials manufactured by a combination of multiple manufacturing conditions, wherein the process is executed by a computer.

[0012] The analysis steps involve analyzing the relationship between the manufacturing process of the material being designed and the mechanical properties of the material being designed.

[0013] The extraction step involves extracting candidate manufacturing processes for the design material that meet the target values ​​of mechanical properties based on the analysis results of the analysis step.

[0014] The calculation steps involve using thermodynamic calculations to calculate data representing the microstructure of the material being designed, which serves as data influencing the mechanical properties; and

[0015] The judgment step determines whether the candidate manufacturing process is suitable based on the data representing the material structure calculated by performing thermodynamic calculations on the candidate manufacturing process in the calculation step.

[0016] The second aspect of this disclosure is the material manufacturing process search method described in the first aspect, wherein...

[0017] The material of the design object is an inorganic material.

[0018] The third aspect of this disclosure is the material manufacturing process search method described in the second aspect, wherein...

[0019] The inorganic material is an alloy material.

[0020] The fourth aspect of this disclosure is the material manufacturing process search method described in the third aspect, wherein...

[0021] The alloy material is a stretched (extended) material or a cast material.

[0022] The fifth aspect of this disclosure is the material manufacturing process search method described in the fourth aspect, wherein...

[0023] The stretching material is an aluminum alloy stretching material.

[0024] The sixth aspect of this disclosure is the material manufacturing process search method described in the fifth aspect, wherein...

[0025] The manufacturing process includes alloy composition and manufacturing conditions.

[0026] The alloy composition includes Si, Mg, Cu, Fe, Zr, and Ti.

[0027] The manufacturing conditions include the homogenization temperature of the homogenization treatment, the pre-extrusion heating temperature of the solution treatment, the thickness of the extrusion section, the extrusion pressure, the extrusion speed, and the cooling water temperature.

[0028] The seventh aspect of this disclosure is the material manufacturing process search method described in the sixth aspect, wherein...

[0029] The mechanical properties include tensile strength, 0.2% yield strength, and elongation.

[0030] The eighth aspect of this disclosure is the material manufacturing process search method described in the seventh aspect, wherein...

[0031] The data representing the microstructure of the material is data representing the microstructure of a metal.

[0032] The data representing the metal microstructure includes liquidus temperature, Mg solid solution content before extrusion, Si solid solution content before extrusion, precipitate size, precipitate density, and volume fraction.

[0033] The ninth aspect of this disclosure is the material manufacturing process search method described in the fourth aspect, wherein...

[0034] The casting material is an aluminum alloy casting material.

[0035] The tenth aspect of this disclosure is the material manufacturing process search method described in the ninth aspect, wherein...

[0036] The manufacturing process includes alloy composition and manufacturing conditions.

[0037] The alloy composition includes Si, Mg, Cu, Fe, Zr, and Ti.

[0038] The manufacturing conditions include any one of the following: melt temperature at casting, solution temperature, solution time, natural aging time, artificial aging temperature, artificial aging time, annealing temperature, and annealing time.

[0039] The eleventh aspect of this disclosure is the material manufacturing process search method described in the tenth aspect, wherein,

[0040] The mechanical properties include any one of the following: tensile strength, 0.2% yield strength, elongation, Young's modulus, coefficient of linear expansion, and fatigue properties.

[0041] The 12th aspect of this disclosure is the material manufacturing process search method described in the 11th aspect, wherein,

[0042] The data representing the microstructure of the material is data representing the microstructure of a metal.

[0043] The data representing the metal microstructure includes any one of the following: liquidus temperature, solid solution content of Si, Fe, Cu, Mn, Mg, Cr, Ni, Zn, Ti, V, Pb, Sn, Bi, B, P, Zr, and Sr during solid solution, precipitate size, precipitate density, and volume ratio.

[0044] The 13th aspect of this disclosure is the material manufacturing process search method described in the 4th aspect, wherein...

[0045] The stretching material is an iron alloy stretching material.

[0046] The 14th aspect of this disclosure is the material manufacturing process search method described in the 13th aspect, wherein,

[0047] The manufacturing process includes alloy composition and manufacturing conditions.

[0048] The alloy composition includes C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, and W.

[0049] The manufacturing conditions include any one of the following: molten temperature when the ferroalloy is cast in, casting speed, cooling water volume, ferroalloy heating temperature during hot working, ferroalloy heating time during hot working, processing speed, reduction rate, hot working temperature, cooling rate after hot working, natural aging time, heat treatment temperature, heat treatment time, and cooling rate during heat treatment.

[0050] The 15th aspect of this disclosure is the material manufacturing process search method described in the 14th aspect, wherein,

[0051] The mechanical properties include any one of the following: tensile strength, 0.2% yield strength, elongation, Young's modulus, coefficient of linear expansion, impact properties, and fatigue properties.

[0052] The 16th aspect of this disclosure is the material manufacturing process search method described in the 15th aspect, wherein,

[0053] The data representing the microstructure of the material is data representing the microstructure of a metal.

[0054] The data representing the metal microstructure include liquidus temperature, solid solution content of C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, and W after processing, precipitate size, precipitate density, and volume ratio.

[0055] The 17th aspect of this disclosure is the material manufacturing process search method described in aspect 4, wherein...

[0056] The casting material is cast iron.

[0057] The 18th aspect of this disclosure is the material manufacturing process search method described in the 17th aspect, wherein,

[0058] The manufacturing process includes alloy composition and manufacturing conditions.

[0059] 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.

[0060] The manufacturing conditions include any one of the following: melt temperature during casting, casting speed, solidification speed, cooling speed after solidification, heat treatment temperature, heat treatment time, and cooling speed during heat treatment.

[0061] The 19th aspect of this disclosure is the material manufacturing process search method described in aspect 18, wherein,

[0062] The mechanical properties include any one of the following: tensile strength, 0.2% yield strength, elongation, Young's modulus, coefficient of linear expansion, impact properties, and fatigue properties.

[0063] The 20th aspect of this disclosure is the material manufacturing process search method described in the 19th aspect, wherein...

[0064] The data representing the microstructure of the material is data representing the microstructure of a metal.

[0065] The data representing the metal microstructure include liquidus temperature, solid solution content 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 ratio.

[0066] The 21st aspect of this disclosure is the material manufacturing process search method described in the 4th aspect, wherein...

[0067] The drawing material is a copper alloy drawing material.

[0068] The 22nd aspect of this disclosure is the material manufacturing process search method described in the 21st aspect, wherein,

[0069] The manufacturing process includes alloy composition and manufacturing conditions.

[0070] The alloy composition includes Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As.

[0071] The manufacturing conditions include any one of the following: molten temperature when the copper alloy is cast, casting speed, cooling water volume, 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.

[0072] The 23rd aspect of this disclosure is the material manufacturing process search method described in aspect 22, wherein...

[0073] The mechanical properties include any one of the following: 0.2% yield strength, tensile strength, elongation, electrical conductivity, thermal conductivity, Young's modulus, and coefficient of linear expansion.

[0074] The 24th aspect of this disclosure is the material manufacturing process search method described in the 23rd aspect, wherein...

[0075] The data representing the microstructure of the material is data representing the microstructure of a metal.

[0076] The data representing the metal microstructure include liquidus temperature, solid solution content of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As before processing, precipitate size, precipitate density, and volume ratio.

[0077] The 25th aspect of this disclosure is the material manufacturing process search method described in aspect 4, wherein...

[0078] The casting material is a copper alloy casting material.

[0079] The 26th aspect of this disclosure is the material manufacturing process search method described in aspect 25, wherein,

[0080] The manufacturing process includes alloy composition and manufacturing conditions.

[0081] The alloy composition includes Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As.

[0082] The manufacturing conditions include any one of the following: melt temperature at casting, solution temperature, solution time, natural aging time, artificial aging temperature, artificial aging time, annealing temperature, and annealing time.

[0083] The 27th aspect of this disclosure is the material manufacturing process search method described in the 26th aspect, wherein,

[0084] The mechanical properties include any one of the following: 0.2% yield strength, tensile strength, elongation, electrical conductivity, thermal conductivity, Young's modulus, and coefficient of linear expansion.

[0085] The 28th aspect of this disclosure is the material manufacturing process search method described in the 27th aspect, wherein...

[0086] The data representing the material composition is data representing the metallic composition.

[0087] The data representing the metal composition include any one of the following: liquidus temperature, solid solution content of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As after solid solution, precipitate size, precipitate density, and volume ratio.

[0088] The 29th aspect of this disclosure is the material manufacturing process search method described in the 3rd aspect, wherein...

[0089] The inorganic material is a titanium alloy.

[0090] The 30th aspect of this disclosure is the material manufacturing process search method described in aspect 29, wherein...

[0091] The manufacturing process includes alloy composition and manufacturing conditions.

[0092] The alloy composition includes Al, Sn, V, Mo, Zr, Pd, Si, Cr, Ru, Ta, Co, and Ni.

[0093] The manufacturing conditions include any one of the following: melt temperature at casting, solution temperature, solution time, artificial aging temperature, artificial aging time, annealing temperature, and annealing time.

[0094] The 31st aspect of this disclosure is the material manufacturing process search method described in aspect 30, wherein...

[0095] The mechanical properties include any one of the following: 0.2% yield strength, tensile strength, elongation, Young's modulus, coefficient of linear expansion, and fatigue properties.

[0096] The 32nd aspect of this disclosure is the material manufacturing process search method described in aspect 31, wherein...

[0097] The data representing the microstructure of the material is data representing the microstructure of a metal.

[0098] The data representing the metal microstructure includes any one of the following: liquidus temperature, solid solution content of Al, Sn, V, Mo, Zr, Pd, Si, Cr, Ru, Ta, Co, and Ni during solid solution, precipitate size, precipitate density, and volume ratio.

[0099] The material manufacturing process search apparatus of the 33rd aspect of this disclosure is a material manufacturing process search apparatus for designing a design object material, including materials composed of multiple components or materials manufactured by a combination of multiple manufacturing conditions, wherein the material manufacturing process search apparatus has:

[0100] The analysis department analyzes the relationship between the manufacturing process of the material of the design object and the mechanical properties of the material of the design object;

[0101] The extraction unit extracts candidate manufacturing processes for the design material that meet the target values ​​of mechanical properties based on the analysis results of the analysis unit.

[0102] The calculation unit calculates, through thermodynamic calculations, data representing the microstructure of the material being designed, which are data affecting the mechanical properties; and

[0103] The judgment unit determines whether the candidate manufacturing process is suitable based on the data representing the material structure calculated by the calculation unit through thermodynamic calculations of the candidate manufacturing process.

[0104] The material manufacturing process search program of the 34th aspect of this disclosure is executed by a computer of a material manufacturing process search device that designs a material including materials composed of multiple components or materials manufactured by a combination of multiple manufacturing conditions:

[0105] The analysis steps involve analyzing the relationship between the manufacturing process of the material being designed and the mechanical properties of the material being designed.

[0106] The extraction step involves extracting candidate manufacturing processes for the design material that meet the target values ​​of mechanical properties based on the analysis results of the analysis step.

[0107] The calculation steps involve using thermodynamic calculations to calculate data representing the microstructure of the material being designed, which serves as data influencing the mechanical properties; and

[0108] The judgment step involves determining whether the candidate manufacturing process is suitable based on the data representing the material structure calculated by performing thermodynamic calculations on the candidate manufacturing process in the calculation step.

[0109] [Beneficial Effects]

[0110] According to this disclosure, a manufacturing process for a design object material that can achieve the desired mechanical properties can be efficiently searched. Attached Figure Description

[0111] [ Figure 1 ] Figure 1 This is a schematic diagram illustrating an example of the manufacturing process for aluminum alloy extruded profiles.

[0112] [ Figure 2 ] Figure 2 This is a schematic diagram illustrating the relationship between manufacturing processes and mechanical properties in the manufacturing process of aluminum alloy extruded profiles.

[0113] [ Figure 3 ] Figure 3 This is a schematic diagram illustrating the relationship between the manufacturing process and the metal structure in the manufacturing process of aluminum alloy extruded profiles.

[0114] [ Figure 4 ] Figure 4 This is a schematic diagram illustrating the processing overview of the manufacturing process search device.

[0115] [ Figure 5 ] Figure 5 This is a schematic diagram illustrating an example of the hardware configuration of a manufacturing process search device.

[0116] [ Figure 6 ] Figure 6 This is a schematic diagram illustrating an example of the functional configuration of a manufacturing process search device.

[0117] [ Figure 7 ] Figure 7 Figure 1 is a specific example of the processing in the analysis department.

[0118] [ Figure 8 ] Figure 8 This is a schematic diagram illustrating an example of validating the results of a regression analysis.

[0119] [ Figure 9 ] Figure 9 This is a schematic diagram illustrating a specific example of the extraction process.

[0120] [ Figure 10 ] Figure 10 Figure 1 is a specific example of the processing of the computing unit.

[0121] [ Figure 11 ] Figure 11 This is a schematic diagram illustrating a specific example of the processing in the decision-making section.

[0122] [ Figure 12 ] Figure 12 This is a flowchart representing the process of searching and processing manufacturing processes.

[0123] [ Figure 13 ] Figure 13 This is an illustration of an example of the search results for manufacturing process search processing.

[0124] [ Figure 14 ] Figure 14 Figure 2 is a specific example of the processing in the analysis department.

[0125] [ Figure 15 ] Figure 15 Figure 2 is a specific example of the processing of the computing unit.

[0126] [ Figure 16 ] Figure 16 Figure 3 is a specific example of the processing in the analysis department.

[0127] [ Figure 17 ] Figure 17 Figure 3 is a specific example of the processing of the computing unit.

[0128] [ Figure 18 ] Figure 18 Figure 4 is a specific example of the processing in the analysis department.

[0129] [ Figure 19 ] Figure 19Figure 4 is a specific example of the processing of the computing unit.

[0130] [ Figure 20 ] Figure 20 Figure 5 shows a specific example of the processing in the analysis department.

[0131] [ Figure 21 ] Figure 21 Figure 5 shows a specific example of the processing by the computing unit.

[0132] [ Figure 22 ] Figure 22 Figure 6 shows a specific example of the processing in the analysis department.

[0133] [ Figure 23 ] Figure 23 Figure 6 shows a specific example of the processing by the computing unit.

[0134] [ Figure 24 ] Figure 24 Figure 7 shows a specific example of the processing in the analysis department.

[0135] [ Figure 25 ] Figure 25 Figure 7 shows a specific example of the processing by the computing unit. Detailed Implementation

[0136] The various embodiments will now be described with reference to the accompanying drawings. It should be noted that the same reference numerals (symbols) are used to assign substantially the same functional configuration to constituent elements in this specification and the accompanying drawings, thereby omitting redundant descriptions.

[0137] [First Implementation]

[0138] The first embodiment describes the search process for manufacturing processes when the design object material is an inorganic material. Specifically, the first embodiment describes the search process for manufacturing processes when an example of an inorganic material, namely an aluminum alloy drawn material, is used as the design object material.

[0139] <Manufacturing process of aluminum alloy extruded profiles>

[0140] First, the manufacturing process of aluminum alloy extruded profiles, which is the manufacturing process of aluminum alloy drawn material searched by the material manufacturing process search device of the first embodiment (hereinafter referred to as "manufacturing process search device" (Note: "" is equivalent to "")), will be described. Figure 1 This is a schematic diagram illustrating an example of the manufacturing process for aluminum alloy extruded profiles.

[0141] like Figure 1As shown, the manufacturing process of aluminum alloy extruded profiles includes casting treatment 111, homogenization treatment 112, billet heating treatment 113, hot extrusion treatment 114, natural aging hardening treatment 115, artificial aging hardening treatment 116, sampling treatment 117, and mechanical property measurement treatment 118. Furthermore, the temperature variations in each process within the manufacturing process of aluminum alloy extruded profiles are shown by symbol 120.

[0142] Casting process 111 refers to the process of dissolving aluminum metal at a temperature above the single-phase liquidus temperature, then mixing and adding other metals and adjusting the mixture, and finally using a casting machine to process it into a cylindrical billet.

[0143] Homogenization treatment 112 refers to the process of heating and holding the billet generated by casting treatment 111 at a temperature below the solidus temperature, followed by slow cooling. The billet generated by casting treatment 111 has problems such as uneven distribution of solute atoms, non-equilibrium compound phases, and strain generated during cooling. The purpose of homogenization treatment 112 is to eliminate these problems. In the first embodiment, the temperature at which the homogenization treatment 112 is heated is called the "homogenization temperature." It should be noted that in the first embodiment, the "homogenization temperature" is used as an element of the manufacturing conditions included in the manufacturing process.

[0144] The billet heat treatment 113 refers to the process of heating the billet that has undergone homogenization treatment 112 and then cutting it into a predetermined length.

[0145] Hot extrusion treatment 114 refers to the process of forming aluminum alloy extruded profiles and the process of rapidly cooling the formed aluminum alloy extruded profiles with cooling water. The aluminum alloy extruded profiles are formed by placing the cut billet, which has been cut by billet heat treatment 113, into an extruder and then extruding it from the die under high pressure.

[0146] 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 an element of the manufacturing conditions included in the manufacturing process.

[0147] Furthermore, in the first embodiment, the thickness of the extruded section is referred to as "extruded section thickness," and the pressure applied by the extruder during extrusion in the die is referred to as "extrusion pressure." In the first embodiment, "extruded section thickness" and "extrusion pressure" are used as elements of the manufacturing conditions included in the manufacturing process.

[0148] Furthermore, in the first embodiment, the speed of the piston (ram) (push rod) when the extruder extrudes the cut billet is referred to as the "extrusion speed (piston speed)". In the first embodiment, "extrusion speed" is used as an element of the manufacturing conditions included in the manufacturing process.

[0149] Furthermore, in the first embodiment, the temperature of the cooling water used for rapid cooling of the aluminum alloy extruded profile is referred to as the "cooling water temperature." In the first embodiment, the "cooling water temperature" is used as an element of the manufacturing conditions included in the manufacturing process.

[0150] Natural aging hardening treatment 115 refers to a process in which the aluminum alloy extruded profile, after rapid cooling in hot extrusion treatment 114, is kept at room temperature, thereby allowing elements in a supersaturated state to precipitate over time. By precipitating elements in a supersaturated state, natural aging hardening treatment 115 can harden the aluminum alloy extruded profile.

[0151] Artificial aging hardening treatment 116 refers to heating an aluminum alloy extruded profile that has undergone natural aging hardening treatment 115 at a temperature above the formation temperature of β”-Mg2Si, thereby artificially precipitating elements in a supersaturated state. By artificially precipitating elements in a supersaturated state, artificial aging hardening treatment 116 can further harden the aluminum alloy extruded profile.

[0152] Sampling process 117 refers to the process of cutting a portion from the aluminum alloy extruded profile that has undergone artificial aging hardening process 116 for measuring mechanical properties.

[0153] Mechanical property measurement process 118 refers to the process of measuring the mechanical properties of a portion of the aluminum alloy extruded profile cut by sampling process 117. In the first embodiment, the mechanical property measurement process 118 measures the "tensile strength", "0.2% yield strength" and "elongation" as mechanical properties.

[0154] <The Relationship Between Manufacturing Process and Mechanical Properties in the Manufacturing Process of Aluminum Alloy Extruded Profiles>

[0155] Next, the relationship between the manufacturing process and mechanical properties in the manufacturing process of aluminum alloy extruded profiles will be explained. Figure 2 This is a schematic diagram illustrating the relationship between manufacturing processes and mechanical properties in the manufacturing process of aluminum alloy extruded profiles.

[0156] As described above, for aluminum alloy extruded profiles manufactured through an aluminum alloy extrusion profile manufacturing process, mechanical properties are measured in mechanical property measurement processing 118. At this time, the tensile strength, 0.2% yield strength, and elongation, which are the mechanical properties being measured, are...

[0157] • The alloy composition of aluminum alloy extruded profiles; and

[0158] Manufacturing conditions for aluminum alloy extruded profiles

[0159] It is related to the manufacturing process.

[0160] Therefore, in the manufacturing process search device of the first embodiment, the relationship between manufacturing process and mechanical properties is analyzed by using the manufacturing process, which includes alloy composition and manufacturing conditions, as "explanatory variables" and mechanical properties as "target variables." It should be noted that, as... Figure 2 As shown, in the first embodiment, the alloy composition of the aluminum alloy extruded profile uses Si mass%, Mg mass%, Cu mass%, Fe mass%, Zr mass%, and Ti mass%.

[0161] Furthermore, in the first embodiment, the homogenization temperature, pre-extrusion heating temperature, extrusion section thickness, extrusion speed, and cooling water temperature are used as the manufacturing conditions for the aluminum alloy extruded profile. It should be noted that... Figure 2 In the context of aluminum castings, solution treatment, which is related to pre-extrusion heating temperature, extrusion section thickness, extrusion speed, and cooling water temperature, refers to the process of dissolving Cu and Mg from the aluminum alloy into the aluminum substrate as one of the heat treatment steps for aluminum castings. Figure 1 The billet heat treatment 113 and hot extrusion treatment 114 in the manufacturing process shown are... Figure 2 The solution treatment shown corresponds to this.

[0162] <The Relationship Between Manufacturing Process and Metal Structure in the Manufacturing Process of Aluminum Alloy Extruded Profiles>

[0163] Next, the relationship between the manufacturing process and the metal structure in the manufacturing process of alloy extruded profiles will be explained. Figure 3 This is a schematic diagram illustrating the relationship between the manufacturing process and the metal structure in the manufacturing process of aluminum alloy extruded profiles. As described above, mechanical property measurement process 118 is performed on the aluminum alloy extruded profiles manufactured through the aluminum alloy extruded profile manufacturing process to measure the mechanical properties.

[0164] Here, the tensile strength, 0.2% yield strength, and elongation—the mechanical properties of aluminum alloy extruded profiles—are affected by the microstructure of the aluminum alloy extruded profiles. Specifically, the mechanical properties of aluminum alloy extruded profiles are influenced by data representing the microstructure of the aluminum alloy extruded profiles.

[0165] • Liquid phase temperature;

[0166] • Mg solid solution content before extrusion;

[0167] • The amount of Si solid solution before extrusion;

[0168] • precipitate size;

[0169] • Density of precipitates; and

[0170] • Volume ratio

[0171] The impact.

[0172] Therefore, in the manufacturing process search device of the first embodiment, the relationship between the manufacturing process and the metal structure is analyzed by using the manufacturing process as an explanatory variable and the metal structure as a target variable.

[0173] <Processing Overview of the Manufacturing Process Search Device>

[0174] Next, a summary of the processing of the manufacturing process search device in the first embodiment will be described. Figure 4 This is a schematic diagram illustrating the processing overview of the manufacturing process search device. For example... Figure 4 As shown, the manufacturing process search device 400 searches for a process through four stages.

[0175] The first stage is the analysis stage. During the analysis stage, the manufacturing process search device 400 acquires historical (past) manufacturing process data and historical mechanical characteristic data as training data (learning data). Furthermore, during the analysis stage, the manufacturing process search device 400 performs a re-regression analysis using the acquired manufacturing process data as explanatory variables and the acquired mechanical characteristic data as target variables from the training data.

[0176] The second stage is the extraction stage. In the extraction stage, the manufacturing process search device 400 acquires the target values ​​of the mechanical characteristic data. Furthermore, in the extraction stage, the manufacturing process search device 400 extracts manufacturing process data that meet the acquired target values ​​of the mechanical characteristic data as candidate manufacturing process data based on the analysis results of the re-regression analysis.

[0177] The third stage is the calculation stage. In the calculation stage, the manufacturing process search device 400 calculates data representing the metal structure for each candidate of the extracted manufacturing process data using thermodynamic calculations.

[0178] Specifically, the elemental solid solution content and the phase fraction of precipitates in aluminum alloys are determined by using a calculated equilibrium state diagram based on the CALPHAD (CALculation of PHAse Diagrams, Computer Coupling of Phase Diagrams and Thermochemistry) method.

[0179] It should be noted that the state of the precipitate, which is formed when the element is dissolved in the aluminum alloy during extrusion and then artificially precipitated, is predicted by the following method:

[0180] • Thermodynamic and kinetic data determined using the CALPHAD method; and

[0181] • Nucleation (nucleation), growth, and coarsening of precipitates are calculated using numerical methods based on Langer-Schwartz theory and the Kampmann-Wagner method.

[0182] Based on this, the size, density, and volume ratio of the precipitate as a precipitate state can be predicted.

[0183] It should be noted that in predicting the aforementioned precipitates, interfacial energy is incorporated into both the growth of the parent phase (base phase) and the precipitate growth. Furthermore, the precipitate formation site is within the parent phase, and the precipitate morphology is needle-like or plate-like. Thus, by conducting simulations under more suitable conditions, the time-dependent changes in precipitates in aluminum alloys can be appropriately predicted.

[0184] The fourth stage is the judgment stage. In this stage, the manufacturing process search device 400 determines the suitability of each candidate manufacturing process data based on the calculated data representing the metal microstructure. Accordingly, the manufacturing process search device 400 can output manufacturing process data that satisfies the target values ​​for mechanical properties and achieves an appropriate metal microstructure. In other words, the manufacturing process search device 400 can screen candidate manufacturing process data.

[0185] Manufacturing process data filtered by the manufacturing process search device 400 is used to manufacture the prototype to verify its mechanical properties. Then, having obtained the target mechanical properties, the corresponding manufacturing process data is applied... Figure 1 The manufacturing process of the aluminum alloy extruded profile is shown.

[0186] <Hardware Composition of the Manufacturing Process Search Device>

[0187] Next, the hardware configuration of the manufacturing process search device 400 will be explained. Figure 5 This is a schematic diagram illustrating an example of the hardware configuration of a manufacturing process search device. For example... Figure 5 As shown, the manufacturing process search device 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. It should be noted that the various hardware components of the manufacturing process search device 400 are interconnected via a bus 470.

[0188] The processor 410 has various computing devices such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit). The processor 410 loads various programs into the memory 420 and executes them. It should be noted that the various programs mentioned here include programs such as the material manufacturing process search program (hereinafter referred to as the "manufacturing process search program"), which will be described later.

[0189] The memory 420 is a main storage device including ROM (Read Only Memory) and RAM (Random Access Memory). The processor 410 and the memory 420 together form a computer. The computer can perform the aforementioned functions by executing various programs read from the memory 420 through the processor 410.

[0190] The auxiliary storage device 430 stores various programs and the various data used when these programs are executed by the processor 410. For example, the training data storage unit 650, which will be described later, can be implemented in the auxiliary storage device 430.

[0191] Input / output device 440 is a connection device connected to operating device 471 and display device 472, which are examples of user interface devices. Communication device 450 is a communication device for communicating with an external device (not shown) via a network.

[0192] The drive device 460 is a device for inserting the storage medium 473. The storage medium 473 referred to here means a medium that stores information electrically, magnetically, or optically, such as a CD-ROM, floppy disk, or optical disc. The storage medium 473 can also be a semiconductor memory such as a ROM or flash memory that stores information electrically.

[0193] It should be noted that the various programs installed in the auxiliary storage device 430 can be installed, for example, by placing the distributed storage medium 473 into the drive device 460, and then reading and installing the various programs stored in the storage medium 473 by the drive device 460. Alternatively, the various programs installed in the auxiliary storage device 430 can also be installed by downloading and installing them from the network via the communication device 450.

[0194] <Functional Composition of Manufacturing Process Search Device>

[0195] Next, the functional configuration of the manufacturing process search device 400 will be explained. Figure 6This is a schematic diagram illustrating an example of the functional configuration of a manufacturing process search device. As described above, a manufacturing process search program can be installed in the manufacturing process search device 400. By executing this manufacturing process search program, the manufacturing process search device 400 can function as an analysis unit 610, an extraction unit 620, a calculation unit 630, and a judgment unit 640.

[0196] The analysis unit 610 plays a role in the analysis phase. The analysis unit 610 reads the training data stored in the training data storage unit 650. The training data storage unit 650 stores:

[0197] • Applied to Figure 1 Historical manufacturing process data for the manufacturing process shown; and

[0198] • Historical mechanical property data, that is, the mechanical property data of aluminum alloy extruded profiles manufactured using manufacturing processes that apply corresponding historical manufacturing process data.

[0199] The analysis unit 610 performs a re-regression analysis by using the manufacturing process data contained in the read training data as explanatory variables and the mechanical characteristic data as the target variable. The analysis unit 610 then notifies the extraction unit 620 of the analysis results of the re-regression analysis.

[0200] The extraction unit 620 plays a role in the extraction stage. The extraction unit 620 acquires the target values ​​of the mechanical characteristic data. Based on the analysis results of the re-regression analysis, the extraction unit 620 extracts manufacturing process data that satisfies the acquired target values ​​of the mechanical characteristic data.

[0201] Specifically, the extraction unit 620 changes the values ​​of each element of the manufacturing process data according to a predetermined step size (increment / decrease), thereby generating a combination of values ​​for each element of the manufacturing process data. The extraction unit 620 inputs the generated combinations into the analysis results of a regression analysis, thereby predicting the mechanical characteristic data of the corresponding combinations. The extraction unit 620 determines whether the predicted mechanical characteristic data meets the target value of the mechanical characteristic data. If it determines that the target value of the mechanical characteristic data is met, the extraction unit 620 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 manufacturing process data.

[0202] The calculation unit 630 functions during the calculation phase. For each candidate of manufacturing process data notified from the extraction unit 620, the calculation unit 630 calculates data representing the metal microstructure of each candidate by performing thermodynamic calculations. The calculation unit 630 then notifies the judgment unit 640 of the calculated data representing the metal microstructure and the candidate manufacturing process data.

[0203] The judgment unit 640 functions during the judgment phase. For each candidate manufacturing process data notified from the calculation unit 630, the judgment unit 640 determines whether the data representing the corresponding metal structure meets the conditions for achieving the target value of the mechanical property data. If the data representing the corresponding metal structure in the candidate manufacturing process data meets the conditions for achieving the target value of the mechanical property data, the judgment unit 640 outputs the candidate manufacturing process data. Conversely, if the data representing the corresponding metal structure in the candidate manufacturing process data does not meet the conditions for achieving the target value of the mechanical property data, the judgment unit 640 does not output the candidate manufacturing process data. Therefore, the candidate manufacturing process data output by the judgment unit 640 is the manufacturing process data that meets the target value of the mechanical property data and achieves an appropriate metal structure.

[0204] <Specific examples of processing by each functional unit of the manufacturing process search device>

[0205] Next, specific examples of the processing of each functional unit of the manufacturing process search device 400, including the analysis unit 610, extraction unit 620, calculation unit 630, and judgment unit 640, will be explained.

[0206] (1) Specific examples of the analysis department's processing

[0207] First, a specific example of the processing of the analysis unit 610 of the manufacturing process search device 400 will be explained. Figure 7 Figure 1 is a specific example of the processing in the analysis department.

[0208] like Figure 7 As shown, the training data 710 read by the analysis unit 610 from the training data storage unit 650 includes "ID", "explanatory variable" and "target variable" as information items.

[0209] The “ID” field stores an identification number, which is used to identify the combination of explanatory and target variables contained in the training data 710.

[0210] The "Explanatory Variables" include "Alloy Composition Data," "Homogenization Temperature," "Pre-extrusion Heating Temperature," "Extrusion Section Thickness," "Extrusion Pressure," "Extrusion Speed," and "Cooling Water Temperature." The "Alloy Composition Data" also includes data applied to... Figure 1 The historical manufacturing process data shown includes alloy composition data for "Si mass%", "Mg mass%", "Cu mass%", "Fe mass%", "Zr mass%", and "Ti mass%". It should be noted that the data stored in each information item within the explanatory variables has already been explained, so their explanation is omitted here.

[0211] The "target variable" is included in Figure 1 The mechanical properties of the aluminum alloy extruded profiles manufactured using the aforementioned historical manufacturing process data are shown in the manufacturing process diagram. These properties include "tensile strength," "0.2% yield strength," and "elongation." It should be noted that the data stored in each information item within the target variable has already been described, so their descriptions are omitted here.

[0212] like Figure 7 As shown, the analysis unit 610 also includes a data input unit 721, a re-regression calculation unit 722, an analysis result output unit 723, and a verification unit 724.

[0213] The data input unit 721 reads the training data 710 and inputs a portion of the training data stored in the training data storage unit 650 into the regression calculation unit 722.

[0214] The regression calculation unit 722 performs regression analysis by using the manufacturing process data contained in the training data 710 input by the data input unit 721 as the explanatory variable and the mechanical characteristic data as the target variable.

[0215] The analysis result output unit 723 notifies the extraction unit 620 of the analysis results of the re-regression analysis performed by the re-regression calculation unit 722, and also notifies the verification unit 724.

[0216] The verification unit 724 reads the training data other than the training data 710 stored in the training data storage unit 650 and read as training data 710, and uses it as verification data 730. The information items of the verification data 730 are the same as those of the training data 710, so its description is omitted here.

[0217] The verification unit 724 uses the manufacturing process data contained in the verification data 730 as explanatory variables and inputs it into the analysis results of the re-regression analysis, thereby predicting the mechanical characteristic data of the corresponding manufacturing process data. The verification unit 724 determines whether the error between the predicted mechanical characteristic data and the mechanical characteristic data contained in the verification data 730 is below a predetermined threshold, and verifies the appropriateness of the analysis results of the re-regression analysis accordingly.

[0218] Specifically, if the error between the predicted mechanical characteristic data and the mechanical characteristic data contained in the verification data 730 is determined to be below a predetermined threshold, the verification unit 724 determines that the analysis result of the re-regression analysis is appropriate.

[0219] Figure 8 This is a schematic diagram illustrating an example of validating the results of a regression analysis. Figure 8 (a) is a graph showing the relationship between the tensile strength in the mechanical property data, the predicted tensile strength, and the tensile strength contained in the validation data 730. Figure 8 In (a), 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 change (deviation) of the plotted point relative to the 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 change of the plotted point relative to the line 801.

[0220] like Figure 8 As shown in (a), the root mean square error (RMSE) for tensile strength is 8.3283, and the coefficient of determination (R²) is... 2 The p-value was 0.68, and the p-value was less than 0.0001. In other words, in terms of tensile strength, the results of the regression analysis were generally satisfactory (good).

[0221] Figure 8 (b) is a graph showing the relationship between the predicted 0.2% yield strength and the 0.2% yield strength contained in the verification data 730 regarding the 0.2% yield strength in the mechanical property data. Figure 8 In (b), the horizontal axis represents the predicted 0.2% yield strength, and the vertical axis represents the 0.2% yield strength included in the validation data 730. The smaller the error between the predicted 0.2% yield strength and the 0.2% yield strength included in the validation data 730, the smaller the change of the plotted point relative to line 802. On the other hand, the larger the error between the predicted 0.2% yield strength and the 0.2% yield strength included in the validation data 730, the greater the change of the plotted point relative to line 802.

[0222] like Figure 8 As shown in (b), for a yield strength of 0.2%, the root mean square error (RMSE) is 8.4107, and the coefficient of determination (R²) is... 2 The p-value was 0.77, and the p-value was less than 0.0001. In other words, for a yield strength of 0.2%, the results of the regression analysis were generally satisfactory.

[0223] Figure 8 (c) is a graph showing the relationship between the elongation in the mechanical property data, the predicted elongation, and the elongation contained in the validation data 730. Figure 8In (c), the horizontal axis represents the predicted elongation rate, and the vertical axis represents the elongation rate contained in the validation data 730. The smaller the error between the predicted elongation rate and the elongation rate contained in the validation data 730, the smaller the change of the plotted point relative to line 803. On the other hand, the larger the error between the predicted elongation rate and the elongation rate contained in the validation data 730, the greater the change of the plotted point relative to line 802.

[0224] like Figure 8 As shown in (c), the root mean square error (RMSE) for elongation is 2.0491, and the coefficient of determination (R²) is... 2 The p-value was 0.63, and the p-value was less than 0.0001. In other words, in terms of elongation, the results of the regression analysis were generally satisfactory.

[0225] (2) Specific examples of the extraction process

[0226] Next, a specific example of the processing of the extraction unit 620 of the manufacturing process search device 400 will be described. Figure 9 This is a schematic diagram illustrating a specific example of the extraction process.

[0227] like Figure 9 As shown, 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.

[0228] The data modification unit 921 changes the values ​​of each element of the manufacturing process data according to a predetermined step size, thereby comprehensively generating a combination of the values ​​of each element of the manufacturing process data. For example... Figure 9 As shown in the change range data 910, for the manufacturing process data, a minimum value, a maximum value, and a step size are preset for each element. The data modification unit 921 changes the value of each element within the range between the minimum and maximum values ​​shown in the change range data 910 using the step size shown in the change range data 910, thereby generating various combinations of the values ​​of each element of the manufacturing process data.

[0229] The data change unit 921 notifies the prediction unit 922 of the combination of values ​​of each element of the comprehensively generated manufacturing process data.

[0230] The prediction unit 922 sets the analysis results of the regression analysis notified by the analysis unit 610. After receiving the combination of values ​​of each element of the manufacturing process data from the data change 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 regression analysis, thereby predicting the mechanical characteristic data of the corresponding combination. The prediction unit 922 correlates the predicted mechanical characteristic data with the combination of values ​​of each element of the manufacturing process data and notifies it to the candidate extraction unit 923.

[0231] The candidate extraction unit 923 acquires the target value of the mechanical characteristic data. The candidate extraction unit 923 compares the mechanical characteristic data notified by the prediction unit 922 with the acquired target value of the mechanical characteristic data.

[0232] • If it is determined that the tensile strength contained in the mechanical property data notified from the prediction unit 922 is greater than the tensile strength contained in the target value of the acquired mechanical property data;

[0233] • If it is determined that the 0.2% yield strength included in the mechanical property data notified from the prediction unit 922 is greater than the 0.2% yield strength included in the target value of the acquired mechanical property data; and

[0234] • If it is determined that the elongation contained in the mechanical property data notified from the prediction unit 922 is greater than the elongation contained in the target value of the acquired mechanical property data,

[0235] The combination of values ​​of each element of the manufacturing process data corresponding to the mechanical characteristic data notified from the forecasting department 922 is extracted as a candidate for manufacturing process data.

[0236] The candidate extraction unit 923 notifies the candidate output unit 924 of the extracted manufacturing process data.

[0237] The candidate output unit 924 will send a candidate notification of the manufacturing process data notified by the candidate extraction unit 923 to the calculation unit 630.

[0238] (3) Specific examples of processing by the computing department

[0239] Next, a specific example of the processing of the calculation unit 630 of the manufacturing process search device 400 will be explained. Figure 10 Figure 1 is a specific example of the processing of the computing unit.

[0240] The Calculation Unit 630 is a general-purpose thermodynamic calculation software, such as Thermo-Calc.

[0241] In this thermodynamic calculation software, the amount of elements dissolved and the phase fraction of precipitates in aluminum alloys are determined by using a calculation equilibrium state diagram based on the CALPHAD method.

[0242] Furthermore, in this thermodynamic calculation software, the state of the precipitate when the element is dissolved in the aluminum alloy during extrusion and then artificially precipitated is predicted using the following method:

[0243] • Thermodynamic and kinetic data determined using the CALPAD method; and

[0244] • Nucleation, growth, and coarsening of precipitates are calculated using a numerical solution method based on Langer-Schwartz theory and the Kampmann-Wagner method.

[0245] Therefore, the size, density, and volume ratio of the precipitate can be predicted using this thermodynamic calculation software.

[0246] It should be noted that when predicting the aforementioned precipitates, the interface can be included in both the parent phase growth and the precipitate growth. Furthermore, the precipitates are formed within the parent phase, and their morphology is needle-like or plate-like. Thus, by performing simulations based on the aforementioned thermodynamic calculation software under more suitable conditions, the time-dependent changes in precipitates in aluminum alloys can be appropriately predicted.

[0247] Figure 10 This is a schematic diagram of the above-mentioned functions of the thermodynamic calculation software, which includes a liquid phase temperature and solid solution calculation unit 1001 and a precipitate calculation unit 1002.

[0248] Figure 10 The example shows a case where the liquid phase temperature and solid solution amount are calculated using the liquid phase temperature and solid solution amount calculation unit 1001 for the candidate manufacturing process data notified from the extraction unit 620.

[0249] Specifically, the following scenario is illustrated: using the liquidus temperature and solid solution amount calculation unit 1001, and inputting alloy composition data included in the candidate manufacturing process data and information on the target phase, thereby outputting...

[0250] • Mg solid solution content [mass %];

[0251] • Si solid solution content [mass %]; and

[0252] • Liquid phase temperature [°C].

[0253] Furthermore, a case is shown in which the precipitate size, precipitate density, and volume ratio are calculated using the precipitate calculation unit 1002 for the manufacturing process data notified from the extraction unit 620.

[0254] Specifically, the following scenario is illustrated: using the precipitate calculation unit 1002, alloy composition data included in the candidate manufacturing process data and the matrix of the target phase, β”-Mg2Si phase data are input, thereby outputting...

[0255] • Precipitate (β”-Mg2Si) size (average particle size) [nm];

[0256] • Density (number density) of precipitate (β”-Mg2Si) [numbers / m³]3 ];and

[0257] • Volume ratio of precipitates (β”-Mg2Si) [volume %].

[0258] Accordingly, the calculation unit 630 can correlate the candidate manufacturing process data notified from the extraction unit 620 with the data representing the metal structure, namely liquidus temperature, solid solution content, precipitate size, precipitate density and volume ratio, and notify the judgment unit 640.

[0259] (4) Specific examples of the processing in the decision-making section

[0260] Next, a specific example of the processing of the determination unit 640 of the manufacturing process search device 400 will be explained. Figure 11 This is a schematic diagram illustrating a specific example of the processing in the decision-making section.

[0261] like Figure 11 As shown, the judgment unit 640 has a calculation result acquisition unit 1101, a condition acquisition unit 1102, a suitability judgment unit 1103, and a candidate output unit 1104.

[0262] The calculation result acquisition unit 1101 acquires candidate data for the manufacturing process data and corresponding metal structure data from the calculation unit 630. Furthermore, the calculation result acquisition unit 1101 also notifies the suitability determination unit 1103 of the acquired candidate data for the manufacturing process data and corresponding metal structure data.

[0263] The condition acquisition unit 1102 acquires the "conditions representing the metal structure data" used to achieve the target value of the mechanical property data. The condition acquisition unit 1102 then notifies the suitability determination unit 1103 of the acquired conditions representing the metal structure data.

[0264] The suitability determination unit 1103 determines whether the data representing the metal structure notified by the calculation result acquisition unit 1101 satisfies the "conditions for data representing the metal structure" notified by the condition acquisition unit 1102. If it is determined that the data representing the metal structure notified by the calculation result acquisition unit 1101 satisfies the "conditions for data representing the metal structure" notified by the condition acquisition unit 1102, the suitability determination unit 1103 determines that the candidate for the corresponding manufacturing process data is suitable. On the other hand, if it is determined that the data representing the metal structure notified by the calculation result acquisition unit 1101 does not satisfy the "conditions for data representing the metal structure" notified by the condition acquisition unit 1102, the suitability determination unit 1103 determines that the candidate for the corresponding manufacturing process data is unsuitable.

[0265] The suitability determination unit 1103 will notify the candidate manufacturing process data that is deemed suitable to the candidate output unit 1104.

[0266] The candidate output unit 1104 determines the manufacturing process data that satisfies the target value of the mechanical property data and achieves an appropriate metal structure from the candidate output of the manufacturing process data notified by the suitability judgment unit 1103.

[0267] <Manufacturing Process Search and Processing Flow>

[0268] Next, the process of manufacturing process search performed by the manufacturing process search device 400 will be explained. Figure 12 This is a flowchart representing the process of searching and processing manufacturing processes.

[0269] In step S1201, the manufacturing process search device 400 acquires training data.

[0270] In step S1202, the manufacturing process search device 400 uses the training data to perform re-regression analysis and outputs the analysis results of the re-regression analysis.

[0271] In step S1203, the manufacturing process search device 400 acquires the target value of the mechanical characteristic data.

[0272] In step S1204, the manufacturing process search device 400 extracts candidate manufacturing process data that meet the target values ​​of the acquired mechanical characteristic data based on the analysis results of the re-regression analysis.

[0273] In step S1205, the manufacturing process search device 400 inputs "1" into counter i, which is used to count the candidates of the extracted manufacturing process data.

[0274] In step S1206, for the i-th candidate of manufacturing process data in the extracted candidate manufacturing process data, the manufacturing process search device 400 calculates the data representing the metal structure through thermodynamic calculation.

[0275] In step S1207, the manufacturing process search device 400 determines whether the data representing the metal structure calculated for the candidate of the i-th manufacturing process data meets the "conditions for the data representing the metal structure" used to achieve the target value of the mechanical property data. Accordingly, the manufacturing process search device 400 determines whether the candidate of the i-th manufacturing process data is suitable, thereby screening the candidates of manufacturing process data.

[0276] In step S1208, the manufacturing process search device 400 determines whether all candidate manufacturing process data have been assessed for suitability. If, in step S1208, it is determined that there are still candidate manufacturing process data for which suitability has not been assessed (NO in step S1208), the process proceeds to step S1209.

[0277] In step S1209, the manufacturing process search device 400 increments the counter i by 1, and then proceeds to step S1206.

[0278] On the other hand, in step S1208, if it is determined that all candidates for manufacturing process data have been judged as appropriate (in step S1208, YES), then proceed to step S1210.

[0279] In step S1210, the manufacturing process search device 400 outputs the candidate manufacturing process data that is determined to be suitable, and then ends the manufacturing process search process.

[0280] <Example>

[0281] Next, an embodiment of the manufacturing process search process performed by the manufacturing process search device 400 will be described. Figure 13 This is an illustration of an example of the search results for manufacturing process search processing, which represents the target value of the mechanical property data.

[0282] • Tensile strength: 300 MPa or higher;

[0283] • 0.2% yield strength: ≥270 [MPa]; and

[0284] • Elongation rate: 9.0% or higher

[0285] And set the "conditions for representing data on metal structure" to

[0286] • Liquid phase temperature: 560°C or higher;

[0287] • Mg solid solution content before extrusion: 0.005 [mass%] or more;

[0288] • Si solution content before extrusion: 0.01 [mass%] or more;

[0289] • Precipitate (β”-Mg2Si) size (average particle size): 50 nm or more and 300 nm or less;

[0290] • Density (number density) of precipitate (β”-Mg2Si): ​​6.0E+27 [cells / m³] 3 The above; and

[0291] • Volume ratio of precipitates (β”-Mg2Si): ​​10 [volume%] or more

[0292] An illustration of an example of search results at that time. Figure 13 The example shows a scenario where 14 types of manufacturing process data are output as manufacturing process data that meet the target values ​​of mechanical property data and achieve appropriate metal structure.

[0293] Summary

[0294] As described above, the manufacturing process search device 400 of the first embodiment includes:

[0295] • The analysis unit 610 analyzes the relationship between the manufacturing process data and the mechanical property data of aluminum alloy extruded profiles through regression analysis;

[0296] • Extraction unit 620 extracts candidate manufacturing process data for aluminum alloy extruded profiles that meet the target values ​​of mechanical property data based on the analysis results of the re-regression analysis performed by analysis unit 610.

[0297] • The calculation unit 630, for each candidate of the extracted manufacturing process data, calculates the data affecting the mechanical properties, i.e., the data representing the metal microstructure, through thermodynamic calculations; and

[0298] • The judgment unit 640, for the extracted candidate manufacturing process data, determines whether the data representing the metal structure calculated by the calculation unit 630 through thermodynamic calculations meets the "conditions for the data representing the metal structure" used to achieve the target value of the mechanical property data. The judgment unit 640 determines whether the corresponding candidate manufacturing process data is appropriate based on whether the "conditions for the data representing the metal structure" are met.

[0299] Thus, the manufacturing process search device 400 of the first embodiment can output manufacturing process data that satisfies the target values ​​of mechanical property data and achieves an appropriate metal structure. In other words, even when there are multiple manufacturing processes for aluminum alloy extruded profiles that can achieve the desired mechanical properties, screening can be performed. Therefore, according to the first embodiment, manufacturing processes for aluminum alloy extruded profiles that can achieve the desired mechanical properties can be searched efficiently.

[0300] [Second Implementation]

[0301] In the first embodiment described above, tensile strength, 0.2% yield strength and elongation are listed as mechanical property data, but the mechanical property data is not limited to these, and other mechanical property data may also be used.

[0302] Furthermore, in the embodiments of the first embodiment described above, the target values ​​of the mechanical property data are set as follows: tensile strength ≥300MPa, 0.2% yield strength ≥270MPa, and elongation ≥9.0%, but the target values ​​of the mechanical property data are not limited to these.

[0303] Furthermore, in the first embodiment described above, the manufacturing conditions were described using homogenization temperature, pre-extrusion heating temperature, extrusion section thickness, extrusion speed, and cooling water temperature. However, the manufacturing conditions are not limited to these, and other manufacturing conditions may also be used.

[0304] Furthermore, although the first embodiment described above describes a case where the data modification unit 921 generates a combination of values ​​of various elements of the manufacturing process data, the method by which the data modification unit 921 generates a combination of values ​​of various elements of the manufacturing process data is not limited to this.

[0305] Furthermore, in the first embodiment described above, the calculation of solid solution content, liquid phase temperature, precipitate size, precipitate density and volume ratio were explained as data representing the metal structure. However, the data representing the metal structure is not limited to this, and other data representing the metal structure may also be calculated.

[0306] Furthermore, in the first embodiment described above, the "conditions for representing the data of the metal structure" are set as follows:

[0307] • Liquid phase temperature: 560°C or higher;

[0308] • Mg solid solution content before extrusion: 0.005 [mass%] or more;

[0309] • Si solution content before extrusion: 0.01 [mass%] or more;

[0310] • Precipitate (β”-Mg2Si) size (average particle size): 50 nm or more and 300 nm or less;

[0311] • Density (number density) of precipitate (β”-Mg2Si): ​​6.0E+27 [cells / m³] 3 The above; and

[0312] • Precipitate (β”-Mg2Si) volume ratio: 10 [volume%] or more,

[0313] However, the conditions for "representing the structure of metals" are not limited to this.

[0314] [Third Implementation]

[0315] In the first embodiment described above, the case where the material to be designed is an alloy material, namely an aluminum alloy drawn material, was described. However, the material to be designed is not limited to aluminum alloy drawn materials; it can also be an aluminum alloy cast material. The differences between the third embodiment and the first embodiment described above will be emphasized below.

[0316] <Specific example of processing in the analysis section of the manufacturing process search device>

[0317] First, a specific example of the processing of the analysis unit 610 of the manufacturing process search device 400 will be explained. Figure 14 Figure 2 is a specific example of the processing in the analysis department.

[0318] and Figure 7 Similarly, such as Figure 14 As shown, the training data 710 read by the analysis unit 610 from the training data storage unit 650 includes "ID", "explanatory variable" and "target variable" as information items.

[0319] The “ID” field stores an identification number, which is used to identify the combination of explanatory and target variables contained in the training data 710.

[0320] In the case of aluminum alloy castings, the "explanatory variables" include "alloy composition data", "melt temperature", "solution temperature", "solution time", "natural aging time", "artificial aging temperature", "artificial aging time", "annealing temperature", and "annealing time". The "alloy composition data" include "Si mass%", "Mg mass%", "Cu mass%", "Fe mass%", "Zr mass%", and "Ti mass%".

[0321] The “target variables” include “tensile strength”, “0.2% yield strength”, “elongation”, “Young’s modulus”, “coefficient of linear expansion” and “fatigue characteristics”, which are mechanical properties of aluminum alloy castings.

[0322] It should be noted that the first embodiment described above has already addressed... Figure 14 The functional units included in the analysis unit 610 have been described, so their descriptions are omitted here. Furthermore, the validation data 730 is similar to the training data 710, so its description is also omitted here.

[0323] <Specific example of processing by the computing unit of the manufacturing process search device>

[0324] Next, a specific example of the processing of the calculation unit 630 of the manufacturing process search device 400 will be explained. Figure 15 Figure 2 is a specific example of the processing of the computing unit.

[0325] like Figure 15 As shown, in the case of aluminum alloy castings, the liquidus temperature and solid solution calculation unit 1001 outputs:

[0326] • The amount of Si dissolved during solution treatment [mass %];

[0327] • Fe solid solution content during solid solution [mass %];

[0328] • Cu solid solution content [mass %] during solution treatment;

[0329] • The amount of Mn dissolved during solution treatment [mass %];

[0330] • Mg solid solution content during solution treatment [mass %];

[0331] • The amount of Cr dissolved during solution treatment [mass %];

[0332] • Ni solid solution content during solid solution [mass %];

[0333] • The amount of Zn dissolved during solution treatment [mass %];

[0334] • The amount of Ti dissolved during solution treatment [mass %];

[0335] • V solid content [mass %] during solution treatment;

[0336] • Pb solid solution content during solid solution [mass %];

[0337] • Sn solid solution content during solution treatment [mass %];

[0338] • Bi solid solution content during solution treatment [mass %];

[0339] • The amount of B dissolved during solution treatment [mass %];

[0340] • The amount of P dissolved during solution treatment [mass %];

[0341] • Zr solid solution content during solution treatment [mass %];

[0342] • Sr solid solution content [mass %] during solution treatment; and

[0343] • Liquid phase temperature [°C].

[0344] Furthermore, in the case of aluminum alloy castings, the precipitate calculation unit 1002 outputs:

[0345] • Precipitate size [nm];

[0346] • Density of precipitates [number / m³] 3 ];and

[0347] • Volume ratio of precipitates [volume %].

[0348] Accordingly, the computing unit 630 can enable

[0349] • "Candidate data for manufacturing process data notified from extraction department 620" and

[0350] • "Data representing the microstructure of the metal, including liquidus temperature, the amount of Si, Fe, Cu, Mn, Mg, Cr, Ni, Zn, Ti, V, Pb, Sn, Bi, B, P, Zr, Sr dissolved during solid solution, precipitate size, precipitate density, and precipitate volume ratio."

[0351] It is associated with and notified to the Judgment Department 640.

[0352] Thus, according to the third embodiment, even when the material of the design object is aluminum alloy casting, similar effects to those of the first embodiment described above can be obtained.

[0353] [Fourth Implementation]

[0354] In the first embodiment described above, the case where the material to be designed is an alloy material, namely an aluminum alloy drawn material, was explained. However, the material to be designed is not limited to aluminum alloy drawn materials; it can also be an iron alloy drawn material. The differences between the fourth embodiment and the first embodiment described above will be emphasized below.

[0355] <Specific example of processing in the analysis section of the manufacturing process search device>

[0356] First, a specific example of the processing of the analysis unit 610 of the manufacturing process search device 400 will be explained. Figure 16 Figure 3 is a specific example of the processing in the analysis department.

[0357] and Figure 7 Similarly, such as Figure 16 As shown, the training data 710 read by the analysis unit 610 from the training data storage unit 650 includes "ID", "explanatory variables", and "target variables" as information items.

[0358] The “ID” field stores an identification number, which is used to identify the combination of explanatory and target variables contained in the training data 710.

[0359] In the case of ferroalloy drawn materials, the "explanatory variables" include "alloy composition data", "melt temperature" during ferroalloy casting, "casting speed", "cooling water volume", "ferroalloy heating temperature" during hot working, "ferroalloy heating time" during hot working, "processing speed", "reduction rate", "hot working temperature", "cooling rate after hot working", "natural aging time", "heat treatment temperature", "heat treatment time", and "cooling rate of heat treatment". "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%", and "W mass%".

[0360] The “target variables” include “tensile strength”, “0.2% yield strength”, “elongation”, “Young’s modulus”, “coefficient of linear expansion”, “impact characteristics” and “fatigue characteristics”, which are mechanical properties of ferroalloy tensile materials.

[0361] It should be noted that the first embodiment described above has already addressed... Figure 16 The functional units included in the analysis unit 610 have been described, so their descriptions are omitted here. Furthermore, the validation data 730 is similar to the training data 710, so its description is also omitted here.

[0362] <Specific example of processing by the computing unit of the manufacturing process search device>

[0363] Next, a specific example of the processing of the calculation unit 630 of the manufacturing process search device 400 will be explained. Figure 17 Figure 3 is a specific example of the processing of the computing unit.

[0364] like Figure 17 As shown, in the case of a ferroalloy drawn material, the liquidus temperature and solid solution content calculation unit 1001 outputs:

[0365] • Solubility of C after processing [mass%];

[0366] • Solubility of B solids after processing [mass%];

[0367] • Solubility of nitrogen (N) after processing [mass %];

[0368] • The amount of Si solid solution after processing [mass %];

[0369] • Solubility of P after processing [mass%];

[0370] • Solubility of S after processing [mass %];

[0371] • Soluble Mn content after processing [mass %];

[0372] • Al solid solution content after processing [mass %];

[0373] • Ti solid solution content after processing [mass %];

[0374] • V solid solution content after processing [mass %];

[0375] • Cr solid solution content after processing [mass%];

[0376] • Solubility of Co after processing [mass %];

[0377] • Ni solid solution content after processing [mass %];

[0378] • Cu solid solution content after processing [mass %];

[0379] • Zr solid solution content after processing [mass %];

[0380] • Nb solid solution content after processing [mass %];

[0381] • Mo solid solution content after processing [mass %];

[0382] • The amount of W solids dissolved after processing [mass %]; and

[0383] • Liquid phase temperature [°C].

[0384] Furthermore, in the case of ferroalloy drawn material, the precipitate calculation unit 1002 outputs:

[0385] • Precipitate size [nm];

[0386] • Density of precipitates [number / m³] 3 ];and

[0387] • Volume ratio of precipitates [volume %].

[0388] Accordingly, the computing unit 630 can

[0389] • "Candidate data for manufacturing process data notified from extraction department 620" and

[0390] • "Data representing the microstructure of the metal include liquidus temperature, solid solution content of C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, and W after processing, precipitate size, precipitate density, and precipitate volume ratio."

[0391] It is associated with and notified to the Judgment Department 640.

[0392] Thus, according to the fourth embodiment, even when the material of the design object is a ferroalloy tensile material, similar effects to those of the first embodiment described above can be obtained.

[0393] [Fifth Implementation]

[0394] In the third embodiment described above, the case where the material of the design object is an alloy material, namely aluminum alloy casting, was explained. However, the material of the design object is not limited to aluminum alloy casting; it can also be cast iron. The differences between the fifth embodiment and the third embodiment described above will be emphasized below.

[0395] <Specific example of processing in the analysis section of the manufacturing process search device>

[0396] First, a specific example of the processing of the analysis unit 610 of the manufacturing process search device 400 will be explained. Figure 18 Figure 4 is a specific example of the processing in the analysis department.

[0397] and Figure 14 Similarly, such as Figure 18 As shown, the training data 710 read by the analysis unit 610 from the training data storage unit 650 includes "ID", "explanatory variable" and "target variable" as information items.

[0398] The “ID” field stores an identification number, which is used to identify the combination of explanatory and target variables contained in the training data 710.

[0399] In the case of cast iron, the "Explanatory Variables" include "Alloy Composition Data", "Molten Temperature at Casting", "Casting Rate", "Solidation Rate", "Cooling Rate after Solidification", "Heat Treatment Temperature", "Heat Treatment Time", and "Cooling Rate during Heat Treatment". "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%".

[0400] The “target variables” include “tensile strength”, “0.2% yield strength”, “elongation”, “Young’s modulus”, “coefficient of linear expansion”, “impact characteristics” and “fatigue characteristics”, which are mechanical properties of cast iron.

[0401] It should be noted that the first embodiment described above has already addressed... Figure 18The functional units included in the analysis unit 610 have been described, so their descriptions are omitted here. Furthermore, the validation data 730 is similar to the training data 710, so its description is also omitted here.

[0402] <Specific example of processing by the computing unit of the manufacturing process search device>

[0403] Next, a specific example of the processing of the calculation unit 630 of the manufacturing process search device 400 will be explained. Figure 19 Figure 4 is a specific example of the processing of the computing unit.

[0404] like Figure 19 As shown, in the case of cast iron, the output of the liquidus temperature and solid solution calculation unit 1001 is as follows:

[0405] • C solid solution content [mass %] before heat treatment;

[0406] • Solubility of B before heat treatment [mass %];

[0407] • Nitrogen solid solution content [mass %] before heat treatment;

[0408] • Si solid solution content [mass %] before heat treatment;

[0409] • P solid solution content [mass %] before heat treatment;

[0410] • Soluble sulfur content [mass %] before heat treatment;

[0411] • Mn solid solution content [mass %] before heat treatment;

[0412] • Al solid solution content [mass %] before heat treatment;

[0413] • Ti solid solution content [mass %] before heat treatment;

[0414] • V solid solution content [mass %] before heat treatment;

[0415] • Cr solid solution content [mass %] before heat treatment;

[0416] • Co solid solution content [mass %] before heat treatment;

[0417] • Ni solid solution content [mass %] before heat treatment;

[0418] • Cu solid solution content [mass %] before heat treatment;

[0419] • Zr solid solution content [mass %] before heat treatment;

[0420] • Nb solid solution content [mass %] before heat treatment;

[0421] • Mo solid solution content [mass %] before heat treatment;

[0422] • Solubility of W solids before heat treatment [mass %];

[0423] • Ca solid solution content [mass %] before heat treatment;

[0424] • Mg solid solution content [mass %] before heat treatment;

[0425] • Ce solution content [mass %] before heat treatment; and

[0426] • Liquid phase temperature [°C].

[0427] Furthermore, in the case of cast iron, the precipitate calculation unit 1002 outputs:

[0428] • Precipitate size [nm];

[0429] • Density of precipitates [number / m³] 3 ];and

[0430] • Volume ratio of precipitates [volume %].

[0431] Accordingly, the computing unit 630 can

[0432] • "Candidate data for manufacturing process data notified from extraction department 620" and

[0433] • "Data representing the microstructure of the metal, including liquidus temperature, solid solution content 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 precipitate volume ratio."

[0434] It is associated with and notified to the Judgment Department 640.

[0435] Thus, according to the fifth embodiment, even when the material of the design object is cast iron, similar effects to those of the first embodiment described above can be obtained.

[0436] [Sixth Implementation]

[0437] In the first embodiment described above, the case where the material to be designed is an alloy material, namely an aluminum alloy drawn material, was explained. However, the material to be designed is not limited to aluminum alloy drawn materials; it can also be a copper alloy drawn material. The differences between the sixth embodiment and the first embodiment described above will be described in detail below.

[0438] <Specific example of processing in the analysis section of the manufacturing process search device>

[0439] First, a specific example of the processing of the analysis unit 610 of the manufacturing process search device 400 will be explained. Figure 20Figure 5 shows a specific example of the processing in the analysis department.

[0440] and Figure 7 Similarly, such as Figure 20 As shown, the training data 710 read by the analysis unit 610 from the training data storage unit 650 includes "ID", "explanatory variable" and "target variable" as information items.

[0441] The “ID” field stores an identification number, which is used to identify the combination of explanatory and target variables contained in the training data 710.

[0442] In the case of copper alloy drawn materials, the "Explanatory Variables" include "Alloy Composition Data", "Molten Temperature" during copper alloy casting, "Casting Speed", "Cooling Water Flow Rate", "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". 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%", and "As mass%".

[0443] The “target variables” include the mechanical properties of copper alloy tensile materials, namely “0.2% yield strength”, “tensile strength”, “elongation”, “electrical conductivity”, “thermal conductivity”, “Young’s modulus” and “coefficient of linear expansion”.

[0444] It should be noted that the first embodiment described above has already addressed... Figure 20 The various functional units included in the analysis unit 610 have been described, so their descriptions are omitted here. Furthermore, the validation data 730 is similar to the training data 710, so its description is also omitted here.

[0445] <Specific example of processing by the computing unit of the manufacturing process search device>

[0446] Next, a specific example of the processing of the calculation unit 630 of the manufacturing process search device 400 will be explained. Figure 21 Figure 5 shows a specific example of the processing by the computing unit.

[0447] like Figure 21 As shown, in the case of a copper alloy drawn material, the liquidus temperature and solid solution content calculation unit 1001 outputs:

[0448] • Zn solid solution content [mass %] before processing;

[0449] • Pb solid solution content [mass %] before processing;

[0450] • Bi solid solution content [mass %] before processing;

[0451] • Sn solid solution content [mass %] before processing;

[0452] • Fe solid solution content [mass %] before processing;

[0453] • Solubility of P before processing [mass %];

[0454] • Al solid solution content [mass %] before processing;

[0455] • Hg solid solution content [mass %] before processing;

[0456] • Ni solid solution content [mass %] before processing;

[0457] • Mn solid solution content [mass %] before processing;

[0458] • Se solid solution content [mass %] before processing;

[0459] • Solubility of Te before processing [mass %];

[0460] • O solid solution content [mass %] before processing;

[0461] • Soluble solids content of S before processing [mass %];

[0462] • Zr solid solution content [mass %] before processing;

[0463] • Soluble Be content [mass %] before processing;

[0464] • Co solid solution content [mass %] before processing;

[0465] • Ti solid solution content [mass %] before processing;

[0466] • As solid solution content [mass %] before processing; and

[0467] • Liquid phase temperature [°C].

[0468] Furthermore, in the case of copper alloy drawn material, the precipitate calculation unit 1002 outputs:

[0469] • Precipitate size [nm];

[0470] • Density of precipitates [number / m³] 3 ];and

[0471] • Volume ratio of precipitates [volume %].

[0472] Accordingly, the computing unit 630 can

[0473] • "Candidate data for manufacturing process data notified from extraction department 620" and

[0474] • "Data representing the liquidus temperature, solid solution content of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As before processing, precipitate size, precipitate density, and precipitate volume ratio."

[0475] It is associated with and notified to the Judgment Department 640.

[0476] Thus, according to the sixth embodiment, even when the material of the design object is a copper alloy drawing material, similar effects to those of the first embodiment described above can be obtained.

[0477] [Seventh Implementation]

[0478] In the third embodiment described above, the case where the material of the design object is an alloy material, namely aluminum alloy casting, was explained. However, the material of the design object is not limited to aluminum alloy casting; it can also be copper alloy casting. The differences between the seventh embodiment and the third embodiment described above will be emphasized below.

[0479] <Specific example of processing in the analysis section of the manufacturing process search device>

[0480] First, a specific example of the processing of the analysis unit 610 of the manufacturing process search device 400 will be explained. Figure 22 Figure 6 shows a specific example of the processing in the analysis department.

[0481] and Figure 14 Similarly, such as Figure 22 As shown, the training data 710 read by the analysis unit 610 from the training data storage unit 650 includes "ID", "explanatory variable" and "target variable" as information items.

[0482] The “ID” field stores an identification number, which is used to identify the combination of explanatory and target variables contained in the training data 710.

[0483] In the case of copper alloy castings, the "explanatory variables" include "alloy composition data," "melt temperature," "solution temperature," "solution time," "natural aging time," "artificial aging temperature," "artificial aging time," "annealing temperature," and "annealing time" at the time of casting. 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%," and "As mass%."

[0484] The “target variables” include the mechanical properties of copper alloy castings, such as “0.2% yield strength”, “tensile strength”, “elongation”, “electrical conductivity”, “thermal conductivity”, “Young’s modulus”, and “coefficient of linear expansion”.

[0485] It should be noted that the functional units included in the analysis unit 610 have already been described in the first embodiment above, so their description is omitted here.

[0486] <Specific example of processing by the computing unit of the manufacturing process search device>

[0487] Next, a specific example of the processing of the calculation unit 630 of the manufacturing process search device 400 will be explained. Figure 23 Figure 6 shows a specific example of the processing by the computing unit.

[0488] like Figure 23 As shown, in the case of copper alloy castings, the liquidus temperature and solid solution calculation unit 1001 outputs:

[0489] • Zn solid solution content [mass %] before processing;

[0490] • Pb solid solution content [mass %] before processing;

[0491] • Bi solid solution content [mass %] before processing;

[0492] • Sn solid solution content [mass %] before processing;

[0493] • Fe solid solution content [mass %] before processing;

[0494] • Solubility of P before processing [mass %];

[0495] • Al solid solution content [mass %] before processing;

[0496] • Hg solid solution content [mass %] before processing;

[0497] • Ni solid solution content [mass %] before processing;

[0498] • Mn solid solution content [mass %] before processing;

[0499] • Se solid solution content [mass %] before processing;

[0500] • Solubility of Te before processing [mass %];

[0501] • O solid solution content [mass %] before processing;

[0502] • Soluble solids content of S before processing [mass %];

[0503] • Zr solid solution content [mass %] before processing;

[0504] • Soluble Be content [mass %] before processing;

[0505] • Co solid solution content [mass %] before processing;

[0506] • Ti solid solution content [mass %] before processing;

[0507] • As solid solution content [mass %] before processing; and

[0508] • Liquid phase temperature [°C].

[0509] Furthermore, in the case of copper alloy castings, the precipitate calculation unit 1002 outputs:

[0510] • Precipitate size [nm];

[0511] • Density of precipitates [number / m³] 3 ];and

[0512] • Volume ratio of precipitates [volume %].

[0513] Accordingly, the computing unit 630 can

[0514] • "Candidate data for manufacturing process data notified from extraction department 620" and

[0515] • "Data representing the microstructure of the metal, including liquidus temperature, solid solution content of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As before processing, precipitate size, precipitate density, and precipitate volume ratio."

[0516] It is associated with and notified to the Judgment Department 640.

[0517] Thus, according to the seventh embodiment, even when the material of the design object is a copper alloy casting, similar effects to those of the first embodiment described above can be obtained.

[0518] [Eighth Implementation]

[0519] In the first embodiment described above, the case where the material of the design object is an alloy material, namely aluminum alloy casting, was explained. However, the material of the design object is not limited to aluminum alloy casting; it can also be titanium alloy. The differences between the eighth embodiment and the first embodiment described above will be explained in detail below.

[0520] <Specific example of processing in the analysis section of the manufacturing process search device>

[0521] First, a specific example of the processing of the analysis unit 610 of the manufacturing process search device 400 will be explained. Figure 24 Figure 7 shows a specific example of the processing in the analysis department.

[0522] and Figure 14 Similarly, such as Figure 24 As shown, the training data 710 read by the analysis unit 610 from the training data storage unit 650 includes "ID", "explanatory variable" and "target variable" as information items.

[0523] The “ID” field stores an identification number, which is used to identify the combination of explanatory and target variables contained in the training data 710.

[0524] In the case of titanium alloys, the "explanatory variables" include "alloy composition data", "melt temperature", "solution temperature", "solution time", "artificial aging temperature", "artificial aging time", "annealing temperature", and "annealing time". The "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%".

[0525] The “target variables” include “0.2% yield strength”, “tensile strength”, “elongation”, “Young’s modulus”, “coefficient of linear expansion” and “fatigue properties”, which are mechanical properties of titanium alloys.

[0526] It should be noted that the first embodiment described above has already addressed... Figure 24 The functional units included in the analysis unit 610 have been described, so their descriptions are omitted here. Furthermore, the validation data 730 is similar to the training data 710, so its description is also omitted here.

[0527] <Specific example of processing by the computing unit of the manufacturing process search device>

[0528] Next, a specific example of the processing of the calculation unit 630 of the manufacturing process search device 400 will be explained. Figure 25 Figure 7 shows a specific example of the processing by the computing unit.

[0529] Figure 25 As shown, in the case of a titanium alloy, the output of the liquidus temperature and solid solution calculation unit 1001 is as follows:

[0530] • Al solid solution content during solution treatment [mass %];

[0531] • Sn solid solution content during solution treatment [mass %];

[0532] • V solid content [mass %] during solution treatment;

[0533] • Mo solid solution content during solution treatment [mass %];

[0534] • Zr solid solution content during solution treatment [mass %];

[0535] • Pd solid solution content during solution treatment [mass %];

[0536] • The amount of Si dissolved during solution treatment [mass %];

[0537] • The amount of Cr dissolved during solution treatment [mass %];

[0538] • The amount of Ru dissolved during solution treatment [mass %];

[0539] • The amount of Ta dissolved during solution treatment [mass %];

[0540] • Co solid solution content during solution treatment [mass %];

[0541] • Ni solid solution content [mass %] during solution treatment; and

[0542] • Liquid phase temperature [°C].

[0543] Furthermore, in the case of titanium alloy castings, the precipitate calculation unit 1002 outputs:

[0544] • Precipitate size [nm];

[0545] • Density of precipitates [number / m³] 3 ];and

[0546] • Volume ratio of precipitates [volume %].

[0547] Accordingly, the computing unit 630 can

[0548] • "Candidate data for manufacturing process data notified from extraction department 620" and

[0549] • "Data representing the microstructure of the metal, including liquidus temperature, the amount of Al, Sn, V, Mo, Zr, Pd, Si, Cr, Ru, Ta, Co, and Ni dissolved during solid solution, precipitate size, precipitate density, and precipitate volume ratio."

[0550] It is associated with and notified to the Judgment Department 640.

[0551] Thus, according to the eighth embodiment, even when the material of the design object is a titanium alloy, similar effects to those of the first embodiment described above can be obtained.

[0552] [Other Implementation Methods]

[0553] The above embodiments illustrate the use of re-regression analysis to analyze the relationship between manufacturing process data and mechanical characteristic data; however, the method for analyzing the relationship between manufacturing process data and mechanical characteristic data is not limited to this. For example, training data can be used to train a machine learning model to analyze the relationship between manufacturing process data and mechanical characteristic data.

[0554] Furthermore, although the manufacturing process search device 400 has been described as being composed of an integral device in the above embodiments, the manufacturing process search device 400 may also be composed of multiple devices.

[0555] It should be noted that this disclosure is not limited to the configurations listed in the above embodiments, combinations with other elements, or the configurations shown herein. Various modifications can be made to these without departing from the spirit of this disclosure, and appropriate determinations can be made based on their application.

[0556] This application claims priority to Japanese Patent Application No. 2023-123470, filed on July 28, 2023, the contents of which are incorporated herein by reference in their entirety.

[0557] [Explanation of reference numerals in the attached figures]

[0558] 400: Manufacturing Process Search Device

[0559] 610: Analysis Department

[0560] 620: Extraction Department

[0561] 630: Computing Department

[0562] 640: Judgment Department

[0563] 710: Training data

[0564] 721: Data Input Department

[0565] 722: Regression Computation Department

[0566] 723: Analysis Results Output Section

[0567] 724: Verification Department

[0568] 730: Validation data

[0569] 921: Data Change Department

[0570] 922: Forecasting Department

[0571] 923: Waiting List Extraction Department

[0572] 924: Backup Output Department

[0573] 1001: Calculation Section for Liquid Phase Temperature and Solid Solution Content

[0574] 1002: Precipitate Calculation Department.

Claims

1. A material manufacturing process search method for designing a design target material including a material composed of a plurality of components or a material manufactured by a combination of a plurality of manufacturing conditions, wherein, by a computer: an analysis step of analyzing a relationship between a manufacturing process of the design target material and a mechanical property of the design target material; an extraction step of extracting a candidate of the manufacturing process of the design target material satisfying a target value of the mechanical property, based on a result of the analysis step; a calculation step of calculating data representing a material structure of the design target material, which is data affecting the mechanical property, by thermodynamic calculation; and a determination step of determining whether the candidate of the manufacturing process is appropriate or not, based on the data representing the material structure calculated by the thermodynamic calculation on the candidate of the manufacturing process in the calculation step.

2. The material manufacturing process search method according to claim 1, wherein the design target material is an inorganic material.

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

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

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

6. The material manufacturing process search method according to claim 5, wherein the manufacturing process includes an alloy composition and a manufacturing condition, the alloy composition includes Si, Mg, Cu, Fe, Zr, and Ti, and the manufacturing condition includes a homogenization temperature of a homogenization treatment, a pre-extrusion heating temperature of a solution treatment, a thickness of an extrusion section, an extrusion pressure, an extrusion speed, and a cooling water temperature.

7. The material manufacturing process search method according to claim 6, wherein the mechanical property includes a tensile strength, a 0.2% yield strength, and an elongation.

8. The material manufacturing process search method according to claim 7, wherein the data representing the material structure is data representing a metal structure, and the data representing the metal structure includes a liquidus temperature, a pre-extrusion Mg solid solution amount, a pre-extrusion Si solid solution amount, a precipitate size, a precipitate density, and a volume fraction.

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

10. The material manufacturing process search method according to claim 9, wherein the manufacturing process includes an alloy composition and a manufacturing condition, the alloy composition includes Si, Mg, Cu, Fe, Zr, and Ti, and the manufacturing condition includes any one of a molten metal temperature at a time of 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.

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

12. The material manufacturing process search method according to claim 11, wherein the data representing the material structure is data representing a metal structure, and the data representing the metal structure includes a liquidus temperature, a pre-extrusion Mg solid solution amount, a pre-extrusion Si solid solution amount, a precipitate size, a precipitate density, and a volume fraction. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ The data indicating the metal structure include any one of liquidus temperature, solid solution amount of Si, Fe, Cu, Mn, Mg, Cr, Ni, Zn, Ti, V, Pb, Sn, Bi, B, P, Zr, Sr after solid solution, precipitate size, precipitate density, and volume fraction.

13. The material manufacturing process search method according to claim 4, wherein The tensile material is a ferrous alloy tensile material.

14. The material manufacturing process search method according to claim 13, wherein The manufacturing process includes an alloy composition and a manufacturing condition, The alloy composition includes C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, and W, The manufacturing condition includes any one of molten metal temperature at casting, casting speed, amount of cooling water, heating temperature of the ferrous alloy at working, heating time of the ferrous alloy at working, working speed, reduction ratio, working temperature, cooling speed after working, natural aging time, heat treatment temperature, heat treatment time, and cooling speed after heat treatment.

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

16. The material manufacturing process search method according to claim 15, wherein The data indicating the material structure are data indicating a metal structure, The data indicating the metal structure include liquidus temperature, solid solution amount of C, B, N, Si, P, S, Mn, Al, Ti, V, Cr, Co, Ni, Cu, Zr, Nb, Mo, W after working, precipitate size, precipitate density, and volume fraction.

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

18. The material manufacturing process search method according to claim 17, wherein The manufacturing process includes an alloy composition and a manufacturing condition, 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, The manufacturing condition includes any one of molten metal temperature at casting, casting speed, solidification speed, cooling speed after solidification, heat treatment temperature, heat treatment time, and cooling speed after heat treatment.

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

20. The material manufacturing process search method according to claim 19, wherein The data indicating the material structure are data indicating a metal structure, The data indicating the metal structure include 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, Ce before heat treatment, precipitate size, precipitate density, and volume fraction.

21. The material manufacturing process search method according to claim 4, wherein The drawn material is a copper alloy drawn material.

22. The material manufacturing process search method according to claim 21, wherein The manufacturing process includes an alloy composition and a manufacturing condition, The alloy composition includes Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, and As, The manufacturing condition includes any one of a melt temperature at the time of casting of the copper alloy, a casting speed, a cooling water amount, a homogenization temperature, a homogenization time, a cooling speed after homogenization, a copper alloy heating temperature at the time of hot working, a working speed, a cooling speed 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.

23. The material manufacturing process search method according to claim 22, wherein The mechanical property includes any one of 0.2% yield strength, tensile strength, elongation, electrical conductivity, thermal conductivity, Young's modulus, and linear expansion coefficient.

24. The material manufacturing process search method according to claim 23, wherein The data indicating the material structure is data indicating a metal structure, The data indicating the metal structure include liquidus temperature, solid solution amounts of Zn, Pb, Bi, Sn, Fe, P, Al, Hg, Ni, Mn, Se, Te, O, S, Zr, Be, Co, Ti, As before working, precipitate size, precipitate density, and volume fraction.

25. The material manufacturing process search method according to claim 4, wherein The cast material is a copper alloy cast material.

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

27. The material manufacturing process search method according to claim 26, wherein The mechanical property includes any one of 0.2% yield strength, tensile strength, elongation, electrical conductivity, thermal conductivity, Young's modulus, and linear expansion coefficient.

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

29. The material manufacturing process search method according to Claim 3, wherein The inorganic material is a titanium alloy.

30. The material manufacturing process search method according to Claim 29, wherein The manufacturing process includes an alloy component and a manufacturing condition, The alloy component includes Al, Sn, V, Mo, Zr, Pd, Si, Cr, Ru, Ta, Co, and Ni, The manufacturing condition includes any one of melt temperature at casting, solid solution temperature, solid solution time, artificial aging temperature, artificial aging time, annealing temperature, and annealing time.

31. The material manufacturing process search method according to Claim 30, wherein The mechanical property includes any one of 0.2% yield strength, tensile strength, elongation, Young's modulus, linear expansion coefficient, and fatigue property.

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

33. A material manufacturing process search apparatus for designing a design target material including a material composed of a plurality of components or a material manufactured by a combination of a plurality of manufacturing conditions, the material manufacturing process search apparatus comprising: an analysis section that analyzes a relationship between a manufacturing process of the design target material and a mechanical property of the design target material; an extraction section that extracts a candidate of the manufacturing process of the design target material satisfying a target value of the mechanical property, based on a result of the analysis by the analysis section; a calculation section that calculates data indicating a material structure of the design target material, which is data affecting the mechanical property, by thermodynamic calculation; and a determination section that determines whether or not the candidate of the manufacturing process is appropriate, based on the data indicating the material structure calculated by the calculation section for the candidate of the manufacturing process.

34. A material manufacturing process search program that causes a computer of a material manufacturing process search apparatus that designs a design target material including a material composed of a plurality of components or a material manufactured by a combination of a plurality of manufacturing conditions to execute: an analysis step of analyzing a relationship between a manufacturing process of the design target material and a mechanical property of the design target material; an extraction step of extracting a candidate of the manufacturing process of the design target material satisfying a target value of the mechanical property, based on a result of the analysis by the analysis step; a calculation step of calculating data indicating a material structure of the design target material, which is data affecting the mechanical property, by thermodynamic calculation; and a determination step of determining whether or not the candidate of the manufacturing process is appropriate, based on the data indicating the material structure calculated by the calculation step for the candidate of the manufacturing process. ​ ​ ​ ​ ​ ​ ​ ​ ​ a judging step of judging whether the candidate of the manufacturing process is appropriate or not, based on data indicating the material structure calculated in the thermodynamic calculation of the candidate of the manufacturing process in the calculating step.

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