Automatic evaluation device and automatic evaluation method for alloy composition search

Abstract

To perform an alloy composition search that enables automatic, high-speed evaluation between evaluation devices for a plurality of evaluation items, this automatic evaluation device (1000) is provided with: a nanoindentation testing machine (50) which measures a mechanical characteristic according to the shape of an indenter mark; a mechanical characteristic distribution memory unit (120) which stores a distribution condition of the mechanical characteristic; a measurement instrument cooperation controller (110) which controls measurement work by measurement instruments upon a sample to be measured; a measurement data storage unit (130) which stores measurement data from the measurement instruments; and a post processing unit (200) which calculates physical property data corresponding to the measurement data.

Description

[Technical Field]

[0001] The present invention relates to an automated evaluation apparatus and automated evaluation method for alloy composition exploration suitable for performance prediction in the field of structural materials, and more particularly to an automated evaluation apparatus and automated evaluation method for alloy composition exploration using a high-speed automated processing evaluation technology for composition-process-structure-property databases to achieve a significant improvement in accuracy and an expansion of the prediction range. [Background technology]

[0002] The acceleration and sophistication of prediction technologies, including numerical simulations of phase diagrams, structures, and properties, are crucial technological elements for shortening the time it takes to develop and implement materials in society. An example of how predictions utilizing physical phenomena and empirical formulas have contributed to the creation of new materials is the alloy design program developed by the applicant [Non-Patent Literature 1], in which a Ni-based single-crystal superalloy (TMS alloy) created with the assistance of prediction results was actually installed as a turbine blade in an aircraft engine. Prediction technologies are also widely used to improve process conditions. A typical example is the software (JMatPro) [Non-Patent Literature 2] developed by Sente Software in the UK, which predicts strain-rate-dependent physical, thermodynamic, and mechanical properties from the chemical composition and structure information of alloys, and has greatly contributed to determining process conditions such as casting, heat treatment, and forging in industrial settings. Many other process-structure-property prediction programs [Non-Patent Literature 3, 4] are also under development. Furthermore, recent advances in AI and machine learning technologies will likely further shorten the time required from material development to practical application.

[0003] On the one hand, whether using physical phenomena and empirical formulas or machine learning for prediction, the accuracy and range of the prediction are determined by the quality and quantity of experimentally obtained material databases. In other words, in order to optimize the process of searching for alloy compositions that are truly socially implementable through prediction, it is essential to acquire a wide range of highly accurate experimental data. On the other hand, in the field of structural materials development, there are no large-scale databases known as big data, and until now, databases have been built through long-term, painstaking experiments by researchers and engineers. For example, in order to design and optimize the process of Ni-based superalloys with a γ-γ' two-phase structure, it is essential to obtain accurate phase diagrams corresponding to countless combinations of elements from systems of more than 10 elements such as Ni-Cr-Co-W-Mo-Al-Ti-Ta-Hf-Re-Ru, to understand the microstructure formation behavior at each process temperature and time, and to acquire databases of microstructure and high-temperature strength properties, as well as creep and fatigue databases. Generally, it takes decades of trial and error, many experiments, and enormous development funds to implement a single candidate alloy in society. Under these circumstances, the development of high-speed processing evaluation systems, including technologies and equipment for acquiring experimental data at high speed (high-speed processing), has become increasingly important in recent years.

[0004] <Composition gradient material technology> As a high-speed processing and evaluation technique for experimental data used to create phase diagrams, evaluation techniques using compositionally graded materials with diffusion pair methods or Bridgman methods have been proposed. This evaluation technique obtains phase diagram and property information for countless alloy compositions by performing microstructure and property evaluation on samples with large compositional gradients within a single sample. Here, the diffusion pair method is a technique that creates a compositional gradient within a single sample by stacking alloy or pure metal plates with different compositions and causing interdiffusion reactions at high temperatures [Non-Patent Literature 5]. Furthermore, the Bridgman method is a technique that utilizes the differences in the initial and final solidification compositions of multicomponent alloys, and achieves a large compositional gradient within a single sample by slowly solidifying the multicomponent alloy in one direction [Non-Patent Literature 6]. Unlike the diffusion pair method, the Bridgman method utilizes metallic solidification, and therefore has the characteristic of being able to achieve a large compositional gradient even with heavy elements such as Re, W, Mo, and Ta, which generally have slow diffusion rates and are used for strengthening advanced Ni-based superalloys.

[0005] <High-speed processing evaluation technology> Nanoindentation is an effective method for evaluating the mechanical properties of such compositionally graded materials. Ikeda et al. [Non-Patent Literature 6] heat-treated compositionally graded samples of Ni-based superalloys at predetermined aging temperatures and times, then performed compositional analysis using an electron probe microanalyzer (EPMA). Subsequently, they observed the crystal orientation and γ' precipitate particles using a field emission scanning electron microscope (FE-SEM) equipped with electron backscatter diffraction (EBSD), and then obtained mechanical properties such as Young's modulus and hardness in a room-temperature nanoindentation apparatus. This has enabled them to successfully construct a composition-structure-property database for countless alloys with various compositions that have been heat-treated at a given aging temperature. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] H. Harada, H. Murakami, Design of Ni-base superalloys, in: T. Saito (Ed.), Computational Materials Design, Springer-Verlag, Berlin, 1999, pp. 39-70, [Non-Patent Document 2] Hideya Kijima, "Calculation of Solidification Properties using the JMatPro Property Calculation Software," Casting Engineering, Vol. 86, pp. 951-956 (2014). [Non-Patent Document 3] T. Osada et al., Virtual heat treatment for gg' two-phase NI-Al alloy on the Materials Integration System, Materials & Design 226 (2023) 111631 [Non-Patent Document 4] L. Wu et al. The temperature dependence of strengthening mechanisms in Ni based superalloys: A newly re-defined cuboidal model and its implications for strength design, Journal of the Alloys and Compounds, 931(2023)167508. [Non-Patent Document 5] K. Goto et al., High-throughput evaluation of stress-strain relationships in Ni-Co-Cr ternary systems via indentation testing of diffusion couples, 910 (2022)164868 [Non-Patent Document 6] A.Ikeda et al. High-throughput mapping method for mechanical properties oxidation resistance, and phase stability in Ni-based superalloys using composition-graded unidirectional solidified alloys, Scripta Materialia, 193(2021) 91-96 [Non-Patent Document 7] K. Goto et al., “Inverse estimation approach for elastoplastic properties using the load-displacement curve and pile-up topography of a single Berkovich indentation”, Materials and Design, 194 (2020) 108925 [Overview of the project] [Problems that the invention aims to solve]

[0007] However, the method for evaluating the mechanical properties of compositionally graded materials described in Non-Patent Literature 6 had the problem that moving between EPMA / FE-SEM / nanomindentation devices required positioning on the order of several millimeters each time, necessitating significant time, effort, and expertise for sample setting between devices. Furthermore, post-processing of the countless data obtained from various devices using analysis software also required significant time and human effort. In addition, the only mechanical property that could be obtained was hardness, and it was not possible to obtain stress-strain curves and high-temperature stress-strain curves, which are generally required when designing alloys. Moreover, for compositionally graded samples, only one heat treatment condition could be performed per sample, limiting the process range.

[0008] On the other hand, the challenges related to obtaining mechanical properties can be solved to some extent by using a method that estimates stress-strain curves by inverse analysis. Goto et al. [Non-Patent Literature 7] have proposed a method that utilizes finite element method simulation results to estimate the stress-strain curve at the location where indentation is performed from the load-displacement curve and indentation shape obtained by the nanoindentation method. By using this inverse analysis method in combination with the method of Non-Patent Literature 6, it becomes possible to obtain a phase diagram-structure-stress-strain curve database from one or a limited number of compositionally graded samples. Here, information on the indentation shape is necessary to estimate the stress-strain curve, and Goto et al. obtain the indentation shape after indentation using AFM with the same indenter. However, the method of estimating the stress-strain curve by inverse analysis described in Non-Patent Document 7 has the drawback that acquiring the indentation shape using AFM is time-consuming, and the indenter tip wears down, affecting the accuracy of stress-strain curve measurement, making it difficult to acquire a large amount of mechanical data at high speed.

[0009] As described above, several elemental technologies exist for high-speed processing and evaluation of necessary databases in the field of structural materials. However, since there is still a lot of manual work and know-how involved in the interaction between evaluation devices, the development of evaluation systems and post-processing software that enable automated and high-speed evaluation across multiple evaluation items is essential.

[0010] Existing high-speed processing evaluation technologies have the following temporal and technical challenges: (1) to (5). (1) Time and technical challenges related to the transfer and setting up of EPMA / FE-SEM / nanoindentation devices, (2) Time and technical challenges related to the estimation of high-temperature stress-strain curves caused by the indentation shape acquisition method, (3) Time and technical challenges related to post-processing of big data using analytical software, (4) Technical challenges that limit the scope of the process, and (5) Since the experiments involve humans, there are limitations on working hours, and the human resource costs are enormous. The present invention solves the problems of the prior art described above and aims to provide an automated evaluation device for alloy composition exploration that enables automatic and high-speed evaluation of multiple evaluation items between evaluation devices. [Means for solving the problem]

[0011] The automated evaluation apparatus for alloy composition exploration of the present invention, as shown in Figure 1 for example, includes a sample holding unit 20 that holds the sample to be measured 10 in a predetermined position, a mechanical property distribution storage unit 120 that stores the distribution of mechanical properties measured by the nanoindentation tester based on the position of the indentation marks on the sample to be measured 10 formed by the nanoindentation tester, a housing unit 30 that holds at least one measuring instrument from among a wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer for analyzing the constituent elements of the alloy, a field-emission scanning electron microscope for measuring the microstructure of the alloy, or an electron beam backscatter diffractometer for analyzing the orientation distribution of crystal grains, texture, or crystal phase distribution, and the housing unit 30 The system comprises a measurement instrument linkage controller 110 that controls the measurement of a sample to be measured using at least one of the following measuring instruments: a wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer for analyzing the constituent elements of the applied alloy; a field-emission scanning electron microscope for measuring the microstructure of the alloy; or an electron beam backscatter diffractometer for analyzing the orientation distribution of crystal grains, texture, or crystal phase distribution; a measurement data storage unit 130 that stores the measurement data of the measuring instrument as measurement data of the sample to be measured by the measuring instrument, based on the position of the indenter marks formed on the sample to be measured 10; and a post-processing unit 200 that calculates physical property data corresponding to the measurement data of the measuring instrument.

[0012] The automatic evaluation method for alloy composition exploration of the present invention, as shown in FIG. 3A for example, performs scanning electron microscope observation or electron backscatter diffraction observation on the sample to be measured 10 (S200), selects the position for mechanical measurement based on the structure, phase, or crystal structure on the obtained image (S220), arbitrarily stores the designed composition ratio of the composition elements at the position of the mechanical measurement in the composition ratio storage unit (S400), performs a high-temperature hardness test (nanoindentation measurement) at a predetermined temperature on the selected position for mechanical measurement of the sample to be measured 10 (S210), performs indentation shape measurement on the position of the mechanical measurement (S220), calculates the high-temperature stress-strain curve for the sample to be measured 10 (S240), and optionally associates the data of the high-temperature stress-strain curve with the position observed by the scanning electron microscope observation or electron backscatter diffraction observation of the sample to be measured 10 from the structure, phase, or crystal structure of the indenter mark of the sample to be measured 10 (S500).

[0013] The automatic evaluation method for alloy composition exploration of the present invention, as shown in FIG. 3B for example, for the sample to be measured 10 on which a high-temperature hardness test (nanoindentation measurement) has been performed at a predetermined temperature by a nanoindentation tester, performs indentation shape measurement on the position where the indenter mark of the sample to be measured 10 is formed (S220), calculates the high-temperature stress-strain curve for the sample to be measured 10 based on the result of the indentation shape measurement (S240), performs scanning electron microscope observation or electron backscatter diffraction observation on the sample to be measured 10 (S300), selects the detailed position for scanning electron microscope observation or electron backscatter diffraction observation using the indenter mark of the sample to be measured 10 on the obtained image as the position for mechanical measurement, measures the structure, phase, or crystal structure of the detailed position by scanning electron microscope observation or electron backscatter diffraction observation (S320), and associates the mechanical measurement data with the position observed by the scanning electron microscope observation or electron backscatter diffraction observation of the sample to be measured 10 from the structure, phase, or crystal structure of the indenter mark of the sample to be measured 10 (S500). [[Effect of the Invention]]

[0014] According to the automatic evaluation device for alloy composition exploration of the present invention, a holding device for a composition gradient sample is provided, for example, inside a field emission scanning electron microscope, and at least one of a wavelength dispersive X-ray spectrometer, an energy dispersive X-ray spectrometer, or electron backscatter diffraction, and a housing that keeps the positional relationship of high-temperature nanoindentation constant is provided. Therefore, it is not necessary to move and precisely align the composition gradient sample among EPMA / field emission scanning electron microscope / nanoindentation device, and the sample setting adjustment among the devices becomes simple.

Brief Description of the Drawings

[0015] [Figure 1] It is a functional block diagram showing an outline of an integrated high-speed processing automatic evaluation system as an automatic evaluation device for alloy composition exploration of the present invention. [Figure 2] It is a diagram showing various auxiliary facilities of the integrated high-speed processing automatic evaluation system and the data flow to the obtained raw measurement data and post-processing software. [Figure 3A] It is a diagram showing an example of an automatic experiment / data analysis flow utilizing the integrated high-speed processing automatic evaluation system, showing the case where a nanoindentation tester is built in. [Figure 3B] It is a diagram showing an example of an automatic experiment / data analysis flow utilizing the integrated high-speed processing automatic evaluation system, showing the case where a nanoindentation tester is not built in. [Figure 4] It is a diagram showing the effect of high-speedization brought about by the integrated high-speed processing automatic evaluation system. [Figure 5] It is a diagram showing an example of a design method for a composition gradient diffusion pair. (a) is a Ni-Al phase diagram, (b) is an external view of a composition gradient single crystal alloy, and (c) is a perspective view of the structure of a composition gradient single crystal alloy. [Figure 6] It is a diagram showing a temperature gradient heat treatment device and the external appearance of a composition / temperature gradient test piece after heat treatment. [Figure 7A] It is a diagram showing an example of 500-point results of nanoindentation (load-displacement curve) in a Ni-Al binary system composition / temperature gradient single crystal sample. [Figure 7B]This figure shows the composition-hardness-Young's modulus map obtained from N=10,000 tests for a Ni-Al binary single crystal sample with a temperature gradient. [Figure 8A] This figure shows the results of automatic measurement of indentation load-depth in a temperature gradient single crystal sample of Ni-Co-Cr-W-Mo-Ti-Al-Ta-Nb 9-component superalloy (when aging time = 3 hours). [Figure 8B] This is an example of transferring coordinate information from indentation measurements to a temperature gradient single crystal sample of a Ni-Co-Cr-W-Mo-Ti-Al-Ta-Nb 9-component superalloy and automatically acquiring the indentation shape at each coordinate position using a scanning electron microscope (SEM). [Figure 8C] This figure shows the results of automated microstructural measurement (with an aging period of 3 hours) of a temperature gradient single crystal sample of a Ni-Co-Cr-W-Mo-Ti-Al-Ta-Nb 9-component superalloy. [Figure 8D] This figure shows the analysis results (for aging time = 3 hours) of (a) hardness H(GPa), (b) damped modulus Er(GPa), and (c) pile-up height Hp, which were analyzed using a shape analysis program (S230 in Figure 3B) based on the indentation height information in Figure 8B for a temperature gradient single crystal sample of Ni-Co-Cr-W-Mo-Ti-Al-Ta-Nb 9-component superalloy (a) H(GPa), (b) damped modulus Er(GPa), and (c) pile-up height Hp (for aging time = 3 hours). [Figure 8E] This figure shows (a) the yield strength σY (MPa), (b) the work hardening rate B (MPa), (c) all the obtained stress-strain curves, and (d) an example of a stress-strain curve, obtained by analyzing a temperature gradient single crystal sample of Ni-Co-Cr-W-Mo-Ti-Al-Ta-Nb 9-component superalloy using an inverse analysis program (S230 in Figure 3B). [Figure 8F] This figure shows (a) the volume fraction fV of aged precipitates and cooled precipitates, (b) the precipitate size, and (c) the precipitate shape (when the aging time is 3 hours) as analyzed using an automated image processing program (S310 in Figure 3B) from SEM image information of a temperature gradient single crystal sample of a Ni-Co-Cr-W-Mo-Ti-Al-Ta-Nb 9-component superalloy. [Figure 9] This figure shows the appearance and indentation of a Ni-W-Ta composition gradient material. [Figure 10] This figure shows an example of high-temperature nanoindentation results in Ni-W-Ta composition gradient samples (e.g., Ni93%-Ta7% region). [Figure 11A] This figure shows an example of automated measurement of indentation shape and the resulting high-temperature stress-strain diagram, where (a) is a SEM image and (b) and (c) show the three-dimensional indentation shape obtained by SEM. [Figure 11B] This figure shows an example of automatic measurement of indentation shape and the resulting high-temperature stress-strain diagram. (d) is an example of the output of the pile-up height measurement program, and (e) is a high-temperature stress-strain curve estimated from the load-displacement curve and indentation shape (e.g., Ni93%-Ta7% region). [Figure 12] This figure shows an example of automated measurement results (example of Ni-W-Ta composition gradient material) using an integrated high-speed processing automated evaluation system. At each measurement point, (a) is the Ni, W, and Ta concentration, (b) is Young's modulus, (c) is hardness, (d) is pile-up height, (e) is the processing effect coefficient, and (f) is the yield stress. [Figure 13A] This figure shows an example of automated measurement results using an integrated high-speed automated evaluation system (example of Ni-Co composition gradient material), and displays SEM images of composition gradient samples. [Figure 13B] This figure shows an example of automated measurement results (example of Ni-Co composition gradient material) using an integrated high-speed automated evaluation system, illustrating the temperature dependence of hardness for each composition. [Figure 14] This figure shows an example of automated measurement results (example of Ni-Co composition gradient material) using an integrated high-speed automated evaluation system, comparing the hardness map and phase diagram for each composition and temperature. [Figure 15A] This figure displays the EPMA map of each element in the Ni-Co-Al-Ti quaternary alloy. [Figure 15B] This is a SEM image showing the crystal grains to be analyzed in the Ni-Co-Al-Ti quaternary alloy, and also indicating the area to be analyzed using EPMA surface analysis. [Figure 15C]This figure shows the tie-line data setting conditions for determining the γ-γ' composition required for the state diagram database using an automated composition analysis program. [Figure 15D] This figure shows the results of the analysis of the composition of the γ and γ' phases in the target area of ​​the EPMA surface analysis. [Figure 15E] This figure shows tie-line data for Ni-Co-Al-Ti quaternary alloys. [Figure 16A] This diagram shows markers corresponding to each measurement point on a location map of the sample being measured. [Figure 16B] This diagram displays the mechanical properties of a sample under test, such as elastic modulus, hardness, pile-up height, yield stress, and processing effect coefficient, at each measurement point, on a state diagram or location map. [Figure 16C] This figure shows the display of characteristic values ​​at any selected point among the measurement points in Figure 16A of the sample being measured, using data reanalysis and mapping software. [Figure 16D] This figure displays raw data from load-displacement curves, microstructure, and pile-up analysis obtained by nanoindentation. [Figure 17A] This is a cross-sectional view of a key part of a weld, which is an example of a sample being measured that exhibits a compositional gradient and / or microstructural variation, showing a weld on a turbine disk made of nickel-based superalloy material. [Figure 17B] Figure 17A shows the dendritic solidification structure of the weld metal. [Figure 17C] Figure 17A is an organizational diagram of the HAZ (heat-affected zone). [Figure 17D] Figure 17A is a microstructure diagram of the base material. [Figure 18A] This is an overall perspective view of a turbine blade, an example of a sample being measured that exhibits a compositional gradient and / or microstructural variation, showing both the thick-walled and thin-walled sections. [Figure 18B] Figure 18A shows the dendritic coagulation structure of the thick-walled section. [Figure 18C] Figure 18A shows the dendritic solidification structure of the thin-walled section. [Figure 19]This is a microstructure diagram of a multiphase polycrystalline material, which is an example of a sample being measured that exhibits a compositional gradient and / or microstructural variation. [Modes for carrying out the invention]

[0016] As a means of solving the above problems, the inventors have conceived of an integrated high-speed automatic evaluation system 1000, which consists of an integrated evaluation device 100, an automatic control system 110, and a post-processor (hereinafter sometimes referred to as the "post-processing unit") 200 that executes post-processing software, as shown in Figure 1. Conventional evaluation systems measured independently using EPMA, EBSD, FE-SEM, or nanoindentation. However, the automatic evaluation device 1000 for alloy composition exploration of the present invention, which is an example of the integrated high-speed automatic evaluation system of the present invention, has a mechanism that enables comprehensive automatic evaluation of alloy composition, phase and orientation information, microstructure shape information, or mechanical properties of composition-graded samples by automatically controlling the integrated evaluation device 100, which is equipped with a wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer or electron beam backscatter diffractometer installed in a field emission scanning electron microscope, and high-temperature nanoindentation, using an automatic control system 110 operated by external software. Therefore, the integrated evaluation apparatus 100 includes, for example, a device for holding compositionally graded samples within a field emission scanning electron microscope, as well as at least one of a wavelength-dispersive X-ray spectrometer, an energy-dispersive X-ray spectrometer, or an electron beam backscatter diffractometer, and a housing that maintains a constant positional relationship of the high-temperature nanoindentation. Furthermore, as shown in Figure 2, the obtained raw data 64, 44, 12, 74, and 54 are converted into information truly necessary for materials engineering experts when designing alloys, such as phase diagram information, precipitation shape information, and high-temperature mechanical properties (elastic constants, stress-strain curve, yield stress, work hardening coefficient, or creep deformation rate), via a post-processing unit 200 that runs post-processing software. In addition, the field emission scanning electron microscope is configured to evaluate the indentation shape non-contact, at high speed, and with high accuracy.

[0017] [1] The automated evaluation apparatus 1000 for alloy composition exploration of the present invention, as shown in Figure 1 for example, includes a sample holding unit 20 that holds the sample to be measured 10 in a predetermined position, a mechanical property distribution storage unit 120 that stores the distribution of mechanical properties measured by the nanoindentation tester based on the position of the indenter marks on the sample to be measured 10 formed by the nanoindentation tester, a housing unit 30 that holds at least one measuring instrument from among a wavelength-dispersive X-ray spectrometer or an energy-dispersive X-ray spectrometer for analyzing the constituent elements of the alloy, a field emission scanning electron microscope for measuring the microstructure of the alloy, or an electron beam backscatter diffractometer for analyzing the orientation distribution of crystal grains, texture, or crystal phase distribution, and the housing unit The system includes a measurement instrument linkage controller 110 that controls the measurement of a sample to be measured using at least one of the following measuring instruments: a wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer for analyzing the constituent elements of the alloy mounted on 30; a field-emission scanning electron microscope for measuring the microstructure of the alloy; or an electron beam backscatter diffractometer for analyzing the orientation distribution of crystal grains, texture, or crystal phase distribution; a measurement data storage unit 130 that stores the measurement data of the measuring instrument as measurement data of the sample to be measured by the measuring instrument, based on the position of the indenter marks formed on the sample to be measured 10; and a post-processing unit 200 that calculates physical property data corresponding to the measurement data of the measuring instrument.

[0018] [1A] The automated evaluation apparatus for alloy composition exploration of the present invention, as shown in Figure 1 for example, includes a sample holding unit 20 that holds the sample to be measured 10 in a predetermined position, a nanoindentation tester 50 that presses an indenter onto the sample to be measured 10 and measures the mechanical properties by the shape of the indenter mark, a mechanical property distribution storage unit 120 that stores the distribution of mechanical properties measured by the nanoindentation tester 50 based on the position of the indenter mark formed on the sample to be measured 10, and at least one of the following measuring instruments: a wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer that analyzes the constituent elements of the alloy, a field-emission scanning electron microscope that measures the microstructure of the alloy, or an electron backscatter diffractometer that analyzes the orientation distribution of crystal grains, texture, or crystal phase distribution. The system comprises a housing 30 for holding the instrument, a nanoindentation tester 50 mounted on the housing 30, and a measurement instrument linkage controller 110 that controls the measurement of a sample to be measured using at least one of the following measuring instruments: a wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer for analyzing the constituent elements of the alloy, a field-emission scanning electron microscope for measuring the microstructure of the alloy, or an electron beam backscatter diffractometer for analyzing the orientation distribution of crystal grains, texture, or crystal phase distribution; a measurement data storage unit 130 that stores the measurement data of the measuring instrument as measurement data of the sample to be measured by the measuring instrument based on the position of the indenter marks formed on the sample to be measured 10; and a post-processing unit 200 that calculates physical property data corresponding to the measurement data of the measuring instrument. [2] In the automated evaluation apparatus for alloy composition exploration of the present invention [1] or [1A], the sample to be measured 10 is a composition gradient material having a composition ratio that is gradient between the upper limit and lower limit of each of the composition elements of the alloy system to be evaluated. A heat treatment temperature gradient material is obtained by heat-treating a material with a composition gradient, or a test material with a uniform composition in place of the material with a composition gradient, in a furnace where the heat treatment temperature is graded between an upper limit and a lower limit. A heat-affected element that includes a region of the base material (metal, thermoplastic material, etc.) where the microstructure and properties of the sample 10 have been altered by welding or thermal cutting operations, even though it has not melted. A metal additive manufacturing material is produced by dissolving metal powder in the necessary parts of a metal component using an electron beam or fiber laser, and then solidifying it to form the metal component. The material may be either a metal powder injection molded material, in which metal fine powder is used as a raw material, the metal fine powder is not melted, a binder is added and injection molded, and the molded body formed by the injection molding is degreased and sintered to obtain a metal part. [3] In the automated evaluation apparatus for alloy composition exploration of the present invention [2], the metal additive fabricated material is, The powder bed method involves irradiating a powder bed, which is covered with metal powder, with a laser beam or electron beam, causing each layer to melt and solidify repeatedly. Directed energy deposition (EDM) is a method of fabricating objects by supplying powder or wire and melting it with a laser or electron beam, then depositing it. A fused deposition modeling (FDM) method is used, in which metal powder is added to a thermoplastic resin, melted by heat and layered, and the degreased molded body is sintered to solidify the metal powder, or This binder jetting method involves spraying a liquid binder onto metal powder through a nozzle to create a molded object. After each layer, once the binder has been sprayed and solidified, the build plate is lowered and more powder is laid down. This process is repeated for each layer, and after molding, the object is sintered in a high-temperature furnace or heater to remove the binder. It would be preferable if it were manufactured in one of the following places. [4] Any of the automated evaluation apparatus for alloy composition exploration of the present invention [1] to [3] and [1A] further includes a nanoindentation tester 50 that presses an indenter onto the sample to be measured 10 and measures the mechanical properties by the shape of the indenter mark, The mechanical property distribution storage unit 120 stores the distribution of mechanical properties measured by the nanoindentation tester 50 based on the position of the indenter marks formed on the sample 10 to be measured. The housing 30, together with the nanoindentation testing machine 50, holds at least one measuring instrument from among the wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer, the field-emission scanning electron microscope, or the electron beam backscatter diffraction apparatus. The measurement instrument linkage controller 110 may control the measurement operation of the sample to be measured using the nanoindentation tester 50 mounted on the housing, and at least one of the following measuring instruments: the wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer, the field-emission scanning electron microscope, or the electron beam backscatter diffraction apparatus.

[0019] [5] In any of the automatic evaluation apparatus for alloy composition exploration of the present invention [1] to [4] and [1A], preferably, the target of the alloy composition exploration is one or more selected from the group consisting of composition gradient materials, heat treatment temperature gradient materials, heat-affected members, metal additive manufactured materials, and metal powder injection molded materials, and in the measurement data storage unit 130, the measurement data of the sample to be measured is preferably at least one of the following: crystal phase equilibrium composition data 64 corresponding to the composition elements 62 of the alloy, fine precipitate identification data 44 and indentation shape high-speed acquisition data 12 corresponding to the microstructure shape 42 of the alloy, polycrystalline orientation data 74 corresponding to the anisotropy 72 of the alloy, or load / displacement information corresponding to the mechanical properties measured by the nanoindentation tester 50. [6] In the automated evaluation apparatus for alloy composition exploration of the present invention [5], preferably, as shown in Figure 2, for example, the target of the alloy composition exploration is a nickel-based superalloy, and in the measurement data storage unit 130, the measurement data of the sample to be measured is preferably at least one of the following: γ / γ' equilibrium composition data 64 corresponding to the constituent elements 62 of the alloy, fine precipitate identification data 44 and indentation shape high-speed acquisition data 12 corresponding to the microstructure shape 42 of the alloy, polycrystalline orientation data 74 corresponding to the anisotropy 72 of the alloy, or load / displacement information corresponding to the mechanical properties measured by the nanoindentation tester 50. [7] In the automated evaluation apparatus for alloy composition exploration of the present invention [5], preferably, as shown in Figure 2, the post-processing unit 200 reads at least one of the following: composition data (66) corresponding to the crystal phase equilibrium composition data 64, precipitation morphology data (46) corresponding to the fine precipitate identification data 44, or indentation shape high-speed acquisition data 12 and polycrystalline orientation data 74, or load / displacement information 54, and calculates at least one of the following: elastic constant, critical resolved shear stress (CRSS), work hardening coefficient, or creep deformation rate. [8] In the automated evaluation apparatus for alloy composition exploration of the present invention [6], preferably, as shown in Figure 2, the post-processing unit 200 reads at least one of the following: composition data (66) corresponding to the γ / γ' equilibrium composition data 64, precipitation morphology data (46) corresponding to the fine precipitate identification data 44, or indentation shape high-speed acquisition data 12 and polycrystalline orientation data 74, or load / displacement information 54, and calculates at least one of the following: elastic constant, critical resolved shear stress (CRSS), work hardening coefficient, or creep deformation rate.

[0020] [9] In any of the automatic evaluation apparatus for alloy composition exploration of the present invention [1] to [8] and [1A], preferably the sample to be measured 10 is a heat treatment temperature gradient material obtained by heat-treating the composition gradient material or a test material with a uniform composition in place of the composition gradient material in a furnace in which the heat treatment temperature is graded between an upper limit and a lower limit, and it is preferable that the position of the indenter marks formed on the sample to be measured 10 is linked to the heat treatment temperature.

[10] In any of the automated evaluation apparatus for alloy composition exploration of the present invention [1] to [9] and [1A], preferably the microstructure of the alloy shows a phase transformation boundary region in which the crystal structure changes between the first crystal structure and the second crystal structure.

[11] In the automated evaluation apparatus for alloy composition exploration of the present invention

[10] , preferably the first crystal structure is a face-centered cubic lattice and the second crystal structure is a hexagonal close-packed lattice.

[12] In the automated evaluation apparatus for alloy composition exploration of the present invention

[10] , preferably, the composition ratio of the constituent elements changes at the boundary between the first crystal structure and the second crystal structure.

[13] In the automated evaluation apparatus for alloy composition exploration of the present invention

[10] , preferably, the mechanical properties measured by the nanoindentation tester show a change in mechanical properties across the boundary line between the first crystal structure and the second crystal structure.

[14] In the automated evaluation apparatus for alloy composition exploration of the present invention

[13] , preferably, the mechanical property is hardness.

[15] In the automated evaluation apparatus for alloy composition exploration of the present invention [9], preferably, the sample to be measured 10 has a composition range that covers the γ / γ' two-phase structure, The post-processing unit may extract tie-line information for the γ / γ' two-phase structure of the nickel-based superalloy from an observation region consisting of a γ / γ' two-phase structure by obtaining an elemental map from the electron backscatter diffractometer and distinguishing between the γ phase and the γ' phase using the scanning electron microscope.

[16] In the automated evaluation apparatus for alloy composition exploration of the present invention [9], preferably, the sample to be measured 10 is a heat treatment temperature gradient material obtained by heat-treating the composition gradient material in a furnace in which the heat treatment temperature is graded between an upper limit and a lower limit, and the γ' precipitated particle volume fraction data is constructed by analyzing the size and amount of the γ' precipitated particles according to the heat treatment temperature.

[0021]

[17] The automated evaluation method for alloy composition exploration of the present invention, as shown in Figure 3A, for example, involves performing scanning electron microscopy observation or electron beam backscatter diffraction observation on the sample to be measured 10 (S200), selecting a position for mechanical measurement based on the microstructure, phase, or crystal structure on the obtained image (S220), optionally storing the design composition ratio of the constituent elements at the position for mechanical measurement in the composition ratio storage unit (S400), performing a high-temperature hardness test (nanoindentation measurement) at a predetermined temperature at the selected position for mechanical measurement of the sample to be measured 10 (S210), performing indentation shape measurement at the position for mechanical measurement (S220), calculating a high-temperature stress-strain curve for the sample to be measured 10 (S240), and optionally linking the microstructure, phase, or crystal structure of the indentation of the sample to be measured 10 with the data of the high-temperature stress-strain curve at the position observed by scanning electron microscopy observation or electron beam backscatter diffraction observation of the sample to be measured 10 (S500).

[0022]

[18] The automated evaluation method for alloy composition exploration of the present invention, as shown in Figure 3B, for example, involves performing a high-temperature hardness test (nanoindentation measurement) on a sample 10 to be measured at a predetermined temperature using a nanoindentation testing machine, measuring the indentation shape at the position where the indentation marks are formed on the sample 10 (S220), calculating a high-temperature stress-strain curve for the sample 10 based on the results of the indentation shape measurement (S240), and performing scanning electron microscope observation or electron beam backscatter diffraction observation on the sample 10. (S300) Using the indenter marks of the sample 10 to be measured on the obtained image, a detailed position to be observed using scanning electron microscopy or electron beam backscatter diffraction is selected as the position for mechanical measurement, and the microstructure, phase, or crystal structure of the detailed position is measured by scanning electron microscopy or electron beam backscatter diffraction (S320), and the data of the mechanical measurement is linked to the location of the sample 10 observed using scanning electron microscopy or electron beam backscatter diffraction from the microstructure, phase, or crystal structure of the indenter marks of the sample 10 to (S500).

[19] In the automated evaluation method for alloy composition exploration of the present invention

[17] or

[18] , preferably, the sample to be measured is Regarding the constituent elements of the alloy system to be evaluated, a composition gradient material having a composition ratio that is sloped between the upper and lower limits of each of the constituent elements, A heat treatment temperature gradient material is obtained by heat-treating a material with a composition gradient, or a test material with a uniform composition in place of the material with a composition gradient, in a furnace where the heat treatment temperature is graded between an upper limit and a lower limit. A heat-affected component includes a region of the base material (metal, thermoplastic material, etc.) that is not melted but whose microstructure and properties have been altered by welding or thermal cutting operations. Metal additive manufacturing materials are produced by dissolving and solidifying metal powder in the required areas using an electron beam or fiber laser to create metal parts. A metal powder injection molded material is obtained by using metal fine powder as a raw material, adding a binder to the metal fine powder without melting it, injection molding, degreasing and sintering the molded body formed by the injection molding, and obtaining a metal part. It would be good if it were one of the following.

[20] In the automated evaluation method for alloy composition exploration of the present invention

[19] , the metal additive manufacturing material is, The powder bed method involves irradiating a powder bed, which is covered with metal powder, with a laser beam or electron beam, causing each layer to melt and solidify repeatedly. Directed energy deposition (EDM) is a method of fabricating objects by supplying powder or wire and melting it with a laser or electron beam, then depositing it. A fused deposition modeling (FDM) method is used, in which metal powder is added to a thermoplastic resin, melted by heat and layered, and the degreased molded body is sintered to solidify the metal powder, or This binder jetting method involves spraying a liquid binder onto metal powder through a nozzle to create a molded object. After each layer, once the binder has been sprayed and solidified, the build plate is lowered and more powder is laid down. This process is repeated for each layer, and after molding, the object is sintered in a high-temperature furnace or heater to remove the binder. It would be preferable if it were manufactured in one of the following places.

[0023]

[21] In the automated evaluation method for alloy composition exploration of the present invention

[17] or

[18] , preferably, as shown in Figures 3A and 3B, the sample to be measured 10 is a heat-treated temperature gradient material obtained by heat-treating (S120) the composition gradient material or a test material with a uniform composition in place of the composition gradient material in a temperature gradient heat treatment furnace in which the heat treatment temperature is graded between an upper limit and a lower limit, and furthermore, the heat treatment temperature is stored in the composition ratio storage unit 66 along with the composition ratio storage of the sample to be measured at the position of the mechanical measurement.

[22] In any of the automated evaluation methods for alloy composition exploration of the present invention

[17] to

[21] , preferably, microstructure information for the sample to be measured 10 is obtained using high-magnification scanning electron microscope observation at the position of the mechanical measurement, and the microstructure information may include at least one of the following: grain size, precipitate particle size, or volume fraction.

[23] In any of the automated evaluation methods for alloy composition exploration of the present invention

[17] to

[22] , preferably, compositional information for the sample to be measured 10 is obtained at the position of the mechanical measurement using a wavelength-dispersive X-ray spectrometer or an energy-dispersive X-ray spectrometer, and the compositional information includes at least one of the coordinate composition of the sample to be measured 10, the matrix composition, or the precipitate composition, and the composition ratio of the constituent elements at the position of the mechanical measurement, as observed by the wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer, is stored in the composition ratio storage unit 66.

[24] In the automated evaluation method for alloy composition exploration of the present invention

[19] , preferably, the sample to be measured 10 is a composition gradient material, and for metal plates made of each constituent element of the alloy system to be evaluated, the metal plates of each constituent element are stacked alternately, and in a vacuum at a first predetermined temperature, while being pressed with a first predetermined stress, and held for a first predetermined time, spark plasma sintering is performed to temporarily bond them, It is preferable that the compositionally graded samples are produced by subjecting the pre-bonded spark plasma sintered material to a diffusion heat treatment using a hot isostatic pressurizing device, pressing it in an Ar atmosphere at a second predetermined temperature with a second predetermined stress, and holding it for a second predetermined time.

[25] In the automated evaluation method for alloy composition exploration of the present invention

[24] , preferably, the metal plates made of each constituent element of the alloy system to be evaluated are a set of three metal plates of a first metal element, a second metal element, and a third metal element, wherein the metal plate of the first metal element is used as an intermediate layer, and the metal plates of the second metal element and the third metal element are laminated above and below the metal plate of the first metal element, respectively. In the composition gradient sample obtained by subjecting the pre-joined spark plasma sintered material to diffusion heat treatment, a binary composition gradient alloy of the first and second metal elements is formed at the first initial interface between the metal plate of the first metal element and the metal plate of the second metal element in a direction perpendicular to the first and second initial interfaces, and a binary composition gradient alloy of the first and third metal elements is formed at the second initial interface between the metal plate of the first metal element and the metal plate of the third metal element, The thickness of the metal plate of the first metal element is preferably such that a ternary composition gradient alloy of the first, second, and third metal elements is formed between the first and second initial interfaces.

[26] In the automated evaluation method for alloy composition exploration of the present invention

[25] , preferably the first metal element is Ni, the second metal element is Ta, and the third metal element is W, and the binary composition gradient alloy of the first and second metal elements is Ni-Ta, the binary composition gradient alloy of the first and third metal elements is Ni-W, and the ternary composition gradient alloy of the first, second and third metal elements is Ni-Ta-W.

[0024] According to the automated evaluation apparatus for alloy composition exploration of the present invention, a device for holding composition-graded samples is provided, for example, within a field emission scanning electron microscope, and a housing is provided that maintains a constant positional relationship between at least one of a wavelength-dispersive X-ray spectrometer, an energy-dispersive X-ray spectrometer, or an electron beam backscatter diffraction device, as well as a high-temperature nanoindentation device. Therefore, movement and precise positioning of composition-graded samples between the EPMA / field emission scanning electron microscope / nanoindentation device are unnecessary, and sample setting adjustments between devices are simplified. As shown in

[24] and

[25] , the sample to be measured is a metal plate made of each constituent element of the alloy system to be evaluated, in which the metal plates of each constituent element are alternately stacked and subjected to diffusion heat treatment, thereby producing a composition gradient sample perpendicular to the joining surface of the metal plates of each constituent element, and further, if the composition gradient material is a heat treatment temperature gradient material obtained by heat treating the composition gradient material in a furnace in which the heat treatment temperature is graded between an upper limit and a lower limit, the constituent element (Al) of the alloy and the size of the γ' precipitate structure differ for each position measured by the nanoindentation tester, and the load-displacement curve has a large distribution with respect to the composition of the composition gradient sample and the heat treatment temperature, so that mechanical property data can be obtained with respect to the composition of the composition gradient sample and the heat treatment temperature over a wide range. Here, the diffusion heat treatment is performed by alternately stacking metal plates made of each constituent element of the alloy system to be evaluated, and then applying diffusion heat treatment in an Ar atmosphere at a second predetermined temperature and under a second predetermined stress using a hot isostatic pressurizing device, while holding for a second predetermined time.

[0025] <Example 1: Development of an integrated high-speed processing automated evaluation system> Figure 1 is a block diagram of the configuration of an integrated high-speed processing automatic evaluation system as an automatic evaluation device 1000 for alloy composition exploration according to the present invention. In Figure 1, the automatic evaluation device 1000 for alloy composition exploration includes a sample holding unit 20 for holding the sample to be measured 10 in a predetermined position, a housing unit 30, an FE-SEM 40, a nanoindentation tester 50, a WDS / EDS 60, an EBSD 70, a measurement instrument linkage controller 110, a mechanical property distribution storage unit 120, a measurement data storage unit 130, and a post-processing unit 200.

[0026] The sample under test 10 is a composition gradient material having a composition ratio that slopes between the upper and lower limits of each of the constituent elements of the alloy system to be evaluated. Furthermore, the sample under test 10 may be a heat treatment temperature gradient material obtained by heat-treating the composition gradient material in a furnace where the heat treatment temperature is sloped between the upper and lower limits. Alternatively, the sample under test 10 may be a heat treatment temperature gradient material obtained by heat-treating a sample under test, which has a uniform composition, in a furnace where the heat treatment temperature is sloped between the upper and lower limits.

[0027] The sample holding unit 20 holds the sample to be measured 10 in a predetermined position and is provided, for example, on the sample table of the FE-SEM 40. The housing 30 holds the measuring instruments, the FE-SEM 40, the nanoindentation tester 50, and, if necessary, the WDS / EDS 60 and / or EBSD 70, and maintains a constant relative positional relationship between these measuring instruments and the sample to be measured 10. The housing 30 is preferably made of a rigid material, such as metal or resin. The FE-SEM40 is a field emission scanning electron microscope used to measure the microstructure of alloys.

[0028] The nanoindentation tester 50 measures the mechanical properties of a sample 10 by pressing an indenter onto it and determining the shape of the indenter mark. When measuring the mechanical properties of a sample 10 based on the shape of the indenter mark, it is preferable to obtain the mechanical properties, such as Young's modulus or hardness, in a room-temperature nanoindentation apparatus, as disclosed in Non-Patent Literature 6, for example. WDS / EDS60 is used to analyze the elemental composition of alloys. WDS stands for Wave Dispersive Spectroscopy, and EDS stands for Energy Dispersive Spectroscopy. EBSD70 is a method used to analyze the orientation distribution, texture, or phase distribution of crystal grains, and is an abbreviation for Electron Back-Scatter Diffraction.

[0029] The instrument linkage controller 110 controls the measurement of the sample 10 to be measured using the FE-SEM 40 for measuring the microstructure of the alloy, and at least one of the following instruments: nanoindentation tester 50, WDS / EDS 60, or EBSD 70. The instrument linkage controller 110 is an example of an automated control system. The mechanical property distribution storage unit 120 stores the distribution of mechanical properties measured by the nanoindentation tester 50 based on the position of the indenter marks formed on the sample 10 to be measured. The measurement data storage unit 130 stores the measurement data from the measuring instrument as measurement data of the sample to be measured by the measuring instrument (excluding the nanoindentation tester 50), in a manner that allows it to be linked with the position of the indenter marks formed on the sample to be measured 10. The post-processing unit 200 calculates physical property data corresponding to the measurement data from the measuring instruments (excluding the nanoindentation tester 50). The physical property data is converted into information that materials engineering experts truly need when designing alloys, such as phase diagram information, precipitation shape information, and high-temperature mechanical properties (elastic constants, stress-strain curves, yield stress, work hardening coefficient, or creep deformation rate).

[0030] Furthermore, if the distribution of mechanical properties of the sample 10 is pre-stored by pressing an indenter onto the sample 10 to form an indenter mark, the automated alloy composition search evaluation device 1000 of the present invention can link the measurement data of the sample 10 measured by a measuring instrument (excluding the nanoindentation tester) with the distribution of mechanical properties of the sample 10, using the indenter mark measured by the FE-SEM 40 within the sample 10.

[0031] Figure 2 shows the various ancillary equipment of the integrated high-speed processing automatic evaluation system 1000, and the data flow to the obtained raw measurement data and post-processing software. The measurement instrument linkage controller 110 obtains the alloy composition 62 measured by WDS / EDS 60, the microstructure 42 measured by FE-SEM 40, the anisotropy 72 measured by EBSD 70, and the mechanical properties 52 measured by the nanoindentation tester 50 as measurement data for the sample under test 10. The post-processing unit 200 obtains γ / γ' equilibrium composition data 64 from the alloy composition 62 as raw measurement data of the sample 10 to be measured by the measuring instrument, fine precipitate identification data 44 and indentation shape high-speed acquisition data 12 from the microstructure 42 as raw measurement data, polycrystalline orientation data 74 from the anisotropy 72 as raw measurement data, and load / displacement information 54 from the mechanical properties 52 as raw measurement data. Furthermore, the post-processing unit 200 constructs a multi-component database from the raw measurement data of the sample 10 measured by the measuring instrument using post-processing software. A composition database 66 is generated from the γ / γ' equilibrium composition data 64. The composition database 66 can also be referred to as the composition ratio storage unit. A precipitation morphology database 46 is generated from the fine precipitate identification data 44. Elastic constants, critical resolved shear stress (CRSS), work hardening coefficient, and creep deformation rate are generated from the indentation shape high-speed acquisition data 12, polycrystalline orientation data 74, and load / displacement information 54, and stored in the physical property data storage unit 140. The information in the multi-system database generated by the post-processing unit 200 is linked with the characteristic prediction program 300 and / or the calculated state diagram.

[0032] Figure 3A is a flowchart showing an example of an evaluation procedure using the integrated high-speed processing automatic evaluation system 1000, illustrating the case where the integrated high-speed processing automatic evaluation system 1000 incorporates a nanoindentation tester. The integrated high-speed processing automatic evaluation system 1000 of the present invention can maximize its effectiveness by using the above-mentioned composition and temperature gradient samples. First, select the alloy system to be evaluated, for example, a nickel-based superalloy (S100). Next, a sample is prepared in which multiple alloying elements constituting the nickel-based superalloy have a compositional gradient (S110). For this preparation step, for example, the diffusion pair method or the Bridgman method may be used. Furthermore, compositional and temperature gradient samples are prepared by setting the compositional gradient material in a temperature gradient heat treatment furnace and subjecting it to heat treatment (S120). The manufacturing process information of the diffusion pair method or Bridgman method used in the production process of the compositional gradient material, or the process information of the temperature gradient heat treatment furnace used for heat treatment, is stored as process information and made available for use in data reanalysis and mapping software (S130).

[0033] Next, as a preliminary step for mechanical measurements, SEM or EBSD observation is performed, and the location for mechanical measurements is selected based on the microstructure, phase, and crystal structure obtained from the image (S200). Next, a high-temperature hardness test (nanoindentation measurement) is performed at an arbitrary temperature (S210). During this process, by maintaining the sample setting used for SEM / EBSD observation and sharing coordinate information with the indenter position, the measurement positioning time is significantly reduced, and positional accuracy is greatly improved. Hardness measurement can be performed automatically at multiple points by setting the loading / unloading speed, maximum load, and holding time to the same conditions, and establishing equally spaced measurement patterns within a two-dimensional space on the sample surface.

[0034] In the subsequent steps (S220, S300, S400), mechanical, structural, and compositional information corresponding to a single indentation location is acquired. First, SEM observation is performed to reset the indentation position with high precision (S220). At this time, using software within the integrated high-speed automatic evaluation system 1000, the user can specify the hardness test range for samples that have undergone tens to tens of thousands of high-temperature hardness tests. Within the hardness test range, the working distance (WD: distance between the SEM lens and the sample) for high-resolution SEM imaging is recorded at several points, and the WD for obtaining in-focus, high-resolution SEM images across the entire hardness test range is estimated in advance. Subsequently, multiple low-magnification SEM images with height information are automatically acquired, and by using software within the integrated high-speed automatic evaluation system 1000 that can automatically detect the (x,y) coordinates indicating the centroid of the indentation from the image contrast and / or height information, a significant speed increase can be achieved compared to manual measurement of the indentation coordinates (x,y). The reset indentation position information can be repeatedly used until the sample is removed and fixed on the SEM stage. This technology can also be applied to samples measured with an indenter independent of the SEM, and it is possible to determine the indentation position on the SEM stage for any sample. Furthermore, when hardness testing is performed at an arbitrary (x,y) coordinate position at high temperature and observation is performed at room temperature, the (x,y) coordinates may change between the high-temperature hardness test and the room-temperature observation due to the effects of thermal expansion of the sample and surrounding equipment. However, by using this automatic coordinate detection software, it is possible to automatically detect the accurate indentation coordinates (x,y) immediately before observation.

[0035] Next, the integrated high-speed processing automatic evaluation system 1000 acquires mechanical information (indentation shape multi-channel SEM measurement) (S220), tissue information (high-magnification SEM observation) (S300), and composition information (WDS / EDS measurement) for the defined indentation coordinates (x,y) (S400). Of this information, the mechanical information is obtained by simultaneously acquiring surface topography images from multiple directions using a multi-channel annular-resolved backscattered electron detector located below the objective lens of the SEM. The obtained SEM images are then reconstructed in three dimensions to measure the height of the pile-up around the indentation. This height measurement method is non-contact (scanning time = approximately 10 seconds), making it significantly faster than conventional methods (scanning time = approximately 120 seconds). Furthermore, it is a method that can suppress the deterioration of the indentation indenter. Regarding the tissue information, the coordinates corresponding to the flat sample surface near the indentation are automatically calculated from the indentation coordinates (x,y) and indentation height information. By performing high-resolution SEM imaging at the estimated working distance WD, tissue information including fine precipitates of several to tens of nanometers can be automatically acquired (S300). Because this tissue observation method is linked to the automatically detected indentation coordinate (x,y) information, it is a method that automatically calculates the appropriate planar position and takes high-resolution tissue images, even when the indentation coordinate pattern is complex and arbitrary, and the indentation size differs depending on the coordinate, which previously required manual coordinate setting. Furthermore, regarding compositional information, similar to the case of tissue information described above, the coordinates corresponding to the flat sample surface near the indentation are automatically calculated from the indentation coordinates (x,y) and indentation height information. By performing wavelength-dispersive X-ray spectrometer analysis or measurement using an energy-dispersive X-ray spectrometer at the estimated working distance WD, highly accurate compositional analysis becomes possible (S400). This compositional measurement method, similar to the case of tissue information described above, is linked to the automatically detected indentation coordinate (x,y) information. Therefore, even when the indentation coordinate pattern is complex and arbitrary, and the indentation size differs depending on the coordinate, it is possible to obtain highly accurate compositional information.

[0036] In the integrated high-speed processing and automatic evaluation system 1000, the data obtained automatically and at high speed through the processes up to this point is automatically analyzed in the post-processing unit 200, which executes post-processing software. Specifically, using the shape analysis / inverse analysis program (S230), the stress-strain curve is inversely estimated based on the indentation height information and the load-displacement curve separately obtained from nanoindentation measurements. Assuming that the inversely estimated information has the same value as the actual measurement, this is estimated to be equivalent to several hundred times greater efficiency. Furthermore, the integrated high-speed processing and automatic evaluation system converts the tissue and composition information obtained in the tissue information (S300) and composition information (S400) processes into the necessary tissue information (precipitation size, precipitate volume fraction, precipitate shape) and composition information via an automatic image processing program (S310) and an automatic composition analysis program (S410). As described above, the raw data obtained is converted into information truly necessary for materials engineering experts when designing alloys, such as equilibrium composition information, precipitation shape information, and high-temperature mechanical properties (elastic constants, stress-strain curve, yield stress, processing effect coefficient, creep deformation rate), via a post-processing unit 200 that runs the developed post-processing software (see Figure 2).

[0037] Furthermore, by storing the mechanical, microstructure, and compositional information together with the process information in the newly developed reanalysis and mapping software (S500), a composition-process-microstructure-property database with clearly defined interrelationships is obtained. The reanalysis and mapping software is part of the post-processing software and allows for the reanalysis of raw data according to the purpose. It also has the function to map the composition-process-microstructure-property data onto a phase diagram in a way that is easy for alloy developers to understand, thus providing maximum support for strategic planning for alloy development. Moreover, by selecting the target alloy in (S100) based on these results, it becomes possible to efficiently acquire a database that is effective for alloy design. These integrated high-speed automated evaluation systems 1000 are highly effective when applied to compositionally graded samples, but they are also effective in rapidly acquiring local composition-structure-properties for general structural materials with complex hierarchical and multiphase structures.

[0038] The effects of the integrated high-speed processing automatic evaluation system and method, configured in this manner as the automatic evaluation apparatus 1000 for alloy composition exploration according to the present invention, will be explained. Figure 4 shows the relationship between the test time and the number of tests N required to obtain mechanical properties (stress-strain curve). In conventional methods, after preparing a test specimen with a predetermined alloy composition through alloy casting, plastic deformation process, and heat treatment, obtaining a high-temperature stress-strain curve, for example, requires an average of one day for specimen sampling by electrical discharge machining, cutting, surface polishing, and tensile testing for N=1. This translates to approximately 21 years of testing if N=10,000 tests are performed. On the other hand, although the following is an estimate focusing only on mechanical properties, using the integrated high-speed automated evaluation system of the present invention, it is possible to obtain high-temperature stress-strain curves for, for example, 10,000 different alloy compositions in just 20 days. This demonstrates that the present invention is a 384 times faster evaluation system compared to conventional methods.

[0039] Thus, the integrated high-speed processing automatic evaluation system 1000 of the present invention is an integrated evaluation device that can acquire alloy composition, phase and orientation information, microstructure shape information, and mechanical properties for each coordinate of a composition and temperature gradient sample within a single device. To achieve this, the FE-SEM is equipped with WDS / EDS, EBSD, and high-temperature nanoindentation, and these are automatically controlled by external software, providing a mechanism for comprehensive automatic evaluation. Figure 3B is a flowchart showing an example of an evaluation procedure using the integrated high-speed processing automatic evaluation system 1000 as a modified example, showing a case where the integrated high-speed processing automatic evaluation system 1000 does not have a built-in nanoindentation tester. In the embodiment shown in Figure 3B, the difference from the embodiment shown in Figure 3A is that the step of performing a high-temperature hardness test (nanoindentation measurement) at an arbitrary temperature (S212) is performed by a nanoindentation tester located outside the integrated high-speed processing automatic evaluation system, but the other evaluation procedures are the same. [Examples]

[0040] <Example 2: Example of high-speed automatic measurement of composition, hardness, and Young's modulus> This section introduces an example of high-speed processing evaluation using Ni-Al binary alloy composition and temperature gradient samples. As examples of single-crystal alloys A and B used as the base material for the composition gradient samples, alloys with Al content = 10 at.% and Al content = 20 at.% were selected. Here, the Al content is, for example, the γ' volume fraction (f) at 600°C, as shown in the Ni-Al phase diagram in Figure 5(a). V ) are each f V =0% and f V This represents the amount of Al in the alloy that makes up 70%. Furthermore, the gray-filled areas on the phase diagram 5(a) indicate the upper and lower limits of composition and temperature for which composition and temperature gradient samples can be prepared using Ni-Al binary alloys. In the automated evaluation apparatus and method for Ni-Al binary alloys, diffusion heat treatment in the γ single-phase region is required, as described later. Therefore, the upper limit of the Al concentration is the concentration at the intersection of the solid phase temperature and the γ' solid solution temperature (C Al The value is 21 at.%). The lower limit concentration corresponds to an Al content of 0 at.% for pure Ni. The upper limit temperature is the solid phase temperature, and the lower limit temperature is 400°C, which is the instrument specification. Here, as an example, results using alloys with Al content of 10 at.% and 20 at.% are shown, but the Al content can be arbitrarily selected. Furthermore, this approach can also be applied to multi-component superalloys, and compositional gradient samples of any multi-component superalloy can be prepared by performing calculations using commercially available thermodynamic phase diagrams.

[0041] Here, Figure 5 shows an example of a design method for compositionally graded diffusion pairs, where (a) is the Ni-Al phase diagram, (b) is the appearance of the compositionally graded single crystal alloy, and (c) is a perspective view of the compositionally graded single crystal alloy. In Figure 5, single crystal rod A11 has an Al content of 10 at.% with the remainder being nickel and unavoidable impurities, single crystal rod B12 has an Al content of 20 at.% with the remainder being nickel and unavoidable impurities, Ni13 is a Ni foil with a film thickness of 0.02 mm, and stainless steel tube 14 is a holding material for HIP treatment of the bonded single crystal rods A11 and B12. Here, HIP is an abbreviation for Hot Isostatic Pressing, which refers to the hot isostatic pressing method. HIP is a process that simultaneously applies high temperatures of several hundred to 2000°C and isotropic pressures of several tens to 200 MPa to the object to be processed. Typically, a gas such as argon is used as the pressure medium to apply isotropic pressure.

[0042] Single-crystal alloy A and single-crystal alloy B were cast using a unidirectional solidification furnace. As shown in Figures 5(b) and (c), the obtained single-crystal rods A11 and B12 were both cut longitudinally along the {001} plane and bonded together, then vacuum-sealed in a stainless steel tube 14 to prepare a sample for HIP. To avoid reaction between the stainless steel tube 14 and the substrate during sealing, a 0.02 mm thick Ni foil 13 was placed between the single-crystal rods A11 and B12 and the stainless steel. The HIP sample was subjected to HIP treatment at 1100°C, 98.6 MPa, and in an Ar atmosphere for 3 hours. This allows the two single-crystal alloys to be joined on the {001} plane. Next, the obtained HIP material was subjected to diffusion heat treatment at 1300°C for 720 hours under furnace cooling conditions to prepare compositional gradient samples. Here, the diffusion heat treatment temperature of 1300°C is the temperature at which both single-crystal alloys A and B become a single γ phase, as shown in Figure 5(a). The obtained compositional gradient samples were subjected to aging heat treatment using a temperature gradient heat treatment furnace as shown in Figure 6 to prepare compositional and temperature gradient samples. The settings for this were a maximum sample temperature of 1280°C, a minimum sample temperature of 981°C, and an aging time of 3 hours. The compositional and temperature gradient samples were cut and polished on the {001} plane to prepare evaluation samples.

[0043] As described above, automated nanoindentation tests were performed on the {001} surface polished of the compositional and temperature gradient samples at intervals of 0.245 mm in the length direction and 0.075 mm in the diameter direction, for a total of 10,000 points. Figure 7A shows the indentation load-displacement curves for 500 points out of the 10,000 tests. It can be seen that the load-displacement curves have a large distribution because the Al composition and γ' precipitate size differ for each test coordinate. Figure 7B shows the Al content C in the sample cross-section. AlThis figure shows the relationship between hardness (at.%), hardness H(GPa), Young's modulus Er(GPa), and aging temperature (981°C to 1280°C). Thus, the integrated high-speed automated evaluation system and method of the present invention can evaluate the mechanical properties of alloys with a wide range of compositions and aging temperatures at high speed.

[0044] <Example 3: Example of high-speed automated measurement of an organizational database> This section presents a high-speed processing evaluation example demonstrating the relationship between the morphology of the γ' precipitated phase and the aging temperature, which significantly influences the high-temperature properties of a newly developed Ni-Co-based superalloy for turbine disks. A single-crystal round bar of Ni Bal.-11.7Cr-27.0Co-1.9W-3.4Mo-3.2Al-4.4Ti-2.2Ta-0.5Hf(wt.%) was used as the evaluation sample. This composition is similar to that of TMP(trademark)-5002 alloy, a powder metallurgy alloy developed for turbine disks. The obtained round bar was homogenized and then water-cooled. Similar to Example 2, it was subjected to aging heat treatment using a temperature gradient heat treatment furnace as shown in Figure 6. The settings were a maximum sample temperature of 1200°C, a minimum sample temperature of 680°C, and an aging time of 3 hours. The obtained temperature gradient samples were cut and polished longitudinally along the {001} plane to prepare evaluation samples.

[0045] Figures 8A to 8F show an example of the results of automated experiments and analyses performed on a {001} surface polished using nanoindentation SEM. In this case, a total of 2400 measurement points were used for the automated experiments and analyses, with 10 automated measurements performed for each of the 240 temperature points. Figure 8A shows an example of automated nanoindentation performed on a temperature gradient heat-treated sample of a multicomponent Ni-based superalloy (S120 in Figure 3B). The heat treatment temperatures of the temperature gradient heat-treated sample can be estimated to be 1164.3°C at measurement point a, 1161.7°C at measurement point b, 1086.9°C at measurement point c, 1014.0°C at measurement point d, 940.1°C at measurement point e, 868.7°C at measurement point f, 793.2°C at measurement point g, 721.7°C at measurement point h, and 647.5°C at measurement point i. Figure 8Aa is an SEM image showing the results of a nanoindentation test (S212 in Figure 3B) performed on the microstructural positions exemplified for measurement points a to i of the temperature gradient heat-treated sample. Figure 8Ab is a three-dimensional diagram showing the indentation load-displacement curves obtained at a total of 240 temperature points, including the illustrative measurement points a to i. The horizontal axis represents depth [nm], the vertical axis represents force [μN], and the depth represents heat treatment temperature [°C]. Figure 8Ac is a two-dimensional diagram showing the obtained indentation load-displacement curves. The horizontal axis represents depth [nm], and the vertical axis represents force [μN]. Furthermore, as shown in Figure 8B, this is an example where the coordinate information from the indentation measurement was transferred to the SEM, and the indentation shape was automatically acquired at each coordinate position (S220 in Figure 3B). By automatically acquiring multiple low-magnification SEM images with height information for measurement points a to i of a temperature gradient heat-treated sample, it is possible to reconstruct an image with height information at each measurement position. Furthermore, Figure 8C shows an example of the results of automatically performing high-resolution SEM imaging using the estimated working distance WD after automatically calculating the coordinates corresponding to the flat sample surface near the indentation. It shows measurement points a to i of a temperature gradient heat-treated sample. It can be confirmed that microstructural information, including fine γ' precipitates of tens to hundreds of nanometers, can be automatically acquired. Figure 8D shows the analysis results of (a) hardness H(GPa), (b) damping modulus Er(GPa) obtained from the indentation load-displacement curve shown in Figure 8A, and (c) pile-up height Hp, which was analyzed using a shape analysis program (S230 in Figure 3B) based on the indentation height information in Figure 8B. It can be confirmed that H, Er, and Hp fluctuate in accordance with the structural changes shown in Figure 8C. Furthermore, Fig. 8E shows (a) the yield strength σ Y (MPa), (b) the work hardening rate B (MPa), (c) all the obtained stress-strain curves, and (d) an example of a stress-strain curve, which were analyzed using the inverse analysis program (S230 in Fig. 3B) with the information shown in Fig. 8D. By using the inverse analysis program, it was confirmed that a large amount of data showing the influence of the microstructure on the stress-strain curve, which is essential for alloy design and component design, can be automatically obtained in a short time. Fig. 8F shows (a) the volume fraction f V of the aging precipitates and cooling precipitates, (b) the precipitate size, and (c) the precipitate shape, which were analyzed using the automatic image processing program (S310 in Fig. 3B) from the SEM image information shown in Fig. 8A. Here, the precipitate shape (Median superellipse) uses the ratio of the major axis to the minor axis in the superellipse. The superellipse is a closed curve similar to an ellipse. As shown in Fig. 8F, it can be seen that the size and amount of the γ' precipitate particles that grow during aging vary greatly for each coordinate of the {001} observation plane. As shown in Fig. 8F(a), it was found that the obtained volume fraction shows a tendency relatively close to the volume fraction estimated using Thermo-Calc (trademark), a commercially available thermodynamics equilibrium calculation software. On the other hand, as shown in Fig. 8F(b), it was possible to comprehensively obtain the precipitate sizes in a wide range of aging temperature regions. These size data become an important database for constructing a prediction formula based on the Ostwald ripening of the precipitates. Specifically, it contributes to the determination of various parameters (reaction time, diffusion coefficient, activation energy, etc.) required for calculating the Ostwald ripening rate. As described above, the automatic evaluation apparatus and method for alloy composition exploration according to the present invention can obtain an experimental data set regarding the microstructure information (volume fraction f V of the aging precipitates and cooling precipitates, precipitate size, and precipitate shape) and mechanical properties (hardness H (GPa), attenuation elastic modulus Er (GPa), yield strength σ Y (MPa), work hardening rate B (MPa), stress-strain curve) at a higher speed compared to the conventional method.

[0046] <Example 4: High-speed automated measurement example of a solid solution strengthening database> This section presents an example of high-temperature, high-speed processing evaluation using Ni-Ta-W composition gradient samples. The evaluation samples were prepared from a Ni plate with a thickness of approximately 1.0 mm, a Ta plate with a thickness of 0.2 mm, and a W plate with a thickness of 0.2 mm. These plates were stacked alternately in a Ni-Ta-Ni-W-Ni pattern and pre-bonded in a spark plasma sintering (SPS) apparatus under conditions of 700°C, 98 MPa, and 5 min in a vacuum. Subsequently, the SPS material was subjected to diffusion heat treatment using a HIP apparatus in an Ar atmosphere under conditions of 1250°C, 50 MPa, and 24 hours to prepare composition gradient samples. SEM images of the obtained composition gradient samples are shown in Figure 9. At the Ni-Ta initial interface and the Ni-W initial interface, Ni-Ta and Ni-W binary composition gradient alloys can be prepared perpendicular to the interface, respectively, and Ni-Ta-W ternary composition gradient alloys can be prepared between the two initial interfaces.

[0047] High-temperature nanoindentation tests were performed on these compositionally graded samples. The test conditions were room temperature, 200°C, and 400°C, with a maximum load of 50 mN and a measurement interval of 0.050 mm in the direction of compositional gradation. Figure 10 shows the indentation load-displacement curve obtained from nanoindentation tests performed on the 93at.%Ni-7at.%Ta coordinate for the compositionally graded samples. Hardness decreases from room temperature to 200°C, but the hardness at 200°C and 400°C are almost equivalent. In addition to such load-displacement curves, by measuring the indentation shape (pile-up height) and utilizing the techniques described in Non-Patent Documents 5 and 7, it is possible to estimate the high-temperature stress-strain curve for the hardness measurement coordinate (in this case, the 93at.%Ni-7at.%Ta alloy).

[0048] Figure 11A shows an example of automated measurement of indentation shape and the resulting high-temperature stress-strain diagram, where (a) is the SEM image and (b) and (c) show the three-dimensional indentation shape obtained by SEM. Figure 11A shows indentation SEM images (Figure 11A(a)) and three-dimensional height profiles (Figures 11A(b), (c)) measured at high speed automatically using the newly introduced SEM-multichannel annular segmentation backscattered electron detector. Previously, shape measurement was performed using AFM, a function of nanoindentation, but non-contact measurement with SEM has made it possible to obtain three-dimensional height profiles with equivalent height accuracy.

[0049] Figure 11B shows an example of automatic measurement of indentation shape and the resulting high-temperature stress-strain diagram. (d) is an example of the output of the pile-up height measurement program, and (e) shows the high-temperature stress-strain curve estimated from the load-displacement curve and indentation shape (e.g., Ni93%-Ta7% region). Figure 11B(d) is an output image of the Pile-up height evaluation program, a post-processing software developed separately. This program identifies the centroid position of the triangular pyramidal indentation from the height profile and determines the pile-up height on 36 equally spaced lines from the centroid. This series of functions has resulted in a 26-fold speed increase compared to conventional shape measurement methods. Figure 11B(e) shows the stress-strain curve estimated from the load-displacement curve and pile-up height.

[0050] Figure 12 shows the results obtained using the integrated high-speed processing automatic evaluation system 1000 and post-processing software, including (a) composition, (b) Young's modulus, (c) hardness, (d) pile-up height, (e) processing effect coefficient, and (f) yield stress. By using the present invention, it is possible to automatically and rapidly evaluate the high-temperature Young's modulus and high-temperature yield stress of Ni alloys with a wide range of Ta and W compositions. The obtained composition-yield stress data can be used as a solid solution strengthening database for Ni-W, Ni-Ta, and Ni-Ta-W alloys. Specifically, by using it as a solid solution strengthening database in commercially available and independently developed strength prediction programs, as shown in Non-Patent Documents 3-5, it is possible to improve the accuracy of property prediction for Ni-based superalloys and expand the range of applicable compositions.

[0051] <Example 5: High-speed automated measurement example of a solid solution strengthening database> This section presents an example of high-temperature, high-speed processing evaluation using Ni-Co binary alloy composition gradient samples. Focusing on the Ni-Co elements, which are important γ-phase constituent elements of Ni-Co-based superalloys, diffusion pair samples were prepared. Ni and Co plates were stacked alternately and joined by SPS sintering. The samples were heat-treated at 1150°C for 3000 hours to sufficiently diffuse the Ni and Co elements, and then subjected to HIP treatment at 1120°C at 98 MPa to prepare composition gradient diffusion pair samples (Figure 13A). Figure 13B shows the results of hardness tests conducted at a total of 630 locations in the temperature range from room temperature to 500°C using the nanoindentation method on compositionally graded samples. In the region where the Ni composition ratio is 1 at% to 23 at%, the properties are generally similar, with hardness H ranging from 3.4 to 4.1 GPa at 300 K. Hardness tests were also conducted at 370 K, 470 K, 570 K, 670 K, and 770 K. Hardness decreased with increasing temperature; at 670 K, hardness H was in the range of 2.2 to 3.0 GPa, but at 770 K, it decreased to the range of 1.2 to 1.7 GPa. Furthermore, in the region where the Ni composition ratio is 44 at% to 91 at%, the hardness H at room temperature (300 K) is in the range of 1.9 to 2.1 GPa, which is about half the hardness H of Ni-Co binary alloys in the region where the Ni composition ratio is 1 at% to 23 at%. Ni-Co binary alloys with Ni composition ratios of 30 at% and 37 at% transition between these two ranges depending on the hardness test temperature. In the Ni-Co binary alloy with a Ni composition ratio of 30 at%, the hardness H is in the range of 3.0 to 3.2 GPa at 570 K, but decreases to the range of 1.5 to 2.0 GPa at 670 K. In the Ni-Co binary alloy with a Ni composition ratio of 37 at%, the hardness H is in the range of 3.0 to 3.2 GPa at 470 K, but decreases to the range of 1.6 to 1.8 GPa at 570 K.

[0052] Figure 14 shows a hardness map superimposed on the Ni-Co binary phase diagram. At room temperature, hardness increased gradually with increasing Co content. Furthermore, it can be observed that hardness increases sharply at the Co content at which the crystal structure transforms from FCC (face-centered cubic) to HCP (hexagonal closest packing) structure on the phase diagram. In addition, hardness gradually decreased with increasing temperature in all Co regions. Of particular note is that while the Co content at which phase transformation occurs tends to increase with increasing temperature on the phase diagram, the hardness results accurately represent these phase transformation behaviors. In summary, the integrated high-speed processing automatic evaluation system 1000 can accurately acquire hardness maps for a wide range of compositions and measurement temperatures in any alloy system, and can also store data showing mechanical properties on a general phase diagram.

[0053] <Automated compositional analysis software for γ-γ' biphase microstructure> This document describes the necessity and application examples of an automated analysis program for tie-line information in γ-γ' two-phase structures. A tie-line is an isotherm that passes through a two-phase region on a phase diagram. Creating phase diagrams for multi-component alloys requires a large number of γ-γ' tie-line composition sets. For example, in a quaternary composition space, the phase boundary is a curved surface in three dimensions, and its determination requires hundreds of points (phase boundary composition data). As the number of constituent elements increases exponentially, the number of required tie-line composition sets increases, making the introduction of an automated analysis program essential. The automated analysis program can determine the composition of the γ and γ' phases from elemental maps obtained by EPMA surface analysis and SEM images of the γ-γ' two-phase structure in a 25x25mm region where local equilibrium exists. It can be applied to some microstructures consisting of 0.4mm γ' phase grains, which exceeds the spatial resolution of EPMA (1mm), and can handle up to octagonal systems, for example. However, it is not limited to this, and the number of dimensions analyzed can be increased or decreased depending on the number of constituent elements of the multidimensional alloy being analyzed. Generally, point analysis using EPMA offers superior accuracy in compositional measurement, but because it requires identifying the location of precipitates smaller than a few micrometers and multiple measurement points, the analysis time is more than three times longer than that of area analysis.

[0054] Figures 15A to 15E show examples of analysis performed by an automated composition analysis program, which is one embodiment of the present invention, and an external view of its interface. In the automated composition analysis program, which is one embodiment of the present invention, surface analysis is employed, and it is not necessary to identify the position of γ' grains. Since data can be acquired in a single measurement, for example, in a quaternary system, the composition can be automatically determined in a measurement time of about 4 minutes for one region. Figure 15A shows the EPMA maps of each element in the Ni-Co-Al-Ti quaternary alloy. Figure 15B is an SEM image showing the crystal grains to be analyzed in the Ni-Co-Al-Ti quaternary alloy, and also indicates the area to be analyzed using EPMA surface analysis. Figure 15C shows the tie-line data setting conditions for determining the γ-γ' composition required for the phase diagram DB by the automatic composition analysis program. The tie-line data setting screen 500 includes a composition folder load instruction unit 501, a current map number display unit 502, an element map feed button 504, an element selection button 506, a noise threshold setting unit 507, a noise reduction instruction unit 508, a first boundary selection button 510, a second boundary selection button 512, a skip button 514, a single-phase specification button 516, and a tie-line registration button 518.

[0055] The element map to be analyzed is specified using the element map advance button 504, the element to be analyzed is selected using the element selection button 506, and the corresponding phase is specified using the pop-up of the first boundary selection button 510 (Boundary1) or the second boundary selection button 512 (Boundary2). The noise threshold setting unit 507LQ is a pop-up for setting parameters to remove noise from the data included in the element map, such as regions affected by voids among the 2400 points. The noise removal instruction unit 508 removes data included in the element map that has been determined to be noise based on the threshold of the noise threshold setting unit 507LQ from the analysis target. Once the phase specification and noise removal for the element map are complete, press the tie line registration button 518, or press the skip button 514 or the single-phase specification button 516.

[0056] Figure 15D shows the analysis results of the γ and γ' phase compositions in the target region of EPMA surface analysis. Information on the standard sample and the two phase boundary compositions obtained from the analysis are displayed. The elemental names of the standard sample and the type and intensity of the characteristic X-rays are displayed. Phase boundary composition Boundary 1 shows Ni at 33%, Co at 55%, Al at 7%, and Ti at 4% (units are atomic %). Phase boundary composition Boundary 2 shows Ni at 46%, Co at 32%, Al at 11%, and Ti at 11% (units are atomic %). Using compositional gradient samples with a compositional range that covers the γ-γ' two-phase region of Ni-Co-Al-Ti quaternary alloys, approximately 1000 sets of EPMA elemental map sets were obtained, and the tie-line composition sets for each were determined. Figure 15E shows tie-line data for a Ni-Co-Al-Ti quaternary alloy. Dark gray circles represent the phase boundary composition on the γ phase side, light gray circles are connected by straight lines to represent the phase boundary compositions of phases other than the γ phase corresponding to the γ phase composition of the dark gray circles, and two black circles connected by thin straight lines represent the phase boundary compositions of phases consisting of combinations other than the γ phase. These tie-line data can be used as a database for computational phase diagrams and will greatly contribute to improving the accuracy of computational phase diagrams for multicomponent alloys.

[0057] <Data reanalysis and mapping software> The mechanical, microstructure, compositional, and process information obtained in the automated alloy composition search and evaluation device of the present invention can be stored in the developed data reanalysis and mapping software (S500 in Figure 3), thereby converting it into a composition-process-microstructure-properties database that clarifies the interrelationships between these elements. Furthermore, by visualizing the big data, it becomes possible to detect data showing outliers from a dataset of several million or more data points.

[0058] Figure 16 shows an example of the interface for data reanalysis and mapping software. Figure 16A shows a map of the locations of the sample being measured, with markers corresponding to each measurement point. This data reanalysis and mapping software is stored, for example, in the post-processing unit 200 or the characteristic prediction program 300. This data reanalysis and mapping software accesses the mechanical property distribution storage unit 120 and the measurement data storage unit 130 to read measurement data, including nanoindentation, WDS / EDS, SEM, and EBSD information at each collected measurement point. This data reanalysis and mapping software can display markers corresponding to each measurement point on a position map of the surface of the sample 10 for any selected data, for example, as shown in Figure 16A. Instead of a position map of the surface of the sample 10, markers corresponding to each measurement point may be displayed on a phase diagram. Here, the phase diagram can be created by reading a TDB file, which is a data file for thermodynamic equilibrium phase diagrams.

[0059] Figures 16B(a) to (e) show the mechanical properties of the sample under test, such as the damped modulus of elasticity Er (GPa), pile-up height Hp, work hardening rate B (MPa), hardness H (GPa), and yield strength sY (MPa), displayed on a phase diagram or position map. As shown in Figures 16B(a) to (e), it is possible to map mechanical properties such as the damped modulus of elasticity Er (GPa), pile-up height Hp, work hardening rate B (MPa), hardness H (GPa), and yield strength sY (MPa) onto a phase diagram or position map, making it easy for alloy developers to understand the measurement data.

[0060] Figure 16C shows the display of characteristic values ​​at any point selected from the measurement points in Figure 16A of the sample being measured, using data reanalysis and mapping software. The sample characteristic value display 600 includes a phase diagram-Ni visualization system display unit 610, a sample / measurement information display unit 630, and a measurement point information display unit 660. The phase diagram-Ni visualization system display unit 610 includes a database selection unit 612, a button to update the TDB / sample list 614, a phase diagram registration button 616, a characteristic sheet creation button 618, a TDB file list button 620, an unregistered sample list button 622, a characteristic data recalculation button 624, a phase diagram recalculation button 626, and a characteristic redisplay button 628. The sample / measurement information display unit 630 includes a sample name display unit 632, a sample type display unit 634, a constituent element display unit 636, a sample preparation date display unit 638, an other display unit 640, an NI folder selection unit 642, an EPMA folder selection unit 644, an Hp folder selection unit 646, an EBSD folder selection unit 648, an EDS folder selection unit 650, and a microstructure folder selection unit 652. The measurement point information display unit 660 has individual composition element display fields 662, mechanical property display fields 664, crystal orientation display fields 666, and microstructure display fields 668. The individual composition element display fields 662 include Ni (nickel), Co (cobalt), Cr (chromium), W (tungsten), Mo (molybdenum), Nb (niobium), Al (aluminum), Ti (titanium), Hf (hafnium), C (carbon), B (boron), and Zr (zirconium). The mechanical properties section 664 contains σY, Er, H, Hp, and B. Here, σY is the yield strength, Er is the damped modulus, H is the hardness, Hp is the pile-up height, and B is the work hardening rate. The microstructure section 668 contains fγ', dγ', and TCP (Topologically Close Packed). fγ' is the γ' phase fraction, dγ' is the average grain size of the γ' phase precipitates, and TCP indicates the presence or absence of TCP.

[0061] Figure 16D displays the raw data of the load-displacement curve, microstructure, and pile-up analysis obtained by nanoindentation. The raw data display screen 700 includes the NI profile screen 710, the microstructure screen 720, and the pile-up screen 730. Re-analysis is possible for the microstructure and pile-up analysis. The NI profile screen 710 displays the SEM image of the indentation 712 and the stress-displacement curve 714. The microstructure screen 720 displays an SEM image with the detected γ' phase region colored black. The pile-up screen 730 displays the maximum height location obtained from the analysis on the indentation image.

[0062] As described above, the data reanalysis and mapping software allows for the reanalysis of raw data according to the purpose, and also has the functionality to map composition-process-structure-property data onto phase diagrams or location maps in a way that is easy for alloy developers to understand, thereby providing maximum support for strategic planning for alloy development.

[0063] The integrated high-speed processing automatic evaluation system for alloy composition exploration according to the present invention has the following effects. This invention relates to a high-speed automated processing and evaluation technology for composition-process-structure-property databases and an all-in-one automated evaluation system that enables it, for achieving a significant improvement in accuracy and expansion of the prediction range in performance prediction in the field of structural materials. For example, it demonstrates its capabilities by using composition-graded materials of multi-component alloys with more than 10 components, such as Ni-based superalloys, or temperature-graded samples with heat treatment temperatures graded in one direction, or composition-temperature-graded materials having both, as evaluation samples. Here, the composition-temperature-graded evaluation sample is a sample in which the composition and heat treatment temperature conditions differ at each coordinate within a single sample. The automated evaluation system for alloy composition exploration of the present invention is characterized by its ability to automatically and rapidly evaluate compositional analysis, microstructure feature analysis, and high-temperature mechanical properties for such special samples. Specifically, it is an evaluation system that combines a high-resolution, dual-beam FE-SEM capable of high-resolution analysis of microstructure and analysis of indentation indenter shape with a WDS / EDS capable of compositional analysis, and a high-temperature nanoindentation device installed inside the SEM to automatically and rapidly acquire the high-temperature Young's modulus and high-temperature hardness at each coordinate.

[0064] Furthermore, the post-processing software linked to the automated alloy composition exploration evaluation device of the present invention—a composition analysis program, an automated microstructure image processing program, and a mechanical property analysis program—automatically analyzes the shape, composition, and stress-strain curve of precipitates at measurement points, respectively. In this way, by setting up a sample for composition-process gradient evaluation in the evaluation system, performing automated experiments, and executing post-processing, the composition-process-microstructure-property database can be evaluated at high speed. For example, in conventional alloy manufacturing, heat treatment, specimen processing, and high-temperature strength evaluation, it takes three weeks to evaluate one composition. In contrast, with the present invention, it is possible to build a database for 10,000 compositions in three weeks, which is expected to be approximately 10,000 times faster than conventional evaluation methods.

[0065] Figure 17A is a cross-sectional view of a key part of a weld, which is an example of a sample being measured that exhibits a compositional gradient and / or microstructural variation, and shows a weld on a turbine disk material made of nickel-based superalloy. In Figure 17A, when a welding rod (torch) 810 is applied to a recess in the base metal 800 of the turbine disk material, molten weld metal 820 is formed. The heat-affected zone (HAZ) is formed in the boundary layer between the base metal 800 and the weld metal 820. Therefore, the weld has a macro-scale compositional gradient / structural variation from the weld metal 820 / HAZ area 830 / base metal 800. Figure 17B is a dendritic solidification structure diagram of the weld metal shown in Figure 17A. The dendritic solidification structure diagram of the weld metal 820 has a primary dendrite arm 822 and a secondary dendrite arm 824. Figure 17C is a microstructure diagram of the heat-affected zone (HAZ) shown in Figure 17A. The HAZ region 830 has a multiphase polycrystalline structure consisting of the matrix phase 832 and the second phase 834. Figure 17D is a microstructure diagram of the base material shown in Figure 17A. The base material 800 has a multiphase polycrystalline structure consisting of a matrix 802, a second phase 804, and an abnormal structure 806. The abnormal structure 806 is an abnormal grain growth or unsintered area, and is a part that determines the quality / macro-characteristics of the component.

[0066] Figure 18A is an overall perspective view of a turbine blade 840, which is an example of a sample under test exhibiting a compositional gradient and / or microstructural variation, showing the thick-walled section 850 and the thin-walled section 860. Examples of turbine blades include single-crystal turbine blades manufactured by precision casting, unidirectional solidification turbine blades, or complex-shaped parts produced as metal additive manufacturing materials using three-dimensional fabrication techniques. Figure 18B is a diagram of the dendritic solidification structure of the thick-walled section shown in Figure 18A. The dendritic solidification structure of the thick-walled section 850 has a primary dendrite arm 852 and a secondary dendrite arm 854. Figure 18C is a diagram of the dendritic solidification structure of the thin-walled section shown in Figure 18A. The dendritic solidification structure of the thin-walled section 860 has a primary dendrite arm 862 and a secondary dendrite arm 864. Compared to the dendritic solidification structure of the thick-walled section 850, the dendritic solidification structure of the thin-walled section 860 is smaller.

[0067] Figure 19 is a microstructure diagram of a multiphase polycrystalline material, which is an example of a sample exhibiting a compositional gradient and / or microstructure variation. In the multiphase polycrystalline material 870, the composition and mechanical properties of the matrix 872, the second phase 874, and the abnormal structure 876 are different. The abnormal structure 876 is an abnormal grain growth or unsintered area, etc., and is a part that determines the quality / macro-characteristics of the component.

[0068] Although the present invention has described nickel-based superalloys as the target of alloy composition exploration, the evaluation target of the automated evaluation device for alloy composition exploration of the present invention is not limited to nickel-based superalloys, but may also be cobalt-based superalloys or iron-based superalloys, as well as high-entropy alloys, and furthermore, aluminum alloys or titanium alloys. In this case, the precipitation morphology data may include, for example, crystal grains, GP zones (Guinier-Preston zones), atomic clusters that are precursors to GP zones, quasicrystals, etc. [Industrial applicability]

[0069] The integrated high-speed processing automatic evaluation system for alloy composition exploration of the present invention is highly effective when applied to compositionally graded samples, but it is also effective in rapidly acquiring local composition-structure-properties for general structural materials with complex hierarchical structures. Furthermore, by heat-treating compositionally graded materials with temperature gradient heat treatment, and applying composition-temperature graded samples that have large compositional and structural gradients within a single sample, it becomes possible to acquire a highly accurate and wide-ranging dataset of composition-process-structure-properties all at once. [Explanation of Symbols]

[0070] 10 Sample to be measured 20 Sample holding section 30. Enclosure 40 FE-SEM (Field Emission-Scanning Electron Microscope) 46 Precipitation Form Database 50 Nanoindentation Testing Machine 60 WDS (Wave Dispersive Spectroscopy, wavelength dispersive X-ray spectrometer) / EDS (Energy Dispersive Spectroscopy, energy dispersive X-ray spectrometer) 66 Composition Database (Composition Ratio Storage Unit) 70 EBSD (Electron Back-Scatter Diffraction) 100 Integrated Evaluation Device 110 Measurement Instrument Interoperability Controller 120 Mechanical property distribution memory section 130 Measurement data storage unit 140 Physical property data storage unit 200 Post-processing unit 300 Characteristics Prediction Program S230 Shape Analysis / Inverse Analysis Program S300 organization information S310 Automatic Image Processing Program S400 composition information S410 Automatic Composition Analysis Program S500 Reanalysis / Mapping Software

Claims

1. A sample holding unit that holds the sample to be measured in a predetermined position, A mechanical properties distribution storage unit that stores the distribution of mechanical properties measured by the nanoindentation tester based on the position of the indenter marks on the sample to be measured formed by the nanoindentation tester, A housing that holds at least one measuring instrument from among a wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer for analyzing the constituent elements of an alloy, a field-emission scanning electron microscope for measuring the microstructure of an alloy, or an electron beam backscatter diffractometer for analyzing the orientation distribution of crystal grains, texture, or crystal phase distribution, A measurement instrument linkage controller controls the measurement operation of a sample to be measured using at least one of the following measuring instruments: a wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer for analyzing the constituent elements of the alloy mounted on the housing; a field-emission scanning electron microscope for measuring the microstructure of the alloy; or an electron beam backscatter diffractometer for analyzing the orientation distribution of crystal grains, texture, or crystal phase distribution. A measurement data storage unit stores the measurement data of the sample being measured by the measuring instrument, based on the position of the indenter mark formed on the sample being measured. A post-processing unit that calculates physical property data corresponding to the measurement data of the measuring instrument, An automated evaluation device for alloy composition exploration, equipped with the following features.

2. The sample to be measured is, Regarding the constituent elements of the alloy system to be evaluated, a composition gradient material having a composition ratio that is sloped between the upper and lower limits of each of the constituent elements, A heat treatment temperature gradient material is obtained by heat-treating a material with a composition gradient, or a test material with a uniform composition in place of the material with a composition gradient, in a furnace where the heat treatment temperature is graded between an upper limit and a lower limit. A heat-affected element that includes a region of the base material in which the microstructure and properties of the sample to be measured have been altered by welding or thermal cutting operations, even though it has not melted. A metal additive manufacturing material is produced by dissolving metal powder in the necessary parts of a metal component using an electron beam or fiber laser, and then solidifying it to form the metal component. A metal powder injection molded material is obtained by using metal fine powder as a raw material, adding a binder to the metal fine powder without melting it, injection molding, degreasing and sintering the molded body formed by the injection molding, and obtaining a metal part. An automated evaluation apparatus for alloy composition exploration according to claim 1, which is any one of the above.

3. The aforementioned metal additively fabricated material is The powder bed method involves irradiating a powder bed, which is covered with metal powder, with a laser beam or electron beam, causing each layer to melt and solidify repeatedly. Directed energy deposition (EDM) is a method of fabricating objects by supplying powder or wire and melting it with a laser or electron beam, then depositing it. A fused deposition modeling (FDM) method is used, in which metal powder is added to a thermoplastic resin, melted by heat and layered, and the degreased molded body is sintered to solidify the metal powder, or This binder jetting method involves spraying a liquid binder onto metal powder through a nozzle to create a molded object. After each layer, once the binder has been sprayed and solidified, the build plate is lowered and more powder is laid down. This process is repeated for each layer, and after molding, the object is sintered in a high-temperature furnace or heater to remove the binder. An automated evaluation apparatus for alloy composition exploration according to claim 2, manufactured by any of the following methods.

4. Furthermore, the device includes a nanoindentation tester that presses an indenter onto the sample to be measured and measures the mechanical properties based on the shape of the indenter mark. The mechanical property distribution storage unit stores the distribution of mechanical properties measured by the nanoindentation tester based on the position of the indenter marks formed on the sample under test. The housing unit, together with the nanoindentation testing machine, holds at least one measuring instrument from among the wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer, the field-emission scanning electron microscope, or the electron beam backscatter diffraction apparatus. The aforementioned measuring instrument linkage controller controls the measurement operation of the sample to be measured using the nanoindentation tester mounted on the housing, and at least one of the following measuring instruments: the wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer, the field emission scanning electron microscope, or the electron beam backscatter diffraction apparatus. An automated evaluation apparatus for alloy composition exploration according to any one of claims 1 to 3.

5. The target of the alloy composition search is one or more selected from the group consisting of composition gradient materials, heat treatment temperature gradient materials, heat-affected element members, metal additive manufacturing materials, and metal powder injection molded materials. In the measurement data storage unit, the measurement data of the sample to be measured is Crystal phase equilibrium composition data corresponding to the constituent elements of the aforementioned alloy, Micro-precipitate identification data and indentation shape high-speed acquisition data corresponding to the microstructure of the alloy, Polycrystalline orientation data corresponding to the anisotropy of the aforementioned alloy, or The automatic evaluation device for alloy composition exploration according to claim 2 or 3, which is at least one type of load and displacement information corresponding to the mechanical properties measured by the nanoindentation testing machine.

6. The target of the aforementioned alloy composition search is nickel-based superalloys, In the measurement data storage unit, the measurement data of the sample to be measured is γ / γ' equilibrium composition data corresponding to the constituent elements of the aforementioned alloy, Micro-precipitate identification data and indentation shape high-speed acquisition data corresponding to the microstructure of the alloy, Polycrystalline orientation data corresponding to the anisotropy of the aforementioned alloy, or The automatic evaluation device for alloy composition exploration according to claim 5, which is at least one type of load and displacement information corresponding to the mechanical properties measured by the nanoindentation tester.

7. In the post-processing unit, the physical property data corresponding to the measurement data of the measuring instrument is: Composition data corresponding to the aforementioned crystal phase equilibrium composition data, Precipitation morphology data corresponding to the aforementioned micro-precipitation identification data, or An automated evaluation device for alloy composition exploration according to claim 5, wherein at least one of the indentation shape high-speed acquisition data and polycrystalline orientation data, or load / displacement information, is read and processed to calculate at least one of the elastic constant, critical resolved shear stress (CRSS), work hardening coefficient, or creep deformation rate.

8. In the post-processing unit, the physical property data corresponding to the measurement data of the measuring instrument is: Composition data corresponding to the aforementioned γ / γ' equilibrium composition data, Precipitation morphology data corresponding to the aforementioned micro-precipitation identification data, or An automated evaluation device for alloy composition exploration according to claim 6, wherein at least one of the indentation shape high-speed acquisition data and polycrystalline orientation data, or load / displacement information is read and processed to calculate at least one of the elastic constant, critical resolved shear stress (CRSS), work hardening coefficient, or creep deformation rate.

9. The sample to be measured is a heat treatment temperature gradient material obtained by heat-treating the composition gradient material or a test material with a uniform composition in place of the composition gradient material in a furnace in which the heat treatment temperature is graded between an upper limit and a lower limit. An automated evaluation apparatus for alloy composition exploration according to claim 2 or 3, wherein the position of the indenter mark formed on the sample to be measured is linked to the heat treatment temperature.

10. The automated evaluation apparatus for alloy composition exploration according to any one of claims 1 to 3, wherein the microstructure of the alloy indicates a phase transformation boundary region where the crystal structure changes between a first crystal structure and a second crystal structure.

11. The automated evaluation apparatus for alloy composition exploration according to claim 10, wherein the first crystal structure is a face-centered cubic lattice and the second crystal structure is a hexagonal close-packed lattice.

12. The automated evaluation apparatus for alloy composition exploration according to claim 10, wherein the composition ratio of constituent elements changes at the boundary between the first crystal structure and the second crystal structure.

13. The automated evaluation apparatus for alloy composition exploration according to claim 10, wherein the mechanical properties measured by the nanoindentation tester show a change in the mechanical properties across the boundary line between the first crystal structure and the second crystal structure.

14. The automatic evaluation apparatus for alloy composition exploration according to claim 13, wherein the mechanical property is hardness.

15. The sample to be measured has a composition range that covers the γ / γ' two-phase structure, The automated evaluation apparatus for alloy composition exploration according to claim 9, wherein the post-processing unit extracts tie-line information of the γ / γ' two-phase structure in an observation region consisting of a γ / γ' two-phase structure in a nickel-based superalloy by obtaining an elemental map from the electron backscatter diffraction apparatus and distinguishing between the γ phase and the γ' phase from the scanning electron microscope.

16. The sample to be measured is a heat treatment temperature gradient material obtained by heat-treating the composition gradient material in a furnace in which the heat treatment temperature is graded between an upper limit and a lower limit. The automated evaluation apparatus for alloy composition exploration according to claim 9, which constructs γ' precipitated particle volume fraction data by analyzing the size and amount of γ' precipitated particles according to the heat treatment temperature.

17. Scanning electron microscopy or electron backscatter diffraction observation is performed on the sample to be measured, and the location for mechanical measurement is selected based on the microstructure, phase, or crystal structure in the obtained image. A high-temperature hardness test (nanoindentation measurement) is performed at a predetermined temperature on the selected location of the sample to be measured. An indentation shape measurement is performed at the position of the aforementioned mechanical measurement, and the high-temperature stress-strain curve for the sample under measurement is calculated. The positions observed by scanning electron microscopy or electron backscatter diffraction observation of the sample under measurement are linked to the data of the high-temperature stress-strain curve. An automated evaluation method for alloy composition exploration.

18. For a sample subjected to a high-temperature hardness test (nanoindentation measurement) at a predetermined temperature using a nanoindentation testing machine, the indentation shape is measured at the location where the indentation marks are formed on the sample. Based on the results of the indentation shape measurement, the high-temperature stress-strain curve for the sample under measurement is calculated. Scanning electron microscopy or electron beam backscatter diffraction observation is performed on the sample to be measured, and using the indenter marks of the sample to be measured in the image obtained from the scanning electron microscopy or electron beam backscatter diffraction observation, the detailed position to be observed by scanning electron microscopy or electron beam backscatter diffraction is selected as the position for mechanical measurement. The microstructure, phase, or crystal structure of the aforementioned detailed location is measured by scanning electron microscopy or electron backscatter diffraction observation. Based on the microstructure, phase, or crystal structure of the indenter marks of the sample under measurement, the location observed by scanning electron microscopy or electron backscatter diffraction of the sample under measurement is linked to the data from the mechanical measurements. An automated evaluation method for alloy composition exploration.

19. The sample to be measured is, Regarding the constituent elements of the alloy system to be evaluated, a composition gradient material having a composition ratio that is sloped between the upper and lower limits of each of the constituent elements, A heat treatment temperature gradient material is obtained by heat-treating a material with a composition gradient, or a test material with a uniform composition in place of the material with a composition gradient, in a furnace where the heat treatment temperature is graded between an upper limit and a lower limit. A heat-affected component includes a region of the base material (metal, thermoplastic material, etc.) that is not melted but whose microstructure and properties have been altered by welding or thermal cutting operations. Metal additive manufacturing materials are produced by dissolving and solidifying metal powder in the required areas using an electron beam or fiber laser to create metal parts. A metal powder injection molded material is obtained by using metal fine powder as a raw material, adding a binder to the metal fine powder without melting it, injection molding, degreasing and sintering the molded body formed by the injection molding, and obtaining a metal part. An automated evaluation method for alloy composition exploration according to claim 17 or 18, which is either of the above.

20. The aforementioned metal additively fabricated material is The powder bed method involves irradiating a powder bed, which is covered with metal powder, with a laser beam or electron beam, causing each layer to melt and solidify repeatedly. Directed energy deposition (EDM) is a method of fabricating objects by supplying powder or wire and melting it with a laser or electron beam, then depositing it. A fused deposition modeling (FDM) method is used, in which metal powder is added to a thermoplastic resin, melted by heat and layered, and the degreased molded body is sintered to solidify the metal powder, or This binder jetting method involves spraying a liquid binder onto metal powder through a nozzle to create a molded object. After each layer, once the binder has been sprayed and solidified, the build plate is lowered and more powder is laid down. This process is repeated for each layer, and after molding, the object is sintered in a high-temperature furnace or heater to remove the binder. An automated evaluation method for exploring alloy compositions according to claim 19, manufactured by any of the following.

21. The sample to be measured is a heat-treated temperature gradient material obtained by heat-treating a composition gradient material or a test material with a uniform composition in place of the composition gradient material in a temperature gradient heat treatment furnace in which the heat treatment temperature is graded between an upper limit and a lower limit, The heat treatment temperature is stored in the composition ratio storage unit along with the composition ratio storage unit for the sample to be measured at the location of the mechanical measurement. An automated evaluation method for alloy composition exploration according to claim 19.

22. Using high-magnification scanning electron microscopy observation at the location of the mechanical measurement, tissue information of the sample to be measured is acquired. The aforementioned structural information includes at least one of the following: grain size, precipitate particle size, or volume fraction. An automated evaluation method for alloy composition exploration according to claim 17 or 18.

23. Compositional information for the sample under measurement is obtained at the location of the aforementioned mechanical measurement using a wavelength-dispersive X-ray spectrometer or an energy-dispersive X-ray spectrometer. The composition information includes at least one of the coordinate composition, matrix composition, or precipitate composition of the sample under measurement. The composition ratio of constituent elements at the position of the mechanical measurement, as observed by the wavelength-dispersive X-ray spectrometer or energy-dispersive X-ray spectrometer, is stored in the composition ratio storage unit. An automated evaluation method for alloy composition exploration according to claim 17 or 18.

24. The sample to be measured is a material with a compositional gradient, For metal plates made of each constituent element of the alloy system to be evaluated, the metal plates of each constituent element are stacked alternately, and in a vacuum, at a first predetermined temperature, while being pressed with a first predetermined stress, and held for a first predetermined time, they are subjected to spark plasma sintering to temporarily bond them. The aforementioned pre-bonded spark plasma sintered material is subjected to diffusion heat treatment using a hot isostatic pressurizing device in an Ar atmosphere, at a second predetermined temperature, with a second predetermined stress, and held for a second predetermined time, thereby producing a compositionally graded sample. An automated evaluation method for alloy composition exploration according to claim 19.

25. The metal plates made up of each constituent element of the alloy system to be evaluated are a set of three metal plates consisting of a first metal element, a second metal element, and a third metal element, with the first metal element plate serving as an intermediate layer, and the second and third metal element plates laminated above and below the first metal element plate, respectively. In the composition gradient sample obtained by subjecting the pre-joined spark plasma sintered material to diffusion heat treatment, a binary composition gradient alloy of the first and second metal elements is formed at the first initial interface between the metal plate of the first metal element and the metal plate of the second metal element in a direction perpendicular to the first and second initial interfaces, and a binary composition gradient alloy of the first and third metal elements is formed at the second initial interface between the metal plate of the first metal element and the metal plate of the third metal element, The thickness of the metal plate of the first metal element is such that a ternary composition gradient alloy of the first, second, and third metal elements is formed between the first and second initial interfaces. An automated evaluation method for alloy composition exploration according to claim 24.

26. The first metal element is Ni, the second metal element is Ta, and the third metal element is W, The binary composition gradient alloy of the first and second metallic elements is Ni-Ta, and the binary composition gradient alloy of the first and third metallic elements is Ni-W. The ternary compositional gradient alloy of the first, second, and third metallic elements is Ni-Ta-W. An automated evaluation method for alloy composition exploration according to claim 25.