CN114974474A - Multi-scale chain type integrated design method of structural material and database system - Google Patents
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Abstract
The application provides a multi-scale chain type integrated design method of a structural material and a database system, and relates to the technical field of structural materials and the technical field of computers. The multi-scale chain type integrated design method of the structural material comprises the following steps: according to the attributes of the basic elements of the preset structural material, a first principle method is adopted, various target transition elements required for manufacturing the preset structural material are screened from the various transition elements on a microscopic level, so that the types of alloying elements doped in the preset structural material are determined, then phase diagram calculation is carried out on the various target transition elements to obtain a phase diagram, multiple groups of target component points meeting a first preset condition are screened from the phase diagram, and the component points/component range doped in each target transition element in the structural material is determined on a mesoscopic level. The target candidate composition of the alloy is then screened by machine learning. And finally, performing phase field calculation on each group of possible target component points to determine the process parameters for manufacturing the preset structural material on the macroscopic level.
Description
Technical Field
The invention relates to the technical field of structural materials and computers, in particular to a multi-scale chain type integrated design method and a database system for structural materials.
Background
The structural material has wide application in the fields of aeroengine manufacturing, gas turbine manufacturing, petroleum and petrochemical industry, automobiles, metallurgy, glass manufacturing, atomic energy and the like, and the development of a new generation of structural material with higher temperature bearing capacity and high strength and corrosion resistance is an urgent need for developing advanced power devices aiming at the harsh service environment of the structural material in the application scene.
In the aspect of the current material calculation design research, single-scale calculation and simulation are mainly adopted, and material calculation models and data under microscopic, mesoscopic and macroscopic scales cannot be effectively compatible with each other, so that the calculation method is not integrated and applied, and the requirements of full-chain design and research and development of novel structural materials from atoms to macroscopic levels cannot be met.
Disclosure of Invention
The present invention aims to provide a multi-scale chain-type integrated design method and a database system for a structural material, so as to provide a multi-scale information chain of the microscopic atomic scale, the mesoscopic phase scale and the macroscopic process scale of the structural material.
In order to achieve the above purpose, the technical solutions adopted in the embodiments of the present application are as follows:
in a first aspect, an embodiment of the present application provides a multi-scale chain type integrated design method for a structural material, including:
screening out a plurality of target transition elements required for manufacturing a preset structural material from the plurality of transition elements by adopting a first principle method according to the attribute of a basic element of the preset structural material;
carrying out phase diagram calculation on the multiple target transition elements to obtain a digital phase diagram;
screening out multiple groups of target component points meeting a first preset condition from the phase diagram according to a preset machine learning algorithm;
and performing phase field calculation on each group of target component points to obtain process parameters required for manufacturing the preset structural material, wherein the process parameters are used for indicating: and (4) carrying out heat treatment on each group of target component points.
Optionally, after the multiple groups of target component points meeting the first preset condition are screened from the phase diagram according to a preset machine learning algorithm, the method further includes:
determining the composition range of each target transition element according to the target composition points;
determining a preset number of groups of target transition element candidate components from the component range;
the phase field calculation is performed on each group of target component points to obtain process parameters required for manufacturing the preset structural material, and the process parameters comprise:
and performing phase field calculation on the preset number of groups of target transition element candidate components to obtain the process parameters.
Optionally, the phase diagram calculation on the component points of the multiple target transition elements to obtain the phase diagram includes:
and carrying out phase diagram calculation on the multiple target transition elements to obtain a digital phase diagram.
Optionally, the selecting, according to the attribute of the base element of the preset structural material, a first principle method to screen out multiple target transition elements required for manufacturing the preset structural material from multiple transition elements includes:
and screening various target transition elements required for manufacturing the preset structural material from the various transition elements corresponding to the base elements by adopting the first principle method according to the attributes of the base elements of the preset structural material.
Optionally, the selecting, according to the attribute of the base element of the preset structural material, multiple target transition elements required for manufacturing the preset structural material from multiple transition elements by using a first principle method includes:
screening out a plurality of target transition elements required for manufacturing the preset structural material from a plurality of transition elements according to a first principle method; the parameter database includes at least one of the following parameters: relaxation structure, energy, elastic constant, differential charge density.
Optionally, the phase diagram calculation on the multiple target transition elements to obtain the phase diagram includes:
and according to a thermodynamic database, performing phase diagram calculation on the multiple target transition elements to obtain a phase diagram.
Optionally, the performing phase field calculation on each group of target component points includes:
and performing phase field calculation on each group of target component points according to a dynamics database.
Optionally, the determining a preset number of target transition element candidate components from the component range includes:
and screening out a preset number of groups of target transition element candidate components from the component range according to a machine learning algorithm.
In a second aspect, an embodiment of the present application further provides a database system, including:
the device comprises a first principle calculation module, a phase diagram calculation module, a machine learning module and a phase field calculation module; the first sexual principle calculating module is in communication connection with the phase diagram calculating module through an input-output interface; the phase diagram calculation module is in communication connection with the machine learning module through an input-output interface; the machine learning module is in communication connection with the phase field calculation module through an input/output interface;
the first principle calculation module is used for screening out multiple target transition elements required for manufacturing the preset structural material from multiple transition elements by adopting a first principle method according to the attributes of the basic elements of the preset structural material;
the phase diagram calculation module is used for carrying out phase diagram calculation on the multiple target transition elements to obtain a phase diagram;
the machine learning module is used for screening out multiple groups of target component points meeting a first preset condition from the phase diagram according to a preset machine learning algorithm;
the phase field calculation module is configured to perform phase field calculation on each group of target component points to obtain process parameters required for manufacturing the preset structural material, where the process parameters are used to indicate: and (4) performing heat treatment on each group of target component points.
Optionally, the calculation method is configured to determine a composition range of each target transition element according to the target composition point; determining a preset number of groups of target transition element candidate components from the component range;
and the phase field calculation module is used for performing phase field calculation on the preset number of groups of target transition element candidate components to obtain the process parameters.
The beneficial effect of this application is: the embodiment of the application provides a multi-scale chain type integrated design method for a structural material, which is characterized in that multiple target transition elements required for manufacturing the preset structural material are screened from the multiple transition elements on a microscopic level by adopting a first principle method according to the attributes of basic elements of the preset structural material, so that the types of alloying elements doped in the preset structural material are determined, then phase diagram calculation is carried out on the multiple target transition elements to obtain a phase diagram, multiple groups of target component points meeting a first preset condition are screened from the phase diagram, and the component points/component range doped in each target transition element in the structural material are determined on a mesoscopic level. The target candidate composition of the alloy is then screened by machine learning. And finally, performing phase field calculation on each group of target component points to determine process parameters required by manufacturing the preset structural material on a macroscopic level. In general, the multi-scale chain type integrated design method of the structural material builds a progressive screening framework of an element-component-process, and realizes the rapid optimization design of the structural material. In addition, the method can flexibly set the screening criterion according to actual needs in the screening of the target transition elements and the screening of the target component points, so that the method has stronger practicability.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a multi-scale chain-type integrated design method for a structural material according to an embodiment of the present application;
fig. 2 is a screening result of a first-principle calculation module of a novel Co-based superalloy for screening transition element types according to an embodiment of the present disclosure;
fig. 3 is a screening result of the transition element type screening performed by the first principle calculation module for an Nb-Si based superalloy provided in an embodiment of the present application;
FIG. 4 is a schematic view of the microstructure of the strengthening phase of the Co-based superalloy after heat treatment according to an embodiment of the present disclosure;
FIG. 5 is a schematic view of the microstructure of the strengthening phase of the Nb-Si based superalloy provided by an embodiment of the present application after heat treatment;
FIG. 6 is a flow chart of a method for multi-scale chain-based integrated design of a structural material according to another embodiment of the present application;
fig. 7 is a schematic diagram of a database system according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention.
In the present application, unless otherwise specifically stated or limited, "a plurality" in the description of the present invention means at least two, for example, two, three, unless otherwise specifically limited. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The structural material is a material used for manufacturing a stressed member on the basis of mechanical properties. Under the industrial 4.0 background, the development of structural materials is moving towards high tensile strength, good plasticity, toughness and long fatigue life. The comprehensive properties of the structural material are closely related to its chemical composition, structure and preparation process. The structural material is widely applied to the fields of industrial production, manufacturing, biomedicine and the like. According to the 'Chinese manufacturing 2025', aiming at the harsh service environment of the structural material in the application scene, the development of a new generation of structural material with better comprehensive performance is an urgent need in China.
The structural material is an alloy consisting of a plurality of materials, and the influence mechanism of the interaction on the structure and the performance of the structural material is very complicated. Because basic data are seriously deficient, the understanding on the alloying principle is insufficient, and an alloy design rule is not established, the comprehensive performance of the structural material needs to be optimized through component optimization and preparation process integration. The measurement and characterization of various properties of the structural material are difficult through the traditional experimental method, and systematic theoretical research between elements, structure, components, temperature, time and performance is lacked, so that the research on the properties of the structural material by adopting a calculation method has great significance.
In the aspect of the current material calculation design research, single-scale calculation and simulation are mainly adopted, and material calculation models and data under microscopic, mesoscopic and macroscopic scales cannot be effectively compatible with each other, so that the calculation method is not integrated and applied, and the requirements of full-chain design and research and development of novel structural materials from atoms to macroscopic levels cannot be met.
In view of the above existing problems, the embodiments of the present application provide multiple possible implementation manners to implement a multi-scale chain-type integrated design for a structural material based on micro-mesoscopic-macroscopic implementation. The following is explained by way of a number of examples in connection with the drawings. Fig. 1 is a flowchart of a multi-scale chain-type integrated design method for a structural material according to an embodiment of the present application, where the multi-scale chain-type integrated design method for a structural material may be implemented by one or more electronic devices operating the method, where the electronic devices may be, for example, terminal devices (computers, supercomputers), or servers. As shown in fig. 1, the method includes:
step 101: and screening out multiple target transition elements required for manufacturing the preset structural material from the multiple transition elements by adopting a first principle method according to the attributes of the basic elements of the preset structural material.
The first principle is that the algorithm of the Schrodinger equation is directly solved after some approximate treatment by applying the quantum mechanics principle according to the principle of interaction between atomic nucleus and electron and the basic motion rule thereof and starting from specific requirements.
A great deal of experimental research reports find that the solid solution strength and the high-temperature performance of the strengthening phase of the structural material can be improved and enhanced by adding the fourth and fifth trace alloy elements, so that the problem that the mechanical performance can not be maintained and is sharply reduced at high temperature is solved. Various properties of the strengthening phase are difficult to describe in the traditional experiment, and the theoretical research on the stability of the multi-component strengthening phase by adding alloy elements is still imperfect. Therefore, the method has important significance in researching the element occupation tendency, elasticity, thermodynamic property and other microscopic information of the multicomponent strengthening precipitated phase through a first principle calculation method.
Therefore, according to the first principle method and the attribute of the basic element of the preset structural material, a plurality of target transition elements required for manufacturing the preset structural material can be screened from the plurality of transition elements. It should be noted that the base element of the preset structural material is a main component element of the structural material generated by the target, for example, if the structural material synthesized by the target is a novel Co-based high-temperature alloy, Co in the structural material is a main component, and the base element of the preset structural material is Co.
After the basic elements of the preset structural material are determined, the influence of various transition elements on the attributes of the basic elements of the preset structural material is calculated according to a first principle, namelyIt is clear that the transition element may occupy the doping element of the base element of the predetermined structural material (i.e. occupy the transition element in the predetermined structural material), L1 2 Phase cell stability, L1 2 The method has the advantages that the method has influence on the mechanical property, the physicochemical property related to the base element of the material and the like, and can screen out various target transition elements required for manufacturing the preset structural material from various transition elements by analyzing the influence of the transition elements and combining the use purpose of the structural material.
Among the properties that may be affected, the calculation of the space occupied by the doping elements in the unit cell is an important prerequisite for obtaining an accurate arrangement of the atoms in the unit cell. The occupancy of doping elements can be determined by the binding energy and the doping formation energy. Taking a structural material system containing three basic elements as an example, the main elements can be numbered according to the names of the system. For example, in the Co-Al-W system, Co is the main element 1, Al is the main element 2, and W is the main element 3. The rule of occupying the alloy elements can be clarified by calculating TM (TM ═ 3d,4d, and 5d TM elements), and the reaction energies of the elements occupying 1, 2, and 3 positions in each system unit cell can be defined as follows
Wherein,
denotes undoped Co 3 The static energy of (X, Y),
represents TM-doped Co 3 Static energy of (X, Y); mu.s i And mu TM Is the chemical potential of the main element and the TM element. The doping atoms will occupy the position of the lowest reaction energy.
Based on the above analysis, it was possible to compare TM-substituted L1 2 Structure and D0 19 Stable formation energy of structure Δ H S Determination of L1 after doping trace elements 2 Thermodynamic stability of the phase.
Wherein mu j Is the chemical potential of each element. For L1 2 And D0 19 The structure is more stable, and the stable formation energy is lower. That is, L1 can be further determined by placeholder analysis for dopant elements 2 Phase unit cell stability.
Further, L1 2 The facies mechanical properties can be calculated as follows:
the mechanical properties such as B, G and E can be calculated from the elastic constants and the stress strain model, and can be expressed as follows:
wherein epsilon i ,C ij And σ i Respectively representing a strain vector, an elastic constant matrix and a stress matrix. The crystal structure is affected by strain epsilon, which results in a set of stress tensors sigma ═ sigma (σ) 1 ,σ 2 ,σ 3 ,σ 4 ,σ 5 ,σ 6 )。σ 1 -σ 3 The stress component representing the normal stress, σ 4 -σ 6 The stress component represents shear stress. In contrast, a set of strain tensors, ε ═ ε 1 ,ε 2 ,ε 3 ,ε 4 ,ε 5 ,ε 6 ) Is applied to a supercell (supercell) and produces a small deformation. The stress value is defined as the first derivative of the total energy calculated by the first principle of linearity with respect to the corresponding strain. The number of independent elastic constants C of different crystals is also different due to the symmetry of the crystals. In the cubic system, there are only three independent elastic constants
And
can be calculated by the formulas (1) to (3).
Wherein S is ij Is a flexible constant matrix composed of an elastic matrix C ij And (5) inversion is carried out to obtain.
Thus, for cubic systems, the elastic matrix can be simplified as:
L1 2 the mechanical properties of the phases include bulk modulus (B), shear modulus (G) and Young's modulus (E), which can be calculated approximately by Voigt-reus-Hill (VRH). The calculation method is shown in formulas (5) to (10).
E v ＝(9B v G v )/(3B v +G v )E R ＝(9B R G R )/(3B R +G R ), (7)
B＝(B v +B R )/2, (8)
G＝(G v +G R )/2, (9)
E＝(E v +E R )/2, (10)
In a specific implementation, if the base element of the predetermined structural material is Co, after determining the base element, according to the existing research data or related analysis, a plurality of transition elements are selected from the transition elements in the periodic table for subsequent screening, for example, for a novel Co-based superalloy, 21 transition elements (Sc, Ti, V, Cr, Mn, Fe, Ni, Y, Zr, Nb, Mo, Tc, Ru, Rh, Pd, Hf, Ta, Re, Os, Ir, and Pt) are doped into L1 (i.e. if the transition elements (i.e. the selected doping elements) are the same as the base elements in the unit cell, the transition elements are removed) 2 The occupation of the doping atoms in the different equivalent positions of the phase cells (for example in the Co-Al-W system, Co1, Co2, Co3, … Co6, Al1, Al2, W1, W2 equivalent positions) is determined by calculation. (it should be noted that the calculation of the generation energy may be performed manually or by computer assistance based on the above calculation formula, or may be implemented by using related generation energy calculation software, which is not limited in the present application). After the occupancy determination is completed, L1 is determined 2 Phase stability, i.e. judgment of L1 according to the above calculation method of stability 2 And its competitive phase D0 19 The enthalpy of formation of the phases is large, so that L1 is screened 2 A stable doped structure.
After the calculation is finished, the L1 of the novel Co-based high-temperature alloy with multiple transition elements can be obtained 2 Fig. 2 shows the screening result of the transition element type screening performed by the first principle calculation module of the Co-based superalloy provided in the embodiment of the present application, as shown in fig. 2, it can be found that Ta, Ti, Ni, Cr, and W elements can effectively improve L1 of the Co-based superalloy 2 The microscopic properties of the phases, and in addition the Cr element, are important for the oxidizability of the alloy, and therefore the targeted transition elements were determined to be: ta, Ti, Ni, W, Cr, Al. In the subsequent process, optimization of Co-based superalloys including these several transition elements is required.
In another embodimentIn the implementation of (1), if the basic elements of the preset structural material are Nb-Si, after determining the basic elements, according to the existing research data or correlation analysis, select multiple transition elements from the transition elements in the periodic table of elements for subsequent screening, for example, for Nb-Si-based superalloy, Nb of Nb-Si-based superalloy is calculated by 30 elements 5 Si 3 Fig. 3 is a screening result of a first principle calculation module of an Nb-Si-based superalloy provided in an embodiment of the present application for screening transition element types, and it is found that Sc, Ti, V, Cr, Mn, Y, Zr, Nb, Mo, Tc, Lu, and Hf elements can effectively improve Nb 5 Si 3 The microscopic properties of the phases, of which the Ti, Si, Cr, V, Hf and Zr elements contribute greatly to the reduction of the oxygen diffusion coefficient. Thus, the target transition element may be determined to be: si, Cr, V, Hf, Zr. In the subsequent process, optimization of Nb-Si based superalloys including these several transition elements is required.
The above implementation manner is only an example, and in an actual implementation, there may be a related calculation manner of the structural material of other base elements, and the present application does not limit the specific type of the base element.
As can be seen from the above two specific implementation manners, on one hand, after the base element of the preset structural material is determined, the calculation manner used in screening the target transition element may be different according to different uses of the structural material, for example, in the above embodiment, the L1 of the novel Co-based superalloy is obtained by using multiple transition elements for the novel Co-based superalloy 2 The influence of phase unit cell stability and mechanical properties is screened, and the Nb-Si based high temperature alloy is screened by using the oxygen diffusion coefficient. On the other hand, the number of the target transition elements finally determined is different for the preset structural materials of different base elements, and generally, in order to meet the computing power of the current computing equipment and the processing technology of the structural materials, the number of the elements of the structural materials is generally limited within the preset structural materials (including the base elements).
In one possible implementation, the first-principle-based computing software (VASP) may be executedThe first principle method calculates the generation energy and judges the position occupation L1 of the transition element atom according to the related criterion 2 The micromechanics performance of the phase cells, etc. The specific computational logic of the first principle when using such software is briefly described below. The VASP software carries out self-consistent iterative computation solution of a Kohn-Sham equation through exchange correlation functional theories such as GGA, LDA and the like based on a pseudopotential plane wave basis set model. The specific calculation process comprises the following steps: firstly, constructing a reasonable initial electron density distribution, and solving an effective correlation potential Veff, an interaction exchange correlation functional Exc and a Kohn-Sham equation orbit phi according to the reasonable initial electron density distribution; then, based on the electronic orbit, VASP software can construct a new electronic density distribution, and after the new electronic density is substituted into an equation, the VASP software can judge whether self-consistent conditions are met; if the self-consistent condition is met, the computing system is always calculated, when the computing system always meets the given convergence precision, the total calculation result is output, and if the self-consistent condition is not met, the VASP software carries out repeated iterative calculation until the new electron density meets the self-consistent condition.
Step 102: and carrying out phase diagram calculation on the multiple target transition elements to obtain a phase diagram.
After the target transition element is determined, the phase diagram calculation can be utilized to realize the screening of the doping component points/component ranges of the target transition element.
The phase diagram is an important basis for researching the relationship among the components, the process, the structure and the performance of the material, and describes mesoscopic information of the structural material. The process of experimental determination of the phase diagram needs to consume a large amount of manpower and material resources, and under the conditions of high temperature, high pressure and reaction participation of corrosive gas, the method also faces the difficulties in aspects of component control, container selection, high temperature measurement and the like, and the experimental determination is always limited and one-sided, so that the phase diagram and thermodynamic properties of a system cannot be completely and comprehensively understood. In order to solve the problems, a CALPHAD method can be used for calculating and extrapolating a phase diagram, wherein the essence of the phase diagram is to establish thermodynamic models of all phases according to information such as crystal structures, magnetic order, chemical order conversion and the like of all phases in a target system, construct Gibbs free energy expressions of all phases by the models, and finally calculate the phase diagram through equilibrium conditions to establish a correlation rule between components, phases and properties.
In a specific implementation manner, for the novel Co-based superalloy mentioned in the above embodiment, after determining Ta, Ti, Ni, W, Cr and A as target transition elements, the predetermined structural material is determined to be Co-Ni-Al-W-Ti-Ta-Cr seven-element alloy. Then, all elements in the structural material are arranged and combined, for example, 20,250 composition points are total in the seven-element alloy Co-Ni-Al-W-Ti-Ta-Cr, and after the possible composition points of the structural material are determined, a phase diagram is further obtained.
In another specific implementation manner, for the Nb-Si based superalloy mentioned in the above embodiment, after Si, Cr, V, Hf, and Zr are determined as target transition elements, the predetermined structural material is determined as a seven-element system alloy Nb-Ti-Si-Cr-V-Hf-Zr. The phase diagram of this seven-element alloy was obtained by arranging and combining all the elements in this alloy, and this alloy had 11,664 composition points in total.
Step 103: and screening multiple groups of target component points meeting a first preset condition from the phase diagram according to a preset machine learning algorithm.
Step 104: and performing phase field calculation on each group of target component points to obtain process parameters required for manufacturing the preset structural material, wherein the process parameters are used for indicating: and (4) performing heat treatment on each group of target component points.
Screening out multiple groups of target component points meeting a first preset condition from a phase diagram, and carrying out phase field calculation on each group of target component points to obtain process parameters required by manufacturing a preset structural material, wherein the process parameters are used for indicating: and (4) performing heat treatment on each group of target component points.
When it is required to be mentioned, after the component points of the arrangement combination of the base elements and the target transition elements of the preset structural material are obtained in step 102, the phase composition information and the melting points of all the component points can be obtained through high-throughput thermodynamic calculation, and then the multiple groups of target component points meeting the first preset condition are screened out according to the calculation results. It should be noted that the first preset condition may be set according to actual structural material usage or design requirements, which is not limited in the present application.
In a specific implementation, the Co-Ni-Al-W-Ti-Ta-Cr seven-element alloy mentioned in the above examples is used. After 20,250 component points are determined, phase composition information and melting points of all the component points are obtained through high-throughput thermodynamic calculation, and then target component points can be screened out by setting a first preset condition that the melting point is higher than 1150 ℃ and no TCP phase exists.
In another specific implementation, the Nb-Ti-Si-Cr-V-Hf-Zr seven-element alloy mentioned in the above embodiment is used. After 11,664 component points are determined, phase composition information and melting points of all the component points are obtained through high-throughput thermodynamic calculation, and further, the first preset conditions of ' melting point higher than 1350 ℃ and ' no Nb ' can be set 3 Si phase "screen out target component points.
It should be noted that the target component points may be a plurality of target component points, and the number of the target component points is not limited in this application.
After the target component point is obtained, the process parameters of the preset structural material need to be determined according to phase field calculation.
Phase field calculation, i.e. phase field simulation, is a simulation method only for the tissue phase transition process, and the process parameters of the structural material are calculated according to the growth condition of the tissue of the structural material. In the field of structural materials, the method mainly simulates the structure evolution of a target phase, the aging structure evolution under different alloy component gradients and the structure evolution of a harmful phase, and provides macroscopic process information of the structural material.
In a specific implementation manner, phase field calculation is performed on screened target component points of the Co-Ni-Al-W-Ti-Ta-Cr seven-element alloy mentioned in the above embodiment, and a proper heat treatment process corresponding to each target component point is determined by using criteria of ' impurity-free phase ' and ' proper gamma ' phase morphology '. As shown in fig. 4, fig. 4 is a schematic view of a microstructure of a strengthening phase of the novel Co-based superalloy after heat treatment according to an embodiment of the present disclosure. By determining elements, components and process parameters, a novel Co-based high-temperature alloy with the components of Co-30Ni-11Al-4W-4Ti-1Ta-5Cr is successfully designed.
In another specific implementationThe phase field calculation was performed for the target composition points of the Nb-Ti-Si-Cr-V-Hf-Zr seven-element alloy mentioned in the above examples, using the criteria "no impurity phase" and "appropriate Nb ss +Nb 5 Si 3 Eutectic phase morphology "determines the appropriate heat treatment process for each target composition point. As shown in fig. 5, fig. 5 is a schematic view of a microstructure of a strengthening phase of a Nb-Si-based superalloy after heat treatment according to an embodiment of the present disclosure, where the left figure is a microstructure of the strengthening phase of the Nb-Si-based superalloy after heat treatment, and the right figure is a microstructure of a Si-element strengthening phase of the Nb-Si-based superalloy after heat treatment. By determining target transition elements, target component points and process parameters, the Nb-Si-based high-temperature alloy with the components of Nb-22 Ti-15 Si-5 Cr-3V-2 Hf-2Zr is successfully designed.
To sum up, the embodiment of the present application provides a multi-scale chain type integrated design method for a structural material, which is implemented by using a first principle method to screen out multiple target transition elements required for manufacturing a preset structural material from multiple transition elements on a microscopic level according to the attributes of base elements of the preset structural material, so as to determine the types of alloying elements doped in the preset structural material, then performing phase diagram calculation on the multiple target transition elements to obtain a phase diagram, screening out multiple groups of target component points satisfying a first preset condition from the phase diagram, and determining the component points/component ranges doped in each target transition element in the structural material on a mesoscopic level. The target candidate composition of the alloy is then screened by machine learning. And finally, performing phase field calculation on each group of target component points to determine process parameters required by manufacturing the preset structural material on a macroscopic level. In general, the multi-scale chain type integrated design method of the structural material builds a progressive screening framework of an element-component-process, and realizes the rapid optimization design of the structural material. In addition, the method can flexibly set the screening criterion according to actual needs in the screening of the target transition elements and the screening of the target component points, so that the method has stronger practicability.
Optionally, on the basis of fig. 1, the present application further provides a possible implementation manner of a multi-scale chain-type integrated design method for a structural material, and fig. 6 is a flowchart of a multi-scale chain-type integrated design method for a structural material according to another embodiment of the present application; as shown in fig. 6, after the groups of target component points satisfying the first preset condition are screened out from the phase diagram according to the preset machine learning algorithm, the method further includes:
step 601: and determining the composition range of each target transition element according to the target composition points.
In the process of determining the parameters of the structural material, the combination space of the element types, the temperature, the composition ranges and the process parameters is very large (for example, in the above embodiment, there are tens of thousands of combined composition points), and experiments and calculations are performed by using methods such as an ergodic method or an inefficient trial-and-error method, which may cause a great amount of waste of manpower, material resources and time. The target component points can therefore be further screened.
Firstly, a plurality of groups of target component points meeting a first preset condition are screened from a phase diagram, and the component range of each target transition element is determined according to the target component points.
In a specific implementation, the Co-Ni-Al-W-Ti-Ta-Cr seven-element alloy mentioned in the above examples is used. From 20,250 composition points, a target composition point was selected by setting a first preset condition, and it was determined that in this alloy, the composition range of Ni was 20 to 40 at.%, the composition range of Cr was 4 to 12 at.%, the composition range of W was 0 to 5 at.%, and the composition ranges of Ta and Ti were 0 to 4 at.%.
In another specific implementation, the Nb-Ti-Si-Cr-V-Hf-Zr seven-element alloy is used. From the 11,664 composition points, the target composition points were selected by setting the first preset condition, and it was determined that in this alloy, the composition range of Ti was 20 to 25 at.%, the composition range of Si was 15 to 25 at.%, the composition range of Cr was 0 to 10 at.%, and the composition ranges of V, Hf and Zr were 0 to 5 at.%.
Step 602: a preset number of sets of target transition element candidate compositions are determined from the composition range.
In one possible implementation, the determination is made from the composition range according to a second preset conditionAnd (4) targeting the candidate components of the transition elements. For example, for the Nb-Ti-Si-Cr-V-Hf-Zr seven-element alloy mentioned in the above examples, after obtaining the composition ranges, "Nb" is added 5 Si 3 Phase volume fraction higher than 25% "and" Nb 5 Si 3 The phase dissolution temperature is higher than 1200 ℃, which is used as a screening criterion, namely screening is carried out under a second preset condition, so as to determine candidate components of the target transition elements, and seven candidate components of the target transition elements are determined according to a screening result. In this case, if the second preset condition can achieve the purpose of screening, the result after screening may be directly used for subsequent phase field calculation without setting the preset number.
In another possible implementation manner, if the result after the second preset condition screening still exceeds the calculation power or the expected quantity, further quantity limitation may be performed on the result after the screening, so as to determine a preset number of sets of target transition element candidate components. For example, for the seven-element Co-Ni-Al-W-Ti-Ta-Cr alloy mentioned in the above examples, after the component ranges are obtained, the ' gamma ' dissolution temperature is higher than 1150 ℃ and the ' gamma ' phase volume fraction is higher than 50% ' are used as the screening criteria, i.e. the second preset condition for screening, and five candidate components of the target transition element are determined from the screening results.
It should be noted that the above determining of the preset number of groups of target transition element candidate components from the component range may be implemented by calculation, and in order to further increase the calculation speed, methods such as machine learning may be adopted, which are not limited in this application.
And performing phase field calculation on each group of target component points to obtain process parameters required by manufacturing the preset structural material, wherein the process parameters comprise:
step 603: and performing phase field calculation on the preset number of groups of target transition element candidate components to obtain process parameters.
After the target transition element candidate components are further screened, phase field calculation is carried out on the target transition element candidate components to obtain process parameters corresponding to each group of target transition element candidate components, so that the calculation is simplified, and the expenses and the time are saved.
By the method, a series of screening criteria of the element types, the component ranges and the process parameters of the structural material are provided, candidate components and process parameters of the alloy can be screened from a huge component space, and a scheme is provided for the design of the structural material. In addition, the calculation amount and the calculation cost are reduced by determining a preset number of groups of target transition element candidate components from the component range.
Optionally, on the basis of fig. 1, the present application further provides a possible implementation manner of the multi-scale chain-type integrated design method for a structural material, where phase diagram calculation is performed on component points of multiple target transition elements to obtain a phase diagram, where the phase diagram includes:
and carrying out phase diagram calculation on the composition points of the various target transition elements to obtain a phase diagram and obtain a digital phase diagram.
It should be noted that, on one hand, the digitized phase diagram can store data in a table, is not limited by the number of components, and can be conveniently and flexibly applied to building a material database and training a machine learning model. On the other hand, since the ternary or higher phase diagram (for example, in the above-described examples, the phase diagram of Co — Ni — Al — W — Ti — Ta — Cr is a seven-element alloy, and the seven-element system) cannot be directly expressed by a figure, it is necessary to obtain a digitized phase diagram by digitizing the phase diagram.
In one possible implementation, step 102 performs phase diagram calculation on multiple target transition elements, and may generate a digitized phase diagram according to the calculated related information (e.g., component points, temperature information, generation phase information, etc.).
Optionally, on the basis of fig. 1, the present application further provides a possible implementation manner of the multi-scale chain-type integrated design method for a structural material, and a first principle method is adopted to screen out multiple target transition elements required for manufacturing a preset structural material from multiple transition elements according to the attributes of the base elements of the preset structural material, including:
according to the attribute of the basic element of the preset structural material, a first principle method is adopted to screen out multiple target transition elements required by manufacturing the preset structural material from multiple transition elements corresponding to the basic element.
For the preset structural materials of different base elements, the number of the multiple transition elements as screening candidates is different, for example, for the novel Co-based superalloy in the above embodiment, the number of the multiple transition elements as screening candidates is 21; and for the Nb-Si-based superalloy, the number of a plurality of transition elements as screening candidates is 30. The difference is determined by the inherent attributes of the transition elements, and after the user determines the base elements, the user can select the transition elements according to the related knowledge to determine a plurality of candidate transition elements for screening. The foregoing is merely an example, and in an actual implementation, there may be other ways to select multiple transition elements corresponding to the base element from all transition elements, which is not limited in this application.
Optionally, on the basis of fig. 1, the present application further provides a possible implementation manner of the multi-scale chain-type integrated design method for a structural material, and a first principle method is adopted to screen out multiple target transition elements required for manufacturing a preset structural material from multiple transition elements according to the attributes of the base elements of the preset structural material, including:
screening out multiple target transition elements required by manufacturing a preset structural material from multiple transition elements according to a parameter database and a first principle method; the parameter database includes at least one of the following parameters: relaxation structure, energy, elastic constant, differential charge density.
It should be noted that the parameter database includes parameters related to the element properties of multiple transition elements, and the parameter database can obtain microscopic information such as the structures and energies of the multiple transition elements, so as to provide a theoretical basis for screening of target transition elements.
Optionally, on the basis of fig. 1, the present application further provides a possible implementation manner of the multi-scale chain-type integrated design method for a structural material, where the phase diagram is obtained by performing phase diagram calculation on multiple target transition elements, and the method includes:
and according to a thermodynamic database, carrying out phase diagram calculation on the multiple target transition elements to obtain a phase diagram.
The target determined by the parameters is a structural material, and when the target component points are calculated and screened through a phase diagram, the target component points can be screened according to the phase composition information and the melting point information of the calculated component points of a thermodynamic database.
The foregoing is merely an example, and in an actual implementation, target component point screening may also be implemented according to other databases, which is not limited in this application.
Optionally, on the basis of fig. 1, the present application further provides a possible implementation manner of a multi-scale chain-type integrated design method for a structural material, where the phase-field calculation is performed on each group of target component points, including:
and performing phase field calculation on each group of target component points according to the dynamic database.
Through dynamics (such as a microstructure evolution database), microstructure evolution can be carried out on each group of target component points, and then the process parameters corresponding to each group of target component points are determined to guide the heat treatment process.
Optionally, on the basis of fig. 6, the present application further provides a possible implementation manner of the multi-scale chain-type integrated design method for structural materials, where the method determines a preset number of groups of target transition element candidate components from a component range, and includes:
and screening out a preset number of groups of target transition element candidate components from the component range according to a machine learning algorithm.
It should be noted that because the number of component points of each structural material is large and the situation is complex, if the component points are manually screened, a lot of time is consumed, and the accuracy of the screening result cannot be ensured, the candidate components of the target transition element can be determined by using a machine learning algorithm.
In one specific implementation, the algorithm "No Free Lunch Theory" may be used, that is, one algorithm (algorithm A) performs better on a specific data set than another algorithm (algorithm B), and at the same time, the algorithm A performs less on a specific data set than the algorithm B. For example, the classification model can be used to build a space-occupying model of the target transition element and L1 2 Phase stability model, using regression model to establish L1 2 Phase micromechanics property predictionAnd (4) modeling.
Establishing an occupancy model of the target transition element by using a classification model and L1 2 In the case of a phase stability model, the following classification models may be used: random Forest Classification (RFC), Gradient Boosting Classification (GBC), AdaBoost Classification (Ada), Support Vector Machine (SVM), Architectural Neural Network (ANN), K-Nearest Neighbors Classification (KNNC), Gaussian Process Classification (GPC).
After the regression model is adopted to establish L1 2 When the phase micromechanics property prediction model is used, the following regression model can be adopted: random Forest Regression (RFR), Gradient Boosting Regression (GBR), AdaBoost Regression (Ada), Support Vector Regression (SVR), Architectural Neural Network (ANN), K-near Neighbors Regression (KNNR), Gaussian Process Regression (GPR).
Therefore, the multi-scale chain type integrated design method of the structural material can realize the cyclic optimization of alloy components, organization and performance based on a data mining and machine learning method of high-throughput multi-scale calculation, and provides theoretical guidance for the preparation of the structural material.
The following describes a parameter determination system, a structural material, and the like for executing the structural material provided in the present application, and specific implementation processes thereof are referred to above and will not be described again below.
The embodiment of the present application provides a possible implementation example of a parameter determination system for a structural material, which is capable of implementing the multi-scale chain type integrated design method for a structural material provided in the above embodiment. Fig. 7 is a schematic diagram of a database system according to an embodiment of the present application. As shown in fig. 7, the database system 100 includes: a first principle calculation module 71, a phase diagram calculation module 73, and a phase field calculation module 75; the first principle calculating module 71 and the phase diagram calculating module 73 are in communication connection through an input and output interface; the phase diagram calculation module 73 is in communication connection with the machine learning module 77 through an input-output interface; the machine learning module 77 is in communication connection with the phase field calculation module 75 through an input-output interface;
the first principle calculating module 71 is configured to screen out multiple target transition elements required for manufacturing a preset structural material from multiple transition elements by using a first principle method according to the attributes of the base elements of the preset structural material;
the phase diagram calculation module 73 is configured to perform phase diagram calculation on multiple target transition elements to obtain a phase diagram;
the machine learning module is used for screening out multiple groups of target component points meeting a first preset condition from the phase diagram according to a preset machine learning algorithm;
a phase field calculation module 75, configured to perform phase field calculation on each group of target component points to obtain a process parameter required for manufacturing a preset structural material, where the process parameter is used to indicate: and (4) performing heat treatment on each group of target component points.
The first principle calculation module, the phase diagram calculation module, the machine learning module, and the phase field calculation module may be implemented by an electronic device running the method corresponding to the device, and the electronic device may be, for example, a terminal device (e.g., a computer), or a server (e.g., a super computer), which is not limited in this application. Generally, since the first principle calculation module and the phase field calculation module have high requirements on the performance of the equipment in the calculation, a supercomputer can be adopted for the calculation.
Therefore, the first principle calculation module, the phase diagram calculation module and the phase field calculation module respectively determine the doping alloying element type (target transition element), the composition point (target composition point) and the heat treatment process (process parameters) of the structural material. A first principle calculation chain, a phase diagram calculation chain and a phase field calculation multi-scale calculation chain are built in the material multi-scale calculation database system, and adjacent modules are connected through input and output interfaces, so that I/O type data transmission is realized.
In one possible implementation, the first principle calculation module, the phase diagram calculation module, and the phase field calculation module may be located in the same computer device (e.g., a computer or a server, etc.) for calculation.
In another possible implementation manner, after the material multi-scale calculation database is established in the material multi-scale calculation database system, all modules may be automatic acquisition modules, and the calculation data calculated by each module is automatically acquired, so as to obtain a complete material multi-scale calculation data chain of the structural material element-component-process.
Alternatively, on the basis of fig. 7, the present application further provides a possible implementation manner of the multi-scale chain-type integrated design method for structural materials, and the machine learning module 77 is configured to determine the composition range of each target transition element according to the target composition point; a preset number of sets of target transition element candidate compositions are determined from the composition range.
The machine learning module 77 thus obtains the composition range of each target transition element of the phase map calculation module 73 through the input-output interface, and determines the target transition element candidate composition on the basis thereof.
Optionally, the phase diagram calculation module 73 is configured to perform phase diagram calculation on multiple target transition elements to obtain a digitized phase diagram.
Optionally, the first principle calculating module 71 is configured to screen out multiple target transition elements required for manufacturing the preset structural material from multiple transition elements corresponding to the base element by using a first principle method according to an attribute of the base element of the preset structural material.
Optionally, the first principle calculating module 71 is configured to screen out multiple target transition elements required for manufacturing a preset structural material from the multiple transition elements according to the parameter database and the first principle method; the parameter database includes at least one of the following parameters: relaxation structure, energy, elastic constant, differential charge density.
Optionally, the phase diagram calculation module 73 is configured to perform phase diagram calculation on multiple target transition elements according to a thermodynamic database to obtain a phase diagram.
Optionally, the phase field calculation module 75 is configured to perform phase field calculation on each group of target component points according to the dynamics database.
In summary, the present application provides a multi-scale information chain of microscopic atomic scale, mesoscopic phase scale, and macroscopic process scale of a structural material; a progressive screening framework of an element-component-process based on an I/O type data communication material design concept is set up, the rapid optimization design of the structural material is realized, the problem of structural material calculation model coupling under the micro, mesoscopic and macro scales is broken through, and the effective data transmission among different models is realized; the method has the advantages that the first principle calculation, the phase diagram calculation, the machine learning and the phase field calculation are skillfully and efficiently integrated, the problem of coupling of calculation models of materials with different scales is solved, and effective data transmission among different models is realized.
In one specific implementation, first microscopic information of the target phase of the structural material is calculated by the first principle of nature. And then transmitting the microscopic information to phase diagram calculation software by using a data interface, calculating the phase composition information and the melting point of all possible composition points of the target alloy, and determining candidate compositions of the structural material by using machine learning. And finally, transmitting the candidate components to phase field calculation software by using a data interface, and determining the proper heat treatment process of the structural material.
Optionally, an embodiment of the present application provides a possible implementation example of a structural material, where the structural material is obtained by a multi-scale chain type integrated design method using the structural material, or by performing parameter determination by using the database system and performing processing according to determined process parameters.
In a specific implementation manner, by a multi-scale chain type integrated design method of a structural material, firstly, the element types (target transition elements) which can effectively improve the reinforcing phase of the structural material are determined; then screening out alloy components (target component points) with higher melting points, larger volume fraction of the strengthening phase and no impurity phase; and finally, screening out proper reinforced phase morphology to determine a proper heat treatment process (process parameter). Through the process parameters, the structural material with excellent performance meeting the service requirements is designed, and the structural material can be generated according to the process parameters. For example, a structural material with excellent performance applied to the performance of blades of an aircraft engine and a ground combustion engine can be developed, and the purpose of improving the efficiency of two types of gas turbines is achieved.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A multi-scale chain type integrated design method of a structural material is characterized by comprising the following steps:
screening out a plurality of target transition elements required for manufacturing a preset structural material from the plurality of transition elements by adopting a first principle method according to the attribute of a basic element of the preset structural material;
carrying out phase diagram calculation on the multiple target transition elements to obtain a phase diagram;
screening out multiple groups of target component points meeting a first preset condition from the phase diagram according to a preset machine learning algorithm;
and performing phase field calculation on each group of target component points to obtain process parameters required for manufacturing the preset structural material, wherein the process parameters are used for indicating: and (4) carrying out heat treatment on each group of target component points.
2. The method of claim 1, wherein after the selecting the plurality of sets of target component points satisfying a first preset condition from the phase diagram according to a preset machine learning algorithm, the method further comprises:
determining the composition range of each target transition element according to the target composition points;
determining a preset number of groups of target transition element candidate components from the component range;
the phase field calculation is performed on each group of target component points to obtain process parameters required for manufacturing the preset structural material, and the process parameters comprise:
and performing phase field calculation on the preset number of groups of target transition element candidate components to obtain the process parameters.
3. The method of claim 1, wherein the phase map calculation of the constituent points of the plurality of target transition elements results in a phase map, comprising:
carrying out phase diagram calculation on the multiple target transition elements to obtain the graphed phase diagram; wherein the phase diagram of the diagrammatizing is a digitized phase diagram.
4. The method according to claim 1, wherein the step of screening out a plurality of target transition elements required for manufacturing the preset structural material from a plurality of transition elements by using a first principle method according to the properties of the base elements of the preset structural material comprises the following steps:
and screening various target transition elements required for manufacturing the preset structural material from the various transition elements corresponding to the base elements by adopting the first principle method according to the attributes of the base elements of the preset structural material.
5. The method according to claim 1, wherein the step of screening out a plurality of target transition elements required for manufacturing the preset structural material from a plurality of transition elements by using a first principle method according to the properties of the base elements of the preset structural material comprises the following steps:
screening out a plurality of target transition elements required for manufacturing the preset structural material from the plurality of transition elements according to a parameter database and a first principle method; the parameter database includes at least one of the following parameters: relaxation structure, energy, elastic constant, differential charge density.
6. The method of claim 1, wherein the performing a phase diagram calculation on the plurality of target transition elements results in a phase diagram comprising:
and according to a thermodynamic database, performing phase diagram calculation on the multiple target transition elements to obtain a phase diagram.
7. The method of claim 1, wherein performing phase field calculations for each set of target constituent points comprises:
and performing phase field calculation on each group of target component points according to a dynamics database.
8. The method of claim 2, wherein said determining a preset number of sets of target transition element candidate compositions from said composition range comprises:
and screening out a preset number of groups of target transition element candidate components from the component range according to a machine learning algorithm.
9. A multi-scale chain-based integrated database system of structural materials, the system comprising: the device comprises a first principle calculation module, a phase diagram calculation module, a machine learning module and a phase field calculation module; the first sexual principle calculating module is in communication connection with the phase diagram calculating module through an input-output interface; the phase diagram calculation module is in communication connection with the machine learning module through an input-output interface; the machine learning module is in communication connection with the phase field calculation module through an input/output interface;
the first principle calculation module is used for screening out multiple target transition elements required for manufacturing the preset structural material from multiple transition elements by adopting a first principle method according to the attributes of the basic elements of the preset structural material;
the phase diagram calculation module is used for carrying out phase diagram calculation on the multiple target transition elements to obtain a phase diagram;
the machine learning module is used for screening out multiple groups of target component points meeting a first preset condition from the phase diagram according to a preset machine learning algorithm;
the phase field calculation module is configured to perform phase field calculation on each group of target component points to obtain process parameters required for manufacturing the preset structural material, where the process parameters are used to indicate: and (4) performing heat treatment on each group of target component points.
10. The system of claim 9, wherein the machine learning module is configured to determine a composition range for each target transition element based on the target composition points; determining a preset number of groups of target transition element candidate components from the component range;
and the phase field calculation module is used for performing phase field calculation on the preset number of groups of target transition element candidate components to obtain the process parameters.
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