Data-driven multi-objective performance reverse design optimization method for deformed nickel-based superalloy

Abstract

The invention relates to a data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method, which belongs to the technical field of metal material design and development, and comprises the following steps: calculating a stable high-strength deformation nickel-based superalloy system based on a first principle, and on this basis, calculating a deformation nickel-based superalloy system; constructing a deformed nickel-based superalloy component design space based on the knowledge of the nickel-based superalloy field; based on a deformed nickel-based superalloy component design space, the deformed nickel-based superalloy component design space is reduced by adopting an empirical formula in combination with thermodynamic high-throughput calculation to obtain the reduced deformed nickel-based superalloy component design space, and based on the reduced deformed nickel-based superalloy component design space, based on machine learning and a genetic algorithm, the deformation nickel-based superalloy component design space is obtained. And a deformed nickel-based high-temperature alloy reverse design model is established, and the deformed nickel-based high-temperature alloy with the target performance is screened. Compared with the prior art, the oriented development method for the high-strength and high-toughness nickel-based high-temperature alloy is achieved by fusing cross-scale calculation, domain knowledge constraint and machine learning reverse design.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of metal material design and development, and particularly relates to a data-driven multi-objective performance reverse design optimization method for deformed nickel-based high-temperature alloy. BACKGROUND

[0002] Nickel-based high-temperature alloy has become the preferred material for key components such as turbine disks and turbine blades of an aero-engine, due to its high strength and creep rupture life, as well as excellent resistance to corrosion, oxidation and fatigue at high temperatures.

[0003] In the process of data-driven design of nickel-based high-temperature alloy, it is difficult to reversely design the alloy composition according to the target performance, because the dimension of the composition vector is much larger than that of the predicted performance vector, and one performance parameter corresponds to multiple alloy compositions.

[0004] The current design method of nickel-based high-temperature alloy mainly includes: first principle calculation (such as density functional theory DFT) to predict the electronic structure, phase stability and basic physical properties of materials from the atomic scale by solving quantum mechanics equation. In the design of high-temperature alloy, this method can be used to evaluate the influence of element doping on thermodynamic stability, lattice distortion, interface energy and defect formation energy; the high-throughput thermodynamic technology based on CALPHAD (phase diagram calculation) can realize rapid phase equilibrium prediction and composition-technology window screening by constructing the Gibbs free energy database of multi-component alloy system. Typical applications include batch evaluation of the change of γ' phase content, phase composition and content with alloy composition and temperature in the thermodynamic calculation module, TCP phase precipitation tendency; the precipitation phase calculation module can also calculate the average particle size, volume fraction and TTT / CCT curve of the precipitated phase; machine learning (Machine Learning, ML) can realize the rapid prediction of material performance by establishing a nonlinear mapping model of “composition-technology-performance” (such as support vector machine, random forest), and the reverse design framework (such as Bayesian optimization, genetic algorithm) can reversely generate candidate alloy composition for target performance (yield strength / tensile strength / elongation).

[0005] However, the first principle calculation consumes huge resources and is only suitable for small-scale models (usually <1000 atoms), and it is difficult to directly correlate macroscopic performance (such as high-temperature yield strength and room-temperature elongation). The thermodynamic phase diagram calculation depends on the accuracy of experimental data, cannot handle non-equilibrium dynamic processes, and has limited prediction ability for complex performance (such as high-temperature yield strength and room-temperature elongation). Machine learning requires high quality of data set, and mainly relies on single performance optimization, lacks systematic coordination ability for multi-objective conflict of high-temperature alloy (such as the trade-off between strength and plasticity), and has insufficient model interpretability.

[0006] CN202310956599.5 discloses a high-temperature alloy composition and process design method, comprising the following steps: collecting data of a certain brand of high-temperature alloy manual, experiment, literature, and constructing a database; using machine learning algorithm to construct corresponding performance prediction model, using Bayesian optimization technology, taking the required optimized alloy composition range as the search range and taking the comprehensive performance index as the optimization target, realizing alloy composition optimization to obtain excellent comprehensive performance, realizing process optimization of small sample data comprehensive performance through machine learning algorithm, using optimized alloy composition and process to cast alloy, verifying the optimized comprehensive performance, training the model by collecting data as the training set, analyzing the influence law of alloy composition and processing technology on the structure performance, designing the composition and process of the optimized alloy, reducing the cost of manpower and material resources, and reducing the research and development cycle of cast nickel-based high-temperature alloy. However, the method in CN202310956599.5 mainly relies on domain knowledge, and the physical interpretability and engineering feasibility of composition design cannot be ensured.

[0007] CN202410138800.3 discloses a method and device for reverse design of target performance nickel-based high-temperature alloy composition, relating to the technical field of nickel-based high-temperature alloy composition design, which comprises: obtaining formed nickel-based high-temperature alloy composition data and performance prediction model; using K-means algorithm to cluster the formed nickel-based high-temperature alloy composition data to obtain several nickel-based high-temperature alloy data sets; using each nickel-based high-temperature alloy data set to train variational autoencoder to obtain nickel-based high-temperature alloy composition generation model; and using the nickel-based high-temperature alloy composition generation model and the performance prediction model to reverse design target performance nickel-based high-temperature alloy composition to obtain nickel-based high-temperature alloy composition design value, which can realize reverse design of nickel-based high-temperature alloy composition with target performance. However, the method in CN202410138800.3 only takes alloy chemical composition as input feature, without incorporating domain knowledge and microstructure parameters as physical constraints, and without proving the effectiveness of the method through iterative optimization and experimental verification.

[0008] Therefore, there is a need for a directional development method for high-strength and high-toughness nickel-based high-temperature alloy. SUMMARY

[0009] The present application aims to overcome the defects of the prior art and provides a data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method, which is a directional development method of high strength and toughness nickel-based superalloy by combining cross-scale calculation, field knowledge constraint and machine learning reverse design. The composition design space is constructed by screening the thermodynamic stable system through the first principle, combining the organizational stability criterion, the strengthening effect regulation and the processing performance hard constraint; the microstructure parameters are calculated by the high-throughput thermodynamic software, the feature dimension of machine learning is expanded, and the performance-component reverse mapping is established based on machine learning and genetic algorithm (XGBoost model and NSGA-II algorithm). Through multiple iterations (EI index driving) optimization and experimental verification, a new deformation nickel-based superalloy with strong plasticity synergy is finally obtained.

[0010] The object of the present application can be achieved by the following technical solutions: The object of the present application is to develop a data-driven deformation nickel-based superalloy multi-scale reverse optimization design method. By combining the first principle and the high-throughput calculation of thermodynamics, the computational materials science method is used to provide a multi-source data basis for the alloy reverse design facing the performance requirements by using the machine learning method. New rules and new knowledge are mined from traceable material data, and a chemical composition design window of the alloy is established based on the machine learning model enhanced by the field professional knowledge. Through the cross-scale fusion design and machine learning reverse optimization, a new deformation nickel-based superalloy with strong plasticity synergy and processability is developed.

[0011] The present application provides a data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method, comprising the following steps: S1, based on the first principle, a stable high-strength deformation nickel-based superalloy system is calculated; S2, based on the deformation nickel-based superalloy system obtained in step S1, a deformation nickel-based superalloy composition design space is constructed based on the field knowledge of nickel-based superalloy; S3, based on the deformation nickel-based superalloy composition design space constructed in step S2, the deformation nickel-based superalloy composition design space is reduced by using the empirical formula combined with the high-throughput calculation of thermodynamics, and the reduced deformation nickel-based superalloy composition design space is obtained; S4, based on the reduced deformation nickel-based superalloy composition design space obtained in step S3, a deformation nickel-based superalloy reverse design model is established based on machine learning and genetic algorithm, and a target performance deformation nickel-based superalloy is screened.

[0012] Further, step S1 includes the following process: Based on the first principle, the thermodynamic stability, the lattice mismatch and the dislocation energy of the alloy are calculated, and a stable high-strength deformation nickel-based superalloy system is screened. Further, step S1 includes the following process: Based on the first principle, the thermodynamic stability, the lattice mismatch and the dislocation energy of the alloy are calculated, and a stable high-strength deformation nickel-based superalloy system is screened.

[0013] Further, the deformed nickel-based superalloy system is a Ni-Fe-Cr-Co-Nb-Ta-Al-Ti-Mo-W alloy system.

[0014] Further, the step S2 comprises the following process: Based on the deformed nickel-based superalloy system obtained in step S1, a deformed nickel-based superalloy composition design space is constructed based on the composition calculation space of hot workability, microstructure stability and oxidation corrosion resistance.

[0015] Further, the step S2 specifically comprises the following process: Based on the deformed nickel-based superalloy system obtained in step S1, the composition design space of the deformed nickel-based superalloy is constructed by setting Ni>40wt% and being a balance element to ensure that Ni is the matrix, Cr>15wt% to ensure oxidation corrosion resistance, Al+Ti<6wt% and Ti / Al≥1.5wt%, and requiring the γ' volume fraction (V γ′ ) of the designed deformed nickel-based superalloy to satisfy the following condition: 20%<(V γ′ )<50%.

[0016] Further, the step S3 comprises the following process: S31, a deformed nickel-based superalloy composition with high-temperature stability of γ and γ' phases is rapidly screened by using an empirical formula, and the deformed nickel-based superalloy composition traversal space is reduced; S32, the microstructure stability of the deformed nickel-based superalloy composition is accurately screened by high-throughput thermodynamic calculation to traverse the composition space of the previous step, and the deformed nickel-based superalloy composition design space is reduced, thereby obtaining the reduced deformed nickel-based superalloy composition design space.

[0017] Further, the empirical formula comprises: A γ phase stability judgment formula for judging the microstructure stability, the formula is shown in the following formula (1): (1) In the formula, x C i is the mole fraction of element i in the alloy, Md i is the d-orbital energy of element i, eV, the value of Md of different deformed nickel-based superalloy compositions at 650℃ is calculated according to formula (1), and the deformed nickel-based superalloy satisfying Md <0.94eV is considered to have γ stability; An empirical judgment formula for judging the microstructure stability of γ' phase, the formula is shown in the following formula (2): (2) In the formula, Mγ′ is the ratio of (Nb+Ta+Ti) to Al content, C Ti , C Nb , C Ta , C Al are the mole fractions of the corresponding metal elements Ti, Nb, Ta, and Al, respectively.

[0018] Further, in the process of narrowing the composition design space of the wrought nickel-based superalloy, the γ volume fraction V γ and the γ' volume fraction V γ′ of the wrought nickel-based superalloy are set to satisfy the following conditions: V γ+γ′ > 99.9 vol%, 20% < V γ′ < 50%, the solidus temperature T s and the γ' solubility temperature T γ′s of the wrought nickel-based superalloy are set to satisfy the following conditions: T γ′s ≤ 1150℃, T s -T γ′s > 50℃.

[0019] Further, formulas (1) and (2) are used to screen the high-temperature stability of γ and γ', that is, the microstructure stability. The strengthening effect is the content relationship of the set elements, and the microstructure stability and the strengthening effect influence each other according to the proportion of the alloy composition. The hot workability is the solidus temperature T s and the γ' solubility temperature T γ′s ≤ 1150℃, T s -T γ′s > 50℃, also known as the hot working window.

[0020] Further, step S4 includes the following process: S41, based on the narrowed composition design space of the wrought nickel-based superalloy obtained in step S3, a composition and microstructure performance quantization relationship database of the wrought nickel-based superalloy is established; S42, based on the established database, data preprocessing and feature engineering are performed, the best machine learning model is selected, and a reverse design model of the wrought nickel-based superalloy is established; S42, based on the reverse design model of the wrought nickel-based superalloy, a genetic algorithm multi-objective optimization is used to search for the target performance composition of the wrought nickel-based superalloy, the model is evaluated and iteratively optimized.

[0021] Further, the best machine learning model is selected from one of Gaussian kernel support vector machine (SVR), Gaussian process regression (GP), random forest (RF), and extreme gradient boosting tree (XGBoost).

[0022] Further preferably, the best machine learning model is an extreme gradient boosting tree (XGBoost).

[0023] Further, step S42 comprises the following process: Based on the prediction results of high-temperature yield strength and room-temperature elongation by the optimal machine learning model, the EI values of each target performance of the alloy are calculated as the fitness function in the execution of the NSGA-II algorithm, and the Pareto frontier of the EI values of the alloy in the composition space is determined. The candidate experimental alloys are screened from the Pareto frontier for experimental preparation and performance test verification. The experimental results of the high-temperature yield strength, tensile strength and room-temperature elongation of the candidate experimental alloys are fed back to the initial data set (machine learning training data set), and iterative optimization is performed to obtain the optimized composition of the deformed nickel-based high-temperature alloy.

[0024] Further, in the experimental preparation and performance test verification, the specific experimental preparation includes vacuum arc melting of alloy ingots + hot rolling, homogenization + solid solution + double-stage aging heat treatment, and the specific performance test verification includes microstructure and phase composition characterization analysis: chemical composition quantitative analysis, grain size and grain size statistics, phase composition and phase content analysis, and room-temperature tensile test, 650℃ tensile test.

[0025] Further, the domain knowledge includes microstructure stability, strengthening effect, and hot workability.

[0026] Further, the microstructure stability: the excellent performance of the nickel-based high-temperature alloy is derived from the microstructure composed of L12 ordered structure γ' precipitated strengthening phase (L12-Ni3Al) and FCC structure γ matrix phase (FCC-Ni), so the primary condition for designing a high-temperature alloy is to ensure that the alloy has a γ+γ' dual-phase structure to avoid the formation of topologically close-packed phases. Therefore, referring to the γ stability judgment method based on the d-orbital energy level (Md) proposed by Morinage et al., the formula is , where Ci is the mole fraction of element i in the alloy, Md i is the d-orbital energy of element i, eV. According to this formula, the Md values of different alloy compositions at 650℃ are calculated, and the nickel-based high-temperature alloy with Md < 0.94eV is considered to have γ stability.

[0027] Further, strengthening effect: each element in the nickel-based superalloy can be used based on its respective characteristics, for example, increasing the Fe content to improve forgeability, adding Cr to improve oxidation corrosion resistance, adding Al, Ti and Ta to achieve precipitation strengthening, adding Mo and W to improve creep resistance, adding C, B and Zr to achieve grain boundary strengthening. However, the interaction between different alloying elements should also be considered when designing the nickel-based superalloy, for example, the geometrically close-packed phases such as η-Ni3Ti and δ-Ni3Nb increase the high-temperature instability of the γ' phase, but in the present method, a small amount of η phase is allowed to exist, which precipitates at the grain boundary during heat treatment to strengthen the grain boundary, thereby improving the stress rupture performance, which is related to the content of Al, Ti, Nb, Ta, and the relationship can be described as Therefore, to maximize the solid solution and precipitation strengthening effect, the proportion of Nb+Mo+Ta solid solution strengthening elements and Al+Ti precipitation strengthening elements is mainly controlled, and the grain boundary strengthening elements such as C, B, Zr, etc. are not considered for the time being.

[0028] Further, processability: according to the amount (mass fraction) of Al+Ti, the alloy can be divided into three types, namely high plastic deformation alloy (Al+Ti<3%), which is in the single-phase γ region within the forging temperature range, that is, the single-phase austenite region without γ' phase, which is beneficial to forging; medium plastic deformation alloy (3%<Al+Ti<6%), which has grain boundary carbides within the hot working temperature range, which is not conducive to hot working; low plastic deformation alloy (Al+Ti>6%), which has grain boundary carbides and unsolved γ' phase within the forging temperature range, and the alloy is in a multi-phase state, and the process plasticity is very low. Therefore, in order to ensure that the alloy has plastic deformation and hot workability during melting and preparation without sacrificing high-temperature strength, the content of Al+Ti elements is controlled to be less than 6% and the volume fraction of γ' phase is less than 50%, the difference between the solidus temperature (T s ) and the complete dissolution temperature (T γ′s ) of the γ' phase is greater than 100°C, and the complete dissolution temperature (T γ′s ) of the γ' phase is less than 1150°C.

[0029] Compared with the prior art, the present application has the following technical advantages: 1) The present application provides a data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method, which is a multi-scale integrated computing method for reverse design and optimization of deformation nickel-based superalloy by combining first principles, domain knowledge, thermodynamic high-throughput calculation and machine learning. Compared with the traditional "trial and error" method for developing new deformation nickel-based superalloy, the efficiency is greatly improved, and the mechanical properties of the alloy obtained by reverse design are verified by experiments to meet the target requirements and theoretical design expectations, which can reduce the research and development period of new deformation nickel-based superalloy.

[0030] 2) The present application provides a data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method, a multi-scale integrated calculation method for reverse design and optimization of deformation nickel-based superalloy is proposed by combining first principle, field knowledge, thermodynamic high-throughput calculation and machine learning, the microstructure parameters are introduced into machine learning features, so that the model can associate the complex mapping of composition-microstructure-mechanical properties; the model deviation is corrected through 5 rounds of "prediction-experiment-data feedback" cycle. Traditional methods usually rely on single verification, while the present application dynamically introduces experimental data into the training set, which significantly improves the optimization efficiency.

[0031] 3) The present application provides a data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method, a multi-scale calculation method is used to construct a series of work flow of "electronic scale screening→thermodynamic scale parameter compensation→machine learning modeling→experimental verification feedback iteration", through embedding the physical constraints of microstructure parameters, the blindness of machine learning "pure data black box" is avoided, not only the design efficiency is significantly improved (5 rounds of iteration to obtain the optimal alloy and the real effectiveness is verified by experiment), but also the internal relationship between chemical composition-microstructure-mechanical properties is established, which provides a generalizable design idea and method for complex alloy system.

[0032] 4) The present application provides a data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method, a new type of deformation nickel-based superalloy is obtained by integrated calculation reverse design, which can be prepared by conventional vacuum arc melting and hot rolling process, the room temperature and high temperature tensile mechanical properties are better than those of GH4706 high temperature alloy for turbine disc, and subsequent iteration optimization can be carried out to develop new deformation nickel-based superalloy with better mechanical properties. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 The flow chart of the data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method.

[0034] Figure 2 The alloy ingot after hot rolling is prepared by experimental melting.

[0035] Figure 3 The #1 alloy single-axis equilibrium phase diagram calculated based on Thermo-Calc software.

[0036] Figure 4 The #1 alloy precipitated phase size distribution diagram calculated based on Pandat software.

[0037] Figure 5 The tensile engineering stress-strain comparison curve of the optimal #1 alloy and GH4706 alloy. DETAILED DESCRIPTION

[0038] The present application will be described in detail below in combination with the drawings and specific embodiments. In this technical solution, if the component model, material name, connection structure, control method, algorithm and other features are not explicitly stated, they are considered as common technical features disclosed in the prior art.

[0039] The technical concept of the present application is as follows: 1) First, through first-principle pre-screening, unstable component systems are quickly excluded by calculating thermodynamic stability, lattice mismatch and stacking fault energy, thereby reducing the component design space (Ni-Fe-Cr-Co-Nb-Ta-Al-Ti-Mo-W system).

[0040] 2) Based on domain knowledge (structural stability, strengthening effect, processability), the component boundary is constructed, and the component design space is further reduced to a controllable range, thereby improving the search efficiency.

[0041] 3) The traditional design method only uses chemical composition and heat treatment to predict performance, ignoring the key influence of microstructure parameters (such as γ' volume fraction, precipitate phase size radius, and antiphase domain boundary energy). The microstructure parameters are calculated by commercial thermodynamic calculation software as input features of machine learning, which enhances the characterization ability of machine learning on the strengthening mechanism of the deformed nickel-based superalloy, significantly improves the physical interpretability of the model, and significantly improves the prediction accuracy.

[0042] 4) There is a trade-off between high-temperature strength and room-temperature plasticity, and it is difficult to optimize them simultaneously by traditional methods. The expected improvement (EI) is used as the fitness function to search for the strength-plasticity balance point on the Pareto frontier, and the experimental data is fed back to the initial data set for iterative optimization, dynamically correcting the model bias, solving the reverse design problem of multi-objective performance constraints, and significantly improving the efficiency compared with the traditional "trial and error method".

[0043] The present application combines first-principle calculation, physical metallurgy rules specific to the field of nickel-based superalloy to construct the component space, and uses the microstructure parameters of thermodynamic calculation as physical constraints as input features of the machine learning model, which significantly improves the material identification ability of the machine learning model. This dual-driving mode of "computational physics + domain knowledge" integrates the design framework combining physical mechanisms and domain knowledge, the machine learning model enhanced by microstructure parameters, and the multi-objective iterative optimization for engineering application requirements, ensuring the physical interpretability and engineering feasibility of the component design. The present application is mainly developed for the component design of deformed nickel-based superalloy, which not only uses alloy chemical composition as input features, but also integrates domain knowledge and microstructure parameters as physical constraints, and proves the effectiveness of the multi-scale reverse design method proposed in this patent through iterative optimization and experimental verification.

[0044] Embodiment As Figure 1As shown, in this embodiment, a data-driven deformation nickel-based superalloy multi-objective performance reverse design optimization method is provided, including the following steps: I. Based on the first principle, a stable high-strength deformation nickel-based superalloy system is calculated, specifically the thermodynamic stability, lattice mismatch and stacking fault energy of the alloy, and the stable high-strength (Ni-Fe-Cr-Co-Nb-Ta-Al-Ti-Mo-W) alloy system is selected.

[0045] II. Based on the deformation nickel-based superalloy system obtained in step I, based on the knowledge in the field of nickel-based superalloy, the organization stability, strengthening effect and hot workability are constructed, the nickel-based superalloy design strategy is constructed, the composition design space of the deformation nickel-based superalloy is constructed, and the composition design space of the deformation nickel-based superalloy is obtained based on the constructed composition design space of the deformation nickel-based superalloy. The composition design space of the deformation nickel-based superalloy is obtained by using empirical formula combined with thermodynamic high-throughput calculation. Specifically, it includes: Based on the deformation nickel-based superalloy system obtained in step S1, set Ni>40wt% and balance element to ensure Ni as matrix, Cr>15wt% to ensure oxidation and corrosion resistance, Al+Ti<6wt% and Ti / Al (wt%)≥1.5, and require the designed deformation nickel-based superalloy γ' volume fraction V γ′ Meet the following conditions: 20%<V γ′ <50%, construct the composition design space of the deformation nickel-based superalloy; Use empirical formula to preliminarily screen alloy components with high-temperature stability of γ and γ' phase, and reduce the composition traversal space of the deformation nickel-based superalloy; Thermodynamic high-throughput calculation traverses the composition space of the last step, selects the organization stability of the deformation nickel-based superalloy, reduces the composition design space of the deformation nickel-based superalloy, and obtains the reduced composition design space of the deformation nickel-based superalloy.

[0046] The specific construction of the nickel-based superalloy design strategy in step II is as follows: ① Organization stability: The excellent performance of nickel-based superalloy comes from the microstructure composed of L12 ordered structure γ' precipitation strengthening phase (L12-Ni3Al) and FCC structure γ matrix phase (FCC-Ni), so the first condition for designing high-temperature alloy is to ensure that the alloy has γ+γ' dual-phase structure and avoid the formation of topological dense packing phase. Therefore, referring to the γ stability judgment method based on d orbital energy level (Md) proposed by Morinage et al., the formula is , in which Ci is the mole fraction of element i in the alloy, Md i is the d orbital energy of element i, eV. According to the formula, the Md value of different alloy compositions at 650℃ is calculated, and the nickel-based superalloy satisfying Md<0.94eV is considered to have γ stability.

[0047] ② Strengthening effect: Each element in the nickel-based superalloy can be used based on its respective characteristics, for example, increasing the Fe content to improve forgeability, adding Cr to improve oxidation corrosion resistance, adding Al, Ti, and Ta to achieve precipitation strengthening, adding Mo and W to improve creep resistance, and adding C, B, and Zr to achieve grain boundary strengthening. However, the interaction between different alloying elements must also be considered when designing nickel-based superalloys, such as the geometrically close-packed phases η-Ni3Ti and δ-Ni3Nb, which increase the high-temperature instability of the γ' phase. However, in this method, a small amount of η phase is allowed to precipitate at the grain boundaries during heat treatment to strengthen the grain boundaries and improve the stress rupture performance, which is related to the content of Al, Ti, Nb, and Ta, and can be described as Therefore, to maximize the solid solution and precipitation strengthening effect, the proportion of Nb+Mo+Ta solid solution strengthening elements and Al+Ti precipitation strengthening elements is mainly controlled, and grain boundary strengthening elements such as C, B, and Zr are not considered.

[0048] ③ Hot working performance (hot workability): According to the amount of Al+Ti (mass fraction, the same below), the alloy can be divided into three types, namely high plastic deformation alloy (Al+Ti<3%), which is in the single-phase γ region within the forging temperature range, that is, the single-phase austenite region without γ' phase, which is beneficial to forging; medium plastic deformation alloy (3%<Al+Ti<6%), which has grain boundary carbides within the hot working temperature range, which is not conducive to hot working; and low plastic deformation alloy (Al+Ti>6%), which has grain boundary carbides and unsolved γ' phase within the forging temperature range, and the alloy is in a multi-phase state, with low process plasticity. Therefore, in order to ensure the plastic deformation and hot workability of the alloy during melting and preparation without sacrificing high-temperature strength, the content of Al+Ti elements is controlled to be less than 6% and the volume fraction of γ' phase is less than 50%, the difference between the solidus temperature (T s ) and the complete dissolution temperature of γ' phase (T γ′s ) is greater than 100°C, and the complete dissolution temperature of γ' phase (T γ′s ) is less than 1150°C.

[0049] In summary, the composition design space of the nickel-based superalloy is constructed, with Ni>40wt% and balanced elements to ensure that Ni is the matrix, Cr>15wt% to ensure oxidation corrosion resistance, Al+Ti<6wt% and Ti / Al (wt%)≥1.5 (Ti / Al ratio>1.5 to allow η phase to precipitate at the grain boundaries during heat treatment), and the volume fraction of γ phase V γ and the volume fraction of γ' phase V γ′ must meet the following conditions: V γ+γ′ >99.9vol%, 20%<V γ′< 50%. The composition design space of wrought nickel-base superalloys is shown in Table 1.

[0050] Table 1 Composition design space of wrought nickel-base superalloys (Ni Bal., wt%) III. Machine learning dataset construction and preprocessing: To eliminate the performance differences caused by forming processes and improve the learning ability of machine learning on strengthening mechanisms, the machine learning training dataset only collects the chemical composition, heat treatment process, room temperature and 650°C tensile property data of the wrought nickel-base superalloys prepared by general processes such as rolling and forging, and does not include the performance data of other special forming processes such as powder metallurgy, directional solidification, and additive manufacturing. The data comes from Chinese High Temperature Alloy Handbook, Chinese Aviation Materials Handbook Volume 2. Wrought High Temperature Alloy. Cast High Temperature Alloy, existing alloy grades at home and abroad, publicly available databases on the Internet, and literature data, after screening, a total of 726 samples are collected. Alloy composition is in mass percentage (wt%), and interval data (such as [a, b] or a ~ b) is converted to average value. Heat treatment includes solution treatment temperature, solution treatment time, aging treatment temperature, and aging treatment time, the test temperature is 650°C, and the design target performance is room temperature yield strength, room temperature elongation, and 650°C yield strength.

[0051] In fact, the performance of nickel-base superalloy materials is closely related to their microstructure, and only considering composition and heat treatment parameters as factors affecting alloy strength will result in the loss of some information. The integration of more strength-related knowledge or information may further improve the search efficiency of the target alloy. Therefore, material parameters related to microstructure need to be added to improve the material recognition ability of the machine learning model, including the volume fraction of γ' phase (V γ′ ), inverse domain boundary energy E APB , γ / γ' lattice mismatch (δ), γ' phase complete dissolution temperature (γ' dissolution temperature, T γ′s ), secondary γ' phase precipitation size radius (r s ), and tertiary γ' phase precipitation size radius (r t ). In addition, some missing values of microstructure material parameters in the collected wrought nickel-base superalloy dataset were calculated by Python scripts through the API interface of commercial thermodynamic calculation software Thermal-Calc and Pandat.

[0052] IV、In order to realize the inverse design of the multi-objective performance of the wrought nickel-based superalloy, data preprocessing and feature engineering are performed, the best machine learning model is selected, and the inverse design model of the wrought nickel-based superalloy is established. Specifically, first, the performance of four machine learning models, including Gaussian kernel support vector machine (SVR), Gaussian process regression (GP), random forest (RF), and extreme gradient boosting tree (XGBoost), is evaluated using the leave-one-out method. The training set and the test set are divided in a ratio of 8:2 for training and modeling. Ten-fold cross-validation method is used to optimize the model parameters. The root mean square error (RMSE) is selected as the evaluation index of the model performance. The smaller the RMSE, the higher the prediction accuracy of the model. Based on the inverse design model of the wrought nickel-based superalloy, the genetic algorithm is used to search for the composition of the wrought nickel-based superalloy with the target performance. The model is evaluated and iteratively optimized. Specifically, based on the prediction results of the high-temperature yield strength and the room-temperature elongation of the best machine learning model (XGBoost), the EI values of the target performance of the alloy are calculated, which are used as the fitness function in the execution of the NSGA-Ⅱ algorithm. Then, the Pareto frontier of the EI values of the alloy in the composition space is determined. Finally, candidate experimental alloys are selected from the Pareto frontier for experimental preparation and performance testing and verification. Figure 2 To prepare the hot-rolled alloy plate for experimental melting, all candidate alloys use metal element pellets with a purity of >99.95% to match the alloy composition. The ingots with a size of about 70mmx45mmx12mm are prepared by vacuum arc melting, and then homogenized at 1150℃ for 24h. After homogenization, the total deformation of hot rolling is 50%, followed by solid solution + double-stage aging heat treatment (980℃ for 1h air cooling to room temperature, 730℃ for 8h, cooling rate of 55℃ / h to 620℃ for 8h, air cooling). Specific performance testing and verification includes microstructure and phase composition characterization analysis: mass spectrometer (IPC-MS) is used for quantitative analysis of alloy chemical composition, optical microscope is used for grain size and grain size statistics by intercept method, X-ray diffraction (XRD) is used for phase composition analysis by comparing XRD standard cards, field emission scanning electron microscope (SEM) is used to observe the volume fraction and size of γ and γ', and room temperature tensile test, 650℃ tensile test. The experimental results of the high-temperature yield strength, tensile strength and room-temperature elongation of the new alloy are fed back to the initial data set for iterative optimization. After 5 iterations, a new wrought nickel-based superalloy is obtained, named "#1". As shown in Figure 3 The single-axis equilibrium phase diagram of #1 alloy calculated based on Thermo-Calc software is shown in FIG. 1. It is shown that #1 alloy only exists in γ and γ' phases (FCC_L12 and FCC_L12#2, respectively) at 650℃, without other phases. Figure 4The size distribution of the multi-peak precipitates of the alloy #1 under the heat treatment regime calculated based on the Pandat software is shown in FIG. 1. The deformed nickel-based superalloy “#1” obtained satisfies the target performance requirements of the room temperature yield strength > 900 MPa, the high temperature 650 °C yield strength > 820 MPa, and the room temperature elongation > 12%, and has a significant improvement in the tensile yield strength at room temperature and 650 °C without a significant reduction in elongation compared to the existing GH4706 brand deformed superalloy (e.g. Figure 5

[0053] The above describes the embodiments of the method of the present application in conjunction with the accompanying drawings, but the present application is not limited to the above-described embodiments, and can be changed in various ways according to the purpose of the inventive idea of the present application, and any parameter change or calculation simplification made according to the principle of the technical solution of the present application, as long as it meets the purpose of the present application, as long as it does not deviate from the principle and concept of the data-driven deformed nickel-based superalloy multi-objective performance reverse design optimization method of the present application, belongs to the protection scope of the present application.

[0054] The above description of the embodiments is for the convenience of the ordinary skilled person in the art to understand and use the application. Those skilled in the art can obviously make various modifications to these embodiments, and apply the general principles described herein to other embodiments without having to go through creative labor. Therefore, the present application is not limited to the above-described embodiments, and any improvement and modification made by those skilled in the art according to the disclosure of the present application without departing from the scope of the present application should be within the protection scope of the present application.​

Claims

1. A data-driven inverse design optimization method for the multi-objective performance of a wrought nickel-based superalloy, characterized in that, The method comprises the following steps: S1, calculating stable high-strength deformation nickel-based superalloy system based on first principles; S2, constructing a deformation nickel-based superalloy composition design space based on the knowledge in the field of nickel-based superalloys on the basis of the deformation nickel-based superalloy system obtained in step S1; S3, narrowing the deformation nickel-based superalloy composition design space by using empirical formula combined with thermodynamic high-throughput calculation based on the deformation nickel-based superalloy composition design space constructed in step S2, to obtain the narrowed deformation nickel-based superalloy composition design space; S4, based on the narrowed deformation nickel-based superalloy composition design space obtained in step S3, establishing a deformation nickel-based superalloy reverse design model based on machine learning and genetic algorithm, and screening target performance deformation nickel-based superalloy.

2. The data-driven inverse design and optimization method of the multi-objective performance of the wrought nickel-based superalloy according to claim 1, characterized in that, Step S1 comprises the following process: Based on first principles, the thermodynamic stability, lattice mismatch and stacking fault energy of the alloy are calculated, and stable high-strength deformation nickel-based superalloy systems are screened. The deformation nickel-based superalloy system is a Ni-Fe-Cr-Co-Nb-Ta-Al-Ti-Mo-W alloy system.

3. The data-driven multi-objective performance inverse design optimization method of a wrought nickel-based superalloy according to claim 2, wherein, Step S2 comprises the following process: Based on the deformation nickel-based superalloy system obtained in step S1, a deformation nickel-based superalloy composition design space is constructed based on hot workability, microstructure stability and oxidation corrosion resistance.

4. The data-driven multi-objective performance inverse design optimization method of a wrought nickel-based superalloy according to claim 3, wherein, Step S2 specifically comprises the following process: Based on the deformed nickel-based superalloy system obtained in step S1, set Ni>40wt% and balance element to ensure Ni as the matrix, Cr>15wt% to ensure oxidation and corrosion resistance, Al+Ti<6wt% and Ti / Al≥1.5wt%, and require the designed deformed nickel-based superalloy to have a γ' volume fraction V γ′ Satisfy the following conditions: 20%<V γ′ <50%, and construct a deformed nickel-based superalloy component design space.

5. The data-driven inverse design and optimization method of property variations of a wrought nickel-based superalloy according to claim 1, wherein Step S3 comprises the following process: S31, using empirical formula to preliminarily screen alloy compositions with high-temperature stability of γ and γ', to narrow the deformation nickel-based superalloy composition traversal space; S32, thermodynamic high-throughput calculation is performed on the composition space in the previous step to screen the microstructure stability of the nickel-based superalloy composition, to narrow the deformation nickel-based superalloy composition design space, and to obtain the narrowed deformation nickel-based superalloy composition design space.

6. The data-driven multi-objective performance inverse design optimization method of a wrought nickel-based superalloy according to claim 5, wherein, The empirical formula includes: The γ phase stability empirical judgment formula for judging the microstructure stability is shown in the following formula (1): （1） wherein C i is the mole fraction of element i in the alloy, Md i is the d-orbital energy of element i in eV, calculated according to equation (1) for the composition of the different wrought nickel-based superalloys at 650 °C Md The value of Md wrought nickel-based superalloys with a value of < 0.94 eV are considered to have gamma stability; The γ' phase stability empirical judgment formula for judging the microstructure stability is shown in the following formula (2): （2） wherein M γ′ is the ratio of (Nb+Ta+Ti) to Al content, C Ti , C Nb , C Ta , C Al are the mole fractions of the respective metal elements.

7. The data-driven multi-objective performance inverse design optimization method of a wrought nickel-based superalloy according to claim 5, wherein, In the process of narrowing the composition design space of wrought nickel-base superalloys, the γ volume fraction V γ and the γ' volume fraction V γ′ are set to satisfy the following conditions: V γ+γ′ > 99.9 vol%, 20% < V γ′ < 50%; the solidus temperature T s and the γ' solvus temperature T γ′s of the wrought nickel-base superalloy are set to satisfy the following conditions: T γ′s ≤ 1150℃, T s - T γ′s > 100℃, to ensure that the alloy has a sufficient hot working window, thereby ensuring the hot workability of the alloy.

8. The data-driven inverse design and optimization method of property variations of a wrought nickel-based superalloy according to claim 1, wherein, Step S4 comprises the following process: S41, based on the narrowed deformation nickel-based superalloy composition design space obtained in step S3, a deformation nickel-based superalloy composition and microstructure performance quantization relationship database is established; S42, based on the established database, data preprocessing and feature engineering are performed, the best machine learning model is selected, and a deformation nickel-based superalloy reverse design model is established; S42, based on the deformation nickel-based superalloy reverse design model, a genetic algorithm is used to search for target performance deformation nickel-based superalloy compositions, the model is evaluated and iteratively optimized.

9. The data-driven, deformation nickel-based superalloy multi-objective performance inverse design optimization method of claim 8, wherein, The best machine learning model is selected from one of a Gaussian kernel support vector machine, a Gaussian process regression, a random forest and an extreme gradient boosting tree.

10. The data-driven, deformation nickel-based superalloy multi-objective performance inverse design optimization method of claim 8, wherein, Step S42 comprises the following process: Based on the prediction results of the target performance of the optimal machine learning model, the EI values of each target performance of the alloy are calculated as the fitness function in the execution of the NSGA-II algorithm, and the Pareto front of the EI values of the alloy in the composition space is determined. The candidate experimental alloys are screened from the Pareto front for experimental preparation and performance test verification. The experimental results of the high-temperature yield strength, tensile strength and room-temperature elongation of the candidate experimental alloys are fed back to the machine learning training data set, and iterative optimization is performed to obtain the optimized deformed nickel-based high-temperature alloy composition. The target performance is selected from one or more of room-temperature yield strength, 650 DEG C yield strength and room-temperature elongation.

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