Aluminum alloy design method based on multi-objective genetic algorithm and high-thermal-conductivity aluminum alloy

Abstract

The invention discloses a high-thermal-conductivity aluminum alloy design method and system, and belongs to the technical field of metal material science. The method comprises the steps that S1, aluminum alloy component elements are set, and a plurality of individuals with different component element contents are generated to form a population; s2, obtaining a fitness score F of each individual in the population; s3, performing genetic manipulation on individuals in the population to generate a next-generation population; and S4, repeatedly executing the steps S2-S3 until a preset number of iterations is reached or the fitness score is converged to a preset target score. According to the method, the basic principle of material science and the optimization strategy of data driving are combined, and the LPBF aluminum alloy material which has excellent heat conductivity and avoids hot cracks can be efficiently designed. Compared with a traditional single machine learning method, a more accurate and reliable alloy design scheme is provided, the thermal conductivity of the aluminum alloy and the adaptability of the aluminum alloy in the LPBF process are remarkably improved, meanwhile, the material development period is shortened, the cost is reduced, and the design accuracy and practicability are improved.

Description

[0001] Divisional application This application is a divisional application of Chinese invention patent application filed on March 11, 2025 [Application No.: 2025102860161] [Title: A Design Method and System for High Thermal Conductivity Aluminum Alloy]. Technical Field

[0002] This invention belongs to the field of metal materials science and technology, specifically relating to an aluminum alloy design method based on a multi-objective genetic algorithm and a high thermal conductivity aluminum alloy. Background Technology

[0003] In modern industry, particularly in the automotive and aerospace sectors, the demand for efficient thermal management systems is growing. These systems require materials that can rapidly and effectively dissipate heat while maintaining structural integrity, thereby improving the overall performance and reliability of the system. Aluminum alloys, due to their high thermal conductivity, low density, and high specific strength, have become an ideal choice for thermal management applications.

[0004] Laser Powder Bed Fusion (LPBF) technology enables integrated design of complex structures, improves heat dissipation efficiency, and provides technical support for the production of thermal management components. However, the rapid solidification characteristics of the LPBF process often increase the susceptibility of aluminum alloys to hot cracking, affecting the structural integrity and thermal conductivity of components. Although optimizing LPBF process parameters can suppress hot cracking to some extent, the fundamental solution lies in developing new alloy systems. However, aluminum alloys face an inherent contradiction between thermal conductivity and strength in performance design. This is because while alloying increases the strength of aluminum, it also increases electron and phonon scattering, significantly reducing thermal conductivity.

[0005] Traditional alloy design relies on experience, which is time-consuming and costly. Computational alloy design, especially the Calculation of Phase Diagrams (CALPHAD) method based on thermodynamic databases, can effectively predict the grain refinement behavior and hot cracking tendency of LPBF aluminum alloys, but it still has limitations in handling complex nonlinear problems and multi-objective optimization problems. The development of machine learning (ML) technology has provided new avenues for material property prediction and multi-objective optimization, but relying solely on data-driven ML methods may ignore fundamental theories in materials science, leading to predictions that deviate from actual needs. Therefore, combining machine learning with the fundamental principles of materials science to construct a comprehensive materials design framework is becoming a rapidly developing research direction. For example, materials genome engineering has successfully developed high-performance high-entropy alloys, copper alloys, and perovskite solar cells through the deep integration of high-throughput thermodynamic calculations and ML technology. However, to date, no technology has integrated high-throughput CALPHAD and ML technologies for optimizing the thermal conductivity, printability, and strength of LPBF aluminum alloys.

[0006] For example, Chinese patent application 202210612466.1 discloses a data-driven aluminum alloy composition design method. This method includes: characterizing and analyzing the alloy composition and performance parameters of the aluminum alloy formed in each micro-region; establishing a database, the database including the alloy composition, corresponding preparation parameters, and corresponding performance parameters of samples in each micro-region; acquiring samples from the database; training an artificial neural network model using the alloy composition and preparation parameters as input and the corresponding performance parameters as output; employing a genetic algorithm for intelligent optimization, using the aluminum alloy's alloy composition and preparation parameters as individuals in the population, and the target performance as the optimization objective; calling the trained artificial neural network model to obtain performance parameters under different alloy compositions and preparation parameters and calculating the corresponding individual fitness; and outputting alloy compositions and preparation parameters whose individual fitness meets the requirements after genetic evolution.

[0007] The existing technologies described above employ a controllable gradient heating environment (e.g., solution treatment 350℃–550℃, aging treatment 50℃–250℃), achieving different temperature distributions by independently adjusting the heating rod power and cooling water channels. The purpose of this patented method is to more efficiently acquire a large amount of effective alloy data (MgZn2 precipitate volume fraction, electrical conductivity, and other properties), and then utilize artificial neural networks and genetic algorithms to design alloy compositions. However, since traditionally cast / forged aluminum alloys do not exhibit hot cracking, the aforementioned technologies also cannot alleviate the hot cracking problem present in aluminum alloys during the LPBF process. Summary of the Invention

[0008] The purpose of this invention is to provide a design method and system for high thermal conductivity aluminum alloys, which partially solves or alleviates the problems of poor printability and difficulty in balancing thermal conductivity and strength in the LPBF process of aluminum alloys in the prior art.

[0009] To solve the aforementioned technical problems, the present invention specifically adopts the following technical solution: A design method for high thermal conductivity aluminum alloys, comprising: S1, set the aluminum alloy composition elements, and generate several individuals with different composition element contents to form a population; S2, obtain the fitness score F of each individual in the population, including: S21, if an individual's theoretical thermal conductivity λ is lower than the thermal conductivity threshold, assign the individual a fitness score F lower than the score threshold; or, In simulated solidification, if an individual does not form a heterogeneous nucleation phase in the early stage of solidification, the individual is assigned a fitness score F below the score threshold. S22, when the individual's theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold and a heterogeneous nucleation phase is formed, the fitness score F of the individual is calculated based on the grain refinement index and the eutectic solidification metallurgical index; the grain refinement index includes the initial slope and the initial solidification range, and the eutectic solidification metallurgical index includes the brittle solidification range and the crack sensitivity factor. S3, genetic operations are performed on individuals within the population to generate the next generation of the population; S4. Repeat steps S2 to S3 until the preset number of iterations is reached or the fitness score converges to the preset target score. Then stop the optimization process and output the final optimal alloy composition.

[0010] As an improvement, the method for calculating the fitness score F of an individual based on grain refinement index and eutectic solidification metallurgical index includes using the formula: ; Calculate the fitness score; where F is the fitness score, IS is the initial slope, and IS... ref ΔT is the slope reference value. IFR For the initial solidification interval, ΔT IFR,ref ΔT is the reference value for the solidification range. BTR For the brittle solidification range, ΔT BTR,ref The CSI is a reference value for the brittle solidification range, and it is also the crack sensitivity factor. ref This is a reference value for the crack sensitivity factor. , , and These are the weighting coefficients.

[0011] As an improvement, the initial slope is calculated using the formula: ; Calculate the initial slope; where IS is the initial slope, f s Here, T represents the solid fraction and temperature.

[0012] As an improvement, the calculation method for the initial solidification interval includes using the formula: ; Calculate the initial solidification interval; where ΔT IFR For the initial solidification interval, T fs1 and T fs2 These are the initial stage of solidification (0≤f) s ≤0.4) The temperature at which the alloy solidifies to a certain solid fraction. Specifically, ΔT IFR The calculation range can be flexibly set according to performance objectives.

[0013] As an improvement, the method for calculating the brittle solidification range includes using the formula: ; Calculate the range of brittle solidification; where ΔT BTR For the brittle solidification range, T ZST At zero intensity temperature, T ZDT It is the zero ductility temperature.

[0014] As an improvement, the method for calculating the crack sensitivity factor includes using the formula: ; Calculate the crack susceptibility factor; where CSI is the crack susceptibility factor, T is the temperature, and f is the crack susceptibility factor. s 1 / 2 It is the square root of the solid fraction.

[0015] As an improvement, the slope reference value and solidification range reference value are derived from Scalmalloy alloy; the brittle solidification range reference value and crack sensitivity factor reference value are derived from AlSi. 10 Mg alloy.

[0016] As an improvement, the aluminum alloy composition includes aluminum and at least one of rare earth elements, magnesium, silicon, iron, zinc, titanium, zirconium, manganese, copper, nickel, chromium, yttrium, molybdenum, and vanadium.

[0017] As an improvement, the steps for generating several individuals with different proportions of component elements include: Set the content range of each component element in the aluminum alloy; Several individuals are generated within the specified content range according to a preset content step size.

[0018] The present invention also provides a high thermal conductivity aluminum alloy design system, comprising: The initialization module is used to set the aluminum alloy composition elements and generate several individuals with different composition element contents to form a population. The fitness score acquisition module is used to obtain the fitness score F of each individual in the population, including: S21, if the individual's theoretical thermal conductivity λ is lower than the thermal conductivity threshold, assign the individual a fitness score F that is lower than the score threshold; In simulated solidification, if an individual fails to form a heterogeneous nucleation phase during long-term solidification, the individual is assigned a fitness score F below the score threshold. S22, when the individual's theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold and a heterogeneous nucleation phase is formed, the fitness score F of the individual is calculated based on the grain refinement index and the eutectic solidification metallurgical index; the grain refinement index includes the initial slope and the initial solidification range, and the eutectic solidification metallurgical index includes the brittle solidification range and the crack sensitivity factor. The mutation genetic module is used to perform genetic operations on individuals within a population to generate the next generation of the population. The iteration module is used to iterate until a preset number of iterations is reached or the fitness score converges to a preset target score, at which point the optimization process stops and the final optimal alloy composition is output.

[0019] Beneficial effects: This invention develops a design method for high thermal conductivity, crack-free aluminum alloys suitable for LPBF (Limited-Boiler) processes by integrating high-throughput CALPHAD computation with multi-objective genetic algorithm technology. This method has the following technical advantages: 1. Optimizing the balance between thermal conductivity and strength: By precisely optimizing the alloy composition, this invention can significantly improve the printability and strength of aluminum alloys while maintaining their high thermal conductivity, overcoming the problem of mutual constraints between thermal conductivity and strength in traditional aluminum alloy design.

[0020] 2. Reduce hot crack formation: By using Scheil-Gulliver solidification simulation, the grain refinement performance and eutectic solidification effect of aluminum alloy are optimized, making the design results more in line with the principles of physical metallurgy, effectively reducing the formation of hot cracks in the LPBF process and improving the structural integrity of the alloy.

[0021] 3. Improve alloy design efficiency: By combining the high-throughput CALPHAD multi-objective genetic algorithm with [other methods], this invention achieves rapid and efficient aluminum alloy composition design, avoiding the time and economic costs of traditional trial-and-error methods, and significantly improving the efficiency and accuracy of the design process.

[0022] 4. Wide applicability and scalability: The method of this invention is applicable to the development of multi-element (such as ternary, quaternary, pentaneous, etc.) aluminum alloy systems, and can meet the optimization design requirements of different alloy element combinations, with strong adaptability and scalability.

[0023] Compared with existing technologies, this invention addresses the issue of hot cracking while ensuring the strength of aluminum alloys by setting grain refinement capability and eutectic solidification effect as constraints. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0025] Figure 1 This is a flowchart of Embodiment 1 of the present invention; Figure 2 A comparison of the Scheil-Gulliver solidification behavior of the Al-1.03Fe-0.39Zr alloy in Example 1 with that of the alloy in Comparative Example 1; whereby... Figure 2 (a) Example 1 shows the Cheil-Gulliver solidification behavior of alloy 1. Figure 2 (b) shows the cheil-Gulliver solidification behavior of the alloy in Comparative Example 1; Figure 3 Example 2: Scheil-Gulliver solidification behavior of Al-2.02Ni-0.13Sc-0.52Zr alloy; Figure 4 The Scheil-Gulliver solidification behavior of the Al-0.45Mg-0.28Si-0.59Sc-0.56Zr alloy in Example 3 is shown. Figure 5 This is a structural diagram of Example 2. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0027] In this document, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" may be used interchangeably.

[0028] In this document, the terms "upper," "lower," "inner," "outer," "front," "rear," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the present invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0029] In this document, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0030] In this document, "and / or" includes any and all combinations of one or more of the listed related items.

[0031] In this article, "multiple" means two or more, that is, it includes two, three, four, five, etc.

[0032] Example 1: To address the problems of poor printability and difficulty in balancing thermal conductivity and strength in existing aluminum alloy LPBF processes, this invention provides an alloy design method that integrates high-throughput CALPHAD computation with a multi-objective genetic algorithm. This method combines fundamental principles of materials science with a data-driven optimization strategy, enabling the efficient design of LPBF aluminum alloys that possess both excellent thermal conductivity and avoid hot cracking. Compared to traditional single machine learning methods, this invention provides a more accurate and reliable alloy design scheme, significantly improving the thermal conductivity of aluminum alloys and their adaptability in LPBF processes, while shortening the material development cycle, reducing costs, and enhancing the accuracy and practicality of the design.

[0033] like Figure 1 As shown, the specific steps of this embodiment include: S1 sets the aluminum alloy composition elements and generates several individuals with different composition element contents to form a population.

[0034] In this invention, the composition elements of the aluminum alloy are set within a predetermined alloy element system. Besides aluminum, the composition elements of the aluminum alloy may also include at least one of the following: magnesium (Mg), silicon (Si), iron (Fe), zinc (Zn), titanium (Ti), zirconium (Zr), manganese (Mn), copper (Cu), nickel (Ni), chromium (Cr), yttrium (Y), molybdenum (Mo), vanadium (V), and rare earth elements (such as Sc, La, Ce, Nd, Sm).

[0035] For example, if an aluminum alloy system includes elements such as aluminum (Al), copper (Cu), magnesium (Mg), zinc (Zn), and zirconium (Zr), then during initialization, the computer program will randomly assign the mass or volume percentage of each element to each individual component based on a preset content range for each element. The range of the number of individuals, N, can be adjusted by setting the allocation step size. That is, first, the content range of each component element in the aluminum alloy is set, and then a number of individuals are generated within that content range according to a preset content step size.

[0036] Let's take a simple ternary aluminum alloy system (Al-Fe-Zr) as an example to illustrate this. The Fe element content is set to be 0 ~ 2 wt%; Zr, as a trace alloying element, is set to be 0 ~ 1 wt%. With an alloy composition step of 0.01 wt%, a population of 200 * 100 = 20,000 individuals can be generated.

[0037] S2, obtain the fitness score F of each individual in the population.

[0038] For each genetic individual, the fitness score F is calculated based on preset constraints and objectives. The fitness score F reflects the alloy's performance under various performance indicators (such as grain refinement ability and eutectic solidification effect). Specifically, it includes the following sub-steps: S21, calculate the individual's theoretical thermal conductivity λ, and assign the individual a fitness score F that is worse than (i.e. lower than) the score threshold if the individual's theoretical thermal conductivity λ is lower than the thermal conductivity threshold.

[0039] Specifically, using thermodynamic calculation software such as Thermo-calc, FactSage, MSE, or any other thermodynamic calculation software with similar functions, a single-point equilibrium calculation is performed to calculate the theoretical thermal conductivity λ of each individual and compare it with a preset standard value. If λ is lower than the standard value, a fitness score F of poor value is assigned.

[0040] In this invention, "assigning a fitness score F lower than the score threshold to the individual" means that when a higher fitness score F is better, the individual's fitness score F is assigned a value lower than the score threshold. Conversely, when a lower fitness score F is better, the individual's fitness score F is assigned a value higher than the score threshold.

[0041] In the population evolution process of genetic algorithms, fitness score is an important indicator for measuring the quality of individuals. Assigning a poor F value to individuals that do not meet the requirements helps to eliminate those individuals that perform poorly in terms of thermal conductivity, solidification properties, etc., during the selection operation. This increases the probability that individuals with better performance potential will be retained and inherited by the next generation, thereby improving the overall quality of the population and accelerating the speed at which the algorithm converges to the optimal solution.

[0042] Similarly, to determine the heterogeneous nucleation condition, if an individual does not form a heterogeneous nucleation phase in the early stage of solidification during the simulated solidification, the individual is assigned a fitness score F that is lower than the score threshold.

[0043] Specifically, based on the Scheil-Gulliver solidification simulation results from the thermodynamic calculation software, it is determined whether each individual first forms a heterogeneous nucleation phase Al3X in the early stage of solidification (where X is any element in the designed alloy that can form a heterogeneous nucleation phase, such as Zr, Sc, Ti, etc.). If the phase is not formed, a fitness score F is assigned as a poor value.

[0044] It is foreseeable that two constraints will be in place: 1) the individual's theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold (e.g., the thermal conductivity value of alloy 6063 as a reference); 2) if either constraint is not met in the initial stage of solidification during simulated solidification, the individual will be directly assigned a poor fitness score F. Consequently, individuals assigned this fitness score are very likely to not be screened in subsequent processes. Furthermore, the order of calculating the individual's theoretical thermal conductivity λ and judging the heterogeneous nucleation condition can be interchanged. For example, if it is determined that the individual's theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold, and it is further determined that the individual did not form a heterogeneous nucleation phase in the initial stage of solidification during simulated solidification, the individual will also be assigned a fitness score F lower than the threshold. Alternatively, if it is determined that the individual formed a heterogeneous nucleation phase in the initial stage of solidification during simulated solidification, but the individual's theoretical thermal conductivity λ is lower than the thermal conductivity threshold, the individual will also be assigned a fitness score F lower than the threshold.

[0045] S22, when the individual's theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold and a heterogeneous nucleation phase is formed in the early stage of solidification, the fitness score F of the individual is calculated based on the grain refinement index and the eutectic solidification metallurgical index.

[0046] Assuming that the individual's theoretical thermal conductivity λ and heterogeneous nucleation conditions both meet the requirements, the individual's performance indicators are calculated. Specifically, based on the Scheil-Gulliver solidification simulation results from the thermodynamic calculation software, the temperature-solid fraction (Tf) of each individual is obtained. s Curve and temperature-solid fraction square root (Tf) s 1 / 2The grain refinement index and eutectic solidification metallurgical index for each individual are calculated using the curve. The grain refinement index includes the initial slope and the initial solidification range, while the eutectic solidification metallurgical index includes the brittle solidification range and the crack sensitivity factor.

[0047] One of the grain refinement indicators, the initial slope IS, reflects the initial rate (r) of compositional supercooling during alloy solidification. A larger IS value corresponds to a higher r value, indicating a faster nucleation rate, which helps to generate more dispersed phases, thereby inhibiting columnar crystal growth and promoting equiaxed crystal formation. In this embodiment, the initial slope is calculated using the formula: ; Calculate the initial slope; where IS is Tf s The initial slope of the curve, f s Here, is the solid fraction, and T is the temperature. This invention aims to improve the IS value by optimizing the alloy composition, thereby improving grain refinement and suppressing crack formation.

[0048] The second grain refinement index is the initial solidification range ΔT. IFR Defined as the temperature range during the primary phase formation stage of the alloy. A larger solidification range indicates higher undercooling of the alloy, thereby promoting grain refinement. In this embodiment, the initial solidification range is calculated using the formula: ; Calculate the initial solidification interval; where ΔT IFR For the initial solidification interval, T fs1 and T fs2 These are the temperatures corresponding to a certain solid fraction of the alloy during the initial solidification stage, and their calculation range can be flexibly set according to performance objectives.

[0049] Specifically, the initial solidification interval ΔT IFR The definition is that in the initial stage of solidification, 0 ≤ f s Adjustable within the range of ≤0.4, with the optimal range being 0≤f s ≤0.2, the recommended range is 0≤f s ≤0.1, to optimize the grain refinement effect of the alloy.

[0050] One of the metallurgical indicators of eutectic solidification is the brittle solidification range ΔT. BTR It measures the temperature range from ductility to brittleness of a material, and is typically most pronounced towards the end of solidification. ΔT BTR The larger the value, the higher the material's tendency to thermal crack. Therefore, optimizing the alloy composition can reduce ΔT. BTR This is crucial for improving printability. In this embodiment, the calculation method for the brittle solidification range includes using the formula: ; Calculate the range of brittle solidification; where ΔT BTR For the brittle solidification range, T ZST At zero intensity temperature, T ZDT It is the zero ductility temperature.

[0051] Brittle solidification range ΔT BTR The definition is that 0.6 ≤ f at the end of solidification. s Adjustable within the range of ≤1, with the optimal range being 0.7≤f. s ≤1, recommended range is 0.85 ≤f s ≤0.95, to optimize the eutectic solidification behavior of the alloy.

[0052] The crack sensitivity factor (CSI), a key indicator in eutectic solidification metallurgy, is crucial for assessing a material's susceptibility to hot cracking. It primarily reflects the balance between the transverse grain growth rate and the liquid feeding capacity during solidification. When the liquid phase feeding rate is lower than the grain bridging rate, the material is more prone to cracking. In this embodiment, the CSI is calculated using the formula: ; Calculate the crack susceptibility factor; where CSI is the crack susceptibility factor, T is the temperature, and f is the crack susceptibility factor. s 1 / 2 d represents the square root of the solid fraction; d represents the differential, and correspondingly, dT is the temperature gradient, reflecting the rate of temperature change at the solidification front; It is the differential of the square root of the solid fraction, characterizing the rate of solidification.

[0053] After obtaining the grain refinement index and the eutectic solidification metallurgical index, the fitness score F of the individual is calculated. In this implementation, a linear weighted method is used to convert the grain refinement index and the eutectic solidification metallurgical index into a fitness score F. The fitness score F is defined as follows: a smaller F indicates a better individual. The specific calculation method includes using the formula: ; Calculate the fitness score; where F is the fitness score, IS is the initial slope, and IS... ref ΔT is the slope reference value. IFR For the initial solidification interval, ΔT IFR,ref ΔT is the reference value for the solidification range. BTR For the brittle solidification range, ΔT BTR,ref The CSI is a reference value for the brittle solidification range, and it is also the crack sensitivity factor. ref This is a reference value for the crack sensitivity factor. , , and The weighting coefficients are the fitness score F. , , and The weighting coefficients can be dynamically adjusted according to the target properties of the alloy to optimize its performance in specific applications. For typical or atypical "grain refinement-eutectic solidification" system characteristics, the four weighting coefficients mentioned above can be adjusted according to actual needs in practical applications. For example, when it is necessary to focus on optimizing the "grain refinement" characteristic, the corresponding weighting coefficients can be adjusted accordingly. , Greater than the weighting coefficient , If the focus is on optimizing the "eutectic solidification" characteristic, the weighting coefficient is... , Less than the weighting coefficient , 。

[0054] The reference values ​​for grain refinement and eutectic solidification can be selected from existing alloy systems (such as Scalmalloy alloys and AlSi). 10 Mg alloys can be used as a reference, but other alloys with similar grain refinement and eutectic solidification characteristics can also be selected to ensure that the designed alloy can achieve optimized grain refinement and eutectic solidification behavior, thereby improving its adaptability in LPBF process.

[0055] S3 involves performing genetic operations on individuals within the population to generate the next generation of the population.

[0056] Specifically, selection, crossover, and mutation genetic operations are performed sequentially to generate the next generation of the population, allowing the population to continuously evolve. The selection operation combines an elitist strategy with a random selection strategy to avoid prematurely getting trapped in local optima. The crossover operation uses a random crossover method, splicing together a portion of the gene segments from each parent to generate new offspring. Crossover effectively maintains population diversity and increases the genetic algorithm's ability to search for the global optimum. To enhance local optimization capabilities, appropriate gene mutation is performed.

[0057] S4. Repeat steps S2 to S3 until the preset number of iterations is reached or the fitness score converges to the preset target score.

[0058] After each iteration, determine whether the stopping condition is met. If the preset number of generations or the fitness score of the population converges to a certain target score, stop the optimization process and output the final optimal alloy composition; otherwise, continue to execute steps S2~S4.

[0059] In this embodiment, a genetic algorithm is used to find printable aluminum alloy components and their optimal ratio range based on high thermal conductivity, while also taking into account the issue of thermal cracking sensitivity. This ensures that the aluminum alloy has appropriate strength while maintaining high thermal conductivity, preventing its strength from becoming too low due to cracking, thereby obtaining an aluminum alloy with high thermal conductivity and suitable strength.

[0060] The invention will be illustrated by the following three examples.

[0061] Example 1: This example selects a ternary Al-Fe-Zr alloy as the design object. This alloy exhibits typical characteristics of a "grain refinement-eutectic solidification" system. In setting the alloy composition, the Fe element content is set to a range of 0 ~ 2 wt%; Zr, as a trace alloying element, is set to a range of 0 ~ 1 wt%. The alloy composition step size is 0.01 wt%.

[0062] S1, randomly generates an initial population of N = 2000 individuals to form the genetic algorithm.

[0063] S2, for each genetic individual, calculate the fitness score F based on preset constraints and objectives. In this embodiment, the weights... , , and Set to 0.2, 0.2, 0.3, and 0.3.

[0064] S33, Selection, Crossover, and Mutation: Select the top 20% of individuals with the best fitness scores from the current population as parents. Simultaneously, randomly select parents from the remaining individuals with a probability of 0.05 to avoid prematurely getting trapped in local optima. The crossover operation uses a random crossover method to generate new offspring individuals. The gene mutation rate is set to 10%.

[0065] (4) Iterative optimization, determine the stopping condition.

[0066] After eight rounds of iterative cycles, the final alloy composition was Al-1.03Fe-0.39Zr, and its Scheil-Gulliver solidification behavior was as follows: Figure 2 As shown in (a): During solidification, as the temperature decreases, the aluminum alloy liquid gradually forms Al3Zr (a heterogeneous nucleation phase), FCC, and Al. 13 Fe4. Specifically, such as Figure 2 (a) During the cooling stage shown by the gray line to the red line, as the temperature decreases, some of the aluminum alloy liquid gradually forms Al3Zr (a heterogeneous nucleation phase); as the temperature further decreases, some of the aluminum alloy liquid forms Al3Zr (a heterogeneous nucleation phase) and FCC aluminum (i.e., aluminum crystals with a face-centered cubic (fcc) structure), respectively; as Figure 2 (a) Blue line: If the temperature decreases further, FCC aluminum will continue to form; as... Figure 2 (a) Yellow line: As the temperature decreases further, in addition to the continued formation of FCC aluminum, some of the molten aluminum alloy will gradually form Al. 13 Fe4. Comparative Example 1: Al-1.02Fe-1.05Zr alloy ( Figure 2 (b) From the literature doi.org / 10.1016 / j.actamat.2023.119199, after direct aging treatment, this alloy exhibits excellent yield strength (310 MPa) and thermal conductivity (180 W / m·K), making it a typical example of strength-thermal conductivity optimization in LPBF aluminum alloys. Figure 2 As shown in (b), the solidification curves of the Al-1.02Fe-1.05Zr alloy all exhibit an "L" shape: Figure 2 (b) From the gray line to the red line, as the temperature decreases, some of the molten aluminum alloy gradually forms Al3Zr (heterogeneous nucleation phase); such as Figure 2 (b) Blue line: As the temperature further decreases, some of the molten aluminum alloy will gradually form FCC aluminum; such as Figure 2 (b) Yellow line: When the temperature decreases further, in addition to forming FCC aluminum, Al will gradually form. 13 Fe4. The results showed that the solidification curves of both the alloy in Example 1 and the alloy in Comparative Example 1 were "L"-shaped, indicating that their grain refinement and eutectic solidification effects were similar.

[0067] Table 1. Comparison of properties and thermal conductivity between the designed alloy Al-1.03Fe-0.39Zr and the literature alloy Al-1.02Fe-1.05Zr. Table 1 lists the various properties of the alloy and the calculated thermal conductivity results. The results show that the fitness score F of Alloy Example 1 is lower than that of Alloy Comparative Example 1, indicating that the designed alloy is superior in terms of the combined mechanism of "grain refinement-eutectic solidification". Meanwhile, the theoretical thermal conductivity of Alloy Example 1 is 232.7 W·m. -1 ·K -1 It is higher than the 231.7 W·m of the comparative alloy. -1 ·K -1 Furthermore, Example 1 has a lower alloy element content, resulting in a greater cost advantage. These results validate the advantages of the method proposed in this invention from multiple perspectives.

[0068] Example 2: This example selects a quaternary Al-Ni-Sc-Zr alloy as the design object. This alloy also exhibits typical "grain refinement-eutectic solidification" system characteristics, differing from Example 1 in that it has a richer variety of elements. In terms of alloy composition, the Ni content is set to a range of 0–7 wt%; the Sc and Zr contents are set to 0–1 wt% to control costs. The alloy composition increment is 0.01 wt%.

[0069] This example includes the following steps: (1) Initialization: Randomly generate an initial population of N = 2000 individuals to form the genetic algorithm.

[0070] (2) Calculate the individual fitness score F: For each genetic individual, calculate the fitness score F based on preset constraints and objectives. In this embodiment, the weights... , , and They were set to 0.2, 0.2, 0.3 and 0.3 respectively.

[0071] (3) Selection, crossover, and mutation. The top 20% of individuals with the best fitness scores in the current population are selected as parents. Simultaneously, individuals from the remaining population are randomly selected as parents with a probability of 0.05 to avoid prematurely falling into local optima. A random crossover method is used to generate new offspring individuals. The gene mutation rate is set at 10%.

[0072] (4) Iterative optimization, determine the stopping condition.

[0073] After 12 rounds of iterative cycles, the final alloy composition was Al-2.02Ni-0.13Sc-0.52Zr, and its Scheil-Gulliver solidification behavior was as follows: Figure 3 As shown, the curve also exhibits an "L" shaped characteristic: as Figure 3 From the gray line to the red line, as the temperature decreases, some of the molten aluminum alloy gradually forms Al3Zr (heterogeneous nucleation phase); with further decreases in temperature, in addition to the continued formation of Al3Zr, FCC aluminum will gradually form; such as Figure 3 In the middle blue line, as the temperature decreases further, FCC aluminum continues to form during this cooling phase; for example... Figure 3 As the temperature drops further, in addition to the continued formation of FCC aluminum, some of the molten aluminum alloy will gradually form Al3Ni; for example... Figure 3 In the mid-purple line, as the temperature decreases further, in addition to the formation of heterogeneous nucleation phases of FCC aluminum and Al3Ni, Al3Sc gradually forms.

[0074] Table 2. Comparison of properties and performance between the designed alloy Al-2.02Ni-0.13Sc-0.52Zr and the reference alloy. Table 2 lists the various properties of the alloy and the calculated thermal conductivity results. The results show that the grain refinement index (initial slope IS) of the alloy in Example 2 is... 、 Initial solidification interval ΔT IFR ） Comparable to Comparative Example 2. Compared to Comparative Example 3, the eutectic solidification index (brittle solidification range ΔT) of the alloy in Example 2 is... BTR The crack sensitivity factor (CSI) was significantly optimized, resulting in stronger resistance to hot cracking. Furthermore, the thermal conductivity of alloy 2 was improved. λ The thermal conductivity is greater than that of common high thermal conductivity aluminum alloys, as shown in Comparative Example 4. The above results verify the applicability of the method proposed in this invention in more complex quaternary systems.

[0075] Example 3: Traditional 6063 aluminum alloy is known for its high thermal conductivity, but it suffers from the problem of being unmachinable due to its low thermal conductivity (LPBF). This example focuses on designing a Sc- and Zr-modified 6063 aluminum alloy (a pentagonal Al-Mg-Si-Sc-Zr alloy), which exhibits atypical "grain refinement-eutectic solidification" system characteristics. Compared to Examples 1 and 2, it contains more elements and has a more complex design. Regarding the alloy composition, based on the national standard requirements for traditional 6063 aluminum, the Mg content is set to range from 0.45 to 0.9 wt%, the Si content from 0.2 to 0.6 wt%, and the Sc and Zr content from 0 to 1 wt%, to control costs. The alloy composition increment is 0.01 wt%.

[0076] This example includes the following steps: (1) Initialization: Randomly generate an initial population of N = 2000 individuals to form the genetic algorithm.

[0077] (2) Calculate the individual fitness score F: For each genetic individual, calculate the fitness score F based on the preset constraints and objectives. (Weight) , , and The values ​​were set to 0.3, 0.3, 0.2 and 0.2 respectively, with a focus on optimizing the ability of grain refinement to suppress cracks.

[0078] (3) Selection, crossover, and mutation. The top 20% of individuals with the best fitness scores from the current population are selected as parents. Simultaneously, individuals with lower fitness scores are selected with a probability of 0.05 to avoid prematurely falling into local optima. A random crossover method is used to generate new offspring individuals. The gene mutation rate is set at 10%.

[0079] (4) Iterative optimization, determine the stopping condition.

[0080] After eight rounds of iterative cycles, the final alloy composition was Al-0.45Mg-0.28Si-0.59Sc-0.56Zr, and its Scheil-Gulliver solidification behavior was as follows: Figure 4 As shown: Figure 4 From the gray line to the red line, as the temperature decreases, some of the molten aluminum alloy gradually forms Al3Zr (heterogeneous nucleation phase); with further decreases in temperature, Al3Sc will gradually form on top of Al3Zr; such as Figure 4 The blue line in the mid-sky indicates that Al3Sc will continue to form as the temperature decreases further; for example... Figure 4 The yellow line indicates that as the temperature decreases further, FCC aluminum will gradually form on top of Al3Sc; for example... Figure 4 In the mid-purple range, as the temperature decreases further, AlSc2Si2 will gradually form on top of the existing FCC aluminum; for example... Figure 4 The blue-green line indicates that as the temperature decreases further, Mg2Si will gradually form in addition to the existing FCC aluminum and AlSc2Si2. Figure 4 In the middle brown line, as the temperature gradually decreases to or approaches 550℃, in addition to the continued formation of FCC aluminum and AlSc2Si2, Mg2Si, Si will also gradually form. The results show that the system did not undergo a typical eutectic solidification reaction, resulting in a steep downward trend in the solidification end curve. Compared with the traditional 6063 aluminum alloy (Comparative Example 4), this alloy is expected to suppress crack formation through in-situ precipitation of Al3(Sc,Zr) nanophases.

[0081] Table 3. Comparison of the properties and performance of the novel Al-0.45Mg-0.28Si-0.59Sc-0.56Zr alloy and the 6063 alloy. Table 3 lists the various properties of the alloy and the calculated thermal conductivity. As can be seen from Table 3, compared with Comparative Example 4, Example 3 shows a significant improvement in grain refinement, while the eutectic solidification index decreases, indicating a significant optimization of the solidification curve. Furthermore, the thermal conductivity of Example 3 is still higher than that of the common high thermal conductivity aluminum alloy, Comparative Example 4. These results fully demonstrate that the method proposed in this invention has strong adaptability and scalability.

[0082] Example 2: like Figure 5 As shown, the present invention also provides a high thermal conductivity aluminum alloy design system, comprising: The initialization module is used to set the aluminum alloy composition elements and generate several individuals with different composition element contents to form a population. The fitness score acquisition module is used to obtain the fitness score F of each individual in the population, including: S21, when the individual's theoretical thermal conductivity λ is lower than the thermal conductivity threshold, assign the individual a fitness score F that is worse than (or lower than) the score threshold. In simulated solidification, if an individual fails to form a heterogeneous nucleation phase during long-term solidification, the individual is assigned a fitness score F that is worse than (or lower than) the score threshold. S22, when the individual's theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold and a heterogeneous nucleation phase is formed, the fitness score F of the individual is calculated based on the grain refinement index and the eutectic solidification metallurgical index; the grain refinement index includes the initial slope and the initial solidification range, and the eutectic solidification metallurgical index includes the brittle solidification range and the crack sensitivity factor. The mutation genetic module is used to perform genetic operations on individuals within a population to generate the next generation of the population. The iteration module is used to iterate until a preset number of iterations is reached or the fitness score converges to a preset target score, at which point the optimization process stops and the final optimal alloy composition is output.

[0083] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0084] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a computer terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0085] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A high thermal conductivity aluminum alloy design method, characterized by, Comprise: S1, set the aluminum alloy composition elements, and generate several different composition element content of individual composition population; specifically including: set the content range of each composition element in the aluminum alloy; generate several individuals in the content range according to the preset content step; The preset content step is 0.01 wt%; S2, obtain the fitness score F of each individual in the population, the preset constraint condition includes: the individual theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold; Or, in the simulation solidification, the individual forms a heterogeneous nucleation phase in the early stage of solidification; Accordingly, this step S2 specifically includes: S21, in the case of individual theoretical thermal conductivity λ lower than the thermal conductivity threshold, the individual is given a fitness score F lower than the score threshold; Or, In the case of individual not forming a heterogeneous nucleation phase in the early stage of solidification in the simulation solidification, the individual is given a fitness score F lower than the score threshold; S22, in the case that the individual theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold value and the heterogeneous nucleation phase is formed, the grain refinement index and the eutectic solidification metallurgical index of each individual are obtained based on the temperature-solid phase fraction (T-f s ) curve and the temperature-solid phase fraction square root (T-f s 1 / 2 ) curve of each individual, and the fitness score F of the individual is calculated based on the grain refinement index and the eutectic solidification metallurgical index; the grain refinement index includes the initial slope and the initial solidification interval, and the eutectic solidification metallurgical index includes the brittle solidification range and the crack sensitivity factor; S3, the individual in the population is operated to generate the next generation population; S4, repeat steps S2-S3 until the preset iteration number is reached or the fitness score converges to the preset target score, then stop the optimization process, and output the final optimal alloy composition.

2. The method of designing a high thermal conductivity aluminum alloy according to claim 1, wherein The method for calculating the fitness score F of the individual based on the grain refinement index and the eutectic solidification metallurgical index includes using the formula: ； calculating a fitness score; wherein F is the fitness score, IS is the initial slope, IS ref is the slope reference value, ΔT IFR is the initial solidification interval, ΔT IFR,ref is the solidification interval reference value, ΔT BTR is the brittle solidification range, ΔT BTR,ref is the brittle solidification range reference value, CSI is the crack sensitivity factor, CSI ref is the crack sensitivity factor reference value, , , and are the weight coefficients of IS, ΔT IFR , ΔT BTR , and CSI, respectively.

3. The method of designing a high thermal conductivity aluminum alloy of claim 1, wherein The calculation method of the initial slope is to use the formula: ； calculating an initial slope; wherein IS is the initial slope, f s is the solid fraction and T is the temperature.

4. The method of designing a high thermal conductivity aluminum alloy of claim 2, wherein The calculation method of the initial solidification interval includes using the formula: ； calculating an initial solidification interval; wherein, ΔT IFR is the initial solidification interval, T fs1 and T fs2 are temperatures at which the alloy solidifies to a certain solid fraction at the beginning of solidification; or, The calculation method of the brittle solidification range includes using the formula: ； calculating a brittle solidification range; wherein, ΔT BTR is the brittle solidification range, T ZST is the zero strength temperature, T ZDT is the zero ductility temperature; or, The calculation method of the crack sensitivity factor includes using the formula: ； calculating a crack sensitivity factor; wherein, CSI is the crack sensitivity factor, T is the temperature, f s 1 / 2 is the solid fraction square root.

5. A high thermal conductivity aluminum alloy characterized by comprising, in mass %, The high thermal conductivity aluminum alloy is a ternary aluminum alloy system, the ternary aluminum alloy system includes Fe, Zr, Al and impurities, wherein the mass percentage of Fe, Zr and Al is determined by the aluminum alloy design method of any one of claims 1 to 4, and the initial content of Fe element is 0-2wt%; The initial content of Zr element is 0-1wt%; Or, the high thermal conductivity aluminum alloy is a quaternary aluminum alloy system, the quaternary aluminum alloy system includes Ni, Sc, Zr and Al and impurities, wherein the mass percentage of Ni, Sc, Zr and Al is determined by the aluminum alloy design method of claims 1 to 9, and the initial content of Ni element is 0-7 wt%; The initial content of Sc and Zr is 0-1wt%; Or, the high thermal conductivity aluminum alloy is a quinary aluminum alloy system, the quinary aluminum alloy system includes Mg, Si, Sc, Zr, Al and impurities, wherein the mass percentage of Mg, Si, Sc, Zr and Al is determined by the aluminum alloy design method of claims 1 to 9, and the initial content of Mg element is 0.45-0.9 wt%, the initial content of Si element is 0.2-0.6 wt%; The initial content of Sc and Zr is 0-1wt%.

6. The high thermal conductivity aluminum alloy of claim 5, wherein, The individual theoretical thermal conductivity λ of the high thermal conductivity aluminum alloy is higher than or equal to the thermal conductivity threshold, and the high thermal conductivity aluminum alloy forms a heterogeneous nucleation phase Al3X in the early stage of solidification in the simulation solidification.

7. The high thermal conductivity aluminum alloy of claim 6, wherein, if the high thermal conductivity aluminum alloy is a ternary or quaternary aluminum alloy system, the high thermal conductivity aluminum alloy has an initial slope IS and / or an initial solidification interval ΔT in the grain refinement index IFR higher than or equal to a reference value, the reference value comprising the initial slope IS and / or the initial solidification interval ΔT of Scalmalloy IFR ; and / or, the eutectic solidification metallurgical index of the high thermal conductivity aluminum alloy comprises a brittle solidification range ΔT BTR and / or a crack sensitivity factor CSI smaller than or equal to a reference value, the reference value comprising a brittle solidification range ΔT of a reference alloy AlSi10Mg BTR and / or a crack sensitivity factor CSI.

8. The high thermal conductivity aluminum alloy of claim 6, wherein, if the high thermal conductivity aluminum alloy is a five-component aluminum alloy system, the high thermal conductivity aluminum alloy has an initial slope IS and / or an initial solidification interval ΔT in the grain refinement index IFR an initial slope IS and / or an initial solidification interval ΔT higher than or equal to Al-0.45Mg-0.28Si-0.59Sc-0.56Zr IFR .

9. The high thermal conductivity aluminum alloy of any one of claims 5-7, wherein, The ternary aluminum alloy system has the mass percentages of Fe, Zr and Al as follows: the content of Fe is 1.03 wt%; the content of Zr is 0.39 wt%; and the rest is Al and impurities; Alternatively, the quaternary aluminum alloy system has the mass percentages of Ni, Sc, Zr and Al as follows: the content of Ni is 2.02 wt%; the content of Sc is 0.13; the content of Zr is 0.52 wt%; and the rest is Al and impurities; Alternatively, the quinary aluminum alloy system has the mass percentages of Mg, Si, Sc, Zr and Al as follows: the content of Mg is 0.45 wt%; the content of Si is 0.28 wt%; the content of Sc is 0.59 wt%; the content of Zr is 0.56 wt%; and the rest is Al and impurities.

10. The aluminum alloy of claim 8, wherein, The high-thermal-conductivity aluminum alloy includes a heterogeneous nucleation phase Al3Zr that is first precipitated at the beginning of solidification, and a eutectic phase Al 13 Fe4; or, The high-thermal-conductivity aluminum alloy includes a heterogeneous nucleation phase Al3Zr that is first precipitated at the initial stage of solidification, and eutectic phases Al3Ni and Al3Sc formed at the end of solidification; Alternatively, the high-thermal-conductivity aluminum alloy includes heterogeneous nucleation phases Al3Zr and Al3Sc that are first precipitated at the initial stage of solidification.

Images