A high thermal conductivity aluminum alloy design method and system

Abstract

The application 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 following steps: S1, setting aluminum alloy component elements and generating a plurality of individual constituent populations with different component element contents; S2, obtaining the fitness score F of each individual in the population; S3, performing genetic operation on the individuals in the population to generate the next generation population; and S4, repeatedly executing steps S2-S3 until the preset iteration number is reached or the fitness score converges to the preset target score. The method combines the basic principles of material science and the optimization strategy of data driving, and can efficiently design LPBF aluminum alloy materials which have excellent thermal conductivity and avoid thermal cracks. Compared with the traditional single machine learning method, the application provides a more accurate and reliable alloy design scheme, significantly improves the thermal conductivity of the aluminum alloy and its adaptability in the LPBF process, shortens the material development cycle, reduces the cost, and improves the accuracy and practicability of the design.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of metal material science and technology, and particularly relates to a high-thermal-conductivity aluminum alloy design method and system. BACKGROUND

[0002] In the modern industrial field, especially in the automobile and aerospace industries, there is an increasing demand for high-efficiency thermal management systems. These systems require materials to quickly and effectively dissipate heat while maintaining structural integrity, in order to improve the overall performance and reliability of the system. Aluminum alloys have become the ideal choice for thermal management applications due to their high thermal conductivity, low density, and high specific strength.

[0003] Laser Powder Bed Fusion (LPBF) technology can achieve integrated design of complex structures and improve heat dissipation efficiency, providing technical support for the production of thermal management components. However, the rapid solidification characteristics in the LPBF process often lead to increased thermal crack sensitivity of aluminum alloys, affecting the structural integrity and thermal conductivity of the components. Although optimizing LPBF process parameters can to some extent inhibit the generation of thermal cracks, the fundamental way to solve this problem is to develop new alloy systems. However, aluminum alloys face inherent contradictions between thermal conductivity and strength in performance design, as alloying improves the strength of aluminum but also increases electron and phonon scattering, significantly reducing thermal conductivity.

[0004] Traditional alloy design relies on experience, is time-consuming and costly. Calculation of Phase Diagrams (CALPHAD) methods based on thermodynamic databases can effectively predict the grain refinement behavior and thermal crack tendency of LPBF aluminum alloys, but there are still limitations in dealing with complex nonlinear problems and multi-objective optimization problems. The development of machine learning (ML) technology provides a new way for material performance prediction and multi-objective optimization, but relying solely on data-driven ML methods may overlook the basic principles of materials science, leading to predictions that deviate from actual needs. Therefore, combining machine learning with the basic principles of materials science to build a comprehensive material design framework is becoming a rapidly developing research direction. For example, material genetic engineering has successfully developed high-performance high-entropy alloys, copper alloys, and perovskite batteries through the deep integration of high-throughput thermodynamic calculations and ML technology. However, so far there is no technology that integrates high-throughput CALPHAD and ML technology to optimize LPBF aluminum alloys for thermal conductivity, printability, and strength.

[0005] For example, the prior art Chinese patent application 202210612466.1 discloses a data-driven aluminum alloy composition design method, which comprises: characterizing and analyzing the alloy composition and performance parameters of the aluminum alloy formed in each micro area, establishing a database, the database includes the alloy composition of each micro area, the corresponding preparation parameters and the corresponding performance parameters; obtaining the sample in the database, taking the alloy composition and the preparation parameter as the input, and taking the corresponding performance parameter as the output to train the artificial neural network model; intelligent optimization is adopted by genetic algorithm, the alloy composition and the preparation parameter of the aluminum alloy are taken as the population individual, and the target performance is taken as the optimization target, the trained artificial neural network model is called to obtain the performance parameter under different alloy composition and preparation parameter and calculate the corresponding individual fitness, and the alloy composition and preparation parameter meeting the requirements are output after genetic evolution.

[0006] The heating environment in the above-mentioned prior art adopts controllable gradient heating (such as solid solution treatment 350℃-550℃, aging treatment 50℃-250℃), and different temperature distributions are realized by independently adjusting the power of the heating rod and the cooling water channel. The purpose of using this method in the patent is to more efficiently obtain a large amount of effective alloy data (MgZn2 precipitated phase volume fraction, electrical conductivity and other performances), and then use artificial neural network and genetic algorithm to design alloy composition. However, since the traditional cast / forged aluminum alloy does not have hot cracks, the above-mentioned technology cannot alleviate the problem of hot cracks in the aluminum alloy in the LPBF process. SUMMARY

[0007] The purpose of the present application is to provide a high-thermal-conductivity aluminum alloy design method and system, which partially solves or alleviates the problem of poor printability, difficulty in balancing thermal conductivity and strength of the aluminum alloy in the LPBF process in the prior art.

[0008] In order to solve the above-mentioned technical problems, the present application specifically adopts the following technical solutions:

[0009] A high-thermal-conductivity aluminum alloy design method, comprising:

[0010] S1, setting aluminum alloy composition elements and generating a plurality of individual compositions with different composition element contents to form a population;

[0011] S2, obtaining the fitness score F of each individual in the population, comprising:

[0012] S21, in the case that the individual theoretical thermal conductivity λ is lower than the thermal conductivity threshold, assigning the individual a fitness score F lower than the score threshold; or,

[0013] in the case that the individual does not form a heterogeneous nucleation phase in the initial stage of solidification in the simulated solidification, assigning the individual a fitness score F lower than the score threshold.

[0014] S22, in the case that the individual theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold value and a heterogeneous nucleation phase is formed, calculating a fitness score F of the individual based on a grain refinement index and a eutectic solidification metallurgical index; the grain refinement index comprises an initial slope and an initial solidification interval, and the eutectic solidification metallurgical index comprises a brittle solidification range and a crack sensitivity factor;

[0015] S3, performing genetic operation on the individuals in the population to generate a next generation population;

[0016] S4, repeating the steps S2-S3 until a preset iteration number is reached or the fitness score converges to a preset target score, then stopping the optimization process and outputting a final optimal alloy composition.

[0017] As an improvement, the method for calculating the fitness score F of the individual based on the grain refinement index and the eutectic solidification metallurgical index comprises using the formula:

[0018] ;

[0019] to calculate the fitness score; wherein F is the fitness score, IS is the initial slope, IS ref is a reference value of the slope, ΔT IFR is the initial solidification interval, ΔT IFR,ref is a reference value of the solidification interval, ΔT BTR is the brittle solidification range, ΔT BTR,ref is a reference value of the brittle solidification range, CSI is the crack sensitivity factor, CSI ref is a reference value of the crack sensitivity factor, 、 、 and are weight coefficients.

[0020] As an improvement, the method for calculating the initial slope comprises using the formula:

[0021] ;

[0022] to calculate the initial slope; wherein IS is the initial slope, f s is the solid phase fraction, and T is the temperature.

[0023] As an improvement, the method for calculating the initial solidification interval comprises using the formula:

[0024] ;

[0025] to calculate the initial solidification interval; wherein ΔT IFR is the initial solidification interval, T fs1 and T fs2respectively, are the temperatures corresponding to the solidification of the alloy with solid fraction f s ≤0.4) to a certain solid fraction. Specifically, ΔT IFR The calculation range of ΔT

[0026] As an improvement, the calculation method of the brittle solidification range comprises using the formula:

[0027] ;

[0028] to calculate the brittle solidification range; wherein ΔT BTR is the brittle solidification range, T ZST is the zero strength temperature, and T ZDT is the zero ductility temperature.

[0029] As an improvement, the calculation method of the crack sensitivity factor comprises using the formula:

[0030] ;

[0031] to calculate the crack sensitivity factor; wherein CSI is the crack sensitivity factor, T is the temperature, and f s 1 / 2 is the square root of the solid fraction.

[0032] As an improvement, the slope reference value and the solidification interval reference value are derived from Scalmalloy alloy; the brittle solidification range reference value and the crack sensitivity factor reference value are derived from AlSi 10 Mg alloy.

[0033] As an improvement, the aluminum alloy component elements include aluminum and at least one of rare earth elements, magnesium, silicon, iron, zinc, titanium, zirconium, manganese, copper, nickel, chromium, yttrium, molybdenum, and vanadium.

[0034] As an improvement, the step of generating a plurality of individuals with different content proportions of component elements comprises:

[0035] setting a content range of each component element in the aluminum alloy;

[0036] generating a plurality of individuals within the content range according to a preset content step.

[0037] The application also provides a high-thermal-conductivity aluminum alloy design system, comprising:

[0038] an initialization module configured to set aluminum alloy component elements and generate a plurality of individuals with different content of component elements to form a population;

[0039] a fitness score acquisition module configured to acquire a fitness score F of each individual in the population, comprising:

[0040] S21, in the case that the individual theoretical thermal conductivity λ is lower than the thermal conductivity threshold, assigning the individual a fitness score F lower than the score threshold;

[0041] In the case that the individual does not form heterogeneous nucleation phase in the simulation of solidification for a long time, the individual is assigned a fitness score F lower than the score threshold;

[0042] S22, in the case that the individual theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold and forms heterogeneous nucleation phase, calculating the fitness score F of the individual based on the grain refinement index and eutectic solidification metallurgical index; the grain refinement index includes initial slope and initial solidification interval, and the eutectic solidification metallurgical index includes brittle solidification range and crack sensitivity factor;

[0043] A variation genetic module is configured to perform genetic operation on the individuals in the population to generate a next generation population;

[0044] An iteration module is configured to iterate until a preset iteration number is reached or the fitness score converges to a preset target score, and then stop the optimization process and output a final optimal alloy composition.

[0045] Beneficial effects:

[0046] The present application fuses high-throughput CALPHAD calculation and multi-objective genetic algorithm technology to develop a high-thermal-conductivity and crack-free aluminum alloy design method suitable for LPBF process, which has the following technical effects:

[0047] 1. Optimizing the balance between thermal conductivity and strength: By accurately optimizing the alloy composition, the present application can significantly improve the printability and strength of the aluminum alloy while maintaining its high thermal conductivity, overcoming the problem of mutual restriction between thermal conductivity and strength in traditional aluminum alloy design.

[0048] 2. Reducing the generation of thermal cracks: Using Scheil-Gulliver solidification simulation, the grain refinement performance and eutectic solidification effect of the aluminum alloy are optimized, making the design results more consistent with the principles of physical metallurgy, effectively reducing the generation of thermal cracks in the LPBF process and improving the structural integrity of the alloy.

[0049] 3. Improving the efficiency of alloy design: By combining high-throughput CALPHAD multi-objective genetic algorithm, the present application realizes fast and efficient aluminum alloy composition design, avoiding the time and economic cost of traditional trial-and-error methods, and greatly improving the efficiency and accuracy of the design process.

[0050] 4. Wide applicability and scalability: The present application is suitable for the development of multi-element (such as ternary, quaternary, quinary, etc.) aluminum alloy systems, and can meet the optimization design needs of different alloy element combinations, with strong adaptability and scalability.

[0051] Compared with the prior art, the present application sets the grain refinement ability and eutectic solidification effect as constraint conditions, and takes into account the problem of hot cracking on the premise of ensuring the strength of the aluminum alloy. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. In all the drawings, similar elements or parts are generally identified by similar reference signs. In the drawings, each element or part is not necessarily drawn according to the actual proportion. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0053] Figure 1 Flow chart for embodiment one of the present application;

[0054] Figure 2 Scheil-Gulliver solidification behavior of the Al-1.03Fe-0.39Zr alloy in Example 1 and the comparative example one alloy; Figure 2 (a) is the Scheil-Gulliver solidification behavior of the alloy in Example 1, Figure 2 (b) is the Scheil-Gulliver solidification behavior of the alloy in the comparative example one;

[0055] Figure 3 Scheil-Gulliver solidification behavior of the Al-2.02Ni-0.13Sc-0.52Zr alloy in Example 2;

[0056] Figure 4 Scheil-Gulliver solidification behavior of the Al-0.45Mg-0.28Si-0.59Sc-0.56Zr alloy in Example 3;

[0057] Figure 5 Structural diagram of embodiment two. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0059] Herein, the suffixes such as "module", "part", or "unit" used for an element are merely used for convenience of explanation of the present application, and have no specific meaning by themselves. Thus, "module", "part", or "unit" can be mixedly used.

[0060] Herein, the terms "upper", "lower", "inner", "outer", "front", "rear", "one end", "the other end", and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are merely for convenience of description of the present application and simplification of the description, and do not indicate or imply that the indicated device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus cannot be understood as a limitation on the present application. In addition, the terms "first", "second" are merely for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0061] Herein, unless otherwise explicitly specified and limited, the terms "mount", "provided with", "connected", and the like should be broadly understood, for example, "connected" can be fixedly connected, can be detachably connected, or integrally connected; can be mechanically connected, can be directly connected, can be indirectly connected through an intermediate medium, or can be internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0062] Herein, "and / or" includes any and all combinations of one or more of the listed related items.

[0063] Herein, "a plurality of" means two or more, that is, it includes two, three, four, five, etc.

[0064] Embodiment one: in order to solve the problems of poor printability, difficult to balance thermal conductivity and strength of existing aluminum alloy in LPBF process, the present application provides an alloy design method of high-throughput CALPHAD calculation combined with multi-objective genetic algorithm. This method combines the basic principles of material science with data-driven optimization strategies, and can efficiently design LPBF aluminum alloy materials with excellent thermal conductivity and avoid thermal cracking. Compared with traditional single machine learning method, the present application provides a more accurate and reliable alloy design scheme, which significantly improves the thermal conductivity of aluminum alloy and its adaptability in LPBF process, shortens the material development cycle, reduces the cost, and improves the accuracy and practicability of the design.

[0065] As shown in Figure 1 , the specific steps of the embodiment include:

[0066] S1, setting aluminum alloy component elements, and generating a population composed of a plurality of individuals with different component element contents.

[0067] The setting of the component elements of the aluminum alloy in the present application is performed within a predetermined alloy element system. The component elements of the aluminum alloy, in addition to aluminum, can include at least one of 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, and Sm).

[0068] For example, if the aluminum alloy system includes elements such as aluminum (Al), copper (Cu), magnesium (Mg), zinc (Zn), and zirconium (Zr), the computer program can randomly assign the mass or volume percentage of each element to each individual within the preset content range of each element during initialization. By setting the step size of the assignment, the range of the number of individuals N can be adjusted. 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 the content range according to the preset content step size.

[0069] A simple ternary aluminum alloy system (Al-Fe-Zr) is taken as an example for specific description. The content range of Fe element is set to 0 ~ 2 wt%; Zr is set as a trace alloying element, and the content range thereof is set to 0 ~ 1 wt%. The alloy component step size is 0.01 wt%, that is, a population of 200*100=20000 individual numbers can be generated.

[0070] S2, obtaining the fitness score F of each individual in the population.

[0071] For each genetic individual, the fitness score F is calculated according to the preset constraint condition and target. The fitness score F reflects the advantages and disadvantages of the alloy under multiple performance indicators (such as grain refinement ability and eutectic solidification effect). Specifically, the following sub-steps are included:

[0072] S21, calculating the theoretical thermal conductivity λ of the individual, and in the case that the theoretical thermal conductivity λ of the individual is lower than the thermal conductivity threshold, the fitness score F of the individual is assigned to be worse (i.e. lower) than the score threshold.

[0073] Specifically, the theoretical thermal conductivity λ of each individual is calculated by using a thermodynamic calculation software such as Thermo-calc, FactSage, MSE, or any thermodynamic calculation software with similar functions, running single-point equilibrium calculation, and comparing with the preset standard value. If λ is lower than the standard value, the fitness score F is assigned to a worse value.

[0074] The so-called "assigning the individual a fitness score F worse than the score threshold" in the present application refers to assigning the individual a fitness score F lower than the score threshold in the case that the higher the fitness score F is the better, and assigning the individual a fitness score F higher than the score threshold in the case that the lower the fitness score F is the better.

[0075] In the population evolution process of the genetic algorithm, the fitness score is an important indicator for measuring the pros and cons of individuals. Assigning a poor F value to an individual that does not meet the requirements helps to eliminate those individuals that perform poorly in thermal conductivity, solidification characteristics, etc. in the selection operation, so that individuals with better performance potential have a greater probability of being retained and inherited to the next generation, thereby improving the overall quality of the population and accelerating the speed of the algorithm converging to the optimal solution.

[0076] Similarly, in the case that the individual does not form a heterogeneous nucleation phase in the initial stage of solidification in the simulation solidification, the individual is assigned a fitness score F worse than the score threshold.

[0077] Specifically, according to the Scheil-Gulliver solidification simulation results of the thermodynamic calculation software, it is determined whether each individual first forms a heterogeneous nucleation phase Al3X (where X is any element in the designed alloy that can form a heterogeneous nucleation phase, such as Zr, Sc, Ti, etc.) in the initial stage of solidification. If not, a poor fitness score F is assigned.

[0078] It can be foreseen that either of the two constraint conditions: 1) the individual theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold; 2) the individual does not form a heterogeneous nucleation phase in the initial stage of solidification in the simulation solidification, does not meet, will directly assign a poor fitness score F to the corresponding individual, and accordingly, the individual assigned with the fitness score will be very likely not selected in the subsequent process. Moreover, the order of calculating the individual theoretical thermal conductivity λ and judging the heterogeneous nucleation condition can be interchanged. For example, when it is judged that the individual theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold, and then it is judged that the individual does not form a heterogeneous nucleation phase in the initial stage of solidification in the simulation solidification, the individual will also be assigned a fitness score F worse than the score threshold. Or, when it is judged that the individual forms a heterogeneous nucleation phase in the initial stage of solidification in the simulation solidification, but it is judged that the individual theoretical thermal conductivity λ is lower than the thermal conductivity threshold, the individual will also be assigned a fitness score F worse than the score threshold.

[0079] S22, in the case that the individual theoretical thermal conductivity λ is higher than or equal to the thermal conductivity threshold and forms a heterogeneous nucleation phase in the initial stage of solidification, the fitness score F of the individual is calculated based on the grain refinement index and the eutectic solidification metallurgical index.

[0080] In the case that the individual theory thermal conductivity λ and the heterogeneous nucleation condition judgment are both in line with the requirements, the performance index of the individual is calculated, that is, according to the Scheil-Gulliver solidification simulation result of the thermodynamic calculation software, 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 are obtained, and the grain refinement index and the eutectic solidification metallurgical index of each individual are calculated. 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.

[0081] The initial slope IS of one of the grain refinement indexes, in the alloy solidification process, the IS value reflects the initial rate (r) of composition undercooling. A larger IS value corresponds to a higher r value, indicating that the nucleation rate is faster, which helps to generate more dispersed phases, thereby inhibiting the growth of columnar crystals and promoting the generation of equiaxed crystals. In the embodiment, the calculation method of the initial slope is to use the formula:

[0082] ;

[0083] to calculate the initial slope; wherein IS is the initial slope of the T-f s curve, f s is the solid phase fraction, and T is the temperature. The present application aims to improve the IS value, and further improves the grain refinement effect and inhibits crack generation by optimizing the alloy composition.

[0084] The initial solidification interval ΔT IFR of the second grain refinement index is defined as the temperature interval of the primary phase formation stage of the alloy. The larger the solidification interval, the higher the undercooling degree of the alloy, thereby promoting grain refinement. In the embodiment, the calculation method of the initial solidification interval includes using the formula:

[0085] ;

[0086] to calculate the initial solidification interval; wherein ΔT IFR is the initial solidification interval, T fs1 and T fs2 are the temperatures corresponding to the solidification of the alloy to a certain solid phase fraction at the initial stage of solidification, and the calculation range can be flexibly set according to the performance target.

[0087] Specifically, the definition of the initial solidification interval ΔT IFR is adjustable within the range of 0≤f s ≤0.4 at the initial stage of solidification, wherein the more optimal range is 0≤f s ≤0.2, and the recommended range is 0≤f s ≤0.1, so as to optimize the grain refinement effect of the alloy.

[0088] Brittle solidification range ΔT, one of the eutectic solidification metallurgical indicators BTR is the temperature interval that measures the transition of material from ductile to brittle, usually most pronounced at the end of solidification. ΔT BTR The greater the ΔT BTR , the higher the tendency of thermal cracking of the material, thus it is essential to reduce ΔT BTR for improving printability. In this embodiment, the method for calculating the brittle solidification range ΔT

[0089] ;

[0090] calculating the brittle solidification range ΔT BTR ; wherein ΔT ZST is the brittle solidification range, T ZDT is the zero strength temperature, and T BTR is the zero ductility temperature.

[0091] Brittle solidification range ΔT BTR is defined in the range of 0.6 ≤ f s ≤ 1 at the end of solidification, wherein the more optimal range is 0.7 ≤ f s ≤ 1, and the recommended range is 0.85 ≤ f s ≤ 0.95, to optimize the eutectic solidification behavior of the alloy.

[0092] Crack sensitivity factor CSI, the second eutectic solidification metallurgical indicator, is a key indicator to evaluate the tendency of thermal cracking of the material, mainly reflecting the balance between the transverse grain growth rate and the liquid feeding ability during solidification. When the liquid feeding rate is lower than the grain bridging rate, the material is more prone to cracking. In this embodiment, the method for calculating the crack sensitivity factor CSI includes using the formula:

[0093] ;

[0094] calculating the crack sensitivity factor CSI; wherein CSI is the crack sensitivity factor, T is the temperature, and f s 1 / 2 is the square root of the solid fraction; d represents differentiation, and accordingly, dT is the temperature gradient, reflecting the rate of temperature change at the solidification front; is the differential of the square root of the solid fraction, representing the rate of solidification progress.

[0095] After obtaining the grain refinement indicator and the eutectic solidification metallurgical indicator, the fitness score F of the individual is calculated. In this embodiment, the grain refinement indicator and the eutectic solidification metallurgical indicator are converted into the fitness score F by using linear weighting. The fitness score F is defined as the smaller the F, the better the individual. The specific calculation method includes using the formula:

[0096] ;

[0097] calculating a fitness score; wherein F is the fitness score, IS is the initial slope, IS ref is the slope reference value, AT IFR is the initial solidification interval, AT IFR,ref is the solidification interval reference value, AT BTR is the brittle solidification range, AT BTR,ref is the brittle solidification range reference value, CSI is the crack sensitivity factor, CSI ref is the crack sensitivity factor reference value, are the weight coefficients. The weight coefficients of the fitness score F can be dynamically adjusted according to the target performance of the alloy to optimize the performance of the alloy in a specific application. For typical or atypical “grain refinement-eutectic solidification” system characteristics, the size of the above four weight coefficients 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 weight coefficient is greater than the weight coefficient ; if the “eutectic solidification” characteristic is focused on, the weight coefficient is less than the weight coefficient 。

[0098] The reference values of the grain refinement and eutectic solidification can be selected from existing alloy systems (such as Scalmalloy alloy and AlSi 10 Mg alloy), but other alloys with similar grain refinement and eutectic solidification characteristics can also be selected as references to ensure that the designed alloy can achieve optimal grain refinement effect and eutectic solidification behavior, thereby improving its adaptability in the LPBF process.

[0099] S3, performing genetic operations on the individuals in the population to generate the next generation of population.

[0100] ​​​​​​​​​​Specifically, the selection, crossover and mutation genetic operations are sequentially performed to generate the next generation population, so that the population is continuously evolved. In the selection operation, an elite strategy is combined with a random selection strategy to avoid the search from falling into a local optimum too early. In the crossover operation, a random crossover method is used to splice a part of each parent gene fragment to generate a new offspring individual. The crossover operation can effectively maintain the diversity of the population and increase the ability of the genetic algorithm to search for a global optimal solution. To enhance the local optimization ability, gene mutation is appropriately performed.

[0101] S4, the steps S2-S3 are repeatedly performed until a preset iteration number is reached or a fitness score converges to a preset target score.

[0102] After each iteration, it is determined whether a stop condition is met. If a preset generation number is reached or the fitness score of the population converges to a certain target score, the optimization process is stopped, and the final optimal alloy composition is output; otherwise, the steps S2-S4 are continuously performed.

[0103] In this embodiment, the genetic algorithm is used to find the printable aluminum alloy composition and its optimal proportioning range on the basis of high thermal conductivity, and the problem of thermal crack sensitivity is also considered, so that the aluminum alloy has appropriate strength on the basis of high thermal conductivity, and the strength is not too low due to the generation of cracks, and thus an aluminum alloy with high thermal conductivity and appropriate strength is obtained.

[0104] The following three examples are used to illustrate the present application.

[0105] Example 1

[0106] In this example, a ternary Al-Fe-Zr alloy is selected as the design object. The alloy has typical "grain refinement-eutectic solidification" system characteristics. In the alloy composition setting, the content of Fe element is set to 0-2 wt%; Zr is set as a trace alloying element, and its content range is set to 0-1 wt%. The alloy composition step is 0.01 wt%.

[0107] S1, an initial population of genetic algorithm is randomly generated and contains N = 2000 individuals.

[0108] S2, for each genetic individual, the fitness score F is calculated according to the preset constraints and targets. In this embodiment, the weights , , and are set to 0.2, 0.2, 0.3 and 0.3.

[0109] S33, selection, crossover and mutation: select the top 20% of individuals with the best fitness score from the current population as parent individuals, and randomly select the remaining individuals as parent individuals with a probability of 0.05 to avoid premature search into local optimum. The random crossover method is used for crossover operation to generate new offspring individuals. The gene mutation rate is set to 10%.

[0110] (4) Iterative optimization, judge the stopping condition.

[0111] After 8 cycles of iteration, the final alloy composition is Al-1.03Fe-0.39Zr, and the Scheil-Gulliver solidification behavior is as shown in Figure 2 (a): During solidification, as the temperature decreases, the aluminum alloy liquid gradually forms Al3Zr (as a heterogeneous nucleation phase), FCC and Al 13 Fe4. Specifically, as shown by the gray line to the red line in Figure 2 (a), as the temperature decreases, part of the aluminum alloy liquid gradually forms Al3Zr (as a heterogeneous nucleation phase); as the temperature further decreases, part of the aluminum alloy liquid forms Al3Zr (as a heterogeneous nucleation phase) and FCC aluminum (i.e., aluminum crystals with face-centered cubic (fcc) structure), respectively; as shown by the blue line in Figure 2 (a), when the temperature is further reduced, FCC aluminum continues to form; as shown by the yellow line in Figure 2 (a), when the temperature is further reduced, on the basis of the continuous formation of FCC aluminum, part of the aluminum alloy liquid will also gradually form Al 13 Fe4. The Al-1.02Fe-1.05Zr alloy of Comparative Example 1 (Example 1) Figure 2 (b) comes from the literature doi.org / 10.1016 / j.actamat.2023.119199, and after direct aging treatment, the alloy exhibits excellent yield strength (310 MPa) and thermal conductivity (180 W / m·K), which is currently the typical strength-thermal conductivity optimization of LPBF aluminum alloys. As shown in Figure 2 (b), the solidification curve of the Al-1.02Fe-1.05Zr alloy is "L" shaped: as shown by the gray line to the red line in Figure 2 (b), as the temperature decreases, part of the aluminum alloy liquid gradually forms Al3Zr (heterogeneous nucleation phase); as shown by the blue line in Figure 2 (b), as the temperature further decreases, part of the aluminum alloy liquid will also gradually form FCC aluminum; as shown by the yellow line in Figure 3 (b), when the temperature is further reduced, in addition to the formation of FCC aluminum, Al 13 Fe4 will also gradually form. The results show that the solidification curves of Example 1 and Comparative Example 1 alloy are both "L" shaped, indicating that their grain refinement and eutectic solidification effects are similar.

[0112] 1Table 1 Comparison of indexes and thermal conductivity of designed alloy Al-1.03Fe-0.39Zr and literature alloy Al-1.02Fe-1.05Zr

[0113]

[0114] Table 1 lists the indexes and thermal conductivity calculation results of the alloys. The results show that the fitness score F of Example 1 alloy is lower than that of Comparative Example 1 alloy, indicating that the designed alloy is better in the "grain refinement-eutectic solidification" comprehensive mechanism. At the same time, the theoretical thermal conductivity of Example 1 alloy is 232.7 W·m -1 ·K -1 , which is higher than 231.7 W·m -1 ·K -1 of Comparative Example 1 alloy. In addition, the element content of Example 1 alloy is lower, which has cost advantage. The above results verify the advantages of the method proposed in the present application from multiple dimensions.

[0115] Example 2:

[0116] In this example, a quaternary Al-Ni-Sc-Zr alloy is selected as the design object. The alloy also has the typical characteristics of "grain refinement-eutectic solidification" system, and the difference from Example 1 is that it has more types of elements. In the setting of alloy composition, the content of Ni element is set to 0 ~ 7 wt%; the contents of Sc and Zr are set to 0 ~ 1 wt% to control the cost. The composition step of the alloy is 0.01 wt%.

[0117] This example includes the following steps:

[0118] (1) Initialization: randomly generate N = 2000 individuals to form the initial population of the genetic algorithm.

[0119] (2) Calculate the fitness score F of the individual: for each genetic individual, calculate the fitness score F according to the preset constraints and objectives. In this embodiment, the weights , , and are set to 0.2, 0.2, 0.3 and 0.3, respectively.

[0120] (3) Selection, crossover and mutation. Select the top 20% of individuals with the best fitness score as parent individuals from the current population, and randomly select the remaining individuals as parent individuals with a probability of 0.05 to avoid premature convergence into local optimum. Random crossover method is used for crossover operation to generate new offspring individuals. The gene mutation rate is set to 10%.

[0121] (4) Iterative optimization, judge the stopping condition.

[0122] 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... Lambda 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.

[0123] Table 2. Comparison of properties and performance between the designed alloy Al-2.02Ni-0.13Sc-0.52Zr and the reference alloy.

[0124]

[0125] 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. Figure 4 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.

[0126] Example 3:

[0127] The conventional 6063 aluminum alloy is known for high thermal conductivity, but it has the problem of being non-processable by LPBF. This example takes the design of Sc, Zr modified 6063 aluminum alloy (five-element Al-Mg-Si-Sc-Zr alloy) as an example. The alloy has the atypical "grain refinement-eutectic solidification" system characteristics. Compared with Example 1 and Example 2, it has more elements and more complex design. In terms of alloy composition setting, according to the national standard requirements of the conventional 6063 aluminum, the Mg element content range is set to 0.45 ~ 0.9 wt%, the Si element content range is set to 0.2 ~ 0.6 wt%, and the Sc and Zr content range is set to 0 ~ 1 wt% to control the cost. The alloy composition step is 0.01 wt%.

[0128] This example includes the following steps:

[0129] (1) Initialization: randomly generate N = 2000 individuals to form the initial population of the genetic algorithm.

[0130] (2) Calculate the individual fitness score F: for each genetic individual, calculate the fitness score F according to the pre-set constraints and objectives. The weights , , and are set to 0.3, 0.3, 0.2 and 0.2 respectively, focusing on optimizing the ability of grain refinement to inhibit cracks.

[0131] (3) Selection, crossover and mutation. Select the top 20% of individuals with the best fitness score from the current population as parent individuals, and select individuals with poor fitness score with a probability of 0.05 to avoid premature search into local optimum. Random crossover method is used for crossover operation to generate new offspring individuals. The gene mutation rate is set to 10%.

[0132] (4) Iterative optimization, judge the stopping condition.

[0133] After 8 cycles of iteration, the final alloy composition is Al-0.45Mg-0.28Si-0.59Sc-0.56Zr, and the Scheil-Gulliver solidification behavior is as shown in Figure 4 : as shown in the gray line to the red line in Figure 4 , with the decrease of temperature, part of the aluminum alloy liquid gradually forms Al3Zr (heterogeneous nucleation phase); with the further decrease of temperature, Al3Zr is formed on the basis of which Al3Sc is gradually formed; as shown in Figure 4 , when the temperature is further reduced, Al3Sc continues to form; as shown in Figure 4 , when the temperature is further reduced, on the basis of the formation of Al3Sc, FCC aluminum is gradually formed; as shown in Figure 4The middle purple line, when the temperature is further reduced, on the basis of the continued formation of FCC aluminum, AlSc2Si2 will also gradually form; such as Figure 4 The middle blue-green line, when the temperature is further reduced, on the basis of the continued formation of FCC aluminum and AlSc2Si2, Mg2Si will also gradually form; such as Figure 5 The middle brown line, when the temperature gradually decreases to or gradually approaches 550℃, on the basis of the continued formation of FCC aluminum and AlSc2Si2, Mg2Si, Si will also gradually form. The results show that the system does not undergo a typical eutectic solidification reaction, resulting in a relatively steep downward trend of the solidification end curve. Compared with the conventional 6063 aluminum alloy (Comparative Example Four), the alloy is expected to suppress the generation of cracks through in-situ precipitation of Al3(Sc, Zr) nano-phase.

[0134] 3Table 3 Comparison of indexes and properties of new Al-0.45Mg-0.28Si-0.59Sc-0.56Zr alloy and 6063 alloy

[0135]

[0136] Table 3 lists the indexes and thermal conductivity calculation results of the alloy. As can be seen from Table 3, compared with Comparative Example Four, Example 3 is significantly improved in grain refinement, and the eutectic solidification index is reduced, indicating that the solidification curve is significantly optimized. In addition, the thermal conductivity of Example 3 is still higher than that of the common high thermal conductivity aluminum alloy Comparative Example Four. The above results fully show that the method proposed in the present application has strong adaptability and expansibility.

[0137] Example Two:

[0138] As ​ shown, the present application also provides a high thermal conductivity aluminum alloy design system, comprising:

[0139] An initialization module for setting aluminum alloy component elements and generating a plurality of individual constituent populations with different component element contents;

[0140] An fitness score acquisition module for acquiring the fitness score F of each individual in the population, comprising:

[0141] S21, in the case that the individual theoretical thermal conductivity λ is lower than the thermal conductivity threshold value, the individual is assigned a fitness score F which is worse (or lower) than the score threshold value;

[0142] In the case that the individual does not form a heterogeneous nucleation phase in the long-term solidification, the individual is assigned a fitness score F which is worse (or lower) than the score threshold value;

[0143] 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, calculating the fitness score F of the individual based on a grain refinement index and a eutectic solidification metallurgical index; the grain refinement index includes an initial slope and an initial solidification interval, and the eutectic solidification metallurgical index includes a brittle solidification range and a crack sensitivity factor;

[0144] a variation genetic module, configured to perform genetic operation on the individuals in the population to generate a next generation population;

[0145] an iteration module, configured to iterate until a preset iteration number is reached or the fitness score converges to a preset target score, and then stop the optimization process and output a final optimal alloy composition.

[0146] It should be noted that in this document, the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a list of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0147] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and necessary general hardware platforms, of course, they can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes a plurality of instructions for making a computer terminal (which can be a mobile phone, computer, server, or network device, etc.) execute the methods described in various embodiments of the present application.

[0148] The embodiments of the present application are described above in combination with the drawings, but the present application is not limited to the above-mentioned specific embodiments, and the above-mentioned specific embodiments are only illustrative, not limiting, and those skilled in the art can make many forms under the inspiration of the present application without departing from the purpose of the present application and the scope protected by the claims. These are all within the protection of the present application.

Claims

1. A high thermal conductivity aluminum alloy design method, characterized by, The method comprises the following steps: S1, setting aluminum alloy component elements and generating a plurality of individual populations with different component element contents; S2, obtaining a fitness score F of each individual in the population, comprising: S21, in the case that the theoretical thermal conductivity λ of the individual is lower than the thermal conductivity threshold, assigning the individual a fitness score F lower than the score threshold; or, in the case that the individual does not form a heterogeneous nucleation phase in the initial stage of simulated solidification, assigning the individual a fitness score F lower than the score threshold; S22, in the case that the theoretical thermal conductivity λ of the individual is higher than or equal to the thermal conductivity threshold and the heterogeneous nucleation phase is formed, calculating the fitness score F of the individual based on a grain refinement index and a eutectic solidification metallurgical index; the grain refinement index comprises an initial slope and an initial solidification interval, and the eutectic solidification metallurgical index comprises a brittle solidification range and a crack sensitivity factor; S3, performing genetic operation on the individuals in the population to generate a next generation population; S4, repeating steps S2-S3 until a preset iteration number is reached or the fitness score converges to a preset target score, then stopping the optimization process and outputting 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 comprises using the following formulas: ； 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 comprises using the following 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 comprises using the following 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 at the beginning of solidification to a certain fraction of solid.

5. The method of designing a high thermal conductivity aluminum alloy of claim 2, wherein The calculation method of the brittle solidification range comprises using the following 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.

6. The method of designing a high thermal conductivity aluminum alloy of claim 2, wherein The calculation method of the crack sensitivity factor comprises using the following 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.

7. The method of designing a high thermal conductivity aluminum alloy of claim 2, wherein: The slope reference value and solidification interval 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.

8. The method of designing a high thermal conductivity aluminum alloy of claim 1, wherein: The aluminum alloy component elements comprise aluminum and at least one of rare earth elements, magnesium, silicon, iron, zinc, titanium, zirconium, manganese, copper, nickel, chromium, yttrium, molybdenum, and vanadium.

9. The method of designing a high thermal conductivity aluminum alloy of claim 1, wherein The step of generating a plurality of individuals with different content proportions of component elements comprises: setting a content range of each component element in the aluminum alloy; generating a plurality of individuals in the content range according to a preset content step size.

10. A high thermal conductivity aluminum alloy design system, characterized by, The method comprises the following steps: an initialization module for setting aluminum alloy component elements and generating a plurality of individual populations with different component element contents; a fitness score acquisition module for obtaining a fitness score F of each individual in the population, comprising: S21, in the case that the theoretical thermal conductivity λ of the individual is lower than the thermal conductivity threshold, assigning the individual a fitness score F lower than the score threshold; in the case that the individual does not form a heterogeneous nucleation phase in the initial stage of simulated solidification, assigning the individual a fitness score F lower than the score threshold; S22, in the case that the theoretical thermal conductivity λ of the individual is higher than or equal to the thermal conductivity threshold and the heterogeneous nucleation phase is formed, calculating the fitness score F of the individual based on a grain refinement index and a eutectic solidification metallurgical index; the grain refinement index comprises an initial slope and an initial solidification interval, and the eutectic solidification metallurgical index comprises a brittle solidification range and a crack sensitivity factor; a variation genetic module for performing genetic operation on the individuals in the population to generate a next generation population; an iteration module for iteration until a preset iteration number is reached or the fitness score converges to a preset target score, then stopping the optimization process and outputting the final optimal alloy composition.

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