A process parameter optimization method for aluminum alloy friction stir solid-phase additive manufacturing, an aluminum alloy component and a preparation method thereof

Abstract

A process parameter optimization method for aluminum alloy friction stir solid-phase additive manufacturing, an aluminum alloy component and a preparation method thereof. The present application places the pre-solution treatment process in the raw material stage, solving the problems of poor solution effect and low efficiency of large and complex components. Secondly, by constructing a quantitative prediction model between process parameters-thermal cycle-precipitated phase / grain size, the two main factors leading to softening, i.e. precipitated phase coarsening and grain growth, are accurately identified and inhibited, realizing the rapid and selective optimization design of process parameters, significantly improving the process optimization efficiency and reducing the cost. Finally, the deep cooling and multi-stage aging synergistic strengthening post-treatment technology is introduced to promote the fine and dispersed precipitation of strengthening phase, realizing the synchronous improvement of the strength and toughness of the component.

Description

Technical Field

[0001] This invention belongs to the field of additive manufacturing technology for metallic materials, and particularly relates to a method for optimizing process parameters for friction stir solid-state additive manufacturing of aluminum alloys, aluminum alloy components and their preparation methods. Background Technology

[0002] As key structural components in aerospace, rail transportation, and other fields develop towards integration, lightweighting, and increasing complexity, more stringent requirements are being placed on the comprehensive mechanical properties, manufacturing costs, and production efficiency of materials. Aluminum alloys, due to their low density, high specific strength, and excellent corrosion resistance, have become one of the most widely used lightweight metal structural materials. Traditional aluminum alloy component manufacturing primarily relies on casting and forging processes. The casting process is prone to solidification defects such as porosity, inclusions, and shrinkage, leading to fluctuations in the mechanical properties of the components. While forging can improve material density, it typically requires multiple forging passes and extensive subsequent machining, resulting in limitations such as low material utilization and difficulty in forming large, complex components.

[0003] Additive manufacturing technology provides a new approach for the high-performance and complex forming of aluminum alloy components. Among them, friction stir solid-state additive manufacturing technology, which is based on the principle of friction stir welding, has significant advantages such as high material utilization, high additive efficiency, and excellent comprehensive performance. It is especially suitable for the preparation of lightweight alloys such as aluminum alloys and magnesium alloys.

[0004] However, this technology faces a core challenge in practical applications: aluminum alloy components produced by solid-state additive manufacturing exhibit significant softening, making it difficult for their microstructure and properties to meet the requirements of high-end equipment. The main reason for this is that under conditions of frictional stirring cycle heat input and strong deformation, dynamic recrystallization leads to significant grain coarsening, and in addition, the precipitated phases continue to grow and coarsen.

[0005] To address the aforementioned softening problem, the industry commonly employs a "solid-phase additive manufacturing-solution-aging" post-treatment approach, aiming to re-dissolve and re-precipitate the coarsened second phase. However, this approach has inherent drawbacks: the subsequent high-temperature solution treatment destroys the fine equiaxed grains and high-density dislocation substructure introduced during additive manufacturing due to large plastic deformation, failing to fully leverage the technological advantages of friction stir solid-phase additive manufacturing. Furthermore, the optimization of additive manufacturing and heat treatment processes still heavily relies on traditional trial-and-error methods, i.e., screening for optimal parameters by batch testing the microstructure and properties of samples under different combinations of process parameters. This approach is not only costly in process screening but also inefficient in optimization, severely hindering the industrial application of this technology.

[0006] Therefore, developing an integrated fabrication technology that can achieve precise control of precipitated phase and grain size, effectively suppress softening, and significantly improve process optimization efficiency and overall component performance has become an urgent problem to be solved in this field. Summary of the Invention

[0007] To address the aforementioned problems in the existing technology, this invention provides a method for optimizing process parameters in friction stir solid-state additive manufacturing of aluminum alloys, as well as aluminum alloy components and their preparation methods. This method enables precise prediction and selective control of precipitated phase size and grain size, thereby achieving high-quality, efficient, and low-cost preparation of high-strength and tough aluminum alloy components.

[0008] To achieve the above objectives, the technical solution provided by the present invention is as follows: In a first aspect, this application provides a method for optimizing process parameters in friction stir solid-state additive manufacturing of aluminum alloys, comprising the following steps: Obtain the thermal cycling curves of the substrate material during the friction stir solid-state additive manufacturing process under different process parameters; Based on the thermal cycling curve, precipitation kinetics simulation was performed to predict the average size of the enhanced precipitate under various process parameters, and the first process window was selected based on the criterion that "the average size of the enhanced precipitate is ≤100nm". Based on the aforementioned thermal cycling curve, a dynamic recrystallization grain size prediction model characterized by Zener-Hollomon parameters is established. Using the dynamic recrystallization grain size prediction model, the average grain size corresponding to each combination of process parameters within the first process window is predicted. Then, the first process window is screened a second time based on the criterion of "average grain size ≤ 10 μm" to obtain an optimized second process window.

[0009] Optionally, obtaining the thermal cycling curves of the substrate material during the friction stir solid additive manufacturing process under different process parameters includes: pre-processing a blind hole array on an aluminum alloy substrate and embedding thermocouples, conducting multiple sets of friction stir solid additive manufacturing experiments under typical process parameters, and monitoring and recording the thermal cycling curves and peak temperatures of the substrate in real time.

[0010] Optionally, precipitation kinetics simulation is performed based on the thermal cycling curve to predict the average size of the enhanced precipitate under various process parameters, and a first process window is selected based on the criterion of "average size of enhanced precipitate ≤ 100 nm", including: The alloy composition and thermal cycling curves are input into the precipitation kinetics calculation software. Based on the classical nucleation theory and the Kampmann-Wagner Numerical model, the dynamic evolution of the particle diameter of different precipitates in the matrix over time is simulated to obtain the particle size distribution curves of each precipitate type. The largest simulated precipitate type and the largest simulated precipitate size are obtained through the particle size distribution curve; the largest precipitate size in a typical sample is obtained by using transmission electron microscopy as the measured maximum precipitate size; the model parameters are calibrated based on the measured maximum precipitate size to control the deviation between the simulation results and the measured results within a preset range. Using the calibrated model, the average particle diameter of the enhanced precipitate at the end of the thermal cycle is extracted as the average size of the enhanced precipitate. Using "average size of enhanced precipitate ≤ 100 nm" as the standard, different combinations of process parameters are screened to obtain the first process window.

[0011] Optionally, the step of establishing a dynamic recrystallization grain size prediction model characterized by Zener-Hollomon parameters based on the thermal cycling curve includes: The characteristic temperature range and corresponding strain rate range are extracted from the thermal cycling curve, and the isothermal compression temperature T and strain rate of the Gleeble thermal simulation test are set accordingly. ; Isothermal constant strain rate hot compression tests were conducted to extract the peak stress σ under each condition. p Substituting this as rheological stress into the hyperbolic sinusoidal Arrhenius constitutive equation: ; in, Strain rate; This refers to the stress level parameter; n Stress index; For the rheological stress, the peak stress is used as the rheological stress here; Q Deformation activation energy; It is the gas constant; For temperature; A These are material constants; α, n, Q, and A were obtained through linear regression analysis; Create the Zener-Hollomon parameter expression: ; Calculate each (T, using the Z-parameter expression) The Z-value under the given conditions was determined, and the hot-compressed samples were characterized by EBSD to statistically average the dynamic recrystallized grain size. ; based on and Linear fitting was performed on the data to obtain the material constants c and m, and a dynamic recrystallization grain size prediction model was established: ; Where c and m are constants related to material properties.

[0012] Optionally, for any combination of process parameters within the first process window, the corresponding deformation temperature and strain rate are extracted, and the Z value is calculated using Equation 2. The Z value is then substituted into the dynamic recrystallization grain size prediction model of Equation 3 to predict the average grain size. The process parameter combinations with a predicted average grain size ≤ 10 μm are selected to form the second process window.

[0013] Secondly, this application provides an integrated preparation method for aluminum alloy friction stir solid-state additive manufacturing and heat treatment based on precise control of precipitated phase and grain size, comprising the following steps: According to the target alloy composition, the raw materials are batched and smelted to obtain the as-cast material, which is then processed to obtain the rod-shaped casting. The rod-shaped casting is subjected to pre-solution treatment; The process parameters for friction stir solid-state additive manufacturing of aluminum alloys are optimized using the method described in the first aspect, and the optimized parameters are used to perform friction stir solid-state additive manufacturing on a pre-solution treated rod casting to obtain an additive part. The resulting additive parts were subjected to cryogenic treatment and multi-stage aging treatment in sequence.

[0014] Optionally, the rod-shaped casting is subjected to pre-solution treatment, including: taking a slice of the rod-shaped casting for differential scanning calorimetry analysis, using the starting temperature of the endothermic peak on the DSC curve as the lower limit of the solution temperature, and the temperature of the intense endothermic peak as the upper limit of the solution temperature; and using the shortest processing time for the remelting of precipitates with particle size greater than a threshold and the grain size change not exceeding a set range as the optimal solution time window.

[0015] Optionally, the cryogenic treatment conditions are as follows: the additive part is placed in a liquid nitrogen environment at -180°C to -220°C for 30-90 minutes.

[0016] Optionally, the multi-stage aging process includes: initially defining the approximate temperature window for aging based on the temperature range corresponding to the precipitation reaction in the DSC; obtaining the maximum size of the precipitated phase under different combinations of aging temperature and aging time by combining precipitation kinetic simulation calculations; and filtering the aging temperature and time based on the target precipitated phase size threshold to obtain multi-stage aging conditions.

[0017] Thirdly, this application provides an aluminum alloy component, characterized in that it is prepared according to the method described in the second aspect.

[0018] Compared with the prior art, the present invention provides at least the following beneficial effects: This invention moves the pre-solution treatment process to the raw material stage, solving the problems of poor solution treatment effect and low efficiency in large and complex components. Secondly, unlike trial-and-error methods relying on experience and numerous experiments, this invention constructs a quantitative prediction model between process parameters, thermal cycling, and precipitate / grain size. This model accurately identifies and suppresses the two main factors leading to softening (precipitate coarsening and grain growth), enabling rapid and selective optimization of process parameters, significantly improving process optimization efficiency and reducing costs. Finally, the introduction of cryogenic and multi-stage aging synergistic strengthening post-treatment techniques promotes the fine and dispersed precipitation of strengthening phases, achieving a simultaneous improvement in the strength and toughness of the components.

[0019] This invention achieves synergistic optimization of aluminum alloy components in terms of production efficiency, manufacturing cost, and overall performance, meeting the stringent requirements for strength, plasticity, and density of key aluminum alloy components in aerospace, rail transportation, and other fields.

[0020] This invention integrates the precise control of precipitates and recrystallization into the entire preparation process. This approach is not limited to aluminum alloys but can also be extended to non-ferrous metals such as magnesium alloys, zinc alloys, and copper alloys, as well as ferrous metals such as austenitic stainless steel. It addresses the need for precipitate or grain size control during the preparation process, resulting in significant economic benefits and broad application prospects. Attached Figure Description

[0021] Figure 1 This is a macroscopic morphology image of the aluminum alloy component prepared in Example 2 of the present invention; Figure 2 Microstructure images of aluminum alloy components prepared for Examples 1 (corresponding to a) and 2 (corresponding to b) of the present invention; Figure 3 The graph shows the mechanical property test results of the aluminum alloy components prepared in Examples 1 and 2 of this invention. Detailed Implementation

[0022] The present invention will now be described in further detail with reference to the accompanying drawings: This invention provides an integrated preparation method for aluminum alloy friction stir solid-phase additive manufacturing and heat treatment based on precise control of precipitated phases and grain size. Its core logic is "pre-solution treatment - solid-phase additive manufacturing process optimization - synergistic strengthening through cryogenics and aging". It includes the following steps: Step 1: Prepare and melt the raw materials according to the target alloy composition to obtain the as-cast material, and then process it to obtain the rod-shaped casting; Specifically, the following steps are taken: Materials are prepared according to the grade and composition requirements of the target aluminum alloy, and then melted and cast to obtain as-cast material. Subsequently, the as-cast material is processed into rod-shaped castings with a diameter of 10mm-30mm and a length of 200mm-400mm through hot extrusion to match the feeding system of the subsequent friction stir solid-phase additive manufacturing equipment.

[0023] Step 2: Perform pre-solution treatment on the rod-shaped casting; Specifically, this includes: performing differential scanning calorimetry (DSC) analysis on slices of the rod-shaped casting. By analyzing the DSC curves, the endothermic peaks of the target strengthening phase are identified, and their phase transformation temperature range is determined. Specifically, the starting temperature of the endothermic peak is used as the lower limit reference for the solution temperature, and the peak temperature of the intense endothermic peak is used as the upper limit reference. Within this range, a solution temperature is selected. Based on this, several samples are held at the selected solution temperature for different times and then quenched. The shortest processing time for remelting precipitates with a size >100nm and a grain size change not exceeding 10% of the original casting (in this embodiment, the precipitate threshold is 100nm, and the set range for grain size change is 10%) is used as the optimal solution time window. Based on the optimal solution temperature and time determined here, the entire rod-shaped raw material is pre-solution treated.

[0024] Step 3: Optimize the process parameters of friction stir solid-state additive manufacturing of aluminum alloy, and use the optimized parameters to perform friction stir solid-state additive manufacturing on the pre-solution treated rod casting to obtain the additive part; The main process parameters for friction stir solid-state additive manufacturing are as follows: spindle speed during the preheating stage is 250-600 r / min, and preheating time is 5-50 s; spindle speed during the deposition stage is 250-600 r / min, tool head feed speed is 50-300 mm / min, and feed speed is 80-500 mm / min.

[0025] Methods for optimizing process parameters in friction stir solid-state additive manufacturing of aluminum alloys include: S1: Obtain the thermal cycling curves of the substrate material during the friction stir solid-state additive manufacturing process under different process parameters; S2: Based on the thermal cycling curve, precipitation kinetics simulation is performed to predict the average size of the enhanced precipitate under each process parameter, and the first process window is selected based on the criterion that "the average size of the enhanced precipitate is ≤100nm". S3: Based on the aforementioned thermal cycling curve, establish a dynamic recrystallization grain size prediction model characterized by Zener-Hollomon parameters; S4: Using the dynamic recrystallization grain size prediction model, predict the average grain size corresponding to each combination of process parameters within the first process window, and use "average grain size ≤ 10μm" as the criterion to perform a second screening of the first process window to obtain an optimized second process window.

[0026] In one possible embodiment, S1: Obtain the thermal cycling curves of the substrate material during the friction stir solid additive manufacturing process under different process parameters, including: pre-processing a blind hole array on an aluminum alloy substrate and embedding thermocouples, conducting multiple sets of friction stir solid additive manufacturing experiments under typical process parameters, and monitoring and recording the thermal cycling curves and peak temperatures of the substrate in real time.

[0027] Specifically, on an aluminum alloy substrate, 3-5 blind holes arranged in a one-dimensional array are machined using a micro drill bit. The holes have a diameter of 0.8-1.5 mm and a depth of 1.0-6.0 mm. K-type armored thermocouples are pre-embedded in the holes and fixed. 3-5 sets of typical process parameters covering different spindle speeds, feed rates, and material feed rates are set, and friction stir solid-state additive manufacturing experiments are performed. The complete thermal cycle curves and peak temperatures of key locations on the substrate during the additive manufacturing process are acquired and recorded in real time.

[0028] In one possible embodiment, S2: Based on the thermal cycling curve, precipitation kinetics simulation is performed to predict the average size of the enhanced precipitate under various process parameters, and a first process window is selected based on the criterion of "average size of enhanced precipitate ≤ 100nm", including: Input the alloy composition (obtained from step one) and thermal cycling curve into the precipitation kinetics calculation software (the kinetics calculation software is MatCalc or TC-PRISMA). Based on the classical nucleation theory and the Kampmann-Wagner Numerical model (KWN model), simulate the kinetic evolution of the particle diameter of different precipitates in the matrix over time to obtain the particle size distribution curves of each precipitate type. The largest simulated precipitate type and the largest simulated precipitate size are obtained through the particle size distribution curve; the largest precipitate size in a typical sample is obtained by using transmission electron microscopy as the measured maximum precipitate size; the model parameters are calibrated based on the measured maximum precipitate size to control the deviation between the simulation results and the measured results within a preset range. Using the calibrated model, the average particle diameter of the enhanced precipitate at the end of the thermal cycle is extracted as the average size of the enhanced precipitate. Using "average size of enhanced precipitate ≤ 100 nm" as the standard, different combinations of process parameters are screened to obtain the first process window.

[0029] In one possible embodiment, S3: The step of establishing a dynamic recrystallization grain size prediction model characterized by Zener-Hollomon parameters based on the thermal cycling curve includes: The characteristic temperature range and corresponding strain rate range are extracted from the thermal cycling curve, and the isothermal compression temperature T and strain rate of the Gleeble thermal simulation test are set accordingly. ; Using a Gleeble thermal simulation testing machine, isothermal constant strain rate hot compression tests were conducted on specimens (e.g., Φ8mm×12mm or Φ10mm×15mm) after pre-solidification treatment in step two to obtain true stress-true strain curves under different deformation conditions. The peak stress σ under each condition was extracted. p Substituting this as rheological stress into the hyperbolic sinusoidal Arrhenius constitutive equation: ; in, Strain rate; This refers to the stress level parameter; n Stress index; For the rheological stress, the peak stress is used as the rheological stress here; Q Deformation activation energy; It is the gas constant; For temperature; A These are material constants; α, n, Q, and A were obtained through linear regression analysis; Create the Zener-Hollomon parameter expression: ; Calculate each (T, using the Z-parameter expression) The Z-value under the given conditions was determined, and the hot-compressed samples were characterized by EBSD to statistically average the dynamic recrystallized grain size. ; based on and Linear fitting was performed on the data to obtain the material constants c and m, and a dynamic recrystallization grain size prediction model was established: .

[0030] Where c and m are constants related to material properties.

[0031] Using this model, the recrystallization grain size of the friction stir solid-state additive manufacturing process under different process parameters can be simulated and predicted. For any combination of process parameters within the first process window, the corresponding deformation temperature and strain rate are extracted, Equation 2 is used to calculate the Z value, and then the Z value is substituted into the dynamic recrystallization grain size prediction model in Equation 3 to predict the average grain size. The process parameter combinations with a predicted average grain size ≤ 10 μm are selected to obtain the second process window that simultaneously satisfies the dual constraints of precipitated phase size and grain size, thus completing the optimization.

[0032] Optionally, the process also includes testing the hardness of the sample according to standard GB / T 4340.1-2024. If the average hardness value is better than that of a sample of the same material prepared using the conventional "solid-phase additive manufacturing-solution-aging" route, the process parameters are confirmed to be feasible; otherwise, the experimental data is fed back to calibrate the parameters of the precipitation kinetics model and recrystallization model, and the above optimization process is iterated again until the required process parameters are obtained.

[0033] Step 4: Perform cryogenic treatment and multi-stage aging treatment on the obtained additive parts in sequence.

[0034] The cryogenic treatment conditions are as follows: the additive part is placed in a liquid nitrogen environment at -180℃ to -220℃ and held for 30-90 minutes. The cryogenic treatment aims to introduce higher dislocation density and residual stress through volume shrinkage effect, thereby increasing nucleation sites for the subsequent aging process.

[0035] The multi-stage aging treatment includes: initially defining the approximate temperature window for aging based on the temperature range corresponding to the precipitation reaction in DSC; calculating the maximum size of the precipitated phase under different combinations of aging temperatures and aging times using precipitation kinetic simulation; and selecting the aging temperature and time based on the target precipitated phase size threshold to obtain multi-stage aging treatment conditions (wherein the multi-stage aging treatment includes low-temperature pre-aging and medium-temperature main aging, and the aging temperature and time parameters for low-temperature pre-aging and medium-temperature main aging are obtained through the above method). The additive material is then aged according to the selected multi-stage aging treatment conditions to promote the fine and dispersed precipitation of the strengthening precipitate, simultaneously improving strength and toughness. After the aging treatment is completed, the aluminum alloy component of this invention is obtained.

[0036] After preparation, the precipitate size and grain size were characterized using electron backscatter diffraction (EBSD) and scanning electron microscopy (SEM), respectively. Simultaneously, according to standard GB / T 228.1-2021, samples were taken from the component using a universal testing machine for room temperature tensile testing to obtain key mechanical properties such as tensile strength, yield strength, and elongation after fracture. This verified whether the following conditions were met: average grain size ≤ 10 μm, average size of strengthening precipitates ≤ 100 nm, and overall strength and toughness (the product of tensile strength and elongation) improved compared to components prepared from the same alloy using the conventional "solid-phase additive manufacturing-solution-aging" route.

[0037] Example 1

[0038] This embodiment provides a method for preparing 6061 aluminum alloy components using the conventional route of "solid-phase additive manufacturing - solution treatment - aging". The steps are as follows: Step 1: Raw material preparation Based on the alloy composition, the raw materials were designed and smelted to prepare 6061 aluminum alloy as-cast material, which was then hot-extruded into rod-shaped materials with a diameter of 10 mm and a length of 350 mm, and used as raw materials for friction stir solid-phase additive manufacturing.

[0039] Step 2: Stir-friction solid-state additive manufacturing Additive manufacturing was performed using a friction stir solid-state additive manufacturing system with a shoulder diameter of 46 mm and a layer height of 2 mm. The additive manufacturing process parameters were as follows: During the preheating stage, the spindle speed was 400 r / min, and the preheating time was 20 s. During the deposition stage, the spindle speed was 400 r / min, the travel speed was 175 mm / min, and the feed speed was 100 mm / min.

[0040] Step 3: Solution treatment + aging followed by heat treatment The plate-shaped component obtained by additive manufacturing was placed in a resistance furnace for solution treatment. The solution treatment parameters were: heating temperature 520℃, holding time 2h, and after holding, it was quickly removed and water-quenched. The solution-treated component was then placed in an aging furnace for artificial aging treatment. The aging parameters were: heating temperature 160℃, holding time 12h, and after holding, it was removed and air-cooled to room temperature to obtain the aluminum alloy component.

[0041] Step 4: Organizational Performance Testing and Comprehensive Evaluation The aluminum alloy components were inspected. The precipitate size and grain size were characterized and analyzed using SEM and EBSD. The results are shown in [Figure number missing]. Figure 2 Simultaneously, tensile mechanical properties were tested using a universal testing machine. The results showed that the average size of the precipitated phase in the additive component after solution treatment and aging was 94 nm, the average grain size was 13.3 μm, the average tensile strength was 218.2 MPa, and the average elongation was 17.8%.

[0042] Example 2

[0043] This embodiment provides an integrated method for preparing 6061 aluminum alloy components using friction stir solid-state additive manufacturing and heat treatment based on precise control of precipitates and grain size, including the following steps: Step 1: Raw material preparation Based on the alloy composition, the raw materials were designed and smelted to prepare 6061 aluminum alloy as-cast material, which was then hot-extruded into rod-shaped materials with a diameter of 10 mm and a length of 350 mm, and used as raw materials for friction stir solid-phase additive manufacturing.

[0044] Step Two: Pre-solution Design and Implementation Differential scanning calorimetry (DSC) analysis was used to determine the solution temperature of the aluminum alloy sample. SEM observation of the changes in intermetallic compounds within the alloy microstructure determined the solution time window. The resulting solution process parameters were: temperature 560 °C, time 2 h. Using these parameters, the raw material from step one was solution-treated followed by water quenching to obtain supersaturated solution-treated bars.

[0045] Step 3: Optimization of Solid-Phase Additive Manufacturing Process During the additive manufacturing process, temperature sensors such as thermocouples are used to measure the temperature in real time, clarifying the relationship between process parameters and heat input (thermal cycle curve). The spindle shoulder diameter in the friction stir additive manufacturing is 46 mm, and the layer height is 2 mm. The optimized process parameters in this embodiment include the spindle rotation speed, feed rate, and travel speed during the deposition stage. The initial process range is as follows: spindle speed 400 r / min, preheating time 20 s during the preheating stage; spindle speed 300 r / min-600 r / min, travel speed 50 mm / min-300 mm / min, and feed rate 150 mm / min-300 mm / min during the deposition stage. Based on the prediction results of precipitation kinetics and grain size models, the range of process parameters is narrowed down to: spindle rotation speed 400 r / min, travel speed 300 mm / min, and feed rate 200 mm / min. The new process has an average hardness of 53 HV, indicating its feasibility.

[0046] Step 4: Post-processing design and implementation For the additively produced plate-shaped component, the component was placed in a liquid nitrogen cryogenic chamber, and liquid nitrogen was introduced to lower the temperature to -196℃ for 60 minutes. After the holding period, the component was removed and allowed to naturally warm to 45℃ in a room temperature environment. Based on the precipitation phase transition temperature obtained from the DSC curve and combined with the precipitation kinetic model, the heat treatment regime was designed, and the multi-stage aging regime was determined to be: 175℃×3h + 160℃ 20h. After aging, the component was cooled to room temperature by air cooling to obtain an aluminum alloy component with the following morphology. Figure 1 As shown.

[0047] Step 5: Tissue Performance Testing and Comprehensive Evaluation A comprehensive evaluation of the microstructure and properties of aluminum alloy components was conducted. Characterization analysis of precipitate size and grain size was performed using SEM and EBSD. The results are shown in [Figure number missing]. Figure 2 Simultaneously, tensile mechanical properties were tested using a universal testing machine. The measured average size of the precipitated phases in the aluminum alloy was 67 nm, the average grain size was 9.2 μm, the average tensile strength was 256 MPa, and the average elongation was 22.8%, all meeting the requirements for microstructure and performance indicators. These results demonstrate that the newly proposed integrated preparation technology of aluminum alloy using friction stir solid-state additive manufacturing combined with heat treatment is effective for 6061 aluminum alloy.

[0048] The aluminum alloy components obtained in Examples 1 and 2 were subjected to mechanical property tests (samples were taken from the bottom layer for testing as controlled variables), and the results are shown in the figure. Figure 3 The aluminum alloy components prepared by the method of the present invention (Example 2, shown in the figure as: pre-solution-solid phase additive manufacturing process optimization-post-treatment) have significantly improved mechanical properties compared with the existing conventional process (Example 1, shown in the figure as: solid phase additive manufacturing-solution-aging).

[0049] Example 3

[0050] This embodiment provides an integrated method for preparing 2219 aluminum alloy components using friction stir solid-state additive manufacturing and heat treatment based on precise control of precipitates and grain size, including the following steps: Step 1: Raw material preparation Based on the alloy composition, the raw materials were designed and smelted to prepare 2219 aluminum alloy as-cast material, which was then hot-extruded into rod-shaped materials with a diameter of 10 mm and a length of 350 mm, as raw materials for friction stir solid-phase additive manufacturing.

[0051] Step Two: Pre-solution Design and Implementation Differential scanning calorimetry (DSC) was used to analyze the aluminum alloy samples to determine the solution temperature. The morphology of intermetallic compounds in the alloy microstructure was observed using SEM to determine the solution time window. The solution process parameters were determined to be a temperature of 535℃ and a time of 1 hour. Using these parameters, the raw material from step one was solution-treated followed by water quenching to obtain supersaturated solution-treated bars.

[0052] Step 3: Optimization of Solid-Phase Additive Manufacturing Process Temperature was measured in real time using thermocouples and other temperature sensing devices during the additive manufacturing process to clarify the relationship between process parameters and heat input. The spindle shoulder diameter in the friction stir additive manufacturing process was 46 mm, and the layer height was 2 mm. The optimized process parameters in this embodiment included the spindle rotation speed, feed rate, and travel speed during the deposition stage. The initial process range was as follows: spindle speed 400 r / min and preheating time 20 s during the preheating stage; spindle speed 300 r / min-600 r / min, travel speed 50 mm / min-300 mm / min, and feed rate 150 mm / min-300 mm / min during the deposition stage. Based on the prediction results of precipitation kinetics and grain size models, the range of process parameters was narrowed down to: spindle rotation speed 300 r / min, travel speed 150 mm / min, and feed rate 100 mm / min. The average hardness value of the new process was 72 HV, indicating that the process is feasible.

[0053] Step 4: Post-processing design and implementation For the additively produced plate-shaped component, the component was placed in a liquid nitrogen cryogenic chamber, and liquid nitrogen was introduced to lower the temperature to -196℃, which was then held for 60 minutes. After the holding period, the component was removed and allowed to naturally warm to 45℃ in a room temperature environment. Based on the precipitation phase transition temperature obtained from the DSC curve and combined with the precipitation kinetic model, the heat treatment regime was designed. The final multi-stage aging regime was determined to be: 220℃ × 20 min + 170℃ × 4 h. After aging, the component was cooled to room temperature by air cooling to obtain the aluminum alloy component.

[0054] Step 5: Tissue Performance Testing and Comprehensive Evaluation A comprehensive evaluation of the microstructure and properties of aluminum alloy components was conducted. The precipitate size and grain size were characterized using SEM and EBSD scanning, and tensile mechanical properties were tested using a universal testing machine. The measured average precipitate size was 45 nm, the average grain size was 5.4 μm, the average tensile strength was 425 MPa, and the average elongation was 14.5%, all meeting the microstructure and performance requirements. These results indicate that the newly proposed integrated friction stir solid-state additive manufacturing technology combined with heat treatment is effective for 2219 aluminum alloy.

[0055] This invention develops an integrated preparation technology for aluminum alloy friction stir solid-phase additive manufacturing and heat treatment based on precise control of precipitated phases and grain size. It innovatively proposes a process route of "pre-solution-solid-phase additive manufacturing process optimization-deep cryogenic and multi-stage aging synergistic strengthening", which realizes simultaneous improvement in production efficiency, manufacturing cost and comprehensive performance of aluminum alloy components, and meets the stringent requirements of key aluminum alloy components in aerospace, transportation and other fields for strength, plasticity and density.

Claims

1. A method for optimizing process parameters in friction stir solid-state additive manufacturing of aluminum alloys, characterized in that, Includes the following steps: Obtain the thermal cycling curves of the substrate material during the friction stir solid-state additive manufacturing process under different process parameters; Based on the thermal cycling curve, precipitation kinetics simulation was performed to predict the average size of the enhanced precipitate under various process parameters, and the first process window was selected based on the criterion that "the average size of the enhanced precipitate is ≤100nm". Based on the aforementioned thermal cycling curve, a dynamic recrystallization grain size prediction model characterized by Zener-Hollomon parameters is established. Using the dynamic recrystallization grain size prediction model, the average grain size corresponding to each combination of process parameters within the first process window is predicted. Then, the first process window is screened a second time based on the criterion of "average grain size ≤ 10 μm" to obtain an optimized second process window.

2. The method for optimizing process parameters in friction stir solid-state additive manufacturing of aluminum alloys according to claim 1, characterized in that, The process of obtaining the thermal cycling curves of the substrate material under different process parameters during the friction stir solid additive manufacturing process includes: pre-processing a blind hole array on an aluminum alloy substrate and embedding thermocouples, conducting multiple sets of friction stir solid additive manufacturing experiments under typical process parameters, and monitoring and recording the thermal cycling curves and peak temperatures of the substrate in real time.

3. The method for optimizing process parameters in friction stir solid-state additive manufacturing of aluminum alloys according to claim 1, characterized in that, Based on the aforementioned thermal cycling curve, precipitation kinetics simulation was performed to predict the average size of the enhanced precipitate under various process parameters. Using "average size of enhanced precipitate ≤ 100 nm" as the criterion, a first process window was selected, including: The alloy composition and thermal cycling curves are input into the precipitation kinetics calculation software. Based on the classical nucleation theory and the Kampmann-Wagner Numerical model, the dynamic evolution of the particle diameter of different precipitates in the matrix over time is simulated to obtain the particle size distribution curves of each precipitate type. The largest simulated precipitate type and the largest simulated precipitate size are obtained through the particle size distribution curve; the largest precipitate size in a typical sample is obtained by using transmission electron microscopy as the measured maximum precipitate size; the model parameters are calibrated based on the measured maximum precipitate size to control the deviation between the simulation results and the measured results within a preset range. Using the calibrated model, the average particle diameter of the enhanced precipitate at the end of the thermal cycle is extracted as the average size of the enhanced precipitate. Using "average size of enhanced precipitate ≤ 100 nm" as the standard, different combinations of process parameters are screened to obtain the first process window.

4. The method for optimizing process parameters in friction stir solid-state additive manufacturing of aluminum alloys according to claim 1, characterized in that, The establishment of a dynamic recrystallization grain size prediction model based on the thermal cycling curve and characterized by Zener-Hollomon parameters includes: The characteristic temperature range and corresponding strain rate range are extracted from the thermal cycling curve, and the isothermal compression temperature T and strain rate of the Gleeble thermal simulation test are set accordingly. ; Isothermal constant strain rate hot compression tests were conducted to extract the peak stress σ under each condition. p Substituting this as rheological stress into the hyperbolic sinusoidal Arrhenius constitutive equation: ； in, Strain rate; This refers to the stress level parameter; n Stress index; For the rheological stress, the peak stress is used as the rheological stress here; Q Deformation activation energy; It is the gas constant; For temperature; A These are material constants; α, n, Q, and A were obtained through linear regression analysis; Create the Zener-Hollomon parameter expression: ； Calculate each (T, using the Z-parameter expression) The Z-value under the given conditions was determined, and the hot-compressed samples were characterized by EBSD to statistically average the dynamic recrystallized grain size. ; based on and Linear fitting was performed on the data to obtain the material constants c and m, and a dynamic recrystallization grain size prediction model was established: ； Where c and m are constants related to material properties.

5. The method for optimizing process parameters in friction stir solid-state additive manufacturing of aluminum alloys according to claim 4, characterized in that, For any combination of process parameters within the first process window, extract the corresponding deformation temperature and strain rate, calculate the Z value using Equation 2, and then substitute the Z value into the dynamic recrystallization grain size prediction model of Equation 3 to predict the average grain size; select process parameter combinations with a predicted average grain size ≤ 10 μm to form the second process window.

6. A method for integrating friction stir solid-state additive manufacturing and heat treatment of aluminum alloys based on precise control of precipitated phases and grain size, characterized in that, Includes the following steps: According to the target alloy composition, the raw materials are batched and smelted to obtain the as-cast material, which is then processed to obtain the rod-shaped casting. The rod-shaped casting is subjected to pre-solution treatment; The process parameters of friction stir solid-state additive manufacturing of aluminum alloy are optimized by the method described in any one of claims 1-5, and the optimized parameters are used to perform friction stir solid-state additive manufacturing on a pre-solution treated rod casting to obtain an additive part. The resulting additive parts were subjected to cryogenic treatment and multi-stage aging treatment in sequence.

7. The integrated preparation method of aluminum alloy friction stir solid-phase additive manufacturing and heat treatment based on precise control of precipitated phase and grain size according to claim 6, characterized in that, The pre-solution treatment of the rod-shaped casting includes: taking a slice of the rod-shaped casting for differential scanning calorimetry analysis, using the starting temperature of the endothermic peak on the DSC curve as the lower limit of the solution temperature, and the temperature of the intense endothermic peak as the upper limit of the solution temperature; and using the shortest treatment time for the remelting of precipitates with particle size greater than a threshold and grain size change not exceeding a set range as the optimal solution time window.

8. The integrated preparation method of aluminum alloy friction stir solid-phase additive manufacturing and heat treatment based on precise control of precipitated phase and grain size according to claim 6, characterized in that, The cryogenic treatment conditions are as follows: the additive part is placed in a liquid nitrogen environment at -180℃ to -220℃ and kept at that temperature for 30-90 minutes.

9. The integrated preparation method of aluminum alloy friction stir solid-phase additive manufacturing and heat treatment based on precise control of precipitated phase and grain size according to claim 7, characterized in that, The multi-stage aging process includes: initially defining the approximate temperature window for aging based on the temperature range corresponding to the precipitation reaction in the DSC; obtaining the maximum size of the precipitated phase under different combinations of aging temperature and aging time by combining precipitation kinetic simulation calculations; and filtering the aging temperature and time based on the target precipitated phase size threshold to obtain the multi-stage aging conditions.

10. An aluminum alloy component, characterized in that, Prepared by the method according to any one of claims 6-9.

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