A method for quickly optimizing process parameters of FSW of die-cast aluminum alloy

Abstract

The present application relates to the field of die-casting aluminum alloy friction stir welding, and specifically discloses a method for quickly and optimally selecting process parameters of die-casting aluminum alloy FSW, which comprises the following steps: determining a process parameter factor set according to the chemical composition and as-cast structure characteristics of the die-casting aluminum alloy, wherein the process parameter factor set comprises the rotation speed of the stirring head, the welding speed, the pressing force, the geometric shape of the stirring head and the shaft shoulder structure; screening the process parameter factor set by using the Plackett-Burman test method to identify the main factors that have a significant impact on the weld quality; establishing a prediction model based on the main factors and the weld quality evaluation index by using the Box-Behnken Design of the response surface method for quadratic regression modeling; verifying the parameter combination output in the prediction model by using a rapid detection method, wherein the rapid detection method comprises microhardness testing and ultrasonic or radiographic nondestructive testing; correcting the prediction model and iteratively optimizing according to the detection results to obtain the optimal combination of process parameters suitable for the die-casting aluminum alloy.

Description

Technical Field

[0001] This invention relates to the field of friction stir welding of die-cast aluminum alloys, and more specifically to a method for rapidly optimizing the process parameters of FSW for die-cast aluminum alloys. Background Technology

[0002] Die-cast aluminum alloys are widely used in new energy vehicle electronic control housings, automotive structural components, and electronic heat sinks due to their light weight, high forming precision, and cost advantages. Die-cast aluminum alloys typically belong to the high-silicon aluminum alloy system, and their composition design often includes a high proportion of elements such as Si and Fe to improve fluidity. In traditional welding processes, these alloys readily form coarse primary silicon phases, brittle Fe-rich phases, and needle-like compounds, resulting in insufficient weld toughness and ductility. Especially in fusion welding, the problems of internal porosity and hot cracking are particularly prominent, severely limiting the connection and service performance of structural components. However, these alloys also present several significant problems in friction stir welding (FSW):

[0003] Firstly, die-cast aluminum alloys generally contain high levels of silicon and defects such as porosity, which makes welded joints prone to defects such as pores and inclusions, thereby affecting the density and mechanical properties of the weld.

[0004] Secondly, the selection window for welding process parameters is relatively narrow, and different rotation speeds, welding speeds, and downward pressures have a significant impact on the microstructure evolution and properties of the weld zone.

[0005] Third, traditional process development usually relies on a large number of experiments, which is both time-consuming and material-intensive, making it difficult to meet the demand for efficient and stable processes in industrial production.

[0006] In existing technologies, some studies adjust parameters through single-factor experiments or empirical formulas, but these methods often fail to fully reveal the coupling effects of multiple parameters, resulting in low optimization efficiency and difficulty in adapting to different die-cast aluminum alloy grades and complex working conditions. Therefore, there is an urgent need for a method that can quickly screen key parameters, build predictive models, and achieve efficient optimization to improve the development efficiency of welding processes and the reliability of joints. Summary of the Invention

[0007] The purpose of this invention is to provide a method for rapidly optimizing the process parameters of friction stir welding of die-cast aluminum alloys, so as to solve the problems of long parameter development cycle, high experimental cost and poor welding stability in the prior art.

[0008] The objective of this invention can be achieved through the following technical solutions:

[0009] A method for rapidly optimizing FSW process parameters for die-cast aluminum alloys, the method comprising:

[0010] S0. Step 1, Scope of application: The thickness of the upper plate is suitable for welding 3-16mm, the thickness of the lower plate is suitable for welding 10-50mm, the applicable range of welding speed is 50-200mm / min, the applicable range of rotation speed is 600-1200r / min, and the applicable range of downward pressure is 3-8KN.

[0011] S1. Second step, parameter factor establishment: Combine the chemical composition and as-cast microstructure characteristics of the die-cast aluminum alloy to determine the process parameter factor set, which includes stirring head rotation speed, welding speed, downward pressure, stirring head geometry and shoulder structure.

[0012] S2. The third step is to use the Plackett-Burman test method to quickly identify the main parameters that have a significant impact on weld performance under multi-factor conditions and reduce invalid experiments. By using the Plackett-Burman test method, the set of process parameter factors is screened, and the main factors that have a significant impact on weld quality are welding speed, rotation speed, and downward pressure.

[0013] S3. Fourth step: Modeling and optimization, using Box-Behnken Design (BBD) based on response surface methodology for experiments;

[0014] Significance assessment is required during the experiment.

[0015] Fifth step, determine whether the selected factor is significant: such as Figure 1 The model diagram shown below indicates that the Model must be significant. A value of 0-0.01 indicates that the selected factor is highly significant, and a value between 0.01-0.05 indicates that the selected factor has a significant effect. The LackofFit below must show the non-significant values.

[0016] Step 6: The analysis shows that the established response surface model is not significant. There are several possible reasons for this:

[0017] Insufficient or low-quality data: Response surface methodology (RSM) requires sufficient sample data to ensure the accuracy and reliability of the model. If the data volume is too small or the data quality is low (e.g., outliers or missing values ​​exist), the model results may be insignificant. Inappropriate model assumptions: RSM is typically based on assumptions such as a linear relationship between the response variable and independent variables, and that the data follows a normal distribution. If these assumptions do not hold true in your dataset, the resulting RSM model may be insignificant. Inappropriate selection of independent variables: In RSM, selecting appropriate independent variables is crucial for building a significant model. If the selected independent variables do not match the actual situation or other important independent variables are not considered, the model's significance may decrease. Consider increasing the sample size, improving data quality, reselecting independent variables, optimizing the experimental design, and performing appropriate preprocessing and denoising on the data to improve the significance of the RSM model. Additionally, other modeling methods or more flexible nonlinear models can be used to capture potential nonlinear relationships.

[0018] The modeling method used in this study is quadratic regression modeling, which establishes a predictive model based on the relationship between key factors and weld quality evaluation indicators; based on step S3, specifically:

[0019] S4. Verify the parameter combination output in the prediction model through rapid detection methods, including microhardness testing and ultrasonic or radiographic non-destructive testing.

[0020] Further rapid verification was conducted: welding tests were carried out under the predicted optimal parameters, and the joint quality was verified by microhardness distribution, X-ray detection, and metallographic analysis; if the results deviated from the prediction, the model was further optimized through iterative correction.

[0021] S5. Based on the test results, revise the prediction model and iteratively optimize it to obtain the optimal combination of process parameters suitable for die-cast aluminum alloys.

[0022] Preferably, the stirring head rotation speed is in the range of 600-1200 r / min, the welding speed is in the range of 50-200 mm / min, the preferred range of the downward pressure is 3-8 KN, and the stirring head geometry and shoulder structure are tapered threads, three-face threads, or composite stirring heads with shoulder grooves.

[0023] Preferably, the Plackett-Burman experimental method in step S2 employs an 8-factor-12 level design to rapidly identify the main process parameters affecting weld strength and forming quality within a limited number of experiments.

[0024] Preferably, step S3 further includes obtaining the optimal combination of the main factors through a multi-objective optimization algorithm, and conducting a limited number of experiments based on the data generated by the optimal combination of the main factors.

[0025] Preferably, the rapid detection method in step S4 also includes tissue observation under a metallographic microscope to assist in analyzing the distribution of pores and defects.

[0026] Preferably, the objective function parameters of the prediction model are set to maximize tensile strength and minimize porosity; the prediction model is a multiple quadratic regression model, with the following formula:

[0027]

[0028] in, As an indicator for evaluating weld quality, This is the first process parameter factor. This is the second process parameter factor; These are the regression coefficients; This is the error term; For the regression intercept, For the first Linear regression coefficients of the independent variables; For the first Linear regression coefficients of the independent variables; For the first The first independent variable and the second independent variable Regression coefficients of interaction effects among independent variables;

[0029] It is the sum of linear effect terms, which captures the individual, linear effect of each factor on the response outcome;

[0030] This is the sum of the square effect terms or curvature effect terms; this part captures the nonlinear effect of each factor itself; for example, as welding speed increases, tensile strength may first increase and then decrease, and this "bending" relationship is described by this term. The sign of the coefficient β_ii determines whether the curve opens upwards or downwards.

[0031] It is the sum of the interaction effect terms (usually) < (to avoid double counting); this part indicates that the synergistic or antagonistic effect between two factors has been captured, that is, the degree of influence of one factor on Y depends on the level of the other factor.

[0032] The above formula serves as the core of the entire optimization method's calculations. Specifically:

[0033] First, a model was established: weld quality data under different combinations of process parameters were collected using Box-Behnken experimental design;

[0034] Then, the model is fitted: using statistical methods such as the least squares method, all coefficients are calculated based on the experimental data. , , , The specific values ​​of ( ) are determined; the process of quadratic regression modeling is implemented.

[0035] Next, the model is used: once the coefficients are determined, this mathematical formula becomes a predictive model. By inputting a new set of process parameters (e.g., welding speed = 120 mm / min, rotation speed = 1000 rpm, downward pressure = 8 kN), the model will output a predicted weld quality result (e.g., predicted tensile strength = 280 MPa, predicted porosity = 0.5%).

[0036] Finally, the optimization objective: the objective function is "to maximize tensile strength ( ) and minimize porosity ( Applying optimization algorithms (such as the expectation function method) to this mathematical model allows for the rapid "simulation" of more parameter combinations on a computer, and the selection of combinations that simultaneously achieve the desired result is found. maximum, The minimum optimal combination of process parameters significantly reduces the number of actual experiments, achieving efficient optimization.

[0037] Preferably, the significance of the selected factor is determined based on the prediction model. The Model is significant, with a value of 0-0.01 indicating that the selected factor is very significant, and a value between 0.01-0.05 indicating that the selected factor has a significant effect. The LackofFit below indicates that the selected factor is not significant.

[0038] Preferably, the weld quality evaluation indicators include one or more of tensile strength, hardness distribution, porosity, and forming integrity; the weld quality evaluation indicators also include the forming integrity of the joint surface, the grain size of the stirring zone, and the hardness gradient of the heat-affected zone.

[0039] Preferably, the optimal combination of process parameters in step S5 includes specific numerical ranges for welding speed, rotation speed, and downward pressure, which is suitable for industrial applications of friction stir welding of die-cast aluminum alloys.

[0040] The beneficial effects of this invention are:

[0041] (1) Reduce the number of experiments: By combining experimental design with mathematical modeling, the high time consumption of the traditional single-factor method is avoided, and the efficiency is improved by more than 50%;

[0042] (2) High prediction accuracy: By using mechanical properties (tensile strength, hardness gradient), weld defects (porosity, crack rate) and microstructure uniformity as comprehensive indicators, the prediction result has an error of less than 5% compared with the actual result;

[0043] (3) Wide applicability: It is not only applicable to high silicon die-cast aluminum alloys, but can also be extended to other die-cast aluminum materials with porosity sensitivity;

[0044] (4) Significant industrial value: It can be used for the development of welding processes for products such as inverter housings, radiators, and automotive structural parts for new energy vehicles, significantly shortening the process verification cycle.

[0045] Of course, any product implementing this invention does not necessarily need to achieve all the advantages described above at the same time. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the steps of a method for rapidly optimizing FSW process parameters for die-casting aluminum alloys according to the present invention.

[0048] Figure 2 This is a comparison and analysis chart of the predicted and actual values ​​of this invention;

[0049] Figure 3 This is a perturbation diagram showing the sensitivity and influence trend of each factor of the present invention to the response variable;

[0050] Figure 4 This is a contour diagram illustrating the interaction between the two factors in this invention.

[0051] Figure 5 This is a three-dimensional response surface diagram of the interaction between the two factors in this invention. Detailed Implementation

[0052] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] Please see Figure 1 As shown, this invention provides a method for rapidly optimizing the process parameters of friction stir welding (FSW) on die-cast aluminum alloys. The specific implementation process is as follows:

[0054] Example 1:

[0055] This example selects the optimal process parameters for lap friction stir welding of A360 and AlSi10Mg die-cast aluminum alloys.

[0056] Material preparation: Upper plate material: A360 die-cast aluminum alloy (4mm thick); Lower plate material: AlSi10Mg die-cast aluminum alloy (20mm thick).

[0057] Parameter factor establishment: Based on the compositional characteristics and as-cast microstructure of die-cast aluminum alloys, the main process factors affecting welding quality are selected, including stirring head rotation speed, welding speed, downward pressure, stirring head geometry and shoulder structure;

[0058] Process factors selection: The Plackett-Burman test method was used to select the stirring head speed (600-1200 r / min), welding speed (50-200 mm / min), and downward pressure (3-8 KN).

[0059] Modeling and Optimization: This paper adopts Box-Behnken Design (BBD) from the Response Surface Methodology (RSM).

[0060] A quadratic regression model was performed; the objective function parameters were set as tensile strength and minimizing porosity.

[0061] Input the process factor range, and the value will be automatically generated;

[0062] Experiments and tests are conducted on product values ​​based on automatically generated values;

[0063] Analyze the variance plot, statistical data plot, disturbance plot, contour plot, and three-dimensional response surface plot of the obtained quadratic model, and comprehensively analyze whether it is reasonable.

[0064] The final equation in terms of actual factors is obtained;

[0065] The optimal combination was obtained by genetic algorithm optimization: stirring head speed 950 r / min, welding speed 120 mm / min, and downward pressure 5.5 KN;

[0066] Rapid verification: The tensile test showed that the joint efficiency reached 85%, X-ray inspection showed that the porosity was less than 0.3%, and microhardness testing showed that the hardness of the weld stirring zone was uniform and the hardness gradient was less than 10%.

[0067] Results and Applications: This embodiment shows that the method can obtain better process parameters in only about 20 experiments.

[0068] Example 2:

[0069] In this example, the optimal process parameters for butt stir friction welding of AlCu4Ti and AlSiCu3 die-cast aluminum alloys are selected.

[0070] Material preparation: Upper plate material: AlCu4Ti die-cast aluminum alloy (thickness 6mm); Lower plate material: AlSiCu3 die-cast aluminum alloy (thickness 26mm);

[0071] Parameter factor establishment: Based on the compositional characteristics and as-cast microstructure of die-cast aluminum alloys, the main process factors affecting welding quality are selected, including stirring head rotation speed, welding speed, downward pressure, stirring head geometry and shoulder structure;

[0072] Process factors selection: The Plackett-Burman test method was used to select the stirring head speed (600-1200 r / min), welding speed (50-200 mm / min), and downward pressure (3-8 KN).

[0073] Modeling and Optimization: This paper adopts Box-Behnken Design (BBD) from the Response Surface Methodology (RSM).

[0074] A quadratic regression model was performed; the objective function parameters were set as tensile strength and minimizing porosity.

[0075] Input the process factor range, and the value will be automatically generated;

[0076] Analyze the variance plot, statistical data plot, disturbance plot, contour plot, and three-dimensional response surface plot of the obtained quadratic model, and comprehensively analyze whether it is reasonable.

[0077] The final equation in terms of actual factors is obtained;

[0078] The optimal combination was obtained by using a genetic algorithm: stirring head speed 800 r / min, welding speed 100 mm / min, and downward pressure 6.0 KN;

[0079] Rapid verification: The tensile test showed that the joint efficiency reached 60%, X-ray inspection showed that the porosity was less than 0.6%, and microhardness testing showed that the hardness of the weld stirring zone was uniform with a hardness gradient of less than 20%.

[0080] Results and Applications: This embodiment shows that the method can obtain better process parameters in only about 20 experiments.

[0081] Example 3:

[0082] In this example, the optimal parameters for the lap friction stir welding process of ADC12 and AlSi12Cu1Fe die-cast aluminum alloy are selected.

[0083] Material preparation: Upper plate material: ADC12 die-cast aluminum alloy (8mm thick); Lower plate material: AlSi10Mg die-cast aluminum alloy (30mm thick).

[0084] Parameter factor establishment: Based on the compositional characteristics and as-cast microstructure of die-cast aluminum alloys, the main process factors affecting welding quality are selected, including stirring head rotation speed, welding speed, downward pressure, stirring head geometry and shoulder structure;

[0085] Process factors selection: The Plackett-Burman test method was used to select the stirring head speed (600-1200 r / min), welding speed (50-200 mm / min), and downward pressure (3-8 KN).

[0086] Modeling and Optimization: This paper adopts Box-Behnken Design (BBD) from the Response Surface Methodology (RSM).

[0087] A quadratic regression model was performed; the objective function parameters were set as tensile strength and minimizing porosity.

[0088] Input the process factor range, and the value will be automatically generated;

[0089] Experiments and tests are conducted on product values ​​based on automatically generated values;

[0090] Analyze the variance plot, statistical data plot, disturbance plot, contour plot, and three-dimensional response surface plot of the obtained quadratic model, and comprehensively analyze whether it is reasonable.

[0091] The final equation in terms of actual factors is obtained;

[0092] The optimal combination was obtained by genetic algorithm optimization: stirring head speed 1000 r / min, welding speed 130 mm / min, and downward pressure 4.0 KN;

[0093] Rapid verification: The tensile test showed that the joint efficiency reached 70%, X-ray inspection showed that the porosity was less than 0.5%, and microhardness testing showed that the hardness of the weld stirring zone was uniform with a hardness gradient of less than 15%.

[0094] Results and Applications: This embodiment demonstrates that the method can obtain optimal process parameters in only about 20 experiments; Result Verification:

[0095] 1. Table 1 is as follows:

[0096] Table 1

[0097]

[0098] Table 1 above is the analysis of variance table for the quadratic model. The model f-value of 39.72 indicates that the model is significant. Due to noise, there is only a 0.01% chance of such a large f-value. A p-value less than 0.0500 indicates that the model terms are significant. In this case, C, A, and B are important model terms. A p-value greater than 0.1000 indicates that the model terms are not important. The f-value of LackofFit is 0.18, which means that LackofFit is not significant relative to the pure error, with a 90.45% chance of this.

[0099] 2. Table 2 is as follows:

[0100] Table 2

[0101]

[0102] Table 2 above presents the statistical data conclusions. R² (R-squared): R² is a statistical indicator that measures the goodness of fit of a model. It represents the proportion of variance of the dependent variable that can be explained by the independent variables. The value of R² ranges from 0 to 1; the closer to 1, the better the model fits the data. In experiments, R² is usually required to reach a certain threshold, such as 0.7 or 0.8 or higher, to ensure that the model can well explain the variability of the data. Adjusted R²: Adjusted R² is a correction of R², taking into account the number of independent variables and sample size in the model. Compared with R², adjusted R² is more stringent and can reduce the impact of overfitting caused by an increase in the number of independent variables. In experiments, in addition to requiring a high R², it is also necessary to ensure that adjusted R² reaches a certain threshold to avoid over-interpreting the model. Predictive R²: Predictive R² is an indicator that measures the predictive ability of a model. It evaluates the model's performance on new data through cross-validation, that is, the model's predictive accuracy on unused data. In experiments, the predicted R-value can be used to verify the model's generalization ability. It typically needs to reach a certain threshold, such as above 0.6, to ensure the model has good predictive power. AdeqPrecision: AdeqPrecision is a metric used in response surface methodology to evaluate the accuracy of model predictions. It represents the comparison error between the model's predicted values ​​and the actual observed values. In experiments, AdeqPrecision needs to reach a certain threshold, usually above 4, to ensure the model's predictive ability is sufficiently accurate.

[0103] 3. Table 2 is as follows:

[0104] Table 3

[0105]

[0106] Table 3 above is the final equation data table represented by the actual factors. The actual factor for weld quality is +7.22. The equation represented by the coded factors can be used to predict the weld quality for each factor (A, B, C, AB, AC, BC, A). 2 B 2 C 2 The response at a given level; by default, the higher level of the factor is coded as +1 and the lower level as -1.

[0107] By comparing the coefficients of each factor, the coding equation helps to determine the relative influence of each factor;

[0108] During the verification process:

[0109] A limited number of experiments are conducted based on the data generated from the selected factors;

[0110] Perform analysis;

[0111] Results analysis: In the statistical data conclusion table, the values ​​of R², AdjustedR², and PredictedR² are close to 1, and the AdeqPrecision value is greater than 4, indicating a better model fit. The predicted R is 0.9369, which is basically consistent with the adjusted R of 0.9561, meaning the difference is less than 0.2. AdeqPrecision measures the signal-to-noise ratio; a ratio greater than 4 is ideal. Your ratio of 16.346 indicates sufficient signal strength, and this model can be used for navigation design space.

[0112] Further, image analysis:

[0113] in, Figure 2 The left side shows the predicted value vs. the actual value: to verify the goodness of fit of the regression model. Points to note: whether the points are close to the diagonal (closeness indicates model reliability), whether there are few points with large deviations, whether they are randomly distributed, whether the R² value is close to 1, and whether the difference between Adj-R² and Pred-R² is reasonable.

[0114] Figure 3 For disturbance plots: show the sensitivity and influence trends of each factor on the response variable. Points to note: which factors have a large impact on the response (factors with steep curves), which factors have a small impact (factors with flatter curves), and the variation pattern of the response parameters near the center point.

[0115] Figure 4 Contour lines: If the contour lines are elliptical, the interaction (mutual influence) between the two factors is stronger; if they are circular, the opposite is true. The greater the density of contour lines, the more significant the influence of the corresponding factors (equivalent to the projection of a 3D surface plot).

[0116] Figure 5It is a 3D response surface plot: showing the effect of the interaction between two factors on the response variable; points to note: whether the surface has obvious "peaks" or "valleys" (i.e., the optimal region), whether the interaction between the two factors is significant (if the surface is curved and non-planar, the interaction is significant), and roughly in which region the optimal combination of process parameters is located.

[0117] Optimized process window: Through optimized calculations and rapid experimental feedback, the optimal combination of process parameters applicable to different grades of die-cast aluminum alloys is obtained, and a generalizable welding parameter database is formed.

[0118] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0119] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended documents. In some cases, the actions or steps described in this application may be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this application, they should all fall within the protection scope of the present invention.

Claims

1. A method for rapidly optimizing FSW process parameters for die-casting aluminum alloys, characterized in that, The method includes: S1. Based on the chemical composition and as-cast microstructure characteristics of the die-cast aluminum alloy, determine the process parameter factor set, which includes stirring head rotation speed, welding speed, downward pressure, stirring head geometry and shoulder structure. S2. Using the Plackett-Burman experimental method, the set of process parameter factors was screened to identify the main factors that have a significant impact on weld quality. The main factors are welding speed, rotation speed and downward pressure. S3. Use Box-Behnken Design with response surface methodology to perform quadratic regression modeling and establish a prediction model based on the relationship between the main factors and weld quality evaluation indicators. S4. Verify the parameter combination output in the prediction model using rapid detection methods, including microhardness testing and ultrasonic or radiographic non-destructive testing. S5. Based on the test results, revise the prediction model and iteratively optimize it to obtain the optimal combination of process parameters suitable for die-cast aluminum alloys; The objective function parameters of the prediction model are set to maximize tensile strength and minimize porosity; the prediction model is a multiple quadratic regression model, and the formula is: in, As an indicator for evaluating weld quality, This is the first process parameter factor. This is the second process parameter factor; These are the regression coefficients; This is the error term; For the regression intercept, For the first Linear regression coefficients of the independent variables; For the first Linear regression coefficients of the independent variables; For the first The first independent variable and the second independent variable Regression coefficients of the interaction between the independent variables.

2. The method for rapidly optimizing FSW process parameters for die-casting aluminum alloys according to claim 1, characterized in that, The stirring head rotation speed ranges from 600 to 1200 r / min, the welding speed ranges from 50 to 200 mm / min, the downward pressure ranges from 3 to 8 KN, and the stirring head geometry and shoulder structure are tapered threads, three-faced threads, or composite stirring heads with shoulder grooves.

3. The method for rapidly optimizing FSW process parameters for die-casting aluminum alloys according to claim 1, characterized in that, The Plackett-Burman test method described in step S2 employs an 8-factor-12 level design to rapidly identify the key process parameters affecting weld strength and forming quality within a limited number of experiments.

4. The method for rapidly optimizing FSW process parameters for die-casting aluminum alloys according to claim 1, characterized in that, The S3 step also includes obtaining the optimal combination of the main factors through a multi-objective optimization algorithm, and conducting a limited number of experiments based on the data generated by the optimal combination of the main factors.

5. The method for rapidly optimizing FSW process parameters for die-casting aluminum alloys according to claim 1, characterized in that, The rapid detection method in step S4 also includes tissue observation under a metallographic microscope, which is used to assist in analyzing the distribution of pores and defects.

6. The method for rapidly optimizing FSW process parameters for die-casting aluminum alloys according to claim 1, characterized in that, The significance of the selected factor is determined based on the prediction model. The Model is significant, with a value of 0-0.01 indicating that the selected factor is very significant, and a value between 0.01-0.05 indicating that the selected factor has a significant effect. LackofFit indicates that it is not significant.

7. The method for rapidly optimizing FSW process parameters for die-casting aluminum alloys according to claim 1, characterized in that, The weld quality evaluation indicators include one or more of tensile strength, hardness distribution, porosity, and forming integrity; the weld quality evaluation indicators also include the forming integrity of the joint surface, the grain size of the stirring zone, and the hardness gradient of the heat-affected zone.

8. The method for rapidly optimizing FSW process parameters for die-casting aluminum alloys according to claim 1, characterized in that, The optimal combination of process parameters mentioned in step S5 includes specific numerical ranges for welding speed, rotation speed, and downward pressure, and is suitable for industrial applications of friction stir welding of die-cast aluminum alloys.

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