An aluminum alloy electric arc additive process optimization method
By establishing a multi-parameter coupled model using the response surface methodology, the aluminum alloy arc additive manufacturing process was optimized, solving the problems of inaccurate heat input control and mechanical property fluctuations. This resulted in efficient and stable welding effects, applicable to various aluminum alloy materials and welding power source types.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANTONG UNIV
- Filing Date
- 2026-03-05
- Publication Date
- 2026-07-21
AI Technical Summary
Existing aluminum alloy arc additive manufacturing processes suffer from problems such as inaccurate heat input control, non-uniform weld structure, and large fluctuations in mechanical properties. In particular, it is difficult to balance production efficiency and quality in thin plate welding, and there is a lack of systematic multi-parameter coupling models.
A multivariate quadratic regression model of welding process parameters and welding performance indicators was constructed using the response surface methodology. Through multi-parameter coupling optimization, a nonlinear coupling model between welding current, wire feed speed and welding speed was established to achieve scientific optimization.
It improves the consistency of weld formation quality and mechanical properties, significantly reduces porosity and thermal deformation, increases production efficiency, and reduces energy consumption and production costs. It is suitable for various welding power source types and aluminum alloy materials.
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Figure CN122433459A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of aluminum alloy welding and arc additive manufacturing technology, and particularly relates to an optimization method for aluminum alloy arc additive manufacturing process. Background Technology
[0002] 5083 aluminum alloy belongs to the Al-Mg series of rust-resistant aluminum alloys. Due to its high specific strength, good plasticity and toughness, excellent fatigue resistance and corrosion resistance, it has been widely used in the automotive, shipbuilding, and aerospace industries, and is a commonly used lightweight metal material for vehicle body structural components. Currently, for joining thin aluminum alloy sheets, the industry mainly uses tungsten inert gas welding (TIG) and metal inert gas welding (MIG). Although MIG welding has advantages such as high production efficiency and no slag, its high heat input makes it prone to welding deformation, hot cracking, and burn-through when welding thin sheets. Furthermore, its high equipment cost limits its application in high-quality thin sheet welding. In contrast, TIG welding has a stable arc and concentrated heat, making it the preferred process for welding thin aluminum alloy sheets in most industries.
[0003] Although TIG welding is widely used in thin plate welding, the following significant problems still exist in actual production and existing research: (1) It is difficult to balance production efficiency and quality: Traditional TIG welding is limited by the current carrying capacity of tungsten electrodes, resulting in slow welding speed and low production efficiency; if the welding speed is blindly increased, it is easy to cause incomplete fusion; if the current is increased to compensate for the heat input, it is easy to cause excessive thermal deformation, hot cracks and burn-through; (2) Limitations of parameter optimization methods: Existing process optimization research is mostly focused on single-factor analysis or simple trial and error. Most of these studies are conducted under open-loop control or fixed parameters; or although the heat input, current, and current are studied, the parameters are not always optimized. The influence of current voltage on weld quality and mechanical properties is often overlooked, but the interaction and coupling between various process parameters are often ignored; (3) lack of a systematic multi-parameter coupling model: in actual welding process, there is a complex nonlinear interaction relationship (coupling effect) between the three key parameters of welding current, wire feed speed and welding speed; for example, the increase of welding speed will reduce heat input, while the change of wire feed speed will change the cooling rate and volume of the molten pool; the optimization of a single parameter cannot reveal this complex coupling mechanism, making it difficult to obtain the global optimal solution, which makes the welded joint prone to problems such as porosity, coarse structure and large fluctuations in mechanical properties (such as tensile strength).
[0004] The main difficulties in solving the above problems are: the thermal fluid behavior of the molten pool is complex, and small changes in parameters may lead to drastic fluctuations in forming quality, which are difficult to predict through simple linear regression; 5083 aluminum alloy is sensitive to heat input, and it is necessary to control the porosity within a reasonable range and refine the grains while ensuring full penetration (no unfused material), which is a difficult point in process optimization. Summary of the Invention
[0005] Technical problem to be solved: In view of the problems of inaccurate heat input control, non-uniform weld structure and large fluctuation of mechanical properties in the existing aluminum alloy electric arc additive manufacturing process, the present invention provides a scientific optimization method based on mathematical statistical model. By establishing a multi-parameter coupled mathematical model, the electric arc additive manufacturing process parameters are scientifically optimized, thereby improving the forming density and mechanical property consistency.
[0006] Technical Solution: The present invention provides an optimization method for aluminum alloy arc additive manufacturing process. This method employs response surface methodology to construct a multivariate quadratic regression model of welding process parameters and welding performance indicators, achieving multi-parameter coupled optimization. Specifically, it includes the following steps: Step 1: Determine the process parameters and their value ranges for arc additive manufacturing, including welding current, wire feed speed, and welding speed; Step 2: Using the process parameters determined in Step 1 as independent variables and the weld formation quality and mechanical performance indicators as response variables, design the experimental scheme using the response surface methodology. Step 3: Conduct electric arc additive manufacturing experiments according to the experimental plan, and measure the response variable values of each group of experiments; Step 4: Based on the experimental results, establish a multiple regression model between process parameters and response variables; Step 5: Set the expected value of the response variable with the optimization objective, solve the multiple regression model, and obtain the optimal combination of process parameters; Step 6: Conduct verification tests using the optimal combination of process parameters.
[0007] Preferably, the arc additive manufacturing is tungsten inert gas welding or metal inert gas welding.
[0008] Preferably, the aluminum alloy is a 5-series or 7-series aluminum alloy; more preferably, it is 5083 aluminum alloy.
[0009] Preferably, the response variable includes at least one of tensile strength, wetting angle, and melt depth ratio.
[0010] Preferably, in step 2, the response surface method employs Box-Behnken design or central composite design.
[0011] Preferably, the multiple regression model in step 4 is a quadratic polynomial model, which includes the main effects, interaction effects, and squared terms of the process parameters; the expression for the tensile strength Rm is: R m =+236.22-4.89×A-5.29×B-0.945×C-9.79×AB-10.68×AC-6.28×BC+3.72×A 2 +10.10×B 2 +16.09×C2 ; In the formula: A is the welding current; B is the wire feed speed; C is the welding speed.
[0012] Preferably, the optimization objective in step 5 includes one or more of maximizing tensile strength, minimizing heat input, minimizing wetting angle, or maximizing melt depth ratio.
[0013] Preferably, for TIG welding of 5083 aluminum alloy thin plates, the range of process parameters is: welding current of 120-140A, wire feed speed of 220-260cm / min, and welding speed of 2-4mm / s; the optimal combination of process parameters is: welding current of 120A, wire feed speed of 254.5cm / min, and welding speed of 4mm / s.
[0014] Preferably, the performance evaluation indicators for the verification test in step 6 include weld tensile strength, grain size, porosity, and wetting angle; wherein the optimized weld tensile strength is ≥274.7MPa, the average grain size is ≤19μm, the pore diameter is ≤50μm, and the wetting angle is ≤22°.
[0015] Preferably, the method further includes performing significance and fit tests on the established multiple regression model, and deleting insignificant terms to optimize the model.
[0016] Compared with the prior art, the present invention has at least the following outstanding advantages: 1. This invention proposes a multi-parameter coupled optimization method based on response surface methodology, which breaks through the limitations of traditional single-factor, experience-based trial-and-error parameter optimization and realizes the scientific optimization of aluminum alloy arc additive manufacturing process; a nonlinear coupling model is established between welding current-wire feed speed-welding speed and penetration depth, wetting angle, and tensile strength, revealing the interactive coupling mechanism between various process parameters; the model inversion achieves accurate prediction of the global optimal process combination, and the reliability and practicality of the model are verified by experiments; 2. Significantly improved forming quality: By establishing a multi-parameter coupled model through the response surface methodology, the matching relationship between welding current, wire feed speed and welding speed is precisely controlled, achieving uniform heat input to the molten pool and stable metal transition; after optimization, the weld surface is smooth, the wetting angle is reduced from about 28° before optimization to about 22°, the weld reinforcement is reduced by about 15%, the surface is smooth and continuous, spatter is significantly reduced, and there are no obvious signs of oxidation. 3. Significantly improved mechanical properties: Under optimal process parameters (welding current 120A, wire feed speed 254.5cm / min, welding speed 4mm / s), the tensile strength of the resulting additive layer reaches 274.7MPa, which is about 10% higher than that of the unoptimized process (about 250MPa), and the elongation after fracture is about 7% higher. Microstructure analysis shows that the grains in the weld pool zone are significantly refined, with the average size decreasing from about 28μm to about 19μm. The microstructure is uniform and dense, the average size of the β(Al3Mg2) precipitate is less than 3μm, and the pore diameter is concentrated within 5μm. Pores within 50μm will not have a significant impact on the mechanical properties of the joint. 4. Enhanced process stability and repeatability: The response surface model enables quantitative prediction and control of the process window, maintaining consistent weld formation within a ±5A current fluctuation range, with additive layer thickness error less than 0.1mm, significantly improving repeatability; and effectively reducing the probability of defects such as porosity, cracks, and lack of fusion during continuous deposition. 5. Production efficiency and energy consumption optimization: By optimizing the coupling of welding speed and wire feeding speed, the deposition time per unit length is reduced by about 12%, which improves production efficiency while ensuring the quality of the microstructure; the precise control of heat input reduces the average energy consumption by about 8%, effectively saving electricity and protective gas consumption, and reducing production costs. 6. Wide range of applications and strong process versatility: It is not only suitable for arc additive manufacturing of 5083 aluminum alloy, but can also be extended to 5-series and 7-series aluminum alloys such as 5356 and 7075; at the same time, it is compatible with various welding power source types such as TIG and MIG, and can be widely used in the field of lightweight structural aluminum alloy welding in automobiles, rail transportation, aerospace and other fields, with high engineering application and promotion value. Attached Figure Description
[0017] Figure 1 Here are schematic diagrams of the sample structure ((a) is a schematic diagram of the sample model structure; (b) is a schematic diagram of the sample cutting structure). Figure 2 The response surface 3D fitting plot and the normal probability distribution plot of the model (the red area is the optimal parameter interval, and the feasibility value of the scheme is 1); Figure 3 A perturbation trend graph of the deviation of tensile strength as a function of the center reference point (showing the influence of welding current, wire feed speed, and welding speed on tensile strength); Figure 4 The surface morphology of aluminum alloy butt welds under 17 sets of experimental parameters is shown. Figure 5 Cross-sectional views of aluminum alloy butt welds under 17 sets of test parameters; Figure 6Metallographic images of the weld ((a) Metallographic images of the matrix and weld at 100x magnification; (b) Microstructure of the weld center at 100x magnification); Figure 7 SEM images of the weld ((a) shows the location of the weld fusion line at 500x magnification; (b) shows the microstructure of the weld center at 500x magnification). Figure 8 SEM images of the pores at the center of the weld ((a) is a 250x magnification; (b) is a 1000x magnification). Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will be described in conjunction with the accompanying drawings. Figures 1-8 The technical solutions of the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.
[0019] like Figure 1 As shown, this invention provides an optimization method for aluminum alloy arc additive manufacturing process. It employs the response surface methodology to construct a multivariate quadratic regression model of welding process parameters and welding performance indicators, achieving multi-parameter coupled optimization. Specifically, the method includes the following steps: (I) Determine the process parameters and their value ranges: Based on the aluminum alloy material to be welded and the additive manufacturing process, determine the key process parameters, including welding current, wire feed speed and welding speed, and initially set the value ranges of each parameter.
[0020] (II) Response surface design: Using the determined process parameters as independent variables and the weld formation quality and mechanical properties (such as tensile strength, wetting angle, penetration ratio, etc.) as response variables, the test scheme is designed using the response surface method (such as Box-Behnken design or center composite design).
[0021] (III) Experiment and data acquisition: Conduct electric arc additive manufacturing experiments according to the experimental plan, and measure the response variable values of each group of experiments.
[0022] (iv) Establishing a regression model: Based on the experimental results, a multiple quadratic regression model between process parameters and response variables is established using the response surface methodology; the typical form is as follows: R=β0+∑β i x i +∑β ii x i 2 +∑β ij x i x j +εR=β0+∑βi x i +∑β ii x i2 +∑β ij x i x j +ε; Where R is the response variable; x i ε represents the process parameters; β represents the regression coefficient; and ε represents the error term.
[0023] (V) Model optimization and testing: The established multiple regression model is subjected to significance test and lack of fit test, and insignificant terms are deleted to optimize the model and improve the prediction accuracy.
[0024] (vi) Parameter optimization solution: Set the expected value of the response variable with the optimization objective (such as maximizing tensile strength, minimizing heat input, etc.), solve the multiple regression model, and obtain the optimal combination of process parameters.
[0025] (vii) Verification test: Verification test is conducted using the optimal combination of process parameters. Weld formation quality, mechanical properties and microstructure are compared and analyzed to confirm the optimization effect.
[0026] Example 1: The present invention will be further described in detail below with reference to the example. This example takes the optimization of the TIG welding arc additive manufacturing process for 3mm thin 5083 aluminum alloy plates as an example (the shape of the structural component can be as follows). Figure 1 The structure shown is not intended to limit the scope of protection of this invention.
[0027] (I) Sample preparation and basic process conditions: In this embodiment, 5083 aluminum alloy is selected as the welding base material, and the plate size is 200mm×100mm×3mm; 5083 aluminum alloy welding wire with a diameter of 1.2mm is selected, and its main chemical composition is shown in Table 1.
[0028] Table 1 Chemical composition of 5083 aluminum alloy wire (mass fraction, %): .
[0029] Before welding, the welding area was mechanically ground with an angle grinder and SiC sandpaper to completely remove the surface oxide film. The welding equipment used was a MasterTig 335ACDC welding machine and a matching automatic wire feeding system. The welding shielding gas was argon with a purity of 99.99% and the gas flow rate was set to 15L / min. TIG welding was used to conduct arc additive deposition tests.
[0030] (II) Process parameter optimization design: To address the characteristics of 3mm thin 5083 aluminum alloy sheets being prone to burn-through and large deformation, three key process parameters—welding current (A), wire feed speed (B, cm / min), and welding speed (C, mm / s)—were selected as independent variables, with tensile strength (R) as the modulus. m Wetting angle (θ) and penetration ratio (D) are the response variables. The value ranges of each parameter are set as follows: welding current 120-140A, wire feed speed 220-260cm / min, welding speed 2-4mm / s; the specific parameter values are set as follows: welding current: 120 / 130 / 140A; wire feed speed: 220 / 240 / 260cm / min; welding speed: 2 / 3 / 4mm / s.
[0031] (III) Box-Behnken Experimental Design and Data Acquisition: A Box-Behnken design was used for a three-factor, three-level experiment, consisting of 17 sets of tests, including 5 sets of center-point repeat tests to eliminate random errors. Arc additive deposition experiments were conducted under the basic process conditions for each set of test parameters. After completion, the tensile strength of the welded joint was determined by a standard tensile test. Simultaneously, the weld penetration and wetting angle were measured using a stereomicroscope. All experimental data were collected for model establishment. The experimental scheme and results are shown in Table 2.
[0032] Table 2. Box-Behnken experimental design and response variable results: .
[0033] (iv) Establishment and optimization of the multiple quadratic regression model for tensile strength: Regression analysis was performed on 17 sets of experimental data using the response surface methodology. Taking tensile strength as an example, multiple regression fitting was performed on the experimental results using Minitab software to obtain the tensile strength (Rt). m The quadratic polynomial regression equations for welding current (A), wire feed speed (B), and welding speed (C) are as follows: R m =+236.22-4.89×A-5.29×B-0.945×C-9.79×AB-10.68×AC-6.28×BC+3.72×A 2 +10.10×B 2 +16.09×C 2 .
[0034] Analysis of variance was performed on the model, and the results showed that the model's coefficient of determination R0 2 =0.5881, the fitting accuracy meets the requirements of engineering applications; the lack-of-fit term P>0.05 is not significant, indicating that the model is stable and reliable; the residuals are normally distributed, and the model prediction bias is small.
[0035] (V) Multi-objective optimization to find the optimal process parameters: To address the technical challenge of easy deformation during welding of 5083 thin plates, and to deeply analyze the influence of three input variables—welding current, wire feed speed, and welding speed—and their interactions on the tensile strength of the weld, a perturbation diagram of the tensile strength model was studied (e.g., Figure 3 (As shown). By Figure 3 It can be seen that the three factors have little difference in their influence on the response quantity—tensile strength. Among them, the welding speed has the most significant impact on the tensile strength, and its disturbance amplitude is the largest. When the welding speed (variable A) deviates from the central reference point, the tensile strength of the weld shows a gradual decreasing trend.
[0036] A multi-objective optimization strategy is set: minimizing welding current (reducing heat input), maximizing welding speed (shortening high-temperature dwell time), optimizing wire feed speed range, and maximizing tensile strength. The feasibility index distribution of the scheme under this condition is as follows: Figure 2 As shown, the optimal parameters are obtained near the red area in the figure, and the feasibility value of the scheme is 1. The above multivariate quadratic regression model is solved by a multi-objective optimization algorithm, and the optimal combination of process parameters is obtained as follows: welding current 120A, wire feed speed 254.5cm / min, welding speed 4mm / s. The model predicts that the tensile strength of the welded joint under these parameters is 274.736MPa.
[0037] (vi) Parameter optimization verification and performance testing: Validation experiments on arc additive deposition were conducted using the optimal combination of process parameters (the validation experiments were repeated three times). Figure 4 The weld formation surface diagrams are shown for 17 sets of parameters. When the welding speed is kept constant, the increase in wire feed speed will lead to an increase in the width of the formed surface, such as in group (4) (wire feed speed 260 cm / min, welding speed 3 mm / s), group (13) (wire feed speed 240 cm / min, welding speed 3 mm / s) and group (14) (wire feed speed 240 cm / min, welding speed 3 mm / s). When the wire feed speed is kept constant, the width of the formed surface increases as the welding speed decreases. Due to the small heat input, there are no obvious signs of oxidation on the weld surface, and the overall tensile strength is high.
[0038] Figure 5 This is a cross-sectional view of a butt weld of 5-series aluminum alloy after polishing and etching. Keller's reagent was used to etch the 5083 aluminum alloy, allowing for direct observation of the connection between the filler material and the two base metal plates. A stereomicroscope can also be used to quantify and statistically analyze the wetting angle. Figure 5 It can be seen that the filler material and the connecting base material are well connected, and no unfused areas are found in any parameters. The overall tensile strength of the weld is good.
[0039] Compared with the test results of traditional empirical parameters without optimization, the results show that the measured tensile strength of the optimized welded joint is 274.7 MPa, which is highly consistent with the model prediction value, and is about 10% higher than that of the unoptimized process. The elongation after fracture is about 7% higher. There is no obvious oxidation on the weld surface, the deformation of the heat-affected zone is significantly reduced, and no defects such as lack of fusion or burn-through are found in the cross section.
[0040] (vii) The effect of optimized parameters on the improvement of weld microstructure: Metallographic testing was performed on the welds of the verification test (e.g.) Figure 6 (as shown) and SEM scan analysis (such as Figure 7 and Figure 8 As shown in the figure, the results show that the cooling rate is different in different regions of the weld. The microstructure of the optimized weld center region is composed of fine equiaxed dendrites, and the average grain size is reduced from 28 μm to 19 μm. The microstructure is uniform and dense, and the grains are significantly refined.
[0041] Figure 7 The images show SEM images at 500x magnification of the weld fusion line and weld center. At the weld fusion line, white particles are uniformly distributed within a gray matrix. Spectroscopic analysis revealed the matrix to be α-aluminum, while the black particles belong to the β(Al3Mg2) crystal structure. Mg segregation creates gray areas around the white particles. SEM analysis shows that, except for some areas with larger precipitates, the β(Al3Mg2) precipitates are uniformly distributed with an average size less than 3 μm. At the weld center, with increasing heat (near the arc center), the precipitate distribution is more uniform, indicating a relatively uniform and continuous growth of the precipitate within the molten pool, with some precipitates appearing as small particles.
[0042] Figure 8 (a) SEM image of the weld center at 250x magnification; Figure 8 (a) shows a 45μm defect and numerous small pores; Figure 8 (b) As shown in the 1000x SEM image, the diameter of the fine pores is mostly concentrated within 5μm and below, which is within the safe range of 50μm and will not have a significant impact on the mechanical properties of the joint. This achieves a synergistic improvement in strength and microstructure refinement.
[0043] (viii) Applicability of different aluminum alloys: The same method was used to optimize the TIG welding parameters of 5356 aluminum alloy (thickness 3mm). The optimal parameter combination was obtained as follows: welding current 125A, wire feed speed 250 cm / min, welding speed 3.8mm / s, and tensile strength reached 285MPa, which is about 8% higher than the empirical parameters, indicating that the method of the present invention has good versatility.
[0044] This invention achieves multi-parameter coupling optimization of aluminum alloy electric arc additive manufacturing process through response surface methodology, solving technical problems such as inaccurate heat input control and large performance fluctuations in existing processes. It has significant improvements in forming quality, mechanical properties, and process stability, and can be widely applied in the field of electric arc additive manufacturing of lightweight aluminum alloy structures.
[0045] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. An optimization method for aluminum alloy electric arc additive manufacturing process, characterized in that, A multivariate quadratic regression model of welding process parameters and welding performance indicators is constructed using the response surface methodology to achieve multi-parameter coupled optimization. The specific steps include: Step 1: Determine the process parameters and their value ranges for arc additive manufacturing, including welding current, wire feed speed, and welding speed; Step 2: Using the process parameters determined in Step 1 as independent variables and the weld formation quality and mechanical performance indicators as response variables, design the experimental scheme using the response surface methodology. Step 3: Conduct electric arc additive manufacturing experiments according to the experimental plan, and measure the response variable values of each group of experiments; Step 4: Based on the experimental results, establish a multiple regression model between process parameters and response variables; Step 5: Set the expected value of the response variable with the optimization objective, solve the multiple regression model, and obtain the optimal combination of process parameters; Step 6: Conduct verification tests using the optimal combination of process parameters.
2. The method for optimizing aluminum alloy arc additive manufacturing process according to claim 1, characterized in that, The arc additive manufacturing is either tungsten inert gas welding or metal inert gas welding.
3. The method for optimizing aluminum alloy arc additive manufacturing process according to claim 1, characterized in that, The aluminum alloy is a 5-series or 7-series aluminum alloy; preferably, it is 5083 aluminum alloy.
4. The method for optimizing aluminum alloy arc additive manufacturing process according to claim 1, characterized in that, The response variables include at least one of tensile strength, wetting angle, and melt depth ratio.
5. The method for optimizing aluminum alloy arc additive manufacturing process according to claim 1, characterized in that, In step 2, the response surface methodology employs either Box-Behnken design or central composite design.
6. The method for optimizing aluminum alloy arc additive manufacturing process according to claim 1, characterized in that, In step 4, the multiple regression model is a quadratic polynomial model, which includes the main effects, interaction effects, and squared terms of the process parameters; the expression for the tensile strength Rm is: R m =+236.22-4.89×A-5.29×B-0.945×C-9.79×AB-10.68×AC-6.28×BC+3.72×A 2 +10.10×B 2 +16.09×C 2 ; In the formula: A is the welding current; B is the wire feed speed; C is the welding speed.
7. The method for optimizing aluminum alloy arc additive manufacturing process according to claim 1, characterized in that, The optimization objectives in step 5 include one or more of the following: maximizing tensile strength, minimizing heat input, minimizing wetting angle, or maximizing melt depth ratio.
8. The method for optimizing aluminum alloy arc additive manufacturing process according to claim 1, characterized in that, For TIG welding of 5083 aluminum alloy thin plates, the range of process parameters is: welding current of 120-140A, wire feed speed of 220-260cm / min, and welding speed of 2-4mm / s; the optimal combination of process parameters is: welding current of 120A, wire feed speed of 254.5cm / min, and welding speed of 4mm / s.
9. The method for optimizing aluminum alloy arc additive manufacturing process according to claim 1, characterized in that, The performance evaluation indicators for the verification test in step 6 include weld tensile strength, grain size, porosity, and wetting angle; among which, the optimized weld tensile strength is ≥274.7MPa, the average grain size is ≤19μm, the pore diameter is ≤50μm, and the wetting angle is ≤22°.
10. The method for optimizing aluminum alloy arc additive manufacturing process according to claim 1, characterized in that, It also includes performing significance and fit tests on the established multiple regression model, and deleting insignificant terms to optimize the model.