A process parameter optimization method for repairing surface grooves of aluminum alloy by friction stack welding

Abstract

A process parameter optimization method for repairing aluminum alloy surface groove by friction stack welding belongs to the field of friction repair optimization. First, a friction stack welding additive repair simulation process is established. A three-dimensional model of friction stack welding additive repair is established using three-dimensional software, including deposition layer, filling layer and substrate with groove. Model Change function is set to realize the deletion and reactivation of deposition layer and filling layer units. On the basis of realizing the additive filling process, heat input is established using a friction stack welding heat source model. The values of rotational speed and traverse speed in the heat source model are modified to simulate the temperature field under different process parameters. The temperature gradient along the normal direction of the friction surface is extracted by setting the node path. According to the lowest temperature at which the groove bottom and the aluminum alloy are in a thermoplastic state and the highest temperature within the melting point range of the aluminum alloy, the process parameter range is obtained. Finally, the optimal process parameters are determined through friction stack welding repair experiments and metallographic structure observation.

Description

Technical Field

[0001] This invention relates to a method for optimizing process parameters, and more particularly to a method for optimizing process parameters for friction welding repair of grooves on the surface of aluminum alloys, belonging to the field of process parameter optimization technology. Background Technology

[0002] During service, components in engineering projects are subjected to complex stress-strain states and various environmental factors, such as vibration, corrosion, and high temperatures. Inevitably, fatigue damage and cracks develop on their surfaces, significantly reducing the overall performance and service life of the components. Therefore, timely repair of surface damage is essential.

[0003] Since the damaged area is generally irregular, grooves of a certain width and depth can be machined according to the size of the damaged area to remove it, and then the area can be completely filled by additive manufacturing. Friction cladding, as a novel solid-state additive manufacturing method, can avoid common problems in fusion-based additive manufacturing technologies such as porosity, shrinkage, cracks, and segregation. Furthermore, the deposited layer is usually fine-grained, which helps improve the microstructure, thereby achieving good hardness, mechanical properties, wear resistance, and corrosion resistance.

[0004] To ensure the deposited material fills to the bottom of the trench, its fluidity must be maintained, keeping it in a thermoplastic state. Therefore, sufficient heat input is required during friction welding repair. However, excessive heat input can lead to severe trench deformation, hindering material filling. Insufficient heat input results in insufficient material fluidity, preventing it from filling to the bottom of the trench. Thus, appropriate heat input is crucial for friction welding trench repair. The process parameters for friction welding include rotational speed, lateral speed, and axial feed rate, with rotational speed and lateral speed having a significant impact on heat input. Obtaining suitable process parameters solely through experiments in the early stages would consume considerable time and resources; therefore, it is essential to replace most experiments with additive repair simulations. Summary of the Invention

[0005] The purpose of this invention is to propose a method for optimizing process parameters in friction welding repair of grooves on aluminum alloy surfaces. First, a simulation process for friction welding additive repair is established. A 3D model of friction welding additive repair is created using 3D software, including a deposition layer, a filler layer, and a substrate with grooves. This model is then imported into Abaqus for assembly and merging. In Abaqus, temperature-dependent material parameters are assigned to the 3D model, meshing is performed, and boundary conditions are set. A Python script is then used to implement the Model Change function, allowing the deletion and reactivation of deposition and filler layer elements. Based on the additive filling process, a friction welding heat source model is used to establish the heat input during the repair process. After the additive repair simulation is set up, the values ​​of rotational speed and lateral velocity in the heat source model are modified to simulate the temperature field under different process parameters. The temperature field during the repair process is combined with node paths to extract the temperature gradient along the normal direction of the friction surface. The range of process parameters is obtained based on the groove bottom temperature and the lowest temperature of the aluminum alloy in its thermoplastic state, and the friction surface temperature and the highest temperature within the melting point range of the aluminum alloy. Finally, the optimal process parameters are determined through friction welding repair experiments and metallographic observation.

[0006] To achieve the above objectives, the technical solution adopted in this invention is a process parameter optimization method combining simulation and experimentation. The specific steps of this method are as follows:

[0007] Step 1: Establishment of a 3D model for friction surfacing additive repair

[0008] The model includes a grooved matrix, a filling layer that fills the grooves in the matrix, and a deposition layer on top of the filling layer. After modeling with 3D software, it is saved as a step format and imported into Abaqus for assembly and merging.

[0009] Step 2: Assigning material parameters, meshing, and setting boundary conditions

[0010] The three-dimensional model established in step 1 needs to be assigned material parameters that vary with temperature, including thermal conductivity, density, and specific heat. The entire model is then meshed using eight-node linear heat transfer hexahedral elements (DC3D8). Boundary conditions are set for the initial temperature and convective heat transfer boundary conditions of the model.

[0011] Step 3: Configure the Model Change function to enable the additive filling process.

[0012] Using a Python script to set up the Model change function, through the mesh generation in step 2, the Model change function enables the deletion and activation of deposition layer units and filling layer units to be performed synchronously, thereby realizing the additive filling process;

[0013] Step 4: Establishment of the friction welding heat source model

[0014] After setting up the additive filling in step 3, a heat source model for friction surfacing is then established to obtain the heat input during the repair process. The establishment process is as follows:

[0015] From the heat source model of friction welding, we can obtain:

[0016]

[0017] Where Q is the heat generated by the friction surface, μ is the friction coefficient, n is the rotational speed of the consumable rod, F is the axial pressure of the consumable rod, and R and r are the radius of the consumable rod and the radius of the friction heating surface, respectively.

[0018] If the heat source (which can be considered as a consumable rod undergoing frictional motion) moves laterally along the substrate at a speed of v and a distance of d, then equation (2) holds:

[0019] d=vt (2)

[0020] Where t is the time it takes for the heat source to move;

[0021] Referring to the heat source model of friction stir welding, it is assumed that the distance between any point D on the bottom end of the shaft shoulder and the center of the heat source is r. d Then we have equation (3):

[0022]

[0023] Where X t With Y t Let ts (referring to seconds) be the x-coordinate and y-coordinate of point D relative to x0 and y0 (the x-axis direction is parallel to the length of the substrate groove), where x0 and y0 are the initial x-coordinate and y-coordinate of the heat source center; then the heat flux density q at point D is... r1 Equation (4):

[0024]

[0025] Where Q is the heat generation power of the friction surface, r1 is the radius of the stirring needle, and r0 is the radius of the shaft shoulder.

[0026] In the friction welding process, there is no stirring pin; only the bottom end of the consumable rod generates heat through friction with the substrate surface. Using the bottom end of the consumable rod as the surface heat source, the heat flux density q at point D is... r2 For equation (5):

[0027]

[0028] Where Q is the heat generated by the friction surface, and R is the radius of the consumable rod.

[0029] In a friction system consisting of a consumable rod and a matrix, a heat distribution problem exists at the interface during temperature field simulation. The heat flow q flowing into the matrix is ​​significant. s Equation (6) is given, and the heat flow distribution coefficient η is given by equation (7):

[0030] q s =ηq r2 (6)

[0031]

[0032] Where λ1 and λ2 represent the thermal conductivity of the consumable rod and the substrate, respectively; ρ1 and ρ2 represent the density of the consumable rod and the substrate, respectively; c1 and c2 represent the specific heat capacity of the consumable rod and the substrate, respectively. When the consumable rod and the substrate are made of the same material, η is 0.5.

[0033] Step 5: Submit the job in Abaqus to simulate the temperature field.

[0034] After creating the job according to steps 1-4, submit the job in Abaqus to simulate the temperature field of friction surfacing additive repair.

[0035] Step 6: Obtaining the temperature gradient

[0036] The temperature field of the friction surfacing additive repair process is obtained through step 5, and the temperature gradient along the normal of the friction surface is extracted by setting the node path of the friction surface normal. The node path includes at least one point on the upper surface of the deposition layer, one point at the interface between the deposition layer and the filler layer, and one point at the bottom of the substrate trench, which are distributed perpendicular to the normal of the friction surface. Furthermore, another point is set between the upper surface of the deposition layer and the interface between the deposition layer and the filler layer, and another point is set between the interface between the deposition layer and the filler layer and the bottom of the substrate trench.

[0037] Step 7: Selection of process parameter range

[0038] Compared to the transverse speed v, the rotational speed n has a greater impact on the heat generation of the consumable rod. Therefore, the rotational speed n is determined first, and then the transverse speed v is determined.

[0039] Further friction welding additive repair is a solid-state repair process, requiring the friction surface temperature to be below its melting point. Since the base material and the corresponding aluminum alloy for the consumable rod in this invention do not have a fixed melting point, the friction surface temperature must be kept below the highest temperature within the aluminum alloy's melting point range during simulation. By controlling variables, the simulation is first performed at the same lateral speed by changing the rotational speed *n* of the heat source model in step 4. The temperature gradient is obtained in step 6, and a judgment is made based on the trench bottom temperature and the lowest temperature within the thermoplastic state of the aluminum alloy. If the trench bottom temperature exceeds the minimum temperature within the thermoplastic state range of the process parameters, it is also necessary to determine whether the friction surface temperature within that range is lower than the highest temperature within the aluminum alloy's melting point range (if lower, then the optimal process parameters are obtained in step 8 using that range; if not lower, then the rotational speed *n* of the heat source model in step 4 needs to be reduced before simulating the temperature field in step 5). If there is no process parameter range in which the temperature at the bottom of the trench is greater than the lowest temperature at which the aluminum alloy is in a thermoplastic state, then at the maximum rotation speed, by changing the transverse speed v of the heat source model in step 4, a repair simulation with different transverse speeds is performed. The temperature gradient is obtained according to step 6, and then the transverse speed range is selected based on the temperature at the bottom of the trench and the lowest temperature at which the aluminum alloy is in a thermoplastic state. Then it is determined whether the friction surface temperature within this process parameter range is less than the highest temperature within the melting point range of the aluminum alloy. This process is repeated until a range that meets the process parameter range is selected.

[0040] Step 8: Obtaining the optimal process parameters

[0041] After selecting the process parameter range according to step 7, conduct actual friction welding repair experiments within the corresponding process parameter range, take samples for metallographic experiments, compare and observe the cross-sectional morphology after repair, and finally determine the optimal process parameters.

[0042] The advantage of this invention is that it establishes a relatively complete simulation process of the temperature field of friction surfacing additive repair, selects the theoretically complete range of process parameters that can fill the trench based on the simulation, and finally determines the optimal process parameters through experiments, which reduces most of the experimental workload and has certain engineering significance. Attached Figure Description

[0043] Figure 1 This is a flowchart of a method for optimizing process parameters in friction welding to repair grooves on the surface of aluminum alloys.

[0044] Figure 2 This is a schematic diagram of a three-dimensional model of the friction welding additive repair method in this invention.

[0045] Figure 3 This is a schematic diagram of the mesh generation of the finite element model in this invention.

[0046] Figure 4 This is a schematic diagram of the node paths required to obtain the temperature gradient in this invention.

[0047] Figure 5 This is a schematic diagram of the temperature gradient along the normal direction of the friction surface in this invention. Detailed Implementation

[0048] like Figure 1 As shown, a specific implementation method for optimizing process parameters of friction welding repair of grooves on aluminum alloy surfaces is as follows:

[0049] Step 1: Establishing the 3D model for friction surfacing additive repair. The model includes the deposit layer, the filler layer, and the substrate with grooves. After modeling using 3D software, save it as a STEP file and import it into Abaqus for assembly and merging.

[0050] Specifically, taking the repair of a 1mm × 1mm (width × depth) rectangular trench as an example, such as... Figure 2 As shown, A represents the sediment layer, B represents the filler layer, C represents the substrate, and D represents the repaired 3D model. The sediment layer is 1mm thick, 12mm wide, and 75mm long. The filler layer is 1mm thick, with the same width as the trench and a length of 75mm. The substrate is 3mm thick, 50mm wide, and 100mm long. The trench is 1mm wide, 1mm deep, and its length matches that of the substrate. The starting point of the sediment layer is 15mm from one end of the substrate, and the ending point of the sediment layer is 10mm from the other end of the substrate.

[0051] like Figure 2 The figure shown is a schematic diagram of the finite element model of friction welding additive repair in this invention.

[0052] Step 2: Assigning Material Parameters, Meshing, and Setting Boundary Conditions. The 3D model established in Step 1 needs to be assigned temperature-dependent material parameters, including thermal conductivity, density, and specific heat. The entire model is meshed using eight-node linear heat transfer hexahedral elements (DC3D8). Boundary conditions are set for the initial temperature and convective heat transfer boundary conditions.

[0053] Specifically, in this example, the 3D model established in step 1 is used to assign temperature-dependent material parameters to the 7075 aluminum alloy, including thermal conductivity, density, and specific heat. An eight-node linear heat transfer hexahedral element (DC3D8) is used for mesh generation, as shown below. Figure 3 As shown, E represents the deposition layer element, F represents the filling layer element, and G represents the matrix element. After meshing, the initial temperature of the element nodes of the entire model is set to 25℃. Convective heat transfer boundary conditions are set on the upper surface of the matrix, the outer surface of the deposition layer, the inner surface of the trench, the four sides of the matrix, and the bottom surface.

[0054] like Figure 3The diagram shown is a schematic diagram of the mesh generation of the finite element model in this invention.

[0055] Step 3: Configure the Model Change feature. Use a Python script to configure the Model Change feature.

[0056] By using the mesh generation in step 2, the Model change function is used to synchronize the deletion and activation of the deposition layer unit E and the filling layer unit F, thereby realizing the additive filling process.

[0057] Step 4: Establishment of the friction surfacing heat source model. After achieving additive filling in Step 3, a friction surfacing heat source model is established to obtain the heat input during the repair process. The establishment process is as follows:

[0058] From the heat source model of friction welding, we can obtain:

[0059]

[0060] Where Q is the heat generation power of the friction surface, μ is the friction coefficient, n is the rotational speed of the consumable rod, F is the axial pressure, and R and r are the radii of the consumable rod and the friction heating surface, respectively.

[0061] If the lateral velocity of the heat source is v and the distance it moves is d, then we have equation (2):

[0062] d=vt (2)

[0063] Where t is the current value of the total analysis time.

[0064] Referring to the heat source model of friction stir welding, it is assumed that the distance between any point D on the bottom end of the shaft shoulder and the center of the heat source is r. d Then we have equation (3):

[0065]

[0066] Where X t With Y t Let x and y be the x and y coordinates of point D at time ts, and x0 and y0 be the initial x and y coordinates of the heat source center. Then the heat flux density q at point D is... r1 Equation (4):

[0067]

[0068] Where Q is the heat generation power of the friction surface, r1 is the radius of the stirring needle, and r0 is the radius of the shaft shoulder.

[0069] In the friction welding process, there is no stirring pin; only the bottom end of the consumable rod generates heat through friction with the substrate surface. Using the bottom end of the consumable rod as the surface heat source, the heat flux density q at point D is... r2 For equation (5):

[0070]

[0071] Where Q is the heat generated by the friction surface, and R is the radius of the consumable rod.

[0072] In a friction system consisting of a consumable rod and a matrix, a heat distribution problem exists at the interface during temperature field simulation. The heat flow q flowing into the matrix is ​​significant. s Equation (6) is given, and the heat flow distribution coefficient η is given by equation (7):

[0073] q s =ηq r2 (6)

[0074]

[0075] Where λ1 and λ2 represent the thermal conductivity of the consumable rod and the substrate, respectively; ρ1 and ρ2 represent the density of the consumable rod and the substrate, respectively; and c1 and c2 represent the specific heat capacity of the consumable rod and the substrate, respectively.

[0076] Step 5: Submit a job in Abaqus to simulate the temperature field. After creating the job according to steps 1-4, submit the job in Abaqus to simulate the temperature field of friction surfacing additive repair.

[0077] Specifically, step 1 involves establishing a 3D model for friction surfacing additive repair; step 2 involves assigning material properties, meshing, and setting boundary conditions; step 3 involves implementing the additive filling process; and step 4 involves implementing the heat input for friction surfacing. After completing these steps, a job is created and submitted in Abaqus to simulate the temperature field of friction surfacing additive repair.

[0078] Step 6: Obtaining the temperature gradient. The temperature field of the friction surfacing additive repair process is obtained through Step 5, and the temperature gradient along the normal to the friction surface is extracted by setting node paths.

[0079] Specifically, the extraction distance is 2mm, with the friction surface at 0mm and the trench bottom at 2mm. When the heat source moves to node 1, node paths 1-5 are created at the cross-section. The node temperatures under these paths are extracted using the temperature field of the friction welding additive repair simulation process in step 5, where node 1 is the friction surface temperature and node 5 is the trench bottom temperature.

[0080] like Figure 4 The diagram shows the node path required to obtain the temperature gradient in this invention.

[0081] Step 7: Selection of Process Parameter Range. Compared with the traverse speed, the rotational speed has a greater impact on the heat generation of the consumable rod. Therefore, the rotational speed is determined first, followed by the traverse speed. The friction welding additive repair process is a solid-state repair process, which requires ensuring that the friction surface temperature is below the melting point. Since aluminum alloy does not have a fixed melting point, the friction surface temperature needs to be kept below the highest temperature within the melting point range of aluminum alloy during the simulation. By controlling variables, firstly, under the same traverse speed, different rotational speeds are used for simulation by changing the rotational speed n of the heat source model in Step 4. The temperature gradient is obtained through Step 6. The process parameter range is judged based on the temperature at the bottom of the trench and the lowest temperature of the aluminum alloy in the thermoplastic state. If there is a process parameter range where the temperature at the bottom of the trench is greater than the lowest temperature of the aluminum alloy in the thermoplastic state, it is also necessary to determine whether the friction surface temperature within this process parameter range is less than the highest temperature within the melting point range of aluminum alloy (if less, the process parameter range is used to obtain the optimal process parameters in Step 8; if not less, the rotational speed n of the heat source model in Step 4 needs to be reduced before simulating the temperature field in Step 5). If there is no process parameter range in which the temperature at the bottom of the trench is greater than the lowest temperature at which the aluminum alloy is in a thermoplastic state, then at the maximum rotation speed, by changing the transverse speed v of the heat source model in step 4, a repair simulation with different transverse speeds is performed. The temperature gradient is obtained according to step 6, and then the transverse speed range is selected based on the temperature at the bottom of the trench and the lowest temperature at which the aluminum alloy is in a thermoplastic state. Then it is determined whether the friction surface temperature within this process parameter range is less than the highest temperature within the melting point range of the aluminum alloy, until the process parameter range is selected.

[0082] Specifically, in this example, at a lateral speed of 900 mm / min, additive repair simulation was performed by changing the rotational speed n of the heat source model in step 4 to 900 rpm, 1200 rpm, and 1500 rpm, respectively, and the temperature gradient was obtained according to step 6. Taking 7075 aluminum alloy as an example, its melting point range is 475-635℃, so the highest temperature within the melting point range of aluminum alloy is 635℃, while the lowest temperature in the thermoplastic state is about 80% of the melting point. Therefore, the lowest temperature of 7075 aluminum alloy in the thermoplastic state is about 380℃. Thus, it is necessary to ensure that the temperature at the bottom of the trench is higher than 380℃ and the temperature of the friction surface is lower than 635℃. According to the temperature gradient obtained in step 6, at a lateral speed of 900 mm / min and rotational speeds of 900 rpm, 1200 rpm, and 1500 rpm, the temperature at the bottom of the trench is lower than 380℃. Therefore, in this embodiment, at a maximum rotation speed of 1500 rpm, additive repair simulations are performed by changing the transverse speed v of the heat source model in step 4 to achieve transverse speeds of 600 mm / min, 750 mm / min, and 1050 mm / min. The temperature gradients are obtained according to step 6. Finally, at a rotation speed of 1500 rpm and a transverse speed of 600-750 mm / min, the temperature at the bottom of the groove is higher than 380°C and the temperature of the friction surface is lower than 635°C.

[0083] like Figure 5 The diagram shown illustrates the temperature gradient along the normal to the friction surface in this invention.

[0084] Step 8: Obtaining the optimal process parameters. Following the selection of the process parameter range in Step 7, friction welding repair experiments were conducted using this range. Metallographic experiments were performed on samples, and the cross-sectional morphology of the repaired specimens was compared and observed to determine the optimal process parameters. Specifically, taking the repair of a 1mm × 1mm (width × depth) rectangular groove as an example, friction welding repair experiments were conducted at a rotation speed of 1500 rpm and a transverse speed of 600-750 mm / min. Metallographic experiments revealed that at a rotation speed of 1500 rpm and a transverse speed of 750 mm / min, the material could completely fill the groove with minimal deformation, resulting in a better repair effect.

Claims

1. A method for optimizing process parameters for friction welding repair of grooves on aluminum alloy surfaces, characterized in that, Includes the following steps: Step 1: Establishment of a 3D model for friction surfacing additive repair The model includes a grooved matrix, a filling layer that fills the grooves in the matrix, and a deposition layer on top of the filling layer. After modeling with 3D software, it is saved as a step format and imported into Abaqus for assembly and merging. Step 2: Assigning material parameters, meshing, and setting boundary conditions The three-dimensional model established in step 1 needs to be assigned material parameters that vary with temperature, including thermal conductivity, density, and specific heat. The entire model is then meshed using eight-node linear heat transfer hexahedral elements. Boundary conditions are set for the initial temperature and convective heat transfer boundary conditions of the model. Step 3: Configure the Model Change function to enable the additive filling process. Using a Python script to set up the Model change function, through the mesh generation in step 2, the Model change function enables the deletion and activation of deposition layer units and filling layer units to be performed synchronously, thereby realizing the additive filling process; Step 4: Establishment of the friction welding heat source model After setting up the additive filling in step 3, a heat source model for friction surfacing is then established to obtain the heat input during the repair process. The establishment process is as follows: From the heat source model of friction welding, we can obtain: (1) in For the heat generated by the friction surface, The coefficient of friction, For the rotational speed of the consumable rod, For the axial pressure of the consumable rod, and These are the radius of the consumable rod and the radius of the friction heating surface, respectively. The heat source moves laterally along the substrate at a speed of _____. The distance moved is Then we have equation (2): (2) in The time it takes for the heat source to move; Referring to the heat source model of friction stir welding, it is assumed that the distance between any point D on the bottom end of the shaft shoulder and the center of the heat source is... Then we have equation (3): (3) in and When ts, point D is relative to and The x and y coordinates, where 's' refers to seconds. and Let D be the initial x-coordinate and y-coordinate of the heat source center; then the heat flux density at point D is... For equation (4): (4) Where Q is the heat generated by the friction surface, r1 is the radius of the stirring needle, and r0 is the radius of the shaft shoulder; In the friction welding process, there is no stirring pin; only the bottom end of the consumable rod generates heat through friction with the substrate surface. Using the bottom end of the consumable rod as the surface heat source, the heat flux density at point D is... For equation (5): (5) Where Q is the heat generated by the friction surface, and R is the radius of the consumable rod; In a friction system consisting of a filament rod and a matrix, a heat distribution problem exists at the interface during temperature field simulation, with heat flowing into the matrix. For equation (6), the heat flow distribution coefficient For equation (7): (6) (7) in, These represent the thermal conductivity of the consumable rod and the substrate, respectively. These represent the densities of the consumable rod and the matrix, respectively. These represent the specific heat capacities of the consumable rod and the substrate, respectively. When the consumable rod and substrate materials are the same, then... It is 0.5; Step 5: Submit the job in Abaqus to simulate the temperature field. After creating the job according to steps 1-4, submit the job in Abaqus to simulate the temperature field of friction surfacing additive repair. Step 6: Obtaining the temperature gradient The temperature field of the friction surfacing additive repair process is obtained through step 5, and the temperature gradient along the normal of the friction surface is extracted by setting the node path of the friction surface normal. The node path includes at least one point on the upper surface of the deposition layer, one point at the interface between the deposition layer and the filler layer, and one point at the bottom of the substrate trench. Step 7: Selection of process parameter range Rotational speed n and lateral speed In comparison, rotational speed has a greater impact on the heat generation of the consumable rod, so the rotational speed n is determined first, and then the transverse speed is determined. The friction surfacing additive repair process is a solid-state repair process, requiring the friction surface temperature to be below its melting point. Since the base material and the corresponding aluminum alloy for the consumable rod do not have a fixed melting point, the simulation process necessitates ensuring the friction surface temperature remains below the highest temperature within the aluminum alloy's melting point range. By controlling variables, the simulation is first performed at the same lateral speed by varying the rotational speed *n* of the heat source model in step 4. The temperature gradient is obtained in step 6, and the process is judged based on the trench bottom temperature and the lowest temperature within the thermoplastic state of the aluminum alloy. If the trench bottom temperature exceeds the minimum temperature within the thermoplastic state range of the process parameters, it is also necessary to determine whether the friction surface temperature within this range is less than the highest temperature within the aluminum alloy's melting point range. If it is less, the optimal process parameters are obtained in step 8 using this range. If it is not less, the rotational speed *n* of the heat source model in step 4 needs to be reduced before performing the temperature field simulation in step 5. If no trench bottom temperature exceeds the minimum temperature within the thermoplastic state range of the process parameters, the simulation is performed at the maximum rotational speed by varying the lateral speed of the heat source model in step 4. Then, perform repair simulations at different lateral movement speeds, obtain the temperature gradient according to step 6, and select the lateral movement speed range based on the temperature at the bottom of the trench and the lowest temperature of the aluminum alloy in the thermoplastic state. Then, determine whether the friction surface temperature within the range of process parameters is lower than the highest temperature within the melting point range of the aluminum alloy. Repeat this process until a range that meets the process parameters is selected. Step 8: Obtaining the optimal process parameters After selecting the process parameter range according to step 7, conduct actual friction welding repair experiments within the corresponding process parameter range, take samples for metallographic experiments, compare and observe the cross-sectional morphology after repair, and finally determine the optimal process parameters.

2. The method according to claim 1, characterized in that, The node path described in step 6 also includes the following: setting another point between the upper surface of the deposition layer and the interface between the deposition layer and the filling layer, and setting another point between the interface between the deposition layer and the filling layer and the bottom of the matrix trench.

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