Co-optimization method for finishing efficiency and residual stress deformation of leading edge reinforcement profile

Abstract

The present invention provides a method for collaboratively optimizing the finishing efficiency and residual stress deformation of the outer shape of a leading edge reinforcement, comprising the following steps: establishing a mesh division and boundary constraints for finite element analysis of the residual stress deformation of the leading edge metal reinforcement, acquiring the residual stress distribution state of the inner cavity surface of the leading edge metal reinforcement, milling the residual stress deformation with a tapered ball end milling cutter, establishing a finishing finite element simulation model, establishing a model between the milling residual stress deformation and the mapping of different milling process parameters, and collaboratively optimizing the process parameters for residual stress deformation and machining efficiency, thereby achieving effective control of the residual stress deformation of the inner and outer surfaces of the leading edge reinforcement while improving the finishing efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of complex thin-wall structure processing, and specifically relates to a method for collaboratively optimizing residual stress deformation and processing efficiency during processing of an outer profile of a metal reinforcement edge at the leading edge of a large composite fan blade of an engine. Background Art

[0002] The fan components of domestic high-performance, high-bypass commercial aircraft engines use large-size composite blades. Their weight reduction and efficiency increase are extremely important for improving the thrust-to-weight ratio of the engine. The leading edge of the composite fan blade must be protected by a leading edge metal reinforcement edge with a complex thin-walled, deep and narrow V-shaped long groove structure of titanium alloy to prevent the leading edge of the composite blade from delamination and improve its ability to resist bird strikes and corrosion. The leading edge metal reinforcement edge is an asymmetric, ultra-weak rigid complex thin-walled structure. At present, its main manufacturing process method is five-axis machine tool milling. The V-shaped inner cavity of the leading edge metal reinforcement edge is deep and narrow. In order to improve the processing efficiency and accuracy, it is necessary to use a tool sequence with different diameters and tapers and different cutting parameters to complete its inner cavity processing. For its appearance, the same specification and good rigidity tools are used for processing, but the residual stress of titanium alloy cutting is very sensitive to process parameters such as tools and cutting amount. The asymmetry of the leading edge metal reinforcement edge structure and the inner and outer processing process parameters makes the deformation caused by the residual stress serious and the regional distribution complex, making it difficult to meet the design requirements.

[0003] The deep, narrow and long characteristics of the inner cavity of the leading edge metal reinforcement edge impose great constraints on the tool size, cutting amount and tool axis vector, making the control window of the inner cavity processing process parameters very limited; at the same time, it is difficult to use symmetrical milling technology for the inner cavity and the outer shape, and it is difficult to achieve symmetrical distribution of residual stress in the inner and outer surfaces, which makes it extremely difficult to control the residual stress deformation. Summary of the invention

[0004] The purpose of the present invention is to provide a method for collaboratively optimizing the finishing efficiency and residual stress deformation of the leading edge reinforcement edge outer profile, so as to effectively control the residual stress deformation of the inner and outer surfaces of the leading edge reinforcement edge while improving the finishing efficiency.

[0005] The technical solution adopted by the present invention to solve the technical problem is: a method for collaboratively optimizing the finishing efficiency and residual stress deformation of the front edge reinforcement edge shape, comprising the following steps:

[0006] Step 1: Establish the mesh division and boundary constraints of the finite element analysis of the residual stress deformation of the leading edge metal reinforcement edge: Use the UG modeling software to establish the three-dimensional physical model of the leading edge metal reinforcement edge workpiece and import it into the Abaqus simulation software. The inner surface of the leading edge metal reinforcement edge workpiece is divided into regions according to the actual processing area of ​​each taper ball end milling cutter in the four specifications. After the regional division, the skin is extracted from the inner and outer sides of the side wall of the leading edge metal reinforcement edge workpiece as the stress application layer, and defined as the bottom surface application. The skin of the leading edge metal reinforcement edge workpiece and the prestress application layer are meshed. The prestress is loaded on the surface of the leading edge metal reinforcement edge workpiece through the SIGINI subroutine to obtain the deformation mesh division and boundary constraints of the leading edge metal reinforcement edge workpiece caused by the residual stress.

[0007] Step 2: Obtain the residual stress distribution state of the inner cavity surface of the leading edge metal reinforcement edge: Milling experiments were conducted on the taper ball-end milling cutters of four specifications used for machining the inner cavity surface of the leading edge metal reinforcement edge workpiece. The thick block was machined under the same working conditions to obtain the residual stress distribution of the cutting surface. After milling, the residual stress test was conducted on the machined thick block. The residual stress distribution data along the depth direction was obtained by electrochemical corrosion stripping of the machined thick block test piece. The experimental measurement values ​​were substituted into the hyperbolic tangent model, and the optimal model coefficients were obtained after optimization by the FA algorithm.

[0008] Step 3: Establish a finishing finite element simulation model for the residual stress deformation caused by milling with a tapered ball end mill: After establishing the mesh division and boundary constraints for the finite element analysis of the residual stress deformation of the leading edge metal reinforcement edge in step 1, perform finite element simulation design on the residual stress deformation caused by milling of the leading edge metal reinforcement edge workpiece outer surface. The design process parameter variable is the cutting speed. V c , Feed per tooth f z , an orthogonal experimental design scheme was used for full-factor simulation; the residual stress on the inner cavity surface of the leading edge metal reinforcement edge measured by the milling experiment in step 2 was directly loaded into the simulation model through the SIGINI subroutine, and the design simulation was calculated using the Matlab-Python-Abaqus software interactive method to solve the residual stress deformation caused by different milling process parameters on the outer surface of the leading edge metal reinforcement edge workpiece;

[0009] Step 4: Establish a model between the milling residual stress deformation and the mapping between different milling process parameters: Process the milling residual stress deformation data of the monitoring point under different milling process parameters obtained in step 3, and establish the mapping relationship between different milling process parameters and the milling residual stress deformation of the monitoring point through multivariate linear regression analysis;

[0010] Step 5: Collaborative optimization of process parameters for residual stress deformation and machining efficiency: The bat optimization algorithm is used to establish a multi-objective optimization model with milling parameters as design variables and milling efficiency and residual stress deformation as objective functions. The model between the milling residual stress deformation and different milling process parameters established in step 4 is combined with the machining efficiency function as the objective function of the optimization model. By setting constraints, the multi-objective optimization function is converted into a single-objective optimization function for solution to obtain the optimized machining process parameters.

[0011] In the above technical solution, in step one, when meshing the leading edge metal reinforcement workpiece and the skin of the prestressed layer, the leading edge metal reinforcement workpiece model uses three-dimensional stress eight-node linear hexahedral units for meshing, and the prestressed layer skin uses four-node curved thin shell units for meshing.

[0012] In the above technical solution, in step 2, the thick block is processed under the same working conditions to obtain the residual stress distribution of the cutting surface, and the residual stress distribution includes σ x and σ y Residual stresses in two directions, where σ x are the residual stresses in the cutting direction. σ y is the residual stress perpendicular to the cutting direction.

[0013] In the above technical scheme, in the step three, the design simulation is calculated by the interactive method of Matlab-Python-Abaqus software to solve the residual stress deformation caused by different milling process parameters of the outer surface of the leading edge metal reinforcement edge workpiece, including: first, the milling process parameters of the outer surface of the leading edge reinforcement edge workpiece are substituted into the residual stress hybrid prediction model, and the average residual stress deformation under different milling process parameters is calculated by Matlab software; then, the residual stress deformation data of the inner cavity surface and the outer surface of the leading edge reinforcement edge workpiece are written into the Fortran subroutine respectively through the Python script, and the Inp file is called and submitted to the Abaqus software for simulation. After calculation, the residual stress deformation caused by different process parameters is obtained and the deformation displacement data of the specified monitoring point is output.

[0014] In the above technical solution, in step 4, after establishing a model between the milling residual stress deformation and the mapping between different milling process parameters, the least squares method is used to solve it, and the optimal coefficient of the model is found by minimizing the sum of squares of the errors. The regression equation of the specified monitoring point is calculated using Matlab software:

[0015] ,

[0016] ,

[0017] in, , Respectively , The deformation of the corresponding monitoring point, , Each independent variable corresponds to the cutting speed V c and feed per tooth f z .

[0018] In the above technical solution, in step 5, a bat optimization algorithm is used to establish a multi-objective optimization model with milling parameters as design variables and milling efficiency and residual stress deformation as objective functions, and an optimization mathematical model is established:

[0019] ,

[0020] In the formula, To optimize the objective function, To optimize the constraints.

[0021] In the above technical solution, in the multi-objective optimization model with milling efficiency and residual stress deformation as objective functions, a linear weighted method of probability statistics is used to construct an evaluation function, and the multi-objective optimization function is converted into a single-objective optimization function for solution.

[0022] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0023] 1. A finite element model of residual stress deformation of the leading edge metal reinforcement under asymmetric process was constructed to predict the processing deformation of the leading edge metal reinforcement;

[0024] 2. A process parameter optimization method for residual stress and deformation control of the leading edge metal reinforcement is proposed, achieving the goal of controlling residual stress and deformation while improving processing efficiency by using an asymmetric process;

[0025] 3. The milling residual stress deformation and process parameter mapping modeling is adopted. The optimized milling process parameters effectively reduce the deformation caused by residual stress. On the basis of controlling deformation, the improvement of processing efficiency can effectively reduce costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Leading edge metal reinforcement edge meshing and boundary constraints;

[0027] Figure 2 Residual stress distribution curve of the inner surface of the leading edge metal reinforcement;

[0028] Figure 3 The inner cavity of the leading metal reinforcement edge is prestressed and loaded in different areas;

[0029] Figure 4 Simulation cloud diagram of residual stress and deformation of the leading edge metal reinforcement edge;

[0030] Figure 5 Multi-objective optimization fitness curve of Bat (BA) optimization algorithm for leading edge metal reinforcement;

[0031] Figure 6 Optimize the deformation cloud diagram of the reinforced edge of workpiece processing;

[0032] Figure 7 Deviation of the profile of the outer surface of the leading edge metal reinforcement;

[0033] Figure 8 Comparison of contour deviation between original process parameters and optimized process parameters of leading edge metal reinforcement. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and effect of the present invention clearer and more specific, the present invention is further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific implementation described herein is only used to explain the present invention and is not used to limit the present invention.

[0035] A method for collaboratively optimizing the finishing efficiency and residual stress deformation of a leading edge reinforcement profile, comprising the following steps:

[0036] Step 1: Establish the mesh division and boundary constraints of the finite element analysis of the residual stress deformation of the leading edge metal reinforcement edge: Use the UG modeling software to establish the three-dimensional physical model of the leading edge metal reinforcement edge workpiece and import it into the Abaqus simulation software. The inner surface of the leading edge metal reinforcement edge workpiece is divided into regions according to the actual processing area of ​​each taper ball end milling cutter in the four specifications. After the regional division, the skin is extracted from the inner and outer sides of the side wall of the leading edge metal reinforcement edge workpiece as the stress application layer, and defined as the bottom surface application. The skin of the leading edge metal reinforcement edge workpiece and the prestress application layer are meshed. The prestress is loaded on the surface of the leading edge metal reinforcement edge workpiece through the SIGINI subroutine to obtain the deformation mesh division and boundary constraints of the leading edge metal reinforcement edge workpiece caused by the residual stress.

[0037] Step 2: Obtain the residual stress distribution state of the inner cavity surface of the leading edge metal reinforcement edge: Milling experiments were conducted on the taper ball-end milling cutters of four specifications used for machining the inner cavity surface of the leading edge metal reinforcement edge workpiece. The thick block was machined under the same working conditions to obtain the residual stress distribution of the cutting surface. After milling, the residual stress test was conducted on the machined thick block. The residual stress distribution data along the depth direction was obtained by electrochemical corrosion stripping of the machined thick block test piece. The experimental measurement values ​​were substituted into the hyperbolic tangent model, and the optimal model coefficients were obtained after optimization by the FA algorithm.

[0038] Step 3: Establish a finishing finite element simulation model for the residual stress deformation caused by milling with a tapered ball end mill: After establishing the mesh division and boundary constraints for the finite element analysis of the residual stress deformation of the leading edge metal reinforcement edge in step 1, perform finite element simulation design on the residual stress deformation caused by milling of the leading edge metal reinforcement edge workpiece outer surface. The design process parameter variable is the cutting speed. V c , Feed per tooth f z , an orthogonal experimental design scheme was used for full-factor simulation; the residual stress on the inner cavity surface of the leading edge metal reinforcement edge measured by the milling experiment in step 2 was directly loaded into the simulation model through the SIGINI subroutine, and the design simulation was calculated using the Matlab-Python-Abaqus software interactive method to solve the residual stress deformation caused by different milling process parameters on the outer surface of the leading edge metal reinforcement edge workpiece;

[0039] Step 4: Establish a model between the milling residual stress deformation and the mapping between different milling process parameters: Process the milling residual stress deformation data of the monitoring point under different milling process parameters obtained in step 3, and establish the mapping relationship between different milling process parameters and the milling residual stress deformation of the monitoring point through multivariate linear regression analysis;

[0040] Step 5: Collaborative optimization of process parameters for residual stress deformation and machining efficiency: The bat optimization algorithm is used to establish a multi-objective optimization model with milling parameters as design variables and milling efficiency and residual stress deformation as objective functions. The model between the milling residual stress deformation and different milling process parameter mapping established in step 4 is combined with the machining efficiency function as the objective function of the optimization model. By setting constraints, the multi-objective optimization function is converted into a single-objective optimization function for solution, and the optimized machining process parameters are obtained to improve the milling efficiency on the basis of reducing the residual stress deformation.

[0041] This embodiment will introduce the details of each step in detail according to the step sequence of the above method.

[0042] Step 1: Establishing mesh division and boundary constraints for finite element analysis of residual stress and deformation of leading edge metal reinforcement:

[0043] The mesh division and boundary constraints of the finite element analysis of the residual stress and deformation of the leading edge metal reinforcement edge were established by Abaqus simulation software. First, the physical model of the leading edge metal reinforcement edge workpiece was established by UG modeling software and imported into Abaqus simulation software. Then, the SIGINI subroutine was used to load the prestress. The skin was extracted as the stress application layer on the inner and outer sides of the side wall of the leading edge metal reinforcement edge workpiece, and defined as the bottom surface application. The workpiece material is Ti-6Al-4V titanium alloy, the material density is 4.43×10-9ton / mm3, the elastic model is 114GPa, and the Poisson's ratio is 0.34. According to the actual processing situation, the position of the reinforcement edge workpiece is completely fixed and constrained, and the strategy of loading residual stress in different regions is adopted to simulate the milling residual stress and deformation. Due to the structural characteristics of the leading edge metal reinforcement edge with complex geometric surface and deep and narrow inner cavity, the inner cavity is processed by 4 types of taper ball head milling cutters under the premise of ensuring processing efficiency. The inner cavity surface of the simulation model is divided according to the actual processing area of ​​each tool. The skin is extracted from the inner cavity surface as the stress application layer and defined as the bottom surface application. The skin thickness is 100μm, and an integral point is created every 10μm, with a total of 11 integral points. After the area division, the reinforced edge workpiece and the prestressed layer skin are meshed. Among them, the leading edge metal reinforced edge workpiece model uses the three-dimensional stress eight-node linear hexahedron unit C3D8R, and the prestressed layer skin uses the four-node curved shell unit S4R. The mesh control attribute is the neutral axis sweep algorithm, and the initial mesh size is 2.5mm; the prestress is loaded on the workpiece surface through the SIGINI subroutine, and only the S11 and S22 stress components are loaded, and the other components are 0. Among them, the S11 direction is the tool feed direction, and the S22 direction is the tool-workpiece contact point perpendicular to the feed direction. After the simulation is completed, the mesh division and boundary constraints of the finite element analysis of the residual stress deformation of the leading edge metal reinforced edge can be obtained, such as Figure 1 shown.

[0044] The second step is to obtain the residual stress distribution state of the inner surface of the leading edge metal reinforcement edge: a milling experiment is carried out on the inner surface of the leading edge metal reinforcement edge workpiece using 4 types of tapered ball end milling cutters. The inner surface of the leading edge metal reinforcement edge is extremely deep and narrow, and needs to be processed using a customized tapered ball end milling cutter. Under the premise of considering the processing efficiency, 4 types of tapered ball end mills are used to process from the upper edge of the side wall to the nose cone arc position. The specific tool geometric parameters and corresponding process parameters are shown in Table 1.

[0045] Table 1 Tool parameters and process parameters for machining the inner cavity of the leading edge metal reinforcement edge

[0046]

[0047] Since the front edge metal reinforcement edge is deformed after processing, the cutting residual stress will be redistributed with the deformation of the workpiece. The redistributed residual stress is not only affected by the process parameters but also related to the geometric structure of the workpiece. It is impossible to obtain the residual stress distribution of the cutting surface by processing the front edge metal reinforcement edge. Therefore, the residual stress distribution of the cutting surface is obtained by processing thick blocks under the same working conditions. The residual stress distribution includes σ x and σ y Residual stresses in two directions, where σ x are the residual stresses in the cutting direction. σ y is the residual stress perpendicular to the cutting direction.

[0048] After milling, the residual stress of the test block was tested. The residual stress distribution data along the depth direction was obtained by electrochemical corrosion stripping of the leading edge metal reinforcement edge workpiece test piece. The experimental measurement values ​​were substituted into the hyperbolic tangent model, and the optimal model coefficients were obtained after optimization by FA algorithm. The model coefficients are shown in Table 2.

[0049] Table 2 Hyperbolic tangent model coefficients of residual stress on the inner surface of the leading edge metal reinforcement

[0050]

[0051] After simulation, the residual stress distribution curve of the inner cavity surface of the leading edge metal reinforcement is obtained as follows: Figure 2 shown.

[0052] Step 3: Establish a finishing finite element simulation model for residual stress and deformation caused by milling with a tapered ball end mill: After establishing the simulation model for residual stress and deformation of the leading edge metal reinforcement edge in step 1, perform finite element simulation design for residual stress and deformation caused by milling of the outer profile surface, and design the process parameter variable as cutting speed V c , Feed per tooth f z According to actual engineering experience, the cutting speed is specified to be between 94m / min and 142m / min, the feed per tooth is between 0.03mm / z and 0.06mm / z, the rake angle is fixed at 60°, and the cutting depth is fixed at 0.2mm. At this time, the orthogonal experimental design scheme L64 (24) is used for full factor simulation, with a total of 16 groups. The specific design scheme is shown in Table 3.

[0053] Table 3 Process parameters and levels of surface deformation simulation design

[0054]

[0055] The design simulation was calculated using the interactive method of Matlab-Python-Abaqus software to solve the residual stress deformation caused by different milling process parameters.

[0056] Firstly, the milling process parameters of the outer surface of the leading edge metal reinforcement are substituted into the residual stress hybrid prediction model, and the average residual stress under different process parameters is calculated by Matlab software; then, the residual stress data of the inner surface and outer surface of the leading edge metal reinforcement are written into the Fortran subroutine through Python script, and the Inp file is called and submitted to the Abaqus software for simulation. The stress distribution state of the inner surface of the leading edge metal reinforcement is shown in Figure 2. Figure 3 After calculation, the residual stress deformation of the leading edge metal reinforcement caused by different process parameters is obtained and the deformation displacement data of the specified monitoring point is output.

[0057] For example: Figure 3 As shown, the prestressed inner cavity of the leading edge metal reinforcement is loaded in different areas, and monitoring points are added to the positions where the maximum deformation may occur on the back side of the reinforcement blade and the blade basin side, and the deformation displacement data in the U3 direction (that is, the Z direction) are extracted.

[0058] All 16 sets of process parameters designed for simulation are simulated, and the residual stress and deformation data of all monitoring points are extracted. After calculation, the residual stress and deformation caused by different process parameters are obtained and the deformation and displacement data of the specified monitoring points are output.

[0059] like Figure 4 This is the simulation cloud diagram of residual stress and deformation during machining of the leading edge metal reinforcement edge under the process parameters of cutting speed 126m / min and feed per tooth 0.04mm / z.

[0060] It can be seen that the maximum deformation area on the blade basin side and the blade back side is mainly concentrated at the blade root. Monitoring points are added to the locations where the maximum deformation may occur on the back side and the blade basin side of the reinforced edge, and the deformation displacement data in the U3 direction (that is, the Z direction) are extracted. All 16 sets of process parameters designed for simulation are simulated, and the residual stress deformation data of all monitoring points are extracted, as shown in Table 4.

[0061] Table 4 Deformation simulation values ​​of monitoring points on the leading edge metal reinforcement

[0062]

[0063] The fourth step: Establish a model between the milling residual stress deformation and the mapping between different milling process parameters: Process the simulated deformation data of the monitoring points under each process parameter obtained in step three, establish the mapping relationship between the process parameters and the deformation of the monitoring points through multivariate linear regression analysis, establish the mapping relationship between the process parameters and the residual stress deformation by using the multivariate quadratic equation linear regression method, solve it by using the least squares method, and find the optimal coefficient of the model by minimizing the sum of squares of the errors. Specifically: Use Matlab software to calculate the regression equations of monitoring point 1 and monitoring point 2:

[0064] (1)

[0065] (2)

[0066] in, , Respectively represent the deformation of monitoring point 1 and monitoring point 2, , Each independent variable corresponds to the cutting speed V c and feed per tooth f z After significant analysis Correlation coefficient R2=0.9873, adjustment coefficient AdjR2=0.9801, The correlation coefficient R2=0.9768 and the adjustment coefficient AdjR2=0.9653 indicate that the regression model established for monitoring points 1 and 2 has a high fitting accuracy.

[0067] Step 5: Coordinated optimization of process parameters for residual stress deformation and processing efficiency: The objective function is solved by the bat (BA) optimization algorithm to obtain the minimum deformation milling process parameters considering the processing efficiency. Since the bat (BA) optimization algorithm searches for the bat position with the best individual pulse frequency, and the objective function is to find the milling process parameters corresponding to the minimum deformation caused by residual stress. The bat position vector in the bat (BA) optimization algorithm is expressed by the milling process parameters, and the individual frequency of the bat can be defined as:

[0068] (3)

[0069] In multi-objective optimization problems, each objective is not independent of each other. They are mutually constrained by decision variables. The optimization of any objective condition will inevitably lead to the degradation of other objectives, and a balance point needs to be found between the objectives. The present invention allocates weights according to the relative importance of each objective function based on the actual production and processing conditions, and obtains milling process parameters that are satisfactory to each objective through the method of multi-objective parameter optimization. Based on the above analysis, an optimization mathematical model is established:

[0070] (4)

[0071] In the formula, To optimize the objective function, To optimize the constraints.

[0072] The objective function is the mapping relationship between the set independent variables and dependent variables, and the dependent variables are solved by giving the range of the independent variables. General optimization models mostly seek the maximum or minimum value of the dependent variable to give the optimal solution that meets the objective function. In the multi-objective optimization process of the leading edge metal reinforcement milling process, the variables that have a greater impact on the objective function should be considered first. The deformation of the leading edge metal reinforcement caused by residual stress has a significant impact on the machining accuracy and is an important indicator for evaluating the high precision of thin-walled parts. Therefore, minimizing the maximum machining deformation should be used as a main objective function. Then the objective function is:

[0073] (5)

[0074] (6)

[0075] in, , Respectively represent the deformation of monitoring point 1 and monitoring point 2, 、 and Represents cutting speed V c and feed per tooth f z and roll angle θ .

[0076] On the basis of controlling deformation, improving processing efficiency can effectively reduce costs. In the actual processing of the leading edge metal reinforcement edge, since the milling area and process plan of the workpiece have been determined, the total volume of material to be removed to complete the workpiece cutting process has been determined, and the actual milling time can be determined.

[0077] According to the tool cutting path planning, the cutting time t of each path is:

[0078] (7)

[0079] in, L is the length of each path, F is the feed rate. The relationship between the feed rate and the feed per tooth is shown in formula (8):

[0080] (8)

[0081] in, n is the machine tool spindle speed, z is the number of tool teeth. Cutting speed V c With machine speed n The relationship between them is as shown in formula (9), where d is the tool diameter:

[0082] (9)

[0083] Combining equations (7), (8) and (9), the cutting time t of each path is:

[0084] (10)

[0085] Number of tool paths planned for workpiece machining area N The width of the processing area W Cutting width The ratio between:

[0086] (11)

[0087] Total cutting time T for:

[0088] (12)

[0089] The objective function of maximizing processing efficiency, i.e. minimizing time, is:

[0090] (13)

[0091] The optimization goal obtained from the above is to find the milling process parameters with the minimum deformation of residual stress, while taking into account the processing efficiency. It is necessary to evaluate the coordination between the objective functions. The linear weighted method commonly used in probability statistics is used to construct an evaluation function to transform the multi-objective optimization function into a single-objective optimization function for solution. According to multiple debugging of the optimization program, the weight coefficients of the residual stress deformation function of the two selected monitoring points are set to be the same, the weight coefficient is 0.4, and the weight coefficient of the processing efficiency function is 0.2. The overall optimization objective function can be expressed as:

[0092] (14)

[0093] The constraints for milling process parameter optimization are: cutting speed V c =[94m / min, 142m / min], feed per tooth f z =[0.03mm / z, 0.06mm / z]. =[-0.15mm, 0.15mm] After a lot of program tests, the main parameters of the BA algorithm were finally selected: population size is 200, number of iterations is 10000, initial loudness is 0.5, initial pulse frequency is 0.6, and decay rate is 1. The BA algorithm is used to perform multi-objective optimization on the milling process parameters of the leading edge metal reinforcement edge. The fitness convergence curve is shown in Figure 5 shown.

[0094] Table 5 shows the multi-objective optimization results of the BA algorithm using the linear regression model as the objective function. After optimization, the minimum deformation caused by residual stress is 0.128 mm, and the corresponding milling process parameters are cutting speed V c =103.76m / min, feed per tooth f z =0.0357mm / z.

[0095] Table 5 BA algorithm multi-objective optimization results

[0096]

[0097] The inner cavity-external surface multi-axis milling experiment was carried out on the leading edge metal reinforcement to verify the accuracy of the optimized process parameters, and the GOM Inspect 2018 blue light detector was used to detect the deformation of the leading edge metal reinforcement after processing.

[0098] Figure 6 This is the deformation cloud map after the leading edge metal reinforcement processed with optimized process parameters is aligned with the theoretical model. The design tolerance of the outer surface profile of the reinforcement edge is set to ±0.200mm. As can be seen from the figure, after processing with optimized milling process parameters, the contour deviation area is significantly reduced, and the contour deviation area is mainly concentrated at the blade root, and the deformation gradually increases from the blade body to the blade root. 15 sections are selected along the direction from the blade root to the blade tip to extract the contour deviation data for analysis, and the maximum upper / lower deviation of the leading edge metal reinforcement at the section is calculated.

[0099] Figure 7In order to strengthen the measurement results of the upper / lower deviation of the profile of each section of the edge, from the root to the tip of the blade, there are sections 1 to 15. As can be seen from the figure, the lower deviation of the profile of all selected sections is less than 0.20mm. Near the root of the blade, the upper deviation of the profile of sections 1 to 6 is large, and the deviation is between 0.2 and 0.5mm. Among them, the upper deviation of the profile of section 1 is the largest, reaching 0.465mm. The upper deviation of the profile gradually decreases from the root of the blade to the blade body, and is reduced to the minimum value of 0.054mm at the position of section 9. Then the upper deviation of the profile gradually increases toward the tip of the blade, and the upper deviation of section 15 is 0.102mm.

[0100] Figure 8 The upper profile deviations of the sections of the leading edge metal reinforcement processed by the optimized process parameters and the original process parameters were compared. It can be found that the upper profile deviations of all measured sections are significantly reduced. At section 1, the upper profile deviation was reduced from 0.805mm to 0.465mm, and the upper profile deviation was reduced by 42.24%. At section 15, the upper profile deviation was reduced from 0.217mm to 0.102mm, and the upper profile deviation was reduced by 52.96%. In the blade root section 1 to section 8 area where residual stress deformation is more sensitive, the maximum upper profile deviation is reduced by an average of 46.93%. Compared with the upper profile deviations of all measured sections, the average reduction is 63.35%. It shows that the use of optimized milling process parameters effectively reduces the deformation caused by residual stress.

Claims

1. A method for collaboratively optimizing the finishing efficiency and residual stress deformation of a leading edge reinforcement profile, characterized in that: The following steps are involved: Step 1: Establish the mesh division and boundary constraints of the finite element analysis of the residual stress deformation of the leading edge metal reinforcement edge: Use the UG modeling software to establish the three-dimensional physical model of the leading edge metal reinforcement edge workpiece and import it into the Abaqus simulation software. The inner surface of the leading edge metal reinforcement edge workpiece is divided into regions according to the actual processing area of ​​each taper ball end milling cutter in the four specifications. After the regional division, the skin is extracted from the inner and outer sides of the side wall of the leading edge metal reinforcement edge workpiece as the stress application layer, and defined as the bottom surface application. The skin of the leading edge metal reinforcement edge workpiece and the prestress application layer are meshed. The prestress is loaded on the surface of the leading edge metal reinforcement edge workpiece through the SIGINI subroutine to obtain the deformation mesh division and boundary constraints of the leading edge metal reinforcement edge workpiece caused by the residual stress. Step 2: Obtain the residual stress distribution state of the inner cavity surface of the leading edge metal reinforcement edge: Milling experiments were conducted on the taper ball-end milling cutters of four specifications used for machining the inner cavity surface of the leading edge metal reinforcement edge workpiece. The thick block was machined under the same working conditions to obtain the residual stress distribution of the cutting surface. After milling, the residual stress test was conducted on the machined thick block. The residual stress distribution data along the depth direction was obtained by electrochemical corrosion stripping of the machined thick block test piece. The experimental measurement values ​​were substituted into the hyperbolic tangent model, and the optimal model coefficients were obtained after optimization by the FA algorithm. Step 3: Establish a finishing finite element simulation model for the residual stress deformation caused by milling with a tapered ball end mill: After establishing the mesh division and boundary constraints for the finite element analysis of the residual stress deformation of the leading edge metal reinforcement edge in step 1, perform finite element simulation design on the residual stress deformation caused by milling of the leading edge metal reinforcement edge workpiece outer surface. The design process parameter variable is the cutting speed. V c , Feed per tooth f z , an orthogonal experimental design scheme was used for full-factor simulation; the residual stress on the inner cavity surface of the leading edge metal reinforcement edge measured by the milling experiment in step 2 was directly loaded into the simulation model through the SIGINI subroutine, and the design simulation was calculated using the Matlab-Python-Abaqus software interactive method to solve the residual stress deformation caused by different milling process parameters on the outer surface of the leading edge metal reinforcement edge workpiece; Step 4: Establish a model between the milling residual stress deformation and the mapping between different milling process parameters: Process the milling residual stress deformation data of the monitoring point under different milling process parameters obtained in step 3, and establish the mapping relationship between different milling process parameters and the milling residual stress deformation of the monitoring point through multivariate linear regression analysis; Step 5: Collaborative optimization of process parameters for residual stress deformation and machining efficiency: The bat optimization algorithm is used to establish a multi-objective optimization model with milling parameters as design variables and milling efficiency and residual stress deformation as objective functions. The model between the milling residual stress deformation and different milling process parameters established in step 4 is combined with the machining efficiency function as the objective function of the optimization model. By setting constraints, the multi-objective optimization function is converted into a single-objective optimization function for solution to obtain the optimized machining process parameters.

2. The method for collaboratively optimizing the finishing efficiency and residual stress deformation of the front edge reinforcement profile according to claim 1, characterized in that: In the step 1, when meshing the leading edge metal reinforcement workpiece and the prestressed layer skin, the leading edge metal reinforcement workpiece model uses three-dimensional stress eight-node linear hexahedral units for meshing, and the prestressed layer skin uses four-node curved thin shell units for meshing.

3. The method for collaboratively optimizing the finishing efficiency and residual stress deformation of the front edge reinforcement profile according to claim 1 is characterized in that: In the step 2, the thick block is processed under the same working conditions to obtain the residual stress distribution of the cutting surface, and the residual stress distribution includes σ x and σ y Residual stresses in two directions, where σ x are the residual stresses in the cutting direction. σ y is the residual stress perpendicular to the cutting direction.

4. The method for collaboratively optimizing the finishing efficiency and residual stress deformation of the front edge reinforcement profile according to claim 1, characterized in that: In the step three, the design simulation is calculated by using the Matlab-Python-Abaqus software interaction method to solve the residual stress deformation caused by different milling process parameters of the outer surface of the leading edge metal reinforcement edge workpiece, including: first, the milling process parameters of the outer surface of the leading edge reinforcement edge workpiece are substituted into the residual stress hybrid prediction model, and the average residual stress deformation under different milling process parameters is calculated by Matlab software; then, the residual stress deformation data of the inner cavity surface and the outer surface of the leading edge reinforcement edge workpiece are written into the Fortran subroutine respectively through the Python script, and the Inp file is called and submitted to the Abaqus software for simulation. After calculation, the residual stress deformation caused by different process parameters is obtained and the deformation displacement data of the specified monitoring point is output.

5. The method for collaboratively optimizing the finishing efficiency and residual stress deformation of the front edge reinforcement profile according to claim 4, characterized in that: In the step 4, after establishing the model between the milling residual stress deformation and the mapping between different milling process parameters, the least square method is used to solve it, and the optimal coefficient of the model is found by minimizing the sum of squares of the errors. The regression equation of the specified monitoring point is calculated using Matlab software: ， ， in, , Respectively , The deformation of the corresponding monitoring point, , Each independent variable corresponds to the cutting speed V c and feed per tooth f z .

6. The method for collaboratively optimizing the finishing efficiency and residual stress deformation of the front edge reinforcement profile according to claim 1, characterized in that: In step 5, a bat optimization algorithm is used to establish a multi-objective optimization model with milling parameters as design variables and milling efficiency and residual stress deformation as objective functions, and an optimization mathematical model is established: ， In the formula, To optimize the objective function, To optimize the constraints.

7. A method for collaboratively optimizing the finishing efficiency and residual stress deformation of the front edge reinforcement profile according to claim 6, characterized in that: In the multi-objective optimization model with milling efficiency and residual stress deformation as objective functions, a linear weighted method of probability statistics is used to construct an evaluation function, and the multi-objective optimization function is converted into a single-objective optimization function for solution.

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