A method for optimizing milling parameters for surface integrity of titanium alloy components

Abstract

This application provides a method for optimizing milling parameters for the surface integrity of titanium alloy components, comprising: establishing a mapping relationship model between surface condition characteristics and milling parameters; performing sensitivity analysis on the established mapping relationship model to determine a stable process parameter domain Ω1 for surface condition characteristics; selecting parameters to process components within the stable process parameter domain Ω1 and conducting fatigue life tests to establish a component fatigue life prediction model based on surface condition characteristics; establishing surface condition constraint conditions based on fatigue life to obtain a surface integrity process parameter domain Ω2; and optimizing the process parameter domain within the surface integrity process parameter domain Ω2 with high efficiency as the goal to obtain a high-efficiency precision milling process parameter domain Ω3 for surface integrity.

Description

Technical Field

[0001] This application belongs to the field of high-efficiency precision milling parameter optimization technology for surface integrity, and specifically relates to a method for optimizing milling parameters for the surface integrity of titanium alloy components. Background Technology

[0002] Titanium alloys, as a high-strength alloy, have excellent properties such as light weight, high strength, high temperature resistance, and fatigue resistance. They are important high-strength alloy materials for manufacturing key components in major equipment in the aerospace field. In commercial aero engines, titanium alloys account for 30% and 40% of their weight, respectively.

[0003] Titanium alloys, as typical high-strength alloys for aerospace applications, are also typical difficult-to-machine materials. Their low thermal conductivity hinders the rapid dissipation of heat during the cutting process, and the heat is concentrated in a small area near the cutting deformation zone and the cutting edge. The cutting force per unit area is large and the cutting temperature is high, which easily leads to tool collapse and chipping. This can easily cause surface morphology defects and surface damage, destroying the surface integrity of the component and thus seriously affecting its fatigue performance.

[0004] Milling not only requires that the surface roughness and geometric accuracy of the components meet the requirements of the process design drawings, but also ensures that the surface condition characteristics of the components meet the service performance requirements.

[0005] Existing milling parameter optimization methods only consider machining efficiency, dimensional accuracy, and positional accuracy, neglecting the integrity of the machined surface. Therefore, establishing an efficient milling process parameter optimization model and solution method for surface integrity, and proposing an efficient precision milling parameter optimization scheme for surface integrity based on fatigue as the criterion and surface condition characteristics as constraints, is essential to ensuring the long service life and high reliability requirements of aerospace components. Summary of the Invention

[0006] Purpose of the invention: To provide a method for optimizing and controlling the surface integrity of titanium alloy components through efficient and precise milling parameters, so as to resolve the contradiction between processing efficiency, surface condition characteristics, and fatigue life in the processing of titanium alloy components.

[0007] This application provides a method for optimizing milling parameters to ensure the surface integrity of titanium alloy components, the method comprising the following steps:

[0008] Step 1: Determine the initial process parameter domain Ω0 based on the survey and literature review, design milling process experiments within the initial process parameter domain Ω0, test the surface condition characteristics of the machined specimens, summarize, statistically analyze, and nonlinearly fit all test results to establish a mapping relationship model between surface condition characteristics and milling parameters.

[0009] Step 2: Perform sensitivity analysis on the established mapping relationship model between surface condition features and milling parameters to determine the stable process parameter domain Ω1 for surface condition features;

[0010] Step 3: Select the parameters to process the component in the stable process parameter domain Ω1 of surface condition characteristics, and conduct fatigue life test to establish a component fatigue life prediction model based on surface condition characteristics; use fatigue life as the criterion to establish surface condition characteristic constraints and obtain the surface integrity process parameter domain Ω2.

[0011] Step 4: With high efficiency as the goal, optimize the process parameter domain within the surface integrity process parameter domain Ω2 to obtain the surface integrity high-efficiency precision milling process parameter domain Ω3.

[0012] Preferably, the initial process parameter domain Ω0 includes v c ,f z ,a p ,a e .

[0013] Preferably, the surface condition characteristics include surface stress concentration factor, surface residual stress, and surface microhardness, specifically represented by the symbol K. stx K represents the surface stress concentration factor of the specimen along the feed direction. sty σ represents the surface stress concentration factor of the specimen along the shear width direction. rx σ represents the surface residual stress of the specimen along the feed direction. rx HV represents the surface residual stress of the specimen along the cutting width direction, and HV represents the surface hardness of the specimen.

[0014] Preferably, the surface condition characteristic constraints include: surface condition characteristic stable process parameter domain constraints, fatigue life constraints, surface stress concentration constraints, surface residual stress constraints, surface microhardness constraints, and damage layer depth constraints.

[0015] Preferably, step 1 includes:

[0016] Step 1.1 Determine the initial process parameter range Ω0 for machining titanium alloy components;

[0017] Step 1.2 Within the initial process parameter domain Ω0, the full factorial response surface methodology is used for experimental design.

[0018] Step 1.3 Based on the response surface test design in Step 1.2, conduct milling tests and test the surface condition characteristic parameters of the test pieces processed by each set of process parameters;

[0019] Step 1.4 Based on the process parameters and the test data of the surface condition characteristics of the specimens processed by each set of parameters, establish a relationship model between the surface condition characteristics of the specimens and the process parameters;

[0020]

[0021] In the formula, SS represents the surface state characteristics, and a i (i = 1, 2, 3, 4, 5) are the fitting coefficients.

[0022] Preferably, in step 2, the established mapping relationship model between surface state features and milling parameters is subjected to sensitivity analysis according to equation (2);

[0023] Input variables x1, ..., x p , ..., x n x in p Divide into m levels M1, ..., M q M m Based on the sensitivity curve, for the input variable x p Calculate sensitivity values ​​in [M1,M2], [M2,M3], ..., [M m-1 M m The amplitudes of change within m-1 horizontal intervals are denoted as A1, A2, ..., A... m-1 ;

[0024] Calculate the amplitude of the change in m-1 sensitivity values ​​A1, ..., A j A m-1 The average value is A0;

[0025] If A j If A > 0, then the interval is an unstable region.

[0026] If A j If A < A0, then the interval is a stable region.

[0027] Obtain the stable process parameter domain Ω1 of the input variables: [x 1min ,x 1max ]、[x 2min ,x 2max ]、…、[x nmin ,x nmax ];

[0028]

[0029] In the formula, n is the number of variables.

[0030] Preferably, in step 3, parameters are selected in the stable process parameter domain Ω1 of the surface condition characteristics to process the component, and fatigue life tests are conducted to establish a component fatigue life prediction model based on surface condition characteristics, as shown in equation (3):

[0031]

[0032] In the formula, N f For fatigue life, N f0 K represents the fatigue life of the matrix material. st σ is the stress concentration factor, HV is the microhardness value, HV0 is the matrix microhardness value, and σ is the stress concentration factor. r The residual stress value is expressed in MPa, σ. b a is the tensile strength of the material in MPa. i i = 0, 1, 2, 3 are model coefficients;

[0033] When the stress concentration factor is 1, the microhardness value is the matrix microhardness, and the residual stress equals the material tensile strength, N f equals N f0 ;

[0034] Using fatigue life as the criterion, surface condition characteristic constraints are established, and the surface integrity process parameter domain Ω2 is obtained by solving. The constraints on process parameters and surface condition characteristics, as well as the fatigue life criterion conditions, are as follows:

[0035] ① Establish stable process parameter domain constraints for surface state characteristics:

[0036] v cmin ≤v c ≤v cmax (4)

[0037] f zmin ≤f z ≤f zmax (5)

[0038] a pmin ≤a p ≤a pmax (6)

[0039] a emin ≤a e ≤a emax (7)

[0040] In the formula: v cmax v is the maximum milling speed. cmin f is the minimum milling speed. zmax f is the maximum feed per tooth. zmin a is the minimum feed per tooth. pmax a is the maximum value of the cutting depth. pmin a is the minimum depth of cut. emax a is the maximum value of the cutting width. emin This is the minimum value of the cutting width;

[0041] ② Fatigue life constraint conditions:

[0042] N f ≥ N f0 (8)

[0043]

[0044] In the formula, N f For fatigue life, N f0 K represents the fatigue life of the matrix material. st σ is the stress concentration factor, HV is the microhardness value, HV0 is the matrix microhardness value, and σ is the stress concentration factor. r The residual stress value is expressed in MPa, σ. b a is the tensile strength of the material in MPa. i (i = 0, 1, 2, 3) are model coefficients; when the stress concentration factor is 1, the microhardness value is the matrix microhardness, and the residual stress equals the material tensile strength, N f equals N f0 ;

[0045] ③ Surface stress concentration constraint conditions:

[0046] K stmin ≤K st (v c ,f z ,a p ,a e )≤K stmax (10)

[0047] In the formula: K st (v c ,f z ,a p ,a e K represents the established mapping model between milling parameters and surface stress concentration. stmin K is the minimum value of the surface stress concentration factor. stmax This represents the maximum value of the surface stress concentration factor;

[0048] ④ Surface residual stress constraint condition:

[0049] σ rmin ≤σ r (v c ,f z ,a p ,a e )≤σ rmax (11)

[0050] In the formula: σ r (v c ,f z ,a p ,a e) represents the established mapping model between milling parameters and surface residual stress, σ rmin σ represents the minimum allowable value of surface residual stress or the maximum allowable value of residual compressive stress. rmax This refers to the maximum allowable value of surface residual stress or the minimum allowable value of residual compressive stress.

[0051] ⑤ Surface microhardness constraint conditions:

[0052] HV min ≤HV(v c ,f z ,a p ,a e )≤HV max (12)

[0053] Where: HV(v) c ,f z ,a p ,a e To establish a mapping model between milling parameters and surface microhardness, HV min HV is the minimum permissible value for surface microhardness. max This represents the maximum permissible value for surface microhardness.

[0054] ⑥ Damage layer depth constraint condition:

[0055] According to the residual stress cosine attenuation function prediction model, as shown in Equation (13), when the residual stress value at depth h is less than or equal to the residual stress of the matrix, the depth h at this time is considered to be the residual stress influence layer depth value, which is numerically equal to the damage layer depth D; the residual stress influence layer depth h is calculated by using the inverse function calculation method, and the damage layer depth D is obtained, as shown in Equation (14).

[0056] Ae -λh cos(wh+θ)+σ0≤σ0 (13)

[0057] Let σ rsb =Ae -λh cos(wh+θ)≤0, then:

[0058] h = f -1 (σ rsb (14)

[0059] In the formula: A, λ, ω, θ are the model control factors in the residual stress prediction model. The values ​​of each model control factor under a given milling parameter can be obtained based on the established relationship model between milling parameters and model control factors.

[0060] D1 p0 (15)

[0061] ​In the formula: D1 is the depth of the damaged layer produced by precision milling, a p0 The material removal depth in the finishing process.

[0062] Preferably, the efficient objective function shown in equation (16) established in step 4 is optimized in the process parameter domain Ω2 using MATLAB mathematical optimization tools to obtain the efficient precision milling process parameter domain Ω3 for surface integrity:

[0063] maxQ=f(v c ,f z ,a p ,a e (16).

[0064] This application has the following technical effects:

[0065] This invention targets the precision milling process. By designing response surface process experiments within the initial process parameter domain, a relationship model between titanium alloy milling process parameters and surface condition characteristics is established, determining the stable process parameter domain for surface condition characteristics. Through fatigue life tests, a blade fatigue life prediction model dependent on stress concentration, considering strain hardening and stress sensitivity is proposed. Finally, with high efficiency as the goal and fatigue life as the criterion, process parameters are optimized within the stable process parameter domain for surface condition characteristics. To verify the effectiveness of this invention, TC17 titanium alloy blades were machined using both optimized and conventional parameters, and surface condition characteristic tests and fatigue tests were conducted on the blades. Compared with conventionally used parameters, the parameters obtained by the high-efficiency precision milling parameter optimization method for titanium alloy component surface integrity improved the machining efficiency of the blades by 4.9%, reduced the surface stress concentration factor, increased residual compressive stress and microhardness, and improved fatigue life by 45%, significantly enhancing fatigue resistance. Attached Figure Description

[0066] Figure 1 This is a graph showing the absolute sensitivity of surface condition characteristics to cutting speed.

[0067] Figure 2 This is a graph showing the absolute sensitivity analysis of surface condition characteristics to the feed rate per tooth.

[0068] Figure 3 This is a graph showing the absolute sensitivity of surface condition characteristics to cutting depth.

[0069] Figure 4 This is a graph showing the absolute sensitivity of surface condition characteristics to cutting width.

[0070] Figure 5 This is a graph showing the iteration curves of the genetic optimization algorithm.

[0071] Figure 6A comparison diagram of the gradient distribution of residual stress along the depth direction in the blade;

[0072] Figure 7 A comparison diagram of the gradient of microhardness along the depth direction of the blade;

[0073] Figure 8 This is a comparison chart of blade fatigue life. Detailed Implementation

[0074] Please see Figures 1-8 The present invention adopts the following technical solution: a method for optimizing and controlling the surface integrity of titanium alloy components through high-efficiency precision milling parameters, specifically including the following steps:

[0075] Step 1: Determine the initial process parameter domain Ω0 (v) based on research and literature review. c ,f z ,a p ,a e A milling process experiment was designed within the initial process parameter domain Ω0. The surface condition characteristics of the machined specimens were tested, and all test results were summarized, statistically analyzed, and nonlinearly fitted to establish a mapping model between surface condition characteristics and milling parameters. Surface condition characteristics (including surface stress concentration factor, surface residual stress, and surface microhardness) are specifically represented by the symbol K. stx K represents the surface stress concentration factor of the specimen along the feed direction. sty σ represents the surface stress concentration factor of the specimen along the shear width direction. rx σ represents the surface residual stress of the specimen along the feed direction. rx HV represents the surface residual stress of the specimen along the cutting width direction, and HV represents the surface hardness of the specimen.

[0076] Step 2: Perform sensitivity analysis on the established mapping relationship model between surface condition features and milling parameters to determine the stable process parameter domain Ω1 for surface condition features.

[0077] Step 3: Select parameters to process components in the stable process parameter domain Ω1 of surface condition characteristics, and conduct fatigue life tests to establish a component fatigue life prediction model based on surface condition characteristics; use fatigue life as the criterion to establish surface condition characteristic constraints and obtain the surface integrity process parameter domain Ω2.

[0078] Step 4: With high efficiency as the goal, optimize the process parameter domain within the surface integrity process parameter domain Ω2 to obtain the surface integrity high-efficiency precision milling process parameter domain Ω3.

[0079] Furthermore, the relationship model between surface condition features and process parameters that needs to be established in step 1 is shown in equation (1):

[0080]

[0081] In the formula, SS represents the surface state characteristics, and a i (i = 1, 2, 3, 4, 5) are the fitting coefficients.

[0082] Furthermore, the specific method of step 1 includes the following steps:

[0083] Step 1.1 Determine the initial process parameter range Ω0 for machining titanium alloy components;

[0084] Step 1.2 Within the initial process parameter domain Ω0, the full factorial response surface methodology is used for experimental design.

[0085] Step 1.3 Conduct milling tests based on the response surface experimental design, and determine the surface stress concentration factor K of the test piece after machining for each set of parameters. st Residual stress σ r Microhardness HV was tested and calculated.

[0086] Step 1.4 Based on the process parameters and the surface condition characteristic parameters of each set of parameters, establish a model relating the surface stress concentration factor, surface residual stress, surface microhardness, and process parameters of the specimen:

[0087]

[0088] In the formula, K stx K is the surface stress concentration factor of the specimen along the feed direction. sty σ is the surface stress concentration factor of the specimen along the shear width direction. rx σ represents the surface residual stress of the specimen along the feed direction. rx The surface residual stress of the specimen along the shear width direction is given by denoted as a, and HV is the surface hardness of the specimen. ij (i,j=1,2,3,4,5) are the fitting coefficients.

[0089] Furthermore, the specific method for determining the surface state characteristic stabilization process parameter domain Ω1 in step 2 is as follows:

[0090] The sensitivity analysis of the established mapping relationship model between surface condition features and milling parameters is performed according to the following formula.

[0091]

[0092] In the formula, n is the number of variables.

[0093] Method for determining the stable process parameter domain Ω1: Input variables (x1, ..., x...) p , ..., x n x in ) pDivide into m levels (M1, ..., M) q M m Based on the sensitivity curve, for the input variable x p Calculate sensitivity values ​​in [M1,M2], [M2,M3], ..., [M m-1 M m The variation amplitudes within m-1 horizontal intervals are denoted as A1, A2, ..., A... m-1 ; Calculate the amplitude of the change in m-1 sensitivity values ​​A1, ..., A j A m-1 The average value is A0; if A j If A > A0, then the interval is an unstable region; if A j <A0, then this interval is the stable region; obtain the stable process parameter domain Ω1 of the input variables: [x 1min ,x 1max ]、[x 2min ,x 2max ]、…、[x imin ,x imax ].

[0094] Furthermore, the specific method for determining the surface integrity process parameter domain Ω2 in step 3 is as follows:

[0095] Step 3.1 Select the parameters to process the component in the surface condition characteristic stable process parameter domain Ω1. Select one specimen in each test group for surface condition testing, and conduct fatigue life tests on the other specimens.

[0096] Step 3.2 Based on the surface condition test results and fatigue life test results, and based on the proposed blade fatigue life prediction model that depends on stress concentration and considers strain hardening and stress sensitivity, the following relationship is established:

[0097]

[0098] In the formula, N f For fatigue life, N f0 K represents the fatigue life of the matrix material. st σ is the stress concentration factor, HV is the microhardness value, HV0 is the matrix microhardness value, and σ is the stress concentration factor. r The residual stress value is expressed in MPa, σ. b a is the tensile strength of the material in MPa. i (i = 0, 1, 2, 3) are model coefficients. When the stress concentration factor is 1, the microhardness value is the matrix microhardness, and the residual stress equals the material tensile strength, N... f equals N f0 .

[0099] Step 3.3 Solve for the surface integrity process parameter domain Ω2 and establish conditions based on fatigue life as the criterion:

[0100] N f ≥ N f0 (5)

[0101]

[0102] In the formula, N f For fatigue life, N f0 K represents the fatigue life of the matrix material. st σ is the stress concentration factor, HV is the microhardness value, HV0 is the matrix microhardness value, and σ is the stress concentration factor. r The residual stress value is expressed in MPa, σ. b a is the tensile strength of the material in MPa. i (i = 0, 1, 2, 3) are model coefficients. When the stress concentration factor is 1, the microhardness value is the matrix microhardness, and the residual stress equals the material tensile strength, N... f equals N f0 .

[0103] Step 3.4 Using fatigue life as the criterion, the surface integrity process parameter domain Ω2 is obtained. The following surface state characteristic constraints also need to be established.

[0104] ① The constraint conditions for the stable process parameter domain of surface state characteristics are established as follows:

[0105] v cmin ≤v c ≤v cmax (7)

[0106] f zmin ≤f z ≤f zmax (8)

[0107] a pmin ≤a p ≤a pmax (9)

[0108] a emin ≤a e ≤a emax (10)

[0109] In the formula: v cmax v is the maximum milling speed. cmin f is the minimum milling speed. zmax f is the maximum feed per tooth. zmin a is the minimum feed per tooth. pmax a is the maximum value of the cutting depth. pmin a is the minimum depth of cut.emax a is the maximum value of the cutting width. emin This represents the minimum cutting width.

[0110] ②The surface stress concentration constraint condition is:

[0111] K stmin ≤K st (v c ,f z ,a p ,a e )≤K stmax (11)

[0112] In the formula: K st (v c ,f z ,a p ,a e K represents the established mapping model between milling parameters and surface stress concentration. stmin K is the minimum value of the surface stress concentration factor. stmax This represents the maximum value of the surface stress concentration factor.

[0113] ③ The surface residual stress constraint condition is:

[0114] σ rmin ≤σ r (v c ,f z ,a p ,a e )≤σ rmax (12)

[0115] In the formula: σ r (v c ,f z ,a p ,a e ) represents the established mapping model between milling parameters and surface residual stress, σ rmin σ represents the minimum allowable value of surface residual stress or the maximum allowable value of residual compressive stress. rmax This represents the maximum allowable value of surface residual stress or the minimum allowable value of residual compressive stress.

[0116] ④ The surface microhardness constraint condition is:

[0117] HV min ≤HV(v c ,f z ,a p ,a e )≤HV max (13)

[0118] Where: HV(v) c,f z ,a p ,a e To establish a mapping model between milling parameters and surface microhardness, HV min HV is the minimum permissible value for surface microhardness. max This represents the maximum permissible value for surface microhardness.

[0119] ⑤ The constraint condition for the depth of the damaged layer is:

[0120] According to the residual stress cosine attenuation function prediction model, as shown in Equation (14), when the residual stress value at depth h is less than or equal to the residual stress of the matrix, the depth h at this time is considered to be the residual stress influence layer depth value, which is numerically equal to the damage layer depth D. The residual stress influence layer depth h is calculated using the inverse function method, thus obtaining the damage layer depth D, as shown in Equation (14).

[0121] Ae -λh cos(wh+θ)+σ0≤σ0 (14)

[0122] Let σ rsb =Ae -λh cos(wh+θ)≤0, then:

[0123] h = f -1 (σ rsb (15)

[0124] In the formula: A, λ, ω, θ are the model control factors in the residual stress prediction model. The values ​​of each model control factor under a given milling parameter can be obtained based on the established relationship model between milling parameters and model control factors.

[0125] D1 p0 (16)

[0126] In the formula: D1 is the depth of the damaged layer produced by precision milling, a p0 The material removal depth in the finishing process.

[0127] In other embodiments of this application, a method for optimizing and controlling the surface integrity of titanium alloy components through efficient precision milling parameters includes the following steps: determining the initial process parameter domain Ω0(v) based on research and literature review. c ,f z ,a p ,a e ​A milling process experiment was designed within the initial process parameter domain Ω0. The surface condition characteristics of the machined specimens were tested, and all test results were summarized, statistically analyzed, and nonlinearly fitted to establish a first relational model. Sensitivity analysis was performed on the established mapping relationship model between surface condition characteristics and milling parameters to determine the stable process parameter domain Ω1 for surface condition characteristics. Within the stable process parameter domain Ω1, parameters were selected to machine components, and fatigue life tests were conducted to establish a component fatigue life prediction model based on surface condition characteristics. Using fatigue life as a criterion, surface condition constraint conditions were established to obtain the surface integrity process parameter domain Ω2. With high efficiency as the goal, the process parameter domain was optimized within the surface integrity process parameter domain Ω2 to obtain the high-efficiency precision milling process parameter domain Ω3 for surface integrity.

[0128] This invention establishes a model relating surface condition characteristics to process parameters through titanium alloy milling experiments, obtaining a stable process parameter domain for the surface condition characteristics of machined specimens. Then, through blade machining and fatigue tests, a model relating fatigue life to surface condition characteristics is established. Finally, based on the goal of high efficiency, efficient precision milling parameters for blade surface integrity are calculated, using blade fatigue life requirements as the criterion and the range of surface condition characteristics as constraints. Machining blades using these efficient precision milling parameters for surface integrity meets the requirements for dimensional and positional accuracy while improving machining efficiency, optimizing surface condition characteristics, and increasing blade fatigue life.

[0129] Step 1: Determine the initial process parameter domain Ω0 (v) based on research and literature review. c ,f z ,a p ,a e A milling process experiment was designed within the initial process parameter domain Ω0. The surface condition characteristics of the machined specimens (including surface stress concentration factor, surface residual stress, and surface microhardness) were tested. All test results were summarized, statistically analyzed, and nonlinearly fitted to establish a mapping model between surface condition characteristics and milling parameters. The specific method is as follows:

[0130] Step 1.1 Under the selected machine tool, cutting tool, and coolant conditions, determine the initial process parameter domain Ω0(v) of this invention based on research and literature review. c ,f z ,a p ,a e A milling process experiment was designed within the initial process parameter domain Ω0. Based on the actual application, the material in this case is TC17 titanium alloy, and the determined initial process parameter domain Ω0 is v. c ∈[75,395]m / min,f z∈[0.02,0.06]mm / z,a p ∈[0.05,0.25]mm,a e ∈[0.1,0.5]mm.

[0131] Step 1.2 Within the initial process parameter domain Ω0 mentioned above, the experimental design is completed using a combination of four-factor, five-level full factor response surface methodology and single-factor experiment. The milling experiment is conducted using fixed machine tools, cutting tools, and cooling and lubrication methods, with a new cutting tool used for each set of parameters.

[0132] Step 1.3 Test the surface condition characteristics of each group of process test pieces. Use a roughness tester to test the surface roughness of the test pieces after processing with each set of parameters, and calculate the surface stress concentration factor K based on the test results. st The residual stress σ was measured using an X-ray stress testing system. r The microhardness HV was tested using a microhardness tester.

[0133] Step 1.4 Based on the process parameters of each group of tests and the surface condition characteristic parameters of the processed specimens, establish a relationship model between the surface condition characteristics of the specimens and the process parameters, as shown in Equation (17);

[0134]

[0135] In the formula, K stx K is the surface stress concentration factor of the specimen along the feed direction. sty σ is the surface stress concentration factor of the specimen along the shear width direction. rx σ represents the surface residual stress of the specimen along the feed direction. rx HV represents the surface residual stress of the specimen along the cutting width direction, and HV represents the surface hardness of the specimen.

[0136] Step 2: Perform sensitivity analysis on the established mapping relationship model between surface condition features and milling parameters to determine the stable process parameter domain Ω1 for surface condition features.

[0137] Step 2.1 Based on the concept of stable parameter domain, perform sensitivity analysis on the mapping relationship model between surface state features and milling parameters established by equation (17) to obtain the absolute sensitivity between surface state features and milling parameters, such as... Figures 1-4 As shown.

[0138] Step 2.2 Based on the absolute sensitivity change, calculate the sensitivity amplitudes of cutting speed, feed rate, depth of cut, and width of cut within different parameter ranges, as shown in Table 1. The amplitudes include the amplitudes of stress concentration factor, surface residual stress, and surface microhardness changes. Calculate the average value based on multiple amplitudes: according to the definition of a stable process parameter domain, the sensitivity change amplitude within the stable process parameter domain must be less than the average of the sensitivity amplitudes across all intervals of that parameter factor.

[0139] For example, sensitivity analysis of stress concentration factor, surface residual stress, and surface microhardness to each process parameter is performed within the initial processing parameter range. Based on the sensitivity curve, the input variables (x1, ..., x...) are... p , ..., x n x in ) p Divide into m levels (M1, ..., M) q M m Based on the sensitivity curve, for the input variable x p Calculate sensitivity values ​​in [M1,M2], [M2,M3], ..., [M m-1 M m The variation amplitudes within m-1 horizontal intervals are denoted as A1, A2, ..., A... m-1 From a mathematical perspective, sensitivity reflects the gradient of the output function with respect to changes in the design variables. When solving for the sensitivity of one design variable, the other design variables take intermediate values.

[0140] Step 2.3 According to Figure 1 As shown in the sensitivity variation curve, the calculated sensitivity variation amplitudes across nine horizontal ranges of milling speed are shown in Table 1. Similarly, based on... Figure 2 As shown in the sensitivity variation curve, the sensitivity variation amplitude within four horizontal ranges of the feed per tooth was calculated; based on Figure 3 As shown in the sensitivity variation curve, the sensitivity variation amplitude within four horizontal ranges of the cutting depth was calculated; based on Figure 4 As shown in the sensitivity variation curve, the amplitude of sensitivity variation within four horizontal ranges of the cutting width was calculated.

[0141] Calculate the amplitude of the change in m-1 sensitivity values ​​A1, ..., A j A m-1 The average value is A0; if A j If A > A0, then the interval is an unstable region; if A j <A0, then this interval is the stable region; obtain the stable process parameter domain Ω1 of the input variables: [x 1min ,x 1max ]、[x 2min ,x 2max ]、…、[ximin ,x imax ].

[0142] Step 2.4 For different intervals of the four process parameters, the average value of the sensitivity change amplitude of stress concentration factor, residual stress, and microhardness in each interval is compared to the average value of the sensitivity change amplitude of all intervals. The stable process parameter domain Ω1 of the surface state characteristics is determined as shown in Table 2, i.e., v c = [195, 395] m / min, f z =[0.03,0.06]mm / z, a p =[0.1,0.25]mm, a e = [0.2, 0.5] mm.

[0143] Table 1 Sensitivity analysis results

[0144]

[0145] Table 2. Analysis results of sensitivity variation amplitude and stability parameter range

[0146]

[0147] Step 3: Select parameters to process components in the stable process parameter domain Ω1 of surface condition characteristics, and conduct fatigue life tests to establish a component fatigue life prediction model based on surface condition characteristics; use fatigue life as the criterion to establish surface condition characteristic constraints and obtain the surface integrity process parameter domain Ω2.

[0148] Step 3.1 Specifically, in the surface condition characteristic stable process parameter domain Ω1, select the five different milling parameters shown in Table 3 to complete the finish milling of the blade unit. Each set of parameters processes 4 blades, for a total of 20 blades. The overall bladed disk finish milling is performed on a Liechti G-mill 1150 five-axis high-speed milling machining center. The cutting tool used is a Zhuzhou drill BR5×3×XD16 tapered ball end mill, four teeth, and the milling method is helical milling.

[0149] Table 3 Blade Milling Parameters

[0150]

[0151] Step 3.2 For the blades processed above, one blade from each group is selected for surface condition characteristic testing, and the other three are subjected to vibration fatigue life testing. Based on the surface condition characteristic test data and vibration fatigue life test results, a fatigue life prediction model based on surface condition characteristics is established, as shown in Equation (18):

[0152]

[0153] Where: Nf For fatigue life, K st σ is the stress concentration factor, HV is the surface microhardness, and σ is the surface microhardness. r This represents the residual stress value on the surface.

[0154] Step 3.3 For this example, using the criterion that fatigue life is greater than the matrix life, the fatigue life coefficient constraint condition is established, which can be expressed as:

[0155]

[0156] Step 3.4 For this example, the constraint conditions for the stable process parameter domain of surface state characteristics are established, which can be expressed as:

[0157] 195≤v c ≤395 (21)

[0158] 0.03≤f z ≤0.06 (22)

[0159] 0.1≤a p ≤0.25 (23)

[0160] 0.2≤a e ≤0.5 (24)

[0161] Step 3.5 For this example, the surface state characteristic constraints are established sequentially, which can be expressed as:

[0162] The surface stress concentration factor constraint condition can be expressed as: K st ≤1.056(25)

[0163]

[0164] The surface residual stress constraint condition can be expressed as: σ r ≤-152.215(27)

[0165]

[0166] The surface microhardness constraint condition can be expressed as: HV≥393.24(29)

[0167]

[0168] The constraint condition for the depth of the damage layer can be expressed as:

[0169] h<0.05 (31)

[0170] Where h = f -1 (σ rsb (32)

[0171] σrsb =Ae -λh cos(wh+θ) and σ rsb ≤0 (33)

[0172]

[0173] Step 4: With high efficiency as the goal, optimize the process parameter domain within the surface integrity process parameter domain Ω2 to obtain the surface integrity high-efficiency precision milling process parameter domain Ω3.

[0174] Step 4.1 In this example, based on the surface integrity high-efficiency precision milling parameter domain optimization model, with high efficiency as the optimization objective, an optimization objective function is established:

[0175]

[0176] Specifically, the ball end mill used in the experiment had a diameter of 10mm and 4 teeth. The objective function for optimization can be transformed into:

[0177]

[0178] Step 4.2 After establishing the optimization objective and constraints, the above nonlinear programming problem is optimized and solved using a mathematical optimization algorithm. The solution results are then sorted to obtain the parameter domain Ω3 for efficient and precise milling of the surface integrity of titanium alloy blades.

[0179] Step 4.3 In this example, a genetic optimization algorithm is used in MATLAB to optimize and solve the above nonlinear programming problem. The initial population size is 80, the crossover probability is 0.9, the mutation probability is 0.1, the elite ratio is 0.1, and the maximum number of iterations is 500. The iteration curve of the genetic algorithm is calculated. After sorting and analyzing the optimization results, the high-efficiency precision milling parameters for the surface integrity of TC17 titanium alloy blades are obtained as follows: v c =395m / min, f z =0.04mm / z, a p =0.13mm, a e =0.2mm.

[0180] Step 4.4 Experimental Verification of Parameter Optimization Results. The blade test pieces were machined using both high-efficiency precision milling parameters and conventional parameters for surface integrity of the TC17 titanium alloy blade. Surface condition tests and fatigue life tests were then conducted to verify that the parameter optimization scheme can achieve the goals of improving machining efficiency, enhancing surface condition characteristics, and increasing blade fatigue life while meeting the requirements for dimensional and positional accuracy of the blade.

[0181] The blade test pieces were machined using the optimized milling parameters and conventional milling parameters shown in Table 4. The material removal rate of the blades machined with different parameters was calculated, and the surface condition characteristics and fatigue life of the two sets of blades were tested.

[0182] Table 4 Comparative Verification Test Plan

[0183]

[0184] Table 5 shows the results of material removal rate, surface roughness, surface residual stress, and surface microhardness of the machined blades. Compared with conventional parameters, the optimized parameters improved the machining efficiency by 156%. The stress concentration factors of the blades machined with optimized parameters were 1.0062 and 1.011 in the feed direction and the cutting width direction, respectively, while those with conventional parameters were 1.0058 and 1.014. Compared with conventional parameters, the stress concentration factors increased by 0.0004 in the feed direction and decreased by 0.003 in the cutting width direction.

[0185] Table 5 Comparison of Surface Condition Characteristics

[0186]

[0187] The distribution of residual stress along the depth direction of the blade is as follows: Figure 6 As shown in the figure. Analysis shows that the depth of the residual stress-affected layer is 25–30 μm. The consistency and stability of the surface residual compressive stress values ​​of the blade processed with optimized parameters are greater than those with conventional processing parameters.

[0188] The distribution of microhardness of the blade along the depth direction is as follows: Figure 7 As shown. The surface hardness of the blade processed with optimized parameters is 408.23 HV, the hardening rate is 6.03%, and the hardened layer depth is approximately 20 μm; the surface hardness of the blade processed with conventional parameters is 394.67 HV, the hardening rate is 2.51%, and the hardened layer depth is approximately 15 μm.

[0189] Fatigue life of blades processed with different process parameters, such as Figure 8 As shown. The median fatigue life of the blade processed with optimized parameters is 9.03 × 10⁻⁶. 5 The median fatigue life of blades processed with conventional parameters is 6.21 × 10⁻⁶. 5 Compared to conventional parameters, the optimized parameters improved the median fatigue life of blades by 45%.

Claims

1. A method for optimizing milling parameters to ensure surface integrity of titanium alloy components, characterized in that, The method includes the following steps: Step 1: Determine the initial process parameter domain Ω0 based on the survey and literature review, design milling process experiments within the initial process parameter domain Ω0, test the surface condition characteristics of the machined specimens, summarize, statistically analyze and nonlinearly fit all test results, and establish a mapping relationship model between surface condition characteristics and milling parameters. Step 2: Perform sensitivity analysis on the established mapping relationship model between surface condition features and milling parameters to determine the stable process parameter domain Ω1 for surface condition features; Step 3: Select the parameters to process the component in the stable process parameter domain Ω1 of surface condition characteristics, and conduct fatigue life test to establish a component fatigue life prediction model based on surface condition characteristics; use fatigue life as the criterion to establish surface condition characteristic constraints and obtain the surface integrity process parameter domain Ω2. Step 4: With high efficiency as the goal, optimize the process parameter domain within the surface integrity process parameter domain Ω2 to obtain the surface integrity high-efficiency precision milling process parameter domain Ω3; Step 1 includes: Step 1.1 Determine the initial process parameter range Ω0 for machining titanium alloy components; Step 1.2 Within the initial process parameter domain Ω0, the full factorial response surface methodology is used for experimental design. Step 1.3 Based on the response surface test design in Step 1.2, conduct milling tests and test the surface condition characteristic parameters of the test pieces processed by each set of process parameters; Step 1.4 Based on the process parameters and the test data of the surface condition characteristics of the specimens processed by each set of parameters, establish a relationship model between the surface condition characteristics of the specimens and the process parameters; In the formula, SS represents the surface state characteristics, and a i (i = 1, 2, 3, 4, 5) are the fitting coefficients; Step 2 involves sensitivity analysis of the established mapping relationship model between surface state features and milling parameters according to equation (2); In the formula, n is the number of variables; In step 3, parameters are selected to process components within the stable process parameter domain Ω1 of the surface condition characteristics, and fatigue life tests are conducted to establish a component fatigue life prediction model based on surface condition characteristics, as shown in equation (3): When the stress concentration factor is 1, the microhardness value is the matrix microhardness, and the residual stress equals the material tensile strength, N f equals N f0 ; In the formula, N f For fatigue life, N f0 K represents the fatigue life of the matrix material. st σ is the stress concentration factor, HV is the microhardness, HV0 is the matrix microhardness value, and σ is the matrix microhardness value. r The residual stress value is expressed in MPa, σ. b a is the tensile strength of the material in MPa. i , i = 0, 1, 2, 3 are model coefficients.

2. The method as described in claim 1, characterized in that, Surface condition characteristics include surface stress concentration factor, surface residual stress, and surface microhardness, with specific characterization symbols as follows: K stx K represents the surface stress concentration factor of the specimen along the feed direction. sty σ represents the surface stress concentration factor of the specimen along the shear width direction. rx σ represents the surface residual stress of the specimen along the feed direction. ry HV represents the surface residual stress of the specimen along the cut width direction, and HV represents the microhardness.

3. The method as described in claim 2, characterized in that, The surface condition characteristic constraints include: surface condition characteristic stable process parameter domain constraints, fatigue life constraints, surface stress concentration constraints, surface residual stress constraints, surface microhardness constraints, and damage layer depth constraints.

4. The method as described in claim 3, characterized in that, Input variables x1, ..., x p , ..., x n x in p Divide into m levels M1, ..., M q M m Based on the sensitivity curve, for the input variable x p Calculate sensitivity values ​​in [M1,M2], [M2,M3], ..., [M m-1 M m The amplitudes of change within m-1 horizontal intervals are denoted as A1, A2, ..., A... m-1 ; Calculate the amplitude of the change in m-1 sensitivity values ​​A1, ..., A j A m-1 The average value is A0; If A j If A > 0, then the interval is an unstable region. If A j If A < A0, then the interval is a stable region. Obtain the stable process parameter domain Ω1 of the input variables: [x 1min ,x 1max ]、[x 2min ,x 2max ]、…、[x nmin ,x nmax ].

5. The method as described in claim 4, characterized in that, Using fatigue life as the criterion, surface condition characteristic constraints are established, and the surface integrity process parameter domain Ω2 is obtained by solving. The constraints on process parameters and surface condition characteristics, as well as the fatigue life criterion conditions, are as follows: ① Establish stable process parameter domain constraints for surface state characteristics: in cmin ≤in c ≤in cmax (4) f zmin ≤f z ≤f zmax (5) a pmin ≤a p ≤a pmax (6) a emin ≤a e ≤a emax (7) In the formula: v cmax v is the maximum milling speed. cmin f is the minimum milling speed. zmax f is the maximum feed per tooth. zmin a is the minimum feed per tooth. pmax a is the maximum value of the milling depth. pmin a is the minimum milling depth. emax a is the maximum value of the milling width. emin This is the minimum value of the milling width; ② Fatigue life constraint conditions: N f ≥N f0 (8) When the stress concentration factor is 1, the microhardness value is the matrix microhardness, and the residual stress equals the material tensile strength, N f equals N f0 ; ③ Surface stress concentration constraint conditions: K stmin ≤K st (v c ,f z ,a p ,a e )≤K stmax (9) In the formula: K st (v c ,f z ,a p ,a e K represents the established mapping model between milling parameters and surface stress concentration. stmin K is the minimum value of the surface stress concentration factor. stmax This represents the maximum value of the surface stress concentration factor; ④ Surface residual stress constraint condition: s rmin ≤σ r (v c ,f z ,a p ,a e )≤σ rmax (10) In the formula: σ r (v c ,f z ,a p ,a e ) represents the established mapping model between milling parameters and surface residual stress, σ rmin σ represents the minimum allowable value of surface residual stress or the maximum allowable value of residual compressive stress. rmax This refers to the maximum allowable value of surface residual stress or the minimum allowable value of residual compressive stress. ⑤ Surface microhardness constraint conditions: HV min ≤HV(v c , f z , a p , a e )≤HV max (11) Where: HV(v) c ,f z ,a p ,a e To establish a mapping model between milling parameters and surface microhardness, HV min HV is the minimum permissible value for surface microhardness. max This represents the maximum permissible value for surface microhardness. ⑥ Damage layer depth constraint condition: According to the residual stress cosine attenuation function prediction model, as shown in Equation (12), when the residual stress value at depth h is less than or equal to the residual stress of the matrix, the depth h at this time is considered to be the residual stress influence layer depth value, which is numerically equal to the damage layer depth D; the residual stress influence layer depth h is calculated by using the inverse function calculation method, and the damage layer depth D is obtained, as shown in Equation (13). Ae -λh cos(ωh+θ)+σ0≤σ0 (12) Let σ rsb = Ae -λh cos(ωh + θ) ≤ 0, then we have: h=f -1 (s rsb ) (13) In the formula: A, λ, ω, θ are the model control factors in the residual stress prediction model. The values ​​of each model control factor under a given milling parameter can be obtained based on the established relationship model between milling parameters and model control factors. D1 p0 (14)​ In the formula: D1 is the depth of the damaged layer produced by precision milling, a p0 The material removal depth in the finishing process.

6. The method as described in claim 5, characterized in that, In step 4, the efficient objective function shown in equation (15) is established. The process parameter domain is optimized within the surface integrity process parameter domain Ω2 using MATLAB mathematical optimization tools to obtain the efficient precision milling process parameter domain Ω3 for surface integrity: maxQ=f(v c ,f z ,a p ,a e ) (15)。

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