Titanium alloy turning surface torsional fatigue performance and distribution characteristic identification method

Abstract

The invention relates to a titanium alloy turning surface torsional fatigue performance and distribution characteristic identification method, and belongs to the technical field of machining. 2, turning a titanium alloy machining surface appearance non-uniform distribution calculation model; 3, characterizing the distribution characteristics of the performance parameters of the surface layer and the subsurface layer of the turned titanium alloy; the invention provides a method for identifying the torsional fatigue performance and the distribution characteristics of the titanium alloy turning surface, and the torsional fatigue performance and the distribution characteristics of the titanium alloy turning surface can be identified under the condition that a sample is not damaged. The fatigue performance of the material is identified through a simulation method and experimental measurement of surface characteristics, and the influence characteristics of machined surface characteristic parameters on the torsional fatigue performance of the material are obtained, so that the loss of a sample in a traditional fatigue test is avoided, and the preliminary evaluation of the torsional fatigue performance of the material can be completed within a relatively short time; the material torsional fatigue performance evaluation period is shortened, and the problems of high cost and long period of a fatigue life test are solved.

Description

Technical Field

[0001] The invention relates to a method for identifying torsional fatigue performance and distribution characteristics of a titanium alloy turning surface, and belongs to the technical field of mechanical processing. Background Art

[0002] Titanium alloys are widely used in aerospace, military, and automotive applications due to their excellent strength-to-lightweight ratio and corrosion resistance. During the turning of titanium alloy components for aviation transmission systems, the instantaneous cutting behavior of the tool changes continuously due to a variety of dynamic random factors. The surface morphology and subsurface properties of the machined workpiece exhibit a non-uniform distribution, directly affecting the torsional strength and torsional fatigue life of the machined surface. Due to the constraints of the workpiece structure and experimental conditions, existing fatigue life analysis and testing methods cannot efficiently and accurately identify the torsional fatigue performance and location of the turned surface.

[0003] In the process of efficient turning of titanium alloy, affected by time-varying factors such as cutting load fluctuation caused by process system vibration, tool wear and serrated chips, the nonlinear contact relationship between the tool and the tool, the multi-strong field coupling distribution in the cutting deformation zone and the instantaneous state parameters of the machined surface and sub-surface are variable, resulting in ambiguity and uncertainty in the distribution characteristics of the titanium alloy processing surface morphology, microstructure, residual stress layer, hardened layer and plastic strain layer. The uneven distribution of the titanium alloy processing surface integrity and the difficulty in identifying potential damage directly affect the torsional fatigue performance evaluation results of the titanium alloy processing surface.

[0004] Under torsional loading, a complex series of mechanical behaviors occur on the material surface, including but not limited to the formation of microcracks, increased surface roughness, and changes in residual stress. These changes can weaken the material's torsional fatigue performance, especially at the weakest points of surface integrity, where damage first occurs and gradually expands, ultimately leading to material failure. Weak points in surface integrity are often where microcracks, tool marks, or other surface defects are located. These areas are more likely to become sources of stress concentration, thereby accelerating the development of fatigue damage. Therefore, identifying and understanding these weak points is crucial to improving the service life of titanium alloy components.

[0005] Existing research on the identification methods of titanium alloy turning surface morphology mostly focuses on the overall surface roughness of the machined surface and ignores the consistency of the machined surface morphology distribution, and cannot fully reflect the unevenness of the machined surface distribution. The fatigue life of the titanium alloy turning surface is affected by the machined surface morphology structure, and the surface roughness alone cannot reflect the changes in the structure of different parts; in the identification of surface and sub-surface performance parameters, existing studies often only consider the effects of residual stress and work hardening on the fatigue performance of titanium alloy turning surfaces and ignore plastic strain. In fatigue performance analysis, the maximum or average value method is often used to solve the torsional strength and fatigue life, resulting in large errors in the solution results and the inability to accurately identify the fracture location. Although experiments can provide direct data, they are costly and time-consuming, and single simulations are limited by the accuracy of model assumptions and boundary conditions. Therefore, there is an urgent need for a comprehensive method combining experiments and simulations to comprehensively reveal the influence of machined surface morphology, surface and subsurface performance parameters, and α+β phase element content distribution on torsional strength and fatigue life, and thus accurately identify the torsional fatigue performance and fracture location of titanium alloy machined surfaces without destroying the workpiece. Summary of the Invention

[0006] This invention proposes a method for identifying the torsional fatigue properties and distribution characteristics of titanium alloy turning surfaces. Based on the analysis of the instantaneous position and cutting mechanical behavior of the turning tool, combined with experimental results of titanium alloy turning surface morphology and finite element analysis of the thermal-mechanical coupling field, this method uses a non-uniform distribution model for titanium alloy turning surface morphology and a method for characterizing the distribution characteristics of surface and subsurface performance parameters. The method obtains the maximum and minimum residual heights and their curvature radii of the titanium alloy turning surface, as well as the distribution characteristics of the plastic strain, residual stress, and work hardening rate of the machined surface along the axial and circumferential directions of the workpiece. Based on this, a model for calculating the torsional strength and torsional fatigue life of the titanium alloy turning surface is constructed. The distribution characteristics of the torsional strength and torsional fatigue life of the titanium alloy turning surface under different turning process conditions are obtained, and the locations of the lowest torsional fatigue strength and minimum torsional fatigue life of the titanium alloy turning surface are identified. The validity of the calculation model and method is verified by mapping the titanium alloy surface structure, α+β phase element content, mechanical properties, torsional strength, and fatigue life. A brief overview of the invention is provided below to provide a basic understanding of certain aspects of the invention. It should be understood that this summary is not an exhaustive overview of the present invention, nor is it intended to identify the key or important parts of the present invention, nor is it intended to limit the scope of the present invention.

[0007] The technical solution of the present invention:

[0008] The method for identifying the torsional fatigue performance and distribution characteristics of the titanium alloy turning surface includes the following steps:

[0009] Step 1, the method for solving the instantaneous cutting behavior of turning tools;

[0010] Step 2, solving the uneven distribution model of the surface morphology of titanium alloy during turning, that is, combining the surface morphology experiment of titanium alloy during turning to construct the uneven distribution model of the surface morphology of titanium alloy during turning;

[0011] Step 3, a method for characterizing the distribution characteristics of performance parameters of the surface and sub-surface layers of the turned titanium alloy is proposed based on the results of the thermal-mechanical coupling field analysis of the turned titanium alloy;

[0012] Step 4, a method for calculating the torsional strength and torsional fatigue life of the surface of titanium alloy during turning, that is, using the maximum residual height, minimum residual height and curvature radius of the surface of titanium alloy during turning and the calculation results of the plastic strain, residual stress and work hardening rate of the surface along the axial and circumferential distribution of the workpiece as model parameters and boundary conditions, a method for calculating the torsional strength and torsional fatigue life of the surface of titanium alloy during turning is proposed, so as to realize the effective correlation between the uneven distribution of the turning surface morphology, surface and sub-surface performance parameters and fatigue performance; obtain the distribution characteristics of the torsional strength and torsional fatigue life of the titanium alloy during turning under different turning process conditions, and identify the position of the lowest torsional fatigue strength and minimum torsional fatigue life of the titanium alloy during turning.

[0013] The present invention has the following beneficial effects:

[0014] 1. During the turning process of titanium alloys, affected by time-varying factors such as process system vibration, tool wear, and cutting load fluctuations caused by serrated chips, the uneven distribution of titanium alloy processing surface integrity and potential damage are difficult to identify, which directly affects the torsional fatigue performance assessment results of titanium alloy processing surfaces. Existing methods for identifying surface characteristic parameters of titanium alloy turning ignore the uneven distribution characteristics of surface characteristic parameters during turning; the present invention proposes a method for characterizing the uneven distribution of titanium alloy processing surface characteristic parameters, using the correlation analysis between parameters and surface characteristic parameters, combined with the finite element analysis method, to reveal the close relationship between different processing surface characteristic parameters and torsional strength and torsional fatigue life, solving the problem that existing methods ignore the differences in the impact of uneven surface distribution on its performance;

[0015] 2. The present invention proposes a method for identifying the torsional fatigue performance and distribution characteristics of the titanium alloy turning surface, which allows the fatigue performance to be identified and the influence of the characteristic parameters of the machining surface on the torsional fatigue performance of the material to be obtained through simulation methods and experimental measurement of surface characteristics without destroying the sample. It avoids the loss of the sample caused by traditional fatigue tests, can complete the preliminary evaluation of the torsional fatigue performance of the material in a relatively short time, shortens the evaluation cycle of the torsional fatigue performance of the material, and solves the problems of high cost and long cycle of fatigue life tests. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of tool structure parameters and its coordinate system according to a specific embodiment of the present invention; Figure 2 Schematic diagram of the instantaneous cutting posture of turning according to a specific embodiment of the present invention; Figure 3 Schematic diagram of the instantaneous cutting layer and instantaneous force of the turning surface according to a specific embodiment of the present invention, wherein a) is a schematic diagram of the instantaneous force at the lowest point on the workpiece surface, b) is a schematic diagram of the cutting layer parameters, and c) is a schematic diagram of the instantaneous force at any point on the transition arc;

[0017] Figure 4 It is a schematic diagram of surface topography feature recognition of simulated surface processing according to a specific embodiment of the present invention; Figure 5 is a schematic diagram of the instantaneous state of the subsurface during the turning process according to a specific embodiment of the present invention; Figure 6 is a schematic diagram of the instantaneous state of the subsurface layer of a turned surface under a torsional load according to a specific embodiment of the present invention; Figure 7 This is a schematic diagram of a measurement site using an ultra-depth-of-field microscope according to a specific embodiment of the present invention; Figure 8 is a schematic diagram of surface topography feature recognition for a modified surface machining process according to a specific embodiment of the present invention; Figure 9 2 is a schematic diagram comparing the surface before and after correction and the experimental surface according to a specific embodiment of the present invention; Figure 10 is a schematic diagram of corrected cross-section feature point recognition according to a specific embodiment of the present invention; Figure 11 Schematic diagram of stress and strain field and characteristic point selection for turning titanium alloy according to a specific embodiment of the present invention, wherein a) is a schematic diagram of stress field and characteristic point selection for turning titanium alloy, and b) is a schematic diagram of strain field and characteristic point selection for turning titanium alloy;

[0018] Figure 12 Schematic diagrams of the plastic strain distribution of cross sections at different characteristic points according to a specific embodiment of the present invention, wherein a) is a schematic diagram of the plastic strain of cross section a1, b) is a schematic diagram of the plastic strain of cross section a2, c) is a schematic diagram of the plastic strain of cross section a3, d) is the plastic strain of cross section a4, and e) is the plastic strain of cross section a5;

[0019] Figure 13 Schematic diagrams of residual stress distribution of cross sections at different characteristic points according to a specific embodiment of the present invention, wherein a) is a schematic diagram of residual stress in cross section a1, b) is a schematic diagram of residual stress in cross section a2, c) is a schematic diagram of residual stress in cross section a3, d) is a schematic diagram of residual stress in cross section a4, and e) is a schematic diagram of residual stress in cross section a5;

[0020] Figure 14It is a schematic diagram of a stress-strain curve of a characteristic point of the cross section a1 described in a specific embodiment of the present invention;

[0021] Figure 15 Schematic diagram of the distribution of work hardening rates of cross sections at different characteristic points according to a specific embodiment of the present invention, wherein a) is a schematic diagram of the work hardening rate of cross section a1, b) is a schematic diagram of the work hardening rate of cross section a2, c) is a schematic diagram of the work hardening rate of cross section a3, d) is a schematic diagram of the work hardening rate of cross section a4, and e) is a schematic diagram of the work hardening rate of cross section a5;

[0022] Figure 16 Schematic diagram of applying initial performance parameters of a titanium alloy workpiece according to a specific embodiment of the present invention; Figure 17 It is a schematic diagram of equivalent stress during the torsion process of the modified surface under the initial performance parameter conditions of the present invention; Figure 18 Schematic diagram of the torsion process of the modified surface under the initial performance parameter conditions of the present invention; Figure 19 Schematic diagram of the torsion process of the simulated surface under the initial performance parameter conditions of the present invention; Figure 20 Schematic diagram of fatigue life (T=90 N·m) and maximum torque under the same torque in different groups according to a specific embodiment of the present invention;

[0023] Figure 21 Schematic diagram of the fracture position under the same torque (T=90 N·m) of different groups according to a specific embodiment of the present invention;

[0024] Figure 22 Schematic diagram of maximum torque and torsional breaking point of groups 1, 2, and 3 according to a specific embodiment of the present invention; Figure 23 Schematic diagram of maximum torque and torsional breaking point of groups 4, 5, and 6 according to a specific embodiment of the present invention; Figure 24 Schematic diagram of maximum torque and torsional breaking point of groups 7, 8, 9, and 10 according to the specific embodiment of the present invention; Figure 25 Schematic diagram of maximum torque and torsional breaking point of groups 10 and 11 according to a specific embodiment of the present invention; Figure 26 This is a schematic diagram of fatigue life distribution of schemes a1 to a5 according to a specific embodiment of the present invention; Figure 27 This is a schematic diagram of the simulated morphology and the corrected morphology of the second solution described in the specific embodiment of the present invention; Figure 28 2 is a schematic diagram comparing characteristic points of the simulated surface and the corrected surface according to the second embodiment of the present invention; Figure 28 Schematic diagram of maximum torque and torsional breaking point of solution 2 according to a specific embodiment of the present invention; Figure 29 Schematic diagram of the twisting result of the second solution according to the specific embodiment of the present invention; Figure 30This is a schematic diagram of fatigue life distribution of scheme 2 a1 to a5 according to a specific embodiment of the present invention;

[0025] Figure 31 1 is a schematic diagram of the surface morphology detection results of characteristic points in solution 1 according to a specific embodiment of the present invention, wherein a) is a schematic diagram of the surface morphology of a1, b) is a schematic diagram of the surface morphology of a2, c) is a schematic diagram of the surface morphology of a3, d) is a schematic diagram of the surface morphology of a4, and e) is a schematic diagram of the surface morphology of a5;

[0026] Figure 32 1 is a schematic diagram of the normalized results of the standard deviation of the surface topography characteristic parameters processed in the first embodiment of the present invention;

[0027] Figure 33 Schematic diagram of surface morphology detection results of characteristic points of solution 2 according to a specific embodiment of the present invention, wherein a) is a schematic diagram of the surface morphology of a1, b) is a schematic diagram of the surface morphology of a2, c) is a schematic diagram of the surface morphology of a3, d) is a schematic diagram of the surface morphology of a4, and e) is a schematic diagram of the surface morphology of a5;

[0028] Figure 34 1 is a schematic diagram comparing the normalized standard deviations of different morphological feature parameters of Scheme 1 and Scheme 2 according to the specific embodiments of the present invention, wherein a) is a schematic diagram of a distance of xk, b) is a schematic diagram of a distance of zk, c) is a schematic diagram of a distance of Δzk, d) is a schematic diagram of a distance of xm, e) is a schematic diagram of a distance of zm, f) is a schematic diagram of a distance of Δzm, g) is a schematic diagram of a distance of Δxkm, h) is a schematic diagram of a distance of Δzkm, and i) is a schematic diagram of a distance of Rm;

[0029] Figure 35 1 is a schematic diagram of the SEM scanning results and surface scanning element distribution of Scheme 1 and Scheme 2 described in the specific embodiments of the present invention, wherein a) is a schematic diagram of the SEM and EDS surface scanning measurement results of Scheme 1, and b) is a schematic diagram of the SEM and EDS surface scanning measurement results of Scheme 2;

[0030] Figure 36 is a schematic diagram of different surface element contents according to a specific embodiment of the present invention; Figure 37 Schematic diagram of point scanning SEM and EDS measurement results of different schemes described in the specific embodiment of the present invention; Figure 38 Schematic diagram of a point scanning test result of the scheme described in a specific embodiment of the present invention, wherein a) is a schematic diagram of the distribution of each element along the feed direction, and b) is a schematic diagram of the element content at different sampling points;

[0031] Figure 39Schematic diagram of the scanning test results of the second scheme according to the specific embodiment of the present invention, wherein a) is a schematic diagram of the distribution of each element along the feed direction, and b) is a schematic diagram of the element content at different sampling points; DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions, and advantages of the present invention more clearly apparent, the present invention is described below using specific embodiments shown in the accompanying drawings. However, it should be understood that these descriptions are merely illustrative and are not intended to limit the scope of the present invention. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present invention.

[0033] Specific implementation method 1: Combination Figure 1-Figure 39 This embodiment describes a method for identifying the torsional fatigue performance and distribution characteristics of a titanium alloy turning surface, including the following steps:

[0034] Step 1, the method for solving the instantaneous cutting behavior of turning tools;

[0035] In order to reveal the instantaneous dynamic characteristics of the cutting edge during turning, the structure of the turning tool and its cutting edge is characterized by geometric modeling, and the corresponding coordinate system is established, such as Figure 1 shown.

[0036] Figure 1 In, o a -x a y a z a is the tool coordinate system, the coordinate origin o of the tool coordinate system a The intersection of the plane where the highest point of the cutting edge of the turning tool is located and the projection of the maximum radial overhang point of the cutting edge, the line connecting the coordinate origin and the highest point of the cutting edge is taken as z a The coordinate origin is the line connecting the maximum radial overhang point of the cutting edge as the x-axis direction. a Axis direction, determine y according to the Cartesian coordinate system principle a Axis. q is the left boundary point of the cutting edge, m is the upper boundary point of the cutting edge, p is any point on the cutting edge, l is the length of point p on the primary and secondary cutting edges, φ a is the arc angle of the turning tool transition, φ a ' is the angle between the line connecting point p, point q and the center of the tool tip arc, l a is the cutting edge equation. B is the handle length, x D is the turning tool height, x B is the shank width, r a is the fillet radius. The main angles of the turning tool include the main deflection angle κ r , secondary deflection angle κ r ', tool tip angle ε r , blade inclination angle λs, radial rake angle γn , radial relief angle α n , radial wedge angle β n , axial rake angle γ0, axial back angle α0, axial wedge angle β0.

[0037] Depend on Figure 1 It can be seen that the solution method of the equation of the main cutting edge in the turning tool coordinate system is:

[0038]

[0039] The solution method for the equation of the tool tip arc of the cutting edge transition section in the tool tooth coordinate system is:

[0040]

[0041] The solution method for the equation of the secondary cutting edge in the tooth coordinate system is:

[0042]

[0043] The solution method for the cutting edge equation in the tooth coordinate system is:

[0044]

[0045] In order to reveal the relative motion relationship between the workpiece and the tool and control the formation process of the machined surface, the relative motion of the tool, workpiece, tool coordinate system, cutting coordinate system, workpiece coordinate system and reference coordinate system is characterized, such as Figure 2 The explanation of the dynamic cutting process variables of the turning tool is shown in the table.

[0046] Table 1 Explanation of variables in the dynamic cutting process of turning tools

[0047]

[0048]

[0049] According to the figure, the origin of the cutting coordinate system is o c The trajectory equation in the machine tool coordinate system can be expressed as:

[0050] L c (x c (t),y c (t),z c (t))=Φ0·[0 0 0 1] T (5)

[0051] Where: The transformation matrix Φ0 is T2T1M1. T2 is the translation matrix between the tool and the reference coordinate system; T1 is the rotation matrix between the tool coordinate system and the cutting coordinate system; M1 is the rotation matrix between the workpiece coordinate system and the reference coordinate system.

[0052] Figure 3 In the figure, point m is the highest point on the cutting edge, point p is any point on the transition arc of the cutting edge, γ0 is the tool rake angle, α0 is the tool back angle, β0 is the friction angle on the rake face, φ p is the shear angle at point p, f is the feed per revolution, κ rp is the instantaneous principal deflection angle at point p, h D is the thickness of the cutting layer, ds is the instantaneous width of the cutting layer at point p, A D is the actual cutting layer area, ΔA D is the cutting residual area, R m is the radius of gyration of point m, R p is the radius of gyration of point p, v c is the cutting speed, F c Main cutting force, v m is the friction speed, F γ is the friction force on the rake face, F γN is the normal force of the friction force on the rake face, F α is the friction force on the flank face, F αN is the normal force of the friction force on the flank face, v sh is the shear velocity, F sh is the shear force, F shN is the normal force of the shear force, and F is the resultant force of the front and rear tool faces friction and their normal forces, the resultant force of the shear force and its normal forces, and the resultant force of the main cutting force and the feed force.

[0053] The calculation method of the instantaneous cutting layer thickness at any point p is shown in formula (6).

[0054] h D =f·sinκ rp ·cosλ s (6)

[0055] Cutting layer area A D The solution method is shown in formula (7).

[0056]

[0057] According to the figure, equations (34) and (35), τ represents the shear stress on the shear surface, and the solution method for each force at point p is shown in equations (8) to (11).

[0058]

[0059] F sh =F·cos(φ p +β0-γ0) (9)

[0060]

[0061] Ignoring cutting edge wear and the extrusion effect of the flank face on the machined transition surface, the final workpiece contour is essentially the result of the superposition of the cutting edge motion paths from two adjacent cutting cycles. While the cutting edge generates the transition surface in the previous cutting cycle, the subsequent cycle partially eliminates the surface formed in the previous cycle through material removal and forms a new transition surface. The intersection of the cutting edge paths from the two cycles constitutes the periodic residual height.

[0062] The solutions for the transition surface during turning are shown in equations (12) to (15).

[0063]

[0064] Where: H a1 The machining transition surface formed by the main cutting edge, H a2 H is the machining transition surface formed by the transition arc. a3 A machined transition surface formed for the secondary cutting edge.

[0065] t s Indicates the initial cutting moment of each workpiece revolution, t e Indicates the moment when the cutting ends per revolution of the workpiece. The equation of the machining transition surface formed when the workpiece rotates i times is:

[0066]

[0067] The machining transition surface formed when the workpiece rotates i+1 times and intersects with the machining transition surface formed by the previous turning is expressed as:

[0068]

[0069] The machining transition surface formed when the workpiece rotates i+2 times and the machining transition surface formed by the previous turning to form the final turning surface can be expressed as:

[0070]

[0071] According to the above model construction, the simulation and solution software used is Matlab, and the surface morphology of the turning surface is simulated, where the feed rate per revolution f is 0.1mm, the spindle speed n is 983r / min, and the cutting depth a is 0.1mm. p The residual feature point distribution recognition method of turning surface is as follows: Figure 4 shown.

[0072] Intercept the intersection line between the current cutting position and the xoz plane, the feature point p k i 、p m i The solution is shown in formula (19) and formula (20):

[0073]

[0074] During the turning process, the performance parameters of the titanium alloy subsurface layer are constantly changing, such as Figure 5 、 Figure 6 As shown. The characteristic parameters of the subsurface are important indicators for evaluating material properties, mainly including plastic strain, residual stress and work hardening rate. These three characteristic parameters not only characterize the deformation and stress state of the material during processing, but also have a major impact on the fatigue life of the material. Plastic strain is the irreversible deformation of the material during loading. The plastic strain generated by titanium alloy during turning can improve fatigue resistance, but too high a strain will cause fatigue cracks and reduce fatigue life. Residual stress is the internal stress generated by uneven deformation and still exists after the external load is removed. Appropriate residual stress can improve fatigue strength, but uneven distribution will cause stress gradients and reduce fatigue life. The work hardening rate indicates the hardness of the material increased due to deformation. A higher work hardening rate helps to improve wear resistance and fatigue resistance.

[0075] The set of design variables that determine the instantaneous cutting posture change during turning is shown in Equation (21). Turning process parameters determine the surface morphology and subsurface performance parameters of titanium alloy turning, both of which play a decisive role in the torsional strength and fatigue life of titanium alloy workpieces. The relationship between the three is shown in Equations (22) to (27).

[0076] D = {κ r ,κ r ',γ,β,r a} (twenty one)

[0077] Q1={x k ,x m ,△x k ,△x m ,R m ,Ra} (22)

[0078] Q1=f1(a p ,n,f) (23)

[0079] Q2={ε r ,h ε ,σ r ,h r ,η,h η} (twenty four)

[0080] Q2=f2(a p ,n,f) (25)

[0081] Q3={τ max ,N f} (26)

[0082] Q3=f3(Q1,Q2) (27)

[0083] Step 2, solving the model of uneven distribution of surface morphology in turning titanium alloy;

[0084] In actual machining, the tool's instantaneous position exhibits dynamic fluctuations due to vibration, precision deviation, and other complex conditions, leading to random evolution of the turned surface microtopography. Through turning experiments, the titanium alloy surface topography was measured, cross-sectional data points were extracted, and multi-scheme regression was performed to modify the titanium alloy turning surface topography simulation model.

[0085] A CNC lathe was used to perform turning experiments on a titanium alloy workpiece. A 55° external turning tool from Walter was used. External turning was performed with a lead angle of 93°. The toolholder was PDJNR2525M1506, the insert was DNMG150604, and the tool tip radius was 0.4 mm. The cutting parameters are shown in Table 2.

[0086] Table 2 Turning experiment plan

[0087]

[0088] In order to characterize the turning surface morphology, the experimental instrument PZ-CS3500A ultra-depth microscope was used to measure the turning surface morphology. Figure 7 shown.

[0089] The surface morphology characteristic parameters measured at different sampling positions on the same surface of the titanium alloy workpiece are all a set of values. According to the change of the characteristic parameters with the detection position, the variable sets shown in formulas (28) to (33) are established.

[0090]

[0091]

[0092] When the process parameters change, the surface topography characteristic parameters of different process parameters and sampling positions are fitted and regressed by equations (28) to (33), and the variable set that determines the turning surface topography can be obtained:

[0093] S a ={x k (z k ,θ k ),x m (z m ,θ m ),△x k (z k ,θk ),△x m (z k ,θ m ),R m (z m ,θ m ),Ra} (34)

[0094] Any characteristic parameter in the set is denoted as s a , we can get the fitting regression equation shown in (35). The fitting regression equation coefficients are shown in Table 5.

[0095]

[0096] Where: β, α1~α5 are the coefficients of the fitting regression equation, s0 is the characteristic parameter value corresponding to the ideal uniform surface, when the machined surface is an ideal uniform surface, β=1, α1~α5 are all 0, that is, s a =s0.

[0097] s0 is related to tool parameters, process parameters, etc., and its variable set is shown in formula (36).

[0098] s0={D,a p ,n,f,L c} (36)

[0099] Table 3 Fitting coefficients of turning surface topography characteristic parameters

[0100]

[0101] Based on the above turning surface solution correction model, Matlab is used to solve the turning topography with the correction coefficient introduced, and the turning topography simulation is performed again at the same position. The simulation results are as follows: Figure 8 shown.

[0102] Compare the cross-sectional curves of the machined surface morphology before and after correction with the experimental measurement cross-sectional curves, such as Figure 9 The correlation degree was analyzed using grey correlation. The correlation between the cross-sectional curve and the experimental curve before correction was 0.81, and the correlation after correction was 0.93, indicating a strong correlation. This demonstrates the feasibility of correcting the turning surface topography based on the fitted regression function.

[0103] The positions and spacings of different characteristic points of the turned surface topography were calculated, and the results are shown in Table 4. The results show that after surface topography correction, the relative errors of the positions and spacings in the feed and depth directions are mostly small, with 50.00% between 0 and 5%, 16.67% between 5 and 10%, and 8.33% between 10 and 15%, totaling 75%. This indicates that the characteristic parameters at most locations are highly correlated.

[0104] Table 4 Positions of the highest and lowest points of the machined surface topography and their errors

[0105]

[0106]

[0107] Step 3, characterizing the distribution characteristics of performance parameters of the surface and subsurface layers of the titanium alloy during turning;

[0108] TC6 titanium alloy contains α-stabilizing element Al, isomorphous β-stabilizing element Mo, eutectoid β-stabilizing element Cr, Fe and neutral element Si, and the β-stabilizing coefficient K p =0.6. The specific material properties are shown in Table 5.

[0109] Table 5 TC6 material properties

[0110]

[0111] A 3D finishing turning model for the external cylindrical turning of a titanium alloy workpiece was established using ABAQUS software. Because the interaction between the workpiece and tool generates significant forces and heat during turning, a thermomechanical coupling simulation was employed. Tool rotational speed and feed rate were added as boundary conditions to ensure the relative motion matched actual turning. The workpiece material was TC6, and the tool material was WC carbide. The physical parameters are shown in Table 6.

[0112] Table 6 WC material parameters

[0113]

[0114] Since the elastic modulus of the tool material (705GPa) is much higher than that of the workpiece material (109.8GPa), the deformation of the tool during the machining process is small, so the tool can be regarded as a rigid body during modeling. The boundary conditions of the model are set as follows: the bottom surface and both ends of the workpiece are fixed, and the tool moves the workpiece at a preset feed rate and speed. The thermal conductivity of titanium alloy is low, and a large amount of heat is generated due to friction during the cutting process, which affects its mechanical properties. During the turning process of titanium alloy TC6, the heat generated by plastic deformation is difficult to dissipate quickly in a short period of time, so the cutting process of titanium alloy can be regarded as an adiabatic shear process.

[0115] Based on the requirements of multi-field coupled cutting simulation, this paper selects the Johnson-Cook dynamic response constitutive model as the benchmark for material behavior analysis, as shown in Equation (37).

[0116]

[0117] Where: Plastic JC stress is a function of strain, strain rate and temperature, A, B, n, C, m are model constants. σ is the flow stress, ε is the equivalent plastic strain, is the strain rate, is the reference true strain, T is the operating temperature, T melt is the melting temperature of the material, and T0 is the ambient temperature.

[0118] In terms of meshing, the workpiece uses a linear reduced integration 8-node hexahedron unit, and the tool uses a tetrahedron temperature-displacement coupling unit. The contact relationship between the tool and the workpiece is defined as a master-slave contact pair, where the tool is the master surface and the workpiece is the slave surface. The tool-workpiece contact interface is parameterized using a kinematic friction model, the friction coefficient μ=0.1 is set, and based on the friction heat theory, 100% of the interface energy dissipation is converted into a heat flow boundary condition. When simulating the traditional turning process, the initial temperature of the workpiece is set to room temperature 20°C. Through the constructed finite element model of turning titanium alloy, the finite element simulation of the titanium alloy outer cylindrical turning process is carried out, and 5 feature points a1 to a5 are selected for the xoz surface. Taking the section A1-A1 at point a1 as an example, the stress and strain field distribution is as follows Figure 11 As shown in a) and b), each feature point is 2.5 mm apart and the total length is 10 mm.

[0119] Post-processing analysis is performed in ABAQUS to extract stress and strain data on the surface and depth directions of the selected feature points and their feature sections, and then the data is exported through the interface between ABAQUS and Excel for analysis and processing. Plastic strain refers to the irreversible deformation of a material under the action of external force, which usually occurs after the yield point of the material. In the metal processing process, the distribution and depth of plastic strain directly affect the mechanical properties and service life of the material. The plastic strain of each feature point on the section a1 to a5 is as follows: Figure 12 shown.

[0120] Residual stress is one of the important indicators to measure the surface quality of finishing turning, and it is also one of the important indicators to judge the cutting performance of workpiece materials. Residual stress has an extremely important influence on the fatigue strength, durability and wear resistance of workpiece materials. Figure 13 It is the residual stress variation curve of each characteristic point of the section a1~a5.

[0121] The work hardening rate characterizes the degree of hardness increase of the material surface due to the combined action of plastic deformation and thermodynamic effects during machining. Taking a1 as an example, the stress-strain curves of each characteristic point are as follows: Figure 14 As shown in Equation (38), it is obtained by derivation of stress with respect to strain in the plastic deformation stage after removing the elastic deformation stage.

[0122]

[0123] Where: σ is the stress in the plastic deformation stage, ε is the strain in the plastic deformation stage.

[0124] By using formula (38) to derive the stress-strain plastic deformation stage, we can obtain Figure 15 The curve showing the change of work hardening rate with depth.

[0125] Step 4: Calculation method for torsional strength and torsional fatigue life of titanium alloy machining surface;

[0126] When evaluating the surface performance of titanium alloys, fatigue life is a key consideration. The quality of the surface performance directly determines the fatigue life of the titanium alloy workpiece's surface. Fatigue life not only reflects the material's endurance under dynamic loads but also reveals the impact of surface properties on the material's long-term reliability. Therefore, by combining surface performance and fatigue life research, it is possible to more accurately predict and optimize the performance of titanium alloys in practical applications, ensuring their reliability and durability in high-stress and complex environments.

[0127] When modeling titanium alloy workpieces, the rough surface generator in ABAQUS is used to generate workpieces with different surface morphologies, and the generated rough surface is applied to the surface of the titanium alloy workpiece. Taking the section a4 as an example, the performance parameter distribution is as follows: Figure 16 shown.

[0128] Figure 16 In the equation (a), g1 is the distribution function of plastic strain and its depth, g2 is the distribution function of residual stress and its depth, g3 is the distribution function of work hardening rate and its depth, and G4(x, y, z) is the turning transition surface equation corresponding to a4.

[0129] The torsional dynamics simulation of the modified surface under the initial performance parameters was performed using ABAQUS. The maximum stress of 670 MPa in the frame before the torsional fracture was extracted and recorded as the torsional strength. The corresponding maximum torque under the current conditions was 360 N·m. When the workpiece was torsionally twisted, the nominal shear stress it was subjected to was approximately 167.5 MPa (≈τ max / 4), when the nominal shear stress is 167.5MPa, the torque T≈90N·m, so in the fatigue life analysis, T is selected as 90N·m for finite element simulation. The equivalent stress and plastic strain of the modified surface torsion process under the maximum torque and the torsion process are as follows Figure 17 and Figure 18 As shown in the figure, the torsion process of the titanium alloy simulation surface under the maximum torque is as follows Figure 19 shown.

[0130] Depend on Figure 18 and Figure 19It can be seen that due to the different initial characteristic parameters applied to the surface and at different locations, the stress and strain distribution of the ferroalloy workpiece during plastic deformation under torque exhibits significant non-uniformity. This non-uniformity can lead to localized stress concentration in certain areas of the workpiece, resulting in early damage or fatigue failure, which in turn affects the mechanical properties and fatigue life of the material. By defining different performance parameters of the machined surface morphology and surface subsurface layers, we analyze the influence of these parameters on the torsional strength, maximum torque, fatigue life, and fracture location of the titanium alloy workpiece. The performance parameter settings are shown in Table 7.

[0131] Table 7 Simulation surface performance parameter settings

[0132]

[0133]

[0134] Based on the fatigue life analysis function of FE-SAFE, an analysis process is established for the torsional fatigue life of TC6 titanium alloy turned workpieces and the key parts of the process are analyzed. The fatigue analysis of FE-SAFE needs to be based on the results of ABAQUS, so it is necessary to use ABAQUS to model the research object and output the required stress and strain parameters. FE-SAFE uses the Rainflow algorithm at each node to count the number of fatigue cycles to determine the fatigue life. The fatigue life of groups 1 to 11 under the same torque is compared with the maximum torque as shown in Figure 2. Figure 20 The fracture location is shown in Figure 21 shown.

[0135] In FE-SAFE, both surface roughness and residual stress can be used to impose corresponding boundary conditions on the workpiece during pre-processing. Material constitutive parameters are calibrated based on the Seeger algorithm, defining key indicators such as elastic modulus and torsional strength, and automatically fitting the SN curve that characterizes fatigue life. After determining the material properties, the stress and strain results in the final torsion state of the workpiece are selected and imported into FE-SAFE. The SN curve algorithm is then used to perform fatigue life analysis on TC6 titanium alloy turned workpieces.

[0136] Groups 1 to 3 are simulation experiments of different processed surface morphologies under the same surface and subsurface performance parameters. Their initial states, torsional stress fields and torsional fracture points are shown in Figure 2. Figure 22As shown in the figure, when the machined surface morphology is an ideal smooth surface and the surface and subsurface performance parameters are all 0, the torsional strength of the titanium alloy workpiece reaches a maximum of 1170MPa, the corresponding maximum torque is 630N·m, and the torsional fracture position is any point on the central section where a3 is located; when the machined surface morphology is a surface with uniform peaks and troughs and their spacing and the surface and subsurface performance parameters are all 0, the maximum torque of the titanium alloy workpiece decreases to 340N·m, a significant decrease, and the torsional fracture point is still any point on the central section where a3 is located; when the machined surface morphology is a modified surface and the surface and subsurface performance parameters are all 0, the maximum torque continues to decrease to 300N·m, but the position of its torsional fracture point is between the sections corresponding to a3 and a4, and the fracture point is unique. The solution results show that when the machined surface morphology is different, the maximum torque, fatigue life and fracture position of the titanium alloy workpiece are different.

[0137] Groups 4, 5, and 6 are the simulation results of single factor analysis of the mean values ​​of plastic strain, work hardening rate, and residual stress. Figure 23 As shown in the figure. The fracture locations are all arbitrary points on the central section where a3 is located. When plastic strain acts alone, the maximum torque is lower than that of group 3, indicating that plastic strain has a negative effect on the maximum torque. When residual stress acts alone, the maximum torque increases by 30.00% compared with group 3, increasing to 390 N·m, a significant improvement. When the work hardening rate acts alone, the maximum torque increases by 16.67% compared with group 3, increasing to 350 N·m, which is a smaller increase than the residual stress.

[0138] The maximum torque, fatigue life and fracture position corresponding to groups 7 to 10 are as follows Figure 24 As shown. The minimum, average, maximum and solution results of the three surface and sub-surface performance parameters under the same processing surface morphology conditions are shown. As can be seen from the figure, when the performance parameter increases from the minimum value to the maximum value, the maximum torque of groups 7 to 9 increases continuously, which are 450N·m, 480N·m and 560N·m respectively, but the torsional fracture position does not change. Under the conditions of group 10, its maximum torque is less than 450N·m of group 7, which is 420N·m. This shows that under uneven distribution, the load capacity and maximum torque decrease. From the solution results, it can be seen that when the processing surface morphology is the same, applying uniform surface and sub-surface performance parameters has different effects on the maximum torque and fatigue life, but has no effect on the location where torsional fracture occurs. Among the three, residual stress has the greatest positive effect on the maximum torque and fatigue life, followed by work hardening rate, and plastic strain will reduce the maximum torque and fatigue life.

[0139] The maximum torque, fatigue life and fracture position corresponding to groups 10 and 11 are as follows Figure 25As shown in the figure, under the same initial performance parameters of the surface and subsurface layers, different machined surface topography results in different maximum torques and torsional fracture points for titanium alloy workpieces, indicating that machined surface topography plays a decisive role in determining the torsional fracture location of titanium alloy workpieces. When the performance parameters of the surface and subsurface layers are the same, and the machined surface topography is more uniformly distributed, the maximum torque and fatigue life of the titanium alloy workpiece are higher than those of the modified titanium alloy machined surface.

[0140] The maximum torque and fatigue life of each point a1 to a5 of the current turning surface are calculated. The results are as follows: Figure 26 As shown in the figure. The solution results show that on the titanium alloy turning surface, the maximum torque and fatigue life distribution of different characteristic points along the axial direction still vary greatly. The characteristic points close to the fracture location have lower maximum torque and fatigue life. According to the solution results, although the characteristic points a1 to a5 show a certain consistency under the same process parameters, the overall consistency is not high, and the fatigue life of the characteristic point a4 is significantly lower than that of other points, indicating potential processing problems and uneven distribution.

[0141] Step 5, verifying the identification method of torsional fatigue performance and distribution characteristics of the titanium alloy turning surface;

[0142] In order to verify the effectiveness of the method for identifying parameters of torsional fatigue performance of titanium alloy turning surface, the turning process parameters in the previous article are used as scheme 1. The same turning tool, workpiece, installation method, cutting method and detection method as scheme 1 are used to keep the cutting efficiency unchanged. Turning simulation and experimental detection are carried out, in which the feed rate per revolution f is 0.1mm, the spindle speed n is 1081r / min, and the cutting depth a is 0.1mm. p 0.1mm.

[0143] The surface morphology of the machined surface of Scheme 2 is solved and corrected and compared, such as Figure 27 、 Figure 28 shown.

[0144] The degree of correlation was analyzed using grey correlation. The correlation between the cross-sectional curve and the experimental curve before correction was 0.88, and the correlation after correction was 0.97, indicating a strong correlation. Both correlations were higher than those in Scheme 1.

[0145] Finite element simulation of titanium alloy turning process under the turning process parameters of Scheme 2 was carried out by ABAQUS, and the same 5 characteristic points as those of Scheme 1 were selected. The initial performance parameters of the titanium alloy workpiece were defined by the same performance parameter definition method as that of Scheme 1, and the torsion load was applied. The torsion results of Scheme 2 are shown as follows: Figure 29 shown.

[0146] The finite element results of the previous frame where torsional fracture occurs are imported into FE-SAFE for fatigue life calculation. The fatigue life calculation result of Scheme 2 is 3.72×10 5 The maximum torque, fatigue life and fracture position of Scheme 1 and Scheme 2 are shown in Table 8.

[0147] Table 8 Maximum torque, fatigue life and fracture position of Scheme 1 and Scheme 2

[0148]

[0149] As shown in Table 8, compared with Scheme 1, the maximum torque of Scheme 2 is increased by 19.44%, the fatigue life is increased by 45.88%, and the axial position of the torsional fracture is closer to the midpoint of the titanium alloy workpiece, indicating that the performance parameters and surface morphology of Scheme 2 are more uniformly distributed.

[0150] The maximum torque and fatigue life of each point a1 to a5 in Scheme 2 are calculated, and the results are as follows: Figure 30 Compared to Option 1, after process parameter optimization, Option 2 achieved improved overall consistency. The maximum torque and fatigue life distribution in the radial direction of the turned surface were more uniform, and both the maximum torque and fatigue life at each feature point were improved. By analyzing the fatigue life at the corresponding angles of different feature points to determine their degree of non-uniformity, the error at feature point a3 was relatively high at all angles, indicating that fatigue fracture is more likely to occur near a3.

[0151] In the surface morphology detection, the detection position is within 1mm of the characteristic point of the titanium alloy workpiece. The detection results of scheme 1 are as follows Figure 31 As shown. The surface morphology at a1 and a2 is relatively uniform and flat. Figure 32 It can be seen that the distribution of characteristic parameters of the surface topography processed in Scheme 1 is more uneven at points a3 and a4 than at other characteristic points. Starting at a3, the surface shows a noticeable increase in roughness and irregular texture, and surface defects are more pronounced at a4, indicating the presence of localized microscopic defects or stress concentration areas in the material. Due to these defects and unevenness, fatigue stress easily accumulates between a3 and a4, inducing crack propagation and leading to fracture in this area.

[0152] The test results of option 2 are as follows Figure 33 As shown. The surface morphology at a1 and a2 is relatively flat and regular in texture, while the surface morphology at a3 shows more obvious roughness and increased microscopic defects. Due to pre-existing defects or stress concentration inside the material, stress is likely to accumulate between a2 and a3 during the fatigue test and expand cracks, causing the fracture point to occur in this area. The surface morphology of the cross section of the different characteristic points of Scheme 2 is shown as follows. The normalized comparison results of the characteristic parameter standard deviations of Scheme 1 and Scheme 2 are shown as follows. Figure 34As shown in the figure, Scheme 2 generally outperforms Scheme 1 in most parameters. In particular, the normalized values ​​of parameters x and z are close to 1, demonstrating Scheme 2's advantage in machining accuracy. When comparing the spacing Δx and Δz in different directions, Scheme 2 also shows significantly more consistent performance across most feature points.

[0153] The change in element content directly reflects the difference in surface characteristic parameters and can effectively reveal the performance differences in different areas of the machined surface. The element composition and content ratio of the titanium alloy turning surface were obtained using SU5000 scanning electron microscope and energy spectrum analyzer, and the element content of different machined surfaces and different positions of the same machined surface were analyzed and compared. The SEM scanning results of Scheme 1 and Scheme 2 and their surface scanning element distribution are shown in the figure. Figure 35 shown.

[0154] Depend on Figure 35 It can be seen that the surface quality of the turning process of Scheme 1 is lower than that of Scheme 2, with more surface defects, local microcracks and holes, indicating that local stress concentration occurred during the turning process. The surface of Scheme 2 is smoother and denser. By changing the turning process parameters, the surface quality of the titanium alloy turning process is effectively improved. The element content of the turning surface of Scheme 1 and Scheme 2 is as follows Figure 36 shown.

[0155] Depend on Figure 36 It can be seen that after turning, the α-phase elements C and N content increased significantly, while the Al content decreased by approximately 25%. The neutral element Si decreased from 0.40% to 0.25%, and the matrix element Ti decreased significantly. Compared with Scheme 1, Scheme 2 had a higher β-phase content, with Cr, Fe, and Mo increasing by 83.87%, 16.28%, and 45%, respectively. The α-phase content of the three different surfaces was 7.13%, 18.25%, and 15.85%, respectively, while the β-phase content was 6.00%, 3.87%, and 5.97%, respectively. The ideal unmachined titanium alloy workpiece has a lower α-phase content and a higher β-phase content, resulting in better mechanical properties. However, after turning, Scheme 1 has a higher α-phase content and a lower β-phase content than Scheme 2, resulting in poorer mechanical properties and a shorter fatigue life.

[0156] The point scanning energy spectrum detection is performed on different characteristic points of the turning surface of Scheme 1. The detection results are as follows Figure 37 、 Figure 38As shown. The proportion of α-phase elements in a1 to a5 is 17.06%, 16.54%, 17.20%, 18.5%, and 15.09%, respectively, and the proportion of β-phase elements is 4.77%, 4.89%, 3.74%, 3.58%, and 5.04%, respectively. The proportion of α-phase elements in a3 and a4 is higher than that of the other points, and the proportion of β-phase elements is lower than that of the other points, which proves that the brittleness between the two points increases and the ductility and toughness decrease, resulting in fatigue fracture first. The element content at different positions on the turning surface under the same process parameters shows obvious unevenness. This uneven distribution of element content on the turning surface of titanium alloy further affects the difference in its mechanical properties.

[0157] Scanning electron microscopy results revealed microcrack initiation and material microdefects on the surface of the fractured area. The surface also contained a certain amount of non-uniform particles and micropores, which could potentially serve as stress concentration points. Before the torsion test, these pre-existing defects and localized stress concentration areas predisposed the material to crack propagation and fracture under subsequent fatigue loading. The formation of these areas is primarily due to the accumulation of microdefects within the material and the localized stress concentration that leads to fatigue fracture.

[0158] The point scanning energy spectrum detection is performed on different characteristic points of the turning surface of Scheme 2. The detection results are as follows Figure 39 As shown. The proportions of α-phase elements in a1-a5 are 13.27%, 14.64%, 16.54%, 12.56%, and 12.06%, respectively, and the proportions of β-phase elements are 8.12%, 5.11%, 4.49%, 7.46%, and 7.96%, respectively. The proportions of α-phase elements at points a2 and a3 are both higher than those at the other points, and the proportions of β-phase elements are both lower than those at the other points, resulting in fatigue fracture occurring earliest within the two-point interval. Compared to the fracture interval detection results of Scheme 1, there is no obvious generation of non-uniform particles and pores on the surface of the titanium alloy workpiece in Scheme 2. Although there are still dislocations and other phenomena in local areas, there are no obvious microcracks.

[0159] It should be noted that in the above embodiments, as long as the technical solutions are not contradictory, they can be permuted and combined. Those skilled in the art can exhaust all possibilities based on the mathematical knowledge of permutations and combinations. Therefore, the present invention will no longer describe the technical solutions after permutations and combinations one by one, but it should be understood that the technical solutions after permutations and combinations have been disclosed by the present invention.

[0160] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for identifying the torsional fatigue performance and distribution characteristics of titanium alloy turning surfaces, characterized in that: The following steps are involved: Step 1, the method for solving the instantaneous cutting behavior of turning tools; Step 2, solving the uneven distribution model of the surface morphology of titanium alloy during turning, that is, combining the surface morphology experiment of titanium alloy during turning to construct the uneven distribution model of the surface morphology of titanium alloy during turning; Step 3, a method for characterizing the distribution characteristics of performance parameters of the surface and sub-surface layers of the turned titanium alloy is proposed based on the results of the thermal-mechanical coupling field analysis of the turned titanium alloy; Step 4, a method for calculating the torsional strength and torsional fatigue life of the surface of titanium alloy during turning, that is, using the maximum residual height, minimum residual height and curvature radius of the surface of titanium alloy during turning and the calculation results of the plastic strain, residual stress and work hardening rate of the surface along the axial and circumferential distribution of the workpiece as model parameters and boundary conditions, a method for calculating the torsional strength and torsional fatigue life of the surface of titanium alloy during turning is proposed, so as to realize the effective correlation between the uneven distribution of the turning surface morphology, surface and sub-surface performance parameters and fatigue performance; obtain the distribution characteristics of the torsional strength and torsional fatigue life of the titanium alloy during turning under different turning process conditions, and identify the position of the lowest torsional fatigue strength and minimum torsional fatigue life of the titanium alloy during turning.

2. The piston-type hydraulic accumulator lever arm booster device according to claim 1, characterized in that: The step 4 also includes identifying the torsional fracture position of the titanium alloy processing surface based on the surface morphology of the turned titanium alloy, SEM scanning electron microscopy and EDS energy spectrum detection results, and utilizing the mapping relationship between the titanium alloy processing surface structure, α+β phase element content and mechanical properties, torsional strength and fatigue life to verify the effectiveness of the solution model and method.

3. The piston-type hydraulic accumulator lever arm assist device according to claim 2, characterized in that: The specific steps of the method for calculating the instantaneous cutting behavior of a turning tool in step 1 are as follows: Reveal the instantaneous dynamic characteristics of the cutting edge during turning, characterize the structure of the turning tool and its cutting edge through geometric modeling, and establish the corresponding coordinate system, namely: a -x a y a z a is the tool coordinate system, the coordinate origin o of the tool coordinate system a The intersection of the plane where the highest point of the cutting edge of the turning tool is located and the projection of the maximum radial overhang point of the cutting edge, the line connecting the coordinate origin and the highest point of the cutting edge is taken as z a The coordinate origin is the line connecting the maximum radial overhang point of the cutting edge as the x-axis direction. a Axis direction, determine y according to the Cartesian coordinate system principle a axis; q is the left boundary point of the cutting edge, m is the upper boundary point of the cutting edge, p is any point on the cutting edge, l is the length of point p on the primary and secondary cutting edges, φ a is the arc angle of the turning tool transition, φ a ' is the angle between the line connecting point p, point q and the center of the tool tip arc, l a is the cutting edge equation; z B is the handle length, x D is the turning tool height, x B is the shank width, r a is the fillet radius; the main angles of the turning tool include the main deflection angle κ r , secondary deflection angle κ r ', tool tip angle ε r , blade inclination angle λs, radial rake angle γ n , radial relief angle α n , radial wedge angle β n , axial rake angle γ0, axial back angle α0, axial wedge angle β0; The solution method for the equation of the main cutting edge in the turning tool coordinate system is: The solution method for the equation of the tool tip arc of the cutting edge transition section in the tool tooth coordinate system is: The solution method for the equation of the secondary cutting edge in the tooth coordinate system is: The solution method for the cutting edge equation in the tooth coordinate system is: In order to reveal the relative motion relationship between the workpiece and the tool and control the formation process of the machined surface, the relative motion of the tool, workpiece, tool coordinate system, cutting coordinate system, workpiece coordinate system and reference coordinate system is characterized; Cutting coordinate system origin o c The trajectory equation in the machine tool coordinate system is expressed as: L c (x c (t),y c (t),z c (t))=Φ0·[0 0 0 1] T (5) Where: the transformation matrix Φ0 is T2T1M1; T2 is the translation matrix of the tool and the reference coordinate system; T1 is the rotation matrix of the tool coordinate system and the cutting coordinate system; M1 is the rotation matrix of the workpiece coordinate system and the reference coordinate system; Assume that point m is the highest point on the cutting edge, point p is any point on the transition arc of the cutting edge, γ0 is the tool rake angle, α0 is the tool back angle, β0 is the friction angle on the rake face, φ p is the shear angle at point p, f is the feed per revolution, κ rp is the instantaneous principal deflection angle at point p, h D is the thickness of the cutting layer, ds is the instantaneous width of the cutting layer at point p, A D is the actual cutting layer area, ΔA D is the cutting residual area, R m is the radius of gyration of point m, R p is the radius of gyration of point p, v c is the cutting speed, F c Main cutting force, v m is the friction speed, F γ is the friction force on the rake face, F γN is the normal force of the friction force on the rake face, F α is the friction force on the flank face, F αN is the normal force of the friction force on the flank face, v sh is the shear velocity, F sh is the shear force, F shN is the normal force of the shear force, F is the resultant force of the friction force and its normal force on the front and rear tool faces, the resultant force of the shear force and its normal force, and the resultant force of the main cutting force and the feed force; The calculation method of the instantaneous cutting layer thickness at any point p is shown in formula (6); h D =f·sinκ rp ·cosλ s (6) Cutting layer area A D The solution method is shown in formula (7); According to equations (6) and (7), τ represents the shear stress on the shear surface, and the solution method of each force at point p is shown in equations (8) to (11); F sh =F·cos(φ p +β0-γ0) (9) Ignoring the cutting edge loss and the extrusion effect of the flank face on the machined transition surface, the final machined contour of the workpiece is essentially the result of the superposition of the cutting edge motion trajectories in two adjacent cutting cycles. While the cutting edge generates the transition surface in the previous cutting cycle, the subsequent cycle will partially eliminate the surface formed in the previous cycle through material removal and form a new transition surface. The intersection area of ​​the cutting edge trajectories of the two cycles constitutes the periodic residual height. The solution of turning transition surface is shown in equations (12) to (15); Where: H a1 The machining transition surface formed by the main cutting edge, H a2 H is the machining transition surface formed by the transition arc. a3 A machined transition surface formed for the secondary cutting edge; t s Indicates the initial cutting moment of each workpiece revolution, t e Indicates the cutting end time of each workpiece revolution; the equation of the machining transition surface formed when the workpiece rotates i times is: The machining transition surface formed when the workpiece rotates i+1 times and intersects with the machining transition surface formed by the previous turning is expressed as: The machining transition surface formed when the workpiece rotates i+2 times and the machining transition surface formed by the previous turning turn form the final turning surface are expressed as:

4. The piston-type hydraulic accumulator lever arm assist device according to claim 3, characterized in that: The specific steps of step 2, solving the uneven distribution model of the surface morphology during turning of titanium alloy, are as follows: During machining, the tool's instantaneous position exhibits dynamic fluctuations due to vibration, precision deviation, and other complex conditions, which in turn induces random evolution of the microscopic topography of the turned surface. Through turning experiments, the titanium alloy's surface topography was measured, cross-sectional data points were extracted, and multi-scheme regression was performed to modify the titanium alloy turning surface topography simulation model. The turning experiment of titanium alloy workpiece was carried out using CNC lathe; In order to characterize the turning surface topography, the turning surface topography was measured; The surface morphology characteristic parameters measured at different sampling positions on the same surface of the titanium alloy workpiece are all a set of values. According to the change of the characteristic parameters with the detection position, the variable sets shown in formulas (28) to (33) are established; When the process parameters change, the surface topography characteristic parameters of different process parameters and sampling positions are fitted and regressed through equations (28) to (33), and the variable set that determines the turning surface topography is obtained: S a ={x k (z k ,θ k ),x m (z m ,θ m ),△x k (z k ,θ k ),△x m (z k ,θ m ),R m (z m ,θ m ),Ra} (25) Any characteristic parameter in the set is denoted as s a , we get the fitting regression equation shown in (35); Where: β, α1~α5 are the coefficients of the fitting regression equation, s0 is the characteristic parameter value corresponding to the ideal uniform surface, when the machined surface is an ideal uniform surface, β=1, α1~α5 are all 0, that is, s a =s0; s0 is related to tool parameters, process parameters, etc., and its variable set is shown in formula (36); s0={D,a p ,n,f,L c } (27) 5. The piston-type hydraulic accumulator lever arm assist device according to claim 4, characterized in that: The specific steps of the method for characterizing the distribution characteristics of performance parameters of the surface and sub-surface layers of the titanium alloy during turning in step 3 are as follows: TC6 titanium alloy contains α-stabilizing element Al, isomorphous β-stabilizing element Mo, eutectoid β-stabilizing element Cr, Fe and neutral element Si, and the β-stabilizing coefficient K p =0.6; A 3D finishing turning model for the external cylindrical turning of a titanium alloy workpiece was established. Because the interaction between the workpiece and the tool generates a large amount of force and heat during the turning process, a thermal-mechanical coupling simulation was used. The tool's rotational speed and feed rate were added as boundary conditions, ensuring that the relative motion conformed to actual turning. The workpiece material was TC6, and the tool material was WC carbide. Since the elastic modulus of the tool material is higher than that of the workpiece material, the tool deformation during machining is small. Therefore, the tool is considered a rigid body during modeling. The boundary conditions of the model are set as follows: the bottom surface and both ends of the workpiece are fixed, and the tool moves the workpiece at a preset feed rate and rotation speed. The cutting process of titanium alloy is considered an adiabatic shear process. Based on the multi-field coupled cutting simulation requirements, the Johnson-Cook dynamic response constitutive model is selected as the material behavior analysis benchmark, as shown in formula (37); Where: Plastic JC stress is a function of strain, strain rate and temperature, A, B, n, C, m are model constants; σ is flow stress, ε is equivalent plastic strain, is the strain rate, is the reference true strain, T is the operating temperature, T melt is the melting temperature of the material, T0 is the ambient temperature; In terms of meshing, the workpiece adopts linear reduced integration 8-node hexahedron unit, and the tool uses tetrahedron temperature-displacement coupling unit; the contact relationship between the tool and the workpiece is defined as a master-slave contact pair, in which the tool is the master surface and the workpiece is the slave surface. The tool-workpiece contact interface is parameterized by the kinematic friction model, and the friction coefficient μ is set to 0.

1. Based on the friction heat theory, the interface energy dissipation is 100% converted into heat flow boundary conditions; when performing simulation calculations of the traditional turning process, the initial temperature of the workpiece is set to room temperature 20°C; the finite element model of turning titanium alloy is constructed, and the finite element simulation of the titanium alloy outer cylindrical turning process is carried out, and 5 feature points a1 to a5 are selected for the xoz surface.

6. The piston-type hydraulic accumulator lever arm assist device according to claim 5, characterized in that: The specific steps of the method for calculating the torsional strength and torsional fatigue life of the titanium alloy machining surface in step 4 are as follows: When modeling a titanium alloy workpiece, a workpiece with different surface morphologies is generated by a rough surface generator, and the generated rough surface is applied to the surface of the titanium alloy workpiece; The modified surface was subjected to torsional dynamics simulation under the conditions of applying initial performance parameters. The maximum stress in the frame before torsional fracture occurred was extracted and recorded as torsional strength. The equivalent stress and plastic strain of the modified surface torsion process under the action of maximum torque were obtained, and the torsion process of the simulated titanium alloy surface under the action of maximum torque was obtained. By defining different surface morphology performance parameters and surface subsurface performance parameters differently, the influence of the two on the torsional strength, maximum torque, fatigue life and fracture position of titanium alloy workpieces is analyzed.

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