Parameter optimization method for ultrasonic vibration rolling strengthening of gas turbine blade tenon material surface

Abstract

This invention discloses a parameter optimization method for ultrasonic vibratory rolling strengthening of gas turbine blade tenon materials. Under set process parameters, the invention obtains the predicted surface roughness and subsurface stress distribution of the material through finite element analysis, obtains the actual subsurface stress distribution through ultrasonic vibratory rolling experiments, calculates the predicted fatigue life of the material, and corrects the process parameters based on the predicted surface roughness and the fatigue life deviation between the predicted and preset fatigue lives. This process is repeated until the predicted fatigue life meets the requirements, thus obtaining the optimal process parameters for strengthening the gas turbine blade tenon material. This invention obtains the optimal process parameters for strengthening gas turbine blade tenon materials through iterative correction, which are then used for ultrasonic vibratory rolling strengthening of gas turbine blade tenons made from this material, thereby improving the fretting fatigue performance of the gas turbine blade tenons made from this material.

Description

Technical Field

[0001] This invention belongs to the field of gas turbine blade surface strengthening technology, specifically relating to a method for optimizing process parameters for ultrasonic vibration rolling strengthening of the surface of gas turbine blade tenon material. Background Technology

[0002] As a critical load-bearing component, the fretting fatigue performance of gas turbine blade tenons directly determines the service life of the unit. Currently, nickel-based superalloys (such as DZ125), martensitic stainless steels (such as the 410 / 420 series), and cobalt-based superalloys are commonly used to manufacture tenons. However, after milling and grinding, these materials generally suffer from surface morphology damage, surface hardness gradient disruption, and uneven distribution of subsurface residual stress, which reduces the fretting fatigue performance of the blade tenons. Therefore, it is necessary to strengthen the surface of the blade tenons to improve their fretting fatigue performance.

[0003] Currently, the common methods for strengthening the surface of blade tenons are shot peening, rolling, and ultrasonic vibratory rolling. Shot peening introduces a residual compressive stress layer to improve fatigue performance by impacting the material surface with high-speed shot. However, the high impact energy generated by the shot on the blade tenon surface during shot peening can lead to decreased surface high-temperature stability and increase surface roughness, exacerbating fretting wear. Rolling uses hard rollers to apply static pressure to the blade tenon surface, improving surface morphology and stress state through plastic deformation. However, rolling generates significant rigidity, affecting the service life of machine tools and rolling cutters, and is costly. Ultrasonic vibratory rolling uses high-frequency ultrasonic vibration energy to act on the blade tenon surface, achieving deep plastic deformation under low load. Deformation to improve fretting fatigue performance, compared to shot peening and rolling, generates less impact energy on the blade tenon surface and does not produce greater rigidity. However, the process parameters (amplitude and rolling force) required for ultrasonic vibratory rolling to strengthen the blade tenon surface are mostly based on experience or charts, which cannot achieve optimal strengthening of the blade tenon surface. At the same time, the materials used to make gas turbine blade tenons are diverse, and the process parameter values ​​for surface strengthening of gas turbine blade tenons made of different materials are different. Therefore, how to adjust the process parameter values ​​to achieve the best fretting fatigue performance for gas turbine blade tenons made of different materials is the main challenge currently facing the technology. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and to propose a method for optimizing the process parameters of ultrasonic vibration rolling to strengthen the surface of the tenon material of gas turbine blades.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The present invention discloses a parameter optimization method for ultrasonic vibratory rolling strengthening of the surface of gas turbine blade tenon material, as detailed below:

[0007] S1. Select a gas turbine blade tenon material to be strengthened as the workpiece, and obtain the material parameter values ​​of the workpiece; set the frequency and initial process parameter values ​​of ultrasonic vibratory rolling, including amplitude and vibratory rolling force.

[0008] S2. Establish a coordinate system for the damage-affected area with the center of the vibratory rolling as the center of the spherical coordinate system of the damage-affected area, and define the damage area of ​​the material workpiece. The damage area of ​​the material workpiece includes the surface damage area, the surface layer damage area, and the subsurface damage area.

[0009] S3. Under the set process parameters, the predicted surface roughness, surface density and subsurface stress distribution of the material workpiece are obtained through finite element analysis, and the actual surface density and subsurface stress distribution of the material workpiece are obtained through ultrasonic vibratory rolling experiments.

[0010] S4. Integrate and correct the predicted surface density and subsurface stress distribution data of the material workpiece with the actual surface density and subsurface stress distribution data of the material workpiece. Calculate the predicted fatigue life of the material workpiece based on the integrated and corrected subsurface stress distribution data, and then calculate the fatigue life deviation between the predicted fatigue life and the preset fatigue life of the material workpiece.

[0011] S5. Correct the amplitude iteratively based on the predicted surface roughness of the workpiece material, and correct the vibratory rolling force iteratively based on the calculated fatigue life deviation. During the correction iteration, return to step S3 until the predicted fatigue life of the workpiece material is not less than the preset fatigue life of the workpiece material in three consecutive iterations, and the difference between the predicted fatigue life of the workpiece material in each iteration and the predicted fatigue life of the workpiece material in the previous iteration is less than 5% of the predicted fatigue life of the workpiece material in this iteration. The process parameters in the last iteration are the optimal process parameters for the workpiece material. A new workpiece material is used when performing ultrasonic vibratory rolling experiments in each iteration.

[0012] S6. Analyze the surface density of the corrected material workpiece. Compare the surface density of the corrected material workpiece in the last three iterations with that in the first iteration. If the surface density of the corrected material workpiece continuously decreases in the last three iterations, and the surface density of the corrected material workpiece in the last iteration is less than that in the first iteration, then it is considered that the material workpiece has cracked in the last iteration. Replace it with a new material made of gas turbine blade tenon material and return to step S1. Otherwise, determine that the material is a gas turbine blade tenon material that can be surface-strengthened by ultrasonic vibratory rolling, and determine the optimal process parameters for ultrasonic vibratory rolling of the material.

[0013] Preferably, the material parameters of the workpiece include elastic modulus, Poisson's ratio, yield strength, fatigue strength coefficient, fatigue ductility coefficient, fatigue strength index, and fatigue ductility index.

[0014] Preferably, the specific process of step S2 is as follows:

[0015] Establish a coordinate system for the damage-affected area with the center of the vibratory roller as the center of the spherical coordinate system of the damage-affected area. Where r is the radial distance, representing the straight-line distance from a point on the material workpiece to the center of the vibratory roller; The polar angle is defined as the angle between the line connecting the point and the center of the vibratory mill and the direction of the normal to the surface of the material workpiece, and when... At that time, the line connecting this point and the center of the vibratory roller is perpendicular to the surface of the workpiece and points towards the interior of the workpiece. At that time, the line connecting the point and the center of the vibratory rolling is parallel to the surface of the material workpiece; θ is the azimuth angle, which indicates the orientation of the point in the tangent plane of the material workpiece surface. When θ = 0°, it corresponds to the feed direction of the vibratory rolling tool, and when θ = 90°, it indicates the feed direction perpendicular to the vibratory rolling tool. Regions with depths of 0-10μm, 10μm-100μm, and greater than 100μm are defined as the surface damage region, surface layer damage region, and subsurface damage region of the material workpiece, respectively.

[0016] Preferably, the predicted surface roughness R of the material workpiece obtained through finite element analysis... a Surface density and subsurface stress distribution The process is as follows:

[0017] A 3D model of the gas turbine blade tenon was created in finite element method (FEM) software. Material and material parameters were set, and the contact area of ​​the 3D model was locally refined (mesh size ≤ 1 μm). The bottom of the 3D model was constrained with fixed boundary conditions. Then, the set process parameters were applied to the surface of the 3D model, and an explicit dynamic solver was used to simulate the transient contact process of vibration at a set ultrasonic rolling frequency. A finite element simulation experiment was conducted. After completing the finite element simulation experiment, the predicted surface roughness R of the material workpiece was calculated using the finite element simulation results. a Surface density and subsurface stress distribution Among them, the predicted surface roughness R of the material workpiece a The calculation formula is

[0018]

[0019] In the formula, Z(x) is the height of the surface profile curve deviating from the average line, and the surface profile curve is obtained by cutting the three-dimensional model along the feed direction of the vibratory milling tool, l is the evaluation length, and x is the distance between the projection of the current profile position in the evaluation length direction and the projection of the origin profile position in the evaluation length direction.

[0020] Predicted surface density of the material workpiece The calculation formula is

[0021]

[0022] In the formula, P i Let N be the porosity of the i-th grid in the damaged area of ​​the material workpiece surface, and N be the total number of grids in the damaged area of ​​the material workpiece surface.

[0023] Predicted subsurface stress distribution of the material workpiece The calculation formula is

[0024]

[0025] Where F is the vibratory rolling force.

[0026] Preferably, the process of obtaining the actual surface density D and subsurface stress distribution σ(r) of the material workpiece through ultrasonic vibration rolling experiment is as follows:

[0027] Ultrasonic vibratory rolling experiments were conducted on the material workpiece using the set ultrasonic vibratory rolling frequency and process parameters. After the ultrasonic vibratory rolling experiments were completed, the actual surface density D and subsurface stress distribution σ(r) of the material workpiece were obtained by detection. Specifically, when detecting the surface density of the material workpiece, the surface porosity was first detected, the original projection data was reconstructed, and a three-dimensional image of the damaged area on the surface of the material workpiece was generated. Then, image segmentation was performed on the three-dimensional image, using a threshold segmentation algorithm to distinguish between the material matrix and pores. Next, the total volume of each pore in the damaged area on the surface of the material workpiece was calculated, thereby calculating the porosity P of the material workpiece. Based on the porosity P, the actual surface density D of the material workpiece was obtained.

[0028]

[0029] In the formula, V pores V represents the total volume of all pores in the damaged area on the surface of the workpiece. total The total volume of the damaged area on the surface of the workpiece material;

[0030] When inspecting the subsurface stress distribution of a material workpiece, X-ray equipment is used for inspection, and the sin2ψ method is used for multi-angle measurement. After the measurement is completed, full spectrum fitting is performed to obtain the subsurface stress distribution σ(r).

[0031] Preferably, the specific process of step S4 is as follows:

[0032] Predicted surface density of the material workpiece Substituting the actual surface density D of the material workpiece into... After integration and correction, the surface density D of the material workpiece is obtained. c The predicted subsurface stress distribution of the material workpiece Substituting the actual subsurface stress distribution σ(r) of the material workpiece into... After integration and correction, the subsurface stress distribution σ of the material workpiece is obtained. c (r);

[0033] Based on the integrated and corrected subsurface stress distribution σ c (r) uses the Manson-Coffin-Basquin equation, modified with the Morrow model, to predict the fatigue life of the material workpiece. The Manson-Coffin-Basquin equation modified with the Morrow model is as follows:

[0034]

[0035] In the formula, Δε a This is the difference between the maximum and minimum strain during cyclic loading, and σ c (r) max To correct the subsurface stress distribution σ of the workpiece material c The maximum stress in, σ c (r) min To correct the subsurface stress distribution σ of the workpiece material c The minimum stress in the equation, E is the elastic modulus of the material, σ m The average stress is, and σ f ′ ε is the fatigue strength coefficient of the material. f ′ denoted as σf, b is the fatigue strength index of the material, and c is the fatigue ductility index of the material.

[0036] The predicted fatigue life N of the material workpiece is obtained through calculation. f And calculate the predicted fatigue life N of the material workpiece. f The preset fatigue life N of the material workpiece target The fatigue life deviation ΔN between them, and ΔN = N target -N f .

[0037] Preferably, in step S5, when the amplitude value is corrected and iterated, if the predicted surface roughness of the material workpiece in the Kth iteration is greater than a preset value, then the amplitude value A in the (K+1)th iteration... K+1 =A K +Ax, otherwise, starting from the (K+1)th iteration, the amplitude value in each iteration is A. K Where K is the iteration number; the step size decay method is used to correct the vibration rolling force value iteratively. When the fatigue life deviation ΔN calculated in the Kth iteration is greater than 0, the vibration rolling force F in the (K+1)th iteration is... K+1 =F K +Fx, when the fatigue life deviation ΔN calculated in the Kth iteration is less than 0, the vibration rolling force F in the K+1th iteration. K+1 =F K -Fx, and during the iteration, if the sign of ΔN is not changed compared to the previous iteration, the value of Fx remains unchanged in this iteration; otherwise, the value of Fx is reduced to half in this iteration.

[0038] The present invention has the following beneficial effects:

[0039] 1. This invention enables the determination of the optimal process parameters for ultrasonic vibratory rolling strengthening of tenons made of different materials for gas turbine blades, thereby improving the fretting fatigue performance of tenons made of different materials for gas turbine blades. Specifically, this invention obtains optimal process parameters for ultrasonic vibratory rolling strengthening of gas turbine blade tenon materials by iteratively modifying the process parameters. These optimal parameters guide the ultrasonic vibratory rolling strengthening of gas turbine blade tenons made from this material, improving the fretting fatigue performance of the tenons and reducing the cost of ultrasonic vibratory rolling strengthening. Each iteration of the process parameter modification involves: first, under the set process parameters, obtaining the predicted material surface roughness and subsurface stress distribution through finite element analysis; second, obtaining the actual material subsurface stress distribution through ultrasonic vibratory rolling experiments; third, integrating and correcting the predicted and actual subsurface stress distributions; fourth, calculating the predicted material fatigue life based on the integrated and corrected subsurface stress distribution data; fifth, calculating the fatigue life deviation between the predicted and preset fatigue life based on the predicted fatigue life; and finally, correcting the amplitude value in the process parameters based on the predicted material surface roughness and the vibratory rolling force value based on the calculated fatigue life deviation.

[0040] 2. This invention, while determining the optimal process parameters for ultrasonic vibratory rolling strengthening of gas turbine blade tenon materials, also enables the screening of gas turbine blade tenon material quality, avoiding the actual use of gas turbine blade tenons that have undergone strengthening but are already damaged. Specifically, in each iteration, this invention obtains the predicted material surface density through finite element analysis and the actual material surface density through ultrasonic vibratory rolling experiments. The predicted and actual material surface density are integrated and corrected. After completing the iteration, the corrected material density from the last three iterations is compared with the corrected material density from the first iteration to determine whether microcracks have occurred in the material. If microcracks appear in the material, it means that the material cannot be surface-strengthened using ultrasonic vibratory rolling. Conversely, if no microcracks appear, it means that the material can be surface-strengthened using ultrasonic vibratory rolling. This allows us to determine whether the material is suitable for ultrasonic vibratory rolling of gas turbine blade tenons and to identify the optimal process parameters for ultrasonic vibratory rolling of this material. This enables the screening of gas turbine blade tenon material quality and guides the use of optimal process parameters in actual production to perform ultrasonic vibratory rolling of gas turbine blade tenons made from this material. The goal is to achieve a gas turbine blade tenon with low surface roughness, no microcracks, good fretting fatigue performance, and long fatigue life after processing. Attached Figure Description

[0041] Figure 1This is a flowchart of the present invention;

[0042] Figure 2 This is a schematic diagram of the coordinate system of the damage-affected area established in this invention. Detailed Implementation

[0043] The present invention will now be further described with reference to the accompanying drawings.

[0044] like Figure 1 As shown, the parameter optimization method for ultrasonic vibration rolling strengthening of the surface of the gas turbine blade tenon material of the present invention is as follows:

[0045] S1. Select a gas turbine blade tenon material as the workpiece to be strengthened (the workpiece can be made into a cuboid shape for ultrasonic vibratory rolling experiment), and obtain the material parameter values ​​of the workpiece, including elastic modulus, Poisson's ratio, yield strength, fatigue strength coefficient, fatigue ductility coefficient, fatigue strength index, and fatigue ductility index; set the ultrasonic vibratory rolling frequency and the initial process parameter values, including amplitude and vibratory rolling force F. In this embodiment, the ultrasonic vibratory rolling frequency is selected in the range of 20-25kHz, the initial amplitude value A1 = 20μm, and the initial vibratory rolling force F1 = 100N.

[0046] S2, such as Figure 2 As shown, a coordinate system for the damage-affected area is established with the center of the vibratory roller as the center of the spherical coordinate system of the damage-affected area. Where r is the radial distance, representing the straight-line distance from a point on the material workpiece to the center of the vibratory roller (on the surface of the material workpiece); The polar angle is defined as the angle between the line connecting the point and the center of the vibratory mill and the direction of the normal to the surface of the material workpiece, and when... At that time, the line connecting this point and the center of the vibratory roller is perpendicular to the surface of the workpiece and points towards the interior of the workpiece. At that time, the line connecting this point and the center of the vibratory mill is parallel to the surface of the workpiece; θ is the azimuth angle, indicating the position of this point in the tangential plane of the workpiece surface, and when θ = 0°, it corresponds to the feed direction of the vibratory milling tool ( Figure 2 The direction of the feed speed v), when θ = 90°, indicates the feed direction perpendicular to the vibratory milling tool.

[0047] The damage regions of a material workpiece are defined as follows: regions with depths of 0-10 μm, 10 μm-100 μm, and greater than 100 μm are defined as the surface damage region, the surface layer damage region, and the subsurface damage region, respectively. The surface damage region is the outermost layer of the workpiece directly in contact with the external environment, typically exhibiting a microscopic geometric shape. Surface roughness directly affects the friction coefficient and fretting wear behavior. The surface layer damage region lies below the surface damage region and relates to the workpiece's density. Surface density reflects the workpiece's porosity, influencing fatigue resistance and crack propagation resistance. The subsurface damage region is the deeper region below the surface damage region; its stress distribution is crucial for fatigue life prediction.

[0048] S3. Under the set process parameters, the predicted surface roughness R of the material workpiece is obtained through finite element analysis. a Surface density and subsurface stress distribution The surface density D and subsurface stress distribution σ(r) of the actual material workpiece were obtained through ultrasonic vibration rolling experiments.

[0049] The predicted surface roughness R of the material workpiece is obtained through finite element analysis. a Surface density and subsurface stress distribution The process is as follows:

[0050] A 3D model of the gas turbine blade tenon is created in finite element software (such as ABAQUS and ANSYS). The material properties, including its elastic modulus, Poisson's ratio, and yield strength, are set. The contact area of ​​the 3D model is locally refined (mesh size ≤ 1 μm). The bottom of the 3D model is constrained with fixed boundary conditions. Then, the set process parameters are applied to the surface of the 3D model, and an explicit dynamics solver (such as ABAQUS / Explicit) is used to simulate the transient contact process under a set ultrasonic vibration frequency. A finite element simulation experiment is then conducted. After completing the finite element simulation experiment, the predicted surface roughness R of the material workpiece is calculated using the finite element simulation results. a Surface density and subsurface stress distribution Among them, the predicted surface roughness R of the material workpiece a The calculation formula is

[0051]

[0052] In the formula, Z(x) is the height of the surface profile curve deviating from the average line, and the surface profile curve is obtained by cutting the three-dimensional model along the feed direction of the vibratory milling tool, l is the evaluation length, and x is the distance between the projection of the current profile position in the evaluation length direction and the projection of the origin profile position in the evaluation length direction.

[0053] Predicted surface density of the material workpiece The calculation formula is

[0054]

[0055] In the formula, P i Let N be the porosity of the i-th grid in the damaged area of ​​the material workpiece surface, and N be the total number of grids in the damaged area of ​​the material workpiece surface.

[0056] Predicted subsurface stress distribution of the material workpiece The calculation formula is

[0057]

[0058] The process of obtaining the actual surface density D and subsurface stress distribution σ(r) of the material workpiece through ultrasonic vibratory rolling experiments is as follows:

[0059] Ultrasonic rolling experiments were conducted on the material workpiece using the set ultrasonic rolling frequency and process parameters. After the ultrasonic rolling experiment, the actual surface density D and subsurface stress distribution σ(r) of the material workpiece were obtained by detection. Specifically, when detecting the surface density of the material workpiece, a Micro-CT device (such as a Bruker SkyScan 1272) was first used to detect the surface porosity of the material workpiece. The scanning process was observed through a real-time monitoring system, the original projection data was reconstructed, and a three-dimensional image (resolution ≥2μm) of the damaged area on the surface of the material workpiece was generated and stored in DICOM or RAW format. Next, image processing software (such as VG Studio MAX, Avizo, or the ImageJ plugin BoneJ) was used to segment the three-dimensional image. During image segmentation, a threshold segmentation algorithm was used to distinguish between the material matrix and pores. Then, the total volume of each pore in the damaged area on the surface of the material workpiece was calculated, thereby calculating the porosity P of the material workpiece. Based on the porosity P, the actual surface density D of the material workpiece was obtained.

[0060]

[0061] In the formula, V pores V represents the total volume of all pores in the damaged area on the surface of the workpiece. total This represents the total volume of the damaged area on the surface of the workpiece.

[0062] In this embodiment, when using a Micro-CT device (such as a Bruker SkyScan 1272) to detect the surface pores of a material workpiece, the scanning parameters are set as follows: voltage 80V, current 300μA, and single-frame exposure time 500ms.

[0063] When inspecting the subsurface stress distribution of a material workpiece, X-ray equipment is used for inspection based on the requirements of standard ASTM E2860-12. The sin2ψ method is used for multi-angle measurement. After the measurement is completed, JADE 6.5 software is used for full-spectrum fitting to obtain the subsurface stress distribution σ(r). Specifically, based on the requirements of standard ASTM E2860-12, a copper target (Cu-Kα radiation, wavelength λ=0.15406nm) X-ray tube is selected, with the tube voltage set to 30kV and the current to 20mA. A scintillation counter or position-sensitive detector is selected, with a step angle of 0.02°. When using the sin2ψ method for multi-angle measurement, the ψ angle range covers 5 angle points (0°, 15°, 25°, 35°, 45°).

[0064] S4. Predicted surface density of the material workpiece and subsurface stress distribution The data is integrated and corrected with the actual surface density D and subsurface stress distribution σ(r) data of the material workpiece. Based on the integrated and corrected subsurface stress distribution data of the material workpiece, the fatigue life of the material workpiece is predicted by calculation. Then, the fatigue life deviation between the predicted fatigue life and the preset fatigue life of the material workpiece is calculated.

[0065] Predicted surface density of the material workpiece Substituting the actual surface density D of the material workpiece into... After integration and correction, the surface density D of the material workpiece is obtained. c The predicted subsurface stress distribution of the material workpiece Substituting the actual subsurface stress distribution σ(r) of the material workpiece into... After integration and correction, the subsurface stress distribution σ of the material workpiece is obtained. c (r);

[0066] Based on the integrated and corrected subsurface stress distribution σ c (r) uses the Manson-Coffin-Basquin equation, modified with the Morrow model, to predict the fatigue life of the material workpiece. The Manson-Coffin-Basquin equation modified with the Morrow model is as follows:

[0067]

[0068] In the formula, Δεa This is the difference between the maximum and minimum strain during cyclic loading, and σ c (r) max To correct the subsurface stress distribution σ of the workpiece material c The maximum stress in, σ c (r) min To correct the subsurface stress distribution σ of the workpiece material c The minimum stress in the equation, E is the elastic modulus of the material, σ m The average stress is, and σ f ′ ε is the fatigue strength coefficient of the material. f ′ denoted as σf, b as σf, and c as σf.

[0069] The predicted fatigue life N of the material workpiece is obtained through calculation. f And calculate the predicted fatigue life N of the material workpiece. f The preset fatigue life N of the material workpiece target The fatigue life deviation ΔN between them, and ΔN = N target -N f .

[0070] S5. Iterate and correct the process parameter values. During each iteration, return to step S3 until the predicted fatigue life N of the material / workpiece is obtained in three consecutive iterations. f All are not less than the preset fatigue life N of the material workpiece target Furthermore, in three consecutive iterations, if the difference between the predicted fatigue life of the material workpiece in each iteration and the predicted fatigue life in the previous iteration is less than 5% of the predicted fatigue life of the material workpiece in this iteration, then the fatigue life of the material workpiece is considered to have reached a stable optimal fatigue life, that is, the fretting fatigue performance of the surface of the material workpiece has reached the best strengthening effect, and the process parameters in the last iteration are the optimal process parameters for the material workpiece.

[0071] In each iteration, a new material workpiece is used during the ultrasonic vibratory rolling experiment (but the material used for the workpiece remains the same). When the process parameter values ​​are corrected and iterated, the influence of amplitude change on the surface roughness of the workpiece is considered (within a reasonable range, a larger amplitude results in a smaller surface roughness, but too small or too large an amplitude will lead to a larger surface roughness; in this embodiment, the initial amplitude value A1 = 20 μm is a smaller value within a reasonable range). The predicted surface roughness of the workpiece is used as the criterion for amplitude correction iteration. According to JB / T 6690-1993, the surface roughness of the tenon of the gas turbine blade is required to be below 1.6 μm. However, considering that the vibratory rolling force also affects the surface roughness of the workpiece, a value smaller than 1.6 μm is selected as the limit. In this embodiment, 1.2 μm is selected. If the predicted surface roughness of the workpiece in the Kth iteration is greater than 1.2 μm, then the amplitude value A1 in the K+1th iteration is adjusted. K+1 =A K +Ax, otherwise, starting from the (K+1)th iteration, the amplitude value in each iteration is A. K Where K is the iteration number, and K = 1, 2, 3, ..., the amplitude value A1 in the first iteration is the initial amplitude value A1 set in step S1. In this embodiment, the amplitude step size Ax = 5 μm; the step size decay method is used to correct the rolling force iteratively. When the fatigue life deviation ΔN calculated in the Kth iteration is > 0, the rolling force F in the (K+1)th iteration is... K+1 =F K +Fx, when the fatigue life deviation ΔN calculated in the Kth iteration is less than 0, the vibration rolling force F in the K+1th iteration. K+1 =F K -Fx, and during the iteration process, if the sign of ΔN is not changed compared to the sign of ΔN in the previous iteration, the value of Fx remains unchanged in this iteration; otherwise, the value of Fx is reduced to half in this iteration. The vibratory rolling force F1 in the first iteration is the initial vibratory rolling force F1 set in step S1. In this embodiment, the initial value of the vibratory rolling force step size Fx is f1 = 10N.

[0072] S6. Considering that in actual reinforced material workpieces, the lower the porosity, the better the quality, i.e., the higher the surface density, the better the quality. However, as the vibratory rolling force increases to a certain large value, it may cause cracks in the material workpiece, leading to a sudden change in surface density and damage. To ensure the quality of the material workpiece and prevent the use of damaged workpieces after reinforcement, it is necessary to analyze the corrected surface density of the material workpiece. The corrected surface density of the material workpiece in the last three iterations will be used as the basis for this analysis. and Compared with the corrected material workpiece surface density in the first iteration Comparative analysis shows that if the surface density of the corrected material workpiece continuously decreases in the last three iterations, and If a micro-crack is found in the workpiece during the last iteration, the workpiece is replaced with a new gas turbine blade tenon material and returned to step S1. Otherwise, it indicates that the material corresponding to the workpiece can be surface-strengthened using ultrasonic vibratory rolling, thus determining that the material is suitable for ultrasonic vibratory rolling of the gas turbine blade tenon and identifying the optimal process parameters for ultrasonic vibratory rolling. This guides the use of optimal process parameters in actual production to perform ultrasonic vibratory rolling on gas turbine blade tenons made from this material, resulting in a low surface roughness, no micro-cracks, good fretting fatigue performance, and long fatigue life after processing. This represents the corrected surface density of the material workpiece in the penultimate iteration. This represents the corrected surface density of the material workpiece in the penultimate iteration. This represents the corrected surface density of the material workpiece in the penultimate iteration.

Claims

1. A parameter optimization method for ultrasonic vibration rolling strengthening of the surface of gas turbine blade tenon material, characterized in that: Specifically as follows: S1. Select a gas turbine blade tenon material to be strengthened as the workpiece, and obtain the material parameter values ​​of the workpiece; set the frequency and initial process parameter values ​​of ultrasonic vibratory rolling, including amplitude and vibratory rolling force; S2. Establish a coordinate system for the damage-affected area with the center of the vibratory rolling as the center of the spherical coordinate system of the damage-affected area, and define the damage area of ​​the material workpiece. The damage area of ​​the material workpiece includes the surface damage area, the surface layer damage area and the subsurface damage area of ​​the material workpiece. S3. Under the set process parameters, the predicted surface roughness, surface density and subsurface stress distribution of the material workpiece are obtained by finite element analysis, and the actual surface density and subsurface stress distribution of the material workpiece are obtained by ultrasonic vibration rolling experiment. S4. Integrate and correct the predicted surface density and subsurface stress distribution data of the material workpiece with the actual surface density and subsurface stress distribution data of the material workpiece. Based on the integrated and corrected subsurface stress distribution data of the material workpiece, calculate the predicted fatigue life of the material workpiece, and then calculate the fatigue life deviation between the predicted fatigue life of the material workpiece and the preset fatigue life of the material workpiece. S5. The amplitude is corrected iteratively based on the predicted surface roughness of the workpiece material, and the vibratory rolling force is corrected iteratively based on the calculated fatigue life deviation. During the correction iteration, return to step S3 until the predicted fatigue life of the workpiece material is not less than the preset fatigue life of the workpiece material in three consecutive iterations. In addition, in three consecutive iterations, the difference between the predicted fatigue life of the workpiece material in each iteration and the predicted fatigue life of the workpiece material in the previous iteration is less than 5% of the predicted fatigue life of the workpiece material in this iteration. The process parameters in the last iteration are the optimal process parameters for the workpiece material. A new workpiece material is used when performing ultrasonic vibratory rolling experiments in each iteration. S6. Analyze the surface density of the corrected material workpiece. Compare the surface density of the corrected material workpiece in the last three iterations with that in the first iteration. If the surface density of the corrected material workpiece continuously decreases in the last three iterations, and the surface density of the corrected material workpiece in the last iteration is less than that in the first iteration, then it is considered that the material workpiece has cracked in the last iteration. Replace it with a new material made of gas turbine blade tenon material and return to step S1. Otherwise, determine that the material is a gas turbine blade tenon material that can be surface-strengthened by ultrasonic vibratory rolling, and determine the optimal process parameters for ultrasonic vibratory rolling of the material.

2. The parameter optimization method for ultrasonic vibration-rolling strengthening of the surface of gas turbine blade tenon material according to claim 1, characterized in that: The material parameters of the workpiece include elastic modulus, Poisson's ratio, yield strength, fatigue strength coefficient, fatigue ductility coefficient, fatigue strength index, and fatigue ductility index.

3. The parameter optimization method for ultrasonic vibration rolling strengthening of the surface of gas turbine blade tenon material according to claim 1, characterized in that: The specific process of step S2 is as follows: Establish a coordinate system (r, ) for the damage-affected area, with the center of the vibratory roller as the center of the spherical coordinate system of the damage-affected area. θ); where r is the radial distance, representing the straight-line distance from a point on the material workpiece to the center of the vibratory roller; The polar angle is defined as the angle between the line connecting the point and the center of the vibratory mill and the direction of the normal to the surface of the material workpiece, and when... At that time, the line connecting this point and the center of the vibratory roller is perpendicular to the surface of the workpiece and points towards the interior of the workpiece. At that time, the line connecting the point and the center of the vibratory rolling is parallel to the surface of the material workpiece; θ is the azimuth angle, which indicates the orientation of the point in the tangent plane of the material workpiece surface. When θ = 0°, it corresponds to the feed direction of the vibratory rolling tool, and when θ = 90°, it indicates the feed direction perpendicular to the vibratory rolling tool. Regions with depths of 0-10μm, 10μm-100μm, and greater than 100μm are defined as the surface damage region, surface layer damage region, and subsurface damage region of the material workpiece, respectively.

4. The parameter optimization method for ultrasonic vibration rolling strengthening of the surface of gas turbine blade tenon material according to claim 3, characterized in that: The surface roughness R of the material workpiece predicted by finite element analysis a Surface density and subsurface stress distribution The process is as follows: A 3D model of the gas turbine blade tenon was created in finite element method (FEM) software. Material and material parameters were set, and the contact area of ​​the 3D model was locally refined into a fine mesh. The bottom of the 3D model was constrained with fixed boundary conditions. Then, the set process parameters were applied to the surface of the 3D model, and an explicit dynamic solver was used to simulate the transient contact process under a set ultrasonic vibration frequency. A finite element simulation experiment was conducted. After completing the finite element simulation experiment, the predicted surface roughness R of the material workpiece was calculated using the finite element simulation results. a Surface density and subsurface stress distribution Among them, the predicted surface roughness R of the material workpiece a The calculation formula is In the formula, Z(x) is the height of the surface profile curve deviating from the average line, and the surface profile curve is obtained by cutting the three-dimensional model along the feed direction of the vibratory milling tool, l is the evaluation length, and x is the distance between the projection of the current profile position in the evaluation length direction and the projection of the origin profile position in the evaluation length direction. Predicted surface density of the material workpiece The calculation formula is In the formula, P i Let N be the porosity of the i-th grid in the damaged area of ​​the material workpiece surface, and N be the total number of grids in the damaged area of ​​the material workpiece surface. Predicted subsurface stress distribution of the material workpiece The calculation formula is Where F is the vibratory rolling force.

5. The parameter optimization method for ultrasonic vibration rolling strengthening of the surface of gas turbine blade tenon material according to claim 4, characterized in that: The process of obtaining the actual surface density D and subsurface stress distribution σ(r) of the material workpiece through ultrasonic vibratory rolling experiments is as follows: Ultrasonic vibratory rolling experiments were conducted on the material workpiece using the set ultrasonic vibratory rolling frequency and process parameters. After the ultrasonic vibratory rolling experiments were completed, the actual surface density D and subsurface stress distribution σ(r) of the material workpiece were obtained by detection. Specifically, when detecting the surface density of the material workpiece, the surface porosity was first detected, the original projection data was reconstructed, and a three-dimensional image of the damaged area on the surface of the material workpiece was generated. Then, image segmentation was performed on the three-dimensional image, using a threshold segmentation algorithm to distinguish between the material matrix and pores. Next, the total volume of each pore in the damaged area on the surface of the material workpiece was calculated, thereby calculating the porosity P of the material workpiece. Based on the porosity P, the actual surface density D of the material workpiece was obtained. In the formula, V pores V represents the total volume of all pores in the damaged area on the surface of the workpiece. total The total volume of the damaged area on the surface of the workpiece material; When inspecting the subsurface stress distribution of a material workpiece, X-ray equipment is used for detection, employing sin... 2 The ψ method is used for multi-angle measurements. After the measurements are completed, full-spectrum fitting is performed to obtain the subsurface stress distribution σ(r).

6. The parameter optimization method for ultrasonic vibration rolling strengthening of the surface of gas turbine blade tenon material according to claim 5, characterized in that: The specific process of step S4 is as follows: Predicted surface density of the material workpiece Substituting the actual surface density D of the material workpiece into... After integration and correction, the surface density D of the corrected material workpiece is obtained. c The predicted subsurface stress distribution of the material workpiece Substituting the actual subsurface stress distribution σ(r) of the material workpiece into... After integration and correction, the subsurface stress distribution σ of the material workpiece is obtained. c (r); Based on the integrated and corrected subsurface stress distribution σ c (r) uses the Manson-Coffin-Basquin equation, modified with the Morrow model, to predict the fatigue life of the material workpiece. The Manson-Coffin-Basquin equation modified with the Morrow model is as follows: In the formula, Δε a This is the difference between the maximum and minimum strain during cyclic loading, and σ c (r) max To correct the subsurface stress distribution σ of the workpiece material c The maximum stress in, σ c (r) min To correct the subsurface stress distribution σ of the workpiece material c The minimum stress in the equation, E is the elastic modulus of the material, σ m The average stress is, and σ f ′ ε is the fatigue strength coefficient of the material. f ′ denoted as σf, b is the fatigue strength index of the material, and c is the fatigue ductility index of the material. The predicted fatigue life N of the material workpiece is obtained through calculation. f And calculate the predicted fatigue life N of the material workpiece. f The preset fatigue life N of the material workpiece target The fatigue life deviation ΔN between them, and ΔN = N target -N f .

7. The parameter optimization method for ultrasonic vibration rolling strengthening of the surface of gas turbine blade tenon material according to claim 6, characterized in that: In step S5, when the amplitude value is corrected and iterated, if the predicted surface roughness of the material workpiece in the Kth iteration is greater than the preset value, then the amplitude value A in the (K+1)th iteration... K+1 =A K +Ax, otherwise, starting from the (K+1)th iteration, the amplitude value in each iteration is A. K Where K is the iteration number; the step size decay method is used to correct the vibration rolling force value iteratively. When the fatigue life deviation ΔN calculated in the Kth iteration is greater than 0, the vibration rolling force F in the (K+1)th iteration is... K+1 =F K +Fx, when the fatigue life deviation ΔN calculated in the Kth iteration is less than 0, the vibration rolling force F in the (K+1)th iteration. K+1 =F K -Fx, and during the iteration, if the sign of Δn has not changed compared to the previous iteration, then the value of Fx remains unchanged in this iteration; otherwise, the value of Fx is reduced to half in this iteration.

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