A method for rapidly predicting fatigue limit based on infrared thermal image energy dissipation characteristics

Abstract

The present application relates to a kind of fatigue limit fast prediction method based on infrared thermal image energy dissipation characteristics, containing sample preparation and pretreatment, fatigue loading stress level upper limit determination, infrared fatigue test and temperature rise data acquisition, thermal image data processing and temperature rise feature extraction, energy dissipation model construction, fatigue limit value prediction, error analysis and method verification.Steady temperature rise rate under different stresses is monitored by infrared thermal imager, the relationship curve of dissipation rate and stress amplitude is established, the fatigue limit is predicted based on least square method, and compared with S N Test results are compared and verified.Compared with traditional method, the method has the advantages of high prediction accuracy, high response sensitivity, low loss, short test cycle, etc., and is especially suitable for fatigue limit fast prediction and strengthening effect determination of high-temperature alloy material after laser-assisted ultrasonic roll strengthening.​

Description

Technical Field

[0001] This invention belongs to the field of fatigue limit prediction and surface strengthening technology, specifically relating to a rapid fatigue limit prediction method based on infrared thermographic energy dissipation characteristics. This method is applicable to difficult-to-machine high-performance alloy materials such as titanium alloys and stainless steel, and is particularly suitable for high-temperature alloy materials after surface strengthening treatments such as laser-assisted ultrasonic rolling. It has good application value and promotion potential in material screening, process optimization, and service evaluation, enabling rapid assessment of fatigue limits and quantitative determination of strengthening effects. Background Technology

[0002] Fatigue fracture is one of the most significant failure mechanisms for high-temperature alloy components under complex service conditions. Especially under alternating stress and cyclic loading, irreversible plastic damage easily occurs on the component surface, gradually accumulating and inducing cracks. The insidious and sudden nature of these cracks severely restricts service safety in critical fields such as aerospace. To improve service reliability, various surface strengthening technologies, including shot peening, rolling, ultrasonic rolling, and laser-assisted ultrasonic rolling, have been widely applied in recent years. Laser-assisted ultrasonic rolling, in particular, significantly enhances the surface plastic deformation capacity and microstructure density through thermo-mechanical coupling, giving the material superior fatigue properties.

[0003] However, existing fatigue performance assessment methods are mostly based on traditional life inference methods (such as single-point methods, gradient methods, and group methods), which suffer from problems such as long testing cycles, high sample consumption, and low efficiency. Furthermore, they lack real-time performance and physical interpretability, making it difficult to meet the needs of rapid and accurate assessment in engineering fields. Taking the widely used gradient method as an example, to determine the fatigue limit of a material, at least 10 or more stress amplitude gradient loading points are typically required, and each sample needs to undergo tens of thousands of cyclic loading cycles to obtain effective failure data for SN curve fitting. To improve prediction confidence, the number of samples needs to be further increased, significantly increasing the testing cycle and testing costs. In addition, for materials with inherently long lifespans, such as high-temperature alloys, the loading time of traditional testing methods is even more lengthy, failing to meet the urgent need for rapid fatigue performance assessment in engineering fields or process feedback. Therefore, how to achieve efficient and reliable fatigue limit prediction under finite loading conditions has become a key technical problem in the service assessment of structural components and the verification of surface strengthening processes.

[0004] Studies have shown that during fatigue loading, the activation of microscopic defects within the metal leads to energy dissipation and localized temperature rise, reflecting the material's plastic damage and energy evolution state. Therefore, temperature rise characteristics can serve as an important indirect indicator for assessing fatigue behavior. Existing methods attempt to monitor temperature through contact methods such as thermocouples and thermistors. While low-cost and based on clear principles, these methods suffer from low sensitivity, response lag, and poor environmental adaptability. In contrast, infrared thermography offers advantages such as non-contact operation, fast response, wide measurement range, and strong visualization capabilities, making it a crucial tool for identifying energy evolution behavior during fatigue. Currently, research has focused on using thermographic image analysis to identify fatigue crack locations, measure crack propagation rates, and quantitatively assess localized damage. For example, Liu Xuesong et al. from Harbin Institute of Technology disclosed a method for predicting the fatigue life of metals based on the surface temperature evolution analysis of specimens in their invention patent with authorization announcement number CN106644781B. By calculating the heat loss rate and establishing its relationship with the temperature rise value, the critical temperature rise is determined to assess the fatigue life. It has a certain predictive accuracy, but it is only applicable to static failure data modeling and cannot cover the dynamic response process under the strengthening state.

[0005] In an invention patent authorized by CN108760546B, Wang Xiaogang et al. of Hunan University disclosed a method for measuring fatigue crack propagation rate based on infrared thermography. The method tracks the temperature field evolution at the crack tip through thermography to achieve non-contact measurement. However, this method mainly focuses on the crack propagation stage and has limited ability to identify and predict the limits of early fatigue micro-damage.

[0006] Shenzhen Nanwang Lan Industrial Co., Ltd. disclosed a structural component life assessment method combining infrared thermography technology in its invention patent authorized by CN101598650B. By implementing graded loading fatigue tests and monitoring the temperature rise response in real time, a mapping relationship between stress, temperature rise and life is established to estimate the working life of the component. However, this method focuses more on the life assessment in the later stage of service, and the prediction accuracy is highly dependent on the load path and parameter settings.

[0007] In their invention patent (CN104007007B), Zhang Hongxia et al. from Taiyuan University of Technology disclosed a fatigue analysis method based on the surface temperature characteristics of magnesium alloy specimens. This method monitors the surface temperature distribution changes of the specimen using an infrared thermal imager, extracts the relationship between the local slope of the temperature field and the fatigue load, and thus indirectly estimates the fatigue limit of the magnesium alloy. This method improves the convenience and accuracy of fatigue limit determination, but it is only applicable to specific alloy types with uneven stress distribution.

[0008] Wei Lingxiao et al. of Taiyuan University of Technology [Wei Lingxiao, Yan Zhifeng, Wang Wenxian, et al. Study on fatigue crack propagation of magnesium alloy based on infrared thermal imaging [J]. Journal of Mechanical Engineering, 2012, 48(6):64-69.] further used infrared thermal imaging technology to predict the location of fatigue crack initiation, but the research focus was still on the crack propagation stage, and no direct quantitative relationship between temperature rise behavior and fatigue limit was established, nor was the influence of material strengthening state on energy dissipation behavior considered.

[0009] In summary, while current research has made contributions to understanding the temperature rise behavior, crack propagation, and life estimation during fatigue processes, existing methods primarily focus on crack propagation monitoring and rely on static data fitting, making it difficult to meet the needs for real-time response assessment and rapid limit determination of strengthened components. In particular, for high-temperature alloy components strengthened by laser-assisted ultrasonic rolling, the surface microstructure and energy dissipation behavior of the material undergo significant changes, making it difficult for existing assessment methods to accurately reflect their fatigue response state.

[0010] Therefore, there is an urgent need to develop a rapid fatigue limit prediction method that combines infrared thermal imaging and energy dissipation model, applicable to surface-strengthened components, and possesses high sensitivity and accuracy, in order to meet the engineering requirements for service safety assessment and quantitative judgment of strengthening effects of key components. Summary of the Invention

[0011] To address the problems of long testing cycles, low evaluation efficiency, high sample consumption, and difficulty in quickly and effectively identifying fatigue performance differences in reinforced regions in existing fatigue performance assessment methods for metallic materials, this invention provides a rapid fatigue limit prediction method based on infrared thermography energy dissipation characteristics. During the material's loading process, infrared thermography captures the temperature change curve of the component under cyclic loading in real time, extracting the evolution law of the steady-state temperature rise curve as an evaluation basis. Combined with energy dissipation theory, a quantitative model between temperature rise and fatigue limit is established, enabling rapid determination of the fatigue limit. Compared with traditional methods, this method has advantages such as high prediction accuracy, sensitive response, high testing efficiency, and low sample consumption.

[0012] To address the above problems, the present invention provides the following technical solution:

[0013] A rapid fatigue limit prediction method based on infrared thermography energy dissipation characteristics is proposed. This method utilizes infrared thermography to monitor the surface temperature rise process under different stress loading conditions in real time, extracts the temperature rise rate in the steady-state phase, calculates the unit energy dissipation rate, constructs an energy dissipation model, and establishes a curve relating the dissipation rate to the stress amplitude. Based on the least squares fitting method, high-precision prediction of the fatigue limit is achieved under a limited number of loading cycles and a limited number of samples. It can also be used to analyze changes in the material's energy response before and after strengthening treatment, enabling a quantitative assessment of the strengthening effect.

[0014] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0015] A rapid fatigue limit prediction method based on infrared thermographic energy dissipation characteristics includes the following steps:

[0016] Step 1: Specimen Preparation and Pretreatment: Prepare standard-compliant metal tensile and fatigue specimens. The dimensions and shape of the specimens should meet the specifications in GB / T 228.1-2010 "Metallic Materials - Tensile Testing" and GB / T3075-2008 "Metallic Materials - Axial Control Method for Fatigue Testing". Perform stepped mechanical grinding on the specimen surface to control the surface roughness Ra to less than 0.4 μm; simultaneously, pre-calibrate the infrared thermography observation area.

[0017] Specifically, both the tensile and fatigue specimens are round bars, but not limited to these; they can also be plate-shaped specimens, and universal clamping can be achieved through adaptable fixtures. To reduce the impact of stress concentration and improve the repeatability of fatigue tests, the fatigue specimens adopt a smooth transition axisymmetric dumbbell shape; the tensile specimens adopt an axisymmetric dumbbell structure. Furthermore, the specimens are made of difficult-to-machine high-performance alloy materials, including high-temperature alloys, titanium alloys, and stainless steel.

[0018] Step 2: Surface strengthening treatment of the samples: For the sample group after surface strengthening treatment, specific process parameter values ​​are set according to the material type, literature review data and experimental optimization results;

[0019] Furthermore, the surface strengthening treatment includes, but is not limited to: conventional rolling strengthening, laser shock strengthening, ultrasonic rolling strengthening, shot peening strengthening, and laser-assisted ultrasonic rolling strengthening. In this step, an untreated sample is also provided as a control group.

[0020] Step 3: Determining the upper limit of fatigue loading stress level: Based on the material's mechanical properties, geometric dimensions, and stress state, a preliminary estimate of the tensile load is made using the following formula:

[0021] F = σ b ·A (1)

[0022] Where: F is the estimated tensile load, in N; σ b A is the tensile strength of the material, in MPa; A is the cross-sectional area of ​​the specimen, in mm. 2 ,

[0023] Untreated tensile specimens are clamped onto a tensile testing machine using fixtures, and tensile tests are conducted under a constant strain rate. A laser extensometer is used to collect stress-strain data of the specimens in real time under each cyclic load, obtaining key parameters such as yield strength and tensile strength. By analyzing the stress-strain curves, stages such as elastic deformation, yielding, plastic flow, and necking to fracture are identified, determining the tensile strength value. This, in turn, determines the fatigue loading stress amplitude range, covering the possible fatigue limit range, and serves as a reference for setting the upper limit of the stress amplitude in subsequent fatigue tests.

[0024] Furthermore, the method for determining the upper limit of fatigue loading stress level described in step three is also applicable to specimens after surface strengthening treatment. However, the residual compressive stress introduced into the strengthening layer must be considered, as it may cause a decrease in apparent yield strength. To avoid early damage or delamination of the strengthening layer, the loading stress should be controlled within a range below the yield limit of the strengthening layer. To optimize the fatigue loading range, quasi-static thermo-mechanical finite element simulations can be further performed using the finite element simulation software ABAQUS on both untreated and surface-strengthened specimens. The specific implementation process is as follows:

[0025] Step 1: Sample Model Establishment: Using the finite element simulation software ABAQUS, a three-dimensional finite element model is constructed based on the actual geometric dimensions of the processed sample. For the sample after surface strengthening treatment, a strengthening area is defined on the surface of the model, and the strengthening layer thickness parameter is set. The strengthening layer thickness is determined based on the process conditions and test data.

[0026] Step 2: Assign material parameters: Set material property parameters for the reinforcement layer and the matrix, including elastic modulus, Poisson's ratio, thermal conductivity, specific heat capacity, and density;

[0027] Step 3: Boundary conditions and load settings: Apply tensile loads using a thermo-mechanical coupling method, and simultaneously set thermal convection boundary and heat source conditions to simulate the infrared environment;

[0028] Step 4: Simulation Result Analysis and Optimization: Extract the Mises equivalent stress distribution inside the untreated sample and the surface-strengthened sample under different loading conditions, focusing on the strengthening layer region;

[0029] Step 5: Determining the fatigue loading range and zone intervals: Based on the equivalent stress distribution law and the safety margin of the yield strength of the reinforced layer, the upper limit of fatigue loading is controlled at about 45% of the yield strength.

[0030] Step Four: Infrared Fatigue Test: Referring to the upper limit of the fatigue loading stress level determined in Step Three, five stress amplitude levels are set in an incremental manner, with an interval of Δσ. The initial stress amplitude is set to σ0. The loading frequency f and stress ratio R are set, and axial fatigue loading is applied to the specimen. The specimen is mounted on the infrared fatigue testing system using a fixture. This system consists of a fatigue testing machine, an infrared thermal imager, a fatigue control system, a laser extensometer, a computer, and an image acquisition card. The fatigue testing machine applies a constant stress cycle through the fatigue control system. The infrared thermal imager acquires infrared thermal images of the specimen surface through the computer and image acquisition card, recording the curve T(t) of the surface temperature change over time in the observed area.

[0031] Furthermore, the infrared thermal imager described in step four is installed directly in front of the sample, with the imaging distance controlled at 35–45 cm to ensure that the imaging field of view completely covers the sample surface and avoids blind spots. The sampling parameters of the infrared thermal imager are: sampling frequency not less than 30 Hz, thermal sensitivity better than 0.05℃, and resolution of 640×480 pixels.

[0032] Furthermore, to verify the physical process of specimen fracture evolution during fatigue loading, the fatigue workpiece used was a GH4169 high-temperature alloy fatigue specimen. The evolution process of its actual fatigue damage state under load was examined to confirm the effectiveness of using energy dissipation rate as an index of fatigue state evolution.

[0033] Specifically, for untreated samples, the infrared thermal imager records each load cycle for approximately 200–300 seconds; for samples after surface strengthening treatment, since the surface strengthening layer causes thermal response hysteresis and a decrease in the rate of temperature rise, the loading duration should be appropriately extended, preferably for each load cycle of no less than 500 seconds, to ensure that temperature rise data in the stable phase is obtained.

[0034] Specifically, steps three and four are performed at room temperature;

[0035] The tensile testing machine mentioned in step three and the fatigue testing machine mentioned in step four belong to the same testing device.

[0036] Step 5: Thermal Image Data Processing and Temperature Rise Feature Extraction: Import the image data recorded by the infrared thermal imager into FLIRResearchIR Max software, extract the temperature test data of the sample observation area under various stresses, perform sliding window smoothing and noise reduction on the temperature-time curve T(t), filter out the initial transient interference, and extract the stable rising stage as the analysis interval. Calculate the steady-state temperature rise rate according to the following formula.

[0037]

[0038] Each test data is saved in TXT format, covering the complete test loading process.

[0039] Where: ΔT S Δt represents the relative temperature rise of the material surface during the steady-state phase, in °C; Δt represents the loading time during the steady-state phase, in s; T1 and T2 represent the temperature values ​​corresponding to the start time of the steady-state phase, in °C; t1 and t2 represent the corresponding times, in s.

[0040] Step Six: Energy Dissipation Model Construction: Based on heat conduction theory and Fourier's law, the heat dissipation power of the material per unit time can be obtained.

[0041]

[0042] In the formula: c is the specific heat capacity of the material, J / (kg·℃); ρ is the density of the material, kg / m³ 3 S represents the area of ​​the thermal imaging monitoring region, in meters. 2 h represents the effective thermally affected layer depth that the infrared imaging system can detect, in meters, approximately estimated as... Where α is the thermal diffusivity, m 2 / s, the calculation formula is as follows:

[0043]

[0044] In the formula: k is the thermal conductivity, W / (m·℃).

[0045] Then, by combining the steady-state temperature rise rate, the energy dissipation rate q per unit area was calculated.

[0046]

[0047] In the formula, q is the energy dissipation rate per unit area, W / m 2 .

[0048] Step 7: Fatigue Limit Prediction: Based on the energy dissipation rate q extracted during the steady-state temperature rise stage at each stress amplitude σ, a q-σ scatter plot is constructed, and the least squares method is used to fit a piecewise function to obtain the parameters a, b, C, and D in the two fitted functions. The expression is as follows:

[0049]

[0050] In the formula: a and b are constants related to the viscous loss and viscoelastic dissipation mechanism of the material obtained by fitting experimental data; C and D are constants related to the heat dissipation mechanism during the plastic deformation process of the material obtained by fitting experimental data; σ is the stress amplitude; σ IR-ED This represents the fatigue limit.

[0051] The stress amplitude corresponding to the intersection of the two fitted lines is the predicted fatigue limit σ. IR-ED The calculation formula is as follows:

[0052]

[0053] Step 8: Error Analysis and Method Validation: The prediction result σ... IR-ED The fatigue limit value σ of the specimen measured by the traditional SN curve method S-N Perform error comparison analysis.

[0054] The traditional SN curve method involves subjecting fatigue loading at different stress levels σ, recording the fatigue life N, performing a logarithmic transformation on the obtained N-σ data, plotting the SN curve, and then using the least squares method for linear fitting to estimate the fatigue limit. The error calculation is as follows:

[0055]

[0056] In the formula, ζ represents the relative error rate of fatigue limit prediction, used to evaluate the degree of deviation between the prediction method of this invention and traditional methods; σ S-N The fatigue limit is measured by the traditional SN curve method; σ IR-ED The fatigue limit value predicted by the method of this invention is given. If the error rate ζ between the two values ​​is within 10%, it indicates that the method of this invention can achieve high-precision and rapid prediction of the fatigue limit under test conditions without relying on full-life fatigue loading, providing a reliable, economical and applicable means of fatigue performance evaluation for engineering practice.

[0057] Beneficial effects:

[0058] This invention provides a rapid fatigue limit prediction method based on infrared thermography energy dissipation characteristics. By capturing the temperature change curve of a workpiece under cyclic loading in real time using infrared thermography, the evolution law of the steady-state temperature rise curve is extracted as an evaluation criterion. Combined with energy dissipation theory, a quantitative correlation model between temperature rise and fatigue limit is established, enabling efficient judgment of the fatigue limit. Compared with existing fatigue performance evaluation methods, this invention has the following advantages:

[0059] (1) Shorter testing cycle, significantly improved prediction efficiency, and less sample loss: The method of this invention is based on monitoring the steady-state temperature rise of materials during fatigue loading using an infrared thermal imager, and combined with an energy dissipation model, it can quickly predict the fatigue limit. The number of stress loading sets required for fatigue limit prediction can be reduced from more than 10 sets in traditional methods to 3-5 sets, and the number of test cycles can be reduced from tens of thousands to less than several thousand. This avoids the dependence of traditional SN tests on full-life data results and significantly shortens the testing cycle. At the same time, sample loss is minimal.

[0060] (2) The method has high sensitivity, a traceable physical model, and high prediction accuracy: The method of this invention uses an infrared thermal imager with a thermal sensitivity better than 0.05℃, which can accurately capture energy dissipation behavior and achieve sensitive identification of material fatigue damage. The constructed energy dissipation model is based on thermophysical parameters such as specific heat capacity c, density ρ, and thermal diffusivity α, with a clear physical mechanism and strong reproducibility.

[0061] (3) Applicable to difficult-to-machine high-performance alloy materials, with a simple system and low cost: This invention is applicable to difficult-to-machine high-performance alloy materials such as titanium alloys and stainless steel, and is especially suitable for high-temperature alloy materials that have undergone surface strengthening treatments such as laser-assisted ultrasonic rolling. The testing system consists of a thermal imager and a standard fatigue loading device, which is simple in structure and easy to operate. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0063] Figure 1 The overall flowchart of a rapid fatigue limit prediction method based on infrared thermal imaging energy dissipation characteristics provided by the present invention;

[0064] Figure 2 This is a schematic diagram of the tensile specimen structure in Embodiment 1 of the present invention;

[0065] Figure 3 This is a schematic diagram of the fatigue specimen structure in Embodiment 1 of the present invention;

[0066] Figure 4 The following are the finite element simulation models and stress distributions of the specimens in Embodiment 1 of the present invention: (a) Finite element simulation model; (b) Mises stress distribution of the untreated specimen in the control group; (c) Mises stress distribution of the laser-assisted ultrasonic rolling specimen.

[0067] Figure 5 This is a schematic diagram of the testing system of the present invention, including a fixture, a fatigue testing machine, a fatigue control system, a fatigue specimen, an infrared thermal imager, a laser extensometer, a computer, and an image acquisition card;

[0068] Figure 6 This is a process evolution diagram of the GH4169 fatigue specimen in Embodiment 1 of the present invention from the initial state to the fracture stage during loading;

[0069] Figure 7 The following are graphs showing the surface temperature of the specimens under different stress levels over time in Embodiment 1 of the present invention: (a) control group; (b) experimental group;

[0070] Figure 8This is a schematic diagram of the data set of energy dissipation rate q and stress amplitude σ of the present invention, as well as the fitted fatigue limit prediction results;

[0071] Figure 9 This is a dataset of energy dissipation rate q per unit area and stress amplitude σ of the sample during the steady-state temperature rise stage in Embodiment 1 of the present invention, along with fatigue limit prediction results.

[0072] Explanation of the main reference numerals in the attached drawings: 1. Fatigue testing machine; 2. Infrared thermal imager; 3. Fixture; 4. Fatigue specimen; 5. Laser extensometer; 6. Fatigue control system; 7. Computer and image acquisition card. Detailed Implementation

[0073] To make the objectives, technical solutions, and effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings, specific implementation processes, and embodiments, enabling those skilled in the art to better understand and use it. Obviously, the described embodiments are only some, not all, of the embodiments of this invention.

[0074] Example 1:

[0075] In this embodiment, GH4169 high-temperature alloy bars with a diameter of Φ12mm were selected and processed into tensile and fatigue specimens. The overall flow chart is shown below. Figure 1 As shown, the specific steps are as follows:

[0076] Step 1: Sample preparation and pretreatment.

[0077] (1) The tensile specimen was prepared according to GB / T 228.1-2010 "Metallic Materials - Tensile Testing Method" as an axisymmetric dumbbell-shaped structure. The total length of the specimen was 140 mm, the diameter of the two clamping sections was Φ10 mm and the length of each was 50 mm, and the middle section was a parallel section with a diameter of Φ6±0.05 mm and a length of 40 mm. The effective deformation zone was 18 mm. The surface strengthening treatment area and the infrared observation area were both located in the central area of ​​the entire parallel section. Figure 2 As shown. The fatigue specimen was prepared in accordance with GB / T 3075-2008 "Methods for Axial Control in Fatigue Testing of Metallic Materials" as a smooth-transition axisymmetric dumbbell shape. The total length of the specimen was 140 mm. The two clamping sections were Φ10 mm in diameter and 48 mm in length, and the middle section was a Φ6±0.05 mm thin neck section with a length of 44 mm. The thin neck section and the clamping section were connected by a large arc with a radius of 120 mm to reduce stress concentration. The surface strengthening area and the infrared observation area were located at the center of the large arc transition section. Figure 3 As shown.

[0078] (2) In order to eliminate residual stress from processing and ensure the consistency of the sample surface condition and the reliability of mechanical property testing, the sample surface was subjected to step-by-step mechanical grinding with 240#, 600#, 800#, 1200# and 2000# sandpaper in sequence, and the final surface roughness Ra was controlled within 0.4μm. At the same time, the infrared thermal imaging observation area was pre-calibrated and the sample surface was coated with high-emissivity matte black paint to improve the sensitivity and accuracy of subsequent infrared thermal imager temperature measurement.

[0079] Step 2: Perform surface strengthening treatment on the sample.

[0080] The experiment consisted of two groups: an untreated group (control group) and a group of samples treated with laser-assisted ultrasonic rolling (experimental group), with five samples in each group. The surface strengthening treatment method for the experimental group was based on previous experimental optimizations, with the following process parameters set: laser spot diameter 50 μm, power 8 W, moving speed 0.8 mm / s, circumferential distance between the laser spot center and the ultrasonic rolling tool center 5 μm; ultrasonic frequency 27.6 kHz, ultrasonic amplitude 3.5 μm, workpiece spindle speed 500 r / min, rolling depth 0.3 mm, rolling cycles 3 times, and feed rate 0.08 mm / r. After laser-assisted ultrasonic rolling treatment, a distinct strengthening layer was formed on the sample surface, with a thickness of approximately 120 μm.

[0081] Step 3: Determine the upper limit of fatigue loading stress level.

[0082] (1) Preliminary estimate of the upper limit of fatigue loading stress level: tensile strength σ of GH4169 high temperature alloy b The tensile strength is 1376 MPa, and the diameter d of the middle section of the tensile specimen is 6 mm. Substituting the above data into equation (1) for estimation:

[0083] F = σ b ·A (1)

[0084] Where: F is the estimated tensile load, in N; σ b A is the tensile strength of the material, in MPa; A is the cross-sectional area of ​​the specimen, in mm. 2 ,

[0085] The tensile load is calculated to be 38.89 N. Therefore, to avoid premature fracture of the specimen during tensile testing, the maximum load should not exceed this estimated value.

[0086] The control group specimens were clamped onto the tensile testing machine 1 using fixture 3 for uniaxial tensile testing. The loading rate was set to 2 mm / min, and stress-strain curve data were acquired in real time using a laser extensometer 5. By analyzing the stress-strain curves, the stages of elastic deformation, yielding, plastic deformation, necking to fracture were identified. The yield strength of the GH4169 high-temperature alloy was obtained as approximately 1048.2 MPa, and the tensile strength as 1380.3 MPa. In contrast, the yield strength of the laser-assisted ultrasonic rolling strengthened specimen increased from 1048.2 MPa to 1196.4 MPa, and the tensile strength increased from 1380.3 MPa to 1497.4 MPa.

[0087] (2) Optimization of the upper limit of loading stress using finite element experiments: Considering that the residual compressive stress introduced by the laser-assisted ultrasonic rolling strengthening process of the experimental group specimens may affect the apparent yield behavior of the strengthening layer, in order to prevent early damage or delamination of the strengthening layer, it is necessary to appropriately reduce the upper limit of fatigue loading, preferably setting the stress level below the yield limit of the strengthening layer. Therefore, quasi-static thermo-mechanical coupling simulation analysis was further carried out on the two types of specimens using the finite element simulation software ABAQUS to assist in optimizing the loading range of the fatigue test. The finite element simulation model is as follows: Figure 4 As shown in (a), the specific implementation process is as follows:

[0088] Step 1: Sample model establishment: Construct a three-dimensional finite element model based on the actual geometric dimensions of the processed sample used in the experiment; for the sample after laser-assisted ultrasonic rolling strengthening treatment, define the strengthening area on the surface of the model and set the strengthening layer thickness to 120μm.

[0089] Step 2: Assigning material parameters: Set the corresponding GH4169 high-temperature alloy material property parameters for the reinforcing layer and the substrate, including elastic modulus, Poisson's ratio, thermal conductivity, specific heat capacity, density, etc. The mechanical parameters of the reinforcing layer are adopted after tensile testing.

[0090] Step 3: Boundary conditions and load settings: Apply axial tensile load using a thermo-mechanical coupling method, and simultaneously set thermal convection boundary and heat source conditions to simulate the infrared environment.

[0091] Step 4: Simulation Result Analysis and Optimization: Extract the Mises equivalent stress distribution inside the untreated sample and the sample after laser-assisted ultrasonic rolling strengthening under different loading conditions, focusing on the strengthening layer region, such as... Figure 4 (b) and Figure 4 As shown in (c).

[0092] Step 5: Based on the equivalent stress distribution law and the safety margin of the yield strength of the strengthening layer, it is recommended that the upper limit of fatigue loading be controlled at about 45% of the yield strength. Finally, it is determined that in the subsequent fatigue test, the maximum stress amplitude of the control group specimen should not exceed 480 MPa; and the maximum stress amplitude of the specimen after laser-assisted ultrasonic rolling strengthening treatment should not exceed 540 MPa.

[0093] Step 4: Infrared fatigue test.

[0094] Referring to the upper limit of the loading stress determined in step three, five levels of fatigue loading stress amplitude were set. The initial stress of the control group specimens was set to 160 MPa, with an increment interval Δσ = 80 MPa; the initial stress of the experimental group specimens after ultrasonic rolling strengthening treatment was set to 220 MPa, with an increment interval Δσ = 80 MPa. The loading frequency f of each group of specimens was set to 20 Hz, and the stress ratio R = -1.

[0095] Fatigue specimen 4 is mounted on the infrared fatigue testing system using clamp 3 for axial fatigue loading. A schematic diagram of the system is shown below. Figure 5 As shown, the infrared fatigue testing system includes a fatigue testing machine 1, an infrared thermal imager 2, a laser extensometer 5, a fatigue control system 6, and a computer and image acquisition card 7. The fatigue testing machine 1 applies a constant stress cycle through the fatigue control system 6, and the infrared thermal imager 2 acquires infrared thermal images of the sample surface through the computer and image acquisition card 7. To ensure that the imaging field of view completely covers the sample surface and avoid blind spots, the infrared thermal imager lens is kept at a distance of 40 cm from the sample observation area.

[0096] The infrared thermal imager 2 is configured with the following parameters: sampling frequency of 60Hz, thermal sensitivity better than 0.03℃, resolution of 640×480 pixels, and real-time acquisition of the temperature change curve T(t) of the sample during fatigue loading.

[0097] Specifically, steps three and four are both performed at room temperature; and the tensile testing machine used in step three and the fatigue testing machine used in step four are the same testing apparatus.

[0098] Furthermore, to verify the physical process of specimen fracture evolution during fatigue loading, such as... Figure 6 As shown, the evolution of the actual fatigue damage state of the GH4169 high-temperature alloy fatigue specimen under load is recorded, which supports the effectiveness of energy dissipation rate as an indicator of fatigue state evolution.

[0099] Step 5: Thermal image data processing and temperature rise feature extraction.

[0100] Image data recorded by an infrared thermal imager was imported into FLIR ResearchIR Max software. Temperature-time curves T(t) under various stress levels within the observation area of ​​the control and experimental samples were extracted. Smoothing was performed using a sliding window (window width of 5 seconds), and noise was removed by combining this with an FFT filtering algorithm to filter out initial transient interference. Figure 7 As shown in the figure, for the control group samples, the temperature rise stabilization range typically occurs around 60–80 s after loading, with a steady-state duration of approximately 100–250 s. For the experimental group, the samples treated with laser-assisted ultrasonic rolling showed a slight lag in the steady-state temperature rise process due to residual compressive stress and hardening effect on the surface layer. The steady-state range occurred around 120 s after loading, with a maximum duration of up to 400 s.

[0101] The analysis interval is defined as the period after the sample enters the steady-state temperature rise phase, and the steady-state temperature rise rate is calculated. The stress amplitude of the control group was 160 MPa, T1 = 18.3℃, T2 = 19.8℃, corresponding to times t1 = 70 s and t2 = 130 s. Substituting these values ​​into equation (2):

[0102]

[0103] The steady-state temperature rise rate can be obtained. Similarly, calculate the steady-state temperature rise rate of the experimental group.

[0104] Step Six: Energy Dissipation Model Construction.

[0105] Since the sample is GH4169 high-temperature alloy, its thermophysical parameters are as follows: specific heat capacity c = 460 J / (kg·℃), density ρ = 8280 kg / m³ 3 The thermal conductivity k = 11.5 W / (m·℃) is given. Substituting this into equation (4), we can obtain the thermal diffusivity α = 3.018 × 10⁻⁶. -6 m 2 / s;

[0106] For the control group, the area monitored by the infrared thermal imager is S = 10mm × 50mm = 0.0005m². 2 The steady-state loading time Δt is 60s, and the approximate estimated effective thermally affected layer depth that the infrared imaging system can detect is:

[0107] Substituting the data obtained above into equations (3) and (5),

[0108]

[0109] In the formula: c is the specific heat capacity of the material, J / (kg·℃); ρ is the density of the material, kg / m³ 3S represents the area of ​​the thermal imaging monitoring region, in meters. 2 h represents the effective thermally affected layer depth that the infrared imaging system can detect, in meters, approximately estimated as... Where α is the thermal diffusivity, m 2 / s, the calculation formula is as follows:

[0110]

[0111] Where: k is the thermal conductivity, W / (m·℃);

[0112] The formula for calculating the energy dissipation rate q per unit volume is as follows:

[0113]

[0114] The energy dissipation rate q during the steady-state temperature rise stage under the stress amplitude level of the control group was obtained. Similarly, the energy dissipation rate q per unit volume of the experimental group was calculated.

[0115] Step 7: Prediction of fatigue limit value.

[0116] Schematic diagram as follows Figure 8 As shown, based on the energy dissipation rate q of the steady-state temperature rise stage at each stress amplitude σ calculated from the five samples in the control and experimental groups in step six, a q-σ scatter plot is constructed, as follows. Figure 9 As shown, the piecewise function (6) is fitted using the least squares method to obtain the parameters a, b, C, and D in the two fitted functions, which are then substituted into equation (7):

[0117]

[0118] Solve for the stress value corresponding to the intersection point, that is, predict the fatigue limit σ. IR-ED The calculation results are as follows: the predicted fatigue limit of the control group. The predicted fatigue limit is 360 MPa; It is 456 MPa.

[0119] Step 8: Error analysis and method validation.

[0120] The fatigue limit was obtained by conducting fatigue tests on the control group specimens using the traditional SN curve method. The pressure is 395 MPa. Substituting this into equation (8):

[0121]

[0122] The calculated error rate ζ = 8.86%.

[0123] Similarly, the fatigue limit of the experimental group was obtained using the traditional SN curve method. The pressure is 498 MPa. Substituting this into equation (8), the error rate ζ is calculated to be 8.43%, with the error controlled within 10%. It can be seen that the method of this invention can achieve high-precision and rapid prediction of fatigue limits under test conditions that do not rely on full-life fatigue loading.

[0124] Although specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, these descriptions should not be construed as limiting the scope of the present invention. The scope of protection of the present invention is defined by the appended claims, and any modifications based on the claims of the present invention are within the scope of protection of the present invention.

Claims

1. A rapid prediction method for fatigue limit based on infrared thermographic energy dissipation characteristics, characterized in that, The steps include the following: Step 1: Specimen Preparation and Pretreatment: Prepare standard-compliant metal tensile and fatigue specimens. The dimensions and shape of the specimens should meet the specifications in GB / T 228.1-2010 "Metallic Materials - Tensile Testing" and GB / T 3075-2008 "Metallic Materials - Axial Control Method for Fatigue Testing". Perform stepped mechanical grinding on the specimen surface to control the surface roughness Ra to be less than 0.4 μm. Simultaneously, pre-calibrate the infrared thermography observation area. Step 2: Surface strengthening treatment of the sample: The surface strengthening treatment includes: ordinary rolling strengthening, laser shock strengthening, ultrasonic rolling strengthening, shot peening strengthening, and laser-assisted ultrasonic rolling strengthening; In this step, an untreated sample is set up as a control group. Step 3: Determining the upper limit of fatigue loading stress level: Based on the material's mechanical properties, geometric dimensions, and stress state, a preliminary estimate of the tensile test load is made; Tensile specimens without surface hardening treatment are clamped onto a tensile testing machine using fixtures, and a constant strain rate is applied for tensile testing. Stress-strain data of the specimens under each cyclic load is collected in real time using a laser extensometer to obtain yield strength and tensile strength. By analyzing the stress-strain curves, the stages of elastic deformation, yielding, plastic flow, and necking to fracture are identified, and the tensile strength value is determined. In addition, the fatigue loading stress amplitude range is determined as a reference for setting the upper limit of stress amplitude in subsequent fatigue tests. For the samples after surface strengthening treatment, in order to avoid early damage or peeling of the strengthening layer, the loading stress is controlled within the range below the yield limit of the strengthening layer, and the fatigue loading range is optimized by combining the finite element simulation software ABAQUS. Step 4: Infrared fatigue test: Referring to the upper limit of fatigue loading stress determined in Step 3, set 5 levels of stress amplitude in an incremental manner, with an interval of Δσ. Set the initial stress amplitude to σ0, set the loading frequency f and stress ratio R, and perform axial fatigue loading on the specimen. The specimen is mounted in the infrared fatigue testing system by a fixture. The fatigue testing machine applies a constant stress cycle through the fatigue control system. The infrared thermal imager acquires infrared thermal images of the specimen surface through a computer and image acquisition card, and records the curve T(t) of the surface temperature of the observation area changing over time. Step 5: Thermal Image Data Processing and Temperature Rise Feature Extraction: Import the image data recorded by the infrared thermal imager into FLIRResearchIR Max software, extract the temperature test data under various stress levels within the sample observation area, perform sliding window smoothing and noise reduction on the temperature-time curve T(t), filter out initial transient interference, and extract the stable rising stage as the analysis interval to calculate the steady-state temperature rise rate. Where: ΔT S Δt represents the relative temperature rise of the material surface during the steady-state phase, in °C; Δt represents the loading time during the steady-state phase, in seconds; T1 and T2 represent the temperature values ​​at the start of the steady-state phase, in °C; t1 and t2 represent the corresponding times, in seconds. Each test data is saved in TXT format, covering the complete test loading process; Step Six: Energy Dissipation Model Construction: Based on heat conduction theory and Fourier's law, the power dissipated by the material per unit time can be obtained as follows: In the formula: c is the specific heat capacity of the material, J / (kg·℃); ρ is the density of the material, kg / m³ 3 S represents the area of ​​the thermal imaging monitoring region, in meters. 2 h represents the effective thermally affected layer depth that the infrared imaging system can detect, in meters, approximately estimated as... Where α is the thermal diffusivity, m 2 / s, the calculation formula is as follows: Where: k is the thermal conductivity, W / (m·℃); Subsequently, combining the steady-state temperature rise rate, the mathematical expression for the energy dissipation rate q per unit area was calculated: In the formula, q is the energy dissipation rate per unit area, W / m 2 ; Step 7: Fatigue Limit Prediction: Based on the energy dissipation rate q extracted during the steady-state temperature rise stage at each stress amplitude σ, a q-σ scatter plot is constructed, and the least squares method is used to fit a piecewise function to obtain the parameters a, b, C, and D in the two fitted functions. The expression is as follows: In the formula: a and b are constants related to the viscous loss and viscoelastic dissipation mechanism of the material obtained by fitting experimental data; C and D are constants related to the heat dissipation mechanism during the plastic deformation process of the material obtained by fitting experimental data; σ is the stress amplitude; σ IR-ED This is the fatigue limit; The stress amplitude corresponding to the intersection of the two fitted lines is the predicted fatigue limit σ. IR-ED The calculation formula is as follows: Step 8: Error Analysis and Method Validation: The prediction result σ... IR-ED The fatigue limit value σ of the specimen measured by the traditional SN curve method S-N Perform error comparison analysis; The traditional SN curve method involves applying fatigue loading at different stress levels σ, recording the fatigue life N, performing a logarithmic transformation on the obtained N-σ data, plotting the SN curve, and then using the least squares method for linear fitting to estimate the fatigue limit. The error calculation is as follows: In the formula, σ S-N The fatigue limit is measured by the traditional SN curve method; σ IR-ED ζ represents the fatigue limit value predicted by the method of this invention; ζ represents the relative error rate of fatigue limit prediction, used to evaluate the degree of deviation of the prediction method of this invention compared with the traditional method; if the error rate of the two is within 10%, it indicates that the method of this invention can achieve high-precision and rapid prediction of fatigue limit under test conditions without relying on full-life fatigue loading.

2. The method for rapid prediction of fatigue limit based on infrared thermal imaging energy dissipation characteristics according to claim 1, characterized in that, The tensile specimens and fatigue specimens mentioned in step one are both round bars or plates, and are clamped using appropriate fixtures; the fatigue specimens adopt a smooth transition axisymmetric dumbbell shape; the tensile specimens adopt an axisymmetric dumbbell structure.

3. The method for rapid prediction of fatigue limit based on infrared thermal imaging energy dissipation characteristics according to claim 1, characterized in that, In step three, the tensile load is initially estimated using the following formula: F=σ b ·A (1) Where: F is the estimated tensile load, in N; σ b A is the tensile strength of the material, in MPa; A is the cross-sectional area of ​​the specimen, in m. 2 , 4. The method for rapid prediction of fatigue limit based on infrared thermographic energy dissipation characteristics according to claim 1, characterized in that, In step three, the finite element simulation software ABAQUS optimizes the fatigue loading range. The specific implementation process is as follows: Step 1: Sample model establishment: Using the finite element simulation software ABAQUS, a three-dimensional finite element model is constructed based on the actual geometric dimensions of the processed sample used in the experiment; for the sample after surface strengthening treatment, a strengthening area is defined on the surface of the model, and the thickness of the strengthening layer is set. The thickness of the strengthening layer is determined based on the process conditions and experimental data. Step 2: Assign material parameters: Set the corresponding material property parameters for the reinforcement layer and the matrix, including elastic modulus, Poisson's ratio, thermal conductivity, specific heat capacity, and density; Step 3: Boundary conditions and load settings: Apply tensile loads using a thermo-mechanical coupling method, and simultaneously set thermal convection boundary and heat source conditions to simulate the infrared environment; Step 4: Simulation Result Analysis and Optimization: Extract the Mises equivalent stress distribution inside the untreated sample and the surface-strengthened sample under different loading conditions, focusing on the strengthening layer region; Step 5: Determining the fatigue loading range and zone intervals: Based on the equivalent stress distribution law and the safety margin of the yield strength of the reinforced layer, the upper limit of fatigue loading is controlled at about 45% of the yield strength.

5. The method for rapid prediction of fatigue limit based on infrared thermal imaging energy dissipation characteristics according to claim 1, characterized in that, The infrared fatigue testing system described in step four includes: a fatigue testing machine, an infrared thermal imager, a fatigue control system, a laser extensometer, a computer, and an image acquisition card; the infrared thermal imager is installed directly in front of the sample, with the imaging distance controlled at 35-45cm to ensure that the imaging field of view completely covers the sample surface and avoids blind spots; the sampling parameters of the infrared thermal imager are: sampling frequency not less than 30Hz, thermal sensitivity better than 0.05℃, and resolution of 640×480 pixels; The tensile testing machine mentioned in step three and the fatigue testing machine mentioned in step four are the same testing apparatus; steps three and four are carried out at room temperature.

6. The method for rapid prediction of fatigue limit based on infrared thermal imaging energy dissipation characteristics according to claim 5, characterized in that, For untreated samples, the infrared thermal imager records each load cycle for 300–400 s; for samples with surface strengthening treatment, since the surface strengthening layer causes thermal response hysteresis and a decrease in the rate of temperature rise, the loading duration is extended, and the loading time for each load is not less than 500 s to ensure that temperature rise data in the stable phase is obtained.

7. The method for rapid prediction of fatigue limit based on infrared thermal imaging energy dissipation characteristics according to claim 1, characterized in that, In step one, the sample material is a difficult-to-machine high-performance alloy material, including high-temperature alloys, titanium alloys, and stainless steel.

8. The method for rapid prediction of fatigue limit based on infrared thermal imaging energy dissipation characteristics according to claim 1, characterized in that, In step two, the surface strengthening treatment includes: rolling strengthening, laser shock strengthening, ultrasonic rolling strengthening, shot peening strengthening, and laser-assisted ultrasonic rolling strengthening.

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