CN109855959B - Prediction method for fatigue strength of metal material - Google Patents
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Abstract
The invention discloses a method for predicting the fatigue strength of metal materials, and belongs to the technical field of metal material performance testing. The steps are as follows: (1) Select the same series of materials for tensile performance test; (2) Fatigue performance test; (3) Parameter fitting: use the measured tensile and fatigue data to obtain the σy/σb and σw/σy values of the material, then draw a σw/σy‑‑σy/σb relationship diagram with σy/σb value as the abscissa and σw/σy value as the ordinate, and obtain the parameters ω and C through linear fitting; (4) Determine the σy/σb value of the material by the tensile properties of the material to be predicted, and further determine the σw/σy value of the material by the fitted straight line, so as to obtain the predicted value of the fatigue strength σw of the corresponding material. The invention establishes the intrinsic relationship between fatigue strength, yield strength and tensile strength, and only needs tensile test and a small amount of fatigue test to realize the fatigue strength prediction of all metal materials of the same series.
Description
Technical Field
The invention relates to the technical field of metal material performance testing, in particular to a method for predicting fatigue strength of a metal material.
Background
Fatigue strength (generally, the material is subjected to 10 times of load) as a key index of the long-term service safety and reliability of the engineering material under the action of cyclic alternating load7Maximum stress at which fatigue fracture does not occur after the action of secondary alternating loads Meyers, M.A.&Chawla,K.K.Mechanical behavior of materials(Cambridge University Press,2009)]Have received constant attention from anti-fatigue design developers. At present, the specific numerical value of the fatigue strength of the material is obtained mainly by means of fatigue testing, namely, load and service life data are obtained in a mode that a fatigue sample is cyclically loaded on a fatigue testing machine, and the fatigue strength is obtained through further calculation. The specific calculation method is mainly divided into two categories, namely a stress-life (S-N) method based on the Basquin formula [ Basquin O.H.the empirical law of end tests, Proceedings, ASTM, ASTEA 10(1910) 625-.]And the other is a confidence limit-reliability-stress (C-R-S) lifting method [ GB/T24176-.](ii) a Various methods for subsequent development [ Yangyongsheng, Liutao Wei, a stress amplitude method for predicting fatigue strength CN200810043469.8.2009-01-14; CN201610486983.3.2016-12-07 parts of evaluation method for the ultra-high cycle fatigue strength and the fatigue life of the steam turbine rotor.]But also basically results from a simplification or a modification of the two above-mentioned ways.
The fatigue strength detection method is rigorous and accurate, can be used for carrying out targeted detection on specific materials and loading modes, and obtains reliable results. However, several practical problems are also raised at the same time: firstly, both the two calculation methods need a large amount of fatigue data as a basis, and the economic cost and the time cost of the fatigue test are high; secondly, the fatigue performances of multiple materials in the same series or multiple states of the same material are necessarily compared with each other in the process of material selection or material development, but the performance results obtained through fatigue detection are mutually independent, the detection process cannot be simplified by utilizing the mutual correlation among the materials, and therefore, the repeated test must be carried out on the state of each material; thirdly, the independent fatigue detection results have very limited contribution to defining the key influence factors of the fatigue strength, and further, the blind trial and error condition caused by lack of clear targets in the material selection and development process is caused. These problems severely restrict the development process of the fatigue-resistant material design, and indirectly reflect the key role of the fatigue strength index. Therefore, how to avoid fatigue detection as much as possible, quickly obtain the fatigue strength of multiple materials in the same series, and really realize the conversion from detection to prediction is a problem with great practical significance.
With regard to the problem of fatigue strength prediction, early attempts resulted from
The study work in 1870 years
A.Z.Versuche über Biegung und Verdrehung von Eisbahnwagen–Achsen
der Fahrt.Z.Bauw.8(1858)641–652.]. Through the arrangement and induction of a large amount of fatigue data,
it is pointed out that there is an approximately linear relationship between the fatigue strength and the tensile strength of the material, and the formula is expressed as: sigmaw/σbWherein C is 0.3-0.5.
The formula correlates the fatigue strength of the material with other mechanical properties for the first time, and thus has a profound effect on the development of the fatigue field. On one hand, according to the empirical relationship, on the premise that the parameter C value is fitted through the existing data, the fatigue strength of the material can be obtained by directly calculating the tensile strength by skipping a testing link, and the method is convenient and quick; on the other hand, the formula shows the positive relation between the fatigue strength and the tensile strength, namely the fatigue strength can be improved by improving the tensile strength, thereby providing a clear definition for the development of the anti-fatigue materialIn the direction of (a).
In recent years, with the continuous development of material science and technology, high strength materials [ Gleiter, h.nanocrystalline materials.prog.mater.sci.33(1989) 223-; aggen, G.et al.ASM handbook properties and selection: Irons, steels, and high-performance alloys Vol.1(ASM International, USA,1990).]The study on fatigue strength thus found a new problem, emerging endlessly: when the strength of the material is improved to a certain degree (about 1400MPa for steel, 480MPa for copper alloy and 340MPa for aluminum alloy), if the tensile strength is continuously improved, the fatigue strength is not linearly improved, or the growth trend is obviously slowed down, or the fatigue strength tends to be saturated, or even has a descending trend; in this case
The formula no longer applies. Therefore, establishing a universal quantitative relationship between fatigue strength and tensile property to realize rapid prediction of fatigue strength becomes a problem to be solved at present.
Disclosure of Invention
The invention aims to provide a method for predicting the fatigue strength of a metal material, which can realize the fatigue strength prediction of all metal materials in the same series by establishing the intrinsic relation among the fatigue strength, the yield strength and the tensile strength and only needing a tensile test and a small amount of fatigue tests. The method can effectively reduce fatigue tests in the engineering material development and selection process, is expected to replace the traditional fatigue-resistant material design mode of repeated trial and error, and realizes the real efficient prediction of the fatigue strength.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for predicting fatigue strength of a metal material comprises the following steps:
(1) selecting a plurality of metal materials of the same series for tensile property test to obtain a plurality of groups of material yield strength sigmayAnd tensile strength sigmabA value;
(2) testing the fatigue performance:
fatigue test prepared by selecting 2-4 materials from the same series of metal materialsThe sample is subjected to fatigue strength test to obtain the fatigue strength sigma of the materialwAn actual measurement value;
(3) parameter fitting:
determining sigma of the material by using the measured tensile property data and fatigue strength datay/σbAnd σw/σyValue, then byy/σbThe value being abscissa, in σw/σyValue plotting sigma for ordinatew/σy--σy/σbObtaining parameters omega and C through linear fitting, wherein omega is the negative reciprocal of the slope of the fitting straight line, and C is the fitting straight line and sigmay/σbThe intercept of the shaft;
(4) and (3) fatigue strength prediction:
determining sigma of a material by tensile properties of the material to be predictedy/σbValue, i.e. sigmaw/σy--σy/σbThe position of the abscissa in the relational graph can be further determined by fitting a straight line to sigma of the materialw/σyValue, i.e. ordinate position, to find the fatigue strength σ of the respective materialwPredicting a value; or else, the tensile properties σ of the material to be predictedy、σbDirectly substituting the parameter values omega and C into the formula (1), and calculating to obtain the fatigue strength sigma of the corresponding materialwPredicting a value;
in the step (1), the selected metal materials of the same series refer to a plurality of materials obtained by different pre-deformation processes or heat treatments on the same metal material.
In the step (1), after the same series of metal materials are uniformly processed into tensile samples, the tensile property test is carried out under the same experimental conditions.
In the step (2), in order to ensure the accuracy of the prediction result and reduce the fatigue test amount, 2 to 4 materials with large differences in tensile properties are selected for fatigue property test.
In the step (2), the selected material is used for processing the fatigue sample, and the surface of the sample needs to be subjected to uniform polishing treatment to ensure the consistency of the surface state; and then selecting the required loading conditions to uniformly perform the fatigue performance test.
The method is suitable for steel, copper alloy, aluminum alloy or magnesium alloy; is suitable for various pre-deformation and heat treatment processes; the applicable loading conditions are tension-compression loading modes, bending-torsion loading modes and different cyclic load ratios and cyclic times.
The invention has the following advantages and beneficial effects:
1. the invention solves the problem of rapidly estimating the fatigue strength by the tensile property. At present, the fatigue strength is obtained mainly by fatigue testing, and the economic cost and the time cost are high. The invention is in
On the basis of theories such as a formula and the like, a large number of fatigue experiment tests in the engineering material development and selection process are effectively reduced by establishing a universal quantitative relation between the fatigue strength and the tensile property, and the efficient prediction of the material fatigue strength is realized.
2. The invention solves the problem of correlation between fatigue strengths of materials in different states in the same series. In the actual material development and selection process, the problem of comparison of fatigue strength among materials in the same series (like the same basic component) and different states (such as different pre-deformation amounts, different heat treatment processes and the like) is often faced, and the fatigue test can be only carried out one by one through a conventional method to obtain respective fatigue strength values. The main parameters of the formula can be regarded as constants for the same series of materials, and specific numerical values can be determined only through a small amount of fatigue tests, so that an efficient prediction formula of the fatigue strength of the series of materials is obtained, and the fatigue strength of the corresponding materials can be obtained by substituting any tensile property. The method not only greatly reduces the necessary fatigue testing amount, but also successfully realizes the mutual correlation of the fatigue strength among materials in different states, thereby providing great convenience for the development and selection of the anti-fatigue material.
3. The invention combines deep understanding of fatigue damage essence and provides brand new fatigue strengthA degree theory model; the key features of this theory are: to be provided with
In the form of basic function, the dynamic fatigue performance predicted by static tensile performance is taken as basic guiding idea, the basic principle of fatigue damage is reflected by integrating and centralizing the great influence factors of the fatigue strength such as elasticity, plasticity, hardening capacity, tissue defect and the like of the material, and the fatigue strength sigma is established in a concise formwAnd yield strength sigmayTensile Strength σbThe method gives consideration to universality and practicability at the same time, and embodies the double value of the theory.
Drawings
FIG. 1 is a flow chart of a method for predicting fatigue strength of a metal material.
Fig. 2 is a theoretical diagram of the fatigue strength "lever law".
FIG. 3 shows the relationship between different fatigue strengths and tensile strengths and the fatigue strength prediction σw/σy--σy/σbA relationship graph; wherein: (a) SPCC and SPRC Material sigmaw--σbA linear relationship graph; (b) SPCC and SPRC Material sigmaw/σy--σy/σbA relationship graph; (c) QBe2 Material sigmaw--σbA non-linear relationship graph; (d) QBe2 Material sigmaw/σy--σy/σbA relationship graph; (e) cu-5Al material sigmaw--σbA non-linear relationship graph; (f) cu-5Al material sigmaw/σy--σy/σbAnd (5) a relational graph.
FIG. 4 shows the fatigue strength prediction σ for materials of different compositions and processesw/σy--σy/σbA relationship graph; wherein: (a) steel; (b) a copper alloy; (c) an aluminum alloy; (d) a magnesium alloy.
FIG. 5 shows the fatigue strength prediction σ for materials under different loading conditionsw/σy--σy/σbA relationship graph; wherein: (a) cycling the cycle change; (b) a change in load ratio; (c) the loading manner changes.
FIG. 6 is a verification of the accuracy of the fatigue strength prediction results.
FIG. 7 shows the predicted fatigue strength for the 5052 aluminum alloy of example 1.
FIG. 8 is a predicted fatigue strength of the Fe-30Mn-0.9C TWIP steel of example 2.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
FIG. 1 is an operation flow of the method for efficiently predicting fatigue strength of a metal material according to the present invention, which comprises five main steps: (1) testing the tensile property; (2) selecting a fatigue test sample; (3) testing fatigue performance; (4) fitting parameters; (5) and (4) predicting fatigue strength. The operation is simple and efficient, and the method is widely applicable to various different metal materials, processes and loading conditions.
The method adopts the fatigue strength 'lever law' to realize prediction, the name is derived from the balance relation similar to a lever among the fatigue strength, the yield strength and the tensile strength, and the basic form of the formula is as follows:
(see fig. 2). Wherein sigmawFor fatigue strength, σyAs yield strength, σbFor tensile strength, parameters relating to defects and modulus of elasticity, respectively, are given. For the same series of materials, ω and C are constants, in which case σw/σyAnd σy/σbAnd the specific numerical values of omega and C can be determined by linear fitting of two or more groups of fatigue and tensile data in a linear relationship. After the parameter values are determined, the yield strength and the tensile strength of any materials in the series are measured, and the yield strength and the tensile strength can be substituted into the formula to obtain the corresponding fatigue strength value, so that efficient prediction is realized.
The scientific principle of the method is as follows:
the fatigue strength 'lever law' adopted in the method is a model based on the fatigue damage basic principle, and the model integrates several large influence factors of the fatigue strength and is embodied in the following aspects:
first, fatigue damage essentially results from the irreversible deformation of a material during cyclic deformationPlastic deformation, thus introducing a yield strength σ which represents the ease of plastic deformationyThe larger the yield strength is, the more favorable the control of the whole plastic deformation amount of the material is, thereby being favorable for improving the fatigue strength.
Secondly, although the damage is caused by plastic deformation, under the load condition close to the fatigue strength, the material is often in the state of local plastic deformation of the whole elastic deformation, which relates to the problem of matching of the elastic-plastic deformation among the parts of the material, so a parameter C reflecting the elastic deformation characteristic is introduced, the parameter is directly related to the elastic modulus of the material, the larger the modulus is, the larger the value of C is, the smaller the whole elastic deformation quantity under the same load condition is, the smaller the corresponding local plastic deformation quantity is, and the fatigue strength is favorably improved.
Third, the matching of bulk elasticity to local plasticity creates a problem of localized fatigue damage, with the more concentrated the damage, the more likely the material will crack at shorter cycle cycles, which is extremely detrimental to high cycle fatigue where the crack initiation phase occupies the majority of the life. Thus, the ability of the material itself to resist local plastic deformation is critical to fatigue strength. Based on the facts, the model introduces the yield ratio sigma capable of reflecting the work hardening capacity of the materialy/σbThe smaller the yield ratio is, the better the work hardening capacity of the material is, and the stronger the strain localization resistance is, the better the fatigue strength is.
Fourth, the problem of localization of fatigue damage is closely related to the degree of homogeneity of the material itself, in addition to the homogeneous deformability mentioned above. Defects such as locally grown grains, coarse second phases, inclusions, loose pores and the like often exist in actual engineering materials, and the existence of the defects directly influences the distribution of fatigue damage and even changes the fatigue cracking behavior. In the model, the parameter omega is the centralized embodiment of the influence of the defect on the fatigue strength, and the higher the degree of localization of the damage caused by the defect is, the larger the value omega is, the more adverse to the improvement of the fatigue strength is.
The four aspects represent key factors influencing the fatigue strength of the metal material; if only the first two points are considered, the model function form is
The formulas are similar, so the model can be regarded as a pair
And (5) developing and perfecting the formula. In addition, the model comprehensively reflects the influence of elasticity, plasticity, hardening capacity, tissue defects and the like of the material on the fatigue performance, covers key indexes for restricting the fatigue strength, intensively reflects the essence of fatigue damage, and is enough to be used as the theoretical basis of the patent to realize the high-efficiency prediction of the fatigue strength.
The invention has the following technical effects: and predicting the fatigue strength under different fatigue strength-tensile strength relations. Statistics of the data show that under different conditions, the fatigue strength and the tensile strength of the material can show an approximately linear (figure 3a) or nonlinear (figure 3c, e) relationship, and
the formula is valid only for linear relations. The model adopted by the method is applicable to various fatigue strength-tensile strength relations, and is specifically expressed in sigmaw/σy--σy/σbGood linearity in the coordinate system (fig. 3b, d, f). Therefore, the method is far beyond the traditional method.
The invention has the following technical effects: and predicting the fatigue strength of the material under different compositions and processes. Through a clear analysis of typical engineering material tensile and fatigue data, the method is applicable to a variety of materials (including but not limited to steel, copper alloys, aluminum alloys, magnesium alloys) and process types (various pre-deformation and heat treatment processes). The values of the parameters omega and C are changed along with the change of materials and processes, but the data is in sigmaw/σy--σy/σbThe coordinate system shows good linear relation, and the model is well matched (figure 4).
The invention has the following technical effects: and predicting the fatigue strength of the material under different loading conditions. In addition to adaptation to changes in the state of the material itself, the methodIt is also possible to cope well with a change in external conditions. As shown in FIG. 5, the values of the parameters ω and C are changed with the change of the loading mode, the cyclic load ratio R and the cyclic frequency, but σ is changedw/σy--σy/σbThe relationship remains linear throughout. It can be seen that fatigue strength values under a variety of external conditions can be obtained using this method.
The invention has the following technical effects: high efficiency and accuracy of fatigue strength prediction. The model related to the method is simple in form, the prediction method is fast and practical, the fatigue strength can be predicted only through the tensile test and a small number of fatigue tests, and the method has the outstanding advantages of low cost and high efficiency. In addition, through a large amount of data statistics, the fatigue strength predicted value obtained by the method is higher in coincidence degree with the experimental value, the estimated deviation is more within 10% (figure 6), and the accuracy and the reliability of the method to a certain degree are reflected.
In conclusion, the method has universality, practicability and reliability, is wide in application range, is simple and feasible to operate, can greatly reduce necessary fatigue tests on the premise of ensuring the prediction accuracy, and is an efficient fatigue strength prediction method.
Example 1:
the method for predicting the fatigue strength of the aluminum alloy comprises the following specific steps:
(1) materials:
5052 aluminum alloy, different pre-deformed and heat treated states (O, H32, H34, H36, H38).
(2) The process comprises the following steps:
step 1: and (5) testing tensile property. Uniformly processing the materials into tensile samples, and testing the tensile property under the same experimental conditions to obtain the yield strength sigmayAnd tensile strength sigmabValues (see table 1 for specific data).
Step 2: and selecting fatigue test samples. In order to ensure the accuracy of the prediction result as much as possible and reduce the fatigue test amount as much as possible, 2-4 materials with large difference in tensile property are selected for fatigue property test. Since the material states involved in this example are few, only 2 materials were selected to test fatigue strength, which are: 5052-O, 5052-H36.
And step 3: and (5) testing the fatigue performance. The selected material is used for processing a fatigue sample, and the surface of the sample needs to be uniformly polished, so that the consistency of the surface state is ensured. Then selecting the required loading conditions to uniformly test the fatigue performance to obtain the fatigue strength sigmawMeasured values (see table 1 for specific data).
And 4, step 4: and (6) parameter fitting. As shown by the solid dots in FIG. 7(a), the σ of 5052-O, 5052-H36 material was determined using the measured tensile and fatigue datay/σbAnd σw/σyValues plotted as abscissa and ordinate, respectively, at σw/σy--σy/σbIn the relation diagram, parameters omega and C are obtained through linear fitting, wherein omega is the negative reciprocal of the slope of the fitting straight line, and C is the fitting straight line and sigmay/σbThe intercept of the shaft (see figure 2). See table 1 for specific data.
And 5: and (4) predicting fatigue strength. As hollow points in FIG. 7(a), the abscissa position (. sigma.) can be determined by the tensile properties of the material to be predicted (5052-H32, H34, H38)y/σb) Extending to the fitted line further determines the ordinate position (σ)w/σy) To thereby find the fatigue strength sigma of the corresponding materialwAnd (5) predicting the value. Also by modifying the tensile properties σy、σbDirectly substituting parameter values omega and C into formula
The method of (1) obtaining the fatigue strength sigmawPredicted values (see table 1 for specific data).
Step 6: and (6) evaluating the prediction accuracy. The actually measured fatigue strength values of 5052-H32, H34 and H38 materials can be substituted into the semi-hollow point shown in FIG. 7(a) to be compared with predicted values, the prediction accuracy is shown in FIG. 7(b), and the deviation values are detailed in Table 1 (note: this step belongs to the verification of the method, and the actual operation process can be omitted).
TABLE 15052 summary of data related to prediction of fatigue strength of aluminum alloys
Example 2:
the embodiment is fatigue strength prediction of TWIP steel, and the specific process is as follows:
(1) materials:
fe-30Mn-0.9C TWIP steel in different pre-stretching deformation states (original state, pre-stretching 30%, pre-stretching 60% and pre-stretching 70%).
(2) The process comprises the following steps:
step 1: and (5) testing tensile property. Uniformly processing the materials into tensile samples, and testing the tensile property under the same experimental conditions to obtain the yield strength sigmayAnd tensile strength sigmabValues (see table 2 for specific data).
Step 2: and selecting fatigue test samples. In order to ensure the accuracy of the prediction result as much as possible and reduce the fatigue test amount as much as possible, 2-4 materials with large difference in tensile property are selected for fatigue property test. Since the material states involved in this example are few, only 2 materials were selected to test fatigue strength, which are: fe-30Mn-0.9C original state (0%) and pre-stretching 70%.
And step 3: and (5) testing the fatigue performance. The selected material is used for processing a fatigue sample, and the surface of the sample needs to be uniformly polished, so that the consistency of the surface state is ensured. Then selecting the required loading conditions to uniformly test the fatigue performance to obtain the fatigue strength sigmawMeasured values (see table 2 for specific data).
And 4, step 4: and (6) parameter fitting. As shown by the solid dots in FIG. 8(a), the tensile and fatigue data were measured to determine the sigma of the material at 70% of Fe-30 Mn-0.9C-0%y/σbAnd σw/σyValues plotted as abscissa and ordinate, respectively, at σw/σy--σy/σbIn the relation diagram, parameters omega and C are obtained through linear fitting, wherein omega is the negative reciprocal of the slope of the fitting straight line, and C is the fitting straight line and sigmay/σbThe intercept of the shaft (see figure 2). See table 2 for specific data.
And 5: and (4) predicting fatigue strength. As shown in FIG. 8(a), the tensile properties of the material to be predicted (Fe-30 Mn-0.9C-30%, 60%) can be determined by the open dotsDetermining the abscissa position (σ)y/σb) Extending to the fitted line further determines the ordinate position (σ)w/σy) To thereby find the fatigue strength sigma of the corresponding materialwAnd (5) predicting the value. Also by modifying the tensile properties σy、σbDirectly substituting parameter values omega and C into formula
The method of (1) obtaining the fatigue strength sigmawPredicted values (see table 2 for specific data).
Step 6: and (6) evaluating the prediction accuracy. For example, the semi-hollow point shown in FIG. 8(a) can be substituted into Fe-30 Mn-0.9C-30%, and the fatigue strength value actually measured by 60% material can be compared with the predicted value, the prediction accuracy is shown in FIG. 8(b), and the deviation value is shown in Table 2 (note: this step belongs to the verification of the method, and the actual operation process can be omitted).
TABLE 2 summary of relevant data for fatigue strength prediction of Fe-30Mn-0.9C TWIP steels
Claims (5)
1. A method for predicting fatigue strength of a metal material is characterized by comprising the following steps: the method comprises the following steps:
(1) selecting a plurality of metal materials of the same series for tensile property test to obtain a plurality of groups of material yield strength sigmayAnd tensile strength sigmabA value; the selected metal materials of the same series refer to a plurality of materials obtained by different pre-deformation processes or heat treatment on the same metal material;
(2) testing the fatigue performance:
selecting 2-4 materials from the same series of metal materials to prepare a fatigue test sample, and testing the fatigue strength of the sample to obtain the fatigue strength sigma of the materialwAn actual measurement value;
(3) parameter fitting:
determining sigma of the material by using the measured tensile property data and fatigue strength datay/σbAnd σw/σyValue, then byy/σbThe value being abscissa, in σw/σyValue plotting sigma for ordinatew/σy--σy/σbObtaining parameters omega and C through linear fitting, wherein omega is the negative reciprocal of the slope of the fitting straight line, and C is the fitting straight line and sigmay/σbThe intercept of the shaft;
(4) and (3) fatigue strength prediction:
determining sigma of a material by tensile properties of the material to be predictedy/σbValue, i.e. sigmaw/σy--σy/σbDetermining the sigma of the material through the straight line fitted in the step (3) at the abscissa position in the relation diagramw/σyValue, i.e. ordinate position, to find the fatigue strength σ of the respective materialwPredicting a value; or else, the tensile properties σ of the material to be predictedy、σbDirectly substituting the parameter values omega and C into the formula (1), and calculating to obtain the fatigue strength sigma of the corresponding materialwPredicting a value;
2. the method for predicting fatigue strength of a metallic material according to claim 1, wherein: in the step (1), tensile property test is carried out under the same experimental conditions after tensile samples are uniformly processed by the same series of metal materials.
3. The method for predicting fatigue strength of a metallic material according to claim 1, wherein: in the step (2), in order to ensure the accuracy of the prediction result and reduce the fatigue test amount, 2-4 materials with larger differences of tensile properties are selected for fatigue property test.
4. The method for predicting fatigue strength of a metallic material according to claim 1, wherein: in the step (2), the selected material is used for processing a fatigue sample, and the surface of the sample needs to be subjected to uniform polishing treatment to ensure the consistency of the surface state; and then selecting the required loading conditions to uniformly perform the fatigue performance test.
5. The method for predicting fatigue strength of a metallic material according to claim 1, wherein: the method is suitable for steel, copper alloy, aluminum alloy or magnesium alloy; is suitable for various pre-deformation and heat treatment processes; the applicable loading conditions are tension-compression loading modes, bending-torsion loading modes and different cyclic load ratios and cyclic times.
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