CN111678821A - Low-cycle fatigue life prediction method based on high-temperature alloy processing surface integrity - Google Patents
Low-cycle fatigue life prediction method based on high-temperature alloy processing surface integrity Download PDFInfo
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Abstract
The invention discloses a low cycle fatigue life prediction method based on the integrity of a high-temperature alloy processing surface, which comprises the following steps: performing a low-cycle fatigue test on a high-temperature alloy sample, acquiring data of fatigue life and total strain amplitude, and drawing a relation graph of the fatigue life and the total strain amplitude under a log-log coordinate system; selecting a high-temperature alloy sample subjected to a low-cycle fatigue test, axially cutting and sampling the high-temperature alloy sample perpendicular to the sample, embedding the sample in black mosaic resin, mechanically polishing the sample to ensure that the roughness of the surface of the sample reaches micron level, and then performing surface chemical corrosion to measure the mean area root mean square of the surface; selecting a fatigue life test sample, and measuring the processed surface unevenness of the sample; obtaining a fatigue life prediction model related to the root mean square of the average grain area of the high-temperature alloy: and carrying out formula correction considering the influence of the unevenness of the machined surface on the fatigue life prediction model, and establishing the high-temperature alloy sample low-cycle fatigue life prediction model based on the surface integrity.
Description
Technical Field
The invention relates to the technical field of fatigue life prediction of high-temperature alloy samples, in particular to a low-cycle fatigue life prediction method based on the integrity of a high-temperature alloy processing surface, which is suitable for high-temperature alloy materials used in the industries of aerospace, nuclear industry, chemical industry and the like.
Background
The high-temperature alloy has excellent high-temperature strength, heat corrosion resistance, high-temperature oxidation resistance and fatigue resistance at high temperature, so that the high-temperature alloy is a key engineering material for manufacturing hot-end parts of working equipment in extreme service environments such as high-temperature high-load, frequent high-low temperature alternation, high-speed airflow scouring and the like. The common high-temperature alloy can be divided into three types of high-temperature alloys of nickel base, cobalt base and iron base according to matrix elements. The nickel-based high-temperature alloy has the optimal comprehensive performance and is widely applied to the manufacturing of hot-end parts of aerospace engines, particularly turbine disks.
However, the high-temperature alloy is very sensitive to stress concentration and strain rate, has complex service environment and variable load history when being used as a hot-end part in actual work, can be subjected to repeated coupling action of mechanical stress and thermal stress, and is very easy to generate low-cycle fatigue to fail. The low-cycle fatigue failure of the high-temperature alloy part can cause equipment failure, cause serious economic loss and even threaten the life safety of operators, so that the accurate prediction of the low-cycle fatigue life of the nickel-based high-temperature alloy is urgently needed.
The existing methods for predicting the low-cycle fatigue life of the high-temperature alloy can be divided into two types: one method is to carry out regression fitting based on fatigue life test data of a high-temperature alloy sample to predict the fatigue life, and although the method has high prediction precision, a large number of tests are required, so that the method is time-consuming, high in cost and limited to be applied only in a test range; the other type is based on the physical and mechanical properties of the high-temperature alloy material, an analytic model is established through theoretical reasoning to predict the fatigue life, the derivation process of the method is very complicated, and meanwhile, the numerical values of a plurality of parameters in the model still need to be obtained through material performance testing, so that the difficulty in obtaining is high, the precision is low, and the fatigue life prediction precision is influenced. Analysis and research show that the fatigue life of the high-temperature alloy is also influenced by the size of the grain size on the surface of the part, and the influence on the aspect is not considered by the conventional fatigue life prediction method. Therefore, a relatively simple and accurate prediction method is urgently needed for the low cycle fatigue life of the high-temperature alloy material.
Disclosure of Invention
Aiming at the defects of the existing low-cycle fatigue life prediction technical method of the high-temperature alloy material, the invention provides a low-cycle fatigue life prediction method of a high-temperature alloy sample with complete processing surface. The method carries out fatigue life prediction through the integrity index parameters of the processed surface of the high-temperature alloy material, considers the influence of the integrity index parameters of the two processed surfaces, namely the unevenness of the processed surface of the high-temperature alloy material and the mean square root of the average grain area, and avoids the calculation of a complex theoretical reasoning model and improves the fatigue life prediction precision.
In order to solve the problems, the invention adopts the following technical scheme:
the low cycle fatigue life prediction method based on the integrity of the machined surface of the high-temperature alloy specifically comprises the following steps:
s1, performing a low-cycle fatigue test on a high-temperature alloy sample, acquiring data of fatigue life and total strain amplitude, and drawing a relation graph of the fatigue life and the total strain amplitude under a dual-logarithmic coordinate system;
s2, selecting a high-temperature alloy sample subjected to a low-cycle fatigue test, axially cutting and sampling the high-temperature alloy sample perpendicular to the sample, embedding the sample in black mosaic resin, mechanically polishing the sample to enable the surface roughness of the sample to be micron-sized, and then performing surface chemical corrosion to measure the mean square root of the surface (the measurement is performed in a range from the surface of the sample to a sub-surface of a few microns);
s3, selecting a fatigue life test sample, and measuring the processed surface unevenness of the sample;
s4, in the step S1, the fatigue life and the total strain amplitude are in a linear function relationship under the log-log coordinate system, so that a power function relationship (1) of the fatigue life and the total strain amplitude is obtained under the Cartesian coordinate system:
Nf＝a(Δ-m)-b(1)
in formula (1), NfIn order to prolong the fatigue life, delta is the total strain amplitude, and a, b and m are coefficients to be determined;
s5. in the formula (1), when the delta is infinitely close to m, NfApproaching infinity. Theoretically, when the applied stress is less than the fatigue limit, the fatigue process will cycle indefinitely without fatigue failure, so assuming that the fatigue limit is equal to the elastic limit, the value of m can be calculated as shown in equation (2):
in the formula (2), the reaction mixture is,-1to elastic strain limit, σ-1Fatigue limit, E elastic modulus;
further, as a high temperature alloy, the fatigue limit and the tensile strength are in the relationship of the formula (3), and the tensile strength and the average grain area root mean square are in the relationship of the Hall-Patch formula (4):
σ-1＝Pσb(3)
in formula (3), P is a scale factor, σbIs the tensile strength; in formula (4), σ0Is the friction stress, c is the coefficient related to the grade of the high-temperature alloy material, A is the mean grain area root mean square;
s6, substituting the expressions (2), (3) and (4) in the step S5 into the expression (1) in the step S1 to obtain a fatigue life prediction model related to the root mean square of the average grain area of the high-temperature alloy:
s7, carrying out formula correction considering the influence of the machined surface unevenness on the formula (5) in the step S6, and establishing the high-temperature alloy sample low-cycle fatigue life prediction model based on the surface integrity:
in formula (6), SaThe arithmetic mean height of the processed surface of the nickel-base superalloy, SzThe maximum height of the processed surface of the nickel-based superalloy.
Further, in the superalloy fatigue test in step S1, suitable processing parameters should be selected according to different superalloy materials of the sample to be processed, and tests should be performed at different stress levels and stress ratios to calculate the parameters a and b in step S4 by performing data fitting on the relationship between the fatigue life and the total strain amplitude.
Further, the etching solution for the polished sample described in step S2 is not unique, and the correct solvent and proportioning scheme should be selected according to the corresponding superalloy grade.
Further, the mean grain area root mean square as described in step S2
Wherein N isAIs the number of grains per square micron;
wherein S is the area of the selected circular area, M is the magnification factor of the microscope, and N is the number of crystal grains in the area S; all measurements should be performed according to the national standard GB/T6394-2017 of the people's republic of China.
Further, the unevenness of the work surface in step S3 is obtained by measuring the arithmetic mean height of the work surface and the maximum height of the work surface.
Furthermore, the number of points selected in each measurement is more than or equal to 5, and the average value is taken as a measurement value to ensure the accuracy and the reliability of the test data.
The test principle of the invention is as follows:
factors that affect the fatigue life of high temperature alloys include: the state of the machined surface, the residual stress of the machined surface, the stress amplitude, the temperature and the like. The integrity of the machined surface, including the surface geometry and the residual stress of the machined surface, are important factors for evaluating the fatigue life of the superalloy specimen. The influence of the geometrical state of the machined surface on the service life of the high-temperature alloy is reflected on a macroscopic scale, and the poorer the geometrical state of the machined surface, the more concentrated the surface stress and the lower the fatigue life of the sample. The influence of the residual stress of the processed surface on the service life of the high-temperature alloy is reflected on a microscopic scale, and corresponding type II (intercrystalline) residual stress can be generated by different grain sizes and distribution, so that the fatigue crack initiation and expansion resistance of the alloy can be further influenced. Therefore, the invention relates to macroscopic and microscopic scales simultaneously, selects the integrity index parameters of the processed surface, namely the unevenness of the processed surface and the mean grain area root-mean-square, to couple and establish a low-cycle fatigue prediction model of the high-temperature alloy sample, and improves the fatigue life prediction precision.
The invention has the following beneficial effects:
(1) the invention provides a method for predicting the low-cycle fatigue life of a high-temperature alloy sample based on the integrity of a machined surface, which is simple in model and has clear physical significance.
(2) The invention considers the influence of the grain size of the material on the low-cycle fatigue life prediction of the high-temperature alloy sample. The mean grain area root mean square influences the fatigue crack initiation and propagation resistance of the high-temperature alloy and is an important microstructure factor influencing the fatigue life, so that the method has higher fatigue life prediction precision. Meanwhile, the influence of the unevenness of the processed surface of the sample on the fatigue life is also considered, and the model prediction precision can be further improved.
(3) The method only needs to consider two parameters of the unevenness of the processed surface and the mean square root of the average grain area of the material in the integrity index of the processed surface, and can meet the rapid and accurate requirements of fatigue life prediction in actual engineering. And a large amount of test repetition and data acquisition are avoided, a large amount of time and material expenditure are saved, and the method has wide scientific research value and engineering application prospect.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
FIG. 2 is a plot of superalloy low cycle fatigue test specimen dimensions.
FIG. 3 is a graph of fatigue life of the superalloy in a low cycle fatigue test in a log-log coordinate system versus the magnitude of total strain.
Detailed Description
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the invention expressly state otherwise, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, it indicates the presence of the stated features, steps, operations, devices, components, and/or combinations thereof.
In order to solve the technical problems in the prior art, the invention provides a low cycle fatigue life prediction method based on the completeness of a high-temperature alloy processing surface, which mainly comprises the following steps:
s1, performing a low-cycle fatigue test on a high-temperature alloy sample, acquiring data of fatigue life and total strain amplitude, and drawing a relation graph of the fatigue life and the total strain amplitude under a dual-logarithmic coordinate system;
s2, selecting a high-temperature alloy sample subjected to a low-cycle fatigue test, axially cutting and sampling the high-temperature alloy sample perpendicular to the sample, embedding the sample in black mosaic resin, mechanically polishing the sample to enable the roughness of the surface of the sample to be micron-sized, and then performing surface chemical corrosion to measure the mean square root of the surface; the solvent of the etching solution comprises HCl and HNO3、H2SO4、C2H5OH, HF, Glycerol, CuCl2And the like. When the processing surface of the alloy is corroded, the corrosion time is controlled, and if the corrosion time is too short or too long, the grain boundary can be blurred, so that the root mean square measurement result of the average grain area is influenced.
S3, selecting a fatigue life test sample, and measuring the processed surface unevenness of the sample;
s4, in a step S1, in a log-log coordinate system, the fatigue life and the total strain amplitude are in a linear function relationship, so that a power function relationship of the fatigue life and the total strain amplitude is obtained in a Cartesian coordinate system, an m value is calculated, and a fatigue limit and tensile strength relationship and a Hall-batch relationship of the tensile strength and the root-mean-square of the average crystal grain area are substituted into the power function relationship; obtaining a fatigue life prediction model related to the root mean square of the average grain area of the high-temperature alloy: and carrying out formula correction considering the influence of the unevenness of the machined surface on the fatigue life prediction model, and establishing the high-temperature alloy sample low-cycle fatigue life prediction model based on the surface integrity.
The invention is further illustrated with reference to the figures and examples.
Example 1
The invention discloses a method for predicting the low cycle fatigue life of a high-temperature alloy sample based on the integrity of a machined surface, which takes a GH4169 nickel-based high-temperature alloy turning sample as an example and introduces the specific implementation steps of the low cycle fatigue prediction method under the high-temperature condition in detail:
s1, adopting a servo hydraulic fatigue testing machine to perform a GH4169 nickel-based high-temperature alloy low-cycle fatigue test at the constant temperature of 650 ℃, wherein the total axial strain is controlled to be 0.2-0.6%, the strain ratio is-1, the loading frequency is 20Hz, and the size of a sample is shown in figure 2. And acquiring data of the fatigue life and the total strain amplitude, and drawing a relation graph 3 of the fatigue life and the total strain amplitude under a double logarithmic coordinate system.
S2, selecting a sample for the fatigue test, axially cutting the sample in a direction perpendicular to the sample, inlaying the cut sample into a black resin inlaying block through an inlaying machine, and mechanically polishing the sample on a polishing machine to a level of 1 micron. The polished sample was placed in 100mL HCl +100mLC2H5OH+5g CuCl2The etching solution is etched for 20 minutes, then the etching solution is taken out, cleaned by alcohol, dried and placed on an optical microscope to observe the microstructure of the etching solution, the mean crystal grain area root mean square A is calculated, and the calculation process is executed according to the national standard GB/T6394-once 2017 of the people's republic of China.
S3, selecting a sample for a fatigue test, and measuring the arithmetic mean height S of the processed surface of the sample through a laser confocal microscopeaAnd maximum height S of the working surfacez。
S4, for the nickel-based alloy, the parameter P in the formula (3) is 0.35, and the relation between the fatigue limit and the tensile strength is as follows:
σ-1＝0.35σb(7)
s5, determining the elastic modulus E of the GH4169 nickel-based high-temperature alloy at 650 ℃ to be 176MPa by using a dynamic method elastic modulus tester, and determining the root mean square relation (4) of the tensile strength and the average crystal grain area as follows:
s6, obtaining a GH4169 nickel-based high-temperature alloy sample low-cycle fatigue life prediction model according to equations (6), (7) and (8):
s7, fitting the data of the fatigue life and the total strain amplitude in step S1 according to equation (1), and obtaining the parameter a equal to 2.4832 × 10-5And b is 2.935, and is substituted into formula (9), finally obtaining a prediction model of the low cycle fatigue life of the GH4169 nickel-based superalloy sample:
example 2
The invention discloses a method for predicting the low cycle fatigue life of a high-temperature alloy sample based on the integrity of a machined surface, which takes GH4169 nickel-based high-temperature alloy finish turning-rolling combined machined sample as an example and introduces the specific implementation steps of the low cycle fatigue prediction method under the normal temperature condition in detail:
s1, carrying out a GH4169 nickel-based superalloy finish turning-rolling combined machining sample low-cycle fatigue test under a normal temperature condition by adopting a servo hydraulic fatigue testing machine, wherein the total axial strain is controlled to be 0.2-0.6%, the strain ratio is 0.1, the loading frequency is 90Hz, and the sample size is shown in figure 2. And acquiring data of the fatigue life and the total strain amplitude, and drawing a relation graph 3 of the fatigue life and the total strain amplitude under a double logarithmic coordinate system.
S2, selecting a sample for the fatigue test, axially cutting the sample in a direction perpendicular to the sample, inlaying the cut sample into a black resin inlaying block through an inlaying machine, and mechanically polishing the sample on a polishing machine to a level of 1 micron. The polished sample was placed in 100mL HCl +100mLC2H5OH+5g CuCl2The etching solution is etched for 20 minutes, then the etching solution is taken out, cleaned by alcohol, dried and placed on an optical microscope to observe the microstructure of the etching solution, the mean crystal grain area root mean square A is calculated, and the calculation process is executed according to the national standard GB/T6394-once 2017 of the people's republic of China.
S3, selecting a sample for a fatigue test, and measuring the arithmetic mean height S of the processed surface of the sample through a laser confocal microscopeaAnd maximum height S of the working surfacez。
S4, for the nickel-based alloy, the parameter P in the formula (3) is 0.35, and the relation between the fatigue limit and the tensile strength is as follows:
σ-1＝0.35σb(11)
s5, determining the elastic modulus E of the GH4169 nickel-based high-temperature alloy under the normal temperature condition as 201MPa by using a dynamic method elastic modulus tester, and determining a root-mean-square relational expression (4) of the tensile strength and the average crystal grain area as follows:
s6, obtaining a GH4169 nickel-based high-temperature alloy low-cycle fatigue life prediction model according to the equations (6), (11) and (12):
s7, fitting the data of the fatigue life and the total strain amplitude in step S1 according to equation (1), and obtaining the parameter a equal to 8.4154 × 10-4B is 2.147, and is substituted into formula (13), finally the GH4169 nickel-base superalloy low-cycle fatigue life is obtainedAnd (3) prediction model:
example 3
The method is a low-cycle fatigue life prediction method of the high-temperature alloy sample based on the integrity of the machined surface, but has guiding significance for low-cycle fatigue life prediction of other alloy samples. Taking a TC4 high-temperature titanium alloy finish turning-rolling combined processed sample as an example, the specific implementation steps of the low cycle fatigue prediction method under the normal temperature condition are described in detail as follows:
s1, performing a TC4 titanium alloy finish turning-rolling combined machining sample low-cycle fatigue test under a normal temperature condition by adopting a servo hydraulic fatigue testing machine, controlling the total axial strain to be 0.4-0.8%, controlling the strain ratio to be 0.481, controlling the loading frequency to be 0.5Hz, and controlling the fatigue limit to be sigma-1407MPa, tensile strength sigmab974MPa, the sample size is shown in FIG. 2. And acquiring data of the fatigue life and the total strain amplitude, and drawing a relation graph 3 of the fatigue life and the total strain amplitude under a double logarithmic coordinate system.
S2, selecting a sample for the fatigue test, axially cutting the sample in a direction perpendicular to the sample, inlaying the cut sample into a black resin inlaying block through an inlaying machine, and mechanically polishing the sample on a polishing machine to a level of 1 micron. The polished sample was placed in 5mL of HNO3+3mLHF+92mLH20, taking out after 20 minutes of corrosion, cleaning with alcohol, drying, placing on an optical microscope to observe the microstructure, calculating the mean crystal grain area root mean square A, and executing the calculation process strictly according to the national standard GB/T6394 of the people's republic of China-2017.
S3, selecting a sample for a fatigue test, and measuring the arithmetic mean height S of the processed surface of the sample through a laser confocal microscopeaAnd maximum height S of the working surfacez。
S4, fatigue limit sigma in pair formula (3)-1And tensile strength sigmabThe data are fitted to obtain a parameter P of 0.43, and the fatigue limit and tensile strength relationship is:
σ-1＝0.43σb(15)
s5, determining the elastic modulus E of the titanium alloy at the normal temperature TC4 as 115MPa by using a dynamic method elastic modulus tester, and determining the root mean square relational expression (4) of the tensile strength and the average crystal grain area as follows:
s6, obtaining a low cycle fatigue life prediction model of the TC4 titanium alloy sample according to the equations (6), (15) and (16):
s7, fitting the data of the fatigue life and the total strain amplitude in step S1 according to equation (1), and obtaining the parameter a equal to 3.255 × 10-4And b is 2.289, and is substituted into formula (17), finally obtaining a low cycle fatigue life prediction model of the TC4 titanium alloy sample:
the above description is only an example of the present invention, but the present invention is not limited to the example, and those skilled in the art can make various equivalent substitutions, modifications, and improvements within the spirit and principle of the present invention. Any equivalent substitutions, modifications, improvements and the like are intended to be included within the scope of the present invention.
Claims (10)
1. The low cycle fatigue life prediction method based on the integrity of the machined surface of the high-temperature alloy is characterized by comprising the following steps of:
s1, performing a low-cycle fatigue test on a high-temperature alloy sample, acquiring data of fatigue life and total strain amplitude, and drawing a relation graph of the fatigue life and the total strain amplitude under a dual-logarithmic coordinate system;
s2, selecting a high-temperature alloy sample subjected to a low-cycle fatigue test, performing axial cutting sampling perpendicular to the sample, treating the sampling surface to enable the roughness of the sampling surface to be micron-sized, and performing surface chemical corrosion to measure the mean area root mean square of the surface;
s3, selecting a fatigue life test sample, and measuring the processed surface unevenness of the sample;
s4, obtaining a fatigue life prediction model related to the root mean square of the average grain area of the high-temperature alloy based on the relation graph of the fatigue life and the total strain amplitude in the step S1 and related calculation: and carrying out formula correction considering the influence of the unevenness of the machined surface on the fatigue life prediction model, and establishing the high-temperature alloy sample low-cycle fatigue life prediction model based on the surface integrity.
2. The method of claim 1, wherein in step S1, the fatigue life and the total strain amplitude are linearly related in log-log coordinate system, and the power function (1) of the two is obtained in cartesian coordinate system:
Nf＝a(Δ-m)-b(1)
in formula (1), NfFor fatigue life, Δ is the total strain amplitude, and a, b, and m are the coefficients to be determined.
3. The method of claim 2, wherein the superalloy fatigue test in step S1 selects suitable processing parameters according to different superalloy materials of the sample to be processed, and tests are performed at different stress levels and stress ratios to calculate the parameters a and b in formula (1) by fitting the data of the fatigue life and the total strain amplitude.
4. The method of claim 2, wherein the m value is calculated by: assuming that the fatigue limit is equal to the elastic limit, the value of m can be calculated according to equation (2):
in the formula (2), the reaction mixture is,-1to elastic strain limit, σ-1For fatigue limit, E is the modulus of elasticity.
5. The method of claim 4, wherein the fatigue limit is related to the tensile strength as a superalloy by the equation (3);
σ-1＝Pσb(3)
in formula (3), P is a scale factor, σbIs the tensile strength.
6. The method of claim 5, wherein a Hall-Patch relationship (4) exists between tensile strength and mean grain area root mean square as the superalloy, wherein the method comprises the following steps:
in formula (4), σ0Is the friction stress, c is the coefficient related to the grade of the high-temperature alloy material, A is the mean grain area root mean square; by substituting the expressions (2), (3), and (4) into the expression (1) in step S1, a fatigue life prediction model relating to the root mean square of the average grain area of the superalloy can be obtained.
7. The method of claim 5, wherein the fatigue life prediction model related to the root mean square of the mean grain area of the superalloy:
and (3) performing formula correction considering the influence of the unevenness of the machined surface on the formula (5), and establishing a low-cycle fatigue life prediction model formula (6) of the high-temperature alloy sample based on the surface integrity:
in formula (6), SaThe arithmetic mean height of the processed surface of the nickel-base superalloy, SzThe maximum height of the processed surface of the nickel-based superalloy.
8. The method of claim 1, wherein the etching solution of the polished sample in step S2 selects the correct solvent and mixture ratio according to the corresponding grade of the superalloy.
9. The method of claim 1, wherein the mean grain area Root Mean Square (RMS) in step S2 is used as a basis for predicting the low cycle fatigue life of the superalloy as it is processed to form a surface
Wherein N isAIs the number of grains per square micron;
wherein S is the area of the selected circular area, M is the magnification of the microscope, and N is the number of the crystal grains in the area S.
10. The method of claim 1, wherein the step S3 includes measuring the mean height of the machined surface and the maximum height of the machined surface.
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| CN113252479A (en) * | 2021-05-14 | 2021-08-13 | 北京理工大学 | Energy method for predicting fatigue life by considering integrity of machined surface |
| CN114458854A (en) * | 2022-02-17 | 2022-05-10 | 中国核电工程有限公司 | Method for reducing vibration fatigue of pipeline and pipeline |
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