CN117740525A - Vermicular cast iron fatigue strength prediction method based on microstructure content and fatigue ratio - Google Patents
Vermicular cast iron fatigue strength prediction method based on microstructure content and fatigue ratio Download PDFInfo
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- 229910001018 Cast iron Inorganic materials 0.000 title claims abstract description 80
- 238000000034 method Methods 0.000 title claims abstract description 34
- 239000000463 material Substances 0.000 claims abstract description 34
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims abstract description 29
- 229910002804 graphite Inorganic materials 0.000 claims abstract description 29
- 239000010439 graphite Substances 0.000 claims abstract description 29
- 229910001562 pearlite Inorganic materials 0.000 claims abstract description 12
- 238000012360 testing method Methods 0.000 claims abstract description 12
- 229910000859 α-Fe Inorganic materials 0.000 claims abstract description 12
- 238000012887 quadratic function Methods 0.000 claims abstract description 9
- 238000009661 fatigue test Methods 0.000 claims abstract description 6
- 230000003068 static effect Effects 0.000 claims abstract description 5
- 244000137852 Petrea volubilis Species 0.000 claims description 10
- 238000005498 polishing Methods 0.000 claims description 3
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 2
- 229910002651 NO3 Inorganic materials 0.000 claims 1
- NHNBFGGVMKEFGY-UHFFFAOYSA-N Nitrate Chemical compound [O-][N+]([O-])=O NHNBFGGVMKEFGY-UHFFFAOYSA-N 0.000 claims 1
- 230000008569 process Effects 0.000 abstract description 5
- 238000011056 performance test Methods 0.000 abstract description 2
- 230000005496 eutectics Effects 0.000 description 5
- 238000013461 design Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 230000001186 cumulative effect Effects 0.000 description 2
- 235000000396 iron Nutrition 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000000877 morphologic effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000000977 initiatory effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
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Abstract
The invention relates to a vermicular cast iron fatigue strength prediction method based on microstructure content and fatigue ratio, and belongs to the field of member fatigue performance test. The main process of the method is as follows: calculating the content of each structure in vermicular cast iron to obtain the area percentage content of vermicular graphite, spheroidal graphite, ferrite and pearlite respectively; carrying out static tensile property test on the vermicular graphite cast iron material to obtain corresponding tensile strength; performing high-cycle fatigue test on the vermicular cast iron material to obtain fatigue strength; at the same time, fatigue is obtained by using the ratio of the measured tensile strength to the fatigue strengthLabor ratio data; fatigue ratio data and parameters M ic And carrying out quadratic function fitting on the values, combining the fitting result with the tensile strength test result, and predicting the fatigue strength of the vermicular cast iron material. According to the invention, through microscopic structure observation and static stretching experimental results of the vermicular cast iron, and by combining the high-cycle fatigue damage characteristics of the vermicular cast iron, the quantitative relation between the microscopic structure content and the fatigue ratio is established.
Description
Technical Field
The invention relates to a vermicular cast iron fatigue strength prediction method based on microstructure content and fatigue ratio, and belongs to the field of member fatigue performance test.
Background
The vermicular graphite cast iron is used as an important material for preparing a cylinder cover of a diesel engine, and is subjected to high-temperature and high-pressure gas high-frequency impact caused by the reciprocating motion of a piston in the working process, so that high-cycle fatigue damage is very easy to generate. The high cycle fatigue fracture process usually does not generate obvious macroscopic plastic deformation, has stronger burst property and destructiveness, and is an important reason for restricting the peak pressure and the further improvement of the thermal efficiency of the diesel engine at present. Therefore, the fatigue strength prediction model with higher accuracy is provided, so that the vermicular cast iron material with higher fatigue strength can be designed in an assisted manner, and the efficient and safe service of the diesel engine can be effectively ensured.
Conventional fatigue strength testing generally requires a lot of time and economic cost, and the measured related data has no clear physical meaning, so that reference is difficult to provide for subsequent fatigue performance optimization. In recent years, the association between easily measured mechanical properties (such as tensile strength, yield strength, hardness, impact toughness, etc.) and fatigue strength has become an effective means for solving the above problems. For vermicular cast iron materials, the primary reason for the difference in tensile strength from fatigue strength is the different degree of sensitivity to tissue homogeneity. Therefore, the microstructure content is used as a bridge to establish a connection between the tensile strength and the fatigue strength, and the method is an effective means for predicting the fatigue strength of the vermicular cast iron through the tensile strength.
Disclosure of Invention
The invention aims to provide a vermicular cast iron fatigue strength prediction method based on microstructure content and fatigue ratio, which can realize accurate prediction of the vermicular cast iron fatigue strength by establishing quantitative relation between microstructure content and tensile strength and fatigue strength. The method summarizes the high-cycle fatigue damage mechanism model of the vermicular cast iron obtained through a large number of experiments, effectively reduces the time and economic cost consumed by the traditional fatigue strength test, and simultaneously provides an optimization direction for the fatigue resistance design of the vermicular cast iron material.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
a vermicular cast iron fatigue strength prediction method based on microstructure content and fatigue ratio comprises the following specific steps:
(1) Polishing and corroding the vermicular cast iron material to obtain a metallographic structure analysis sample of the vermicular cast iron;
(2) Observing metallographic structure of the vermicular cast iron, and calculating the content of each structure in the vermicular cast iron to obtain area percentage contents of vermicular graphite, spheroidal graphite, ferrite and pearlite respectively;
(3) Static tensile property test is carried out on the vermicular graphite cast iron material to obtain corresponding tensile strength sigma b ；
(4) Performing high-cycle fatigue test on vermicular graphite cast iron material to obtain fatigue strength sigma w The method comprises the steps of carrying out a first treatment on the surface of the At the same time, using the measured tensile strength sigma b And fatigue strength sigma w Obtaining fatigue ratio data from the ratio of (2);
(5) The vermicular graphite content w measured in the step (2) v And ferrite content w f Summing to obtain w v +w f A value; content w of spheroidal graphite s And pearlite content w p Summing to obtain w s +w p A value;
calculating the tissue content parameter M according to the formula (1) ic A value;
(6) Combining the fatigue ratio data obtained in step (4) with the tissue content parameter M obtained in step (5) ic Performing quadratic function fitting on the values according to a formula (2) to obtain corresponding primary termsCoefficient a, quadratic coefficient b and constant term c;
(7) And (3) combining the fitting result of the step (6) with a tensile strength test result to predict the fatigue strength of the vermicular cast iron material.
In the method for predicting the fatigue strength of the vermicular cast iron based on the microstructure content and the fatigue ratio, in the step (1), the surface of the vermicular cast iron material is firstly sequentially polished by 400# sand paper, 800# sand paper, 1200# sand paper, 1500# sand paper and 2000# sand paper, then fine polishing is carried out by adopting swan flannelette, and finally the polished surface is immersed in nitric alcohol solution with the concentration of 4wt% to be corroded for 15 seconds to obtain a sample.
In the method for predicting the fatigue strength of the vermicular cast iron based on the microstructure content and the fatigue ratio, in the step (2), the vermicular cast iron is regarded as a multiphase material comprising graphite, pearlite and ferrite.
According to the method for predicting the fatigue strength of the vermicular cast iron based on the microstructure content and the fatigue ratio, graphite is divided into a spherical shape and a vermicular shape, and specific shape division standard reference GB/T26655-2011.
In the method for predicting the fatigue strength of the vermicular cast iron based on the microstructure content and the fatigue ratio, in the step (2), the area percentage content of different phases is measured through Image Pro Plus software, and the area of each phase is determined according to the contrast difference of the different phases in the cast iron material under a metallographic microscope, so that the corresponding area of the area is obtained.
In the method for predicting the fatigue strength of the vermicular cast iron based on the microstructure content and the fatigue ratio, in the step (4), the tensile strength and the fatigue strength used are measured in the same experimental environment, and the experimental environment comprises temperature and humidity.
In the method for predicting the fatigue strength of the vermicular cast iron based on the microstructure content and the fatigue ratio, in the step (6), the fatigue ratio used for performing quadratic function fitting is obtained in the same experimental environment.
The design idea of the invention is as follows:
in the cyclic loading process, the cumulative damage behavior of the vermicular cast iron mainly depends on the difference of the splitting action of vermicular graphite and spheroidal graphite on a matrix and the mechanical property difference between ferrite and pearlite, and the cumulative damage caused by the combined action of the vermicular cast iron and the spheroidal graphite and the mechanical property difference between ferrite and pearlite in a property transition region is an essential cause of fatigue crack initiation of the vermicular cast iron. Based on this, the invention provides a tissue uniformity parameter M based on the content ratio of each microstructure ic With M ic The fatigue resistance dominant mechanism of the vermicular cast iron gradually changes from the structural uniformity to the strength, and therefore, the fatigue resistance dominant mechanism and the fatigue ratio accord with a quadratic function relation.
The invention has the following advantages and beneficial effects:
1. the invention utilizes the microstructure content and the tensile strength to establish an equivalent relation with the vermicular cast iron fatigue strength, thereby effectively reducing the time and the economic cost consumed by the traditional fatigue strength test of the vermicular cast iron material.
2. According to the method, through analyzing the high cycle fatigue damage rule, key factors influencing the high cycle fatigue performance of the vermicular cast iron are ascertained, and the precision and universality of fatigue strength prediction are improved.
3. The invention relates to several key microstructures in a vermicular cast iron material, and provides an optimization direction for the production process and fatigue resistance design of the vermicular cast iron material.
Drawings
FIG. 1 is a flow chart of a method for predicting fatigue strength of vermicular cast iron.
FIG. 2 is a schematic diagram of the fatigue damage behavior of vermicular cast iron based on vermicular eutectic masses. Wherein, (a) positional factors, (b) dimensional factors, (c) morphological factors.
FIG. 3 is a diagram of a different type of vermicular cast iron material sigma w /σ b -M ic A relationship diagram. In the figure, the abscissa M ic For tissue content parameters, the ordinate sigma w /σ b Is the ratio of fatigue strength to tensile strength.
FIG. 4 is a graph showing the predicted fatigue strength of the vermicular cast iron material according to the example.
Detailed Description
In a specific implementation process, as shown in fig. 1, the invention provides a vermicular cast iron fatigue strength prediction method based on microstructure content and fatigue ratio, which comprises the following specific steps:
step (1): selecting vermicular cast iron, preparing a metallographic structure analysis sample, carrying out metallographic observation on the sample, and shooting metallographic pictures.
Step (2): selecting at least five metallographic structure observation areas, respectively measuring the area percentage contents of vermicular graphite, ferrite, spheroidal graphite and pearlite in the metallographic structure of each area by Image Pro Plus (IPP) software (refer to GB/T26655-2011), taking an average value, and calculating a structure content parameter M according to a formula (1) ic ：
Wherein w is v Represents the vermicular graphite content (%), w f Represents ferrite content (%), w s Represents the content (%), w of spheroidal graphite p Represents pearlite content (%)
Step (3): testing the tensile property of the selected vermicular cast iron sample to obtain the tensile strength sigma of the corresponding material b 。
Step (4): preparing a fatigue test sample, and performing a high cycle fatigue test according to GB/T3075-2008 to obtain a sample fatigue strength sigma w Is a measured value of (2).
Step (5): calculating sigma by using the tensile strength and high cycle fatigue strength data measured in the step (3) and the step (4) w /σ b Value of sigma w /σ b On the ordinate, in M ic And performing quadratic function fitting on the abscissa, and obtaining corresponding values of a, b and c.
Step (6): m to be obtained ic The values and a, b and c are substituted into the formula (2), and the fatigue strength of the material can be predicted by combining the corresponding tensile strength values.
In sigma w Representing fatigue strength (MPa), sigma b Represents tensile strength (MPa), M ic Representing tissue content parameters.
The tensile strength and the fatigue strength used should be measured under the same experimental environment (such as temperature, humidity and the like), and the fatigue ratio used for performing quadratic function fitting should be obtained under the same experimental environment (such as temperature, humidity and the like).
As shown in fig. 2, the fatigue damage behavior of vermicular cast iron based on vermicular eutectic masses includes location factor, size factor and morphological factor, and is specifically as follows:
fig. 2 (a): fatigue cracks of vermicular cast iron are more prone to initiate along vermicular eutectic cells at the edges of the test specimen;
fig. 2 (b): fatigue cracks of vermicular cast iron are more prone to initiate along vermicular eutectic cells that extend more inward;
fig. 2 (c): when the location and size are the same, fatigue fracture of vermicular cast iron is more prone to occur at vermicular eutectic cells with greater end stress strength factors.
The invention will be further described with reference to examples and figures.
Example 1:
as shown in fig. 1, the method for predicting the fatigue strength of the vermicular cast iron material comprises the following steps:
step one, vermicular cast iron materials are taken from a diesel engine cylinder cover, and high cycle fatigue tests are respectively carried out at room temperature, 400 ℃ and 500 ℃.
In the second example, five vermicular cast irons with different microstructures were selected, and their vermicular graphite, ferrite and pearlite area percentage contents were obtained by IPP software, respectively (specific data are shown in table 1).
TABLE 1 microstructure content summary of several vermicular cast iron materials
| Materials | w p +w s (％) | w f +w v (％) | M ic |
| RuT300 | 19.80 | 80.20 | 4.05 |
| RuT350 | 32.50 | 67.50 | 2.08 |
| RuT400 | 58.60 | 41.40 | 0.71 |
| RuT450 | 60.24 | 39.76 | 0.66 |
Step three, measuring the tensile property and the high cycle fatigue property of the selected vermicular cast iron material to obtain the corresponding tensile strength sigma b Fatigue strength sigma w Actual measurement value and find the ratio sigma of fatigue strength to tensile strength w /σ b (see Table 2 for specific data).
TABLE 2 sigma of several vermicular cast iron materials at different temperatures w /σ b Value and sigma b Value of
Step four, the ratio of fatigue strength to tensile strength (fatigue ratio) sigma at different temperatures according to the formula (2) w /σ b And tissue content parameter M ic Performing quadratic function fitting between the two parameters, wherein the fitting result is shown in fig. 3, and the obtained corresponding parameter values are respectively as follows: 25 ℃, a= -0.026, b=0.154, c=0.242; 400 ℃, a= -0.020, b= 0.101, c= 0.346;500 ℃, a= -0.042, b=0.215, c=0.320.
And fifthly, predicting the fatigue strength of other vermicular cast irons with different tensile strengths and different tissue contents according to the parameters obtained in the step four. As shown in fig. 4, the relationship between the prediction result and the test result is displayed, and the accuracy of the prediction result is verified.
The implementation result shows that the quantitative relation between the microstructure content and the fatigue ratio is established by combining the microstructure observation and static stretching experimental results of the vermicular cast iron and the high-cycle fatigue damage characteristics of the vermicular cast iron. The method can not only effectively predict the fatigue strength of the vermicular graphite cast iron, but also remarkably reduce the time and economic cost required by conventional fatigue strength measurement.
Claims (7)
1. A vermicular cast iron fatigue strength prediction method based on microstructure content and fatigue ratio is characterized by comprising the following specific steps:
(1) Polishing and corroding the vermicular cast iron material to obtain a metallographic structure analysis sample of the vermicular cast iron;
(2) Observing metallographic structure of the vermicular cast iron, and calculating the content of each structure in the vermicular cast iron to obtain area percentage contents of vermicular graphite, spheroidal graphite, ferrite and pearlite respectively;
(3) Static tensile property test is carried out on the vermicular graphite cast iron material to obtain corresponding tensile strength sigma b ；
(4) Performing high-cycle fatigue test on vermicular graphite cast iron material to obtain fatigue strength sigma w The method comprises the steps of carrying out a first treatment on the surface of the At the same time, using the measured tensile strength sigma b And fatigue strength sigma w Obtaining fatigue ratio data from the ratio of (2);
(5) The vermicular graphite content w measured in the step (2) v And ferrite content w f Summing to obtain w v +w f A value; content w of spheroidal graphite s And pearlite content w p Summing to obtain w s +w p A value;
calculating the tissue content parameter M according to the formula (1) ic A value;
(6) Combining the fatigue ratio data obtained in step (4) with the tissue content parameter M obtained in step (5) ic Performing quadratic function fitting on the values according to a formula (2) to obtain a corresponding primary term coefficient a, a corresponding quadratic term coefficient b and a corresponding constant term c;
(7) And (3) combining the fitting result of the step (6) with a tensile strength test result to predict the fatigue strength of the vermicular cast iron material.
2. The method for predicting the fatigue strength of the vermicular cast iron based on the microstructure content and the fatigue ratio according to claim 1, wherein in the step (1), the surface of the vermicular cast iron material is firstly sequentially polished by 400# sand paper, 800# sand paper, 1200# sand paper, 1500# sand paper and 2000# sand paper, then is finely polished by swan flannelette, and finally the polished surface is immersed in a nitrate alcohol solution with the concentration of 4wt% for 15 seconds to obtain a sample.
3. The method for predicting fatigue strength of vermicular cast iron based on microstructure content and fatigue ratio of claim 1, wherein the vermicular cast iron is regarded as a multi-phase material including graphite, pearlite and ferrite in the step (2).
4. A method of predicting fatigue strength of vermicular cast iron based on microstructure content and fatigue ratio as recited in claim 3, wherein the graphite is divided into spheroidal and vermicular, and the specific shape division standard is referred to in GB/T26655-2011.
5. The method for predicting the fatigue strength of the vermicular cast iron based on the microstructure content and the fatigue ratio according to claim 1, wherein in the step (2), the area percentage content of different phases is measured by Image Pro Plus software, and the area of each phase is determined according to the contrast difference of the different phases in the cast iron material under a metallographic microscope, so as to obtain the corresponding area of the area.
6. The method for predicting fatigue strength of vermicular cast iron based on microstructure content and fatigue ratio of claim 1, wherein in the step (4), the tensile strength and the fatigue strength used should be measured in the same experimental environment, and the experimental environment includes temperature and humidity.
7. The method for predicting fatigue strength of vermicular cast iron based on microstructure content and fatigue ratio according to claim 1, wherein in the step (6), the fatigue ratio used for performing quadratic function fitting is obtained under the same experimental environment.
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