A method for predicting fatigue strength of compacted graphite cast iron by pearlite content and tensile strength

Abstract

The present invention discloses a method for predicting the fatigue strength of vermicular graphite cast iron by pearlite content and tensile strength, and belongs to the technical field of fatigue performance testing of materials and components. The method first selects at least two vermicular graphite cast irons with different pearlite contents, then performs microstructure detection, tensile and high-cycle fatigue experiments, and establishes the relationship between pearlite content, tensile strength and fatigue strength by formula derivation. The method can accurately predict the fatigue strength of vermicular graphite cast iron, and greatly saves the manpower, material and financial resources required for fatigue experiments, and can be extended to the field of fatigue strength prediction of vermicular graphite cast iron with different loading modes (axial symmetry, rotational bending, etc.) and other types of cast iron materials (ductile iron and gray cast iron).

Description

Technical Field

[0001] The invention relates to the technical field of material and component fatigue performance testing, in particular to a method for predicting the fatigue strength of vermicular graphite cast iron through pearlite content and tensile strength. Background Art

[0002] Vermicular graphite cast iron has become the preferred material for diesel engine cylinder heads due to its good thermal, mechanical and casting properties. Due to the cyclic load during service, high-cycle fatigue damage of the cylinder head is inevitable. This not only causes safety problems, but also leads to economic losses. Therefore, it is particularly important to improve the mechanical properties of the cylinder head. As a key factor affecting the mechanical properties of vermicular graphite cast iron, constructing the relationship between organization and fatigue performance can not only achieve a simple and accurate prediction of fatigue strength, but also provide certain theoretical guidance for the organization optimization of vermicular graphite cast iron, making this work of great significance. Summary of the invention

[0003] In order to reduce the cost required to obtain the fatigue limit of the material, the present invention provides a method for predicting the fatigue strength of vermicular graphite cast iron by pearlite content and tensile strength, by selecting at least two vermicular graphite cast irons with different pearlite contents, using IPP software to perform tissue ratio analysis, obtaining the content of pearlite in the sample matrix, and then performing room temperature tensile and high cycle fatigue tests to obtain the tensile strength and fatigue strength values ​​of vermicular graphite cast irons with different pearlite contents, and by fitting with the least squares method, establishing the relationship between fatigue ratio and pearlite content, the accurate prediction of fatigue strength can be achieved. This method greatly reduces the demand for experimental quantity, the prediction results are relatively accurate, and there is no complex formula derivation and calculation.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is:

[0005] A method for predicting fatigue strength of compacted graphite cast iron by pearlite content and tensile strength, the method specifically comprising the following steps:

[0006] (1) Select at least two types of vermicular graphite cast iron with different pearlite contents, use IPP software to analyze the area percentage of pearlite and graphite in the samples, and select no less than 5 areas. Calculate the pearlite content in the matrix according to formula (1):

[0007]

[0008] (2) Room temperature tensile and high cycle fatigue tests were carried out on vermicular graphite cast iron with different pearlite contents to obtain the corresponding tensile strength and fatigue strength values. The fatigue ratio was calculated according to formula (2):

[0009]

[0010] In formula (2): σ w is fatigue strength, σ b is the tensile strength and A is the fatigue ratio.

[0011] (3) Substitute the fatigue ratio of vermicular graphite cast iron with different pearlite contents into formula (3) and perform fitting:

[0012]

[0013] In formula (3), m and n are both material-related parameters, which are obtained through fitting.

[0014] Transforming formula (3) into formula (4):

[0015] σ w =(mC P +n)·σ b (4);

[0016] (4) Based on the two material related parameters m and n obtained in step (3) and formula (4), the fatigue strength of compacted graphite cast iron at various pearlite contents and tensile strengths can be calculated.

[0017] In step (4), m and n are both material-related parameters and are only applicable to compacted graphite cast iron materials with different pearlite contents and tensile strengths.

[0018] The advantages and beneficial effects of the present invention are as follows:

[0019] 1. The method of the present invention can achieve a relatively accurate prediction of the high-cycle fatigue strength of vermicular graphite cast iron through macroscopic static tensile properties and microscopic microstructure, which can not only save materials but also improve work efficiency, and at the same time has a certain reference role in the prediction of fatigue strength of cast iron materials.

[0020] 2. The present invention analyzes the influence of tissue content on material strength, and the physical meaning is clear.

[0021] 3. The fatigue strength prediction model of the present invention has good accuracy and universality, and can more accurately predict the high cycle fatigue strength of vermicular graphite cast iron with different pearlite contents.

[0022] 4. The prediction method of the present invention is simple to calculate and has high accuracy. By establishing the relationship between pearlite content, static tensile strength and fatigue strength, fatigue strength prediction is performed. It takes less time and saves time, manpower and money costs to a great extent.

[0023] 5. The method of the present invention can be extended to the field of fatigue strength prediction of compacted graphite cast iron under different loading modes (axial symmetry, rotational bending, etc.) and other types of cast iron materials (ductile iron and gray cast iron). BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is the fitting result of the experimental data according to formula (3).

[0025] Figure 2 It is the fitting result of the collected data on compacted graphite cast iron according to formula (3).

[0026] Figure 3 These are the fatigue strength prediction results of compacted graphite cast iron with different pearlite content and tensile strength. DETAILED DESCRIPTION

[0027] The present invention is further described below in conjunction with embodiments and drawings.

[0028] Embodiment 1:

[0029] This embodiment predicts the fatigue strength of vermicular graphite cast iron with different tissue proportions. At least two vermicular graphite cast irons with different pearlite contents are selected for room temperature tensile and high cycle fatigue tests (experimental data). The fitting results are compared with the actual experimental results to evaluate the accuracy of the method.

[0030] Step 1: Adjust the content of pearlite in the vermicular graphite cast iron matrix by annealing at 760℃ for 4h with furnace cooling, normalizing at 900℃ for 1.5h with air cooling, and normalizing at 990℃ for 2.5h with air cooling. Use IPP software to calculate the area percentage of pearlite and graphite in the unheat-treated and heat-treated samples, and substitute them into formula (1) to calculate the pearlite content. The pearlite contents of the annealed sample, unheat-treated sample, low-temperature normalized sample, and high-temperature normalized sample are 2.84%, 58.80%, 82.94%, and 92.48%, respectively.

[0031] Step 2: Perform axial tensile and high cycle fatigue tests on samples with different pearlite contents. As the pearlite content increases, the tensile strength values ​​obtained are 284MPa, 400MPa, 533MPa and 604MPa, respectively; the fatigue strength values ​​obtained are 112MPa, 129MPa, 145MPa and 158MPa, respectively. The fatigue ratio value is calculated according to formula (2).

[0032] Step 3: Substitute the pearlite content and fatigue ratio values ​​obtained in steps 1 and 2 into formula (3) for fitting. The fitting error is 5%, as shown in Figure 1 As shown; collect data related to the pearlite content, tensile strength and fatigue strength of vermicular graphite cast iron, repeat the above operation, and the fitting error is 10%, as shown Figure 2 This shows that the universal relationship has a high fitting accuracy.

[0033] Step 4: Transform formula (3) to obtain formula (4), namely:

[0034] σ w =(mC P +n)·σ b (4)

[0035] m and n are both material-related parameters and can be obtained by fitting formula (3). The fatigue strength value calculated by formula (4) is compared with the experimental value, and the error is 10%. Figure 3 It is shown that this method has good prediction accuracy for the high cycle fatigue strength of compacted graphite cast iron.

[0036] The above embodiments are merely illustrative of the principles and performances of the present invention and are not exhaustive. People can also obtain other embodiments based on the present embodiments without creative work, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A method for predicting fatigue strength of compacted graphite cast iron by pearlite content and tensile strength, characterized in that: The method specifically comprises the following steps: (1) Select at least two vermicular graphite cast iron samples with different pearlite contents, use Image Pro Plus (IPP) software to measure and count the area percentages of pearlite and graphite in each sample, and calculate the pearlite content in the sample according to formula (1): In formula (1): C P Indicates the pearlite content, Area P (%) represents the area percentage of pearlite. G (%) represents the area percentage of graphite; (2) Tensile tests and axially symmetric high-cycle fatigue tests were performed on samples with different pearlite contents to obtain the tensile strength and fatigue strength values ​​of each sample, and the fatigue ratio was calculated according to formula (2): In formula (2): σ w is fatigue strength, σ b is the tensile strength, A is the fatigue ratio, and the relationship is; (3) Substituting the pearlite content obtained in step (1) and the tensile strength and fatigue strength values ​​obtained in step (2) into formula (3), the parameters m and n related to the material are obtained by fitting; (4) Transform formula (3) to obtain formula (4): s w =(mC P +n)·s b (4); According to the m and n parameters fitted in step (3) and formula (4), the fatigue strength of the same material with different pearlite contents and tensile strengths can be calculated.

2. The method for predicting fatigue strength of compacted graphite cast iron by pearlite content and tensile strength according to claim 1, characterized in that: In step (1), a metallographic sample is prepared by polishing and etching, and pearlite and graphite in the compacted graphite cast iron sample are analyzed according to GB / T 26656-2011 (national standard).

3. The method for predicting fatigue strength of compacted graphite cast iron by pearlite content and tensile strength according to claim 1, characterized in that: In step (2), tensile strength and fatigue strength values ​​of samples with different pearlite contents are obtained through tensile and high cycle fatigue tests; the tensile test is carried out on a universal testing machine, the high cycle fatigue test is generally carried out on a high frequency fatigue testing machine, and the fatigue strength is calculated using the lifting and lowering method according to GB / T 24176-2009.

4. The method for predicting fatigue strength of compacted graphite cast iron by pearlite content and tensile strength according to claim 1, characterized in that: In step (3), the values ​​of the material-related parameters m and n can be obtained by fitting using software such as Origin.

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