A method for predicting fatigue strength of compacted graphite cast iron based on microstructure content and tensile strength

Abstract

This invention discloses a method for predicting the fatigue strength of compacted graphite cast iron based on microstructure content and tensile strength, belonging to the technical field of component fatigue performance testing. By combining microstructural observations and static tensile test results of compacted graphite cast iron with the characteristics of high-cycle fatigue damage, this method establishes a quantitative relationship between microstructure content, tensile strength, and fatigue strength. This method not only effectively predicts the fatigue strength of compacted graphite cast iron but also significantly reduces the time and cost required for conventional fatigue strength determination.

Description

Technical Field

[0001] The invention relates to the field of material science and engineering application technology, and in particular to a method for predicting the fatigue strength of compacted graphite cast iron based on microstructure content and tensile strength. Background Art

[0002] Compacted graphite cast iron, a key material for diesel engine cylinder heads, is constantly subjected to high-frequency impacts from high-temperature, high-pressure gases caused by the reciprocating motion of the piston during operation, which can easily lead to high-cycle fatigue damage. High-cycle fatigue fracture, which typically does not exhibit significant macroscopic plastic deformation and is highly sudden and destructive, is a major constraint on further improvements in diesel engine peak pressure and thermal efficiency. Therefore, developing a more accurate fatigue strength prediction model would not only aid in the design of higher fatigue strength compacted graphite cast iron materials, but also effectively ensure the efficient and safe service of diesel engines.

[0003] Traditional fatigue strength testing usually takes a lot of time and economic costs, and the relevant data after measurement do not have a clear physical meaning, making it difficult to provide a reference for subsequent fatigue performance optimization. In recent years, establishing a connection between easily measurable mechanical properties (such as tensile strength, yield strength, hardness, impact toughness, etc.) and fatigue strength has become an effective means to solve the above problems. However, for compacted graphite cast iron materials, due to their complex organizational structure and obvious differences in mechanical properties between various tissues, changes in tissue content will have a significant impact on fatigue strength. Therefore, comprehensively considering tissue content and mechanical properties, and establishing a universal quantitative relationship between the two and the fatigue strength of compacted graphite cast iron has become an urgent need in the current field of fatigue research. Summary of the Invention

[0004] This invention provides a method for predicting the fatigue strength of compacted graphite cast iron based on microstructure content and tensile strength. By establishing a quantitative relationship between microstructure content, tensile strength, and fatigue strength, accurate prediction of the fatigue strength of compacted graphite cast iron can be achieved. This method summarizes the high-cycle fatigue damage mechanism model of compacted graphite cast iron derived from extensive experiments. While effectively reducing the time and cost of traditional fatigue strength testing, it also provides optimization guidelines for the fatigue resistance design of compacted graphite cast iron materials.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A method for predicting fatigue strength of compacted graphite cast iron based on microstructure content and tensile strength, the specific steps are as follows:

[0007] Step (1): Select vermicular graphite cast iron and prepare a metallographic analysis sample, perform metallographic observation on the sample, and take metallographic photos.

[0008] Step (2): Select at least five metallographic observation areas, and determine the area percentage of vermicular graphite, ferrite, and pearlite in the metallographic structure of each area using Image Pro Plus (IPP) software (refer to GB / T26655-2011), and take the average value (defined as w v 、w f and w p ). Calculate the tissue content parameter w according to formula (2) m .

[0009] Step (3): Perform tensile test on the selected compacted graphite cast iron samples to obtain the tensile strength σ of the corresponding materials. b .

[0010] Step (4): Prepare fatigue test samples and perform high cycle fatigue test according to GB / T 3075-2008 to obtain the fatigue strength σ of the samples. w Measured value.

[0011] Step (5): Using the tensile strength and high cycle fatigue strength data measured in steps (3) and (4), calculate σ w / σ b Value, with σ w / σ b As the vertical axis, σ b Perform a linear fit on the horizontal axis, set the negative number of the slope of the fitted line as the parameter P value, and set the intercept of the fitted line as the parameter C value.

[0012] Step (6): Perform a linear fit between the C and P values ​​in step (5) to obtain a quantitative relationship between the two.

[0013] Step (7): Using the area percentage of each metallographic structure measured in step (2), calculate the structure content parameter w according to formula (2): m , and perform quadratic function fitting with the parameter C value in step (5) to obtain the corresponding quadratic term, linear term coefficient and constant term.

[0014] Step (8): Obtain the predicted values ​​of parameters P and C according to formulas (3) and (4), and substitute them into formula (1) to calculate the predicted value of fatigue strength of the material.

[0015] The advantages and beneficial effects of the present invention are as follows:

[0016] 1. By establishing an equivalent relationship between microstructure content and tensile strength and the fatigue strength of compacted graphite cast iron, the time and economic cost of traditional fatigue strength testing of compacted graphite cast iron materials are effectively reduced.

[0017] 2. By analyzing the high-cycle fatigue damage law, the key factors affecting the high-cycle fatigue performance of compacted graphite cast iron were identified, and the accuracy and universality of fatigue strength prediction were improved.

[0018] 3. The present invention relates to several key microstructures in compacted graphite cast iron materials, providing an optimization direction for the production process and fatigue resistance design of compacted graphite cast iron materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the fatigue strength prediction method for compacted graphite cast iron.

[0020] Figure 2 Schematic diagram of high cycle fatigue damage mechanism of compacted graphite cast iron with different microstructures.

[0021] Figure 3 For different types of compacted graphite cast iron materials σ w / σ b --σ b Relationship diagram.

[0022] Figure 4 This is the fatigue strength prediction of the compacted graphite cast iron material in the embodiment. DETAILED DESCRIPTION

[0023] The present invention is further described below with reference to the embodiments and accompanying drawings.

[0024] Example 1:

[0025] This embodiment predicts the fatigue strength of compacted graphite cast iron material. The process is as follows Figure 1 The specific process is as follows:

[0026] First, the compacted graphite cast iron material was taken from the diesel engine cylinder head, and the high cycle fatigue test was carried out at room temperature, 400℃ and 500℃ respectively ( Figure 2 ).

[0027] Second, in this embodiment, five types of vermicular graphite cast irons with different microstructures were selected, and the area percentages of vermicular graphite, ferrite, and pearlite were obtained using IPP software (see Table 1 for specific data).

[0028] Table 1 Summary of microstructure contents of several compacted graphite cast iron materials

[0029]

[0030] Third, measure the tensile properties and high cycle fatigue properties of the selected compacted graphite cast iron material to obtain the corresponding tensile strength σ b and fatigue strength σ w Measured value, and calculate the ratio of fatigue strength to tensile strength σ w / σ b(See Table 2 for specific data).

[0031] Table 2 σ of several compacted graphite cast iron materials at different temperatures w / σ b Value and σ b value

[0032]

[0033]

[0034] Fourth, based on the PC model obtained by Pang et al.:

[0035] σ w =(CP·σ b )·σ b (5)

[0036] Where: C is defined as the damage capacity; P is defined as the damage weight coefficient. When the parameters C and P are constants, the ratio σ w / σ b and σ b There is a linear relationship between them. In order to verify the applicability of the model in vermicular graphite cast iron materials, data of vermicular graphite cast iron at different temperatures were selected for verification. The results show that the σ of vermicular graphite cast iron materials with different pearlite content, ferrite content and creep rate at different temperatures is w / σ b and σ b The values ​​basically satisfy the linear relationship (such as Figure 3 The linear fitting results are:

[0037] RuT300:σ w / σ b =-0.00064σ b +0.66 (6)

[0038] RuT350:σ w / σ b =-0.00092σ b +0.80 (7)

[0039] RuT400-1:σ w / σ b =-0.00084σ b +0.68 (8)

[0040] RuT400-2:σ w / σ b =-0.00080σ b +0.70 (9)

[0041] RuT450:σ w / σ b =-0.00046σ b +0.56 (10)

[0042] The specific values ​​of parameters P and C can be directly obtained through the above fitting results. By establishing a relationship between the two, it is found that they basically satisfy a linear relationship. The linear fitting result is:

[0043] C=435.56×P+0.36 (11)

[0044] According to the high cycle fatigue damage mechanism of compacted graphite cast iron, the parameter C is combined with the microstructure parameter w m Establishing an equivalence relationship, the corresponding expression can be expressed as:

[0045] C=m·w m 2 +n·w m +k (12)

[0046] After fitting the above five materials, the parameter values ​​obtained are m=-0.04, n=0.23, and k=0.53.

[0047] Fifth, based on the parameters obtained in step 4, the fatigue strength of other compacted graphite irons with different tensile strengths and different microstructure contents can be predicted. Figure 4 The relationship between the predicted results and the experimental results is shown, verifying the accuracy of the predicted results.

Claims

1. A method for predicting fatigue strength of compacted graphite cast iron based on microstructure content and tensile strength, characterized by: The method comprises the following steps: (1) Polishing and etching the compacted graphite cast iron sample to obtain a metallographic analysis sample of the compacted graphite cast iron; (2) Observe the metallographic structure of the vermicular graphite cast iron, calculate the content of each structure in the vermicular graphite cast iron, and obtain the area percentage of vermicular graphite, ferrite and pearlite phases respectively; (3) Conduct static tensile test on the compacted graphite cast iron material to obtain the corresponding tensile strength s b ; (4) Perform high cycle fatigue test on the compacted graphite cast iron material to obtain the fatigue strength value s w ; At the same time, the measured tensile strength and fatigue strength data are fitted according to formula (1) to obtain the specific values ​​of the corresponding parameters P and C; (1)； (5) The worm-like graphite content obtained in step (2) w v , ferrite content w f and pearlite content w p Substitute into formula (2) to calculate the tissue content parameter w m ; (2)； (6) The parameter C obtained in step (4) and step (5) is compared with w m The C value and the P value are fitted with a quadratic function to obtain the corresponding linear term, quadratic term coefficient and constant term; the C value and the P value are fitted linearly to obtain the P value and w m The quantitative relationship between (7) Compare the P value of step (6) with w m The quantitative relationship between them is substituted into formula (1), and the fatigue strength of the compacted graphite cast iron material is predicted in combination with the corresponding tensile property test results; The method for predicting fatigue strength of compacted graphite cast iron based on microstructure content and tensile strength is characterized in that: in formula (1), the constants P and C are determined by formulas (3)-(4): (3)； (4)； Where: w m is the tissue content parameter; m 、 n 、 k 、 、 is a constant.

2. The method for predicting fatigue strength of compacted graphite cast iron based on microstructure content and tensile strength according to claim 1, characterized in that: In step (1), the surface of the compacted graphite cast iron sample is first polished in sequence with 400#, 800#, 1200#, 1500#, and 2000# sandpaper, then finely polished with velvet cloth, and finally the polished surface is immersed in a 4wt% nitric acid alcohol solution for corrosion for 15 seconds to obtain a metallographic structure analysis sample of the compacted graphite cast iron.

3. The method for predicting fatigue strength of compacted graphite cast iron based on microstructure content and tensile strength according to claim 1, characterized in that: In step (2), the compacted graphite cast iron includes graphite, pearlite and ferrite.

4. The method for predicting fatigue strength of compacted graphite cast iron based on microstructure content and tensile strength according to claim 1, wherein: In step (2), the area percentages of different phases are measured using Image Pro Plus software; the regions where each phase is located are determined based on the contrast differences of different phases in the cast iron material under a metallographic microscope, and the corresponding regional areas are obtained.

5. The method for predicting fatigue strength of compacted graphite cast iron based on microstructure content and tensile strength according to claim 1, characterized in that: In step (4), the tensile strength and fatigue strength used should be measured under the same experimental environment.

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