Method, system and equipment for predicting fracture toughness of steel material and medium

Abstract

The invention provides a steel material fracture toughness prediction method, system, equipment and medium, and the method comprises the steps: obtaining carbon content distribution data of a to-be-tested steel material, selecting a carbon content from the carbon content distribution data, and recording the selected carbon content as a target carbon content; calculating the upper platform impact absorption energy and the temperature offset of the target carbon content through a first calculation model; generating a fracture toughness curve of the target carbon content based on the upper platform impact absorption energy and the temperature offset; the tensile strength of the target carbon content is calculated through a second calculation model, and the yield strength of the target carbon content is calculated through a third calculation model; generating a passivation line equation of the target carbon content based on the tensile strength and the yield strength; based on the fracture toughness curve and the passivation line equation, plane strain fracture toughness corresponding to the target carbon content is generated; the plane strain fracture toughness of each target carbon content is counted, and fracture toughness data of the to-be-detected steel material is generated. The method can provide input for ductile tearing fracture evaluation of the carbon-containing segregation structure.

Description

Technical Field

[0001] The invention relates to the technical field of fracture toughness testing, and in particular to a method, system, equipment and medium for predicting the fracture toughness of a steel material. Background Art

[0002] In nuclear power plants, Mn-Ni-Mo low alloy high strength steel is a common material for nuclear grade equipment. However, this type of steel material has cold brittleness, which must be avoided in the operation of nuclear power plant pressure equipment and hypothetical accident scenarios to prevent the pressure boundary from rupturing due to brittleness and ensure that the equipment performs its functions safely and reliably.

[0003] Carbon segregation occurs during the smelting and casting process of steel, resulting in uneven distribution of alloying elements. This non-uniformity reduces the hardenability of steel, thereby affecting the fracture toughness of the material in the carbon segregation area. Currently, there is a lack of effective prediction models to evaluate the effect of carbon segregation on structural ductile tearing. Therefore, there is room for improvement. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the object of the present invention is to provide a method, system, equipment and medium for predicting the fracture toughness of steel materials, so as to solve the technical problem that there is a lack of effective prediction model in the prior art to evaluate the effect of carbon segregation on structural ductile tearing.

[0005] To achieve the above-mentioned and other related purposes, the present invention provides a method for predicting the fracture toughness of a steel material, comprising:

[0006] Acquire carbon content distribution data of the steel material to be tested, select multiple carbon contents from the carbon content distribution data, and record them as target carbon contents;

[0007] Calculating the upper platform impact absorption energy and temperature offset corresponding to each target carbon content by a first calculation model, wherein the first calculation model characterizes the relationship between carbon content and impact absorption energy;

[0008] Based on the upper platform impact absorption energy and temperature offset of each target carbon content, a fracture toughness curve corresponding to each target carbon content is generated;

[0009] Calculating the tensile strength corresponding to each target carbon content by a second calculation model, and calculating the yield strength corresponding to each target carbon content by a third calculation model, wherein the second calculation model represents the relationship between the carbon content and the tensile strength, and the third calculation model represents the relationship between the carbon content and the yield strength;

[0010] Based on the tensile strength and yield strength of each target carbon content, a passivation line equation corresponding to each target carbon content is generated;

[0011] Based on the fracture toughness curve and the passive line equation of each target carbon content, the plane strain fracture toughness corresponding to each target carbon content is generated;

[0012] The plane strain fracture toughness of each target carbon content is counted to generate fracture toughness data of the steel material to be tested.

[0013] In one embodiment of the present invention, the step of obtaining carbon content distribution data of the steel material to be tested, selecting a carbon content from the carbon content distribution data, and recording it as the target carbon content comprises:

[0014] Sampling at multiple locations of the steel material to be tested and measuring the carbon content of the samples;

[0015] Generate carbon content distribution data of the steel material to be tested based on the extreme values ​​of carbon content of all samples;

[0016] A plurality of carbon contents are selected from the carbon content distribution data and recorded as target carbon contents.

[0017] In one embodiment of the present invention, the first calculation model is generated by the following steps:

[0018] Acquiring impact performance data of a plurality of carbon contents, wherein the impact performance data characterizes the relationship between the impact absorption energy of the carbon content and the temperature;

[0019] The impact absorption energy and temperature of multiple carbon contents are fitted respectively to generate the ductile-brittle transition temperature curve corresponding to each carbon content;

[0020] Based on the ductile-brittle transition temperature curves corresponding to multiple carbon contents, a calculation model of impact absorption energy and carbon content is constructed and recorded as the first calculation model.

[0021] In one embodiment of the present invention, the first calculation model satisfies the following formula:

[0022]

[0023] Among them, KV represents the impact absorption energy, C represents the carbon content, k1, k2, k3 represent the fitting coefficients, b2, b3, θ1 represent constant parameters, and T represents the temperature.

[0024] In one embodiment of the present invention, the fracture toughness curve satisfies the following formula:

[0025]

[0026] Where J represents the J integral value at the crack tip, k4, k5, k6 represent the fitting coefficients, b4 represents the constant parameter, ΔT 41J represents the temperature offset, USE represents the upper platform impact absorption energy, and Δa represents the crack extension.

[0027] In one embodiment of the present invention, the step of generating the plane strain fracture toughness corresponding to each target carbon content based on the fracture toughness curve and the passivation line equation of each target carbon content comprises:

[0028] Obtain the intersection of the fracture toughness curve of each target carbon content and the straight line corresponding to its passivation line equation, and record the J integral value of the crack tip corresponding to the intersection as the crack initiation fracture toughness of the target carbon content;

[0029] The crack initiation fracture toughness of the target carbon content is converted to generate the plane strain fracture toughness corresponding to the target carbon content. In one embodiment of the present invention, the slope of the passivation line equation satisfies the following formula:

[0030] M=R m +R p0.2 =(k7·C+b7)+(k8·C+b8)

[0031] Among them, M represents the slope of the passivation line equation, R m Represents the third computational model, R p0.2 represents the fourth calculation model, C represents the carbon content, k7 and k8 represent fitting coefficients, and b7 and b8 represent constant parameters.

[0032] The present invention also provides a prediction system for the fracture toughness of steel materials, characterized in that it comprises:

[0033] A carbon content acquisition module is used to acquire carbon content distribution data of the steel material to be tested, select multiple carbon contents from the carbon content distribution data, and record them as target carbon contents;

[0034] A fracture toughness calculation module, used to calculate the upper platform impact absorption energy and temperature offset corresponding to each target carbon content through a first calculation model, wherein the first calculation model characterizes the relationship between carbon content and impact absorption energy;

[0035] The fracture toughness calculation module is further used to generate a fracture toughness curve corresponding to each target carbon content based on the upper platform impact absorption energy and temperature offset of each target carbon content;

[0036] The fracture toughness calculation module is further used to calculate the tensile strength corresponding to each target carbon content through a second calculation model, and calculate the yield strength corresponding to each target carbon content through a third calculation model, wherein the second calculation model represents the relationship between the carbon content and the tensile strength, and the third calculation model represents the relationship between the carbon content and the yield strength;

[0037] The fracture toughness calculation module is also used to generate a passivation line equation corresponding to each target carbon content based on the tensile strength and yield strength of each target carbon content;

[0038] The fracture toughness calculation module is further used to generate the plane strain fracture toughness corresponding to each target carbon content based on the fracture toughness curve and the passivation line equation of each target carbon content;

[0039] The data statistics module is used to count the plane strain fracture toughness of each target carbon content and generate the fracture toughness data of the steel material to be tested.

[0040] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that when the processor executes the computer program, the steps of the method for predicting the fracture toughness of steel materials as described in any one of the above items are implemented.

[0041] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of the method for predicting the fracture toughness of a steel material as described in any one of the above items are implemented.

[0042] As described above, a method, system, device and medium for predicting fracture toughness of steel materials of the present invention have the following beneficial effects: the present invention provides important input for the evaluation of ductile tear fracture of carbon-containing segregation structures, while saving evaluation costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic flow chart of a method for predicting fracture toughness of a steel material provided by an embodiment of the present invention;

[0044] Figure 2 The graph showing the relationship between the carbon content and the ductile-brittle transition temperature curve of the material in one embodiment of the present invention;

[0045] Figure 3 Shown is a graph showing the relationship between carbon content and material fracture toughness curve in one embodiment of the present invention;

[0046] Figure 4 A schematic diagram showing carbon content, tensile strength and yield strength in one embodiment of the present invention is shown;

[0047] Figure 5 Shown is a structural block diagram of a prediction system for fracture toughness of steel materials provided by an embodiment of the present invention;

[0048] Figure 6 Shown is a structural schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0050] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0051] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0052] First of all, it should be noted that during the solidification process of the material, due to the difference in solubility of the solute elements in the solid phase and the liquid phase and their diffusion behavior in the solid phase, the solute elements are continuously discharged from the solid phase to the liquid phase, thus forming segregation, and the closer to the end of solidification, the more serious the enrichment of the solute elements in the liquid phase. When carbon and alloy elements in steel are heated and dissolved into austenite, the austenite isothermal transformation curve moves to the right, increasing the stability of austenite and reducing the critical cooling rate, which significantly improves the hardenability of the steel. However, when there is segregation in the steel, the alloy elements segregate in a certain place, which reduces the actual effective components in other areas of the steel, thereby partially changing the position of the isothermal transformation curve, increasing the critical cooling rate and prompting the phase change reaction to start in advance. The alloy elements in the steel have been completely dissolved in the austenite, but the distribution is uneven. During the phase change, the diffusion rate is increased due to the difference in concentration, and the nucleation rate of the new phase is also increased. Therefore, when there is segregation and uneven composition in the steel, the hardenability of the steel will be reduced. According to relevant research, the fracture toughness of materials in the carbon segregation area will decrease, but the corresponding prediction model has not been published at home and abroad. Therefore, it is necessary to carry out relevant research to provide input for the evaluation of carbon segregation on structural ductile tearing fracture.

[0053] The present invention provides a method and system for predicting the fracture toughness of low alloy steel materials. By introducing a carbon content factor, the material ductile-brittle transition temperature curve (FATT) and tensile properties are predicted and analyzed to obtain relevant parameters of the material, and by establishing a relationship between the impact performance and the material fracture JR resistance curve, an important input parameter for evaluating the structural integrity of large forgings containing carbon segregation, namely the material fracture toughness, is obtained, thereby providing technical guarantee for the structural integrity and safe operation of the primary circuit of a nuclear power plant. This is described in detail below through a specific embodiment.

[0054] See also Figure 1 The present invention provides a method for predicting the fracture toughness of a steel material, which may include the following steps:

[0055] Step S100, obtaining carbon content distribution data of the steel material to be tested, selecting a plurality of carbon contents from the carbon content distribution data, and recording them as target carbon contents;

[0056] Step S200, calculating the upper platform impact absorption energy and temperature offset corresponding to each target carbon content by using a first calculation model, wherein the first calculation model represents the relationship between the carbon content and the impact absorption energy;

[0057] Step S300, generating a fracture toughness curve corresponding to each target carbon content based on the upper platform impact absorption energy and temperature offset of each target carbon content;

[0058] Step S400, calculating the tensile strength corresponding to each target carbon content by a second calculation model, and calculating the yield strength corresponding to each target carbon content by a third calculation model, wherein the second calculation model represents the relationship between the carbon content and the tensile strength, and the third calculation model represents the relationship between the carbon content and the yield strength;

[0059] Step S500, generating a passivation line equation corresponding to each target carbon content based on the tensile strength and yield strength of each target carbon content;

[0060] Step S600, generating a plane strain fracture toughness corresponding to each target carbon content based on the fracture toughness curve and the passivation line equation of each target carbon content;

[0061] Step S700: Count the plane strain fracture toughness of each target carbon content to generate fracture toughness data of the steel material to be tested.

[0062] In one embodiment of the present invention, when executing step S100, the carbon content distribution data of the steel material to be tested is obtained, a carbon content is selected from the carbon content distribution data, and recorded as the target carbon content. Specifically, the following steps may be included:

[0063] Step S110, sampling at multiple locations of the steel material to be tested and measuring the carbon content of the samples;

[0064] Step S120, generating carbon content distribution data of the steel material to be tested based on the extreme values ​​of the carbon content of all samples;

[0065] Step S130: Select multiple carbon contents from the carbon content distribution data and record them as target carbon contents.

[0066] In one embodiment of the present invention, when executing step S110 to step S130. Specifically, first, multiple samples are collected from different positions (such as the surface, center, edge, etc.) of the steel material to be tested to ensure that the collected data can represent the carbon content distribution of the entire material. Then, the carbon content in each sample is measured by spectral analysis, chemical analysis or thermal conductivity method. According to the carbon content values ​​measured by all samples, the maximum and minimum values ​​are found to determine the carbon content distribution data of the steel material to be tested. For example, C_min represents the lowest carbon content in all samples, and C_max represents the highest carbon content in all samples, then the carbon content distribution data can be recorded as [C_min, C_max]. Finally, a series of representative carbon content values ​​are selected in the interval [C_min, C_max], and one of the carbon content values ​​is recorded as the target carbon content.

[0067] In one embodiment of the present invention, when step S200 is executed, the upper platform impact absorption energy and temperature offset corresponding to each target carbon content are calculated by the first calculation model, and the first calculation model characterizes the relationship between the carbon content and the impact absorption energy. Specifically, the first calculation model characterizes the relationship between the carbon content and the impact absorption energy, and therefore, the upper platform impact absorption energy and temperature offset corresponding to each target carbon content can be calculated by the first calculation model. In this embodiment, the first calculation model can satisfy the following relationship:

[0068]

[0069] Wherein, KV represents the impact absorption energy at a given temperature T, and its unit is J, C represents the carbon content, k1, k2, k3 represent fitting coefficients, b2, b3, θ1 represent constant parameters, and T represents temperature. As can be seen from the above, k1C represents the relationship function between the impact absorption energy A1 of the lower platform and the carbon content C, k2C+b2 represents the relationship function between the impact absorption energy A2 of the upper platform and the carbon content C, k3C+b3 represents the relationship function between the turning point temperature T0 and the carbon content C, and θ1 is the constant parameter of the Charpy impact energy-temperature fitting curve, that is, the width ΔT of the turning area. In some embodiments, the value of k1 can be 44.8, the value of k2 can be 586.8, the value of b2 can be 363.4, the value of k3 can be 81.5, the value of b3 can be 84.3, and the value of θ1 can be 17.5.

[0070] In one embodiment of the present invention, the first calculation model is generated by the following steps:

[0071] Step S210, acquiring impact performance data of a plurality of carbon contents, where the impact performance data represents the relationship between the impact absorption energy of the carbon content and the temperature;

[0072] Step S220, performing fitting processing on the impact absorption energy and temperature of multiple carbon contents respectively, and generating a ductile-brittle transition temperature curve corresponding to each carbon content;

[0073] Step S230: constructing a calculation model of impact absorption energy and carbon content based on multiple ductile-brittle transition temperature curves corresponding to carbon contents, and recording it as a first calculation model.

[0074] In one embodiment of the present invention, when step S210 is executed, multiple carbon content impact performance data are obtained, and the impact performance data represent the relationship between the impact absorption energy of the carbon content and the temperature. Specifically, the impact performance data set represents the relationship between the impact absorption energy of the carbon content and the temperature, and the data set can be based on the performance test data of the existing carbon-containing segregation material, and can also be generated by the Charpy impact test.

[0075] In this embodiment, when performing the Charpy impact test, firstly, a series of representative carbon content values ​​are selected within the determined carbon content distribution data. For each selected carbon content, a sample that meets the Charpy impact test standard is prepared. Then, according to the Charpy impact test standard procedure, the Charpy impact test is performed on each sample under different temperature conditions, and the impact absorption energy and other relevant performance indicators after each test are recorded. The impact absorption energy of each sample at different temperatures is sorted and recorded to form an impact performance data set corresponding to each carbon content.

[0076] In one embodiment of the present invention, when step S220 is executed, the impact absorption energy and temperature of multiple carbon contents are fitted respectively to generate a ductile-brittle transition temperature curve corresponding to each carbon content. Specifically, the carbon content factor is introduced into the Boltzmann model to predict the ductile-brittle transition temperature curve (FATT) of steel materials. The Boltzmann function is an S-shaped curve that can be used to describe transition behaviors in a variety of physical phenomena, especially in materials science to describe property changes with temperature changes. In this embodiment, for each specific carbon content, the impact absorption energy obtained at different temperatures is used to fit a Boltzmann function. The ductile-brittle transition temperature curve corresponding to each carbon content conforms to the following form:

[0077]

[0078] Wherein, T represents temperature, KV represents the impact absorption energy at a given temperature T, A1 represents the impact absorption energy of the lower platform, A2 represents the impact absorption energy of the upper platform, T0 represents the turning point temperature, that is, the temperature at which the impact absorption energy begins to change significantly, ΔT represents the width of the turning area, and represents the speed at which the impact absorption energy changes with temperature.

[0079] See also Figure 2 In one embodiment of the present invention, when step S230 is executed, a calculation model of impact absorption energy and carbon content is constructed based on multiple ductile-brittle transition temperature curves corresponding to carbon contents, and recorded as the first calculation model. Specifically, all ductile-brittle transition temperature curves established with different carbon contents are integrated to form a composite function that can simultaneously consider the effects of carbon content and temperature on impact absorption energy, and the composite function is recorded as the first calculation model. In this embodiment, the lower platform impact absorption energy A1, the upper platform impact absorption energy A2, the turning point temperature T0 and the carbon content C in the ductile-brittle transition temperature curve are linearly fitted, respectively, to express the effect of carbon content on impact absorption energy.

[0080] In this embodiment, the impact absorption energy A1 of the lower platform and the carbon content C can satisfy the following relationship:

[0081] A1=k1C;

[0082] The upper platform impact absorption energy A2 and carbon content C can satisfy the following relationship:

[0083] A2=k2C+b2;

[0084] The turning point temperature T0 and the carbon content C can satisfy the following relationship:

[0085] T0=k3C+b3;

[0086] Combining the above formulas, a relationship function between the impact absorption energy KV and the carbon content C can be generated, thereby constructing the first calculation model.

[0087] In one embodiment of the present invention, according to the first calculation model established above, the upper platform impact absorption energy USE and the temperature offset ΔT of the Charpy impact curve corresponding to each carbon content C can be calculated. 41J . Among them, the upper platform impact absorption energy (Upper Shelf Energy, USE) is the maximum impact energy that the material can absorb at a higher temperature, at which time the material exhibits the best toughness. In the Charpy impact test, as the temperature increases, the material changes from a brittle state to a ductile state, and the impact absorption energy also increases until it reaches a relatively stable high level. This high level of energy absorption value is the upper platform impact absorption energy. Temperature offset ΔT 41JIt refers to the temperature change of the Fracture Appearance Transition Temperature (FATT) under a certain reference temperature or standard conditions when the Charpy impact energy of the material reaches 41 joules (J). This offset reflects the change of the ductile-brittle transition temperature of the material under different carbon contents or other factors.

[0088] In one embodiment of the present invention, when step S300 is executed, a fracture toughness curve corresponding to each target carbon content is generated based on the upper platform impact absorption energy and temperature offset of each target carbon content. Specifically, first, the JR curve of the target carbon content is obtained. The JR curve (J-Resistance Curve) is a graph used in elastic-plastic fracture mechanics to describe the ability of a material to resist crack propagation. It shows the relationship between energy release J and crack extension Δa. The JR curve is crucial for evaluating and predicting the safety and life of cracked structures. In this embodiment, the JR curve can satisfy the following formula:

[0089] J=m(Δa) n

[0090] Among them, J integral is a parameter related to the energy release rate near the crack tip, R represents the ability of the material to resist crack propagation, m represents the energy release rate parameter, and n represents the slope exponent parameter.

[0091] Then, the upper platform impact absorption energy and temperature offset are introduced into the JR curve to construct the fracture toughness curve corresponding to the target carbon content. Specifically, first, the energy release rate parameter m and the temperature offset ΔT can be 41J Perform fitting processing to construct the energy release rate parameter m and temperature offset ΔT 41J In this embodiment, the energy release rate parameter m and the temperature offset ΔR 41J The calculation model can satisfy the following formula:

[0092] m=k4·ΔT 41J +b4;

[0093] Among them, k4 represents the fitting coefficient and b4 represents the constant parameter.

[0094] Then, the slope index parameter n and the upper platform impact absorption energy USE can be fitted to construct a calculation model of the slope index parameter n and the upper platform impact absorption energy USE. In this embodiment, the calculation model of the slope index parameter n and the upper platform impact absorption energy USE can satisfy the following formula:

[0095]

[0096] Among them, k5 and k6 represent fitting coefficients.

[0097] Finally, according to the above JR curve formula, the energy release rate parameter m and the temperature offset ΔT 41J The calculation model of the slope index parameter n and the calculation model of the upper platform impact absorption energy USE is used to construct the fracture toughness curve of the target carbon content. In this embodiment, the fracture toughness curve represents the calculation model of the crack tip J integral value of the target carbon content and the upper platform impact absorption energy and temperature offset. In this embodiment, the fracture toughness curve can satisfy the following formula:

[0098]

[0099] Where J represents the J integral value at the crack tip, k4, k5, k6 represent the fitting coefficients, b4 represents the constant parameter, ΔT 41J represents the temperature offset, USE represents the upper platform impact energy. In some embodiments, the value of k4 is -2.84, the value of b4 is 406.4, the value of k5 is 0.342, and the value of k6 is 0.0021.

[0100] See also Figure 4 In one embodiment of the present invention, when step S400 is executed, the tensile strength corresponding to each target carbon content is calculated by the second calculation model, and the yield strength corresponding to each target carbon content is calculated by the third calculation model. The second calculation model characterizes the relationship between the carbon content and the tensile strength, and the third calculation model characterizes the relationship between the carbon content and the yield strength. Specifically, the second calculation model characterizes the relationship between the carbon content and the tensile strength, and the third calculation model characterizes the relationship between the carbon content and the yield strength. It can be seen that the tensile strength corresponding to each target carbon content can be calculated by the second calculation model, and the yield strength corresponding to each target carbon content can be calculated by the third calculation model.

[0101] In this embodiment, a plurality of material strength data sets corresponding to carbon contents may be obtained, and the material strength data sets may include tensile strength data and yield strength data at different carbon content levels. A regression analysis is performed on the carbon content and tensile strength data to find a function form suitable for describing the relationship between the two. Possible function forms may include linear relationships, polynomial relationships, or exponential relationships. A calculation model between tensile strength and carbon content is established based on the fitting results and recorded as a third calculation model. In this embodiment, the third calculation model may satisfy the following formula:

[0102] R m =k7·C+b7;

[0103] Among them, R mrepresents tensile strength, C represents carbon content, k7 represents fitting coefficient, and b7 represents constant parameter. It can be seen that the third calculation model can accept carbon content as input and output predicted tensile strength value. In some embodiments, the value of k7 can be 409.68, and the value of b7 can be 600.56.

[0104] A regression analysis is performed on the carbon content and yield strength data to find a function form suitable for describing the relationship between the two. A calculation model between yield strength and carbon content is established based on the fitting results and recorded as the fourth calculation model. In this embodiment, the fourth calculation model can satisfy the following formula:

[0105] R p0.2 =k8·C+b8;

[0106] Among them, R p0.2 represents the tensile strength of the steel material to be tested, which refers to the stress value corresponding to 0.2% permanent plastic deformation of the material in the tensile test, C represents the carbon content, k8 represents the fitting coefficient, and b8 represents the constant parameter. It can be seen that the fourth calculation model can accept carbon content as input and output the predicted yield strength value. In some embodiments, the value of k8 can be 362.09, and the value of b8 can be 489.39.

[0107] In one embodiment of the present invention, when step S500 is executed, the passivation line equation corresponding to each target carbon content is generated based on the tensile strength and yield strength of each target carbon content. Specifically, first, the slope of the passivation line equation is calculated based on the sum of the tensile strength and yield strength of the target carbon content. Then, the passivation line equation is constructed according to the slope of the passivation line equation and the preset intercept. The passivation line equation defines the energy consumption characteristics of the material in the process of transitioning from elastic deformation to plastic deformation and finally fracture in the material fracture toughness test. In fracture mechanics, the passivation line is usually used to characterize the energy that a material can absorb without obvious crack extension. The passivation line refers to a straight line on the fracture toughness curve, which reflects the relationship between the fracture energy (J) and the crack extension Δa when the material undergoes plastic flow (i.e., passivation) at the crack tip without further crack extension. The fracture toughness curve shows how the energy absorbed by the material changes as the crack extension increases. The passivation line can be obtained by fitting experimental data, usually using a linear regression method. In this embodiment, the passivation line equation may satisfy the following formula:

[0108] J=M·Δa+b

[0109] Where J represents the fracture energy, Δa represents the crack extension, M and b represent the slope and Y-intercept respectively. The slope M of the passivation line equation is defined to satisfy the following formula:

[0110] M=R m +R p0.2

[0111] Substitute the third and fourth calculation models mentioned above into:

[0112] M=R m +R p0.2 =(k7·C+b7)+(k8·C+b8)

[0113] According to the requirements of ASTM E1820, the intercept of the passivation line on the X-axis is 0.2 mm. Therefore, the passivation line equation can be established based on the slope calculated above and the preset intercept.

[0114] In one embodiment of the present invention, when executing step S600, the plane strain fracture toughness corresponding to each target carbon content is generated based on the fracture toughness curve and the passivation line equation of each target carbon content. Specifically, the following steps may be included:

[0115] Step S610, obtaining the intersection of the fracture toughness curve of each target carbon content and the straight line corresponding to its passivation line equation, and recording the J integral value of the crack tip corresponding to the intersection as the crack initiation fracture toughness of the target carbon content;

[0116] Step S620: converting the crack initiation fracture toughness of the target carbon content to generate the plane strain fracture toughness corresponding to the target carbon content.

[0117] In one embodiment of the present invention, when executing step S610, the intersection of the fracture toughness curve of each target carbon content and the straight line corresponding to its passivation line equation is obtained, and the crack tip J integral value corresponding to the intersection is recorded as the initiation fracture toughness of the target carbon content. Specifically, according to the requirements of ASTM E1821, the passivation line is offset by a predetermined value along the crack extension Δa axis. In this embodiment, the offset is 0.2mm. Find the intersection of the translated passivation line and the JR curve. The Δa value at the intersection is the crack extension of the material under the critical state. The fracture energy value corresponding to the intersection is the initiation fracture toughness J of the material. IC .

[0118] In one embodiment of the present invention, when step S620 is executed, the crack initiation fracture toughness of the target carbon content is converted to generate the plane strain fracture toughness corresponding to the target carbon content. Specifically, according to the principle of fracture mechanics, the crack initiation fracture toughness obtained in step S610 is converted into the plane strain fracture toughness K IC . Plane strain fracture toughness K IC and crack initiation fracture toughness J IC The relationship between them satisfies the following formula:

[0119]

[0120] The above conversion process involves using the fracture mechanics formula to convert the J integral value at the crack tip into a stress intensity factor. The resulting plane strain fracture toughness data reflects the material's ability to resist crack propagation under plane stress conditions, which is very important for evaluating the fracture toughness of the material in practical applications.

[0121] In one embodiment of the present invention, when step S700 is executed, the plane strain fracture toughness of each target carbon content is counted to generate the fracture toughness data of the steel material to be tested. Specifically, according to steps S200 to S600, the plane strain fracture toughness of each target carbon content is calculated. By counting the plane strain fracture toughness of all target carbon contents, the fracture toughness data of the steel material to be tested can be generated.

[0122] See also Figure 5 The present invention also provides a prediction system for the fracture toughness of steel materials, which corresponds to the processing method in the above embodiment. The prediction system may include a carbon content acquisition module 101, a fracture toughness calculation module 102, and a data statistics module 103. The functional modules are described in detail as follows:

[0123] The carbon content acquisition module 101 can be used to obtain the carbon content distribution data of the steel material to be tested, select multiple carbon contents in the carbon content distribution data, and record them as target carbon contents. Further, the carbon content acquisition module 101 can be specifically used to take samples at multiple locations of the steel material to be tested and measure the carbon content of the samples; generate carbon content distribution data based on the extreme values ​​of the carbon content of all samples; select multiple carbon contents in the carbon content distribution data, and record them as target carbon contents.

[0124] The fracture toughness calculation module 102 can be used to calculate the upper platform impact absorption energy and temperature offset corresponding to each target carbon content through a first calculation model, and the first calculation model characterizes the relationship between carbon content and impact absorption energy; based on the upper platform impact absorption energy and temperature offset of each target carbon content, generate a fracture toughness curve corresponding to each target carbon content; calculate the tensile strength corresponding to each target carbon content through a second calculation model, and calculate the yield strength corresponding to each target carbon content through a third calculation model, and the second calculation model characterizes the relationship between carbon content and tensile strength, and the third calculation model characterizes the relationship between carbon content and yield strength; based on the tensile strength and yield strength of each target carbon content, generate a passivation line equation corresponding to each target carbon content; based on the fracture toughness curve and passivation line equation of each target carbon content, generate the plane strain fracture toughness corresponding to each target carbon content.

[0125] The data statistics module 103 can be used to count the plane strain fracture toughness of each target carbon content and generate the fracture toughness data of the steel material to be tested. Further, the data statistics module 103 can be specifically used to calculate the plane strain fracture toughness of each target carbon content through the fracture toughness calculation module 102, and count the plane strain fracture toughness of all target carbon contents to generate the fracture toughness data of the steel material to be tested.

[0126] The specific definition of the prediction system for the fracture toughness of steel materials can be found in the definition of the prediction method above, which will not be repeated here. Each module in the above prediction system can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0127] An embodiment of the present invention further provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device implements the method for predicting the fracture toughness of steel materials provided in the above-mentioned embodiments.

[0128] See also Figure 6 The electronic device 200 may include a memory 210, a processor 220 and a bus, and may also include a computer program stored in the memory 210 and executable on the processor 220, such as a prediction program for the fracture toughness of steel materials.

[0129] The memory 210 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 210 may be an internal storage unit of the electronic device 200, such as a mobile hard disk of the electronic device 200. In other embodiments, the memory 210 may also be an external storage device of the electronic device 200, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 200. Further, the memory 210 may also include both an internal storage unit of the electronic device 200 and an external storage device. The memory 210 may not only be used to store application software and various types of data installed in the electronic device 200, such as the code for predicting the fracture toughness of steel materials, but may also be used to temporarily store data that has been output or is to be output.

[0130] In some embodiments, the processor 220 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 220 is the control core (Control Unit) of the electronic device 200, and uses various interfaces and lines to connect various components of the entire electronic device 200, and executes or executes programs or modules (such as a prediction program for the fracture toughness of steel materials, etc.) stored in the memory 210, and calls data stored in the memory 210 to execute various functions of the electronic device 200 and process data.

[0131] The processor 220 executes the operating system and various installed application programs of the electronic device 200. The processor 220 executes the application programs to implement the steps in the above-mentioned method for predicting the fracture toughness of steel materials.

[0132] Exemplarily, the computer program may be divided into one or more modules, one or more modules are stored in the memory 210, and are executed by the processor 220 to complete the present application. One or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device 200. For example, the computer program may be divided into a carbon content acquisition module 101, a fracture toughness calculation module 102, and a data statistics module 103.

[0133] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium, and the computer-readable storage medium can be non-volatile or volatile. The above-mentioned software function module is stored in a storage medium, and includes a number of instructions for enabling a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to perform part of the functions of the method for predicting the fracture toughness of steel materials in various embodiments of the present application.

[0134] In summary, the present invention discloses a method, system, device and medium for predicting the fracture toughness of steel materials, which are suitable for predicting the change of fracture toughness of large Mn-Ni-Mo low alloy steel forging materials containing carbon segregation (C%<0.4%) in nuclear power plants with carbon content. The present invention introduces the carbon content factor of the material, innovatively proposes a method for predicting the change of impact performance and tensile performance of the material, and establishes the relationship between the impact performance and the JR resistance curve of the material fracture to obtain the fracture toughness K of the material. This can solve the technical problem of no performance prediction model for carbon segregated materials, provide important input for the evaluation of ductile tear fracture of carbon segregated structures, and save evaluation costs. Therefore, the present invention effectively overcomes various shortcomings in the prior art and has a high industrial utilization value.

[0135] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for predicting the fracture toughness of a steel material, characterized in that: include: Acquire carbon content distribution data of the steel material to be tested, select multiple carbon contents from the carbon content distribution data, and record them as target carbon contents; Calculating the upper platform impact absorption energy and temperature offset corresponding to each target carbon content by a first calculation model, wherein the first calculation model characterizes the relationship between carbon content and impact absorption energy; Based on the upper platform impact absorption energy and temperature offset of each target carbon content, a fracture toughness curve corresponding to each target carbon content is generated; Calculating the tensile strength corresponding to each target carbon content by a second calculation model, and calculating the yield strength corresponding to each target carbon content by a third calculation model, wherein the second calculation model represents the relationship between the carbon content and the tensile strength, and the third calculation model represents the relationship between the carbon content and the yield strength; Based on the tensile strength and yield strength of each target carbon content, a passivation line equation corresponding to each target carbon content is generated; Based on the fracture toughness curve and the passive line equation of each target carbon content, the plane strain fracture toughness corresponding to each target carbon content is generated; The plane strain fracture toughness of each target carbon content is counted to generate fracture toughness data of the steel material to be tested.

2. The method for predicting fracture toughness of steel material according to claim 1, characterized in that: The step of obtaining carbon content distribution data of the steel material to be tested, selecting a plurality of carbon contents from the carbon content distribution data, and recording them as target carbon contents comprises: Sampling at multiple locations of the steel material to be tested and measuring the carbon content of the samples; Generate carbon content distribution data of the steel material to be tested based on the extreme values ​​of carbon content of all samples; A plurality of carbon contents are selected from the carbon content distribution data and recorded as target carbon contents.

3. The method for predicting fracture toughness of steel material according to claim 1, characterized in that: The first computational model is generated by the following steps: Acquiring impact performance data of a plurality of carbon contents, wherein the impact performance data characterizes the relationship between the impact absorption energy of the carbon content and the temperature; The impact absorption energy and temperature of multiple carbon contents are fitted respectively to generate the ductile-brittle transition temperature curve corresponding to each carbon content; Based on the ductile-brittle transition temperature curves corresponding to multiple carbon contents, a calculation model of impact absorption energy and carbon content is constructed and recorded as the first calculation model.

4. The method for predicting fracture toughness of steel material according to claim 1, characterized in that: The first calculation model satisfies the following formula: Among them, KV represents the impact absorption energy, C represents the carbon content, k1, k2, k3 represent the fitting coefficients, b2, b3, θ1 represent constant parameters, and T represents the temperature.

5. The method for predicting fracture toughness of steel material according to claim 1, characterized in that: The fracture toughness curve satisfies the following formula: Where J represents the J integral value at the crack tip, k4, k5, k6 represent the fitting coefficients, b4 represents the constant parameter, ΔT 41J represents the temperature offset, USE represents the upper platform impact absorption energy, and Δa represents the crack extension.

6. The method for predicting fracture toughness of steel material according to claim 1, characterized in that: The step of generating the plane strain fracture toughness corresponding to each target carbon content based on the fracture toughness curve and the passivation line equation of each target carbon content comprises: Obtain the intersection of the fracture toughness curve of each target carbon content and the straight line corresponding to its passivation line equation, and record the J integral value of the crack tip corresponding to the intersection as the crack initiation fracture toughness of the target carbon content; The crack initiation fracture toughness of the target carbon content is converted to generate the plane strain fracture toughness corresponding to the target carbon content.

7. The method for predicting fracture toughness of steel material according to claim 1, characterized in that: The slope of the passivation line equation satisfies the following formula: M=R m +R p0.2 =(k7·C+b7)+(k8·C+b8) Among them, M represents the slope of the passivation line equation, R m Represents the third computational model, R p0.2 represents the fourth calculation model, C represents the carbon content, k7 and k8 represent fitting coefficients, and b7 and b8 represent constant parameters.

8. A prediction system for fracture toughness of steel material, characterized in that: include: A carbon content acquisition module is used to acquire carbon content distribution data of the steel material to be tested, select multiple carbon contents from the carbon content distribution data, and record them as target carbon contents; A fracture toughness calculation module, used to calculate the upper platform impact absorption energy and temperature offset corresponding to each target carbon content through a first calculation model, wherein the first calculation model characterizes the relationship between carbon content and impact absorption energy; The fracture toughness calculation module is further used to generate a fracture toughness curve corresponding to each target carbon content based on the upper platform impact absorption energy and temperature offset of each target carbon content; The fracture toughness calculation module is further used to calculate the tensile strength corresponding to each target carbon content through a second calculation model, and calculate the yield strength corresponding to each target carbon content through a third calculation model, wherein the second calculation model represents the relationship between the carbon content and the tensile strength, and the third calculation model represents the relationship between the carbon content and the yield strength; The fracture toughness calculation module is also used to generate a passivation line equation corresponding to each target carbon content based on the tensile strength and yield strength of each target carbon content; The fracture toughness calculation module is further used to generate the plane strain fracture toughness corresponding to each target carbon content based on the fracture toughness curve and the passivation line equation of each target carbon content; The data statistics module is used to count the plane strain fracture toughness of each target carbon content and generate the fracture toughness data of the steel material to be tested.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method for predicting the fracture toughness of a steel material according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting the fracture toughness of a steel material according to any one of claims 1 to 7 are implemented.

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