Method and system for predicting fracture toughness of steel material, device, and medium
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- SUZHOU NUCLEAR POWER RES INST CO LTD
- Filing Date
- 2024-12-06
- Publication Date
- 2026-06-04
Smart Images
Figure CN2024137543_04062026_PF_FP_ABST
Abstract
Description
A method, system, equipment, and medium for predicting the fracture toughness of steel materials. Technical Field
[0001] This invention relates to the field of fracture toughness testing technology, and in particular to a method, system, equipment and medium for predicting the fracture toughness of steel materials. Background Technology
[0002] In nuclear power plants, Mn-Ni-Mo low-alloy high-strength steel is a commonly used material for nuclear-grade equipment. However, this type of steel is prone to cold brittleness, which must be avoided in the operation of pressure-bearing equipment in nuclear power plants and in simulated accident scenarios to prevent pressure boundary fracture due to brittleness and ensure that the equipment can perform its functions safely and reliably.
[0003] Carbon segregation occurs during steel smelting and casting, leading to uneven distribution of alloying elements. This non-uniformity reduces the hardenability of steel, thus affecting the fracture toughness of the material in the carbon segregated regions. Currently, there is a lack of effective predictive models to assess the impact of carbon segregation on ductile tearing in structures. Therefore, there is room for improvement. Summary of the Invention
[0004] In view of the shortcomings of the prior art described above, the purpose of this invention is to provide a method, system, device and medium for predicting the fracture toughness of steel materials, in order to solve the technical problem of the lack of an effective prediction model to evaluate the impact of carbon segregation on structural ductile tearing in the prior art.
[0005] To achieve the above and other related objectives, the present invention provides a method for predicting the fracture toughness of steel materials, comprising:
[0006] Obtain 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] The upper platform impact absorption energy and temperature offset corresponding to each target carbon content are calculated using the first calculation model, which 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] The tensile strength corresponding to each target carbon content is calculated by a second calculation model, and the yield strength corresponding to each target carbon content is calculated by a third calculation model. The second calculation model represents the relationship between carbon content and tensile strength, and the third calculation model represents the relationship between carbon content and yield strength.
[0010] Based on the tensile strength and yield strength of each target carbon content, the passivation line equation corresponding to each target carbon content is generated;
[0011] Based on the fracture toughness curve and passivation line equation for 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 statistically analyzed to generate fracture toughness data of the steel material under test.
[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 includes:
[0014] Samples were taken from multiple locations on the steel material to be tested, and the carbon content of the samples was measured.
[0015] Carbon content distribution data of the steel material to be tested are generated based on the extreme values of carbon content in all samples.
[0016] Select multiple carbon contents from the carbon content distribution data and denot them as target carbon contents.
[0017] In one embodiment of the present invention, the first computational model is generated through the following steps:
[0018] Obtain impact performance data for multiple carbon contents, wherein the impact performance data characterizes the relationship between the impact absorbed energy of carbon content and temperature;
[0019] The impact absorption energy and temperature were fitted separately for multiple carbon contents to generate ductile-brittle transition temperature curves for each carbon content.
[0020] Based on the ductile-brittle transition temperature curves corresponding to multiple carbon contents, a calculation model for impact absorption energy and carbon content is constructed and denoted as the first calculation model.
[0021] In one embodiment of the present invention, the first calculation model satisfies the following formula:
[0022] Where KV represents the impact absorption energy, C represents the carbon content, k1, k2, and k3 represent the fitting coefficients, b2, b3, and θ1 represent constant parameters, and T represents the temperature.
[0023] In one embodiment of the present invention, the fracture toughness curve satisfies the following formula:
[0024] Where J represents the integral value of J at the crack tip, k4, k5, and k6 represent fitting coefficients, b4 represents a constant parameter, and ΔT 41J Δa represents the temperature offset, USE represents the impact absorption energy of the upper platform, and Δa represents the crack propagation amount.
[0025] 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 passivation line equation of each target carbon content includes:
[0026] Obtain the intersection point of the fracture toughness curve of each target carbon content with the straight line corresponding to its passivation line equation, and record the crack tip J integral value corresponding to the intersection point as the crack initiation fracture toughness of the target carbon content.
[0027] The fracture toughness at 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: M = R m +R p0.2 =(k7·C+b7)+(k8·C+b8)
[0028] Where M represents the slope of the passivation line equation, R m Represents the third computational model, R p0.2 This represents the fourth calculation model, where C represents the carbon content, k7 and k8 represent the fitting coefficients, and b7 and b8 represent constant parameters.
[0029] The present invention also provides a system for predicting the fracture toughness of steel materials, characterized in that it includes:
[0030] The 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 the target carbon content.
[0031] The fracture toughness calculation module is used to calculate the upper platform impact absorption energy and temperature offset corresponding to each target carbon content through a first calculation model. The first calculation model characterizes the relationship between carbon content and impact absorption energy.
[0032] The fracture toughness calculation module is also used to generate fracture toughness curves corresponding to each target carbon content based on the upper platform impact absorption energy and temperature offset of each target carbon content.
[0033] The fracture toughness calculation module is also used to calculate the tensile strength corresponding to each target carbon content through the second calculation model and to calculate the yield strength corresponding to each target carbon content through the third calculation model. The second calculation model represents the relationship between carbon content and tensile strength, and the third calculation model represents the relationship between carbon content and yield strength.
[0034] The fracture toughness calculation module is also used to generate the passivation line equation corresponding to each target carbon content based on the tensile strength and yield strength of each target carbon content.
[0035] The fracture toughness calculation module is also used to generate the plane strain fracture toughness corresponding to each target carbon content based on the fracture toughness curve and passivation line equation for each target carbon content.
[0036] The data statistics module is used to statistically analyze the plane strain fracture toughness of each target carbon content and generate fracture toughness data of the steel material to be tested.
[0037] The present invention also provides a computer device, including 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, it implements the steps of the method for predicting the fracture toughness of steel as described in any of the preceding claims.
[0038] The present invention also provides a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it implements the steps of the method for predicting the fracture toughness of steel as described in any of the preceding claims.
[0039] As described above, the method, system, device, and medium for predicting the fracture toughness of steel materials according to the present invention have the following beneficial effects: the present invention provides important input for the evaluation of ductile tearing fracture of carbon segregated structures, while saving evaluation costs. Attached Figure Description
[0040] Figure 1 is a flowchart illustrating a method for predicting the fracture toughness of steel materials according to an embodiment of the present invention.
[0041] Figure 2 shows the relationship between carbon content and the ductile-brittle transition temperature curve of the material in one embodiment of the present invention;
[0042] Figure 3 shows the relationship between carbon content and material fracture toughness curve in one embodiment of the present invention;
[0043] Figure 4 shows a schematic diagram of carbon content and tensile strength and yield strength in one embodiment of the present invention;
[0044] Figure 5 shows a structural block diagram of a steel fracture toughness prediction system provided in an embodiment of the present invention;
[0045] Figure 6 shows a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation
[0046] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.
[0047] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.
[0048] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.
[0049] First, it's important to note that during solidification, due to differences in solubility and diffusion behavior of solute elements in the solid and liquid phases, solute elements continuously migrate from the solid to the liquid phase, leading to segregation. The enrichment of solute elements in the liquid phase is more pronounced closer to the end of solidification. When carbon and alloying elements in steel dissolve into austenite upon heating, the isothermal transformation curve shifts to the right, increasing austenite stability and decreasing the critical cooling rate, thus significantly improving the hardenability of the steel. However, when segregation occurs in steel, the segregation of alloying elements in certain areas reduces the actual effective composition in other regions, partially altering the position of the isothermal transformation curve, increasing the critical cooling rate, and causing the phase transformation reaction to begin earlier. Even if alloying elements in steel are completely dissolved in austenite, their uneven distribution during phase transformation increases the diffusion rate due to concentration differences, also increasing the nucleation rate of new phases. Therefore, segregation and uneven composition in steel reduce its hardenability. According to relevant studies, the fracture toughness of materials in the carbon segregation region will decrease, but no corresponding prediction model has been published at home and abroad. Therefore, it is necessary to carry out relevant research to provide input for the assessment of the effect of carbon segregation on structural ductile tearing fracture.
[0050] This invention provides a method and system for predicting the fracture toughness of low-alloy steel materials. By introducing a carbon content factor, the method predicts and analyzes the material's ductile-brittle transition temperature curve (FATT) and tensile properties to obtain relevant material parameters. Furthermore, by establishing the relationship between impact performance and the material's fracture resistance curve (JR curve), a crucial input parameter for assessing the structural integrity of large carbon-segregated forgings—namely, the material's fracture toughness—is obtained. This provides technical support for the structural integrity and safe operation of the primary loop in nuclear power plants. Detailed explanations are provided below through specific embodiments.
[0051] Referring to Figure 1, this invention provides a method for predicting the fracture toughness of steel materials, which may include the following steps:
[0052] Step S100: Obtain the 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 the target carbon contents;
[0053] Step S200: Calculate the upper platform impact absorption energy and temperature offset corresponding to each target carbon content using the first calculation model. The first calculation model characterizes the relationship between carbon content and impact absorption energy.
[0054] Step S300: Based on the upper platform impact absorption energy and temperature offset of each target carbon content, generate the fracture toughness curve corresponding to each target carbon content.
[0055] Step S400: Calculate the tensile strength corresponding to each target carbon content using the second calculation model, and calculate the yield strength corresponding to each target carbon content using the third calculation model. The second calculation model represents the relationship between carbon content and tensile strength, and the third calculation model represents the relationship between carbon content and yield strength.
[0056] Step S500: Based on the tensile strength and yield strength of each target carbon content, generate the passivation line equation corresponding to each target carbon content;
[0057] Step S600: Based on the fracture toughness curve and passivation line equation for each target carbon content, generate the plane strain fracture toughness corresponding to each target carbon content.
[0058] Step S700: Calculate the plane strain fracture toughness of each target carbon content to generate fracture toughness data of the steel material to be tested.
[0059] In one embodiment of the present invention, when step S100 is executed, namely, acquiring 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, the process may include the following steps:
[0060] Step S110: Take samples from multiple locations on the steel material to be tested and measure the carbon content of the samples;
[0061] Step S120: Generate carbon content distribution data of the steel material to be tested based on the extreme values of carbon content of all samples;
[0062] Step S130: Select multiple carbon contents from the carbon content distribution data and record them as the target carbon contents.
[0063] In one embodiment of the present invention, when performing steps S110 to S130, specifically, firstly, multiple samples are collected from different locations (e.g., 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 using methods such as spectral analysis, chemical analysis, or thermal conductivity method. Based on the carbon content values measured from all samples, the maximum and minimum values are identified to determine the carbon content distribution data of the steel material to be tested. For example, C_min represents the lowest carbon content among all samples, and C_max represents the highest carbon content among all samples; the carbon content distribution data can then be recorded as [C_min, C_max]. Finally, a series of representative carbon content values are selected within the interval [C_min, C_max], and one of these carbon content values is recorded as the target carbon content.
[0064] 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 using a first calculation model. The first calculation model characterizes the relationship between carbon content and impact absorption energy. Specifically, the first calculation model characterizes the relationship between carbon content and impact absorption energy; therefore, the upper platform impact absorption energy and temperature offset corresponding to each target carbon content can be calculated using the first calculation model. In this embodiment, the first calculation model can satisfy the following relationship:
[0065] Where KV represents the impact absorption energy at a given temperature T, in J; C represents the carbon content; k1, k2, and k3 represent fitting coefficients; b2, b3, and θ1 represent constant parameters; and T represents temperature. As shown above, k1C represents the relationship between the lower platform impact absorption energy A1 and the carbon content C; k2C+b2 represents the relationship between the upper platform impact absorption energy A2 and the carbon content C; k3C+b3 represents the relationship between the inflection point temperature T0 and the carbon content C; and θ1 is a constant parameter of the Charpy impact energy-temperature fitting curve, i.e., the width ΔT of the inflection region. In some embodiments, k1 can be 44.8, k2 can be 586.8, b2 can be 363.4, k3 can be 81.5, b3 can be 84.3, and θ1 can be 17.5.
[0066] In one embodiment of the present invention, the first computational model is generated through the following steps:
[0067] Step S210: Obtain impact performance data for multiple carbon contents. The impact performance data characterizes the relationship between the impact absorption energy of carbon content and temperature.
[0068] Step S220: Fit the impact absorption energy and temperature for multiple carbon contents respectively to generate the ductile-brittle transition temperature curve corresponding to each carbon content.
[0069] Step S230: Based on the ductile-brittle transition temperature curves corresponding to multiple carbon contents, construct a calculation model of impact absorption energy and carbon content, and denote it as the first calculation model.
[0070] In one embodiment of the present invention, when step S210 is executed, impact performance data of multiple carbon contents are acquired. These impact performance data characterize the relationship between the impact absorption energy of the carbon content and temperature. Specifically, the impact performance dataset characterizes the relationship between the impact absorption energy of the carbon content and temperature. This dataset can be based on existing performance test data of carbon-segregated materials or generated through Charpy impact testing.
[0071] In this embodiment, during the Charpy impact test, a series of representative carbon content values are first selected from the defined carbon content distribution data. For each selected carbon content, a sample conforming to the Charpy impact test standard is prepared. Then, according to the Charpy impact test standard procedure, Charpy impact tests are conducted 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 compiled and recorded to form an impact performance dataset corresponding to each carbon content.
[0072] In one embodiment of the present invention, when step S220 is executed, the impact absorption energy and temperature are fitted to multiple carbon contents respectively to generate a ductile-brittle transition temperature curve corresponding to each carbon content. Specifically, a carbon content factor is introduced into the Boltzmann model to predict the ductile-brittle transition temperature curve (FATT) of steel. The Boltzmann function is an S-shaped curve that can be used to describe the transition behavior in various physical phenomena, especially in materials science to describe the property transformation with temperature. In this embodiment, for each specific carbon content, a Boltzmann function is fitted using the impact absorption energy obtained at different temperatures. The ductile-brittle transition temperature curve corresponding to each carbon content conforms to the following form:
[0073] Where 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 inflection point temperature, that is, the temperature at which the impact absorption energy begins to change significantly, and ΔT represents the width of the inflection region, which represents the rate at which the impact absorption energy changes with temperature.
[0074] Referring to 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 different carbon contents, and is denoted as the first calculation model. Specifically, all ductile-brittle transition temperature curves established for different carbon contents are integrated to form a composite function that can simultaneously consider the influence of carbon content and temperature on impact absorption energy, and this composite function is denoted as the first calculation model. In this embodiment, the lower plateau impact absorption energy A1, the upper plateau impact absorption energy A2, and the inflection point temperature T0 in the ductile-brittle transition temperature curve are linearly fitted with the carbon content C, thereby expressing the influence of carbon content on impact absorption energy.
[0075] In this embodiment, the impact absorption energy A1 of the lower platform and the carbon content C can satisfy the following relationship: A1=k1C;
[0076] The impact absorption energy A2 of the upper platform and the carbon content C can satisfy the following relationship: A2=k2C+b2;
[0077] The inflection point temperature T0 and the carbon content C can satisfy the following relationship: T0 = k3C + b3;
[0078] By combining the above formulas, the relationship function between the impact absorption energy KV and the carbon content C can be generated, thereby constructing the first calculation model.
[0079] In one embodiment of the present invention, based on the first calculation model established above, the upper plateau impact absorption energy (USE) and the temperature shift ΔT of the Charpy impact curve corresponding to each carbon content C can be calculated. 41J The upper shelf energy (USE) is the maximum impact energy a material can absorb at higher temperatures, at which point it exhibits optimal toughness. In Charpy impact tests, as temperature increases, the material transitions from a brittle to a ductile state, and the impact absorption energy increases accordingly until it reaches a relatively stable high level. This high level of energy absorption is the upper shelf energy. Temperature offset ΔT 41JThis refers to the temperature change relative to the ductile-brittle transition temperature (FATT) under certain reference or standard conditions when the Charpy impact energy of a material reaches 41 joules (J). This offset reflects the variation of the ductile-brittle transition temperature of the material under different carbon contents or other influences.
[0080] 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 plateau impact absorption energy and temperature offset for each target carbon content. Specifically, firstly, the JR curve for the target carbon content is obtained. The JR curve (J-Resistance Curve) is a graph used in elastoplastic fracture mechanics to describe a material's ability to resist crack propagation. It represents the relationship between energy release J and crack propagation Δa. The JR curve is crucial for evaluating and predicting the safety and lifespan of cracked structures. In this embodiment, the JR curve satisfies the following formula: J = m(Δa) n
[0081] Here, J integral is a parameter related to the energy release rate near the crack tip, while R represents the material's ability to resist crack propagation, m represents the energy release rate parameter, and n represents the slope exponent parameter.
[0082] Then, by introducing the upper plateau impact absorption energy and temperature shift into the JR curve, a fracture toughness curve corresponding to the target carbon content is constructed. Specifically, firstly, the energy release rate parameter m and the temperature shift ΔT can be compared... 41J A fitting process is performed to construct the relationship between the energy release rate parameter m and the temperature offset ΔT. 41J The calculation model is as follows. In this embodiment, the energy release rate parameter m and the temperature offset ΔT are used. 41J The computational model can satisfy the following formula: m=k4·ΔT 41J +b4;
[0083] Where k4 represents the fitting coefficient and b4 represents the constant parameter.
[0084] Then, the slope exponent parameter n and the upper platform impact absorption energy USE can be fitted to construct a calculation model for the slope exponent parameter n and the upper platform impact absorption energy USE. In this embodiment, the calculation model for the slope exponent parameter n and the upper platform impact absorption energy USE can satisfy the following formula:
[0085] Where k5 and k6 represent the fitting coefficients.
[0086] Finally, based on the JR curve formula, the energy release rate parameter m, and the temperature offset ΔT... 41J The calculation model, along with the calculation model of the slope exponent parameter n and the upper plateau impact absorption energy USE, is used to construct the fracture toughness curve for the target carbon content. In this embodiment, the fracture toughness curve represents the calculation model of the crack tip J integral value, upper plateau impact absorption energy, and temperature offset for the target carbon content. In this embodiment, the fracture toughness curve can satisfy the following formula:
[0087] Where J represents the integral value of J at the crack tip, k4, k5, and k6 represent fitting coefficients, b4 represents a constant parameter, and ΔT 41J The value represents the temperature offset, and USE represents the impact energy of the upper platform. In some embodiments, k4 is -2.84, b4 is 406.4, k5 is 0.342, and k6 is 0.0021.
[0088] Referring to 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 using a second calculation model, and the yield strength corresponding to each target carbon content is calculated using a third calculation model. The second calculation model represents the relationship between carbon content and tensile strength, and the third calculation model represents the relationship between carbon content and yield strength. Specifically, the second calculation model represents the relationship between carbon content and tensile strength, and the third calculation model represents the relationship between carbon content and yield strength. Therefore, the second calculation model can calculate the tensile strength corresponding to each target carbon content, and the third calculation model can calculate the yield strength corresponding to each target carbon content.
[0089] In this embodiment, multiple material strength datasets corresponding to different carbon contents are available. These datasets may include tensile strength and yield strength data at different carbon content levels. Regression analysis is performed on the carbon content and tensile strength data to identify a suitable functional form describing their relationship. Possible functional forms may include linear, polynomial, or exponential relationships. A calculation model between tensile strength and carbon content is established based on the fitting results and denoted as the third calculation model. In this embodiment, the third calculation model satisfies the following formula: R m =k7·C+b7;
[0090] Among them, R m Let C represent the tensile strength, C represent the carbon content, k7 represent the fitting coefficient, and b7 represent a constant parameter. Therefore, the third calculation model can accept carbon content as input and output the predicted tensile strength value. In some embodiments, k7 can be 409.68 and b7 can be 600.56.
[0091] Regression analysis was performed on the carbon content and yield strength data to find a suitable functional form to describe their relationship. Based on the fitting results, a calculation model between yield strength and carbon content was established and denoted as the fourth calculation model. In this embodiment, the fourth calculation model satisfies the following formula: R p0.2 =k8·C+b8;
[0092] Among them, R p0.2 The tensile strength of the steel material under test refers to the stress value corresponding to 0.2% permanent plastic deformation in a tensile test. C represents the carbon content, k8 represents the fitting coefficient, and b8 represents constant parameters. Therefore, the fourth calculation model can accept carbon content as input and output the predicted yield strength value. In some embodiments, k8 can be 362.09 and b8 can be 489.39.
[0093] In one embodiment of the present invention, when step S500 is executed, a 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, firstly, 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 and a preset intercept of the passivation line equation. The passivation line equation defines the energy consumption characteristics of a material during the transition from elastic deformation to plastic deformation and finally fracture in material fracture toughness testing. In fracture mechanics, the passivation line is usually used to characterize the energy that a material can absorb without significant crack propagation. The passivation line is a straight line on the fracture toughness curve, which reflects the relationship between the fracture energy (J) and the crack propagation amount Δa when the material undergoes plastic flow (i.e., passivation) at the crack tip without further crack propagation. The fracture toughness curve shows how the energy absorbed by the material changes as the crack propagation amount increases. The passivation line can be obtained by fitting experimental data, usually using a linear regression method. In this embodiment, the passivation line equation can satisfy the following formula: J=M·Δa+b
[0094] Where J represents the fracture energy, Δa represents the crack propagation, and M and b represent the slope and Y-intercept, respectively. The slope M of the passivation line equation is defined by the following formula: M = R m +R p0.2
[0095] Substituting into the third and fourth calculation models above: M = R m +R p0.2 =(k7·C+b7)+(k8·C+b8)
[0096] According to ASTM E1820, the passivation line has an X-axis intercept of 0.2 mm. Therefore, based on the slope and preset intercept calculated above, the passivation line equation can be established.
[0097] In one embodiment of the present invention, when step S600 is executed, that is, based on the fracture toughness curve and passivation line equation for each target carbon content, the plane strain fracture toughness corresponding to each target carbon content is generated. Specifically, it may include the following steps:
[0098] Step S610: Obtain the intersection point of the fracture toughness curve of each target carbon content and the straight line corresponding to its passivation line equation, and record the crack tip J integral value corresponding to the intersection point as the crack initiation fracture toughness of the target carbon content.
[0099] Step S620: Convert the crack initiation fracture toughness of the target carbon content to generate the plane strain fracture toughness corresponding to the target carbon content.
[0100] In one embodiment of the present invention, when step S610 is executed, the intersection point of the fracture toughness curve of each target carbon content and the corresponding straight line of its passivation line equation is obtained, and the crack tip J integral value corresponding to the intersection point is recorded as the crack 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 propagation Δa axis. In this embodiment, the offset is 0.2 mm. The intersection point of the shifted passivation line and the JR curve is found. The Δa value at the intersection point is the crack propagation amount of the material in the critical state. The fracture energy value corresponding to the intersection point is the crack initiation fracture toughness J of the material. IC .
[0101] 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 principles of fracture mechanics, the crack initiation fracture toughness obtained in step S610 is converted into plane strain fracture toughness K. IC Plane strain fracture toughness K IC With fracture toughness J IC The relationship between them satisfies the following formula:
[0102] The above conversion process involves using fracture mechanics formulas to transform the crack tip J-integral value 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 crucial for evaluating the fracture toughness of materials in practical applications.
[0103] In one embodiment of the present invention, when step S700 is executed, the plane strain fracture toughness of each target carbon content is statistically analyzed to generate 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 statistically analyzing the plane strain fracture toughness of all target carbon contents, the fracture toughness data of the steel material to be tested can be generated.
[0104] Referring to Figure 5, this invention also provides a prediction system for the fracture toughness of steel materials, which corresponds one-to-one with the processing methods in the above embodiments. This prediction system may include a carbon content acquisition module 101, a fracture toughness calculation module 102, and a data statistics module 103. Detailed descriptions of each functional module are as follows:
[0105] The carbon content acquisition module 101 can be 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. Further, the carbon content acquisition module 101 can be specifically used to sample and measure the carbon content of the samples at multiple locations on the steel material to be tested; generate carbon content distribution data based on the extreme values of the carbon contents of all samples; and select multiple carbon contents from the carbon content distribution data and record them as target carbon contents.
[0106] 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, where 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; the tensile strength corresponding to each target carbon content is calculated through a second calculation model, and the yield strength corresponding to each target carbon content is calculated through a third calculation model, where 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, a passivation line equation corresponding to each target carbon content is generated; and based on the fracture toughness curve and passivation line equation of each target carbon content, the plane strain fracture toughness corresponding to each target carbon content is generated.
[0107] The data statistics module 103 can be used to statistically analyze the plane strain fracture toughness of each target carbon content, generating fracture toughness data of the steel material to be tested. Further, the data statistics module 103 can specifically be used to calculate the plane strain fracture toughness of each target carbon content through the fracture toughness calculation module 102, and statistically analyze the plane strain fracture toughness of all target carbon contents, generating fracture toughness data of the steel material to be tested.
[0108] Specific limitations regarding the prediction system for the fracture toughness of steel can be found in the limitations of the prediction method described above, and will not be repeated here. Each module in the above prediction system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independent of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the corresponding operations of each module.
[0109] Embodiments of the present invention also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the method for predicting the fracture toughness of steel provided in the above embodiments.
[0110] Referring to 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 program for predicting the fracture toughness of steel.
[0111] The memory 210 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 210 can be an internal storage unit of the electronic device 200, such as the portable hard drive of the electronic device 200. In other embodiments, the memory 210 can be an external storage device of the electronic device 200, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 200. Furthermore, the memory 210 can include both internal and external storage units of the electronic device 200. The memory 210 can be used not only to store application software and various types of data installed on the electronic device 200, such as code for predicting the fracture toughness of steel materials, but also to temporarily store data that has been output or will be output.
[0112] In some embodiments, processor 220 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. Processor 220 is the control unit of electronic device 200, connecting various components of the entire electronic device 200 via various interfaces and lines. It executes various functions of electronic device 200 and processes data by running or executing programs or modules stored in memory 210 (e.g., programs predicting the fracture toughness of steel materials) and calling data stored in memory 210.
[0113] The processor 220 executes the operating system of the electronic device 200 and various installed application programs. The processor 220 executes the application programs to implement the steps in the above-described method for predicting the fracture toughness of steel materials.
[0114] For example, a computer program may be divided into one or more modules, one or more of which are stored in memory 210 and executed by processor 220 to complete this application. One or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in 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.
[0115] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium, which can be non-volatile or volatile. The software functional module, stored in the storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute part of the functions of the steel fracture toughness prediction method of the various embodiments of this application.
[0116] In summary, the present invention discloses a method, system, equipment, and medium for predicting the fracture toughness of steel materials. This method is applicable to predicting the fracture toughness of large Mn-Ni-Mo low-alloy steel forgings containing carbon segregation (C% < 0.4%) in nuclear power plants as a function of carbon content. By introducing a material carbon content factor, this invention innovatively proposes a method for predicting changes in the impact and tensile properties of materials. Furthermore, by establishing the relationship between impact performance and the JR resistance curve of the material fracture, the fracture toughness K of the material is calculated. This solves the technical problem of lacking a performance prediction model for carbon segregated materials, providing important input for the evaluation of ductile tear fracture of carbon segregated structures, while simultaneously saving evaluation costs. Therefore, this invention effectively overcomes the various shortcomings of existing technologies and has high industrial application value.
[0117] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can 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 those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.
Claims
1. A method of predicting fracture toughness of a steel material, characterized by, include: Obtain 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; The upper platform impact absorption energy and temperature offset corresponding to each target carbon content are calculated using the first calculation model, which 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. The tensile strength corresponding to each target carbon content is calculated by a second calculation model, and the yield strength corresponding to each target carbon content is calculated by a third calculation model. The second calculation model represents the relationship between carbon content and tensile strength, and the third calculation model represents the relationship between carbon content and yield strength. Based on the tensile strength and yield strength of each target carbon content, the passivation line equation corresponding to each target carbon content is generated; Based on the fracture toughness curve and passivation line equation for 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 statistically analyzed to generate fracture toughness data of the steel material under test.
2. The method of predicting the fracture toughness of a steel material according to claim 1, characterized by, The step of obtaining carbon content distribution data of the steel material to be tested, and selecting multiple carbon contents from the carbon content distribution data and recording them as the target carbon contents, includes: Samples were taken from multiple locations on the steel material to be tested, and the carbon content of the samples was measured. Carbon content distribution data of the steel material to be tested are generated based on the extreme values of carbon content in all samples. Select multiple carbon contents from the carbon content distribution data and denot them as target carbon contents.
3. The method of predicting the fracture toughness of a steel material according to claim 1, characterized by, The first computational model is generated through the following steps: Obtain impact performance data for multiple carbon contents, wherein the impact performance data characterizes the relationship between the impact absorbed energy of carbon content and temperature; The impact absorption energy and temperature were fitted separately for multiple carbon contents to generate the ductile-brittle transition temperature curves corresponding to each carbon content. Based on the ductile-brittle transition temperature curves corresponding to multiple carbon contents, a calculation model for impact absorption energy and carbon content is constructed and denoted as the first calculation model.
4. The method of predicting the fracture toughness of a steel material according to claim 1, characterized by, The first computational model satisfies the following equation: Where KV represents the impact absorption energy, C represents the carbon content, k1, k2, and k3 represent the fitting coefficients, b2, b3, and θ1 represent constant parameters, and T represents the temperature.
5. The method of predicting the fracture toughness of a steel material according to claim 1, characterized by, The fracture toughness curve satisfies the following equation: wherein J represents a crack tip J integral value, k4, k5, k6 represent fitting coefficients, b4 represents a constant parameter, ΔT 41J represents a temperature offset, USE represents an upper plateau impact absorbed energy, and Δa represents a crack propagation amount.
6. The method of predicting the fracture toughness of a steel material according to claim 1, characterized by, The step of generating the plane strain fracture toughness corresponding to each target carbon content based on the fracture toughness curve and passivation line equation for each target carbon content includes: Obtain the intersection point of the fracture toughness curve of each target carbon content with the straight line corresponding to its passivation line equation, and record the crack tip J integral value corresponding to the intersection point 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 of predicting the fracture toughness of a steel material according to claim 1, characterized by, The slope of the passivation line equation satisfies the following formula: M = R m + R p0.2 = (k7 · C + b7) + (k8 · C + b8) where M represents the slope of the passivation line equation, R m represents the third calculation model, R p0.2 represents the fourth calculation model, C represents the carbon content, k7, k8 represent fitting coefficients, and b7, b8 represent constant parameters.
8. A system for predicting fracture toughness of a steel material, characterized by, include: The 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 the target carbon content. The fracture toughness calculation module is used to calculate the upper platform impact absorption energy and temperature offset corresponding to each target carbon content through a first calculation model. The first calculation model characterizes the relationship between carbon content and impact absorption energy. The fracture toughness calculation module is also used to generate fracture toughness curves 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 also used to calculate the tensile strength corresponding to each target carbon content through the second calculation model and to calculate the yield strength corresponding to each target carbon content through the third calculation model. The second calculation model represents the relationship between carbon content and tensile strength, and the third calculation model represents the relationship between carbon content and yield strength. The fracture toughness calculation module is also used to generate the 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 also used to generate the plane strain fracture toughness corresponding to each target carbon content based on the fracture toughness curve and passivation line equation for each target carbon content. The data statistics module is used to statistically analyze the plane strain fracture toughness of each target carbon content and generate 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, it implements the steps of the method for predicting the fracture toughness of steel as described in any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. When the computer program is executed by the processor, it implements the steps of the method for predicting the fracture toughness of steel as described in any one of claims 1 to 7.