Method for detecting ductile-brittle transition temperature of metal material based on thermal method

Abstract

The invention relates to the technical field of metal material performance detection, and discloses a method for detecting ductile-brittle transition temperature of a metal material based on a thermal method. The method comprises the following steps: acquiring the average grain size of a material through three-dimensional characterization, quantitatively estimating the movable dislocation density generated by grain boundary thermal activation emission at a specific temperature in combination with a preset dislocation emission coefficient, and linearly superposing the movable dislocation density with the inherent pre-stored movable dislocation density of the material to obtain a total movable dislocation density estimated value. And comparing the total movable dislocation density calculated at different temperatures with the ductile-brittle transition critical threshold obtained from the material knowledge base to determine the critical temperature meeting the condition, namely the ductile-brittle transition temperature. According to the method, the mechanical prediction of the ductile-brittle transition temperature is realized by quantifying the contribution of a key dislocation source from a plastic deformation microphysical mechanism, and the dependence of a traditional method on a large number of destructive experiments is reduced.

Description

Technical Field

[0001] This invention relates to the field of metal material performance testing technology, specifically a method for detecting the ductile-brittle transition temperature of metal materials based on thermal methods. Background Technology

[0002] The ductile-brittle transition temperature of metallic materials is a key indicator for assessing their safety during low-temperature service. Traditional testing methods primarily rely on a series of macroscopic mechanical property tests. This method involves measuring the impact absorption energy or fracture toughness of the material at different temperatures, plotting its variation with temperature, and determining the ductile-brittle transition temperature from the curve according to specific standards. In addition, some studies have attempted to establish statistical empirical relationships between microstructure parameters such as grain size and the transition temperature.

[0003] Existing technical solutions have shortcomings. Macroscopic testing methods require the preparation of a large number of standard samples and destructive testing at different temperatures, a process that is time-consuming, labor-intensive, and costly, and the results are heavily dependent on sample geometry and testing standards. Empirical statistical relationships lack universality, fail to reveal the physical nature of the ductile-brittle transition, and are not applicable to materials with novel or complex microstructures. The fundamental reason is that existing methods do not directly link to and quantify the core microscopic physical process controlling the transition of materials from brittle to ductile, namely the generation and movement of mobile dislocations.

[0004] There is a need to develop a method that can directly quantify key parameters to predict the ductile-brittle transition temperature from the perspective of microscopic deformation mechanisms. This method needs to be able to quantitatively assess how temperature affects the source and density of mobile dislocations within the material, especially quantifying the contribution of grain boundaries as dislocation sources under thermal activation, and establishing a direct physical link between these contributions and the critical conditions for the ductile-brittle transition, thereby overcoming the dependence on large-scale macroscopic experiments. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting the ductile-brittle transition temperature of metallic materials based on thermal methods, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for detecting the ductile-brittle transition temperature of metallic materials based on a thermal method, the method comprising: Obtain a set of microstructure parameters for the target metallic material, including the three-dimensional average grain size measured by three-dimensional characterization technology and the pre-stored mobile dislocation density calculated by a preset dislocation analysis model. The total area of ​​grain boundaries per unit volume is calculated based on the three-dimensional average grain size. Combined with the preset dislocation emission coefficient, the density of movable dislocations emitted by grain boundaries under preset temperature conditions is estimated. The estimated total mobile dislocation density of the target metal material under the preset temperature condition is calculated by linearly superimposing the grain boundary emitted mobile dislocation density with the pre-stored mobile dislocation density. Obtain the minimum mobile dislocation density threshold required for the target metallic material to undergo ductile-brittle transition from a preset material knowledge base; Within a preset temperature range, the total mobile dislocation density is estimated at different temperature test points using a thermal method. The total mobile dislocation density estimate corresponding to each temperature test point is then compared with the minimum mobile dislocation density threshold. A critical temperature test point is determined that satisfies the condition that the estimated total mobile dislocation density is greater than or equal to the minimum mobile dislocation density threshold, and the temperature corresponding to the critical temperature test point is determined as the ductile-brittle transition temperature of the target metallic material.

[0007] Preferably, the calculation of the total grain boundary area per unit volume based on the three-dimensional average grain size includes: Construct an equal-volume sphere geometric model to characterize polycrystalline metallic materials based on the three-dimensional average grain size; Calculate the surface area of ​​a single equivalent grain based on the geometric model of the equal-volume sphere; Based on the three-dimensional average grain size and unit volume, calculate the equivalent number of grains contained in the unit volume; Multiply the surface area of ​​the single equivalent grain by the number of equivalent grains contained in the unit volume to obtain the total grain boundary area in the unit volume.

[0008] Preferably, estimating the density of movable dislocations emitted from grain boundaries under preset temperature conditions, in conjunction with a preset dislocation emission coefficient, includes: Data on dislocation nucleation activation energies of the target metallic material at different temperatures were retrieved from a materials thermodynamics database. Based on the dislocation nucleation activation energy data and the preset temperature conditions, the nucleation probability of dislocation sources at grain boundaries under the preset temperature conditions is calculated. Multiplying the nucleation probability by the preset dislocation emission coefficient yields the temperature-corrected grain boundary dislocation emission efficiency. The grain boundary emitted mobile dislocation density is calculated by multiplying the total grain boundary area per unit volume, the temperature-corrected grain boundary dislocation emission efficiency, and the preset dislocation source density per unit area.

[0009] Preferably, obtaining the minimum mobile dislocation density threshold required for the target metallic material to undergo the ductile-brittle transition from a preset material knowledge base includes: Retrieve a dataset of reference materials with the same crystal structure and chemical composition as the target metallic material from the preset material knowledge base; Multiple sets of known ductile-brittle transition temperatures and corresponding microstructure parameters were extracted from the reference material dataset. Based on the extracted microstructure parameters, the critical mobile dislocation density corresponding to each set of data is fitted by inverse calculation. Statistical analysis was performed on all the critical mobile dislocation densities obtained from the fitting, and their mathematical average value was taken as the minimum mobile dislocation density threshold required for the target metallic material to undergo ductile-brittle transition.

[0010] Preferably, the step of using a thermal method to simulate and calculate the estimated total mobile dislocation density at different temperature test points within a preset temperature range includes: Multiple discrete temperature test points are uniformly selected within the preset temperature range; For each selected temperature test point, the dislocation nucleation activation energy corresponding to the temperature test point is recalculated, and the grain boundary dislocation emission efficiency is updated accordingly. Based on the updated grain boundary dislocation emission efficiency, the density of the grain boundary emitted mobile dislocations corresponding to the temperature test point is calculated. The grain boundary emission mobile dislocation density calculated at each temperature test point is superimposed with the pre-stored mobile dislocation density to generate the estimated total mobile dislocation density corresponding to the temperature test point. Collect the estimated total mobile dislocation density corresponding to all temperature test points to form an estimated data sequence of total mobile dislocation density as a function of temperature.

[0011] Preferably, comparing the estimated total mobile dislocation density for each temperature test point with the minimum mobile dislocation density threshold includes: The estimated data sequence of the total mobile dislocation density as a function of temperature was plotted as a curve showing the relationship between the estimated total mobile dislocation density and temperature. In the coordinate system where the relationship curve is located, draw a horizontal threshold line representing the minimum movable dislocation density threshold. Calculate the coordinates of the intersection point between the relationship curve and the horizontal threshold line; Extract the temperature values ​​corresponding to the intersection coordinates to obtain candidate critical temperature points.

[0012] Preferably, determining the critical temperature test point that satisfies the condition that the estimated total mobile dislocation density is greater than or equal to the minimum mobile dislocation density threshold, and determining the temperature corresponding to the critical temperature test point as the ductile-brittle transition temperature of the target metallic material, includes: On the relationship curve, all temperature test points whose total mobile dislocation density estimate is greater than or equal to the minimum mobile dislocation density threshold are selected to form a temperature range above the threshold. The lowest temperature test point in the above-threshold temperature range is determined, and the temperature test point is determined as the critical temperature test point; The temperature value corresponding to the critical temperature test point is taken as the final determination result of the ductile-brittle transition temperature of the target metallic material.

[0013] Preferably, calculating the surface area of ​​a single equivalent grain based on the geometric model of the equal-volume sphere includes: The shape of the equivalent grain is set as an ideal sphere, and the three-dimensional average grain size is used as the diameter of the ideal sphere; By applying the formula for calculating the surface area of ​​a sphere, the value of the three-dimensional average grain size is substituted into the diameter variable in the formula; Perform multiplication and squaring operations to calculate the surface area value of the ideal sphere. This surface area value is the calculated surface area value of a single equivalent grain. Record the calculated surface area of ​​a single equivalent grain and use it as one of the input parameters for calculating the total grain boundary area per unit volume.

[0014] Preferably, the step of calculating the nucleation probability of dislocation sources at grain boundaries under the preset temperature condition based on the dislocation nucleation activation energy data and the preset temperature condition includes: Query the materials thermodynamics database to obtain the reference value of dislocation nucleation activation energy corresponding to the grain boundary characteristics of the target metallic material; Read the value of the preset temperature condition and convert the temperature value from the Celsius unit to the thermodynamic temperature scale unit; Substitute the reference value of dislocation nucleation activation energy, the temperature value expressed in thermodynamic temperature scale units, and the Boltzmann constant into the exponential probability formula describing the thermal activation process. By using exponential and constant multiplication operations, the probability of a single potential dislocation source successfully overcoming the energy barrier and undergoing a nucleation event under preset temperature conditions is calculated. The calculated probability value is defined as the nucleation probability of dislocation sources at grain boundaries under a preset temperature condition, and this nucleation probability is used to estimate the density of movable dislocations emitted from grain boundaries.

[0015] Preferably, the steps for constructing the dislocation analysis model include: Collect raw mechanical property data of the target metallic material, including stress-strain curves obtained by uniaxial tensile tests and hardness distribution data obtained by microhardness tests. The collected stress-strain curves were analyzed to extract the work hardening rate during the plastic deformation stage. Spatial statistical analysis was performed on the hardness distribution data to calculate the average hardness and the variance of the fluctuation. A first mathematical relationship is established based on the physical relationship between work hardening rate and movable dislocation density, and a second mathematical relationship is established based on the relationship between hardness statistical characteristics and dislocation strengthening contribution. By combining the first and second mathematical relations, a system of two equations in two variables is constructed regarding the pre-existing density of movable dislocations; The system of two equations is solved by numerical iteration, and the obtained pre-stored mobile dislocation density is compared and verified with the known theoretical range of dislocation density for metallic materials. When the obtained pre-stored mobile dislocation density value is within the theoretical range, the constructed dislocation analysis model is confirmed to be effective.

[0016] Compared with the prior art, the beneficial effects of the present invention are: By employing a technique that calculates the total grain boundary area based on the three-dimensional average grain size and introduces a preset dislocation emission coefficient to estimate the density of movable dislocations emitted from grain boundaries at a specific temperature, an independent and quantitative characterization of the contribution of grain boundaries as thermally activated dislocation sources is achieved. This allows the prediction model to be directly rooted in the physical mechanism of plastic deformation initiation, transforming the previously empirical and indirect influence of grain size into a clear and physically meaningful parameter of dislocation multiplication rate. Consequently, the prediction process is freed from dependence on specific macroscopic experimental data and empirical formulas, enabling it to reveal the intrinsic principles of temperature-induced plastic deformation initiation from more fundamental microstructural parameters. This approach is particularly suitable for mechanistic studies and early-stage evaluation of novel materials.

[0017] By constructing a model that linearly superimposes pre-stored mobile dislocation densities with temperature-dependent grain boundary emission mobile dislocation densities to calculate the estimated total mobile dislocation density, the contributions of the material's initial state and thermal activation process to dislocation supply are clearly separated. This model provides a clear physical picture and more accurately reflects the evolution of the total mobile dislocation density with increasing temperature. Combined with the critical minimum mobile dislocation density threshold for the ductile-brittle transition obtained from a knowledge base, this scheme achieves a physical criterion-based determination of the transition temperature. Compared to traditional methods that search for feature points on macroscopic performance curves, this scheme directly targets the microscopic critical conditions for the ductile-brittle transition, giving the prediction results a more solid physical basis and enabling the analysis of the influence mechanism of the material's initial state on the transition temperature. Attached Figure Description

[0018] Figure 1 This is a schematic diagram illustrating the working principle of the thermal method-based method for detecting the ductile-brittle transition temperature of metallic materials as described in this invention. Figure 2 A flowchart for calculating the total area of ​​grain boundaries per unit volume; Figure 3 A flowchart for estimating the density of mobile dislocations emitted from grain boundaries; Figure 4 This is a graph showing the relationship between the total mobile dislocation density of a metallic material and temperature. Figure 5 This is a thermogram showing the effect of metal material type and temperature on performance. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1 This invention provides a method for detecting the ductile-brittle transition temperature of metallic materials based on a thermal method. The method includes: simulating the generation mechanism of mobile dislocations in the material at different temperatures using the thermal activation principle, quantitatively evaluating the mobile dislocation density in combination with microstructural parameters, and then determining the ductile-brittle transition temperature by comparing it with a critical threshold. The implementation process first requires obtaining a set of microstructural parameters of the target metallic material. This set includes the three-dimensional average grain size measured by three-dimensional characterization techniques such as electron backscatter diffraction or X-ray tomography, and a pre-stored mobile dislocation density calculated using a preset dislocation analysis model. Based on the three-dimensional average grain size, the total grain boundary area per unit volume is calculated. Combined with the dislocation emission coefficient preset from the intrinsic properties of the material, the grain boundary emission mobile dislocation density generated by thermally activated emission at a specific preset temperature is estimated. The calculated grain boundary emission mobile dislocation density is linearly superimposed with the pre-stored mobile dislocation density to obtain an estimated total mobile dislocation density of the target metallic material at that preset temperature. From a pre-defined materials knowledge base, the minimum mobile dislocation density threshold required for the target metallic material to undergo the ductile-brittle transition can be obtained. Within a pre-defined temperature range, thermal simulations are used to calculate the estimated total mobile dislocation density at different temperature test points, and the estimated total mobile dislocation density for each temperature test point is compared with the minimum mobile dislocation density threshold. The critical temperature test point where the estimated total mobile dislocation density is greater than or equal to the minimum mobile dislocation density threshold is determined, and the temperature corresponding to this critical temperature test point is identified as the ductile-brittle transition temperature of the target metallic material.

[0021] In one embodiment of the present invention, see [reference] Figure 2A geometric model of an equivalent sphere representing polycrystalline metallic materials is constructed based on the three-dimensional average grain size. The surface area of ​​a single equivalent grain is calculated based on this model. This process involves setting the shape of the equivalent grain as an ideal sphere, using the three-dimensional average grain size as the diameter of the ideal sphere, applying the formula for calculating the surface area of ​​a sphere, substituting the value of the three-dimensional average grain size into the diameter variable in the formula, performing multiplication and squaring operations to calculate the surface area of ​​the ideal sphere. This surface area value is the calculated value of the surface area of ​​a single equivalent grain. This calculated value is recorded and used as one of the input parameters for calculating the total grain boundary area per unit volume. Based on the three-dimensional average grain size and the unit volume, the number of equivalent grains contained within the unit volume is calculated. The surface area of ​​a single equivalent grain is multiplied by the number of equivalent grains contained within the unit volume to obtain the total grain boundary area per unit volume.

[0022] In practice, the calculation process begins with constructing an equal-volume spherical geometric model to characterize the microstructure of polycrystalline metallic materials. The basic assumption of this model is that each actual grain is equivalent to a sphere of equal volume. The model is constructed based on the three-dimensional average grain size. This model simplifies irregular grains into spheres with a diameter equal to the three-dimensional average grain size, which is the core input parameter. Calculating the surface area of ​​a single equivalent grain based on this model involves setting the shape of the equivalent grain as an ideal sphere. Using the three-dimensional average grain size as the diameter of the ideal sphere, the surface area formula is applied, substituting the value of the three-dimensional average grain size into the diameter variable. Multiplication and squaring operations are performed to calculate the surface area of ​​the ideal sphere; this surface area value is the calculated surface area of ​​a single equivalent grain. The calculated surface area of ​​a single equivalent grain is recorded and used as one of the input parameters for calculating the total grain boundary area per unit volume.

[0023] In practical implementation, the number of equivalent grains per unit volume is calculated based on the three-dimensional average grain size and unit volume. The calculation requires determining the volume of a single equivalent spherical grain, which is calculated from the three-dimensional average grain size using the sphere volume formula. The number of equivalent grains per unit volume is obtained by dividing the selected unit volume by the volume of a single equivalent spherical grain. In practical implementation, a unit volume of 1 cubic millimeter or 1 cubic centimeter is typically used as the calculation benchmark. The total grain boundary area per unit volume is obtained by multiplying the surface area of ​​a single equivalent grain by the number of equivalent grains per unit volume. This multiplication is a simple arithmetic operation. The calculated product physically represents the sum of the areas of all grain boundaries within a unit volume of material. Since each grain boundary is shared by two adjacent grains, a coefficient correction is needed for the product result in precise calculations; however, under a first-order approximation, this product can be directly used as the total grain boundary area per unit volume.

[0024] In some embodiments, the entire calculation process can be integrated into a concise mathematical expression. There is an inverse relationship between the total grain boundary area per unit volume and the three-dimensional average grain size. The specific relationship is: in: This represents the calculated total area of ​​grain boundaries per unit volume. This represents the three-dimensional average grain size measured using three-dimensional characterization techniques. This represents the selected unit volume. This formula is directly derived from the model of an equal-volume sphere.

[0025] Optionally, in practice, once the specific measured value of the three-dimensional average grain size is obtained, it can be directly substituted into the above formula for calculation. For example, for a low-carbon steel material, the three-dimensional average grain size is measured to be 30 micrometers by three-dimensional electron backscatter diffraction analysis. 30 micrometers can be converted to 30 × 10⁻⁶ micrometers. -6 meters, and take the unit volume For a volume of 1 cubic meter, substitute the values ​​into the formula to calculate the total grain boundary area per unit volume. The value is 200,000 square meters. This value can be compared with the grain boundary area results obtained directly from other theoretical models or microscopic images. It can be understood that calculating the total grain boundary area per unit volume using the aforementioned equal-volume sphere model is an effective macroscopic approximation method. The calculated total grain boundary area per unit volume is a key geometric parameter for subsequent estimation of the density of movable dislocations emitted from grain boundaries. The accuracy of the measurement of the three-dimensional average grain size directly determines the accuracy of the calculated total grain boundary area per unit volume.

[0026] In one embodiment of the present invention, see [reference] Figure 3The dislocation nucleation activation energy data of the target metallic material at different temperatures is retrieved from a materials thermodynamics database. Based on the dislocation nucleation activation energy data and preset temperature conditions, the nucleation probability of dislocation sources at grain boundaries under preset temperature conditions is calculated. This calculation process includes retrieving the dislocation nucleation activation energy baseline value corresponding to the grain boundary characteristics of the target metallic material from the materials thermodynamics database, reading the numerical value of the preset temperature conditions, converting the temperature value from degrees Celsius to thermodynamic temperature scale units, substituting the dislocation nucleation activation energy baseline value, the temperature value expressed in thermodynamic temperature scale units, and the Boltzmann constant into the exponential probability formula describing the thermal activation process, and calculating the probability value of a single potential dislocation source successfully overcoming the energy barrier and undergoing a nucleation event under preset temperature conditions through exponential operations and constant multiplication operations. The calculated probability value is defined as the nucleation probability of dislocation sources at grain boundaries under preset temperature conditions, and this nucleation probability is used to subsequently estimate the density of mobile dislocations emitted at grain boundaries. Multiplying the nucleation probability by the preset dislocation emission coefficient yields the temperature-corrected grain boundary dislocation emission efficiency. The density of movable dislocations emitted from grain boundaries is calculated by multiplying the total grain boundary area per unit volume, the temperature-corrected grain boundary dislocation emission efficiency, and the preset dislocation source density per unit area.

[0027] In practice, the calculation process begins by retrieving dislocation nucleation activation energy data for the target metallic material at different temperatures from a pre-established materials thermodynamics database. This database stores intrinsic energy parameters for various metallic material systems obtained through experimental measurements or first-principles calculations. For a ferritic steel material, the operation requires inputting the material grade and crystal structure information. The database then retrieves and calls a list of dislocation nucleation activation energy data corresponding to the grain boundary characteristics of the ferritic steel. This list may contain discrete values ​​or fitted parameters showing how the activation energy varies with temperature or grain boundary type. In some embodiments, the nucleation probability of dislocation sources at grain boundaries under preset temperature conditions is calculated based on the dislocation nucleation activation energy data and preset temperature conditions. The calculation process includes querying the materials thermodynamics database to obtain a baseline value of dislocation nucleation activation energy corresponding to the grain boundary characteristics of the target metallic material. The value of the preset temperature condition is read, and the temperature value is converted from Celsius to thermodynamic temperature scale units. The baseline value of the dislocation nucleation activation energy, the temperature value expressed in thermodynamic temperature scale units, and the Boltzmann constant are substituted into the exponential probability formula describing the thermal activation process. By employing exponential and constant multiplication operations, the probability of a single potential dislocation source successfully overcoming the energy barrier and undergoing nucleation under a preset temperature condition is calculated. This calculated probability is defined as the nucleation probability of a dislocation source at a grain boundary under the preset temperature condition, and is used to subsequently estimate the density of movable dislocations emitted from grain boundaries. For example, assuming a base value of 1.5 electron volts for dislocation nucleation activation energy at a preset temperature of -50°C for ferritic steel, after unit conversion and formula calculation, a value on the order of 10⁻⁶ electron volts might be obtained. -8The nucleation probability value.

[0028] In practice, the nucleation probability is multiplied by a preset dislocation emission coefficient to obtain the temperature-corrected grain boundary dislocation emission efficiency. The dislocation emission coefficient is a dimensionless coefficient related to the atomic structure of the material's grain boundaries and the state of impurity segregation; its value is between 0 and 1 and needs to be obtained through prior experiments or simulations. For the aforementioned ferritic steel, assuming its dislocation emission coefficient is calibrated to 0.1, then the nucleation probability of 10... -8 Multiplying by 0.1 yields a temperature-corrected grain boundary dislocation emission efficiency of 10. -9 In specific implementation, the grain boundary emitted mobile dislocation density is calculated by multiplying the total grain boundary area per unit volume, the temperature-corrected grain boundary dislocation emission efficiency, and the preset dislocation source density per unit area. The total grain boundary area per unit volume is calculated using the method described in Example 1. The preset dislocation source density per unit area can be set based on statistical observations of grain boundaries in similar materials using transmission electron microscopy, for example, set to 10 per square meter. 16 Each dislocation source. Multiplying these three values ​​yields an estimated value for the mobile dislocation density generated purely by the grain boundary thermally activated emission mechanism under the current preset temperature conditions.

[0029] In some embodiments, the above query and calculation process can be repeated for multiple discrete preset temperature conditions to observe the change in grain boundary emission movable dislocation density with temperature. For example, for the same ferritic steel, the grain boundary emission movable dislocation density can be calculated at -100°C, 0°C, and 100°C. At the low temperature of -100°C, due to the extremely low nucleation probability, the calculated grain boundary emission movable dislocation density may be negligible; as the temperature increases to 100°C, the nucleation probability increases exponentially, and the calculated grain boundary emission movable dislocation density may increase significantly by several orders of magnitude. This comparison of trends intuitively demonstrates the thermal activation effect of temperature on the emission capability of grain boundary dislocations.

[0030] Optionally, the nucleation probability can be calculated using a standardized thermal activation formula: in: This represents the calculated nucleation probability of dislocation sources at grain boundaries under a preset temperature condition. This represents a pre-exponential factor related to the frequency of attempts. This represents the baseline value of dislocation nucleation activation energy obtained from a materials thermodynamics database. Represents Boltzmann's constant. This represents the preset temperature condition expressed in a thermodynamic temperature scale. In specific calculations, the pre-exponential factor... The value is usually taken to be on the order of magnitude of the atomic vibration frequency.

[0031] It is understandable that querying the dislocation nucleation activation energy benchmark from a materials thermodynamics database is the fundamental input step for the entire calculation. The accuracy of the dislocation nucleation activation energy benchmark directly determines the reliability of the nucleation probability calculation result. It is also understandable that the cascade calculation method, which multiplies the nucleation probability by the dislocation emission coefficient, the dislocation source density per unit area, and the total grain boundary area, physically simulates the average number of dislocations that can successfully emit from grain boundaries and become mobile dislocations within a unit volume of material at a specific temperature. The entire calculation chain establishes a quantitative bridge from microscopic thermal activation parameters to macroscopic mobile dislocation density estimation.

[0032] In one embodiment of the present invention, a reference material dataset with the same crystal structure and chemical composition as the target metallic material is retrieved from a preset material knowledge base. Multiple sets of known ductile-brittle transition temperatures and corresponding microstructural parameters are extracted from the reference material dataset. Based on the extracted microstructural parameters, the critical mobile dislocation density corresponding to each set of data is fitted by inverse calculation. Statistical analysis is performed on all fitted critical mobile dislocation densities, and their mathematical average is taken as the minimum mobile dislocation density threshold required for the target metallic material to undergo the ductile-brittle transition. An implementation method for simulating and calculating the estimated total mobile dislocation density at different temperature test points within a preset temperature range using a thermal method involves uniformly selecting multiple discrete temperature test points within the preset temperature range. For each selected temperature test point, the dislocation nucleation activation energy corresponding to that temperature test point is recalculated, and the grain boundary dislocation emission efficiency is updated accordingly. Based on the updated grain boundary dislocation emission efficiency, the grain boundary emission mobile dislocation density corresponding to that temperature test point is calculated. The calculated grain boundary emission mobile dislocation density at each temperature test point is superimposed with the pre-stored mobile dislocation density to generate an estimated total mobile dislocation density for that temperature test point. All estimated total mobile dislocation densities for all temperature test points are collected to form an estimated data sequence of total mobile dislocation density varying with temperature.

[0033] In practice, the main operations are to obtain the minimum movable dislocation density threshold required for the target metallic material to undergo the ductile-brittle transition, and to use thermal methods to simulate and calculate the estimated total movable dislocation density at different temperature test points within a preset temperature range. In practice, the operation of obtaining the minimum movable dislocation density threshold begins by retrieving a reference material dataset with the same crystal structure and chemical composition as the target metallic material from a preset material knowledge base. For example, for a low alloy steel with a body-centered cubic structure, the filtering conditions in the material knowledge base need to be set as the crystal structure "body-centered cubic" and the composition range of the main alloying elements. The system then returns a reference material dataset containing a variety of similar low alloy steel grades. Each record in the reference material dataset contains the known ductile-brittle transition temperature and the corresponding microstructure parameters such as the three-dimensional average grain size and the pre-stored movable dislocation density.

[0034] In some embodiments, multiple sets of known ductile-brittle transition temperatures and corresponding microstructure parameters are extracted from a reference material dataset. The extracted data includes the ductile-brittle transition temperature of the reference material under specific heat treatment conditions determined by a standard Charpy impact test, and the three-dimensional average grain size and pre-stored mobile dislocation density values ​​for the corresponding conditions obtained through independent measurement or model calculation. Based on the extracted microstructure parameters, the critical mobile dislocation density corresponding to each set of data is fitted by inverse calculation. Inverse calculation means assuming that the estimated total mobile dislocation density of the reference material at its known ductile-brittle transition temperature is exactly equal to a critical value to be determined. The grain boundary emission mobile dislocation density is recalculated using the three-dimensional average grain size at this known temperature, and then combined with the pre-stored mobile dislocation density of the material. A critical mobile dislocation density is then derived by inversely using the formula for calculating the estimated total mobile dislocation density. Statistical analysis is performed on all fitted critical mobile dislocation densities, and their mathematical average is taken as the minimum mobile dislocation density threshold required for the target metallic material to undergo the ductile-brittle transition. For example, five critical mobile dislocation density values ​​are fitted from the reference material dataset, namely... , , , and Calculate the mathematical average of these five values ​​to obtain This value is set as the minimum movable dislocation density threshold for the current target low alloy steel.

[0035] In practical implementation, the operation involves using a thermal method to simulate and calculate the estimated total mobile dislocation density at different temperature test points within a preset temperature range. This involves uniformly selecting multiple discrete temperature test points within the preset temperature range. The preset temperature range can be set according to the expected operating temperature or transformation temperature range of the target material; for example, for low-alloy steel, a temperature range from -150°C to 150°C is set, and temperature test points are uniformly selected in 10°C increments. For each selected temperature test point, the dislocation nucleation activation energy corresponding to that temperature test point is recalculated, and the grain boundary dislocation emission efficiency is updated accordingly. Recalculation requires querying the material thermodynamics database or calling the activation energy-temperature relationship function again based on the specific values ​​of the temperature test points. Based on the updated grain boundary dislocation emission efficiency, the grain boundary emitted mobile dislocation density corresponding to the temperature test point is calculated. The grain boundary emission mobile dislocation density calculated at each temperature test point is superimposed with the pre-stored mobile dislocation density to generate an estimated total mobile dislocation density corresponding to the temperature test point. The estimated total mobile dislocation density corresponding to all temperature test points is collected to form an estimated data sequence of the total mobile dislocation density changing with temperature. The estimated data sequence is a list or array containing two columns of data: temperature and the corresponding estimated total mobile dislocation density.

[0036] Optionally, the mathematical average of the minimum movable dislocation density threshold can be expressed as: in: This represents the final determined minimum movable dislocation density threshold. This indicates the number of valid data sets extracted from the reference material dataset. This indicates the first number of cells fitted by reverse calculation. The critical mobile dislocation density corresponding to the set of data. Summation and division operations are used to obtain all... Execute after setting the value.

[0037] In some embodiments, calculations are performed on low-alloy steel in 10°C increments within the range of -150°C to 150°C, resulting in 31 temperature test points and their corresponding estimated total mobile dislocation densities. The estimated data from these 31 temperature test points are then compared with the minimum mobile dislocation density threshold obtained from a reference material dataset. By comparing the data side-by-side, it can be observed that in the low-temperature range, such as below -100°C, most of the estimated total mobile dislocation densities are lower than this threshold, while in the high-temperature range, such as above 50°C, the estimated total mobile dislocation densities are generally higher than this threshold. This data comparison intuitively shows the temperature range in which the estimated total mobile dislocation density crosses the critical threshold.

[0038] It is understandable that backfitting the critical mobile dislocation density from a reference material dataset is a method of calibrating key parameters using existing knowledge. The representativeness and data quality of the reference material dataset directly affect the accuracy of the minimum mobile dislocation density threshold calibration. It is also understandable that generating a total mobile dislocation density estimation data sequence within a preset temperature range is a core step in simulating the continuous change of material properties with temperature. The estimated data sequence provides a complete input data foundation for subsequent determination of the critical temperature point through comparison.

[0039] See Figure 4 This is a graph showing the relationship between the total mobile dislocation density of a metallic material and temperature, belonging to the category of material mechanical property analysis charts. The green dashed line represents the "minimum threshold: 5.85," indicating the minimum mobile dislocation density required for the metal to undergo the ductile-brittle transition. The intersection of the curve and the threshold line corresponds to a temperature of approximately 50°C, which is the ductile-brittle transition temperature of the material. This graph is a key visualization result in the detection of the ductile-brittle transition temperature of metallic materials, used to intuitively determine the critical temperature at which a material transitions from brittle to ductile. It is commonly used for low-alloy steel, body-centered cubic metals, and other materials for low-temperature performance evaluation, providing a basis for material selection in engineering applications such as cryogenic equipment and pressure vessels.

[0040] In one embodiment of the present invention, the estimated data sequence of total mobile dislocation density changing with temperature is plotted as a curve showing the relationship between the estimated total mobile dislocation density and temperature. A horizontal threshold line representing the minimum mobile dislocation density threshold is drawn in the coordinate system containing the curve. The coordinates of the intersection point between the curve and the horizontal threshold line are calculated. The corresponding temperature values ​​in the intersection coordinates are extracted to obtain candidate critical temperature points. An operation for determining critical temperature test points and classifying them as the ductile-brittle transition temperature involves filtering all temperature test points on the curve whose estimated total mobile dislocation density is greater than or equal to the minimum mobile dislocation density threshold, forming a temperature range above the threshold. The lowest temperature test point in the temperature range above the threshold is determined and identified as the critical temperature test point. The temperature value corresponding to the critical temperature test point is used as the final determination result for the ductile-brittle transition temperature of the target metallic material.

[0041] In practice, the operation of comparing the estimated total movable dislocation density with the minimum movable dislocation density threshold for each temperature test point, and the operation of determining the critical temperature test point and identifying the critical temperature test point as the ductile-brittle transition temperature, in practice, the operation of comparing the estimated total movable dislocation density with the minimum movable dislocation density threshold for each temperature test point begins by plotting the estimated data sequence of the total movable dislocation density changing with temperature into a curve showing the relationship between the estimated total movable dislocation density and temperature. For example, for a sequence containing 31 temperature test points from -150°C to 150°C in 10°C steps and their corresponding estimated total movable dislocation density, using plotting software or program library, with temperature as the horizontal axis and the estimated total movable dislocation density as the vertical axis, the 31 data points are connected to form a smooth or broken line curve.

[0042] In some embodiments, a horizontal threshold line representing the minimum movable dislocation density threshold is drawn in the coordinate system containing the relationship curve, for example, a value of The threshold, in a coordinate system where the vertical axis represents the estimated total mobile dislocation density, is determined from the vertical axis value. Draw a horizontal straight line parallel to the x-axis at the specified position. This horizontal line is the horizontal threshold line. The relationship curve and the horizontal threshold line may intersect at one or more points, or they may not intersect at all. Calculate the coordinates of the intersection point between the relationship curve and the horizontal threshold line. When the relationship curve is a broken line connecting discrete data points, the calculation of the intersection point coordinates is completed by linear interpolation between two adjacent data points. Find the temperature range where one of the estimated total mobile dislocation densities of two adjacent points is less than the threshold, and the other is greater than the threshold. Within this range, calculate the precise intersection temperature using the linear interpolation formula, extract the corresponding temperature value from the intersection point coordinates, and obtain the candidate critical temperature point. The candidate critical temperature point may be a specific temperature value, such as -22.5°C.

[0043] In practice, determining the critical temperature test point and classifying it as the ductile-brittle transition temperature involves screening the relationship curve for all temperature test points whose estimated total mobile dislocation density is greater than or equal to the minimum mobile dislocation density threshold. This forms a temperature range above the threshold, which can be a set of multiple continuous or discontinuous temperature test points. The lowest temperature test point within this range is then identified as the critical temperature test point. For example, within this range, test points might include -20°C, -10°C, 0°C, and 10°C. The lowest temperature test point is -20°C, which is then designated as the critical temperature test point. The temperature value corresponding to the critical temperature test point is used as the final determination of the ductile-brittle transition temperature of the target metallic material. In the example above, the final determination of the ductile-brittle transition temperature for low-alloy steel is -20°C.

[0044] In some embodiments, a data table can be created to visually display the correspondence between partial temperature test points and the estimated total mobile dislocation density, as well as their comparison with the minimum mobile dislocation density threshold. See Table 1.

[0045] Table 1: Comparison of Estimated Total Movable Dislocation Density with Threshold Values ​​at Selected Temperature Test Points in Low Alloy Steel Optionally, when the relationship curve is composed of discrete data points, the linear interpolation process for calculating the coordinates of the intersection points can be expressed by the following formula: in: This represents the calculated temperature value of the candidate critical temperature point. This indicates the temperature at which the estimated total mobile dislocation density is lower than a threshold between two adjacent temperature test points. This indicates the temperature at which the estimated total mobile dislocation density of two adjacent temperature test points exceeds a threshold. This represents the minimum movable dislocation density threshold. Indicates temperature The corresponding estimated total mobile dislocation density, Indicates temperature The corresponding estimated total movable dislocation density. The formula calculation relies on two adjacent data points that cross a threshold.

[0046] It is understandable that plotting the relationship curve and the horizontal threshold line is a fundamental step in achieving data visualization and comparison. Graphical representation helps to intuitively determine the trend of the estimated total mobile dislocation density changing with temperature and its relative position to the threshold line. It is also understandable that by filtering the temperature range above the threshold and selecting the lowest temperature point as the critical temperature test point, this operation physically corresponds to determining the lower limit of the critical temperature at which a material transitions from a brittle to a ductile state. The temperature value corresponding to the critical temperature test point is the ductile-brittle transition temperature calculated based on thermal methods and microstructural parameters.

[0047] In one embodiment of the present invention, raw mechanical property data of the target metallic material are collected. This raw mechanical property data includes stress-strain curves obtained through uniaxial tensile testing and hardness distribution data obtained through microhardness testing. The collected stress-strain curves are analyzed to extract the work hardening rate during the plastic deformation stage. Spatial statistical analysis is performed on the hardness distribution data to calculate the average hardness and its variance. A first mathematical relationship is established based on the physical relationship between the work hardening rate and the movable dislocation density. Simultaneously, a second mathematical relationship is established based on the relationship between the statistical characteristics of hardness and the contribution of dislocation strengthening. The first and second mathematical relationships are combined to construct a system of two equations concerning the pre-existing movable dislocation density. The constructed system of two equations is solved using a numerical iteration method. The solved value of the pre-existing movable dislocation density is compared and verified with the known theoretical range of dislocation density for the metallic material. When the solved value of the pre-existing movable dislocation density is within the theoretical range, the constructed dislocation analysis model is confirmed to be effective.

[0048] In practice, the dislocation analysis model construction step for calculating the pre-stored movable dislocation density begins with collecting the original mechanical property data of the target metallic material. The original mechanical property data includes the stress-strain curve obtained by uniaxial tensile testing and the hardness distribution data obtained by microhardness testing. For example, for a ferritic steel material, the uniaxial tensile test is carried out on a standard mechanical testing machine to obtain the engineering stress-engineering strain curve, and the microhardness test is obtained by indenting multiple regular grid points on the material surface using a Vickers hardness tester to form a set of hardness distribution data.

[0049] In some embodiments, the collected stress-strain curves are analyzed to extract the work hardening rate during the plastic deformation stage. Spatial statistical analysis is performed on the hardness distribution data to calculate the average hardness and its variance. When analyzing the stress-strain curves, the engineering stress-strain needs to be converted into true stress-strain, and the slope is calculated during the uniform plastic deformation stage to obtain the work hardening rate. Spatial statistical analysis of the hardness distribution data involves calculating the arithmetic mean of hardness at all measurement points and the average of the squares of the differences between the hardness values ​​at each point and the average, i.e., the variance. A first mathematical relationship is established based on the physical relationship between the work hardening rate and the movable dislocation density. The relationship between the work hardening rate and the movable dislocation density is usually described by the Taylor hardening relation, which can be expressed as the work hardening rate being proportional to the product of the material shear modulus and the Burgers vector, multiplied by the square root of the movable dislocation density. Simultaneously, a second mathematical relationship is established based on the relationship between the statistical characteristics of hardness and the contribution of dislocation strengthening. The average hardness and the variance of the hardness together reflect the contribution of dislocation strengthening to the material strength. Their relationship can be correlated with the hardness value and the dislocation density through empirical formulas or micromechanical models.

[0050] In practical implementation, a system of two equations is constructed by simultaneously establishing the first and second mathematical relations regarding the pre-existing movable dislocation density. This system contains two equations sharing the same unknown, the pre-existing movable dislocation density. However, measured values ​​of work hardening rate obtained analytically from stress-strain curves and measured values ​​of the average and variance of hardness obtained statistically from hardness distribution data are introduced as known parameters. The constructed system of two equations is solved using a numerical iteration method. The obtained value of the pre-existing movable dislocation density is then compared and verified with the known theoretical range of dislocation density for metallic materials. The numerical iteration method can employ the Newton-Raphson method or a simple bisection method to search for solutions within a reasonable initial value range. The known theoretical range of dislocation density for annealed metals is typically within a certain range. arrive The magnitude is so high that for deformable metals it can reach [amount missing]. That concludes the above. When the calculated pre-stored mobile dislocation density value is within the theoretical range, the constructed dislocation analysis model is confirmed to be effective.

[0051] Optionally, the second mathematical relationship established based on the relationship between hardness statistical characteristics and dislocation strengthening contribution can be specifically expressed in one form: in: This represents the increment in yield strength obtained from the average hardness value; this increment is primarily attributed to dislocation strengthening. Represents the Taylor factor. This represents a proportionality constant related to the material. Indicates the shear modulus of a material. Represents the magnitude of the Burgers vector. This represents the pre-existing density of movable dislocations to be determined. The average hardness is obtained through empirical relationships or calibration curves with the yield strength increment. Related.

[0052] In some embodiments, taking a specific ferritic steel as an example, the work hardening rate is analytically obtained from the uniaxial tensile curve. The average hardness was calculated from the hardness distribution data as follows: and variance Substituting the work hardening rate and hardness statistics into the two established relationships, and solving the system of two equations through numerical iteration, a possible solution can be obtained. This solution is compared with the range of dislocation density theories commonly found in ferritic steels (e.g., arrive For comparison, because The values ​​fall within this range, therefore it can be confirmed that the dislocation analysis model constructed in this study is effective for this ferritic steel. If the calculated values ​​significantly exceed the theoretical range, for example, below... or higher Then it is necessary to check the quality of the original data or the applicability of the established relation.

[0053] It is understandable that collecting stress-strain curves and hardness distribution data is the basis for inferring microstructural parameters from macroscopic mechanical test results. These two types of data reflect the material's resistance to plastic deformation and local strength uniformity, respectively, both of which are physically related to dislocation density. It is also understandable that establishing and solving a system of two equations concerning the pre-existing movable dislocation density is the core computational process of the dislocation analysis model, and the model's validity needs to be verified by comparing the solution with known theoretical ranges.

[0054] See Figure 5 This is a heatmap showing the effect of temperature on the performance of different metal materials, illustrating the "performance retention rate" of various materials at different temperatures. This graph serves as a reference tool for engineering material selection, allowing for the rapid matching of stable materials based on the operating temperature scenario (e.g., cryogenic containers, ambient temperature machinery, high-temperature components). It also reflects the "temperature-sensitive characteristics" of different materials; for example, ferritic steel is "low-temperature adaptable," while copper alloys are "medium- and high-temperature adaptable." It visually presents the "temperature-sensitive range" of various materials: for example, martensitic steel maintains high performance only at room temperature (0-50℃), with performance plummeting outside this range; copper alloys, on the other hand, exhibit greater stability in the high-temperature range (100-150℃). Adding test data of newly developed materials to the heatmap allows for rapid comparison of their temperature adaptability with traditional materials.

[0055] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0056] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting the ductile-brittle transition temperature of metallic materials based on thermal methods, characterized in that, The method includes: Obtain a set of microstructure parameters for the target metallic material, including the three-dimensional average grain size measured by three-dimensional characterization technology and the pre-stored mobile dislocation density calculated by a preset dislocation analysis model. The total area of ​​grain boundaries per unit volume is calculated based on the three-dimensional average grain size. Combined with the preset dislocation emission coefficient, the density of movable dislocations emitted by grain boundaries under preset temperature conditions is estimated. The estimated total mobile dislocation density of the target metal material under the preset temperature condition is calculated by linearly superimposing the grain boundary emitted mobile dislocation density with the pre-stored mobile dislocation density. Obtain the minimum mobile dislocation density threshold required for the target metallic material to undergo ductile-brittle transition from a preset material knowledge base; Within a preset temperature range, the total mobile dislocation density is estimated at different temperature test points using a thermal method. The total mobile dislocation density estimate corresponding to each temperature test point is then compared with the minimum mobile dislocation density threshold. A critical temperature test point is determined that satisfies the condition that the estimated total mobile dislocation density is greater than or equal to the minimum mobile dislocation density threshold, and the temperature corresponding to the critical temperature test point is determined as the ductile-brittle transition temperature of the target metallic material.

2. The method for detecting the ductile-brittle transition temperature of metallic materials based on a thermal method according to claim 1, characterized in that, The calculation of the total grain boundary area per unit volume based on the three-dimensional average grain size includes: Construct an equal-volume sphere geometric model to characterize polycrystalline metallic materials based on the three-dimensional average grain size; Calculate the surface area of ​​a single equivalent grain based on the geometric model of the equal-volume sphere; Based on the three-dimensional average grain size and unit volume, calculate the equivalent number of grains contained in the unit volume; Multiply the surface area of ​​the single equivalent grain by the number of equivalent grains contained in the unit volume to obtain the total grain boundary area in the unit volume.

3. The method for detecting the ductile-brittle transition temperature of metallic materials based on a thermal method according to claim 1, characterized in that, The step of estimating the density of movable dislocations emitted from grain boundaries under preset temperature conditions, in conjunction with a preset dislocation emission coefficient, includes: Data on dislocation nucleation activation energies of the target metallic material at different temperatures were retrieved from a materials thermodynamics database. Based on the dislocation nucleation activation energy data and the preset temperature conditions, the nucleation probability of dislocation sources at grain boundaries under the preset temperature conditions is calculated. Multiplying the nucleation probability by the preset dislocation emission coefficient yields the temperature-corrected grain boundary dislocation emission efficiency. The grain boundary emitted mobile dislocation density is calculated by multiplying the total grain boundary area per unit volume, the temperature-corrected grain boundary dislocation emission efficiency, and the preset dislocation source density per unit area.

4. The method for detecting the ductile-brittle transition temperature of metallic materials based on a thermal method according to claim 1, characterized in that, The step of obtaining the minimum mobile dislocation density threshold required for the target metallic material to undergo the ductile-brittle transition from a preset material knowledge base includes: Retrieve a dataset of reference materials with the same crystal structure and chemical composition as the target metallic material from the preset material knowledge base; Multiple sets of known ductile-brittle transition temperatures and corresponding microstructure parameters were extracted from the reference material dataset. Based on the extracted microstructure parameters, the critical mobile dislocation density corresponding to each set of data is fitted by inverse calculation. Statistical analysis was performed on all the critical mobile dislocation densities obtained from the fitting, and their mathematical average value was taken as the minimum mobile dislocation density threshold required for the target metallic material to undergo ductile-brittle transition.

5. The method for detecting the ductile-brittle transition temperature of metallic materials based on a thermal method according to claim 1, characterized in that, The step of using a thermal method to simulate and calculate the estimated total mobile dislocation density at different temperature test points within a preset temperature range includes: Multiple discrete temperature test points are uniformly selected within the preset temperature range; For each selected temperature test point, the dislocation nucleation activation energy corresponding to the temperature test point is recalculated, and the grain boundary dislocation emission efficiency is updated accordingly. Based on the updated grain boundary dislocation emission efficiency, the density of the grain boundary emitted mobile dislocations corresponding to the temperature test point is calculated. The grain boundary emission mobile dislocation density calculated at each temperature test point is superimposed with the pre-stored mobile dislocation density to generate the estimated total mobile dislocation density corresponding to the temperature test point. Collect the estimated total mobile dislocation density corresponding to all temperature test points to form an estimated data sequence of total mobile dislocation density as a function of temperature.

6. The method for detecting the ductile-brittle transition temperature of metallic materials based on a thermal method according to claim 5, characterized in that, The step of comparing the estimated total mobile dislocation density for each temperature test point with the minimum mobile dislocation density threshold includes: The estimated data sequence of the total mobile dislocation density as a function of temperature was plotted as a curve showing the relationship between the estimated total mobile dislocation density and temperature. In the coordinate system where the relationship curve is located, draw a horizontal threshold line representing the minimum movable dislocation density threshold. Calculate the coordinates of the intersection point between the relationship curve and the horizontal threshold line; Extract the temperature values ​​corresponding to the intersection coordinates to obtain candidate critical temperature points.

7. The method for detecting the ductile-brittle transition temperature of metallic materials based on a thermal method according to claim 6, characterized in that, The step of determining the critical temperature test point that satisfies the condition that the estimated total mobile dislocation density is greater than or equal to the minimum mobile dislocation density threshold, and determining the temperature corresponding to the critical temperature test point as the ductile-brittle transition temperature of the target metallic material, includes: On the relationship curve, all temperature test points whose total mobile dislocation density estimate is greater than or equal to the minimum mobile dislocation density threshold are selected to form a temperature range above the threshold. The lowest temperature test point in the above-threshold temperature range is determined, and the temperature test point is determined as the critical temperature test point; The temperature value corresponding to the critical temperature test point is taken as the final determination result of the ductile-brittle transition temperature of the target metallic material.

8. The method for detecting the ductile-brittle transition temperature of metallic materials based on a thermal method according to claim 2, characterized in that, The calculation of the surface area of ​​a single equivalent grain based on the geometric model of the equal-volume sphere includes: The shape of the equivalent grain is set as an ideal sphere, and the three-dimensional average grain size is used as the diameter of the ideal sphere; By applying the formula for calculating the surface area of ​​a sphere, the value of the three-dimensional average grain size is substituted into the diameter variable in the formula; Perform multiplication and squaring operations to calculate the surface area value of the ideal sphere. This surface area value is the calculated surface area value of a single equivalent grain. Record the calculated surface area of ​​a single equivalent grain and use it as one of the input parameters for calculating the total grain boundary area per unit volume.

9. The method for detecting the ductile-brittle transition temperature of metallic materials based on a thermal method according to claim 3, characterized in that, The calculation of the nucleation probability of dislocation sources at grain boundaries under the preset temperature condition, based on the dislocation nucleation activation energy data and the preset temperature condition, includes: Query the materials thermodynamics database to obtain the reference value of dislocation nucleation activation energy corresponding to the grain boundary characteristics of the target metallic material; Read the value of the preset temperature condition and convert the temperature value from the Celsius unit to the thermodynamic temperature scale unit; Substitute the reference value of dislocation nucleation activation energy, the temperature value expressed in thermodynamic temperature scale units, and the Boltzmann constant into the exponential probability formula describing the thermal activation process. By using exponential and constant multiplication operations, the probability of a single potential dislocation source successfully overcoming the energy barrier and undergoing a nucleation event under preset temperature conditions is calculated. The calculated probability value is defined as the nucleation probability of dislocation sources at grain boundaries under a preset temperature condition, and this nucleation probability is used to estimate the density of movable dislocations emitted from grain boundaries.

10. The method for detecting the ductile-brittle transition temperature of metallic materials based on a thermal method according to claim 1, characterized in that, The steps for constructing the dislocation analysis model include: Collect raw mechanical property data of the target metallic material, including stress-strain curves obtained by uniaxial tensile tests and hardness distribution data obtained by microhardness tests. The collected stress-strain curves were analyzed to extract the work hardening rate during the plastic deformation stage. Spatial statistical analysis was performed on the hardness distribution data to calculate the average hardness and the variance of the fluctuation. A first mathematical relationship is established based on the physical relationship between work hardening rate and movable dislocation density, and a second mathematical relationship is established based on the relationship between hardness statistical characteristics and dislocation strengthening contribution. By combining the first and second mathematical relations, a system of two equations in two variables is constructed regarding the pre-existing density of movable dislocations; The system of two equations is solved by numerical iteration, and the obtained pre-stored mobile dislocation density is compared and verified with the known theoretical range of dislocation density for metallic materials. When the obtained pre-stored mobile dislocation density value is within the theoretical range, the constructed dislocation analysis model is confirmed to be effective.

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