Method and system for predicting non-uniform distribution of microstructure and mechanical properties in metal heat treatment

Abstract

The application provides a kind of metal heat treatment microstructure and mechanical property non-uniform distribution prediction method and system, comprising: step 1: obtaining the internal temperature distribution and evolution history of metal parts during heat treatment;Step 2: based on sample heat treatment experiment to obtain the change data of metal organization performance with temperature;Step 3: using polynomial and power function fitting organization performance evolution with temperature and temperature gradient;Step 4: extract temperature information and geometric coordinates at different positions, use polynomial and power function to predict the corresponding organization performance, and visualize the spatial distribution of organization performance.The application starts from the temperature field which is easier to predict or measure, combines the correlation between temperature field, temperature gradient field and organization performance, solves the problem of direct calculation or measurement of organization performance distribution, and realizes the visualization of its spatial distribution, which has strong engineering practical significance for optimizing and improving heat treatment process and product performance.

Description

Technical Field

[0001] This invention relates to the field of metal heat treatment and the prediction of the distribution of microstructure and mechanical properties. Specifically, it relates to a method and system for predicting the non-uniform distribution of microstructure and mechanical properties in metal heat treatment. Background Technology

[0002] Heat treatment is an important process in the processing of metallic materials. By controlling processes such as heating, holding, and cooling, the internal crystal structure, grain size, and microstructure of the material are altered, thereby adjusting its mechanical properties, corrosion resistance, and machinability. This process is crucial for improving the performance and extending the service life of metallic materials, and is an important processing step for key components in fields such as aerospace, automotive manufacturing, and building structures.

[0003] Due to factors such as material properties, complex structures, or geometric dimensions, significant temperature differences exist within or on the surface of metal parts during actual heat treatment. This temperature unevenness leads to different heat treatment histories within the parts. The uneven microstructure distribution caused by the temperature field severely affects the mechanical properties and service life of the material, resulting in premature cracking and fracture failure. Because sampling and measuring the temperature field distribution within large-sized or complex-structured parts is difficult, and effective experimental methods are lacking, static single-point detection is usually the only option, making it difficult to dynamically and comprehensively monitor the internal temperature and microstructure properties of the material. For example, heat treatment experiments on large ring forgings are challenging; the deep oxide scale on the surface hinders accurate temperature measurement, making it difficult to optimize the cooling process using experimental methods alone. Furthermore, significant temperature history differences may exist between the surface and core of large ring forgings during heat treatment, leading to significant differences in properties such as fracture toughness. Meanwhile, the rapid development of finite element methods (FEM) that accommodate convection and radiation effects provides an effective tool for predicting the temperature evolution of parts during heat treatment. Based on finite element method (FEM) simulation of heat treatment and experimental heat treatment of material samples, this study aims to predict the microstructure and properties of parts under non-uniform temperature histories. This is of great significance for guiding the optimization of heat treatment processes, improving economic efficiency in production, and ensuring product quality. According to the survey results, there is currently a lack of predictive methods that can consider the evolution of microstructure and properties under non-uniform temperature fields during heat treatment. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the purpose of this invention is to provide a method and system for predicting the non-uniform distribution of microstructure and mechanical properties in metal heat treatment.

[0005] The method for predicting the non-uniform distribution of microstructure and mechanical properties in metal heat treatment according to the present invention includes: Step 1: Obtain the internal temperature distribution and evolution history of the metal parts during the heat treatment process; Step 2: Obtain data on the changes in metal microstructure and properties with temperature based on sample heat treatment experiments; Step 3: Fit the evolution of tissue properties with temperature and temperature gradient using polynomial and power functions; Step 4: Extract temperature information and geometric coordinates at different locations, use polynomials and power functions to predict the corresponding tissue properties, and visualize the spatial distribution of tissue properties.

[0006] Preferably, in step 1, the temperature distribution includes the temperature field and temperature gradient field during the heat treatment process, which are obtained through experimental measurement or finite element simulation.

[0007] Preferably, in step 2, the representative characteristics of the selected microstructure properties include yield strength and grain size; Experimental measurements for each type of feature include at least three different rates of temperature change; each rate of temperature change covers no fewer than eight temperature points within the measurement range to reflect the evolutionary pattern; the measurement error for each type of feature is less than 10%. The boundary conditions of the heat treatment experiment of the sample cover the temperature evolution range during the heat treatment of metal parts.

[0008] Preferably, in step 3, based on the dominant role of temperature in heat treatment, the evolution of the microstructure and properties of the metal is simplified as a function of temperature and temperature gradient, thereby establishing the relationship between the evolution of microstructure and properties and the heat treatment temperature; wherein, a polynomial function is used to fit the relationship between microstructure and properties and temperature, a power function is used to fit the relationship between microstructure and properties and temperature gradient, and the combined influence of temperature and temperature gradient on microstructure and properties is considered in a multiplicative form.

[0009] Preferably, in step 4, the metal parts are discretized into nodes, the temperature, temperature gradient and geometric coordinates of each node are derived, and the microstructure parameters on the nodes are predicted according to the polynomial function and power function fitted in step 3. Then, the microstructure distribution is plotted according to the node geometric coordinates. The microstructure and mechanical properties of all node locations at a specified time are displayed in the form of cloud maps, and the corresponding values ​​can be exported.

[0010] The prediction system for non-uniform distribution of microstructure and mechanical properties in metal heat treatment provided by the present invention includes: Module M1: Acquires the internal temperature distribution and evolution history of metal parts during heat treatment; Module M2: Obtains data on the change of metal microstructure properties with temperature based on sample heat treatment experiments; Module M3: Employs polynomials and power functions to fit the evolution of tissue properties with temperature and temperature gradient; Module M4: Extracts temperature information and geometric coordinates at different locations, uses polynomials and power functions to predict the corresponding tissue properties, and visualizes the spatial distribution of tissue properties.

[0011] Preferably, in module M1, the temperature distribution includes the temperature field and temperature gradient field during the heat treatment process, which are obtained through experimental measurement or finite element simulation.

[0012] Preferably, in module M2, the representative characteristics of the selected microstructure properties include yield strength and grain size; Experimental measurements for each type of feature include at least three different rates of temperature change; each rate of temperature change covers no fewer than eight temperature points within the measurement range to reflect the evolutionary pattern; the measurement error for each type of feature is less than 10%. The boundary conditions of the heat treatment experiment of the sample cover the temperature evolution range during the heat treatment of metal parts.

[0013] Preferably, in module M3, based on the dominant role of temperature in heat treatment, the evolution of the microstructure and properties of the metal is simplified as a function of temperature and temperature gradient, thereby establishing the relationship between the evolution of microstructure and properties and the heat treatment temperature; wherein, a polynomial function is used to fit the relationship between microstructure and properties and temperature, a power function is used to fit the relationship between microstructure and properties and temperature gradient, and the combined influence of temperature and temperature gradient on microstructure and properties is considered in a multiplicative form.

[0014] Preferably, in module M4, the metal parts are discretized into nodes, the temperature, temperature gradient and geometric coordinates of each node are derived, and the microstructure parameters on the nodes are predicted according to the polynomial function and power function fitted in module M3. Then, the microstructure distribution is plotted according to the node geometric coordinates. The microstructure and mechanical properties of all node locations at a specified time are displayed in the form of cloud maps, and the corresponding values ​​can be exported.

[0015] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention designs a clear and explicit prediction process for the microstructure properties after heat treatment. Starting from the temperature field, which is relatively easy to predict or measure, and combining the correlation between the temperature field, temperature gradient field and microstructure properties, it solves the problem of difficulty in directly calculating or measuring the distribution of microstructure properties and realizes the visualization of its spatial distribution. It has strong practical engineering significance for optimizing and improving heat treatment processes and product performance. (2) This invention uses polynomials and power functions to fit the evolution relationship of microstructure properties with temperature and temperature gradient. This combination of functions accurately reflects the main characteristics and laws of microstructure properties changing with temperature during heat treatment, and provides a convenient and feasible dynamic prediction method for the distribution evolution of microstructure properties. (3) The present invention is highly operable and easy to implement. It is applicable to the prediction of the distribution of microstructure properties of various materials and is not limited to metals or non-metals. Attached Figure Description

[0016] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the ring component's geometry; Figure 2 The temperature field of a certain cross section inside the ring obtained from heat treatment simulation; Figure 3a and Figure 3b The temperature gradient field of a certain cross section inside the ring obtained from heat treatment simulation; Figure 4a and Figure 4b The figures show the evolution curves of grain size and yield strength as a function of temperature at different cooling rates, as measured experimentally. Figure 5a and Figure 5b The distribution of grain size and yield strength in the cross section, as predicted by polynomial and power functions, respectively; Figure 6 This is a flowchart of a method for predicting the non-uniform distribution of microstructure and mechanical properties in metal heat treatment. Detailed Implementation

[0017] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0018] Example 1 like Figure 6 This invention provides a method for predicting the non-uniform distribution of microstructure and mechanical properties in metal heat treatment, comprising the following steps: Step 1: Obtaining the internal temperature distribution and evolution of metal parts during heat treatment; a schematic diagram of the ring geometry is shown below. Figure 1 The distribution of temperature and temperature gradient (temperature change rate) during the heat treatment process is obtained based on finite element simulation, such as... Figure 2 And Figure 3. Models are established for the heat-treated components and the cooling, heating, or insulation systems. The heat dissipation and heat transfer characteristics of the components are analyzed, appropriate mathematical physics equations are selected, and then the temperature field and temperature gradient field during heat treatment are obtained through finite element analysis. Furthermore, the evolution and distribution of temperature can also be obtained through experimental measurements.

[0019] Step Two: Obtain the relationship between metal microstructure and properties and temperature based on sample heat treatment experiments. Standard-sized metal samples are prepared and placed in a thermal analysis instrument or similar device for heat treatment. The temperature change rate is controlled to obtain the evolution curves of the metal's microstructure and mechanical properties as a function of temperature, as shown in Figure 4. In the heat treatment experiments, the microstructure and properties are measured under multiple temperature conditions with different temperature change rates.

[0020] Step 3: Fit the evolution of microstructure and mechanical properties with temperature and temperature gradient using polynomials and power functions. Given the dominant role of the temperature field in the evolution of microstructure and properties during heat treatment, a fitting function is used to establish the microstructure and mechanical properties with respect to temperature and temperature gradient. Specifically, a power function is employed (…). , Constant exponent) fits the relationship between tissue properties and temperature gradient, polynomial function ( , n Let the degree be a polynomial. The relationship (where the coefficients are polynomials) is used to fit the relationship between tissue properties and temperature.

[0021] Step 4: Extract temperature information and geometric coordinates at different locations, and use the fitted polynomial and power functions to predict and display the spatial distribution of microstructure properties. Discretize the predicted metal parts into nodes and their corresponding elements. Extract the temperature, temperature gradient, and geometric coordinates of each node at a specific time, and calculate the microstructure property parameters at the nodes using polynomial and power functions. Visualize the spatial distribution of microstructure properties based on the node geometric coordinates, and draw a cloud map showing the distribution of microstructure and mechanical properties after heat treatment.

[0022] Example 2 Example 2 is a preferred example of Example 1.

[0023] This embodiment uses the air-cooled normalizing process of a ring as an example to predict the non-uniform distribution of microstructure and properties caused by the temperature field during heat treatment. This example is achieved through the following steps: Step 1: Finite element simulation of the temperature distribution and evolution inside the ring during heat treatment. A three-dimensional physical model of the ring and the air-cooling system is constructed. Then, based on the heat dissipation characteristics, the heat conduction differential equation for heat dissipation of the ring is determined. Since this simulation involves fluid-structure interaction analysis, the heat transfer control equations for the fluid are introduced, namely the mass conservation equation, momentum conservation equation, and energy conservation equation. The model is meshed, with refinement of the mesh near the heat transfer interface and near the fluid inlet. After determining the boundary conditions, iterative calculation of the differential equations on the discretized model yields the temperature and temperature gradient distribution of the ring.

[0024] Step 2: Measure the microstructure and properties of the ring material as a function of temperature through sample heat treatment experiments. and temperature gradient The evolution of [the material] was investigated. The ring material used in the experiment was S355NL alloy steel, and cylindrical samples measuring 2.8 mm × 2 mm were prepared for thermal analysis. Grain sizes were selected accordingly. and yield strength As descriptive features of microstructure and mechanical properties, the grain size and yield strength were measured at eight temperature points under three different cooling rates to obtain the evolution relationship.

[0025] Step 3: Fit the evolution of microstructure properties with temperature and temperature gradient using polynomials and power functions. This invention simplifies the interaction between the temperature field and the microstructure field during heat treatment. Considering the dominant role of temperature, the microstructure properties of the ring material are treated as functions of temperature and temperature gradient. Specifically, a fourth-order polynomial function is used to consider the evolution of microstructure properties with temperature, a power function is used to consider its evolution with temperature gradient, and the interaction between the two is considered through multiplication. The specific definitions are as follows:

[0026]

[0027] In the formula, , , and These are the coefficients of a polynomial or power function fitted based on experimental data; The temperature gradient is used as a reference temperature gradient, with the temperature gradient variable in the regularization formula as the reference temperature gradient. Step 4: Extract temperature information and geometric coordinates from various locations on the ring model, and use polynomials and power functions to predict and display the spatial distribution of microstructure properties. Discretize the ring model into a certain number of nodes and record the corresponding elements. Extract the temperature values, cooling rates, and spatial coordinates of each node at a certain moment during the heat treatment process, and calculate the corresponding yield strength and grain size using the polynomials and power functions obtained in Step 3. Visualize the calculation results according to the geometric coordinates of the nodes, and draw the distribution cloud map of yield strength and grain size in the cross section, as shown in Figure 5. Interpolation smoothing is performed based on the color and geometric coordinates of each node to enhance the visual effect and guide the optimization of the heat treatment process.

[0028] Example 3 This invention provides a prediction system for the non-uniform distribution of microstructure and mechanical properties in metal heat treatment, comprising: module M1: acquiring the internal temperature distribution and evolution history of metal parts during heat treatment; module M2: acquiring data on the change of metal microstructure and properties with temperature based on sample heat treatment experiments; module M3: fitting the evolution of microstructure and properties with temperature and temperature gradient using polynomials and power functions; and module M4: extracting temperature information and geometric coordinates at different locations, predicting the corresponding microstructure and properties using polynomials and power functions, and visualizing the spatial distribution of microstructure and properties.

[0029] In module M1, the temperature distribution includes the temperature field and temperature gradient field during the heat treatment process, which are obtained through experimental measurement or finite element simulation.

[0030] In module M2, the representative characteristics of the selected microstructure properties include yield strength and grain size. Experimental measurements for each type of feature include at least three different rates of temperature change; each rate of temperature change covers no fewer than eight temperature points within the measurement range to reflect the evolutionary pattern; the measurement error for each type of feature is less than 10%. The boundary conditions of the heat treatment experiment of the sample cover the temperature evolution range during the heat treatment of metal parts.

[0031] In module M3, based on the dominant role of temperature in heat treatment, the evolution of the microstructure and properties of metals is simplified as a function of temperature and temperature gradient, thereby establishing the relationship between the evolution of microstructure and properties and the heat treatment temperature. Specifically, a polynomial function is used to fit the relationship between microstructure and properties and temperature, a power function is used to fit the relationship between microstructure and properties and temperature gradient, and the combined influence of temperature and temperature gradient on microstructure and properties is considered in a multiplicative form.

[0032] In module M4, the metal parts are discretized into nodes, the temperature, temperature gradient and geometric coordinates of each node are derived, and the microstructure parameters on the nodes are predicted based on the polynomial function and power function fitted in module M3. Then, the microstructure distribution is plotted based on the node geometric coordinates. The microstructure and mechanical properties of all node locations at a specified time are displayed in the form of cloud maps, and the corresponding values ​​can be exported.

[0033] Those skilled in the art will understand that, in addition to implementing the system, apparatus, and their modules provided by this invention in purely computer-readable program code, the same program can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system, apparatus, and their modules provided by this invention can be considered a hardware component, and the modules included therein for implementing various programs can also be considered structures within the hardware component; alternatively, modules for implementing various functions can be considered both software programs implementing the method and structures within the hardware component.

[0034] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A method for predicting the non-uniform distribution of microstructure and mechanical properties in metal heat treatment, characterized in that, include: Step 1: Obtain the internal temperature distribution and evolution history of the metal parts during the heat treatment process; Step 2: Obtain data on the changes in metal microstructure and properties with temperature based on sample heat treatment experiments; Step 3: Fit the evolution of tissue properties with temperature and temperature gradient using polynomial and power functions; Step 4: Extract temperature information and geometric coordinates at different locations, use polynomials and power functions to predict the corresponding tissue properties, and visualize the spatial distribution of tissue properties; Based on the dominant role of temperature in heat treatment, the evolution of the microstructure and properties of metals is simplified as a function of temperature and temperature gradient, thereby establishing the relationship between the evolution of microstructure and properties and heat treatment temperature. Specifically, a polynomial function is used to fit the relationship between microstructure and properties and temperature, a power function is used to fit the relationship between microstructure and properties and temperature gradient, and the combined influence of temperature and temperature gradient on microstructure and properties is considered in a multiplicative form. Temperature distribution includes the temperature field and temperature gradient field during the heat treatment process; The representative characteristics of the selected microstructure properties include yield strength and grain size; Using power functions , The constant exponent is used to fit the relationship between tissue properties and temperature gradient, using a polynomial function. , n Let the degree be a polynomial. The coefficients are polynomials used to fit the relationship between tissue properties and temperature. The evolution of tissue properties with temperature is considered using a quartic polynomial function, its evolution with temperature gradient is considered using a power function, and the interaction between the two is considered through multiplication; the specific definitions are as follows: In the formula, Grain size, For yield strength, , , , These are the coefficients of the polynomial function and the coefficients of the power function fitted based on the experimental data, respectively. The temperature gradient is used as a reference temperature gradient, with the temperature gradient variable in the regularization formula as the reference temperature gradient.

2. The method for predicting the non-uniform distribution of microstructure and mechanical properties in metal heat treatment according to claim 1, characterized in that, Temperature distribution includes the temperature field and temperature gradient field during heat treatment, which are obtained through experimental measurement or finite element simulation.

3. The method for predicting the non-uniform distribution of microstructure and mechanical properties in metal heat treatment according to claim 1, characterized in that, Experimental measurements for each type of feature include at least three different rates of temperature change; each rate of temperature change covers no fewer than eight temperature points within the measurement range to reflect the evolutionary pattern; the measurement error for each type of feature is less than 10%. The boundary conditions of the heat treatment experiment of the sample cover the temperature evolution range during the heat treatment of metal parts.

4. The method for predicting the non-uniform distribution of microstructure and mechanical properties in metal heat treatment according to claim 1, characterized in that, In step 4, the metal parts are discretized into nodes, the temperature, temperature gradient and geometric coordinates of each node are derived, and the microstructure parameters on the nodes are predicted based on the polynomial function and power function fitted in step 3. Then, the microstructure distribution is plotted based on the node geometric coordinates. The microstructure and mechanical properties of all node locations at a specified time are displayed in the form of cloud maps, and the corresponding values ​​can be exported.

5. A prediction system for non-uniform distribution of microstructure and mechanical properties in metal heat treatment, characterized in that, include: Module M1: Acquires the internal temperature distribution and evolution history of metal parts during heat treatment; Module M2: Obtains data on the change of metal microstructure properties with temperature based on sample heat treatment experiments; Module M3: Employs polynomials and power functions to fit the evolution of tissue properties with temperature and temperature gradient; Module M4: Extracts temperature information and geometric coordinates at different locations, uses polynomials and power functions to predict the corresponding tissue properties, and visualizes the spatial distribution of tissue properties; Based on the dominant role of temperature in heat treatment, the evolution of the microstructure and properties of metals is simplified as a function of temperature and temperature gradient, thereby establishing the relationship between the evolution of microstructure and properties and heat treatment temperature. Specifically, a polynomial function is used to fit the relationship between microstructure and properties and temperature, a power function is used to fit the relationship between microstructure and properties and temperature gradient, and the combined influence of temperature and temperature gradient on microstructure and properties is considered in a multiplicative form. Temperature distribution includes the temperature field and temperature gradient field during the heat treatment process; The representative characteristics of the selected microstructure properties include yield strength and grain size; Using power functions , The constant exponent is used to fit the relationship between tissue properties and temperature gradient, using a polynomial function. , n Let the degree be a polynomial. The coefficients are polynomials used to fit the relationship between tissue properties and temperature. The evolution of tissue properties with temperature is considered using a quartic polynomial function, its evolution with temperature gradient is considered using a power function, and the interaction between the two is considered through multiplication; the specific definitions are as follows: In the formula, Grain size, For yield strength, , , , These are the coefficients of the polynomial function and the coefficients of the power function fitted based on the experimental data, respectively. The temperature gradient is used as a reference temperature gradient, with the temperature gradient variable in the regularization formula as the reference temperature gradient.

6. The prediction system for non-uniform distribution of microstructure and mechanical properties in metal heat treatment according to claim 5, characterized in that, Temperature distribution includes the temperature field and temperature gradient field during heat treatment, which are obtained through experimental measurement or finite element simulation.

7. The prediction system for non-uniform distribution of microstructure and mechanical properties in metal heat treatment according to claim 5, characterized in that, Experimental measurements for each type of feature include at least three different rates of temperature change; each rate of temperature change covers no fewer than eight temperature points within the measurement range to reflect the evolutionary pattern; the measurement error for each type of feature is less than 10%. The boundary conditions of the heat treatment experiment of the sample cover the temperature evolution range during the heat treatment of metal parts.

8. The prediction system for non-uniform distribution of microstructure and mechanical properties in metal heat treatment according to claim 5, characterized in that, In module M4, the metal parts are discretized into nodes, the temperature, temperature gradient and geometric coordinates of each node are derived, and the microstructure parameters on the nodes are predicted based on the polynomial function and power function fitted in module M3. Then, the microstructure distribution is plotted based on the node geometric coordinates. The microstructure and mechanical properties of all node locations at a specified time are displayed in the form of cloud maps, and the corresponding values ​​can be exported.

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