Method and system for rapidly calculating morphology characteristic parameters of alloy solidification structure

Abstract

The invention discloses a method and system for rapidly calculating morphology characteristic parameters of an alloy solidification structure, and relates to the technical field of metal material solidification structure characterization, and the method comprises the following steps: selecting an alloy with any component, obtaining solidification structure samples under different solidification conditions through a plurality of casting experiments, and calculating the morphology characteristic parameters of the alloy solidification structure; measuring the primary dendritic crystal arm spacing of each sample and at least two characteristic parameters representing the overall morphology; establishing a parameter relation graph based on measurement data, and further constructing a multivariate nonlinear fitting model of the primary dendritic crystal arm spacing and each morphological characteristic parameter; and measuring the primary dendritic crystal arm spacing of the target alloy, and substituting the primary dendritic crystal arm spacing into the model to calculate required morphology parameter values. The system comprises a data acquisition module, a model construction module and a parameter calculation module. According to the method, the problem of tedious parameter calculation is solved, the model applicability is wide, rapid evaluation is achieved, and casting process optimization and casting quality control are assisted.

Description

Technical Field

[0001] This invention relates to the field of solidification microstructure characterization technology for metallic materials, and in particular to a method and system for rapidly calculating the morphological characteristic parameters of alloy solidification microstructure. Background Technology

[0002] Alloy composition, microstructure, and forming process are three important factors affecting alloy properties. For alloys with specific compositions, the microstructure significantly influences their properties, including mechanical properties, corrosion resistance, oxidation resistance, and damping performance. Alloy parts typically have complex structures with varying wall thicknesses and different cooling rates during solidification, resulting in significantly different microstructure characteristics formed during solidification and forming. Therefore, the study of solidification microstructure is inseparable from the description of its morphology; solidification microstructure morphology parameters can reflect the solidification characteristics of a certain aspect or stage of solidification.

[0003] Currently, commonly used characteristic parameters for characterizing the morphology of solidified structures include: dendrite arm spacing, specific surface area, fractal dimension, shape factor, and dimensionless perimeter. Dendrite arm spacing refers to the vertical distance between adjacent dendrites of the same order; it characterizes the degree of refinement of the dendritic structure. Specific surface area, also known as surface area density, is a measure of the overall morphological characteristics of the structure, describing the degree of dendrite coarsening and morphological complexity. Fractal dimension can quantitatively characterize the complexity and irregularity of solidified structures. Shape factor and dimensionless perimeter are dimensionless parameters that characterize the geometric complexity of solidified structures, depicting the degree to which the solidified structure's outline approximates a circle.

[0004] Dendrite arm spacing is one of the most studied and important morphological parameters of solidified structures. It is convenient and quick to measure, such as using the linear intercept method. Dendrite arm spacing can quickly provide some information about the solidified structure. However, this parameter mainly describes the length scale of the microstructure and is difficult to characterize the complex overall morphology of dendrites. Therefore, it is necessary to use overall characteristic parameters such as specific surface area, fractal dimension, shape factor, and dimensionless perimeter for further description. Calculating specific surface area, shape factor, and dimensionless perimeter all require obtaining the perimeter and area of ​​the target solidified structure. However, the dendrite morphology varies in solidified structures under different solidification conditions, with many dendrites interlinking and not forming clear boundaries, making the calculation of the perimeter and area of ​​different solidified structures difficult and time-consuming. Calculating the fractal dimension requires covering the solidified structure with a series of boxes of different sizes, and then taking the logarithm of the box size and the number of boxes needed to cover the solidified structure; the slope of this logarithm is the fractal dimension. It is evident that the calculation process for these overall morphological feature parameters is usually quite cumbersome, requiring the use of software to obtain a great deal of information about the solidified structure before calculation can be performed. When it is necessary to statistically analyze the morphological feature parameters of a large number of different solidified structures, the process is time-consuming and cannot meet the demand for low-cost, rapid detection. Summary of the Invention

[0005] The purpose of this invention is to provide a method for rapidly calculating the characteristic parameters of the solidification microstructure of alloys. Addressing the problems of complex and time-consuming calculations of characteristic parameters such as alloy specific surface area and fractal dimension, this invention establishes a model using a small amount of data to achieve efficient prediction of the microstructure characteristic parameters under various solidification conditions and compositions.

[0006] To achieve the above objectives, this invention provides a method for rapidly calculating the characteristic parameters of the solidification microstructure of an alloy, comprising the following steps: S1. Data acquisition: Select a target alloy of arbitrary composition, and obtain alloy solidification structure samples of the target alloy under different solidification conditions through multiple casting experiments. For each alloy solidification structure sample, measure its primary dendrite arm spacing and at least two morphological characteristic parameters characterizing the overall morphology of the alloy solidification structure. S2. Model Construction: Based on the measurement data of the primary dendrite arm spacing and the morphological feature parameters obtained in S1, a relationship diagram between the primary dendrite arm spacing and each of the morphological feature parameters is established. Based on the relationship diagram and the measurement data, a multivariate nonlinear fitting model between the primary dendrite arm spacing and each of the morphological feature parameters is established. S3. Parameter estimation: For the solidification structure of the target alloy under other solidification conditions or with other compositions, measure the spacing of its primary dendrite arms, substitute the spacing of the primary dendrite arms into the multivariate nonlinear fitting model established in S2, and estimate the value of a morphological characteristic parameter corresponding to the solidification structure of the target alloy.

[0007] Preferably, when the different solidification conditions include furnace cooling, graphite mold casting, air cooling, wedge mold casting, and water cooling, and the measured morphological characteristic parameters are specific surface area, fractal dimension, shape factor, and dimensionless perimeter, the multivariate nonlinear fitting model satisfies the following functional relationship:

[0008] Among them, S v For specific surface area, F d Let F be the fractal dimension, F be the shape factor, and P be the fractal dimension. d The perimeter is dimensionless. <aij>For generalized fitting parameters, λ1 is the primary dendrite arm spacing, C is the alloy solidification composition, and Const is a constant. Specific surface area, fractal dimension, and dimensionless perimeter are negatively correlated with the primary dendrite arm spacing, while the shape factor is positively correlated with the primary dendrite arm spacing.

[0009] Preferably, the linear intercept method is used to measure the dendrite arm spacing; when measuring specific surface area, shape factor, and dimensionless perimeter, the perimeter and area of ​​the alloy solidification sample are first obtained; when measuring fractal dimension, a series of boxes of different sizes are used to cover the solidification, and then the logarithm of the box size and the number of boxes required to cover the solidification is taken, and the slope of the logarithm is used as the fractal dimension.

[0010] A system for rapidly calculating characteristic parameters of solidification microstructure in alloys, comprising: The data acquisition module is used to acquire alloy solidification microstructure samples with different solidification conditions and compositions, and to measure the primary dendrite arm spacing and at least two morphological characteristic parameters of each sample. The model building module is used to receive the primary dendrite arm spacing measurement data and morphological feature parameter measurement data output by the data acquisition module, and to establish a multivariate nonlinear fitting model between the primary dendrite arm spacing and each of the morphological feature parameters. The parameter calculation module is used to measure the spacing of the primary dendrite arms in the solidified structure of the target alloy, substitute the spacing of the primary dendrite arms into the multivariate nonlinear fitting model established by the model construction module, and output the estimated value of at least one morphological feature parameter corresponding to the solidified structure of the target alloy.

[0011] Preferably, the data acquisition module includes a metallographic microscope and an image analysis unit. The metallographic microscope is used to observe the microstructure of the alloy solidification sample, and the image analysis unit is used to measure the primary dendrite arm spacing and morphological characteristic parameters based on the observed microstructure data.

[0012] According to specific embodiments provided by the present invention, the following technical effects are disclosed: Addressing the difficulty and time-consuming calculation of characteristic parameters such as specific surface area, fractal dimension, shape factor, and dimensionless perimeter, a method for rapidly calculating the morphological characteristic parameters of alloy solidification structures is provided. A reliable model is established using a small amount of data. Based on this model, only the simple local characteristic parameter, first-order dendrite arm spacing, is needed to predict the relatively complex overall morphological characteristic parameters. Furthermore, it can predict the characteristic parameters of solidification structures under various solidification conditions and with different compositions, significantly reducing the computational load and enabling rapid evaluation of the solidification structure quality of castings using multiple solidification morphological characteristic parameters.

[0013] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating an embodiment of a method for rapidly calculating the morphological characteristics of alloy solidification structures according to the present invention. Figure 2 This is a schematic diagram showing the relationship between the primary dendrite arm spacing and other parameters in an embodiment of the present invention; in the figure, (a) represents the relationship between specific surface area and primary dendrite arm spacing; (b) represents the relationship between fractal dimension and primary dendrite arm spacing; (c) represents the relationship between shape factor and primary dendrite arm spacing; and (d) represents the relationship between dimensionless perimeter and primary dendrite arm spacing.

[0016] Figure 3 The figures show a comparison between the model predictions and actual values ​​of various parameters in the embodiments of the present invention. In the figures, (a) is the comparison between the model predictions and actual values ​​of the fractal dimension, (b) is the comparison between the model predictions and actual values ​​of the specific surface area, (c) is the comparison between the model predictions and actual values ​​of the shape factor, and (d) is the comparison between the model predictions and actual values ​​of the dimensionless perimeter. Detailed Implementation

[0017] 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.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Example

[0020] A method for rapidly calculating characteristic parameters of alloy solidification microstructure, such as... Figure 1 As shown, the steps are as follows: S1. Data Acquisition: Taking the solidification structure of Mg-Al alloys obtained under furnace cooling, graphite mold casting, air cooling, wedge mold casting, and water cooling conditions as an example, after grinding, polishing, and etching, multiple typical fields of view were selected using a metallographic microscope. Using the linear intercept method, the intersection points of dendrite arms and the intercept line were statistically analyzed using image analysis software to calculate the primary dendrite arm spacing (PDAS). The average value across multiple fields of view was taken as the primary dendrite arm spacing data for this sample.

[0021] Then, the morphological characteristic parameters are measured, specifically including: Specific surface area: Based on metallographic images, the perimeter (P) and area (A) of the dendritic region are extracted using image analysis software, and then calculated according to the formula... (unit: μm -1 ) Calculate and take the average value of multiple fields of view.

[0022] Fractal dimension: Using the box counting method, square boxes of different sizes are generated with the help of software to cover the dendritic region, and the number of boxes covering the dendrites N(r) under each size is counted. The natural logarithm of the box size (r) and N(r) is taken and linearly fitted, and the slope of the fitted line is the fractal dimension.

[0023] Shape factor: based on the formula The average value of multiple views is calculated using the extracted dendrite perimeter (P) and area (A).

[0024] Dimensionless perimeter: According to the formula The dendrite perimeter (P) and area (A) are combined and the average value of multiple fields of view is taken.

[0025] S2. Model Construction: Data on the primary dendrite arm spacing (PDAS), specific surface area, fractal dimension, shape factor, and dimensionless perimeter of Mg-Al alloys under different compositions and solidification conditions are compiled and a scatter plot of the relationships is drawn, such as... Figure 2 As shown in the figure, (a) represents the relationship between specific surface area and primary dendrite arm spacing; (b) represents the relationship between fractal dimension and primary dendrite arm spacing; (c) represents the relationship between shape factor and primary dendrite arm spacing; and (d) represents the relationship between dimensionless perimeter and primary dendrite arm spacing.

[0026] Depend on Figure 2 It is evident that specific surface area, fractal dimension, and dimensionless perimeter all decrease with increasing primary dendrite arm spacing, exhibiting a negative correlation; while the shape factor increases with increasing primary dendrite arm spacing, showing a positive correlation. Primary dendrite arm spacing reflects the size of the dendrites. These relationships indicate that in Mg-Al alloys, smaller dendrites typically possess more complex overall morphology and lower coarsening, while larger dendrites have less complex morphology, tend to be more circular, and exhibit higher coarsening. Therefore, changes in overall morphology parameters can be predicted by measuring changes in local morphology characteristic parameters. Although the relationships between parameters are often nonlinear, the trend of one parameter can still be predicted by measuring the changes in another.

[0027] Based on measurement data, a multivariate nonlinear fitting model was established for dendrite arm spacing, specific surface area, fractal dimension, shape factor, and dimensionless perimeter, satisfying the following functional relationships:

[0028] Among them, S v For specific surface area, F d Let F be the fractal dimension, F be the shape factor, and P be the fractal dimension. d The perimeter is dimensionless. <aij>λ1 is the generalized fitting parameter, C is the primary dendrite arm spacing, and Const is a constant. <aij>C represents the functional relationship between the two, not a multiplication.

[0029] When the influence of alloy composition on the four quantitative parameters is ignored, i.e., assuming that all alloy compositions are consistent, it can be seen that the specific surface area, fractal dimension, and dimensionless perimeter all decrease with increasing primary dendrite arm spacing, i.e., they exhibit a negative correlation, while the shape factor increases with increasing primary dendrite arm spacing, i.e., it exhibits a positive correlation. Grouping by composition, the functional relationship between primary dendrite arms and other morphological characteristic parameters is tested separately, which is called the "primary relationship". The constant term in the primary relationship is plotted as a function of composition according to different compositions, and a secondary fit is performed based on its trend, which is called the "secondary relationship". Finally, the primary and secondary relationships are coupled based on their fit degree and equation form, and the coupling equation is adjusted to continuously improve the fitting accuracy until the relationship between different morphological characteristic parameters and primary dendrite arm spacing and alloy composition is finally obtained, as shown in Table 1.

[0030] Table 1. Fitting relationships between different morphological characteristic parameters and primary dendrite arm spacing λ1 and alloy composition C

[0031] As can be seen from Table 1, the model's fitting coefficient is above 0.89, indicating that the fitting results are highly reliable.

[0032] To further verify the reliability of the model, the model's predicted values ​​were compared with the measured values ​​of various morphological feature parameters, such as... Figure 3 As shown in the figure, (a) compares the model predictions and measured values ​​of fractal dimension, (b) compares the model predictions and measured values ​​of specific surface area, (c) compares the model predictions and measured values ​​of shape factor, and (d) compares the model predictions and measured values ​​of dimensionless perimeter. The slopes of the regression lines in the figure are all above 0.89, indicating that the predicted values ​​and measured values ​​of the fitted equations are in excellent agreement. This further demonstrates that the model can accurately estimate the morphological characteristics of solidified structures, such as specific surface area, fractal dimension, shape factor, and dimensionless perimeter, based on the spacing of the primary dendrite arms.

[0033] S3. For the target sample of the Mg-Al alloy to be studied, after measuring its primary dendrite arm spacing, the corresponding morphological characteristic parameters such as specific surface area, fractal dimension, shape factor, and dimensionless perimeter can be quickly calculated by substituting them into the constructed multivariate nonlinear fitting model, thereby achieving efficient evaluation of the morphological characteristics of the alloy solidification structure.

[0034] Therefore, this invention establishes a relationship model between primary dendrite arm spacing and complex morphological characteristic parameters, allowing the calculation of cumbersome characteristic parameter data using only the primary dendrite arm spacing. Compared with existing methods for obtaining solidified microstructure morphological characteristic parameters, this invention has the following advantages: 1. Minimal data required for model building: Only a few relevant parameters need to be obtained to build a reliable model and predict the solidification morphology characteristics of alloys with multiple compositions under different solidification conditions.

[0035] 2. Quickly achieve overall evaluation of solidified microstructure morphology: Only the easy and quick calculation of the first dendrite arm spacing is needed to obtain the values ​​of the more cumbersome and complex characteristic parameters through the model.

[0036] The remaining technical features in the above embodiments can be flexibly selected by those skilled in the art to meet different specific practical needs. However, it is obvious to those skilled in the art that these specific details are not necessary to implement the present invention. In other instances, to avoid obscuring the present invention, well-known components, structures, or parts are not specifically described, and all are within the scope of technical protection defined by the claims of the present invention.

[0037] Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of this invention should be within the protection scope of the appended claims. In the above description, numerous specific details have been set forth to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, to avoid obscuring the invention, well-known techniques, such as specific construction details, operating conditions, and other technical conditions, have not been specifically described.

[0038] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.< / aij> < / aij> < / aij>

Claims

1. A method for rapidly calculating a feature parameter of a solidification structure morphology of an alloy, characterized in that, The steps are as follows: S1, data acquisition: selecting a target alloy of any composition, obtaining alloy solidification structure samples of the target alloy under different solidification conditions through multiple casting experiments, and measuring the primary dendrite arm spacing and at least two morphology characteristic parameters of each alloy solidification structure sample; S2, model construction: based on the primary dendrite arm spacing measurement data and the morphology characteristic parameter measurement data obtained in S1, a relationship diagram between the primary dendrite arm spacing and each of the morphology characteristic parameters is established, and based on the relationship diagram and the measurement data, a multivariate nonlinear fitting model between the primary dendrite arm spacing and each of the morphology characteristic parameters is established; S3, parameter calculation: for the solidification structure of a target alloy of other composition or other solidification condition, the primary dendrite arm spacing thereof is measured, the primary dendrite arm spacing is substituted into the multivariate nonlinear fitting model established in S2, and the numerical value of a corresponding morphology characteristic parameter of the target alloy solidification structure is calculated.

2. The method for quickly calculating the solidification microstructure feature parameters of an alloy according to claim 1, characterized in that: The different solidification conditions include at least two of furnace cooling, graphite mold casting, air cooling, wedge-shaped mold casting, and water cooling.

3. The method for quickly calculating the solidification microstructure feature parameters of an alloy according to claim 2, characterized in that: When the measured morphology characteristic parameter is specific surface area, fractal dimension, shape factor, or dimensionless perimeter, the multivariate nonlinear fitting model satisfies the function relationship: where S v is the specific surface area, F d is the fractal dimension, F is the shape factor, P d is the dimensionless perimeter, <aij>is a general fitting parameter, λ1 is the primary dendrite arm spacing, C is the composition of the alloy solidification, and Const is a constant. The specific surface area, the fractal dimension, and the dimensionless perimeter each have a negative correlation with the primary dendrite arm spacing, and the shape factor has a positive correlation with the primary dendrite arm spacing.< / aij> 4. The method of claim 3, wherein the method further comprises: The linear intercept method is used to measure the primary dendrite arm spacing. When the specific surface area, the shape factor, and the dimensionless perimeter are measured, the perimeter and the area of the alloy solidification structure sample are first obtained. When the fractal dimension is measured, a series of boxes of different sizes are used to cover the solidification structure, and the logarithm of the box size and the number of boxes required to cover the solidification structure are taken, and the slope of the logarithm is used as the fractal dimension.

5. A system for implementing the method of any one of claims 1 to 4 for rapid calculation of a parameter of the solidification microstructure morphology of an alloy, characterized in that, It comprises: a data acquisition module configured to obtain alloy solidification structure samples under different solidification conditions and of different compositions, and to measure the primary dendrite arm spacing and at least two morphology characteristic parameters of each sample; a model construction module configured to receive the primary dendrite arm spacing measurement data and the morphology characteristic parameter measurement data output by the data acquisition module, and to establish a multivariate nonlinear fitting model between the primary dendrite arm spacing and each of the morphology characteristic parameters; a parameter calculation module configured to measure the primary dendrite arm spacing of a target alloy solidification structure, to substitute the primary dendrite arm spacing into the multivariate nonlinear fitting model established by the model construction module, and to output the calculated numerical value of at least one morphology characteristic parameter of the target alloy solidification structure.

6. The system for rapid calculation of solidification microstructure feature parameters of an alloy according to claim 5, characterized in that, The data acquisition module comprises a metallographic microscope and an image analysis unit. The metallographic microscope is used to observe the microstructure of the alloy solidification structure sample, and the image analysis unit is used to measure the primary dendrite arm spacing and the morphology characteristic parameter data based on the observed microstructure data.

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