Method for predicting iron-rich phase and dendritic crystal spacing distribution of casting

Abstract

The invention relates to a prediction method for iron-rich phase and dendritic crystal spacing distribution of a casting. The prediction method comprises the following steps: obtaining cooling rate distribution of different parts of the casting under different process parameters; processing microstructure samples at a plurality of positions of the casting under different process parameters; based on the statistical distribution data of the iron-rich phases and the dendritic crystal spacing in the microstructure samples, constructing a relation model of the number density of the iron-rich phases and the cooling rate and a relation model of the dendritic crystal spacing and the cooling rate; and the relation model of the number density of the iron-rich phases and the cooling rate and the relation model of the dendritic crystal spacing and the cooling rate are combined with the position coordinates of all the parts of the casting, and the distribution characteristics of the iron-rich phases and the dendritic crystal spacing of the different parts of the casting are obtained. According to the method, mold flow analysis and microstructure characterization are combined, the relation between the cooling rates of different parts of the casting and the iron-rich phase and the dendritic crystal spacing is constructed, and reference is provided for prediction of mechanical property distribution of the casting.

Description

Technical Field

[0001] The present application relates to the field of metal materials and computer-aided design, and in particular to a method for predicting the distribution of iron-rich phase and dendrite spacing of a casting. Background Art

[0002] At present, material weight reduction is an effective way to increase the range of automobiles. Aluminum alloys have low density, high specific strength and good corrosion resistance. The use of low-pressure casting technology to produce integrated aluminum alloy castings can effectively reduce the weight of the vehicle body, reduce carbon emissions, and increase the range of the vehicle. However, low-pressure aluminum castings are affected by multiple factors such as process parameters, size, shape, etc., and the microstructure and mechanical properties show non-uniform distribution characteristics, which poses a great challenge to the prediction of the service performance and service life of the castings. Studies have shown that casting defects are the main reason affecting the mechanical properties of castings. However, for parts with only a small number of defects, the characteristic parameters of the microstructure are the main reason for the fluctuation of their mechanical properties.

[0003] In the related art, the defect distribution in the casting can be characterized by CT, for example, using a CT scanner to obtain the internal defect distribution of the casting, the average pore size and the pore volume fraction; however, this method cannot statistically analyze the distribution of microstructure characteristic parameters, and has limitations for areas with only a small number of defects.

[0004] Therefore, it is necessary to design a prediction method for the distribution of iron-rich phase and dendrite spacing in castings to overcome the above problems. Summary of the invention

[0005] The present application provides a method for predicting the distribution of the iron-rich phase and dendrite spacing of a casting, which can solve the technical problem that the methods in the related art are limited to parts with only a small number of defects.

[0006] In a first aspect, an embodiment of the present application provides a method for predicting the distribution of iron-rich phase and dendrite spacing of a casting, the prediction method comprising:

[0007] Obtain the cooling rate distribution of different parts of the casting under different process parameters;

[0008] Microstructure specimens were machined at multiple locations of the casting under different process parameters;

[0009] Based on the statistical distribution data of iron-rich phase and dendrite spacing in multiple microstructure samples, the relationship model between the number density of iron-rich phase and cooling rate, as well as the relationship model between dendrite spacing and cooling rate were constructed;

[0010] The relationship model between the number density of iron-rich phase and cooling rate and the relationship model between dendrite spacing and cooling rate are combined with the position coordinates of various parts of the casting to obtain the distribution characteristics of iron-rich phase and dendrite spacing in different parts of the casting.

[0011] In combination with the first aspect, in one embodiment, processing microstructure samples at multiple positions of the casting under different process parameters includes:

[0012] According to the cooling rate distribution of different parts of the casting, the positions with large cooling rate differences are selected on the casting to process the microstructure samples.

[0013] In combination with the first aspect, in one embodiment, after processing microstructure samples at multiple positions of the casting under different process parameters, the method further includes:

[0014] The microstructure sample is cut along its thickness direction to prepare the metallographic sample;

[0015] The metallographic samples were photographed to obtain metallographic images, and the distribution data of the dendrite spacing and the distribution data of the iron-rich phase in the metallographic images were statistically analyzed.

[0016] In combination with the first aspect, in one embodiment, photographing the metallographic sample to obtain a metallographic picture, and counting the distribution data of the dendrite spacing in the metallographic picture, includes:

[0017] Take metallographic pictures of the surface layer, segregation zone and core area of ​​the metallographic sample respectively, and enlarge the metallographic pictures uniformly;

[0018] The dendrite spacing in the metallographic images of the surface layer, segregation zone and core area was determined by the straight line intercept method;

[0019] The average value of the dendrite spacing in the surface, segregation zone and core area is calculated as the dendrite spacing of the microstructure sample.

[0020] In combination with the first aspect, in one embodiment, photographing the metallographic sample to obtain a metallographic picture, and statistically analyzing the distribution data of the dendrite spacing and the distribution data of the iron-rich phase in the metallographic picture, include:

[0021] Metallographic images of the core of the metallographic sample, the two segregation zones on both sides of the core, and the two surface layers on both sides of the core are taken along the thickness direction of the metallographic sample, and then seamless mosaic is performed to obtain the distribution map of the iron-rich phase;

[0022] The number density of the iron-rich phase is identified and quantitatively counted on the iron-rich phase distribution diagram to obtain the distribution data of the iron-rich phase.

[0023] In combination with the first aspect, in one embodiment, based on the statistical distribution data of the iron-rich phase and the dendrite spacing in multiple microstructure samples, a relationship model between the number density of the iron-rich phase and the cooling rate, and a relationship model between the dendrite spacing and the cooling rate are constructed, including:

[0024] According to the statistical distribution data of dendrite spacing and cooling rate distribution in multiple microstructure samples, a scatter plot of the relationship between dendrite spacing and cooling rate is established;

[0025] The relationship model between dendrite spacing and cooling rate was constructed based on the scatter plot of the relationship between dendrite spacing and cooling rate.

[0026] In combination with the first aspect, in one embodiment, based on the statistical distribution data of the iron-rich phase and the dendrite spacing in multiple microstructure samples, a relationship model between the number density of the iron-rich phase and the cooling rate, and a relationship model between the dendrite spacing and the cooling rate are constructed, including:

[0027] According to the statistical number density of iron-rich phase and the distribution of cooling rate in multiple microstructure samples, a scatter plot of the relationship between the number density of iron-rich phase and cooling rate is established;

[0028] Based on the scatter plot of the relationship between the number density of iron-rich phase and cooling rate, a relationship model between the number density of iron-rich phase and cooling rate was constructed.

[0029] In combination with the first aspect, in one embodiment, after constructing the relationship model between the number density of the iron-rich phase and the cooling rate, and the relationship model between the dendrite spacing and the cooling rate, the method further includes:

[0030] The relationship model between dendrite spacing and cooling rate was tested using the test data of cooling rate and dendrite spacing;

[0031] If the test error is less than the set value, the relationship model between dendrite spacing and cooling rate meets the requirements;

[0032] The relationship model between the number density of the iron-rich phase and the cooling rate was tested using the test data of the cooling rate and the number density of the iron-rich phase;

[0033] If the test error is less than the set value, the relationship model between the number density of the iron-rich phase and the cooling rate meets the requirements.

[0034] In combination with the first aspect, in one embodiment, obtaining the cooling rate distribution of different parts of the casting under different process parameters includes:

[0035] Obtain the solidification time and solid-liquid phase temperature distribution of castings under different process parameters;

[0036] The solidification time and the solid-liquid phase temperature distribution are substituted into the cooling rate calculation formula to obtain the cooling rate distribution of different parts of the casting under different process parameters.

[0037] In combination with the first aspect, in one embodiment, before obtaining the cooling rate distribution of different parts of the casting under different process parameters, it also includes: adjusting the casting mold temperature and the casting temperature to obtain multiple sets of different process parameters.

[0038] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0039] By combining the cooling rate distribution of different parts of the casting under different process parameters with the dendrite spacing and iron-rich phase number density characterized by the microstructure, we can construct a relationship model between the iron-rich phase number density and the cooling rate, as well as a relationship model between the dendrite spacing and the cooling rate. Then, we can obtain the distribution characteristics of these microstructure characteristic parameters such as the iron-rich phase and the dendrite spacing, which provides data support for the accurate prediction of the distribution of mechanical properties of castings and solves the technical problem that the methods in related technologies are limited to parts with only a small number of defects. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 A flow chart of a method for predicting the distribution of iron-rich phase and dendrite spacing of a casting provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of the sampling position of the microstructure sample on the casting provided in the embodiment of the present application;

[0043] Figure 3 OM images of dendrite spacing in different regions provided in the embodiments of the present application;

[0044] Figure 4 This is a picture of the distribution of the iron-rich phase along the thickness direction of the sample provided in the embodiment of the present application;

[0045] Figure 5 A scatter plot of the relationship between dendrite spacing and cooling rate provided in the embodiments of the present application;

[0046] Figure 6 A scatter plot of the relationship between the number density of the iron-rich phase and the cooling rate provided in the examples of the present application. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] The microstructure of the material is a key factor affecting its mechanical properties. Studies have shown that casting defects are the main reason affecting the mechanical properties of castings. However, for parts with only a small number of defects, characteristic parameters such as iron-rich phase and dendrite spacing are the main reasons for the fluctuation of its mechanical properties. Therefore, in order to predict the distribution of mechanical properties of castings, it is necessary to consider defects, iron-rich phases and dendrite spacing characteristic parameters at the same time. However, the prediction methods in related technologies are limited to considering casting defects, and do not consider the distribution characteristics of iron-rich phases and dendrite spacing. ProCAST is a mold flow analysis software based on finite elements, which can be used for flow field, temperature field, and casting defect distribution prediction in low-pressure casting processes. However, it is also impossible to directly predict the distribution of microstructure characteristic parameters such as iron-rich phases and dendrite spacing. Therefore, the currently disclosed method for predicting the distribution of microstructure and mechanical properties of aluminum alloy castings based on mold flow analysis software is also limited to considering casting defects.

[0049] The embodiment of the present application provides a method for predicting the distribution of the iron-rich phase and dendrite spacing of a casting, which can solve the technical problem that the methods in the related art are limited to areas with only a small number of defects.

[0050] See also Figure 1 As shown, a method for predicting the distribution of iron-rich phase and dendrite spacing of a casting provided in an embodiment of the present application may include the following steps:

[0051] S1: Obtain the cooling rate distribution of different parts of the casting under different process parameters.

[0052] In this step, the mold temperature and the casting temperature can be adjusted on the basis of the low-pressure casting production process parameters to obtain multiple groups of different process parameters; for example, the mold temperature is set to four parameters of 300°C, 330°C, 360°C, and 390°C, and the casting temperature is set to three parameters of 690°C, 720°C, and 740°C, and the other parameters remain unchanged, and a total of 12 groups of casting process parameters are obtained, numbered 1#-12# respectively. The cooling rate distribution of different parts of the casting under different process parameters can be obtained from the casting simulation software. Casting simulation software can, for example, select ProCAST software or Magmasoft software and AnyCasting software. This embodiment preferably uses ProCAST software for low-pressure casting simulation, obtains the casting simulation process of the casting under 12 groups of process parameters, and obtains the cooling rate distribution of different parts of the casting.

[0053] S2: Microstructure specimens processed at multiple locations of the casting under different process parameters.

[0054] In this step, casting is performed under the above 12 sets of process parameters to obtain 12 castings, and microstructure samples are processed at multiple positions of each casting. Figure 2As shown, in this embodiment, 10 locations are preferably selected on each casting to process 10 microstructure samples, and 120 microstructure samples are obtained for the 12 castings in total. Moreover, the sampling locations of the microstructure samples on different castings should be kept consistent as much as possible.

[0055] S3: Based on the statistical distribution data of iron-rich phase and dendrite spacing in multiple microstructure samples, a relationship model between the number density of iron-rich phase and cooling rate, as well as a relationship model between dendrite spacing and cooling rate are constructed.

[0056] S4: The relationship model between the number density of the iron-rich phase and the cooling rate and the relationship model between the dendrite spacing and the cooling rate are combined with the position coordinates of each part of the casting to obtain the distribution characteristics of the iron-rich phase and the dendrite spacing in different parts of the casting.

[0057] In step S4, the relationship model between the number density of the iron-rich phase and the cooling rate and the relationship model between the dendrite spacing and the cooling rate are both two-dimensional plane curves. By combining the two-dimensional plane curves with the position coordinates of each part of the casting, the distribution characteristics of the iron-rich phase and the dendrite spacing at different parts of the casting under the three-dimensional coordinates can be obtained. Among them, the relationship model between the number density of the iron-rich phase and the cooling rate and the relationship model between the dendrite spacing and the cooling rate can be directly input into the ProCAST post-processing module, combined with the position coordinates of each part of the casting in the ProCAST software, to obtain the distribution characteristics of the iron-rich phase and the dendrite spacing at different parts of the casting. The relationship model between the number density of the iron-rich phase and the cooling rate and the relationship model between the dendrite spacing and the cooling rate can also be created with Python, and the distribution characteristics of the iron-rich phase and the dendrite spacing under different position coordinates (X, Y, Z) can be obtained in Python.

[0058] In this embodiment, by obtaining the cooling rate distribution of different parts of the casting under different process parameters, and processing microstructure samples at multiple positions of the casting, the distribution data of the iron-rich phase and the dendrite spacing can be statistically obtained from the microstructure samples, and then a relationship model between the number density of the iron-rich phase and the cooling rate and a relationship model between the dendrite spacing and the cooling rate are constructed. By combining the relationship model with the position coordinates of each part of the casting, the distribution characteristics of the iron-rich phase and the dendrite spacing in different parts of the casting can be obtained. This method combines mold flow analysis and microstructure characterization, constructs the relationship between the cooling rate of different parts of the casting and the characteristic parameters of the iron-rich phase and dendrite spacing, and can predict the distribution of microstructure characteristic parameters such as iron-rich phase and dendrite spacing, providing data support for the accurate prediction of the distribution of mechanical properties of castings, and solving the technical problem that the methods in the related technology are limited to parts with only a small number of defects.

[0059] Further, in one embodiment, obtaining the cooling rate distribution of different parts of the casting under different process parameters may include the following steps:

[0060] S11: Obtaining the solidification time and solid-liquid phase temperature distribution of the casting under different process parameters. In this step, casting simulation under different process parameters can be performed in casting simulation software to obtain the solidification time and solid-liquid phase temperature distribution of the casting under different process parameters.

[0061] S12: Substitute the solidification time and the solid-liquid phase temperature distribution into the cooling rate calculation formula to obtain the cooling rate distribution of different parts of the casting under different process parameters.

[0062] In this embodiment, the cooling rate calculation formula of each node on the casting is: C = T / t. Where C is the cooling rate, T is the solidus-liquidus temperature difference, and t is the solidification time. Enter the cooling rate calculation formula in the ProCAST software, and the cooling rate distribution of the casting can be displayed in the ProCAST software.

[0063] Further, in some optional embodiments, processing microstructure samples at multiple positions of the casting under different process parameters may include: selecting positions on the casting with larger cooling rate differences to process microstructure samples according to the cooling rate distribution of different parts of the casting.

[0064] In this embodiment, according to the cooling rate distribution, 10 positions with large cooling rate differences are selected on the casting to process microstructure samples. The sampling positions of microstructure samples on different castings are kept consistent as much as possible, so that samples with different microstructure characteristic parameters at different positions on the casting can be obtained, which is convenient for constructing a relationship model between cooling rate and microstructure characteristic parameters. In addition, by using positions with large cooling rate differences to process microstructure samples, the sample volume can cover a wide range, so that the application range of the relationship model constructed subsequently is larger, the precision is higher, and the relationship model is more accurate.

[0065] Based on the above technical solution, in one embodiment, after processing microstructure samples at multiple positions of the casting under different process parameters, the following steps may also be included:

[0066] Step a: Cut the microstructure sample along its thickness direction to prepare the metallographic sample.

[0067] Step b: photographing the metallographic sample to obtain a metallographic image, and statistically analyzing the distribution data of the dendrite spacing and the distribution data of the iron-rich phase in the metallographic image.

[0068] In this embodiment, the microstructure sample is cut to prepare the metallographic sample, and the distribution data of the iron-rich phase and the dendrite spacing in each metallographic sample are statistically analyzed; at the same time, when cutting, the sample is cut along the thickness direction, so that the dendrite spacing of the metallographic sample shows an increasing trend from the surface to the core.

[0069] Furthermore, in some embodiments, photographing the metallographic sample to obtain a metallographic picture and collecting distribution data of the dendrite spacing in the metallographic picture may include:

[0070] Step b1: Take metallographic pictures of the surface, segregation zone and core area of ​​the metallographic sample respectively, and enlarge the metallographic pictures uniformly.

[0071] Step b2: The dendrite spacing in the metallographic images of the surface layer, segregation zone and core area is determined by the straight line intercept method.

[0072] Step b3: Calculate the average value of the dendrite spacing in the surface, segregation zone and core region as the dendrite spacing of the microstructure sample.

[0073] In this embodiment, OM can be used (in other embodiments, other shooting tools can also be used for shooting) to respectively shoot metallographic images of the surface layer, segregation zone and core area of ​​the metallographic sample (such as Figure 3 As shown), the magnification is uniformly 200x, and the straight line intercept method is used to determine the dendrite arm spacing DAS (dendrite arm spacing). The average value of the DAS of the three regions of the surface, segregation zone and core is calculated to represent the average DAS of the microstructure sample. Since the dendrite arm spacing of the metallographic sample shows an increasing trend from the surface to the core after being cut along the thickness direction, the values ​​of these three regions represent the characteristics of the selected area, including both the maximum and the minimum, as well as the values ​​of defects and other characteristics (i.e., abnormal values). This embodiment adopts the method of taking the average value after separately shooting the surface, segregation zone and core regions, so that the dendrite arm spacing distribution data of the microstructure sample finally calculated is more accurate.

[0074] Further, in some optional embodiments, photographing the metallographic sample to obtain a metallographic picture, and collecting statistics on the distribution data of the dendrite spacing and the distribution data of the iron-rich phase in the metallographic picture, may include:

[0075] Step b4: Take metallographic pictures of the core of the metallographic sample, two segregation zones on both sides of the core, and two surface layers on both sides of the core along the thickness direction of the metallographic sample, and then perform seamless mosaic to obtain the iron-rich phase distribution map.

[0076] Step b5: Identify and quantitatively count the number density of the iron-rich phase on the iron-rich phase distribution diagram to obtain the distribution data of the iron-rich phase.

[0077] In this embodiment, the distribution of the iron-rich phase can be characterized by SEM-BSE (backscattered electrons in a scanning electron microscope). The magnification is uniformly 700x, and the observation area of ​​each picture is 400×290 μm. The pictures are taken in sequence along the thickness direction (surface-segregation zone-core-segregation zone-surface), and then seamless mosaic is performed (such as Figure 4 By taking multiple shots of different areas and synthesizing them into one image, the distribution of the iron-rich phase in each part of the metallographic sample can be reflected. Then, the image recognition program is used to automatically identify and quantitatively count the number density parameters of the iron-rich phase in the spliced ​​image. The calculation formula for the number density of the iron-rich phase is: N =N / S, where ρ N is the number density of the iron-rich phase, N is the number of iron-rich phase particles in the observation area, and S is the area of ​​the observation area.

[0078] Further, in one embodiment, based on the statistical distribution data of the iron-rich phase and the dendrite spacing in multiple microstructure samples, a relationship model between the number density of the iron-rich phase and the cooling rate, and a relationship model between the dendrite spacing and the cooling rate are constructed, which may include:

[0079] S31: establishing a scatter plot of the relationship between the dendrite spacing and the cooling rate according to the statistical distribution data of the dendrite spacing and the cooling rate distribution in multiple microstructure samples.

[0080] S32: A relationship model between dendrite spacing and cooling rate is constructed based on a scatter plot of the relationship between dendrite spacing and cooling rate.

[0081] In this embodiment, the relationship model between the characteristic parameter of the dendrite spacing and the cooling rate is established by an empirical formula method. That is, according to the test results of the microstructure samples in the aforementioned 1#-10# castings, a scatter plot of the relationship between the dendrite spacing and the cooling rate of different samples can be established, such as Figure 5 As shown. Then the empirical formula of the relationship between cooling rate and dendrite spacing was established: L = AC n , where L is the dendrite spacing, A and n are material constants, and C is the cooling rate. The relationship model between the dendrite spacing and the cooling rate is obtained from the data in the relationship scatter diagram: L = 40.5C -0.31 In this embodiment, the relationship model is constructed in the form of a scatter plot, which can not only save the sample volume, but also find out the distribution law of the scatter points.

[0082] Further, in one embodiment, based on the statistical distribution data of the iron-rich phase and the dendrite spacing in multiple microstructure samples, a relationship model between the number density of the iron-rich phase and the cooling rate, and a relationship model between the dendrite spacing and the cooling rate are constructed, which may include:

[0083] S33: Based on the statistical number density of the iron-rich phase and the cooling rate distribution in multiple microstructure samples, a scatter plot of the relationship between the number density of the iron-rich phase and the cooling rate is established.

[0084] S34: A relationship model between the number density of the iron-rich phase and the cooling rate is constructed based on the scatter plot of the relationship between the number density of the iron-rich phase and the cooling rate.

[0085] In this embodiment, the relationship model between the characteristic parameters of the iron-rich phase and the cooling rate is also established by the empirical formula method. That is, according to the test results of the microstructure samples in the aforementioned 1#-10# castings, a scatter plot of the relationship between the number density of the iron-rich phase and the cooling rate of different samples can be established, such as Figure 6 As shown. Then the empirical formula for the relationship between cooling rate and the number density of iron-rich phase was established: ρ N =a+kC, where a and k are material-related constants and C is the cooling rate. The relationship model between the number density of the iron-rich phase and the cooling rate is obtained from the data in the relationship scatter plot as ρ N =5.77+0.069C.

[0086] Based on the above technical solution, in one embodiment, after constructing the relationship model between the number density of the iron-rich phase and the cooling rate, and the relationship model between the dendrite spacing and the cooling rate, the following may also be included:

[0087] Step c: using the test data of cooling rate and dendrite spacing to test the relationship model between dendrite spacing and cooling rate; if the test error is less than the set value, the relationship model between dendrite spacing and cooling rate meets the requirements.

[0088] Step d: Use the test data of cooling rate and iron-rich phase number density to test the relationship model between the iron-rich phase number density and cooling rate; if the test error is less than the set value, the relationship model between the iron-rich phase number density and cooling rate meets the requirements.

[0089] In this embodiment, the cooling rates of the 11# and 12# process parameter samples can be respectively substituted into the relationship model between dendrite spacing and cooling rate and the relationship model between the number density of iron-rich phase and cooling rate to obtain the model results of dendrite spacing and iron-rich phase number density, and then the model results of the 11# and 12# process parameter samples are compared and analyzed with the experimental results of dendrite spacing and iron-rich phase number density obtained by shooting statistics to determine whether the error between the model results and the experimental results is less than 10%. If the error between the model results and the experimental results of dendrite spacing is less than 10%, it is considered that the relationship model between dendrite spacing and cooling rate has a higher accuracy; if the error between the model results and the experimental results of iron-rich phase number density is less than 10%, it is considered that the relationship model between iron-rich phase number density and cooling rate has a higher accuracy.

[0090] Furthermore, in one embodiment, before combining the relationship model between the number density of the iron-rich phase and the cooling rate and the relationship model between the dendrite spacing and the cooling rate with the position coordinates of each part of the casting to obtain the distribution characteristics of the iron-rich phase and the dendrite spacing in different parts of the casting, it may also include: exporting the position coordinates of each part of the casting from the casting simulation software. In this embodiment, the position coordinates of each part of the casting are exported from the casting simulation software, and the position coordinates are combined with the relationship model to obtain the distribution characteristics of the iron-rich phase and the dendrite spacing. In other embodiments, the position coordinates do not need to be exported, and the relationship model is directly input into the casting simulation software and combined with the position coordinates in the casting simulation software. Among them, after the 3D data model and the pouring system are imported into the casting simulation software, the pouring process and the gravity direction are set, and the position coordinates of each part of the casting can be automatically generated.

[0091] This method combines microscopic characterization with casting simulation. First, ProCAST is used to perform casting simulation to obtain the distribution of temperature field and solidification time in different parts of the casting. Based on the simulation results, microstructure samples are processed at different positions. OM and SEM-BSE tests are performed on the microstructure samples to obtain the distribution data of the iron-rich phase and dendrite spacing of the samples, and a relationship model between the iron-rich phase, dendrite spacing and cooling rate is constructed. Combining the relationship model with ProCAST casting simulation can realize the prediction of the distribution of iron-rich phase and dendrite spacing of low-pressure cast aluminum alloy castings, which can provide a basis for the prediction of elongation and yield strength of low-pressure aluminum castings, and has important application value for improving the mechanical properties of castings.

[0092] In the description of the present application, it should be noted that the terms "upper", "lower", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application. Unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0093] It should be noted that, in this application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0094] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A method for predicting the distribution of iron-rich phase and dendrite spacing of a casting, characterized in that: The prediction method comprises: Obtain the cooling rate distribution of different parts of the casting under different process parameters; Microstructure specimens were machined at multiple locations of the casting under different process parameters; Based on the statistical distribution data of iron-rich phase and dendrite spacing in multiple microstructure samples, the relationship model between the number density of iron-rich phase and cooling rate, as well as the relationship model between dendrite spacing and cooling rate were constructed; The relationship model between the number density of iron-rich phase and cooling rate and the relationship model between dendrite spacing and cooling rate are combined with the position coordinates of various parts of the casting to obtain the distribution characteristics of iron-rich phase and dendrite spacing in different parts of the casting.

2. The prediction method according to claim 1, characterized in that: The microstructure samples are processed at multiple positions of the casting under different process parameters, including: According to the cooling rate distribution of different parts of the casting, the positions with large cooling rate differences are selected on the casting to process the microstructure samples.

3. The prediction method according to claim 1, characterized in that: After machining microstructure specimens at multiple locations of the casting under different process parameters, it also includes: The microstructure sample is cut along its thickness direction to prepare the metallographic sample; The metallographic samples were photographed to obtain metallographic images, and the distribution data of the dendrite spacing and the distribution data of the iron-rich phase in the metallographic images were statistically analyzed.

4. The prediction method according to claim 3, characterized in that: The metallographic sample is photographed to obtain a metallographic picture, and distribution data of the dendrite spacing in the metallographic picture is counted, including: Take metallographic pictures of the surface layer, segregation zone and core area of ​​the metallographic sample respectively, and enlarge the metallographic pictures uniformly; The dendrite spacing in the metallographic images of the surface layer, segregation zone and core area was determined by the straight line intercept method; The average value of the dendrite spacing in the surface, segregation zone and core area is calculated as the dendrite spacing of the microstructure sample.

5. The prediction method according to claim 3, characterized in that: The metallographic sample is photographed to obtain a metallographic picture, and distribution data of the dendrite spacing and the distribution data of the iron-rich phase in the metallographic picture are counted, including: Metallographic images of the core of the metallographic sample, the two segregation zones on both sides of the core, and the two surface layers on both sides of the core are taken along the thickness direction of the metallographic sample, and then seamless mosaic is performed to obtain the distribution map of the iron-rich phase; The number density of the iron-rich phase is identified and quantitatively counted on the iron-rich phase distribution map to obtain the distribution data of the iron-rich phase.

6. The prediction method according to claim 1, characterized in that: Based on the statistical distribution data of iron-rich phase and dendrite spacing in multiple microstructure samples, the relationship model between the number density of iron-rich phase and cooling rate, as well as the relationship model between dendrite spacing and cooling rate are constructed, including: According to the statistical distribution data of dendrite spacing and cooling rate distribution in multiple microstructure samples, a scatter plot of the relationship between dendrite spacing and cooling rate is established; The relationship model between dendrite spacing and cooling rate was constructed based on the scatter plot of the relationship between dendrite spacing and cooling rate.

7. The prediction method according to claim 1, characterized in that: Based on the statistical distribution data of iron-rich phase and dendrite spacing in multiple microstructure samples, the relationship model between the number density of iron-rich phase and cooling rate, as well as the relationship model between dendrite spacing and cooling rate are constructed, including: According to the statistical number density of iron-rich phase and the distribution of cooling rate in multiple microstructure samples, a scatter plot of the relationship between the number density of iron-rich phase and cooling rate is established; Based on the scatter plot of the relationship between the number density of iron-rich phase and cooling rate, a relationship model between the number density of iron-rich phase and cooling rate was constructed.

8. The prediction method according to claim 1, characterized in that: After constructing the relationship model between the number density of the iron-rich phase and the cooling rate, and the relationship model between the dendrite spacing and the cooling rate, the method further includes: The relationship model between dendrite spacing and cooling rate was tested using the test data of cooling rate and dendrite spacing; If the test error is less than the set value, the relationship model between dendrite spacing and cooling rate meets the requirements; The relationship model between the number density of the iron-rich phase and the cooling rate was tested using the test data of the cooling rate and the number density of the iron-rich phase; If the test error is less than the set value, the relationship model between the number density of the iron-rich phase and the cooling rate meets the requirements.

9. The prediction method according to claim 1, characterized in that: The method of obtaining the cooling rate distribution of different parts of the casting under different process parameters includes: Obtain the solidification time and solid-liquid phase temperature distribution of castings under different process parameters; The solidification time and the solid-liquid phase temperature distribution are substituted into the cooling rate calculation formula to obtain the cooling rate distribution of different parts of the casting under different process parameters.

10. The prediction method according to claim 1, characterized in that: Before obtaining the cooling rate distribution of different parts of the casting under different process parameters, the method further includes: Adjust the casting mold temperature and casting temperature to obtain multiple sets of different process parameters.

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