Method for predicting aging precipitated phase distribution and structure characteristics of aluminum alloy casting

Abstract

The invention provides a method for predicting aging precipitated phase distribution and structure characteristics of an aluminum alloy casting, and the method comprises the following steps: S1, carrying out finite element modeling and heat treatment process numerical simulation on a to-be-tested aluminum alloy casting, and determining the heating time t for enabling the whole casting to reach an aging temperature T; s2, discrete value taking is carried out on the heating time t, node temperature distribution curves at different discrete moments are drawn, interpolation processing is carried out on the temperature interval of the temperature rapid change stage, and a temperature range is obtained; and S3, establishing corresponding precipitated phase microcell databases in different temperature ranges through phase field simulation, establishing a structure information database of the precipitated phase, and obtaining a three-dimensional cloud picture of precipitated phase distribution and structure characteristic information by utilizing visualization processing. Precipitated phase distribution and structure characteristic prediction is carried out on the overall situation of the aluminum alloy casting, the casting aging process exploration period can be shortened, the multi-turn trial and error test cost is reduced, and the casting quality can be improved.

Description

Technical Field

[0001] The invention belongs to the technical field of digital prediction of metal material casting structure, and in particular relates to a method for predicting the distribution and structure characteristics of aging precipitation phases of aluminum alloy castings. Background Art

[0002] The performance of aluminum alloy castings depends not only on their chemical composition, but also on the heat treatment process, especially the formation and distribution of precipitated phases during aging treatment. Aging treatment is to promote the precipitation of specific phases (such as GP zone, θ′, η′, etc.) in its internal microstructure by keeping the aluminum alloy under specific temperature and time conditions, thereby optimizing the mechanical properties of the material.

[0003] Traditionally, the development of aging process for aluminum alloy castings relies on a large number of trial and error tests, which is not only time-consuming and labor-intensive, but also costly. In addition, due to the complexity of casting shape, size and cooling conditions, the aging response of different parts often varies, resulting in uneven distribution of precipitates, which in turn affects the overall performance of the casting. Therefore, the accurate prediction of the distribution and organizational characteristics of precipitates during the aging process of aluminum alloy castings has become the key to improving casting quality, shortening product development cycle and reducing production costs.

[0004] In recent years, with the development of computational materials science, digital technology has been increasingly used in the prediction of the microstructure and properties of metal materials. In particular, the introduction of multi-field coupling simulation technology (such as thermal-mechanical coupling, phase field simulation, etc.) has provided a powerful tool for in-depth understanding of the evolution mechanism of the precipitated phase during the aging process of aluminum alloys. These technologies can simulate the evolution of the microstructure of materials under specific process conditions and provide a theoretical basis for optimizing heat treatment process parameters. Invention content Aiming at the problems of difficulty in accurately predicting the distribution and organizational characteristics of aging precipitation phases of aluminum alloy castings, long exploration cycle of aging process, and high experimental cost, the purpose of the present invention is to provide an efficient and accurate prediction method. Through the simulation of the heat treatment process of aluminum alloy castings, the heating time for the entire casting to reach the aging temperature is determined; by discretizing the time, the temperature set of each node of the casting model at each discrete moment is obtained, and the node coordinates are matched with the temperature one by one; by interpolating the temperature distribution of the nodes at the initial discrete moments of heating, several groups of temperature ranges are obtained; through phase field simulation, a corresponding precipitation phase micro-area database within different temperature ranges is established; through matching node temperatures and visualization processing, a precipitate phase organizational information database is established, and a three-dimensional cloud map of the precipitation phase distribution and organizational characteristic information is obtained.

[0006] To achieve the above object, the technical solution created by the present invention is implemented as follows: A method for predicting the distribution and organizational characteristics of aging precipitation phases of aluminum alloy castings, which specifically includes the following steps: Step 1: Perform finite element modeling and numerical simulation of the heat treatment process of the aluminum alloy casting to determine the heating time t (unit: s) for the entire casting to reach the aging temperature T (unit: °C); Step 2: Discrete the heating time t, the discrete interval is t / n, n is the number of discrete values, and according to the finite element simulation results, obtain the temperature set A of each node of the casting model at each discrete moment i ={T j}, i is 1~n, j is the node number of the casting model; Step 3: Read the data files of each node of the macroscopic temperature field at each discrete moment, and correspond the node coordinates of the casting model to the temperature values ​​one by one; Step 3.1: At each discrete time t / n, 2t / n, 3t / n, …, t, count the number of nodes corresponding to each temperature in ascending order; Step 3.2: Focus on the discrete moments when most nodes of the casting are in the initial stage of heating at the stage of rapid temperature change, i.e., t / n and 2t / n, ignore the discrete moments when the minimum global node temperature exceeds 0.8T, and draw the node temperature distribution curve at the discrete moments; Step 4: Interpolate the temperature intervals during the rapid temperature change phase. Interpolate at 5°C intervals for the temperature interval below T / 2 and at 2.5°C intervals for the temperature interval above T / 2 to obtain m groups of temperature ranges (0~T q ), q ranges from 1 to m; Step 5: According to the microstructure image and thermodynamic parameters of the precipitation phase of the aluminum alloy material, the evolution process of the precipitation phase is simulated using the phase field-based numerical simulation method to calculate the experience m groups (0~T q ) The distribution and organizational characteristics of the precipitated phases in the temperature range change, and the establishment of the precipitated phase micro-area database; Step 6: Establishing a database of organizational information of the precipitated phase; Step 6.1: Match data according to node coordinates to form a database, and the file types in the database are .txt files; Step 6.2: Classify the data and output four data text files, such as Figure 4 As shown, they are area fraction (Area Fraction.txt), aspect ratio (Aspect Ratio.txt), average length (Average Length.txt) and number density (Number Density.txt); Step 7: Visualize the three-dimensional temperature distribution of the casting and the distribution of the organizational information of the precipitated phase; Step 7.1: Based on a programming language framework (such as MATLAB program), the corresponding relationship between the organizational information database of the precipitated phase and the coordinates of each node of the casting model is obtained; Step 7.2: Call the function to obtain a three-dimensional cloud map reflecting the distribution of the precipitated phase and the organizational characteristics.

[0007] Compared with the prior art, the method for predicting the aging precipitation phase distribution and organizational characteristics of aluminum alloy castings created by the present invention has the following advantages: 1. The present invention discloses a method for predicting the distribution and organizational characteristics of aging precipitation phases of aluminum alloy castings. The method comprehensively utilizes phase field simulation, data interpolation method, information database construction and visualization processing, matches the three-dimensional temperature field simulation results with the precipitation phase micro-region database obtained by phase field simulation, and constructs a three-dimensional cloud map for predicting the global aging precipitation phase distribution and organizational characteristics of aluminum alloy castings by compiling model node coordinates.

[0008] 2. The present invention discloses a method for predicting the distribution of precipitation phases and organizational characteristics of aluminum alloy castings during aging. The method predicts the distribution of precipitation phases and organizational characteristics of aluminum alloy castings as a whole, which can shorten the exploration cycle of casting aging process, reduce the cost of multiple rounds of trial and error tests, and is conducive to improving the quality of castings. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The drawings constituting part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments and descriptions of the present invention are used to explain the present invention and do not constitute an improper limitation on the present invention. In the drawings:

[0010] Figure 1 A flowchart of a method for predicting the distribution and organizational characteristics of aging precipitation phases of aluminum alloy castings disclosed in the present invention; Figure 2 : The temperature distribution curves of each node of the aluminum alloy cylinder head model of this embodiment at different aging times (a is 30s, b is 60s, c is 90s, and d is 120s); Figure 3 The phase field simulation results of the precipitation phase distribution and organizational characteristics of 42 groups of temperature interval changes of the aluminum alloy cylinder head model in this embodiment; Figure 4 The data files included in the organizational information database of the precipitated phase in this embodiment; Figure 5 The three-dimensional cloud diagram of the temperature field and the aspect ratio of the precipitate phase of the aluminum alloy cylinder head model in this embodiment: (a1 is the temperature field at 30s, a2 is the aspect ratio of the precipitate phase at 30s, b1 is the temperature field at 60s, and b2 is the aspect ratio of the precipitate phase at 60s). DETAILED DESCRIPTION

[0011] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0012] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0013] like Figure 1 As shown, the present embodiment discloses a method for predicting the distribution and organizational characteristics of aging precipitation phases of aluminum alloy castings, and predicts the distribution, area fraction, aspect ratio, number density and organizational morphology of precipitation phases at different node positions of aluminum alloy cylinder head. The implementation process is as follows Figure 1 As shown, the specific implementation steps are as follows: Step 1: Perform finite element modeling and numerical simulation of the heat treatment process of the Al-Si-Cu-Mg aluminum alloy cylinder head to determine the heating time of 180s for the entire casting to reach the aging temperature of 170°C; Step 2: Discrete the heating time of 180s, with a discrete interval of 30s, and obtain discrete moments of 30s, 60s, 90s, 120s, 150s, and 180s. According to the finite element simulation results, the temperature of each node of the casting model at each discrete moment is obtained; Step 3: Read the data files of each node of the macroscopic temperature field at each discrete moment, and correspond the node coordinates of the casting model to the temperature values ​​one by one; Step 3.1: At each discrete time 30s, 60s, 90s, 120s, 150s, and 180s, count the number of nodes corresponding to each temperature in ascending order; Step 3.2: According to the node temperature distribution curve at each discrete moment ( Figure 2 ) It can be seen that at the initial moment of 30s, the cylinder head wall has reached the aging temperature (170℃), and at the moment of 90s, almost all the node temperatures of the cylinder head have reached the aging temperature (169℃-170℃); considering only the temperature moments of 30s and 60s can reflect the difference in the organizational distribution of the precipitated phase caused by temperature; the temperature distribution at 30s is taken as the initial moment, and the temperature distribution at 60s is taken as the intermediate moment; Step 4: Interpolate the temperature intervals during the rapid temperature change stage, interpolate at 5°C intervals in the temperature range of 55°C to 80°C, and interpolate at 2.5°C intervals for the temperature range above 80°C, to obtain 42 sets of temperature ranges; Step 5: The aging precipitation structure of Al-Si-Cu-Mg aluminum alloy is mainly θ′-Al2Cu needle-shaped precipitation phase and Q′-Al5Cu2Mg8Si6 blocky precipitation phase. According to the microstructure picture and thermodynamic parameters of the precipitation phase, the evolution process of the precipitation phase is simulated by the numerical simulation method based on the phase field, and the distribution and organizational characteristics of the precipitation phase that undergoes 42 sets of temperature intervals are calculated to establish a precipitation phase micro-area database, such as Figure 3 As shown; Step 6: Establishing a database of organizational information of the precipitated phase; Step 6.1: Match data according to node coordinates to form a database, and the file types in the database are .txt files; Step 6.2: Classify the data and output four data text files, such as Figure 4 As shown, they are area fraction (Area Fraction.txt), aspect ratio (Aspect Ratio.txt), average length (Average Length.txt) and number density (Number Density.txt); Step 7: Visualize the three-dimensional temperature distribution of the casting and the distribution of the organizational information of the precipitated phase; Step 7.1: Based on the MATLAB program, obtain the corresponding relationship between the organizational information database of the precipitated phase and the coordinates of each node of the casting model; Step 7.2: Call the function to obtain Figure 5 The three-dimensional cloud map shown reflects the distribution of precipitation phase and organizational characteristics.

[0014] The present invention proposes a method for predicting the distribution and organizational characteristics of precipitation phases during aging of aluminum alloy castings. This method innovatively integrates heat treatment process simulation, discretization, interpolation technology, phase field simulation and visualization technology, aiming to achieve accurate prediction of the distribution and organizational characteristics of precipitation phases in the global range of aluminum alloy castings. Through this method, the number of trial and error tests can be significantly reduced, the optimization process of the aging process can be accelerated, and at the same time, the castings can be ensured to obtain uniform and ideal microstructures, thereby improving their comprehensive mechanical properties and meeting the high-quality requirements of aluminum alloy castings in high-end application fields.

[0015] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting the distribution and organizational characteristics of aging precipitation phases of aluminum alloy castings, characterized in that: The steps include: S1: Conduct finite element modeling and numerical simulation of the heat treatment process of the aluminum alloy casting to determine the heating time t for the entire casting to reach the aging temperature T; S2: Discrete the heating time t, draw the node temperature distribution curve at different discrete moments, interpolate the temperature interval in the stage of rapid temperature change, and obtain the temperature range; S3: According to the microstructure image and thermodynamic parameters of the precipitation phase of aluminum alloy materials, a corresponding precipitation phase micro-area database in different temperature ranges is established through phase field simulation; after matching the node temperature, a precipitate phase organization information database is established, and visualization processing is used to obtain a three-dimensional cloud map of the precipitate phase distribution and organizational characteristic information.

2. The method for predicting the distribution and organizational characteristics of aging precipitation phases of aluminum alloy castings according to claim 1, characterized in that: The discrete value selection of the heating time t in step S2 includes: The discrete interval is t / n, where n is the number of discrete values. According to the finite element simulation results, the temperature set A of each node of the casting model at each discrete moment is obtained. i ={T j }, Where i is 1~n, and j is the node number of the casting model.

3. A method for predicting the distribution and organizational characteristics of aging precipitation phases of aluminum alloy castings according to claim 2, characterized in that: Step S2 also includes reading the data files of each node of the macroscopic temperature field at each discrete moment, and corresponding the node coordinates of the casting model to the temperature value one by one; at each discrete moment t / n, 2t / n, 3t / n, ..., t, according to the ascending order of temperature, counting the number of nodes corresponding to each temperature, and drawing the node temperature distribution curve at different discrete moments.

4. The method for predicting the distribution and organizational characteristics of aging precipitation phases of aluminum alloy castings according to claim 2, characterized in that: Step S2 also includes reading the data files of each node of the macroscopic temperature field at each discrete moment, and corresponding the node coordinates of the casting model to the temperature value one by one; at each discrete moment t / n, 2t / n, 3t / n, ..., t, according to the ascending temperature, counting the number of nodes corresponding to each temperature, focusing on the discrete moment of the initial heating (t / n, 2t / n) when most nodes of the casting are in the stage of rapid temperature change, ignoring the discrete moment when the minimum global node temperature exceeds 0.8T, and drawing the node temperature distribution curve at the discrete moment.

5. The method for predicting the distribution and organizational characteristics of aging precipitation phases of aluminum alloy castings according to claim 1, characterized in that: In step S2, the temperature intervals in the rapid temperature change stage are interpolated, and the temperature intervals below the aging temperature T / 2 are interpolated at intervals of 5°C, and the temperature intervals above the aging temperature T / 2 are interpolated at intervals of 2.5°C, to obtain m groups of temperature ranges (0~T q ), q ranges from 1 to m.

6. The method for predicting the distribution and structural characteristics of aging precipitation phases of aluminum alloy castings according to claim 1, characterized in that: In step S3, according to the microstructure image and thermodynamic parameters of the precipitation phase of the aluminum alloy material, the evolution process of the precipitation phase is simulated by a numerical simulation method based on phase field, and the m groups (0~T q ) The distribution and organizational characteristics of the precipitated phases in different temperature ranges are analyzed, and a precipitated phase micro-area database is established.

7. The method for predicting the distribution and structural characteristics of aging precipitation phases of aluminum alloy castings according to claim 1, characterized in that: In step S3, establishing a precipitate phase organization information database according to establishing a precipitate phase micro-area database includes matching the precipitate phase number density, area fraction and aspect ratio data in the precipitate phase micro-area database according to the temperature field distribution and the node coordinates to establish a precipitate phase organization information database.

8. The method for predicting the distribution and structural characteristics of aging precipitation phases of aluminum alloy castings according to claim 7, characterized in that: The data is matched according to the node coordinates to form a database. The file type in the database is a .txt file, and a total of four data files are output, namely area fraction, aspect ratio, average length and number density.

9. The method for predicting the distribution and structural characteristics of aging precipitation phases of aluminum alloy castings according to claim 1, characterized in that: In step S3, based on the organizational information database of the precipitated phase and the node coordinates of the casting model, the three-dimensional temperature distribution of the casting and the organizational information distribution of the precipitated phase are visualized to establish a three-dimensional cloud map reflecting the distribution of the precipitated phase and the organizational characteristic information.

10. The method for predicting the distribution and structural characteristics of aging precipitation phases of aluminum alloy castings according to claim 1, characterized in that: In step S3, the correspondence between the organizational information database of the precipitated phase and the coordinates of each node of the casting model is obtained based on the programming language framework, and then the function is called to obtain a three-dimensional cloud map reflecting the distribution and organizational characteristic information of the precipitated phase.

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