Method for obtaining structure distribution of aluminum alloy part according to manufacturing process
By establishing dynamic and thermodynamic models of manufacturing process parameters and the structure of aluminum alloy parts, combining simulation and experiments, the tissue distribution of aluminum alloy parts is obtained, and the problems of inefficiency and high cost in the existing technology are solved, and efficient and accurate tissue distribution research is achieved.
Patent Information
- Application Number
- CN202510227266.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-27
- Publication Date
- 2025-07-18
AI Technical Summary
The prior art is difficult to efficiently and accurately obtain the tissue distribution of various parts of aluminum alloy parts, especially on parts with complex scales and structures, resulting in inefficient research and high cost.
By establishing dynamic and thermodynamic models of manufacturing process parameters and aluminum alloy parts structure, combining simulation and experiments, a material-level tissue distribution with minimal error and meeting engineering requirements is obtained, a database is established and the organization of each position of the part is obtained using mapping relationships.
It improves the accuracy and efficiency of the structure distribution research of aluminum alloy parts, reduces costs, saves time and resources, and is suitable for the tissue distribution research of complex parts.
Smart Images
Figure CN120337487A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the technical field of the microstructure distribution of aluminum alloy parts, and particularly relates to a method for obtaining the microstructure distribution of aluminum alloy parts according to the manufacturing process. Background Art
[0002] The use of aluminum instead of steel to manufacture parts is widely applied in fields such as aerospace and automotive manufacturing. Studying the microstructure of parts is of utmost importance for evaluating the applicability of parts, directly determining the yield rate, service conditions, and lifespan of parts, etc. The microstructures of different parts of aluminum alloy parts are different. Taking the secondary dendrite microstructure of an aluminum alloy single-cylinder head of a diesel engine as an example, the secondary dendrite arm spacing between the firing surface and the airway wall differs by 40 - 80 μm. Studying the microstructure distribution of aluminum alloy parts will improve the accuracy of evaluating the applicability of parts, which has very important reasons.
[0003] Regarding the microstructure distribution of aluminum alloy parts, in the prior art, the methods for studying the microstructure of aluminum alloy parts are all experimental, and only the microstructure data of some parts of the parts can be obtained. It is impossible to obtain the microstructure distribution of aluminum alloy parts. Some parts of the parts have small sizes and complex structures, which do not meet the requirements for experimental sample preparation, and thus the microstructure data cannot be obtained. Taking an aluminum alloy engine block of a diesel engine as an example, samples are taken at key positions such as partitions and water cavities, and the grain microstructure is observed using a metallurgical microscope to count the grain size, thereby obtaining the grain microstructure data of key positions such as partitions and water cavities, but it is impossible to obtain the grain microstructure distribution of the aluminum alloy engine block of the diesel engine. Since the parts are in the millimeter scale and above, while the microstructure is in the micrometer scale or even nanometer scale, although it is very important to obtain the microstructure distribution of aluminum alloy parts, it is very difficult.
[0004] Regarding the microstructure at the aluminum alloy material level, in the prior art, there are many methods for studying the microstructure at the material level. For example, the microstructure at the material level is studied using experiments, the eutectic phase microstructure of aluminum alloy is calculated using the phase field method, the grains of aluminum alloy are calculated using the cellular automaton, the secondary dendrites of aluminum alloy are calculated using molecular dynamics, and the precipitation phases of aluminum alloy are calculated using the first-principles method. These methods can only study the microstructure of aluminum alloy materials at a specific manufacturing process.
[0005] The difficulty in applying the research method of aluminum alloy material-level microstructure to the study of the microstructure distribution of aluminum alloy parts lies in the following aspects: ① For aluminum alloy parts, for a specific part, although the manufacturing process is the same, the specific processes in different parts of the part are completely different. Taking an aluminum alloy cylinder head as an example, for the same cylinder head with the same manufacturing process, the solidification processes of the cylinder head top plate, bottom plate, and airway wall are completely different. The solidification rate of the airway wall is the slowest, resulting in a larger microstructure size. ② At the aluminum alloy material level, the experimental method has a long cycle, requiring sample preparation, observation with special instruments (such as metallographic microscopes, transmission electron microscopes, etc.), and data analysis (such as IPP statistical software, Origin analysis software, etc.), with a relatively high cost. If all the specific processes corresponding to different parts of the aluminum alloy part are studied through experiments to obtain the microstructure distribution of the aluminum alloy part, the experimental cycle will be very long and the cost will be very high. Moreover, it is impossible to completely use experimental research because the specific processes of different parts of the part gradually transition rather than in a stepped manner, and it is impossible to distinguish them one by one. Computational methods such as phase field method, molecular dynamics, and first-principles method need to solve many kinetic and thermodynamic equations, with a long calculation cycle. Each time, only a certain manufacturing process can be calculated, such as the size of the secondary dendritic microstructure at a solidification rate of 3 K / s. Since the specific processes of different parts of the part are different and cover a wide range, if calculated one by one, it will take a very long time, even several months. In addition, attention should be paid to the calculation accuracy of the computational method.
[0006] Improving the research efficiency and ensuring accuracy will greatly improve the efficiency of the study of the microstructure distribution of aluminum alloy parts, saving manpower and material resources. In the existing technology, neural network and self-learning methods are widely used. These methods are highly efficient, and the more data available for training and learning, the more accurate the prediction results. However, these methods are only data sorting. A large amount of experimental data is accumulated in the early stage, and then the self-learning method is used to find the mathematical relationship between the data, so as to predict the microstructure under other conditions. These methods have no actual physical or chemical meaning and have certain limitations.
[0007] Based on this, the present invention combines efficiency, accuracy, physical and chemical meaning, and provides a method for obtaining the microstructure distribution of aluminum alloy parts according to the manufacturing process. Contents of the Invention In view of this, the present invention aims to propose a method for obtaining the microstructure distribution of aluminum alloy parts according to the manufacturing process to solve at least one technical problem in the background technology.
[0009] To achieve the above object, the technical solution of the present invention is realized as follows: A method for obtaining the microstructure distribution of aluminum alloy parts according to the manufacturing process includes the following steps: S1: Obtain the manufacturing process parameters of the aluminum alloy part microstructure and establish a model for the relationship between the aluminum alloy part microstructure and the manufacturing process parameters; S2: Modify the model obtained in step S1 to obtain the microstructure of the material-level aluminum alloy parts with the minimum error and meeting the engineering requirements. S3: Establish a database of manufacturing process parameters and the microstructure of the material-level aluminum alloy parts obtained in step S2, and obtain the mapping relationship between the manufacturing process parameters and the material microstructure according to the database. S4: Obtain the manufacturing process parameters at each position of the part. S5: Obtain the distribution of the aluminum alloy part microstructure according to the manufacturing process parameters obtained in step S4 and the mapping relationship in step S3.
[0010] Further, the manufacturing process parameters in step S1 include one or more of solidification rate, aging temperature, and solution time.
[0011] Further, the aluminum alloy part microstructure in step S1 includes one or more of secondary dendrites, grains, and eutectic silicon.
[0012] Further, the modeling in step S1 includes establishing a kinetic equation model and a thermodynamic equation model based on the aluminum alloy part microstructure and manufacturing process parameters.
[0013] Further, the kinetic equation model in step S1 includes one or more of field equations, molecular dynamics equations, and cellular automaton equations.
[0014] Further, in step S2, the model obtained in step S1 is modified to obtain the microstructure of the material-level aluminum alloy parts with the minimum error and meeting the engineering requirements.
[0015] Further, establishing the database of manufacturing process parameters and the microstructure of the material-level aluminum alloy parts obtained in step S2 in step S3 includes changing the manufacturing process parameters, obtaining the material-level microstructure under this process condition, and repeating the above operations to establish the manufacturing process parameters and the microstructure of the material-level aluminum alloy parts.
[0016] Further, obtaining the manufacturing process parameters at each position of the part in step S4 includes using a combination of simulation and experiment to obtain the specific manufacturing process parameters at each position of the part.
[0017] Further, each position of the part in step S4 is one of each region, each part, each grid cell, or each node.
[0018] Compared with the prior art, the method for obtaining the distribution of the aluminum alloy part microstructure according to the manufacturing process of the present invention has the following advantages: (1) Regarding the relationship between the manufacturing process parameters in step S1 of this application and the aluminum alloy part microstructure, there are a large number of basic research papers, which can be directly summarized from the papers without the need for a large number of basic experiments.
[0019] (2) This application calculates the material-level microstructure using equations such as kinetics and thermodynamics, taking into account physical and chemical meanings, rather than just the application of mathematical relationships.
[0020] (3) In step S2 of this application, the calculation results are experimentally corrected, and the results have high accuracy. In step S3 of the present invention, as the amount of calculation increases and the amount of data in the database increases, the relationship in step S5 will become more and more accurate. The results of the present invention have very good accuracy.
[0021] (4) In step S3 of the present invention, the database is established from the calculation results. Compared with the database established from the experimental results, it has lower cost, larger amount of data, and wider coverage of data types.
[0022] (5) In steps S2 and S3 of the present invention, although it takes a long period to calculate the material microstructure using equations such as kinetics and thermodynamics, first of all, the time required for these equation calculations can be arranged in daily life, without occupying other transaction times, and it is not necessarily necessary to perform the calculations only when studying this part, which will lengthen the part research cycle. Secondly, with the establishment of the database and mapping relationship in step S3, when studying aluminum alloy parts of the same type, the mapping relationship can be directly applied, skipping the calculation process of equations such as kinetics and thermodynamics, which will greatly save time and resources. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] The drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The schematic embodiments and descriptions thereof of the present invention are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings: Figure 1 It is the calculation result of the secondary dendrite at the material level of the aluminum-silicon alloy in Example 1; Figure 2 It is the experimentally corrected result of the secondary dendrite at the material level of the aluminum-silicon alloy in Example 1; Figure 3 It is the solidification rate nephogram of the aluminum-silicon alloy cylinder head in Example 1; Figure 4 It is a partial screenshot of the data file of the grid nodes and solidification rate of the aluminum-silicon alloy cylinder head in Example 1; Figure 5 It is the secondary dendrite nephogram of the aluminum-silicon alloy cylinder head in Example 1; Figure 6 It is a partial screenshot of the data file of the grid nodes and secondary dendrites of the aluminum-silicon alloy cylinder head in Example 1; Figure 7 It is a screenshot during the calculation of the phase field equation; Figure 8 It is the solidification rate nephogram of the aluminum-silicon alloy engine block in Example 2; Figure 9 Partial screenshot of the data file of the grid nodes and solidification rate of the aluminum-silicon alloy body in the second embodiment; Figure 10 Continuous eutectic silicon aspect ratio nephogram of the aluminum-silicon alloy body in the second embodiment; Figure 11 Partial screenshot of the data file of the grid nodes and continuous eutectic silicon of the aluminum-silicon alloy body in the second embodiment. Specific implementation manners
[0024] It should be noted that, without conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.
[0025] The present invention will be described in detail below with reference to the drawings and in combination with embodiments.
[0026] A method for obtaining the tissue distribution of aluminum alloy parts according to the manufacturing process, including the following steps: S1. Study the relationship between manufacturing process parameters and tissue: For a certain tissue, determine the manufacturing process parameters of the tissue.
[0027] S2. Obtain the material-level tissue: According to the relationship in S1, establish equations such as kinetics and thermodynamics, and calculate the material-level tissue under a certain manufacturing process condition.
[0028] S3. Modify the result of step S2: Use experiments to modify the kinetics, thermodynamics and other equations in S2 to obtain the material-level tissue with the smallest error and meeting the engineering requirements.
[0029] S4. Establish a database of manufacturing process parameters and material-level tissue: According to the result of S3, change the manufacturing process parameters, obtain the material-level tissue under this process condition, and repeat this way to establish a database of manufacturing process parameters and material-level tissue.
[0030] S5. Establish a mapping relationship between manufacturing process parameters and material tissue: Use the database in S4 to establish a mapping relationship between manufacturing process parameters and material tissue.
[0031] S6. Obtain the specific manufacturing process parameters of each position of the part: Use a combination of simulation and experiments to obtain the specific manufacturing process parameters of each position of the part.
[0032] S7. Obtain the tissue distribution of the aluminum alloy part: According to the specific manufacturing process parameters of each position of the part in S6, use the mapping relationship between manufacturing process parameters and material tissue in S5 to obtain the tissue distribution of the aluminum alloy part.
[0033] In step S1, the manufacturing process parameters include but are not limited to solidification rate, aging temperature, solution time, etc.
[0034] In step S1, the tissues include but are not limited to secondary dendrites, grains, eutectic silicon, etc.
[0035] In step S2, the equations such as kinetics and thermodynamics include but are not limited to field equations, molecular dynamics equations, cellular automaton equations, etc.
[0036] In step S6, each position of the part can be each region (partition evaluation), or each part, or even each grid cell and node.
[0037] Example 1: Select the aluminum alloy part as an aluminum-silicon alloy cylinder head and select the tissue as secondary dendrites.
[0038] S1. Study the relationship between manufacturing process parameters and tissues: For the secondary dendrite tissue, determine the manufacturing process parameter of this tissue as the solidification rate.
[0039] S2. Obtain the material-level tissue: According to the relationship in S1, establish a phase field equation and calculate the secondary dendrites of the aluminum-silicon alloy at the material level with a solidification rate of 1.2 K / s, as Figure 1 shown in the calculation results.
[0040] S3. Modify the result of step S2: Use experiments to modify the phase field equation in S2 to obtain secondary dendrites of the aluminum-silicon alloy at the material level with small errors, as Figure 2 shown.
[0041] S4. Establish a database of manufacturing process parameters and material-level tissues: According to the results in S3, change the solidification rate and obtain the secondary dendrites of the aluminum-silicon alloy at the material level at this solidification rate. Repeat this to establish a database of solidification rate and secondary dendrites.
[0042] S5. Establish a mapping relationship between manufacturing process parameters and material tissues: Use the database in S4 to establish a mapping relationship between the solidification rate and secondary dendrites.
[0043] S6. Obtain the specific manufacturing process parameters at each position of the part: Use a combination of simulation and experiments to obtain the solidification rate of each grid node of the aluminum-silicon alloy cylinder head, as Figure 3 shown in the solidification rate contour map, and as Figure 4 shown in a partial screenshot of the data file of grid nodes and solidification rate.
[0044] S7. Obtain the tissue distribution of the aluminum alloy part: Based on the specific solidification rate of each node of the aluminum-silicon alloy cylinder head in S6, use the mapping relationship between the solidification rate and secondary dendrites in S5 to obtain the secondary dendrite distribution of the aluminum-silicon alloy cylinder head, as Figure 5 shown in the secondary dendrite contour map, and as Figure 6 shown in a partial screenshot of the data file of grid nodes and secondary dendrites.
[0045] In this embodiment, the research on the secondary dendrite distribution of the aluminum-silicon alloy cylinder head has been completed. When conducting research on the secondary dendrite distribution of other parts of the aluminum-silicon alloy, only steps S6 and S7 need to be executed, saving a large amount of time.
[0046] As Figure 7 shown in the screenshot during the calculation of the phase field equation, the cooling rate (solidification rate) is approximately 0.09 K / s. At this time, there is still 1 day and 23 hours of calculation time remaining, that is, it takes about 2 days for one solidification rate calculation, and the cycle is very long.
[0047] Example 2: Select the aluminum alloy part as the aluminum-silicon alloy engine block, and select the structure as eutectic silicon.
[0048] S1. Study the relationship between manufacturing process parameters and structure: For the eutectic silicon structure, determine the manufacturing process parameter of this structure as the solidification rate.
[0049] S2. Obtain the material-level structure: According to the relationship in S1, establish a phase field equation and calculate the eutectic silicon at the material level of the aluminum-silicon alloy when the solidification rate is 1.2 K / s.
[0050] S3. Correct the result of step S2: Use experiments to correct the phase field equation in S2 to obtain the eutectic silicon at the material level of the aluminum-silicon alloy with small errors.
[0051] S4. Establish a database of manufacturing process parameters and material-level structures: According to the result of S3, change the solidification rate to obtain the eutectic silicon at the material level of the aluminum-silicon alloy at this solidification rate, and repeat this process to establish a database of solidification rate and eutectic silicon.
[0052] S5. Establish a mapping relationship between manufacturing process parameters and material structures: Use the database in S4 to establish a mapping relationship between the solidification rate and eutectic silicon.
[0053] S6. Obtain the specific manufacturing process parameters at each position of the part: Use a combination of simulation and experiments to obtain the solidification rate of each grid node of the aluminum-silicon alloy engine block. As Figure 8 shown in the solidification rate contour map, and as Figure 9 shown in a partial screenshot of the data file of grid nodes and solidification rate.
[0054] S7. Obtain the tissue distribution of the aluminum alloy part: Based on the specific solidification rate of each node of the aluminum-silicon alloy engine block in S6, use the mapping relationship between the solidification rate and eutectic silicon in S5 to obtain the eutectic silicon distribution of the aluminum-silicon alloy cylinder head. As Figure 10 shown in the aspect ratio contour map of eutectic silicon, and as Figure 11 shown in a partial screenshot of the data file of grid nodes and eutectic silicon.
[0055] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.
Claims
1. A method for obtaining the tissue distribution of aluminum alloy parts according to the manufacturing process, characterized in that: The method includes the following steps: S1: Obtain the manufacturing process parameters of the aluminum alloy part structure, and establish a model for the relationship between the aluminum alloy part structure and the manufacturing process parameters; S2: Modify the model obtained in step S1 to obtain the aluminum alloy part structure at the material level with the minimum error and meeting the engineering requirements; S3: Establish a database of the manufacturing process parameters and the aluminum alloy part structure obtained in step S2, and obtain the mapping relationship between the manufacturing process parameters and the material structure according to the database; S4: Obtain the manufacturing process parameters of each position of the part; S5: Obtain the distribution of the aluminum alloy part structure according to the manufacturing process parameters obtained in step S4 and the mapping relationship in step S3.
2. The method for obtaining the microstructure distribution of an aluminum alloy part according to the manufacturing process as claimed in claim 1, wherein: The manufacturing process parameters in step S1 include one or more of the solidification rate, aging temperature, and solution time.
3. A method for obtaining the microstructure distribution of aluminum alloy parts according to the manufacturing process as claimed in claim 1, characterized in that: The aluminum alloy part structure in step S1 includes one or more of secondary dendrites, grains, and eutectic silicon.
4. A method for obtaining the microstructure distribution of aluminum alloy parts according to the manufacturing process as claimed in claim 1, characterized in that: The modeling in step S1 includes establishing a kinetic equation model and a thermodynamic equation model based on the aluminum alloy part structure and the manufacturing process parameters.
5. A method for obtaining the tissue distribution of an aluminum alloy part according to the manufacturing process as described in claim 4, characterized in that: The kinetic equation model in step S1 includes one or more of the field equation, molecular dynamics equation, and cellular automaton equation.
6. A method for obtaining the microstructure distribution of an aluminum alloy part according to the manufacturing process as claimed in claim 1, characterized in that: In step S2, the model obtained in step S1 is modified to obtain the aluminum alloy part structure at the material level with the minimum error and meeting the engineering requirements.
7. A method for obtaining the tissue distribution of an aluminum alloy part according to the manufacturing process as claimed in claim 1, characterized in that: In step S3, establishing a database of the manufacturing process parameters and the aluminum alloy part structure obtained in step S2 includes changing the manufacturing process parameters, obtaining the material structure under this process condition, and repeating the above operations to establish the mapping between the manufacturing process parameters and the aluminum alloy part structure.
8. A method for obtaining the microstructure distribution of an aluminum alloy part according to the manufacturing process as claimed in claim 1, characterized in that: In step S4, obtaining the manufacturing process parameters of each position of the part includes using a combination of simulation and experiment to obtain the specific manufacturing process parameters of each position of the part.
9. A method for obtaining the microstructure distribution of an aluminum alloy part according to the manufacturing process as claimed in claim 1, characterized in that: Each position of the part in step S4 is one of each region, each part, each grid cell, or each node.