CN113239587A - Shrinkage cavity and shrinkage porosity prediction method for hot chamber die casting - Google Patents
Shrinkage cavity and shrinkage porosity prediction method for hot chamber die casting Download PDFInfo
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Abstract
The invention provides a shrinkage cavity and shrinkage porosity prediction method for hot chamber die casting, which comprises the following steps: step S1, importing configuration files and model information, performing solver pretreatment, setting control parameters of a shrinkage cavity shrinkage porosity algorithm, and predicting the total shrinkage of the casting; step S2, calculating the position and shape information of the isolated thermal node at every preset time step; step S3, calculating the shrinkage value of the current thermal node according to the total shrinkage, and filtering unreasonable thermal node information; step S4, when each thermal node shrinks below the limit value, counting the thermal node information; and step S5, summarizing all recorded hot spot information to form a shrinkage distribution map and outputting the shrinkage distribution map to a result file.
Description
Technical Field
The invention relates to the technical field of pressure injection molding, in particular to a shrinkage cavity and shrinkage porosity prediction method for hot chamber die casting.
Background
In hot chamber die casting, in the solidification process after mold filling, metal liquid shrinks when being cooled, so that shrinkage cavities and shrinkage porosity are generated, and the service life and the product quality of castings are seriously influenced by the shrinkage cavities and the shrinkage porosity. Due to the limitation of the technical level, qualitative speculation is usually carried out by means of years of experience of workers or some empirical formulas when shrinkage cavities and shrinkage porosities are predicted, and prediction is not accurate. If quantitative simulation is needed, numerical simulation needs to be performed by using modular flow software, and meanwhile, the numerical simulation can be realized only by using hardware facilities such as efficient parallel computing algorithm and high-performance computing, and various limitations make it difficult to make an accurate algorithm for shrinkage cavity and shrinkage porosity prediction.
Disclosure of Invention
The object of the present invention is to solve at least one of the technical drawbacks mentioned.
Therefore, the invention aims to provide a shrinkage cavity and shrinkage porosity prediction method for hot-chamber die casting.
In order to achieve the above object, an embodiment of the present invention provides a shrinkage cavity and shrinkage porosity prediction method for hot chamber die casting, including:
step S1, importing configuration files and model information, performing solver pretreatment, setting control parameters of a shrinkage cavity shrinkage porosity algorithm, and predicting the total shrinkage of the casting;
step S2, calculating the position and shape information of the isolated thermal node at every preset time step;
step S3, calculating the shrinkage value of the current thermal node according to the total shrinkage, and filtering unreasonable thermal node information;
step S4, when each thermal node shrinks below the limit value, counting the thermal node information;
and step S5, summarizing all recorded hot spot information to form a shrinkage distribution map and outputting the shrinkage distribution map to a result file.
Further, in step S2, the position and shape information of the isolated hot spot is calculated by using a breadth-first search or a depth-first search algorithm.
Further, in the step S3, the filtered unreasonable hot spot information includes: isolated thermal junctions, unreasonably small thermal junctions of the runner section.
Further, in the step S4, the hot spot information includes: the method comprises the steps of obtaining the position and shape information of the thermal node, and calculating the temperature centroid and shrinkage cavity fraction of the thermal node according to the position and shape information of the thermal node.
Further, in the step S4, the statistical hot spot information includes the following steps:
(1) acquiring the distribution conditions of a maximum isolated domain, a current isolated hot spot and an isolated domain at the last moment in the current state;
(2) constructing a topological relation between the current isolated hot spot and the isolated domain at the last moment;
(3) calculating the minimum volume of each isolated thermal node, generating a new maximum isolated domain array, and assigning to the isolated domain at the last moment;
(4) and repeating iteration until the current thermal section number is calculated to be 0, stopping counting, and starting to output data.
Further, in the step (3), calculating the volume of a shrinkage cavity generated after each thermal joint is shrunk according to the thermal expansion coefficient of the currently filled molten metal; when the thermal node shrinks below the minimum volume, the thermal node is regarded as a space domain and is stored in a space domain sequence; if a hot spot shrinks to 0 directly from the last time to the current time, the hot spot is also regarded as an airspace; eliminating the airspace in the current isolated hot section to obtain a current isolated domain; and adding the space domain at the last moment, the newly generated space domain at the current moment and the current isolated domain to obtain the maximum isolated domain.
Further, in step S5, a multi-thread parallel computation is adopted, and each computation thread writes out data of its own block structure to the result file at the same time.
According to the shrinkage cavity and shrinkage cavity prediction method for hot chamber die casting, a data framework with parallel multilayer and block structures is adopted, and a quantitative shrinkage cavity and shrinkage cavity prediction method with high performance and high accuracy is developed on the basis of a CAE solver. The invention aims to realize the prediction of shrinkage cavity and shrinkage porosity in the hot chamber die casting process by using the CAE technology. By using a highly parallelized data architecture, the calculation precision is ensured, and meanwhile, the calculation time of the algorithm is greatly reduced, so that the method has industrial application value.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a block flow diagram of a shrinkage cavity and shrinkage porosity prediction method for hot cell die casting according to an embodiment of the present invention;
FIG. 2 is a flow chart of a shrinkage cavity and shrinkage porosity prediction method for hot cell die casting according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an example of a Lenz sphere according to an embodiment of the invention;
FIG. 4 is a schematic diagram of computing isolated hotspots according to an embodiment of the invention;
FIG. 5 is a diagram illustrating statistical hot spot information according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
As shown in fig. 1, a shrinkage cavity and shrinkage porosity prediction method for hot-chamber die casting according to an embodiment of the present invention includes:
and step S1, importing the configuration file and the model information, carrying out pre-treatment by a solver, setting control parameters of a shrinkage cavity shrinkage porosity algorithm, and predicting the total shrinkage of the casting.
Specifically, as shown in the example of the lenger sphere in fig. 3, the temperature change process of the sphere after high-temperature condensation was simulated. The estimated grid quantity before calculation is 5,369,976. The frequency of calculating the solidification defect is set to be once every 10 time steps, and variable parameters such as the thermal expansion coefficient of the calculation example are set. Because the surface of the casting is not condensed in the early solidification stage, and the shrinkage generated by the solidification of the molten metal cannot be wrapped in the casting, a time node for starting to count the thermal nodes needs to be arranged and is led into the model.
Step S2, calculating the position and shape information of the isolated thermal segment at every preset time step.
And when the set time node is reached, starting to calculate. The isolated hotspots are calculated based essentially on an isolated domain algorithm, which may be calculated using a breadth-first search or a depth-first search algorithm. Calculation process as shown in fig. 4, during the solidification shrinkage process of the casting, the liquid parts of the casting gradually become isolated from each other from complete communication, and shrinkage cavities and shrinkage porosity are generated.
In the embodiment of the invention, the position and shape information of the isolated hot spot is calculated by adopting an breadth-first search algorithm or a depth-first search algorithm.
And step S3, calculating the shrinkage value of the current thermal node according to the total shrinkage, and filtering unreasonable thermal node information.
In the calculation process, because the mesh subdivision is too small, a large number of extremely small isolated thermal nodes can be generated in the calculation, and the thermal nodes have no significance for guiding production work, even have an interference effect and are required to be removed. At the same time, the isolated hot spot of the runner section should also be deleted, and the hot spot of the runner section is generally solidified at the latest.
In this step, the filtered unreasonable hot spot information includes: isolated thermal junctions, unreasonably small thermal junctions of the runner section.
In step S4, when each thermal node shrinks below the limit value, the thermal node information is counted.
In an embodiment of the invention, the hot spot information comprises: the position and shape information of the thermal node, and the temperature centroid and the shrinkage cavity fraction of the thermal node calculated according to the position and shape information of the thermal node.
Iterative solution of isolated hot section information is required. To store isolated thermal section information, we need at least three arrays to complete the operation: last time step isolated domain, maximum isolated domain, current isolated domain. In order to count the positions of thermal nodes with possible defects, a certain memory space needs to be opened up for storing the thermal nodes which are shrunk and disappeared, and the thermal nodes are marked as a 'space domain'. The maximum isolated domain stores the union set of the current isolated domain and the airspace, and after each calculation is finished, the value of the maximum isolated domain is assigned to the isolated domain of the last time step, and the iteration is repeated in such a way. Therefore, it is easy to easily estimate that the proportion of the airspace in the maximum isolated domain gradually increases with the lapse of the solidification time, and after the calculation is completed, all the airspaces are stored in the entire maximum isolated domain. These airspaces are the distribution of the computed shrinkage cavities and shrinkage porosity.
Referring to FIG. 5, taking a one-step iteration as an example, briefly state the process from isolated hot spot statistics to isolated domain arrays:
(1) and acquiring the distribution conditions of the maximum isolated domain, the current isolated hot section and the isolated domain at the last moment in the current state.
(2) And constructing a topological relation between the current isolated hot section and the isolated domain at the last moment. When a hot spot shrinks, it may be split into multiple parts, so that the isolated domain at the previous time may correspond to several current isolated hot spots, and the isolated domains at the previous time may not correspond to one current isolated hot spot. Therefore, a one-to-many topology table needs to be established to describe the corresponding relationship.
(3) And calculating the minimum volume of each isolated thermal node, generating a new maximum isolated domain array, and assigning to the isolated domain at the last moment. According to the thermal expansion coefficient of the current filling molten metal, the volume of the shrinkage cavity generated after each thermal joint is shrunk can be accurately calculated. When the thermal node shrinks below the minimum volume we have calculated, it is meaningless to calculate further down, and the thermal node should be considered as "spatial domain" and stored in the spatial domain sequence. A hot spot should also be considered as spatial if it shrinks directly to 0 from the last time to the current time. Therefore, the airspace in the current isolated hot section is removed to form the current isolated domain; and adding the space domain at the last moment, the newly generated space domain at the current moment and the current isolated domain to obtain the maximum isolated domain.
(4) And repeating iteration until the current thermal section number is calculated to be 0, and stopping counting. The data output is started.
And step S5, summarizing all recorded hot spot information to form a shrinkage distribution map and outputting the shrinkage distribution map to a result file.
Namely, all the recorded values are summarized, and after the overall calculation is finished, a shrinkage porosity distribution map is formed and output to a result file.
In the step, binary calculation result files are output to a hard disk for storage at intervals of a plurality of time steps, and due to the fact that multi-thread parallel calculation is adopted, each calculation thread writes out data of own block structure to the calculation result files at the same time.
As shown in fig. 2, the method for predicting shrinkage cavity and shrinkage porosity of hot chamber die casting according to the embodiment of the present invention includes the following steps:
(1) counting the number of grids on the surface of the casting;
(2) judging whether the grid number of the surface of the casting is larger than the initial percentage, and if so, executing (3); otherwise, executing (1);
(3) judging whether the number of the isolated domains is larger than 0, if so, executing (4), otherwise, stopping iteration;
(4) filtering undersized isolated domains;
(5) filtering the isolated region of the pouring channel;
(6) calculating a maximum isolated domain;
(7) calculating the shrinkage porosity volume of each isolated domain;
(8) calculating a temperature centroid;
(9) calculating the shrinkage cavity fraction of each unit, and then returning to the step (3); and iteratively calculating an output result file according to the specified frequency.
According to the shrinkage cavity and shrinkage cavity prediction method for hot chamber die casting, a data framework with parallel multilayer and block structures is adopted, and a quantitative shrinkage cavity and shrinkage cavity prediction method with high performance and high accuracy is developed on the basis of a CAE solver. The invention aims to realize the prediction of shrinkage cavity and shrinkage porosity in the hot chamber die casting process by using the CAE technology. By using a highly parallelized data architecture, the calculation precision is ensured, and meanwhile, the calculation time of the algorithm is greatly reduced, so that the method has industrial application value.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A shrinkage cavity and shrinkage porosity prediction method for hot chamber die casting is characterized by comprising the following steps:
step S1, importing configuration files and model information, performing solver pretreatment, setting control parameters of a shrinkage cavity shrinkage porosity algorithm, and predicting the total shrinkage of the casting;
step S2, calculating the position and shape information of the isolated thermal node at every preset time step;
step S3, calculating the shrinkage value of the current thermal node according to the total shrinkage, and filtering unreasonable thermal node information;
step S4, when each thermal node shrinks below the limit value, counting the thermal node information;
and step S5, summarizing all recorded hot spot information to form a shrinkage distribution map and outputting the shrinkage distribution map to a result file.
2. A shrinkage cavity and shrinkage cavity prediction method for hot chamber die casting according to claim 1, wherein in said step S2, position and shape information of an isolated thermal node is calculated using a breadth-first search or a depth-first search algorithm.
3. A shrinkage cavity and shrinkage porosity prediction method for die casting of a hot chamber as claimed in claim 1, wherein in said step S3, the filtered unreasonable thermal section information includes: isolated thermal junctions, unreasonably small thermal junctions of the runner section.
4. A shrinkage cavity and shrinkage porosity prediction method for hot-chamber die casting according to claim 1, wherein in said step S4, said thermal section information includes: the method comprises the steps of obtaining the position and shape information of the thermal node, and calculating the temperature centroid and shrinkage cavity fraction of the thermal node according to the position and shape information of the thermal node.
5. A shrinkage cavity and shrinkage porosity prediction method for hot-chamber die casting according to claim 1, wherein in said step S4, said statistical thermal section information includes the steps of:
(1) acquiring the distribution conditions of a maximum isolated domain, a current isolated hot spot and an isolated domain at the last moment in the current state;
(2) constructing a topological relation between the current isolated hot spot and the isolated domain at the last moment;
(3) calculating the minimum volume of each isolated thermal node, generating a new maximum isolated domain array, and assigning to the isolated domain at the last moment;
(4) and repeating iteration until the current thermal section number is calculated to be 0, stopping counting, and starting to output data.
6. A shrinkage cavity and shrinkage porosity prediction method for hot chamber die casting according to claim 5, wherein in said step (3),
calculating the volume of a shrinkage cavity generated after each thermal joint is shrunk according to the thermal expansion coefficient of the current filling molten metal; when the thermal node shrinks below the minimum volume, the thermal node is regarded as a space domain and is stored in a space domain sequence; if a hot spot shrinks to 0 directly from the last time to the current time, the hot spot is also regarded as an airspace; eliminating the airspace in the current isolated hot section to obtain a current isolated domain; and adding the space domain at the last moment, the newly generated space domain at the current moment and the current isolated domain to obtain the maximum isolated domain.
7. The method according to claim 1, wherein in step S5, a plurality of threads of parallel computing are used, and each thread of computing writes data of its own block structure to the result file at the same time.
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