Metal casting cooling control method and system

Abstract

The present invention discloses a metal casting cooling control method and system, which relates to the field of metal casting. The method comprises: collecting basic casting information; constructing a first screening constraint; constructing a second screening constraint based on cooling acceptance criteria; filtering data in a cooling record database according to the first screening constraint; performing qualified screening on the memory database according to the second screening constraint to generate a qualified memory database and an abnormal memory database; performing fine screening of cooling control parameters to generate a target cooling control parameter adjustable interval set; performing target optimization of control parameters to obtain a target cooling control parameter set; synchronizing the system with a control unit to execute cooling control. The method solves the technical problem of low control accuracy and inability to meet high-precision requirements in existing metal casting cooling control, thereby achieving the technical effect of achieving high-precision cooling control and meeting high-precision requirements.

Description

Technical Field

[0001] The present application relates to the field of metal casting, and in particular to a cooling control method and system for metal castings. Background Art

[0002] After casting, metal castings require a precise cooling process to achieve the desired physical and mechanical properties. The cooling process of metal castings is influenced by a variety of factors, such as the casting material, geometry, and dimensions. These factors together determine the cooling rate and temperature distribution, thereby affecting the quality and performance of the casting. Traditional metal casting cooling control methods are mostly based on fixed control parameters or empirical formulas. These methods cannot accurately reflect the real-time status of the casting during the cooling process and are difficult to adapt to the differences between different castings.

[0003] In the current related technologies, the cooling control of metal castings has the technical problem of low control accuracy and cannot meet high-precision requirements. Summary of the Invention

[0004] The present application provides a metal casting cooling control method and system, which adopts the following technical means: constructing a first screening constraint based on the basic casting information of the target metal casting, constructing a second screening constraint based on the cooling acceptance standard of the target metal casting, using the first screening constraint and the second screening constraint to perform qualified screening on the cooling record database and the memory database respectively, performing fine screening of cooling control parameters based on the qualified memory database and the abnormal memory database, and optimizing the control parameter target in the adjustable interval set of the target cooling control parameter, thereby achieving the technical effect of realizing high-precision cooling control and meeting high-precision requirements.

[0005] The present application provides a metal casting cooling control method, comprising:

[0006] Collect basic casting information of the target metal casting, wherein the basic casting information includes casting material data, casting geometry and casting geometry; construct a first screening constraint based on the casting material data, casting geometry and casting geometry; construct a second screening constraint based on the cooling acceptance standard of the target metal casting; retrieve a cooling record database, and screen data in the cooling record database according to the first screening constraint to obtain a memory database; perform qualified screening on the memory database according to the second screening constraint to generate a qualified memory database and an abnormal memory database; perform fine screening of cooling control parameters based on the qualified memory database and the abnormal memory database to generate a target cooling control parameter adjustable interval set; perform control parameter target optimization in the target cooling control parameter adjustable interval set to obtain a target cooling control parameter set; synchronize the target cooling control parameter set to a control unit to execute cooling control of the target metal casting.

[0007] In a possible implementation, the following processing is performed:

[0008] Perform tolerance interval screening on K qualified cooling control parameter sets among the K qualified cooling record data in the qualified memory database to generate a cooling control parameter tolerance interval set; perform abnormal interval screening on L abnormal cooling control parameter sets among the L abnormal cooling record data in the abnormal memory database to generate an abnormal control parameter tolerance interval set; use the abnormal control parameter tolerance interval set to perform parameter fine screening on the cooling control parameter tolerance interval set to generate a target cooling control parameter adjustable interval set.

[0009] In a possible implementation, K qualified cooling control parameter sets in the K qualified cooling record data in the qualified memory database are screened for tolerance intervals to generate a cooling control parameter tolerance interval set, and the following processing is performed:

[0010] The K qualified cooling record data are retrieved using the cooling control parameter as an index to obtain a plurality of qualified cooling control parameter cluster sets, wherein each qualified cooling control parameter cluster set corresponds to a cooling control parameter; a plurality of qualified box plots are constructed based on the plurality of qualified cooling control parameter cluster sets; the interquartile ranges of the plurality of qualified box plots are calculated, and the calculated results are multiplied by a preset filtering step to obtain a plurality of tolerance filtering steps; based on the plurality of tolerance filtering steps, the plurality of qualified cooling control parameter cluster sets are retrieved to obtain a plurality of qualified cooling control parameter cluster intervals; and the plurality of qualified cooling control parameter cluster intervals are used as a cooling control parameter tolerance interval set.

[0011] In a possible implementation, based on the multiple tolerance screening steps, a search is performed in the multiple qualified cooling control parameter cluster sets to obtain multiple qualified cooling control parameter cluster intervals, and the following processing is performed:

[0012] Calculate multiple cluster centers of the multiple qualified cooling control parameter cluster sets respectively; construct multiple first search intervals with the multiple cluster centers as starting points and the multiple tolerance screening steps as search radius; again use the multiple cluster centers as starting points and expand the multiple first search intervals according to the multiple tolerance screening steps to generate multiple second search intervals; perform interval density transition authentication on the multiple first search intervals and the multiple second search intervals respectively, stop expanding when the authentication is passed, and use the multiple search intervals corresponding to the multiple interval density maximum values ​​as the multiple qualified cooling control parameter cluster intervals.

[0013] In a possible implementation, the following processing is performed:

[0014] Perform similarity analysis on multiple cooling record data in the memory database to obtain a database consistency factor; determine whether the consistency factor meets the preset requirements, and if not, obtain a database expansion instruction; and supplement the memory database with data according to the database expansion instruction.

[0015] In a possible implementation, the following processing is performed:

[0016] A cooling record database is constructed, wherein the cooling record database includes multiple sample points; a target constraint point is obtained in the cooling record database according to the first screening constraint; and multiple neighboring sample points whose distances to the target constraint point are within a preset distance threshold from the multiple sample points are collected, and the memory database is obtained based on the multiple neighboring sample points.

[0017] In a possible implementation, the following processing is performed:

[0018] Acquire a plurality of sample cooling record data, wherein the plurality of sample cooling record data include a plurality of casting material data, a plurality of casting geometric structures, and a plurality of casting geometric dimensions; input the plurality of casting material data, the plurality of casting geometric structures, and the plurality of casting geometric dimensions into a three-dimensional space coordinate system to generate a plurality of sample points; and construct the cooling record database based on the plurality of sample points.

[0019] The present application also provides a metal casting cooling control system, comprising:

[0020] A basic casting information acquisition module, the basic casting information acquisition module is used to acquire basic casting information of the target metal casting, wherein the basic casting information includes casting material data, casting geometry and casting geometry; a first screening constraint construction module, the first screening constraint construction module is used to construct a first screening constraint based on the casting material data, casting geometry and casting geometry; a second screening constraint construction module, the second screening constraint construction module is used to construct a second screening constraint based on the cooling acceptance standard of the target metal casting; a memory database acquisition module, the memory database acquisition module is used to call a cooling record database, perform data screening in the cooling record database according to the first screening constraint, and obtain a memory database; a qualified screening module, the qualified A grid screening module is used to perform qualified screening on the memory database according to the second screening constraint, and generate a qualified memory database and an abnormal memory database; a target cooling control parameter adjustable interval set generation module, the target cooling control parameter adjustable interval set generation module is used to perform fine screening of cooling control parameters according to the qualified memory database and the abnormal memory database, and generate a target cooling control parameter adjustable interval set; a target cooling control parameter set acquisition module, the target cooling control parameter set acquisition module is used to perform control parameter target optimization in the target cooling control parameter adjustable interval set, and obtain a target cooling control parameter set; a cooling control module, the cooling control module is used to synchronize the target cooling control parameter set to a control unit, and execute cooling control of the target metal casting.

[0021] A metal casting cooling control method and system proposed in this application first collects basic casting information of the target metal casting, wherein the basic casting information includes casting material data, casting geometric structure and casting geometric dimensions, then constructs a first screening constraint based on the casting material data, casting geometric structure and casting geometric dimensions, and then constructs a second screening constraint based on the cooling acceptance criteria of the target metal casting, then retrieves the cooling record database, and performs data screening in the cooling record database according to the first screening constraint to obtain a memory database, and then performs qualified screening on the memory database according to the second screening constraint to generate a qualified memory database and an abnormal memory database, and then performs fine screening of cooling control parameters based on the qualified memory database and the abnormal memory database to generate a target cooling control parameter adjustable interval set, and then performs control parameter target optimization in the target cooling control parameter adjustable interval set to obtain a target cooling control parameter set, and finally synchronizes the target cooling control parameter set to the control unit to execute cooling control of the target metal casting, thereby achieving the technical effect of realizing high-precision cooling control and meeting high-precision requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0023] Figure 1 A schematic flow chart of a metal casting cooling control method provided in an embodiment of the present application.

[0024] Figure 2 A schematic structural diagram of a metal casting cooling control system provided in an embodiment of the present application.

[0025] Explanation of the accompanying drawings: basic casting information acquisition module 10, first screening constraint construction module 20, second screening constraint construction module 30, memory database acquisition module 40, qualified screening module 50, target cooling control parameter adjustable interval set generation module 60, target cooling control parameter set acquisition module 70, cooling control module 80. DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0029] The present application embodiment provides a method for controlling cooling of metal castings, such as Figure 1 As shown, the method includes:

[0030] Step S100 collects basic casting information of a target metal casting, where the basic casting information includes casting material data, casting geometry, and casting dimensions. Specifically, the casting material data refers to the chemical composition, physical properties, and other attributes of the metal material used in the target metal casting; the casting geometry refers to the shape characteristics, such as the external shape and internal structure, of the target metal casting; and the casting dimensions refer to the specific dimensions, such as the length, width, and height, of each part of the target metal casting. Specifically, the target metal casting is identified and appropriate measuring tools and equipment, such as a metal analyzer, calipers, gauges, and 3D scanners, are prepared. Using a metal analyzer or chemical composition testing equipment, the target metal casting is sampled and analyzed for its chemical composition, including the content of major alloying elements and impurity content. This data constitutes the casting material data. A 3D scanner or measuring equipment is used to scan or measure the target metal casting to obtain its 3D geometry data. Using measuring tools such as calipers and gauges, the target metal casting's key dimensions, including length, width, height, and wall thickness, are accurately measured and the measurement results are recorded. The collected casting material data, casting geometric structure and casting geometric dimensions are integrated to form complete basic casting information.

[0031] Step S200 constructs a first screening constraint based on the casting material data, casting geometry, and casting dimensions. Specifically, the casting material data is analyzed to determine the primary alloying elements and impurity content of the target metal casting, as well as their impact on the casting's thermophysical properties. Key performance parameters of the casting material, such as thermal conductivity, melting point, and thermal expansion coefficient, are evaluated. The casting geometry is analyzed to determine characteristics such as the casting's overall shape, internal structure, and wall thickness. The impact of the casting geometry on the heat transfer path and temperature distribution is evaluated. For example, thicker wall sections require longer cooling times. Based on the casting's dimensions, key parameters such as the casting's volume and surface area are calculated, and their impact on the casting's cooling rate and uniformity is analyzed. The effects of the casting's material, geometry, and dimensions on the cooling process are comprehensively analyzed to generate a series of screening conditions, including material type, thermal conductivity range, geometric features (e.g., wall thickness, structural complexity), and dimension range. These screening conditions are then converted into an executable mathematical model or algorithm to obtain the first screening constraint, which is used to filter cooling records similar to or related to the target metal casting from the cooling record database.

[0032] Step S300: Construct a second screening constraint based on the cooling acceptance criteria of the target metal casting. Specifically, consult relevant technical standards, process specifications or customer requirements to determine the various performance indicators that the target metal casting needs to achieve after cooling, including the temperature distribution, hardness, microstructure, residual stress, etc. of the casting. Analyze the various indicators in the cooling acceptance criteria, determine the impact of each indicator on the performance of the casting and the relationship between them, evaluate the importance and priority of each indicator, and determine which indicators are critical and must be met, and which indicators are secondary and can have a certain tolerance. Based on the cooling acceptance criteria, generate a series of screening conditions, including temperature distribution range, hardness range, microstructure characteristics, residual stress level, etc., to filter out qualified cooling records that meet the cooling acceptance criteria from the memory database, and convert the screening conditions into quantifiable indicators or parameters for numerical comparison and screening. For example, the temperature distribution range is converted into a specific temperature threshold, and the hardness range is converted into a specific hardness value range. The quantified screening conditions are integrated into a model to form the second screening constraint.

[0033] Step S400: Retrieve a cooling record database and filter data in the cooling record database according to the first filtering constraint to obtain a memory database. Specifically, the cooling record database is a database that stores a large amount of cooling record data for metal castings, including the casting's material, geometry, dimensions, cooling process parameters, and performance data after cooling. A database query tool or programming language (such as SQL) is used to connect to the cooling record database, convert the first filtering constraint (a filtering condition based on the casting's material data, geometry, and dimensions) into a filter condition in a database query statement, execute the query statement, and filter out records from the cooling record database that meet the first filtering constraint. The filtered records are further cleaned and organized to extract cooling record data similar to or related to the current target metal casting, including cooling process parameters and performance data after cooling. The filtered and extracted cooling record data is then stored in a new database, which is the memory database.

[0034] In one possible implementation, step S400 further includes step S410, performing a similarity analysis on the multiple cooling record data in the memory database to obtain a database consistency factor. Specifically, multiple cooling record data are extracted from the memory database, each of which includes information such as different cooling conditions, temperature records, and timestamps. Key features are extracted from each cooling record data, including temperature change trends during the cooling process, maximum / minimum temperature values, and cooling time. Similarity between these cooling record data is calculated using a similarity measurement method (such as cosine similarity and Euclidean distance). Based on the similarity calculation results between all cooling record data, a database consistency factor is obtained through weighted average, median, and other comprehensive methods. The consistency factor reflects the overall similarity between the cooling record data in the memory database. Step S420 determines whether the consistency factor meets preset requirements. If not, a database expansion instruction is obtained. Specifically, based on actual needs or experience, one or more preset requirements for the consistency factor are set (such as the consistency factor must be less than a certain threshold), and the calculated consistency factor is compared with the preset requirements. If the consistency factor does not meet the preset requirements (i.e., the consistency is high), a database expansion instruction is generated to guide the data supplement operation of the memory database. Step S430, the memory database is supplemented with data according to the database expansion instruction. Specifically, according to the requirements of the database expansion instruction, new cooling record data is collected. These new cooling record data can come from new experiments, tests or cooling systems in actual operation. The new data is added to the memory database to enrich the content of the memory database. This implementation method can timely discover possible data deficiencies or consistency problems in the memory database by performing similarity analysis and consistency evaluation on the cooling record data in the memory database. When the consistency factor does not meet the preset requirements, it means that the data in the memory database is insufficient to fully reflect different cooling conditions. At this time, the integrity and accuracy of the memory database are enhanced by data supplementation, thereby achieving the technical effect of improving the accuracy and reliability of subsequent analysis.

[0035] In one possible implementation, step S400 further includes step S440, constructing a cooling record database, wherein the cooling record database includes multiple sample points. Specifically, historical cooling record data from cooling systems in experiments, tests, or actual operations is collected, the collected data is cleaned, deduplicated, and formatted to ensure data quality and consistency, and the organized data is stored as sample points in the cooling record database. Each sample point is a data record in the cooling record database, representing recorded data under a specific cooling condition, and may include multiple attributes, such as cooling conditions, temperature records, timestamps, etc. Step S450, obtaining a target constraint point in the cooling record database according to the first screening constraint. Specifically, the first screening constraint is applied to the cooling record database to screen out sample points that meet the conditions as target constraint points. Step S460, collecting multiple neighboring sample points from the multiple sample points whose distance to the target constraint point is within a preset distance threshold, and obtaining the memory database based on the multiple neighboring sample points. Specifically, the distance from each sample point in the cooling record database to the target constraint point is calculated (based on the similarity between the sample point attributes or the actual distance). Based on a preset distance threshold, sample points that are closer to the target constraint point are selected as neighboring sample points. These selected neighboring sample points are then used as the content of the memory database. This means that the memory database contains the target constraint point and cooling record data similar to it. This implementation method expands the content of the memory database by introducing neighboring sample points, allowing it to include more records that are similar to the target constraint point but not identical to it, thus enriching the memory database and helping to capture potential patterns.

[0036] In one possible implementation, step S440 further includes step S441 of acquiring a plurality of sample cooling record data, wherein the plurality of sample cooling record data includes a plurality of casting material data, a plurality of casting geometric structures, and a plurality of casting geometric dimensions. Specifically, the plurality of sample cooling record data is collected from a production or testing environment, and the sample cooling record data comes from different casting batches, different production conditions, or different time periods. Each sample cooling record data includes three parts: casting material data, casting geometric structures, and casting geometric dimensions. Step S442 of inputting the plurality of casting material data, the plurality of casting geometric structures, and the plurality of casting geometric dimensions into a three-dimensional coordinate system to generate a plurality of sample points. Specifically, the collected casting material data, the plurality of casting geometric structures, and the plurality of casting geometric dimensions are converted into numerical values ​​or vectors that can be represented in the three-dimensional coordinate system. Based on the converted numerical values ​​or vectors, a unique coordinate point is assigned to each sample in the three-dimensional coordinate system. This coordinate point represents the comprehensive characteristics of the sample in terms of material, structure, and dimensions. The coordinate points of all samples in the three-dimensional coordinate system are aggregated to form a plurality of sample points. Step S443 constructs the cooling record database based on the multiple sample points. Specifically, the generated multiple sample points are stored in the database to form the cooling record database. This implementation reduces data redundancy and storage space requirements by combining multiple feature dimensions into a single three-dimensional coordinate point, achieving the technical effect of efficiently storing and managing large amounts of sample data in the cooling record database.

[0037] Step S500, the memory database is subjected to qualification screening according to the second screening constraint, and a qualified memory database and an abnormal memory database are generated. Specifically, the memory database is loaded into a data processing environment, the second screening constraint is converted into data processing logic or query statements, each record in the memory database is traversed, and it is checked whether it meets the performance index requirements in the second screening constraint. According to whether each record meets the second screening constraint, it is classified as a qualified record or an abnormal record. A qualified record refers to a record that meets all cooling acceptance criteria, and an abnormal record refers to a record that does not meet at least one cooling acceptance criterion. All qualified records are extracted and stored in a new database, which is the qualified memory database. The qualified memory database contains records that are similar to the cooling process of the target metal casting and meet the performance standards; all abnormal records are extracted and stored in another database, which is the abnormal memory database.

[0038] Step S600, performing fine screening of cooling control parameters according to the qualified memory database and the abnormal memory database, and generating a target cooling control parameter adjustable interval set. Specifically, analyzing the cooling control parameter records in the qualified memory database, such as cooling rate, temperature set point, cooling medium flow, etc., analyzing the relationship between these cooling control parameters and the cooling performance of the target metal casting, and determining the key control parameters that affect the casting performance. Comparing the cooling control parameter records in the qualified memory database and the abnormal memory database, identifying the key control parameters with significant differences between the qualified and abnormal records, and for the screened key control parameters, determining the feasible or effective interval of each cooling control parameter based on the records in the qualified memory database and the value range or distribution of the key control parameters in the qualified records. Combining the feasible intervals of all key control parameters to form a target cooling control parameter adjustable interval set, the target cooling control parameter adjustable interval set is the basis for cooling process control and optimization, and is used to guide the cooling process of the target metal casting.

[0039] In one possible implementation, step S600 further includes step S610, performing tolerance interval screening on K qualified cooling control parameter sets among the K qualified cooling record data in the qualified memory database to generate a cooling control parameter tolerance interval set. Specifically, K qualified cooling record data are read from the qualified memory database, and a corresponding cooling control parameter set is extracted from each qualified cooling record data. Based on the K qualified cooling control parameter sets, the tolerance interval of each cooling control parameter is calculated according to the distribution of parameter values ​​(such as mean ± standard deviation) or expert experience, i.e., the interval within the qualified parameter range within which the parameter value is allowed to fluctuate to a certain extent without affecting the cooling effect. The tolerance intervals of each cooling control parameter are integrated into a set, which is the cooling control parameter tolerance interval set. Step S620, performing abnormal interval screening on L abnormal cooling control parameter sets among the L abnormal cooling record data in the abnormal memory database to generate an abnormal control parameter tolerance interval set. Specifically, L abnormal cooling record data are read from the abnormal memory database, and the corresponding cooling control parameter set is extracted from each abnormal cooling record data. The abnormal cooling control parameter set is analyzed to find the parameter or parameter combination that causes the cooling abnormality. Based on the analysis results of the abnormal parameters, the parameter value interval that causes the cooling abnormality is determined. This interval may be a specific range or one or more discrete points. The determined abnormal intervals are integrated into a set, which is the abnormal control parameter tolerance interval set. Step S660, the cooling control parameter tolerance interval set is finely screened using the abnormal control parameter tolerance interval set to generate a target cooling control parameter adjustable interval set. Specifically, the cooling control parameter tolerance interval set is compared with the abnormal control parameter tolerance interval set, and the part overlapping with the abnormal control parameter tolerance interval set is excluded from the cooling control parameter tolerance interval set. After excluding the abnormal interval, the remaining tolerance interval is the target cooling control parameter adjustable interval set. This implementation method ensures that the generated target cooling control parameter adjustable interval set not only includes the qualified parameter range but also excludes parameter values ​​that may cause abnormalities through analysis of qualified and abnormal cooling record data, achieving the technical effect of improving the accuracy and reliability of cooling control.

[0040] In one possible implementation, step S610 further includes step S611, searching the K qualified cooling record data using the cooling control parameter as an index to obtain multiple qualified cooling control parameter cluster sets, where each qualified cooling control parameter cluster set corresponds to a cooling control parameter. Specifically, in the qualified memory database, using each cooling control parameter as an index, the corresponding qualified cooling record data is retrieved. The retrieved qualified cooling record data is clustered according to its corresponding cooling control parameter to form multiple qualified cooling control parameter cluster sets, each cluster set containing records of cooling control parameter values ​​corresponding to the same cooling control parameter. Step S612, constructing multiple qualified boxplots based on the multiple qualified cooling control parameter cluster sets. Specifically, a boxplot is a graphic used to display data distribution, using information such as quartiles and outliers to display data distribution. For each qualified cooling control parameter cluster set, the quartile is calculated and the corresponding boxplot is plotted. Step S613, calculating the interquartile range of the multiple qualified boxplots and multiplying the calculated result by a preset screening step size to obtain multiple tolerance screening step sizes. Specifically, the interquartile range is the difference between the third quartile and the first quartile in the boxplot. The calculated interquartile range is multiplied by a preset screening step size (a preset coefficient used to adjust the strictness of the screening; a larger screening step size results in a wider screening interval, while a smaller screening step size results in a narrower screening interval) to obtain a tolerance screening step size for each cooling control parameter. In step S614, a search is performed within the multiple qualified cooling control parameter cluster sets based on the multiple tolerance screening step sizes to obtain multiple qualified cooling control parameter cluster intervals. Specifically, within each qualified cooling control parameter cluster set, data is searched based on the corresponding tolerance screening step size. Based on the search results, qualified cluster intervals for each cooling control parameter are determined. These intervals represent relatively dense and reasonable portions of the data distribution. In step S615, the multiple qualified cooling control parameter cluster intervals are used as a cooling control parameter tolerance interval set. Specifically, the qualified cluster intervals for each cooling control parameter are integrated to form a complete cooling control parameter tolerance interval set. This implementation method performs separate analysis and screening for each cooling control parameter, more finely controls the screening results, and achieves the technical effect of improving the accuracy and reliability of tolerance interval screening.

[0041] In one possible implementation, step S614 further includes step S6141, calculating multiple cluster centers for each of the multiple qualified cooling control parameter cluster sets. Specifically, a clustering algorithm (such as K-means or DBSCAN) is used to calculate a cluster center for each qualified cooling control parameter cluster set, resulting in multiple cluster centers. A cluster center represents the "center of mass" or "center point" of a cluster and is obtained by calculating the average or other statistical value of all points in the cluster. Step S6142, constructing multiple first search intervals using the multiple cluster centers as starting points and the multiple tolerance screening steps as search radii. Specifically, constructing multiple circular (or square) first search intervals within the qualified cooling control parameter cluster set using each cluster center as a circle center (or center point) and the corresponding tolerance screening step as a radius. Step S6143, again using the multiple cluster centers as starting points, expanding the multiple first search intervals using the multiple tolerance screening steps to generate multiple second search intervals. Specifically, based on the cluster center, the first search interval is expanded using the same tolerance screening step size. This expansion can be achieved by increasing the radius or adjusting the shape or direction of the search interval. After expansion, multiple second search intervals are generated. Step S6144: Interval density transition authentication is performed on each of the multiple first and second search intervals. If authentication is successful, expansion is stopped, and the multiple search intervals corresponding to the multiple interval density maxima are selected as the multiple qualified cooling control parameter clustering intervals. Specifically, for each first and second search interval, the data point density is calculated by, for example, calculating the ratio of the number of data points within the interval to the interval area (or volume). A determination is made as to whether the density difference between adjacent search intervals reaches a preset threshold. If the density difference is large, indicating that an interval with relatively concentrated density has been retrieved, authentication is successful. Once authentication is successful, expansion is stopped, and the search interval corresponding to the maximum interval density is selected as the qualified cooling control parameter clustering interval. This implementation method, by gradually expanding the search interval and performing density transition authentication, accurately locates the clustering intervals of qualified cooling control parameters, achieving the technical effect of quickly locating areas with relatively concentrated data distribution and improving search efficiency.

[0042] Step S700 involves performing a target optimization search for control parameters within the target cooling control parameter adjustable range set to obtain a target cooling control parameter set. Specifically, an optimization target is determined, i.e., a desired performance indicator or target value for the cooling process of the target metal casting, including the casting's temperature distribution uniformity, hardness distribution range, residual stress level, and the like. Based on the physical model of the cooling process and known data, a mathematical model is constructed to describe the relationship between the cooling control parameters and casting performance. This model can reflect the impact of the control parameters on casting performance and predict casting performance within a given control parameter range. Constraints within the optimization model are set based on actual conditions and process requirements, including the range of control parameter values ​​and constraints on casting performance, to ensure the feasibility and effectiveness of the optimization results. Within the target cooling control parameter adjustable range set, an optimization algorithm, such as a genetic algorithm, a particle swarm optimization algorithm, or a gradient descent algorithm, is used to perform iterative calculations. By continuously adjusting the control parameter values, a control parameter combination is found that optimizes casting performance or meets a specific target. The cooling control parameter set obtained through the optimization calculations is evaluated using experimental or simulation methods to verify whether it meets the optimization target and constraints. The verified target cooling control parameters that meet the optimization objectives and constraints are combined and organized into a target cooling control parameter set.

[0043] Step S800: Synchronize the target cooling control parameter set to the control unit to execute cooling control of the target metal casting. Specifically, the target cooling control parameter set is transmitted to the control unit via wired or wireless means. The control unit is an automated control system or device for receiving and executing cooling control parameters. It can be a PLC (programmable logic controller), an industrial control computer, or other control system with corresponding functions. The corresponding control parameters are set or updated in the control unit to make them consistent with the target cooling control parameter set. Start the control unit to execute cooling control of the target metal casting. The control unit automatically adjusts relevant parameters of the cooling system, such as temperature, flow rate, time, etc., according to the set target cooling control parameter set. During the cooling process, the cooling condition of the target metal casting and the operating status of the control system are continuously monitored. If necessary, the control parameters are adjusted in real time according to the monitoring results to ensure that the cooling process of the casting meets the expected requirements. The embodiment of the present application adopts technical means such as constructing a first screening constraint based on the basic casting information of the target metal casting, constructing a second screening constraint based on the cooling acceptance standard of the target metal casting, using the first screening constraint and the second screening constraint to perform qualified screening on the cooling record database and the memory database respectively, performing fine screening of cooling control parameters based on the qualified memory database and the abnormal memory database, and optimizing the control parameter target in the adjustable interval set of the target cooling control parameter, thereby achieving the technical effect of realizing high-precision cooling control and meeting high-precision requirements.

[0044] In the above, refer to Figure 1 A method for controlling cooling of a metal casting according to an embodiment of the present invention is described in detail. Figure 2 A metal casting cooling control system according to an embodiment of the present invention is described.

[0045] A metal casting cooling control system according to an embodiment of the present invention is designed to address the technical problem of low control accuracy and inability to meet high-precision requirements in existing metal casting cooling control systems, thereby achieving high-precision cooling control and meeting high-precision requirements. The metal casting cooling control system includes: a basic casting information acquisition module 10, a first screening constraint construction module 20, a second screening constraint construction module 30, a memory database acquisition module 40, a qualified screening module 50, a target cooling control parameter adjustable interval set generation module 60, a target cooling control parameter set acquisition module 70, and a cooling control module 80.

[0046] The basic casting information acquisition module 10 is used to acquire basic casting information of the target metal casting, wherein the basic casting information includes casting material data, casting geometry and casting geometry; the first screening constraint construction module 20 is used to construct a first screening constraint based on the casting material data, casting geometry and casting geometry; the second screening constraint construction module 30 is used to construct a second screening constraint based on the cooling acceptance standard of the target metal casting; the memory database acquisition module 40 is used to call the cooling record database, and perform data screening in the cooling record database according to the first screening constraint to obtain a memory database; the qualified screening module 50 It is used to perform qualified screening on the memory database according to the second screening constraint, and generate a qualified memory database and an abnormal memory database; the target cooling control parameter adjustable interval set generation module 60 is used to perform fine screening of cooling control parameters according to the qualified memory database and the abnormal memory database, and generate a target cooling control parameter adjustable interval set; the target cooling control parameter set acquisition module 70 is used to perform control parameter target optimization in the target cooling control parameter adjustable interval set, and obtain a target cooling control parameter set; the cooling control module 80 is used to synchronize the target cooling control parameter set to the control unit, and execute cooling control of the target metal casting.

[0047] The specific configuration of the target cooling control parameter adjustable interval set generation module 60 will be described in detail below. As described above, the target cooling control parameter adjustable interval set generation module 60 may further include: a tolerance interval screening unit for performing tolerance interval screening on K qualified cooling control parameter sets among the K qualified cooling record data in the qualified memory database to generate a cooling control parameter tolerance interval set; an abnormal interval screening unit for performing abnormal interval screening on L abnormal cooling control parameter sets among the L abnormal cooling record data in the abnormal memory database to generate an abnormal control parameter tolerance interval set; and a parameter fine screening unit for performing parameter fine screening on the cooling control parameter tolerance interval set using the abnormal control parameter tolerance interval set to generate a target cooling control parameter adjustable interval set.

[0048] Among them, tolerance interval screening is performed on K qualified cooling control parameter sets among the K qualified cooling record data in the qualified memory database to generate a cooling control parameter tolerance interval set. The tolerance interval screening unit may further include: a qualified cooling control parameter cluster set acquisition subunit for retrieving the K qualified cooling record data with the cooling control parameter as an index to obtain multiple qualified cooling control parameter cluster sets, wherein each qualified cooling control parameter cluster set corresponds to one cooling control parameter; a qualified box plot construction subunit for constructing multiple qualified box plots based on the multiple qualified cooling control parameter cluster sets; a tolerance screening step acquisition subunit for calculating the interquartile range of the multiple qualified box plots, and multiplying the calculation result by the preset screening step to obtain multiple tolerance screening steps; a qualified cooling control parameter cluster interval acquisition subunit for searching in the multiple qualified cooling control parameter cluster sets based on the multiple tolerance screening steps to obtain multiple qualified cooling control parameter cluster intervals, and using the multiple qualified cooling control parameter cluster intervals as the cooling control parameter tolerance interval set.

[0049] Among them, based on the multiple tolerance screening steps, a search is performed in the multiple qualified cooling control parameter cluster sets to obtain multiple qualified cooling control parameter cluster intervals. The qualified cooling control parameter cluster interval acquisition subunit may further include: a cluster center calculation microunit for respectively calculating the multiple cluster centers of the multiple qualified cooling control parameter cluster sets; a first search interval construction microunit for taking the multiple cluster centers as the starting point and constructing multiple first search intervals according to the multiple tolerance screening steps as the search radius; a second search interval generation microunit for again taking the multiple cluster centers as the starting point and expanding the multiple first search intervals according to the multiple tolerance screening steps to generate multiple second search intervals; an interval density transition authentication microunit for respectively performing interval density transition authentication on the multiple first search intervals and the multiple second search intervals. When the authentication is passed, the expansion is stopped, and the multiple search intervals corresponding to the multiple interval density maximum values ​​are used as multiple qualified cooling control parameter cluster intervals.

[0050] The specific configuration of the memory database acquisition module 40 will be described in detail below. As described above, the memory database acquisition module 40 may further include: a similarity analysis unit for performing similarity analysis on the plurality of cooling record data in the memory database to obtain a database consistency factor; a database expansion instruction acquisition unit for determining whether the consistency factor meets a preset requirement and, if not, obtaining a database expansion instruction; and a data supplementation unit for supplementing the memory database with data according to the database expansion instruction.

[0051] Among them, the memory database acquisition module 40 can further include: a cooling record database construction unit for constructing a cooling record database, wherein the cooling record database includes multiple sample points; a target constraint point acquisition unit for obtaining a target constraint point in the cooling record database according to the first screening constraint; a memory database generation unit for collecting multiple neighboring sample points from the multiple sample points whose distance to the target constraint point is within a preset distance threshold, and obtaining the memory database based on the multiple neighboring sample points.

[0052] Among them, the cooling record database construction unit may further include: a sample cooling record data acquisition subunit for acquiring multiple sample cooling record data, wherein the multiple sample cooling record data include multiple casting material data, multiple casting geometric structures and multiple casting geometric dimensions; a sample point generation subunit for inputting the multiple casting material data, multiple casting geometric structures and multiple casting geometric dimensions into a three-dimensional space coordinate system to generate multiple sample points; a cooling record database generation subunit for constructing the cooling record database based on the multiple sample points.

[0053] A metal casting cooling control system provided by an embodiment of the present invention can execute a metal casting cooling control method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0054] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0055] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for controlling cooling of metal castings, characterized in that: The method comprises: Collecting basic casting information of the target metal casting, wherein the basic casting information includes casting material data, casting geometric structure and casting geometric dimensions; Constructing a first screening constraint according to the casting material data, the casting geometric structure and the casting geometric size; constructing a second screening constraint based on the cooling acceptance criteria of the target metal casting; Retrieving a cooling record database, filtering data in the cooling record database according to the first screening constraint, and obtaining a memory database; Loading the memory database into a data processing environment, performing qualification screening on the memory database according to the second screening constraint, generating a qualified memory database and an abnormal memory database, wherein qualified records refer to records that meet all cooling acceptance criteria, and abnormal records refer to records that do not meet at least one cooling acceptance criterion; Performing a fine screening of cooling control parameters according to the qualified memory database and the abnormal memory database, determining a feasible or effective interval for each cooling control parameter based on the value range or distribution of key control parameters in the qualified records, and generating a target cooling control parameter adjustable interval set; Performing control parameter target optimization in the target cooling control parameter adjustable interval set to obtain a target cooling control parameter set; Synchronizing the target cooling control parameter set to a control unit to perform cooling control of the target metal casting; The method comprises: Performing tolerance interval screening on K qualified cooling control parameter sets in the K qualified cooling record data in the qualified memory database to generate a cooling control parameter tolerance interval set; Performing abnormal interval screening on L abnormal cooling control parameter sets in the L abnormal cooling record data in the abnormal memory database to generate an abnormal control parameter tolerance interval set; Using the abnormal control parameter tolerance interval set, the cooling control parameter tolerance interval set is finely screened to generate a target cooling control parameter adjustable interval set; The method of performing tolerance interval screening on K qualified cooling control parameter sets in the K qualified cooling record data in the qualified memory database to generate a cooling control parameter tolerance interval set includes: Retrieving the K qualified cooling record data using the cooling control parameter as an index to obtain a plurality of qualified cooling control parameter cluster sets, wherein each qualified cooling control parameter cluster set corresponds to one cooling control parameter; constructing a plurality of qualified box plots based on the plurality of qualified cooling control parameter cluster sets; Calculating the interquartile ranges of the plurality of qualified box plots, and multiplying the calculated results by a preset screening step length to obtain a plurality of tolerant screening step lengths; Searching the plurality of qualified cooling control parameter cluster sets based on the plurality of tolerance screening steps to obtain a plurality of qualified cooling control parameter cluster intervals; Aggregating the plurality of qualified cooling control parameter intervals as a cooling control parameter tolerance interval set; The method includes searching the plurality of qualified cooling control parameter cluster sets based on the plurality of tolerance screening steps to obtain a plurality of qualified cooling control parameter cluster intervals. respectively calculating a plurality of cluster centers of the plurality of qualified cooling control parameter cluster sets; Taking the plurality of cluster centers as starting points and the plurality of tolerance screening steps as search radiuses, constructing a plurality of first search intervals; Again taking the plurality of cluster centers as starting points, the plurality of first search intervals are expanded according to the plurality of tolerance screening steps to generate a plurality of second search intervals; Performing interval density transition authentication on the plurality of first search intervals and the plurality of second search intervals respectively, stopping expansion when the authentication is passed, and using the plurality of search intervals corresponding to the maximum values ​​of the plurality of interval densities as the plurality of qualified cooling control parameter aggregation intervals; The method comprises: Performing similarity analysis on multiple cooling record data in the memory database to obtain a database consistency factor; Determine whether the consistency factor meets the preset requirements, and if not, obtain a database expansion instruction; The memory database is supplemented with data according to the database expansion instruction.

2. A metal casting cooling control method according to claim 1, characterized in that: The method comprises: Constructing a cooling record database, wherein the cooling record database includes a plurality of sample points; Obtaining a target constraint point in the cooling record database according to the first screening constraint; A plurality of neighboring sample points whose distances to the target constraint point are within a preset distance threshold from the plurality of sample points are collected, and the memory database is obtained according to the plurality of neighboring sample points.

3. A metal casting cooling control method according to claim 2, characterized in that: The method comprises: Acquire a plurality of sample cooling record data, wherein the plurality of sample cooling record data includes a plurality of casting material data, a plurality of casting geometric structures, and a plurality of casting geometric dimensions; Inputting the plurality of casting material data, the plurality of casting geometric structures and the plurality of casting geometric dimensions into a three-dimensional space coordinate system to generate a plurality of sample points; The cooling record database is constructed according to the multiple sample points.

4. A metal casting cooling control system, characterized in that: The system is used to implement a metal casting cooling control method according to any one of claims 1 to 3, and the system comprises: A basic casting information acquisition module, wherein the basic casting information acquisition module is used to acquire basic casting information of a target metal casting, wherein the basic casting information includes casting material data, casting geometric structure and casting geometric dimensions; a first screening constraint building module, the first screening constraint building module being configured to build a first screening constraint according to the casting material data, the casting geometric structure and the casting geometric dimensions; a second screening constraint building module, the second screening constraint building module being configured to build a second screening constraint based on a cooling acceptance criterion of the target metal casting; a memory database acquisition module, the memory database acquisition module being used to retrieve a cooling record database, filter data in the cooling record database according to the first filtering constraint, and obtain a memory database; a qualified screening module, the qualified screening module being configured to load a memory database into a data processing environment, perform qualified screening on the memory database according to the second screening constraint, and generate a qualified memory database and an abnormal memory database, wherein a qualified record refers to a record that meets all cooling acceptance criteria, and an abnormal record refers to a record that does not meet at least one cooling acceptance criterion; a target cooling control parameter adjustable interval set generation module, the target cooling control parameter adjustable interval set generation module being used to perform a fine screening of cooling control parameters according to the qualified memory database and the abnormal memory database, determine a feasible or valid interval for each cooling control parameter based on the value range or distribution of key control parameters in the qualified records, and generate a target cooling control parameter adjustable interval set; A target cooling control parameter set acquisition module, the target cooling control parameter set acquisition module is used to perform control parameter target optimization in the target cooling control parameter adjustable interval set to obtain a target cooling control parameter set; A cooling control module is configured to synchronize the target cooling control parameter set to a control unit to execute cooling control of the target metal casting.

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