Machine learning based aluminum alloy extrusion prediction optimization method and related devices

Abstract

The application provides an aluminum alloy extrusion prediction optimization method based on machine learning and a related device, and relates to the field of material plastic processing. In the method, an electronic device acquires a simulation model of an aluminum alloy extrusion process, wherein the simulation model can simulate the maximum extrusion stress and outlet temperature of the aluminum alloy by using input simulation process parameters; a plurality of simulation data is generated through the simulation model; and the aluminum alloy extrusion model is obtained through model training by using the plurality of simulation data. In this way, a large amount of simulation data is generated by using a finite element model and is used to train the aluminum alloy extrusion model. The aluminum alloy extrusion model can predict the maximum extrusion stress and outlet temperature of the aluminum alloy according to input design process parameters, thereby providing a guidance basis for actual extrusion production and facilitating quick adjustment of a production plan.

Description

Technical Field

[0001] This application relates to the field of plastic processing of materials, and more specifically, to a machine learning-based method for predicting and optimizing aluminum alloy extrusion and related apparatus. Background Technology

[0002] Hot extrusion is primarily used to manufacture profiles, pipes, bars, and various ultra-wide, thin-walled profiles and small, high-precision profiles. Due to the diverse types of extruded products, their high precision, and great production flexibility, hot extrusion production has become a key area of ​​industrial manufacturing. With the continuous development of manufacturing technology, hot extruded products have been widely used in aerospace, transportation, and other fields.

[0003] For aluminum extrusion technology, extrusion stress and exit temperature are key factors determining whether product defects occur. Therefore, effectively predicting the maximum extrusion stress and exit temperature and determining the optimal parameters is crucial. Extrusion stress and exit temperature for aluminum alloy extrusion are typically obtained through extrusion tests, but these tests require extensive preparation and are time-consuming.

[0004] For example, before conducting an extrusion test, preheating of the extruder and the die blank are required. During the test, the die needs to be adjusted and repaired multiple times, which not only prolongs the entire test but also increases the workload. Furthermore, the extrusion test consumes a large amount of aluminum, resulting in high material costs.

[0005] Therefore, the finite element method (FEM) has been adopted in related technologies. FEM can systematically solve boundary value problems and, compared to experiments, can more accurately analyze components with complex geometries. Thus, using finite element models, multiple models can be simulated simultaneously without conducting experiments, eliminating the need for preheating steps in experimental processes, thereby significantly reducing experimental time and costs, and providing an important engineering foundation for achieving efficient aluminum extrusion production. This method is particularly important for predicting the maximum extrusion stress and exit temperature in aluminum alloy extrusion and reducing time costs.

[0006] However, in practice, it has been found that large enterprises need to use more than 2,000 different molds every month, of which more than 50% are new molds. The cost of a single mold trial ranges from 1 million to 10 million yuan, and the annual mold trial cost is as high as about 40 billion yuan. Faced with such a huge demand for molds, traditional finite element analysis methods are inadequate due to the large amount of computation and long calculation time. Even simulation using finite element tools still requires a lot of time. Summary of the Invention

[0007] To overcome at least one deficiency in the prior art, this application provides a machine learning-based method and related apparatus for predicting and optimizing aluminum alloy extrusion, specifically including:

[0008] In a first aspect, this application provides a machine learning-based method for predicting and optimizing aluminum alloy extrusion, the method comprising:

[0009] A simulation model of aluminum alloy extrusion process is obtained, wherein the simulation model can simulate the maximum extrusion stress and exit temperature of aluminum alloy using the input simulation process parameters;

[0010] Multiple simulation data points are generated using the simulation model;

[0011] The model is trained using multiple sets of simulation data to obtain a trained aluminum alloy extrusion model, which is used to predict the maximum extrusion stress and exit temperature of the aluminum alloy based on the input design process parameters.

[0012] In conjunction with the optional implementation of the first aspect, the simulation model is a finite element model verified by measured data.

[0013] In conjunction with the optional implementation method of the first aspect, a model is trained using multiple sets of simulation data to obtain a trained aluminum alloy extrusion model, including:

[0014] Multiple training models are trained using multiple sets of simulation data to obtain multiple candidate models.

[0015] The aluminum alloy extrusion model is selected from the multiple candidate models based on its best prediction performance.

[0016] In conjunction with the optional implementation of the first aspect, each piece of simulation data includes mold type and extrusion parameters;

[0017] Multiple training models are trained using the aforementioned simulation data to obtain multiple candidate models, including:

[0018] The process parameters of each simulation data are preprocessed to obtain preprocessed data for each simulation data. The preprocessing method for the mold category is thermal unique encoding, and the preprocessing method for the extrusion parameters is standardization.

[0019] Multiple sets of preprocessed data are input into the multiple models to be trained for training, resulting in multiple candidate models.

[0020] In conjunction with the optional implementation of the first aspect, the extrusion parameters include extrusion speed, extrusion temperature, extrusion cylinder temperature, and coefficient of friction.

[0021] In conjunction with an optional embodiment of the first aspect, the coefficient of friction in the extrusion parameters is obtained by mapping the extrusion temperature in the extrusion parameters.

[0022] In conjunction with the optional implementation of the first aspect, the relationship between the friction coefficient and the extrusion cylinder temperature in the extrusion parameters is as follows:

[0023]

[0024] In the formula, μ(T) represents the friction coefficient when the extrusion cylinder temperature is T, T0 represents room temperature, μ(T0) represents the friction coefficient at room temperature, and a, b, and n represent preset coefficients, respectively.

[0025] Secondly, this application also provides a machine learning-based aluminum alloy extrusion prediction and optimization device, the method comprising:

[0026] The data generation module is used to obtain a simulation model of the aluminum alloy extrusion process, wherein the simulation model can simulate the maximum extrusion stress and exit temperature of the aluminum alloy using the input simulation process parameters.

[0027] The data generation module is also used to generate multiple simulation data through the simulation model;

[0028] The model training module is used to train the model using multiple sets of simulation data to obtain a trained aluminum alloy extrusion model. The aluminum alloy extrusion model is used to predict the maximum extrusion stress and exit temperature of the aluminum alloy based on the input design process parameters.

[0029] In conjunction with the optional implementation method of the second aspect, the simulation model is a finite element model verified by measured data.

[0030] In conjunction with the optional implementation of the second aspect, the model training module is further specifically used for:

[0031] Multiple training models are trained using multiple sets of simulation data to obtain multiple candidate models.

[0032] The aluminum alloy extrusion model is selected from the multiple candidate models based on its best prediction performance.

[0033] In conjunction with the optional implementation of the second aspect, each piece of simulation data includes a mold type and extrusion parameters; the model training module is further specifically used for:

[0034] The process parameters of each simulation data are preprocessed to obtain preprocessed data for each simulation data. The preprocessing method for the mold category is thermal unique encoding, and the preprocessing method for the extrusion parameters is standardization.

[0035] Multiple sets of preprocessed data are input into the multiple models to be trained for training, resulting in multiple candidate models.

[0036] In conjunction with the optional implementation of the second aspect, the extrusion parameters include extrusion speed, extrusion temperature, extrusion cylinder temperature, and coefficient of friction.

[0037] In conjunction with the optional implementation of the second aspect, the coefficient of friction in the extrusion parameters is obtained by mapping the extrusion temperature in the extrusion parameters.

[0038] In conjunction with the optional implementation of the second aspect, the relationship between the friction coefficient and the extrusion cylinder temperature in the extrusion parameters is as follows:

[0039]

[0040] In the formula, μ(T) represents the friction coefficient when the extrusion cylinder temperature is T, T0 represents room temperature, μ(T0) represents the friction coefficient at room temperature, and a, b, and n represent preset coefficients, respectively.

[0041] Thirdly, this application also provides a storage medium storing a computer program, which, when executed by a processor, implements the aforementioned machine learning-based aluminum alloy extrusion prediction and optimization method.

[0042] Fourthly, this application also provides an electronic device, which includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, it implements the aforementioned machine learning-based aluminum alloy extrusion prediction and optimization method.

[0043] Compared with the prior art, this application has the following beneficial effects:

[0044] This application provides a machine learning-based method and apparatus for predicting and optimizing aluminum alloy extrusion. In this method, an electronic device acquires a simulation model of the aluminum alloy extrusion process. This simulation model can simulate the maximum extrusion stress and exit temperature of the aluminum alloy using input process parameters. Multiple simulation data points are generated from the simulation model. These data points are then used to train the model, resulting in a trained aluminum alloy extrusion model. Thus, a large amount of simulation data is generated using a finite element model and used to train the aluminum alloy extrusion model. This aluminum alloy extrusion model can predict the maximum extrusion stress and exit temperature of the aluminum alloy based on the input design process parameters, thereby providing guidance for actual extrusion production and facilitating rapid adjustments to production plans. Attached Figure Description

[0045] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 A flowchart illustrating the machine learning-based aluminum alloy extrusion prediction and optimization method provided in this application embodiment;

[0047] Figure 2 A comparative test diagram of extrusion stress provided for embodiments of this application;

[0048] Figure 3 A comparison chart of outlet temperatures provided for embodiments of this application;

[0049] Figure 4 A schematic diagram of the structure of the machine learning-based aluminum alloy extrusion prediction and optimization method provided in the embodiments of this application;

[0050] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0052] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0053] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0054] In the description of this application, it should be noted that the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0055] Based on the above statement, as introduced in the background section, although finite element models can simulate multiple models simultaneously without conducting experiments and without the preheating steps required in experimental processes, thus significantly reducing experimental time and costs, traditional finite element analysis methods are inadequate for the massive mold demands of large enterprises due to their large computational load and long computation time. Even simulations using finite element tools still require a significant amount of time.

[0056] Based on the discovery of the aforementioned technical problems, the inventors, through creative labor, proposed the following technical solutions to solve or improve these problems. It should be noted that the deficiencies in the solutions of the prior art are all results derived by the inventors after practical experience and careful research. Therefore, the discovery process of the aforementioned problems and the solutions proposed in the embodiments of this application below should be considered contributions made by the inventors to this application during the inventive process, and should not be construed as technical content known to those skilled in the art.

[0057] In view of the above problems, this embodiment provides a machine learning-based prediction and optimization method for aluminum alloy extrusion. In this method, an electronic device acquires a simulation model of the aluminum alloy extrusion process. This simulation model can simulate the maximum extrusion stress and exit temperature of the aluminum alloy using input simulation process parameters. Multiple simulation data points are generated from the simulation model. These data points are then used to train the model, resulting in a trained aluminum alloy extrusion model. Thus, a large amount of simulation data is generated using the finite element model and used to train the aluminum alloy extrusion model. This aluminum alloy extrusion model can predict the maximum extrusion stress and exit temperature of the aluminum alloy based on the input design process parameters, thereby providing a guiding basis for actual extrusion production and facilitating rapid adjustments to production plans.

[0058] It should be noted that the electronic device implementing the above-described machine learning-based aluminum alloy extrusion prediction and optimization method can be, but is not limited to, mobile terminals, tablet computers, laptop computers, desktop computers, servers, etc., as long as they can provide sufficient computing power. The server can be a single server or a group of servers. The server group can be centralized or distributed (e.g., the servers can be a distributed system). In some embodiments, the server can be local or remote relative to the user terminal. In some embodiments, the server can be implemented on a cloud platform; by way of example only, the cloud platform can include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud, multi-cloud, etc., or any combination thereof. In some embodiments, the server can be implemented on an electronic device having one or more components.

[0059] To make the solution provided in this embodiment clearer, a server is used as the electronic device for implementing the method below, and in conjunction with... Figure 1 Each step of the method is described in detail. However, it should be understood that the operations in the flowchart may not be implemented in sequence, and steps without logical contextual relationships may be reversed in order or implemented simultaneously. Furthermore, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowchart, or remove one or more operations from the flowchart. Figure 1 As shown, the method includes:

[0060] S1, obtain the simulation model of aluminum alloy extrusion process.

[0061] The simulation model can simulate the maximum extrusion stress and exit temperature of the aluminum alloy using the input simulation process parameters. In this embodiment, the simulation model can be a finite element model verified by measured data.

[0062] To validate the constructed finite element model, as an optional implementation, an actual aluminum alloy extrusion test can be conducted first to collect data such as the maximum extrusion stress and exit temperature during the extrusion process. This data is used to verify the accuracy of subsequent simulations. Next, the billet size is set according to actual requirements, and a three-dimensional geometric model of the extrusion cylinder, billet, and extrusion bar is established. Then, the established three-dimensional geometric model is input into finite element simulation software (such as Deform-3D), and the material processing parameters, material parameters, and simulation control parameters are set according to actual experimental requirements. These parameters include extrusion speed, extrusion temperature, die temperature, friction coefficient, thermal conductivity, and mesh refinement size. The simulation is run through the finite element simulation software to generate simulation data, which is then compared with the experimental results for verification. If there is a discrepancy between the two, the finite element model is continuously adjusted until the results are within the error range, thus ensuring the accuracy of the model.

[0063] In this embodiment, a finite element model is constructed using 7N01 aluminum alloy as the research object. The material parameters of 7N01 are shown in the table below:

[0064] element Si Fe Cu Mn Mg Cr Zn Ti Al Content / w% <0.030 <0.35 0.20 0.15 1.80 0.20 5.0 0.10 Bal.

[0065] In addition, the material properties of 7N01 are shown in the table below:

[0066]

[0067] Of course, the method provided in this embodiment is not limited to 7N01 aluminum alloy, but can also be applied to other types of aluminum alloy.

[0068] Based on the above description of the simulation model in step S1, please refer to... Figure 1 The method also includes:

[0069] S2 generates multiple simulation data points through a simulation model.

[0070] It should be noted that the final prediction performance of the neural network model is directly related to the quality and quantity of the training samples. During the research, it was found that there was very little data available regarding the hot extrusion forming process of aluminum alloys. Therefore, in order to obtain a sufficient number of training samples, this embodiment uses a simulation model to generate a large number of samples.

[0071] For example, continuing with the finite element model described above, the server can receive different parameters set by the user according to actual experimental requirements, simulate and record the generated extrusion stress and outlet temperature, forming multiple simulation data sets to constitute a dataset for subsequent machine learning training. As an optional implementation, each simulation data set includes a die type and extrusion parameters. The die type in each simulation data set is one of profiles, bars, tubes, or plates. The extrusion parameters include extrusion speed, extrusion temperature, die temperature, extrusion barrel temperature, and friction coefficient.

[0072] It is worth noting that, during the research, it was found that, unlike other parameters in the extrusion parameters which can be arbitrarily set by the user, the friction coefficient is not a constant about the material, but rather a parameter that varies with temperature. Further research revealed that the friction coefficient is affected by the extrusion cylinder temperature; therefore, the friction coefficient in the extrusion parameters is obtained by mapping it to the extrusion cylinder temperature. To obtain this mapping relationship, this embodiment uses a large amount of measured data for fitting, resulting in the following relationship between the friction coefficient and the extrusion cylinder temperature in the extrusion parameters:

[0073]

[0074] In the formula, μ(T) represents the friction coefficient when the extrusion cylinder temperature is T, T0 represents room temperature, μ(T0) represents the friction coefficient at room temperature, and a, b, n represent preset coefficients, which are all obtained through fitting.

[0075] Based on the above description of the multiple simulation data in step S2, please refer to [link / reference]. Figure 1 The method also includes:

[0076] S3 uses multiple simulation data to train the model and obtain the trained aluminum alloy extrusion model.

[0077] The aluminum alloy extrusion model is used to predict the maximum extrusion stress and exit temperature of the aluminum alloy based on the input design process parameters. Given that a large number of machine learning models are available, but not every model is suitable for the application scenario of this embodiment, this embodiment provides the following optional implementation methods for step S3:

[0078] S3-1 uses multiple simulation data to train multiple models to obtain multiple candidate models.

[0079] During the research, it was learned that Scikit-learn, as an open-source machine learning library, is built upon open-source projects such as NumPy, SciPy, and matplotlib. It provides a series of simple and effective tools for data mining and data analysis. Therefore, Scikit-learn has extensive machine learning and data mining capabilities, including but not limited to classification, regression, clustering, data preprocessing, model selection, and evaluation.

[0080] Therefore, the aforementioned multiple models to be trained can include nine machine learning models from Scikit-learn, such as K-Nearest Neighbors, RIDGE Regression, LASSO Regression, Decision Tree, Random Forest, eXtreme Gradient Boosting, LinearSupport Vector Machine (with linear kernel), Support Vector Machine_RBF (with Gaussian kernel), and Adaptive boosting.

[0081] Based on the above implementation of multiple models to be trained, as an optional implementation method, the server can preprocess the process parameters of each simulation data to obtain preprocessed data for each simulation data. The preprocessing method for mold type is thermal unique encoding, and the preprocessing method for extrusion parameters is standardization. Multiple preprocessed data are input into multiple models to be trained for training to obtain multiple candidate models trained.

[0082] Regarding the above implementation method, it should be understood that mold categories such as profiles, bars, pipes, and plates are not continuous numerical features and cannot be directly used in machine learning model training. This implementation uses one-hot encoding for them. Specifically, the server can use the OneHotEncoder function in sklearn.preprocessing to perform one-hot encoding, encoding profiles as 1000, plates as 0100, pipes as 0010, and bars as 0001, and adding them to the feature combination.

[0083] Furthermore, it should be understood that in machine learning algorithms, features should not be highly correlated, and if the magnitudes of the features differ significantly, the algorithm will struggle to converge or require a very long training time. Therefore, in this embodiment, Z-Score normalization can be used to preprocess the squeezing parameters to obtain standardized data.

[0084] Z-Score standardization, also known as standardization or standard score standardization, scales features by adjusting their mean and standard deviation, ensuring that each feature has a mean of 0 and a standard deviation of 1. The formula used for Z-Score standardization for each parameter in the squeezing parameters is as follows:

[0085]

[0086] In the formula, x z-score σ represents the extrusion parameters after standard deviation, where x represents the original extrusion parameters, μ represents the mean of the characteristic data, and σ represents the standard deviation of the extrusion parameters.

[0087] In this way, multiple preprocessed data are obtained using the above implementation method, and then input into different training models to obtain multiple candidate models.

[0088] S3-2: Select the model with the best prediction performance from multiple candidate models as the aluminum alloy extrusion model.

[0089] To continue using the above nine models as examples, we will refer to them as KNN, RIDGE, LASSO, DT, RF, XGBOOST, SVM LINEAR, SVM RBF, and ADA, respectively. In this example, the coefficient of determination R is used. 2 As an evaluation metric for generalization performance, R 2 A value closer to 1 indicates a better fit. The above nine models were tested using a test set, and the test results are as follows: Figure 2 , Figure 3 As shown. Among them, Figure 2 The test set R of each model after 10 training cycles under compressive stress is shown. 2 Comparison chart of mean results Figure 3 The test set R for each model trained 10 times shows the outlet temperature. 2 The mean results are compared in the graph. After comparison, it's clear that the Random Forest (RF) model has a lower R-value in the two comparison graphs. 2 The values ​​were 0.88 and 0.9 respectively, therefore, this model performed the best among the nine models.

[0090] Finally, this embodiment also uses the trained aluminum alloy extrusion model to predict random parameters, conducts experiments on the prediction results, and compares and analyzes the error and optimization effect between the prediction results of the aluminum alloy extrusion model and the experimental data. Some parameters in the experimental process are shown in the table below:

[0091] Extrusion temperature ℃ mold temperature Extrusion barrel temperature Extrusion speed 490 480 440 1.5

[0092] During the experiment, the billet undergoes rapid deformation after extrusion, generating a large amount of heat and causing a sharp rise in the exit temperature. As the extrusion reaches a steady state, the billet's exit temperature gradually stabilizes. The average exit temperature obtained from the experiment is approximately 513.5 degrees Celsius. Under the same parameters, the aluminum alloy extrusion model predicts an exit temperature of 507.5 degrees Celsius, with an error of 1.1%, thus meeting the requirements.

[0093] Based on the same inventive concept as the machine learning-based aluminum alloy extrusion prediction and optimization method provided in this embodiment, this embodiment also provides a machine learning-based aluminum alloy extrusion prediction and optimization device. This device includes at least one software functional module that can be stored in a memory or embedded in an electronic device. The processor in the electronic device executes the executable module stored in the memory. For example, the software functional modules and computer programs included in this device. Please refer to... Figure 4 Functionally, the device may include:

[0094] The data generation module 11 is used to obtain a simulation model of the aluminum alloy extrusion process. The simulation model can simulate the maximum extrusion stress and exit temperature of the aluminum alloy using the input simulation process parameters.

[0095] The data generation module 11 is also used to generate multiple simulation data through the simulation model;

[0096] The model training module 12 is used to train the model using multiple simulation data to obtain the trained aluminum alloy extrusion model. The aluminum alloy extrusion model is used to predict the maximum extrusion stress and exit temperature of the aluminum alloy based on the input design process parameters.

[0097] In this implementation, the data generation module 11 is used to achieve... Figure 1 Steps S1 and S2 in the model training module 12 are used to implement... Figure 1 Step S3 in the above describes the process. Therefore, for a detailed description of each module, please refer to the specific implementation of the corresponding step. Of course, since it shares the same inventive concept as the machine learning-based aluminum alloy extrusion prediction and optimization method, this machine learning-based aluminum alloy extrusion prediction and optimization device can also implement other steps or sub-steps of the method through the above modules, which will not be elaborated further in this embodiment.

[0098] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0099] It should also be understood that if the above embodiments are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0100] Therefore, this embodiment also provides a storage medium, which is a computer-readable storage medium. This storage medium stores a computer program, which, when executed by a processor, implements the machine learning-based aluminum alloy extrusion prediction and optimization method provided in this embodiment. The storage medium can be any medium capable of storing program code, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0101] This embodiment provides an electronic device that implements a machine learning-based aluminum alloy extrusion prediction and optimization method. For example... Figure 5 As shown, the electronic device may include a processor 22 and a memory 21. The memory 21 stores a computer program, and the processor reads and executes the computer program corresponding to the above embodiments in the memory 21 to implement the machine learning-based aluminum alloy extrusion prediction and optimization method provided in this embodiment.

[0102] See also Figure 5 The electronic device may also include a communication unit 23. The memory 21, processor 22 and communication unit 23 are electrically connected to each other directly or indirectly through system bus 24 to realize data transmission or interaction.

[0103] The memory 21 can be an information recording device based on any electronic, magnetic, optical, or other physical principles, used to record execution instructions, data, etc. In some embodiments, the memory 21 can be, but is not limited to, volatile memory, non-volatile memory, memory drive, etc.

[0104] In some embodiments, the volatile memory may be random access memory (RAM); in some embodiments, the non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, etc.; in some embodiments, the storage drive may be a disk drive, solid-state drive, any type of storage disk (such as optical disc, DVD, etc.), or similar storage media, or a combination thereof.

[0105] The communication unit 23 is used to send and receive data over a network. In some embodiments, the network may include a wired network, a wireless network, a fiber optic network, a telecommunications network, an intranet, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN), a Bluetooth network, a ZigBee network, or a near field communication (NFC) network, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network may include wired or wireless network access points, such as base stations and / or network switching nodes, through which one or more components of the service request processing system can connect to the network to exchange data and / or information.

[0106] The processor 22 may be an integrated circuit chip with signal processing capabilities, and may include one or more processing cores (e.g., a single-core processor or a multi-core processor). By way of example only, the processor described above may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction-set Processor (ASIP), a Graphics Processing Unit (GPU), a Physics Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computing (RISC) computer, or a microprocessor, or any combination thereof.

[0107] Understandable. Figure 5 The structure shown is for illustrative purposes only. The electronic device 100 may also have... Figure 5 Showing more or fewer components, or having with Figure 5 The different configurations shown. Figure 5 The components shown can be implemented using hardware, software, or a combination thereof.

[0108] It should be understood that the apparatus and methods disclosed in the above embodiments can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0109] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A machine learning-based method for predicting and optimizing aluminum alloy extrusion, characterized in that, The method includes: A simulation model of aluminum alloy extrusion process is obtained, wherein the simulation model can simulate the maximum extrusion stress and exit temperature of aluminum alloy using the input simulation process parameters; Multiple simulation data points are generated using the simulation model; The model is trained using multiple sets of simulation data to obtain a trained aluminum alloy extrusion model. This model is used to predict the maximum extrusion stress and exit temperature of the aluminum alloy based on input design process parameters. Each set of simulation data includes the die type and extrusion parameters. This step specifically includes: The process parameters of each simulation data are preprocessed to obtain preprocessed data for each simulation data. The preprocessing method for the mold category is thermal unique encoding, and the preprocessing method for the extrusion parameters is standardization. Multiple preprocessed data are input into multiple models to be trained to obtain multiple candidate models; The aluminum alloy extrusion model is selected from the multiple candidate models based on its best prediction performance.

2. The machine learning-based aluminum alloy extrusion prediction and optimization method according to claim 1, characterized in that, The simulation model is a finite element model that has been verified by measured data.

3. The machine learning-based aluminum alloy extrusion prediction and optimization method according to claim 1, characterized in that, The extrusion parameters include extrusion speed, extrusion temperature, extrusion cylinder temperature, and coefficient of friction.

4. The machine learning-based aluminum alloy extrusion prediction and optimization method according to claim 3, characterized in that, The friction coefficient in the extrusion parameters is obtained by mapping the extrusion temperature in the extrusion parameters.

5. The machine learning-based aluminum alloy extrusion prediction and optimization method according to claim 4, characterized in that, The relationship between the friction coefficient and the extrusion cylinder temperature in the extrusion parameters is as follows: In the formula, This indicates that the temperature of the extrusion cylinder is... The coefficient of friction at that time Indicates room temperature. Indicates the coefficient of friction at room temperature. These represent preset coefficients.

6. A machine learning-based aluminum alloy extrusion prediction and optimization device, characterized in that, The device includes: The data generation module is used to obtain a simulation model of the aluminum alloy extrusion process, wherein the simulation model can simulate the maximum extrusion stress and exit temperature of the aluminum alloy using the input simulation process parameters. The data generation module is also used to generate multiple simulation data through the simulation model; The model training module is used to train the model using multiple sets of simulation data to obtain a trained aluminum alloy extrusion model. The aluminum alloy extrusion model is used to predict the maximum extrusion stress and exit temperature of the aluminum alloy based on input design process parameters. Each set of simulation data includes the die type and extrusion parameters. The model training module is also specifically used for: The process parameters of each simulation data are preprocessed to obtain preprocessed data for each simulation data. The preprocessing method for the mold category is thermal unique encoding, and the preprocessing method for the extrusion parameters is standardization. Multiple preprocessed data are input into multiple models to be trained to obtain multiple candidate models; The aluminum alloy extrusion model is selected from the multiple candidate models based on its best prediction performance.

7. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the machine learning-based aluminum alloy extrusion prediction and optimization method as described in any one of claims 1-5.

8. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing a computer program, which, when executed by the processor, implements the machine learning-based aluminum alloy extrusion prediction and optimization method according to any one of claims 1-5.

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