Online optimization method and device for hot die forging process parameters

Abstract

The invention discloses a hot die forging process parameter online optimization method and device which can be applied to the technical field of hot forming. According to the method, after the grain size simulation result is obtained by performing multi-scale analogue simulation on the hot forging based on the full-factor experimental design, the grid corresponding to the preset final forging is obtained as the base grid, and the grain size quick prediction data set is constructed in combination with the grain size simulation result; then, after a hot forging grain size rapid prediction model and a visual model are constructed according to the grain size rapid prediction data set, hot forging process parameters are input into the hot forging grain size rapid prediction model to predict and obtain real-time grain size data, and the real-time grain size data are subjected to visual processing through the visual model; and finally, the technological parameters of the hot forging piece are optimized through a bionic intelligent optimization algorithm, and then the technological parameters can be rapidly optimized.

Description

Technical Field

[0001] The present application relates to the field of hot forming technology, and in particular to a method and device for online optimization of hot die forging process parameters. Background Art

[0002] In the related technology, the grain size of hot forgings has a profound impact on their final performance. From mechanical properties to processing and service performance, grain size control is a key factor in ensuring that the material meets the design requirements. In the hot forging process, the grain size can be controlled by appropriate process parameters to optimize and balance the material properties. At present, the process parameter optimization method of hot forgings cannot quickly visualize and predict the global grain size of hot forgings, and cannot quickly optimize the process parameters.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention

[0004] The main purpose of the embodiments of the present application is to propose a method and device for online optimization of hot die forging process parameters, which can perform online visual prediction of the global grain size of hot forgings and quickly optimize process parameters.

[0005] To achieve the above object, one aspect of an embodiment of the present application provides an online optimization method for hot die forging process parameters, the method comprising the following steps:

[0006] Based on the full factorial experimental design, multi-scale simulation of hot forgings was carried out to obtain the grain size simulation results;

[0007] Obtain the mesh corresponding to the preset final forging as the base mesh;

[0008] Constructing a grain size rapid prediction data set according to the grain size simulation result and the base grid;

[0009] Constructing a hot forging grain size rapid prediction model according to the grain size rapid prediction data set, and constructing a visualization model;

[0010] Inputting hot forging process parameters into the hot forging grain size rapid prediction model to predict and obtain real-time grain size data;

[0011] Performing visualization processing on the real-time grain size data through the visualization model;

[0012] The hot forging process parameters are optimized by a bionic intelligent optimization algorithm.

[0013] In some embodiments, the multi-scale simulation of hot forgings based on full factorial experimental design to obtain grain size simulation results includes:

[0014] Construct material constitutive equations;

[0015] Constructing a dynamic recrystallization model according to the material constitutive equation;

[0016] After the material constitutive equation and the dynamic recrystallization model are integrated into the finite element analysis software, a multi-scale simulation of hot forgings is performed based on a full factorial experimental design to obtain a grain size simulation result.

[0017] In some embodiments, the material constitutive equation is as follows:

[0018]

[0019] In the formula, is the strain rate; α is the stress factor; σ is the flow stress; Q is the thermal deformation activation energy; R is the molar gas parameter; T is the absolute temperature; and n is a constant related to the material of the hot mold.

[0020] In some embodiments, the dynamic recrystallization model includes a critical strain model, a volume percentage model, and a grain size model; the formula of the critical strain model is as follows:

[0021]

[0022] In the formula, d0 is the initial grain size; Q1 is the recrystallization activation energy; T is the thermodynamic temperature during deformation; ε p is the peak strain; ε c is the critical strain; n1, m1 and a1 are all constants to be regressed;

[0023] The formula of the volume percentage model is as follows:

[0024]

[0025] In the formula, X drex is the dynamic recrystallization percentage; ε 0.5 is the strain value when 50% dynamic recrystallization occurs; Q5 is the activation energy corresponding to 50% dynamic recrystallization; h5, m5, k d , a5 and β d All represent the regression constants; ε represents the equivalent strain;

[0026] The formula of the grain size model is as follows:

[0027]

[0028] In the formula, d rex is the average grain size of dynamic recrystallization; Q3 is the activation energy of grain growth; h3, m3 and a3 are all constants to be regressed.

[0029] In some embodiments, constructing a fast grain size prediction data set according to the grain size simulation result and the base grid includes:

[0030] Mapping mesh vertex values ​​in the grain size simulation result to vertices of the base mesh;

[0031] The mold preheating temperature, the blank heating temperature, and the pressing rate are used as input values, and the values ​​of the vertices are used as output values ​​to construct the grain size rapid prediction data set.

[0032] In some embodiments, the formula of the hot forging grain size rapid prediction model is as follows:

[0033]

[0034] In the formula, Represents the predicted value f at the predicted position * The mean of Represents the predicted value f at the predicted position * Y is the output value corresponding to the input value X in the grain size fast prediction data set; K XX Indicates that for all input values ​​X and predicted points X * The N*N covariance matrix evaluated at K X*X It's K X*X The transposed matrix of X*X* Represents the covariance matrix between the new set of input points.

[0035] In some embodiments, the visualizing the real-time grain size data by using the visualization model includes:

[0036] Reading vertex coordinates and face index data of the base mesh;

[0037] Constructing a virtual three-dimensional model of the hot forging according to the vertex coordinates, the facet index data and the real-time grain size data;

[0038] Get temperature color map data;

[0039] The virtual three-dimensional model is controlled to display a color corresponding to the real-time grain size data according to the temperature color mapping data.

[0040] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides an online optimization device for hot die forging process parameters, the device comprising:

[0041] The first module is used to perform multi-scale simulation of hot forgings based on full factorial experimental design to obtain grain size simulation results;

[0042] The second module is used to obtain the mesh corresponding to the preset final forging as the base mesh;

[0043] A third module is used to construct a grain size rapid prediction data set according to the grain size simulation result and the base grid;

[0044] A fourth module is used to construct a hot forging grain size rapid prediction model based on the grain size rapid prediction data set, and to construct a visualization model;

[0045] The fifth module is used to input the hot forging process parameters into the hot forging grain size rapid prediction model to predict and obtain real-time grain size data;

[0046] A sixth module is used to perform visualization processing on the real-time grain size data through the visualization model;

[0047] The seventh module is used to optimize the hot forging process parameters through a bionic intelligent optimization algorithm.

[0048] To achieve the above-mentioned purpose, another aspect of the embodiment of the present application provides an online optimization device for hot die forging process parameters, comprising:

[0049] at least one processor;

[0050] at least one memory for storing at least one program;

[0051] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0052] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0053] The embodiments of the present application include at least the following beneficial effects: The present application provides a method and device for online optimization of hot die forging process parameters. After obtaining the grain size simulation results by performing multi-scale simulation of hot forgings based on full-factor experimental design, the grid corresponding to the preset final forging is obtained as the base grid to unify the vertex coordinates and facet data of the three-dimensional model of the final forging, and then a grain size rapid prediction data set is constructed in combination with the grain size simulation results. Subsequently, a hot forging grain size rapid prediction model is constructed based on the grain size rapid prediction data set, and after constructing a visualization model, the hot forging process parameters are input into the hot forging grain size rapid prediction model to predict the real-time grain size data, and the real-time grain size data is visualized through the visualization model, thereby realizing the online visualization prediction of the global grain size of the hot forging, and finally the hot forging process parameters are optimized through the bionic intelligent optimization algorithm, so that the process parameters can be quickly optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a flow chart of the online optimization method of hot die forging process parameters provided in an embodiment of the present application;

[0055] Figure 2 is a schematic diagram of a mold analyzed by the finite element analysis software provided in an embodiment of the present application;

[0056] Figure 3 is a schematic diagram of a control arm model analyzed by the finite element analysis software provided in an embodiment of the present application;

[0057] Figure 4 This is a schematic diagram of the key part selection of the blank provided in the embodiment of the present application;

[0058] Figure 5 It is a structural schematic diagram of an online optimization device for hot die forging process parameters provided in an embodiment of the present application;

[0059] Figure 6 It is a schematic diagram of the hardware structure of the online optimization device for hot die forging process parameters provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application, they are only examples of devices and methods consistent with some aspects of the embodiments of the present application.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0062] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following interpretations:

[0063] Forging: An object in which metal is subjected to pressure and plastic deformation to form a required shape or appropriate compression force.

[0064] Cold forging: refers to the process of processing metal at room temperature.

[0065] Hot forging: A process in which the metal is processed at a temperature above the recrystallization temperature of the blank metal.

[0066] The embodiment of the present application provides a method and device for online optimization of hot die forging process parameters. After obtaining the grain size simulation result by performing multi-scale simulation of hot forgings based on full factorial experimental design, the grid corresponding to the preset final forging is obtained as the base grid to unify the vertex coordinates and facet data of the three-dimensional model of the final forging, and then a grain size rapid prediction data set is constructed in combination with the grain size simulation result. Then, a hot forging grain size rapid prediction model is constructed according to the grain size rapid prediction data set, and after constructing a visualization model, the hot forging process parameters are input into the hot forging grain size rapid prediction model to predict the real-time grain size data, and the real-time grain size data is visualized through the visualization model, thereby realizing the online visualization prediction of the global grain size of the hot forging, and finally, the hot forging process parameters are optimized through the bionic intelligent optimization algorithm, so as to quickly optimize the process parameters.

[0067] The online optimization method of hot die forging process parameters provided in the embodiment of the present application relates to the field of hot forming technology. The online optimization method of hot die forging process parameters provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or distributed system composed of multiple physical servers, and can also be configured to provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms and other basic cloud computing services. The cloud server, the server can also be a node server in the blockchain network; the software can be an application that implements the online optimization method of hot die forging process parameters, etc., but is not limited to the above forms.

[0068] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0069] Figure 1 is an optional flow chart of the online optimization method for hot die forging process parameters provided in the embodiment of the present application, Figure 1 The method may include but is not limited to steps S110 to S180:

[0070] Step S110, performing multi-scale simulation of hot forgings based on full factorial experimental design to obtain grain size simulation results;

[0071] Step S120, obtaining a mesh corresponding to a preset final forging as a base mesh;

[0072] Step S130, constructing a grain size rapid prediction data set according to the grain size simulation results and the base grid;

[0073] Step S140, constructing a hot forging grain size rapid prediction model according to the grain size rapid prediction data set, and constructing a visualization model;

[0074] Step S150, inputting the hot forging process parameters into the hot forging grain size rapid prediction model to predict and obtain real-time grain size data;

[0075] Step S160, visualizing the real-time grain size data through the visualization model;

[0076] Step S170, optimizing the hot forging process parameters by using a bionic intelligent optimization algorithm.

[0077] In the embodiment of the present application, the hot compression experiment can be combined with the metallographic observation of the high-temperature hot compression sample to obtain the material constitutive equation and the dynamic recrystallization model, and then the material constitutive equation and the dynamic recrystallization model are integrated into the finite element analysis software, and a full-factor experimental design table is designed, and the mold preheating temperature, billet heating temperature, and pressing rate are selected as factors of the design table. Finally, a multi-scale simulation of the hot die forging process is performed with different parameter combinations to obtain the grain size simulation results. It can be understood that the Arrhenius relationship in the form of hyperbolic sine can be used to describe the constitutive relationship, where the formula of the material constitutive equation is as follows:

[0078]

[0079] In the formula, is the strain rate; a is the stress factor; σ is the flow stress; Q is the thermal deformation activation energy; R is the molar gas parameter; T is the absolute temperature; n is a constant related to the material of the hot mold; A is a constant.

[0080] It can be understood that the dynamic recrystallization model includes a critical strain model, a volume percentage model and a grain size model; the formula of the critical strain model is as follows:

[0081]

[0082] In the formula, d0 is the initial grain size; Q1 is the recrystallization activation energy; T is the thermodynamic temperature during deformation; ε p is the peak strain; ε c is the critical strain; n1, m1 and a1 are all constants to be regressed;

[0083] The formula for the volume percentage model is as follows:

[0084]

[0085] In the formula, X drex is the dynamic recrystallization percentage; ε 0.5 is the strain value when 50% dynamic recrystallization occurs; Q5 is the activation energy corresponding to 50% dynamic recrystallization; h5, m5, k d , a5 and β d All represent the regression constants; ε represents the equivalent strain;

[0086] The formula for the grain size model is as follows:

[0087]

[0088] In the formula, d rex is the average grain size of dynamic recrystallization; Q3 is the activation energy of grain growth; h3, m3 and a3 are all constants to be regressed.

[0089] In the embodiment of the present application, when constructing a rapid prediction data set for grain size, the grid of a certain section of the forging can be selected as the base grid, and the key parts of the blank can be selected as vertices in the grid to unify the vertex coordinates and facet data of the three-dimensional model of the forging. Then, the KNN algorithm and RBF model are used to map the grid vertex values ​​in the grain size simulation results to the vertices of the base grid, and then the mold preheating temperature, blank heating temperature, and pressing rate are used as input values, and the vertex values ​​are used as output values ​​to construct a rapid prediction data set for grain size.

[0090] In the embodiment of the present application, a Gaussian process regression model can be constructed to quickly predict the grain size of hot forgings. The Gaussian process is represented by a mean function and a covariance function, as shown in the following formula:

[0091]

[0092] Among them, m(x) is the mean function, k(x, x′) is the covariance function, that is, f(x)~N(m(x), k(x, x′)).

[0093] In the Gaussian process regression model, the relationship between the output value and the input value is as follows:

[0094] y(X)=f(X)+ε;

[0095] Where X represents the input vector, f(X) represents the potential function, y(X) represents the output function, and ε is a Gaussian noise random variable.

[0096] The key prediction equation of Gaussian process regression is used as a rapid prediction model for hot forging grain size, and its formula is as follows:

[0097]

[0098] In the formula, Represents the predicted value f at the predicted position * The mean of Represents the predicted value f at the predicted position * Y is the output value corresponding to the input value X in the grain size fast prediction data set; K XX Indicates that for all input values ​​X and predicted points X * The N*N covariance matrix evaluated at K X*X It's K X*X The transposed matrix of K X*X* Represents the covariance matrix between the new set of input points.

[0099] At the same time, the parameters of the hot forgings are visualized through the visualization model. Specifically, after reading the vertex coordinates and facet index data of the base grid, this embodiment can build a virtual three-dimensional model of the hot forging according to the vertex coordinates, facet index data and real-time grain size data, and after obtaining the temperature color mapping data, control the virtual three-dimensional model to display the color corresponding to the real-time grain size data according to the temperature color mapping data, so as to display the parameter change process of the forging in real time in the virtual three-dimensional model.

[0100] In the embodiment of the present application, the hot forging process parameters can also be optimized by a bionic intelligent optimization algorithm. It can be understood that the whale optimization algorithm is selected as the bionic intelligent optimization algorithm. Among them, the bionic intelligent optimization algorithm has the characteristics of simplicity and efficiency, and can find the global optimal solution in a complex high-dimensional space. Specifically, the whale optimization algorithm first identifies the position of the prey and gradually approaches the prey. The mathematical formula can be expressed as the following formula:

[0101]

[0102] In the formula, is the current position of the whale, is the optimal solution found in the current iteration, and is the coefficient vector used to control the position update.

[0103] When approaching prey, humpback whales will form a spiral trajectory around the prey. The model can be expressed as the following formula:

[0104]

[0105] In the formula, represents the distance between the i-th whale and its prey, which is the current optimal solution, b is a constant, and l is a random number.

[0106] In order to avoid local optimal solutions, whales will randomly select other locations for search to enhance global search capabilities. The mathematical model is as follows:

[0107]

[0108] In the formula, is a randomly selected position vector that helps the whale explore the entire search space.

[0109] By confirming the die preheating temperature, billet heating temperature and pressing rate as design variables, the average grain size at the key parts of the hot forging and the global average grain size as optimization targets, the multi-objective optimization is transformed into a single-objective optimization by assigning weights, and the fitness function is designed based on the grain size prediction model. The number of humpback whale individuals is initialized, the range of design variables is determined, and the maximum number of iterations is set, and then the hot die forging process parameters are optimized.

[0110] In some embodiments, the method of the present application is applied to Figure 2 The mold shown is prepared as Figure 3 The automobile control arm model shown in the figure is studied, and multi-scale simulation modeling, artificial intelligence algorithm model, computer graphics and other technologies are combined to realize the online rapid visualization prediction of grain size and the optimization of hot die forging process parameters. The specific implementation process includes but is not limited to the following steps:

[0111] Step 1: Multi-scale simulation of the hot die forging process is performed and a data set is constructed in combination with the base grid.

[0112] Step 1.1, taking the aluminum alloy control arm as an example, take samples for hot compression test and metallographic test, and fit the material constitutive equation and dynamic recrystallization equation according to the experimental data. In this example, taking 6082 aluminum alloy as an example, the corresponding material constitutive equation under high temperature rheology is as follows:

[0113]

[0114] The critical strain model of dynamic recrystallization of 6082 aluminum alloy is:

[0115]

[0116] The dynamic recrystallization volume percentage model of 6082 aluminum alloy is:

[0117]

[0118] The dynamic recrystallization grain size model of 6082 aluminum alloy is:

[0119]

[0120] This embodiment uses Deform simulation software to perform multi-scale simulation of the hot forging process. In the pre-processing interface, the obtained material constitutive equation of 6082 aluminum alloy and the parameters solved by the dynamic recrystallization model are passed into the model integrated by the software to perform macroscopic and mesoscopic multi-scale hot forging simulation. Each group of simulations mainly includes two processes: pre-forging and final forging. Among them, the friction coefficient between the die and the billet is 0.3, the heat transfer coefficient between the billet and the die is 11N / s / mm / ℃, the environmental convection heat transfer coefficient is 0.02N / s / mm / ℃, and the initial grain size is 105 microns. A full factorial experimental design is performed, and the levels and factors are shown in Table 1. According to the table, 27 groups of process parameters are obtained, and 27 groups of hot forging simulations can be performed.

[0121] Table 1

[0122]

[0123] Step 1.2, in the 27 groups of simulations, select the hot forging with the least number of meshes after the hot forging simulation is completed, and extract its mesh as the base mesh. Then attach the global average grain size results obtained by simulating different parameter combinations to the base mesh. Taking a certain group of parameter simulations as an example, first extract the vertex coordinates of the base mesh, extract the vertex coordinates and vertex values ​​of the hot forgings in this group of simulations, where the vertex value refers to the value at the mesh vertex when the variable is the average grain size in the Deform post-processing. Then use the KNN algorithm to obtain the index of the 15 points closest to each vertex of the base mesh in the vertex coordinates of the final hot forgings in this group of simulations, and obtain the 15 vertex coordinates and corresponding vertex values ​​based on the index. Then use the 15 vertex coordinates as input and the 15 vertex values ​​as output to train the RBF model, so as to interpolate the results corresponding to each vertex of the base mesh. Perform the above processing on all 27 groups of simulation results, and you can get the input and output size of the unified data set. The input of the data set is the hot forging process parameters, namely the die preheating temperature, the billet heating temperature, and the pressing rate, and the output is all the vertex values ​​of the base mesh. Finally, in order to eliminate the differences between different dimensions, the minimum-maximum normalization method was used for data preprocessing.

[0124] Step 2: Establish a rapid prediction model and visualization model for hot forging grain size.

[0125] Step 2.1: Based on the grain size rapid prediction data set constructed in 1.2, multiple Gaussian process regression models are established. For small sample data, GPR can well capture the complex patterns and uncertainties of the data and provide accurate predictions and uncertainty measures. In this embodiment, a Gaussian process regression model is created for all vertices of the base grid. Assuming there are m nodes, it can be expressed as follows:

[0126]

[0127] Each prediction model is trained and tested using 27 data samples, each of which contains three inputs (die preheating temperature, billet heating temperature, and pressing rate) and one output (grain size). The following effect can be achieved in the end: passing a set of input parameters into the Gaussian process regression model will instantly obtain a set of base grid corresponding grain size values ​​(real-time grain size data).

[0128] Step 2.2, combined with computer graphics technology, the grain size prediction results are displayed in real time in the form of cloud maps. First, read the vertex and patch index data of the base grid, extract the grain size prediction results output by Gaussian process regression, and use the Mesh3d object of the Plotly library to create a three-dimensional grid graphics object by passing in parameters such as vertex coordinates, patch index, and grain size value. In order to convert the grain size value into color display, a temperature color map is defined, which maps it to different colors according to the size of the grain size, so as to more intuitively display the grain size distribution.

[0129] Step 3: Use the bionic intelligent optimization algorithm to optimize the hot die forging process parameters. The multi-objective optimization problem is transformed into a single-objective optimization problem using the linear weighted method. The weight coefficient of the objective function is determined according to the importance of the problem analysis. In this embodiment, the importance of the average grain size at the key position is equal to the average grain size of the entire domain. The weight factors of both are set to 0.5. For the average grain size of the entire domain, the mean of the predicted results can be taken for calculation; for the average grain size at the key position, the average grain size at the key position can be taken as Figure 4 Taking the middle position as an example, the base mesh vertex order of the key position is obtained in advance. After the prediction model outputs the prediction result, the prediction result at the key position can be directly extracted by programming, and the mean can be directly calculated. First, a set of whale positions are randomly generated, and they are ensured to be within the given boundary range. The fitness of each whale's current position is evaluated and the global optimal solution is updated. The position of each whale is gradually updated according to the strategies of encircling prey, attacking with bubble nets, and exploring prey, and the updated position is ensured to be within the predefined boundary range (the boundary is the boundary value of the factor in Table 1). Set the maximum number of iterations to 100. After reaching this number, the optimal process parameters can be obtained.

[0130] In summary, the method provided in the embodiment of the present application combines digital twin technologies such as multi-scale modeling, machine learning, and three-dimensional visualization with parameter optimization, thereby improving the efficiency of hot die forging process parameter optimization and providing reliable technical support for the manufacture of high-performance hot die forgings.

[0131] Reference Figure 5 , the embodiment of the present application provides a device for online optimization of hot die forging process parameters, the device comprising:

[0132] The first module 510 is used to perform multi-scale simulation of hot forgings based on full factorial experimental design to obtain grain size simulation results;

[0133] The second module 520 is used to obtain a mesh corresponding to a preset final forging as a base mesh;

[0134] The third module 530 is used to construct a grain size fast prediction data set according to the grain size simulation results and the base grid;

[0135] The fourth module 540 is used to construct a hot forging grain size rapid prediction model based on the grain size rapid prediction data set, and to construct a visualization model;

[0136] The fifth module 550 is used to input the hot forging process parameters into the hot forging grain size rapid prediction model to predict and obtain real-time grain size data;

[0137] A sixth module 560 is used to visualize the real-time grain size data through a visualization model;

[0138] The seventh module 570 is used to optimize the hot forging process parameters through a bionic intelligent optimization algorithm.

[0139] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0140] The embodiment of the present application also provides an online optimization device for hot die forging process parameters, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned online optimization method for hot die forging process parameters when executing the computer program. The online optimization device for hot die forging process parameters can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0141] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0142] See also Figure 6 , Figure 6 The hardware structure of another embodiment of the hot die forging process parameter online optimization device is shown, and the hot die forging process parameter online optimization device includes:

[0143] The processor 610 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0144] The memory 620 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 620 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 620, and the processor 610 calls and executes the online optimization method of hot die forging process parameters in the embodiment of this application;

[0145] Input / output interface 630, used to implement information input and output;

[0146] Communication interface 640, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0147] bus 650 , which transmits information between the various components of the device (e.g., processor 610 , memory 620 , input / output interface 630 , and communication interface 640 );

[0148] The processor 610 , the memory 620 , the input / output interface 630 , and the communication interface 640 are connected to each other in communication within the device via a bus 650 .

[0149] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned online optimization method for hot die forging process parameters is implemented.

[0150] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0151] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0152] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0153] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0154] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0155] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0156] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0157] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0158] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0159] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0160] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0161] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0162] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for online optimization of hot die forging process parameters, characterized in that: The method comprises the following steps: Based on the full factorial experimental design, multi-scale simulation of hot forgings was carried out to obtain the grain size simulation results; Obtain the mesh corresponding to the preset final forging as the base mesh; Constructing a grain size rapid prediction data set according to the grain size simulation result and the base grid; Constructing a hot forging grain size rapid prediction model according to the grain size rapid prediction data set, and constructing a visualization model; Inputting hot forging process parameters into the hot forging grain size rapid prediction model to predict and obtain real-time grain size data; Performing visualization processing on the real-time grain size data through the visualization model; The hot forging process parameters are optimized by a bionic intelligent optimization algorithm.

2. The method according to claim 1, characterized in that The multi-scale simulation of hot forgings based on full factorial experimental design is performed to obtain grain size simulation results, including: Construct material constitutive equations; Constructing a dynamic recrystallization model according to the material constitutive equation; After the material constitutive equation and the dynamic recrystallization model are integrated into the finite element analysis software, a multi-scale simulation of hot forgings is performed based on a full factorial experimental design to obtain a grain size simulation result.

3. The method according to claim 2, characterized in that The formula of the material constitutive equation is as follows: In the formula, is the strain rate; α is the stress factor; σ is the flow stress; Q is the thermal deformation activation energy; R is the molar gas parameter; T is the absolute temperature; and n is a constant related to the material of the hot mold.

4. The method according to claim 3, characterized in that The dynamic recrystallization model includes a critical strain model, a volume percentage model and a grain size model; the formula of the critical strain model is as follows: In the formula, d0 is the initial grain size; Q1 is the recrystallization activation energy; T is the thermodynamic temperature during deformation; ε p is the peak strain; ε c is the critical strain; n1, m1 and a1 are all constants to be regressed; The formula of the volume percentage model is as follows: In the formula, X drex is the dynamic recrystallization percentage; ε 0.5 is the strain value when 50% dynamic recrystallization occurs; Q5 is the activation energy corresponding to 50% dynamic recrystallization; h5, m5, k d , a5 and β d All represent the regression constants; ε represents the equivalent strain; The formula of the grain size model is as follows: In the formula, d rex is the average grain size of dynamic recrystallization; Q3 is the activation energy of grain growth; h3, m3 and a3 are all constants to be regressed.

5. The method according to claim 1, characterized in that: The method of constructing a fast grain size prediction data set according to the grain size simulation result and the base grid comprises: Mapping mesh vertex values ​​in the grain size simulation result to vertices of the base mesh; The mold preheating temperature, the blank heating temperature, and the pressing rate are used as input values, and the values ​​of the vertices are used as output values ​​to construct the grain size rapid prediction data set.

6. The method according to claim 1, characterized in that The formula of the hot forging grain size rapid prediction model is as follows: In the formula, Represents the predicted value f at the predicted position * The mean of Represents the predicted value f at the predicted position * Y is the output value corresponding to the input value X in the grain size fast prediction data set; K XX Indicates that for all input values ​​X and predicted points X * The N*N covariance matrix evaluated at K X*X It's K X*X The transposed matrix of Represents the covariance matrix between the new set of input points.

7. The method according to claim 1, characterized in that The visualizing the real-time grain size data by using the visualization model includes: Reading vertex coordinates and face index data of the base mesh; Constructing a virtual three-dimensional model of the hot forging according to the vertex coordinates, the facet index data and the real-time grain size data; Get temperature color map data; The virtual three-dimensional model is controlled to display a color corresponding to the real-time grain size data according to the temperature color mapping data.

8. An online optimization device for hot die forging process parameters, characterized in that: The device comprises: The first module is used to perform multi-scale simulation of hot forgings based on full factorial experimental design to obtain grain size simulation results; The second module is used to obtain the mesh corresponding to the preset final forging as the base mesh; A third module is used to construct a grain size rapid prediction data set according to the grain size simulation result and the base grid; A fourth module is used to construct a hot forging grain size rapid prediction model based on the grain size rapid prediction data set, and to construct a visualization model; The fifth module is used to input the hot forging process parameters into the hot forging grain size rapid prediction model to predict and obtain real-time grain size data; A sixth module is used to perform visualization processing on the real-time grain size data through the visualization model; The seventh module is used to optimize the hot forging process parameters through a bionic intelligent optimization algorithm.

9. An online optimization device for hot die forging process parameters, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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