Vacuum isothermal forging method and system
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- GUIZHOU ANDA AVIATION FORGING
- Filing Date
- 2024-12-23
- Publication Date
- 2026-07-02
Smart Images
Figure CN2024141559_02072026_PF_FP_ABST
Abstract
Description
A vacuum isothermal forging method and system Technical Field
[0001] This application belongs to the field of intelligent manufacturing technology, specifically relating to a vacuum isothermal forging method and system. Background Technology
[0002] Vacuum isothermal forging is a metal processing method that utilizes isothermal heating and forging processes in a vacuum environment to plastically deform metal forgings. Its main principle is: the forging is placed in a vacuum heating furnace, heated to the required temperature in a vacuum environment, and held isothermally for a certain period to allow the forging to reach a suitable plastic state. Then, forging processing is performed to ultimately obtain high-precision, high-strength metal parts.
[0003] Vacuum isothermal forging reduces deformation resistance and flow resistance during the forging process, and also solves the oxidation problem of dies and forgings. Forgings produced by vacuum isothermal forging include high-temperature alloys, titanium alloys, and other difficult-to-deform materials, and are widely used in the aerospace field, such as in the manufacture of important structural parts like turbine disks for aircraft engines. The forged products have excellent microstructure and properties.
[0004] Although vacuum isothermal forging has the advantages mentioned above, the forging process parameters play a crucial role in the forging, but it is difficult to master the optimal forging process parameters, resulting in low mechanical properties of the forging, and even fracture. In other words, the forming quality of the forging is difficult to guarantee.
[0005] Meanwhile, even if the optimal forging process parameters for the forging are designed through theoretical calculations or simulation software, in actual forging production, the control system of vacuum isothermal forging is usually controlled by a servo control system. The low control accuracy of the control system and the unstable leveling state of the crossbeam can also affect the quality of the forging. Summary of the Invention
[0006] This application provides a vacuum isothermal forging method and system to solve the above-mentioned technical problems in the prior art.
[0007] The first aspect of this application provides a vacuum isothermal forging method, including:
[0008] Based on the material properties of the forgings, mechanical property evaluation indicators and forging process parameters are selected, and a mathematical model between the mechanical property evaluation indicators and forging process parameters is constructed.
[0009] Using forging process parameters as optimization variables, mechanical performance evaluation indicators as optimization objectives, and a mathematical model as the objective function model, the Pareto optimal solution set is solved using a preset algorithm to obtain the optimal forging process parameters.
[0010] The surface flatness coefficient of the forging to be forged is detected. The current detection value and the optimal forging process parameters are used as inputs, and the control parameters of the vacuum isothermal forging control system are used as outputs. A nonlinear model is established by applying a preset neural network, and the optimal output of the nonlinear model is obtained by calculating through a genetic algorithm.
[0011] The control system of vacuum isothermal forging operates according to the current output to complete the forging of the forging.
[0012] As can be seen from the above, the embodiments of this application obtain the optimal forging process parameters determined by the material properties of the forging based on the material properties of the forging and the preset algorithm. Then, the optimal forging process parameters and the geometric features (surface flatness coefficient) of the forging are combined as input quantities, and the preset neural network and genetic algorithm are applied to obtain the optimal control parameters of the vacuum isothermal forging control system. Obviously, the material properties and geometric features of the forging are taken into account, adapting to the actual forging scenario of the forging, and also taking into account the control error of the vacuum isothermal forging control system. Therefore, it can effectively improve the utilization rate of metal materials and the forming quality of forgings, and also improve the intelligence level of vacuum isothermal forging.
[0013] In some embodiments, based on the material properties of the forging, mechanical performance evaluation indicators and forging process parameters of the forging are selected, and a mathematical model is constructed between the mechanical performance evaluation indicators and forging process parameters, wherein:
[0014] The mechanical properties of forgings are evaluated using tensile strength and elongation.
[0015] The forging process parameters are selected as forging temperature, deformation rate, and forging speed.
[0016] In some embodiments, the construction of a mathematical model between mechanical performance evaluation indicators and forging process parameters includes the following:
[0017] Obtain several sets of sample data on the tensile strength and elongation of forgings, and their relationship with forging temperature, deformation rate, and forging speed;
[0018] The tensile strength and elongation data were fitted with regression models to obtain the corresponding regression equations for tensile strength and elongation.
[0019] In some embodiments, the tensile strength and elongation data are fitted with regression models to obtain regression equations for tensile strength and elongation, wherein the regression equations for tensile strength and elongation are as follows:
[0020]
[0021]
[0022] In the above formula, and Tensile strength and elongation The response value, Forging temperature, For deformation rate, Forging speed.
[0023] In some embodiments, the optimal forging process parameters are obtained by using forging process parameters as optimization variables, mechanical performance evaluation indicators as optimization objectives, and a mathematical model as the objective function model, and by applying a preset algorithm to solve for the Pareto optimal solution set.
[0024] When the forging process parameters are selected as forging temperature, deformation rate, and forging speed, and the mechanical property evaluation indicators of the forging are selected as tensile strength and elongation, the forging process parameters are used as optimization variables, that is:
[0025]
[0026] In the above formula, Forging temperature, For deformation rate, For forging speed;
[0027] The mechanical performance evaluation index is used as the optimization objective, namely:
[0028]
[0029] In the above formula, and These are objective function models for tensile strength and elongation, respectively.
[0030] In some embodiments, the application of a preset algorithm to solve for the Pareto optimal solution set yields the optimal forging process parameters. The preset algorithm is a multi-objective optimization genetic algorithm, specifically including the following steps:
[0031] Step 21: Based on the preset algorithm parameter table and the range of values for the forging process parameters of the forging, randomly generate an initial population of a certain size, and calculate the objective function value corresponding to each individual in the initial population.
[0032] Step 22: Based on the objective function values corresponding to the individuals in the initial population, perform non-dominated sorting on the individuals in the initial population to determine the dominance level of each individual in the initial population, and then perform selection, crossover, and mutation operations to obtain a subpopulation.
[0033] Step 23: Merge the initial population with the subpopulation to obtain a new population. Perform non-dominated sorting on the new population to obtain the front-end individuals of the non-dominated solutions. Calculate the crowding degree of the front-end individuals of the non-dominated solutions.
[0034] Select N new individuals with high dominance and high crowding as the first new individuals; randomly select some individuals from the initial population for mutation as the second new individuals;
[0035] The first and second new individuals are merged into the initial population to form a new generation population. The new generation population is then subjected to non-dominated sorting and individual crowding calculation, followed by selection, crossover, and mutation operations to obtain a new generation of subpopulations.
[0036] Step 24: Determine whether the maximum number of generations has reached the upper limit of the set value. If it has, the operation stops and the final population individuals are used as the Pareto optimal solution set for multi-objective optimization of mechanical performance. Otherwise, proceed to step 23 and perform the calculation again.
[0037] In some embodiments, the process of non-dominated sorting of individuals in the initial population based on the objective function values corresponding to individuals in the initial population, determining the dominance level of each individual in the initial population, and then performing selection, crossover, and mutation operations to obtain a subpopulation, wherein the non-dominated sorting of individuals in the initial population to determine the dominance level of each individual in the initial population, i.e., the dominance level of the optimization objective value, specifically involves:
[0038] Individuals in the initial population that are not dominated by any other individual are classified as Level 1, Level 2 as individuals dominated only by Level 1, Level 3 as individuals dominated only by Level 2, and so on, to determine the dominance level of all individuals in the initial population.
[0039] In some embodiments, the individual crowding degree is calculated using the following formula:
[0040]
[0041] In the above formula, For the first The first generation of the population The objective function value, For the first The first generation of the population The objective function value, and They are respectively The maximum and minimum values among the objective function values. For the first The first generation of the population Crowding distance at each objective function value For the first The overall crowding of the population The number of objective function values. For the first Non-dominated ranking of generations of populations For the first Non-dominated ranking of generations of populations For the first The overall crowding of the population.
[0042] In some embodiments, the detection of the surface flatness coefficient of the current forging workpiece involves using the current detection value and optimal forging process parameters as inputs, and the control parameters of the vacuum isothermal forging control system as outputs. A nonlinear model is established using a preset neural network, and the optimal output of the nonlinear model is calculated using a genetic algorithm. Specifically:
[0043] The surface flatness coefficient of the forging to be forged is detected by laser rangefinder method;
[0044] Using the current detection value and optimal forging process parameters as inputs, and forging load and slide speed as outputs, a model is trained by nonlinear fitting of a BP neural network. The difference between the model and the output is used as the individual fitness value. A genetic algorithm is then used to globally optimize within the input quantization space to obtain the optimal forging load and slide speed.
[0045] A second aspect of this application provides a vacuum isothermal forging system, comprising:
[0046] The acquisition unit is used to select mechanical property evaluation indicators and forging process parameters of forgings based on the material properties of forgings, and to construct a mathematical model between the mechanical property evaluation indicators and forging process parameters.
[0047] The calculation unit is used to solve the Pareto optimal solution set by applying a preset algorithm with forging process parameters as optimization variables, mechanical performance evaluation index as optimization objective, and mathematical model as objective function model, so as to obtain the optimal forging process parameters.
[0048] The control unit is used to detect the surface flatness coefficient of the forging to be forged. It takes the current detection value and the optimal forging process parameters as inputs and the control parameters of the vacuum isothermal forging control system as outputs. It applies a preset neural network to establish a nonlinear model and calculates the optimal output of the nonlinear model through a genetic algorithm.
[0049] The execution unit, used in the vacuum isothermal forging control system, operates according to the current output to complete the forging of the forging work.
[0050] A third aspect of this application provides a terminal including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described in the first aspect.
[0051] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in the first aspect.
[0052] The fifth aspect of this application provides a computer program product that, when run on a terminal, causes the terminal to perform the steps of the method described in the first aspect. Attached Figure Description
[0053] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0054] Figure 1 is a schematic flowchart of a vacuum isothermal forging method provided in an embodiment of this application;
[0055] Figure 2 is a schematic diagram of a vacuum isothermal forging system provided in an embodiment of this application;
[0056] Figure 3 is a schematic diagram of the structure of a terminal provided in an embodiment of this application. Detailed Implementation
[0057] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.
[0058] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.
[0059] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.
[0060] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.
[0061] It should also be understood that the sequence number of each step in this embodiment does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of this application embodiment.
[0062] To illustrate the technical solution described in this application, specific embodiments are provided below.
[0063] Referring to Figure 1, Figure 1 is a schematic flowchart of a vacuum isothermal forging method provided in an embodiment of this application. As shown in Figure 1, a vacuum isothermal forging method includes the following steps:
[0064] Step 1: Based on the material properties of the forgings, select the mechanical property evaluation indexes and forging process parameters of the forgings, and construct a mathematical model between the mechanical property evaluation indexes and forging process parameters;
[0065] Step 2: Using forging process parameters as optimization variables, mechanical performance evaluation indicators as optimization objectives, and mathematical models as objective function models, apply a preset algorithm to solve for the Pareto optimal solution set to obtain the optimal forging process parameters.
[0066] Step 3: Detect the surface flatness coefficient of the current forging, take the current detection value and the optimal forging process parameters as input, take the control parameters of the vacuum isothermal forging control system as output, apply a preset neural network to establish a nonlinear model, and calculate the optimal output of the nonlinear model through a genetic algorithm.
[0067] Step 4: The vacuum isothermal forging control system operates according to the current output to complete the forging of the forging.
[0068] As can be seen, the embodiments of this application obtain the optimal forging process parameters determined by the material properties of the forging based on the material properties of the forging and the preset algorithm. Then, the optimal forging process parameters and the geometric features (surface flatness coefficient) of the forging are combined as input quantities, and the preset neural network and genetic algorithm are applied to obtain the optimal control parameters of the vacuum isothermal forging control system. Obviously, the material properties and geometric features of the forging are taken into account, adapting to the actual forging scenario of the forging, and also taking into account the control error of the vacuum isothermal forging control system. Therefore, it can effectively improve the utilization rate of metal materials and the forming quality of forgings, and also improve the intelligence level of vacuum isothermal forging.
[0069] In this embodiment of the application, step 1 involves selecting mechanical property evaluation indicators and forging process parameters of the forging based on the material properties of the forging, and constructing a mathematical model between the mechanical property evaluation indicators and the forging process parameters, wherein:
[0070] Since most of the materials that require vacuum isothermal forging are difficult-to-deform metals, in this embodiment, the mechanical property evaluation indexes of the forgings can be tensile strength and elongation.
[0071] Meanwhile, the forging temperature determines the plasticity of the forging during the forging process; the deformation rate is related to the uniformity and refinement of the grain structure of the forging. If the deformation rate is too small, the grain refinement of the forging cannot be effectively achieved, and if the deformation rate is too large, the metal will crack during the forging process, affecting the forming quality; and the forging speed is mainly related to the work hardening of the metal material during deformation. Therefore, in this embodiment, the forging process parameters can be selected as forging temperature, deformation rate, and forging speed.
[0072] It is evident that the embodiments of this application, based on the actual situation of vacuum isothermal forging and the selection of mechanical property evaluation indicators and forging process parameters for key influencing factors of vacuum isothermal forging, are beneficial to improving the forming quality of forgings.
[0073] Accordingly, constructing a mathematical model between mechanical performance evaluation indicators and forging process parameters can include the following:
[0074] Obtain several sets of sample data on the tensile strength and elongation of forgings, and their relationship with forging temperature, deformation rate, and forging speed;
[0075] The tensile strength and elongation data were fitted with regression models respectively, resulting in the regression equations for tensile strength and elongation, as shown below:
[0076]
[0077]
[0078] In the above formula, and Tensile strength and elongation The response value, Forging temperature, For deformation rate, Forging speed.
[0079] Clearly, a regression equation is a mathematical expression obtained from sample data through regression analysis that accurately reflects the regression relationship between one variable (dependent variable) and another variable or a group of variables (independent variables). Therefore, using a regression equation can accurately reflect the relationship between tensile strength and elongation, and forging temperature, deformation rate, and forging speed.
[0080] In this embodiment of the application, step 2 involves using forging process parameters as optimization variables, mechanical performance evaluation indicators as optimization objectives, and a mathematical model as the objective function model. A preset algorithm is then applied to solve for the Pareto optimal solution set to obtain the optimal forging process parameters, wherein:
[0081] When the forging process parameters are selected as forging temperature, deformation rate, and forging speed, and the mechanical property evaluation indicators of the forging are selected as tensile strength and elongation, the forging process parameters are used as optimization variables, that is:
[0082]
[0083] In the above formula, Forging temperature, For deformation rate, For forging speed;
[0084] The mechanical performance evaluation index is used as the optimization objective, namely:
[0085]
[0086] In the above formula, and The objective function models are for tensile strength and elongation, respectively.
[0087] For example, a pre-defined algorithm is applied to solve for the Pareto optimal solution set to obtain the optimal forging process parameters. This pre-defined algorithm can be a multi-objective optimization genetic algorithm, which may specifically include the following steps:
[0088] Step 21: Based on the preset algorithm parameter table and the range of values for the forging process parameters of the forging, randomly generate an initial population of a certain size, and calculate the objective function value corresponding to each individual in the initial population.
[0089] Step 22: Based on the objective function values corresponding to the individuals in the initial population, perform non-dominated sorting on the individuals in the initial population to determine the dominance level of each individual in the initial population, and then perform selection, crossover, and mutation operations to obtain a subpopulation.
[0090] Step 23: Merge the initial population with the subpopulation to obtain a new population. Perform non-dominated sorting on the new population to obtain the front-end individuals of the non-dominated solutions. Calculate the crowding degree of the front-end individuals of the non-dominated solutions.
[0091] Select N new individuals with high dominance and high crowding as the first new individuals; randomly select some individuals from the initial population for mutation as the second new individuals;
[0092] The first and second new individuals are merged into the initial population to form a new generation population. The new generation population is then subjected to non-dominated sorting and individual crowding calculation, followed by selection, crossover, and mutation operations to obtain a new generation of subpopulations.
[0093] Step 24: Determine whether the maximum number of generations has reached the upper limit of the set value. If it has, the operation stops and the final population individuals are used as the Pareto optimal solution set for multi-objective optimization of mechanical performance. Otherwise, proceed to step 23 and perform the calculation again.
[0094] Clearly, the aforementioned multi-objective optimization genetic algorithm, by selecting individuals with high dominance and high crowding levels and merging them into the initial population, can accurately and quickly select the optimal combination of forging process parameters through continuous iteration.
[0095] For example, in step 21, according to the preset algorithm parameter table and the range of values of the forging process parameters of the forging, an initial population of a certain size is randomly generated, and the objective function value corresponding to the individual in the initial population is calculated. The algorithm parameter table is shown in Table 1, but is not limited to this number, and can be set as needed.
[0096] Table 1 Algorithm Parameter Table:
[0097]
[0098] For example, in step 22, based on the objective function values corresponding to the individuals in the initial population, the individuals in the initial population are sorted non-dominated to determine the dominance level of each individual. Then, selection, crossover, and mutation operations are performed to obtain a subpopulation. Specifically, sorting the individuals in the initial population non-dominated to determine the dominance level of each individual, which is also the dominance level for optimizing the objective value, can be done as follows:
[0099] Individuals in the initial population that are not dominated by any other individual are classified as Level 1, Level 2 as individuals dominated only by Level 1, Level 3 as individuals dominated only by Level 2, and so on, to determine the dominance level of all individuals in the initial population.
[0100] For example, in step 23, the initial population and the subpopulation are merged to obtain a new population. The new population is then sorted using non-dominated methods to obtain the front-end individuals of the non-dominated solutions. The crowding degree of the front-end individuals of the non-dominated solutions is then calculated.
[0101] Select N new individuals with high dominance and high crowding as the first new individuals; randomly select some individuals from the initial population for mutation as the second new individuals;
[0102] The first and second new individuals are merged into the initial population to form a new generation population. The new generation population is then subjected to non-dominated sorting and crowding calculations, followed by selection, crossover, and mutation operations to obtain a new generation of subpopulations, where:
[0103] Individual crowding can be calculated using the following formula:
[0104]
[0105] In the above formula, For the first The first generation of the population The objective function value, For the first The first generation of the population The objective function value, and They are respectively The maximum and minimum values among the objective function values. For the first The first generation of the population Crowding distance at each objective function value For the first The overall crowding of the population The number of objective function values. For the first Non-dominated ranking of generations of populations For the first Non-dominated ranking of generations of populations For the first The overall crowding of the population.
[0106] In this embodiment, step 3 involves detecting the surface flatness coefficient of the forging to be forged, using the current detected value and the optimal forging process parameters as inputs, and the control parameters of the vacuum isothermal forging control system as outputs. A nonlinear model is established using a preset neural network, and the optimal output of the nonlinear model is calculated using a genetic algorithm, wherein:
[0107] Since the material properties of forgings are basically the same, the forging process parameters determined by the material properties of forgings can be determined before forging. However, the surface flatness coefficient of forgings with the same material properties is different, and the surface flatness coefficient of forgings affects the uniformity of the crossbeam under force during forging, which has a great impact on the surface quality and dimensional accuracy of forgings. Therefore, it is necessary to detect the surface flatness coefficient of the forging to be forged during forging.
[0108] In this embodiment, in order to quickly, accurately, and non-contactly detect the surface flatness coefficient of the forging to adapt to the actual conditions of the forging site, the surface flatness coefficient of the forging to be forged can be detected by laser rangefinder method.
[0109] It should be noted that vacuum isothermal forging is typically used to process high-temperature alloys and other difficult-to-deform materials for aerospace applications. By maintaining a constant temperature in the mold and forging under vacuum conditions, the forging is formed at a relatively low strain rate. The forming quality of difficult-to-deform materials is affected not only by the forging process parameters calculated by simulation software or theory, but also by the actual operating control parameters of the specific vacuum isothermal forging control system under actual forging conditions.
[0110] To clearly describe the above issues, the process of vacuum isothermal forging is briefly described below:
[0111] The preheated and lubricated forgings are placed in a vacuum isothermal forging die with heating and heat preservation. The die and forgings are then uniformly heated to the forging temperature using an induction furnace.
[0112] Before the pressure head applies pressure to the forging, the slide block descends rapidly at a speed of 100 mm / s to 200 mm / s. When the pressure head contacts the forging, the piston cylinders at the four corners of the crossbeam are simultaneously leveled to ensure that the pressure head applies pressure evenly to the forging. The slide block is controlled to descend slowly at a speed of 0.001 mm / s to 1 mm / s to uniformly upset the forging.
[0113] The pressure head further compresses the forging through the upper die, and the sliding block slow descent speed and crossbeam state are adjusted to precisely control the strain rate of the forging, gradually forming the forging.
[0114] It is evident that during the forging process, it is necessary to precisely control the forging load and slide speed within a limited slide stroke, dynamically compensate for the uneven stress on the crossbeam caused by the unevenness of the forging billet surface, and thus improve the impact of material and shape differences on the surface quality and dimensional accuracy of the forging.
[0115] For example, using the current detection value and optimal forging process parameters as inputs, and the control parameters of the vacuum isothermal forging control system as outputs, a nonlinear model is established using a pre-set neural network. The optimal output of the nonlinear model is then calculated using a genetic algorithm. This can include the following:
[0116] Using the current detection value and optimal forging process parameters as inputs, and forging load and slide speed as outputs, a model is trained by nonlinear fitting of a BP neural network. The difference between the model and the output is used as the individual fitness value. A genetic algorithm is then used to globally optimize within the input quantization space to obtain the optimal forging load and slide speed.
[0117] As can be seen, this embodiment combines BP neural network and genetic algorithm to obtain the optimal forging load and slide speed based on the forging material and geometric characteristics. This not only reduces the overall control error of the vacuum isothermal forging control system, but also improves material utilization and forging quality.
[0118] Referring to Figure 2, which is a schematic diagram of a vacuum isothermal forging system provided in an embodiment of this application, only the parts related to the embodiment of this application are shown for ease of explanation.
[0119] The vacuum isothermal forging system includes:
[0120] Unit 1 is used to select mechanical property evaluation indicators and forging process parameters of forgings based on the material properties of forgings, and to construct a mathematical model between the mechanical property evaluation indicators and forging process parameters.
[0121] Calculation unit 2 is used to solve the Pareto optimal solution set by using forging process parameters as optimization variables, mechanical performance evaluation index as optimization objective, and mathematical model as objective function model, and to obtain the optimal forging process parameters.
[0122] Control unit 3 is used to detect the surface flatness coefficient of the forging to be forged. The current detection value and the optimal forging process parameters are used as inputs, and the control parameters of the vacuum isothermal forging control system are used as outputs. A nonlinear model is established by applying a preset neural network, and the optimal output of the nonlinear model is calculated by a genetic algorithm.
[0123] Execution unit 4 is used for the control system of vacuum isothermal forging to control the operation according to the current output to complete the forging of the forging.
[0124] As can be seen, the embodiments of this application obtain the optimal forging process parameters determined by the material properties of the forging based on the material properties of the forging and the preset algorithm. Then, the optimal forging process parameters and the geometric features (surface flatness coefficient) of the forging are combined as input quantities, and the preset neural network and genetic algorithm are applied to obtain the optimal control parameters of the vacuum isothermal forging control system. Obviously, the material properties and geometric features of the forging are taken into account, adapting to the actual forging scenario of the forging, and also taking into account the control error of the vacuum isothermal forging control system. Therefore, it can effectively improve the utilization rate of metal materials and the forming quality of forgings, and also improve the intelligence level of vacuum isothermal forging.
[0125] Figure 3 is a structural diagram of a terminal provided in an embodiment of this application. As shown in the figure, the terminal 3 of this embodiment includes: at least one processor 30 (only one is shown in Figure 3), a memory 31, and a computer program 32 stored in the memory 31 and executable on the at least one processor 30. When the processor 30 executes the computer program 32, it implements the steps in any of the above-described method embodiments.
[0126] The terminal 3 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The terminal 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that Figure 3 is merely an example of the terminal 3 and does not constitute a limitation on the terminal 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.
[0127] The processor 30 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.
[0128] The memory 31 can be an internal storage unit of the terminal 3, such as a hard disk or memory of the terminal 3. The memory 31 can also be an external storage device of the terminal 3, such as a plug-in hard disk, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard equipped on the terminal 3. Furthermore, the memory 31 can include both internal storage units and external storage devices of the terminal 3. The memory 31 is used to store the computer program and other programs and data required by the terminal. The memory 31 can also be used to temporarily store data that has been output or will be output.
[0129] In specific implementations, the terminals described in the embodiments of this application include, but are not limited to, other portable devices such as mobile phones, laptop computers, or tablet computers with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads). It should also be understood that in some embodiments, the device is not a portable communication device, but a desktop computer with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads).
[0130] The terminal supports a variety of applications, such as one or more of the following: drawing applications, presentation applications, word processing applications, website creation applications, disc burning applications, spreadsheet applications, game applications, telephone applications, video conferencing applications, email applications, instant messaging applications, exercise support applications, photo management applications, digital camera applications, digital camcorder applications, web browsing applications, digital music player applications, and / or digital video player applications.
[0131] Various applications that can run on a terminal can use at least one common physical user interface device, such as a touch-sensitive surface. One or more functions of the touch-sensitive surface and the corresponding information displayed on the terminal can be adjusted and / or changed between and / or within applications. In this way, the terminal's common physical architecture (e.g., the touch-sensitive surface) can support various applications with user interfaces that are intuitive and transparent to the user.
[0132] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0133] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0134] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0135] In the embodiments provided in this application, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.
[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0137] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0138] If the integrated module / unit is implemented as 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, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.
[0139] The methods described in this application can be implemented in whole or in part by a computer program product. When the computer program product is run on a terminal, the terminal executes the steps in the various method embodiments described above.
[0140] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.
Claims
1. A vacuum isothermal forging method, characterized by, include: Based on the material properties of the forgings, mechanical property evaluation indicators and forging process parameters are selected, and a mathematical model between the mechanical property evaluation indicators and forging process parameters is constructed. Using forging process parameters as optimization variables, mechanical performance evaluation indicators as optimization objectives, and a mathematical model as the objective function model, the Pareto optimal solution set is solved by applying a preset algorithm to obtain the optimal forging process parameters. The surface flatness coefficient of the forging to be forged is detected. The current detection value and the optimal forging process parameters are used as inputs, and the control parameters of the vacuum isothermal forging control system are used as outputs. A nonlinear model is established by applying a preset neural network, and the optimal output of the nonlinear model is calculated by a genetic algorithm. The control system of vacuum isothermal forging operates according to the current output to complete the forging of the forging.
2. The vacuum isothermal forging method according to claim 1, wherein Based on the material properties of the forgings, mechanical property evaluation indicators and forging process parameters are selected, and a mathematical model is constructed between the mechanical property evaluation indicators and forging process parameters, wherein: The mechanical properties of forgings are evaluated using tensile strength and elongation. The forging process parameters are selected as forging temperature, deformation rate, and forging speed.
3. The vacuum isothermal forging method according to claim 2, characterized in that, The mathematical model connecting the mechanical performance evaluation index and the forging process parameters includes the following: Obtain several sets of sample data on the tensile strength and elongation of forgings, and their relationship with forging temperature, deformation rate, and forging speed; The tensile strength and elongation data were fitted with regression models to obtain the corresponding regression equations for tensile strength and elongation.
4. The method of vacuum isothermal forging according to claim 3, wherein The tensile strength and elongation data are fitted with regression models to obtain the corresponding regression equations for tensile strength and elongation. The regression equations for tensile strength and elongation are shown below: In the above formulae, and respectively tensile strength and elongation response value of the sensor, for forging temperature, for the deformation rate, Forging speed.
5. The method of isothermal forging in vacuum according to claim 1, characterized in that, The optimal forging process parameters are obtained by using forging process parameters as optimization variables, mechanical performance evaluation indicators as optimization objectives, and a mathematical model as the objective function model, and applying a preset algorithm to solve for the Pareto optimal solution set. When the forging process parameters are selected as forging temperature, deformation rate, and forging speed, and the mechanical property evaluation indicators of the forging are selected as tensile strength and elongation, the forging process parameters are used as optimization variables, that is: In the above formulae, for forging temperature, for the deformation rate, For forging speed; The mechanical performance evaluation index is used as the optimization objective, namely: In the above formulae, and These are objective function models for tensile strength and elongation, respectively.
6. The method of isothermal forging in a vacuum according to claim 5, wherein The application of a preset algorithm solves for the Pareto optimal solution set to obtain the optimal forging process parameters. The preset algorithm is a multi-objective optimization genetic algorithm, which specifically includes the following steps: Step 21: Based on the preset algorithm parameter table and the range of values for the forging process parameters of the forging, randomly generate an initial population of a certain size, and calculate the objective function value corresponding to each individual in the initial population. Step 22: Based on the objective function values corresponding to the individuals in the initial population, perform non-dominated sorting on the individuals in the initial population to determine the dominance level of each individual in the initial population, and then perform selection, crossover, and mutation operations to obtain a subpopulation. Step 23: Merge the initial population with the subpopulation to obtain a new population. Perform non-dominated sorting on the new population to obtain the front-end individuals of the non-dominated solutions. Calculate the crowding degree of the front-end individuals of the non-dominated solutions. Select N new individuals with high dominance and high crowding as the first new individuals; randomly select some individuals from the initial population for mutation as the second new individuals; The first and second new individuals are merged into the initial population to form a new generation population. The new generation population is then subjected to non-dominated sorting and individual crowding calculation, followed by selection, crossover, and mutation operations to obtain a new generation of subpopulations. Step 24: Determine whether the maximum number of generations has reached the upper limit of the set value. If it has, the operation stops and the final population individuals are used as the Pareto optimal solution set for multi-objective optimization of mechanical performance. Otherwise, proceed to step 23 and perform the calculation again.
7. The method of isothermal forging in vacuum according to claim 6, characterized in that, The process involves sorting the individuals in the initial population according to their corresponding objective function values to determine the dominance level of each individual. Then, selection, crossover, and mutation operations are performed to obtain a subpopulation. Specifically, the process of sorting the individuals in the initial population according to their non-dominated values to determine the dominance level of each individual, which is also the dominance level for optimizing the objective value, is as follows: Individuals in the initial population that are not dominated by any other individual are classified as Level 1, Level 2 as individuals dominated only by Level 1, Level 3 as individuals dominated only by Level 2, and so on, to determine the dominance level of all individuals in the initial population.
8. The method of isothermal forging in vacuum according to claim 6, characterized in that, The individual crowding degree is calculated using the following formula: In the above formula, For the first Generation of the cohort a target function value, For the first The first generation of the population a target function value, and respectively a maximum and a minimum of the values of the objective function, For the first The first generation of the population crowding distance at an objective function value, For the first total crowding of the demes, The number of objective function values. For the first Non-dominated ranking of generations of populations For the first Non-dominated ranking of generations of populations For the first The overall crowding of the population.
9. The vacuum isothermal forging method according to claim 1, characterized in that, The process involves detecting the surface flatness coefficient of the forging workpiece, using the current detection value and optimal forging process parameters as inputs, and the control parameters of the vacuum isothermal forging control system as outputs. A nonlinear model is established using a pre-set neural network, and the optimal output of the nonlinear model is calculated using a genetic algorithm. Specifically: The surface flatness coefficient of the forging to be forged is detected by laser rangefinder method; Using the current detection value and optimal forging process parameters as inputs, and forging load and slide speed as outputs, a model is trained by nonlinear fitting of a BP neural network. The difference between the model and the output is used as the individual fitness value. A genetic algorithm is then used to globally optimize within the input quantization space to obtain the optimal forging load and slide speed.
10. A vacuum isothermal forging system, characterized in that, include: The acquisition unit is used to select mechanical property evaluation indicators and forging process parameters of forgings based on the material properties of forgings, and to construct a mathematical model between the mechanical property evaluation indicators and forging process parameters. The calculation unit is used to solve the Pareto optimal solution set by applying a preset algorithm with forging process parameters as optimization variables, mechanical performance evaluation index as optimization objective, and mathematical model as objective function model, so as to obtain the optimal forging process parameters. The control unit is used to detect the surface flatness coefficient of the forging to be forged. It takes the current detection value and the optimal forging process parameters as inputs and the control parameters of the vacuum isothermal forging control system as outputs. It applies a preset neural network to establish a nonlinear model and calculates the optimal output of the nonlinear model through a genetic algorithm. The execution unit, used in the vacuum isothermal forging control system, operates according to the current output to complete the forging of the forging work.