A material data processing method and device for forging process simulation, computer equipment and medium

Abstract

Embodiments of the present application provide a kind of material data processing method, device, computer equipment and medium for forging process simulation, wherein the method comprises the following steps: importing the original experimental data of forging material from multiple sources to carry out automatic analysis and identify physical dimension and data format, and store as structured data object;Based on structured data object, execute the first parameter set generated by constitutive parameter inversion, and record the first inversion iteration process curve generated, execute the second parameter set generated by organization evolution parameter inversion, and record the second inversion iteration process curve generated;Based on the first parameter set, the second parameter set and the basic physical properties of forging material, logical association and encapsulation are carried out, and material model file is generated;First inversion iteration process curve and second inversion iteration process curve are displayed, and error evaluation report containing key goodness-of-fit index is output. Realize the integrated and structured uniform management of forging material data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a material data processing method, apparatus, computer equipment, and medium for forging process simulation. Background Technology

[0002] Forging process simulation technology is a core tool for optimizing forging processes, predicting product forming quality, shortening R&D cycles, and reducing production costs. Its simulation accuracy and reliability highly depend on the accuracy of the material model and its parameters. These parameters typically include constitutive model parameters describing the high-temperature deformation behavior of materials, recrystallization kinetic parameters predicting microstructure evolution, and hot working diagrams guiding process design.

[0003] Currently, in the field of forging process simulation, the management of material data and the acquisition of model parameters face the following prominent problems:

[0004] Data management is fragmented and unstructured. Fundamental material performance data (such as mechanical and thermophysical properties), high-temperature rheological experimental data, and microstructure observation data often originate from various experimental reports, research papers, or third-party databases, stored in diverse formats (such as Excel spreadsheets, text files, and images), lacking a unified standard and structure. This leads to difficulties in data retrieval, cumbersome updates and maintenance, and makes it difficult to guarantee data consistency and integrity.

[0005] Model parameter acquisition is manual and inefficient. Extracting and calibrating material model parameters from raw experimental data (i.e., parameter inversion) is a complex optimization calculation process. Currently, the common approach is for researchers to manually import data into specialized mathematical software (such as Origin, MATLAB scripts) or write their own programs for separation and processing: first, rheological data is processed to fit constitutive parameters, and then metallographic data is processed to calibrate microstructure model parameters. This process is not only time-consuming and labor-intensive, but also heavily reliant on the operator's experience; results from different personnel may vary, resulting in poor repeatability and consistency.

[0006] The data flow is disconnected from the simulation workflow. The acquired model parameters need to be manually organized and compiled into material cards or input files according to the format required by specific simulation software (such as Simufact, DEFORM, Abaqus). This process is prone to human error, and once the material model or simulation software version is updated, all related work needs to be started from scratch, lacking an automated and traceable data conversion and encapsulation chain.

[0007] The lack of a collaborative and knowledge accumulation platform hinders the effective sharing, version control, and access management of material data and related model parameters, which are important knowledge assets for enterprises or research institutions. This lack of an integrated management platform further impedes the accumulation and formation of a standardized materials database, thus restricting the inheritance and reuse of knowledge.

[0008] Therefore, there is an urgent need for an integrated solution that can integrate material data management, automated parameter inversion, visualization analysis, and simulation-ready file generation to overcome the problems of data dispersion, inefficient processing, and application disconnect, thereby truly improving the efficiency of pre-modeling and the overall reliability of forging process simulation. Summary of the Invention

[0009] In view of this, embodiments of the present invention provide a material data processing method for forging process simulation, to solve the technical problems of scattered forging material data, inefficient processing, and disconnected application in the prior art. The method includes: The raw experimental data of forging materials are imported from multiple sources. The raw experimental data is automatically parsed and the physical dimensions and data formats are identified. All the raw experimental data are converted and stored as structured data objects with preset field labels. The raw experimental data includes rheological stress-strain data stored in the form of discrete point sequences. Based on the structured data object, constitutive parameter inversion is performed to generate a first parameter set, and the first inversion iteration process curve generated by the constitutive parameter inversion is recorded. Then, tissue evolution parameter inversion is performed to generate a second parameter set, and the second inversion iteration process curve generated by the tissue evolution parameter inversion is recorded. Based on the material model data structure and file format of the target forging simulation software, the first parameter set, the second parameter set, and the basic physical properties of the forging material are logically associated and encapsulated to generate a material model file for driving the target forging simulation software to perform forging process simulation. The graphical user interface displays the curves of the first inversion iteration process and the curves of the second inversion iteration process, and outputs an error evaluation report containing key goodness-of-fit indices.

[0010] This invention also provides a material data processing device for forging process simulation, to solve the technical problems of scattered forging material data, inefficient processing, and disconnected application in the prior art. The device includes: The data preprocessing module is used to import raw experimental data of forging materials from multiple sources, automatically parse the raw experimental data and identify physical dimensions and data formats, and convert and store all the raw experimental data into structured data objects with preset field labels. The raw experimental data includes rheological stress-strain data stored in the form of discrete point sequences. A parameter set construction module is used to generate a first parameter set by constitutive parameter inversion based on the structured data object, and record the first inversion iteration process curve generated by the constitutive parameter inversion; and to generate a second parameter set by tissue evolution parameter inversion, and record the second inversion iteration process curve generated by the tissue evolution parameter inversion. A material model file module is constructed to logically associate and encapsulate the first parameter set, the second parameter set, and the basic physical properties of the forging material based on the material model data structure and file format of the target forging simulation software, and generate a material model file for driving the target forging simulation software to perform forging process simulation. The output evaluation report module is used to display the first inversion iteration process curve and the second inversion iteration process curve in the graphical user interface, and output an error evaluation report containing key goodness-of-fit indicators.

[0011] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned material data processing methods for forging process simulation, thereby solving the technical problems of scattered data, inefficient processing, and disconnected application of forging materials in the prior art.

[0012] This invention also provides a computer-readable storage medium storing a computer program that executes any of the above-described material data processing methods for forging process simulation, in order to solve the technical problems of scattered forging material data, inefficient processing, and disconnected applications in the prior art.

[0013] Compared with the prior art, the beneficial effects that at least one technical solution adopted in the embodiments of this specification can achieve include at least: It achieves integrated and structured unified management of forging material data, solving the problem that existing technologies have scattered mechanical, thermal, rheological, and microstructure data of materials in different files and formats, making it difficult to find and update them. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments 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.

[0015] Figure 1 This is a flowchart of a material data processing method for forging process simulation provided by an embodiment of the present invention; Figure 2 This is a structural block diagram of a computer device provided in an embodiment of the present invention; Figure 3 This is a structural block diagram of a material data processing device for forging process simulation provided in an embodiment of the present invention. Detailed Implementation

[0016] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0017] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] In this embodiment of the invention, a material data processing method for forging process simulation is provided, such as... Figure 1 As shown, the method includes: Step S101: Import raw experimental data of forging materials from multiple sources, automatically parse the raw experimental data and identify physical dimensions and data formats, and convert and store all the raw experimental data into structured data objects with preset field labels. The raw experimental data includes rheological stress-strain data stored in the form of discrete point sequences. Step S102: Based on the structured data object, perform constitutive parameter inversion to generate a first parameter set, and record the first inversion iteration process curve generated by the constitutive parameter inversion; perform tissue evolution parameter inversion to generate a second parameter set, and record the second inversion iteration process curve generated by the tissue evolution parameter inversion. Step S103: Based on the material model data structure and file format of the target forging simulation software, logically associate and encapsulate the first parameter set, the second parameter set, and the basic physical properties of the forging material to generate a material model file for driving the target forging simulation software to perform forging process simulation; Step S104: In the graphical user interface, display the first inversion iteration process curve and the second inversion iteration process curve, and output an error evaluation report containing key goodness-of-fit indices.

[0019] In specific implementation, the constitutive parameter inversion is performed to generate the first parameter set through the following steps, and the curve of the first inversion iteration process generated by the constitutive parameter inversion is recorded: Multiple sets of true stress-strain curves at different temperatures and strain rates are extracted from the structured data object; a hyperbolic sine constitutive model is determined as the framework of the physical constitutive equation, wherein the hyperbolic sine constitutive model is used to characterize the plastic deformation of the material during the forging process; using the hyperbolic sine constitutive model, based on the given parameters to be inverted, the model stress values ​​corresponding to various experimental conditions are calculated, and a first objective function is constructed with the goal of minimizing the sum of squared global residuals between the experimental stress data points and the model stress values ​​calculated by the hyperbolic sine constitutive model; using a particle swarm optimization algorithm, a global search is performed within the parameter feasible region to obtain the material constants, stress levels, and activation energies that minimize the first objective function, and these are used as the first optimal parameters, and a first parameter set is constructed based on the first optimal parameters; during the constitutive parameter inversion process, a comparison graph of the stress-strain fitting curve corresponding to the optimal parameters and the true stress-strain curve is recorded, and the convergence curve of the objective function value with the number of iterations is recorded, and the comparison graph and the convergence curve are output as the first inversion iteration process curve.

[0020] In specific implementation, the second parameter set is generated by performing tissue evolution parameter inversion through the following steps, and the second inversion iteration process curve generated by the tissue evolution parameter inversion is recorded: The recrystallization fraction under different thermal deformation conditions is calculated from the rheological stress-strain data of the structured data object as a sequence of data. A cellular automata model is used as the microstructure evolution model. Based on the set inversion parameters and the sequence data, the evolution curve of the recrystallization fraction is calculated. A second objective function is constructed with the goal of maximizing the agreement between the experimentally observed tissue evolution data and the predicted curve of the microstructure evolution model. The kinetic parameters in the microstructure evolution model are fitted using the Levenberg-Marquardt algorithm to invert and obtain the recrystallization activation energy, exponential factor, and grain boundary migration rate that minimize the second objective function. These are then used as the second optimal parameters, and a second parameter set is constructed based on these second optimal parameters. During the inversion of the tissue evolution parameters, a scatter plot and residual distribution plot comparing the predicted and experimental values ​​of the recrystallization fraction are recorded. The scatter plot and residual distribution plot are output as the second inversion iteration process curve.

[0021] In specific implementation, the following steps are used to automatically parse the raw experimental data, identify the physical dimensions and data format, and convert and store all the raw experimental data into structured data objects with preset field labels: Based on the file extension and internal data structure characteristics of the original experimental data, the source format of the original experimental data is identified, and the original experimental data is read using a data parser corresponding to the source format. The header information of the original experimental data is parsed to identify the physical quantities and original units corresponding to the data columns of the original experimental data. Based on the original units and a preset unit conversion rule library, the physical quantities in the original experimental data are converted and unified to the International System of Units (SI) to generate unified experimental data. Based on preset rules, the unified experimental data is interpolated or removed to generate processed original experimental data. The processed original experimental data is organized and stored according to a preset field label system to generate structured data objects.

[0022] In specific implementation, the following steps are used to realize the material model data structure and file format based on the target forging simulation software, and to logically associate and encapsulate the first parameter set, the second parameter set, and the basic physical properties of the forging material: Based on the target simulation software type specified by the user on the interface, load the material card XML template corresponding to the target simulation software type; map and fill the first parameter set, the second parameter set, and the basic physical properties into the predefined field positions of the material card XML template, wherein the basic physical properties include material density, specific heat capacity, and thermal conductivity; perform XML syntax validation and keyword format validation on the filled material card XML template, and serialize the validated data structure into the material file of the target forging simulation software.

[0023] In practice, the following steps are used to output an error assessment report containing key goodness-of-fit indices: The system calculates and outputs a constitutive goodness-of-fit index generated by performing the constitutive parameter inversion, which includes the correlation coefficient between the predicted stress value and the experimental value, and the mean absolute percentage error; it also calculates and outputs a microstructure evolution goodness-of-fit index generated by performing the microstructure evolution parameter inversion, which includes the coefficient of determination between the predicted recrystallization fraction or grain size value and the experimental value, and the root mean square error; and displays the error assessment report in the graphical user interface through tables and / or charts.

[0024] In specific implementation, the original experimental data of the forging material are obtained through the following steps: The rheological stress-strain data used for constitutive parameter inversion and the sequence data of recrystallization fraction as a function of strain used for microstructure evolution parameter inversion; the rheological stress-strain data is stored in the form of a discrete point sequence and includes the true stress and true strain measured at different temperatures and strain rates; the recrystallization kinetic data used for microstructure evolution parameter inversion, the sequence data is stored in tabular form and includes data on the recrystallization fraction as a function of strain and grain size measured under different hot deformation conditions.

[0025] In one embodiment of the present invention, a material data processing method for forging process simulation is provided. The core of this method lies in automatically processing raw forging material experimental data from various sources and in different formats into structured data through an integrated system. Then, a dedicated parameter inversion algorithm is used to obtain the key parameter set of the material model, and finally, the data is packaged into a material model file that can directly drive forging process simulation software. The entire process is visualized, interactive, and monitored in a graphical user interface (GUI).

[0026] 1. Overall architecture and modular design.

[0027] This invention utilizes MATLAB App Designer to construct a graphical user interface, employing a modular design concept. The main interface of the material database (corresponding to the "graphical user interface" in the claims) adopts a grid layout divided into two columns: one column is the control window, and the other column is the attribute window.

[0028] The control window bar features customized functional areas for user identity switching, material selection, chemical composition display, data export, and creating new material entries. The property window bar integrates five core functional modules in the form of tab groups: mechanical properties, thermal properties, rheological curves, microscopic models (corresponding to the "microstructure evolution parameter inversion" module in the claims), and heat treatment diagrams.

[0029] In addition, the system also includes a separate interface for creating new materials. This interface also uses a grid layout, with the left side being the input area for basic material information (such as material name and chemical composition) and the right side being the input area for material performance parameters. This area further integrates mechanical properties, thermal properties, rheological curves, microstructure, and a dedicated parameter inversion module (corresponding to the function set for generating the "first parameter set" and "second parameter set" in the claims).

[0030] 2. Data acquisition and structuring (generating structured data objects).

[0031] The system supports importing raw experimental data of forging materials from multiple sources, including but not limited to Excel files, XML files, and text files. The raw experimental data mainly includes two categories: 1) rheological stress-strain data stored as a discrete point sequence (used for constitutive parameter inversion); 2) recrystallization kinetic data stored in tabular form (used for microstructure evolution parameter inversion). The rheological stress-strain data typically includes the true stress and true strain measured at different temperatures and strain rates; the recrystallization kinetic data includes data on the change in recrystallization fraction with strain (or time) and grain size data measured under different hot deformation conditions.

[0032] After the data is imported, the system performs automatic parsing and standardization preprocessing: Format recognition and parsing: Based on the file extension and internal data structure characteristics, it automatically identifies the data source format (such as .xlsx, .xml) and calls the corresponding data parser to read the original data.

[0033] Physical quantity and unit identification: Parses the header information or preset markers of the data file, automatically identifies the physical quantities (such as stress, strain, temperature, recrystallization fraction) and their original units (such as MPa, s) represented by each data column. -1 K, %).

[0034] Unit unification and cleaning: Based on a pre-set unit conversion rule library, all identified physical quantity data are uniformly converted to the International System of Units (SI). Subsequently, the converted data sequence is subjected to integrity verification and outlier detection. Data points that are missing or obviously exceed the physical reasonable range are marked and prompted, and linear interpolation, elimination, or retention operations can be performed according to preset rules or user instructions.

[0035] Structured storage maps data that has undergone unit unification and cleaning to structured data templates with preset field labels (such as temperature, strain_rate, stress, recrystallization_fraction) based on the meaning of their physical quantities, generating standardized structured data objects for subsequent use by all modules.

[0036] 3. Parameter inversion and implementation of core algorithms.

[0037] The parameter inversion algorithm of this invention automatically extracts key parameters of the material model from experimental data.

[0038] 3.1 Constitutive parameter inversion (generating the first parameter set).

[0039] Data extraction involves extracting multiple sets of true stress-strain curves at different temperatures and strain rates from structured data objects.

[0040] Model selection: The hyperbolic sinusoidal constitutive model, which is suitable for describing the high-temperature plastic deformation of materials, was chosen as the framework of the physical constitutive equations.

[0041] The objective function is constructed based on the aforementioned hyperbolic sinusoidal constitutive model. For a given set of parameters to be inverted (such as material constant A, stress level α, exponent n, and activation energy Q), the model-predicted stress values ​​corresponding to various experimental conditions (temperature, strain rate, strain) are calculated. The first objective function is constructed with the goal of minimizing the sum of squared global residuals between all experimental stress data points and the corresponding model-calculated stress values.

[0042] The optimization solution employs the Particle Swarm Optimization (PSO) algorithm as the global search algorithm. Iterative optimization is performed within the defined feasible region of parameters to find the set of parameters that minimizes the value of the first objective function, i.e., the optimal material constants, stress level, and activation energy are obtained. These parameters together constitute the first parameter set.

[0043] The process is visualized. During the inversion iteration, the graphical interface updates and displays in real time the comparison between the stress-strain fitting curve corresponding to the current optimal parameters and the experimental curve, as well as the convergence curve of the objective function value with the number of iterations. This is the first inversion iteration process curve.

[0044] 3.2 Tissue evolution parameter inversion (generating a second parameter set).

[0045] Data extraction involves extracting sequence data of recrystallization fraction as a function of strain under different thermal deformation conditions (different temperatures and strain rates) from structured data objects.

[0046] The model was selected as either the cellular automata (CA) model or the Johnson-Meyer-Avramie (JMA) equation as the microorganism evolution model.

[0047] The objective function is constructed based on the selected microstructure evolution model mentioned above. For given kinetic parameters to be inverted, the evolution curve of recrystallization fraction with strain is calculated. A second objective function is constructed with the objective of maximizing the agreement between the experimentally observed recrystallization fraction data and the model prediction curve (e.g., minimizing the sum of squared residuals).

[0048] To optimize the solution, the Levenberg-Marquardt algorithm is used as a local fine-fit algorithm. The dynamic parameters in the model (such as recrystallization activation energy, exponential factor, and grain boundary migration rate) are iteratively optimized, and the set of parameters that minimizes the second objective function is obtained by inversion, which constitutes the second parameter set.

[0049] The process is visualized by displaying a scatter plot comparing the model predictions and experimental values ​​of recrystallization fraction, as well as a residual distribution plot, on the graphical interface during parameter fitting. This is the curve of the second inversion iteration process.

[0050] 3.3 Simulation file packaging and export (generating material model files) After obtaining the first set of parameters (constitutive parameters) and the second set of parameters (organization evolution parameters), the system logically associates them with the basic physical properties of the material (such as density, specific heat capacity, and thermal conductivity).

[0051] Template loading: Based on the target simulation software type (such as Simufact, DEFORM) specified by the user in the graphical interface, load the corresponding material card XML template or keyword template.

[0052] Parameter mapping accurately maps and fills the first parameter set, the second parameter set, and the basic physical attributes into the corresponding positions according to the predefined field structure and semantics of the template.

[0053] The validation and generation process involves rigorous XML syntax or keyword format validation of the filled template data to ensure it is free of syntax errors and conforms to the target software specifications. Once validation is successful, the data structure is serialized into specific material files (such as .mfd, .dat, .inp formats) that the target forging simulation software can recognize, thus enabling "one-click export".

[0054] 4. User interaction, permission management, and output results The graphical interface and interactive features allow all material performance data (mechanical, thermal, and rheological curves) to be displayed and edited in both graphical and tabular formats within the corresponding modules of the property window, enhancing data readability and operability. When creating a new material, the identification, reading, importing, and plotting of various performance data are all completed through the graphical interface.

[0055] Error assessment and reporting: The system automatically calculates the goodness of fit of the inversion results. For constitutive parameter inversion, it calculates the correlation coefficient (R²) and mean absolute percentage error (MAPE) between predicted and experimental stress values; for microstructure evolution parameter inversion, it calculates the coefficient of determination (R²) and root mean square error (RMSE) between predicted and experimental recrystallization fraction or grain size values. These key indicators, along with the inversion process curves, are presented in a graphical interface as charts and / or tables to generate an intuitive error assessment report, facilitating user evaluation of the reliability of the inversion results.

[0056] For access control and data security, the system supports hierarchical access management for users and administrators. In the user module of the control window, users can switch identities. Administrators have the highest privileges, including the ability to add, delete, modify, and query material data, perform parameter inversion calculations, and export files. Regular users (operators) typically have limited permissions to data querying, graphical display, and result export. The system supports identity verification via username and password or higher-level authentication methods (such as MAC address binding), and can encrypt and store critical data to ensure data security.

[0057] In this embodiment, a computer device is provided, such as... Figure 2 As shown, it includes a memory 201, a processor 202, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned material data processing methods for forging process simulation.

[0058] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0059] In this embodiment, a computer-readable storage medium is provided, which stores a computer program that performs any of the above-described material data processing methods for forging process simulation.

[0060] Specifically, computer-readable storage media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media do not include transient media, such as modulated data signals and carrier waves.

[0061] Based on the same inventive concept, this invention also provides a material data processing apparatus for forging process simulation, as described in the following embodiments. Since the principle of the material data processing apparatus for forging process simulation is similar to that of the material data processing method for forging process simulation, the implementation of the material data processing apparatus for forging process simulation can refer to the implementation of the material data processing method for forging process simulation, and will not be repeated. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0062] Figure 3 This is a structural block diagram of a material data processing device for forging process simulation according to an embodiment of the present invention, such as... Figure 3 As shown, it includes: a data preprocessing module 301, a parameter set construction module 302, a material model file construction module 303, and an evaluation report output module 304. The structure is described below.

[0063] The data preprocessing module 301 is used to import raw experimental data of forging materials from multiple sources, automatically parse the raw experimental data and identify physical dimensions and data formats, and convert and store all the raw experimental data into structured data objects with preset field labels. The raw experimental data includes rheological stress-strain data stored in the form of discrete point sequences. The parameter set construction module 302 is used to perform constitutive parameter inversion to generate a first parameter set based on the structured data object, and record the first inversion iteration process curve generated by the constitutive parameter inversion; and to perform tissue evolution parameter inversion to generate a second parameter set, and record the second inversion iteration process curve generated by the tissue evolution parameter inversion. The material model file construction module 303 is used to logically associate and encapsulate the first parameter set, the second parameter set, and the basic physical properties of the forging material based on the material model data structure and file format of the target forging simulation software, and generate a material model file for driving the target forging simulation software to perform forging process simulation. The output evaluation report module 304 is used to display the first inversion iteration process curve and the second inversion iteration process curve in the graphical user interface, and output an error evaluation report containing key goodness-of-fit indicators.

[0064] In one embodiment, the data preprocessing module includes: Data building units are used to perform constitutive parameter inversion with rheological stress-strain data and to perform microstructure evolution parameter inversion with sequence data of recrystallization fraction as a function of strain. A stress-strain data storage unit is used to store the rheological stress-strain data in the form of a discrete point sequence, including the actual stress and actual strain measured at different temperatures and strain rates; A kinetic data storage unit is used for recrystallization kinetic data for tissue evolution parameter inversion. The sequence data is stored in tabular form and includes data on the recrystallization fraction as a function of strain and grain size data measured under different thermal deformation conditions.

[0065] In one embodiment, the data preprocessing module further includes: The experimental data reading unit is used to identify the source format of the original experimental data based on the file extension and internal data structure characteristics of the original experimental data, and read the original experimental data through the data parser corresponding to the source format; The data identification unit is used to parse the header information of the original experimental data and identify the physical quantities and original units corresponding to the data columns of the original experimental data. A unified experimental data unit is used to convert and unify the physical quantities in the original experimental data to the International System of Units (SI) based on the original units and a pre-set unit conversion rule library, thereby generating unified experimental data. The data processing unit is used to perform interpolation or elimination processing on the unified experimental data based on preset rules to generate the processed original experimental data. A structured data unit is generated to organize and store the processed raw experimental data according to a preset field label system, thereby generating a structured data object.

[0066] In one embodiment, the parameter set construction module includes: The curve extraction unit is used to extract multiple sets of real stress-strain curves at different temperatures and strain rates from the structured data object. A framework building unit is used to determine a hyperbolic sine constitutive model as a framework for physical constitutive equations, wherein the hyperbolic sine constitutive model is used to characterize the plastic deformation of materials during the forging process; The first objective function construction unit is used to calculate the model stress value corresponding to various experimental conditions based on the given parameters to be inverted using the hyperbolic sine constitutive model, with the objective of minimizing the global residual sum of squares between the experimental stress data points and the model stress value calculated by the hyperbolic sine constitutive model; A first parameter set unit is constructed to perform a global search within the parameter feasible region using a particle swarm optimization algorithm to obtain the material constants, stress levels, and activation energies that minimize the first objective function, and these are used as the first optimal parameters. The first parameter set is then constructed based on the first optimal parameters. The first curve output unit is used to record a comparison graph of the stress-strain fitting curve corresponding to the optimal parameters and the actual stress-strain curve during the constitutive parameter inversion process, and to record the convergence curve of the objective function value with the number of iterations. The comparison graph and the convergence curve are output as the first inversion iteration process curve.

[0067] In one embodiment, the parameter set construction module further includes: Extract sequence data units are used to calculate sequence data of recrystallization fraction as a function of strain under different hot deformation conditions from the rheological stress-strain data of the structured data object; Determine the evolutionary model unit to use the cellular automata model as a micro-organization evolution model; A second objective function unit is constructed to calculate the evolution curve of the recrystallization fraction based on the set parameters to be inverted using the microstructure evolution model and the sequence data. The objective is to maximize the degree of agreement between the experimentally observed tissue evolution data and the predicted curve of the microstructure evolution model. A second parameter set unit is constructed to fit the kinetic parameters in the microstructure evolution model using the Levenberg-Marquardt algorithm, and to invert the recrystallization activation energy, exponential factor and grain boundary migration rate that minimize the second objective function, and use them as the second optimal parameters. A second parameter set is constructed based on the second optimal parameters. The second curve output unit is used to record the comparison scatter plot and residual distribution plot of the predicted value and experimental value of the recrystallization fraction during the tissue evolution parameter inversion process, and output the comparison scatter plot and residual distribution plot as the curve of the second inversion iteration process.

[0068] In one embodiment, the material model file module includes: The template loading unit is used to load the material card XML template corresponding to the target simulation software type specified by the user in the interface. A fill field unit is used to map and fill the first parameter set, the second parameter set, and the basic physical properties to the predefined field positions of the material card XML template, wherein the basic physical properties include material density, specific heat capacity, and thermal conductivity. The format verification unit is used to perform XML syntax verification and keyword format verification on the filled material card XML template, and serialize the verified data structure into the material file of the target forging simulation software.

[0069] In one embodiment, the output evaluation report module includes: A generation index unit is used to calculate and output the constitutive goodness index generated by performing the constitutive parameter inversion. The constitutive goodness index includes the correlation coefficient between the predicted stress value and the experimental value, and the mean absolute percentage error. The goodness-of-fit index calculation unit is used to calculate and output the goodness-of-fit index of the tissue evolution generated by the inversion of the tissue evolution parameters. The goodness-of-fit index of the tissue evolution includes the coefficient of determination and root mean square error between the predicted recrystallization fraction or grain size and the experimental value. An interface display unit is used to display the error assessment report in the graphical user interface through tables and / or charts.

[0070] The embodiments of the present invention achieve the following technical effects: This invention achieves integrated and structured unified management of forging material data, solving the problem of existing technologies where mechanical, thermal, rheological, and microstructure data of materials are scattered across different files and formats, making searching and updating difficult. By constructing a unified graphical database platform and designing automated data parsing, unit identification, and conversion processes, it automatically cleans, converts, and stores multi-source heterogeneous raw experimental data into standardized structured data objects, ensuring data consistency, integrity, and traceability, laying a solid foundation for subsequent processing. Furthermore, it automates and intelligently inverts key material model parameters, significantly improving accuracy and efficiency. This invention integrates two parallel parameter inversion engines: for high-temperature deformation behavior, it uses the Particle Swarm Optimization (PSO) algorithm to globally optimize hyperbolic sine constitutive model parameters to generate the first parameter set; for microstructure evolution, it uses the Levenberg-Marquardt (LM) algorithm to locally finely fit cellular automata and other model parameters to generate the second parameter set. The optimized algorithm ensures the objectivity and optimality of the inversion results, reducing human error. This invention, through pre-configured XML templates for material cards in various simulation software (such as Simufact and DEFORM), can automatically and accurately map and encapsulate the first and second parameter sets and basic material properties obtained from the inversion into directly executable material model files that meet the format requirements of the target software. This achieves seamless integration of data flow to simulation flow, eliminating bottlenecks and errors associated with manual conversion. It provides full-process visualization, interaction, and transparent evaluation, greatly enhancing user experience and result credibility. The user-friendly graphical interface (GUI) developed based on MATLAB AppDesigner presents complex data processing, inversion calculations, and other background operations with intuitive charts, curves, and controls. Users can monitor the inversion process in real time, compare fitting results, and ultimately obtain an error assessment report containing key indicators such as correlation coefficient (R²) and mean absolute percentage error (MAPE). This makes advanced parameter inversion technology easy to use and controllable even for ordinary process engineers, while enhancing the transparency and credibility of the entire technical process. A foundational platform for collaborative work and knowledge accumulation is built, facilitating the assetization and standardized management of enterprise material data. Built-in user identity switching and permission management mechanisms support hierarchical operations for administrators and ordinary users. Combined with data encryption storage, the security of core data assets is ensured. The unified database platform facilitates team sharing of material data, model parameters, and process knowledge, enabling continuous accumulation and consolidation of the enterprise's material database, promoting knowledge transfer and reuse, and driving the standardization and normalization of material research and development and process design.

[0071] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0072] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A material data processing method for forging process simulation, characterized in that, include: The raw experimental data of forging materials are imported from multiple sources. The raw experimental data is automatically parsed and the physical dimensions and data formats are identified. All the raw experimental data are converted and stored as structured data objects with preset field labels. The raw experimental data includes rheological stress-strain data stored in the form of discrete point sequences. Based on the structured data object, constitutive parameter inversion is performed to generate a first parameter set, and the first inversion iteration process curve generated by the constitutive parameter inversion is recorded. Then, tissue evolution parameter inversion is performed to generate a second parameter set, and the second inversion iteration process curve generated by the tissue evolution parameter inversion is recorded. Based on the material model data structure and file format of the target forging simulation software, the first parameter set, the second parameter set, and the basic physical properties of the forging material are logically associated and encapsulated to generate a material model file for driving the target forging simulation software to perform forging process simulation. The graphical user interface displays the curves of the first inversion iteration process and the curves of the second inversion iteration process, and outputs an error evaluation report containing key goodness-of-fit indices.

2. The material data processing method for forging process simulation as described in claim 1, characterized in that, Perform constitutive parameter inversion to generate the first parameter set, and record the first inversion iteration process curve generated by the constitutive parameter inversion, including: Multiple sets of real stress-strain curves at different temperatures and strain rates are extracted from the structured data object; A hyperbolic sine constitutive model is established as the framework of the physical constitutive equations, wherein the hyperbolic sine constitutive model is used to characterize the plastic deformation of materials during the forging process; Using the hyperbolic sine constitutive model, based on the given parameters to be inverted, the model stress values ​​corresponding to various experimental conditions are calculated. The first objective function is constructed with the goal of minimizing the sum of squared global residuals between the experimental stress data points and the model stress values ​​calculated by the hyperbolic sine constitutive model. The particle swarm optimization algorithm is used to perform a global search within the parameter feasible region to obtain the material constants, stress levels and activation energies that minimize the first objective function, and these are used as the first optimal parameters. A first parameter set is then constructed based on the first optimal parameters. During the constitutive parameter inversion process, a comparison graph of the stress-strain fitting curve corresponding to the optimal parameters and the actual stress-strain curve is recorded, and the convergence curve of the objective function value with the number of iterations is recorded. The comparison graph and the convergence curve are output as the first inversion iteration process curve.

3. The material data processing method for forging process simulation as described in claim 1, characterized in that, Perform tissue evolution parameter inversion to generate a second parameter set, and record the second inversion iteration process curve generated by the tissue evolution parameter inversion, including: The sequence data of recrystallization fraction as a function of strain under different hot deformation conditions are calculated from the rheological stress-strain data of the structured data object; Using cellular automata models as models for microscopic organizational evolution; Using the microstructure evolution model and the sequence data, and based on the set parameters to be inverted, the evolution curve of the recrystallization fraction is calculated. The second objective function is constructed with the goal of maximizing the degree of agreement between the experimentally observed tissue evolution data and the predicted curve of the microstructure evolution model. The kinetic parameters in the microstructure evolution model are fitted using the Levenberg-Marquardt algorithm, and the recrystallization activation energy, exponential factor, and grain boundary migration rate that minimize the second objective function are obtained by inversion. These parameters are then used as the second optimal parameters, and a second parameter set is constructed based on the second optimal parameters. During the inversion of tissue evolution parameters, a scatter plot and a residual distribution plot comparing the predicted and experimental values ​​of the recrystallization fraction are recorded, and the scatter plot and the residual distribution plot are output as the curves of the second inversion iteration process.

4. The material data processing method for forging process simulation as described in claim 1, characterized in that, The raw experimental data is automatically parsed and its physical dimensions and data format are identified. All raw experimental data is then converted and stored as structured data objects with preset field labels, including: Based on the file extension and internal data structure characteristics of the original experimental data, the source format of the original experimental data is identified, and the original experimental data is read through the data parser corresponding to the source format. Parse the header information of the original experimental data to identify the physical quantities and original units corresponding to the data columns of the original experimental data; Based on the original units and the preset unit conversion rule library, the physical quantities in the original experimental data are converted and unified to the International System of Units (SI) to generate unified experimental data. Based on preset rules, the unified experimental data is interpolated or removed to generate the processed original experimental data. The processed raw experimental data is organized and stored according to a preset field labeling system to generate structured data objects.

5. The material data processing method for forging process simulation as described in claim 1, characterized in that, Based on the material model data structure and file format of the target forging simulation software, the first parameter set, the second parameter set, and the basic physical properties of the forging material are logically associated and encapsulated, including: Based on the target simulation software type specified by the user in the interface, load the material card XML template corresponding to the target simulation software type; The first parameter set, the second parameter set, and the basic physical properties are mapped and filled into the predefined field positions of the material card XML template, wherein the basic physical properties include material density, specific heat capacity, and thermal conductivity; The filled material card XML template is subjected to XML syntax validation and keyword format validation. The validated data structure is serialized into the material file of the target forging simulation software.

6. The material data processing method for forging process simulation as described in any one of claims 1 to 5, characterized in that, The output includes an error assessment report containing key goodness-of-fit metrics, including: Calculate and output the constitutive goodness of fit index generated by performing the constitutive parameter inversion. The constitutive goodness of fit index includes the correlation coefficient between the predicted stress value and the experimental value, and the mean absolute percentage error. Calculate and output the goodness-of-fit index of the tissue evolution generated by the inversion of the tissue evolution parameters. The goodness-of-fit index of the tissue evolution includes the coefficient of determination and root mean square error between the predicted recrystallization fraction or grain size and the experimental value. The error assessment report is displayed in the graphical user interface using tables and / or charts.

7. The material data processing method for forging process simulation as described in any one of claims 1 to 5, characterized in that, The original experimental data for the forging material include: Rheological stress-strain data used to perform constitutive parameter inversion and sequence data of recrystallization fraction as a function of strain used to perform microstructure evolution parameter inversion; The rheological stress-strain data is stored in the form of a discrete point sequence and includes the actual stress and actual strain measured at different temperatures and strain rates. Recrystallization kinetic data used for inversion of tissue evolution parameters, the sequence data is stored in tabular form and includes data on the recrystallization fraction as a function of strain and grain size data measured under different thermal deformation conditions.

8. A material data processing device for forging process simulation, characterized in that, include: The data preprocessing module is used to import raw experimental data of forging materials from multiple sources, automatically parse the raw experimental data and identify physical dimensions and data formats, and convert and store all the raw experimental data into structured data objects with preset field labels. The raw experimental data includes rheological stress-strain data stored in the form of discrete point sequences. A parameter set construction module is used to generate a first parameter set by constitutive parameter inversion based on the structured data object, and record the first inversion iteration process curve generated by the constitutive parameter inversion; and to generate a second parameter set by tissue evolution parameter inversion, and record the second inversion iteration process curve generated by the tissue evolution parameter inversion. A material model file module is constructed to logically associate and encapsulate the first parameter set, the second parameter set, and the basic physical properties of the forging material based on the material model data structure and file format of the target forging simulation software, and generate a material model file for driving the target forging simulation software to perform forging process simulation. The output evaluation report module is used to display the first inversion iteration process curve and the second inversion iteration process curve in the graphical user interface, and output an error evaluation report containing key goodness-of-fit indicators.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the material data processing method for forging process simulation as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that performs a material data processing method for forging process simulation as described in any one of claims 1 to 7.

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