A forging heat treatment optimization method based on time coupling model

Abstract

This invention discloses a forging heat treatment optimization method based on a time-coupled model, belonging to the field of digital heat treatment optimization technology. The method includes: integrating pre-heat treatment temperature state information into the initial state field after forging to determine the initial temperature field of heat treatment; continuously defining the heat treatment process time nodes of the forging according to the heat treatment stage time axis to form a piece-by-piece time-coupled digital twin model; defining microstructure path constraints based on the basic process requirement dataset; using the heat treatment process parameter sequence as the decision variable to be optimized; performing time-progression calculations through the piece-by-piece time-coupled digital twin model to obtain heat treatment evolution process data; and generating the optimal heat treatment control trajectory through multiple rounds of optimization. This invention achieves individualized and high-precision control of the heat treatment process for each forging, improving the accuracy of microstructure evolution prediction and the consistency of final mechanical properties.

Description

Technical Field

[0001] This invention relates to the field of digital heat treatment optimization technology, and in particular to a method for optimizing the heat treatment of forgings based on a time-coupled model. Background Technology

[0002] Heat treatment of forgings, as a crucial process after metal plastic forming that determines the material's microstructure and properties, has long relied on empirical rules, offline testing, and finite element simulation for process optimization. With the development of computer technology and industrial informatization, heat treatment process-aided design methods based on numerical simulation and data processing are gradually being applied in actual production. For example, existing technologies typically establish a geometric model of the forging and, combined with preset material thermophysical parameters and boundary conditions, use numerical calculation methods to simulate and analyze the heating, holding, and cooling processes, thereby providing a reference for setting heat treatment parameters.

[0003] However, in the aforementioned related technologies, forgings are usually treated as batch-consistent objects, and their initial state is often based on idealized or averaged assumptions, making it difficult to fully reflect the true geometric differences, temperature distribution differences, and process history differences that individual forgings possess at the end of the forging process. Especially in the optimization of heat treatment parameters, existing technologies often focus on calculation and analysis under single-stage or static conditions, failing to continuously couple and model the forging end state, heat treatment start state, and subsequent process evolution in the time dimension. This, to some extent, limits the refined calculation and optimization of the microstructure evolution path and heat treatment control trajectory. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a forging heat treatment optimization method based on a time-coupled model, which solves the problem in the prior art that it is impossible to achieve continuous time-coupled modeling and optimization calculation of the entire forging heat treatment process.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides a method for optimizing the heat treatment of forgings based on a time-coupled model, which includes: acquiring a basic process requirement dataset, collecting basic data for each target forging and establishing a basic data record for the forging; the basic data record for the forging includes forging process information, three-dimensional scanning data and temperature state information before heat treatment;

[0008] The geometry of the forging is reconstructed based on the 3D scanning data to generate a numerical model of the forging, and the forging process information is mapped to the numerical model of the forging to generate the initial state field after the forging is completed.

[0009] By incorporating the temperature state information before heat treatment into the initial state field after forging, the starting temperature field of heat treatment is determined, and the time nodes of the heat treatment process of the forging are continuously defined according to the time axis of the heat treatment stage, forming a piece-by-piece time-coupled digital twin model.

[0010] Based on the basic process requirement dataset, organizational path constraints are defined, and the heat treatment process parameter sequence is used as the decision variable to be optimized. Time-progression calculation is performed through a piece-by-piece time-coupled digital twin model to obtain heat treatment evolution process data. After multiple rounds of optimization and solution, the optimal heat treatment control trajectory is generated.

[0011] Heating is performed piece by piece according to the optimal heat treatment control trajectory and the process is tracked and adjusted in real time to generate a record of the heating process status of the forging.

[0012] As a preferred embodiment of the time-coupled model-based optimization method for forging heat treatment described in this invention, the basic process requirement dataset is obtained by organizing the forging heat treatment process route, heat treatment equipment operating information, forging process requirements, and target heat treatment quality indicators.

[0013] As a preferred embodiment of the time-coupled model-based optimization method for forging heat treatment described in this invention, the specific steps for collecting basic data for each target forging and establishing forging basic data records are as follows:

[0014] Based on the basic process requirements dataset, determine the basic data items of the forgings that need to be collected, and perform basic data collection operation for each target forging to obtain forging process information, three-dimensional scanning data and temperature state information before heat treatment;

[0015] Forging process information, 3D scanning data, and temperature status information before heat treatment are collected and stored according to the unique identifier of the forging to form a basic data record of the forging.

[0016] As a preferred embodiment of the time-coupled model-based forging heat treatment optimization method of the present invention, the specific steps for generating a forging numerical model by reconstructing the forging geometry based on three-dimensional scanning data are as follows:

[0017] The 3D scanning data is denoised and registered to form 3D point cloud data. The Poisson surface reconstruction algorithm is used to perform surface fitting on the 3D point cloud data and output a closed triangular mesh.

[0018] Hole repair and acute angle correction are performed on a closed triangular mesh to form a geometric model of the forging. Mesh size control parameters are set, and a three-dimensional finite element mesh is automatically generated through hexahedral hybrid generation to form a numerical model of the forging.

[0019] As a preferred embodiment of the time-coupled model-based optimization method for forging heat treatment described in this invention, the specific steps for mapping forging process information to the forging numerical model to generate the initial state field at the end of forging are as follows:

[0020] According to the forging process time sequence and contact area location, the forging process information is mapped to various spatial distribution locations of the forging numerical model;

[0021] Based on the forging process information at each spatial distribution location, numerical calculations are performed on the numerical model of the forging to obtain the stress state, strain state, and temperature state at each spatial distribution location at the end of the forging process, thus forming the initial state field at the end of forging.

[0022] As a preferred embodiment of the time-coupled model-based optimization method for forging heat treatment described in this invention, the specific steps for integrating the temperature state information before heat treatment into the initial state field after forging to determine the initial temperature field for heat treatment are as follows:

[0023] The temperature state information before heat treatment is matched with the corresponding spatial distribution positions of the forging numerical model to obtain the aligned temperature state information.

[0024] The aligned temperature state information is loaded one-to-one into the same spatial position in the initial state field after forging, the temperature value before heat treatment is obtained, and the temperature data at the corresponding spatial position in the initial state field after forging is replaced to determine the starting temperature field of heat treatment.

[0025] As a preferred embodiment of the time-coupled model-based forging heat treatment optimization method of the present invention, the specific steps of defining the time nodes of the forging heat treatment process continuously according to the time axis of the heat treatment stage to form a piece-by-piece time-coupled digital twin model are as follows.

[0026] Using the initial temperature field of heat treatment as the starting point of time definition, the time nodes of the heat treatment process of forgings are continuously determined according to the time axis of heat treatment stages.

[0027] The initial temperature field of heat treatment is advanced sequentially according to the time nodes of the heat treatment process of forgings and the state of forgings is continuously updated to form a time-coupled digital twin model for each piece.

[0028] As a preferred embodiment of the time-coupled model-based optimization method for forging heat treatment described in this invention, the specific steps of defining microstructure path constraints based on the basic process requirement dataset and using the heat treatment process parameter sequence as the decision variable to be optimized are as follows.

[0029] The target heat treatment quality index is retrieved from the basic process requirement dataset, and the direction of microstructure evolution and sequence of microstructure changes of the target forging during the heat treatment process are determined based on the target heat treatment quality index, thus forming a microstructure path constraint.

[0030] Collect the setpoint of the heat treatment furnace temperature, the holding time, and the cooling stage time, obtain the heat treatment process parameter sequence, combine them in chronological order, and set them as decision variables to be optimized.

[0031] As a preferred embodiment of the time-coupled model-based forging heat treatment optimization method described in this invention, the steps include: obtaining heat treatment evolution process data through time-progression calculations using a piece-by-piece time-coupled digital twin model, and generating the optimal heat treatment control trajectory through multiple rounds of optimization; the specific steps are as follows.

[0032] The decision variables to be optimized are input into the piece-by-piece time-coupled digital twin model, and time-progression calculations are performed sequentially from the heat treatment initiation temperature field according to the heat treatment process time node order. At each time node, the temperature state, stress state and microstructure of each spatial position of the target forging are updated to obtain heat treatment evolution process data.

[0033] The heat treatment evolution process data is compared with the organizational path constraints node by node, the organizational evolution deviation corresponding to each time node is calculated, and the degree of organizational path satisfaction of the decision variable to be optimized is evaluated based on the organizational evolution deviation to obtain organizational path deviation data.

[0034] Based on the organizational path deviation data, the parameters of the decision variables to be optimized are adjusted in multiple rounds, and the time-coupled digital twin model is repeatedly called to perform time-progression calculations. Through iterative optimization, the deviation between the decision variables and the organizational path constraints is gradually reduced, and the optimal heat treatment control trajectory is generated.

[0035] As a preferred embodiment of the time-coupled model-based forging heat treatment optimization method of the present invention, the specific steps of performing piece-by-piece heating and real-time tracking and adjustment according to the optimal heat treatment control trajectory to generate a forging heating process state record are as follows.

[0036] Heating operations are performed piece by piece according to the optimal heat treatment control trajectory, and the actual temperature status of the target forging is continuously collected at each time point during the heating process.

[0037] The actual temperature state at each time point is compared with the optimal heat treatment control trajectory, and the heating action is adjusted in real time. The temperature state and adjustment information of the target forging are recorded in chronological order throughout the entire heating process to generate a forging heating process status record.

[0038] The beneficial effects of this invention are as follows: by accurately integrating the measured temperature state information before heat treatment into the initial state field after forging, a high-fidelity starting condition is constructed, and on this basis, a piece-by-piece time-coupled digital twin model is established. Combined with the microstructure path type constraint, the heat treatment process parameters are dynamically optimized in multiple rounds, thereby realizing individualized and high-precision control of the heat treatment process for each forging, improving the accuracy of microstructure evolution prediction and the consistency of final mechanical properties. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart of a heat treatment optimization method for forgings based on a time-coupled model.

[0041] Figure 2 This is a flowchart for collecting basic data on forgings.

[0042] Figure 3 The flowchart for generating a numerical model of a forging.

[0043] Figure 4 A flowchart for optimizing the solution of heat treatment. Detailed Implementation

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0045] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0047] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for optimizing the heat treatment of forgings based on a time-coupled model, comprising the following steps:

[0048] S1. Obtain the basic process requirement dataset, collect basic data for each target forging, and establish basic data records for the forgings.

[0049] It should be noted that the basic process requirements dataset was obtained by organizing the heat treatment process routes for forgings, the operating conditions of heat treatment equipment, forging process requirements, and target heat treatment quality indicators.

[0050] S1.1. Based on the basic process requirement dataset, determine the basic data items of the forgings to be collected, and perform basic data collection operation for each target forging to obtain forging process information, three-dimensional scanning data and temperature status information before heat treatment.

[0051] Furthermore, a list of basic data items for forgings is established, specifying the acquisition items and formats for forging process information, 3D scanning data, and pre-heat treatment temperature status information. Using the unique identifier of the forging as an index, forging process information acquisition, 3D scanning data acquisition, and pre-heat treatment temperature status information acquisition are performed sequentially for each target forging. After acquisition, the continuity of the time series and the rationality of the parameters of the forging process information are checked; the point cloud coverage, the ratio of holes and noise points, and the registration error of the 3D scanning data are checked; and the matching of measurement points, the rationality of the temperature range, and abnormal jumps of the pre-heat treatment temperature status information are checked. Finally, the consistency of the unique identifier of the forgings in the three types of data is verified to confirm completeness, ensuring that all the above data are consistent with the list of basic data items for forgings.

[0052] It should be noted that the list of basic data items for forgings is formed by extracting the key data fields that must be collected item by item based on the requirements for geometry, process and temperature in the basic process requirements dataset, and classifying and organizing them according to data type; then, combined with the actual fields that can be collected on site, the collection method and format of each data item are clarified.

[0053] The unique identifier for a forging is obtained by combining the forging batch number, material grade, specification code, and production serial number according to a fixed rule to generate a unique code.

[0054] S1.2 Collect and store forging process information, 3D scanning data and temperature status information before heat treatment according to the unique identifier of the forging to form a basic data record of the forging.

[0055] Furthermore, using the unique identifier of the forging as an index, the storage path or data content of the forging process information file, 3D scanning data file, and pre-heat treatment temperature state information file corresponding to each target forging are collected and written into the forging basic data record in the order of fields.

[0056] S2. Reconstruct the geometry of the forging based on the 3D scanning data to generate a numerical model of the forging, and map the forging process information to the numerical model of the forging to generate the initial state field at the end of forging.

[0057] S2.1. The 3D scanning data is denoised and registered to form 3D point cloud data. The surface of the 3D point cloud data is fitted by the Poisson surface reconstruction algorithm to output a closed triangular mesh.

[0058] Furthermore, by statistically analyzing the number of neighboring points and the distribution of neighboring distances for each 3D point cloud data point, noisy 3D point cloud data points with insufficient neighboring points or significantly deviating from the overall distribution are eliminated, thus retaining valid 3D point cloud data points that satisfy spatial continuity. Feature points from multiple sets of 3D scanning data from different perspectives are selected, and the minimum distance matching and rigid body transformation matrix between feature points are continuously calculated using the iterative nearest point registration method. This unifies multiple sets of 3D scanning data into the same spatial coordinate system, forming spatially aligned 3D point cloud data without repetition or misalignment. The spatially aligned 3D point cloud data is then input into a Poisson surface reconstruction algorithm. By solving the corresponding Poisson equation based on the normal information and positional relationship of the 3D point cloud data, a smooth and continuous implicit surface is generated. The implicit surface is then subjected to isosurface extraction, outputting a closed triangular mesh composed of triangular facets, so that the closed triangular mesh completely covers the forging outline represented by the 3D point cloud data.

[0059] It should be noted that the minimum distance matching between feature points is achieved by searching for the nearest corresponding point in the reference point cloud for each feature point in the point cloud to be registered in the iterative nearest point (ICP) algorithm, thereby obtaining the minimum distance matching pair between feature points;

[0060] Rigid transformation matrix between feature points: Based on minimum distance matching pairs, the optimal rotation matrix and translation vector are obtained by minimizing the distance error between corresponding points and solving the problem using singular value decomposition (SVD), thus forming the rigid transformation matrix.

[0061] S2.2. Hole repair and acute angle correction are performed on the closed triangular mesh to form the geometric model of the forging. Mesh size control parameters are set, and a three-dimensional finite element mesh is generated by automatic hexahedral mixing to form the numerical model of the forging.

[0062] Furthermore, the boundary rings on the closed triangular mesh are identified, and new triangular patches are automatically generated inside the boundary rings to fill in the missing areas, ensuring that the closed triangular mesh as a whole no longer has perforations or openings. By detecting sharp edges in the closed triangular mesh with included angles smaller than a preset acute angle threshold and performing local smoothing or local re-division operations in the neighborhood of the sharp edges, overly sharp geometric features that are detrimental to mesh quality (such as sharp convex corners, thin blade-like edges, local sharp protrusions, and abrupt geometric polylines) are eliminated, resulting in the forging geometric model. Based on the curvature changes and key stress areas of the forging geometric model (referring to the areas subjected to stress during the service or processing of the forging), the forging geometric model is obtained. For structural parts subject to large loads and prone to stress concentration, mesh size control parameters are set. The forging geometric model and mesh size control parameters are input into a hexahedral hybrid automatic meshing method. Based on the overall shape and local curvature of the forging geometric model, the main direction of the forging geometric model is identified, and the target element size of each region is determined according to the mesh size control parameters. Hexahedral elements are automatically generated in geometrically regular regions along the main direction, and prism elements or tetrahedral elements are automatically inserted in regions with geometric abrupt changes to form a continuous transition according to the mesh size control parameters, thereby generating a three-dimensional finite element mesh throughout the entire forging geometric model.

[0063] It should be noted that the hexahedral hybrid automatic meshing method is a meshing method that automatically generates three-dimensional meshes based on hexahedral elements. By identifying the main directional regions of the geometric model, regular hexahedral elements are preferentially arranged, and prism elements or tetrahedral elements are automatically inserted as transitions in geometric transition regions or regions where it is difficult to form standard hexahedral elements. This ensures that the overall mesh maintains a high proportion of hexahedral structure while adapting to changes in complex geometric shapes.

[0064] The acute angle threshold of the grid is set by statistically analyzing the distribution of the edge-face angles of the entire closed triangular grid and combining it with the grid quality requirements. Based on the main peak position of the dihedral angle distribution and the abnormally sharp angle range corresponding to a certain proportion of the interval below the main peak, the example value of the acute angle threshold of the grid is set to 25°~35°.

[0065] The critical stress areas are obtained by performing rapid finite element pre-analysis of the stress distribution of forgings under typical service loads or forging loads and extracting high stress concentration areas.

[0066] S2.3. According to the forging process time sequence and contact area location, map the forging process information to various spatial distribution locations of the forging numerical model.

[0067] Furthermore, based on the contact area coordinates in the forging process information, the contact area location is mapped to the specific grid cell of the forging numerical model through coordinate system transformation; according to the forging process time sequence, the contact pressure, contact temperature, and friction conditions in the forging process information are loaded into the corresponding spatial grid cells or nodes; for intermediate moments not included in the time series, time interpolation is performed using data from adjacent time points to complete the continuous loading; finally, the complete mapping of the forging process information to the various spatial distribution locations of the forging numerical model is completed on the entire forging process time axis.

[0068] It should be noted that the forging process time sequence is formed by real-time data collection and recording from the forging equipment or historical process monitoring data, and arranged in chronological order using timestamps.

[0069] The contact area position is obtained by generating the contact area coordinates through the displacement sensor, pressure sensor or forging simulation system of the forging equipment, and then accurately locating these contact area positions to the surface mesh position of the forging numerical model by transforming the equipment coordinate system and the coordinate system of the forging numerical model.

[0070] S2.4. Based on the forging process information at each spatial distribution location, perform numerical calculations on the numerical model of the forging to obtain the stress state, strain state, and temperature state at each spatial distribution location at the end of the forging process, thus forming the initial state field at the end of forging.

[0071] Furthermore, based on the forging process information at each spatial distribution location, the contact pressure, contact temperature, and friction conditions at each forging process time node are loaded as boundary conditions into the corresponding three-dimensional finite element mesh elements and nodes of the forging numerical model. The time steps are divided according to the forging process time sequence to perform thermo-mechanical coupled finite element numerical calculations. Within each time step, the stress state, strain state, and temperature state at each spatial distribution location are gradually updated based on the material constitutive relation and heat conduction equation. When the numerical calculation progresses to the forging process end time node, the stress state, strain state, and temperature state of all three-dimensional finite element mesh elements in the forging numerical model are read, and these stress states, strain states, and temperature states are combined according to their spatial distribution locations to form the initial state field at the end of forging.

[0072] S3. Integrate the temperature state information before heat treatment into the initial state field after forging, determine the starting temperature field of heat treatment, and continuously define the time nodes of the heat treatment process of the forging according to the time axis of the heat treatment stage to form a piece-by-piece time-coupled digital twin model.

[0073] It should be noted that existing methods typically use nominal geometric models and empirical initial values, directly using empirical temperature fields or furnace temperature settings as the starting conditions for heat treatment simulation, without spatially corresponding to the actual temperature state of individual forgings; the time nodes of forging heat treatment processes are mostly set according to fixed process cards, without taking into account the actual state changes of individual forgings; digital simulations are usually performed on typical parts and do not have the ability to continuously couple time.

[0074] This invention precisely integrates the temperature state information before heat treatment into the initial state field after forging by spatial location, and dynamically determines the heat treatment process time nodes of the forging based on the continuous time axis of the heat treatment stage. This makes the heat treatment start conditions and time progression no longer dependent on experience settings, but can reflect the real state of the individual forgings, thereby significantly improving the accuracy and timing consistency of the heat treatment process simulation and providing a more reliable basis for subsequent process optimization.

[0075] S3.1 Match the temperature state information before heat treatment according to the spatial distribution positions of the forging numerical model to obtain the aligned temperature state information.

[0076] Furthermore, based on the spatial coordinates and temperature values ​​of the temperature measurement points recorded in the temperature state information before heat treatment, the spatial coordinates of the temperature measurement points are transformed into the coordinate system used by the forging numerical model through coordinate transformation. In the three-dimensional finite element mesh of the forging numerical model, the distance relationship between the spatial coordinates of the temperature measurement points and the spatial coordinates of the three-dimensional finite element mesh nodes is used to assign a corresponding temperature value to each three-dimensional finite element mesh node through nearest neighbor interpolation or multi-point weighted interpolation. The temperature values ​​of all three-dimensional finite element mesh nodes are subjected to integrity checks and outlier smoothing to eliminate isolated abrupt changes and blank areas. The temperature values ​​that have undergone coordinate alignment and interpolation are then organized into aligned temperature state information that corresponds one-to-one with the spatial distribution positions of each location in the forging numerical model.

[0077] S3.2. Load the aligned temperature state information into the same spatial position in the initial state field after forging, according to the spatial position, obtain the temperature value before heat treatment, and replace the temperature data of the corresponding spatial position in the initial state field after forging to determine the starting temperature field of heat treatment.

[0078] Furthermore, based on the correspondence between each spatial distribution position recorded in the aligned temperature state information and the three-dimensional finite element mesh node of the forging numerical model, the spatial distribution position in the initial state field after forging that is the same as the aligned temperature state information is located; the pre-heat treatment temperature value of each spatial distribution position in the aligned temperature state information is written into the temperature data field of the corresponding three-dimensional finite element mesh node in the initial state field after forging according to the spatial position, while keeping the stress state and strain state of the corresponding three-dimensional finite element mesh node in the initial state field after forging unchanged; after replacing the temperature data field of all three-dimensional finite element mesh nodes, the updated temperature data together with the original stress state and strain state constitute a new spatial distribution state field, which is defined as the heat treatment initiation temperature field.

[0079] S3.3. Using the initial temperature field of heat treatment as the starting point of time definition, the time nodes of the heat treatment process of forgings are continuously determined according to the time axis of the heat treatment stage.

[0080] Furthermore, the moment corresponding to the initial temperature field of heat treatment is set as the initial time node of the heat treatment process time axis, serving as the starting point for time reference. Based on the duration and key control time of the heating, holding, and cooling stages provided in the basic process requirement dataset, multiple consecutive forging heat treatment process time nodes are sequentially divided on the heat treatment process time axis according to their chronological order. During the division process, the time interval between the forging heat treatment process time nodes is appropriately increased according to the temperature change rate and the sensitive range of microstructure evolution, ensuring that the forging heat treatment process time nodes can accurately depict the key change stages in the heat treatment process.

[0081] S3.4. The heat treatment starting temperature field is sequentially advanced according to the heat treatment process time nodes of the forging and the state of the forging is continuously updated to form a piece-by-piece time-coupled digital twin model.

[0082] Furthermore, the initial temperature field of heat treatment is used as the initial state for numerical calculation, and the temperature state, stress state, and strain state of each three-dimensional finite element mesh element in the initial temperature field of heat treatment are used as the initial calculation input. According to the time sequence of the heat treatment process nodes of the forging, at each heat treatment process node of the forging, the furnace temperature condition, holding condition, and cooling condition corresponding to the heat treatment process node of the forging are set as boundary conditions, and thermo-mechanical coupled numerical calculation is performed on the three-dimensional finite element mesh. The temperature state, stress state, and strain state of each three-dimensional finite element mesh element are updated by solving the heat conduction equation and the constitutive equation of material mechanics. After the calculation of each heat treatment process node of the forging is completed, the updated temperature state, stress state, and strain state continue to be used as the initial state of the next heat treatment process node of the forging for time progression, so that the state of the forging evolves continuously along the entire heat treatment process time axis. When all heat treatment process nodes of the forging have completed the time progression calculation, the temperature state, stress state, and strain state generated during the continuous time progression process form a piece-by-piece time-coupled digital twin model.

[0083] It should be noted that the temperature state, stress state, and strain state of each three-dimensional finite element mesh are updated by solving the heat conduction equation and the constitutive equation of mechanics of materials, as shown in the following expressions:

[0084] ;

[0085] In the formula, It is a three-dimensional finite element mesh element at time step Time to step Between these, the rate of change of heat storage per unit volume caused by changes in temperature state, corresponding to the term of temperature state change over time; It is the product of material density and material specific heat capacity, used to quantify the heat storage capacity of a unit volume of material during temperature changes. It is a three-dimensional finite element mesh element at time step Temperature state; It is a three-dimensional finite element mesh element at time step Temperature state; It is the time step, which is the time step. With time step The time interval between; It is a spatial gradient operator used to calculate the rate of change of temperature state in three-dimensional space; It is the thermal conductivity of a material, used to describe the ability of a temperature state to be conducted within the material; It is a three-dimensional finite element mesh element at time step The volumetric heat source term is used to represent the heat source at time step. Internal heat sources or phase change heat release that affect the temperature state; It is the index of the current time step in the time progression sequence, corresponding to the time node where the updated temperature state is located; It is the index of the previous time step in the time progression sequence, corresponding to the time node where the known temperature state is located;

[0086] Where: spatial gradient operator The thermal conductivity of the material is obtained by discretizing the spatial derivative of each node using the spatial coordinates of the nodes in a three-dimensional finite element mesh and the corresponding finite element shape functions; The thermal conductivity is measured by conducting steady-state or transient thermal conductivity experiments on material samples and the experimental results at different temperature points are fitted.

[0087] The stress state of each three-dimensional finite element mesh is updated by solving the heat conduction equation and the constitutive equation of mechanics of materials, as shown in the following expression:

[0088] ;

[0089] In the formula, It is a three-dimensional finite element mesh element at time step The stress state; It is a three-dimensional finite element mesh element at time step The stress state; Based on time step Temperature status The defined constitutive stiffness matrix of materials is used to convert strain increments into stress increments; It is the total strain increment of the three-dimensional finite element mesh element within the time step, which is the strain state increment calculated from the displacement field change; It is the thermal strain increment of a three-dimensional finite element mesh element within a time step, which is the strain state increment caused by the change in temperature state. It is the plastic strain increment of a three-dimensional finite element mesh element within a time step, which is the strain state increment formed by the accumulation of plastic deformation;

[0090] The strain state of each three-dimensional finite element mesh is updated by solving the heat conduction equation and the constitutive equation of mechanics of materials, as shown in the following expression:

[0091] ;

[0092] In the formula, It is the coefficient of linear expansion of a material, a fundamental parameter describing the ability of a material to expand or contract linearly when the temperature changes, and its unit is per Kelvin (1 / K). It is a second-order unit tensor used to represent the isotropic distribution of thermal expansion along the principal directions;

[0093] Wherein: coefficient of linear expansion of material The second-order unit tensor is obtained by measuring the length change of a material sample under different temperature conditions and calculating the rate of length change through a heating-cooling expansion test. It is obtained directly by defining the standard constant tensor form in Cartesian coordinates with diagonal elements set to 1 and off-diagonal elements set to 0.

[0094] Training the piece-by-piece time-coupled digital twin model: By uniformly collecting historical heat treatment data of multiple actual forgings, real-time temperature-stress-microstructure evolution data, and corresponding numerical simulation results, the material parameters, boundary condition response functions, and thermo-mechanical-microstructure coupling coefficients in the piece-by-piece time-coupled digital twin model are iteratively calibrated using these real-simulation paired data. During the training process, the simulation output is compared with the measured data for error, and the model parameters are continuously adjusted using methods such as parameter inversion, least squares fitting, or gradient optimization, so that the prediction results of the piece-by-piece time-coupled digital twin model at different forgings, different time nodes, and different spatial locations gradually conform to the real evolution law.

[0095] S4. Define organizational path constraints based on the basic process requirement dataset, and use the heat treatment process parameter sequence as the decision variable to be optimized. Perform time-progression calculations through a piece-by-piece time-coupled digital twin model to obtain heat treatment evolution process data. After multiple rounds of optimization, generate the optimal heat treatment control trajectory.

[0096] S4.1. Retrieve the target heat treatment quality index from the basic process requirement dataset, and determine the direction of microstructure evolution and sequence of microstructure changes of the target forging during the heat treatment process based on the target heat treatment quality index, thus forming a microstructure path constraint.

[0097] Furthermore, the target heat treatment quality indicators (including target hardness requirements, microstructure grade requirements, and phase transformation control requirements) are retrieved from the basic process requirement dataset. Based on the target hardness requirements, microstructure grade requirements, and phase transformation control requirements, the necessary microstructure states of the target forging during the heat treatment process are analyzed. According to the phase transformation laws, temperature ranges, and time sequences corresponding to these necessary microstructure states, the direction of microstructure evolution and the sequence of microstructure changes of the target forging during the heating, holding, and cooling stages are determined. The direction of microstructure evolution and the sequence of microstructure changes are combined according to their temporal relationship to form a microstructure path constraint for subsequent optimization calculations.

[0098] It should be noted that the target hardness requirement is obtained by reading the limit value of the final hardness range or lower hardness limit in the target heat treatment quality index; the microstructure grade requirement is obtained by reading the judgment clause of the target heat treatment quality index on the final microstructure grade, grain size grade or microstructure morphology; and the phase transformation control requirement is obtained by analyzing the control clause of the target heat treatment quality index on the phase transformation stage, phase transformation temperature range or specific phase ratio.

[0099] S4.2 Collect the setpoint of the heat treatment furnace temperature, the holding time and the cooling stage time, obtain the heat treatment process parameter sequence and combine them in time order, and set them as decision variables to be optimized.

[0100] Furthermore, according to the time sequence of the heating stage, the holding stage, and the cooling stage, the furnace temperature setpoint, stage duration, and stage switching time corresponding to each stage are arranged sequentially to form a structured heat treatment process parameter sequence. The heat treatment process parameter sequence is aligned with the time axis to ensure that all parameters express their effective period with a unified time reference. The heat treatment process parameter sequence arranged in time sequence is set as a decision variable to be optimized.

[0101] S4.3 Input the decision variables to be optimized into the piece-by-piece time-coupled digital twin model, and perform time-progression calculations sequentially from the heat treatment initiation temperature field according to the heat treatment process time node order. Update the temperature state, stress state and microstructure of each spatial position of the target forging at each time node to obtain heat treatment evolution process data.

[0102] Furthermore, the decision variables to be optimized are input into the piece-by-piece time-coupled digital twin model in chronological order, serving as boundary conditions and control parameters for each forging heat treatment process time node. Starting from the forging heat treatment process time node corresponding to the initial heat treatment temperature field, the piece-by-piece time-coupled digital twin model sequentially calls the thermo-mechanical-microstructure coupled numerical calculation process at each subsequent forging heat treatment process time node. It uses the temperature, stress, and microstructure output from the previous forging heat treatment process time node as the initial state for the current forging heat treatment process time node, updating the temperature, stress, and microstructure at each spatial location in the target forging numerical model. After completing the time-progression calculation for all forging heat treatment process time nodes, the temperature, stress, and microstructure at each spatial location of the target forging at each forging heat treatment process time node are collected in chronological and spatial order to form heat treatment evolution process data for subsequent optimization and evaluation.

[0103] S4.4. Compare the heat treatment evolution process data with the organizational path constraints node by node, calculate the organizational evolution deviation corresponding to each time node, and evaluate the degree of organizational path satisfaction of the decision variable to be optimized based on the organizational evolution deviation, and obtain the organizational path deviation data.

[0104] Furthermore, at each heat treatment process time node of the forging, the microstructure of each spatial location of the target forging is extracted and compared node by node with the target microstructure of the corresponding time node in the microstructure path constraint, and the microstructure evolution deviation of each time node is calculated. Within the entire heat treatment process time axis of the forging, the microstructure evolution deviation of all heat treatment process time nodes of the forging is statistically and weighted to quantify the degree of satisfaction of the decision variables to be optimized in terms of microstructure evolution direction and microstructure change sequence. The microstructure evolution deviation of each time node and the comprehensive satisfaction evaluation results are compiled into microstructure path deviation data.

[0105] It should be noted that the expression for calculating the organizational evolution deviation at each time point is as follows:

[0106] ;

[0107] In the formula, Indicates the time node of the heat treatment process for forgings. The tissue evolution deviation after integrating all spatial distribution locations is a dimensionless quantity used to characterize the degree of deviation from the tissue path; This indicates the number of spatially distributed locations involved in the calculation of organizational deviation; It is an index of spatial distribution location; Indicates spatial distribution location The weighting coefficient is a dimensionless quantity used to emphasize the influence of key areas in the overall deviation; Indicates the time node of the heat treatment process for forgings. Upper spatial distribution location The organizational state vector is expressed in the form of dimensionless organizational fraction or normalized grain size; Indicates at a time node Upper spatial position The L2 distance between the current organizational state and the target organizational state is a dimensionless quantity used to quantify the deviation in organizational evolution.

[0108] S4.5. Based on the organizational path deviation data, perform multiple rounds of parameter adjustments on the decision variables to be optimized, and repeatedly call the piece-by-piece time-coupled digital twin model to perform time-progression calculations. Through iterative optimization, gradually reduce the deviation from the organizational path type constraints and generate the optimal heat treatment control trajectory.

[0109] Furthermore, based on the organization path deviation data, the decision variables that have a significant impact on the organization deviation are identified and their parameters are adjusted. The adjusted heat treatment process parameter sequence is then re-input into the piece-by-piece time-coupled digital twin model to advance the calculation, obtain new heat treatment evolution data, and recalculate the organization path deviation data. Through continuous and repeated parameter adjustment and time-advanced calculation, the organization path deviation data is gradually reduced, and the optimal heat treatment control trajectory is obtained.

[0110] S5. Perform piece-by-piece heating according to the optimal heat treatment control trajectory and track and adjust in real time to generate a record of the heating process status of the forging.

[0111] S5.1. Perform heating operations on each piece according to the optimal heat treatment control trajectory, and continuously collect the actual temperature status of the target forging at each time point during the heating process.

[0112] Furthermore, the furnace temperature setpoints corresponding to each time node in the optimal heat treatment control trajectory are applied to the heating equipment where the target forging is located, so that the heating process strictly follows the optimal heat treatment control trajectory. During the heating process, the actual temperature state of the target forging at each time node is obtained using temperature measuring devices arranged on or inside the target forging, with the time nodes of the optimal heat treatment control trajectory as the acquisition benchmark, and the actual temperature state of the target forging at all time nodes is sorted out in chronological order.

[0113] S5.2 Compare the actual temperature state at each time point with the optimal heat treatment control trajectory and adjust the heating action in real time. Record the temperature state and adjustment information of the target forging in the entire heating process in chronological order to generate a forging heating process status record.

[0114] Furthermore, the actual temperature state at each time point is compared with the target temperature state at the corresponding time point in the optimal heat treatment control trajectory node by node to identify heating deviation or heat preservation deviation; the furnace temperature output of the heating equipment is adjusted in real time according to the comparison results to make the heating process fit the optimal heat treatment control trajectory again; during the heating process, the actual temperature state at each time point and the corresponding heating adjustment parameters are recorded in chronological order to generate a complete record of the forging heating process status.

[0115] In summary, this invention achieves individualized and high-precision control of the heat treatment process for each forging by accurately integrating the measured temperature state information before heat treatment into the initial state field after forging, constructing high-fidelity starting conditions, and establishing a piece-by-piece time-coupled digital twin model based on this. Combined with microstructure path constraints, the heat treatment process parameters are dynamically optimized in multiple rounds, thereby improving the accuracy of microstructure evolution prediction and the consistency of final mechanical properties.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing heat treatment of a forged piece based on a time coupling model, characterized in that: include, Obtain the basic process requirements dataset, collect basic data for each target forging, and establish a basic data record for the forgings; The basic data record of the forging includes forging process information, three-dimensional scanning data, and temperature state information before heat treatment; Reconstruct the geometry of the forging based on the 3D scanning data and generate a numerical model of the forging. Map the forging process information to the numerical model of the forging and generate the initial state field after the forging is completed. The specific steps for mapping forging process information to the numerical model of the forging to generate the initial state field after forging completion are as follows: According to the forging process time sequence and contact area location, the forging process information is mapped to various spatial distribution locations of the forging numerical model; Based on the forging process information at each spatial distribution location, numerical calculations are performed on the numerical model of the forging to obtain the stress state, strain state, and temperature state at each spatial distribution location at the end of the forging process, thus forming the initial state field at the end of forging. By incorporating the temperature state information before heat treatment into the initial state field after forging, the starting temperature field of heat treatment is determined, and the time nodes of the heat treatment process of the forging are continuously defined according to the time axis of the heat treatment stage, forming a piece-by-piece time-coupled digital twin model. The steps for defining the heat treatment process time nodes of forgings continuously according to the time axis of the heat treatment stages to form a piece-by-piece time-coupled digital twin model are as follows. Using the initial temperature field of heat treatment as the starting point of time definition, the time nodes of the heat treatment process of forgings are continuously determined according to the time axis of heat treatment stages. The heat treatment initiation temperature field is advanced sequentially according to the heat treatment process time nodes of the forging and the state of the forging is continuously updated to form a piece-by-piece time-coupled digital twin model. Based on the basic process requirement dataset, organizational path constraints are defined, and the heat treatment process parameter sequence is used as the decision variable to be optimized. Time-progression calculation is performed through a piece-by-piece time-coupled digital twin model to obtain heat treatment evolution process data. After multiple rounds of optimization and solution, the optimal heat treatment control trajectory is generated. The process involves using a piece-by-piece time-coupled digital twin model to perform time-progression calculations, acquiring data on the heat treatment evolution process, and generating the optimal heat treatment control trajectory through multiple rounds of optimization. The specific steps are as follows. The decision variables to be optimized are input into the piece-by-piece time-coupled digital twin model, and time-progression calculations are performed sequentially from the heat treatment initiation temperature field according to the heat treatment process time node order. At each time node, the temperature state, stress state and microstructure of each spatial position of the target forging are updated to obtain heat treatment evolution process data. The heat treatment evolution process data is compared with the organizational path constraints node by node, the organizational evolution deviation corresponding to each time node is calculated, and the degree of organizational path satisfaction of the decision variable to be optimized is evaluated based on the organizational evolution deviation to obtain organizational path deviation data. Based on the organizational path deviation data, the parameters of the decision variables to be optimized are adjusted in multiple rounds, and the time-coupled digital twin model is repeatedly called to perform time-progression calculations. Through iterative optimization, the deviation between the decision variables and the organizational path constraints is gradually reduced, and the optimal heat treatment control trajectory is generated. Heating is performed piece by piece according to the optimal heat treatment control trajectory and the process is tracked and adjusted in real time to generate a record of the heating process status of the forging.

2. The time coupled model based forging heat treatment optimization method of claim 1, wherein: The basic process requirements dataset was obtained by organizing the heat treatment process routes for forgings, the operating conditions of heat treatment equipment, forging process requirements, and target heat treatment quality indicators.

3. The time coupled model based forging heat treatment optimization method of claim 1, wherein: The specific steps for collecting basic data for each target forging and establishing basic data records for each forging are as follows: Based on the basic process requirements dataset, determine the basic data items of the forgings that need to be collected, and perform basic data collection operation for each target forging to obtain forging process information, three-dimensional scanning data and temperature state information before heat treatment; Forging process information, 3D scanning data, and temperature status information before heat treatment are collected and stored according to the unique identifier of the forging to form a basic data record of the forging.

4. The time coupled model based forging heat treatment optimization method of claim 3, wherein: The specific steps for reconstructing the geometry of the forging and generating a numerical model of the forging based on 3D scanning data are as follows. The 3D scanning data is denoised and registered to form 3D point cloud data. The Poisson surface reconstruction algorithm is used to perform surface fitting on the 3D point cloud data and output a closed triangular mesh. Hole repair and acute angle correction are performed on a closed triangular mesh to form a geometric model of the forging. Mesh size control parameters are set, and a three-dimensional finite element mesh is automatically generated through hexahedral hybrid generation to form a numerical model of the forging.

5. The optimization method for heat treatment of forgings based on a time-coupled model as described in claim 1, characterized in that: The steps for integrating the temperature state information before heat treatment into the initial state field after forging to determine the starting temperature field for heat treatment are as follows: The temperature state information before heat treatment is matched with the corresponding spatial distribution positions of the forging numerical model to obtain the aligned temperature state information. The aligned temperature state information is loaded one-to-one into the same spatial position in the initial state field after forging, the temperature value before heat treatment is obtained, and the temperature data at the corresponding spatial position in the initial state field after forging is replaced to determine the starting temperature field of heat treatment.

6. The optimization method for heat treatment of forgings based on a time-coupled model as described in claim 1, characterized in that: The steps for defining organizational path constraints based on the basic process requirement dataset and using the heat treatment process parameter sequence as the decision variable to be optimized are as follows: The target heat treatment quality index is retrieved from the basic process requirement dataset, and the direction of microstructure evolution and sequence of microstructure changes of the target forging during the heat treatment process are determined based on the target heat treatment quality index, thus forming a microstructure path constraint. Collect the setpoint of the heat treatment furnace temperature, the holding time, and the cooling stage time, obtain the heat treatment process parameter sequence, combine them in chronological order, and set them as decision variables to be optimized.

7. The time coupled model based forging heat treatment optimization method of claim 6, wherein: The process involves heating each forging piece according to the optimal heat treatment control trajectory and adjusting it in real time to generate a record of the forging heating process status. The specific steps are as follows: Heating operations are performed piece by piece according to the optimal heat treatment control trajectory, and the actual temperature status of the target forging is continuously collected at each time point during the heating process. The actual temperature state at each time point is compared with the optimal heat treatment control trajectory, and the heating action is adjusted in real time. The temperature state and adjustment information of the target forging are recorded in chronological order throughout the entire heating process to generate a forging heating process status record.

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