Process parameter simulation analysis system based on isothermal forging and pressing of forging press

Abstract

The invention relates to the technical field of metal processing, in particular to a process parameter simulation analysis system based on isothermal forging and pressing of a forging press, and aims to solve the problems that temperature, stress and material flow behaviors in the isothermal forging and pressing process cannot be accurately predicted, potential risks cannot be recognized and prevented in advance, and the working efficiency is low in the prior art. And the production risk is improved. The prediction accuracy of temperature, stress and material flow behaviors in the isothermal forging and pressing process is remarkably improved through the parameter simulation module, forging process parameters are optimized, the trial and error cost is reduced, potential defects are recognized and prevented in advance, the production risk is reduced, the requirement for a physical prototype is reduced through virtual testing, and the production efficiency is improved. The development of new products is accelerated, and meanwhile, the understanding of an isothermal forging and pressing mechanism is deepened.

Description

Technical Field

[0001] The present invention relates to the technical field of metal processing, and more specifically, to a process parameter simulation analysis system based on isothermal forging of a forging press. Background Art

[0002] Isothermal forging is a forging process carried out at a constant temperature, which has the advantages of uniform deformation, refined structure, and good mechanical properties. However, the selection of isothermal forging process parameters has an important impact on the quality of forgings, including deformation temperature, deformation rate, die design, etc. The traditional trial-and-error method is not only time-consuming and laborious, but also difficult to obtain the optimal combination of process parameters.

[0003] The patent application with the publication number CN107120328A discloses a process control system and control method for an isothermal forging hydraulic press, which is characterized in that it at least includes: a displacement sensor for collecting slider position data; a pump outlet electromagnet for opening and closing the pump outlet; a fixed-displacement pump for supplying a constant oil fluid to the oil cylinder, thereby enabling the slider to move; a proportional pump for supplying an adjustable and controllable oil fluid to the oil cylinder, thereby enabling the slider to move; a proportional servo valve located at the front end of a branch of the proportional pump for supplying an adjustable and controllable oil fluid to the oil cylinder, thereby enabling the slider to move; a programmable controller for reading the slider position data and calculating the real-time speed of the slider, controlling the flow rate of the output proportional pump, and controlling the flow rate of the proportional servo valve; the present invention achieves the purpose of constant speed, constant strain, and variable strain process control required in the isothermal forging hydraulic press by outputting the real-time changing flow rate of the proportional pump or the proportional servo valve by the programmable controller;

[0004] However, the above reference patent realizes stepless speed regulation, high precision, and load stability of the die forging hydraulic press through the cooperation of a fixed-displacement and variable-displacement pump and the precise control of a proportional pump and a servo valve, in combination with a closed-loop algorithm, but it cannot accurately predict the temperature, stress, and material flow behavior during the isothermal forging process, cannot identify and prevent potential risks in advance, increases the production risk, cannot find the process parameter combination with the lowest energy consumption while meeting the process requirements, increases the total power consumption and production cost, and at the same time cannot monitor and dynamically optimize the temperature, pressure, speed, and material properties in real time, reduces the energy efficiency and production stability, and cannot ensure the product quality consistency and process efficiency.

[0005] Therefore, in view of the above problems, we propose a process parameter simulation analysis system based on isothermal forging of a forging press. Summary of the Invention

[0006] The object of the present invention is to provide a process parameter simulation and analysis system based on isothermal forging of a forging press, which solves the problems in the prior art that the temperature, stress and material flow behavior during isothermal forging cannot be accurately predicted, potential risks cannot be identified and prevented in advance, the production risk is increased, the process parameter combination with the lowest energy consumption cannot be found while meeting the process requirements, the total power consumption and production cost are increased, and at the same time, the temperature, pressure, speed and material properties cannot be monitored in real time and dynamically optimized, the energy efficiency and production stability are reduced, and the consistency of product quality and process efficiency cannot be guaranteed.

[0007] The object of the present invention is achieved through the following technical solutions:

[0008] A process parameter simulation and analysis system based on isothermal forging of a forging press, comprising a cloud management platform, a data acquisition module, a parameter simulation module, an energy consumption optimization module and a process parameter adjustment module;

[0009] The data acquisition module is used to collect the process parameters of the forging press during isothermal forging, perform preprocessing operations on the collected process parameters, and send the preprocessed process parameters to the cloud management platform;

[0010] The parameter simulation module performs high-precision numerical simulation of the isothermal forging process based on the collected process parameters;

[0011] The energy consumption optimization module is used to collect the energy consumption parameters of the forging press during operation, construct a mathematical model between energy consumption and process parameters, and use an optimization algorithm to optimize the energy consumption during forging;

[0012] The process parameter adjustment module is used to monitor the process parameters of the forging press during isothermal forging in real time and dynamically adjust the process parameters according to the simulation results and optimization results.

[0013] As a preferred embodiment of the present invention, the specific process of the parameter simulation module performing high-precision numerical simulation of the isothermal forging process is as follows:

[0014] Obtain the process parameters of the forging press during isothermal forging, and the process parameters include forging temperature T D 、forging pressure P, forging speed V, material elongation ε, material surface hardness H and ambient temperature T H , and perform preprocessing operations on the process parameters;

[0015] The numerical simulation process of the isothermal forging process is as follows:

[0016] Take the process parameters as input, and based on physical laws and material properties, construct a mathematical model of the isothermal forging process;

[0017] Use the finite element method to solve the model to obtain the temperature field, stress field and strain field;

[0018] Output the simulation results, including temperature distribution, stress distribution, and material flow.

[0019] As a preferred embodiment of the present invention, the specific process of constructing the mathematical model is as follows:

[0020] During the isothermal forging process, the change of the temperature field is described by the heat conduction equation:

[0021]

[0022] where ρ is the material density, c p is the specific heat capacity, t is the time, k is the thermal conductivity, Q is the heat source term, is the gradient operator, T is the temperature of the material, η is the heat conversion efficiency, σ is the flow stress, is the strain rate, h is the current height of the workpiece;

[0023] The material flow stress σ is related to the strain ε, the strain rate and the temperature T, and is expressed by the following model expression:

[0024]

[0025] where K is the material constant, n is the strain hardening index, m is the strain rate sensitivity index, and exp represents the exponential function e x ,Q act is the activation energy, R is the gas constant, and T is the temperature of the material;

[0026] The stress field satisfies the stress equilibrium equation:

[0027]

[0028] where σ is the stress tensor, f is the body force, and ▽ is the divergence operator;

[0029] The relationship between the strain ε and the displacement u is:

[0030]

[0031] where ε is the strain tensor, u is the displacement tensor, is the displacement gradient tensor, is the transpose of the displacement gradient tensor;

[0032] The ambient temperature T H is the temperature boundary condition, the forging pressure P is the force boundary condition, and the forging speed v is the velocity boundary condition.

[0033] As a preferred embodiment of the present invention, the specific process of the numerical solution method is as follows:

[0034] The continuous domain is discretized into a finite number of elements using the finite element method, and the above equations are solved within each element. The specific steps include:

[0035] The forging die and the workpiece are divided into finite element meshes;

[0036] Local equations are established within each element;

[0037] The local equations are assembled into a global system of equations;

[0038] The global system of equations is solved using the Newton iteration method;

[0039] For transient problems, time discretization methods are used for solution:

[0040]

[0041] where T n is the temperature at the nth time step, T n+1 is the temperature at the (n + 1)th time step, and Δt is the time step size;

[0042] The specific process of outputting the simulation results is as follows:

[0043] By solving the heat conduction equation, the temperature distributions T(x, y, z) of the workpiece and the die are obtained;

[0044] By solving the stress equilibrium equation and the strain-displacement relationship, the stress distribution σ(x, y, z) is obtained;

[0045] By analyzing the strain field ε(x, y, z), the flow behavior of the material is predicted, including the deformation mode and the formation of defects.

[0046] As a preferred embodiment of the present invention, the specific process of the energy consumption optimization module constructing a mathematical model between energy consumption and process parameters and using an optimization algorithm to optimize the energy consumption during the forging process is as follows:

[0047] Obtain the energy consumption parameters during the operation of the forging press. The energy consumption parameters include the total power consumption E Z , the hydraulic system efficiency η h , the heat treatment energy consumption E r and the process cycle time t c ;

[0048] The relationship between energy consumption and process parameters is described by the following mathematical model:

[0049] Total power consumption model: E z = E j + E r + E l ;

[0050] Among which E j is the mechanical energy consumption, and E l is the energy loss;

[0051] Mechanical energy consumption model:

[0052] Heat treatment energy consumption model: E r = c p ·m·(T D - T H );

[0053] Among which c p is the specific heat capacity of the material, m is the mass of the workpiece, T D is the forging temperature, and T H is the ambient temperature;

[0054] Energy loss model:

[0055] Among which E ys is the hydraulic system loss, E rs is the heat loss, h is the heat transfer coefficient, and A is the surface area of the workpiece.

[0056] As a preferred embodiment of the present invention, the genetic algorithm is selected to optimize the energy consumption in the forging process. The optimization goal is to minimize the total power consumption while satisfying the process constraint conditions. The objective function is expressed as: min E z = E j + E r + E l ;

[0057] Constraint conditions:

[0058] The optimization steps are as follows:

[0059] S1: Initialization: Randomly generate a set of process parameters (T D , P, V, ε, H);

[0060] S2: Calculate the objective function: Calculate the total power consumption E Z according to the mathematical model;

[0061] S3: Evaluate the constraint conditions: Check whether the process parameters satisfy the constraint conditions;

[0062] S4: Update the parameters: Update the process parameters using the optimization algorithm;

[0063] S5: Iteration: Repeat the above steps S2 - S4 until the convergence condition is reached or the maximum number of iterations is reached.

[0064] As a preferred embodiment of the present invention, the specific process of the process parameter adjustment module dynamically adjusting process parameters according to the simulation results and optimization results is as follows:

[0065] Obtain the real-time process parameters of the forging press during isothermal forging. The process parameters include forging temperature T D , forging pressure P, forging speed V, material elongation ε, material surface hardness H, and ambient temperature T H . Obtain the simulation results. The simulation results are the temperature distribution T(x, y, z), stress distribution σ(x, y, z), and strain field ε(x, y, z) of the workpiece and the die. Obtain the optimization result. The optimization result is to minimize the total power consumption E Z ;

[0066] Dynamically adjust the process parameters according to the simulation results and optimization results. The specific content of the dynamic adjustment strategy is as follows:

[0067] Temperature adjustment strategy: Control the temperature distribution T(x, y, z) of the workpiece and the die to keep it within the target temperature range;

[0068] Too high temperature: Reduce the forging temperature T D . According to the temperature distribution T(x, y, z), if the local temperature exceeds the upper limit, gradually reduce the heating power or shorten the heating time;

[0069] Increase the cooling time, extend the time of the cooling stage, or increase the flow rate of the cooling medium;

[0070] Adjust the ambient temperature T H . If the ambient temperature is too high, start the ambient cooling system;

[0071] Too low temperature: Increase the forging temperature T D . According to the temperature distribution T(x, y, z), if the local temperature is lower than the lower limit, gradually increase the heating power or extend the heating time;

[0072] Reduce the cooling time, shorten the time of the cooling stage, or reduce the flow rate of the cooling medium;

[0073] Adjust the ambient temperature T H . If the ambient temperature is too low, start the ambient heating system;

[0074] The adjustment formula of the forging temperature T D is as follows:

[0075] T D ′ = T D + k T ·(T target - T avg );

[0076] where TD ′ is the adjusted forging temperature, k T is the temperature adjustment coefficient, T target is the target temperature, T avg is the average value of the current temperature distribution.

[0077] As a preferred embodiment of the present invention, the pressure adjustment strategy: control the stress distribution σ(x, y, z);

[0078] Excessive stress: reduce the forging pressure P. According to the stress distribution σ(x, y, z), if the local stress exceeds the material yield strength, gradually reduce the pressure;

[0079] Increase the forging speed V. Increasing the forging speed can reduce stress concentration;

[0080] Adjust the die shape: if the stress concentration area is fixed, optimize the die design to reduce stress concentration;

[0081] Insufficient stress: increase the forging pressure P. According to the stress distribution σ(x, y, z), if the local stress is too low, gradually increase the pressure;

[0082] Reduce the forging speed V. Reducing the forging speed can increase the material deformation time;

[0083] The adjustment formula for the forging pressure P is as follows:

[0084] P′ = P + k P ·(σ target - σ max );

[0085] where P′ is the adjusted forging pressure, σ target is the target stress, σ max is the maximum value of the current stress distribution, k P is the pressure adjustment coefficient.

[0086] As a preferred embodiment of the present invention, the speed adjustment strategy: control the strain field ε(x, y, z);

[0087] Strain non-uniformity: adjust the forging speed V. According to the strain field ε(x, y, z), if the local strain is too large, reduce the forging speed, if the local strain is too small, increase the forging speed;

[0088] Adjust the material elongation rate ε. By adjusting the material preheating temperature or cooling rate, optimize the material deformation performance;

[0089] Excessive strain: reduce the forging speed V to reduce the material deformation rate;

[0090] Increase the forging pressure P. Increasing the pressure can evenly distribute the strain;

[0091] Too small strain: Increase the forging speed V to increase the material deformation rate;

[0092] Reduce the forging pressure P. Reducing the pressure can reduce the material deformation resistance;

[0093] The adjustment formula for the forging speed V is as follows:

[0094] V′ = V + k v ·(ε target - ε avg );

[0095] Where V′ is the adjusted forging speed, k v is the speed adjustment coefficient, ε target is the target strain, and ε avg is the average value of the current strain field.

[0096] As a preferred embodiment of the present invention, the material parameter adjustment strategy: control the material elongation rate ε and the surface hardness H;

[0097] Too low elongation rate: Increase the forging temperature T D , increase the material plasticity, and increase the elongation rate;

[0098] Reduce the forging speed V to reduce the material deformation rate;

[0099] Too high elongation rate: Reduce the forging temperature T D , reduce the material plasticity, and reduce the elongation rate;

[0100] Increase the forging speed V to increase the material deformation rate;

[0101] Insufficient surface hardness: Increase the cooling rate to increase the material surface hardness by rapid cooling;

[0102] Adjust the forging pressure P. Increasing the pressure can increase the material density and thus increase the hardness;

[0103] Too high surface hardness: Reduce the cooling rate to reduce the material surface hardness by slow cooling;

[0104] Adjust the forging pressure P. Reducing the pressure can reduce the material density and thus reduce the hardness;

[0105] The adjustment formula for the material elongation rate ε is as follows:

[0106] ε′ = ε + k ε ·(ε target - ε current );

[0107] Where ε′ is the adjusted material elongation rate, k ε is the elongation rate adjustment coefficient, and ε targetis the target elongation, ε current is the current elongation;

[0108] Ambient temperature adjustment strategy: Control the ambient temperature T H ;

[0109] Too high ambient temperature: Start the ambient cooling system and increase the flow rate of the cooling medium;

[0110] Too low ambient temperature: Start the ambient heating system and reduce the flow rate of the cooling medium;

[0111] The adjusted formula for the ambient temperature T H is as follows:

[0112] T H ′ = T H + k TH ·(T H，target - T H,current );

[0113] where T H ′ is the adjusted ambient temperature, T H,target is the target ambient temperature, T H,current is the current ambient temperature, and k TH is the ambient temperature adjustment coefficient.

[0114] Compared with the prior art, the advantages of the present invention are as follows:

[0115] (1) In the present invention, the prediction accuracy of temperature, stress, and material flow behavior during isothermal forging is significantly improved through the parameter simulation module, which not only optimizes the forging process parameters, reduces the trial-and-error cost, but also identifies and prevents potential defects in advance, reduces the production risk, virtual testing reduces the need for physical prototypes, accelerates the development of new products, and at the same time deepens the understanding of the isothermal forging mechanism;

[0116] (2) In the present invention, through the energy consumption optimization module using accurate modeling and genetic algorithms, the combination of process parameters with the lowest energy consumption is found on the premise of meeting the process requirements, which not only significantly reduces the total power consumption and production cost, but also ensures the product quality and production stability. Continuous iterative optimization enables the system to dynamically adjust the process parameters to adapt to different production tasks, enhancing the flexibility and response speed;

[0117] (3) In the present invention, through the process parameter adjustment module, the temperature, pressure, speed, and material properties are monitored in real time and dynamically optimized to ensure the optimal state of the workpiece and the die. It can quickly respond to temperature changes, precisely adjust heating and cooling, flexibly optimize pressure and speed, and precisely control the material hardness by regulating the cooling rate and forging pressure, which improves the energy efficiency and production stability, and ensures the product quality consistency and process efficiency. Brief Description of the Drawings

[0118] Figure 1 It is the system block diagram of the first embodiment in the present invention;

[0119] Figure 2 It is the system block diagram of the second embodiment in the present invention;

[0120] Figure 3 It is the schematic diagram of the numerical simulation process of the isothermal forging process in the present invention;

[0121] Figure 4 It is the schematic diagram of the optimization steps for optimizing the energy consumption in the forging process in the present invention. Detailed Embodiments

[0122] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0123] Embodiment 1: As shown in Figure 1 、 Figure 3 and Figure 4 shown, a process parameter simulation and analysis system based on isothermal forging of a forging press proposed by the present invention includes a cloud management platform, a data acquisition module, a parameter simulation module, an energy consumption optimization module, and a process parameter adjustment module;

[0124] The data acquisition module is used to collect the process parameters of the forging press during the isothermal forging process, perform preprocessing operations on the collected process parameters, and the preprocessing operations include but are not limited to data cleaning, filtering, and normalization, and send the preprocessed process parameters to the cloud management platform;

[0125] The data acquisition module improves the quality of process parameters through preprocessing, reduces noise interference, optimizes transmission efficiency, and supports real-time monitoring and dynamic adjustment. High-quality data enhances the accuracy of the model, promotes in-depth analysis, ensures the stability and robustness of the system, and thus improves the overall efficiency of intelligent manufacturing and production optimization.

[0126] The parameter simulation module performs high-precision numerical simulation of the isothermal forging process based on the collected process parameters;

[0127] The specific process of the parameter simulation module performing high-precision numerical simulation of the isothermal forging process is as follows:

[0128] Obtain the process parameters of the forging press during the isothermal forging process, and the process parameters include the forging temperature T D, forging pressure P, forging speed V, material elongation ε, material surface hardness H, and ambient temperature T H , perform preprocessing operations on the process parameters;

[0129] The numerical simulation process of the isothermal forging process is as follows:

[0130] Take the process parameters as input, and based on physical laws and material properties, construct a mathematical model of the isothermal forging process;

[0131] Use the finite element method to solve the model to obtain the temperature field, stress field, and strain field;

[0132] Output the simulation results, including temperature distribution, stress distribution, and material flow;

[0133] The specific process of constructing the mathematical model is as follows:

[0134] During the isothermal forging process, the change of the temperature field is described by the heat conduction equation:

[0135]

[0136] where ρ is the material density, c p is the specific heat capacity, t is the time, k is the thermal conductivity, Q is the heat source term, ▽ is the gradient operator, T is the temperature of the material, η is the heat conversion efficiency, σ is the flow stress, is the strain rate, and h is the current height of the workpiece;

[0137] The material flow stress σ is related to the strain ε, strain rate and temperature T, and is expressed by the following model expression:

[0138]

[0139] where K is the material constant, n is the strain hardening index, m is the strain rate sensitivity index, and exp represents the exponential function e x , Q act is the activation energy, R is the gas constant, and T is the temperature of the material;

[0140] The stress field satisfies the stress equilibrium equation:

[0141]

[0142] where σ is the stress tensor, f is the body force, and ▽ is the divergence operator;

[0143] The relationship between the strain ε and the displacement u is:

[0144]

[0145] where ε is the strain tensor and u is the displacement tensor, is the displacement gradient tensor, is the transpose of the displacement gradient tensor;

[0146] The ambient temperature T H is the temperature boundary condition, the forging pressure P is the force boundary condition, and the forging speed v is the velocity boundary condition;

[0147] The specific process of the numerical solution method is as follows:

[0148] Use the finite element method to discretize the continuous domain into a finite number of elements, and solve the above equations within each element. The specific steps include:

[0149] Divide the forging die and the workpiece into finite element meshes;

[0150] Establish local equations within each element;

[0151] Assemble the local equations into a global system of equations;

[0152] Use the Newton iteration method to solve the global system of equations;

[0153] For transient problems, use the time discretization method to solve:

[0154]

[0155] where T n is the temperature at the nth time step, T n+1 is the temperature at the (n + 1)th time step, and Δt is the time step size;

[0156] The specific process of the simulation result output is as follows:

[0157] By solving the heat conduction equation, obtain the temperature distribution T(x, y, z) of the workpiece and the die;

[0158] By solving the stress balance equation and the strain-displacement relationship, obtain the stress distribution σ(x, y, z);

[0159] By analyzing the strain field ε(x, y, z), predict the flow behavior of the material, including the deformation mode and the formation of defects;

[0160] The parameter simulation module significantly improves the prediction accuracy of the temperature, stress, and material flow behavior during the isothermal forging process. This not only optimizes the forging process parameters, reduces the trial-and-error cost, but also identifies and prevents potential defects in advance, reduces the production risk. Virtual testing reduces the need for physical prototypes, accelerates the new product development, and at the same time deepens the understanding of the isothermal forging mechanism.

[0161] The energy consumption optimization module is used to collect the energy consumption parameters of the forging press during operation, construct a mathematical model between the energy consumption and process parameters, and use an optimization algorithm to optimize the energy consumption during the forging process;

[0162] The specific process of the energy consumption optimization module constructing a mathematical model between the energy consumption and process parameters and using an optimization algorithm to optimize the energy consumption during the forging process is as follows:

[0163] Obtain the energy consumption parameters of the forging press during operation. The energy consumption parameters include the total power consumption E Z , the hydraulic system efficiency η h , the heat treatment energy consumption E r , and the process cycle time t c ;

[0164] The relationship between the energy consumption and process parameters is described by the following mathematical model:

[0165] Total power consumption model: E z =E j +E r +E l ;

[0166] where E j is the mechanical energy consumption, and E l is the energy loss;

[0167] Mechanical energy consumption model:

[0168] Heat treatment energy consumption model: E r =c p ·m·(T D -T H );

[0169] where c p is the specific heat capacity of the material, m is the mass of the workpiece, T D is the forging temperature, and T H is the ambient temperature;

[0170] Energy loss model:

[0171] where E ys is the hydraulic system loss, E rs is the heat loss, h is the heat transfer coefficient, and A is the surface area of the workpiece;

[0172] Select the genetic algorithm to optimize the energy consumption during the forging process. The optimization goal is to minimize the total power consumption while satisfying the process constraint conditions. The objective function is expressed as: min E z =E j +E r +E l ;

[0173] Constraints:

[0174] The optimization steps are as follows:

[0175] S1: Initialization: Randomly generate a set of process parameters (T D , P, V, ε, H);

[0176] S2: Calculate the objective function: Calculate the total power consumption E according to the mathematical model Z ;

[0177] S3: Evaluate the constraints: Check whether the process parameters meet the constraints;

[0178] S4: Update the parameters: Use the optimization algorithm to update the process parameters;

[0179] S5: Iteration: Repeat the above steps S2 - S4 until the convergence condition is reached or the maximum number of iterations;

[0180] Through the energy consumption optimization module, using accurate modeling and genetic algorithms, on the premise of meeting the process requirements, find the process parameter combination with the lowest energy consumption. This not only significantly reduces the total power consumption and production costs, but also ensures product quality and production stability. Continuous iterative optimization enables the system to dynamically adjust process parameters to adapt to different production tasks, enhancing flexibility and response speed.

[0181] Example 2: The technical solution of this embodiment of the present invention is different from that of Example 1 in that:

[0182] As Figure 2 shown, the process parameter adjustment module is used to monitor the process parameters of the forging press in the isothermal forging process in real time and dynamically adjust the process parameters according to the simulation results and optimization results;

[0183] The specific process of the process parameter adjustment module dynamically adjusting the process parameters according to the simulation results and optimization results is as follows:

[0184] Obtain the real-time process parameters of the forging press in the isothermal forging process. The process parameters include the forging temperature T D , forging pressure P, forging speed V, material elongation ε, material surface hardness H, and ambient temperature T H , obtain the simulation results. The simulation results are the temperature distribution T(x, y, z), stress distribution σ(x, y, z), and strain field ε(x, y, z) of the workpiece and the die, and obtain the optimization results. The optimization result is to minimize the total power consumption E Z ;

[0185] Dynamically adjust the process parameters according to the simulation results and optimization results. The specific content of the dynamic adjustment strategy is as follows:

[0186] Temperature adjustment strategy: Control the temperature distribution T(x, y, z) of the workpiece and the die to keep it within the target temperature range;

[0187] Too high temperature: Reduce the forging temperature T D , according to the temperature distribution T(x, y, z), if the local temperature exceeds the upper limit, gradually reduce the heating power or shorten the heating time;

[0188] Increase the cooling time, extend the time of the cooling stage, or increase the flow rate of the cooling medium;

[0189] Adjust the ambient temperature T H , if the ambient temperature is too high, start the ambient cooling system;

[0190] Too low temperature: Increase the forging temperature T D , according to the temperature distribution T(x, y, z), if the local temperature is below the lower limit, gradually increase the heating power or extend the heating time;

[0191] Reduce the cooling time, shorten the time of the cooling stage, or reduce the flow rate of the cooling medium;

[0192] Adjust the ambient temperature T H , if the ambient temperature is too low, start the ambient heating system;

[0193] Forging temperature T D The adjustment formula of is as follows:

[0194] T D ′ = T D + k T ·(T target - T avg );

[0195] Where T D ′ is the adjusted forging temperature, k T is the temperature adjustment coefficient, T target is the target temperature, T avg is the average value of the current temperature distribution;

[0196] Pressure adjustment strategy: Control the stress distribution σ(x, y, z) to ensure sufficient deformation of the material;

[0197] Too high stress: Reduce the forging pressure P. According to the stress distribution σ(x, y, z), if the local stress exceeds the yield strength of the material, gradually reduce the pressure;

[0198] Increase the forging speed V. Increasing the forging speed can reduce stress concentration, but it is necessary to avoid uneven material deformation caused by too high speed;

[0199] Adjust the die shape: If the stress concentration area is fixed, optimize the die design to reduce stress concentration;

[0200] Too low stress: Increase the forging pressure P. According to the stress distribution σ(x, y, z), if the local stress is too low, gradually increase the pressure;

[0201] Reduce the forging speed V. Reducing the forging speed can increase the material deformation time, but avoid the speed being too slow resulting in temperature drop;

[0202] The adjustment formula for the forging pressure P is as follows:

[0203] P′ = P + k P ·(σ target - σ max );

[0204] Where P′ is the adjusted forging pressure, σ target is the target stress, σ max is the maximum value of the current stress distribution, and k P is the pressure adjustment coefficient;

[0205] Speed adjustment strategy: Control the strain field ε(x, y, z) to ensure uniform material deformation;

[0206] Non-uniform strain: Adjust the forging speed V. According to the strain field ε(x, y, z), if the local strain is too large, reduce the forging speed; if the local strain is too small, increase the forging speed;

[0207] Adjust the material elongation ε. Optimize the material deformation performance by adjusting the material preheating temperature or cooling rate;

[0208] Too large strain: Reduce the forging speed V to reduce the material deformation rate and avoid material fracture;

[0209] Increase the forging pressure P. Increasing the pressure can evenly distribute the strain, but avoid the pressure being too large resulting in stress concentration;

[0210] Too small strain: Increase the forging speed V to increase the material deformation rate and ensure sufficient material deformation;

[0211] Reduce the forging pressure P. Reducing the pressure can reduce the material deformation resistance, but avoid the pressure being too small resulting in insufficient deformation;

[0212] The adjustment formula for the forging speed V is as follows:

[0213] V′ = V + k v ·(ε target - ε avg );

[0214] Where V′ is the adjusted forging speed, and k vis the speed adjustment coefficient, ε target is the target strain, ε avg is the average value of the current strain field;

[0215] Material parameter adjustment strategy: Control the material elongation ε and surface hardness H to ensure that the material properties meet the process requirements;

[0216] Too low elongation: Increase the forging temperature T D , increase the plasticity of the material and improve the elongation;

[0217] Reduce the forging speed V, reduce the material deformation rate, and avoid material fracture;

[0218] Too high elongation: Reduce the forging temperature T D , reduce the plasticity of the material and lower the elongation;

[0219] Increase the forging speed V, increase the material deformation rate, and avoid excessive material deformation;

[0220] Insufficient surface hardness: Increase the cooling rate and improve the material surface hardness through rapid cooling;

[0221] Adjust the forging pressure P. Increasing the pressure can increase the material density and thus improve the hardness;

[0222] Too high surface hardness: Reduce the cooling rate and lower the material surface hardness through slow cooling;

[0223] Adjust the forging pressure P. Reducing the pressure can reduce the material density and thus lower the hardness;

[0224] The adjustment formula for the material elongation ε is as follows:

[0225] ε′ = ε + k ε ·(ε target - ε current );

[0226] where ε′ is the adjusted material elongation, k ε is the elongation adjustment coefficient, ε target is the target elongation, ε current is the current elongation;

[0227] Ambient temperature adjustment strategy: Control the ambient temperature T H , ensure the stability of the process;

[0228] Too high ambient temperature: Start the ambient cooling system and increase the flow rate of the cooling medium;

[0229] Too low ambient temperature: Start the ambient heating system and reduce the flow rate of the cooling medium;

[0230] Ambient temperature T HThe adjustment formula is as follows:

[0231] T H ′ = T H + k TH ·(T H，target - T H,current );

[0232] Where T H ′ is the adjusted ambient temperature, T H,target is the target ambient temperature, T H,current is the current ambient temperature, and k TH is the ambient temperature adjustment coefficient;

[0233] The process parameter adjustment module monitors and dynamically optimizes temperature, pressure, speed, and material properties in real time to ensure the optimal state of the workpiece and the mold. It can quickly respond to temperature changes, accurately adjust heating and cooling, flexibly optimize pressure and speed, precisely control the material hardness by regulating the cooling rate and forging pressure, which improves energy efficiency and production stability, and ensures product quality consistency and process efficiency.

[0234] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its improved concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A process parameter simulation and analysis system based on isothermal forging of a forging press, characterized in that It includes a cloud management platform, a data acquisition module, a parameter simulation module, an energy consumption optimization module, and a process parameter adjustment module; The data acquisition module is used to collect the process parameters of the forging press during isothermal forging, perform preprocessing operations on the collected process parameters, and send the preprocessed process parameters to the cloud management platform; The parameter simulation module performs high-precision numerical simulation of the isothermal forging process based on the collected process parameters; The energy consumption optimization module is used to collect the energy consumption parameters of the forging press during operation, construct a mathematical model between energy consumption and process parameters, and use optimization algorithms to optimize the energy consumption during forging; The process parameter adjustment module is used to monitor the process parameters of the forging press during isothermal forging in real time and dynamically adjust the process parameters according to the simulation results and optimization results.

2. The process parameter simulation analysis system based on isothermal forging of a forging press according to claim 1, wherein The specific process of the parameter simulation module performing high-precision numerical simulation of the isothermal forging process is as follows: Obtain the process parameters of the forging press during isothermal forging, where the process parameters include forging temperature T D , forging pressure P, forging speed V, material elongation ε, material surface hardness H, and ambient temperature T H , and perform preprocessing operations on the process parameters; The numerical simulation process of the isothermal forging process is as follows: Taking the process parameters as input, based on physical laws and material properties, construct a mathematical model of the isothermal forging process; Use the finite element method to solve the model to obtain the temperature field, stress field, and strain field; Output the simulation results, including temperature distribution, stress distribution, and material flow.

3. The process parameter simulation analysis system based on isothermal forging of a forging press according to claim 2, characterized in that The specific process of constructing the mathematical model is as follows: During the isothermal forging process, the change of the temperature field is described by the heat conduction equation: where ρ is the material density, c p is the specific heat capacity, t is time, k is the thermal conductivity, Q is the heat source term, is the gradient operator, T is the temperature of the material, η is the heat conversion efficiency, σ is the flow stress, is the strain rate, h is the current height of the workpiece; The material flow stress σ is related to the strain ε, strain rate and temperature T, and is expressed by the following model expression: where K is the material constant, n is the strain hardening index, m is the strain rate sensitivity index, and exp represents the exponential function e x ,Q act is the activation energy, R is the gas constant, and T is the temperature of the material; The stress field satisfies the stress equilibrium equation: where σ is the stress tensor and f is the body force, is the divergence operator; The relationship between strain ε and displacement u is: where ε is the strain tensor and u is the displacement tensor, is the displacement gradient tensor, is the transpose of the displacement gradient tensor; Ambient temperature T H is the temperature boundary condition, the forging pressure P is the force boundary condition, and the forging speed v is the velocity boundary condition.

4. The process parameter simulation analysis system based on isothermal forging of a forging press according to claim 3, characterized in that The specific process of the numerical solution method is as follows: Use the finite element method to discretize the continuous domain into a finite number of elements and solve the above equations within each element. The specific steps include: Divide the forging die and workpiece into finite element meshes; Establish local equations within each element; Assemble the local equations into a global system of equations; Use the Newton iteration method to solve the global system of equations; For transient problems, use the time discretization method to solve: where T n is the temperature at the nth time step, T n+1 is the temperature at the (n + 1)th time step, and Δt is the time step size; The specific process of outputting the simulation results is as follows: By solving the heat conduction equation, obtain the temperature distribution T(x, y, z) of the workpiece and die; By solving the stress equilibrium equation and the strain-displacement relationship, obtain the stress distribution σ(x, y, z); By analyzing the strain field ε(x, y, z), predict the material flow behavior, including deformation mode and defect formation.

5. The process parameter simulation analysis system based on isothermal forging of a forging press according to claim 1, wherein The specific process of the energy consumption optimization module constructing a mathematical model between energy consumption and process parameters and using optimization algorithms to optimize the energy consumption during forging is as follows: Obtain the energy consumption parameters of the forging press during operation, where the energy consumption parameters include the total power consumption E Z , the hydraulic system efficiency η h , the heat treatment energy consumption E r and the process cycle time t c ; The relationship between energy consumption and process parameters is described by the following mathematical model: Total power consumption model: E z = E j + E r + E l ; where E j is the mechanical energy consumption, and E l is the energy loss; Mechanical energy consumption model: Heat treatment energy consumption model: E r = c p · m · (T D - T H ); where c p is the specific heat capacity of the material, m is the mass of the workpiece, T D is the forging temperature, T H is the ambient temperature; Energy loss model: where E ys is the loss of the hydraulic system, E rs is the heat loss, h is the heat transfer coefficient, and A is the surface area of the workpiece.

6. The process parameter simulation analysis system based on isothermal forging of a forging press according to claim 5, characterized in that Select the genetic algorithm to optimize the energy consumption during forging. The optimization goal is to minimize the total power consumption while satisfying the process constraint conditions. The objective function is expressed as: min E z = E j + E r + E l ; Constraints: The optimization steps are as follows: S1: Initialization: Randomly generate a set of process parameters (T D , P, V, ε, H); S2: Calculate the objective function: Calculate the total power consumption E according to the mathematical model Z ; S3: Evaluate the constraint conditions: Check whether the process parameters meet the constraint conditions; S4: Update the parameters: Use the optimization algorithm to update the process parameters; S5: Iteration: Repeat the above steps S2 - S4 until the convergence condition or the maximum number of iterations is reached.

7. A process parameter simulation and analysis system based on isothermal forging of a forging press according to claim 1, characterized in that, The specific process of the process parameter adjustment module dynamically adjusting the process parameters according to the simulation results and optimization results is as follows: Obtain the real-time process parameters of the forging press during isothermal forging, where the process parameters include the forging temperature T D , the forging pressure P, the forging speed V, the material elongation ε, the material surface hardness H, and the ambient temperature T H , obtain the simulation results, where the simulation results are the temperature distribution T(x, y, z), the stress distribution σ(x, y, z), and the strain field ε(x, y, z) of the workpiece and the die, and obtain the optimization results, where the optimization results are to minimize the total power consumption E Z ; Dynamically adjust the process parameters according to the simulation results and optimization results. The specific content of the dynamic adjustment strategy is as follows: Temperature adjustment strategy: Control the temperature distribution T(x, y, z) of the workpiece and the die to keep it within the target temperature range; Excessive temperature: Lower the forging temperature T D , according to the temperature distribution T(x, y, z), if the local temperature exceeds the upper limit, gradually reduce the heating power or shorten the heating time; Increase the cooling time, extend the time of the cooling stage, or increase the flow rate of the cooling medium; Adjust the ambient temperature T H , if the ambient temperature is too high, start the environmental cooling system; Too low temperature: Increase the forging temperature T D , according to the temperature distribution T(x, y, z), if the local temperature is lower than the lower limit, gradually increase the heating power or extend the heating time; Reduce the cooling time, shorten the time of the cooling stage, or reduce the flow rate of the cooling medium; Adjust the ambient temperature T H , if the ambient temperature is too low, start the ambient heating system; Forging temperature T D The adjustment formula is as follows: T D ′ = T D + k T ·(T target - T avg ); where T D ′ is the adjusted forging temperature, k T is the temperature adjustment coefficient, T target is the target temperature, T avg is the average value of the current temperature distribution.

8. A process parameter simulation and analysis system based on isothermal forging of a forging press according to claim 7, characterized in that, Pressure adjustment strategy: Control the stress distribution σ(x, y, z); Excessive stress: Reduce the forging pressure P. According to the stress distribution σ(x, y, z), if the local stress exceeds the material yield strength, gradually reduce the pressure; Increase the forging speed V. Increasing the forging speed can reduce stress concentration; Adjust the die shape: If the stress concentration area is fixed, optimize the die design to reduce stress concentration; Insufficient stress: Increase the forging pressure P. According to the stress distribution σ(x, y, z), if the local stress is too low, gradually increase the pressure; Reduce the forging speed V. Reducing the forging speed can increase the material deformation time; The adjustment formula for the forging pressure P is as follows: P′ = P + k P ·(σ target - σ max ); where P′ is the adjusted forging pressure, σ target is the target stress, σ max is the maximum value of the current stress distribution, k P is the pressure adjustment coefficient.

9. The process parameter simulation analysis system based on isothermal forging of a forging press according to claim 8, characterized in that, Speed adjustment strategy: Control the strain field ε(x, y, z); Uneven strain: Adjust the forging speed V. According to the strain field ε(x, y, z), if the local strain is too large, reduce the forging speed, and if the local strain is too small, increase the forging speed; Adjust the material elongation ε. Optimize the material deformation performance by adjusting the material preheating temperature or the cooling rate; Excessive strain: Reduce the forging speed V to reduce the material deformation rate; Increase the forging pressure P. Increasing the pressure can evenly distribute the strain; Insufficient strain: Increase the forging speed V to increase the material deformation rate; Reduce the forging pressure P. Reducing the pressure can reduce the material deformation resistance; The adjustment formula for the forging speed V is as follows: V′ = V + k v ·(ε target - ε avg ); where V′ is the adjusted forging speed, k v is the speed adjustment coefficient, ε target is the target strain, and ε avg is the average value of the current strain field.

10. The process parameter simulation analysis system based on isothermal forging of a forging press according to claim 9, characterized in that, Material parameter adjustment strategy: Control the material elongation ε and the surface hardness H; Elongation rate is too low: Increase the forging temperature T D , increase the plasticity of the material and improve the elongation rate; Reduce the forging speed V to reduce the material deformation rate; Elongation rate too high: Lower the forging temperature T D , reduce the plasticity of the material and lower the elongation rate; Increase the forging speed V to increase the material deformation rate; Insufficient surface hardness: Increase the cooling rate to increase the material surface hardness by rapid cooling; Adjust the forging pressure P. Increasing the pressure can increase the material density and thus increase the hardness; Excessive surface hardness: Reduce the cooling rate to reduce the material surface hardness by slow cooling; Adjust the forging pressure P. Reducing the pressure can reduce the material density and thus reduce the hardness; The adjustment formula for the material elongation ε is as follows: ε′ = ε + k ε ·(ε target - ε current ); where ε′ is the adjusted material elongation, and k ε is the elongation adjustment coefficient, ε target is the target elongation, and ε current is the current elongation; Ambient temperature adjustment strategy: Control the ambient temperature T H ; Excessive ambient temperature: Start the ambient cooling system and increase the flow rate of the cooling medium; Low ambient temperature: Start the ambient heating system and reduce the flow rate of the cooling medium; Ambient temperature T H The adjustment formula is as follows: T H ′ = T H + k TH ·(T H，target - T H,current ); where T H ′ is the adjusted ambient temperature, T H,target is the target ambient temperature, T H,current is the current ambient temperature, k TH is the ambient temperature adjustment coefficient.

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