A method and system for reducing quenching deformation of parts based on finite element modeling

Abstract

The present invention relates to the technical field of part quenching process, and proposes a method and system for reducing part quenching deformation based on finite element modeling. The specific implementation steps include: the present invention first obtains the geometric parameters, thermophysical property parameters and mechanical property parameters of the part to be quenched, and uses CAD software to build a three-dimensional model of the part; then, establishes a temperature-physical property function and a stress-strain function, and performs meshing and thermo-mechanical coupling analysis through finite element analysis software to obtain the thermal map, stress distribution map and deformation map of the part; then, constructs a temperature difference optimization function, a stress difference optimization function and a deformation optimization function, and further forms a quenching process optimization function; finally, a genetic algorithm is used to iteratively solve the optimization function to determine the optimal process parameters in the vacuum quenching process, thereby effectively reducing the deformation of the part during the quenching process and improving the accuracy and efficiency of the quenching process.

Description

Technical Field

[0001] The invention relates to the technical field of part quenching technology, and in particular to a method and system for reducing part quenching deformation based on finite element modeling. Background Art

[0002] The vacuum quenching process of parts is a heat treatment technology that uses a vacuum environment to quench metal parts. Compared with traditional air quenching or oil quenching, vacuum quenching can avoid surface defects such as oxidation and decarburization, ensure the surface quality of parts, and improve hardness and wear resistance. In addition, vacuum quenching can also reduce the erosion of the gas medium on the workpiece surface, and is suitable for high-precision and high-quality parts processing, such as high-end molds, aerospace parts, and precision machinery parts. However, during the heating and cooling process, parts may cause shape deformation due to factors such as temperature gradient, uneven thermal expansion, and excessive cooling rate. Especially for parts with high dimensional accuracy requirements, this deformation may result in unqualified products. Therefore, reducing the deformation of parts during quenching not only helps to improve the processing accuracy of parts and reduce the scrap rate, but also reduces the cost of subsequent processing and finishing.

[0003] At present, the existing technology often uses thermal-mechanical coupling analysis to adjust the heating process of the quenching process, without comprehensively considering the impact of the heating and cooling stages on the deformation of the parts. Local uneven cooling may occur, which in turn causes stress accumulation and causes part deformation, reducing the production quality of the parts and increasing subsequent processing costs.

[0004] Therefore, a method and system for reducing quenching deformation of parts based on finite element modeling are proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for reducing the quenching deformation of parts based on finite element modeling, which is used to optimize the quenching process parameters to reduce the quenching deformation. In order to solve the problems existing in the prior art, the present invention first obtains the geometric parameters, thermophysical property parameters and mechanical property parameters of the parts to be quenched, and uses CAD software to build a three-dimensional model of the parts; then, a temperature-physical property function and a stress-strain function are established, and meshing and thermal-mechanical coupling analysis are performed through finite element analysis software to obtain the thermal map, stress distribution map and deformation map of the parts; then, a temperature difference optimization function, a stress difference optimization function and a deformation optimization function are constructed to further form a quenching process optimization function; finally, a genetic algorithm is used to iteratively solve the optimization function to determine the optimal process parameters in the vacuum quenching process, thereby effectively reducing the deformation of the parts during the quenching process and improving the accuracy and efficiency of the quenching process.

[0006] A method for reducing quenching deformation of parts based on finite element modeling, comprising:

[0007] Acquire geometric parameters, thermophysical property parameters and mechanical property parameters of the part to be quenched; and construct a three-dimensional model of the part to be quenched by CAD software according to the geometric parameters;

[0008] According to the thermophysical property parameters, a temperature-physical property function is constructed; in combination with the mechanical property parameters, an elastic stress-strain function and a plastic stress-strain function are constructed;

[0009] Importing the three-dimensional model of the part into finite element analysis software, meshing the three-dimensional model of the part, and encrypting the mesh in key areas of the part; setting quenching temperature boundary conditions, quenching mechanical boundaries and quenching process parameters, combining the temperature-physical property function, the elastic stress-strain function and the plastic stress-strain function, performing thermal-mechanical coupling analysis on the part to be quenched, and obtaining a part heat map, a part stress distribution map and a part deformation map;

[0010] According to the part heat map, a temperature difference optimization function is constructed; through the part stress distribution map, a stress difference optimization function is constructed; through the part deformation map, a deformation optimization function is determined; combining the temperature difference optimization function, the deformation optimization function and the deformation optimization function, a quenching process optimization function is constructed; the quenching process optimization function is optimized by a genetic algorithm to determine the optimal process parameters during vacuum quenching.

[0011] Preferably, the specific process of constructing the temperature-physical property function includes:

[0012] The thermophysical property parameters are obtained, including thermal conductivity, specific heat capacity, density and expansion coefficient; the temperature-physical property function is constructed, and the specific formula is:

[0013] ;

[0014] in, Indicates the next said temperature-physical property function; Indicates reference temperature Next The thermophysical property parameters; Represents an exponential function with a natural constant as base; Indicates the current temperature; and Both indicate The temperature influence coefficient of the thermophysical property parameter.

[0015] Preferably, the specific implementation process of constructing the elastic stress-strain function and the plastic stress-strain function includes:

[0016] The mechanical property parameters are obtained, including elastic modulus, strength coefficient and strain hardening exponent; according to the elastic modulus, the elastic stress-strain function is constructed, and the specific formula is:

[0017] ;

[0018] in, represents the elastic stress-strain function; Indicates the current temperature; Indicates strain; Indicates reference temperature The elastic modulus under Indicates is the power function of the exponential; and are the temperature influence coefficients of the elastic stress-strain function;

[0019] According to the strength coefficient and strain hardening exponent, the plastic stress-strain function is constructed, and the specific formula is:

[0020]

[0021] in, represents the plastic stress-strain function; Indicates reference temperature The strength coefficient under Represents a logarithmic function with a natural constant as base; Indicates reference temperature The strain hardening exponent under represents the temperature influence coefficient of the strength coefficient, represents the temperature influence factor of the strain hardening exponent.

[0022] Preferably, the key areas of the part include the outer surface area of ​​the part, the hole area of ​​the part and the connection area of ​​the part; the quenching temperature boundary conditions include heat flow boundary conditions, convection boundary conditions and radiation boundary conditions; the quenching mechanical boundaries include fixed boundary conditions, symmetric boundary conditions and free boundary conditions.

[0023] Preferably, the quenching process optimization function is optimized by a genetic algorithm, and the specific process of determining the optimal process parameters includes:

[0024] Step S401, obtaining the node position coordinates, node temperature, node stress and node deformation of all grid nodes through the part heat map, the part stress distribution map and the part deformation map, and constructing a quenching process optimization function;

[0025] Step S402, initializing a quenching process population , Indicates The quenching process parameters are grouped; a multi-objective fitness function is constructed to calculate the individual fitness of the quenching process parameters;

[0026] Step S403, updating the quenching process population through selection operation, crossover operation and mutation operation according to the individual fitness of different quenching process parameters in the quenching process population;

[0027] Step S404, calculating the average fitness of the individuals in the quenching process population, and determining whether to terminate the optimization.

[0028] Preferably, the specific process of constructing the quenching process optimization function includes:

[0029] The node temperature, the node stress and the node deformation are standardized, and the quenching process optimization function is constructed by combining the node position coordinates under different quenching process parameters. The specific formula is:

[0030] ;

[0031] in, Represents the optimization function for the quenching process Perform minimum value optimization; represents the temperature difference optimization function; represents the stress difference optimization function; represents the deformation optimization function; Indicates the quenching process parameters , Indicates the heating temperature, Indicates the heating time. represents the cooling rate, Indicates the holding time after quenching; Representing the constraints of the quenching process parameters; express The parameters; Representation parameters The maximum value constraint of Representation parameters The minimum value constraint.

[0032] Preferably, the specific implementation process of constructing the multi-objective fitness function includes:

[0033] The multi-objective fitness function is constructed by combining the temperature difference optimization function, the stress difference optimization function and the deformation optimization function, and the individual fitness of each group of the quenching process parameters in the quenching process population is calculated; the specific formula of the multi-objective fitness function is:

[0034] ;

[0035] in, represents the multi-objective fitness function; represents a weight coefficient of the temperature difference optimization function; represents a weight coefficient of the stress difference optimization function; represents a weight coefficient of the deformation optimization function; , and The values ​​are all in the interval and .

[0036] Preferably, the specific process of updating the quenching process population through selection operation, crossover operation and mutation operation includes:

[0037] By using a roulette wheel selection strategy, select from the quenching process population The quenching process parameters are grouped as a parent set; two parent individuals are determined according to the individual fitness of each group of the quenching process parameters in the parent set; and the fitness of the two parent individuals is crossovered. The quenching process parameters are numerically exchanged to obtain two offspring individuals; the mutation probability is obtained to determine the individual to be mutated; the mutation operation is performed according to the mutation position to obtain the mutated individual, and the quenching process population is updated in combination with the offspring individuals.

[0038] Preferably, the specific process of determining whether to end the optimization includes:

[0039] If the individual average fitness is less than the predetermined fitness threshold, the individual corresponding to the minimum individual fitness in the quenching process population is the optimal process parameter; if the individual average fitness is greater than or equal to the predetermined fitness threshold, return to step S403 and continue to update the quenching process population.

[0040] A system for reducing quenching deformation of parts based on finite element modeling, comprising:

[0041] A three-dimensional model building module is used to obtain geometric parameters, thermophysical property parameters and mechanical property parameters of the part to be quenched; and a three-dimensional model of the part to be quenched is built by CAD software according to the geometric parameters;

[0042] A part property determination module constructs a temperature-physical property function according to the thermophysical property parameters; and constructs an elastic stress-strain function and a plastic stress-strain function in combination with the mechanical property parameters;

[0043] The finite element analysis module imports the three-dimensional model of the part into the finite element analysis software, meshes the three-dimensional model of the part, and encrypts the mesh in the key area of ​​the part; sets the quenching temperature boundary conditions, quenching mechanical boundaries and quenching process parameters, combines the temperature-physical property function, the elastic stress-strain function and the plastic stress-strain function, performs thermal-mechanical coupling analysis on the part to be quenched, and obtains a part heat map, a part stress distribution map and a part deformation map;

[0044] The quenching process parameter optimization module constructs a temperature difference optimization function according to the part heat map; constructs a stress difference optimization function through the part stress distribution map; determines a deformation optimization function through the part deformation map; constructs a quenching process optimization function by combining the temperature difference optimization function, the deformation optimization function and the deformation optimization function; optimizes the quenching process optimization function through a genetic algorithm to determine the optimal process parameters during vacuum quenching.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The present invention can effectively simulate and predict the deformation behavior of parts during quenching by accurately constructing temperature-physical property functions and stress-strain functions. By obtaining thermophysical property parameters (such as thermal conductivity, specific heat capacity, density and expansion coefficient) and mechanical property parameters (such as elastic modulus, strength coefficient and strain hardening index), the precise relationship between temperature and stress-strain is constructed, which can provide a more accurate description of material behavior for the finite element model, thereby improving the accuracy of the simulation.

[0047] 2. The quenching process optimization function constructed by the present invention can effectively optimize the quenching process parameters of parts, reduce the deformation during the quenching process, and significantly improve the processing accuracy and quality of parts. This method analyzes the thermal map, stress distribution map and deformation map of the parts, constructs a quenching process optimization function through the node position, temperature, stress and deformation of the grid nodes, and adjusts and optimizes the quenching process parameters by minimizing the optimization function. It can accurately control the temperature, stress and deformation changes of parts in various links such as heating and cooling, avoid deformation caused by improper process, and thus improve the stability of parts.

[0048] 3. The present invention optimizes the quenching process parameters through finite element modeling analysis and genetic algorithm, which can effectively reduce the deformation of parts during the quenching process and improve the quality and precision of parts. By constructing a multi-objective fitness function and combining the three factors of temperature difference, stress difference and deformation degree, the quenching process parameters are comprehensively optimized, which can significantly reduce the dimensional changes and deformation caused by improper process while ensuring the performance of parts; at the same time, the genetic algorithm can find the optimal solution among multiple candidate solutions through selection, crossover and mutation operations, thereby achieving efficient process parameter optimization and improving the stability of the quenching process and the final quality of parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flow chart of a method for reducing quenching deformation of parts based on finite element modeling provided in an embodiment of the present invention;

[0050] Figure 2 An example diagram of mesh division of a three-dimensional front view of a part provided in an embodiment of the present invention;

[0051] Figure 3 A flow chart of the quenching process parameter optimization process provided by the embodiment of the present invention;

[0052] Figure 4 A system structure diagram of a system for reducing part quenching deformation based on finite element modeling provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Vacuum quenching of parts is a heat treatment method that heats and cools metal materials in a vacuum environment to achieve metal hardening and improve its mechanical properties. It is widely used in materials and parts with high performance requirements, such as aircraft engine rotating bearings, automobile engine gears, automobile crankshafts and connecting rods. However, during the quenching process, parts often deform to varying degrees due to temperature gradients, uneven cooling rates, or internal stresses in the material, which seriously affects the accuracy and performance of the parts. Therefore, reducing the deformation of parts during vacuum quenching has become one of the key factors in improving manufacturing accuracy and part quality. Finite element modeling and analysis can effectively simulate the deformation of parts during vacuum quenching, thereby optimizing the quenching process parameters and reducing part deformation caused by incompatible parameter settings.

[0055] The present invention proposes a method and system for reducing part quenching deformation based on finite element modeling to simulate the part quenching process to reduce the part quenching deformation. In order to illustrate the effectiveness of the method and system of the present invention, a specific description will be given in combination with the accompanying drawings of this embodiment and the following two embodiments.

[0056] Embodiment 1

[0057] The embodiment of the present application discloses a method for reducing part quenching deformation based on finite element modeling, so as to effectively reduce the deformation of part A during vacuum quenching. Figure 1 , Figure 1 A flow chart of a method for reducing part quenching deformation based on finite element modeling provided in an embodiment of the present application; specific implementation steps include: S1. Obtaining geometric parameters, thermophysical property parameters and mechanical property parameters of the part to be quenched, and constructing a three-dimensional model of the part; S2. Constructing a temperature-physical property function based on the thermophysical property parameters; constructing an elastic stress-strain function and a plastic stress-strain function based on the mechanical property parameters; S3. Importing the three-dimensional model of the part into the finite element analysis software, setting parameters and performing a thermal-mechanical coupling analysis on the part to be quenched to obtain a part heat map, a part stress distribution map and a part deformation map; S4. Constructing a quenching process optimization function, and optimizing it through a genetic algorithm to determine the optimal process parameters for vacuum quenching.

[0058] Furthermore, the geometric parameters, thermophysical property parameters and mechanical property parameters of part A are obtained; based on the geometric parameters, a three-dimensional model of part A is constructed through CAD software, corresponding to the above-mentioned step S1; wherein the geometric parameters include geometric shapes and dimensional data; geometric shapes include circles, rectangles, holes and grooves, etc.; dimensional data include length, width, thickness, aperture, groove shape and angle, etc., which are obtained through the engineering drawings of part A.

[0059] Further, according to the thermophysical property parameters, a temperature-physical property function is constructed; corresponding to the above step S2, the specific process includes:

[0060] The thermophysical property parameters are obtained, including thermal conductivity, specific heat capacity, density and expansion coefficient; the temperature-physical property function is constructed, and the specific formula is:

[0061] ;

[0062] in, Indicates the next said temperature-physical property function; Indicates reference temperature Next The thermophysical property parameters; Represents an exponential function with a natural constant as base; Indicates the current temperature; and Both indicate The temperature influence coefficient of each of the thermophysical property parameters is obtained by fitting the experimental data.

[0063] The embodiment of the present application can effectively improve the temperature control accuracy and physical property prediction ability in the vacuum quenching process through the temperature-physical property function. This process comprehensively considers the temperature dependence of thermal conductivity, specific heat capacity, density and expansion coefficient, so as to accurately describe the thermophysical behavior of the material under different temperature conditions.

[0064] Furthermore, in combination with the mechanical property parameters, an elastic stress-strain function and a plastic stress-strain function are constructed; corresponding to the above step S2, the specific implementation process includes:

[0065] The mechanical property parameters are obtained, including elastic modulus, strength coefficient and strain hardening exponent; according to the elastic modulus, the elastic stress-strain function is constructed, and the specific formula is:

[0066] ;

[0067] in, represents the elastic stress-strain function; Indicates the current temperature; Indicates strain; Indicates reference temperature The elastic modulus under Indicates is the power function of the exponential; and are the temperature influence coefficients of the elastic stress-strain function, obtained by fitting experimental data;

[0068] According to the strength coefficient and strain hardening exponent, the plastic stress-strain function is constructed, and the specific formula is:

[0069]

[0070] in, represents the plastic stress-strain function; Indicates reference temperature The strength coefficient under Represents a logarithmic function with a natural constant as base; Indicates reference temperature The strain hardening exponent under represents the temperature influence coefficient of the strength coefficient, The temperature influence factor representing the strain hardening exponent is obtained by fitting experimental data.

[0071] The embodiments of the present application can accurately describe the mechanical behavior of materials under different temperature conditions, especially the effect of temperature on the elastic and plastic deformation of materials, through elastic stress-strain function and plastic stress-strain function. By using mechanical parameters such as elastic modulus, strength coefficient and strain hardening index, the stress-strain model can more accurately reflect the mechanical properties of materials under actual working conditions and accurately describe the deformation response of materials under different temperatures.

[0072] Furthermore, the three-dimensional model of the part is imported into the finite element analysis software, the three-dimensional model of the part is meshed, and the mesh is encrypted in the key area of ​​the part. Figure 2 , Figure 2 This is an example of meshing of the front view of a three-dimensional mold of a part; set the quenching temperature boundary conditions, quenching mechanical boundaries and quenching process parameters, combine the temperature-physical property function, the elastic stress-strain function and the plastic stress-strain function, perform thermal-mechanical coupling analysis on the part A, and obtain the part heat map, part stress distribution map and part deformation map, corresponding to Figure 1 S3 step; wherein the key areas of the part include the outer surface area of ​​the part, the hole area of ​​the part and the connection area of ​​the part; the quenching temperature boundary conditions include heat flow boundary conditions, convection boundary conditions and radiation boundary conditions; the quenching mechanical boundaries include fixed boundary conditions, symmetric boundary conditions and free boundary conditions.

[0073] Optionally, when meshing the three-dimensional model of part A, the areas where the temperature gradient changes drastically, the stress concentration areas and the part deformation areas in the three-dimensional model of the part can be determined based on the part heat map, part stress distribution map and part deformation map obtained by the finite element analysis software; when the finite element analysis software is used next time, the meshes of these areas of part A are encrypted to obtain more accurate simulation data.

[0074] The embodiment of the present application can accurately simulate the deformation behavior of parts during vacuum quenching by encrypting operations on key areas of parts and accurately setting quenching temperature boundary conditions and quenching mechanical boundaries.

[0075] Further, according to the part heat map, a temperature difference optimization function is constructed; through the part stress distribution map, a stress difference optimization function is constructed; through the part deformation map, a deformation optimization function is determined; combining the temperature difference optimization function, the deformation optimization function and the deformation optimization function, a quenching process optimization function is constructed; the quenching process optimization function is optimized by a genetic algorithm to determine the optimal process parameters during vacuum quenching, corresponding to Figure 1 S4 step of Figure 3, is a flow chart of the quenching process parameter optimization process provided in the embodiment of the present application; the specific implementation process includes:

[0076] Step S401, obtaining the node position coordinates, node temperature, node stress and node deformation of all grid nodes through the part heat map, the part stress distribution map and the part deformation map, and constructing a quenching process optimization function; the specific implementation process includes:

[0077] The node temperature, the node stress and the node deformation are standardized, and the quenching process optimization function is constructed by combining the node position coordinates under different quenching process parameters. The specific formula is:

[0078] ;

[0079] in, Represents the optimization function for the quenching process Perform minimum value optimization; represents the temperature difference optimization function; represents the stress difference optimization function; represents the deformation optimization function; Indicates the quenching process parameters , Indicates the heating temperature (unit ), Indicates the heating time (in hours), Indicates the cooling rate (unit ), Indicates the holding time after quenching (in hours); Representing the constraints of the quenching process parameters; express The parameters; Representation parameters The maximum value constraint of Representation parameters The minimum value constraint.

[0080] By constructing the above-mentioned quenching process optimization function, it is possible to effectively control the temperature, stress and deformation of parts during vacuum quenching. By standardizing operations, the node temperature, node stress and node deformation are uniformly scaled, which helps to improve the calculation efficiency and accuracy of the optimization process, thereby minimizing deformation and improving the structural stability and quality of parts while ensuring part performance.

[0081] Step S402, initializing a quenching process population , Indicates The quenching process parameters are grouped; a multi-objective fitness function is constructed to calculate the individual fitness of the quenching process parameters; the specific implementation process includes:

[0082] The multi-objective fitness function is constructed by combining the temperature difference optimization function, the stress difference optimization function and the deformation optimization function, and the individual fitness of each group of the quenching process parameters in the quenching process population is calculated; the specific formula of the multi-objective fitness function is:

[0083] ;

[0084] in, represents the multi-objective fitness function; represents a weight coefficient of the temperature difference optimization function; represents a weight coefficient of the stress difference optimization function; represents a weight coefficient of the deformation optimization function; , and The values ​​are all in the interval and ;

[0085] The temperature difference optimization function The specific formula is expressed as:

[0086] ;

[0087] in, Indicates The grid nodes and The distance weight between grid nodes, Indicates The grid nodes and The distance between the grid nodes, Indicates the maximum distance between grid nodes; Represents the total number of grid nodes; Indicates the quenching process parameters Next The temperature of each grid node; Indicates the quenching process parameters Next The temperature of each grid node;

[0088] The stress difference optimization function The specific formula is:

[0089] ;

[0090] in, Indicates the quenching process parameters Next The stress of each mesh node; Indicates the quenching process parameters Next The stress of each mesh node;

[0091] The deformation optimization function The formula is:

[0092] ;

[0093] in, Indicates the quenching process parameters Next The distance between a mesh node and the edge of part A; Indicates all The maximum value in ; Indicates the quenching process parameters Next The grid nodes are Offset on the coordinate axis; Indicates the quenching process parameters Next The grid nodes are Offset on the coordinate axis; Indicates the quenching process parameters Next The grid nodes are Offset on the axis.

[0094] Constructing a multi-objective fitness function can comprehensively and accurately optimize the quenching process parameters. By introducing temperature difference optimization function, stress difference optimization function and deformation optimization function, the uniformity of heat treatment of parts, the reasonable distribution of internal stress of the material and the minimization of deformation can be taken into account in the optimization process, which can effectively reduce the deformation caused by uneven cooling or temperature gradient during quenching, and further improve the dimensional accuracy and surface quality of parts.

[0095] Step S403, updating the quenching process population through selection operation, crossover operation and mutation operation according to the individual fitness of different quenching process parameters in the quenching process population; further, the specific process of updating the quenching process population through selection operation, crossover operation and mutation operation includes:

[0096] By using a roulette wheel selection strategy, select from the quenching process population The quenching process parameters are grouped as a parent set; two parent individuals are determined according to the individual fitness of each group of the quenching process parameters in the parent set; and the fitness of the two parent individuals is crossovered. The quenching process parameters are numerically exchanged to obtain two offspring individuals; the mutation probability is obtained to determine the individual to be mutated; the mutation operation is performed according to the mutation position to obtain the mutated individual, and the quenching process population is updated in combination with the offspring individuals.

[0097] In order to clearly illustrate the specific role of the crossover operation, the embodiments of the present application exemplarily provide individuals of different crossover operations. Please refer to some examples of different crossover operations listed in Table 1.

[0098] Table 1. Some examples of different crossover operations

[0099]

[0100] By updating the quenching process parameters through the above genetic algorithm, the optimization effect of the quenching process can be effectively improved. The roulette selection strategy ensures that individuals with high fitness have a higher selection probability, the crossover operation further enhances the diversity of the search space, and the mutation operation combines fitness and mutation probability to fine-tune the individuals in a targeted manner, preventing excessive convergence in the optimization process, effectively improving the stability and accuracy of the quenching process, and reducing deformation.

[0101] Step S404, calculating the average fitness of the individuals in the quenching process population, and determining whether to end the optimization; the specific implementation process includes:

[0102] The individual average fitness of the quenching process population is calculated, and the specific formula is:

[0103] ;

[0104] in, represents the average fitness of the individuals in the quenching process population; Indicates The offspring The quenching process parameters The individual fitness under represents the number of offspring in the quenching process population;

[0105] If the individual average fitness is less than the predetermined fitness threshold, the individual corresponding to the minimum individual fitness in the quenching process population is the optimal process parameter; if the individual average fitness is greater than or equal to the predetermined fitness threshold, return to step S403 and continue to update the quenching process population.

[0106] Through the iterative optimization process, the accuracy and reliability of the quenching process can be effectively improved. When the individual average fitness is less than the predetermined fitness threshold, the optimal process parameters are automatically determined, thereby ensuring that each variable in the vacuum quenching process is optimized, making the final quenching process more stable and efficient.

[0107] The embodiment of the present application uses a genetic algorithm to optimize and solve the quenching process optimization function, which can effectively improve the quality of parts during the quenching process, especially playing an important role in reducing deformation. By analyzing the thermal map, stress distribution map and deformation map of the parts, the relevant information of each grid node can be accurately obtained, so as to establish a quenching process optimization function, providing a reliable theoretical basis for optimization; by initializing the quenching process population and constructing a multi-objective fitness function, the genetic algorithm can comprehensively consider multiple factors, such as temperature, stress and deformation, to ensure that the optimization direction of the process parameters is more in line with actual needs; by using selection, crossover and mutation operations, the optimal quenching process parameters can be efficiently searched, so that the parts can maintain higher stability and precision during the quenching process, reduce the occurrence of deformation and cracks, improve the mechanical properties and surface quality of the parts, and ensure that the final quenching process parameters can minimize the deformation of the parts.

[0108] In order to further illustrate the role of the quenching process parameter optimization proposed in the present invention, the deformation vectors of different grid nodes of part A under the optimal process parameters are given as an example. The deformation vectors of different grid nodes of part A are listed in Table 2.

[0109] Table 2. Deformation vectors of different mesh nodes of part A

[0110]

[0111] According to the data in Table 2, under the optimal process parameters, the deformation of the mesh nodes of part A on different coordinate axes is very small, which is within the allowable error range of the vacuum quenching calibration of the part.

[0112] The embodiment of the present application optimizes the process parameters in the vacuum quenching process of parts based on finite element modeling and genetic algorithm to reduce the deformation of parts. The specific implementation process mainly includes the following steps: (a) constructing temperature-physical property function and stress-strain function; (b) importing the three-dimensional model of the part into the finite element analysis software for thermal-mechanical coupling analysis to obtain the thermal map, stress distribution map and deformation map of the part; (c) optimizing the quenching process parameters by constructing a quenching process optimization function and using a genetic algorithm. The present invention respectively describes the material properties of the parts, performs finite element simulation analysis and optimizes the quenching process parameters for the above three processes; describes the material properties of the parts, realizes the accurate simulation of the material behavior of the parts, and improves the accuracy of the finite element analysis; performs finite element simulation analysis to improve the accuracy of the simulated deformation, stress and temperature distribution of the parts; optimizes the quenching process parameters, further improves the selection accuracy of the quenching process parameters, and improves the stability of the vacuum quenching process and the final quality of the parts.

[0113] Embodiment 2

[0114] In the first embodiment, the present invention proposes a method for reducing the quenching deformation of parts based on finite element modeling, so as to optimize the process parameters of part A during vacuum quenching, thereby reducing the quenching deformation of the parts. Figure 4 , which is a system structure diagram of a system for reducing part quenching deformation based on finite element modeling provided in an embodiment of the present application; the system includes: a three-dimensional model construction module, a part attribute determination module, a finite element analysis module and a quenching process parameter optimization module.

[0115] Furthermore, the three-dimensional model construction module obtains the geometric parameters, thermophysical property parameters and mechanical property parameters of the part to be quenched; according to the geometric parameters, a three-dimensional model of the part to be quenched is constructed through CAD software; the part property determination module constructs a temperature-physical property function according to the thermophysical property parameters; and constructs an elastic stress-strain function and a plastic stress-strain function in combination with the mechanical property parameters.

[0116] Furthermore, the finite element analysis module imports the three-dimensional model of the part into the finite element analysis software, meshes the three-dimensional model of the part, and encrypts the mesh in the key area of ​​the part; sets the quenching temperature boundary conditions, quenching mechanical boundaries and quenching process parameters, combines the temperature-physical property function, the elastic stress-strain function and the plastic stress-strain function, and performs thermal-mechanical coupling analysis on the part to be quenched to obtain a part thermal map, a part stress distribution map and a part deformation map.

[0117] Furthermore, the quenching process parameter optimization module constructs a quenching process optimization function according to the part heat map, the part stress distribution map and the part deformation map; optimizes the quenching process optimization function through a genetic algorithm to determine the optimal process parameters during vacuum quenching; the specific implementation process includes:

[0118] Step S401, obtaining the node position coordinates, node temperature, node stress and node deformation of all grid nodes through the part heat map, the part stress distribution map and the part deformation map, and performing a standardization operation, and constructing the quenching process optimization function according to the node position coordinates, the node temperature, the node stress and the node deformation under different quenching process parameters;

[0119] Step S402, initializing a quenching process population , Indicates The quenching process parameters are grouped; a multi-objective fitness function is constructed to calculate the individual fitness of the quenching process parameters; the specific implementation process includes:

[0120] The multi-objective fitness function is constructed by combining the temperature difference optimization function, the stress difference optimization function and the deformation optimization function, and the individual fitness of each group of the quenching process parameters in the quenching process population is calculated; the specific formula of the multi-objective fitness function is:

[0121] ;

[0122] in, represents the multi-objective fitness function; represents a weight coefficient of the temperature difference optimization function; represents a weight coefficient of the stress difference optimization function; represents a weight coefficient of the deformation optimization function; , and The values ​​are all in the interval and ;

[0123] Step S403, updating the quenching process population through selection operation, crossover operation and mutation operation according to the individual fitness of different quenching process parameters in the quenching process population; further, the specific process of updating the quenching process population through selection operation, crossover operation and mutation operation includes:

[0124] By using a roulette wheel selection strategy, select from the quenching process population The quenching process parameters are grouped as a parent set; two parent individuals are determined according to the individual fitness of each group of the quenching process parameters in the parent set; and the fitness of the two parent individuals is crossovered. The quenching process parameters are numerically exchanged to obtain two offspring individuals; the mutation probability is obtained to determine the individual to be mutated; the mutation operation is performed according to the mutation position to obtain the mutated individual, and the quenching process population is updated in combination with the offspring individuals;

[0125] In order to clearly illustrate the specific role of the crossover operation, the embodiments of the present application exemplarily provide individuals of different mutation operations, see Table 3 for some examples of different mutation operations.

[0126] Table 3. Some examples of different mutation operations

[0127]

[0128] Step S404, calculating the average fitness of the individuals in the quenching process population, and determining whether to end the optimization; the specific implementation process includes:

[0129] The individual average fitness of the quenching process population is calculated, and the specific formula is:

[0130] ;

[0131] in, represents the average fitness of the individuals in the quenching process population; Indicates The offspring The quenching process parameters The individual fitness under represents the number of offspring in the quenching process population;

[0132] If the individual average fitness is less than the predetermined fitness threshold, the individual corresponding to the minimum individual fitness in the quenching process population is the optimal process parameter; if the individual average fitness is greater than or equal to the predetermined fitness threshold, return to step S403 and continue to update the quenching process population.

[0133] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for reducing quenching deformation of parts based on finite element modeling, characterized in that: include: Obtaining geometric parameters, thermophysical property parameters and mechanical property parameters of the parts to be quenched; According to the geometric parameters, a three-dimensional model of the part to be quenched is constructed by CAD software; According to the thermophysical property parameters, a temperature-physical property function is constructed; in combination with the mechanical property parameters, an elastic stress-strain function and a plastic stress-strain function are constructed; Importing the three-dimensional model of the part into finite element analysis software, meshing the three-dimensional model of the part, and encrypting the mesh in key areas of the part; Setting quenching temperature boundary conditions, quenching mechanical boundaries and quenching process parameters, combining the temperature-physical property function, the elastic stress-strain function and the plastic stress-strain function, performing thermal-mechanical coupling analysis on the part to be quenched, and obtaining a part heat map, a part stress distribution map and a part deformation map; Constructing a temperature difference optimization function according to the part heat map; Constructing a stress difference optimization function through the stress distribution diagram of the part; The deformation optimization function is determined through the part deformation diagram; the quenching process optimization function is constructed by combining the temperature difference optimization function, the deformation optimization function and the deformation optimization function; the quenching process optimization function is optimized through a genetic algorithm to determine the optimal process parameters during vacuum quenching.

2. The method for reducing part quenching deformation based on finite element modeling according to claim 1 is characterized in that: The specific process of constructing the temperature-physical property function includes: The thermophysical property parameters are obtained, including thermal conductivity, specific heat capacity, density and expansion coefficient; the temperature-physical property function is constructed, and the specific formula is: ； in, Indicates the next a temperature-physical property function; Indicates reference temperature Next The thermophysical property parameters; Represents an exponential function with a natural constant as base; Indicates the current temperature; and Both indicate The temperature influence coefficient of the thermophysical property parameter.

3. The method for reducing part quenching deformation based on finite element modeling according to claim 1 is characterized in that: The specific implementation process of constructing the elastic stress-strain function and the plastic stress-strain function includes: The mechanical property parameters are obtained, including elastic modulus, strength coefficient and strain hardening exponent; according to the elastic modulus, the elastic stress-strain function is constructed, and the specific formula is: ； in, represents the elastic stress-strain function; Indicates the current temperature; Indicates strain; Indicates reference temperature The elastic modulus under Indicates is the power function of the exponential; and are the temperature influence coefficients of the elastic stress-strain function; According to the strength coefficient and strain hardening exponent, the plastic stress-strain function is constructed, and the specific formula is: in, represents the plastic stress-strain function; Indicates reference temperature The strength coefficient under Represents a logarithmic function with a natural constant as base; Indicates reference temperature The strain hardening exponent under represents the temperature influence coefficient of the strength coefficient, represents the temperature influence factor of the strain hardening exponent.

4. The method for reducing part quenching deformation based on finite element modeling according to claim 1 is characterized in that: The key areas of the parts include the outer surface area of ​​the parts, the hole area of ​​the parts and the connection area of ​​the parts; the quenching temperature boundary conditions include heat flow boundary conditions, convection boundary conditions and radiation boundary conditions; the quenching mechanical boundaries include fixed boundary conditions, symmetric boundary conditions and free boundary conditions.

5. The method for reducing part quenching deformation based on finite element modeling according to claim 1 is characterized in that: The specific process of optimizing the quenching process optimization function by using a genetic algorithm and determining the optimal process parameters includes: Step S401, obtaining the node position coordinates, node temperature, node stress and node deformation of all grid nodes through the part heat map, the part stress distribution map and the part deformation map, and constructing a quenching process optimization function; Step S402, initializing a quenching process population , Indicates The quenching process parameters are grouped; a multi-objective fitness function is constructed to calculate the individual fitness of the quenching process parameters; Step S403, updating the quenching process population through selection operation, crossover operation and mutation operation according to the individual fitness of different quenching process parameters in the quenching process population; Step S404, calculating the average fitness of the individuals in the quenching process population, and determining whether to terminate the optimization.

6. The method for reducing part quenching deformation based on finite element modeling according to claim 5 is characterized in that: The specific process of constructing the quenching process optimization function includes: The node temperature, the node stress and the node deformation are standardized, and the quenching process optimization function is constructed by combining the node position coordinates under different quenching process parameters. The specific formula is: ； in, Represents the optimization function for the quenching process Perform minimum value optimization; represents the temperature difference optimization function; represents the stress difference optimization function; represents the deformation optimization function; Indicates the quenching process parameters , Indicates the heating temperature, Indicates the heating time. represents the cooling rate, Indicates the holding time after quenching; Representing the constraints of the quenching process parameters; express The parameters; Representation parameters The maximum value constraint of Representation parameters The minimum value constraint.

7. The method for reducing part quenching deformation based on finite element modeling according to claim 5 is characterized in that: The specific implementation process of constructing the multi-objective fitness function includes: The multi-objective fitness function is constructed by combining the temperature difference optimization function, the stress difference optimization function and the deformation optimization function, and the individual fitness of each group of the quenching process parameters in the quenching process population is calculated; the specific formula of the multi-objective fitness function is: ； in, represents the multi-objective fitness function; represents a weight coefficient of the temperature difference optimization function; represents a weight coefficient of the stress difference optimization function; represents a weight coefficient of the deformation optimization function; , and The values ​​are all in the interval and .

8. The method for reducing part quenching deformation based on finite element modeling according to claim 5 is characterized in that: The specific process of updating the quenching process population through selection operation, crossover operation and mutation operation includes: By using a roulette wheel selection strategy, select from the quenching process population The quenching process parameters are grouped as a parent set; two parent individuals are determined according to the individual fitness of each group of the quenching process parameters in the parent set; and the fitness of the two parent individuals is crossovered. The quenching process parameters are numerically exchanged to obtain two offspring individuals; the mutation probability is obtained to determine the individual to be mutated; the mutation operation is performed according to the mutation position to obtain the mutated individual, and the quenching process population is updated in combination with the offspring individuals.

9. The method for reducing part quenching deformation based on finite element modeling according to claim 5, characterized in that: The specific process of determining whether to end the optimization includes: If the individual average fitness is less than the predetermined fitness threshold, the individual corresponding to the minimum individual fitness in the quenching process population is the optimal process parameter; if the individual average fitness is greater than or equal to the predetermined fitness threshold, return to step S403 and continue to update the quenching process population.

10. A system for reducing quenching deformation of parts based on finite element modeling, characterized in that: include: A three-dimensional model building module to obtain the geometric parameters, thermophysical property parameters and mechanical property parameters of the parts to be quenched; According to the geometric parameters, a three-dimensional model of the part to be quenched is constructed by CAD software; A part property determination module constructs a temperature-physical property function according to the thermophysical property parameters; and constructs an elastic stress-strain function and a plastic stress-strain function in combination with the mechanical property parameters; A finite element analysis module, which imports the three-dimensional model of the part into a finite element analysis software, meshes the three-dimensional model of the part, and encrypts the mesh in key areas of the part; Setting quenching temperature boundary conditions, quenching mechanical boundaries and quenching process parameters, combining the temperature-physical property function, the elastic stress-strain function and the plastic stress-strain function, performing thermal-mechanical coupling analysis on the part to be quenched, and obtaining a part heat map, a part stress distribution map and a part deformation map; A quenching process parameter optimization module constructs a temperature difference optimization function according to the part heat map; Constructing a stress difference optimization function through the stress distribution diagram of the part; The deformation optimization function is determined through the part deformation diagram; the quenching process optimization function is constructed by combining the temperature difference optimization function, the deformation optimization function and the deformation optimization function; the quenching process optimization function is optimized through a genetic algorithm to determine the optimal process parameters during vacuum quenching.

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