A method and apparatus for identifying j-c constitutive parameters for titanium alloy machining

Abstract

A method and device for identifying J-C constitutive parameters for titanium alloy cutting, comprising: 1) titanium alloy turning experiment is carried out by using numerical control lathe and dynamometer, and experimental cutting force is obtained; 2) J-C constitutive model is selected, and a plurality of groups of parameters are adopted by using multi-level orthogonal design, the parameters are brought into two-dimensional turning simulation model to obtain simulation cutting force under different groups of parameters; 3) parameter identification takes minimizing simulation error as the target, regression analysis and iterative optimization are carried out, the parameters of the J-C constitutive model are continuously adjusted in the iterative optimization process, the cutting force corresponding to each group of parameters is compared with the experimental cutting force to obtain error, when the error is less than the set standard value, the algorithm stops iteration, and the parameters in the optimized J-C constitutive model are obtained. The present application not only saves time and cost, but also effectively improves the material constitutive parameter acquisition precision, and ensures the accuracy of the simulation result.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of titanium alloy cutting, in particular to a method and device for identifying J-C constitutive parameters for titanium alloy cutting. BACKGROUND

[0002] High-speed cutting may generate high-frequency vibration cutting force, and the high-frequency vibration further causes tool system vibration, aggravating tool wear; the cutting force size also affects the machining surface roughness, and generally the larger the cutting force, the larger the machining surface roughness. At present, cutting force and tool wear prediction research mainly focuses on empirical method, analytical method and numerical simulation. The empirical method depends on a large number of accurate experimental measurement data, and accidental measurement errors have a great influence on the accurate establishment of the empirical formula, and the empirical method does not consider the influence of material properties on the cutting force. The analytical method uses the material constitutive model to calculate the shear flow stress, and can consider the material properties, but needs to calibrate the shear angle, friction angle between chip and tool and other difficult-to-obtain physical quantities through experiments, which affects the prediction accuracy and efficiency of the analytical method. Numerical simulation can consider material properties and calculate shear angle and other difficult-to-obtain physical quantities. Compared with the empirical method and the analytical method, numerical simulation can reflect the dynamic evolution law of stress, strain, temperature and the like in the cutting process.

[0003] The accuracy of numerical simulation analysis depends on the accuracy of the input parameters of the simulation model, especially the material constitutive parameters. The flow stress of metal material cutting is related to strain, strain rate and temperature. The J-C constitutive model is a material constitutive model proposed by Johnson and Cook for materials under large strain, high strain rate and high temperature, which can comprehensively describe the strain hardening, strain rate hardening and temperature softening effects of the material. Since the cutting process involves large strain, high strain rate and high temperature, the model is often used to describe the material mechanical behavior in the cutting process. The split Hopkinson pressure bar compression test (SHPB) is commonly used to reproduce the material deformation law in the cutting process, but the flow stress data obtained by the test has certain limitations, the strain rate is much lower than that in the cutting process, and special experimental equipment is needed, which is time-consuming and costly.

[0004] The static mechanical properties and creep properties of the material can be tested according to the corresponding national standards; and the dynamic mechanical properties of the metal material have no national standard and no corresponding specification, which makes the dynamic mechanical property parameters of the same material measured by different research units different. In order to accurately describe the dynamic mechanical properties of the material and ensure the accuracy of numerical simulation, it is necessary to carry out accurate identification research on the constitutive model parameters of titanium alloy.

[0005] At present, scholars have proposed different methods for indirectly obtaining the parameters of the constitutive model. The cutting test method indirectly obtains the parameters of the constitutive model by minimizing the average relative error of the predicted flow stress and the flow stress obtained through the test, but this method replaces the cutting force into the cutting model to calculate the stress and other physical quantities, thereby increasing the parameter identification time. In addition, the error minimization between the dynamic shear test and the corresponding simulation output force-displacement curve is also used to indirectly obtain the parameters of the constitutive model, but this method requires a special sample for shear test, and the time and economic cost are high. SUMMARY

[0006] The main purpose of the present application is to overcome the above-mentioned defects in the prior art, and to provide a method and device for identifying J-C constitutive parameters for titanium alloy cutting. The constitutive model parameters are obtained by minimizing the error between the average cutting force of the test and the simulation output through titanium alloy turning test, thereby reducing the parameter identification time.

[0007] The present application adopts the following technical solutions:

[0008] A method for identifying J-C constitutive parameters for titanium alloy cutting, characterized in that it comprises:

[0009] 1) titanium alloy turning test is carried out by using a numerical control lathe and a dynamometer to obtain experimental cutting force;

[0010] 2) J-C constitutive model is selected and multiple sets of parameters are obtained by using multi-level orthogonal design, and the parameters are brought into a two-dimensional turning simulation model to obtain simulation cutting force under different sets of parameters;

[0011] 3) parameter identification is carried out to minimize the simulation error, regression analysis and iterative optimization are carried out, the parameters of the J-C constitutive model are adjusted in the iterative optimization process, the cutting force corresponding to each set of parameters is compared with the experimental cutting force to obtain the error, and when the error is less than a set standard value, the algorithm stops iteration, and the parameters in the optimized J-C constitutive model are obtained.

[0012] Preferably, the J-C constitutive model is as follows:

[0013]

[0014] wherein σ is the flow stress, ε is the plastic strain, is the strain rate, is the reference strain rate, T is the workpiece material temperature, T r is the room temperature, T m is the melting point of the workpiece, A, B, C, m and n are model constants, respectively representing the yield stress, the hardening modulus, the strain rate sensitivity coefficient, the thermal softening coefficient and the hardening coefficient.

[0015] Preferably, the multiple sets of parameters are determined by using three sets of constitutive parameters commonly used in literature, the upper and lower limits of each parameter are the maximum and minimum values of the parameter in the three sets of constitutive parameters, and five levels of each parameter are determined within the range of values to perform orthogonal test of five factors and five levels.

[0016] Preferably, in step 3), the error is the absolute error of the simulated cutting force and the experimental cutting force: f(A, B, C, m, n) = min{|F test -F avg |}

[0017] where F test is the simulated cutting force, F avg is the experimental average cutting force.

[0018] Preferably, it further comprises step 4) evaluating the accuracy of the optimized J-C constitutive model parameters in predicting the cutting force of titanium alloy, which is specifically as follows:

[0019] 4.1) titanium alloy milling experiments are performed using a five-axis machining center and a dynamometer to obtain experimental cutting forces;

[0020] 4.2) the parameters in the optimized J-C constitutive model obtained in step 3) are substituted into the three-dimensional milling finite element simulation model to obtain simulated milling forces, which are compared with the experimental cutting forces obtained in step 4.1) to evaluate the accuracy of the identified J-C constitutive parameters for titanium alloy cutting.

[0021] Preferably, the two-dimensional turning simulation model is constructed as follows:

[0022] (a) establishing a tool and workpiece model;

[0023] (b) inputting material parameters of the tool and workpiece, and defining the plane stress / strain thickness;

[0024] (c) setting the element type as quadrilateral element;

[0025] (d) meshing, and encrypting the mesh of the machined part;

[0026] (e) defining the analysis step time, and selecting the field output and history output data to be output;

[0027] (f) setting the boundary conditions, defining the turning tool as a rigid body, and assigning a moving speed;

[0028] (g) simulating and solving, and outputting the force analysis results.

[0029] Preferably, the three-dimensional milling finite element simulation model is constructed as follows:

[0030] (a) Establishing tool and workpiece models;

[0031] (b) Inputting material parameters of tool and workpiece;

[0032] (c) Setting the unit type as tetrahedral unit;

[0033] (d) Grid division, encrypting the grid of the machined part;

[0034] (e) Defining analysis step time, selecting field output and history output data required to be output;

[0035] (f) Setting boundary conditions, defining the milling tool as a rigid body and giving moving and rotating speeds;

[0036] (g) Simulation solving, outputting force analysis results.

[0037] Preferably, the error back propagation neural network is used for regression in step 3).

[0038] Preferably, in step 3), the parameter identification takes minimizing simulation error as the target, and the particle swarm algorithm is used to accurately obtain the parameters.

[0039] A device for identifying J-C constitutive parameters for titanium alloy cutting, characterized in that:

[0040] A titanium alloy turning experiment module is used to perform titanium alloy turning experiments by using a numerical control lathe and a dynamometer to obtain experimental cutting forces;

[0041] A two-dimensional turning simulation module is used to select a J-C constitutive model and adopt multiple sets of parameters in a multi-level orthogonal design, and the parameters are brought into a two-dimensional turning simulation model to obtain simulation cutting forces under different sets of parameters;

[0042] A parameter optimization module is used to take minimizing simulation error as the target for parameter identification, perform regression analysis and iterative optimization, continuously adjust the parameters of the J-C constitutive model in the iterative optimization process, compare the cutting forces corresponding to each set of parameters with the experimental cutting forces to obtain errors, and stop iteration when the errors are less than a set standard value to obtain the parameters in the optimized J-C constitutive model.

[0043] As can be seen from the above description of the present application, compared with the prior art, the present application has the following beneficial effects:

[0044] 1. According to the simulation of small samples and two-dimensional turning experiment data, combined with regression analysis and optimization algorithm, the present application can quickly and accurately identify the parameters of the J-C constitutive model.

[0045] 2, The milling simulation model based on the optimal J-C constitutive model parameters is constructed, compared with the milling experimental results, and it is proved that the optimal parameters obtained based on the turning simulation-experiment comparison can be extended to the milling process, so that an accurate cutting force prediction model is established.

[0046] 3, The method saves time and cost, effectively improves the material constitutive parameter acquisition precision, and ensures the accuracy of the simulation results. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The flowchart of the method of the application is shown in the figure;

[0048] Figure 2 The turning experiment diagram is shown in the figure;

[0049] Figure 3 The two-dimensional turning simulation process cloud chart is shown in the figure;

[0050] Figure 4 The turning experiment-two-dimensional simulation cutting force change curve comparison chart before and after the J-C constitutive model parameter optimization is shown in the figure;

[0051] Figure 5 The milling experiment diagram is shown in the figure;

[0052] Figure 6 The three-dimensional milling simulation process cloud chart is shown in the figure;

[0053] Figure 7 The milling experiment-three-dimensional simulation cutting force change curve comparison chart before and after the J-C constitutive model parameter optimization is shown in the figure;

[0054] Among them:

[0055] 10, workpiece, 20, tool, 30, dynamometer, 40, machining plane.

[0056] The application will be further described in detail below in combination with the drawings and specific embodiments. DETAILED DESCRIPTION

[0057] The application will be further described below through specific embodiments.

[0058] Reference Figure 1 A method for identifying J-C constitutive parameters for titanium alloy cutting, comprising:

[0059] 1) titanium alloy turning experiment is carried out by using numerical control lathe and dynamometer, and experimental cutting force is obtained.

[0060] 2) J-C constitutive model is selected, and multiple groups of parameters are obtained by using multi-level orthogonal design, and the multiple groups of parameters are brought into the two-dimensional turning simulation model to obtain simulation cutting force under different groups of parameters.

[0061] Where, J-C constitutive model is as follows:

[0062]

[0063] Where, sigma is flow stress, epsilon is plastic strain, is strain rate, is reference strain rate, T is workpiece material temperature, T r is room temperature, T m is workpiece melting point. A, B, C, m and n are model parameters, respectively representing yield stress, hardening modulus, strain rate sensitivity coefficient, thermal softening coefficient and hardening coefficient. Five parameters A, B, n, m and C are used as five factors of simulation design, and a multi-level orthogonal design is adopted. Multiple parameters are as follows: three groups of constitutive parameter values commonly used in literature are referred to, the upper and lower limits of each parameter are the maximum and minimum values of the parameter in the three groups of constitutive parameters, and five levels of each parameter are determined in the value range, and an orthogonal test of five factors and five levels is carried out.

[0064] The modeling method is not only used for turning simulation to obtain training samples, but also used in three-dimensional milling finite element simulation, and the accuracy of J-C parameters obtained by the method in titanium alloy cutting force prediction is evaluated.

[0065] Once the starting criteria are met, Abaqus will use the relevant damage evolution law to determine the material failure process, and the Johnson-Cook damage initiation criterion is selected. Johnson-Cook damage initiation criterion assumes that metals are broken due to the nucleation, growth and coalescence of voids. The criterion for the initiation of damage is the equivalent plastic strain defined as

[0066]

[0067] Where, d1-d5 are failure constants, is reference strain rate, is plastic strain rate, eta is stress triaxiality

[0068] Eta =-p / q

[0069] Where, p is average stress, q is Mises stress. is dimensionless temperature, which is defined as follows:

[0070]

[0071] In the formula, theta is current temperature, theta melt is melting temperature, theta transitionis the transition temperature. The transition temperature refers to the temperature below which there is no temperature dependence of the representation of the damage strain. When used in conjunction with the J-C plasticity constitutive model, the prescribed values of the melting temperature and the transition temperature should be consistent with the values prescribed in the plasticity definition. In the present method, both the melting temperature and the transition temperature are selected as 1650 DEG C and 20 DEG C, respectively.

[0072] Then, the damage evolution mechanism of the ductile metal is defined. In Abaqus, the damage of the material is assumed to be a gradual degradation of the stiffness of the material, which eventually leads to the failure of the material, and it must be used in conjunction with the damage initiation criterion of the ductile metal. In the present method, the damage evolution type is selected as displacement, and the softening method is selected as linear, i.e. the damage variable of the material changes linearly with the deformation.

[0073] In the present application, the construction method of the two-dimensional turning simulation model is as follows:

[0074] (a) establishing a tool and a workpiece model;

[0075] (b) inputting material parameters of the tool and the workpiece, and defining a plane stress / strain thickness;

[0076] (c) setting the unit type as a quadrilateral element;

[0077] (d) meshing, and encrypting the mesh of the machined part;

[0078] (e) defining the analysis step time, and selecting the field output and the history output data to be outputted;

[0079] (f) setting the boundary condition, defining the turning tool as a rigid body, and assigning a moving speed;

[0080] (g) simulating and solving, and outputting the force analysis result.

[0081] 3) Parameter identification, regression analysis and iterative optimization are performed to minimize the simulation error, and in the iterative optimization process, the parameters of the J-C constitutive model are continuously adjusted, the cutting force corresponding to each set of parameters is compared with the experimental cutting force to obtain the error, and when the error is less than the set standard value, the algorithm stops iteration, and the parameters in the optimized J-C constitutive model are obtained.

[0082] In the present application, the operation amount of numerical simulation is large, and it takes too long to obtain more training samples. In the case of "small sample, poor information" data, the method uses error back propagation neural network for regression under the condition of "small sample, poor information". The error back propagation neural network is abbreviated as BP (Back Propagation) network, which is a multi-layer neural network with three or more layers, and each layer is composed of a plurality of neurons. With the continuous error back propagation training, the accuracy of the network response to the input mode will also be continuously improved. An accurate mapping model of force and constitutive equation parameters is constructed. The input parameters are defined as 5, the output parameters are defined as 1, the number of hidden layers is defined as 6, and the number of output layers is defined as 1.

[0083] In order to minimize the simulation error, a particle swarm optimization (PSO) algorithm is used to accurately obtain the parameters. The particle swarm optimization algorithm is an optimization algorithm based on the theory of swarm intelligence, which seeks optimal solution through the cooperation and information sharing between individuals in the group. Compared with genetic algorithm, it can converge to the optimal solution faster. The number of particles is defined as 50, the maximum speed of particles is defined as 0.5, the learning factor c1 is defined as 1.5, the learning factor c2 is defined as 2.5, the inertia weight is defined as 0.6, and the fitness function is defined as:

[0084] f(A,B,C,m,n)=min{|F test -F avg |}

[0085] Where F test is the simulation cutting force, and F avg is the experimental average cutting force.

[0086] In this step, the error is the absolute error of the simulation cutting force and the experimental cutting force, that is, the fitness function.

[0087] The initial J-C constitutive parameter value range A is [800, 1200], B is [200, 600], C is [0.15, 0.55], m is [0.6, 1.4], and n is [0.01, 0.04]. In the iterative optimization process, the five parameters in the constitutive model are continuously adjusted, the cutting force corresponding to each group of parameters is compared with the cutting force obtained by the experiment to obtain the error, and when the error is less than the set standard value, the algorithm stops iteration, and the optimized parameters in the J-C constitutive model are obtained.

[0088] The present application also includes step 4) evaluating the accuracy of the optimized parameters in the J-C constitutive model in predicting the cutting force of titanium alloy, which is as follows:

[0089] 4.1) titanium alloy milling experiment is carried out with a five-axis machining center and a dynamometer to obtain experimental cutting force;

[0090] 4.2) Substitute the parameters in the optimized J-C constitutive model obtained in step 3) into the three-dimensional milling finite element simulation model to obtain the simulation milling force, and compare it with the experimental cutting force obtained in step 4.1) to evaluate the accuracy of the identified J-C constitutive parameters for titanium alloy cutting.

[0091] In this step, the construction method of the three-dimensional milling finite element simulation model is as follows:

[0092] (a) Establishing a tool and workpiece model;

[0093] (b) Inputting the material parameters of the tool and workpiece;

[0094] (c) Setting the unit type as a tetrahedral unit;

[0095] (d) Grid division, and encrypting the grid of the machined part;

[0096] (e) Defining the analysis step time, selecting the field output and history output data required to be output;

[0097] (f) Setting the boundary conditions, defining the milling cutter as a rigid body, and assigning the moving and rotating speed;

[0098] (g) Simulation solving, and outputting the force analysis result.

[0099] The present application uses the turning process to identify the constitutive equation parameters, and uses the milling process to evaluate the parameter accuracy, which proves that the method can better predict the cutting force in the cutting process.

[0100] The present application also provides a device for identifying J-C constitutive parameters for titanium alloy cutting, which adopts the above-mentioned method for identifying J-C constitutive parameters for titanium alloy cutting, and comprises the following:

[0101] A titanium alloy turning experiment module, which uses a numerical control lathe and a dynamometer to perform titanium alloy turning experiments to obtain experimental cutting forces;

[0102] A two-dimensional turning simulation module, which selects a J-C constitutive model and adopts multiple sets of parameters in a multi-level orthogonal design, and brings the parameters into a two-dimensional turning simulation model to obtain simulation cutting forces under different sets of parameters;

[0103] A parameter optimization module, which identifies parameters with the goal of minimizing simulation errors, performs regression analysis and iterative optimization, continuously adjusts the parameters of the J-C constitutive model in the iterative optimization process, compares the cutting force corresponding to each set of parameters with the experimental cutting force to obtain errors, and stops iteration when the error is less than a set standard value, thereby obtaining the parameters in the optimized J-C constitutive model.

[0104] The apparatus of this invention further includes a titanium alloy milling experiment module, a three-dimensional milling finite element simulation model, and an evaluation and identification module. The titanium alloy milling experiment module uses a five-axis machining center and a force gauge to conduct titanium alloy milling experiments and obtain experimental cutting forces. The three-dimensional milling finite element simulation model is used to substitute the parameters from the optimized JC constitutive model obtained by the parameter optimization module into the three-dimensional milling finite element simulation model to obtain simulated milling forces. The evaluation and identification module is used to compare the simulated milling forces obtained from the three-dimensional milling finite element simulation model with the experimental cutting forces obtained from the titanium alloy milling experiment module to evaluate and identify the accuracy of the JC constitutive parameters used for titanium alloy cutting.

[0105] This invention does not require the use of the Split Hopkinson Bar Test (SHPB) to obtain material constitutive model parameters. The strain rate obtained from the SHPB test often fails to reach the high strain rates encountered during cutting, particularly for large strains (greater than 1) and large strain rates (10⁻⁶) during the cutting process. 3 -10 6 s -1 Plastic deformation at high temperatures is difficult to describe accurately, and SHPB requires specialized equipment for extensive testing. Therefore, this invention not only saves time and cost but also effectively improves the accuracy of material constitutive parameters, ensuring the accuracy of simulation results.

[0106] The following examples illustrate the present invention.

[0107] 1) Conduct titanium alloy turning experiments using a CNC lathe and a force gauge to obtain the experimental cutting force, such as... Figure 2 As shown, the apparatus includes workpiece 10 and cutting tool 20. The equipment used in this embodiment is a Colchester Tornado A50 CNC lathe. The material of the workpiece being cut is Ti6Al4V titanium alloy, and the experimental conditions are set as shown in Table 1.

[0108] Table 1

[0109]

[0110] 2) The JC constitutive model was selected, and multiple sets of parameters were designed using a multi-level orthogonal approach. These parameters were then substituted into the two-dimensional turning simulation model to obtain the simulated cutting forces under different sets of parameters. As shown in Table 2, the five parameters A, B, n, m, and C in the constitutive equation were used as the five factors in the simulation design. A multi-level orthogonal design of the constitutive parameter set was adopted, and five levels were determined for each of the five factors according to a certain gradient. These parameters were then substituted into the two-dimensional turning simulation, as shown in Table 2. Figure 3 As shown, 25 turning simulations were performed to obtain simulation cutting force data under different sets of parameters.

[0111] Table 2

[0112]

[0113] The training sample simulation error L 25 (5 5 )orthogonal table as shown in Table 3.

[0114] Table 3

[0115]

[0116]

[0117]

[0118] 3) Parameter identification aims to minimize simulation error, regression analysis and iterative optimization are carried out, and the parameters of the J-C constitutive model are adjusted in the iterative optimization process. The error is obtained by comparing the cutting force corresponding to each group of parameters with the experimental cutting force. When the error is less than the set standard value, the algorithm stops iteration, and the optimized parameters in the J-C constitutive model are obtained.

[0119] According to the samples in Table 3, a regression model of cutting force simulation error and constitutive equation parameters is constructed using BP network. According to the regression model, the particle swarm algorithm is used to identify the constitutive equation parameters corresponding to the minimum cutting force simulation error. Table 4 is the parameter identification result. The optimized parameters and the unoptimized parameters are respectively substituted into the original two-dimensional turning simulation model, and the simulation cutting force is output and compared with the experimental results, as shown in Figure 4 The average cutting force error of simulation using the unoptimized parameters is 20.42%, and the error is reduced to 0.5% after optimization. It can be seen that the optimized constitutive equation parameters are more accurate in predicting the turning cutting force.

[0120] Table 4

[0121]

[0122] 4) Evaluate the accuracy of the optimized J-C constitutive model parameters in predicting the cutting force of titanium alloy.

[0123] Titanium alloy milling experiments are carried out, as shown in Figure 5 The workpiece 10 (titanium alloy workpiece) is installed on the top of the dynamometer 30 (Kistler 9257B), and the dynamometer 30 is installed on the machining plane 40 of the milling center. The cutting force in three directions is measured. The equipment used in the case is Deckel Maho five-axis computer numerical control (CNC) milling center, and the machined workpiece material is Ti6Al4V titanium alloy. The experimental conditions are set as shown in Table 5.

[0124] Table 5

[0125]

[0126] Step (5), the optimal solution obtained in step (3) is substituted into the three-dimensional milling finite element simulation, and the model is as shown in Figure 6 The simulation results of the milling force are compared with the experimental results of the milling force ( Figure 7 ), and the identification accuracy of the J-C constitutive parameters for titanium alloy cutting is evaluated. The simulation error of the main milling force is 5.1%, the simulation error of the radial milling force is 5.2%, and the simulation error of the axial milling force is 19.7%. It can be found that the simulation of each component of the titanium alloy milling force has high accuracy in the prediction of the change trend and the average force size, proving that the identification of the constitutive equation parameters is carried out by using the turning process, and the parameter accuracy is evaluated by using the milling, and the method can better predict the cutting force in the cutting process.

[0127] The above is only a specific embodiment of the present application, but the design concept of the present application is not limited thereto, and any non-essential modification of the present application using this concept shall be deemed to be an act of infringing the protection scope of the present application.​

Claims

1. A method for identifying JC constitutive parameters for cutting titanium alloys, characterized in that, include: 1) A titanium alloy turning experiment was conducted using a CNC lathe and a force gauge to obtain the experimental cutting force; 2) The JC constitutive model is selected and multiple sets of parameters are designed using a multi-level orthogonal approach. These parameters are then substituted into the two-dimensional turning simulation model to obtain the simulated cutting force under different sets of parameters. The JC constitutive model is as follows: ； Where σ is the flow stress and ε is the plastic strain. It is the strain rate. This is the reference strain rate, and T is the workpiece material temperature. r It is room temperature, T m is the melting point of the workpiece, and A, B, C, m and n are model constants, representing yield stress, hardening modulus, strain rate sensitivity coefficient, thermal softening coefficient and hardening coefficient, respectively. 3) Parameter identification aims to minimize simulation error through regression analysis and iterative optimization. During the iterative optimization process, the parameters of the JC constitutive model are continuously adjusted. The cutting force corresponding to each set of parameters is compared with the experimental cutting force to obtain the error. When the error is less than a set standard value, the algorithm stops iterating, yielding the optimized parameters in the JC constitutive model. The error is the absolute error between the simulated cutting force and the experimental cutting force. ; Where F test It simulates the cutting force, F avg It is the average cutting force observed in the experiment; Step 4) Evaluate the accuracy of the parameters in the optimized JC constitutive model for predicting cutting forces in titanium alloys, as follows: 4.1) A titanium alloy milling experiment was conducted using a five-axis machining center and a force gauge to obtain the experimental cutting force; 4.2) Substitute the parameters in the optimized JC constitutive model obtained in step 3) into the three-dimensional milling finite element simulation model to obtain the simulated milling force, and compare it with the experimental cutting force obtained in step 4.1) to evaluate the accuracy of identifying the JC constitutive parameters for titanium alloy cutting.

2. The method for identifying JC constitutive parameters for titanium alloy cutting as described in claim 1, characterized in that, The method of using a multi-level orthogonal design with multiple sets of parameters involves: consulting the three sets of constitutive parameter values ​​commonly used in the literature; the upper and lower limits of each parameter are the maximum and minimum values ​​of the parameter in the three sets of constitutive parameters; and determining five levels for each parameter within the range of values, and conducting a five-factor, five-level orthogonal experiment.

3. The method for identifying JC constitutive parameters for titanium alloy cutting as described in claim 1, characterized in that, The method for constructing the two-dimensional turning simulation model is as follows: (a) Create tool and workpiece models; (b) Input the material parameters of the tool and the workpiece, and define the plane stress / strain thickness; (c) Set the element type to quadrilateral element; (d) Mesh generation, refining the mesh of the cut portion; (e) Define the analysis step time and select the field output and process output data to be output; (f) Set boundary conditions, define the cutting tool as a rigid body, and assign it a movement speed; (g) Simulate and solve the problem, and output the force analysis results.

4. The method for identifying JC constitutive parameters for titanium alloy cutting as described in claim 1, characterized in that, The method for constructing the three-dimensional milling finite element simulation model is as follows: (a) Create tool and workpiece models; (b) Input the material parameters for the tool and the workpiece; (c) Set the element type to tetrahedral element; (d) Mesh generation, refining the mesh of the cut portion; (e) Define the analysis step time and select the field output and process output data to be output; (f) Set boundary conditions, define the milling cutter as a rigid body, and assign it movement and rotation speeds; (g) Simulate and solve the problem, and output the force analysis results.

5. The method for identifying JC constitutive parameters for titanium alloy cutting as described in claim 1, characterized in that, In step 3), an error backpropagation neural network is used for regression.

6. The method for identifying JC constitutive parameters for titanium alloy cutting as described in claim 1, characterized in that, In step 3), parameter identification aims to minimize simulation error, and the particle swarm optimization algorithm is used to accurately obtain the parameters.

7. A device for identifying JC constitutive parameters for cutting titanium alloys, characterized in that: A method for identifying JC constitutive parameters for cutting titanium alloys according to any one of claims 1 to 6, comprising: The titanium alloy turning experiment module uses a CNC lathe and a force gauge to conduct titanium alloy turning experiments and obtain experimental cutting forces. The two-dimensional turning simulation module uses the JC constitutive model and adopts a multi-level orthogonal design with multiple sets of parameters. The parameters are then substituted into the two-dimensional turning simulation model to obtain the simulated cutting force under different sets of parameters. The parameter optimization module identifies parameters with the goal of minimizing simulation error. It performs regression analysis and iterative optimization, continuously adjusting the parameters of the JC constitutive model during the iterative optimization process. The cutting force corresponding to each set of parameters is compared with the experimental cutting force to obtain the error. When the error is less than the set standard value, the algorithm stops iterating and obtains the parameters in the optimized JC constitutive model.

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