A method for predicting the cutting performance of a precision complex tool

Abstract

The application discloses a kind of precision complex cutter cutting performance prediction method.The method is as follows: one, in finite element simulation software, build the thermal coupling cutting simulation model including measured cutter and workpiece, and carry out mesh division, assembly positioning and parameter setting.Two, build the modified constitutive model and tool chip friction model.Three, thermal coupling cutting simulation model is solved using updated material flow stress and yield strength, and cutting simulation result is obtained;Cutting simulation result includes temperature, contact area stress, chip distribution and chip morphology.Four, according to the cutting simulation result, the performance of the measured precision complex cutter is judged.The application builds the modified constitutive model and tool chip friction model, improves the accuracy of the simulation result of thermal coupling cutting simulation model, so that the cutting performance of the precision complex cutter can be predicted and simulated by simulation test.

Description

Technical Field

[0001] This invention relates to the field of cutting performance prediction methods for cutting tools, and more specifically to a method for predicting the cutting performance of precision and complex cutting tools. Background Technology

[0002] Precision cutting tools such as fir tree broaches and dovetail end mills are widely used in aerospace, energy, mold making, and automotive industries due to their high production efficiency, wide machining range, and high machining accuracy. They are frequently used to machine difficult-to-machine materials such as superalloys. However, manufacturing precision cutting tools is difficult, costly, and requires high precision. Furthermore, the cutting process involves high cutting loads and high temperatures in the tool-workpiece-chip contact area, factors that can easily affect the surface finish. Therefore, it is necessary to study the cutting process to control the cutting quality of precision cutting tools. However, using traditional experimental methods to study precision cutting tools is difficult, costly, and inefficient, and it is difficult to capture the changes in various parameters during the dynamic cutting process. Finite element simulation of the cutting process can effectively compensate for the shortcomings of traditional methods, using simulation software to predict the high performance of cutting tools.

[0003] High-performance modeling of precision and complex cutting tools mainly includes models of the tool itself, material constitutive model, and chip friction model. These models can effectively simulate and predict the temperature, stress, tool wear, and chip adhesion during the cutting process of precision and complex cutting tools, thereby optimizing the prediction of cutting performance based on the relevant data. Although cutting performance prediction simulation of cutting tools has many advantages, its prediction accuracy still needs further optimization to accelerate the research and development and production of precision and complex cutting tools. Achieving these goals requires more in-depth research on tool models, material constitutive models, chip friction models, dynamic rheological properties of materials, and time-varying thermo-mechanical coupling adhesion-slip models between tools and chips. Currently, many scholars use incomplete types of tool models and have low model accuracy when performing broaching and other cutting simulations, resulting in an inability to accurately represent the temperature, stress, tool wear, and chip adhesion during the cutting process, and thus failing to complete the prediction of the cutting performance of precision and complex cutting tools. Summary of the Invention

[0004] This invention addresses the problems of low accuracy and inability to reflect real-world machining processes in current cutting simulations with small depths of cut, by providing a method for predicting the cutting performance of precision and complex cutting tools. This invention is a simulation method that cascades and modifies the material constitutive model and the tool-chip friction model; a method based on dislocation density theory to correct the dynamic rheological properties of surface materials; a simulation method that further refines the tool-chip friction model based on an already corrected constitutive model; and a simulation method that proposes a new time-varying thermo-mechanical coupling adhesive slip friction model between tools and chips based on the SCG equation.

[0005] A method for predicting the cutting performance of precision and complex cutting tools uses a thermo-mechanical coupled cutting simulation model containing a modified constitutive model and a chip friction model to test the cutting performance of the cutting tools. The method includes the following steps:

[0006] Step 1: Construct a thermo-mechanical coupling cutting simulation model containing the precision and complex cutting tool and workpiece under test in the finite element simulation software, and perform mesh generation, assembly positioning and parameter setting.

[0007] Step 2: Construct a constitutive model and a tool-chip friction model. Use a thermo-coupled cutting simulation model to simulate the cutting process of the workpiece by the tested tool, and use the constitutive model to obtain the updated material rheological stress σ. ref The updated yield strength σ is obtained using the tool-chip friction model.

[0008] Continuously update the rheological stress σ of the material of the precision and complex cutting tool being tested. ref and yield strength σ.

[0009] 2-1. Construct the constitutive model, whose expression is as follows:

[0010]

[0011] Among them, the material rheological stress σ ref ;σ JC For the uniaxial tensile thermo-visco-plastic rheological stress of the material; α C η is a constant coefficient for plastic materials; G is the shear modulus of the workpiece; b is the magnitude of the Burgers vector; η is the strain gradient; and μ is the coefficient of friction.

[0012] Uniaxial tensile thermo-visco-plastic rheological stress σ of material JC The expression is as follows:

[0013]

[0014] Where, ρ SSD To calculate the dislocation density.

[0015] 2-2. Construct the chip friction model, the expression of which is as follows:

[0016]

[0017] Where, yield strength σ; τ is frictional stress; τ s σ is the critical shear yield strength; n σ0 is the normal stress; σ0 is the yield strength under the reference state; β and n are two work hardening parameters; ε is the strain rate; εn i σ′ represents the initial equivalent plastic strain. P G′ is the derivative of the yield strength under reference pressure; P is the pressure exerted on the tested tool; G′T G0 is the derivative of the shear modulus at the reference temperature; T is the shear modulus at the reference state; and T is the temperature.

[0018] Step 3: Thermo-coupled cutting simulation model utilizes updated material rheological stress σ ref The cutting simulation results are obtained by solving for the yield strength σ; the cutting simulation results include temperature, contact area stress, chip distribution and chip morphology.

[0019] Step 4: Determine the performance of the precision and complex cutting tool under test based on the cutting simulation results.

[0020] Preferably, the parameters set in step one include material properties, analysis steps and output variables, contact constraints between the precision and complex tool and the workpiece, motion characteristics of the precision and complex tool, and load.

[0021] Preferably, during the mesh division process, the mesh density of the contact area between the precision and complex cutting tool and the workpiece, as well as the area of ​​its surrounding preset width, is greater than the mesh density of other areas.

[0022] As a preferred option, in step three, if the simulation results do not converge, or if the converged results are greater than the difference threshold, then step one is restarted to perform mesh generation, assembly positioning, and parameter setting.

[0023] The beneficial effects of this invention are as follows:

[0024] 1. This invention constructs a modified constitutive model and a tool-chip friction model, which improves the accuracy of the simulation results of the thermo-coupling cutting simulation model, thereby enabling the prediction and simulation testing of the cutting performance of precision and complex tools through simulation.

[0025] 2. Based on finite element simulation software, this invention realizes and establishes a cascaded correction material constitutive model and chip, corrects the dynamic rheological properties of the surface material based on dislocation density theory, and establishes a new friction model method for time-varying thermo-mechanical coupling adhesion and slip between chips, thereby realizing the prediction of the cutting performance of precision and complex tools, and thus achieving accurate simulation testing of precision and complex tools.

[0026] 3. This invention uses finite element simulation software to simulate the cutting of workpieces with precision and complex tools. The simulation results are accurate and highly reliable, and can obtain data that is difficult to obtain in experiments. Moreover, it can predict the entire machining process and has high guiding value for practice. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the simulation model of precision and complex cutting tools and workpieces according to the present invention;

[0028] Figure 2 This is a flowchart of the present invention.

[0029] Figure label: Figure 1 In the image, 1-a precision and complex tool model; 2-a workpiece model. Detailed Implementation

[0030] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0031] like Figure 2 As shown, a method for predicting the cutting performance of precision and complex cutting tools uses a thermo-mechanical coupling cutting simulation model containing a modified constitutive model and a tool-chip friction model to test the cutting performance of precision and complex cutting tools. The method includes the following steps:

[0032] Step S1: Pre-processing of precision and complex cutting tools:

[0033] For precision and complex cutting tools, a thermo-mechanical coupling cutting simulation model is constructed, and material properties, mesh generation, assembly positioning, creation of analysis steps and output variables are defined, contact constraints between the precision and complex cutting tool and the workpiece are set, motion characteristics are set and loads are applied to the precision and complex cutting tool.

[0034] Step S2, Modified Constitutive Model and Chip Friction Model: Based on dislocation density theory, the dynamic rheological properties of the surface material are modified, the material constitutive model is modified, and a modified chip friction model is established based on the SCG equation. This chip friction model fully considers the characteristics of time-varying thermo-mechanical coupling and bonding between chips to establish a cascaded modified material constitutive model and chip model, and based on dislocation density theory, the dynamic rheological properties of the surface material are modified, as well as a new friction model of time-varying thermo-mechanical coupling bonding and slip between chips.

[0035] Step S2.1: Correct the constitutive model: Introduce the strain gradient theory based on the Taylor dislocation mechanism to describe the scale effect of the cutting process, so that the corrected material constitutive relation is a function containing the strain gradient term, expressed as follows:

[0036] σ=f(σ JC Equation (1)

[0037] Where η is the strain gradient term, which incorporates higher-order strain gradients and dislocation densities to govern the material's behavior at small scales; σ JCThe uniaxial tensile thermoviscoelastic-plastic rheological stress of the material is obtained from the traditional thermoviscoelastic-plastic JC constitutive equation; σ is the total rheological stress after considering the size effect.

[0038] The traditional Johnson-Cook model, due to its simplicity, can be used to describe the flow stress-strain relationship of metallic materials under high strain rates and is widely used to describe the constitutive relations of materials during machining processes. The strain hardening term, strain rate strengthening term, and temperature softening term are expressed in product form, as detailed below:

[0039]

[0040] Where ε is strain; For strain rate; T is the reference strain rate; T0 is the reference temperature; T me lt is the melting temperature of the material; A, B, C, n, and m are all constants, and the values ​​of each JC constitutive parameter are shown in Table 3.

[0041] Taylor's dislocation density theory shows that statistically stored dislocations associated with plastic strain and geometrically necessary dislocations that influence the plastic strain gradient jointly contribute to the plastic hardening of materials and increase the shear stress of the materials, as specifically expressed below:

[0042]

[0043] In the formula, α C ρ is a constant coefficient for plastic materials; G is the shear modulus; b is the magnitude of the Burgers vector; ρ total ρ is the total dislocation density. SSD To calculate the statistical dislocation density, ρ GND The geometrically necessary dislocation density.

[0044] To achieve a more accurate expression of rheological stress σ ref Introducing coefficients k and μ, for anisotropic materials like Inconel 718 with γ-strengthening terms, k is taken as 3, then equation (3) becomes:

[0045]

[0046] Where, σ ref For the material's rheological stress, σ JC Let be the uniaxial tensile thermo-visco-plastic rheological stress of the material, and its value is related to the statistical stored dislocations, as shown in equation (5):

[0047]

[0048] For polycrystalline materials, the geometrically required dislocation density ρ GND The strain gradient η satisfies the relationship (6):

[0049]

[0050] Substituting equations (5) and (6) into equation (4), we obtain the modified JC constitutive model based on the MSG strain gradient theory, whose expression is as follows:

[0051]

[0052] Step S2.2: Correct the chip friction model: according to equations (8) and (9)

[0053]

[0054]

[0055] Where τ and τ s σ is the shear stress; μ is the friction coefficient; σ n σ is the normal stress; σ is the yield strength; σ0 is the yield strength under the reference state; β and n are work hardening parameters; ε is the strain rate; ε i σ′ represents the initial equivalent plastic strain. P G′ is the derivative of the yield strength under the reference pressure. T G0 is the derivative of the shear modulus at the reference temperature; T is the shear modulus at the reference state; and T is the temperature.

[0056] During the shearing process, a significant amount of heat and pressure are generated that varies over time. These heat and stresses affect the shear stress and yield stress, thus influencing friction. This method continuously acquires various parameters during the simulation process to update the adhesion and slip effects and friction between the cutting tools and chips in real time.

[0057] Step S3: Submit the thermo-mechanical coupling cutting simulation model to the finite element simulation software solver for calculation, and obtain the calculation results. In the thermo-mechanical coupling cutting simulation model, the material rheological stress σ obtained in step S2 is... ref The thermo-coupled cutting simulation model, with input yield strength σ, replaces the rheological stress parameters and yield strength parameters in the original model; cutting simulation results are obtained; due to the material rheological stress σ... ref The data on yield strength σ are more accurate, so the temperature, contact zone stress, chip distribution, and chip morphology in the cutting simulation results are all more accurate.

[0058] Compare the obtained results with the actual results. If the simulation does not converge, or the difference exceeds the threshold, re-enter step S1 and adjust the parameters of the thermo-coupling cutting simulation model.

[0059] In this embodiment, a fine mesh is used in the simulation of the contact area between the precision tool and the workpiece, as well as a small area around the contact area, while a coarse mesh is used further away from the contact area. During the cutting process, the workpiece model is clamped on the machine tool by a fixture, and the bottom surface of the workpiece model can be considered completely fixed. Fully constrained boundary conditions are applied to the bottom surface of the workpiece model during finite element simulation.

[0060] The cutting process of precision and complex cutting tools is simulated using finite element simulation software. It is necessary to pay attention to their stress-strain, temperature, tool-workpiece-chip distribution, and tool-chip adhesion. Therefore, the prediction of the cutting performance of precision and complex cutting tools mainly relies on simulation software to realize and establish a cascaded correction material constitutive model and tool-chip friction, correct the dynamic rheological properties of the surface material based on dislocation density theory, and establish a new friction model of time-varying thermo-mechanical coupling adhesion and slip between tools and chips, thereby further realizing the method of predicting the cutting performance of precision and complex cutting tools.

[0061] In step S1, as Figure 1 As shown, a simulation model of the cutting process of a precision complex tool is established. The simulation model includes a precision complex tool model 1 and a workpiece model 2. The tool-related dimensions are modeled according to the actual precision complex tool produced, and then imported into the finite element simulation software. In this embodiment, the length is selected as mm as the unit, and all subsequent quantities use the same dimension. After the three-dimensional models of the precision complex tool and the workpiece are fully established, the material properties of the two three-dimensional models need to be defined in the material properties function module of the finite element simulation software so that the simulation analysis of physical quantities can be performed.

[0062] Considering the performance requirements of precision and complex cutting tools, the tool material is T15 powder metallurgy high-speed steel, and the workpiece material is GH4169 steel. The material parameters of the tool model and workpiece model in this example are shown in Tables 1 and 2.

[0063] Table 1 Material Parameters for Precision and Complex Cutting Tools

[0064]

[0065] Table 2 Workpiece Material Parameters

[0066]

[0067] This model uses the JC material constitutive model to describe the stress-strain relationship of GH 4169 steel, where the JC damage model is shown below:

[0068]

[0069] in, Dimensionless plastic strain rate The strain rate for a quasi-static experiment;

[0070] T* =(TT) r ) / (T m -T) Dimensionless temperature, where T is the ambient temperature of the specimen;

[0071] Indicates stress triaxiality, where σ m For spherical stress, This is the Mises equivalent stress.

[0072] Its JC constitutive parameters and JC damage parameters are shown in Table 3-4:

[0073] Table 3 JC constitutive model parameters for GH 4169 steel

[0074]

[0075] Table 4 JC damage model parameters for GH 4169 steel

[0076]

[0077] Then, a fine mesh is used in the area where the precision and complex tool model and the workpiece model come into contact, while a coarse mesh is used in the more remote areas, such as... Figure 1 As shown. Next, the pre-defined mesh of the precision complex tool model and workpiece model is assembled and positioned. A dynamic analysis step is selected based on the cutting motion characteristics. Furthermore, the cutting force during the cutting process is defined in the history variables, and displacement, velocity, acceleration, stress, and strain are defined in the field variables. Then, the friction and constraints between the precision complex tool and the workpiece are defined based on their contact characteristics. Since this invention primarily focuses on the stress, strain, temperature, chip distribution, and chip adhesion of the precision complex tool during the cutting process, the tool is set as a rigid body. This reduces analysis calculation time and improves the accuracy of the calculation results. Based on the contact state between the tool and workpiece after the precision complex tool cuts the workpiece, a modified Coulomb's friction law is applied to define the friction characteristics between them, with a friction coefficient f = 0.24. Next, boundary conditions with six-directional constraints on the bottom surface are applied to the base model; the tool movement is controlled, such as... Figure 1 As shown, displacement constraints are applied in the X direction; in addition, the cutting force during the cutting process is defined in the history variables, and the output of the history variables only needs to be for the tool reference point; displacement, velocity, acceleration, stress, and strain are defined in the field variables, and the output of the field variables is for the entire three-dimensional model.

[0078] The JC constitutive parameters used in step S2 are shown in Table 3.

[0079] In step S3, the previously established simulation model is submitted to the solver of the finite element simulation software for calculation. After the calculation is completed, the cutting force, stress-strain, material damage, chip distribution, and chip adhesion are viewed in the post-processing module of the finite element simulation software. The calculation results are analyzed and evaluated in conjunction with experiments and actual cutting. If the simulation results differ significantly from the actual results, or if the results do not converge during the calculation process, or if the simulation results are close to the actual results, then return to step S1, change the simulation model, and find the most suitable combination of cutting process parameters by adjusting the precision mesh, cutting force, and material damage.

[0080] The simulation method of this invention differs from the coarse and inaccurate nature of conventional simulations. It only requires the use of finite element simulation software to realize the cascaded correction of the material constitutive model and the tool-chip friction, the correction of the dynamic rheological properties of the surface material based on the dislocation density theory, and the new friction model of time-varying thermo-mechanical coupling adhesive slip between the tool and the chip, thereby realizing the prediction of the cutting performance of precision and complex tools.

[0081] As described above, although the invention has been shown and described with reference to specific preferred embodiments, it should not be construed as limiting the invention itself. Various changes in form and detail may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A method for predicting the cutting performance of precision and complex cutting tools, characterized in that: Includes the following steps: Step 1: Construct a thermo-mechanical coupling cutting simulation model containing the tested tool and workpiece in the finite element simulation software, and perform mesh generation, assembly positioning, and parameter setting; Step 2: Construct a constitutive model and a tool-chip friction model. Use a thermo-coupled cutting simulation model to simulate the cutting process of the workpiece by the tested tool, and use the constitutive model to obtain the updated material rheological stress σ. ref The updated yield strength σ is obtained using the chip friction model; Continuously update the material rheological stress σ of the tested tool ref and yield strength σ; 2-1. Construct the constitutive model, whose expression is as follows: Among them, the material rheological stress σ ref ;σ JC For the uniaxial tensile thermo-visco-plastic rheological stress of the material; α C η is a constant coefficient for plastic materials; G is the shear modulus of the workpiece; b is the magnitude of the Burgers vector; η is the strain gradient; μ is the coefficient of friction. Uniaxial tensile thermo-visco-plastic rheological stress σ of material JC The expression is as follows: Where, ρ SSD For statistical dislocation density; 2-2. Construct the chip friction model, the expression of which is as follows: Where, yield strength σ; τ is frictional stress; τ s σ is the critical shear yield strength; n σ0 is the normal stress; σ0 is the yield strength under the reference state; β and n are two work hardening parameters; ε is the strain rate; εn i σ′ represents the initial equivalent plastic strain. P G′ is the derivative of the yield strength under reference pressure; P is the pressure exerted on the tested tool; G′ T G0 is the derivative of the shear modulus at the reference temperature; T is the shear modulus at the reference state; Step 3: Thermo-coupled cutting simulation model utilizes updated material rheological stress σ ref The cutting simulation results are obtained by solving for the yield strength σ; the cutting simulation results include temperature, contact zone stress, chip distribution and chip morphology; Step 4: Determine the performance of the tested tool based on the cutting simulation results.

2. The method for predicting the cutting performance of a precision complex tool according to claim 1, characterized in that: The parameters set in step one include material properties, analysis steps and output variables, contact constraints between the precision and complex tool and the workpiece, motion characteristics of the precision and complex tool, and load.

3. The method for predicting the cutting performance of a precision complex tool according to claim 1, characterized in that: During the mesh generation process, the mesh density of the contact area between the precision and complex cutting tool and the workpiece, as well as the area of ​​its surrounding preset width, is greater than the mesh density of other areas.

4. The method for predicting the cutting performance of a precision complex tool according to claim 1, characterized in that: In step three, if the simulation results do not converge, or if the converged results are greater than the difference threshold, then step one is restarted to perform mesh generation, assembly positioning, and parameter setting.

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