Cutting analogue simulation method and system based on dynamic characteristics of machine tool

Abstract

The invention discloses a cutting simulation method and system based on dynamic characteristics of a machine tool, and the method comprises the steps: constructing a cutting power simulation model which comprises a workpiece geometric model and a tool geometric model, and constructing a dynamic characteristic equivalent model based on the cutting power simulation model, thereby achieving the cutting simulation of a workpiece; meanwhile, in the process of constructing the dynamic characteristic equivalent model, a multi-degree-of-freedom spring-damper multi-node space distribution subprogram is adopted to simulate the vibration characteristic in the cutting process, and modal parameters in the actual cutting process are calibrated through a multi-layer perceptron neural network algorithm; iterative simulation of dynamic coupling of the cutting force and the cutting mode is achieved, the problem that an existing cutting simulation method ignores real dynamic characteristics of a machine tool-tool-workpiece system is solved, and the cutting force, vibration response and temperature prediction precision are improved.

Description

Technical Field

[0001] This invention belongs to the field of numerical simulation and machine tool machining simulation technology, specifically relating to a cutting simulation method and system based on the dynamic characteristics of machine tools. Background Technology

[0002] In existing cutting simulation technologies, cutting simulations typically assume ideal rigid constraints, neglecting the actual dynamic characteristics of the machine tool spindle, tool holder, fixture, and other clamping systems. This leads to discrepancies between simulation results and actual machining in terms of cutting force, temperature field, and chip morphology, and it fails to capture the vibration response characteristics of actual machine tool machining. Although patented methods utilize linear rod elements to simulate the dominant characteristics of the machine tool, they lack simulation of the dynamic characteristics of the actual machine tool's spatial distribution, failing to meet the requirements for predicting the vibration characteristics and cutting performance of high-precision machine tools. Therefore, there is an urgent need for a cutting simulation method that can characterize the dynamic characteristics of the machine tool clamping system and consider the coupled effects of force, vibration, and heat, in order to comprehensively improve simulation accuracy and engineering application value. Summary of the Invention

[0003] The purpose of this invention is to provide a cutting simulation method and system based on the dynamic characteristics of machine tools.

[0004] In a first aspect, the present invention provides a cutting simulation method considering the dynamic characteristics of machine tools, the method comprising:

[0005] A dataset is constructed by acquiring the state parameters of the workpiece under continuous cutting at different times; the state parameters include vibration displacement, vibration velocity, elastic force, and damping force.

[0006] Elastic force prediction model and damping force prediction model are constructed respectively, and the elastic force prediction model and damping force prediction model are trained using dataset;

[0007] A cutting dynamics simulation model is constructed, comprising a workpiece geometry model and a tool geometry model. Based on this model, a dynamic characteristic equivalent model is built to simulate the cutting of the workpiece. The method for constructing the dynamic characteristic equivalent model is as follows:

[0008] Arrange spring dampers on the cutting dynamic simulation model; input the elastic force and damping force in the current increment step into the spring dampers to calculate the vibration displacement and vibration velocity of the spring dampers; input the vibration displacement and vibration velocity into the elastic force mathematical model and the damping force mathematical model respectively to obtain the elastic force and damping force in the next increment step; repeat the above process to complete the construction of the dynamic characteristic equivalent model.

[0009] Preferably, the method for obtaining the vibration displacement and vibration velocity is as follows: obtain the vibration acceleration signal of the workpiece under test, and perform integral calculation on the vibration acceleration signal to obtain the vibration displacement and vibration velocity.

[0010] Preferably, the elastic force and damping force are obtained by decomposing the collected time-domain cutting force.

[0011] Preferably, the separation process of the time-domain cutting force is as follows:

[0012] A fast Fourier transform is performed on the time-domain cutting force to obtain the frequency domain function of the cutting force; the elastic force spectrum and the damping force spectrum are obtained based on the real part and the imaginary part of the frequency domain function of the cutting force, respectively; the inverse Fourier transform is performed on the elastic force spectrum and the damping force spectrum, respectively, to obtain the time-domain elastic force and the time-domain damping force.

[0013] Preferably, the elastic force prediction model is trained using a dataset constructed from vibration displacement and the corresponding elastic force; the damping force prediction model is trained using a dataset constructed from vibration velocity and the corresponding damping force.

[0014] Preferably, both the elastic force prediction model and the damping force prediction model include an input layer, a first hidden layer, a second hidden layer, and an output layer connected in sequence; the first hidden layer and the second hidden layer each include a fully connected layer, an activation function, and a batch normalization layer connected in sequence; the second hidden layer also includes a random dropout layer; the random dropout layer is used to perform regularization processing on the batch normalization layer processing results.

[0015] Preferably, the spring damper is arranged at the tool coupling control point; the tool coupling control point is any node on the tool mesh, used to couple and constrain all mesh nodes of the tool.

[0016] Secondly, the present invention provides a cutting simulation system considering the dynamic characteristics of machine tools, which is used to execute the cutting simulation method described above; the cutting simulation system includes a cutting data acquisition module, a vibration characteristic prediction module, and a model building module; the model building module is used to construct a cutting dynamic simulation model and a dynamic characteristic equivalent model; the cutting data acquisition module is used to acquire real cutting data to guide the vibration characteristic prediction module in predicting elastic force and damping force; the vibration characteristic prediction module is used to predict elastic force and damping force based on vibration displacement and vibration velocity, and assist in the construction of the dynamic characteristic equivalent model.

[0017] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the memory stores the computer program; and the processor executes the cutting simulation method described above.

[0018] Fourthly, the present invention provides a readable storage medium storing a computer program; when the computer program is executed by a processor, it is used to implement the above-described cutting simulation method.

[0019] The beneficial effects of this invention are:

[0020] 1. This invention uses a spring-damper system to simulate the dynamic characteristics of a machine tool in a cutting dynamics model, introducing the actual dynamic characteristics of the machine tool and considering the loads and vibration factors experienced during dynamic cutting, which helps to improve the accuracy of cutting force, vibration response and temperature prediction.

[0021] 2. Based on cutting process experimental data, this invention inverts the vibration characteristics of the machine tool system and proposes an equivalent model of the machine tool vibration system. By establishing a spatially distributed multi-degree-of-freedom spring-damper, the dynamic characteristics of the machine tool system are simplified. At the same time, this invention establishes an equivalent model of the machine tool clamping system for multi-factor coupled simulation of the dynamic characteristics of a specific machine tool system, and realizes a simulation method that considers the dynamic vibration characteristics of the machine tool cutting process. Attached Figure Description

[0022] Figure 1 This is the overall flowchart of the present invention.

[0023] Figure 2 This is a flowchart of the neural network model of the present invention.

[0024] Figure 3 This is a schematic diagram of the cutting dynamics model of the present invention.

[0025] Figure 4 This is a schematic diagram of the equivalent model of the spring damper of the present invention. Detailed Implementation

[0026] The present invention will be further described below with reference to the accompanying drawings.

[0027] A cutting simulation method considering the dynamic characteristics of machine tools employs a cutting simulation system comprising a cutting data acquisition module, a vibration characteristic prediction module, and a model building module. The model building module is used to construct a cutting dynamic simulation model and a dynamic characteristic equivalent model. The cutting data acquisition module collects real cutting data to guide the vibration characteristic prediction module in predicting elastic and damping forces. The vibration characteristic prediction module predicts elastic and damping forces based on vibration displacement and vibration velocity, assisting in the construction of the dynamic characteristic equivalent model.

[0028] like Figure 1 As shown, the cutting simulation method includes the following steps:

[0029] Step S1, Data Acquisition

[0030] Continuous cutting tests were conducted on the workpiece under the same CNC machine tool and cutting conditions. Triaxial force sensors and triaxial accelerometers were respectively arranged at the tool holder and workpiece fixture. Data acquisition was performed synchronously using a sampling frequency greater than 10 kHz to obtain time-domain cutting force and vibration acceleration signals to ensure complete capture of high-frequency vibration and cutting pulse processes.

[0031] Step S2: Separate the time-domain cutting force into elastic force and damping force.

[0032] The collected vibration acceleration signal was integrated twice to obtain the vibration displacement data. Its expression is:

[0033]

[0034]

[0035] in, This is a vibration velocity signal; This is a vibration acceleration signal.

[0036] Time-domain cutting force and vibration displacement data Perform Fast Fourier Transform (FFT) separately:

[0037]

[0038] in, Represents the frequency domain function of the cutting force; Represents the frequency domain function of vibration displacement; This represents the Fast Fourier Transform.

[0039] For a single-degree-of-freedom (SDOF) spring-damped system, in the frequency domain, the frequency domain function of the cutting force is... satisfy:

[0040]

[0041] in, It is the elastic modulus; is the damping coefficient.

[0042] The system's amplitude-frequency response characteristics satisfy:

[0043]

[0044] in, The real component of the amplitude-frequency response characteristic; This is the imaginary component of the amplitude-frequency response characteristic.

[0045] In a spring-damped system, the elastic force With vibration displacement In phase, damping force With vibration velocity In phase. In a vibration system, the first differential calculation corresponds to a 90° phase lead, that is, the vibration velocity in the frequency domain. Relative to vibration displacement The phase leads by 90°; the imaginary part of the frequency domain function leads the real part by 90°. Therefore, the real and imaginary parts of the cutting force frequency domain function physically correspond to the elastic and damping components, respectively.

[0046]

[0047]

[0048] After separating the elastic and damping components of the frequency domain function of the cutting force, an inverse transform is performed to obtain the elastic force spectrum. and damping force spectrum It is represented as:

[0049]

[0050]

[0051] elastic force spectrum and damping force spectrum Perform inverse Fourier transform (IFFT) on each part to obtain the time-domain elastic force. and time-domain damping force It is represented as:

[0052]

[0053] in, This represents the inverse Fourier transform.

[0054] Step S3: Establish an equivalent model of the machine tool vibration system.

[0055] like Figure 2 As shown, through time-domain elastic force and damping force And its corresponding displacement and velocity, and construct independent mathematical models (elastic force mathematical model and damping force mathematical model) for the X, Y and Z directional components respectively. The specific process is as follows:

[0056] Step S3-1: Construct the dataset

[0057] For each directional component, a displacement-elastic force dataset and a velocity-damping force dataset are constructed separately. The displacement-elastic force dataset includes vibration displacements. and elastic force The velocity-damping force dataset includes vibration velocities. and damping force Vibration displacement and vibration velocity are used as input features, and elastic force and damping force are used as output labels.

[0058] Step S3-2, Data Preprocessing

[0059] Statistical analysis was performed on the data samples from both datasets. First, outliers outside the physical measurement range, such as those caused by sensor saturation or sudden changes, were removed. Then, the remaining data underwent moving median filtering to remove potential high-frequency measurement noise, ensuring that subsequent training data is both realistic and smooth. Standardization was applied to each input feature and output label to improve the numerical stability of the standardized data, which aids in network convergence.

[0060]

[0061] in, and These are the standardized input features and output labels, respectively. and These are the input features and output labels before standardization; and These are the mean values ​​of the input features and the output labels, respectively. These are the standard deviations of the input features and the output labels, respectively.

[0062] Step S3-3: Construct a neural network model

[0063] Elastic force prediction models and damping force prediction models were constructed separately. Since the sample input dimensions of both models are low, with each sample containing only two scalar features, and these are nonlinearly fitted into lightweight models, this ensures sufficient expressive power while minimizing inference computation costs to avoid impacting simulation efficiency. Therefore, both models employ a shallow multilayer perceptron (MLP), consisting of a sequentially connected input layer, a first hidden layer, a second hidden layer, and an output layer. Both the first and second hidden layers include a sequentially connected fully connected layer, a ReLU activation function, and a batch normalization (BN) layer. The second hidden layer also includes a random dropout layer, which regularizes the batch normalization layer's processing results to improve generalization ability.

[0064] In this embodiment, the fully connected layer includes 32 neurons; the dropout rate of the random dropout layer is 0.2.

[0065] Step S3-4, Model Training

[0066] The elastic force prediction model and the damping force prediction model were trained using displacement-elastic force datasets and velocity-damped force datasets, respectively. During training, a mean squared error loss function was used to guide the training process of both models until the ratio of the mean squared error to the average force value was less than 5%, while simultaneously ensuring... The model's loss function Represented as:

[0067]

[0068] in, The loss function; For the predicted results; For tags; This represents the number of samples.

[0069] In this embodiment, the Adam optimizer is used to iteratively update the model parameters, with a learning rate of 1e-3, a batch size of 32, and 100 training epochs.

[0070] Step S3-5: Construct mathematical models for elastic force and damping force.

[0071] Export all weights and biases from the prediction model to obtain the mathematical model. Based on the network's forward computation flow, this mathematical model can be directly embedded into subsequent subroutines without further debugging. The expression of the mathematical model is:

[0072]

[0073] in, The result is the prediction of the neural network; For the hidden layer The output weights of each neuron to the output layer; For input features; The weights corresponding to the input features; For the hidden layer The bias of each neuron; Neuron bias; This represents the number of neurons in the hidden layer.

[0074] Step S4: Construct a cutting dynamics simulation model

[0075] Step S4-1: Construct the simulation geometric model

[0076] like Figure 3 As shown, the geometric models of the workpiece and the tool are imported into the ABAQUS software, and the completed geometric models of the workpiece and the tool are assembled and positioned according to the actual machining dimensions.

[0077] Step S4-2: Set material parameters

[0078] Material parameters include workpiece material parameters and tool material parameters. The workpiece material parameters are the thermophysical properties and constitutive model of GH4169, while the tool material parameters only require the thermophysical properties of M42 high-speed steel. In this embodiment, the material parameters of GH4169 and M42 high-speed steel are shown in Table 1.

[0079] Table 1 Material Parameters

[0080] Material parameters <![CDATA[Density / (t / mm 3 )]]> Young's modulus (MPa) Poisson's ratio <![CDATA[Conductivity / (W·m -1 °C -1 )]]> <![CDATA[Coefficient of thermal expansion / (°C -1 )]]> <![CDATA[Specific heat capacity / (MJ·t -1 ·°C -1 )]]> M42 8.1e-9 2.3e6 0.23 20 1.23e-5 4.9e8 GH4169 8.28e-9 2.05e6 0.303 13.4 1.18e-5 4.7e8

[0081] The stress-strain relationship of the workpiece is described using the JC constitutive model and the JC damage criterion, as shown in the following equations:

[0082]

[0083]

[0084] in, The rheological stress obtained from the JC constitutive equation; In response to the situation; This is the equivalent rate of change; For reference strain rate; For reference temperature; The melting temperature of the material; Constituent parameters; The actual temperature of the workpiece; Equivalent fracture strain; Indicates stress triaxiality, ; For spherical stress, For Mises equivalent stress; These are damage parameters.

[0085] The JC constitutive model and damage parameters of the workpiece are shown in Tables 2 and 3:

[0086] Table 2 JC Constitutive Parameters of the Workpiece

[0087] A / MPa B / MPa n C m <![CDATA[T melt / ℃]]> <![CDATA[T0 / ℃]]> 485 904 0.777 0.015 1.689 0.001 1800 25

[0088] Table 3 JC damage parameters of the workpiece

[0089] <![CDATA[D1]]> <![CDATA[D2]]> <![CDATA[D3]]> <![CDATA[D4]]> <![CDATA[D5]]> 0.04 0.75 -1.45 0.04 0.89

[0090] Step S4-3: Set up the solver and generate the mesh

[0091] The analysis step of the cutting dynamics simulation model is set to temperature-displacement, an explicit dynamic analysis step. Appropriate field output variables and frame numbers are set as needed. Stress, strain, displacement, temperature, contact stress, and STATUS status outputs are defined in the field output definition. Cutting force outputs are established in the historical outputs based on the tool stress conditions during the cutting process.

[0092] In the cutting dynamics simulation model, the workpiece is divided into a cutting region and a non-cutting region. The cutting region uses a fine mesh, while the non-cutting region uses a coarse mesh. The workpiece is meshed using a hexahedral mesh as the primary mesh type, and the mesh element type is specified as C3D8RT. The element deletion function must be enabled for the workpiece mesh elements. At the same time, the tool mesh elements must be specified as thermally coupled elements.

[0093] Step S4-4, Contact Model Settings

[0094] A node is selected on the workpiece mesh to create a reference point RP1, which serves as the coupling control point for the subsequent workpiece. A node is selected on the tool mesh to create a tool reference point RP2, which serves as the coupling control point for the subsequent tool. After the control points are created, the motion of the tool and workpiece is controlled by kinematic coupling constraints. Specifically, the tool coupling control point requires coupling constraints on all tool mesh nodes, while the workpiece coupling control point only requires coupling constraints on the bottom of the workpiece and the side mesh nodes furthest from the tool, to achieve rigid tool constraints and workpiece clamping simulation effects. The Coulomb friction model is used to define the friction characteristics between the tool and workpiece, with the friction coefficient set to 0.4. A face-to-face contact pair is established between the tool surface and the workpiece machining area, and a motion contact algorithm is used to define the contact characteristics during the cutting process.

[0095] Step S5: Construct an equivalent model of dynamic characteristics

[0096] Step S5-1: Arrange the spring damper

[0097] like Figure 4 As shown, at the tool coupling control point RP2, offset reference nodes N1, N2, and N3 are generated along the global X, Y, and Z axes, respectively, with an offset distance of 1 mm for each. The tool coupling control point is connected to the offset reference nodes using connecting lines to form three geometric rods, which serve as the geometric carriers of the spring-damping unit.

[0098] Step S5-2: Define connector unit cross-sectional properties

[0099] Create connector sections for the geometric carrier of the spring-damped element, named "CON_X", "CON_Y", and "CON_Z" respectively. In each connector section, set two behavior modules: elastic and damping, and set both behavior modules to nonlinearity, with the controllable variable set to 1.

[0100] Step S5-3: Customize nonlinear spring-damping properties

[0101] The Abaqus / Explicit user subroutine VUSDELD is used to implement the dynamic nonlinear coupling update of spring-damping properties. Specifically, the VUSDELD subroutine interface defines the mathematical expressions for material properties, and the spring-damping properties are coupled with cutting force fluctuations and vibration signal responses during dynamic cutting processes. The implementation logic includes:

[0102] The elastic force and damping force in the current increment step are input into the predefined connector unit (spring-damped system) to calculate the vibration displacement and velocity of the connector unit, i.e., the vibration displacement and velocity of the tool. The vibration displacement and velocity are then input into the elastic force mathematical model and the damping force mathematical model, respectively, to obtain the elastic force and damping force in the next increment step. This process is repeated to enable the model to iteratively couple the cutting output with the system's dynamic characteristics during dynamic cutting, achieving timely, nonlinear, and data-driven reflection of the machine tool vibration system's true dynamic characteristics during the cutting process.

[0103] Step S6: Complete the cutting dynamics model and perform calculations to solve it.

[0104] After constructing the simulation model, boundary conditions are set in the Abaqus / Explicit interface. In this embodiment, the bottom and side surfaces of the workpiece model are subject to fully fixed constraints with six degrees of freedom. A constant X-axis velocity boundary condition is set for the tool feed towards the workpiece. Based on the actual cutting environment, the ambient temperature is set to the initial temperature of all mesh nodes of the workpiece and tool. In this embodiment, the velocity boundary conditions need to be applied to points N1, N2, and N3 of the connector unit, and cannot be directly applied to the coupling control points; similarly, the fixed boundary conditions must also be applied to points N4, N5, and N6 of the connector unit of the workpiece model.

[0105] After setting the boundary conditions, in the Abaqus Job module, specify the input file and the compiled VUSDELD subroutine as user subroutines to ensure that the solver is automatically called in each explicit incremental step and performs cutting simulation.

Claims

1. A cutting simulation method based on the dynamic characteristics of machine tools, characterized in that: The method includes: A dataset is constructed by acquiring the state parameters of the workpiece under continuous cutting at different times; the state parameters include vibration displacement, vibration velocity, elastic force, and damping force. Elastic force prediction model and damping force prediction model are constructed respectively, and the elastic force prediction model and damping force prediction model are trained using dataset; A cutting dynamics simulation model is constructed, comprising a workpiece geometry model and a tool geometry model. Based on this model, a dynamic characteristic equivalent model is built to simulate the cutting of the workpiece. The method for constructing the dynamic characteristic equivalent model is as follows: Arrange spring dampers on the cutting dynamic simulation model; input the elastic force and damping force in the current increment step into the spring dampers to calculate the vibration displacement and vibration velocity of the spring dampers; input the vibration displacement and vibration velocity into the elastic force mathematical model and the damping force mathematical model respectively to obtain the elastic force and damping force in the next increment step; repeat the above process to complete the construction of the dynamic characteristic equivalent model.

2. The cutting simulation method based on the dynamic characteristics of machine tools according to claim 1, characterized in that: The method for obtaining the vibration displacement and vibration velocity is as follows: obtain the vibration acceleration signal of the workpiece under test, and perform integral calculation on the vibration acceleration signal to obtain the vibration displacement and vibration velocity.

3. The cutting simulation method based on the dynamic characteristics of machine tools according to claim 1, characterized in that: The elastic force and damping force are obtained by decomposing the collected time-domain cutting force.

4. The cutting simulation method based on the dynamic characteristics of machine tools according to claim 3, characterized in that: The separation process of the time-domain cutting force is as follows: A fast Fourier transform is performed on the time-domain cutting force to obtain the frequency domain function of the cutting force; the elastic force spectrum and the damping force spectrum are obtained based on the real part and the imaginary part of the frequency domain function of the cutting force, respectively; the inverse Fourier transform is performed on the elastic force spectrum and the damping force spectrum, respectively, to obtain the time-domain elastic force and the time-domain damping force.

5. The cutting simulation method based on the dynamic characteristics of machine tools according to claim 1, characterized in that: The elastic force prediction model is trained using a dataset constructed from vibration displacement and the corresponding elastic force; the damping force prediction model is trained using a dataset constructed from vibration velocity and the corresponding damping force.

6. The cutting simulation method based on the dynamic characteristics of machine tools according to claim 1, characterized in that: Both the elastic force prediction model and the damping force prediction model include an input layer, a first hidden layer, a second hidden layer, and an output layer connected in sequence; the first hidden layer and the second hidden layer each include a fully connected layer, an activation function, and a batch normalization layer connected in sequence; the second hidden layer also includes a random dropout layer; the random dropout layer is used to perform regularization processing on the results of the batch normalization layer.

7. The cutting simulation method based on the dynamic characteristics of machine tools according to claim 1, characterized in that: The spring damper is positioned at the tool coupling control point; the tool coupling control point is any node on the tool mesh, used to couple and constrain all mesh nodes of the tool.

8. A cutting simulation system based on the dynamic characteristics of machine tools, characterized in that: The system is used to execute a cutting simulation method based on the dynamic characteristics of a machine tool as described in claim 1. The cutting simulation system includes a cutting data acquisition module, a vibration characteristic prediction module, and a model building module. The model building module is used to construct a cutting dynamic simulation model and a dynamic characteristic equivalent model. The cutting data acquisition module is used to acquire real cutting data to guide the vibration characteristic prediction module in predicting elastic force and damping force. The vibration characteristic prediction module is used to predict elastic force and damping force based on vibration displacement and vibration velocity, thereby assisting in the construction of the dynamic characteristic equivalent model.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The memory stores computer programs; the processor executes a cutting simulation method based on the dynamic characteristics of a machine tool as described in any one of claims 1-7.

10. A readable storage medium storing a computer program; characterized in that: When the computer program is executed by the processor, it is used to implement a cutting simulation method based on the dynamic characteristics of a machine tool as described in any one of claims 1-7.

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