Machine tool profile error dynamic prediction method and device based on digital twinning

Abstract

The present disclosure provides a machine tool profile error dynamic prediction method and device based on digital twinning, electronic equipment and computer readable storage medium, relates to the technical field of intelligent manufacturing, and further relates to the field of machine tool profile error prediction. The machine tool profile error dynamic prediction method based on digital twinning comprises: establishing a digital twinning model of the machine tool with time-varying dynamics characteristics; establishing a single-axis servo control model of each axis of the machine tool; and establishing a profile error dynamic prediction model of the machine tool to realize real-time dynamic prediction of the profile error in the machining process of the machine tool. A machine tool profile error dynamic prediction device based on digital twinning is also provided. An electronic equipment is also provided. A computer readable storage medium is also provided.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent manufacturing technology, further to the field of machine tool contour error prediction, and particularly to a method, apparatus, electronic device, and computer-readable storage medium for dynamic prediction of machine tool contour errors based on digital twins. Background Technology

[0002] As high-end equipment in fields such as aerospace and rail transportation develops towards greater precision and complexity, the precision requirements for various cavities and complex curved surfaces are becoming increasingly stringent. Contour error, as a crucial indicator for evaluating machining quality, requires dynamic prediction to ensure online adjustment of machining parameters and high-quality, efficient machining of parts.

[0003] Factors affecting contour errors include the time-varying dynamic characteristics of the machine tool, uncertainties in the machining process, controller errors, and ambient temperature. Among these, the machine tool's dynamic characteristics are the key factor influencing contour errors. During machining, the time-varying dynamic characteristics of the machine tool indirectly lead to dynamic mismatch between machine tool axes, causing nonlinear interference and inducing machine tool vibration and deformation, which persists throughout the entire machining process. Therefore, the influence of time-varying dynamics must be considered in contour error prediction for multi-axis CNC machining.

[0004] Current methods for predicting multi-axis machine tool contour errors all assume that the machine tool's dynamic characteristics are time-invariant, relying mainly on traditional methods such as theoretical calculations, virtual simulations, and parameter identification. These methods cannot completely solve the dynamic prediction of multi-axis CNC machining contour errors under the influence of time-varying dynamic characteristics. Therefore, there is an urgent need to propose a dynamic prediction method for machine tool contour errors based on digital twins. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the aforementioned issues, this disclosure provides a method, apparatus, electronic device, and computer-readable storage medium for dynamic prediction of machine tool contour errors based on digital twins, thereby alleviating the technical problems in the prior art, such as the inability to dynamically control dynamic prediction of machine tool contour errors.

[0007] (II) Technical Solution

[0008] This disclosure provides a method, apparatus, electronic device, and computer-readable storage medium for dynamic prediction of machine tool contour errors based on digital twins.

[0009] According to one aspect of this disclosure, a method for dynamic prediction of machine tool contour errors using digital twins is provided, comprising:

[0010] Based on the feed system parameters of the machine tool's feed system, a digital twin model of the machine tool with time-varying dynamic characteristics is established;

[0011] Based on the digital twin model, a single-axis servo control model for each axis of the machine tool is established to achieve dynamic prediction of following errors during machining; and

[0012] Based on the servo control models and contour error theoretical calculation formulas, a dynamic prediction model for the contour error of the machine tool is established through a cross-coupling control algorithm, thereby realizing real-time dynamic prediction of the contour error during the machining process of the machine tool.

[0013] According to embodiments of this disclosure, establishing a digital twin model of the machine tool with time-varying dynamic characteristics based on the feed system parameters of the machine tool's feed system includes:

[0014] Based on the feed system parameters of the machine tool's feed system, the time-varying dynamic characteristics of the machine tool's feed system are obtained and a dynamic structural model is established;

[0015] The time-varying dynamic reduced-order model is obtained by reducing the order of the dynamic structure model using Ritz series.

[0016] Based on the time-varying dynamic reduced-order model, the axial and torsional transfer function models of the feed system are obtained;

[0017] Acquire a dataset of natural frequencies under different poses during the machine tool machining process; based on the dataset, establish a data-driven model using machine learning algorithms; and

[0018] The data-driven model is integrated with the transfer function model to obtain the digital twin model.

[0019] According to embodiments of this disclosure, obtaining a dataset of natural frequencies at different poses during the machine tool machining process includes:

[0020] Based on the FRF curve, a qualitative analysis is performed on the variation law of the natural frequency of the machine tool with the pose.

[0021] The variation law of the natural frequency of the machine tool with pose was quantitatively analyzed through multiple sets of modal experiments; and

[0022] The qualitative analysis guides the quantitative analysis, resulting in a dataset of natural frequencies under different poses during the machine tool processing.

[0023] According to embodiments of this disclosure, establishing a single-axis servo control model for each axis of the machine tool based on the digital twin model includes:

[0024] A standard control model is established based on the digital twin model, and the standard control model can be applied to the machine tool axes of the digital twin model; and

[0025] The standard control model establishes a single-axis servo control model for each axis of the machine tool through a data-driven model.

[0026] According to embodiments of this disclosure, the single-axis servo control model includes:

[0027] A position loop is used to obtain the position information of the machine tool axis in the digital twin model;

[0028] A speed loop is used to acquire the speed information of the machine tool axes in the digital twin model; and

[0029] A current loop is used to acquire acceleration information of the machine tool axis in the digital twin model.

[0030] According to embodiments of this disclosure, the step of establishing a dynamic prediction model for the contour error of the machine tool using a cross-coupled control algorithm based on the servo control models and contour error theoretical calculation formulas includes:

[0031] Based on the servo control models described above, the contour error is calculated in real time using the theoretical formula for contour error calculation, resulting in the real-time calculated contour error; and

[0032] The real-time calculated contour error is input into the respective servo control models of each motion axis to establish the contour error dynamic prediction model.

[0033] According to embodiments of this disclosure, the digital twin-based machine tool contour error dynamic prediction method further includes:

[0034] The contour error dynamic prediction model is integrated into the machine tool visualization monitoring system, which can display the real-time dynamic prediction results of the contour error dynamic prediction model.

[0035] According to another aspect of this disclosure, a machine tool contour error dynamic prediction device based on digital twin is provided, comprising:

[0036] The digital twin module is used to establish a digital twin model of the machine tool with time-varying dynamic characteristics based on the feed system parameters of the machine tool's feed system.

[0037] The control model module is used to establish single-axis servo control models for each axis of the machine tool based on the digital twin model, enabling dynamic prediction of following errors during machining; and

[0038] The dynamic prediction module is used to establish a dynamic prediction model of the machine tool's contour error based on the servo control models and the contour error theoretical calculation formula, through a cross-coupled control algorithm, so as to realize real-time dynamic prediction of the contour error during the machining process of the machine tool.

[0039] According to another aspect of this disclosure, an electronic device is provided, comprising:

[0040] At least one processor; and

[0041] A memory communicatively connected to the at least one processor; wherein,

[0042] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the above-described dynamic prediction method for machine tool contour errors based on digital twins.

[0043] According to another aspect of this disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to perform the above-described method for dynamic prediction of machine tool contour errors based on digital twins.

[0044] (III) Beneficial Effects

[0045] As can be seen from the above technical solutions, the present disclosure of a method, apparatus, electronic device, and computer-readable storage medium for dynamic prediction of machine tool contour errors based on digital twins has at least one or a portion of the following beneficial effects:

[0046] (1) Dynamic prediction of contour error was achieved; and

[0047] (2) It can reduce reliance on human experience and improve process stability. Attached Figure Description

[0048] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0049] Figure 1 This is a schematic diagram of the method flow for the dynamic prediction method of machine tool contour error based on digital twin according to an embodiment of this disclosure.

[0050] Figure 2 This is a schematic diagram of the method for establishing a digital twin model according to an embodiment of the present disclosure.

[0051] Figure 3 This is a schematic diagram of the axial tension of the dynamic structural model of the feed system according to an embodiment of the present disclosure.

[0052] Figure 4 This is a schematic diagram of the dynamic structural model of the feed system in an embodiment of the present disclosure, rotated about an axis.

[0053] Figure 5 A schematic flowchart illustrating the method for establishing a single-axis servo control model according to an embodiment of this disclosure.

[0054] Figure 6This is a schematic diagram of the principle of the single-axis servo control model driven by the "mechanism + data" hybrid drive in an embodiment of this disclosure.

[0055] Figure 7 A schematic flowchart illustrating the method for establishing a dynamic prediction model for contour error according to an embodiment of this disclosure.

[0056] Figure 8 This is a block diagram of a machine tool contour error dynamic prediction device based on digital twin according to an embodiment of this disclosure.

[0057] Figure 9 This is a schematic diagram of the overall technical route of the embodiments of this disclosure.

[0058] Figure 10 This is a block diagram of an electronic device for a machine tool contour error dynamic prediction method based on digital twins, according to an embodiment of this disclosure. Detailed Implementation

[0059] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0060] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0061] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0062] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0063] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0064] The technical problem solved by this invention is to provide a dynamic prediction method for the contour error of a multi-axis machine tool based on digital twins, so as to make up for the shortcomings of traditional methods in dynamic prediction of contour error under the influence of time-varying dynamic characteristics. This invention comprehensively utilizes theories and technologies from multiple disciplines such as mechanical dynamics, modal identification, and artificial intelligence to provide a digital representation method for time-varying dynamic characteristics during the movement of a multi-axis machine tool that is operable and a dynamic prediction method for the contour error of a multi-axis machine tool with higher prediction efficiency.

[0065] In this embodiment of the disclosure, a method for dynamic prediction of machine tool contour errors based on digital twins is provided, such as... Figures 1 to 10 As shown, the method includes: a digital twin-based dynamic prediction method for machine tool contour errors according to embodiments of this disclosure, comprising: establishing a digital twin model of the machine tool with time-varying dynamic characteristics based on the feed system parameters of the machine tool's feed system; establishing single-axis servo control models for each axis of the machine tool based on the digital twin model to achieve dynamic prediction of following errors during machining; and establishing a dynamic prediction model for the machine tool's contour errors through a cross-coupling control algorithm based on each servo control model and the theoretical calculation formula for contour errors to achieve real-time dynamic prediction of contour errors during machine tool machining.

[0066] like Figure 1 As shown, the method includes operations S100 to S300.

[0067] When operating S100, a digital twin model of the machine tool with time-varying dynamic characteristics is established based on the feed system parameters of the machine tool's feed system.

[0068] When operating the S200, a single-axis servo control model for each axis of the machine tool is established based on the digital twin model, enabling dynamic prediction of following errors during the machining process.

[0069] When operating the S300, based on the servo control models and the theoretical calculation formula of contour error, a dynamic prediction model of the machine tool's contour error is established through a cross-coupling control algorithm, thereby realizing real-time dynamic prediction of contour error during machine tool processing.

[0070] For example, the feed system is a key actuator in multi-axis CNC machine tools. Accurately establishing a dynamic model of the ball screw feed system is fundamental to achieving high-speed, high-acceleration, and high-precision motion control. Therefore, this embodiment selects the X and Z axis feed systems of a five-axis CNC milling machine as the research object, and establishes a hybrid model of "mechanism + data" based on its time-varying dynamic characteristics.

[0071] According to the embodiments of this disclosure, a multi-axis feed system is selected as the research object, its time-varying dynamic characteristics are obtained to establish a dynamic structural model, and its dynamic equations are listed according to the Lagrange method.

[0072] According to embodiments of this disclosure, a time-varying dynamic model is established by reducing the order of the dynamic equations using Ritz series. The method for reducing the order of the time-varying dynamic model based on Ritz series is as follows:

[0073] The Ritz series method is often used to discretize the continuous elastic deformation of a leadscrew. It is also an effective method for reducing the order of n-dimensional dynamic equations. By selecting appropriate basis functions based on the elastic deformation displacement field, the n-dimensional dynamic equations can be reduced to equivalent lower-order equations. This method effectively avoids the difficulties in handling n degrees of freedom and n-dimensional matrices caused by continuous elastic deformation.

[0074] First, N basis functions are selected to form a series expansion expressing the elastic deformation displacement field of the lead screw, where N represents the elastic deformation displacement field of the lead screw, and the coefficients of the basis functions are generalized coordinates. Generally, the selection of basis functions should follow three principles: ① the selected basis functions must be linearly independent; ② the selected basis functions must be continuous functions; ③ the selected basis functions must satisfy all geometric boundary conditions. Furthermore, an important consideration when selecting basis functions is whether the rigid body motion is kinematically permissible. If so, then the selected basis functions should include a part describing the rigid body motion. By definition, all strain components in rigid body motion are zero. If the two ends of the lead screw cannot restrict the movement of the lead screw, rigid body motion is possible. In this case, the basis function related to rigid body motion in the Ritz series should be ψ1 = 1. Selecting appropriate basis functions can ensure the convergence of the series expansion. The larger the number of terms N in the series, the smaller the discretization error, and the closer the calculation result is to the true value. Next, it is substituted into the energy expression to obtain the matrix form. Next, appropriate basis functions are selected to solve the dynamic equations using the time-varying dynamic model reduction method based on Ritz series. Finally, substituting the above equations into the dynamic equations yields the time-varying dynamic model based on Ritz series.

[0075] According to embodiments of this disclosure, the time-varying dynamic reduced-order model is converted into the axial and torsional transfer functions of the multi-axis feed system, and the variation law of the natural frequency with pose is qualitatively analyzed based on its FRF curve.

[0076] According to embodiments of this disclosure, the variation law of natural frequencies with pose is quantitatively analyzed using multiple sets of modal experiments. A dataset of natural frequencies under different poses during multi-axis machining is recorded, and a mapping relationship model between the two is established using machine learning algorithms—that is, a data-driven model. The construction of this data-driven model differs from traditional methods. This disclosure simulates real-world working conditions through experiments and collects on-site experimental data as the training source for the dataset, then trains the data-driven model using machine learning algorithms. This disclosure uses more realistic and reliable experimental data from actual working conditions for model training, which, compared to traditional methods, better reflects the characteristics of realistic mapping and mirror fidelity of digital twin models.

[0077] According to embodiments of this disclosure, a data-driven model that integrates time-varying natural frequency and a transfer function model are integrated to obtain a mechanism-data hybrid driven digital twin model, which is then used as the controlled object to establish a single-axis servo control model, thereby realizing dynamic prediction of following error and true mapping of time-varying dynamic characteristics during CNC machining.

[0078] According to embodiments of this disclosure, a contour error prediction model for multi-axis machine tools is established based on a single-axis servo control model with time-varying dynamic characteristics and a contour error theoretical calculation formula, thereby realizing dynamic prediction of contour errors during multi-axis CNC machining.

[0079] Figure 2 This is a schematic diagram of the method for establishing a digital twin model according to an embodiment of the present disclosure.

[0080] like Figure 2 As shown, the method includes operations S101 to S105.

[0081] In operation S101, based on the feed system parameters of the machine tool's feed system, the time-varying dynamic characteristics of the machine tool's feed system are obtained and a dynamic structural model is established.

[0082] According to embodiments of this disclosure, the dynamic structural model of the X and Z axis ball screw feed system of a multi-axis machine tool is as follows: Figure 3 and Figure 4 As shown.

[0083] According to embodiments of this disclosure, the mechanistic model first establishes a dynamic structural model based on the dynamic characteristics of the research object. To fully consider the time-varying dynamic characteristics of the ball screw feed system, the ball screw is treated as a continuously elastic deformable body during dynamic modeling. When quantitatively expressing the energy of each part according to the dynamic equations, it is necessary to perform an integral expression based on the effective length of the screw. The dynamic equations are then derived using the Lagrange method, as shown below.

[0084]

[0085] Where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, q represents the generalized coordinate vector, and Q represents the generalized force vector.

[0086] In operation S102, the dynamic structure model is reduced in order using the Ritz series to obtain a time-varying dynamic reduced-order model.

[0087] According to embodiments of this disclosure, a method for reducing the order of a time-varying dynamic model based on Ritz series is used to reduce the order of the dynamic equations and establish a time-varying dynamic model with reduced order, including:

[0088] According to embodiments of this disclosure, firstly, N basis functions are selected to form a series expansion expressing the elastic deformation displacement field of the lead screw body, where N represents the elastic deformation displacement field of the lead screw, and the coefficients of the basis functions are generalized coordinates. Generally, the selection of basis functions should follow these three principles: ① the selected basis functions must be linearly independent; ② the selected basis functions must be continuous functions; ③ the selected basis functions must satisfy all geometric boundary conditions. Furthermore, an important consideration when selecting basis functions is whether rigid body motion is kinematically permissible. If so, then the selected basis functions should include a part describing rigid body motion. By definition, all strain components in rigid body motion are zero. If neither end of the lead screw can restrict its motion, rigid body motion is possible. In this case, the basis function related to rigid body motion in the Ritz series should be ψ1 = 1.

[0089] According to embodiments of this disclosure, based on the time-varying dynamic model reduction method of Ritz series, it is known that to express the elastic deformation displacement field of the lead screw body using a series expansion, N basis functions need to be selected to form a series expansion. Therefore, N are selected respectively. u N θ The axial deformation displacement u of the lead screw body is approximated by a series expansion using several basis functions. b (x, t), torsional deformation angular displacement θ b (x, t):

[0090]

[0091]

[0092] In the above formula, ψ u (x), ψ θ (x) are all basis function vectors, q u (t), q θ (t) are all generalized coordinate vectors.

[0093] According to embodiments of this disclosure, selecting appropriate basis functions can guarantee the convergence of the series expansion. The larger the number of terms N in the series, the smaller the discrete error, and the closer the calculation result is to the true value. Next, it is substituted into the energy expression to obtain matrix form.

[0094] According to the embodiments of this disclosure, the dynamic structural model of the ball screw feed system is analyzed. For the axial elastic deformation displacement field of the screw body, the rigid body motion of the screw body is only kinematically permissible. Therefore, the first basis function ψ is selected. 1u =1. Since neither end of the leadscrew can restrict the axial movement of the leadscrew body, there are no geometric boundary conditions that need to be satisfied, so any set of basis functions can be chosen. Similarly, for the elastic deformation displacement field in the torsional direction of the leadscrew body, the rigid body motion of the leadscrew body is only kinematically permissible; therefore, the first basis function ψ is chosen. 1θ =1, and since neither end of the lead screw can restrict the rotation of the lead screw body, there are no geometric boundary conditions that need to be satisfied. Therefore, any set of basis functions can be chosen. In summary, the basis functions selected for the axial and torsional elastic deformation displacement fields of the lead screw body are as follows:

[0095]

[0096]

[0097] In summary, the dynamic equations are solved using the time-varying dynamic model reduction method based on Ritz series, resulting in a time-varying dynamic model with reduced order based on Ritz series.

[0098] By operating S103, the axial and torsional transfer function models of the feed system are obtained based on the time-varying dynamic reduced-order model.

[0099] According to embodiments of this disclosure, based on the established time-varying dynamic reduced-order model, the axial and torsional transfer functions of the X and Z axis feed systems are listed respectively, and the variation law of the natural frequency with pose is qualitatively analyzed based on its FRF curve.

[0100] Since many high-order systems can be approximated as second-order systems, we will also simplify this transfer function to a second-order system for now. Its general form is as follows.

[0101]

[0102] Where w n This represents the natural frequency, where w n As a time-varying parameter, its specific value is determined by the data-driven model. ζ represents the damping ratio, and the specific value of ζ is [0, 1].

[0103] In operation S104, a dataset of natural frequencies under different poses during machine tool processing is acquired; based on the dataset, a data-driven model is established using machine learning algorithms.

[0104] According to embodiments of this disclosure, the variation law of natural frequencies with pose is further investigated, and quantitative analysis is performed using multiple sets of modal experiments. A dataset of natural frequencies under different poses during multi-axis machining is recorded, and a mapping relationship model between the two is established using machine learning algorithms, i.e., a data-driven model.

[0105] According to embodiments of this disclosure, a neural network algorithm mapping relationship model is selected. Through the neural network model, a prediction model is established between the input multi-axis feed system pose and the measured natural frequencies at different output poses. Multiple sets of poses and natural frequencies measured in the field beforehand are divided into training and testing sets. Then, through neural network structure design, neural network training, and modification of the weights and parameters between neurons, the neural network can predict unknown samples.

[0106] In operation S105, the data-driven model and the transfer function model are integrated to obtain the digital twin model.

[0107] According to embodiments of this disclosure, a single-axis servo control model can be established using a mechanism-data hybrid driven digital twin model as the controlled object. The control model is built using MATLAB-Simulink software.

[0108] For example, this embodiment builds a standard PPI control model based on the schematic diagram of a PID control module. The position loop is controlled by K using proportional regulation. p This indicates that the speed loop is controlled by proportional and integral regulation, with the proportional regulation using K. vp K represents integral adjustment. vi Indicated; the current loop can be simplified to proportional control, using K ip This indicates that once the single-axis servo control model based on PPI control is built, the servo parameters can be tuned by inputting step signals, sine signals, etc.

[0109] According to embodiments of this disclosure, the data-driven model that incorporates the time-varying natural frequency and the transfer function model are ultimately integrated to obtain a mechanism-data hybrid driven digital twin model.

[0110] According to embodiments of this disclosure, obtaining a dataset of natural frequencies under different poses during machine tool machining includes: qualitatively analyzing the variation law of the machine tool's natural frequencies with pose based on the FRF curve; quantitatively analyzing the variation law of the machine tool's natural frequencies with pose through multiple sets of modal experiments; and obtaining a dataset of natural frequencies under different poses during machine tool machining by using the qualitative analysis to guide the quantitative analysis.

[0111] Figure 5 A schematic flowchart illustrating the method for establishing a single-axis servo control model according to an embodiment of this disclosure.

[0112] like Figure 5 As shown, the method includes operations S201 to S202.

[0113] In operation S201, a standard control model is established based on the digital twin model. The standard control model can be applied to the machine tool axes of the digital twin model.

[0114] In operation S202, the standard control model establishes a single-axis servo control model for each axis of the machine tool through a data-driven model.

[0115] According to embodiments of this disclosure, based on the above, a data-driven model is integrated. After the system inputs the command trajectory into the data-driven model, the natural frequency under this pose is predicted by the data-driven model and input to the controlled object, realizing the dynamic update of the servo control model; simultaneously, it sequentially passes through the position loop, velocity loop, and current loop, and is finally converted into the motor shaft torque input to the controlled object, thereby outputting the predicted trajectory. The "mechanism + data" hybrid drive principle, as follows... Figure 6 As shown.

[0116] In summary, the single-axis servo control model driven by a hybrid approach of "mechanism + data" can dynamically predict the trajectory displacement, velocity, acceleration, jerk, and following error at every moment during the machining process.

[0117] Figure 7 A schematic flowchart illustrating the method for establishing a dynamic prediction model for contour error according to an embodiment of this disclosure.

[0118] like Figure 7 As shown, the method includes operations S301 to S302.

[0119] When operating S301, the contour error is calculated in real time based on the contour error theoretical calculation formula of each servo control model.

[0120] According to embodiments of this disclosure, based on a single-axis servo control model with time-varying dynamic characteristics and a contour error theoretical calculation formula, a dynamic prediction model for the contour error of a multi-axis machine tool can be established according to a cross-coupled control algorithm, thereby realizing dynamic prediction of contour error during multi-axis CNC machining.

[0121] In operation S302, the real-time calculated contour error is input to the corresponding servo control model of each motion axis, thereby establishing a dynamic prediction model for the contour error.

[0122] According to embodiments of this disclosure, the cross-coupling control algorithm typically consists of two parts: one part is the real-time calculation of the contour error using a contour error calculation formula, and the other part is the input of the obtained control quantities to contour error distribution controllers for each motion axis according to a certain relationship. The contour error distribution controller can comprehensively consider various coupling factors such as parameter mismatches and motion incoordination among the translational axes, as well as unstable interference sources. Therefore, in multi-axis control, cross-coupling controllers and their improved models are often considered for dynamic error prediction. Based on the feedback information from each independent axis, the magnitude of the contour error is calculated in real time, and a dynamic prediction signal is output through a control strategy. Then, the output signal is distributed to each motion axis according to a certain relationship, thereby reducing the contour error and controlling the positioning accuracy.

[0123] According to embodiments of this disclosure, the digital twin-based method for dynamic prediction of machine tool contour errors further includes:

[0124] Operation S400: Integrate the contour error dynamic prediction model into the machine tool visualization monitoring system. The machine tool visualization monitoring system can display the real-time dynamic prediction results of the contour error dynamic prediction model.

[0125] According to embodiments of this disclosure, based on the established multi-axis machine tool digital twin model and contour error dynamic prediction model, it is embedded into the intelligent manufacturing system's adaptable planning and simulation platform software (VE) using the Python language. 2 Develop a visual monitoring system using VE. 2 The software establishes signal communication interfaces for each axis feed system, uses the Modbus communication protocol to realize data transmission and bidirectional interaction between the physical entity and the digital twin, and uses C# language to quantify and visualize the natural frequency, tracking error and contour error, thereby realizing virtual-physical synchronization between digital and physical spaces.

[0126] According to embodiments of this disclosure, a virtual-physical synchronous motion experiment was conducted using typical parametric curve trajectories such as the Archimedes' spiral and goggles as examples. The dynamic characteristic parameters, following error, and contour error of the XZ motion platform were measured using the LMS Test.Lab vibration testing and analysis system and Control Desk software and hardware. By comparing and analyzing the operating results in digital and physical spaces, the digital twin modeling method, the digital-physical space virtual-physical synchronization, and the contour error dynamic prediction method were verified. Furthermore, the effectiveness of the contour error dynamic prediction method proposed in this embodiment was verified by comparing it with a contour error prediction method that considers time-invariant factors.

[0127] Figure 8 This is a block diagram of a machine tool contour error dynamic prediction device based on digital twin according to an embodiment of this disclosure.

[0128] like Figure 8 As shown, the production process management device 500 may include a digital twin module 510, a control model module 520, and a dynamic prediction module 530.

[0129] The digital twin module 510 is used to establish a digital twin model of the machine tool with time-varying dynamic characteristics based on the feed system parameters of the machine tool's feed system.

[0130] The control model module 520 is used to establish single-axis servo control models for each axis of the machine tool based on the digital twin model, so as to realize dynamic prediction of the following error during the machining process.

[0131] The dynamic prediction module 530 is used to establish a dynamic prediction model of the machine tool's contour error based on each servo control model and the contour error theoretical calculation formula, through a cross-coupled control algorithm, so as to realize real-time dynamic prediction of the contour error during the machine tool machining process.

[0132] According to embodiments of this disclosure, the digital twin module 510 may include a first twin module, a second twin module, a third twin module, a fourth twin module, and a fifth twin module.

[0133] The first twin module is used to obtain the time-varying dynamic characteristics of the machine tool's feed system and establish a dynamic structural model based on the feed system parameters of the machine tool's feed system.

[0134] The second twin module is used to reduce the order of the dynamic structure model through Ritz series, resulting in a time-varying dynamic reduced-order model.

[0135] The third twin module is used to obtain the axial and torsional transfer function models of the feed system based on the time-varying dynamics reduced-order model.

[0136] The fourth twin module is used to acquire a dataset of natural frequencies under different poses during machine tool processing; based on the dataset, a data-driven model is established using machine learning algorithms.

[0137] The fifth twin module is used to integrate the data-driven model with the transfer function model to obtain a digital twin model.

[0138] According to embodiments of this disclosure, the control model module 520 may include a first control module and a second control module.

[0139] The first control module is used to establish a standard control model based on the digital twin model. The standard control model can be applied to the machine tool axes of the digital twin model.

[0140] The second control module is used to establish single-axis servo control models for each axis of the machine tool through a data-driven model based on the standard control model.

[0141] According to embodiments of this disclosure, the dynamic prediction module 530 may include a first prediction module and a second prediction module.

[0142] The first prediction module is used to perform real-time calculations based on the contour error theoretical calculation formula of each servo control model to obtain the real-time calculated contour error.

[0143] The second prediction module is used to input the real-time calculated contour error into the respective servo control models of each motion axis, thereby establishing a dynamic prediction model for the contour error.

[0144] According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described above.

[0145] According to embodiments of the present disclosure, a non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions are used to cause a computer to perform the method described above.

[0146] According to an embodiment of this disclosure, a computer program product includes a computer program that, when executed by a processor, implements the method described above.

[0147] According to embodiments of this disclosure, a multi-axis machine tool feed system is selected as the research object, and a digital twin model of the time-varying dynamic characteristics of the machine tool driven by a hybrid mechanism-data approach is established. Secondly, based on the digital twin model, servo control models for each single axis are established to achieve dynamic prediction of following errors during machining. Thirdly, based on the theoretical calculation formula for contour errors, a dynamic prediction model for the contour errors of a multi-axis machine tool is established using a cross-coupling control algorithm to achieve real-time dynamic prediction of contour errors during multi-axis CNC machining. Finally, a multi-axis machine tool visualization monitoring system is developed, and the contour error prediction results are experimentally verified and evaluated, forming a dynamic prediction scheme for multi-axis machine tool contour errors based on digital twins, such as... Figure 9 As shown.

[0148] Figure 10A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0149] like Figure 7 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0150] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0151] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as speech processing methods. For example, in some embodiments, the speech processing method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the speech processing method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform speech processing methods by any other suitable means (e.g., by means of firmware).

[0152] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0153] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0154] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0155] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0156] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0157] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, distributed system servers, or servers incorporating blockchain technology.

[0158] Based on the above description, those skilled in the art should have a clear understanding of the method, apparatus, electronic equipment, and computer-readable storage medium for dynamic prediction of machine tool contour errors based on digital twins disclosed herein.

[0159] In summary, this disclosure provides a method, apparatus, electronic device, and computer-readable storage medium for dynamic prediction of machine tool contour errors based on digital twins, overcoming the shortcomings of traditional methods in dynamically predicting contour errors under the influence of time-varying dynamic characteristics. The focus is on elucidating a digital representation method for time-varying dynamic characteristics during the motion of multi-axis machine tools, forming a dynamic prediction method for multi-axis machine tool contour errors based on digital twins, developing a multi-axis machine tool visualization monitoring system, and verifying the theoretical research through multi-axis motion experiments using parametric curve trajectories. Compared with traditional contour error prediction methods, the prediction accuracy is expected to be significantly improved.

[0160] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0161] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for dynamic prediction of machine tool contour error based on digital twin, comprising: Based on the feed system parameters of the machine tool's feed system, a digital twin model of the machine tool with time-varying dynamic characteristics is established; Based on the digital twin model, a single-axis servo control model for each axis of the machine tool is established to achieve dynamic prediction of the following error during the machining process; as well as Based on the servo control models and contour error theoretical calculation formulas, a dynamic prediction model for the contour error of the machine tool is established through a cross-coupled control algorithm to realize real-time dynamic prediction of the contour error during the machining process of the machine tool. The step of establishing a digital twin model of the machine tool with time-varying dynamic characteristics based on the feed system parameters of the machine tool's feed system includes: Based on the feed system parameters of the machine tool's feed system, the time-varying dynamic characteristics of the machine tool's feed system are obtained and a dynamic structural model is established; The time-varying dynamic reduced-order model is obtained by reducing the order of the dynamic structure model using Ritz series. Based on the time-varying dynamic reduced-order model, the axial and torsional transfer function models of the feed system are obtained; Acquire a dataset of natural frequencies under different poses during the machine tool machining process; based on the dataset, establish a data-driven model using machine learning algorithms; and The data-driven model is integrated with the transfer function model to obtain the digital twin model.

2. The method according to claim 1, wherein, The acquisition of the dataset of natural frequencies under different poses during the machine tool machining process includes: Based on the FRF curve, a qualitative analysis is performed on the variation law of the natural frequency of the machine tool with the pose. The variation law of the natural frequency of the machine tool with pose was quantitatively analyzed through multiple sets of modal experiments; and The qualitative analysis guides the quantitative analysis, resulting in a dataset of natural frequencies under different poses during the machine tool machining process.

3. The method according to claim 1, wherein, The step of establishing a single-axis servo control model for each axis of the machine tool based on the digital twin model includes: A standard control model is established based on the digital twin model, and the standard control model can be applied to the machine tool axes of the digital twin model; and The standard control model establishes a single-axis servo control model for each axis of the machine tool through a data-driven model.

4. The method according to claim 3, wherein, The single-axis servo control model includes: A position loop is used to obtain the position information of the machine tool axis in the digital twin model; A speed loop is used to acquire the speed information of the machine tool axes in the digital twin model; and A current loop is used to acquire acceleration information of the machine tool axis in the digital twin model.

5. The method according to claim 1, wherein, The step of establishing a dynamic prediction model for the machine tool's contour error based on the servo control models and contour error theoretical calculation formulas, using a cross-coupled control algorithm, includes: Based on the servo control models described above, the contour error is calculated in real time using the theoretical formula for contour error calculation, resulting in the real-time calculated contour error; and The real-time calculated contour error is input into the respective servo control models of each motion axis to establish the contour error dynamic prediction model.

6. The method according to claim 1, further comprising: The contour error dynamic prediction model is integrated into the machine tool visualization monitoring system, which can display the real-time dynamic prediction results of the contour error dynamic prediction model.

7. A machine tool contour error dynamic prediction device based on digital twin, comprising: The digital twin module is used to establish a digital twin model of the machine tool with time-varying dynamic characteristics based on the feed system parameters of the machine tool's feed system. The control model module is used to establish a single-axis servo control model for each axis of the machine tool based on the digital twin model, so as to realize dynamic prediction of the following error during the machining process; as well as The dynamic prediction module is used to establish a dynamic prediction model of the machine tool's contour error based on the servo control models and contour error theoretical calculation formulas, through a cross-coupled control algorithm, so as to realize real-time dynamic prediction of contour error during the machining process of the machine tool. The digital twin module includes a first twin module, a second twin module, a third twin module, a fourth twin module, and a fifth twin module; The first twin module is used to obtain the time-varying dynamic characteristics of the machine tool's feed system and establish a dynamic structure model based on the feed system parameters of the machine tool's feed system. The second twin module is used to reduce the order of the dynamic structure model using Ritz series to obtain a time-varying dynamic reduced-order model; The third twin module is used to obtain the axial and torsional transfer function models of the feed system based on the time-varying dynamic order reduction model. The fourth twin module is used to acquire a dataset of natural frequencies under different poses during the machine tool processing; and to establish a data-driven model based on the dataset using machine learning algorithms. The fifth twin module is used to integrate the data-driven model with the transfer function model to obtain the digital twin model.

8. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

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