CN114137907A - Modeling method, device, equipment and medium for predicting vibration amplitude of numerical control machine tool - Google Patents
Modeling method, device, equipment and medium for predicting vibration amplitude of numerical control machine tool Download PDFInfo
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Abstract
The application discloses a modeling method, a device, equipment and a medium for predicting vibration amplitude of a numerical control machine tool, wherein the method comprises the following steps: establishing an integral rigidity model of the numerical control machine tool, and acquiring the dynamic rigidity of each joint surface of the machine tool; based on the dynamic stiffness of each joint surface, obtaining dynamic stiffness data of the numerical control machine tool under different frequencies, and generating a dynamic stiffness curve of the numerical control machine tool; and substituting the average cutting force of the numerical control machine tool into the overall stiffness model, and obtaining a final overall stiffness model for predicting the relative amplitude between the main shaft and the workbench during machining of the machine tool by adopting fast Fourier transform based on the dynamic stiffness curve. The method adopts a machine tool integral modeling mode combining a multi-body theory, dynamic stiffness, machine learning and fast Fourier transform, and uses the model for predicting the relative amplitude between a main shaft and a workbench during machine tool machining, thereby improving the accuracy of prediction.
Description
Technical Field
The application relates to the technical field of machine tool vibration amplitude prediction, in particular to a modeling method, a device, equipment and a medium for predicting vibration amplitudes of a numerical control machine tool.
Background
The surface quality of the part is an important representation of the product performance and also an important index for reflecting the machining precision of a machine tool, and mainly comprises surface roughness, surface layer hardening, surface waviness, surface residual stress and the like. In actual machining, there are many factors that affect the surface quality of the part, including cutting conditions, material expansion and recovery, material accumulation, crystal orientation, tool position and orientation, tool wear, and machine tool vibration, among others. And the influence of machine tool vibration on the surface quality is of great importance. The influence of machine tool vibration on the surface quality of parts mainly comes from two sources: firstly, high-frequency vibration caused by cutting force; second, low frequency vibration of the structure. For machine tool structures composed of multiple components, the vibrations of sensitive structures and structures at the dominant frequencies both have a significant impact on part surface formation as a weak link in the machine tool system. The low-frequency vibration caused by the structure depends on the self-characteristics of the machine tool such as the self-assembly error, and the amplitude caused by the low-frequency vibration can be regarded as a constant value related to the machine tool. And the vibrations caused by the cutting forces are generally the direction of investigation.
During the machining process, the interaction of the various influencing factors is presented in the form of vibration in the machining process system, so that the cutting force fluctuates obviously, and the parameters of the cutting layer are changed. The resulting change in relative position between the tool and the workpiece causes the tool to move away from the desired position, which results in "under-cutting" or "over-cutting" of the workpiece, resulting in an uneven surface topography on the surface of the machined workpiece. Vibrations in the cutting not only degrade the surface quality of the workpiece, but also increase tool wear and destroy the coupling characteristics between the machine parts. In order to ensure the required workpiece processing quality in actual production, the cutting amount has to be reduced in many cases, so that the cutting performance of a machine tool and a cutter can not be fully exerted, and the improvement of the mechanical processing production efficiency is limited to a great extent.
Therefore, it is important to establish a structural model of the machine tool and predict the amplitude of the machine tool for actual machining.
Content of application
The present application is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, the first purpose of the present application is to provide a modeling method for numerically controlled machine tool vibration amplitude prediction.
The second purpose of this application is to propose a modeling device for numerical control machine vibration amplitude prediction.
A third object of the present application is to provide an electronic device.
A fourth object of the present application is to propose a computer readable storage medium.
In order to achieve the above object, an embodiment of the first aspect of the present application provides a modeling method for predicting a vibration amplitude of a numerically-controlled machine tool, including the following steps:
establishing an integral rigidity model of the numerical control machine tool, and acquiring the dynamic rigidity of each joint surface of the machine tool;
based on the dynamic stiffness of each joint surface, obtaining dynamic stiffness data of the numerical control machine tool under different frequencies, and generating a dynamic stiffness curve of the numerical control machine tool; and
and substituting the average cutting force of the numerical control machine tool into the overall stiffness model, and obtaining a final overall stiffness model for predicting the relative amplitude between the main shaft and the workbench during machining of the machine tool by adopting fast Fourier transform based on the dynamic stiffness curve.
According to the modeling method for predicting the vibration amplitude of the numerical control machine tool, the overall stiffness model of the numerical control machine tool is established, the dynamic stiffness of each joint surface of the machine tool is obtained, the dynamic stiffness data of the numerical control machine tool under different frequencies are obtained based on the dynamic stiffness of each joint surface, the dynamic stiffness curve of the numerical control machine tool is generated, the average cutting force of the numerical control machine tool is substituted into the overall stiffness model, and meanwhile, the final overall stiffness model for predicting the relative amplitude between the main shaft and the workbench of the machine tool during machining is obtained based on the dynamic stiffness curve by adopting fast Fourier transform. Therefore, the overall machine tool modeling mode combining the multi-body theory, dynamic stiffness, machine learning and fast Fourier transform is adopted, the model is used for predicting the relative amplitude between the main shaft and the workbench during machine tool machining, and the prediction accuracy is improved.
In addition, the modeling method for predicting the vibration amplitude of the numerical control machine according to the above embodiment of the present application may further have the following additional technical features:
optionally, the establishing of the overall stiffness model of the numerically-controlled machine tool includes:
analyzing the main shaft of the numerical control machine tool to obtain a matrix transformation formula of the main shaft;
and calculating the deformation deviation of the main shaft through the cutting force component and the dynamic stiffness of each part, and establishing the integral stiffness model.
Optionally, the acquiring the dynamic stiffness of each joint surface of the machine tool includes:
and adjusting the rotation angle of the main shaft, acquiring dynamic stiffness values of the workbench of the machine tool, which contain amplitude-frequency characteristics and phase-frequency characteristics, at different angles, adjusting the position of the machine tool, and acquiring dynamic stiffness values of the main shaft of the machine tool, which contain amplitude-frequency characteristics and phase-frequency characteristics, at different positions.
Optionally, the obtaining dynamic stiffness data of the machine tool at different frequencies includes:
and measuring dynamic stiffness data of the machine tool under different frequencies by adopting an orthogonal test method, and learning the change relation between a dynamic stiffness curve and a position by utilizing a machine learning model to obtain the dynamic stiffness data.
Optionally, the obtaining, by using a fast fourier transform based on the dynamic stiffness curve, a final overall stiffness model for predicting a relative amplitude between a spindle and a table of a machine tool during machining includes:
and performing frequency domain conversion on the time domain signal of the cutting force by adopting the fast Fourier transform, and bringing the cutting force with different frequencies into corresponding dynamic stiffness values.
In order to achieve the above object, an embodiment of a second aspect of the present application provides a modeling apparatus for predicting a vibration amplitude of a numerically-controlled machine tool, including:
the first acquisition module is used for establishing an integral rigidity model of the numerical control machine tool and acquiring the dynamic rigidity of each joint surface of the machine tool;
the generating module is used for obtaining dynamic stiffness data of the numerical control machine tool under different frequencies based on the dynamic stiffness of each joint surface and generating a dynamic stiffness curve of the numerical control machine tool; and
and the second acquisition module is used for substituting the average cutting force of the numerical control machine tool into the overall stiffness model and simultaneously obtaining a final overall stiffness model for predicting the relative amplitude between the main shaft and the workbench during machining of the machine tool by adopting fast Fourier transform based on the dynamic stiffness curve.
According to the modeling device for predicting the vibration amplitude of the numerical control machine tool, the overall stiffness model of the numerical control machine tool is built, the dynamic stiffness of each joint surface of the machine tool is obtained, the dynamic stiffness data of the numerical control machine tool under different frequencies are obtained based on the dynamic stiffness of each joint surface, the dynamic stiffness curve of the numerical control machine tool is generated, the average cutting force of the numerical control machine tool is substituted into the overall stiffness model, and meanwhile, the final overall stiffness model for predicting the relative amplitude between the main shaft and the workbench of the machine tool during machining is obtained based on the dynamic stiffness curve by adopting fast Fourier transform. Therefore, the overall machine tool modeling mode combining the multi-body theory, dynamic stiffness, machine learning and fast Fourier transform is adopted, the model is used for predicting the relative amplitude between the main shaft and the workbench during machine tool machining, and the prediction accuracy is improved.
Optionally, the first obtaining module is specifically configured to:
analyzing the main shaft of the numerical control machine tool to obtain a matrix transformation formula of the main shaft;
and calculating the deformation deviation of the main shaft through the cutting force component and the dynamic stiffness of each part, and establishing the integral stiffness model.
Optionally, the first obtaining module is specifically configured to:
and adjusting the rotation angle of the main shaft, acquiring dynamic stiffness values of the workbench of the machine tool, which contain amplitude-frequency characteristics and phase-frequency characteristics, at different angles, adjusting the position of the machine tool, and acquiring dynamic stiffness values of the main shaft of the machine tool, which contain amplitude-frequency characteristics and phase-frequency characteristics, at different positions.
Optionally, the generating module is specifically configured to:
and measuring dynamic stiffness data of the machine tool under different frequencies by adopting an orthogonal test method, and learning the change relation between a dynamic stiffness curve and a position by utilizing a machine learning model to obtain the dynamic stiffness data.
Optionally, the second obtaining module is specifically configured to:
and performing frequency domain conversion on the time domain signal of the cutting force by adopting the fast Fourier transform, and bringing the cutting force with different frequencies into corresponding dynamic stiffness values.
To achieve the above object, an embodiment of a third aspect of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being configured to perform a modeling method for numerical control machine vibration magnitude prediction as described in the above embodiments.
In order to achieve the above object, a fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, the program being executed by a processor for implementing the modeling method for vibration amplitude prediction of a numerically controlled machine tool as described in the above embodiment.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a modeling method for numerically controlled machine tool vibration amplitude prediction according to an embodiment of the present application;
FIG. 2 is a simplified illustration of a five-axis numerically controlled machine tool according to one embodiment of the present application;
FIG. 3 is a simulation and simplified schematic diagram of two axes A, C of a machine tool according to one embodiment of the present application;
FIG. 4 is a three-dimensional illustration of an experimental setup for dynamic stiffness of a spindle of a machine tool according to an embodiment of the present application;
FIG. 5 is an exemplary graph of a stiffness experiment for quantity A according to one embodiment of the present application;
FIG. 6 is an exemplary graph of a stiffness experiment for an amount a according to one embodiment of the present application;
FIG. 7 is an exemplary graph of a stiffness test in the θ direction according to one embodiment of the present application;
FIG. 8 is an exemplary plot of stiffness experiments in the alpha direction according to one embodiment of the present application;
FIG. 9 is a schematic diagram of the Y-direction stiffness of a spindle of a machine tool according to one embodiment of the present application;
FIG. 10 is an exemplary graph of stiffness in the X direction of the machine tool spindle according to one embodiment of the present application;
FIG. 11 is a schematic diagram of stiffness in the X direction of the machine tool spindle according to one embodiment of the present application;
FIG. 12 is a graph illustrating an example of the results of frequency domain conversion of a time domain signal of a cutting force using a fast Fourier transform, according to one embodiment of the present application;
FIG. 13 is a block diagram of a modeling apparatus for vibration amplitude prediction of a numerically controlled machine tool according to an embodiment of the present application;
fig. 14 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
Hereinafter, a modeling method, an apparatus, a device, and a medium for predicting vibration amplitude of a numerical control machine according to an embodiment of the present application will be described with reference to the accompanying drawings, and first, a modeling method for predicting vibration amplitude of a numerical control machine according to an embodiment of the present application will be described with reference to the accompanying drawings.
Before introducing the modeling method for predicting the vibration amplitude of the numerical control machine tool in the embodiment of the application, a multi-body system theory in the related art is briefly introduced.
The vibration phenomenon is often generated during the working process of the machine tool, and the rigidity and the precision of a machined part are adversely affected. With the continuous development of the machine manufacturing industry, the requirement of modern manufacturing on the machining quality of products is continuously improved, so that a machine tool is required to have good overall performance, and the reliability, the machining precision and the vibration resistance of the machine tool must meet the machining standard. Therefore, modeling analysis is carried out on the machine tool, the dynamic characteristics of the machine tool are researched, and the optimization and improvement of the weak link of the machine tool structure are of great significance. Modal analysis is the main method for studying the performance of the complete machine and the substructure thereof. Reducing machine tool vibration and improving machine tool reliability are important links to be considered in the research process.
Generally, a modal analysis method is adopted for the dynamic characteristics of a machine tool, and the modal analysis is to obtain a series of important parameters of a structure through experiments: the main vibration mode, the natural frequency, the modal mass, the rigidity and the like, and the overall performance of the structure is researched by analyzing the modal parameters. There are three applications of modal analysis in specific conditions: (1) a finite element analysis method based on Modalanysis software such as Hypermesh, MESCOPVES, EDM model and the like; (2) establishing a test mode analysis method of a motion equation based on vibration test; (3) based on a condition modal analysis method for identifying modal parameters under response measurable conditions. The three methods have advantages and disadvantages, the finite element method can predict the overall performance and dynamic characteristics of the structure in advance through finite element analysis, but important parameters such as related joint surface characteristics cannot be determined and can only be used as reference, and the data precision is poor. The experimental modal analysis obtains more accurate data through actually carrying out overall test and analysis on the structure, but the method still has limitations in the specific application process, and has defects when carrying out modeling analysis and result prediction on the machine tool. The working condition modal analysis only needs to collect vibration response data, does not need to test an input mechanism, has a relatively simple process, has limitations on a solving method, and cannot solve a complex problem.
The multi-body system theory is a subject for researching multi-body system problems, has good universality and systematicness, and particularly makes full use of computer algorithms and programs to carry out complex system dynamics and kinematics calculation nowadays when the electronic computer is developed at high speed, thereby meeting the analysis and calculation requirements of complex problems which cannot be solved by the classical dynamics and kinematics theory. At present, a multi-body system is mainly used for modeling geometric errors of a machine tool, a topological structure and an open-loop kinematic chain of the machine tool are generally established according to the structure of a numerical control machine, then a transformation relation between a differential motion matrix and a homogeneous transformation matrix between two coordinate systems is established according to a differential transformation relation between different rectangular coordinate systems, and then the geometric errors of the machine tool are calculated. Such geometric errors may be caused by temperature, by average cutting forces, or by thermal deformation of the machine tool. The multi-body system theory has advantages in the aspect of researching the overall modeling of the machine tool, but at present, the multi-body system theory is mainly applied to the geometric errors of the machine tool, and the research on the vibration of the machine tool is lacked.
Specifically, fig. 1 is a schematic flowchart of a modeling method for predicting a vibration amplitude of a numerically-controlled machine tool according to an embodiment of the present application.
As shown in fig. 1, the modeling method for predicting the vibration amplitude of the numerical control machine comprises the following steps:
in step S101, an overall stiffness model of the numerical control machine tool is established, and dynamic stiffness of each joint surface of the machine tool is obtained.
Optionally, in some embodiments, establishing a global stiffness model of the numerically controlled machine tool comprises: analyzing a main shaft of the numerical control machine tool to obtain a matrix transformation formula of the main shaft; and calculating the deformation deviation of the main shaft according to the cutting force component and the dynamic stiffness of each part, and establishing an integral stiffness model.
Specifically, taking a five-axis coriolis numerical control machine tool as an example, fig. 2 is a simplified structural diagram of the five-axis coriolis numerical control machine tool, where 2, 3, and 4 represent X, Y, and Z axes of the machine tool, respectively, and the other 5 and 6 represent a and C axes of the machine tool, respectively. For a machine tool, the main weak link of the mechanical structure is the joint connecting the parts of the machine tool, so that the deformation of the machine tool can be totally concentrated at the connection part of the machine tool.
First, the A, C two axes of the machine tool can be analyzed in the embodiment of the present application, where fig. 3(a) is a simulation diagram of the A, C axis of the machine tool, and fig. 3(b) is a simplified diagram of the A, C axis of the machine tool. In the above description, the angle of rotation of the a axis is represented by θ, the angle of rotation of the C axis is represented by α, and the angular deviations of the two axes due to the stiffness are represented by d θ and d α, respectively. In addition, due to the stiffness of the A, C axis of the machine tool, other deformation deviations than angle occur, specifically the length variations represented as a and a, denoted as dA and dA, respectively.
The parameters and the deviation thereof are all substituted into a matrix transformation formula of an A axis, and the matrix transformation formula can be written into a form of a formula (1) after a second-order small quantity is removed, wherein the formula (1) is as follows:
similarly, the matrix transformation formula of the C axis is processed, and after the second order small quantity is removed, the matrix transformation formula can be expressed as formula (2), where formula (2) is as follows:
for the same principle, the transformation matrix can be obtained as formula (3), where formula (3) is as follows:
and dA, dA, d theta and d alpha can be obtained by calculating the cutting force component and the dynamic stiffness of each part respectively, and for the main shaft, the overall stiffness model of the machine tool is built by the method.
Further, in some embodiments, obtaining the dynamic stiffness of each joint surface of the machine tool includes: and adjusting the rotation angle of the main shaft, acquiring dynamic stiffness values of the workbench of the machine tool containing amplitude-frequency characteristics and phase-frequency characteristics at different angles, adjusting the position of the machine tool, and acquiring dynamic stiffness values of the main shaft of the machine tool containing amplitude-frequency characteristics and phase-frequency characteristics at different positions.
It should be understood that the above modeling has classified the deformation of the joint surface of the machine tool into seven categories A, a, α, θ, x, y, z, and the magnitude of the deformation can be obtained by the cutting force and the dynamic stiffness curve of the machine tool, which can be obtained by experiment and machine learning together.
The simulation diagram of the experimental device for the dynamic stiffness of the machine tool is shown in fig. 4, a cutter bar of the machine tool is connected with a force sensor through a pair of self-made clamps made of stainless steel, the force sensor is connected with a vibration exciter through the self-made clamps made of stainless steel, and in addition, in order to avoid the force sensor from being subjected to too large radial force, a long stainless steel rod is adopted for connection.
In order to verify the relation between the dynamic stiffness of the machine tool spindle and the spindle position, four different spindle positions are selected at will, the spindle of the machine tool is excited respectively, and the dynamic stiffness of the machine tool spindle is calculated. As shown in fig. 5 to 11, fig. 5(a) is a front view of a stiffness test of a volume, and fig. 5(b) is a plan view of a stiffness test of a volume, in which an excitation force F is applied by an exciter at a position of an arrow; fig. 6(a) is a front view of a stiffness test performed by a, and fig. 6(b) is a plan view of a stiffness test performed by a, in which an excitation force F is applied by an exciter at a position of an arrow; fig. 7(a) is a front view of a stiffness test in the θ direction, and fig. 7(b) is a plan view of the stiffness test in the θ direction, in which an excitation force F is applied by an exciter at a position indicated by an arrow; fig. 8(a) is a front view of a stiffness test in the α direction, and fig. 8(b) is a plan view of the stiffness test in the α direction, in which an excitation force F is applied by an exciter at a position indicated by an arrow; fig. 9(a) is a front view of a Y-direction stiffness test of a machine tool spindle, and fig. 9(b) is a left view of the Y-direction stiffness test of the machine tool spindle, wherein an excitation force F is applied by an exciter at an arrow position; fig. 10(a) is a front view of an X-direction stiffness test of a machine tool spindle, and fig. 10(b) is a left view of the X-direction stiffness test of the machine tool spindle, wherein an exciting force F is applied by an exciter at an arrow position; fig. 11(a) is a front view of an X-direction rigidity test of a machine tool spindle, fig. 11(b) is a left view of the X-direction rigidity test of the machine tool spindle, an exciting force F is applied by an exciter at an arrow position, and an exciting range is selected to be 50 to 600 in consideration of a spindle rotation speed range and the number of blades of a tool in machining the machine tool.
In addition, during the experiment of the worktable, the alpha angle and the theta angle are respectively adjusted, and the dynamic stiffness values including the amplitude-frequency characteristic and the phase-frequency characteristic of the machine tool worktable under different angles are measured. And for the main shaft, respectively adjusting x, y and z values in the same way, and measuring the dynamic stiffness values of the machine tool main shaft containing amplitude-frequency characteristics and phase-frequency characteristics at different positions.
In step S102, dynamic stiffness data of the numerical control machine tool at different frequencies is obtained based on the dynamic stiffness of each joint surface, and a dynamic stiffness curve of the numerical control machine tool is generated.
Optionally, in some embodiments, obtaining dynamic stiffness data of the machine tool at different frequencies comprises: and (3) measuring dynamic stiffness data of the machine tool under different frequencies by adopting an orthogonal test method, and learning the change relation between a dynamic stiffness curve and a position by utilizing a machine learning model to obtain the dynamic stiffness data.
It should be understood that the dynamic stiffness values of different positions of the machine tool have certain differences, so that the position of the spindle and the position of the workbench need to be continuously adjusted when the dynamic stiffness of the machine tool is measured. For example, for the workbench, the parameters which can be selected are the angle of the cradle shaft and the angle of the rotary table, the corresponding angle is selected in the working range of the machine tool, and the dynamic stiffness of the machine tool under different frequencies is respectively measured. For the main shaft of the machine tool, the same principle is used, different values of x, y and z are selected in the working range, the dynamic stiffness of the machine tool is respectively calculated, and the dynamic stiffness data of the machine tool under different frequencies can be obtained through measurement.
Because the experiment is carried out by selecting a plurality of angles and positions, no matter how dense the experimental points are selected, the finally obtained discrete data of the dynamic stiffness are necessarily obtained. In general, when the situation is met in an experiment, interpolation can be directly used, and data of any position can be simply and quickly acquired. However, when measuring the dynamic stiffness of the machine tool, there are serious drawbacks if interpolation is simply used.
If the experimental measurement is carried out on the workbench, the A-axis angle and the C-axis angle are respectively selected. Assuming that one measurement value is selected every 20 °, the angle values of the a axis are 0 °, 20 °, 40 °, 60 °, 80 °, 100 °, 120 °, 140 °, 160 °, 180 °, and the angle values of the C axis are 0 °, 20 °, 40 ° … 340 °, so that the number of experiments required is 10 × 18 — 180. The main working range of the machine tool for the spindle is 400 × 400 × 400, and if the dynamic stiffness test is performed every 20mm, 20 × 20 × 20 is 8000 times. Obviously, the interpolation method needs to measure each test point, so that the dynamic stiffness test by the traditional interpolation method needs a large amount of experiments.
In order to reduce the number of experiments, the embodiments of the present application may adopt an orthogonal test method, thereby greatly reducing the number of experiments. However, the orthogonal test method causes the deletion of a plurality of data points, so that the traditional interpolation method cannot be used for acquiring the dynamic stiffness of the machine tool at any position.
In order to solve the problem that the conventional interpolation method cannot be used, the embodiment of the application adopts a machine learning model to learn the change of the dynamic stiffness curve along with the position. Since machine learning is used to determine the dynamic stiffness curve change of the machine tool, a regression algorithm needs to be adopted. The most commonly used regression algorithms are: the method comprises a decision tree regression algorithm, a linear regression algorithm, an SVM regression algorithm, a KNN regression algorithm, a random forest regression algorithm, an Adaboost regression algorithm, a GBRT regression algorithm, a Bagging regression algorithm and an ExtraTree extreme random tree regression algorithm, and the total number of the algorithms is nine. In order to determine the best algorithm among them, several algorithms are tried all over. The five algorithms of decision tree regression, Bagging regression, KNN regression, random deep forest regression and extreme random tree regression are good in running time and accuracy, and therefore the five algorithms are selected for regression operation. In addition, in order to ensure the accuracy of the result, 90% of the data is selected for training, and 10% of the data is used for verifying the final score of the algorithm. All five algorithms are used for calculating the dynamic stiffness data once each time, then three algorithms with the highest scores are selected from the five algorithms, the stiffness values of the three algorithms are averaged to obtain the best result, the difference between the result and the interpolated result is not more than 3%, and the method is accurate and effective.
In step S103, the average cutting force of the numerical control machine tool is substituted into the global stiffness model, and a final global stiffness model for predicting the relative amplitude between the spindle and the table during machining of the machine tool is obtained by fast fourier transform based on the dynamic stiffness curve.
Optionally, in some embodiments, obtaining a final global stiffness model that predicts a relative amplitude between the spindle and the table of the machine tool during machining using a fast fourier transform based on the dynamic stiffness curve includes: and performing frequency domain conversion on the time domain signal of the cutting force by adopting fast Fourier transform, and bringing the cutting force with different frequencies into corresponding dynamic stiffness values.
In particular, the model can only be used to deal with the static deformation of the machine tool if the signal of the cutting force is not specifically processed. That is, the average cutting force of the machine tool is calculated and then is included in the overall model of the machine tool, and the magnitude of the static deformation of the machine tool (also referred to as the relief size of the machine tool) can be calculated. In actual production, however, factories already have well-established technologies and means for compensating for tool back-off, and can measure a machined workpiece after machining and compensate the workpiece according to actual measured quantities. Therefore, if the model can only predict and compensate the static deformation of the machine tool, the actual engineering significance is insufficient, and good guidance and help can not be provided for the existing engineering problem.
Due to actual engineering requirements, the model needs to make predictions about the dynamic deformation of the machine tool, wherein the amplitude of the machine tool has the greatest influence on the surface quality in actual machining in the dynamic characteristics of the machine tool, so the model needs to be capable of making accurate predictions about the amplitude of the machine tool.
However, the dynamic characteristics of the machine tool are difficult to analyze because various signal impurities in the cutting force time domain signal of the machine tool are mixed therein, and accurate analysis is difficult to perform. In addition, the dynamic stiffness value of the machine tool is different under the cutting forces of different frequencies, and the dynamic stiffness value is close to the static stiffness value of the machine tool at low frequencies, and is obviously different from the static stiffness value at high frequencies. Therefore, if the time domain data of the cutting force is directly processed by directly using the established machine tool overall stiffness model, the predicted result and the actual result are necessarily greatly different.
In order to fully utilize the dynamic stiffness curve of the machine tool and improve the prediction accuracy of the amplitude of the machine tool, the time domain signal of the cutting force is subjected to frequency domain conversion by adopting fast Fourier transform, as shown in FIG. 12,
when the machine tool is in steady cutting, the cutting force exhibits several distinct spikes in the frequency domain, all corresponding to the fundamental and double frequency of the cutting force calculated from the spindle speed. For cutting forces of different frequencies, the machine tool stiffness is different and needs to be calculated separately. After fast Fourier transform, the cutting forces with different frequencies can be completely separated, the cutting forces with different frequencies are brought into respective dynamic stiffness values, and then the final vibration value of the machine tool can be calculated by using a multi-body model. However, the cutting force has phase change, the dynamic stiffness of the machine tool also has phase problem, and the superposition result of two different forces is quite different when the two different forces have the same phase and the phase is different by 180 degrees. Therefore, when predicting the vibration, the amplitudes cannot be simply added, and the phase factor must be sufficiently considered.
The fast fourier transform may calculate the phase of the cutting force signal in the frequency domain, in addition to the amplitude of the cutting force signal in the frequency domain. In addition, the dynamic stiffness (the ratio of force to displacement) of the machine tool has amplitude-frequency characteristics and also phase-frequency characteristics. Therefore, the vibration displacement including the phase factor can be calculated, and the vibration displacement is substituted into the machine tool overall stiffness model, so that the predicted vibration value of the machine tool can be calculated.
According to the modeling method for predicting the vibration amplitude of the numerical control machine tool, provided by the embodiment of the application, the overall stiffness model of the numerical control machine tool is established, the dynamic stiffness of each joint surface of the machine tool is obtained, the dynamic stiffness data of the numerical control machine tool under different frequencies are obtained based on the dynamic stiffness of each joint surface, the dynamic stiffness curve of the numerical control machine tool is generated, the average cutting force of the numerical control machine tool is substituted into the overall stiffness model, and meanwhile, the final overall stiffness model for predicting the relative amplitude between the main shaft and the workbench of the machine tool during machining is obtained by adopting fast Fourier transform based on the dynamic stiffness curve. Therefore, the overall machine tool modeling mode combining the multi-body theory, dynamic stiffness, machine learning and fast Fourier transform is adopted, the model is used for predicting the relative amplitude between the main shaft and the workbench during machine tool machining, and the prediction accuracy is improved.
Next, a modeling apparatus for predicting vibration amplitude of a numerically controlled machine tool according to an embodiment of the present application will be described with reference to the accompanying drawings.
Fig. 13 is a block diagram schematically illustrating a modeling apparatus for predicting vibration amplitude of a numerical control machine according to an embodiment of the present application.
As shown in fig. 13, the modeling apparatus 10 for predicting the vibration amplitude of the numerical control machine includes: a first acquisition module 100, a generation module 200 and a second acquisition module 300.
The first obtaining module 100 is used for establishing an overall stiffness model of the numerical control machine tool and obtaining dynamic stiffness of each joint surface of the machine tool;
the generating module 200 is configured to obtain dynamic stiffness data of the numerical control machine tool at different frequencies based on the dynamic stiffness of each joint surface, and generate a dynamic stiffness curve of the numerical control machine tool; and
the second obtaining module 300 is configured to substitute the average cutting force of the numerical control machine tool into the overall stiffness model, and obtain a final overall stiffness model for predicting the relative amplitude between the spindle and the table of the machine tool during machining by using fast fourier transform based on the dynamic stiffness curve.
Optionally, the first obtaining module 100 is specifically configured to:
analyzing a main shaft of the numerical control machine tool to obtain a matrix transformation formula of the main shaft;
and calculating the deformation deviation of the main shaft according to the cutting force component and the dynamic stiffness of each part, and establishing an integral stiffness model.
Optionally, the first obtaining module 100 is specifically configured to:
and adjusting the rotation angle of the main shaft, acquiring dynamic stiffness values of the workbench of the machine tool containing amplitude-frequency characteristics and phase-frequency characteristics at different angles, adjusting the position of the machine tool, and acquiring dynamic stiffness values of the main shaft of the machine tool containing amplitude-frequency characteristics and phase-frequency characteristics at different positions.
Optionally, the generating module 200 is specifically configured to:
and (3) measuring dynamic stiffness data of the machine tool under different frequencies by adopting an orthogonal test method, and learning the change relation between a dynamic stiffness curve and a position by utilizing a machine learning model to obtain the dynamic stiffness data.
Optionally, the second obtaining module 300 is specifically configured to:
and performing frequency domain conversion on the time domain signal of the cutting force by adopting fast Fourier transform, and bringing the cutting force with different frequencies into corresponding dynamic stiffness values.
It should be noted that the explanation of the embodiment of the modeling method for predicting the vibration amplitude of the numerical control machine tool is also applicable to the modeling apparatus for predicting the vibration amplitude of the numerical control machine tool in the embodiment, and details are not repeated here.
According to the modeling device for predicting the vibration amplitude of the numerical control machine tool, which is provided by the embodiment of the application, the overall stiffness model of the numerical control machine tool is established, the dynamic stiffness of each joint surface of the machine tool is obtained, the dynamic stiffness data of the numerical control machine tool under different frequencies are obtained based on the dynamic stiffness of each joint surface, the dynamic stiffness curve of the numerical control machine tool is generated, the average cutting force of the numerical control machine tool is substituted into the overall stiffness model, and meanwhile, the final overall stiffness model for predicting the relative amplitude between the main shaft and the workbench during machining of the machine tool is obtained based on the dynamic stiffness curve by adopting fast Fourier transform. Therefore, the overall machine tool modeling mode combining the multi-body theory, dynamic stiffness, machine learning and fast Fourier transform is adopted, the model is used for predicting the relative amplitude between the main shaft and the workbench during machine tool machining, and the prediction accuracy is improved.
Fig. 14 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device may include:
a memory 1401, a processor 1402, and a computer program stored on the memory 1401 and executable on the processor 1402.
The processor 1402, when executing the program, implements the modeling method for predicting the vibration amplitude of the numerical control machine provided in the above-described embodiments.
Further, the electronic device further includes:
a communication interface 1403 for communication between the memory 1401 and the processor 1402.
A memory 1401 for storing a computer program that is executable on the processor 1402.
The memory 1401 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 1401, the processor 1402, and the communication interface 1403 are implemented independently, the communication interface 1403, the memory 1401, and the processor 1402 can be connected to each other via a bus and communication with each other can be accomplished. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 14, but this is not intended to represent only one bus or type of bus.
Optionally, in a specific implementation, if the memory 1401, the processor 1402 and the communication interface 1403 are integrated into a chip, the memory 1401, the processor 1402 and the communication interface 1403 may complete communication with each other through an internal interface.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the modeling method for vibration amplitude prediction of a numerically controlled machine tool as above.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of implementing the embodiments of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. A modeling method for predicting vibration amplitude of a numerical control machine tool is characterized by comprising the following steps:
establishing an integral rigidity model of the numerical control machine tool, and acquiring the dynamic rigidity of each joint surface of the machine tool;
based on the dynamic stiffness of each joint surface, obtaining dynamic stiffness data of the numerical control machine tool under different frequencies, and generating a dynamic stiffness curve of the numerical control machine tool; and
and substituting the average cutting force of the numerical control machine tool into the overall stiffness model, and obtaining a final overall stiffness model for predicting the relative amplitude between the main shaft and the workbench during machining of the machine tool by adopting fast Fourier transform based on the dynamic stiffness curve.
2. The method of claim 1, wherein said establishing a global stiffness model of the numerically controlled machine tool comprises:
analyzing the main shaft of the numerical control machine tool to obtain a matrix transformation formula of the main shaft;
and calculating the deformation deviation of the main shaft through the cutting force component and the dynamic stiffness of each part, and establishing the integral stiffness model.
3. The method of claim 1, wherein the obtaining the dynamic stiffness of each joint surface of the machine tool comprises:
and adjusting the rotation angle of the main shaft, acquiring dynamic stiffness values of the workbench of the machine tool, which contain amplitude-frequency characteristics and phase-frequency characteristics, at different angles, adjusting the position of the machine tool, and acquiring dynamic stiffness values of the main shaft of the machine tool, which contain amplitude-frequency characteristics and phase-frequency characteristics, at different positions.
4. The method of claim 1, wherein the obtaining dynamic stiffness data of the machine tool at different frequencies comprises:
and measuring dynamic stiffness data of the machine tool under different frequencies by adopting an orthogonal test method, and learning the change relation between a dynamic stiffness curve and a position by utilizing a machine learning model to obtain the dynamic stiffness data.
5. The method according to any one of claims 1 to 4, wherein the obtaining of the final global stiffness model for predicting the relative amplitude between the spindle and the table of the machine tool during machining by using fast Fourier transform based on the dynamic stiffness curve comprises:
and performing frequency domain conversion on the time domain signal of the cutting force by adopting the fast Fourier transform, and bringing the cutting force with different frequencies into corresponding dynamic stiffness values.
6. A modeling device for predicting vibration amplitude of a numerical control machine tool is characterized by comprising:
the first acquisition module is used for establishing an integral rigidity model of the numerical control machine tool and acquiring the dynamic rigidity of each joint surface of the machine tool;
the generating module is used for obtaining dynamic stiffness data of the numerical control machine tool under different frequencies based on the dynamic stiffness of each joint surface and generating a dynamic stiffness curve of the numerical control machine tool; and
and the second acquisition module is used for substituting the average cutting force of the numerical control machine tool into the overall stiffness model and simultaneously obtaining a final overall stiffness model for predicting the relative amplitude between the main shaft and the workbench during machining of the machine tool by adopting fast Fourier transform based on the dynamic stiffness curve.
7. The apparatus of claim 6, wherein the first obtaining module is specifically configured to:
analyzing the main shaft of the numerical control machine tool to obtain a matrix transformation formula of the main shaft;
and calculating the deformation deviation of the main shaft through the cutting force component and the dynamic stiffness of each part, and establishing the integral stiffness model.
8. The apparatus of claim 6, wherein the first obtaining module is specifically configured to:
adjusting the rotation angle of the main shaft, acquiring dynamic stiffness values of a workbench of the machine tool under different angles, wherein the dynamic stiffness values comprise amplitude-frequency characteristics and phase-frequency characteristics, adjusting the position of the machine tool, and acquiring dynamic stiffness values of the main shaft of the machine tool at different positions, wherein the amplitude-frequency characteristics and the phase-frequency characteristics are included;
the generation module is specifically configured to:
measuring dynamic stiffness data of the machine tool under different frequencies by adopting an orthogonal test method, and learning the change relation between a dynamic stiffness curve and a position by utilizing a machine learning model to obtain the dynamic stiffness data;
the second obtaining module is specifically configured to:
and performing frequency domain conversion on the time domain signal of the cutting force by adopting the fast Fourier transform, and bringing the cutting force with different frequencies into corresponding dynamic stiffness values. .
9. An electronic device, comprising: a memory, a processor and a computer program stored on said memory and executable on said processor, said processor executing said program to implement the modeling method of numerical control machine vibration amplitude prediction according to any one of claims 1 to 5.
10. A computer-readable storage medium on which a computer program is stored, the program being executed by a processor for implementing the modeling method for vibration amplitude prediction of a numerically controlled machine tool according to any of claims 1 to 5.
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