Modeling method, device, equipment and medium for predicting vibration amplitude of CNC machine tools

Abstract

The present application discloses a modeling method, device, equipment and medium for predicting the vibration amplitude of a CNC machine tool, wherein the method includes: establishing an overall stiffness model of the CNC machine tool and obtaining the dynamic stiffness of each joint surface of the machine tool; based on the dynamic stiffness of each joint surface, obtaining the dynamic stiffness data of the CNC machine tool at different frequencies, and generating the dynamic stiffness curve of the CNC machine tool; substituting the average cutting force of the CNC machine tool into the overall stiffness model, and using the fast Fourier transform based on the dynamic stiffness curve to obtain the final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during processing. The method adopts a machine tool overall modeling method that combines multi-body theory, dynamic stiffness, machine learning and fast Fourier transform, and uses the model to predict the relative amplitude between the spindle and the worktable during machine tool processing, thereby improving the accuracy of the prediction.

Description

Technical Field

[0001] The present application relates to the technical field of machine tool vibration amplitude prediction, and in particular to a modeling method, device, equipment and medium for predicting the vibration amplitude of a numerically controlled machine tool. Background Art

[0002] The surface quality of parts is an important representation of product performance and an important indicator of machine tool processing accuracy. It mainly includes surface roughness, surface layer hardening, surface waviness and surface residual stress. In actual processing, there are many factors that affect the surface quality of parts, including cutting conditions, material expansion and recovery, material accumulation, crystal direction, tool position and direction, tool wear and machine tool vibration. The influence of machine tool vibration on surface quality is crucial. The influence of machine tool vibration on part surface quality mainly comes from two sources: one is the high-frequency vibration caused by cutting force; the other is the low-frequency vibration of the structure. For machine tool structures composed of multiple components, the vibration of sensitive structures and structures at the main frequencies as the weak links of the machine tool system will have an important impact on the surface formation of parts. The low-frequency vibration caused by the structure depends on the inherent characteristics of the machine tool such as the assembly error, and the amplitude caused by it can be considered as a constant value related to the machine tool itself. The vibration caused by cutting force is generally the direction of research.

[0003] During the machining process, the interaction of various influencing factors will appear in the form of vibration in the machining process system, which will cause the cutting force to fluctuate and the cutting layer parameters to change. The relative position change between the tool and the workpiece causes the tool to deviate from the ideal position, resulting in "undercutting" or "overcutting" on the workpiece, leaving an uneven surface morphology on the machined workpiece surface. Vibration during cutting will not only reduce the surface quality of the workpiece, but also aggravate tool wear and destroy the connection characteristics between machine tool components. In actual production, in order to ensure the required workpiece processing quality, in many cases, the cutting amount has to be reduced, so that the cutting performance of the machine tool and tool cannot be fully utilized, which greatly limits the improvement of mechanical processing production efficiency.

[0004] Therefore, it is very important to establish a structural model of the machine tool and predict the amplitude of the machine tool for actual processing.

[0005] Application Contents

[0006] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0007] To this end, the first purpose of the present application is to propose a modeling method for predicting the vibration amplitude of a CNC machine tool.

[0008] The second objective of the present application is to propose a modeling device for predicting the vibration amplitude of a CNC machine tool.

[0009] The third objective of the present application is to provide an electronic device.

[0010] A fourth objective of the present application is to provide a computer-readable storage medium.

[0011] To achieve the above-mentioned purpose, the first embodiment of the present application provides a modeling method for predicting the vibration amplitude of a CNC machine tool, comprising the following steps:

[0012] Establishing an overall stiffness model of a CNC machine tool and obtaining the dynamic stiffness of each joint surface of the machine tool;

[0013] Based on the dynamic stiffness of each of the joint surfaces, dynamic stiffness data of the CNC machine tool at different frequencies are obtained to generate a dynamic stiffness curve of the CNC machine tool; and

[0014] The average cutting force of the CNC machine tool is substituted into the overall stiffness model, and a final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during machining is obtained by fast Fourier transform based on the dynamic stiffness curve.

[0015] According to the modeling method for predicting the vibration amplitude of the CNC machine tool of the embodiment of the present application, the overall stiffness model of the CNC machine tool is established, and the dynamic stiffness of each joint surface of the machine tool is obtained. Based on the dynamic stiffness of each joint surface, the dynamic stiffness data of the CNC machine tool at different frequencies is obtained, and the dynamic stiffness curve of the CNC machine tool is generated. The average cutting force of the CNC machine tool is substituted into the overall stiffness model, and the final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during processing is obtained by fast Fourier transform based on the dynamic stiffness curve. Thus, the overall modeling method of the machine tool combining multi-body theory, dynamic stiffness, machine learning and fast Fourier transform is adopted, and the model is used to predict the relative amplitude between the spindle and the worktable during machine tool processing, thereby improving the accuracy of the prediction.

[0016] In addition, the modeling method for predicting the vibration amplitude of a CNC machine tool according to the above embodiment of the present application may also have the following additional technical features:

[0017] Optionally, establishing the overall stiffness model of the CNC machine tool includes:

[0018] Analyze the spindle of the CNC machine tool to obtain a matrix transformation formula of the spindle;

[0019] The deformation deviation of the spindle is calculated through the cutting force components and the dynamic stiffness of each part, and the overall stiffness model is established.

[0020] Optionally, obtaining the dynamic stiffness of each joint surface of the machine tool includes:

[0021] The rotation angle of the spindle is adjusted to obtain the dynamic stiffness values ​​of the worktable of the machine tool at different angles, including the amplitude-frequency characteristics and the phase-frequency characteristics; and the position of the machine tool is adjusted to obtain the dynamic stiffness values ​​of the spindle of the machine tool at different positions, including the amplitude-frequency characteristics and the phase-frequency characteristics.

[0022] Optionally, obtaining dynamic stiffness data of the machine tool at different frequencies includes:

[0023] While the orthogonal test method is used to measure the dynamic stiffness data of the machine tool at different frequencies, a machine learning model is used to learn the relationship between the dynamic stiffness curve and the position change to obtain the dynamic stiffness data.

[0024] Optionally, the final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during machining is obtained by using fast Fourier transform based on the dynamic stiffness curve, including:

[0025] The fast Fourier transform is used to perform frequency domain conversion on the time domain signal of the cutting force, and the cutting forces of different frequencies are brought into corresponding dynamic stiffness values.

[0026] To achieve the above-mentioned purpose, the second aspect of the present application provides a modeling device for predicting the vibration amplitude of a CNC machine tool, comprising:

[0027] A first acquisition module is used to establish an overall stiffness model of a CNC machine tool and acquire the dynamic stiffness of each joint surface of the machine tool;

[0028] A generating module, configured to obtain the dynamic stiffness data of the CNC machine tool at different frequencies based on the dynamic stiffness of each joint surface, and generate a dynamic stiffness curve of the CNC machine tool; and

[0029] The second acquisition module is used to substitute the average cutting force of the CNC machine tool into the overall stiffness model, and use fast Fourier transform based on the dynamic stiffness curve to obtain a final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during processing.

[0030] According to the modeling device for predicting the vibration amplitude of the CNC machine tool of the embodiment of the present application, the overall stiffness model of the CNC machine tool is established, and the dynamic stiffness of each joint surface of the machine tool is obtained. Based on the dynamic stiffness of each joint surface, the dynamic stiffness data of the CNC machine tool at different frequencies is obtained, and the dynamic stiffness curve of the CNC machine tool is generated. The average cutting force of the CNC machine tool is substituted into the overall stiffness model, and the final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during processing is obtained by fast Fourier transform based on the dynamic stiffness curve. Thus, the overall modeling method of the machine tool combining multi-body theory, dynamic stiffness, machine learning and fast Fourier transform is adopted, and the model is used to predict the relative amplitude between the spindle and the worktable during machine tool processing, thereby improving the accuracy of the prediction.

[0031] Optionally, the first acquisition module is specifically configured to:

[0032] Analyze the spindle of the CNC machine tool to obtain a matrix transformation formula of the spindle;

[0033] The deformation deviation of the spindle is calculated through the cutting force components and the dynamic stiffness of each part, and the overall stiffness model is established.

[0034] Optionally, the first acquisition module is specifically configured to:

[0035] The rotation angle of the spindle is adjusted to obtain the dynamic stiffness values ​​of the worktable of the machine tool at different angles, including the amplitude-frequency characteristics and the phase-frequency characteristics; and the position of the machine tool is adjusted to obtain the dynamic stiffness values ​​of the spindle of the machine tool at different positions, including the amplitude-frequency characteristics and the phase-frequency characteristics.

[0036] Optionally, the generating module is specifically used to:

[0037] While the orthogonal test method is used to measure the dynamic stiffness data of the machine tool at different frequencies, a machine learning model is used to learn the relationship between the dynamic stiffness curve and the position change to obtain the dynamic stiffness data.

[0038] Optionally, the second acquisition module is specifically used to:

[0039] The fast Fourier transform is used to perform frequency domain conversion on the time domain signal of the cutting force, and the cutting forces of different frequencies are brought into corresponding dynamic stiffness values.

[0040] To achieve the above-mentioned objectives, the third aspect of the present application provides an electronic device, comprising: 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, and the instructions are configured to execute the modeling method for predicting the vibration amplitude of a CNC machine tool as described in the above-mentioned embodiment.

[0041] To achieve the above-mentioned purpose, the fourth aspect of the present application provides a computer-readable storage medium on which a computer program is stored, and the program is executed by a processor to implement the modeling method for predicting the vibration amplitude of a CNC machine tool as described in the above-mentioned embodiment.

[0042] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0044] Figure 1 A flowchart of a modeling method for predicting vibration amplitude of a CNC machine tool provided according to an embodiment of the present application;

[0045] Figure 2 A simplified example diagram of a five-axis CNC machine tool according to an embodiment of the present application;

[0046] Figure 3 A simulation and simplified schematic diagram of the A and C axes of a machine tool according to an embodiment of the present application;

[0047] Figure 4 A three-dimensional example diagram of an experimental device for dynamic stiffness of a machine tool spindle according to an embodiment of the present application;

[0048] Figure 5 This is an example diagram of a stiffness experiment of quantity A according to an embodiment of the present application;

[0049] Figure 6 An example diagram of a stiffness experiment of a quantity according to an embodiment of the present application;

[0050] Figure 7 This is an example diagram of a stiffness experiment in the θ direction according to an embodiment of the present application;

[0051] Figure 8 This is an example diagram of a stiffness experiment in the α direction according to an embodiment of the present application;

[0052] Fig. 9 Schematic diagram of the Y-direction stiffness of a machine tool spindle according to an embodiment of the present application;

[0053] Fig.10 This is an example diagram of the stiffness of the spindle of a machine tool in the X direction according to one embodiment of the present application;

[0054] Fig.11 Schematic diagram of the stiffness of the spindle of a machine tool in the X direction according to one embodiment of the present application;

[0055] Fig.12 This is an example diagram of the result of frequency domain conversion of the time domain signal of the cutting force using fast Fourier transform according to one embodiment of the present application;

[0056] Fig.13 A block diagram of a modeling device for predicting vibration amplitude of a CNC machine tool according to an embodiment of the present application;

[0057] Fig.14 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0058] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0059] The following describes the modeling method, device, equipment and medium for predicting the vibration amplitude of a CNC machine tool proposed in accordance with the embodiments of the present application with reference to the accompanying drawings. First, the modeling method for predicting the vibration amplitude of a CNC machine tool proposed in accordance with the embodiments of the present application will be described with reference to the accompanying drawings.

[0060] Before introducing the modeling method for predicting the vibration amplitude of a CNC machine tool according to an embodiment of the present application, a multi-body system theory in the related art is briefly introduced.

[0061] Vibration often occurs during the operation of machine tools, which has an adverse effect on the stiffness and precision of machined parts. With the continuous development of the machinery manufacturing industry, modern manufacturing has continuously improved the requirements for product processing quality, which requires machine tools to have good overall performance, and their reliability, processing accuracy, and vibration resistance must meet processing standards. Therefore, it is of great significance to model and analyze machine tools, study their dynamic characteristics, and optimize and improve the weak links of machine tool structures. Modal analysis is the main method for studying the performance of machine tools and their substructures. Reducing machine tool vibration and improving machine tool reliability are important links that need to be considered during the research process.

[0062] Generally, modal analysis is used to study the dynamic characteristics of machine tools. Modal analysis is to obtain a series of important parameters of the structure through experiments: main vibration mode, natural frequency, modal mass, stiffness, etc., and to study the overall performance of the structure by analyzing these modal parameters. There are three types of applications of modal analysis in specific working conditions: (1) finite element analysis based on ModalAnalysis software such as HyperMesh, MESCOPVES and EDMModel; (2) experimental modal analysis based on vibration testing to establish motion equations; (3) working condition modal analysis based on identifying modal parameters under measurable response conditions. The three methods have their own 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 the characteristics of the joint surface involved cannot be determined and can only be used as a reference, and the data accuracy is poor. Experimental modal analysis obtains more accurate data by actually testing and analyzing the structure as a whole, but it still has limitations in the specific application process and is insufficient when modeling and analyzing machine tools and predicting results. Operating modal analysis only requires the collection of vibration response data without the need to test the input mechanism. The process is relatively simple, but there are limitations in the solution method and it cannot solve complex problems.

[0063] Multi-body system theory is a discipline that studies multi-body system problems. It has good versatility and systematicity. Especially in today's era of rapid development of electronic computers, multi-body system theory makes full use of computer algorithms and programs to perform dynamics and kinematics calculations of complex systems, thereby meeting the needs of complex problem analysis and calculation that cannot be solved by classical dynamics and kinematic theory. At present, multi-body systems are mainly used to model the geometric errors of machine tools. Generally, the topological structure and open-loop motion chain of machine tools are established according to the structure of CNC machine tools, and then the transformation relationship between the differential motion matrix and the homogeneous transformation matrix between the two coordinate systems is established according to the differential transformation relationship between different rectangular coordinate systems, and then the geometric error of the machine tool is calculated. This geometric error can be caused by temperature, average cutting force, or thermal deformation of the machine tool. Multi-body system theory has advantages in studying the overall modeling of machine tools, but at present, multi-body system theory is mainly applied to the geometric errors of machine tools, and lacks research on the vibration of machine tools.

[0064] Specifically, Figure 1 A flow chart of a modeling method for predicting vibration amplitude of a CNC machine tool provided in an embodiment of the present application.

[0065] like Figure 1 As shown, the modeling method for predicting the vibration amplitude of a CNC machine tool includes the following steps:

[0066] In step S101, an overall stiffness model of a CNC machine tool is established, and the dynamic stiffness of each joint surface of the machine tool is obtained.

[0067] Optionally, in some embodiments, an overall stiffness model of a CNC machine tool is established, including: analyzing the spindle of the CNC machine tool to obtain a matrix transformation formula of the spindle; calculating the deformation deviation of the spindle through cutting force components and the dynamic stiffness of each part to establish an overall stiffness model.

[0068] Specifically, taking Kede five-axis CNC machine tool as an example, Figure 2 This is a simplified schematic diagram of the structure of the Kede five-axis CNC machine tool, where 2, 3, and 4 represent the X-axis, Y-axis, and Z-axis of the machine tool, respectively, and the other 5 and 6 represent the A-axis and C-axis of the machine tool, respectively. For a machine tool, the main weak link of its mechanical structure is the joint where the various parts of the machine tool are connected, so the deformation of the machine tool can be concentrated entirely at the joint of the machine tool.

[0069] First, the embodiment of the present application can analyze the A and C axes of the machine tool. Figure 3 (a) is the simulation diagram of the A and C axes of the machine tool. Figure 3(b) is a simplified schematic diagram of the A and C axes of the machine tool. The rotation angle of the A axis is represented by θ, the rotation angle of the C axis is represented by α, and the angle deviations of the two axes due to stiffness are represented by dθ and dα, respectively. In addition, due to the stiffness of the A and C axes of the machine tool, other deformation deviations will occur in addition to the angle, which are specifically manifested as the length changes of A and a, represented by dA and da, respectively.

[0070] Substituting all the above parameters and their deviations into the matrix transformation formula of the A-axis, after removing the second-order small quantity, it can be written in the form of formula (1), which is as follows:

[0071]

[0072] Similarly, the matrix transformation formula of the C axis can be processed and expressed as formula (2) after removing the second-order small quantity. Formula (2) is as follows:

[0073]

[0074] The same is true for the main axis. The transformation matrix can be obtained as formula (3). Formula (3) is as follows:

[0075]

[0076]

[0077]

[0078] Among them, dA, da, dθ, and dα can be obtained by calculating the cutting force components and the dynamic stiffness of each part, respectively. The same is true for the spindle. The overall stiffness model of the machine tool is established through this method.

[0079] Furthermore, in some embodiments, the dynamic stiffness of each joint surface of the machine tool is obtained, including: adjusting the rotation angle of the main shaft to obtain the dynamic stiffness values ​​of the worktable of the machine tool at different angles including the amplitude-frequency characteristics and the phase-frequency characteristics, and adjusting the position of the machine tool to obtain the dynamic stiffness values ​​of the main shaft of the machine tool at different positions including the amplitude-frequency characteristics and the phase-frequency characteristics.

[0080] 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, and z. The deformation size can be obtained through the cutting force and the dynamic stiffness curve of the machine tool. The dynamic stiffness of the machine tool can be obtained through experiments and machine learning.

[0081] The simulation diagram of the experimental device for dynamic stiffness of machine tools is shown in the figure below. Figure 4As shown, the tool bar of the machine tool is connected to the force sensor through a pair of homemade clamps made of stainless steel, and the force sensor is also connected to the exciter through a homemade clamp made of stainless steel. In addition, in order to prevent the force sensor from being subjected to too much radial force, a long stainless steel rod is used for connection.

[0082] In order to verify the relationship between the dynamic stiffness of the machine tool spindle and the spindle position, four different spindle positions are randomly selected, the machine tool spindle is excited respectively, and the dynamic stiffness of the machine tool spindle is calculated. Figures 5 to 11 As shown, Figure 5 (a) is the front view of the stiffness test of quantity A. Figure 5 (b) is a top view of the stiffness test of quantity A, where an exciter applies an exciting force F at the arrow position; Figure 6 (a) is the front view of the stiffness test of quantity a. Figure 6 (b) is a top view of the stiffness test of quantity a, and the excitation force F is applied by the exciter at the arrow position; Figure 7 (a) is the front view of the stiffness test in the θ direction. Figure 7 (b) is a top view of the stiffness test in the θ direction, and an excitation force F is applied by an exciter at the arrow position; Figure 8 (a) is the front view of the stiffness test in the α direction. Figure 8 (b) is a top view of the stiffness test in the α direction, and the excitation force F is applied by the exciter at the arrow position; Fig. 9 (a) is the front view of the Y-direction stiffness test of the machine tool spindle. Fig. 9 (b) is the left view of the Y-direction stiffness test of the machine tool spindle, and the excitation force F is applied by the exciter at the arrow position; Fig.10 (a) is the front view of the X-direction stiffness test of the machine tool spindle. Fig.10 (b) is the left view of the X-direction stiffness test of the machine tool spindle, and the excitation force F is applied by the exciter at the arrow position; Fig.11 (a) is the front view of the X-direction stiffness test of the machine tool spindle. Fig.11 (b) is the left view of the X-direction stiffness test of the machine tool spindle. The excitation force F is applied by the exciter at the arrow position. Considering the spindle speed range and the number of cutting edges of the tool during machine tool processing, the excitation range is selected to be 50-600.

[0083] In addition, when conducting experiments on the workbench, the angles α and θ are adjusted respectively to measure the dynamic stiffness values ​​of the machine tool workbench at different angles, including the amplitude-frequency characteristics and the phase-frequency characteristics. For the spindle, the same method is used to adjust the x, y, and z values ​​respectively, and measure the dynamic stiffness values ​​of the machine tool spindle at different positions, including the amplitude-frequency characteristics and the phase-frequency characteristics.

[0084] In step S102, based on the dynamic stiffness of each joint surface, the dynamic stiffness data of the CNC machine tool at different frequencies is obtained, and a dynamic stiffness curve of the CNC machine tool is generated.

[0085] Optionally, in some embodiments, dynamic stiffness data of a machine tool at different frequencies is obtained, including: while measuring the dynamic stiffness data of the machine tool at different frequencies using an orthogonal test method, a machine learning model is used to learn the relationship between the dynamic stiffness curve and the position change to obtain the dynamic stiffness data.

[0086] It should be understood that the dynamic stiffness values ​​at different positions of the machine tool will have certain differences. Therefore, when measuring the dynamic stiffness of the machine tool, it is necessary to constantly adjust the position of the spindle and the position of the worktable. For example, for the worktable, the parameters that can be selected are the angle of the cradle axis and the angle of the turntable. Select the corresponding angle within the working range of the machine tool and measure the dynamic stiffness of the machine tool at different frequencies. The same is true for the spindle of the machine tool. Select different values ​​of x, y, and z within the working range to calculate the dynamic stiffness of the machine tool. The dynamic stiffness data of the machine tool at different frequencies can be obtained through measurement.

[0087] Since the experiment is conducted by selecting some angles and positions, no matter how densely the experimental points are selected, the final data obtained must be discrete data of dynamic stiffness. Generally speaking, when encountering such a situation in the experiment, interpolation can be used directly to obtain data at any position simply and quickly. However, when measuring the dynamic stiffness of machine tools, if interpolation is simply used, there will be serious defects.

[0088] If the workbench is to be experimentally measured, the A-axis angle and the C-axis angle should be selected separately. Assuming that a 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 angles of the C-axis are 0°, 20°, 40°…340°, so the number of experiments required is 10×18=180 times. For the spindle, the main working range of the machine tool is 400×400×400. If a dynamic stiffness test is performed every 20mm, 20×20×20=8000 experiments are required. It can be clearly seen that because the interpolation method requires measurement of each test point, the amount of experiments required for dynamic stiffness experiments using the traditional interpolation method is very large.

[0089] In order to reduce the number of experiments, the embodiment of the present application can adopt the orthogonal test method, thereby greatly reducing the number of experiments. However, because the orthogonal test method causes the loss of many data points, it is impossible to use the traditional interpolation method to obtain the dynamic stiffness of the machine tool at any position.

[0090] In order to solve the problem that the traditional interpolation method cannot be used, the embodiment of the present application intends to use a machine learning model to learn the change of the dynamic stiffness curve with position. Because machine learning is used to determine the change of the dynamic stiffness curve of the machine tool, a regression algorithm is required. The most commonly used regression algorithms are: decision tree regression algorithm, linear regression algorithm, SVM regression algorithm, KNN regression algorithm, random forest regression algorithm, Adaboost regression algorithm, GBRT regression algorithm, Bagging regression algorithm, ExtraTree extreme random tree regression algorithm, a total of nine algorithms. In order to determine the best algorithm, all several algorithms are tried once. Among them, the five algorithms of decision tree regression, Bagging regression, KNN regression, random deep forest regression, and extreme random tree regression perform well in both running time and accuracy. Therefore, these five algorithms are selected for regression operations. In addition, in order to ensure the accuracy of the results, 90% of the data are selected for training, and 10% of the data are used to verify the final score of the algorithm. Each time, all five algorithms are used to calculate the dynamic stiffness data once, and then the three algorithms with the highest scores are selected. The stiffness values ​​of the three algorithms are averaged to obtain the best result. The difference between the result and the interpolated result is no more than 3%, indicating that this method is accurate and effective.

[0091] In step S103, the average cutting force of the CNC machine tool is substituted into the overall stiffness model, and a final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during machining is obtained by fast Fourier transform based on the dynamic stiffness curve.

[0092] Optionally, in some embodiments, a final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during machining is obtained based on the dynamic stiffness curve using fast Fourier transform, including: using fast Fourier transform to convert the time domain signal of the cutting force into the frequency domain, and bringing the cutting forces of different frequencies into corresponding dynamic stiffness values.

[0093] Specifically, if the cutting force signal is not specially processed, the model can only be used to process the static deformation of the machine tool. That is, by calculating the average cutting force of the machine tool and then bringing it into the overall model of the machine tool, the static deformation of the machine tool (also called the tool yield size of the machine tool) can be calculated. However, in actual production, the factory already has very mature technology and means to compensate for tool yield. The machined workpiece can be measured after processing and compensated according to the actual measurement. Therefore, if the model can only predict and compensate for the static deformation of the machine tool, it has insufficient practical engineering significance and cannot provide good guidance and help for existing engineering problems.

[0094] Due to actual engineering needs, the model needs to predict the dynamic deformation of the machine tool. Among the dynamic characteristics of the machine tool, the amplitude of the machine tool has the greatest impact on the surface quality in actual processing. Therefore, the model needs to be able to make accurate predictions on the amplitude of the machine tool.

[0095] However, the difficulty of analyzing the dynamic characteristics of machine tools lies in the fact that various signals are mixed in the time domain signal of the cutting force of the machine tool, making it difficult to make an accurate analysis. In addition, under different cutting forces, the dynamic stiffness value of the machine tool is different. At low frequencies, it is close to the static stiffness value of the machine tool, and at high frequencies, it is significantly different from the low frequencies. Therefore, if the above-mentioned machine tool overall stiffness model is directly used to directly process the time domain data of the cutting force, the predicted results will inevitably differ greatly from the actual results.

[0096] In order to make full use of the dynamic stiffness curve of the machine tool and improve the prediction accuracy of the machine tool amplitude, it is proposed to use fast Fourier transform to convert the time domain signal of the cutting force into the frequency domain, such as Fig.12 As shown,

[0097] When the machine tool is in stable cutting, the cutting force has several obvious peaks in the frequency domain, all corresponding to the fundamental frequency and multiple frequency of the cutting force calculated by the spindle speed. For cutting forces of different frequencies, the stiffness of the machine tool is different and needs to be calculated separately. After fast Fourier transform, the cutting forces of different frequencies can be completely separated, and the cutting forces of different frequencies can be brought into their respective dynamic stiffness values. Then, the final vibration value of the machine tool can be calculated using the multi-body model. However, there are phase changes in the cutting force, and there are also phase problems in the dynamic stiffness of the machine tool. When two different forces are in the same phase and 180° apart in phase, the superposition results are completely different. Therefore, when predicting vibration, the amplitude cannot be simply added, and its phase factor must be fully considered.

[0098] In addition to calculating the amplitude of the cutting force signal in the frequency domain, the fast Fourier transform can also calculate the phase of the cutting force signal in the frequency domain. In addition, the dynamic stiffness of the machine tool (the ratio of force to displacement) has not only amplitude-frequency characteristics but also phase-frequency characteristics. From this, the vibration displacement including the phase factor can be calculated, and by bringing it into the above-mentioned machine tool overall stiffness model, the vibration prediction value of the machine tool can be calculated.

[0099] According to the modeling method for predicting the vibration amplitude of a CNC machine tool proposed in the embodiment of the present application, an overall stiffness model of the CNC machine tool is established, and the dynamic stiffness of each joint surface of the machine tool is obtained. Based on the dynamic stiffness of each joint surface, the dynamic stiffness data of the CNC machine tool at different frequencies is obtained, and the dynamic stiffness curve of the CNC machine tool is generated. The average cutting force of the CNC machine tool is substituted into the overall stiffness model, and the final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during processing is obtained by fast Fourier transform based on the dynamic stiffness curve. Thus, a machine tool overall modeling method combining multi-body theory, dynamic stiffness, machine learning and fast Fourier transform is adopted, and the model is used to predict the relative amplitude between the spindle and the worktable during machine tool processing, thereby improving the accuracy of the prediction.

[0100] Next, a modeling device for predicting the vibration amplitude of a CNC machine tool proposed in an embodiment of the present application will be described with reference to the accompanying drawings.

[0101] Fig.13 It is a block diagram of a modeling device for predicting vibration amplitude of a CNC machine tool according to an embodiment of the present application.

[0102] like Fig.13 As shown, the modeling device 10 for predicting the vibration amplitude of a numerically controlled machine tool includes: a first acquisition module 100 , a generation module 200 and a second acquisition module 300 .

[0103] The first acquisition module 100 is used to establish an overall stiffness model of the CNC machine tool and obtain the dynamic stiffness of each joint surface of the machine tool;

[0104] The generating module 200 is used to obtain the dynamic stiffness data of the CNC machine tool at different frequencies based on the dynamic stiffness of each joint surface, and generate a dynamic stiffness curve of the CNC machine tool; and

[0105] The second acquisition module 300 is used to substitute the average cutting force of the CNC machine tool into the overall stiffness model, and obtain a final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during machining by using fast Fourier transform based on the dynamic stiffness curve.

[0106] Optionally, the first acquisition module 100 is specifically used for:

[0107] Analyze the spindle of CNC machine tools and obtain the matrix transformation formula of the spindle;

[0108] The deformation deviation of the spindle is calculated through the cutting force components and the dynamic stiffness of each part, and the overall stiffness model is established.

[0109] Optionally, the first acquisition module 100 is specifically used for:

[0110] The rotation angle of the spindle is adjusted to obtain the dynamic stiffness values ​​of the machine tool's worktable at different angles, including the amplitude-frequency characteristics and the phase-frequency characteristics; and the position of the machine tool is adjusted to obtain the dynamic stiffness values ​​of the machine tool's spindle at different positions, including the amplitude-frequency characteristics and the phase-frequency characteristics.

[0111] Optionally, the generating module 200 is specifically used for:

[0112] While the orthogonal test method is used to measure the dynamic stiffness data of the machine tool at different frequencies, the machine learning model is used to learn the changing relationship between the dynamic stiffness curve and the position to obtain the dynamic stiffness data.

[0113] Optionally, the second acquisition module 300 is specifically used for:

[0114] Fast Fourier transform is used to convert the time domain signal of cutting force into frequency domain, and the cutting forces of different frequencies are brought into the corresponding dynamic stiffness values.

[0115] It should be noted that the aforementioned explanation of the embodiment of the modeling method for predicting the vibration amplitude of a CNC machine tool is also applicable to the modeling device for predicting the vibration amplitude of a CNC machine tool in this embodiment, and will not be repeated here.

[0116] According to the modeling device for predicting the vibration amplitude of a CNC machine tool proposed in the embodiment of the present application, an overall stiffness model of the CNC machine tool is established, and the dynamic stiffness of each joint surface of the machine tool is obtained. Based on the dynamic stiffness of each joint surface, the dynamic stiffness data of the CNC machine tool at different frequencies is obtained, and the dynamic stiffness curve of the CNC machine tool is generated. The average cutting force of the CNC machine tool is substituted into the overall stiffness model, and the final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during processing is obtained by fast Fourier transform based on the dynamic stiffness curve. Thus, a machine tool overall modeling method combining multi-body theory, dynamic stiffness, machine learning and fast Fourier transform is adopted, and the model is used to predict the relative amplitude between the spindle and the worktable during machine tool processing, thereby improving the accuracy of the prediction.

[0117] Fig.14 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0118] A memory 1401 , a processor 1402 , and a computer program stored in the memory 1401 and executable on the processor 1402 .

[0119] When the processor 1402 executes the program, the modeling method for predicting the vibration amplitude of a numerically controlled machine tool provided in the above embodiment is implemented.

[0120] Furthermore, the electronic device further comprises:

[0121] The communication interface 1403 is used for communication between the memory 1401 and the processor 1402 .

[0122] The memory 1401 is used to store computer programs that can be executed on the processor 1402 .

[0123] The memory 1401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0124] 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 through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.14 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0125] Optionally, in a specific implementation, if the memory 1401, the processor 1402 and the communication interface 1403 are integrated on a chip, the memory 1401, the processor 1402 and the communication interface 1403 can communicate with each other through an internal interface.

[0126] The processor 1402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0127] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned modeling method for predicting the vibration amplitude of a CNC machine tool is implemented.

[0128] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0129] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0130] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0131] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0132] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0133] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0134] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0135] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A modeling method for predicting the vibration amplitude of a CNC machine tool, characterized in that: The following steps are involved: Establishing an overall stiffness model of a CNC machine tool and obtaining the dynamic stiffness of each joint surface of the machine tool; Based on the dynamic stiffness of each joint surface, dynamic stiffness data of the CNC machine tool at different frequencies are obtained, and a dynamic stiffness curve of the CNC machine tool is generated; as well as Substituting the average cutting force of the CNC machine tool into the overall stiffness model, a final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during machining is obtained by fast Fourier transform based on the dynamic stiffness curve; The obtaining of the dynamic stiffness of each joint surface of the machine tool comprises: The rotation angle of the spindle is adjusted to obtain the dynamic stiffness values ​​of the worktable of the machine tool at different angles, including the amplitude-frequency characteristics and the phase-frequency characteristics; and the position of the machine tool is adjusted to obtain the dynamic stiffness values ​​of the spindle of the machine tool at different positions, including the amplitude-frequency characteristics and the phase-frequency characteristics.

2. The method according to claim 1, characterized in that The overall stiffness model of the CNC machine tool is established, comprising: Analyze the spindle of the CNC machine tool to obtain a matrix transformation formula of the spindle; The deformation deviation of the spindle is calculated through the cutting force components and the dynamic stiffness of each part, and the overall stiffness model is established.

3. The method according to claim 1, characterized in that: The obtaining of dynamic stiffness data of the machine tool at different frequencies comprises: While the orthogonal test method is used to measure the dynamic stiffness data of the machine tool at different frequencies, a machine learning model is used to learn the relationship between the dynamic stiffness curve and the position change to obtain the dynamic stiffness data.

4. The method according to any one of claims 1 to 3, characterized in that: The final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during machining is obtained by using fast Fourier transform based on the dynamic stiffness curve, including: The fast Fourier transform is used to perform frequency domain conversion on the time domain signal of the cutting force, and the cutting forces of different frequencies are brought into corresponding dynamic stiffness values.

5. A modeling device for predicting the vibration amplitude of a CNC machine tool, characterized in that: include: A first acquisition module is used to establish an overall stiffness model of a CNC machine tool and obtain the dynamic stiffness of each joint surface of the machine tool; A generating module, used for obtaining the dynamic stiffness data of the CNC machine tool at different frequencies based on the dynamic stiffness of each joint surface, and generating a dynamic stiffness curve of the CNC machine tool; as well as A second acquisition module is used to substitute the average cutting force of the CNC machine tool into the overall stiffness model, and obtain a final overall stiffness model for predicting the relative amplitude between the spindle and the worktable of the machine tool during machining by using a fast Fourier transform based on the dynamic stiffness curve; The first acquisition module is specifically used to: The rotation angle of the spindle is adjusted to obtain the dynamic stiffness values ​​of the worktable of the machine tool at different angles, including the amplitude-frequency characteristics and the phase-frequency characteristics; and the position of the machine tool is adjusted to obtain the dynamic stiffness values ​​of the spindle of the machine tool at different positions, including the amplitude-frequency characteristics and the phase-frequency characteristics.

6. The device according to claim 5, characterized in that The first acquisition module is specifically used to: Analyze the spindle of the CNC machine tool to obtain a matrix transformation formula of the spindle; The deformation deviation of the spindle is calculated through the cutting force components and the dynamic stiffness of each part, and the overall stiffness model is established.

7. The device according to claim 5, characterized in that The generating module is specifically used for: While measuring the dynamic stiffness data of the machine tool at different frequencies by using an orthogonal test method, a machine learning model is used to learn the relationship between the dynamic stiffness curve and the position change to obtain the dynamic stiffness data; The second acquisition module is specifically used for: The fast Fourier transform is used to perform frequency domain conversion on the time domain signal of the cutting force, and the cutting forces of different frequencies are brought into corresponding dynamic stiffness values.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the modeling method for predicting the vibration amplitude of a CNC machine tool as described in any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the modeling method for predicting the vibration amplitude of a CNC machine tool as described in any one of claims 1 to 4.

Images