Self-excitation machine tool frequency characteristic detection system and method

Abstract

The invention relates to the technical field of numerical control machine tool frequency domain characteristic testing, in particular to a self-excitation machine tool frequency characteristic detection system and method. The detection system is arranged on a machine tool and comprises an excitation signal acquisition device, a response signal acquisition device, a signal collection device and a data processing device, the excitation signal acquisition device is arranged between a tool and a tool rest of the machine tool and measures actual turning force as an excitation signal; the response signal acquisition devices are arranged on a tool rest, all moving parts and a lathe body cross beam of the machine tool and measure acceleration vibration signals as response signals; the signal collecting device collects the excitation signals and the response signals and sends the excitation signals and the response signals to the data processing device for analysis and processing. According to the detection method, on the basis of excitation signals and response signals measured by a detection system, the frequency characteristics of the machine tool are calculated through an average period method and singular spectrum analysis. According to the detection system and method, the structure is simple, mounting and dismounting are convenient, the detection efficiency is improved, and meanwhile the detection result is more accurate.

Description

Technical Field

[0001] This invention relates to the field of frequency domain characteristic testing technology for CNC machine tools, and in particular to a self-excited machine tool frequency characteristic testing system and method. Background Technology

[0002] Vibration is one of the main factors affecting the surface roughness of workpieces during CNC machine tool machining. Vibration between the workpiece and the cutting tool can affect the normal cutting process of the machine tool. Strong vibration can accelerate the wear of the machine tool and cutting tool, shorten their service life, and significantly reduce the machining accuracy. The vibration of CNC machine tool spindles, guideways, and cutting tools is generally characterized by high frequency and small amplitude. Although ultra-precision machine tools have high rigidity, vibration can still cause slight changes in the relative position between the workpiece and the cutting tool during actual machining, ultimately leading to increased workpiece surface roughness and reduced machining surface quality. Accurate machine tool vibration signals can directly reflect the frequency characteristics of various machine tool components, allowing for intuitive identification of components that generate vibration responses during machining. This enables rapid measures to prevent or reduce the impact of vibration on the surface quality of ultra-precision machine tools. Therefore, acquiring accurate machine tool vibration signals is of great practical significance for CNC machine tool accuracy compensation, improving machine tool operational stability, and enhancing industrial performance and production efficiency.

[0003] In existing technologies, the vibrator test method is complex to set up, the test process is complicated and time-consuming, the test conditions are demanding and there are problems with additional mass effects; the hammer test method concentrates energy on a point in a short time, which can easily cause overload, local response and nonlinear problems, and the excitation energy is relatively insufficient, which can easily cause double-click.

[0004] There is an urgent need for a testing system that is simple in structure, easy to implement, convenient to install and disassemble, and capable of testing the frequency characteristics of various components of CNC machine tools during actual cutting processes. Summary of the Invention

[0005] Based on the above, the purpose of this invention is to provide a detection system and method that is simple in structure, easy to implement, convenient to install and disassemble, and capable of testing the frequency characteristics of various components of a CNC machine tool during actual cutting.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A self-excited machine tool frequency characteristic detection system, wherein the detection system is installed on the machine tool, characterized in that the detection system comprises: an excitation signal acquisition device, a response signal acquisition device, a signal collection device, and a data processing device;

[0008] The excitation signal acquisition device is located between the cutting tool and the tool holder of the machine tool, and measures the actual turning force as the excitation signal.

[0009] The response signal acquisition device is installed at the tool post, various moving parts and bed crossbeam of the machine tool, and measures the acceleration vibration signal as the response signal;

[0010] The signal collection device is connected to the excitation signal acquisition device, the response signal acquisition device, and the data processing device. The signal collection device collects the excitation signal and the response signal and sends them to the data processing device for analysis and processing to obtain the frequency characteristics of the machine tool.

[0011] As a preferred embodiment of a self-excited machine tool frequency characteristic detection system, the excitation signal acquisition device is specifically a three-dimensional force measurement sensor, which can simultaneously detect the magnitude of forces acting in three mutually perpendicular directions and output real-time data, thereby realizing the acquisition of the excitation signal.

[0012] As a preferred embodiment of a self-excited machine tool frequency characteristic detection system, the response signal acquisition device is specifically a triaxial piezoelectric accelerometer, which can simultaneously detect the magnitude of vibration acceleration in three mutually perpendicular directions and output real-time data, thereby realizing the acquisition of the response signal.

[0013] As a preferred embodiment of a self-excited machine tool frequency characteristic detection system, the response signal acquisition device is installed at the tool post, various moving parts, and bed crossbeam of the machine tool. Specifically, the triaxial piezoelectric acceleration sensors are evenly distributed on both sides of the tool post, both sides of the bed crossbeam, around the spindle and sub-spindle, around the slide saddle, and around the transition plate.

[0014] As a preferred solution for a self-excited machine tool frequency characteristic detection system, during actual testing, multiple measuring points are set on the machine tool. The measuring point set at the bottom corner of the machine tool is fixed as a reference point. During the measurement process of installing the triaxial piezoelectric accelerometer in batches, the reference point is kept stationary, and the response signals of other measuring point positions are measured multiple times.

[0015] As a preferred embodiment of a self-excited machine tool frequency characteristic detection system, the signal collection device is specifically a dynamic signal collector, which is equipped with collection and analysis software.

[0016] As a preferred embodiment of a self-excited machine tool frequency characteristic detection system, the data processing device is specifically a computer, and the collection and analysis software is installed on the computer to analyze and process the excitation signal and the response signal.

[0017] A self-excited machine tool frequency characteristic detection method, applied to a self-excited machine tool frequency characteristic detection system as described in any of the above-mentioned schemes, uses the turning force detected under the actual operating conditions of the machine tool as the excitation signal and the vibration acceleration as the response signal, and calculates the frequency characteristics of the machine tool using the average period method, specifically including the following steps:

[0018] The excitation signal and the response signal are divided into several segments using a fast Fourier transform.

[0019] Perform a fast Fourier transform on the autocorrelation function of the excitation signal to obtain the autopower spectral density function of the excitation signal;

[0020] Perform a fast Fourier transform on the cross-correlation function of the excitation signal and the response signal to obtain the cross-power spectral density function of the excitation signal and the response signal;

[0021] The frequency response function is obtained by dividing the cross power spectral density function by the self power spectral density function.

[0022] A preferred method for detecting the frequency characteristics of a self-excited machine tool includes the following steps:

[0023] The step of dividing the excitation signal and the response signal into several segments using Fast Fourier Transform specifically includes: segmenting the excitation signal and the response signal, and determining the length of each data segment for Fourier Transform to be N. F It is determined by the data sampling frequency f and the frequency resolution Δf, that is:

[0024]

[0025] Perform a Fast Fourier Transform on each obtained data signal, and use the transformation result of each data segment to calculate the auto-power spectral density function of the excitation signal, as well as the cross-power spectral density function of the excitation signal and the response signal.

[0026] The step of performing a Fast Fourier Transform on the autocorrelation function of the excitation signal to obtain the autopower spectral density function of the excitation signal specifically includes: the calculation formula for the autopower spectral density function of the excitation signal F(t) is as follows:

[0027]

[0028] Where: M is the total number of segments in the excitation signal and the response signal; F i (k) is the Fourier transform of the i-th segment of F(t); F i * (k) is F i The conjugate negative of (t);

[0029] The step of performing a Fast Fourier Transform on the cross-correlation function of the excitation signal and the response signal to obtain the cross-power spectral density function of the excitation signal F(t) and the response signal X(t) specifically includes: the cross-power spectral density function of the excitation signal F(t) and the response signal X(t) can be calculated by the following formula:

[0030]

[0031] Where: M is the total number of segments in the excitation signal and the response signal; F i (k) and X i (k) is the Fourier transform of the i-th segment of F(t) and X(t); It is X i The conjugate negative of (t);

[0032] The frequency response function can be calculated using the following formula:

[0033]

[0034] A preferred embodiment of a self-excited machine tool frequency response detection method further includes deharmonicization processing of the detected frequency response function using singular spectrum analysis. The specific processing steps are as follows:

[0035] The data length of the excitation signal and the response signal is T. The minimum embedding dimension M (2 ≤ M ≤ T) is determined, and the data of the excitation signal and the response signal are transformed into a multidimensional sequence X1, X2, ..., X... K ,X i =(y i ,…,y i+M+1 ), K = T - M + 1, to obtain the time delay matrix.

[0036] Construct the covariance matrix XX T Calculate XX T Eigenvalues ​​and eigenvectors: symmetric matrices X×m T Perform eigenvalue decomposition to obtain m eigenvalues, and arrange these eigenvalues ​​in descending order as λ1 > λ2 > ... > λ M >λ0, and simultaneously obtain the corresponding m orthogonal eigenvectors U1, U2, ... U m ;

[0037] The original time delay matrix can then be expressed as: in U represents the singular values ​​of matrix X; i It is an orthogonal function; V i As the main component; It is the i-th triple eigenvector of matrix X;

[0038] Divide matrix X into several different groups, select a finite number of points with large singular values ​​as principal components, and reconstruct the excitation signal and the response signal:

[0039]

[0040] In the formula: i r =1,2...r, where r are the first r singular vectors selected.

[0041] The beneficial effects of this invention are as follows:

[0042] (1) The machine tool structure is subjected to measurable self-excitation by cutting the workpiece. The frequency characteristics of the machine tool can be obtained by calculation based on the response of the structure, thereby accurately collecting the vibration signal generated by the machine tool in the actual cutting process.

[0043] (2) The detection system has a simple overall structure, and the excitation signal acquisition device and response signal acquisition device are easy to install and disassemble, making it highly practical and convenient;

[0044] (3) It replaces the existing vibrator test method and hammer test method, simplifies the testing process, shortens the testing time, improves the testing efficiency, and makes the results obtained from testing actual working conditions more accurate. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the content of the embodiments of the present invention and these drawings without creative effort.

[0046] Figure 1 This is an installation diagram of a self-excited machine tool frequency characteristic detection system provided by the present invention;

[0047] Figure 2 yes Figure 1 A magnified view of a section at point A in the middle;

[0048] Figure 3 This is a structural connection diagram of a self-excited machine tool frequency characteristic detection system provided by the present invention;

[0049] Figure 4 This is a flowchart of a self-excited machine tool frequency characteristic detection method provided by the present invention;

[0050] Figure 5 This is a diagram of the excitation signals for spindle turning under the main roughing and auxiliary roughing conditions of a dual-spindle machine tool.

[0051] Figure 6 This is a diagram of the excitation signals for the secondary spindle turning under the main roughing and secondary roughing conditions of a twin-spindle machine tool.

[0052] Figure 7 This is a diagram of the vibration response signal in the X direction under the roughing conditions of the main and auxiliary spindle machines;

[0053] Figure 8 This is a diagram of the vibration response signal in the Y direction under the roughing conditions of the main and auxiliary spindle machine tools.

[0054] Figure 9 This is a diagram of the vibration response signal in the Z direction under the roughing conditions of the main and auxiliary spindle machine tools.

[0055] Figure 10 This is the frequency response diagram in the X direction of a dual-spindle machine tool under the main roughing and auxiliary roughing conditions;

[0056] Figure 11 This is the frequency response diagram in the Y direction of a dual-spindle machine tool under the main roughing and auxiliary roughing conditions;

[0057] Figure 12 This is the frequency response diagram in the Z direction of a dual-spindle machine tool under the main roughing and auxiliary roughing conditions.

[0058] Figure 13 This is a frequency response function curve of a dual-spindle machine tool under the main roughing and auxiliary roughing conditions;

[0059] Figure 14 This is a diagram of the excitation signals for spindle turning under the main finishing and auxiliary roughing conditions of a dual-spindle machine tool.

[0060] Figure 15 This is a diagram of the excitation signals for secondary spindle turning under the main spindle finishing and secondary spindle roughing conditions on a dual-spindle machine tool.

[0061] Figure 16 This is a diagram of the vibration response signal in the X direction under the main and auxiliary roughing conditions of a dual-spindle machine tool;

[0062] Figure 17 This is a diagram of the vibration response signal in the Y direction under the main and auxiliary roughing conditions of a dual-spindle machine tool;

[0063] Figure 18 This is a diagram of the vibration response signal in the Z direction under the main-finishing and auxiliary-roughing conditions of a dual-spindle machine tool;

[0064] Figure 19 This is the frequency response diagram in the X direction of a dual-spindle machine tool under main-finishing and auxiliary-roughing conditions;

[0065] Figure 20 This is the frequency response diagram in the Y direction of a dual-spindle machine tool under main-finishing and auxiliary-roughing conditions;

[0066] Figure 21This is the frequency response diagram in the Z direction of a dual-spindle machine tool under main-finishing and auxiliary-roughing conditions;

[0067] Figure 22 It is a frequency response function curve of a dual-spindle machine tool under the main finishing and auxiliary roughing conditions.

[0068] Figure label:

[0069] 1-Three-dimensional force measurement sensor; 2-Tool; 3-Tool post; 4-Bed; 5-Triaxial piezoelectric accelerometer; 6-Spindle; 7-Sub-spindle; 8-Slide saddle; 9-Transition plate. Detailed Implementation

[0070] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0071] In the description of this invention, unless otherwise explicitly specified and limited, the terms "connected," "linked," and "fixed" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0072] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0073] In the description of this embodiment, the terms "upper," "lower," "left," and "right," etc., refer to the orientation or positional relationship shown in the accompanying drawings. They are used solely for ease of description and simplification of operation, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. In the description of the present invention, unless otherwise stated, "a plurality of" means two or more. Furthermore, the terms "first" and "second" are merely used for descriptive distinction and have no special meaning.

[0074] This embodiment provides a self-excited machine tool frequency characteristic detection system, such as... Figure 1 As shown, the specific object of the test is the BK-204D dual-spindle CNC machine tool produced by Hubei Yixing Intelligent Equipment Co., Ltd. The test system is installed on the machine tool and specifically includes: an excitation signal acquisition device, a response signal acquisition device, a signal collection device, and a data processing device.

[0075] Preferably, such as Figure 2 As shown, the excitation signal acquisition device is a three-dimensional force measurement sensor 1, which is set between the cutting tool 2 and the tool holder 3 of the machine tool. The three-dimensional force measurement sensor 1 measures the actual turning force as the excitation signal, and can simultaneously detect the magnitude of the forces in three mutually perpendicular directions and output real-time data, thereby realizing the acquisition of the excitation signal.

[0076] Specifically, the main spindle 6 and the sub-spindle 7 of the BK-204D dual-spindle CNC machine tool are arranged in parallel. The three mutually perpendicular directions are referred to as the XYZ directions below. According to the naming rules of the international standard "Machine Tool Numerical Control - Coordinate System and Motion Naming", the Z direction is the direction parallel to the main axis of the machine tool main spindle 6 and the sub-spindle 7, the X direction is the direction of horizontal movement of the machine tool main spindle 6 and the sub-spindle 7, and the Y direction is the direction perpendicular to the plane formed by the X and Z directions.

[0077] More specifically, the three-dimensional force measurement sensor 1 adopts the RDF-3W7000D-2000N three-dimensional force measurement sensor customized by Shenzhen Ruilide Technology Co., Ltd., which can detect the magnitude and direction of turning force in three directions in real time.

[0078] Preferably, the response signal acquisition device is installed at the tool post 3, various moving parts and the crossbeam of the bed 4 of the machine tool, and measures the acceleration vibration signal as the response signal;

[0079] Specifically, the response signal acquisition device is a triaxial piezoelectric accelerometer 5, which can simultaneously detect the magnitude of vibration acceleration in three mutually perpendicular directions and output real-time data to achieve the acquisition of response signals. The three mutually perpendicular directions are the aforementioned XYZ directions.

[0080] Furthermore, triaxial piezoelectric accelerometers 5 are evenly distributed on both sides of the tool post 3, both sides of the bed 4 crossbeam, around the spindle 6 and sub-spindle 7, around the slide saddle 8, and around the transition plate 9. Two triaxial piezoelectric accelerometers 5 are evenly distributed on each moving component, and four triaxial piezoelectric accelerometers 5 are evenly distributed on the tool post 3 and the bed 4 crossbeam. For example, "evenly distributed" means that two sensors are placed within a 1m length, meaning one sensor is placed every 0.33cm. Simultaneously, due to the limited number of triaxial piezoelectric accelerometers 5 and the limitation on the number of data processing device channels, multiple measuring points are set on the machine tool during actual testing. The measuring point located at the bottom corner of the machine tool is fixed as a reference point. During the measurement process of installing the triaxial piezoelectric accelerometers 5 in batches, this reference point remains stationary, and the response signals of other measuring point positions are measured multiple times, thus ultimately completing the acquisition of the response signals of the measuring points of each component of the aforementioned dual-spindle CNC machine tool.

[0081] More specifically, the triaxial piezoelectric accelerometer 5 adopts the SAE30005 piezoelectric triaxial IEPE sensor from Wuxi Shiao Technology Co., Ltd., which features high sensitivity, good cost performance, low noise and strong anti-interference, and is suitable for vibration acceleration measurement in the X, Y and Z directions.

[0082] Preferably, such as Figure 3 As shown, the signal collection device is connected to the excitation signal acquisition device, the response signal acquisition device, and the data processing device. The signal collection device collects the excitation signal and the response signal and sends them to the data processing device for analysis and processing to obtain the frequency characteristics of the machine tool.

[0083] Specifically, the signal collection device is a dynamic signal collector, which is equipped with collection and analysis software; the data processing device is a computer, and the collection and analysis software is installed on the computer to analyze and process the excitation signal and the response signal.

[0084] More specifically, the dynamic signal collector uses the SA1816A dynamic signal analyzer, which has a built-in 4mA / 24V constant current circuit. It can be directly connected to the RDF-3W7000D-2000N three-dimensional force measurement sensor and the IEPE accelerometer for signal collection and transmission. It features a 24-bit high-precision A / D converter, 8-channel parallel synchronous sampling, and a maximum sampling rate of 128kHz / channel. It has advantages such as low noise and high accuracy. The dynamic signal collector is equipped with comprehensive acquisition and analysis software. After installing the acquisition and analysis software on the computer, parameters such as the input and output types, range, sensor sensitivity, and sampling rate of the dynamic signal collector can be set. It can transmit, display, and analyze data in real time, and can record multi-channel signals in real time and without interruption for a long period of time using the computer hard drive.

[0085] This embodiment also provides a self-excited machine tool frequency response detection method. This detection method is applied to the aforementioned detection system and uses the average period method to calculate the frequency response function.

[0086] The excitation signal and response signal are divided into several segments using the Fast Fourier Transform;

[0087] The autocorrelation function of the excitation signal is subjected to a fast Fourier transform to obtain the autopower spectral density function of the excitation signal.

[0088] Perform a fast Fourier transform on the cross-correlation function of the excitation signal and the response signal to obtain the cross-power spectral density function of the excitation signal and the response signal;

[0089] The frequency response function is obtained by dividing the cross power spectral density function by the self power spectral density function.

[0090] Specifically, it includes the following steps:

[0091] S1. Divide the excitation signal and response signal into several segments using Fast Fourier Transform (FFT). Perform segmentation processing on the excitation signal and response signal, and determine the length of each data segment to be N for the FFT. F It is determined by the data sampling frequency f and the frequency resolution Δf, that is:

[0092]

[0093] Perform a Fast Fourier Transform on each data signal obtained, and use the transformation results of each data segment to calculate the self power spectral density function of the excitation signal, as well as the cross power spectral density function of the excitation signal and the response signal.

[0094] S2. Perform a Fast Fourier Transform on the autocorrelation function of the excitation signal to obtain the autopower spectral density function of the excitation signal. Specifically, the formula for calculating the autopower spectral density function of the excitation signal F(t) is as follows:

[0095]

[0096] In the formula: M is the total number of segments in the excitation signal and the response signal; F i (k) is the Fourier transform of the i-th segment of F(t); F i * (k) is F i The conjugate negative of (t);

[0097] Performing a Fast Fourier Transform on the cross-correlation function of the excitation signal and the response signal yields the cross-power spectral density function of the excitation signal F(t) and the response signal X(t). Specifically, the cross-power spectral density function of the excitation signal F(t) and the response signal X(t) can be calculated using the following formula:

[0098]

[0099] In the formula: M is the total number of segments in the excitation signal and the response signal; F i (k) and X i (k) is the Fourier transform of the i-th segment of F(t) and X(t); It is X i The conjugate negative of (t);

[0100] S3, The frequency response function can be calculated using the following formula:

[0101]

[0102] More specifically, the frequency response function of a CNC machine tool under turning conditions, obtained by detecting turning force and vibration acceleration, inevitably contains a large number of harmonic components. To obtain accurate experimental results, singular spectrum analysis is used to deharmonicize the frequency response function. The components with larger singular values ​​contribute more and contain more information, thus being treated as the main components. After grouping and reconstructing the data, the corresponding characteristic components can represent its inherent frequency characteristics, avoiding the influence of harmonic components and other interference signals contained in the turning load. Furthermore, dynamic related information is obtained to derive the frequency characteristics of the CNC machine tool under actual working conditions. The specific processing steps are as follows:

[0103] S1. The data length of the excitation and response signals acquired is T. Determine the minimum embedding dimension M (2≤M≤T), and transform the excitation and response signal data into a multidimensional sequence X1, X2, ..., X K ,X i =(y i ,…,y i+M+1 ), K = T - M + 1, to obtain the time delay matrix.

[0104] S2. Construct the covariance matrix XX T Calculate XX T Eigenvalues ​​and eigenvectors: symmetric matrices X×m T Perform eigenvalue decomposition to obtain m eigenvalues, and arrange these eigenvalues ​​in descending order as λ1 > λ2 > ... > λ M >λ0, and simultaneously obtain the corresponding m orthogonal eigenvectors U1, U2, ... U m ;

[0105] S3. Determine the singular values ​​V i And the left singular vector V:

[0106] i=1,2,...,r (when i>r, σ i =0).

[0107] By XX T The orthogonal matrix U formed by the eigenvectors is the left singular vector matrix in SVD:

[0108] U = [u1, u2, ..., u m ];

[0109] S4. Determine the right singular vector V and matrix V:

[0110] For each nonzero singular value σ i (i = 1, 2, ..., r), the corresponding right singular vector v i It can be calculated using the left singular vector and matrix X:

[0111]

[0112] The right singular vector matrix V is composed of all these vectors:

[0113] V = [v1, v2, ..., v n ];

[0114] S5. Construct a diagonal matrix Σ:

[0115] Create an m×n matrix Σ.

[0116] Set the first r elements of its main diagonal (from top left to bottom right) to the singular values ​​σ1, σ2, ..., σ in descending order. r All other elements of matrix Σ are set to 0.

[0117]

[0118] S6. Obtain the desired SVD decomposition:

[0119] The above-obtained U,Σ,V T Multiplying them together allows us to reconstruct the original time delay matrix X:

[0120] X=UΣV T

[0121] The matrix expression of SVD is X=UΣV T Item by item:

[0122]

[0123] Using the matrix multiplication rule, this is equivalent to:

[0124]

[0125] because so:

[0126]

[0127] That is, the original time delay matrix can be expressed as in U represents the singular values ​​of matrix X; i It is an orthogonal function; V i As the main component; It is the i-th triple eigenvector of matrix X;

[0128] Divide matrix X into several different groups, select the first finite number of points with larger singular values ​​as principal components, and reconstruct the excitation signal and response signal:

[0129]

[0130] In the formula: i r =1,2...r, where r are the first r singular vectors selected.

[0131] In the specific implementation process, two different detection schemes for machining parameters were developed for the BK-204D dual-spindle CNC machine tool, as shown in the table below:

[0132]

[0133] Based on the turning scheme in the table above, by changing the tools and three cutting parameters (depth of cut, feed rate, and cutting speed) required for roughing and finishing, and using the above-mentioned detection system and method, the following results are obtained:

[0134] The excitation signal for spindle turning on the BK-204D twin-spindle CNC machine tool under main roughing and auxiliary roughing conditions is as follows: Figure 5 As shown, the excitation signal for the secondary spindle turning is as follows: Figure 6 The vibration response signals in the XYZ directions are shown below. Figures 7 to 9 As shown, by inputting the excitation signal and response signal into the analysis software, the frequency response diagrams in the XYZ directions can be obtained. Figures 10 to 12 As shown, further processing yields the machine tool frequency response function curve under this operating condition, as shown in the figure. Figure 13 As shown, the specific values ​​of the first three natural frequencies of the machine tool under this working condition can be obtained from the frequency response function curve.

[0135] The excitation signal for spindle turning on the BK-204D twin-spindle CNC machine tool under main finishing and auxiliary roughing conditions is as follows: Figure 14 As shown, the excitation signal for the secondary spindle turning is as follows: Figure 15 The vibration response signals in the XYZ directions are shown below. Figures 16 to 18 As shown, by inputting the excitation signal and response signal into the analysis software, the frequency response diagrams in the XYZ directions can be obtained. Figures 19 to 21 As shown, further processing yields the machine tool frequency response function curve under this operating condition, as shown in the figure. Figure 22 As shown, the specific values ​​of the first three natural frequencies of the machine tool under this working condition can be obtained from the frequency response function curve.

[0136] The specific values ​​of the first three natural frequencies of the machine tool under the two operating conditions are shown in the table below:

[0137]

[0138] This allows for the detection of the machine tool's frequency characteristics, enabling accurate acquisition of vibration signals generated during actual cutting. Consequently, measures can be quickly taken to prevent or reduce the impact of vibration on the surface quality of ultra-precision machine tools.

[0139] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0140] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A self-excited machine tool frequency characteristic detection system, wherein the detection system is installed on a machine tool, characterized in that, The detection system includes: an excitation signal acquisition device, a response signal acquisition device, a signal collection device, and a data processing device; The excitation signal acquisition device is set between the cutting tool (2) and the tool holder (3) of the machine tool, and measures the actual turning force as the excitation signal; The response signal acquisition device is installed at the tool post (3), each moving part and bed (4) crossbeam of the machine tool, and measures the acceleration vibration signal as the response signal; The signal collection device is connected to the excitation signal acquisition device, the response signal acquisition device, and the data processing device. The signal collection device collects the excitation signal and the response signal and sends them to the data processing device for analysis and processing to obtain the frequency characteristics of the machine tool.

2. A self-excited machine tool frequency characteristic detection system based on claim 1, characterized in that, The excitation signal acquisition device is specifically a three-dimensional force measurement sensor (1), which can simultaneously detect the magnitude of forces in three mutually perpendicular directions and output real-time data to realize the acquisition of the excitation signal.

3. A self-excited machine tool frequency characteristic detection system based on claim 1, characterized in that, The response signal acquisition device is specifically a triaxial piezoelectric accelerometer (5), which can simultaneously detect the magnitude of vibration acceleration in three mutually perpendicular directions and output real-time data to realize the acquisition of the response signal.

4. A self-excited machine tool frequency characteristic detection system based on claim 3, characterized in that, The response signal acquisition device is set at the tool post (3), each moving part and bed (4) crossbeam of the machine tool, specifically: the triaxial piezoelectric acceleration sensor (5) is evenly arranged on both sides of the tool post (3), both sides of the bed (4) crossbeam, around the spindle (6) and sub-spindle (7), around the slide saddle (8) and around the transition plate (9).

5. A self-excited machine tool frequency characteristic detection system based on claim 4, characterized in that, In actual testing, multiple measuring points are set on the machine tool. The measuring point set at the bottom corner of the machine tool is fixed as a reference point. During the measurement process of installing the triaxial piezoelectric acceleration sensor (5) in batches, the reference point is kept stationary, and the response signals of other measuring point positions are measured multiple times.

6. A self-excited machine tool frequency characteristic detection system based on claim 1, characterized in that, The signal collection device is specifically a dynamic signal collector, which is equipped with collection and analysis software.

7. A self-excited machine tool frequency characteristic detection system based on claim 6, characterized in that, The data processing device is specifically a computer, and the collection and analysis software is installed on the computer to analyze and process the excitation signal and the response signal.

8. A self-excited machine tool frequency characteristic detection method, applied to the self-excited machine tool frequency characteristic detection system according to any one of claims 1-7, characterized in that, The frequency response function is calculated using the average period method: The excitation signal and the response signal are divided into several segments using a fast Fourier transform. Perform a fast Fourier transform on the autocorrelation function of the excitation signal to obtain the autopower spectral density function of the excitation signal; Perform a fast Fourier transform on the cross-correlation function of the excitation signal and the response signal to obtain the cross-power spectral density function of the excitation signal and the response signal; The frequency response function is obtained by dividing the cross power spectral density function by the self power spectral density function.

9. A method for detecting the frequency characteristics of a self-excited machine tool based on claim 8, characterized in that, The turning force detected under the actual operating conditions of the machine tool is used as the excitation signal, and the vibration acceleration is used as the response signal. The frequency characteristics of the machine tool are calculated using the average period method, specifically including the following steps: The step of dividing the excitation signal and the response signal into several segments using Fast Fourier Transform specifically includes: segmenting the excitation signal and the response signal, and determining the length of each data segment for Fourier Transform to be N. F It is determined by the data sampling frequency f and the frequency resolution Δf, that is: Perform a Fast Fourier Transform on each obtained data signal, and use the transformation result of each data segment to calculate the auto-power spectral density function of the excitation signal, as well as the cross-power spectral density function of the excitation signal and the response signal. The step of performing a Fast Fourier Transform on the autocorrelation function of the excitation signal to obtain the autopower spectral density function of the excitation signal specifically includes: the calculation formula for the autopower spectral density function of the excitation signal F(t) is as follows: Where: M is the total number of segments in the excitation signal and the response signal; F i (k) is the Fourier transform of the i-th segment of F(t); It is F i The conjugate negative of (t); The step of performing a Fast Fourier Transform on the cross-correlation function of the excitation signal and the response signal to obtain the cross-power spectral density function of the excitation signal F(t) and the response signal X(t) specifically includes: the cross-power spectral density function of the excitation signal F(t) and the response signal X(t) can be calculated by the following formula: Where: M is the total number of segments in the excitation signal and the response signal; F i (k) and X i (k) is the Fourier transform of the i-th segment of F(t) and X(t); It is X i The conjugate negative of (t); The frequency response function can be calculated using the following formula:

10. A method for detecting the frequency characteristics of a self-excited machine tool based on claim 9, characterized in that, It also includes using singular spectrum analysis to perform deharmonicization processing on the detected frequency response function. The specific processing steps are as follows: The data length of the excitation signal and the response signal is T. The minimum embedding dimension M (2 ≤ M ≤ T) is determined, and the data of the excitation signal and the response signal are transformed into a multidimensional sequence X1, X2, ..., X... K ,X i =(y i ,…,y i+M+1 ), K = T - M + 1, to obtain the time delay matrix. Construct the covariance matrix XX T Calculate XX T Eigenvalues ​​and eigenvectors: symmetric matrices X×m T Perform eigenvalue decomposition to obtain m eigenvalues, and arrange these eigenvalues ​​in descending order as λ1 > λ2 > ... > λ M >λ0, and simultaneously obtain the corresponding m orthogonal eigenvectors U1, U2, ... U m ; The original time delay matrix can then be expressed as: in U represents the singular values ​​of matrix X; i It is an orthogonal function; V i As the main component; It is the i-th triple eigenvector of matrix X; Divide matrix X into several different groups, select a finite number of points with large singular values ​​as principal components, and reconstruct the excitation signal and the response signal: In the formula: i r =1,2...r, where r are the first r singular vectors selected.

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