TWI638251B - Modal detection system - Google Patents

Abstract

A modality detection system includes a machine tool, a detection unit, and a processing unit. The machine tool includes a machine table, a table that moves along a random path relative to the machine table, and a spindle device that is disposed on the machine table and generates self-vibration when the table is displaced. The maximum displacement of the flat table in the same direction is not more than 100 mm. The detecting unit includes a plurality of detectors dispersed on the spindle device, and each of the detectors measures an amplitude and a vibration frequency of the spindle device to generate an autogenous vibration, and outputs a sensing signal. The processing unit and the detectors communicate with each other, and after receiving the sensing signals, obtain a plurality of modal parameters according to the amplitude and the frequency. Thereby, the small and rapid movement back and forth in different directions by the worktable enables the spindle device to simulate the real processing state to generate automatic vibration, and can improve the accuracy of the modal analysis result, and the equipment cost is low.

Description

Modal detection system

The invention relates to a detection system, in particular to a modal detection system for obtaining modal parameters.

In the process of machining, the machine tool will have a resonance effect due to the vibration frequency approaching the natural frequency of the structure, which makes the response of the machine tool increase and unstable, and affects the machining accuracy. Therefore, by using the Modal Analysis (OMA), understanding the modal frequency, mode shape, and modal damping ratio of the structural members will help improve the machining efficiency of the machine tool.

Traditionally, to perform a Modal Analysis (OMA) on a machine tool, it is necessary to provide a force of known and accurate magnitude. The current main practice is to use a hammer to strike the structural components of the machine tool, or to excite the structural components of the machine tool with an exciter.

However, the method of using the impact hammer to strike can only be carried out in the state of shutdown, and the modal analysis result will have an error due to the difference with the actual processing mode. The use of the exciter will result in random excitation due to the fixed frequency period. The modal analysis results will also produce errors, and the exciter used in the machine tool must be combined with an amplifier with a large output power to increase the amount. Measuring costs.

Accordingly, it is an object of the present invention to provide a modality detection system capable of improving the accuracy of modal analysis results and having a low equipment cost.

Therefore, the modality detecting system of the present invention is configured to obtain a plurality of modal parameters for performing operational modal analysis, and the modal detecting system comprises: a machine tool, a detecting unit, and a processing unit.

The machine tool includes a machine table, a table moving along a random path relative to the machine table, and a spindle device disposed on the machine table and generating self-vibration when the table is displaced, the table is in the same direction The maximum displacement is no more than 100mm.

The detecting unit includes a plurality of detectors dispersed on the spindle device, and each of the detectors measures an amplitude and a vibration frequency of the spindle device to generate an autogenous vibration, and outputs a sensing signal.

The processing unit communicates with the detectors, and after receiving the sensing signals, obtains modal parameters according to the amplitude and frequency.

The utility model has the advantages of: using the worktable to make small and rapid back and forth movements in different directions, so that the spindle device can simulate the real processing state to generate automatic vibration, and can improve the accuracy of the modal analysis result, and the equipment cost is low. .

Referring to FIG. 1, FIG. 2 and FIG. 3, an embodiment of the modality detecting system of the present invention comprises a power tool 1, a detecting unit 2, and a processing unit 3.

The machine tool 1 comprises a machine table 11, a table 12 moving along a random path S relative to the machine table 11, a spindle device 13 disposed on the machine table 11, and a motor unit for driving the table 12 to be displaced. 14. In this embodiment, the table 12 can be displaced along an X-axis direction and a Y-axis direction, so that the random path S is confined in a two-dimensional space, and the maximum displacement of the table 12 in the same direction during the displacement process. The amount is not more than 100 mm, and the moving speed is m0.6 m/s. The spindle unit 13 has a column 131 disposed on the table 11 along a Z-axis direction, and a spindle head 132 that is displaced relative to the table 12 in the Z-axis direction in accordance with the column 131.

The detecting unit 2 includes a plurality of detectors 21. In this embodiment, the detectors 21 have a total of 24, and the numbers 1 to 24 are respectively distributed on the column 131 and the spindle head 132 of the spindle device 13, and are respectively adjacent to the edges of the column 131. A number of edges with the spindle head 132. Each of the detectors 21 measures the amplitude and vibration frequency of the column 131 or the spindle head 132 when the self-vibration is generated, and outputs a sensing signal M. Each of the detectors 21 can be a piezoelectric accelerometer or a three-axis accelerometer with a sampling frequency of 4 Hz to 1 kHz and a measurable bandwidth of 0 to 500 Hz.

The processing unit 3 communicates with the detectors 21, and after receiving the sensing signals M, obtains modal parameters such as a modal frequency, a mode shape, and a modal damping ratio according to the amplitude and the frequency.

It should be noted that, in this embodiment, the random path S is randomly generated by the processing unit 3, and then input into the machine tool 1 by a path command (NC). In addition, the processing unit 3 can be built in at the remote computer or built into the machine tool 1.

During the modal analysis, the machine tool 1 drives the table 12 according to the trajectory of the random path S along the X-axis direction, or the Y-axis direction, with a maximum displacement of no more than 100 mm, and a moving rate of m0.6 m/ The speed of s, the rapid backward and backward displacement and the pause, the column 131 and the main shaft 132 generate self-vibration, and the excitation force of the self-vibration is mainly derived from the vibration generated by the motor unit 14, and The displacement speed of the table 12 is changed, or the moment of pause, or the inertial force when the direction is changed.

Thereby, during the displacement of the table 12, each of the detectors 21 measures the amplitude and the vibration frequency of the respective positions, and outputs a sensing signal M close to white noise.

After receiving the sensing signals M, the processing unit 3 obtains the response distribution of the X-axis direction-Y-axis direction of the respective positions, and the measurement results of the detectors 21 of No. 2, 6, and 12, and The time domain diagram of the response signal in the direction of FIG. 4X is compared with the correlation function diagram of the response signal of the X direction in FIG. 5, and the data of Table 1 is described as follows:  <TABLE border="1" borderColor="#000000" width="85%"><TBODY><tr><td> Table 1 </td></tr><tr><td> Detector number< /td><td> Axial</td><td><img wi="9" he="24" file="TWI638251B_D0001.tif" img-format="jpg"></img> (g) < /td><td><img wi="38" he="24" file="02_image003.jpg" img-format="jpg"></img></td><td><img wi="47 " he="24" file="02_image005.jpg" img-format="jpg"></img></td><td><img wi="47" he="24" file="02_image007.jpg " img-format="jpg"></img></td><td> skew coefficient </td><td> kurtosis coefficient </td></tr><tr><td> 2 </ Td><td> X-axis direction</td><td> 0.023 </td><td> 55.39 </td><td> 92.11 </td><td> 98.87 </td><td> 0.068 </ Td><td> 3.329 </td></tr><tr><td> 6 </td><td> X-axis direction</td><td> 0.014 </td><td> 49.99 </td ><td> 92.56 </td><td> 99.83 </td><td> -0.015 </td><td> 2.412 </td></tr><tr><td> 12 </td>< Td> X-axis direction </td><td> 0.016 </td><td> 61.87 </td><td> 95.76 </td><td> 99.94 </td><td> -0.016 </td> <td> 2.512 </td></tr><tr><td> 2 </td><td> Y-axis direction</td><td> 0.016 </td><td> 70.24 </td>< Td> 94.78 </td><td> 99.380 </td><td> 0.088 </td><td> 3.360 </td></tr><tr><td> 6 </td><td> Y-axis direction</td><td> 0.013 </td><td> 70.15 </td><td> 94.48 </td><td> 99.45 </td><td> 0.038 </td><td> 3.420 </td></tr><tr ><td> 12 </td><td> Y-axis direction</td><td> 0.016 </td><td> 70.17 </td><td> 94.47 </td><td> 99.32 </td ><td> 0.067 </td><td> 3.565 </td></tr></TBODY></TABLE>

Where μ is the average value, As the standard deviation, it indicates the degree of dispersion of data data, which is defined as the degree of dispersion of each variance and the average value in a set of data. Therefore, the larger standard deviation represents a larger difference between most of the data and the mean; on the contrary, the smaller standard deviation means that most of the data differs from the mean, indicating that most of the data The data is closer to the average.

The Cofficient of Skewness is used to describe the degree to which the data set deviates from the mean. The Cofficient of Kurtosis is used to describe the peak degree of the distribution. When the skewness coefficient = 0, it indicates that the distribution state is a symmetric distribution. When the skewness coefficient is >0, it indicates that the distribution state is right-bias or positive-biased distribution. When the skewness coefficient is <0, it indicates that the distribution state is left-biased or negative-biased. When the kurtosis coefficient=3, it indicates that the kurtosis of the distribution state is a normal peak. When the kurtosis coefficient is >3, it indicates that the kurtosis of the distribution state is a high-narrow peak. When the kurtosis coefficient is <3, it indicates that the kurtosis of the distribution state is low. Broad peaks.

The measurement results of Table 1 show that the distribution state distribution of the present invention is substantially consistent with the normal distribution curve, but the kurtosis coefficient is not completely equivalent to the normal distribution, and the distribution exhibits a high peak or a low peak. However, the distribution is still random as a whole, and it is presumed that the excitation force applied to the power tool 2 when the table 2 is moved is random excitation, and it is indeed possible to guess the sensing signal M which is closer to white noise. Further, the modal parameters such as the modal frequency, the mode shape, and the modal damping ratio of the machine tool 1 can be identified by using Stochastic Subspace Identification (SSI). Since the general knowledge in the art can infer the details of the expansion based on the above description, it will not be explained.

Through the above description, the advantages of the foregoing embodiments can be summarized as follows:

The invention utilizes the worktable 12 to make small and rapid back and forth movements in different directions, so that the spindle device 13 can simulate the real processing state to generate automatic vibration, and can improve the accuracy of the modal analysis result, and the equipment cost is low.

The invention can more accurately measure the modal frequency and the mode shape of the machine tool 1 under different working points, and the foregoing measurement data can be transmitted to the remote end, so that the remote tool machine factory can know the condition of the products around the world. , thereby reducing the down-time.

However, the above is only the embodiment of the present invention, and the scope of the invention is not limited thereto, and all the simple equivalent changes and modifications according to the scope of the patent application and the patent specification of the present invention are still Within the scope of the invention patent.

<TABLE border="1" borderColor="#000000" width="85%"><TBODY><tr><td> 1········ Machine Tool 11······ Machine 12 ······ Workbench 13······ Spindle unit 131····· Column 132····· Spindle head 14······ Motor unit </td><td> 2· ······· Detecting unit 21······ Detector X······· X-axis direction Y······· Y-axis direction Z······· · Z-axis direction S········ Random path M······· Sensing signal</td></tr></TBODY></TABLE>

Other features and advantages of the present invention will be apparent from the embodiments of the present invention, wherein: FIG. 1 is a perspective view illustrating an embodiment of the modality detection system of the present invention; FIG. 2 is an embodiment of the present invention; Figure 3 is a trajectory diagram illustrating a table in this embodiment displaced along a random path; Figure 4 is a time domain diagram of a response signal in the X direction; and Figure 5 is a A correlation function diagram of the response signal with respect to the aforementioned X direction.

Claims (9)

A modality detection system for acquiring a plurality of modal parameters for performing operational modal analysis, the modality detection system comprising: a machine tool comprising a machine table, moving relative to the machine along a random path a work table, and a spindle device disposed on the machine and generating self-vibration when the table is displaced, the maximum displacement of the table in the same direction is not more than 100 mm; a detecting unit, including the main body a plurality of detectors of the device, each of the detectors measuring amplitude and vibration frequency of the self-oscillation of the spindle device, and outputting a sensing signal; and a processing unit communicating with the detectors After receiving the sensing signals, modal parameters are obtained according to the amplitude and frequency. The modality detecting system of claim 1, wherein the table is displaceable along an X-axis direction and a Y-axis direction to limit the random path to a two-dimensional space. The modality detecting system of claim 2, wherein the spindle device has a column disposed on the machine along a Z-axis direction, and a spindle that is displaced relative to the table along the Z-axis direction Heads, the detectors are interspersed on the column and the spindle head. The modality detection system of claim 3, wherein the detectors are adjacent to at least one edge of the post and at least one edge of the spindle head. The modality detecting system of claim 3, wherein there are 12 detectors scattered on the column. The modality detecting system of claim 3, wherein there are 12 detectors scattered on the spindle head. The modality detection system of claim 1, wherein the detectors are respectively one of a piezoelectric accelerometer and a three-axis accelerometer. The modality detecting system according to claim 1, wherein the moving speed of the table is m0.6 m/s. The modality detecting system according to claim 1, wherein each of the detectors has a sampling frequency of 4 Hz to 1 kHz, and the measurable bandwidth is 0 to 500 Hz.