CN115555920B - Online chatter detection method and system based on adaptive variation modal decomposition - Google Patents
Online chatter detection method and system based on adaptive variation modal decomposition Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23Q—DETAILS, COMPONENTS, OR ACCESSORIES FOR MACHINE TOOLS, e.g. ARRANGEMENTS FOR COPYING OR CONTROLLING; MACHINE TOOLS IN GENERAL CHARACTERISED BY THE CONSTRUCTION OF PARTICULAR DETAILS OR COMPONENTS; COMBINATIONS OR ASSOCIATIONS OF METAL-WORKING MACHINES, NOT DIRECTED TO A PARTICULAR RESULT
- B23Q17/00—Arrangements for observing, indicating or measuring on machine tools
- B23Q17/12—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring vibration
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C3/00—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles
- G07C3/005—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles during manufacturing process
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Abstract
The invention provides an on-line chatter detection method and system based on self-adaptive variation modal decomposition, which are used for collecting vibration signals in the processing process, identifying the vibration signals by using a trained support vector machine model and determining the processing state; the training process of the support vector machine model comprises the following steps: acquiring natural frequency of a processing system to be analyzed, and determining a flutter frequency band; acquiring a vibration signal of a history machining process; the history signal is adaptively decomposed into a certain number of modal components distributed in different frequency bands by utilizing the adaptive variation modal decomposition, and all the modal components are taken for reconstruction to obtain a filtering signal; calculating the energy ratio of the flutter modal components in the flutter frequency band and the dispersion entropy of the filtering signals to form a feature vector; and training the support vector machine by using the feature vectors in the normal processing and flutter states to obtain a trained support vector machine model. The invention has higher detection precision, timeliness and sensitivity to the chatter, and can effectively identify the chatter in an early stage.
Description
Technical Field
The invention belongs to the technical field of detection in a machining process, and relates to an online chatter detection method and system based on adaptive variation modal decomposition.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Machining chatter is a notoriously unstable phenomenon in the machining process that results in poor workpiece surface quality, reduced tool wear and life, and even machine tool damage. The production efficiency is severely limited by processing chatter, and the method has important significance in avoiding chatter in the processing process.
According to the knowledge of the inventor, students at home and abroad have proposed a plurality of processing stability prediction methods based on analytical models and stability lobe diagrams. However, chatter may still occur at the selected stable cutting parameters due to the presence of system uncertainty and the time-varying nature of the machining process. Therefore, under complex actual processing conditions, it is practical and necessary to employ an on-line chatter method to detect early chatter, avoiding its adverse effects.
Signal acquisition, signal processing and flutter index extraction are key components of flutter detection. Along with the development of sensor technology, scholars at home and abroad propose a lot of chatter detection methods based on different sensors, such as acceleration, force, microphone and the like.
Regardless of the signal employed, efficient signal processing is essential for accurate chatter detection. The traditional frequency spectrum analysis method based on Fourier transform conceals time domain information, is blind to state conversion of non-stationary signals, and is not suitable for extracting flutter characteristics. The variational modal decomposition is a self-adaptive analysis method for nonlinear and non-stationary signals, and has the characteristics of firm theoretical basis, excellent self-adaptability and noise robustness, and is widely applied. However, the variation modal decomposition is computationally inefficient and highly dependent on the decomposition parameters (modal component numbers and penalty factors), which severely limits the application of variation modal decomposition in online chatter detection.
Disclosure of Invention
In order to solve the problems, the invention provides an online chatter detection method and system based on adaptive variation modal decomposition.
According to some embodiments, the present invention employs the following technical solutions:
an online chatter detection method based on adaptive variation modal decomposition comprises the following steps:
collecting vibration signals in the processing process, and identifying the vibration signals by using a trained support vector machine model to determine a processing state;
the training process of the support vector machine model comprises the following steps:
acquiring natural frequency of a processing system to be analyzed, and determining a flutter frequency band;
Acquiring a vibration signal of a history machining process;
The history signal is adaptively decomposed into a certain number of modal components distributed in different frequency bands by utilizing the adaptive variation modal decomposition, and all the modal components are taken for reconstruction to obtain a filtering signal;
calculating the energy ratio of the flutter modal components in the flutter frequency band and the dispersion entropy of the filtering signals to form a feature vector;
and training the support vector machine by using the feature vectors in the normal processing and flutter states to obtain a trained support vector machine model.
As an alternative embodiment, the natural frequency of the machining system is obtained through a mode experiment, and the frequency band where the natural frequency is located is determined as the flutter frequency band.
As an alternative embodiment, the specific process of adaptively decomposing the history signal into a number of modal components distributed in different frequency bands by using adaptive variational modal decomposition includes: and extracting modal components one by one in a recursion frame by utilizing adaptive variation modal decomposition, subtracting each modal component from an original signal after extracting the modal components to obtain a residual signal, extracting the next modal component by taking the residual signal as a new initial signal, and repeatedly and circularly updating until all the signal components are obtained.
As a further defined embodiment, the adaptive variational modal decomposition uses the residual signal energy ratio as a criterion for the stopping algorithm until the residual signal energy ratio is less than a set stopping threshold.
Alternatively, the adaptive variation modal decomposition adaptively adjusts the penalty factor in the iterative process according to the iteration information to reduce the modulo mixing until the spectral overlap ratio is less than a set threshold.
As an alternative embodiment, the energy ratio of the dither modal components is calculated as the ratio of the total energy of the modal components having a center frequency in the dither band to the sum of the energies of the modal components.
As an alternative implementation mode, the support vector machine model is obtained by adopting a radial basis function, and penalty parameters and kernel parameters are reduced by 5 times through cross verification error optimization by using a Bayesian optimization algorithm.
An online chatter detection system based on adaptive variational modal decomposition, comprising:
the acquisition module is configured to acquire vibration signals in the processing process;
The processing module is configured to identify the vibration signal by using the trained support vector machine model and determine a processing state;
Further comprises:
the parameter acquisition module is configured to acquire the natural frequency of the processing system to be analyzed and determine a flutter frequency band;
a signal acquisition module configured to acquire a vibration signal of a history of processing;
The signal decomposition module is configured to adaptively decompose the history signal into a certain number of modal components distributed in different frequency bands by utilizing adaptive variation modal decomposition, and reconstruct all the modal components to obtain a filtered signal;
a feature vector construction module configured to calculate an energy ratio of a dither modal component located within a dither band and a dispersion entropy of a filtered signal, constituting a feature vector;
The model construction and training module is configured to train the support vector machine by utilizing the feature vectors in normal processing and flutter states to obtain a trained support vector machine model.
A computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the steps in the method.
A terminal device comprising a processor and a computer readable storage medium, the processor configured to implement instructions; the computer readable storage medium is for storing a plurality of instructions adapted to be loaded by a processor and to perform the steps in the method.
Compared with the prior art, the invention has the beneficial effects that:
The invention establishes the self-adaptive variation modal decomposition method for the on-line flutter detection, and compared with variation modal decomposition, the self-adaptive variation modal decomposition has higher calculation efficiency, better filtering performance and better convergence, and improves the real-time performance and accuracy of the flutter detection.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
FIG. 1 is an overview of an online chatter detection framework;
FIG. 2 is a schematic diagram of the adaptive variation modal decomposition result of the acceleration signal during the flutter phase;
FIG. 3 is a schematic view of the quality of the work piece processing surface;
fig. 4 is a schematic diagram of the detection result of the method of the one-time variable depth cutting process test.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the invention. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Example 1
Referring to fig. 1, the method for detecting the online chatter vibration based on the adaptive variation modal decomposition comprises the following steps:
Step S1, for a given machine tool workpiece system, the chatter frequency can be predicted by a modal experiment of the machine tool workpiece system. The chatter frequency is typically 0% -15% greater than the lowest order natural frequency of the machine tool workpiece system. In an example, the natural frequency of the processing system is obtained through a modal experiment, and a frequency band where the natural frequency is located is determined as a flutter frequency band;
step S2, vibration signals in the machining process are collected through an acceleration sensor (model Dytran3263A2, sensitivity of 100 mv/g) arranged on a machine tool spindle and a data collection system (DH 5930), and the collected signals are transmitted to a computer through a data line;
S3, adaptively decomposing the acquired signals into a certain number of modal components distributed in different frequency bands by utilizing adaptive variation modal decomposition, and reconstructing all the modal components to obtain filtered signals;
and S3.1, extracting modal components one by one in a recursion frame by the adaptive variation modal decomposition, subtracting the modal components from an original signal after extracting each modal component to obtain a residual signal, extracting the next modal component by taking the residual signal as a new initial signal, and repeatedly and circularly updating until all signal components are obtained. The specific formula for extracting the kth pattern component is as follows:
Wherein m k (t) and Represents the kth mode component and its resolved signal, ω k represents the center frequency of the kth mode component, symbol/>Represents the partial derivative of time t, λ (t) represents the lagrangian multiplier, a is a penalty factor,Is the residual signal after extracting the first k-1 modes, f (t) represents the original signal. The optimization problem is solved by an alternate direction multiplier method. The modal component m k (t) and the center frequency ω k of the n+1th iteration in the frequency domain are given by the following formulas:
And S3.2, the adaptive variation modal decomposition adopts the residual signal energy ratio as a criterion of a stopping algorithm. The residual signal energy ratio e is expressed as:
The adaptive variational modal decomposition extracts modal components one by one in the recursive framework until the residual signal energy ratio is less than the set stop threshold μ 1.
And S3.3, adaptively adjusting a penalty factor according to iteration information in the iteration process by using the adaptive variation modal decomposition, wherein the specific formula is as follows:
Wherein, I is 1,2, & gt, k-1 is except/>Other modal components outside.
SOR is the spectral overlap ratio that measures the degree of modal mixing of the results of each iteration and directs the adjustment of the penalty factor α, i.e., the penalty factor α, to reduce modulo mixing until SOR is less than a set threshold μ 2. The adaptive variation modal decomposition result of the acceleration signal in the flutter stage is shown in fig. 2.
S4, calculating the energy ratio of the flutter modal components in the flutter frequency band and the dispersion entropy of the filtering signals to form a feature vector;
S4.1, the energy ratio calculation method of the flutter modal component is as follows:
the energy of each modal component is calculated:
Calculating the energy ratio of the flutter modal components:
Where Σe c is the total energy of the mode components with center frequency in the dither band.
Step S5, as shown in FIG. 3, the state of the processing surface of the workpiece is checked and the signal type is marked. Selecting a feature vector training support vector machine in normal processing and flutter states to obtain a training model;
and S5.1, the support vector machine adopts a radial basis function, and penalty parameters and nuclear parameters are obtained by reducing 5 times of cross verification errors through a Bayesian optimization algorithm.
And S6, recognizing the processing state on line by adopting the trained support vector machine model and vibration signals acquired in real time in the processing process. In a cutting test of changing axial depth, the identification result of the support vector machine is shown in fig. 4, and the method timely and accurately detects the chatter after the actual chatter occurs, so that the effectiveness of the method is verified.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the foregoing description of the embodiments of the present invention has been presented in conjunction with the drawings, it should be understood that it is not intended to limit the scope of the invention, but rather, it is intended to cover all modifications or variations within the scope of the invention as defined by the claims of the present invention.
Claims (7)
1. An online chatter detection method based on adaptive variation modal decomposition is characterized by comprising the following steps:
collecting vibration signals in the processing process, and identifying the vibration signals by using a trained support vector machine model to determine a processing state;
the training process of the support vector machine model comprises the following steps:
acquiring natural frequency of a processing system to be analyzed, and determining a flutter frequency band;
Acquiring a vibration signal of a history machining process;
The history signal is adaptively decomposed into a certain number of modal components distributed in different frequency bands by utilizing the adaptive variation modal decomposition, and all the modal components are taken for reconstruction to obtain a filtering signal;
calculating the energy ratio of the flutter modal components in the flutter frequency band and the dispersion entropy of the filtering signals to form a feature vector;
Training a support vector machine by using the feature vectors in normal processing and flutter states to obtain a trained support vector machine model;
The specific process of adaptively decomposing the history signal into a certain number of modal components distributed in different frequency bands by using the adaptive variational modal decomposition comprises the following steps: extracting modal components one by one in a recursion frame by utilizing adaptive variation modal decomposition, subtracting each modal component from an original signal after extracting the corresponding modal component to obtain a residual signal, extracting the next modal component by taking the residual signal as a new initial signal, and repeatedly and circularly updating until all signal components are obtained;
The adaptive variation modal decomposition adopts the residual signal energy ratio as a criterion of a stopping algorithm until the residual signal energy ratio is smaller than a set stopping threshold;
The adaptive variation modal decomposition adaptively adjusts the penalty factor according to iteration information in the iteration process to reduce the modulo mixing until the spectrum overlap ratio is smaller than a set threshold.
2. The on-line chatter detection method based on adaptive variation modal decomposition as defined in claim 1, wherein the natural frequency of the machining system is obtained by using a modal experiment, and the frequency band where the natural frequency is located is determined as the chatter frequency band.
3. The on-line chatter detection method based on adaptive variation modal decomposition as defined in claim 1, wherein the energy ratio of the chatter modal components is calculated as a ratio of the total energy of the modal components with the center frequency in the chatter frequency band to the sum of the energies of the modal components.
4. The online chatter detection method based on adaptive variation modal decomposition as defined in claim 1, wherein the support vector machine model is obtained by reducing 5-fold cross validation error optimization of penalty parameters and kernel parameters by using a bayesian optimization algorithm by adopting a radial basis function.
5. An on-line chatter detection system based on adaptive variation modal decomposition is characterized by comprising:
the acquisition module is configured to acquire vibration signals in the processing process;
The processing module is configured to identify the vibration signal by using the trained support vector machine model and determine a processing state;
Further comprises:
the parameter acquisition module is configured to acquire the natural frequency of the processing system to be analyzed and determine a flutter frequency band;
a signal acquisition module configured to acquire a vibration signal of a history of processing;
The signal decomposition module is configured to adaptively decompose the history signal into a certain number of modal components distributed in different frequency bands by utilizing adaptive variation modal decomposition, and reconstruct all the modal components to obtain a filtered signal;
The specific process of adaptively decomposing the history signal into a certain number of modal components distributed in different frequency bands by using the adaptive variational modal decomposition comprises the following steps: extracting modal components one by one in a recursion frame by utilizing adaptive variation modal decomposition, subtracting each modal component from an original signal after extracting the corresponding modal component to obtain a residual signal, extracting the next modal component by taking the residual signal as a new initial signal, and repeatedly and circularly updating until all signal components are obtained;
The adaptive variation modal decomposition adopts the residual signal energy ratio as a criterion of a stopping algorithm until the residual signal energy ratio is smaller than a set stopping threshold;
The self-adaptive variation modal decomposition adaptively adjusts a penalty factor according to iteration information in the iteration process so as to reduce mode mixing until the spectrum overlap ratio is smaller than a set threshold value;
a feature vector construction module configured to calculate an energy ratio of a dither modal component located within a dither band and a dispersion entropy of a filtered signal, constituting a feature vector;
The model construction and training module is configured to train the support vector machine by utilizing the feature vectors in normal processing and flutter states to obtain a trained support vector machine model.
6. A computer readable storage medium, characterized in that a plurality of instructions are stored, which instructions are adapted to be loaded by a processor of a terminal device and to perform the steps of the method of any of claims 1-4.
7. A terminal device, comprising a processor and a computer readable storage medium, the processor configured to implement instructions; a computer readable storage medium for storing a plurality of instructions adapted to be loaded by a processor and to perform the steps of the method of any of claims 1-4.
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