CN114273974A  Vibration signalbased tool runout parameter online estimation method in highspeed milling  Google Patents
Vibration signalbased tool runout parameter online estimation method in highspeed milling Download PDFInfo
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 CN114273974A CN114273974A CN202111560927.7A CN202111560927A CN114273974A CN 114273974 A CN114273974 A CN 114273974A CN 202111560927 A CN202111560927 A CN 202111560927A CN 114273974 A CN114273974 A CN 114273974A
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Abstract
The invention discloses a cutter bounce parameter online estimation method based on vibration signals in highspeed milling, which comprises the steps of firstly preprocessing acquired original vibration signals to acquire time domain vibration signals; secondly, obtaining a frequency domain displacement signal by performing secondary integration on the time domain vibration signal, and converting the frequency domain displacement signal into a time domain displacement signal by performing inverse discrete Fourier transform; and finally, estimating according to the time domain displacement signal to obtain the average tool runout amount, and taking the average tool runout amount as the tool runout length to realize the online estimation of the tool runout parameter based on the vibration signal in the highspeed milling process. The invention quickly estimates the tool jumping distance on line based on realtime vibration signals in the highspeed milling process, greatly improves the tool parameter identification efficiency, thereby accurately analyzing the tool wear characteristics in the highspeed milling process, improving the tool wear prediction efficiency, prolonging the service life of the tool and improving the processing quality of workpieces.
Description
Technical Field
The invention relates to a tool bounce parameter online estimation method in highspeed milling, in particular to a tool bounce parameter online estimation method in highspeed milling based on vibration signals.
Background
The highspeed milling has the main characteristics of high cutting speed, high feeding speed and high processing precision, has the capabilities of high comprehensive benefit, high market response speed and the like, and is widely applied to the industries of automobile industry, household appliances, aerospace production, electronics, optics, biomedicine and the like. Currently, based on methods such as a highspeed cutting theory, a lot of researchers have conducted extensive research and deep analysis on modeling of a milling process and characteristic wear of a tool in highspeed milling.
During high speed machining, the tool inevitably experiences runout. Tool runout is one of the key parameters for modeling the milling process and analyzing the tool wear characteristics. The cutter jump can cause different cutter teeth of the cutter to be worn inconsistently, and further the service life of the cutter and the processing quality of a workpiece are influenced. Current estimation methods can be classified into physical theory methods and datadriven methods according to the type of data and information required for tool runout estimation. The physical theory method estimates the specific tool wear amount through an established mechanism and an empirical formula, wherein physical parameters need to be determined through an experimental method. The datadriven approach is another, more common approach that predicts the future state of wear from the current state of tool wear on a numerical basis. Specifically, the datadriven methods are further classified into Artificial Intelligence (AI) methods (e.g., support vector machines, artificial neural networks, fuzzy theory, longandshort term memory, and the like) and statistical methods (e.g., multiple linear regression, gaussian mixture regression, hidden markov models, and the like). In recent years, various hybrid artificial intelligence methods for predicting tool wear have been proposed by various scholars. However, the above method requires a large amount of multilayer data and redundant time for training, which affects the tool wear prediction efficiency.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a vibration signalbased tool bounce parameter online estimation method in highspeed milling, which carries out tool wear estimation based on time sequence analysis according to realtime vibration signals, thereby more accurately analyzing tool wear characteristics in the highspeed milling process, improving tool wear prediction efficiency, prolonging the service life of a tool and improving the processing quality of a workpiece.
The invention adopts the following technical scheme for realizing the purpose of the invention:
the cutter bounce parameter online estimation method based on the vibration signal in the highspeed milling process is characterized in that:
firstly, the original vibration signal A obtained by collection is subjected to_{R}Preprocessing the signal to obtain a time domain vibration signal A_{P}；
Then aiming at the time domain vibration signal A_{P}Obtaining a frequency domain displacement signal D by integration_{λ}And then converting the frequency domain displacement signal into a time domain displacement signal d by inverse discrete Fourier transform_{i}；
And finally, estimating according to the time domain displacement signal to obtain the average tool runout amount, and taking the average tool runout amount as the tool runout length to realize the online estimation of tool runout parameters based on vibration signals in the highspeed milling process.
The cutter bounce parameter online estimation method based on the vibration signal in the highspeed milling process is also characterized in that:
the preprocessing of the acquired original vibration signal refers to: obtaining original vibration signal A by monitoring with accelerometer_{R}For said original vibration signal A_{R}And smoothing in a time domain to weaken highfrequency random interference signals and eliminate irregular trend terms.
The cutter bounce parameter online estimation method based on the vibration signal in the highspeed milling process is also characterized in that: the method comprises the following steps:
step 1: acquiring and preprocessing an original vibration signal:
obtaining original vibration signal A by collection_{R}For said original vibration signal A_{R}Preprocessing by adopting a fivepoint threetime smoothing method to obtain a preprocessed time domain vibration signal A_{P}；
Step 2: obtaining a time domain displacement signal through signal processing:
for the time domain vibration signal A_{P}Firstly, discrete Fourier transform is carried out to obtain frequency domain vibration signal X_{λ}For the frequency domain vibration signal X_{λ}Integrating to obtain frequency domain displacement signal D_{λ}(ii) a For the frequency domain displacement signal D_{λ}Obtaining a time domain displacement signal d through inverse discrete Fourier transform_{i}；
And step 3: estimating a cutter bounce parameter according to the time domain displacement signal:
according to the time domain displacement signal d_{i}Obtaining a displacement track fitting curve by a nonlinear least square method;
the tool runout position is recorded as (O)_{x},O_{y}) Mean the center point of the cutter point of the milling cutter (O)_{x},O_{y})；
The sampling frequency of the sample is omega, the time length of the milling cutter rotating for one period is T, and the sampling number in one period is omega T;
let the displacement signal sequence in each period be denoted as θ_{q}；
For the displacement signal sequence theta_{q}And (3) solving by a nonlinear least square method: constructed with the toolrunout position (O)_{x},O_{y}) And the tool runout length r is a multielement nonlinear target function group of multitarget variables, the target function group is optimally solved by adopting a least square method, the optimal solution of each target variable is obtained and is used as a tool runout parameter, and the tool runout parameter estimation based on vibration signals in highspeed milling is realized.
The cutter bounce parameter online estimation method based on the vibration signal in the highspeed milling process is also characterized in that:
the original vibration signal A_{R}In turn, each element is represented as a discrete number series { a_{Ri}}；
The time domain vibration signal A_{P}In turn, each element is represented as a discrete number series { a_{Pi}}；
Wherein i is 0,1, …, (U1); u is the number of sampling samples;
obtained according to formula (1)Domain vibration signal A_{P}；
In formula (1): u2, 3, …, (U3);
for sequence { a_{Pi}Performing discrete Fourier transform to obtain frequencydomain vibration signal X represented by formula (2)_{λ}：
For frequency domain vibration signal X_{λ}Integrating to obtain a frequency domain displacement signal D characterized by the formula (3)_{λ}：
In the formula (3), ω_{λ}2 pi λ Δ f; Δ f is the frequency resolution, Δ f is 1/T_{s}，T_{s}The total time length of sample data;
obtaining a time domain displacement signal d characterized by the formula (4) through inverse discrete Fourier transform_{i}：
In the formula (4), L_{λ}Is the frequency characteristic of the bandpass filter characterized by equation (5):
in the formula (5), f_{u}Is a lower cutoff frequency, f_{d}Is the upper cutoff frequency;
then, the displacement signal sequence θ in each period_{q}As characterized by formula (6):
for a displacement signal sequence theta_{q}Performing nonlinear least square solution, and decomposing each group of displacement signal sequence into component theta of x axis_{xq}And the component theta of the yaxis_{yq}Characterized by formula (7):
constructing a multielement nonlinear target function group f (O) of multitarget variables_{x},O_{y}And r) is of formula (8):
first, each target variable in the formula (8) is assigned with an initial value, and then a nonlinear least square method is used for solving, so that an optimal solution of each target variable is obtained.
Compared with the prior art, the invention has the beneficial effects that:
the invention quickly estimates the tool jumping distance on line based on realtime vibration signals in the highspeed milling process, greatly improves the tool parameter identification efficiency, thereby accurately analyzing the tool wear characteristics in the highspeed milling process, improving the tool wear prediction efficiency, prolonging the service life of the tool, and improving the processing quality of workpieces
Drawings
FIG. 1 is a flow chart of a tool runout parameter estimation method of the present invention;
FIG. 2a is a schematic view of a threeedge ballend milling cutter for experiments;
FIG. 2b is a schematic view of the threeedged ballend mill shown in FIG. 2 a;
FIG. 3 is a schematic diagram of the results of preprocessing the original vibration signal using a fivepoint threepass smoothing method;
FIG. 4 is a schematic diagram of obtaining displacement signals in the time domain and the frequency domain from the preprocessed vibration signals;
FIG. 5 is a schematic diagram of a tool runout length estimation result obtained by solving the displacement signal by a nonlinear least square method.
Detailed Description
In this embodiment, the online estimation method for the tool runout parameter based on the vibration signal in the highspeed milling process is as follows:
firstly, the original vibration signal A obtained by collection is subjected to_{R}Preprocessing the signal to obtain a time domain vibration signal A_{P}；
Then aiming at the time domain vibration signal A_{P}Obtaining a frequency domain displacement signal D by integration_{λ}And then converting the frequency domain displacement signal into a time domain displacement signal d by inverse discrete Fourier transform_{i}；
And finally, estimating according to the time domain displacement signal to obtain the average tool runout amount, and taking the average tool runout amount as the tool runout length to realize the online estimation of tool runout parameters based on vibration signals in the highspeed milling process.
In this embodiment, the tool runout parameter online estimation method based on vibration signals in highspeed milling includes the following steps:
step 1: acquiring and preprocessing an original vibration signal:
obtaining original vibration signal A by monitoring with accelerometer_{R}For the original vibration signal A_{R}Smoothing in the time domain by adopting a fivepoint threetime smoothing method to obtain a time domain vibration signal A subjected to smoothing in the time domain_{P}So as to weaken highfrequency random interference signals and eliminate irregular trend items;
step 2: obtaining a time domain displacement signal through signal processing:
for time domain vibration signal A_{P}Firstly, discrete Fourier transform is carried out to obtain frequency domain vibration signal X_{λ}Then to the frequency domain vibration signal X_{λ}Integrating to obtain frequencydomain velocity signal V_{λ}And then obtaining a frequency domain displacement signal D by quadratic integration_{λ}(ii) a For frequency domain velocity signal V_{λ}And frequency domain displacement signal D_{λ}Obtaining time domain velocity signal v through inverse discrete Fourier transform_{i}And a time domain displacement signal d_{i}；
And step 3: estimating the average tool runout according to the time domain displacement signal:
according to the time domain displacement signal d_{i}Obtaining a displacement track fitting curve by a nonlinear least square method;
the tool runout position is recorded as (O)_{x},O_{y}) Mean the center point of the cutter point of the milling cutter (O)_{x},O_{y}) (ii) a Aiming at a vertical machine tool, establishing a plane coordinate system XOY by taking the center point of the tool nose of a milling cutter before the start of processing as a coordinate origin, wherein the x axis is parallel to the feeding direction of a workpiece, and the y axis is perpendicular to the feeding direction of the workpiece; the coordinate of the center point of the cutter point of the milling cutter is recorded as (O) due to the cutter bounce in the machining process_{x},O_{y}) I.e. the tool runout position (O)_{x},O_{y})；O_{x}Is the xaxis coordinate, O, of the center point of the cutter nose of the milling cutter in a coordinate system XOY_{y}The yaxis coordinate of the center point of the cutter point of the milling cutter in a coordinate system XOY;
the sampling frequency of the sample is omega, the time length of the milling cutter rotating for one period is T, and the sampling number in one period is omega T;
let the displacement signal sequence in each period be denoted as θ_{q}；
For the displacement signal sequence theta_{q}And (3) solving by a nonlinear least square method: constructed with the toolrunout position (O)_{x},O_{y}) And the tool runout length r is a multielement nonlinear target function group of multitarget variables, the target function group is optimally solved by adopting a least square method, the optimal solution of each target variable is obtained and is used as a tool runout parameter, and the tool runout parameter based on vibration signals in highspeed milling is realizedIs estimated.
In specific implementation, the original vibration signal A is used_{R}In turn, each element is represented as a discrete number series { a_{Ri}}; the time domain vibration signal A_{P}In turn, each element is represented as a discrete number series { a_{Pi}}; wherein i is 0,1, …, (U1); u is the number of sampling samples;
obtaining a timedomain vibration signal A according to equation (1)_{P}；
In formula (1): u is 2,3, …, (U3), and each element a_{Pu}The corresponding coefficients are the same and are all 1/70 × [ 62434246]；
For sequence { a_{Pi}Performing discrete Fourier transform to obtain frequencydomain vibration signal X represented by formula (2)_{λ}：
For frequency domain vibration signal X_{λ}Integrating to obtain a frequency domain displacement signal D characterized by the formula (3)_{λ}：
In the formula (3), ω_{λ}2 pi λ Δ f; Δ f is the frequency resolution, Δ f is 1/T_{s}，T_{s}The total time length of sample data;
obtaining a time domain displacement signal d characterized by the formula (4) through inverse discrete Fourier transform_{i}：
In the formula (4), L_{λ}Is the frequency characteristic of the bandpass filter characterized by equation (5):
in the formula (5), f_{u}Is a lower cutoff frequency, f_{d}Is the upper cutoff frequency;
lower cutoff frequency f_{u}And an upper cutoff frequency f_{d}Selecting according to the inherent frequency nN/60 of cutter milling; selecting a minimum closed neighborhood by taking the natural frequency as a center, wherein the minimum closed neighborhood can contain a complete frequency domain waveform, and taking a left end point value of the minimum closed neighborhood as a lower limit cutoff frequency f_{u}Taking the right end point value of the minimum closed neighborhood as the upper limit cutoff frequency f_{d}。
Then, the displacement signal sequence θ in each period_{q}As characterized by formula (6):
for a displacement signal sequence theta_{q}And (3) solving by a nonlinear least square method: setting the included angle between the initial angle of the displacement signal and the xaxis to be 0, and decomposing the component theta of each group of displacement signal sequence to the xaxis_{xq}And the component theta of the yaxis_{yq}Characterized by formula (7):
constructing a multielement nonlinear target function group f (O) of multitarget variables_{x},O_{y}And r) is of formula (8):
firstly, assigning initial values to each target variable in the formula (8), and solving by using a nonlinear least square method to obtain an optimal solution of each target variable;
variable O_{x}Initial value of (1)_{x_guess}Variable O_{y}Initial value of (1)_{y_guess}And an initial value r of the variable r_{guess}Characterized by formula (9):
and (3) experimental verification:
a threeedge ballend milling cutter is selected to continuously mill the nickelbased hightemperature alloy Inconel 718, the diameter of the cutter is 0.6mm, and the sampling frequency is 50 KHz. The tool and the machining platform are shown in fig. 2a and 2 b.
The method is adopted to carry out online estimation on the cutter bounce parameter in the highspeed milling process based on the vibration signal, and the flow of the method is shown in figure 1.
Fig. 3 (a) is an xaxis original vibration signal acquired, fig. 3 (b) is a yaxis original vibration signal acquired, fig. 3 (c) is an xaxis preprocessed vibration signal, and fig. 3 (d) is a yaxis preprocessed vibration signal. The original vibration signal in the graphs (a) and (b) in fig. 3 contains highfrequency random noise mixed in during data acquisition, so that a discrete data sequence of the vibration signal presents a plurality of burrs in a time domain graph and is not smooth; in order to reduce the influence of these random interference signals, the original vibration signal is smoothed by a fivepointthree smoothing method, and the vibration signals after being preprocessed are shown in (c) diagram and (d) diagram in fig. 3. After the noise signals in the highfrequency components are eliminated, the amplitude of the curve in the time domain spectrum is reduced.
Fig. 4 (a) is an xaxis time domain displacement signal, fig. 4 (b) is a yaxis time domain displacement signal, fig. 4 (c) is an xaxis frequency domain displacement signal, and fig. 4 (d) is a yaxis frequency domain displacement signal. As can be seen in fig. 4 (a) and (b), after the discrete fourier transform, the time domain vibration signal amplitude in the xaxis and yaxis is substantially flat within the sample interval. As can be seen from the graphs (c) and (d) in fig. 4, the frequency spectrum has a distinct peak near the natural frequency.
Finally, cutter bounce parameters are obtained by solving an optimized objective function group, as shown in fig. 5, the time domain vibration signal amplitudes of the x axis and the y axis are kept stable in a sampling interval, and the center of the cutter tip presents an approximate circular track in an xy plane diagram. And fitting the track by adopting a nonlinear least square method to obtain a fitting circle with the tool runout length r as the radius. The center of the fitting circle is very close to the origin (0,0), which indicates that the inclination angle of the tool during machining is very small.
The method of the invention indirectly estimates the tool runout quantity on the basis of vibration signals, and the method of the invention completes the online realtime estimation of the tool runout quantity by means of the vibration signals, thereby being beneficial to the online monitoring and characteristic analysis of physical parameters of processes such as tool abrasion and the like in the highspeed milling process.
Claims (4)
1. A cutter bounce parameter online estimation method based on vibration signals in highspeed milling processing is characterized in that:
firstly, the original vibration signal A obtained by collection is subjected to_{R}Preprocessing the signal to obtain a time domain vibration signal A_{P}；
Then aiming at the time domain vibration signal A_{P}Obtaining a frequency domain displacement signal D by integration_{λ}And then converting the frequency domain displacement signal into a time domain displacement signal d by inverse discrete Fourier transform_{i}；
And finally, estimating according to the time domain displacement signal to obtain the average tool runout amount, and taking the average tool runout amount as the tool runout length to realize the online estimation of tool runout parameters based on vibration signals in the highspeed milling process.
2. The method for online estimation of tool runout parameters based on vibration signals in highspeed milling according to claim 1, wherein:
the preprocessing of the acquired original vibration signal refers to: obtaining original vibration signal A by monitoring with accelerometer_{R}For said original vibration signal A_{R}And smoothing in a time domain to weaken highfrequency random interference signals and eliminate irregular trend terms.
3. The method for online estimation of tool runout parameters based on vibration signals in highspeed milling according to claim 2, characterized by comprising the steps of:
step 1: acquiring and preprocessing an original vibration signal:
obtaining original vibration signal A by collection_{R}For said original vibration signal A_{R}Preprocessing by adopting a fivepoint threetime smoothing method to obtain a preprocessed time domain vibration signal A_{P}；
Step 2: obtaining a time domain displacement signal through signal processing:
for the time domain vibration signal A_{P}Firstly, discrete Fourier transform is carried out to obtain frequency domain vibration signal X_{λ}For the frequency domain vibration signal X_{λ}Integrating to obtain frequency domain displacement signal D_{λ}(ii) a For the frequency domain displacement signal D_{λ}Obtaining a time domain displacement signal d through inverse discrete Fourier transform_{i}；
And step 3: estimating a cutter bounce parameter according to the time domain displacement signal:
according to the time domain displacement signal d_{i}Obtaining a displacement track fitting curve by a nonlinear least square method;
the tool runout position is recorded as (O)_{x},O_{y}) Mean the center point of the cutter point of the milling cutter (O)_{x},O_{y})；
The sampling frequency of the sample is omega, the time length of the milling cutter rotating for one period is T, and the sampling number in one period is omega T;
let the displacement signal sequence in each period be denoted as θ_{q}；
For the displacement signal sequenceColumn θ_{q}And (3) solving by a nonlinear least square method: constructed with the toolrunout position (O)_{x},O_{y}) And the tool runout length r is a multielement nonlinear target function group of multitarget variables, the target function group is optimally solved by adopting a least square method, the optimal solution of each target variable is obtained and is used as a tool runout parameter, and the tool runout parameter estimation based on vibration signals in highspeed milling is realized.
4. The method for online estimation of tool runout parameters based on vibration signals in highspeed milling according to claim 3, wherein:
the original vibration signal A_{R}In turn, each element is represented as a discrete number series { a_{Ri}}；
The time domain vibration signal A_{P}In turn, each element is represented as a discrete number series { a_{Pi}}；
Wherein i is 0,1, …, (U1); u is the number of sampling samples;
obtaining a timedomain vibration signal A according to equation (1)_{P}；
In formula (1): u2, 3, …, (U3);
for sequence { a_{Pi}Performing discrete Fourier transform to obtain frequencydomain vibration signal X represented by formula (2)_{λ}：
For frequency domain vibration signal X_{λ}Integrating to obtain a frequency domain displacement signal D characterized by the formula (3)_{λ}：
In the formula (3), ω_{λ}2 pi λ Δ f; Δ f is the frequency resolution, Δ f is 1/T_{s}，T_{s}The total time length of sample data;
obtaining a time domain displacement signal d characterized by the formula (4) through inverse discrete Fourier transform_{i}：
In the formula (4), L_{λ}Is the frequency characteristic of the bandpass filter characterized by equation (5):
in the formula (5), f_{u}Is a lower cutoff frequency, f_{d}Is the upper cutoff frequency;
then, the displacement signal sequence θ in each period_{q}As characterized by formula (6):
θ_{q}＝{d_{(q1)ωT+1},d_{(q1)ωT+2},…,d_{qωT}} (6)
for a displacement signal sequence theta_{q}Performing nonlinear least square solution, and decomposing each group of displacement signal sequence into component theta of x axis_{xq}And the component theta of the yaxis_{yq}Characterized by formula (7):
constructing a multielement nonlinear target function group f (O) of multitarget variables_{x},O_{y}And r) is of formula (8):
first, each target variable in the formula (8) is assigned with an initial value, and then a nonlinear least square method is used for solving, so that an optimal solution of each target variable is obtained.
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