CN114273974A - Vibration signal-based tool runout parameter online estimation method in high-speed milling - Google Patents
Vibration signal-based tool runout parameter online estimation method in high-speed milling Download PDFInfo
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Abstract
The invention discloses a cutter bounce parameter online estimation method based on vibration signals in high-speed 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 on-line estimation of the tool runout parameter based on the vibration signal in the high-speed milling process. The invention quickly estimates the tool jumping distance on line based on real-time vibration signals in the high-speed milling process, greatly improves the tool parameter identification efficiency, thereby accurately analyzing the tool wear characteristics in the high-speed 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 high-speed milling, in particular to a tool bounce parameter online estimation method in high-speed milling based on vibration signals.
Background
The high-speed 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 high-speed 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 high-speed milling.
During high speed machining, the tool inevitably experiences run-out. Tool run-out 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 data-driven methods according to the type of data and information required for tool run-out 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 data-driven 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 data-driven methods are further classified into Artificial Intelligence (AI) methods (e.g., support vector machines, artificial neural networks, fuzzy theory, long-and-short 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 multi-layer 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 signal-based tool bounce parameter online estimation method in high-speed milling, which carries out tool wear estimation based on time sequence analysis according to real-time vibration signals, thereby more accurately analyzing tool wear characteristics in the high-speed 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 high-speed milling process is characterized in that:
firstly, the original vibration signal A obtained by collection is subjected toRPreprocessing the signal to obtain a time domain vibration signal AP；
Then aiming at the time domain vibration signal APObtaining a frequency domain displacement signal D by integrationλAnd then converting the frequency domain displacement signal into a time domain displacement signal d by inverse discrete Fourier transformi；
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 on-line estimation of tool runout parameters based on vibration signals in the high-speed milling process.
The cutter bounce parameter online estimation method based on the vibration signal in the high-speed 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 accelerometerRFor said original vibration signal ARAnd smoothing in a time domain to weaken high-frequency random interference signals and eliminate irregular trend terms.
The cutter bounce parameter online estimation method based on the vibration signal in the high-speed 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 collectionRFor said original vibration signal ARPreprocessing by adopting a five-point three-time smoothing method to obtain a preprocessed time domain vibration signal AP；
Step 2: obtaining a time domain displacement signal through signal processing:
for the time domain vibration signal APFirstly, discrete Fourier transform is carried out to obtain frequency domain vibration signal XλFor the frequency domain vibration signal XλIntegrating to obtain frequency domain displacement signal Dλ(ii) a For the frequency domain displacement signal DλObtaining a time domain displacement signal d through inverse discrete Fourier transformi；
And step 3: estimating a cutter bounce parameter according to the time domain displacement signal:
according to the time domain displacement signal diObtaining a displacement track fitting curve by a nonlinear least square method;
the tool run-out position is recorded as (O)x,Oy) Mean the center point of the cutter point of the milling cutter (O)x,Oy)；
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 θq；
For the displacement signal sequence thetaqAnd (3) solving by a nonlinear least square method: constructed with the tool-run-out position (O)x,Oy) And the tool runout length r is a multi-element nonlinear target function group of multi-target 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 high-speed milling is realized.
The cutter bounce parameter online estimation method based on the vibration signal in the high-speed milling process is also characterized in that:
the original vibration signal ARIn turn, each element is represented as a discrete number series { aRi}；
The time domain vibration signal APIn turn, each element is represented as a discrete number series { aPi}；
Wherein i is 0,1, …, (U-1); u is the number of sampling samples;
obtained according to formula (1)Domain vibration signal AP；
In formula (1): u-2, 3, …, (U-3);
for sequence { aPiPerforming discrete Fourier transform to obtain frequency-domain vibration signal X represented by formula (2)λ：
In formula (2), λ ═ 0,1, …, (U-1); j is an imaginary number in units
For frequency domain vibration signal XλIntegrating to obtain a frequency domain displacement signal D characterized by the formula (3)λ：
In the formula (3), ωλ2 pi λ Δ f; Δ f is the frequency resolution, Δ f is 1/Ts，TsThe total time length of sample data;
obtaining a time domain displacement signal d characterized by the formula (4) through inverse discrete Fourier transformi：
In the formula (4), LλIs the frequency characteristic of the band-pass filter characterized by equation (5):
in the formula (5), fuIs a lower cut-off frequency, fdIs the upper cut-off frequency;
then, the displacement signal sequence θ in each periodqAs characterized by formula (6):
in the formula (6), the reaction mixture is,
for a displacement signal sequence thetaqPerforming nonlinear least square solution, and decomposing each group of displacement signal sequence into component theta of x axisxqAnd the component theta of the y-axisyqCharacterized by formula (7):
constructing a multi-element nonlinear target function group f (O) of multi-target variablesx,OyAnd 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 real-time vibration signals in the high-speed milling process, greatly improves the tool parameter identification efficiency, thereby accurately analyzing the tool wear characteristics in the high-speed 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 three-edge ball-end milling cutter for experiments;
FIG. 2b is a schematic view of the three-edged ball-end mill shown in FIG. 2 a;
FIG. 3 is a schematic diagram of the results of preprocessing the original vibration signal using a five-point three-pass 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 run-out length estimation result obtained by solving the displacement signal by a nonlinear least square method.
Detailed Description
In this embodiment, the on-line estimation method for the tool runout parameter based on the vibration signal in the high-speed milling process is as follows:
firstly, the original vibration signal A obtained by collection is subjected toRPreprocessing the signal to obtain a time domain vibration signal AP；
Then aiming at the time domain vibration signal APObtaining a frequency domain displacement signal D by integrationλAnd then converting the frequency domain displacement signal into a time domain displacement signal d by inverse discrete Fourier transformi；
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 on-line estimation of tool runout parameters based on vibration signals in the high-speed milling process.
In this embodiment, the tool runout parameter online estimation method based on vibration signals in high-speed milling includes the following steps:
step 1: acquiring and preprocessing an original vibration signal:
obtaining original vibration signal A by monitoring with accelerometerRFor the original vibration signal ARSmoothing in the time domain by adopting a five-point three-time smoothing method to obtain a time domain vibration signal A subjected to smoothing in the time domainPSo as to weaken high-frequency random interference signals and eliminate irregular trend items;
step 2: obtaining a time domain displacement signal through signal processing:
for time domain vibration signal APFirstly, discrete Fourier transform is carried out to obtain frequency domain vibration signal XλThen to the frequency domain vibration signal XλIntegrating to obtain frequency-domain velocity signal VλAnd then obtaining a frequency domain displacement signal D by quadratic integrationλ(ii) a For frequency domain velocity signal VλAnd frequency domain displacement signal DλObtaining time domain velocity signal v through inverse discrete Fourier transformiAnd a time domain displacement signal di；
And step 3: estimating the average tool runout according to the time domain displacement signal:
according to the time domain displacement signal diObtaining a displacement track fitting curve by a nonlinear least square method;
the tool run-out position is recorded as (O)x,Oy) Mean the center point of the cutter point of the milling cutter (O)x,Oy) (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 processx,Oy) I.e. the tool runout position (O)x,Oy)；OxIs the x-axis coordinate, O, of the center point of the cutter nose of the milling cutter in a coordinate system XOYyThe y-axis 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 θq；
For the displacement signal sequence thetaqAnd (3) solving by a nonlinear least square method: constructed with the tool-run-out position (O)x,Oy) And the tool run-out length r is a multi-element nonlinear target function group of multi-target 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 run-out parameter, and the tool run-out parameter based on vibration signals in high-speed milling is realizedIs estimated.
In specific implementation, the original vibration signal A is usedRIn turn, each element is represented as a discrete number series { aRi}; the time domain vibration signal APIn turn, each element is represented as a discrete number series { aPi}; wherein i is 0,1, …, (U-1); u is the number of sampling samples;
obtaining a time-domain vibration signal A according to equation (1)P；
In formula (1): u is 2,3, …, (U-3), and each element aPuThe corresponding coefficients are the same and are all 1/70 × [ -6243424-6]；
For sequence { aPiPerforming discrete Fourier transform to obtain frequency-domain vibration signal X represented by formula (2)λ：
In formula (2), λ ═ 0,1, …, (U-1); j is an imaginary number in units
For frequency domain vibration signal XλIntegrating to obtain a frequency domain displacement signal D characterized by the formula (3)λ：
In the formula (3), ωλ2 pi λ Δ f; Δ f is the frequency resolution, Δ f is 1/Ts，TsThe total time length of sample data;
obtaining a time domain displacement signal d characterized by the formula (4) through inverse discrete Fourier transformi：
In the formula (4), LλIs the frequency characteristic of the band-pass filter characterized by equation (5):
in the formula (5), fuIs a lower cut-off frequency, fdIs the upper cut-off frequency;
lower cut-off frequency fuAnd an upper cut-off frequency fdSelecting 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 cut-off frequency fuTaking the right end point value of the minimum closed neighborhood as the upper limit cut-off frequency fd。
Then, the displacement signal sequence θ in each periodqAs characterized by formula (6):
in the formula (6), the reaction mixture is,
for a displacement signal sequence thetaqAnd (3) solving by a nonlinear least square method: setting the included angle between the initial angle of the displacement signal and the x-axis to be 0, and decomposing the component theta of each group of displacement signal sequence to the x-axisxqAnd the component theta of the y-axisyqCharacterized by formula (7):
constructing a multi-element nonlinear target function group f (O) of multi-target variablesx,OyAnd 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 OxInitial value of (1)x_guessVariable OyInitial value of (1)y_guessAnd an initial value r of the variable rguessCharacterized by formula (9):
and (3) experimental verification:
a three-edge ball-end milling cutter is selected to continuously mill the nickel-based high-temperature 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 high-speed milling process based on the vibration signal, and the flow of the method is shown in figure 1.
Fig. 3 (a) is an x-axis original vibration signal acquired, fig. 3 (b) is a y-axis original vibration signal acquired, fig. 3 (c) is an x-axis pre-processed vibration signal, and fig. 3 (d) is a y-axis pre-processed vibration signal. The original vibration signal in the graphs (a) and (b) in fig. 3 contains high-frequency 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 five-point-three 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 high-frequency components are eliminated, the amplitude of the curve in the time domain spectrum is reduced.
Fig. 4 (a) is an x-axis time domain displacement signal, fig. 4 (b) is a y-axis time domain displacement signal, fig. 4 (c) is an x-axis frequency domain displacement signal, and fig. 4 (d) is a y-axis 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 x-axis and y-axis 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 x-y plane diagram. And fitting the track by adopting a nonlinear least square method to obtain a fitting circle with the tool run-out 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 real-time 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 high-speed milling process.
Claims (4)
1. A cutter bounce parameter on-line estimation method based on vibration signals in high-speed milling processing is characterized in that:
firstly, the original vibration signal A obtained by collection is subjected toRPreprocessing the signal to obtain a time domain vibration signal AP；
Then aiming at the time domain vibration signal APObtaining a frequency domain displacement signal D by integrationλAnd then converting the frequency domain displacement signal into a time domain displacement signal d by inverse discrete Fourier transformi；
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 on-line estimation of tool runout parameters based on vibration signals in the high-speed milling process.
2. The method for on-line estimation of tool runout parameters based on vibration signals in high-speed milling according to claim 1, wherein:
the preprocessing of the acquired original vibration signal refers to: obtaining original vibration signal A by monitoring with accelerometerRFor said original vibration signal ARAnd smoothing in a time domain to weaken high-frequency random interference signals and eliminate irregular trend terms.
3. The method for on-line estimation of tool runout parameters based on vibration signals in high-speed 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 collectionRFor said original vibration signal ARPreprocessing by adopting a five-point three-time smoothing method to obtain a preprocessed time domain vibration signal AP；
Step 2: obtaining a time domain displacement signal through signal processing:
for the time domain vibration signal APFirstly, discrete Fourier transform is carried out to obtain frequency domain vibration signal XλFor the frequency domain vibration signal XλIntegrating to obtain frequency domain displacement signal Dλ(ii) a For the frequency domain displacement signal DλObtaining a time domain displacement signal d through inverse discrete Fourier transformi；
And step 3: estimating a cutter bounce parameter according to the time domain displacement signal:
according to the time domain displacement signal diObtaining a displacement track fitting curve by a nonlinear least square method;
the tool run-out position is recorded as (O)x,Oy) Mean the center point of the cutter point of the milling cutter (O)x,Oy)；
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 θq；
For the displacement signal sequenceColumn θqAnd (3) solving by a nonlinear least square method: constructed with the tool-run-out position (O)x,Oy) And the tool runout length r is a multi-element nonlinear target function group of multi-target 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 high-speed milling is realized.
4. The method for on-line estimation of tool runout parameters based on vibration signals in high-speed milling according to claim 3, wherein:
the original vibration signal ARIn turn, each element is represented as a discrete number series { aRi}；
The time domain vibration signal APIn turn, each element is represented as a discrete number series { aPi}；
Wherein i is 0,1, …, (U-1); u is the number of sampling samples;
obtaining a time-domain vibration signal A according to equation (1)P；
In formula (1): u-2, 3, …, (U-3);
for sequence { aPiPerforming discrete Fourier transform to obtain frequency-domain vibration signal X represented by formula (2)λ：
In formula (2), λ ═ 0,1, …, (U-1); j is an imaginary number in units
For frequency domain vibration signal XλIntegrating to obtain a frequency domain displacement signal D characterized by the formula (3)λ：
In the formula (3), ωλ2 pi λ Δ f; Δ f is the frequency resolution, Δ f is 1/Ts，TsThe total time length of sample data;
obtaining a time domain displacement signal d characterized by the formula (4) through inverse discrete Fourier transformi：
In the formula (4), LλIs the frequency characteristic of the band-pass filter characterized by equation (5):
in the formula (5), fuIs a lower cut-off frequency, fdIs the upper cut-off frequency;
then, the displacement signal sequence θ in each periodqAs characterized by formula (6):
θq＝{d(q-1)ωT+1,d(q-1)ωT+2,…,dqωT} (6)
in the formula (6), the reaction mixture is,
for a displacement signal sequence thetaqPerforming nonlinear least square solution, and decomposing each group of displacement signal sequence into component theta of x axisxqAnd the component theta of the y-axisyqCharacterized by formula (7):
constructing a multi-element nonlinear target function group f (O) of multi-target variablesx,OyAnd 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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| CN115647440A (en) * | 2022-11-10 | 2023-01-31 | 哈尔滨理工大学 | Method for solving milling infinitesimal energy consumption characteristic parameters of main and auxiliary cutting edges of square shoulder milling cutter |
| CN117270455A (en) * | 2023-11-20 | 2023-12-22 | 成都飞机工业（集团）有限责任公司 | Digital filtering-based groove cavity milling tool path optimization method |
| CN117270455B (en) * | 2023-11-20 | 2024-03-15 | 成都飞机工业（集团）有限责任公司 | Digital filtering-based groove cavity milling tool path optimization method |
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