CN103264317A - Evaluation method for operation reliability of milling cutter - Google Patents
Evaluation method for operation reliability of milling cutter Download PDFInfo
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- CN103264317A CN103264317A CN2013101813902A CN201310181390A CN103264317A CN 103264317 A CN103264317 A CN 103264317A CN 2013101813902 A CN2013101813902 A CN 2013101813902A CN 201310181390 A CN201310181390 A CN 201310181390A CN 103264317 A CN103264317 A CN 103264317A
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Abstract
The invention discloses an evaluation method for the operation reliability of a milling cutter. According to the method, machining tool spindle and feed shaft three-phase current signals are acquired through a Hall sensor, time domain mean characteristics and tooth frequency energy characteristics are extracted through wavelet analysis, an observation vector output function: a mixed Gaussian distribution density function, is obtained through a mean algorithm aimed at characteristic vectors, a continuous 'hidden semi-Markov model' is trained through a Baum-Welch algorithm to obtain a parameter estimation result, and the operation reliability level of the middling cutting is calculated through a Chapman-Kolmogorov differential equation. According to the evaluation method, decision support is provided for preventive maintenance of milling cutters.
Description
Technical field
The present invention relates to a kind of appraisal procedure of Milling Process cutter operational reliability.
Technical background
In the modern times manufacturing, cutting tool state is to guaranteeing crudy and boosting productivity most important.But cutter wearing and tearing work in-process is inevitable again, and it can directly influence machining accuracy and the surface roughness of workpiece, not only reduces crudy, also can influence safety and the normal operation of system of processing when serious.Therefore, good cutting tool state has become the necessary condition in modern mechanical manufacturing and the automation processing, and it also is a key technology that guarantees the processing work quality.And the artificial cognition cutting tool state has become the important bottleneck of restriction process industry development, therefore, press for a kind of new method of carrying out intellectual monitoring and automatic assessment for the cutter running status of design and development, it does not need to wait for that the Milling Process cutter breaks down through long-play, just can analyze, assess Milling Process Tool Reliability change conditions exactly, thereby guides user is taked rational preventive measure in advance, and preventing influences processing work quality and system of processing safe operation because of the cutter problem.
Summary of the invention
For solving the above-mentioned technical problem that exists in the prior art, the invention provides a kind of Milling Process cutter Analysis of Running Reliability method that decision support can be provided for preventative maintenance.
The technical scheme that solves the problems of the technologies described above may further comprise the steps into:
1) historical according to design data and the use of Milling Process cutter, determine its running status sum M; Milling Process cutter running status set representations is S={S
1, S
2..., S
m..., S
M, s
MFor the Milling Process cutter entirely ineffective.
2) at milling cutter processing operating mode, the record cutting parameter utilizes Hall element to gather machine tool chief axis and feed shaft three-phase current signal, therefrom extracts the energy value of average on the time domain and tooth frequency signal as characteristic quantity; Specifically comprise: 1. frequency analysis, the machine motor current signal is carried out wavelet decomposition, the signal that extracts the tooth frequency content is reconstructed, and obtains the tooth frequency curve, carries out the extraction of energy feature value; 2. time-domain analysis: get feeding current signal three-phase current root-mean-square value and carry out wavelet analysis, extract the characteristic quantity on the signal time domain.
3) according to Milling Process cutter running status sum M, determine the corresponding gauss of distribution function number of each Milling Process cutter running status k
mAt a certain running status of Milling Process cutter, characteristic signal to above-mentioned collection averages segmentation, and big matrix of parameter composition of a section will be belonged in the characteristic signal, vector to each section carries out the K mean cluster, obtains the corresponding continuous mixed Gaussian probability density function of certain state of Milling Process cutter
Wherein:
Be t moment observation vector, k
mBe state s
mThe number of corresponding Gaussian component,
Be Gaussian distribution Mean Parameters, Σ
MkBe Gaussian distribution covariance matrix, c
MkBe state s
mThe weight of corresponding each Gaussian component; Repeating step 3), obtain the continuous mixed Gaussian probability density function of all running statuses of Milling Process cutter.
4) adopting continuously latent half Markov model to come that the cutter running status is carried out reliability level estimates; At first to the continuously latent given initial value of half Markov model CHSMM, then adopt the Baum-Welch algorithm to the model training, finish parameter Estimation, carry out Milling Process cutter operational reliability level calculation;
Wherein, the state duration model adopts Gamma to distribute, namely
v
m, w
m0, v
mBe scale parameter, w
mBe form parameter; The observation model adopts mixed Gaussian to distribute, namely
Be t moment observation vector, k
mBe state s
mThe number of corresponding Gaussian component,
Be Gaussian distribution Mean Parameters, Σ
MkBe Gaussian distribution covariance matrix, c
MkBe state s
mThe weight of corresponding each Gaussian component.
Above-mentioned steps 3) detailed process is as follows:
1. model definition: latent-semi-Markov model is expressed as continuously
Wherein, M is cutter running status sum,
mBe Milling Process cutter running status, initial state distribution π
m=(π
1..., π
m), state transition probability matrix a
MnThe expression cutter is from motion state s
mJump to motion state s
nProbability.
2. model training: adopt Bao Mu-Wei Erqi (Baum-Welch) algorithm to the model training, namely solve the parameter Estimation problem of model, obtain model parameter
Estimated value
Successively all running statuses of cutter are trained, obtain the latent-semi-Markov model of every kind of running status.
3. operational reliability assessment: after model training is finished, according to the result of model parameter estimation, adopt Qie Puman-Andrei Kolmogorov differential equation, calculate Milling Process cutter operational reliability level.
Beneficial effect of the present invention has: the present invention is by gathering machining tool spindle motor current and feed shaft current signal, adopt wavelet analysis method to extract time domain and the frequency domain character information of above-mentioned signal, recycling is concealed half Markov model and Qie Puman-Andrei Kolmogorov differential equation continuously and is calculated Milling Process cutter operational reliability level.This maintenance decision analysis for the Milling Machining Center cutter provides important reference information.
Description of drawings
Fig. 1 is flow chart of the present invention.
Fig. 2 is machine tool chief axis current signal time domain average time domain waveform among the present invention.
Fig. 3 is machine tool chief axis current signal frequency-domain waveform among the present invention.
Fig. 4 is the reliable linearity curve of Milling Process cutter of the present invention.
The specific embodiment
Below in conjunction with drawings and Examples the present invention is done explanation in further detail.The inventive method is at first gathered machining tool main shaft and feed shaft current signal by Hall element, then adopt wavelet analysis method to carry out feature extraction, carry out parameter Estimation by continuously latent half Markov model then, and adopt Qie Puman-Andrei Kolmogorov differential equation to calculate Milling Process cutter operational reliability level, thereby provide decision support for preventative maintenance.
As shown in Figure 1, the present invention includes following steps:
The first step is determined Milling Process cutter running status.
Design data and use according to the Milling Process cutter are historical, determine its running status sum M.Milling Process cutter running status set representations is S={S
1, S
2..., S
m..., S
M, s
MFor the Milling Process cutter entirely ineffective.
The second step Milling Process cutter running status Characteristic Extraction.
Utilize Hall element to gather machining tool main shaft and feed shaft three-phase current signal, sampling interval and each group number of gathering signal can be decided according to enterprise practical conditions.Therefrom extract the energy value of average on the time domain and tooth frequency signal then as characteristic quantity.Detailed process is: (1) frequency analysis, the machine motor current signal is carried out wavelet decomposition, and the signal that extracts the tooth frequency content is reconstructed, and obtains the tooth frequency curve, carries out the extraction of energy feature value.(2) time-domain analysis: get feeding current signal three-phase current root-mean-square value and carry out wavelet analysis, extract the characteristic quantity on the signal time domain.
The 3rd step observation sample sequence probability density function extracts.
According to Milling Process cutter running status sum M, determine the corresponding gauss of distribution function number of each Milling Process cutter running status k
mAt a certain running status of Milling Process cutter, the characteristic signal of above-mentioned collection is averaged segmentation, and the parameter that will belong to a section in the characteristic signal forms a big matrix, the vector of each section is carried out the K mean cluster, calculate three key parameter c
Mk,
And Σ
Mk, obtain the corresponding continuous mixed Gaussian probability density function of certain state of Milling Process cutter
And the like, obtain the continuous mixed Gaussian probability density function of all running statuses of Milling Process cutter.
The Milling Process cutter operational reliability assessment of the 4th step.
HSMM (Hidden Semi-Markov Model, latent-semi-Markov model) is the expansion of HMM (Hidden Markov Model, hidden Markov model).The state residence time is the limitation of exponential distribution in the hidden Markov model in order to improve, and on the basis of hidden Markov model, latent-semi-Markov model allows to distribute according to the self-defined residence time of practical problem.From the use history of Milling Process cutter, adopt Gamma to distribute as state residence time probability-distribution function.Simultaneously, the observation sequence output function adopts the mixed Gaussian distribution to carry out match.
Step (a1): model training.At first, continuously latent half Markov model parameter is carried out initialization, wherein initial state distribution adopts evenly and distributes, and sets iterations and convergence error.Adopt the Baum-Welch algorithm to the model training then, obtain model parameter
Estimated value
Successively all running statuses of cutter are trained, obtain the latent-semi-Markov model continuously of every kind of running status.
Step (a2): Milling Process cutter operational reliability assessment.Make P
j(t)=P (q
t=s
j) represent that being equipped in t is in s constantly
jThe probability of state.According to Qie Puman-Andrei Kolmogorov differential equation P'(t is arranged)=P (t) A, wherein P (t)=(p
0(t), P
1(t), L, P
k(t), P
K+1(t)) being state vector, P'(t) is the single order differential state vector of P (t),
Be state-transition matrix.The differential equation is carried out Laplace transformation, can get:
Wherein the Milling Process cutter is in normal condition when primary condition, can get P (0)=(p
0(0), P
1(0), L, P
k(0), P
K+1(0))=(1,0,0,0).
P (s)=(p is arranged
0(s), P
1(s), L, P
k(s), P
K+1(s)), P (s) is carried out the Laplace inverse transformation and obtain being equipped in probability P (t)=(p that t is in different conditions constantly
0(t), P
1(t), L, P
k(t), P
K+1(t)), thus can calculate Milling Process cutter t reliability R (t)=1-P constantly
K+1(t).
The present invention below in conjunction with object lesson makes further elaboration:
1: historical according to certain Milling Process Tool Design data and use, determine that its running status adds up to 4, s
1Be normal condition, s
2Be minor degradation state, s
3Be severe deterioration state, s
4Be total failure mode.
2: this cutter is actual to add man-hour, and cutting parameter is speed of mainshaft 130r/min, feed speed 140mm/min, cutting depth 1mm, slotting cutter processing, 6 teeth, sample frequency 1000Hz.Sampling interval is 48 hours, samples total time T=960 hour.Adopt wavelet analysis to carry out time domain and the extraction of frequency domain character amount.Fig. 2 is machine tool chief axis current signal time domain average time domain waveform; Fig. 3 is machine tool chief axis current signal frequency-domain waveform;
3: above-mentioned characteristic signal is equally divided into four sections, and corresponding 4 gauss of distribution function of each state adopt the K mean algorithm, obtain the observation vector probability density function: the mixed Gaussian density function.For saving space, here only list the some time and carve t, cutter is in the corresponding mixed Gaussian distribution function of normal condition parameter: the weight parameter matrix is weight=[0.345 0.287 0.129 0.239], equal value matrix mean=[0.0099 0.0079 0.5568 0.3234], covariance matrix cov=[3.9448 3.8648 6.3945 7.4736].
4: the assessment of Milling Process cutter operational reliability at first to the continuously latent given initial value of half Markov model CHSMM, is evenly to distribute cutter running status sum M=4 as initial distributions.Then adopt the Baum-Welch algorithm to the model training, finish parameter Estimation, carry out Milling Process cutter operational reliability level calculation, Fig. 4 is milling cutter operational reliability horizontal line graph for this reason.
The present invention not only is confined to the above-mentioned specific embodiment; persons skilled in the art are according to content disclosed by the invention; can adopt other multiple specific embodiment to implement the present invention; therefore; every employing project organization of the present invention and thinking; do some simple designs that change or change, all fall into the scope of protection of the invention.
Claims (4)
1. the appraisal procedure of a Milling Process cutter operational reliability the steps include:
1) historical according to design data and the use of Milling Process cutter, determine its running status sum M; Milling Process cutter running status set representations is S={S
1, S
2..., S
m..., S
M, s
MFor the Milling Process cutter entirely ineffective;
2) at milling cutter processing operating mode, the record cutting parameter utilizes sensor to gather machine tool chief axis and feed shaft three-phase current signal, therefrom extracts the energy value of average on the time domain and tooth frequency signal as characteristic quantity;
3) according to Milling Process cutter running status sum M, determine the corresponding gauss of distribution function number of each Milling Process cutter running status k
m,
mBe Milling Process cutter running status s
mSubscript, be used for representing s
mAt a certain running status of Milling Process cutter, characteristic signal to above-mentioned collection averages segmentation, and big matrix of parameter composition of a section will be belonged in the characteristic signal, vector to each section carries out the K mean cluster, obtains the corresponding continuous mixed Gaussian probability density function of certain state of Milling Process cutter
Wherein:
Be t moment observation vector, t is constantly, k
mBe state s
mCorresponding gauss of distribution function number,
Be Gaussian distribution Mean Parameters, Σ
MkBe Gaussian distribution covariance matrix, c
MkBe state s
mThe weight of corresponding each Gaussian component; Repeating step 3), obtain the continuous mixed Gaussian probability density function of all running statuses of Milling Process cutter;
4) adopting continuously latent half Markov model to come that the cutter running status is carried out reliability level estimates; At first to the continuously latent given initial value of half Markov model CHSMM, then adopt the Baum-Welch algorithm to the model training, finish parameter Estimation, carry out Milling Process cutter operational reliability level calculation;
2. the appraisal procedure of a kind of Milling Process cutter operational reliability as claimed in claim 1 is characterized in that: step 2) comprising:
1) frequency analysis is carried out wavelet decomposition to the machine motor current signal, and the signal that extracts the tooth frequency content is reconstructed, and obtains the tooth frequency curve, carries out the extraction of energy feature value;
2) time-domain analysis: get feeding current signal three-phase current root-mean-square value and carry out wavelet analysis, extract the characteristic quantity on the signal time domain.
3. the appraisal procedure of a kind of Milling Process cutter operational reliability as claimed in claim 1 is characterized in that: step 2) described in sensor be Hall element.
4. as the appraisal procedure of the described a kind of Milling Process cutter operational reliability of arbitrary claim among the claim 1-3, it is characterized in that: the detailed process of step 4) is as follows:
1) model definition: latent-semi-Markov model is expressed as continuously
Wherein, latent state is that the quantity of cutter running status is M, initial state distribution π
m=(π
1..., π
m); State transition probability matrix a
MnThe expression cutter is from motion state s
mJump to motion state s
nProbability, c
MkBe state s
mThe weight of corresponding each Gaussian component,
Be Gaussian distribution Mean Parameters, Σ
MkBe Gaussian distribution covariance matrix, v
mBe scale parameter, w
mBe form parameter;
2) model training: adopt Bao Mu-Wei Erqi (Baum-Welch) algorithm to the model training, namely solve the parameter Estimation problem of model, obtain model parameter
Estimated value
Successively all running statuses of cutter are trained, obtain the latent-semi-Markov model of every kind of running status;
3) operational reliability assessment: after model training is finished, according to the result of model parameter estimation, adopt Qie Puman-Andrei Kolmogorov differential equation, calculate Milling Process cutter operational reliability level.
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