CN107363645B - Milling machine process tool abrasion prediction technique based on power detection - Google Patents
Milling machine process tool abrasion prediction technique based on power detection Download PDFInfo
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- CN107363645B CN107363645B CN201710719144.6A CN201710719144A CN107363645B CN 107363645 B CN107363645 B CN 107363645B CN 201710719144 A CN201710719144 A CN 201710719144A CN 107363645 B CN107363645 B CN 107363645B
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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
- B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
- B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
- B23Q17/0957—Detection of tool breakage
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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
- B23Q17/09—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool
- B23Q17/0952—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining
- B23Q17/0961—Arrangements for observing, indicating or measuring on machine tools for indicating or measuring cutting pressure or for determining cutting-tool condition, e.g. cutting ability, load on tool during machining by measuring power, current or torque of a motor
Abstract
The milling machine process tool abrasion prediction technique based on power detection that the invention discloses a kind of, it mainly includes data collection steps, characteristic extraction step, feature post-processing step and prediction model step.Data acquisition is carried out by power signal acquisition and wear measurement, then the extraction and correlation calculations of the effective value tag of power are carried out, obtain the sensitive features of selection, carry out isotonic regression and exponential smoothing again to realize that feature post-processes, last training sample, SBL model prediction is carried out, is verified after being verified sample.The present invention is able to achieve accurately and efficiently cutter operating status Urine scent and automatic early-warning, improves the level of intelligence of lathe, effectively save the cost, improves efficiency.
Description
Technical field
The present invention relates to cutter wear amount being detected and being predicted field in milling machine process, more particularly to one kind
Milling machine process tool abrasion prediction technique based on power detection.
Background technique
With universal, core one of of the process equipment as wisdom factory of Intelligent Production System, to operating status oneself
I identifies, self-teaching and self ability are its important features.In process tool changing and to knife account for about equipment operation when
Between 20%.Accurately and efficiently cutter operating status Urine scent and automatic early-warning have the level of intelligence for improving lathe important
Meaning, and can effectively save the cost, improve efficiency.
There is following limitation in the use of common force signal, acoustic emission signal, e.g., since workpiece size and cutting fluid are to survey
The reasons such as the damage of power instrument, measurement cutting force is more difficult in actual processing, and the range of work is restricted；Acoustic emission signal
Since its mechanism of production is complex, influence and noise jamming of the signal strength vulnerable to propagation path are big, often result in sensor
Installation and the difficulty on signal processing.
In existing literature: (1) it is general only individually test it is a kind of with the associated signal of cutting-tool wear state, as vibration signal,
Temperature signal, sound emission, spindle motor current signal etc. extract feature relevant to tool wear by this single signal,
Cutting-tool wear state identification is carried out based on these features；(2) although acquisition two kinds of signals, extract signal characteristic
It is not sensitive enough to the state of wear of cutter or signal characteristic is extracted only with single analytical technology to different signals, because
This, it is not high to the accuracy of identification and the monitoring of cutting tool state；(3) in addition, instrument needed for monitoring system is special, price compared with
Height such as monitors acoustic emission signal equipment, monitoring cost is caused to increase；(4) it is temporarily recorded without research and is based on management loading side
The tool abrasion prediction model of method, Cutter wear amount are accurately and effectively predicted.
Summary of the invention
The milling machine process tool abrasion prediction technique based on power detection that the object of the present invention is to provide a kind of, makes
Lathe power signal is acquired with power sensor, difficult cutting and the impacted problem of signal strength can be improved；Extraction and cutter
The strong signal characteristic of abrasion loss correlation, and feature is post-processed to improve the sensibility of feature Cutter wear, it establishes
Tool abrasion prediction model based on management loading method, prediction model is accurate, and effectively save cost simultaneously improves effect
Rate.
A kind of milling machine process tool abrasion prediction technique based on power detection provided by the invention, step
Are as follows:
S1, data acquisition: acquisition power signal and measurement tool abrasion；
S2, feature extraction: the effective value tag of power is extracted, the effective value tag of the power and the tool abrasion are calculated
Related coefficient, obtain sensitive features；
S3, feature post-processing: first carrying out isotonic regression to all characteristic values of the sensitive features, then carries out index and put down
It is sliding；
S4, prediction model: by training sample, the SBL model prediction of management loading method is carried out, verifying sample is carried out
This.
Preferably, the power signal of milling machine process is acquired by power sensor；
Measuring tool abrasion is to be measured after each feed using microscope Cutter wear.
Preferably, the effective value tag of the power includes valid value maximum value, virtual value minimum value and virtual value mean value；
Virtual value maximum value, virtual value minimum value and virtual value mean value after extracting feed each time, calculate separately out whole
The mean value of the mean value of the virtual value maximum value of a feed process, the mean value of virtual value minimum value and virtual value mean value, then count respectively
Calculate the related coefficient of virtual value maximum value, virtual value minimum value and virtual value mean value and the tool abrasion.
Preferably, the formula of the related coefficient of the effective value tag of any one power and the tool abrasion is calculated are as follows:
Wherein, i-feed time；xiThe virtual value characteristic value of-i-th；The mean value of the validity feature value of-entire feed process；VaiThe tool abrasion generated when the feed of-i-th；- entire mistake
The tool abrasion average value of journey；The related coefficient of effective value tag and tool abrasion after ρ-i-th feed.
Preferably, compare related coefficient, virtual value minimum value and the tool wear of virtual value maximum value and tool abrasion
The size of the related coefficient of the related coefficient and virtual value mean value and tool abrasion of amount, will be corresponding to maximum correlation coefficient value
The effective value tag of power as the sensitive features.
Preferably, the isotonic regression is the trend for making all characteristic values of sensitive features keep monotonic nondecreasing；The guarantor
The step of sequence returns are as follows:
If a, having f for arbitrary i=1,2 ..., Ni≤fi+1, then
B, i makes f if it existsi> fi+1, enable
Simultaneously willIt is updated to
C, repeating said steps b, until obtainMeet
Then export result
Wherein, fiFor the ith feature value of sensitive features, N is the number of all characteristic values of sensitive features, f*iIt is fi's
Isotonic regression,It is the sequence of N number of characteristic value,It is the isotonic regression sequence that N number of characteristic value keeps monotonic nondecreasing.
Preferably, exponential smoothing is carried out to all characteristic values for the sensitive features for keeping monotonic nondecreasing, keeps its variation smooth；
The exponential smoothing are as follows:
Given time sequence xt,xt-1,...,x2,x1, sequence after single exponential smoothing are as follows:
yi=α xi+(1-α)yi-1(2≤i≤t),Wherein, 0 < α < 1；α is smoothing factor, yi
For in the smooth value at moment i second, yi-1For in the smooth value at moment i-1 second；y1For initial smoothing value, x1It is the spy at first second moment
Value indicative, x2It is the characteristic value at second second moment, x3It is the characteristic value at moment third second.
Preferably, the smoothing factor α is set as 0.3.
Preferably, by the management loading method SBL model prediction, the characteristic value sample of sensitive features is inputted
This, predicts corresponding tool abrasion；
The management loading method SBL model prediction step are as follows:
S41: given input Yn×k=[y1,y2,...,yk]；
Wherein y1,y2,...,ykThe characteristic value of k sensitive features respectively after post treatment, every a line y of matrix Yi
=[yi1,yi2,…,yik] (i=1 ..., n) correspondingly export as the tool abrasion t being measured microscopicallyi；
S42: according to the characteristic value sample for the sensitive features for giving input in step S41, Gaussian noise variance σ is initialized2
With hyper parameter vector α=(α1,α2,…,αN), set hyper parameter iteration upper limit αmax；Wherein, if assuming tiBe a unknown function and
Measurement error εiCombination, then obtain formula: ti=F (yi,W)+εi；In formula: εiIt is independent error term, and Gaussian distributed
N(0,σ2)；tiObedience is desired for F (yi, W), Gaussian noise variance σ2Gaussian Profile, W=(w0,w1,…,wn) be weight to
Amount；
S43: according to formula u=σ1 -2∑ΦTT, Σ=(σ1 -2ΦTΦ+A)-1Calculate separately weight vector W expectation u and
Variances sigma1 2；Wherein t is tool abrasion, Φ=[Φ (y1),Φ(y2),…,Φ(yn)]TIt is basic function matrix, Φ (yi)=
[1,K(yi,y1),K(yi,y2),…,K(yi,yn)]T；W obeys Gaussian prior distribution, andIn formula:
α=(α0,α1,...,αn) it is hyper parameter vector, αiIt is to wiSpecified hyper parameter；A=diag (α0,α1,...,αn)；
S44: being updated, and recalculates Gaussian noise variance σ2；
S45: repeating step S43 and step S44, until reaching specified the number of iterations, or until Gaussian noise variance
σ2And hyper parameter vector α=(α0,α1,...,αN) reach specified computational accuracy；
S46: being screened, and is retained and is less than α in hyper parameter vectormaxHyper parameter corresponding to weight and basic function；
S47: corresponding tool wear value t is predicted*。
Preferably, hyper parameter iteration upper limit α is set in the step S42max=1.0e4。
Compared with prior art, beneficial effects of the present invention are that prediction model is accurate, and sensor installation is simple, are effectively saved
About cost, and improve efficiency.
Detailed description of the invention
Tool abrasion prediction technique of the Fig. 1 based on power；
Fig. 2 milling machine cutting time and power virtual value curve graph.
Specific embodiment
The milling machine process tool abrasion prediction technique based on power detection that the present invention provides a kind of, to make this hair
Bright to be clearer and more comprehensible, the present invention will be further described with specific embodiment with reference to the accompanying drawing.
Milling machine process tool abrasion prediction technique based on power detection of the invention, step specifically:
Step 1: data acquire；
The power signal of milling machine process is acquired using power sensor, and using micro- after each completion of processing
Mirror Cutter wear is taken pictures, and tool abrasion is measured or calculate, such as the tool wear value V generated when i-th feedaiWith it is whole
The tool wear average value of a process
The embodiment of the present invention is related to a CNC milling machine, work piece (No. 45 steel), milling cutter, power sensor and shows
Micro mirror.
Cutting parameter is as shown in table 1:
1 cutting parameter of table
Wherein, n is cutter revolving speed, and f is cutter linear velocity, apWide, a for millingeIt is deep for milling.
The present embodiment acquires the power signal of CNC milling machine using power instrument (HIOKI PW3360), and power instrument is mounted on
Machine power point of incoming cables, the frequency of power instrument sampling are 10.24KHz, while being calculated automatically and to export each second power effective
Value.
After each feed processing, tool abrasion is measured by microscope.
It is processed according to each feed, the virtual value characteristic value x of available i-thiIt (include virtual value mean value, virtual value
Maximum value and virtual value minimum value), the validity feature value mean value of entire feed process(the virtual value mean value comprising whole process
Average value, whole process virtual value maximum value average value and whole process virtual value minimum value average value).
It is illustrated in figure 2 the curve graph of milling machine cutting time S (abscissa) Yu power virtual value RMS (ordinate).
Step 2: feature extraction；
(1) feature of cutting power virtual value is extracted, respectively virtual value maximum value, virtual value minimum value and virtual value is equal
Value.
(2) meter is extracted to three kinds of above-mentioned virtual value maximum value, virtual value minimum value and virtual value mean value characteristic values
It calculates, calculating extraction is carried out using formula (1):
Wherein, i-feed time；xiThe virtual value characteristic value of-i-th (includes virtual value mean value, virtual value maximum value
With virtual value minimum value)；The mean value of the validity feature value of-entire feed process be (the virtual value mean value comprising whole process
Average value, whole process virtual value maximum value average value and whole process virtual value minimum value average value)；Vai- the
The tool abrasion generated when i feed；The tool abrasion average value of-whole process；It is each after ρ-i-th feed to have
The related coefficient of valid value feature and tool abrasion.
(3) according to the calculating of formula (1), virtual value mean value related coefficient, virtual value maximum value related coefficient are obtained respectively
With virtual value minimum value related coefficient, wherein correlation coefficient ρ value is taken to grind closest to 1 effective value tag as cutter of the invention
The sensitive features of damage amount.
Using formula (1), the correlation coefficient ρ between virtual value maximum value tag and tool abrasion can be calculated1, effectively
The correlation coefficient ρ being worth between minimum value tag and tool abrasion2And between virtual value characteristics of mean and tool abrasion
Correlation coefficient ρ3。
The numerical value specifically calculated is as shown in table 2, because of ρ1<ρ2<ρ3, so selecting virtual value characteristics of mean for the present embodiment
Sensitive features.
The related coefficient of table 2 each feature and tool abrasion
Step 3: feature post-processes；
In order to overcome power network fluctuation to the influence of characteristic value, need to post-process all characteristic values of sensitive features,
The process of post-processing mainly includes isotonic regression and exponential smoothing.
(1) isotonic regression is used, all characteristic values of sensitive features is made to keep the trend of monotonic nondecreasing.The institute of sensitive features
If characteristic value has been monotonic nondecreasing trend in order, the former sequence of all characteristic values is kept.Next spy if it exists
Value indicative is greater than a upper characteristic value, then needs to carry out isotonic regression to it；Circulation carries out, until making all features of sensitive features
Value keeps the trend of monotonic nondecreasing, completes isotonic regression.
Steps are as follows for its specific calculating:
If a, having f for arbitrary i=1,2 ..., Ni≤fi+1, then
B, i makes f if it existsi> fi+1, enable
Simultaneously willIt is updated to
C, repeat step b, until obtained sequenceMeetThen export resultWherein, fiFor the ith feature value of sensitive features, N is the number of all characteristic values of sensitive features,
f*iIt is fiIsotonic regression,It is the sequence of N number of characteristic value,It is the order-preserving of the holding monotonic nondecreasing of N number of characteristic value
Recursive sequence.
Wherein the principle of isotonic regression method is as follows:
If mutually independent Variables Sequence x1,x2…,xnMeet relationship x1≤x2≤…≤xn；Define g (xi) it is xiLetter
It counts, then g (xi) isotonic regression g (xi)*Meet relationship: g (x1)*≤g(x2)*≤…≤g(xn)*.Wherein g (xi)*Using as follows
Formula (2) is calculated:
In formula:Wherein, 1≤i≤n；
(2) double smoothing method is used, when signal characteristic is integrally in increase or reduces trend, uses secondary finger
Number smoothing model is smoothed feature.When being handled for the characteristic value of power sensitive feature of the invention, smoothly
Coefficient takes α=0.3.
Wherein the principle of exponential smoothing method is as follows:
Given time sequence xt,xt-1,...,x2,x1, then shown in such as formula of the sequence after single exponential smoothing (3):
In formula: 0 < α < 1 is smoothing factor, yiFor the smooth value of moment i, yi-1For the smooth value of moment i-1, y1It is initial
Smooth value, t are the time；And pass through the average determining y of first three moment value1。
Step 4: prediction model；
Using management loading method, Abrasion prediction model is constructed, steps are as follows:
1, input Y is givenn×k=[y1,y2,...,yk], wherein y1,y2,...,ykK respectively after post treatment is sensitive
The characteristic value of feature.
Every a line y of matrix Yi=[yi1,yi2,…,yik] (i=1 ..., n), correspondingly output is measured microscopically
Tool abrasion ti。
According to the characteristic value sample data of the sensitive features of input given herein above, Gaussian noise variance σ is initialized2With super ginseng
Number vector α=(α1,α2,...,αN), set hyper parameter iteration upper limit αmax。
It is assumed that tiIt is a unknown function and measurement error εiCombination, it may be assumed that
ti=F (yi；W)+εi (4)
In formula: εiIt is independent error term, and Gaussian distributed N (0, σ2), then tiObedience is desired for F (yi, W), Gauss
Noise variance is σ2Gaussian Profile, W=(w0,w1,...,wn) it is weight vector.
2, according to formula u=σ1 -2∑ΦTT and Σ=(σ1 -2ΦTΦ+A)-1Calculate separately expectation u and the side of weight vector W
Poor σ1 2, wherein t is tool abrasion；Φ=[Φ (y1),Φ(y2),…,Φ(yn)]TIt is basic function matrix, Φ (yi)=[1,
K(yi,y1),K(yi,y2),…,K(yi,yn)]T, A=diag (α0,α1,...,αn)。
W obeys Gaussian prior distribution, shown in following formula (5):
In formula: α=(α0,α1,...,αn) it is hyper parameter vector, αiIt is to wiSpecified hyper parameter.
3, it is updated, that is, recalculates Gaussian noise variance σ2；
4, step 2 and step 3 are repeated, until reaching specified the number of iterations or Gaussian noise variance σ2With hyper parameter to
Measure α=(α0,α1,...,αN) reach specified computational accuracy；
5, it is screened, i.e. reservation hyper parameter vector α=(α0,α1,...,αN) in be less than αmaxHyper parameter corresponding to
Weight and basic function；
It needs that reasonable α is arranged in step 1max, otherwise may generate extra associated vector.Theoretically αmaxIt should be
Infinity sets α in the calculating of this examplemax=1.0e4。
Hyper parameter vector α and Gaussian noise variance σ are obtained by model training2Afterwards, for signal characteristic vector y*, prediction
Corresponding tool wear value t*。
It is discussed in detail although the contents of the present invention have passed through above preferred embodiment, but it should be appreciated that above-mentioned
Description is not considered as limitation of the present invention.After those skilled in the art have read above content, for of the invention
A variety of modifications and substitutions all will be apparent.Therefore, protection scope of the present invention should be limited to the appended claims.
Claims (10)
1. a kind of milling machine process tool abrasion prediction technique based on power detection, which is characterized in that the steps include:
S1, data acquisition: acquisition power signal and measurement tool abrasion；
S2, feature extraction: the effective value tag of power is extracted, the phase of the power effective value tag and the tool abrasion is calculated
Relationship number, obtains sensitive features；
S3, feature post-processing: isotonic regression first is carried out to all characteristic values of the sensitive features, then carries out exponential smoothing；
S4, prediction model: by training sample, the SBL model prediction of management loading method is carried out, verifying sample is carried out.
2. a kind of milling machine process tool abrasion prediction technique based on power detection as described in claim 1, special
Sign is,
The power signal of milling machine process is acquired by power sensor；
Measuring tool abrasion is to be measured after each feed using microscope Cutter wear.
3. a kind of milling machine process tool abrasion prediction technique based on power detection as claimed in claim 2, special
Sign is,
The effective value tag of power includes valid value maximum value, virtual value minimum value and virtual value mean value；
Virtual value maximum value, virtual value minimum value and virtual value mean value after extracting feed each time, calculate separately out and entirely walk
The mean value of the mean value of the virtual value maximum value of knife process, the mean value of virtual value minimum value and virtual value mean value, then calculated separately
The related coefficient of valid value maximum value, virtual value minimum value and virtual value mean value and the tool abrasion.
4. a kind of milling machine process tool abrasion prediction technique based on power detection as claimed in claim 3, special
Sign is,
Calculate the formula of the related coefficient of the effective value tag of any one power and the tool abrasion are as follows:
Wherein, i-feed time；xiThe virtual value characteristic value of-i-th；- entirely the validity feature value of feed process is equal
Value；VaiThe tool abrasion generated when the feed of-i-th；The tool abrasion average value of-whole process；ρ-i-th is walked
The related coefficient of effective value tag and tool abrasion after knife.
5. a kind of milling machine process tool abrasion prediction technique based on power detection as claimed in claim 4, special
Sign is,
Compare the related coefficient of virtual value maximum value and tool abrasion, the related coefficient of virtual value minimum value and tool abrasion
And the size of the related coefficient of virtual value mean value and tool abrasion, by power virtual value corresponding to maximum correlation coefficient value
Feature is as sensitive features.
6. a kind of milling machine process tool abrasion prediction technique based on power detection as claimed in claim 5, special
Sign is,
The isotonic regression is the trend for making all characteristic values of sensitive features keep monotonic nondecreasing；
The step of isotonic regression are as follows:
If a, having f for arbitrary i=1,2 ..., Ni≤fi+1, then
B, i makes f if it existsi> fi+1, enableSimultaneously willIt is updated to
C, repeating said steps b, until obtainMeetThen export result
Wherein, fiFor the ith feature value of sensitive features, N is the number of all characteristic values of sensitive features, f*iIt is fiOrder-preserving
It returns,It is the sequence of N number of characteristic value,It is the isotonic regression sequence that N number of characteristic value keeps monotonic nondecreasing.
7. a kind of milling machine process tool abrasion prediction technique based on power detection as claimed in claim 6, special
Sign is,
Exponential smoothing is carried out to all characteristic values for the sensitive features for keeping monotonic nondecreasing, keeps its variation smooth；
The exponential smoothing are as follows:
Given time sequence xt,xt-1,…,x2,x1, sequence after single exponential smoothing are as follows: yi=α xi+(1-α)yi-1(2≤
I≤t),Wherein, 0 < α < 1；α is smoothing factor, yiFor in the smooth value at moment i second, yi-1For when
Carve i-1 seconds smooth values；y1For initial smoothing value, x1It is the characteristic value at first second moment, x2It is the characteristic value at second second moment,
x3It is the characteristic value at moment third second.
8. a kind of milling machine process tool abrasion prediction technique based on power detection as claimed in claim 7, special
Sign is,
The smoothing factor α is set as 0.3.
9. a kind of milling machine process tool abrasion prediction technique based on power detection as claimed in claim 7 or 8,
It is characterized in that,
By management loading method SBL model prediction, the characteristic value sample of sensitive features is inputted, to predict corresponding knife
Has abrasion loss；
The management loading method SBL model prediction step are as follows:
S41: given input Yn×k=[y1,y2,…,yk]；
Wherein y1,y2,…,ykThe characteristic value of k sensitive features respectively after post treatment, every a line y of matrix Yi=[yi1,
yi2,…,yik] (i=1 ..., n) correspondingly export as the tool abrasion t being measured microscopicallyi；
S42: according to the characteristic value sample for the sensitive features for giving input in step S41, Gaussian noise variance σ is initialized2With super ginseng
Number vector α=(α1,α2,…,αN), set hyper parameter iteration upper limit αmax；Wherein, if assuming tiIt is that a unknown function and measurement miss
Poor εiCombination, then obtain formula: ti=F (yi,W)+εi；In formula: εiIt is independent error term, and Gaussian distributed Ν (0,
σ2)；tiObedience is desired for F (yi, W), Gaussian noise variance σ2Gaussian Profile, W=(w0,w1,…,wn) it is weight vector；
S43: according to formula u=σ1 -2∑ΦTT, Σ=(σ1 -2ΦTΦ+A)-1Calculate separately the expectation u and variance of weight vector W
σ1 2；Wherein, t is tool abrasion, Φ=[Φ (y1),Φ(y2),…,Φ(yn)]TIt is basic function matrix, Φ (yi)=[1, K
(yi,y1),K(yi,y2),…,K(yi,yn)]T；W obeys Gaussian prior distribution, andIn formula: α=
(α0,α1,…,αn) it is hyper parameter vector, αiIt is to wiSpecified hyper parameter, A=diag (α0,α1,…,αn)；
S44: being updated, and recalculates Gaussian noise variance σ2；
S45: repeating step S43 and step S44, until reaching specified the number of iterations, or until Gaussian noise variance σ2And it is super
Parameter vector α=(α0,α1,…,αN) reach specified computational accuracy；
S46: being screened, and is retained and is less than α in hyper parameter vectormaxHyper parameter corresponding to weight and basic function；
S47: corresponding tool wear value t is predicted*。
10. a kind of milling machine process tool abrasion prediction technique based on power detection as claimed in claim 9, special
Sign is,
Hyper parameter iteration upper limit α is set in the step S42max=1.0e4。
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| CN108907896B (en) * | 2018-06-06 | 2019-08-09 | 上海交通大学 | A kind of cutter remaining life on-line prediction method and system |
| CN109605126B (en) * | 2018-12-30 | 2021-09-03 | 深圳市五湖智联实业有限公司 | Digit control machine tool on-line measuring cutter life-span system |
| CN109940462B (en) * | 2019-04-12 | 2020-11-03 | 哈尔滨理工大学 | Method for detecting cutting vibration change characteristics of milling cutter and constructing Gaussian process model |
| CN110458606A (en) * | 2019-07-19 | 2019-11-15 | 西北工业大学 | Nonstandard cutter price expectation method based on deep learning |
| CN111323072B (en) * | 2020-03-18 | 2021-10-22 | 博深普锐高（上海）工具有限公司 | Novel woodworking tool wear online diagnosis method |
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| CN105234746A (en) * | 2015-11-25 | 2016-01-13 | 苏州多荣自动化科技有限公司 | Online tool wear monitoring system and detection method thereof |
| EP3001265A1 (en) * | 2014-09-26 | 2016-03-30 | Palo Alto Research Center, Incorporated | Computer-implemented method and system for machine tool damage assessment, prediction, and planning in manufacturing shop floor |
| CN106514434A (en) * | 2016-12-30 | 2017-03-22 | 华中科技大学 | Milling tool abrasion monitoring method based on data |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104050340A (en) * | 2014-07-07 | 2014-09-17 | 温州大学 | Method for recognizing tool abrasion degree of large numerical control milling machine |
| EP3001265A1 (en) * | 2014-09-26 | 2016-03-30 | Palo Alto Research Center, Incorporated | Computer-implemented method and system for machine tool damage assessment, prediction, and planning in manufacturing shop floor |
| CN104741638A (en) * | 2015-04-20 | 2015-07-01 | 江苏师范大学 | Turning cutter wear state monitoring system |
| CN105234746A (en) * | 2015-11-25 | 2016-01-13 | 苏州多荣自动化科技有限公司 | Online tool wear monitoring system and detection method thereof |
| CN106514434A (en) * | 2016-12-30 | 2017-03-22 | 华中科技大学 | Milling tool abrasion monitoring method based on data |
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