CN106217130B - Milling cutter state on_line monitoring and method for early warning during complex surface machining - Google Patents
Milling cutter state on_line monitoring and method for early warning during complex surface machining Download PDFInfo
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- CN106217130B CN106217130B CN201610668278.5A CN201610668278A CN106217130B CN 106217130 B CN106217130 B CN 106217130B CN 201610668278 A CN201610668278 A CN 201610668278A CN 106217130 B CN106217130 B CN 106217130B
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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
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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
- B23Q17/0995—Tool life management
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- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The present invention provides milling cutter state on_line monitoring and method for early warning during a kind of complex surface machining, belong to field of diagnosis about equipment fault.The present invention demonstrate Milling Force and handle of a knife vibration current vortex displacement signal between there are linear relationship on the basis of, the machine tool chief axis end X that is measured using three-dimensional acceleration transducer, the secondary Frequency Domain Integration of the acceleration signal of Y-direction are as handle of a knife vibration displacement signal, fundamental frequency and its harmonic signal in extraction integral displacement signal is as monitoring signals, on the one hand the various drawbacks for directly carrying out displacement detecting in Machining of Curved Surface by eddy current displacement sensor are overcome, on the other hand can effectively remove the influence of interference signal；Monitoring signals are further processed with Synchronous time average cumulant, synchronized averaging cumulant waveform is then quantitatively portrayed using characteristic quantity, the monitoring to milling cutter state is realized according to characteristic value variation.
Description
Technical field
The method of milling cutter state on_line monitoring belongs to field of diagnosis about equipment fault during Impeller Machining of the present invention,
It is related to the selection of monitoring signals type and processing, the extraction of characteristic information and the selection of characteristic quantity calculates three aspects.
Background technology
As the large-scale precisions such as aero-engine, compression mechanical device is increasingly to high load capacity, high efficiency and high reliability side
To development, the suface processing quality of critical component-impeller in them is required higher and higher.Since spoon of blade structure is answered
Miscellaneous, the reasons such as narrow space between blade, knife bar is needed using larger Mold processing in process, and this easily causes knife
Tipping, abrasion, disrepair phenomenon occur for the fixed milling cutter in bar bottom.
The suface processing quality of blade and milling cutter state have a substantial connection, tipping, abrasion, breakage milling cutter and blade surface
Between frictional force can increased dramatically, cutting temperature can steeply rise, the processing quality of blade surface can be remarkably decreased, therefore blade
The on-line monitoring of milling cutter state there is very important reality to anticipate processing quality, the processing efficiency of raising blade in process
Justice.
Good fortune occasion in Japanese scholars village are in document cutting component ratio injury detection based on cutting component than reflection cutter mill
Damage situation, but force snesor exists that installation is inconvenient, loaded area is small, the unfavorable factors such as of high cost, can not be applied to practical prison
It surveys.Dynamic milling force measuring method of the document based on vibration cutting displacement points out that milling cutter in process can be by interrupted cut
The excitation of power, driving frequency are cutter tooth by frequency, and when the speed of mainshaft is relatively low, driving frequency is far below tooling system first
Rank intrinsic frequency, it is believed that the stiffness characteristics of cutter with it is static when it is essentially identical, i.e., milling cutter acted on by Milling Force after be elasticity
Deformation, dynamic milling force on milling cutter and surveys the vibration displacement of handle of a knife and meets linear relationship, thus can will measurement physical quantity from
The measures conversion of Milling Force is the measurement of handle of a knife vibration displacement, and the present invention is demonstrating Milling Force and handle of a knife swing current vortex displacement
Between signal there are linear corresponding relation on the basis of, consider using current vortex displacement signal replace force signal as monitor letter
Number, but because current vortex sensor is there is also the problem of installation inconvenience, then propose by acquiring lathe spindle nose X, Y-direction adds
Speed signal, then to acceleration signal, secondary Frequency Domain Integration obtains displacement signal, extract integral displacement signal fundamental frequency and its
On the one hand harmonic signal solves the problems, such as that sensor installation is inconvenient, while has effectively removed interference signal as monitoring signals
Influence.
Invention content
The present invention demonstrate Milling Force and handle of a knife swing current vortex displacement signal between there are linear relationship on the basis of,
Machine tool chief axis end X, the acceleration signal of Y-direction measured with three-dimensional acceleration transducer, to its secondary Frequency Domain Integration conduct
Handle of a knife displacement signal overcomes the various drawbacks for directly carrying out displacement detecting in Machining of Curved Surface by eddy current displacement sensor,
On the basis of interference processing is carried out to the handle of a knife vibration displacement signal integrated out with Synchronous time average the Cumulant Method Using, carry
Go out using the fundamental frequency in integral displacement signal and its harmonic signal as monitoring signals, solved milling during complex surface machining
Knife state on_line monitoring and early warning problem.
Technical scheme of the present invention：
Milling cutter state on_line monitoring and method for early warning, step are as follows during a kind of complex surface machining：
Step A. obtains the acceleration signal stage online：Three-dimensional acceleration transducer is fixed on machine tool chief axis end, is marked
Determine to vibrate X-direction to three-dimensional acceleration transducer measurement direction and milling handle and Y-direction is completely superposed, three-dimensional acceleration sensing
Device is used to measure the acceleration signal of milling handle vibration X-direction and Y-direction；
Step B. acceleration signals are converted to the monitoring signals stage：The milling handle vibration X-direction and Y side that step A is obtained
To acceleration signal, monitoring signals be milling handle vibration displacement signal, to step A obtain milling handle vibrate X side
Quadratic integral is carried out to the acceleration signal with Y-direction and is converted into displacement signal, represents milling handle vibration displacement；Due to accelerating
Spend signal band range it is wide, comprising signal component it is complex, integral displacement signal is caused to be mixed with more interference signal, in order to
Remove interference signal influence, using Fourier transform (FFT, Fast Fourier Transformation) to integration after
Displacement signal carries out spectrum analysis, filters out other frequency signals and only retains fundamental frequency and its harmonic components, and inverse Fourier is recycled to become
It changes (IFFT, Inverse Fast Fourier Transformation) and becomes filtered signal again time-domain signal as prison
Signal is surveyed, in order to verify that monitoring signals measure the validity of vibration cutting displacement, to current vortex displacement signal and monitoring signals pair
The cumulant answered carries out cross-correlation analysis, using the fundamental frequency of integral displacement signal and its harmonic signal as monitoring signals, one side
Solve the problems, such as that sensor installation is inconvenient, while can effectively remove the influence of interference signal.
The step C. monitoring signals online processing stages：The monitoring vibrated with Synchronous time average the Cumulant Method Using to milling handle
Signal is further processed, and enhances the reliability and stability of monitoring signals.
Synchronous time average the Cumulant Method Using principle is as follows：The periodic waveform of vibration cutting displacement signal and cutting tool state are close
Correlation, during Impeller Machining, operating mode variation causes monitoring signals waveforms amplitude to be changed greatly in different time sections, time domain
Therefore synchronized averaging, Synchronous time average can be carried out to monitoring signals with the effective attenuation frequency signal unrelated with gyration period
The stability of monitoring signals periodic waveform can be enhanced, this is extremely important to carrying out feature extraction by signal waveform；Time domain is same
Step average accumulated amount is the entirety summary to signal, in order to describe the integrality of signal and from different perspectives extraction signal letter
Breath, is described monitoring signals using Synchronous time average cumulant, compared with original signal, the periodic waveform of cumulant is more
Add stabilization, thus monitoring result is relatively reliable.
It is as follows that Synchronous time average the Cumulant Method Using is treated journey to milling handle vibration monitoring signal：If rotating machinery
Vibration signal be xt, speed f0, the sampling interval is Δ, vibration signal xtCorresponding discrete signal is x (n Δ), will be shaken
Dynamic signal is according to speed f0Corresponding vibration signal is extracted, if by xnIntercept is P sections, then each section of period isAssuming that every section of sampling number is equal and value is N, synchronized averagingIt can be represented with following formula：
Algorithm principle based on Synchronous time average with reference to the calculation formula of statistics cumulant, derives Domain Synchronous
The calculation formula of average accumulated amount, the single order derived, second order Synchronous time average cumulant formula are as follows：
Single order cumulant：
Second-order cumulant：
Step D. cutting tool states judge the stage online：On the basis of step C, Synchronous time average is tired out by characteristic quantity
Accumulated amount waveform is quantitatively described, and judges cutting tool state.
Before and after milling cutter breakage, there is very big difference in the signal waveform of the Synchronous time average cumulant of handle of a knife vibration displacement,
Cumulant waveform is quantitatively portrayed using time domain index, the different corresponding time domain index values of waveform certainly exist difference, by time domain index
As characteristic quantity, tool condition monitoring is realized according to the difference of index value before and after tool failure, using variance, degree of skewness, kurtosis
Value, absolute value mean value and virtual value portray signal waveform as characteristic quantity, wherein, variance is used to describe the discrete journey of signal waveform
Degree, degree of skewness are used to describe the skew direction and degree of data, and kurtosis value is especially sensitive to impact signal, and absolute value mean value embodies
Whole amplitude size, for feature extraction thinking as shown in flow chart 1, it is as follows that each time domain index corresponds to formula：
Variance：
Degree of skewness：
Kurtosis value：
Absolute value mean value：
Virtual value：
Wherein yiSingle order or second order sync average accumulated amount are represented,For single order or second order sync average accumulated amount mean value.
Beneficial effects of the present invention：The present invention acquires signal using acceleration transducer, and acceleration transducer has installation
The characteristics of freely measuring simplicity, available for actual monitoring；Using the characteristic information of synchronized averaging cumulant extraction milling cutter state, then
The feature extracting method that synchronized averaging cumulant is quantitatively described in binding characteristic amount has that monitoring result is good, programming letter
Singly, the characteristics of speed of service is fast meets the requirement of on-line monitoring real-time.
Description of the drawings
Fig. 1 is milling cutter status monitoring flow chart.
Fig. 2 a are X-direction force signal and current vortex displacement linearly relationship displacement diagram.
Fig. 2 b are Y-direction force signal and current vortex displacement linearly relationship displacement diagram.
Fig. 3 is point of a knife frequency response function figure at displacement measurement.
Fig. 4 a are the 14th group of data current vortex displacement signal single order accumulation spirograms.
Fig. 4 b are the 14th group of data current vortex displacement signal second-order cumulant figures.
Fig. 4 c are the 14th group of data integral displacement signal single order accumulation spirograms.
Fig. 4 d are the 14th group of data integral displacement signal second-order cumulant figures.
Fig. 5 a are linear regression scatterplots between the 14th group of data current vortex displacement signal and integral displacement signal single order cumulant
Figure.
Fig. 5 b are linear regression scatterplots between the 14th group of data current vortex displacement signal and integral displacement signal second-order cumulant
Figure.
Fig. 6 a are the 64th group of data current vortex displacement signal single order accumulation spirograms.
Fig. 6 b are the 64th group of data current vortex displacement signal second-order cumulant figures.
Fig. 6 c are the 64th group of data integral displacement signal single order accumulation spirograms.
Fig. 6 d are the 64th group of data integral displacement signal second-order cumulant figures.
Fig. 7 a are linear regression scatterplots between the 64th group of data current vortex displacement signal and integral displacement signal single order cumulant
Figure.
Fig. 7 b are linear regression scatterplots between the 64th group of data current vortex displacement signal and integral displacement signal second-order cumulant
Figure.
Fig. 8 a are 1-80 group data single order cumulant absolute value mean value figures.
Fig. 8 b are 1-80 group data single order cumulant variance yields figures.
Fig. 8 c are 1-80 group data single order cumulant virtual value figures.
Fig. 8 d are 1-80 group data single order cumulant kurtosis value figures.
Fig. 8 e are 1-80 group data single order cumulant deflection angle value figures.
Fig. 9 a are 1-80 group data second-order cumulant absolute value mean value figures.
Fig. 9 b are 1-80 group data second-order cumulant variance yields figures.
Fig. 9 c are 1-80 group data second-order cumulant virtual value figures.
Fig. 9 d are 1-80 group data second-order cumulant kurtosis value figures.
Fig. 9 e are 1-80 group data second-order cumulant deflection angle value figures.
Specific embodiment
Below in conjunction with technical solution and attached drawing, the specific embodiment further illustrated the present invention.
Totally 80 groups of experimental data, is collected in certain domestic enterprise's compressor impeller Milling Process production scene, in gatherer process,
Observation milling cutter state is shut down at interval of a period of time, finds the 61st group of milling cutter tipping, experiment parameter is as shown in table 1.
1 experiment parameter of table
14th group, the 64th group of data be respectively data before and after milling cutter tipping, the synchronized averaging period is 10 speed cycles,
Fig. 4 a, Fig. 4 b correspond to the result of calculation of the 14th group of data current vortex displacement signal single order, second-order cumulant, Fig. 4 c, Fig. 4 d respectively
The result of calculation of the 14th group of integral displacement signal single order, second-order cumulant is corresponded to respectively；Fig. 6 a, Fig. 6 b correspond to the 64th group of number respectively
According to the result of calculation of current vortex displacement signal single order, second-order cumulant；Fig. 6 c, Fig. 6 d correspond to the 64th group of integral displacement signal respectively
The result of calculation of single order, second-order cumulant.
In order to describe the wave-form similarity between current vortex displacement signal and integral displacement signal, the 14th group and the 64th is calculated
Related coefficient between group data current vortex displacement signal cumulant corresponding with integral displacement signal, and scatter plot is drawn, Fig. 5 a, figure
5b corresponds to the regression result between the 14th group of two class displacement signal single order of data, second-order cumulant respectively, and Fig. 7 a, Fig. 7 b are corresponded to respectively
Regression result between 64th group of two class displacement signal single order of data, second-order cumulant.
Find out that related coefficient reaches more than 0.9 by Fig. 5 a, Fig. 5 b and Fig. 7 a, Fig. 7 b, show current vortex displacement signal
The linear relationship highly significant between integral displacement signal, the two have similar monitoring effect, can utilize integral displacement signal
It substitutes current vortex displacement signal and carries out tool condition monitoring；Corresponding comparison diagram 4a, Fig. 4 b, Fig. 4 c, Fig. 4 d and Fig. 6 a, Fig. 6 b, figure
6c, Fig. 6 d find out that the single order of two class displacement signals, second-order cumulant amplitude increased after tool failure, wherein with two
The amplitude of rank cumulant increases notable；Further, it is also possible to finding out the second-order cumulant amplitude of integral displacement signal increases than electric whirlpool
Stream displacement signal is more obvious, this shows second-order cumulant of the second-order cumulant than current vortex displacement signal of integral displacement signal
It is sensitiveer to cutting tool state reacting condition.
Fig. 8 a, Fig. 8 b, Fig. 8 c, Fig. 8 d, Fig. 8 e and Fig. 9 a, Fig. 9 b, Fig. 9 c, Fig. 9 d, Fig. 9 e represent that integral displacement is believed respectively
The feature extraction of number single order, second-order cumulant by Fig. 8 a, Fig. 8 b, Fig. 8 c, Fig. 8 d, Fig. 8 e as a result, find out integral displacement signal
Each characterizing magnitudes of single order cumulant integrally increase after the 55th group, and Fig. 9 a, Fig. 9 b, Fig. 9 c, Fig. 9 d, Fig. 9 e will become apparent from
Each characterizing magnitudes of single order cumulant of integral displacement signal are notable in the 55th group of increase, and characteristic value is basicly stable later, the two knot
Conjunction can be determined that tool failure is happened at the 55th group, and experimental record tool failure is happened at the 61st group, which is in
Before 61 groups, thus monitoring result meets the requirements.
Claims (1)
1. milling cutter state on_line monitoring and method for early warning during a kind of complex surface machining, which is characterized in that step is as follows：
Step A. obtains the acceleration signal stage online：Three-dimensional acceleration transducer is fixed on machine tool chief axis end, calibration is extremely
Three-dimensional acceleration transducer measurement direction vibrates X-direction with milling handle and Y-direction is completely superposed, and three-dimensional acceleration transducer is used
In the acceleration signal for measuring milling handle vibration X-direction and Y-direction；
Step B. acceleration signals are converted to the monitoring signals stage：The milling handle vibration X-direction and Y-direction that step A is obtained
Acceleration signal, monitoring signals are the displacement signals of milling handle vibration, to the step A milling handle vibration X-directions obtained and Y
The acceleration signal in direction carries out quadratic integral and is converted into displacement signal, represents milling handle vibration displacement；Become using Fourier
It changes FFT and spectrum analysis is carried out to displacement signal, filter out other frequency signals and only retain fundamental frequency and its harmonic components, recycle inverse Fu
Vertical leaf transformation IFFT becomes filtered signal again time-domain signal as monitoring signals；
The step C. monitoring signals online processing stages：The monitoring signals vibrated with Synchronous time average the Cumulant Method Using to milling handle
It is further processed, enhances the reliability and stability of monitoring signals；
It is as follows that Synchronous time average the Cumulant Method Using is treated journey to milling handle vibration monitoring signal：If rotating machinery shakes
Dynamic signal is xt, speed f0, the sampling interval is Δ, vibration signal xtCorresponding discrete signal is x (n Δ), and vibration is believed
Number according to speed f0Corresponding vibration signal is extracted, if by xnIntercept is P sections, then each section of period isIt is false
If every section of sampling number is equal and value is N, synchronized averagingIt is represented with following formula：
Algorithm principle based on Synchronous time average with reference to the calculation formula of statistics cumulant, derives Synchronous time average
The calculation formula of cumulant, the single order derived, second order Synchronous time average cumulant formula are as follows：
Single order cumulant：
Second-order cumulant：
Step D. cutting tool states judge the stage online：Before and after milling cutter breakage, the Synchronous time average cumulant of handle of a knife vibration displacement
Signal waveform there is very big difference, quantitatively portray cumulant waveform using time domain index, the different corresponding time domain index values of waveform
It has differences, using time domain index as characteristic quantity, tool condition monitoring is realized according to the difference of index value before and after tool failure, is adopted
Signal waveform is portrayed as characteristic quantity by the use of variance, degree of skewness, kurtosis value, absolute value mean value and virtual value, wherein, variance is used to retouch
The dispersion degree of signal waveform is stated, degree of skewness is used to describe the skew direction and degree of data, and kurtosis value is special to impact signal
Sensitivity, absolute value mean value embody whole amplitude size, and it is as follows that each time domain index corresponds to formula：
Variance：
Degree of skewness：
Kurtosis value：
Absolute value mean value：
Virtual value：
Wherein yiSingle order or second order sync average accumulated amount are represented,For single order or second order sync average accumulated amount mean value.
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| JP6499621B2 (en) * | 2016-08-08 | 2019-04-10 | ファナック株式会社 | Control device and control system |
| CN110008784B (en) * | 2018-01-04 | 2021-02-26 | 西安交通大学 | Milling force identification method and system based on conjugate gradient least square algorithm |
| JP7196473B2 (en) * | 2018-09-05 | 2022-12-27 | 日本電産株式会社 | Amount of wear estimation system, correction system, anomaly detection system, life detection system, machine tool, and method of estimating amount of wear |
| WO2021015075A1 (en) * | 2019-07-24 | 2021-01-28 | シチズン時計株式会社 | Machining device, control device used thereby, and machining device control method |
| CN110842648A (en) * | 2019-11-28 | 2020-02-28 | 南京科技职业学院 | Online cutter wear prediction and monitoring method |
| CN111251070A (en) * | 2020-03-20 | 2020-06-09 | 苏州森鼎高端装备有限公司 | Combined intelligent detection method and cutting device |
| CN112710358B (en) * | 2021-03-29 | 2021-06-25 | 南京诚远高新科技有限公司 | Intelligent machine tool state monitoring device and state monitoring method thereof |
| CN114273974B (en) * | 2021-12-14 | 2023-07-25 | 中国科学院合肥物质科学研究院 | Vibration signal-based cutter runout parameter online estimation method in high-speed milling |
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| US4636779A (en) * | 1984-10-24 | 1987-01-13 | General Electric Company | Acoustic detection of tool break events in machine tool operations |
| JP2754760B2 (en) * | 1989-07-13 | 1998-05-20 | オムロン株式会社 | Tool damage detection device |
| JPH0885047A (en) * | 1994-09-13 | 1996-04-02 | Sumitomo Metal Ind Ltd | Cutter tip abrasion detecting method for cutting tool |
| CN102490086B (en) * | 2011-10-28 | 2013-11-27 | 浙江大学 | System for monitoring working state of boring rod in real time |
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