CN1084795A - A kind of cutter failure comprehensive monitoring and controlling method and device - Google Patents
A kind of cutter failure comprehensive monitoring and controlling method and device Download PDFInfo
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Abstract
A kind of cutter failure comprehensive monitoring and controlling method and device, the integrated information of its monitoring is the following three groups of characteristic parameters as real time control variables with acoustic emission and vibration signal, first group, characterize the parameter of tool failure and blade flow, second group, characterize the parameter that tool wear/breakage is merged, the 3rd group, the parameter of sign blade wearing and tearing self similarity.Monitor tool failure by method of the present invention and can reduce rate of failing to report and rate of false alarm.Device of the present invention can be used for the tool failure monitoring of multiple machine tooling, and jamming performances such as its anti-scene is mechanical, electrical, magnetic, sound are good.
Description
The present invention relates to the method for real-time monitoring and the device of working angles tool failure.
Online, real-time, the high accuracy of working angles tool failure, the research and development of method for supervising and device reliably are to adapt at a high speed, efficiently and the development of flexible automation process technology and modern manufacturing system, the monitoring of realization working angles, procedure quality ensures, the working angles dynamic optimization, the problem that Self Adaptive Control and large-scale, valuable 's process equipment and workpiece safety guarantee etc. are essential, important and anxious to be solved.
Utilized acoustic emission signal to realize the damaged real time monitoring of cutter tipping in recent years, but the real time monitoring of cutter wearing and tearing can't satisfy operating accuracy, stability and be easy to requirement such as The field.Based on optical image, contact still is difficult to practicability with the surveillance of the direct method of radioactive ray, maybe can't finish real time monitoring; With power/moment is the wearing and tearing supervision method of representative, though feasible through experimental verification or its principle, but still the difficult point of unresolved sensing detection device practicability and definite threshold value; It is low to be confined to sensitivity based on the wearing and tearing supervision method of power/current method; Acoustic emission or vibration cutter wearing and tearing supervision method also do not find effective signal characteristic parameter, still can't realize high accuracy, reliably real time monitoring; Many heat transfer agents that the later stage eighties begins merge (fusion) though the research of method can realize cutter wearing and tearing supervision in the laboratory, but used DMGH(group method of data handling) and the NN(neutral net) the monitors identified method also can't practicability, its distinct issues are how to reduce frequency of training, how to adapt to multi-varieties and small-batch or single-piece work applications.Another basic problem is how they realize supervision and the forecast of tool wear value real time calibration (demarcation) to finish attrition value.Existing simultaneously many perception detect transducing signal and signal is handled and the information calculations amount of identification is big, the microcomputer that certainly will require to increase hardware cost and adopt higher class, and the totle drilling cost that is directed at monitor increases.Therefore,, can't meet the demands though the research of tool wear real time monitoring is being carried out without a break always, can't practicability.Chinese patent, patent No. 89100419.x utilizes fuzzy diagnosis, monitor when hierarchical statistics and scoring function therapy have reached the high-precision real of acoustic emission (AE) turning cutting tool tipping breakage, and inordinate wear has certain function for monitoring to cutter.This patent is by the AE sensor, multiple filter, preceding, main amplifier, micro-computer interface circuit, report to the police output and crosslinked interface circuit and utilize the microcomputer of fuzzy Judgment model program to form.Its operation principle is for obtaining the AE signal of working angles AE sound source with the AE sensor, obtain " ring numeration " after this signal handled, " incident numeration ", " one; twice-enveloping detection crest maximum amplitude ", " twice-enveloping detection ripple perdurabgility ", " first enveloped waveform detection rate of change " " wait six parameters; and the fuzzy diagnosis judgment rule of setting up according to expertise and experience carries out the fuzzy classification statistics to the numerical value of six parameters; provide the fractional value of each grade respectively by scoring function therapy; according to the program of each parameter, judge then, find the solution conclusion by regular expression to the tool failure influence.Chinese patent, the patent No. 89108832.6 is utilized the acoustic emission signal of tool failure when unusual to carry out changeable parametric statistics pattern-recognition and is monitored the cutter operating mode.This patent is detected and pretreatment unit by acoustic emission signal, signal and general purpose microprocessor interface unit, and microprocessor, display, crosslinked transmission are formed with warning output unit and anti-jamming power-supply.Its operation principle is, obtain the AE signal in working angles AE source with the AE sensor, obtain " ring numeration " after this signal handled, " peak value of first enveloped signal ", " numeration of first enveloped signal peak ", " average of first enveloped signal amplitude ", " the differential peak-to-peak value of first enveloped signal ", " peak value of twice-enveloping signal ", the numerical value of seven parameters such as " twice-enveloping signal wave perdurabgilities " carries out the statistical model identification of changeable parameter, according to decision-making line (face) different Y values is divided into two classes (normal and unusual), realizes the integrated supervision of multiple tool failure or its work condition abnormality.These two patents can realize the reliable real time monitoring of high accuracy to the tipping breakage or the cutting work condition abnormality of lathe tool and other multiple cutter, but their common weak points are: (1) can't the real time monitoring tool wear, (2) fail three kinds of main inefficacy pattern-wearing and tearing of a cutter, damaged and blade flow (turned) carries out comprehensive real time monitoring, and (3) can not forecast that tool wear is with damaged.
The object of the present invention is to provide a kind of multiple inefficacy pattern of automated machine tool running hours cutter-wearing and tearing that are applicable to multiple cutter, the comprehensive real time monitoring of damaged blade flow, response speed is fast, the operating accuracy height, anti-glitch power is strong, can realize tool wear and damaged acoustic emission and the cutting vibration of forecasting, cutter integrated supervision method and monitoring arrangement.
Technical solution of the present invention provides a kind of tool failure of using, the method that acoustic emission when blade flow and wearing and tearing and working angles vibration signal monitor the multiple inefficacy operating mode of cutter, its particular content is, by acoustic emission and the vibration by sensor (the compound or integrated sensor) acoustic emission of obtaining and the sensing of vibration signal, amplify, frequency-selecting, filtering, acoustic emission and vibration signal form the step that comprehensively (fusion) information and microcomputer utilizes integrated signal that the multiple inefficacy operating mode of cutter is discerned judgement and form, it is characterized in that its monitoring integrated information is the following three groups of characteristic signal parameters as real time control variables by acoustic emission signal and cutting vibration signal, that is: characterize parameter-" ring numeration " N of tool failure and blade flow (turned), " average of first enveloped signal amplitude " A
1, " peak value of first enveloped signal " A
1M, " the differential peak-to-peak value of first enveloped signal " dA
1M, " peak value of twice-enveloping signal " A
2M, " numeration of first enveloped signal peak " NA
1, " twice-enveloping signal wave perdurabgility " tA
2; Characterize tool wear, the parameter that wearing and tearing/breakage is merged-" accumulated value of ring numeration " ∑ N, " accumulated value of first enveloped signal amplitude " ∑ A
1, " accumulated value of first enveloped signal differentiation " ∑ dA
1" accumulated value of twice-enveloping signal amplitude " ∑ A
2"; Characterize characteristic parameter one " the unit interval accumulated value (ring counting rate) of ring numeration of tool wear self similarity
, the unit interval accumulated value of first enveloped signal amplitude "
, " the unit interval accumulated value of first enveloped signal differentiation "
, " the unit interval accumulated value of twice-enveloping signal amplitude " A
2Form.
Cutter failure comprehensive monitoring and controlling device of the present invention comprises acoustic emission and gives processing unit, the microcomputer interface unit, microsystem, crosslinked transmission and sound and light alarm output unit, and anti-jamming power-supply, said acoustic emission signal detects and gives processing unit and comprises calibrate AE sensor, preamplifier, high-pass filter, main amplifier, bandpass filter, it is characterized in that vibration signal detects and gives processing unit in addition, this vibration signal detects and gives processing unit involving vibrations sensor (or acoustic emission one vibration compound sensor), preamplifier, main amplifier, bandpass filter (or low pass filter) amplitude discriminator, signal (or signal fused), compound or integrated sensor signal detection of said acoustic emission one vibration and pretreatment unit comprise: ring forms circuit, first enveloped forms circuit, programmable amplifier, twice-enveloping forms circuit, the first enveloped signal differentiation forms circuit, power one threshold value comparator able to programme is formed, this microprocessor system is made up of the two-level network of microcomputer or microprocessor, it is to " ring numeration ", " first enveloped signal peak ", " the differential peak-to-peak value of first enveloped signal ", " twice-enveloping signal peak ", " the peak value numeration of first enveloped signal ", " twice-enveloping ripple perdurabgility ", " average of first enveloped signal amplitude " and " ring numeration ", " first enveloped signal amplitude ", " first enveloped signal differentiation " handled with the process accumulated value and the accumulated value of unit interval (or title is to rate of change of time) thereof of " twice-enveloping signal amplitude ", the computer system of the mini-computer of information fusion (integrated) and intelligent identification or microprocessor identification judgement program curing.
The main pattern of working angles tool failure has: wearing and tearing, breakage and blade flow (claiming the volume cutter again), the AE signal of its failure procedure has highly sensitive, and bandwidth and the advantages such as cutter operating mode abundant information that contain have generally acknowledged that the preferred signal of the damaged real time monitoring of cutter tipping is the AE signal.N.Alborti points out in the Interdependence of CIRP Annals 34/1/1985 between tool fracture and wear one literary composition: the wearing and tearing of cutter and broken invalid are not to be with phenomenon independently, so AE signal of working angles, the information that must contain the cutter various working, should be wearing and tearing with damaged, and then comprise the blade flow, the Fusion Model of three kinds of unified inefficacy patterns is promptly set up in the consideration of uniting of several inefficacy patterns.Research to the working angles vibration signal also shows that cutter mill/damaged information also contains in its vibration signal.Because the influence factor of working angles is many, characterize the AE signal of cutter operating mode and the noise severe contamination that vibration signal is subjected to other factors, its primary signal usually is low signal-to-noise ratio (S/N).Up to now, though existing people attempts with the supervision of wearing and tearing in real time of acoustic emission (AE) signal and vibration signal, but also do not find effective signal to handle and feature extracting method, do not find out with tool wear, blade flow and attrition value and demarcate (calibration) closely-related characteristic parameter automatically; Fail three kinds of pattern unified Modeling of a tool failure, thereby still can not realize wearing and tearing, real-time automatic calibrating (demarcation) method of tool wear value is not also found in the forecast of breakage or blade flow, thereby can't realize the real time monitoring and the forecast of attrition value.
The present invention utilizes spectral analysis method and the multi-level analytic approach of time domain (multistage feature extraction method) combination, extract the time and frequency domain characteristics parameter set of monitor signal-acoustic emission and vibration signal, the three stack features parameters that characterize tool failure have been found, that is: sign cutter tipping breakage, the characteristic parameter-N of blade flow, A
1, A
2, dA
1, A
1, NA1 and t
A2; Characterize the characteristic parameter-∑ N of tool wear, ∑ A
1, ∑ dA
1With ∑ A
2; The characteristic parameter of sign self-similarity nature-
,
1, d
1With
2On the orthogonal cutting experiment basis, through correlation analysis, trend analysis and least square regression have been constructed with the parameter of above-mentioned characteristic parameter collection and tool wear, the high-order moment tool failure that variable element damaged and the blade flow is higher than secondary merges (comprehensively) model, again according to the method for mutationism (CatastropheTheory), built tool failure potential function one catastrophic model based on above-mentioned Fusion Model, utilize its Differential Manifold (profile of equilibrium, be the differential curved surface of cutter life potential function) the sudden change feature, proposing the sudden change of tool failure process has multi-modal, kick, lag behind, unreachable and the condition feature such as disperse, thus the converting characteristic value of tool failure pattern sudden change found | Uo|.When | U|＜| during Uo|, cutter can not undergone mutation in working angles, be tipping breakage or blade flow, only exist | U|＞| during Uo|, state mutation one tipping breakage or blade flow will just may take place in cutter near degenerate critical point (second dervative of cutter life potential function is zero place).Utilize the Differential Manifold of above-mentioned Fusion Model potential function and intersect collection and can forecast the generation of breakage of cutter tipping or blade flow, and auxilliary the cutter attrition value is demarcated the forecast that (calibration) just can realize tool wear value VB automatically in the self similarity parser.
Illustrate that accompanying drawing is as follows:
Fig. 1 is that acoustic emission (AE) is extracted principle and Parameter Map with vibration signal multistage (stage)
Fig. 2 is car/slotting cutter wearing and tearing polynomial regression matched curve figure
A is that a regression curve b is the quadratic regression curve
C is that cubic regression curve d is four regression curves
Fig. 3 is car/slotting cutter wearing and tearing-breakage (flow) polynomial regression matched curve figure
A is that a regression curve b is the quadratic regression curve
C is that cubic regression curve d is four regression curves
Fig. 4 is the popular figure of the differential of tool failure catastrophic model
Fig. 5 is that car/vertical milling cutter failure comprehensive monitoring and controlling instrument fundamental diagram is merged in acoustic emission/vibration
Fig. 6 is acoustic emission car/vertical milling cutter failure comprehensive monitoring and controlling instrument fundamental diagram
Fig. 7 is that car/vertical milling cutter failure comprehensive monitoring and controlling instrument software block diagram is merged in acoustic emission/vibration
Fig. 8 acoustic emission car/vertical milling cutter failure comprehensive monitoring and controlling letter instrument software block diagram
Fig. 9 is car/vertical milling cutter failure comprehensive monitoring and controlling instrument system block diagram
Figure 10 is the preamplifier circuit schematic diagram
Figure 11 is the main amplifier circuit schematic diagram
Figure 12 is the bandwidth-limited circuit schematic diagram
Figure 13 forms circuit theory diagrams for ring
Figure 14 forms circuit theory diagrams for first enveloped
Figure 15 is the signal processing circuit unit schematic diagram.Wherein
I is the programmable amplifier circuit theory diagrams
II is that twice-enveloping forms circuit theory diagrams
III is the differential circuit schematic diagram
IV is first enveloped peak value numeration circuit theory diagrams
Accompanying drawings operation principle of the present invention is as follows:
The characteristic parameter collection of acoustic emission of the present invention (AE) signal and working angles vibration signal divides following three groups, 1 is illustrated in conjunction with the accompanying drawings:
First group: characterize the breakage of cutter tipping, the characteristic parameter of blade flow
1, ring numeration N: promptly surpass the AE or the vibration signal number of preset threshold value in sampling time interval, this signal is lower than threshold level (V through amplification, filtering, shaping and removal
01) signal after obtain.
2, first enveloped signal: be that AE or vibration signal are carried out the signal that obtained after the envelope detection.
The average A of 3 first enveloped signal amplitudes
1: be in sampling time interval, the mean value of first enveloped signal amplitude.
4, first enveloped signal peak A
1M: the peak value that is first enveloped rectified signal amplitude in the sampling time.
5, the differential peak-to-peak value dA of first enveloped signal
1M: be that the first enveloped signal is handled resulting signal positive peak and negative peak absolute value sum with differential circuit.
6, twice-enveloping signal: be the signal that the first enveloped signal is formed behind LPF.
7, the peak A of twice-enveloping signal
2M: be the peak value of twice-enveloping rectified signal amplitude in the sampling time, the i.e. maximum amplitude of twice-enveloping signal.
8, the peak value of first enveloped signal numeration NA
1: be in the sampling time with the K of twice-enveloping signal amplitude doubly (K is determined by operating mode) as threshold value V
H1, when the first enveloped peak value surpasses threshold value, obtain
Once numeration is designated as 1, is designated as 0 during less than threshold value.
9, twice-enveloping signal wave t perdurabgility
A2: determine preset threshold value V according to operating mode
H2, with the resulting time span of its intercepting twice-enveloping signal wave.
Above-mentioned AE signal and vibration signal characteristics parameter, promptly show breakage of cutter tipping or blade flow (volume of determining), otherwise are nominal situation after several characteristic parameters comprehensively reach certain value all having reflected the operating mode of cutter in working angles aspect certain.
Second group: characterize tool wear, the characteristic parameter that mill/breakage is merged
1, ring numeration accumulated value ∑ N: be from the initial moment of Tool in Cutting, increase progressively the accumulated value of ring numeration with the cutting time.
2, first enveloped amplitude accumulated value ∑ A
1: be from the initial moment of Tool in Cutting, increase progressively the summation of first enveloped sample magnitude with the cutting time.
3, first enveloped signal differentiation accumulated value ∑ dA
1: be from the initial moment of Tool in Cutting, increase progressively the accumulated value of the differential amplitude absolute value of first enveloped signal with the cutting time.
4, the accumulated value ∑ A of twice-enveloping amplitude
2: be from the initial moment of Tool in Cutting, increase progressively the summation of twice-enveloping amplitude with the cutting time.
The 3rd group: the characteristic parameter that characterizes tool wear value calibration (demarcation)
1, ring counting rate
: at the accumulated value of the ring numeration of Nei Caide at interval of given unit interval (1/ sampling time length).
2, the accumulated value of first enveloped signal amplitude unit interval
1: in given unit interval, adopt the accumulated value of first enveloped amplitude.
3, the unit interval accumulated value d of first enveloped signal differentiation
1: in given unit interval, adopt the accumulated value of differential signal amplitude absolute value.
4, the unit interval accumulated value of twice-enveloping signal amplitude
2: in given unit interval, adopt the accumulated value of twice-enveloping signal amplitude.
Above-mentioned second group of parameter both can characterize tool wear, can characterize cutter mill/breakage (blade flow) again and merge (comprehensively); The 3rd group of parameter demarcated (calibration) automatically for attrition value and used; When setting up cutter mill/damaged Fusion Model, the combination of selecting at least one parameter in second group or two above parameters for use is as variable, by least square regression foundation and cutter wear of the tool flank value VB, or tool failure/cutter [mathematical relationship of the equivalent attrition value [VB] during the sword flow (the equivalent attrition value is with the average height value of breakage or flow area conversion) is as mill/breakage/flow operating mode real-time model, automatic real-time calibration tool wear value under the 3rd group of parameter supported, calibration result is combined with Fusion Model, can real time monitoring tool wear value, extrapolation forecast attrition value, second group of two control alter amount that combine with first group of parameter as the tool failure catastrophic model, by obtaining tool failure to the intersection collection of catastrophic model and the analytical calculation of Differential Manifold, the forecast result of cutter flow.
Feature extraction by Fig. 1 acoustic emission (AE) and cutting vibration signal is divided into following Pyatyi: the first order is that the AE that will be obtained by sensor obtains ring numeration and first enveloped rectified signal with the vibration signal of telecommunication after amplify, frequency-selective filtering and amplitude discrimination are handled; Second level extraction is at the amplitude thresholds V that sets first enveloped
H1, extract first enveloped signal peak numeration NA
1, first enveloped amplitude A
1, after differential is handled, from the first enveloped signal, obtain the differential amplitude dA of first enveloped signal
1, after twice-enveloping is handled, obtain twice-enveloping signal amplitude A
2; The third level is extracted: from A
1The middle peak A of extracting the first enveloped signal
1M, the average A of first enveloped signal amplitude
1, first enveloped signal differentiation peak-to-peak value dA
1M is from A
2The middle peak A of extracting the twice-enveloping signal
2M is provided with threshold value V
H2After obtain ripple t perdurabgility of twice-enveloping signal
A2; What the fourth stage extracted is to ask for N in the given unit interval, A
1, dA
1With A
2Accumulated value get: ring scale of notation time accumulated value (ring counting rate)
, the accumulated value of first enveloped signal amplitude unit interval
1, the unit interval accumulated value d of first enveloped signal differentiation
1 and the unit interval accumulated value of twice-enveloping amplitude
2; What level V extracted is: from the initial moment of Tool in Cutting, increase progressively N A along with the cutting time
1, dA
1With A
2Constantly add up: ring numeration accumulated value ∑ N, first enveloped amplitude accumulated value ∑ A
1, first enveloped signal differentiation accumulated value ∑ dA
1Accumulated value ∑ A with the twice-enveloping amplitude
2
Fig. 2, ordinate y is a ∑ in 3, be the accumulated value of first enveloped (or twice-enveloping, or first enveloped signal differentiation or ring numeration) amplitude, abscissa is equivalent attrition value [VB], (or breakage/plastic yield is very little when only there are wearing and tearing in cutter, can ignore) time [VB]=VB, when tool failure or flow, [VB] the equivalent attrition value that breakage or flow area are converted out of serving as reasons, studies have shown that y and x are following variable element high-order moment least square regression fitting function relation, that is:
In the formula: b
jFor with the cutter workpiece material, the variable coefficient that machining condition changes, j=0,1,2 ..., m.i=1,2 ... n, ε
iBe residual error, ε
i=y
i-
i
According to the monitoring technique requirement, control remaining mean square deviation S
In the formula: (y
i-
i) be residual error, N (N-2) is the free degree, y for the sampling number of packages
iBe experiment value,
iBe regressand value.By existing tool monitoring requirement, the minimum regression result proof on a large amount of orthogonal cutting experimental result bases is generally got i.e. 3 order polynomials of j=3() enough, as in turning or End Milling Process, when error of fitting≤2～7% of j=4 than j=3.So the Fusion Model of general car/slotting cutter wearing and tearing/breakage/blade flow is desirable.
y＝bo+b
1x+b
2x
2+b
3x
3（3）
The tool failure potential function V that it is corresponding, the i.e. standard type of elementary catastrophe-antithesis cusp sudden change
V＝V（x）＝-x
4+ux
2+v
x（4）
In the formula: x is the coordinate transform value of tool wear value or equivalent attrition value; U is the time-varying function of the characteristic parameter that characterizes each factor of antagonism tool failure, and as variable element, it has comprised the influence of factors such as the dynamic characteristic of cutter wood property and geometric properties, lathe-cutter-workpiece system and machining condition in equation (4); V is for characterizing the time-varying function of the characteristic parameter that promotes each factor of tool failure, in equation (4) also as variable element, it comprises cutting load, cutting data, cutter material defective, the machinability of workpiece, working angles vibration, the influence of factors such as cutting region temperature rise V(x) is that (3) formula is through the integral result after the necessary conversion.Experimental study obtains: u can select for use the combination of the aforementioned first stack features parameter to express, one or two above characteristic parameter among then available second group of the v makes up expresses, its essence is with one, the time variate (or combined value) of two stack features parameters has characterized the influence of two class tool failure factors, and need not deeply ask for the relation of each influence factor and [VB], must determine that each factor could express the thinking of [VB] to the influence and the dependency relation thereof of [VB] thereby break away from, connect and use time dependent u, v value to express high-order moment function in real time about [VB].
M curved surface among Fig. 4 is the Differential Manifold of tool failure catastrophic model.Curved surface M has three equilbrium positions, upper, middle and lower, inferior lobe represents that attrition value is little, the cutting tool state that cutter still can work on, last leaf represents that cutter loses the state of cutting power because of blunt or tipping breakage, blade flow, the middle part is near the degenerate critical point of V, the sudden change of condition can appear because of certain little disturbance (change) in cutting tool state, so it is the range of instability, this has represented the multi-modal characteristic of tool failure; On the M face, when the path I reaches the C point, cutting tool state value (as equivalent attrition value [VB]) increases to the d point from the C point suddenly, it has represented the kick characteristic of tool failure state, the corresponding points e of f is to the distance of c among the figure, b ≠ 0 has represented that tool failure is not strict state time lag that reversible process occurred.Therefore the existence of b explanation cutting tool state is that forecast feature and time are arranged before arriving C.Near degenerate critical point II, the available small variations of III point because of initial parameter, i.e. the perturbation in control variables path can cause that cutting tool state along II, the development of III path, is the drastic change feature, it has characterized the diversity of cutter condition.In addition, along the path I, the state value between C and d (being the value of X) can not obtain, and it has characterized the inaccessibility of cutting tool state.Above-mentioned five sudden change features have important directive significance to the real-time monitoring of tool failure, that is: (1), when | U|＜| during Uo|, cutting tool state is not near degenerate critical point, state mutation can not take place, just gradual change so cutter can fade to the blunt state by sharp state in time lentamente following of this kind state, keeps | U|＜| it is damaged that Uo| can avoid cutter to occur; (2), when | U|＞| Uo|, and v＞
The time cutting tool state will undergo mutation, promptly occur damaged, the blade flow, or other drastic change state (as, burn blade etc.), claim Uo threshold value for the sudden change indication, (3), can forecast the generation of tool failure or blade flow, be selected in the value [e of v between its forecast period according to hysteresis quality, c) in, its calculated value can utilize following formula
In the formula:
For the predicted value before breakage or the blade flow takes place, promptly work as v=
The time send the sudden change warning signal; v
fBe the v value of Fig. 3 f point correspondence, k is a coefficient,
k = (Vg - Ve)/(Vc - Ve) (5)
v
c, v
e, v
gBe c, e, the v value of g point correspondence, the g point determines according to experiment, it [e, c) during, (4) are to (4) formula, or its general expression V(x)=± x
4+ ux
2The perturbation of+vX proves, V(x) is constitutionally stable.Therefore it has guaranteed that above-mentioned tool failure phenomenon and condition feature thereof can repeated measures, promptly guaranteed tool failure based on above-mentioned principle monitor with control be to observe, can reappear.
Mill/damaged integrated supervision instrument operation principle as shown in Figure 5 and Figure 6 for the acoustic emission of making by above-mentioned principle of the present invention/vibration cutter.
This instrument is finished every processing by hardware, software as shown in Figure 5.
Receive AE signal and cutting vibration signal through amplifying by sensor, high low pass, filtering are carried out preliminary treatment by hardware and are obtained ring and count first enveloped signal, twice-enveloping signal, the differential of first enveloped signal.Carry out feature extraction again, by obtaining behind the software sampling: ring counter value, the average of first enveloped signal amplitude, the peak value of first enveloped signal, the peak value of twice-enveloping signal, the differential peak-to-peak value of first enveloped signal, the numeration of the peak value of first enveloped signal, twice-enveloping signal wave perdurabgility.Under software control, obtain again: the accumulated value of ring numeration through computing, the accumulated value of first enveloped signal amplitude, the accumulated value of twice-enveloping signal amplitude, the unit interval accumulated value of the accumulated value of first enveloped signal differentiation and ring numeration, the unit interval accumulated value of first enveloped signal amplitude, the unit interval accumulated value of twice-enveloping signal amplitude, the unit interval accumulated value of first enveloped signal differentiation, three groups characterize tool failure (flow), wearing and tearing and wearing and tearing self similarity characteristic parameter.Signal fused is partly pressed characteristic ginseng value and is sorted out, and legal by set of weights, and integrated decision-making is carried out in the judgement of making according to AE and vibration signal respectively.Recognizer is preferably to carry out breakage, flow identification, when confirming no breakage or flow (normally), and the identification of wearing and tearing again.Damaged and flow identification can be considered the weighted array decision-making of AE and two kinds of judgements of vibration signal, also can discern by the AE signal.In the wearing and tearing decision-making, be main basis of characterization with the AE signal, and auxilliary discerning in vibration signal can reach high accuracy, so take combination decision.To the tool failure and the recognition principle of flow is the variable element statistical pattern recognition method identical with Chinese patent CN89108832.6.And wearing and tearing are determined x value, i.e. attrition value by the model of (3) formula from the y value.The tool wear value adopts the 3rd stack features parameter to demarcate, and the basis of its scaling method is the self similarity analysis.In Tool Wear Process, the back knife face of workpiece and cutter has the deformation of material under contact condition, and by the fracture mechanics rule repeatedly skin-material breaking releasing can take place.This physical phenomenon shows as the variation along with the attrition value VB of cutter in the characteristic signal of AE and vibration, there is autocorrelation in their unit interval accumulated value, i.e. self-similarity.In other words, as long as when corresponding self-similarity nature appears in the unit interval accumulated value, just can find the VB value of correspondence.Its algorithm principle is: if with Pi represent i unit interval the signal accumulated value (i=1,2 ... n), be designated as a minor peaks when Pi＞Pi-1 and Pi＞Pi+1, the accumulated value h 〉=H(H of peak value is corresponding to VB
0The accumulated value of peak value) time, V
BOReach the VB that trial test is determined
0Value.Cutting facts have proved that this calibrates (demarcation) method automatically can reach high calibration accuracy.In the decision-making of cutter operating mode is damaged, when flow or blunt, starts alarm and carries out sound and light alarm, and transmit alarm signal by crosslinked (data communication) interface to Digit Control Machine Tool, circulates for normally then repeating to monitor as decision-making.
Fig. 6 is the theory diagram of instrument when only adopting the AE signal.
Finish software block diagram such as Fig. 7, shown in Figure 8 of above-mentioned functions.
Shown in Fig. 7 block diagram, open interruption after the main program initialization, time-delay is prepared to accept damaged identification and is interrupted, carry out AE signal sampling and damaged, flow identification, if press the AE signal determining for damaged, during flow, the sampling of starting vibration signal is again interrupted with damaged identification, make decisions with the weighted array model, the processing if breakage, flow are then reported to the police starts warning device; If decision-making is for normal, then return damaged identification interrupt routine, changing wearing and tearing over to handles, start AE wearing and tearing identification interrupt routine and carry out self similarity analysis and wearing and tearing identification, when judging that attrition value reaches predetermined value, starting shakes advises signal wearing and tearing identification to interrupt, and repeats identification by vibration signal, and its decision-making is undertaken by the weighted array model of AE and vibration signal.When if decision-making is wearing and tearing, start warning device, otherwise interrupt returning, repeat to monitor circulation next time.
Fig. 8 is the software block diagram of instrument when only adopting the AE signal.
For finishing above-mentioned functions, the present invention's surveillance block diagram as shown in Figure 9.
This instrument is divided into five major parts: (I) signal detection and pretreatment unit, comprising AE signal detection and pretreatment unit (1～11) and vibration signal detection and pretreatment unit (21～31); (II) signal and mini-computer interface unit (12); (III) mini-computer system (13), (IV) crosslinked transmission I/O and warning output unit (14,15); (V) anti-interference power supply system (16,17).
[1] be broadband piezo-electric crystal AE sensor, frequency show 100KHz～1MHz(≤± 10dB).
[21] be the piezo-electric crystal vibrating sensor, frequency be 0～300Hz(≤± 10dB).
[2], [3] are the AE signal preamplifier, and [2] are the amplifier of gain 20dB or 40dB, and [3] are cut-off frequency 100KHz, the high-pass filter of decay gradient 18dB/OCT.[2] the preamplifier input conversion noise of [3] composition is less than 4.5 μ v(RMS), its circuit theory diagrams are Figure 10.
[22] be the vibration signal preamplifier, gaining is 20db or 40db, and its input conversion noise is less than 4.5 μ vb(RMS), its circuit theory diagrams are Figure 10.
[4], [24] are main amplifier, and gain is 10～40dB, (Figure 11).
[5] be bandpass filter, cut-off frequency 300KHz(high pass), the 1MHz(low pass), decay gradient 24dB/OCT, (Figure 12).
[25] be low pass filter, cut-off frequency 300KHz, decay gradient 24dB/OCT(is with Figure 12 low pass).
[6], [26] are ring numeration circuit (Figure 13).
[7], [27] form circuit (Figure 14) for first enveloped.
[8], [28] are to guarantee that signal is in the suitable range of A/D for programmable amplifier (I of Figure 15) is provided with purpose.
[9], [29] form circuit (II of Figure 15) for twice-enveloping.
[10], [30] form circuit (III of Figure 15) for differential signal.
[11], [31] are the programmable threshold comparator, form envelope counting (IV of Figure 15).
[12] interface of signal and microcomputer.
[13] microcomputer system.
[14] with the crosslinked interface of lathe NC system, can be RS232, also can be relay.
[15] sound and light alarm delivery outlet is delivered to alarm signal the alarm lamp and the buzzer of instrument panel.
[16] exchange anti-jamming power-supply (commercially available).
[17] D.C. regulated power supply (commercially available).
To the AE signal, the various features of vibration signal is carried out the method for comprehensive analysis and judgement cutter operating mode according to the present invention, the tool monitoring instrument of making can reduce cutter broken/rate of failing to report and the error rate of wear working condition identification.On-the-spot mechanical, electrical, magnetic is produced in acoustic emission of the present invention/vibration cutter synthesis monitoring instrument antibiosis, the acoustic jamming ability is strong, can be used for NC, CNC, and FMC, multiple car among the FMS mills machining tool and machining center.
Claims (2)
1, a kind of cutter failure comprehensive monitoring and controlling method, sensing by sound wave and vibration signal, amplify, frequency-selecting, filtering, acoustic emission signal forms comprehensively (fusion) information and microcomputer and utilizes integrated signal to carry out the step that the multiple inefficacy identification of cutter judges to form, it is characterized in that its monitoring integrated information is the following three groups of characteristic signal parameters as real time control variables by acoustic emission signal and cutting vibration signal, characterizes parameter-" ring numeration " N of tool failure and blade flow that is:, " average of first enveloped signal amplitude " A, " peak value of first enveloped signal " dA
1M, " the differential peak-to-peak value of first enveloped signal " dA
1M, " peak value of twice-enveloping signal " A
2M, " numeration of first enveloped signal peak " N
A1, " twice-enveloping signal wave perdurabgility " t
A2Table sheet tool wear, the parameter that wearing and tearing/breakage is merged-" accumulated value of ring numeration " ∑ N, " accumulated value of first enveloped signal amplitude " ∑ A
1, " accumulated value of first enveloped signal differentiation " ∑ dA
1, " accumulated value of twice-enveloping signal amplitude " ∑ A
3Characterize characteristic parameter-" the unit interval accumulated value (ring counting rate) of ring numeration " N, " the unit interval accumulated value of first enveloped signal amplitude " A of tool wear self similarity
1, " the unit interval accumulated value of first enveloped signal differentiation " dA
1, " the unit interval accumulated value of twice-enveloping signal amplitude " A
2Form.
2, a kind of cutter failure comprehensive monitoring and controlling device, comprise acoustic emission and give processing unit, the microcomputer interface unit, microsystem, crosslinked transmission and sound and light alarm output unit, and anti-jamming power-supply, said acoustic emission signal detects and gives processing unit and comprises calibrate AE sensor, preamplifier, high-pass filter, main amplifier, bandpass filter, it is characterized in that vibration signal detects and pretreatment unit in addition, this vibration signal detects and gives processing unit involving vibrations sensor (or the compound or integrated sensor of acoustic emission one vibration), preamplifier, main amplifier, bandpass filter (or low pass filter), amplitude discriminator, signal (or signal fused), compound or integrated sensor signal detection of said acoustic emission one vibration and pretreatment unit comprise: ring forms circuit, first enveloped forms circuit, programmable amplifier, twice-enveloping forms circuit, the first enveloped signal differentiation forms circuit, power one threshold value comparator able to programme is formed, this microprocessor system is made up of the two-level network of microcomputer or microprocessor, it is to " ring numeration ", " first enveloped signal peak ", " the differential peak-to-peak value of first enveloped signal ", the peak value numeration of first enveloped signal "; " average of first enveloped signal amplitude "; " twice-enveloping signal peak "; " twice-enveloping signal wave perdurabgility "; and " ring numeration ", " first enveloped signal amplitude ", " first enveloped signal differentiation "; the process accumulated value of " twice-enveloping signal amplitude " and the accumulated value of unit interval thereof are handled, information fusion (integrated) is discerned the computer system of judging program curing with the mini-computer or the microprocessor of identification.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN92111137A CN1045738C (en) | 1992-09-29 | 1992-09-29 | Cutter failure comprehensive monitoring and controlling method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN92111137A CN1045738C (en) | 1992-09-29 | 1992-09-29 | Cutter failure comprehensive monitoring and controlling method and device |
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| CN1084795A true CN1084795A (en) | 1994-04-06 |
| CN1045738C CN1045738C (en) | 1999-10-20 |
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| CN92111137A Expired - Fee Related CN1045738C (en) | 1992-09-29 | 1992-09-29 | Cutter failure comprehensive monitoring and controlling method and device |
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| CN (1) | CN1045738C (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1484607A1 (en) * | 2003-06-02 | 2004-12-08 | Denso Corporation | Abnormality determination and estimation method for product of plastic working, and an abnormality determination and estimation device |
| CN1298506C (en) * | 2002-07-01 | 2007-02-07 | 株式会社迪斯科 | Cutting tip monitoring device for cutting device |
| CN105058165A (en) * | 2015-08-08 | 2015-11-18 | 电子科技大学 | Tool abrasion loss monitoring system based on vibration signals |
| CN109696478A (en) * | 2018-11-27 | 2019-04-30 | 福建省嘉泰智能装备有限公司 | A kind of monitoring method of combination acoustic emission energy and lathe information |
| CN113369994A (en) * | 2021-06-30 | 2021-09-10 | 温州大学 | Cutter state monitoring method in high-speed milling process |
| CN114850969A (en) * | 2022-07-08 | 2022-08-05 | 成都飞机工业（集团）有限责任公司 | Cutter failure monitoring method based on vibration signals |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS60207744A (en) * | 1984-03-29 | 1985-10-19 | Sumitomo Metal Ind Ltd | Detection of breakage of tool |
| CN1015093B (en) * | 1989-11-30 | 1991-12-18 | 清华大学 | Method and apparatus for monitoring and controlling acoustic emission cutter |
| CN1058164A (en) * | 1990-11-24 | 1992-01-29 | 西北工业大学 | Cutting-tool by sound emission inefficacy monitor |
-
1992
- 1992-09-29 CN CN92111137A patent/CN1045738C/en not_active Expired - Fee Related
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1298506C (en) * | 2002-07-01 | 2007-02-07 | 株式会社迪斯科 | Cutting tip monitoring device for cutting device |
| EP1484607A1 (en) * | 2003-06-02 | 2004-12-08 | Denso Corporation | Abnormality determination and estimation method for product of plastic working, and an abnormality determination and estimation device |
| US7054789B2 (en) | 2003-06-02 | 2006-05-30 | Denso Corporation | Abnormality determination and estimation method for product of plastic working, and an abnormality determination and estimation device |
| CN105058165A (en) * | 2015-08-08 | 2015-11-18 | 电子科技大学 | Tool abrasion loss monitoring system based on vibration signals |
| CN109696478A (en) * | 2018-11-27 | 2019-04-30 | 福建省嘉泰智能装备有限公司 | A kind of monitoring method of combination acoustic emission energy and lathe information |
| CN113369994A (en) * | 2021-06-30 | 2021-09-10 | 温州大学 | Cutter state monitoring method in high-speed milling process |
| CN114850969A (en) * | 2022-07-08 | 2022-08-05 | 成都飞机工业（集团）有限责任公司 | Cutter failure monitoring method based on vibration signals |
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| CN1045738C (en) | 1999-10-20 |
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