CN1128040C - Intelligent in-situ machine tool cutting flutter controlling method and system - Google Patents
Intelligent in-situ machine tool cutting flutter controlling method and system Download PDFInfo
- Publication number
- CN1128040C CN1128040C CN 01144486 CN01144486A CN1128040C CN 1128040 C CN1128040 C CN 1128040C CN 01144486 CN01144486 CN 01144486 CN 01144486 A CN01144486 A CN 01144486A CN 1128040 C CN1128040 C CN 1128040C
- Authority
- CN
- China
- Prior art keywords
- flutter
- omen
- cutting
- signal
- input
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000005520 cutting process Methods 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000011065 in-situ storage Methods 0.000 title claims description 4
- 239000000463 material Substances 0.000 claims abstract description 37
- 230000001133 acceleration Effects 0.000 claims abstract description 32
- 230000005684 electric field Effects 0.000 claims description 30
- 230000008569 process Effects 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 8
- 230000002463 transducing effect Effects 0.000 claims description 7
- 238000013507 mapping Methods 0.000 claims description 6
- 108010076504 Protein Sorting Signals Proteins 0.000 claims description 4
- 230000004069 differentiation Effects 0.000 claims description 4
- 101150066718 FMOD gene Proteins 0.000 claims description 2
- 238000013528 artificial neural network Methods 0.000 claims description 2
- 230000008676 import Effects 0.000 claims description 2
- 230000001932 seasonal effect Effects 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 17
- 238000004519 manufacturing process Methods 0.000 abstract description 8
- 230000033228 biological regulation Effects 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 11
- 238000005516 engineering process Methods 0.000 description 9
- 230000004044 response Effects 0.000 description 7
- 230000008859 change Effects 0.000 description 6
- 238000003754 machining Methods 0.000 description 6
- 238000013461 design Methods 0.000 description 5
- 238000001514 detection method Methods 0.000 description 4
- 238000002474 experimental method Methods 0.000 description 4
- 230000007704 transition Effects 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000011217 control strategy Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 230000005764 inhibitory process Effects 0.000 description 3
- 239000002184 metal Substances 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000009413 insulation Methods 0.000 description 2
- 230000008929 regeneration Effects 0.000 description 2
- 238000011069 regeneration method Methods 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 238000003756 stirring Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- RSWGJHLUYNHPMX-UHFFFAOYSA-N Abietic-Saeure Natural products C12CCC(C(C)C)=CC2=CCC2C1(C)CCCC2(C)C(O)=O RSWGJHLUYNHPMX-UHFFFAOYSA-N 0.000 description 1
- 206010038743 Restlessness Diseases 0.000 description 1
- KHPCPRHQVVSZAH-HUOMCSJISA-N Rosin Natural products O(C/C=C/c1ccccc1)[C@H]1[C@H](O)[C@@H](O)[C@@H](O)[C@@H](CO)O1 KHPCPRHQVVSZAH-HUOMCSJISA-N 0.000 description 1
- 229920002472 Starch Polymers 0.000 description 1
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 239000000654 additive Substances 0.000 description 1
- 230000004323 axial length Effects 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 239000011551 heat transfer agent Substances 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 230000010358 mechanical oscillation Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000005086 pumping Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 235000019698 starch Nutrition 0.000 description 1
- 239000008107 starch Substances 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
- WWJZWCUNLNYYAU-UHFFFAOYSA-N temephos Chemical compound C1=CC(OP(=S)(OC)OC)=CC=C1SC1=CC=C(OP(=S)(OC)OC)C=C1 WWJZWCUNLNYYAU-UHFFFAOYSA-N 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- KHPCPRHQVVSZAH-UHFFFAOYSA-N trans-cinnamyl beta-D-glucopyranoside Natural products OC1C(O)C(O)C(CO)OC1OCC=CC1=CC=CC=C1 KHPCPRHQVVSZAH-UHFFFAOYSA-N 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Landscapes
- Automatic Control Of Machine Tools (AREA)
Abstract
The present invention relates to an intelligent on-line machine tool cutting flutter controlling method and a system thereof, which is used for the field of precision flexible manufacturing processing. The system comprises an intelligent cutter spindle, a voltage changer, a computer, a charge amplifier and an acceleration sensor, wherein the cutter spondle is designed by utilizing an electric rheopectic material and can quickly on-line regulate dynamic characteristics, the voltage changer is used for exerting a high-voltage field on the electric rhepectic material, and the acceleration sensor is used for inputting acceleration signals. The dynamic characteristic on-line regulation of the processing system is carried out mainly by a mechanical structure comprising the electric rheopectic material. The control method comprises system initialization, flutter identification and flutter control. The method uses a CDM method to identify the flutter on line, and the generated flutter omen is identified in 50 ms. The present invention can quickly forecast the cutting flutter omen according to cutting flutter signals, and regulate the dynamic characteristics of the cutting system on line and in time according to the implication information of acceleration signals to restrain the cutting flutter in the blade. The processing flutter marks can not be generated on the workpiece surface, and the processing quality and the efficiency can be ensured.
Description
Technical field:
Intelligent in-situ machine tool cutting flutter controlling method belongs to automation of machinery manufacture and intelligent manufacturing field, the processing stability that mainly belongs to cut for the guaranty money, avoid occurring in the process autovibration-flutter, guarantee processing parts quality and cutting tool life-span.This technology is applicable to the accurate flexible manufacturing system of processing that automaticity is high, can be widely used in precision machined Aeronautics and Astronautics of needs and automobile and other industries.
Background technology:
Cutting-vibration belongs to autovibration, is ferocious relative vibration between the cutter that produces in working angles of Metal Cutting Machine Tool and the workpiece.The rule of reason that it produces and generation, development and cutting process itself and Metal Cutting Machine Tool dynamic perfromance all have inherent essential connection, and influence factor is a lot, is a very complicated mechanical oscillation phenomenon.Along with the factory automation development, the flexibility of machining requires cut to carry out to different workpiece with under the different operating condition, therefore the appearance that often can not fundamentally stop the flutter phenomenon by the method for flutter prevention and control is so carry out the in-service monitoring forecast and control becoming a guardian technique that improves cutting system stability to it.
Because it is sudden and uncertain that cutting system generation flutter has, very of short duration from normally being cut to the time history that flutter takes place, generally within the hundreds of millisecond.So cutting-vibration is carried out in-service monitoring and controls is very difficult.Comparatively successful method can be classified as two classes at present: class employing is carried out Flutter Control to the method that cutting system carries out system modelling, and class employing is carried out online adjustment to cut parameter (speed of mainshaft, the amount of feeding, cutting depth etc.) and suppressed flutter.But, the complicacy of cutting processing system sets up accurately very difficulty of system mathematic model because making, the serious hysteresis of cut machinery system response simultaneously makes above-mentioned two class methods all can not carry out On-line Control to cutting stability well, can not fully the flutter phenomenon be eliminated.
Summary of the invention:
Fundamental purpose of the present invention just is aimed at flexible manufacturing cell, develops a kind of method of the general cutting-vibration On-line Control that does not influence manufacturing cell's flexibility degree, to improve crudy and production efficiency.The present invention is primarily aimed at and overcomes the obstacle that the technology of flutter On-line Control in the past is difficult to break through, and utilizes intellectual material according to the On-line Control of sensor acquisition cutting vibration information regulation and control physical construction dynamic perfromance with the realization cutting-vibration.
Design philosophy of the present invention is based on theory that control cutting system time change step response improves cutting stability as its theoretical foundation, and is very little to the change of original machine cut system when specifically implementing.Original cutting system mainly is made up of lathe, workpiece and tooling system three parts, the present invention is just in the design of the key components and parts that is influencing cutting system stability (for example boring bar, handle of a knife etc.), adopted the method for embedded intellectual material, the characteristics of utilizing the intellectual material dynamic perfromance to regulate and control real-time, the dynamic perfromance of whole system of processing is carried out online quick regulation and control, and the condition of eliminating the flutter generation is to avoid the generation of cutting-vibration.
Control method of the present invention is referring to process flow diagram 1, and CDM ONLINE RECOGNITION flutter prediction is referring to CDM information flow Fig. 3 and CDM ONLINE RECOGNITION flutter omen process flow diagram 2, this method is characterised in that the cutting vibration acceleration signal of gathering according in real time, the dynamic perfromance of automatically controlled rheological characteristics rapid adjustment cutting vibration system of utilizing er material in the cutting system is to avoid cutting the generation of autovibration one flutter, and it may further comprise the steps successively:
It may further comprise the steps successively:
(1) system initialization is set: the initialization electric field strength E
0,
Sample frequency F
s
(2) flutter identification: acceleration signal input flutter recognizer module CDM, circulation is differentiated and operation, until identify the flutter omen;
(3) Flutter Control:
(a) in a single day identify the flutter omen, calculate and get flutter frequency F this moment
C, order: F
Post=F
C, F
Post: the flutter frequency when not applying control signal;
(b) apply control electric field intensity: E=E
0KV/mm;
(c) again acceleration signal is imported above-mentioned CDM module, whether differentiation flutter this moment omen still exists, disappear as the flutter omen, and electric field strength E=0 that order applies, program changes step (2) continuation operation over to then.Still exist as the flutter omen, program is moved downwards;
(d) calculate flutter frequency F this moment
C
(e) differentiate F
CWhether greater than F
Post
If F
C＞F
Post, then making E=E-0.1 KV/mm, measuring acceleration moves by (c)～(d) again;
If F
C＜F
Post, then making E=E+0.1 KV/mm, measuring acceleration moves by (c)～(d) again;
(4) said procedure flow process circular flow always in cutting process finishes up to cutting process, and this control program finishes;
Above-mentioned CDM module is carried out following steps successively:
(1) input sample acceleration signal r
i
(2) with r
iInput LO-RBF type neural network structure program module, operation according to the following steps successively:
(a) calculate transducing signal seasonal effect in time series probability density function at r
iEstimated value f (the r at place
i) and probability density function single order differentiation function at r
iThe estimated value at place
Promptly calculate:
(b) structure new signal sequence G
n, contrast imports transducing signal sampled value r with each
iThe time get G
nMiddle element g
iFor:
(3) with g
iInput Fuzzy ARTMap flutter omen identification module, operation according to the following steps successively:
(a) whether differentiate i greater than 128, differentiate and circular flow,
(b) be under the 1000Hz in sample frequency, differentiate i＞128 o'clock, judge every 50 milliseconds whether a flutter takes place, promptly fmod (i, 50)=0 o'clock carries out a flutter omen differentiation,
(c) take-off time burst: promptly take out preceding 127 points in the G sequence, with currency g
iConstitute 128 timing signal sequence G
128, i.e. G
128={ g
I-127, g
I-126..., g
I-1, g
i,
(d) with G
128Input fast fourier transform subroutine, promptly the FFT subroutine obtains G
128Fourier transform sequence F
128, F
128=FFT (G
128),
(e) get F
128The mould of preceding 64 elements of sequence constitutes new sequence S
64, S
64In the energy density S of each element representative each Frequency point in analyzing frequency range
i=| F
i| (i=1,2,3 ... 64),
(f) 64 dimension sequence S
64Input Fuzzy ARTMap neuroid subroutine,
(g) differentiate the bivector C that Fuzzy ARTMap neuroid is exported
2:
C
2＝Fuzzy?ARTMap(S
64)
If: C
2=0,1} represents no flutter omen,
If: C
2=1, and 0}, expression has the flutter omen,
In the above-mentioned FuzzyARTMap supervised learning stage, the M that when the cutting-vibration omen exists, collects 64 dimensional signal S
64With the M that represents the flutter omen two-dimensional vector C
2, be set at 1,0}, the ART of fan-in network respectively simultaneously
aSubmodule and ART
bSubmodule; The 64 dimensional signal S that collect when equally, N flutter omen do not exist
64N two-dimensional vector C with the no flutter omen of representative
2, be set at 0,1}, the ART of fan-in network respectively simultaneously
aSubmodule and ART
bSubmodule; Here, { 0,1} is corresponding to no flutter omen, and { 1,0} is corresponding to the flutter omen is arranged.By such supervised learning, at ART
aSubmodule and ART
bForm with weights between the submodule is set up mapping relations, guarantees that like this network can be online according to ART
aThe input of submodule differentiates in the acceleration sampled signal whether have the flutter omen.
The present invention introduces intellectual material in the On-line Control of metal cutting processing flutter, use the instant response ability of intellectual material inherent characteristic to electric control signal, not only overcome the shortcoming of cutting system physical construction to the control signal low-response, and this system carries out the online inhibition of flutter according to the heat transfer agent that obtains, and overcome the cutting system complicacy and is difficult to set up the difficulty of controlling models accurately.Therefore this invention has strengthened machining equipment and has adapted to the different processing objects and the ability of processing conditions, has improved the flexibility degree of machining equipment widely.
CDM ONLINE RECOGNITION flutter Application in Prediction mainly shows that based on lot of experiments the evolution of flutter has following characteristics in the process among the present invention:
(1) the flutter waveform is similar to resonance wave, and the growth of amplitude is the process of a gradual change, can be divided into initial flutter stage, flutter developing stage and abundant flutter stage.
(2) cutting vibration frequency is stabilized to the natural frequency near system gradually with the development of flutter.Vibrational energy distributed in frequency domain and transferred the arrowband energy distribution to by the broadband distribution this moment.
(3) in the initial flutter stage, vibration frequency has been stabilized to the natural frequency place of system, and this moment, vibration amplitude did not reach the maximum amplitude of flutter as yet.Reaching abundant flutter stage precontract in the flutter amplitude has 400 to 600 milliseconds or longer, and this just provides the quality time of identification and FEEDBACK CONTROL to supervisory system.
By These characteristics as can be known: can be used as the important omen that flutter takes place by the resonance wave characteristic of flutter and the arrowband feature of frequency domain.Flutter forecasting technique among the present invention just is based on above-mentioned analysis, adopts local optimum signal detection technology and neuroid technology in flutter developing stage identification flutter omen, carries out the flutter forecast, to reach purpose of the present invention.
Description of drawings:
Fig. 1: control method process flow diagram among the present invention, F
S=sample frequency, E=electric field intensity, E
0=initial electric field intensity, AS=acceleration signal, CDM=flutter recognizer module, F
C=flutter frequency, the flutter frequency when Fpost=does not apply control signal;
Fig. 2: CMD ONLINE RECOGNITION flutter omen process flow diagram;
Fig. 3: CMD information flow chart;
Fig. 4: system chart of the present invention, 1, computing machine, 2, mainboard PCI slot, 3, analog/digital digital-to-analog transition card, 4, analog input mouth, 5, the simulation delivery outlet, 6, charge amplifier, 7, acceleration transducer, 8, voltage changer, 9, intelligent boring bar (containing er material);
Fig. 5: system principle diagram of the present invention, a, interference, b, dynamic cutting force, c, steering order, d, vibratory response, 10, controller, 11, the mechanical vibrating system formed of lathe-workpiece-cutter, 12, working angles signal module;
Fig. 6: intelligent boring bar synoptic diagram, 13, support set, 14, positive electrode, 15, O shape circle, 16, insulation sleeve, 17, boring bar, 18, boring cutter, L
1, the boring bar length that is installed, L
2, the boring bar length that overhangs;
Fig. 7: acceleration transducer is settled and the boring system schematic, and 19, chuck, 20, the cutter head anchor clamps, 21, workpiece, 22, boring tool holder;
Fig. 8: surveillance signal transmission configuration schematic diagram of the present invention, the flat adapter of 23,8 passages;
Fig. 9: utilize intelligent boring bar to carry out the system and device synoptic diagram of boring processing;
Figure 10: information input synoptic diagram during the training of Fuzzy-ARTMAP neuroid off-line learning;
Figure 11: the information input and output synoptic diagram during Fuzzy-ARTMAP neuroid online forecasting flutter omen.
Embodiment:
The present invention can use in boring processing.Because boring is endoporus processing, so boring bar generally is designed to elongated cantilever beam structure, and poor rigidity is easy to stressed occuring bending and deformation, and flutter often can't be avoided when being subjected to dynamic cutting force.The present invention is in order to overcome the weakness that boring bar rigidity can't improve at all, when its structural design, added a kind of intellectual material---er material, by er material being applied the dynamic perfromance of the online change boring bar of electric field integral body, regulate and control the boring bar dynamic perfromance to avoid the generation of flutter according to transducing signal in conjunction with flutter online forecasting technology is online.
System wherein adopts accessory and mutual relationship to be described below according to Fig. 4 technology assembling routinely in the present embodiment:
(1) the computer CPU model is PII233, and mainboard has 3 PCI slots, in system as the carrier of data acquisition, flutter forecasting controlling software.
(2) analog/digital, the digital-to-analog transition card: model is HY-6070, inserts in the computer PCI slot, and its analog input end links to each other with the voltage output end of charge amplifier, and analog output links to each other with the Input voltage terminal of high-voltage variable parallel operation.
(3) charge amplifier: model is YE5858, and the quantity of electric charge of its input end degree of will speed up sensor output inserts, and is output as the voltage signal of representing acceleration signal, and output inserts A/D, the analog input end of D/A card.
(4) acceleration transducer: model is YE14103, and acceleration transducer is fixed on the cantilever end of boring bar by dowel screw, and output links to each other with the electric charge input end of charge amplifier.
(5) voltage changer: model is GYW-010, its function be 0～10000 volt high voltage for 0～5 volt low voltage transition will being exported by computing machine, its Input voltage terminal and A/D, the analog output of D/A card links to each other, and the two poles of the earth of its high-voltage output terminal link to each other with the both positive and negative polarity of er material in the boring bar respectively.
Of the present inventionly in the structural design of cutting system, er material is introduced the machining system critical component, develop a kind of intelligent boring bar that can directly control mechanical Structure dynamic characteristics by external electric signal.The structural design of intelligent boring bar is at changed the limited shortcoming of variation range that obtains based on the laminated girder construction of er material or the dynamic perfromance of hollow beam structure with control electric field intensity in the past, utilize er material to change the local support rigidity of boring bar root, improve the variation range of Structure dynamic characteristics greatly, do not influenced normal boring processing simultaneously.Boring bar as shown in Figure 6, positive electricity is the Steel Thin-Wall circle very, the support set part relative with positive electrode is as the negative pole (ground connection just) of er material, the electrode axial length is 20 millimeters, two electrode gaps are 0.5 millimeter.Between positive electrode and boring bar insulation sleeve is arranged, er material is fed in the cavity between positive electrode and negative electrode, and the sealing of er material guarantees by 2 O type circles.Support set and boring bar are fixed from orthogonal both direction by four hexagon socket head cap screws.Support set is installed in the knife rest in the boring system, L
1Equal 100 millimeters, L
2Equal 180 millimeters, boring bar overhang the part L/D ratio be 6: 1.Boring cutter is installed in end at boring bar.Adopt vibrator to boring bar exciting test shows the control electric field intensity variation range of er material during at 0 to 2000 volt/every millimeter the boring bar natural frequency 30 hertz variable quantity is arranged.
Boring system, acceleration transducer are settled as shown in Figure 7.Why acceleration transducer all adopts horizontal direction, mainly be to consider that cutting-vibration mostly is the flutter of regeneration type just, can know to have only just remarkable influence cutting force of the relative vibration displacement component of cutting depth direction cutter with workpiece by regeneration type flutter mechanism of production perpendicular to this direction of cutting surface.The evolution of the reflection cutting-vibration that proof cutting depth direction vibratory response and cutting force dynamic component can be sensitiveer than the transducing signal of other direction in the enforcement.Acceleration transducer is installed on the end of boring bar, because obvious in the vibratory response of end.
The digital signal flow process figure that computing machine is directly handled as shown in Figure 8.In process, the acceleration sensing signal is converted to voltage signal through the YE5853 charge amplifier with charge signal through the collection of YE14103 accelerometer, through 12 A/D transition cards of 8 passage flat cable adapters input, becoming can be by the direct digital signal of handling of computing machine then.
The configuration of er material considers that mainly er material is operated in the room temperature range, material system will have high dispersion stabilization, material will have big viscosity, elastic modulus change scope, and the electric rheological effect stability of material is high, and material will have low electric conductivity in addition.The basic configuration process is: starch and pumping fluid by mixing, are added an amount of rosin derivative and additional additives again, at room temperature stir minute with electric blender, stirring the back is uniform brown suspension liquid.This kind material is placed under blow-by, 15～40 ℃ ambient temperature range, layering, deposited phenomenon do not occur, and electric rheological effect is obvious.
Cutting-vibration control system device synoptic diagram is shown in 9.The boring system is based upon on the CA6140 lathe, and the holding of workpiece cantilever is on main shaft, and boring cutter is installed on the knife rest.Supervisory system is the PII233 of an association computing machine of being furnished with the HY6070 data collecting card.The vibration signal of gathering is the acceleration signal of boring bar end horizontal direction.According to vibration signal and the cutting-vibration On-line Control strategy gathered, the output channel of capture card is sent control signal and is given the GYW-010 voltage changer, voltage changer is applied to the electric field of a certain size electric field intensity between both positive and negative polarity in the boring bar according to control signal, regulates and control the dynamic perfromance of boring bar with this by changing er material performance between both positive and negative polarity.Such configuration can guarantee that adjusting electric power output voltage according to the cutting vibration signal very easily carries out the online inhibition of cutting-vibration with the dynamic perfromance of control boring bar.
The operating process of system control method as shown in Figure 1.System is made up of three parts: system initialization, flutter identification and Flutter Control.
System initialization part mainly be with comprise sample frequency, by the parameter input systems such as initial electric field intensity of experience decision.Sample frequency is according to required signal frequency range and sampling thheorem decision, and initial electric field intensity is selected, determined by experiment according to the cut condition.Arrive in case the flutter omen is predicted, initial electric field intensity is applied in to er material.Like this, can the saving system be used to search for the time of optimum electric field intensity.
Giving of flutter identification division involving vibrations signal (for example acceleration, vibration displacement etc.) handled and two sport technique segments of the seizure of flutter omen in spectrogram.Giving of transducing signal handled along with working angles carries out in real time.The spectrogram of vibration signal is transfused to the network to Fuzzy ARTMap every 50 milliseconds, to be used for the identification of flutter omen.System's continuous monitoring vibration signal in working angles, in case the flutter omen is identified, flutter On-line Control program will be activated.
Third part is finished the function of flutter On-line Control, is divided into two modules.In first module, at first a peak value searching program begins to find out flutter frequency F in spectrogram
C, make F
PostEqual F
CThen, computing machine sends steering order and allows the control power supply of er material apply initial electric field intensity to give er material.Although initial electric field intensity is according to machining condition, by the optimum value that experiment is obtained, because the complicacy of cutting system, the on-line search optimum electric field intensity is still necessary sometimes.Therefore, when the flutter omen still existed after initial electric field intensity applies, second module was activated the continuous online adjustment electric field intensity of beginning and disappears up to the flutter omen.If the flutter omen disappears, program jumps to flutter ONLINE RECOGNITION part, and the electric field intensity that imposes on er material simultaneously reverts to zero electric field intensity.
In second module, adopted a feedback control strategy to adjust the generation of electric field intensity, inhibition flutter.Control strategy is as shown in Figure 7: as the flutter omen still exists after applying initial electric field intensity, then at first calculates flutter frequency F
C, as flutter frequency F this moment
CGreater than the flutter frequency F that applies before the electric field intensity
Post, then reduce to put on the electric field intensity of er material, if F
CBe not more than F
Post, then increase the electric field intensity that puts on er material.And then gather the cutting vibration signal, and judge whether the flutter omen exists, still have the possibility of generation as there being the explanation flutter, calculate flutter frequency so once more, according to said process repeated calculation, adjusting electric field intensity, disappear up to the flutter omen.
The information flow of flutter identification forecasting technique as shown in Figure 3.At first, degree of will speed up signal is adopted into computing machine by the A/D conversion, and it is because the boring flutter mainly occurs near the boring bar natural frequency that sample frequency is set at 1000Hz, is generally between 150～400Hz, so in order to reach satisfied time-frequency transformation result, sample frequency is taken as 1000Hz.
Then, degree of will speed up burst gives processing through the LO-RBF detection techniques, produces new burst.Adopting LO-RBF detection techniques purpose is to increase flutter omen signal---the intensity of resonance signal in the ground unrest.When the burst of LO-RBF detection techniques reconstruct is being done fast fourier transform, can get less sampling number and can manifest its arrowband feature among the signal spectrum figure in the flutter starting stage.Can reduce a large amount of sampling times that data are got that is used for like this.The present invention can reach the arrowband energy distribution feature that manifests the flutter omen in the acceleration frequency spectrum in the boring flutter starting stage through testing the fast fourier transform that determine to adopt at 128.Then, utilize Fuzzy Artmap network model that fuzzy theory and adaptive resonance neuroid technology combine, come the stability of working angles is judged as pattern classifier.When the supervised learning of network, the input of network be respectively the analysis frequency range that obtains after the FFT conversion (the mould vector A (64 dimension) of 1～500Hz) each Frequency point and represent the appearance of flutter omen and do not have flutter bivector B (0, and 1} with 1,0}).Behind learning success, the mapping relations of determining between vectorial A and vectorial B, have been set up.In the online forecasting stage, the real-time online acquired signal that is input as of network is carried out mould vector A after FFT changes after treatment, is output as the result that the flutter omen is forecast.
This technology is made accurate forecast can reaching on the flutter forecast speed in back 50ms appears in the flutter omen.
What adopted among the present invention is Fuzzy ARTMap ARTOICAL NEURAL NETWORK MODEL.The Fuzzy-ARTMap network that adopts in the supervised learning stage is (to be respectively ART by a pair of Fuzzy-ART neuroid
a, ART
b) and ART
a, ART
bBetween the mapping controller form.Here, ART
aInput signal be 64 dimensional signal S
64, the frequency range (energy density of each Frequency point in 1～500Hz) is analyzed in representative respectively.ART
bBe input as 2 dimensional signal C
2, be respectively 0,1} and 1,0}, they have represented flutter omen and no flutter omen respectively.
The study of network is carried out under off-line state, referring to Figure 10.By the cutting experiment under different machining conditions, obtain flutter by not having to the sample sequence that acceleration signal in the process is arranged, it is become G through the LO-RBF network reconfiguration
nSequence is to G
n128 FFT conversion are carried out in the sequence segmentation, with M 64 dimensional vector S of gained
64As ART
aThe input vector group.Judge the moment that the flutter omen occurs according to the depth of surface of the work cutting chatter mark then, the input vector component is become two parts: preceding K is steadily cutting, ART when promptly not having the flutter phenomenon
aThe input vector group; The ART that (M-K) is individual in the back when existing for the flutter omen
aInput vector.With above-mentioned two parts ART
aThe input vector group corresponding, ART
bInput vector group C
2Be respectively 0,1} and 1,0}.Fuzzy-ARTMAP adopts fuzzy minimax learning rules to increase progressively study, behind the learning success, passes through ART
a, ART
bAnd ART
a, ART
bBetween the weight vector W that couples together of mapping controller
j a, W
k bAnd W
j AbAt ART
aAnd ART
bBetween set up mapping relations.
The online forecasting stage is referring to Figure 11.This stage, ART
aInput signal be the 64 dimensional signal S that online in real time is obtained
64, ART
bWhen no flutter omen export C as output terminal this moment
2So that 0,1}, output C when the flutter omen
2So that 1,0}.
Claims (1)
1, a kind of intelligent in-situ machine tool cutting flutter controlling method, it is characterized in that cutting vibration acceleration signal according to real-time collection, the dynamic perfromance of automatically controlled rheological characteristics rapid adjustment cutting vibration system of utilizing er material in the cutting system is to avoid cutting the generation of autovibration-flutter, and it may further comprise the steps successively:
(1) system initialization is set: the control electric field strength E of initialization er material
0,
Sample frequency F
s
(2) flutter identification: acceleration signal input flutter recognizer module CDM, circulation is differentiated and operation, until identify the flutter omen;
(3) Flutter Control:
(a) in case identify the flutter omen, calculating flutter frequency F at this moment
C, order: F
Post=F
C, F
Post: the flutter frequency when not applying control signal;
(b) er material is applied control electric field intensity: E=E
0KV/mm;
(c) again acceleration signal is imported above-mentioned CDM module, differentiate flutter this moment omen and whether still exist, disappear as the flutter omen, electric field strength E=0 that order applies, program changes step (2) continuation operation over to then, still exists as the flutter omen, and program is moved downwards;
(d) calculate flutter frequency F this moment
C
(e) differentiate F
CWhether greater than F
PostIf F
C＞F
Post, then making E=E-0.1KV/mm, measuring acceleration moves by (c)～(d) again; If F
C＜F
Post, then making E=E+0.1KV/mm, measuring acceleration moves by (c)～(d) again;
(4) said procedure flow process circular flow always in cutting process finishes up to cutting process, and this control program finishes; Above-mentioned CDM module is carried out following steps successively:
(1) input sample acceleration signal r
i
(2) with r
iInput LO-RBF type neural network structure program module, operation according to the following steps successively:
(a) calculate transducing signal seasonal effect in time series probability density function at r
iEstimated value f (the r at place
i) and probability density function single order differentiation function at r
iThe estimated value at place
Promptly calculate:
(b) structure new signal sequence G
n, contrast imports transducing signal sampled value r with each
iThe time get G
nMiddle element g
iFor:
(3) with g
iInput fuzzy self-adaption resonance neuroid, promptly the FuzzyARTMap neuroid carries out the identification of flutter omen, successively operation according to the following steps:
(a) whether differentiate i greater than 128, differentiate and circular flow,
(b) be under the 1000Hz in sample frequency, differentiate i＞128 o'clock, judge every 50 milliseconds whether a flutter takes place, promptly work as fmod (i, 50)=0 o'clock, cutting-vibration is once differentiated,
(c) take-off time burst: promptly take out preceding 127 points in the G sequence, with currency g
iConstitute 128 timing signal sequence G
128, i.e. G
128={ g
I-127, g
I-126..., g
I-1, g
i,
(d) with G
128Input fast fourier transform subroutine, promptly the FFT subroutine obtains G
128Fourier transform sequence F
128, F
128=FFT (G
128),
(e) get F
128The mould of preceding 64 elements of sequence constitutes new sequence S
64, S
64In the energy density degree S of each element representative each Frequency point in analyzing frequency range
i=| F
i| (i=1,2,3 ... 64),
(f) 64 dimension sequence S
64Input FuzzyARTMap neuroid subroutine,
(g) differentiate the bivector C that the FuzzyARTMap neuroid is exported
2:
C
2＝Fuzzy?ARTMap(S
64)
If: C
2=0,1} represents no flutter omen,
If: C
2=1, and 0}, expression has the flutter omen,
In the above-mentioned FuzzyARTMap supervised learning stage, the M that when the cutting-vibration omen exists, collects 64 dimensional signal S
64With the M that represents the flutter omen two-dimensional vector C
2, be set at 1,0}, the ART of fan-in network respectively simultaneously
aSubmodule and ART
bSubmodule; The 64 dimensional signal S that collect when equally, N flutter omen do not exist
64N two-dimensional vector C with the no flutter omen of representative
2, be set at 0,1}, the ART of fan-in network respectively simultaneously
aSubmodule and ART
bSubmodule; Here, { 0,1} is corresponding to no flutter omen, and { 1,0} is corresponding to the flutter omen is arranged; By such supervised learning, at ART
aSubmodule and ART
bForm with weights between the submodule is set up mapping relations, guarantees that like this network can be online according to ART
aThe input of submodule differentiates in the acceleration sampled signal whether have the flutter omen.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 01144486 CN1128040C (en) | 2001-12-19 | 2001-12-19 | Intelligent in-situ machine tool cutting flutter controlling method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 01144486 CN1128040C (en) | 2001-12-19 | 2001-12-19 | Intelligent in-situ machine tool cutting flutter controlling method and system |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNA031370462A Division CN1515382A (en) | 2001-12-19 | 2001-12-19 | Machine cutting flutter on-line intelligent control system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1349877A CN1349877A (en) | 2002-05-22 |
| CN1128040C true CN1128040C (en) | 2003-11-19 |
Family
ID=4677618
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 01144486 Expired - Fee Related CN1128040C (en) | 2001-12-19 | 2001-12-19 | Intelligent in-situ machine tool cutting flutter controlling method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN1128040C (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110202170A (en) * | 2019-06-10 | 2019-09-06 | 北京工业大学 | A kind of variation rigidity vibration self-inhibiting intelligent live center |
Families Citing this family (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE10325746A1 (en) * | 2003-03-17 | 2004-10-21 | Infineon Technologies Ag | Operating method for detecting an operating status in direct current voltage motor e.g. in passenger safety system in motor vehicle, involves making an analog signal available for an operating status |
| DE102005023317A1 (en) * | 2005-05-20 | 2006-11-23 | P & L Gmbh & Co. Kg | Method for vibration optimization of a machine tool |
| CN100355521C (en) * | 2005-12-23 | 2007-12-19 | 浙江大学 | Vibration self-suppressed intelligent boring bar component based on magnetorheological fluid |
| JP5234772B2 (en) * | 2008-10-28 | 2013-07-10 | オークマ株式会社 | Vibration suppression method and apparatus for machine tool |
| JP4942839B2 (en) * | 2010-09-10 | 2012-05-30 | 株式会社牧野フライス製作所 | Chatter vibration detection method, chatter vibration avoidance method, and machine tool |
| US9421657B2 (en) | 2011-09-14 | 2016-08-23 | Jtekt Corporation | Machining control apparatus and machining control method thereof |
| US10191017B2 (en) | 2012-07-06 | 2019-01-29 | Jtekt Corporation | Dynamic characteristic calculation apparatus and its method for machine tool |
| EP3052997B1 (en) * | 2013-10-01 | 2018-12-12 | Robe Lighting, Inc | Resonance movement dampening system for an automated luminaire |
| CN105793765B (en) | 2014-10-01 | 2019-12-13 | 罗布照明公司 | Collimation and homogenization system for LED lighting device |
| CN105436981B (en) * | 2015-09-28 | 2018-02-16 | 上海诺倬力机电科技有限公司 | Flutter Control method and numerical control processing apparatus based on vibration monitoring |
| CN105739438A (en) * | 2016-04-28 | 2016-07-06 | 上海交通大学 | Method for intelligently inhibiting machining vibration |
| CN106363450B (en) * | 2016-09-07 | 2018-10-09 | 北京理工大学 | A kind of online suppressing method of milling parameter |
| CN114489167B (en) * | 2021-12-17 | 2023-04-18 | 中国船舶重工集团公司第七一九研究所 | Warship rotary mechanical equipment feedforward vibration control system based on supervised learning |
| CN115570160A (en) * | 2022-06-22 | 2023-01-06 | 湖南工业大学 | Slender shaft turning flutter stability prediction method with follow-up tool rest |
-
2001
- 2001-12-19 CN CN 01144486 patent/CN1128040C/en not_active Expired - Fee Related
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110202170A (en) * | 2019-06-10 | 2019-09-06 | 北京工业大学 | A kind of variation rigidity vibration self-inhibiting intelligent live center |
| CN110202170B (en) * | 2019-06-10 | 2020-07-03 | 北京工业大学 | Variable-rigidity self-vibration-restraining intelligent live center |
Also Published As
| Publication number | Publication date |
|---|---|
| CN1349877A (en) | 2002-05-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN1128040C (en) | Intelligent in-situ machine tool cutting flutter controlling method and system | |
| Schmitz et al. | Improving high-speed machining material removal rates by rapid dynamic analysis | |
| Liu et al. | On-line chatter detection using servo motor current signal in turning | |
| Teti et al. | Advanced monitoring of machining operations | |
| CN110153801A (en) | A kind of cutting-tool wear state discrimination method based on multi-feature fusion | |
| CN102069245B (en) | Interval type-2 fuzzy logic-based two-order fuzzy control method for micro electrical discharge | |
| CN110263474A (en) | A kind of cutter life real-time predicting method of numerically-controlled machine tool | |
| CN111618658B (en) | Main shaft rotating speed self-adaptive adjusting method for flutter-free efficient milling | |
| CN108638076B (en) | Six-degree-of-freedom serial robot milling three-dimensional stability prediction method | |
| CN112405072B (en) | On-line monitoring method and device for cutting chatter of machine tool | |
| Wang et al. | Early chatter identification of robotic boring process using measured force of dynamometer | |
| CN112372371B (en) | Method for evaluating abrasion state of numerical control machine tool cutter | |
| CN1515382A (en) | Machine cutting flutter on-line intelligent control system | |
| CN103323200B (en) | Acquirement method of tool nose point modal parameters relative to speed in principal shaft dry running stimulation | |
| CN110346130A (en) | A kind of boring flutter detection method based on empirical mode decomposition and time-frequency multiple features | |
| Sener et al. | Intelligent chatter detection in milling using vibration data features and deep multi-layer perceptron | |
| CN111723765A (en) | Micro milling cutter abrasion monitoring method based on variational modal decomposition and improved BP neural network | |
| CN115755758A (en) | Machine tool machining control method based on neural network model | |
| CN112733298B (en) | Machining performance evaluation method of series-parallel robot at different poses based on spiral hole milling | |
| Tonshoff et al. | Application of fast Haar transform and concurrent learning to tool-breakage detection in milling | |
| CN114227382A (en) | Cutter damage monitoring system and method based on novel capsule network | |
| CN114819311B (en) | Construction method of numerical control machining surface roughness prediction model | |
| CN109446721B (en) | Machine tool process interaction algorithm based on identifier software thread execution sequence arrangement | |
| CN109531270B (en) | Modal testing method of numerical control machine tool feeding system based on built-in sensor | |
| CN1247233A (en) | Intelligent method and equipment for removing stress by harmonic oscillation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C06 | Publication | ||
| PB01 | Publication | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C17 | Cessation of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20031119 Termination date: 20111219 |