CN104076734A - Milling flutter online optimizing method - Google Patents

Abstract

A milling flutter online optimizing method includes the following steps that S10, a milling regeneration flutter kinetic model is established; S20, a system starts to work, online monitoring based on the stability prediction technology is conducted according to the kinetic model, whether the system predicts an unstable machining phenomenon is judged, and the S30 step is executed if the system predicts the unstable machining phenomenon; S30, a control system automatically conducts stable zone optimizing according to the rotating speed of a spindle; S40, after the control system finishes stable zone optimizing, the step S20 is executed again to judge the optimizing control effect until machining is finished. According to the method, by establishing the milling kinetic model, stability prediction machining parameter automatic search control is achieved, milling parameters do not need to be repeatedly adjusted, the probability that stability theoretical analysis does not conform to an actual work state is avoided, and the defects of theoretical research are overcome.

Description

A kind of Milling Process flutter online optimizing method

[technical field]

The invention belongs to field of machining, relate to a kind of online optimizing method, be specifically related to a kind of Milling Process flutter online optimizing method.

[background technology]

In recent years, along with the development of control technology and deepening continuously of cutting stability mechanism research, stability control technology is developed to from early stage vibration compensation control method, cutting system dynamics modification method stability prediction, the forecast control method of adjusting based on cutting parameter.

Adjust cutting parameter and control method because it is workable, inhibition of vibration is good, that cutting stability is controlled emphasis and the focus of studying in recent years, its goal in research for by the online adjustment speed of mainshaft, axially, radially, the tangential degree of depth and cutter be milled into cutting parameters such as milling out angle, guarantees that cutting carries out in conditional stability district.Development along with on-line monitoring technique, study hotspot has turned to cutting parameter to control the stability Fast Prediction control technology combining with online monitoring chatter, prediction based on cutting stability limiting threshold value and limit of stability carry out stability distinguishing and control response fast, realize Real-Time Monitoring and control to the flutter that happens suddenly in processing.

On-line monitoring adopts the stepless change of driven by servomotor to control the automatic search that numerically-controlled machine is realized machined parameters at present, this on-line monitoring method lacks the theoretical direction of stablizing machined parameters, exist blindly and adjust, repeatedly machined parameters automatic search possibility not in place still, affects the opportunity of controlling flutter adversely.And cutting parameter is adjusted, can follow the drastic change of motor momentary current, therefore must make a choice to the load-bearing capacity of the ability of supply line, power amplifier and motor.In addition, repeatedly the adjustment of cutting parameter has increased the workload of digital control system, causes a series of chain reactions and the not expected emergency situations such as other control tasks cannot complete on time.

[summary of the invention]

The present invention is directed to the problems referred to above a kind of Milling Process flutter online optimizing method is provided, the method is by setting up Milling Process kinetic model, realizing the automatic search of the machined parameters of prediction of stability controls, without cutting parameter is repeatedly adjusted, avoid the possibility that stability theory analysis and actual working state are not inconsistent, made up the deficiency of theoretical research.

In order to achieve the above object, a kind of Milling Process flutter online optimizing method of the present invention, comprises the following steps:

S10: set up milling Regenerative Chatter kinetic model;

S20: system is started working, carries out the on-line monitoring based on prediction of stability technology according to described kinetic model, judges the system unstable processing phenomenon of whether reporting for work in advance, and the unstable processing phenomenon if system is reported for work in advance, performs step S30;

S30: control system is carried out stable region optimizing according to the speed of mainshaft automatically;

S40: control system completes after the optimizing of stable region, returns to effect judgement that step S20 carries out optimizing control until process finishing.

Especially, described kinetic model according to following Formula:

$a_{\mathrm{plim}}=\frac{1}{2Z{K}_t\left|A_0\mathrm{Re}\right[H{\left(\mathrm{j\ω}\right)}_r\left]\right|}---\left(2\right)$

$n=\frac{{60\mathrm{\ω}}_C}{Z[({2J}_r+1)\mathrm{\π}-2\arctan |\frac{\mathrm{Re}\left[H{\left(\mathrm{j\ω}\right)}_r\right]}{\mathrm{Im}\left[H{\left(\mathrm{j\ω}\right)}_r\right]}\left|\right]}---\left(3\right)$

Wherein, a _plimfor axial cutting-in; Z is cutter tooth number; A ₀for dynamic milling force matrix of coefficients, dimensionless; K _tfor tangential Milling Force coefficient, N/m; N is the speed of mainshaft; ω _cfor flutter frequency, HZ; Jr is the whole wave number that cutter tooth is stayed the whole chatter marks of cutting surface in cycle T; Re[H (j ω) _r] be cutter workpiece coupled system r rank transport function real parts, m/N; Im[H (j ω) _r] be cutter workpiece coupled system r rank transport function imaginary parts, m/N.

Especially, described step S20 specifically comprises the following steps:

S201: if do not report for work in advance unstable processing phenomenon, proceed the on-line monitoring based on prediction of stability technology, judge the system unstable processing phenomenon of whether reporting for work in advance, the unstable processing phenomenon if system is reported for work in advance, perform step S202, if do not report for work in advance unstable processing phenomenon, proceed the on-line monitoring based on prediction of stability technology, until machine;

S202: control system is carried out stable region optimizing according to the speed of mainshaft automatically;

Especially, described step S30 specifically comprises the following steps:

S301: system is reported for work after unstable processing phenomenon in advance, if when the speed of mainshaft is positioned at Liang Er lobe intersection, carries out stabilized zone optimizing by reducing axial cutting-in method;

When if the speed of mainshaft is positioned at the rising edge place of theoretical prediction ear lobe, by increasing rotating speed, carry out stabilized zone optimizing;

When if the speed of mainshaft is positioned at the falling edge of theoretical prediction ear lobe, by reducing rotating speed, carry out stabilized zone optimizing.

Especially, described step S40 specifically comprises the following steps:

S401: if control system searches optimum stable region, return to the judgement that step S20 carries out optimizing effect, if control system does not search optimum stable region, return to step S30 and proceed stable region optimizing, until machine.

Compared to prior art, it is under process on-line monitoring state that the online forecasting of cutting stability is controlled, the signals such as the vibration of obtaining, power are carried out to the Characteristic Extraction based on determination of stability, to the flutter of reporting for work in advance, take the control strategy of Cutting Parameters automatic search to suppress flutter.Its advantage is: by the real-time of on-line monitoring lathe duty, avoided the possibility that stability theory analysis and actual working state are not inconsistent, made up the deficiency of theoretical research.

[accompanying drawing explanation]

Fig. 1 is the constant lower consideration a of the Milling Force coefficient of a kind of Milling Process flutter of the present invention online optimizing embodiment of the method _p, n multiple degrees of freedom Regenerative Chatter stability diagram;

When being the Milling Force coefficient of a kind of Milling Process flutter of the present invention online optimizing embodiment of the method, Fig. 2 becomes the lower a of consideration _p, n, a _ethree-dimensional stability figure;

When being the Milling Force coefficient of a kind of Milling Process flutter of the present invention online optimizing embodiment of the method, Fig. 3 becomes the lower a of consideration _p, n, f _zthree-dimensional stability figure;

Fig. 4 is MRR and n and a of a kind of Milling Process flutter of the present invention online optimizing embodiment of the method _pgraph of a relation;

Fig. 5 is MRR ' and a of a kind of Milling Process flutter of the present invention online optimizing embodiment of the method _pand a _egraph of a relation;

Fig. 6 is the process flow diagram of a kind of Milling Process flutter of the present invention online optimizing method;

Fig. 7 is the machined parameters online optimizing policy map of a kind of Milling Process flutter of the present invention online optimizing embodiment of the method.

[embodiment]

In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with accompanying drawing, the present invention is described in more detail.

The flutter online optimizing in Milling Process stage, is to make as far as possible cutting data parameter matching in stablizing cutting zone and obtain maximal value according to actual processing request, and described maximal value is maximum material-removal rate Max[MRR], material-removal rate MRR is expressed as follows:

MRR＝a _plim·a _elim·n _s·Z·f _z (1)

In formula, ns is the speed of mainshaft; a _plimfor axial cutting-in; a _elimfor cutting-in radially; Z is cutter tooth number; f _zfor feed engagement.

At workpiece stiffness, in the heavy section casting milling process of tool stiffness, it is very micro-on the impact of cutter-workpiece system dynamics that material is removed workpiece stiffness variation variation little and rigidity, the milling Regenerative Chatter kinetic model based on this foundation.While considering Milling Force and rigidity, become the impact on processing stability, limit of stability a _plimwith rotation speed n and transfer function H (j ω) _rrelation be expressed as follows:

$a_{\mathrm{plim}}=\frac{1}{2Z{K}_t\left|A_0\mathrm{Re}\right[H{\left(\mathrm{j\ω}\right)}_r\left]\right|}---\left(2\right)$

$n=\frac{{60\mathrm{\ω}}_C}{Z[({2J}_r+1)\mathrm{\π}-2\arctan |\frac{\mathrm{Re}\left[H{\left(\mathrm{j\ω}\right)}_r\right]}{\mathrm{Im}\left[H{\left(\mathrm{j\ω}\right)}_r\right]}\left|\right]}---\left(3\right)$

In formula, a _plimfor axial cutting-in; Z is cutter tooth number; A ₀for dynamic milling force matrix of coefficients, dimensionless; K _tfor tangential Milling Force coefficient, N/m; N is the speed of mainshaft; ω _cfor flutter frequency, HZ; Jr is the whole wave number that cutter tooth is stayed the whole chatter marks of cutting surface in cycle T; Re[H (j ω) _r] be cutter workpiece coupled system r rank transport function real parts, m/N; Im[H (j ω) _r] be cutter workpiece coupled system r rank transport function imaginary parts, m/N.

The present invention considers the impact of High-order Transfer Functions on Regenerative Chatter stability, selects conventional machined parameters, speed of mainshaft n=0～8000r/min, a _p=10mm, a _e=2mm, f _z=0.2mm/ tooth, the transport function that mode experiment is obtained is brought in formula (2) and (3), by matrix experiment chamber (Matrix Laboratory, hereinafter to be referred as MATLAB) draw the multiple degrees of freedom stability diagram (obtaining first order curve under each degree of freedom) of Milling Force Model, as shown in Figure 1, solid line wherein, dotted line and dot-and-dash line represent respectively the 1st, 2 and 3 rank lobe curves, consider that the stability curve under multiple degrees of freedom consists of three rank lobe envelope of curves lines, total lobe curve represents with heavy line, shown in stability lobe curve upper area be range of instability, curve below is relatively stable district to the region between limit of stability cutting-in line, limit of stability cutting-in line lower zone is absolute stability district.

Below stability region is analyzed, the machined parameters in Fig. 1 of take is n _a=7400r/min, a _pAa point (the n of=19mm _a, a _pA) be example, if only consider the steadiness of single-degree-of-freedom, this machined parameters is positioned at the first lobe curve below, rank, thinks that this machined parameters is positioned at cutting stability district.In real work, due to the impact of second-order dynamics on system stability, this machined parameters has been positioned at cutting range of instability, if now adopt single-degree-of-freedom stability prediction method to carry out flutter differentiation, what can cause predicting the outcome is inaccurate.Research shows, under multiple degrees of freedom, relative stability region is little compared with single-degree-of-freedom, also more accurate.

Below to speed of mainshaft n and axial cutting-in a _pmachined parameters coupling, consider the multivariant stability prediction theoretical research of cutting system, selective analysis k _t, k _rtime become the impact on stability, k _t, k _rrelational expression be expressed as follows:

K _t＝a ₀+a ₁f _z+a ₂a _p+a ₃a _e+a ₄f _z ²+a ₅a _p ²+a ₆a _e ²+a ₇f _za _p+a ₈f _za _e+a ₉a _pa _e (4)

K _r＝b ₀+b ₁f _z+b ₂a _p+b ₃a _e+b ₄f _z ²+b ₅a _p ²+b ₆a _e ²+b ₇f _za _p+b ₈f _za _e+b ₉a _pa _e (5)

In formula, K _tfor tangential Milling Force coefficient, N/m; K _rfor Milling Force coefficient radially, N/m; a ₀, a ₁..., a ₉and b ₀, b ₁..., b ₉represent respectively regression coefficient tangential and radially.K _t, k _rbetween relational expression be expressed as follows:

$\left[A\right]=\frac{{\mathrm{ZK}}_t}{2\mathrm{\π}}\left[A_0\right]---\left(6\right)$

In formula, A ₀for dynamic milling force matrix of coefficients, dimensionless, A ₀by following relational expression, represent:

$\begin{array}{c}{A}_{0\mathrm{xx}}=\frac{1}{2}{[\cos 2\mathrm{\α}-2K_r\mathrm{\α}+K_r\sin 2\mathrm{\α}]}_{{\mathrm{\α}}_{\mathrm{en}}}^{{\mathrm{\α}}_{\mathrm{ex}}}\\ A_{0\mathrm{xy}}=\frac{1}{2}{[-\sin 2\mathrm{\α}-2\mathrm{\α}+2K_r\cos 2\mathrm{\α}]}_{{\mathrm{\α}}_{\mathrm{en}}}^{{\mathrm{\α}}_{\mathrm{ex}}}\\ A_{0\mathrm{yx}}=\frac{1}{2}{[-\sin 2\mathrm{\α}+2\mathrm{\α}+K_r\cos 2\mathrm{\α}]}_{{\mathrm{\α}}_{\mathrm{en}}}^{{\mathrm{\α}}_{\mathrm{ex}}}\\ A_{0\mathrm{yy}}=\frac{1}{2}{[-\cos 2\mathrm{\α}-2K_r\mathrm{\α}-K_r\sin 2\mathrm{\α}]}_{{\mathrm{\α}}_{\mathrm{en}}}^{{\mathrm{\α}}_{\mathrm{ex}}}\end{array}---\left(7\right)$

The Milling Force Model multiple degrees of freedom stability diagram of drawing by MATLAB, adds A ₀time change effect rear stability figure from two-dimensional curve, expand to three-dimension curved surface.According to formula (4) and (5), with lobe representation of a surface cutting parameter a _p, n, a _eand a _p, n, f _zthe situation of the cutter-workpiece system stability under coupling, the present invention has only drawn the three-dimensional stability figure in single-degree-of-freedom situation.

Refer to Fig. 2, Fig. 2 becomes a drawing while considering Milling Force coefficient _p, n, a _ethree-dimensional stability figure, as shown in Figure 2, a _eincrease can cause a _psharply reduction, increase gradually a _erear a _pthe trend that is slow decreasing,, there is significantly separation and cause shock effect to cause in the responsive phenomenon of cutting-in variation axially because cutter tooth-workpiece in cutter rotation one-period contacts.The susceptibility of this machined parameters shows, cutting-in a radially in process _eadjustment (when especially path is to cutting-in) cutter-workpiece system dynamics is caused to larger impact, in stability optimizing, need to consider the impact of this adjustable parameter on stability.

Refer to Fig. 3, Fig. 3 becomes a drawing while considering Milling Force coefficient _p, n, f _zthree-dimensional stability figure, as seen from the figure, with f _zincrease, lobe line slightly rises but change very is small, analysis result shows, f _zimpact on stability is insensitive, adjusts this parameter and can not cause large impact to cutter-workpiece system dynamics in process, therefore, take to stablize and cuts the stability optimizing that maximum material removing rate is object to select as far as possible larger f _z.

Refer to Fig. 4, Fig. 4 is at cutting-in a radially according to formula (2) and (3) _e=2mm, feed engagement f _zthe material removing rate of drawing during=0.2mm/ tooth and speed of mainshaft n and axially cutting-in a _pgraph of a relation, as shown in Figure 4, when radially cutting-in and feed engagement are constant, maximum material removing rate is positioned at two ear lobe intersections of high speed area (wave on lobe curve represents an ear lobe) as far as possible.

Below the selection of maximum material removing rate is analyzed, in actual process, cutter tooth number Z is steady state value, therefore the judgement of MRR is undertaken by its reduced form MRR ', relational expression is expressed as follows:

MRR′＝a _plim·a _elim·n _s (8)

According to formula (8), obtain MRR ' with a _p, a _echanging Pattern as shown in Figure 5, by Fig. 5 analysis, shown, with cutting-in a radially _eincrease, therefore MRR ' is increase trend, in guaranteeing without flutter instability processing, should increase radially cutting-in to obtain maximum material-removal rate as far as possible.

Based on foregoing description, the invention provides a kind of Milling Process flutter online optimizing method, the flutter online optimizing in Milling Process stage, be to make as far as possible cutting data parameter matching in stablizing cutting zone and obtain maximal value according to actual processing request, according to the on-line monitoring thinking of controlling without flutter instability cutting optimizing, carry out rotation speed n and axial cutting-in a _pstability technological parameter search, refer to Fig. 6, this Milling Process flutter online optimizing method comprises the following steps:

S10: set up milling Regenerative Chatter kinetic model;

S20: system is started working, carries out the on-line monitoring based on prediction of stability technology according to described kinetic model, judges the system unstable processing phenomenon of whether reporting for work in advance, and the unstable processing phenomenon if system is reported for work in advance, performs step S30;

S30: control system is carried out stable region optimizing according to the speed of mainshaft automatically;

S40: control system completes after the optimizing of stable region, returns to effect judgement that step S20 carries out optimizing control until process finishing.

Especially, described kinetic model according to following Formula:

$a_{\mathrm{plim}}=\frac{1}{2Z{K}_t\left|A_0\mathrm{Re}\right[H{\left(\mathrm{j\ω}\right)}_r\left]\right|}---\left(2\right)$

$n=\frac{{60\mathrm{\ω}}_C}{Z[({2J}_r+1)\mathrm{\π}-2\arctan |\frac{\mathrm{Re}\left[H{\left(\mathrm{j\ω}\right)}_r\right]}{\mathrm{Im}\left[H{\left(\mathrm{j\ω}\right)}_r\right]}\left|\right]}---\left(3\right)$

In formula, a _plimfor axial cutting-in; Z is cutter tooth number; A ₀for dynamic milling force matrix of coefficients, dimensionless; K _tfor tangential Milling Force coefficient, N/m; N is the speed of mainshaft; ω _cfor flutter frequency, HZ; Jr is the whole wave number that cutter tooth is stayed the whole chatter marks of cutting surface in cycle T; Re[H (j ω) _r] be cutter workpiece coupled system r rank transport function real parts, m/N; Im[H (j ω) _r] be cutter workpiece coupled system r rank transport function imaginary parts, m/N.

Especially, described step S20 specifically comprises the following steps:

S201: if do not report for work in advance unstable processing phenomenon, again carry out the on-line monitoring based on prediction of stability technology, judge the system unstable processing phenomenon of whether reporting for work in advance, the unstable processing phenomenon if system is reported for work in advance, perform step S202, if do not report for work in advance unstable processing phenomenon, carry out again the on-line monitoring based on prediction of stability technology, until machine;

S202: control system is carried out stable region optimizing according to the speed of mainshaft automatically;

Refer to Fig. 7, Fig. 7 is the machined parameters online optimizing policy map of a kind of Milling Process flutter of the present invention online optimizing embodiment of the method.

Especially, described step S30 specifically comprises the following steps:

S301: system is reported for work after unstable processing phenomenon in advance, if the speed of mainshaft is positioned at the Liang Er lobe A of intersection ₀time, by reducing axial cutting-in method, carry out stabilized zone A optimizing;

If the speed of mainshaft is positioned at the B of rising edge place of theoretical prediction ear lobe ₀time, by increasing rotating speed, carry out stabilized zone B optimizing;

If the speed of mainshaft is positioned at the falling edge C of theoretical prediction ear lobe ₀time, by reducing rotating speed, carry out stabilized zone C optimizing.

Especially, described step S40 specifically comprises the following steps:

S401: if control system searches optimum stable region, return to the judgement that step S20 carries out optimizing effect, if control system does not search optimum stable region, return to step S30 and proceed stable region optimizing, until machine.

It should be noted that, for those of ordinary skills, can be improved according to the above description or convert, and all these improvement and conversion all should belong to the protection domain of claims of the present invention.

Claims (5)

1. a Milling Process flutter online optimizing method, is characterized in that, comprises the following steps:

S10: set up milling Regenerative Chatter kinetic model;

S20: system is started working, carries out the on-line monitoring based on prediction of stability technology according to described kinetic model, judges the system unstable processing phenomenon of whether reporting for work in advance, and the unstable processing phenomenon if system is reported for work in advance, performs step S30;

S30: control system is carried out stable region optimizing according to the speed of mainshaft automatically;

S40: control system completes after the optimizing of stable region, returns to effect judgement that step S20 carries out optimizing control until process finishing.

2. Milling Process flutter online optimizing method according to claim 1, is characterized in that, described kinetic model according to following Formula:

$a_{\mathrm{plim}}=\frac{1}{2Z{K}_t\left|A_0\mathrm{Re}\right[H{\left(\mathrm{j\ω}\right)}_r\left]\right|}---\left(1\right)$

$n=\frac{{60\mathrm{\ω}}_C}{Z[({2J}_r+1)\mathrm{\π}-2\arctan |\frac{\mathrm{Re}\left[H{\left(\mathrm{j\ω}\right)}_r\right]}{\mathrm{Im}\left[H{\left(\mathrm{j\ω}\right)}_r\right]}\left|\right]}---\left(2\right)$

Wherein, a _plimfor axial cutting-in; Z is cutter tooth number; A ₀for dynamic milling force matrix of coefficients, dimensionless; K _tfor tangential Milling Force coefficient, N/m; N is the speed of mainshaft; Jr is the whole wave number that cutter tooth is stayed the whole chatter marks of cutting surface in cycle T; Re[H (j ω) _r] be cutter workpiece coupled system r rank transport function real parts, m/N; Im[H (j ω) _r] be cutter workpiece coupled system r rank transport function imaginary parts, m/N.

3. Milling Process flutter online optimizing method according to claim 1, is characterized in that, described step S20 specifically comprises the following steps:

S201: if do not report for work in advance unstable processing phenomenon, proceed the on-line monitoring based on prediction of stability technology, judge the system unstable processing phenomenon of whether reporting for work in advance, the unstable processing phenomenon if system is reported for work in advance, perform step S202, if do not report for work in advance unstable processing phenomenon, proceed the on-line monitoring based on prediction of stability technology, until machine;

S202: control system is carried out stable region optimizing according to the speed of mainshaft automatically.

4. Milling Process flutter online optimizing method according to claim 1, is characterized in that, described step S30 specifically comprises the following steps:

S301: system is reported for work after unstable processing phenomenon in advance, if when the speed of mainshaft is positioned at Liang Er lobe intersection, carries out stabilized zone optimizing by reducing axial cutting-in method;

When if the speed of mainshaft is positioned at the rising edge place of theoretical prediction ear lobe, by increasing rotating speed, carry out stabilized zone optimizing;

When if the speed of mainshaft is positioned at the falling edge of theoretical prediction ear lobe, by reducing rotating speed, carry out stabilized zone optimizing.

5. Milling Process flutter online optimizing method according to claim 1, is characterized in that, described step S40 specifically comprises the following steps:

S401: if control system searches optimum stable region, return to the judgement that step S20 carries out optimizing effect, if control system does not search optimum stable region, return to step S30 and proceed stable region optimizing, until machine.