CN106372337A - Thermal deformation prediction method of preheating stage of numerical control machine tool - Google Patents
Thermal deformation prediction method of preheating stage of numerical control machine tool Download PDFInfo
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Abstract
The invention belongs to the field of numerical control machine tool machining, and discloses a thermal deformation prediction method of a preheating stage of a numerical control machine tool. According to the thermal deformation prediction method of the preheating stage of the numerical control machine tool, an experimental G code is operated, thermal deformation quantities of the X axis, the Y axis, the Z axis and the principal axis of a to-be-compensated component at the preheating stage are collected, the relation between the thermal deformation quantities and the working time and the stop time of the component is found out, then a thermal expansion model 1 and a thermal contraction model 2 are respectively established, and the working time and the stop time of the to-be-compensated component are counted by a numerical control system and then are substituted into the two models, so that the thermal deformation of the preheating stage of the numerical control machine tool is predicted in real time. By adopting the thermal deformation prediction method, the production cost and the maintenance cost of machine tool manufacturers can be effectively reduced, the tool setting time is reduced, and the production efficiency of enterprises is improved.
Description
Technical field
The invention belongs to Digit Control Machine Tool manufacture field, more particularly, to a kind of thermal deformation in Digit Control Machine Tool heat engine stage
Forecasting Methodology.
Background technology
When processing 3c product, the feed shaft at the small-sized Zuan Gong center of Digit Control Machine Tool moves back and forth at a high speed, the asking of thermal deformation
Topic is very prominent: deflection is big, more than 0.13mm during highest；Heat balance time is long, and average warm-up times are 100 minutes.
For problem above, mainly there are two kinds of solutions: (1) mounting temperature sensor at present, in the heat of every lathe
The correct position deforming larger part installs one or more temperature sensors, the temperature of collection is substituted into and is based on weighted temperature
Thermal deformation forecast model, realize prediction to thermal deformation, this method is relatively suitable for being worth high, bulky heavy duty machine tools,
Attack center for being worth low, small volume high speed drill, increasedd Machine Manufacture cost and maintenance cost account for the ratio that lathe is worth
Example is higher, is difficult to be accepted by manufacturer；(2) tool setting gauge is installed, before every knife starts cutting, cutter is first corrected on tool setting gauge
Length dimension, at that time the heat distortion amount in z-axis direction just automatically counted in the length dimension of cutter and be compensated.With this
Method process during part it is desirable to the working time of every knife will short it is ensured that the heat distortion amount recording during to knife will not be in this cutter
Working angles in there is too big change.The deficiency of the method is, during the part processing frequently knife process being increased
Between account for total elapsed time ratio higher, reduce production efficiency, and which only compensate for z-axis axially in cutter and tool setting gauge
The thermal deformation of contact point, the thermal deformation for the other positions, x-axis and y-axis of z-axis axial direction does not possess compensation ability.
Content of the invention
Disadvantages described above for prior art or Improvement requirement, the invention provides a kind of heat in Digit Control Machine Tool heat engine stage
Deformation Prediction method, by using time-based thermal deformation forecast model model 1 and model 2, carrying out the real-time estimate course of processing
In thermal deformation, thus solve the Cost Problems that cause of mounting temperature sensor in production process and frequently knife process caused
Working (machining) efficiency problem.
For achieving the above object, it is proposed, according to the invention, provide a kind of thermal deformation Forecasting Methodology in Digit Control Machine Tool heat engine stage,
It is characterized in that, the method comprises the following steps:
A () measures part to be predicted in different working times and time of having a rest corresponding heat distortion amount respectively, and set up
The model 1 of relation, relation between described time of having a rest and described heat distortion amount between described working time and described heat distortion amount
Model 2；
B parameter preset that () is inputted by digital control system obtains model after described model 1 and described model 2 are adjusted
1 ' and model 2 '；
C () judges the state that part to be predicted is presently in:
(c1) when part to be predicted is in running order, by work at present time τi+1' be input in described model 1 ', obtain
Heat distortion amount σ to required predictioni+1；
(c2) when part to be predicted is in resting state, by current time of having a rest τj+1' be input in described model 2 ', obtain
Heat distortion amount σ to required predictionj+1.
As it is further preferred that it is characterized in that, for described model 1, it preferably employs following formula, wherein σ
For heat distortion amount, τ is node sample time, a1、b1、a2And b2It is the coefficient of model curve,
For described model 2, it preferably employs following formula,
As it is further preferred that preferably employing following formula for described model 1 ',
Wherein, σiFor the heat distortion amount in i moment, δaFor heat error compensation value regulation coefficient, δbAdjust system for the Thermal Error time
Number,
a1'=a1δa
b1'=b1δb
Following formula is preferably employed for described model 2 ',
Wherein, σjHeat distortion amount for the j moment
a2'=a2δa.
As it is further preferred that in step (c), described τi+1' preferably employ following formula
τi+1'=τi+1+τi',
Described τj+1' preferably employ following formula
τj+1'=τj+1+τj′
τj+1'=τj+1+τj′
Wherein, τi' it is to substitute into current heat distortion amount in model 1 ' to calculate, τi+1Between the i moment to i+1 moment
Working time section, τj' it is to substitute into current heat distortion amount in model 2 ' to calculate, τj+1For the j moment to j+1 moment rest when
Between section.
In general, by the contemplated above technical scheme of the present invention compared with prior art, can obtain down and show
Beneficial effect:
1st, the Forecasting Methodology that the present invention provides is divided into three steps, and holistic approach is simple, with low cost, and prediction process is just
In control, after the thermal deformation result predicted is compensated, the position error of lathe and warm-up times substantially reduce, and machining accuracy carries
High；
2nd, the present invention passes through using thermal deformation forecast model 1 and model 2, simultaneously take account of in application heat error compensation value and
Thermal Error matter of time, by regulation coefficient δaAnd δbTo adjust model 1 and model 2 heat later in the real-time estimate course of processing
Deformation, and this model to be included x-axis, y-axis, z-axis and main shaft by the main thermal deformation part of lathe general, all possess and meet precision
The predictive ability requiring；
3rd, the thermal deformation Forecasting Methodology of the present invention is by setting up time-based model, not on thermal deformation part
Extra mounting temperature sensor, the method is not only suitable for being worth high, bulky lathe, is also applied for being worth low, small volume
Lathe, can effectively save the production cost of lathe manufacturer and maintenance cost it is easy to be received by manufacturer；
4th, by using the time-based model 1 set up and model 2, prediction is carried out the present invention in real time, when any
The thermal deformation carved can be calculated by model, and does not install tool setting gauge on lathe, save to the knife time, improve enterprise
Production efficiency, simultaneously save production cost.
Brief description
Fig. 1 is the flow chart of the Deformation Prediction method enforcement of the present invention；
Fig. 2 is the relation of the thermal deformation according to the part to be compensated constructed by the preferred embodiments of the present invention and working time
Curve and model；
Fig. 3 is the relation of the thermal deformation according to the part to be compensated constructed by the preferred embodiments of the present invention and time of having a rest
Curve and model；
Fig. 4 is the Forecasting Methodology effect contrast figure according to the preferred embodiments of the present invention.
Specific embodiment
In order that the objects, technical solutions and advantages of the present invention become more apparent, below in conjunction with drawings and Examples, right
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only in order to explain the present invention, and
It is not used in the restriction present invention.As long as additionally, involved technical characteristic in each embodiment of invention described below
The conflict of not constituting each other just can be mutually combined.
Fig. 1 is the flow chart of the thermal deformation Forecasting Methodology enforcement of the present invention, and Fig. 2 is according to the preferred embodiments of the present invention institute
The thermal deformation of part to be compensated building and the relation curve of working time and model, Fig. 3 is being preferable to carry out according to the present invention
The thermal deformation of part to be compensated constructed by example and the relation curve of time of having a rest and model, as shown in Figure 1, Figure 2 and Figure 3, this
Invent the time-based thermal deformation Forecasting Methodology being adopted and mainly include model foundation, model adjustment and model application three
Point.
(1) model is set up
Here model includes the model 2 of thermal deformation and the model 1 of working time and thermal deformation and time of having a rest.
The measuring method of model 1 thermal deformation is: measure the position of z-axis using tool setting gauge after, allows lathe do certain number of times
Move back and forth, then tool setting gauge surveys the position of z-axis again, and this process is referred to as a cycle.Repeat the action in this cycle, directly
Thermal deformation to z-axis reaches stably.Deduct the position of a cycle z-axis with the position of each cycle z-axis, you can obtain each
The heat distortion amount σ in cyclei, and obtain run time τ corresponding to each deflectioni, wherein i represents i-th cycle, model 1
Model of fit isFeature according to this model of fit can obtain: a1It is the maximum of thermal deformation
Value.So, σi、τi、a1Bring model 1 into and can obtain a series of b1Value, averages and can get the b of matching1Value.As figure
The actual thermomechanical curve of z-axis shown in 2 and model of fit curve.
The measuring method of model 2 thermal deformation is: after allowing thermal deformation of machine tool to reach stably by the measuring method of model 1, allows
The static rest of lathe, utilizes tool setting gauge to survey the position of a z-axis every 1min (cycle), stablizes until z-axis thermal deformation reaches.With
The position that the position of each cycle z-axis deducts last cycle z-axis can get the heat distortion amount σ in each cyclej, and obtain
Time of having a rest τ corresponding to each cyclej, wherein j represents j-th cycle, and the model of fit of model 2 is
Feature according to this model can obtain: a2It is the maximum of thermal deformation.So, σj、τj、a2Bring model 1 into can obtain
A series of b2Value, averages and can get the b of matching2Value.Z-axis as shown in Figure 3 actual thermal contraction curve and model of fit
Curve.
(2) model adjustment
The lathe of same brand is external condition (ambient temperature, air flow etc.) is identical and processing conditionss (part, processing
Material, processor etc.) in the case of identical, because lathe assembles qualitative difference, parameter a of model 11、b1With model 2
Parameter a2Have certain change, then " heat error compensation value regulation coefficient δ is seta" and " Thermal Error time adjustment factor δb" come
Model is adjusted:
a1'=a1δa
b1'=b1δb
a2'=a2δa
By a1′、b1' set up model 1:By a2′、b2Set up model 2:
Same lathe is operated under different ambient temperatures, and thermal deformation would also vary from, then arrange " model environment temperature
Degree t0" and " current environmental temperature t " model is adjusted.Digital control system utilizes current environmental temperature and model environment temperature
Difference δ t=t-t0, " the heat error compensation value regulation coefficient δ that adjust automatically is arranged manuallya" and " Thermal Error time adjustment factor
δb":
δa'=δaf(δt)
δb'=δbg(δt)
b2'=b2g(δt)
Wherein, f (δ t) and g (δ t) is ambient temperature Tuning function；
By the more new model 1 of the parameter after adjusting and model 2, thus the thermal deformation prediction at a temperature of realizing varying environment.
(3) model application
Using model 1 and model 2 carry out thermal deformation prediction flow chart as shown in figure 1,
I () digital control system is according to the heat error compensation value regulation coefficient δ of inputa, Thermal Error time adjustment factor δb, model
Ambient temperature t0, current environmental temperature t adjusting model 1 and the model 2 of internal system setting, the model 1 ' after being adjusted and
Model 2 ', concrete adjustment mode is shown in the model adjustment in technical scheme；
(ii) digital control system starts to process specified part when τ=0, runs τ1After time, τ1It is input in model 1, obtain
To prediction of distortion amountAnd enter specified link and compensate；
(iii) digital control system is in τ=τiWhen prediction of distortion amount be σiIf continuing to run with τi+1Time, σiIt is input to mould
In type 1 ', calculate σiThe corresponding time in model 1 'Again τi’+τi+1It is input to model 1 '
In, obtain required prediction of distortion amount
(iv) digital control system is in τ=τiWhen prediction of distortion amount be σiIf, rest τj+1Time, σiIt is input to model 2 '
In, calculate σiThe corresponding time in model 2 'Again τj’+τj+1It is input in model 2, obtain institute
Need prediction of distortion amount
Machine tool motion and stopping are carried out at random, and the above-mentioned flow process of execution can achieve and the heat in whole process is become in order
Shape is predicted.
According to a preferred embodiment of the present invention, experiment lathe model td-500a used, digital control system model
hnc-8.Lathe z-axis maximum rapid traverse speed is 48000mm/s.
(1) set up model
The determination of model 1: measure the position of z-axis using tool setting gauge after, allow lathe to move reciprocatingly, until tool setting gauge again
Survey the position of z-axis, this process is referred to as a cycle.Repeat the action in this cycle, stablize until the thermal deformation of z-axis reaches.
Deduct the position of a cycle z-axis with the position of each cycle z-axis, you can obtain the heat distortion amount σ in each cyclei:
Run time corresponding to each deflection
The model of fit of model 1 isFeature according to this model of fit can obtain: a1I.e.
Maximum 0.1252 for thermal deformation.So, σi、τi、a1Bring model 1 into and can obtain a series of b1Value:
Average the b that can get in model of fit1Value, b1=0.0009942.So model 1 is defined as: σ=
0.1252-0.1252e-0 . 0009942τ.The actual thermomechanical curve of z-axis as shown in Figure 2 and model of fit curve.
The determination of model 2: after allowing thermal deformation of machine tool to reach stably by the measuring method of model 1, make lathe static, every
1min (cycle) utilizes tool setting gauge to survey the position of a z-axis, stablizes until z-axis thermal deformation reaches.Position with each cycle z-axis
The position deducting last cycle z-axis can get the heat distortion amount σ in each cyclei
Time of having a rest corresponding to each cycle
The model of fit of model 2 isFeature according to this model can obtain: a2It is thermal deformation
Maximum 0.14063.So, σi、τi、a2Bring model 1 into and can obtain a series of b2:
Average and can get the b of matching2Value, b2=0.0006.So model 2 is defined as: σ=0.14063e-0.0006τ.Z-axis as shown in Figure 3 actual thermal contraction curve and model of fit curve.
(2) model adjustment
In the implementation case, the regulation coefficient of delivery type: heat error compensation value regulation coefficient δa=1, the Thermal Error time
Regulation coefficient δb=1.
(3) model application
The flow process carrying out thermal deformation prediction using model 1 and model 2 is as follows:
I () digital control system adjusts, according to regulation coefficient, model 1 and the model 2 that internal system sets, after adjustment, model 1 ' and
Model 2 ' expression formula is respectively as follows: σ=0.1252-0.1252e-0.0009942τ, and σ=0.14063e-0.0006τ.
(ii) digital control system starts to process specified part when τ=0, after running the 45.63s time, the time is input to mould
In type 1 ', obtain prediction of distortion amountAnd enter specified link and compensate；
(iii) the prediction of distortion amount in τ=45.63s for the digital control system is σi=0.0056mm, if continue to run with τi+1=
The 57.94s time, σiIt is input in model 1 ', calculate σiThe corresponding time in model 1 'Again τi’+τi+1=103.97s is input in model 1 ', obtains required prediction
Deflection
(iv) the prediction of distortion amount in τ=45.63s for the digital control system is σi=0.0056mm, if rest τj+1During=68.47s
Between, σiIt is input in model 2 ', calculate σiThe corresponding time in model 2 'Again
τj’+τj+1=68.47537s is input in model 2 ', obtains required prediction of distortion amount
Fig. 4 is the Forecasting Methodology effect contrast figure according to the preferred embodiments of the present invention, as shown in figure 4, to heat distortion amount
After being predicted, then carry out heat error compensation, result shows, after compensating using prediction thermal deformation, the positioning of lathe is by mistake
Difference and warm-up times substantially reduce, and machining accuracy improves, and has saved processing cost.
As it will be easily appreciated by one skilled in the art that the foregoing is only presently preferred embodiments of the present invention, not in order to
Limit the present invention, all any modification, equivalent and improvement made within the spirit and principles in the present invention etc., all should comprise
Within protection scope of the present invention.
Claims (4)
1. a kind of thermal deformation Forecasting Methodology in Digit Control Machine Tool heat engine stage is it is characterised in that the method comprises the following steps:
A () measures part to be predicted in different working times and time of having a rest corresponding heat distortion amount respectively, and set up described
The model 1 of relation between working time and described heat distortion amount, the mould of relation between described time of having a rest and described heat distortion amount
Type 2；
B parameter preset that () is inputted by digital control system obtain after described model 1 and described model 2 are adjusted model 1 ' and
Model 2 '；
C () judges the state that part to be predicted is presently in:
(c1) when part to be predicted is in running order, by work at present time τi+1' be input in described model 1 ', obtain institute
The heat distortion amount σ that need to predicti+1；
(c2) when part to be predicted is in resting state, by current time of having a rest τj+1' be input in described model 2 ', obtain institute
The heat distortion amount σ that need to predictj+1.
2. Forecasting Methodology as claimed in claim 1 is it is characterised in that for described model 1, it preferably employs following expression
Formula, wherein σ are heat distortion amount, and τ is node sample time, a1、b1、a2And b2It is the coefficient of model curve,
For described model 2, it preferably employs following formula,
3. Forecasting Methodology as claimed in claim 1 or 2 is it is characterised in that preferably employ following expression for described model 1 '
Formula,
Wherein, σiFor the heat distortion amount in i moment, δaFor heat error compensation value regulation coefficient, δbFor Thermal Error time adjustment factor,
a1'=a1δa
b1'=b1δb
Following formula is preferably employed for described model 2 ',
Wherein, σjHeat distortion amount for the j moment
a2'=a2δa.
4. the Forecasting Methodology as described in any one of claim 1-3 is it is characterised in that in step (c), described τi+1' preferably adopt
Use following formula
τi+1'=τi+1+τi′
Described τj+1' preferably employ following formula
τj+1'=τj+1+τj′
Wherein, τi' it is to substitute into current heat distortion amount in model 1 ' to calculate, τi+1For during work between the i moment to i+1 moment
Between section, τj' it is to substitute into current heat distortion amount in model 2 ' to calculate, τj+1Time of having a rest section for the j moment to j+1 moment.
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| CN201610802011.0A CN106372337A (en) | 2016-09-05 | 2016-09-05 | Thermal deformation prediction method of preheating stage of numerical control machine tool |
| CN201710760498.5A CN107798160A (en) | 2016-09-05 | 2017-08-30 | The method and apparatus that thermal deformation for Digit Control Machine Tool is predicted |
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| CN108490883A (en) * | 2018-03-19 | 2018-09-04 | 成都飞机工业（集团）有限责任公司 | A kind of numerically-controlled machine tool pre-heating mean |
| CN108490883B (en) * | 2018-03-19 | 2020-12-01 | 成都飞机工业（集团）有限责任公司 | Preheating method of numerical control machine tool |
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| CN108857280A (en) * | 2018-07-05 | 2018-11-23 | 大连理工大学 | A kind of exemplar processing method for visual inspection horizontal lathe Thermal Error |
| CN108803486A (en) * | 2018-08-16 | 2018-11-13 | 重庆理工大学 | Numerical control machining tool heat error prediction based on deep learning network in parallel and compensation method |
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| CN110262393A (en) * | 2019-07-03 | 2019-09-20 | 西安交通大学 | Gray theory segmented with lag data processing weights thermal error modeling method |
| CN110262393B (en) * | 2019-07-03 | 2020-08-18 | 西安交通大学 | Gray theory sectional weighted thermal error modeling method with lag data processing |
| CN110515347A (en) * | 2019-08-13 | 2019-11-29 | 大族激光科技产业集团股份有限公司 | A kind of numerical control machine heat transmitting measurement method, device, storage medium and equipment |
| CN117300184A (en) * | 2023-11-29 | 2023-12-29 | 山东正祥工矿设备股份有限公司 | Control system for processing lathe for copper casting production |
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