CN102566492B - Method for forecasting maximum milling force for plunge milling of metal difficult-to-cut materials - Google Patents
Method for forecasting maximum milling force for plunge milling of metal difficult-to-cut materials Download PDFInfo
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- 238000003801 milling Methods 0.000 title claims abstract description 171
- 238000000034 method Methods 0.000 title claims abstract description 54
- 239000000463 material Substances 0.000 title claims abstract description 51
- 229910052751 metal Inorganic materials 0.000 title claims abstract description 22
- 239000002184 metal Substances 0.000 title claims abstract description 22
- 238000005520 cutting process Methods 0.000 claims abstract description 39
- 230000008569 process Effects 0.000 claims abstract description 23
- 238000012937 correction Methods 0.000 claims abstract description 12
- 238000001914 filtration Methods 0.000 claims abstract description 4
- 238000003754 machining Methods 0.000 claims description 21
- 238000002474 experimental method Methods 0.000 claims description 16
- 238000013461 design Methods 0.000 claims description 8
- 239000000956 alloy Substances 0.000 claims description 5
- 238000012417 linear regression Methods 0.000 claims description 4
- 238000012942 design verification Methods 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 4
- 238000012360 testing method Methods 0.000 abstract description 4
- PXHVJJICTQNCMI-UHFFFAOYSA-N Nickel Chemical compound [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 description 14
- 229910000601 superalloy Inorganic materials 0.000 description 8
- 229910052759 nickel Inorganic materials 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 5
- 229910001069 Ti alloy Inorganic materials 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 239000007769 metal material Substances 0.000 description 2
- 229910000838 Al alloy Inorganic materials 0.000 description 1
- 206010019233 Headaches Diseases 0.000 description 1
- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 1
- 229910000831 Steel Inorganic materials 0.000 description 1
- 229910000797 Ultra-high-strength steel Inorganic materials 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000000418 atomic force spectrum Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
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- 231100000869 headache Toxicity 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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Abstract
The invention discloses a method for forecasting a maximum milling force for plunge milling of metal difficult-to-cut materials. The method comprises the following steps that: (1) a forecasting model capable of reflecting the maximum milling force for plunge milling can be built for the difficult-to-cut materials, and parameters of lateral step distance, cut width and feeding and cutting speed in a plunge milling process are used as forecasting factors by the forecasting model; (2) a plunge milling processing test of the difficult-to-cut materials can be designed and processed, and milling force data curves of the difficult-to-cut materials in the plunge milling process can be collected; (3) test data can be obtained through processes of filtering the data curves and taking an extreme value, a correction coefficient and a correction index in the forecasting model can be calculated, and thus an index model can be determined; and (4) a forecasting process of the maximum milling force value can be processed by using the index model. According to the method for forecasting the maximum milling force for plunge milling of the metal difficult-to-cut materials, disclosed by the invention, since the lateral step distance used as a parameter is added in the forecasting model, the maximum milling force of the difficult-to-cut materials in the plunge milling process can be more accurately and comprehensively forecasted, and effective guidance can be provided for the efficient processing of the metal difficult-to-cut materials.
Description
Technical field
The present invention relates to insert milling manufacture field, more specifically, relate to a kind of for predicting that the slotting milling of metal difficult-to-machine material adds the method for the maximum Milling Force in man-hour.
Background technology
For metal cutting manufacture field, difficult-to-machine material possesses clear and definite implication, refers to comprise the metal material that is difficult to cut of nickel base superalloy, potassium steel, titanium alloy, iron-base superalloy, rare insoluble metal, ultra-high strength steel, compound substance etc.Particularly, the quality of metal material machinability, is mainly that the quality of tool life from when cutting, machined surface and cutting form and three aspects of complexity of getting rid of are weighed.The difficult-to-machine material of above-mentioned these particular types has obtained frequent utilization at a plurality of manufacture manufacture fields, on the impeller of nickel base superalloy wherein in Aeronautics and Astronautics field especially aeromotor, apply very extensively, therefore just day by day become the study hotspot place of recent academia.
Because difficult-to-machine material possesses that coefficient of heat conductivity is low, the hard intensity of heat is high, cutting deformation coefficient is large and the physical characteristics such as work hardening is serious, so at present to the cut of difficult-to-machine material or an international headache.Insert milling (plunge milling) and be called again Z axis milling method, it is applicable to the roughing under case of heavy load, therefore can improve the clearance of material and improve working (machining) efficiency, under current technical conditions, inserting milling and be and realize one of the most effective job operation of high resection rate metal cutting.Particularly, for the cut of difficult-to-machine material, insert the efficiency far of milling method higher than conventional face milling method.Yet at present at difficult-to-machine material manufacture field, insert milling processing method and apply to such an extent that be not also very extensive, and also do not have systematic, Forecasting Methodology more accurately for inserting maximum Milling Force in milling process.
Existing slotting milling Prediction Method of Milling Forces is all based on Ahmed Damir, Eu-Gene Ng, the Milling Force forecast model that Mohamed Elbestawi proposes carry out (specifically referring to, 2011, Force prediction and stability analysis of plunge milling of systems with rigid and flexible workpiece, Int J Adv Manuf Technol54:853-877), it is generally to predict Milling Force by exponential model, to liking aluminium alloy, the rapidoprints such as titanium alloy, the work of inserting milling Milling Force prediction aspect for relating to of metal difficult-to-machine material especially nickel base superalloy at present also seldom, and, the factors such as wide, feeding and cutting speed at present in the maximum process of inserting milling Milling Force of prediction, have only been considered laterally to cut, so still comprehensive not and accurate for the maximum Milling Force prediction of inserting in milling motion process, can not reflect to greatest extent the truth in the slotting milling process of metal difficult-to-machine material, correspondingly, can not insert choosing of technological parameter in milling process foundation is more accurately provided for plugging in milling cutter, be also difficult to for cutter select and the prediction in serviceable life provides guidance.
Summary of the invention
Defect for prior art, the object of the present invention is to provide a kind of for predicting the method for the slotting maximum Milling Force of milling process of metal difficult-to-machine material, it comprises that by foundation side direction step pitch predicts that as the slotting milling Milling Force Model of parameter metal difficult-to-machine material inserts the Milling Force in milling process, thereby effective guidance can be provided for the highly-efficient processing of metal difficult-to-machine material.
According to the present invention, provide a kind of for predicting the method for the maximum Milling Force of the slotting milling of nickel-base high-temperature alloy material, the method comprises the following steps:
(1), for nickel-base high-temperature alloy material is set up the predictive index model that the maximum Milling Force of milling is inserted in reflection, this model is used the side direction step pitch of inserting in milling process, laterally cuts wide, per tooth feeding and these parameters of cutting speed as predictor as follows:
Wherein, F
xexpression is with respect to the Milling Force of machining tool X-direction, F
yexpression is with respect to the Milling Force of machining tool Y direction, F
zexpression is with respect to the Milling Force of machining tool Z-direction, a
pexpression is laterally cut wide, f
zrepresent per tooth feeding, v
crepresent cutting speed, s represents side direction step pitch,
with
represent respectively by material and the determined correction factor of machining condition, a
1～a
4, b
1～b
4and c
1～c
4the index that represents respectively this model;
(2) design and carry out the slotting milling machining experiment of metal difficult-to-machine material, and gather the Milling Force data and curves in its slotting milling process;
(3) by the data and curves to gathering in step (2), carry out filtering and get extreme value and process to obtain the experimental data that the maximum Milling Force of milling is inserted in reflection, and these data acquisitions are processed to calculate each correction factor and the index in forecast model with matching or interpolation method, be identified for thus predicting the exponential model of the maximum Milling Force of the slotting milling of metal difficult-to-machine material; And
(4) use the determined exponential model of step (3), carry out the forecasting process of metal difficult-to-machine material being inserted to maximum Milling Force value in milling process.
As further preferably, described slotting milling machining experiment adopts orthogonal experiment method and designs., and adopt the mode of multiple linear regression to calculate described correction factor and index.
As further preferably, in described step (3) afterwards, can design verification experimental group check determined slotting milling Milling Force Model.
As further preferably, the mode of described slotting milling is that milling is inserted in numerical control.
By of the present invention for predicting that difficult-to-machine material inserts the method for the maximum Milling Force of milling process, owing to having increased side direction step pitch as parameter in the maximum Milling Force forecast model of slotting milling, so this method can be predicted the maximum Milling Force size in the slotting milling process of difficult-to-machine material more accurately, all sidedly; In addition, due in whole prediction and computation process based on carrying out onset index model to inserting the analysis of the maximum milling Milling Force of milling, and used the technical characterictic of reflection cutting processes such as laterally cutting wide, per tooth feeding, cutting speed and side direction step pitch as Prediction Parameters, correspondingly solve the technical matters that for example machined parameters in the cutting of hardworking material field of nickel base superalloy is selected, therefore can produce process is controlled and technique effect to aspects such as the more excellent uses of cutter better.
Accompanying drawing explanation
Fig. 1 is for predicting the method flow block scheme of the slotting milling Milling Force of difficult-to-machine material according to of the present invention;
Fig. 2 by the embodiment of the present invention the slotting milling Milling Force data and curves figure that obtains of actual measurement;
The F of Fig. 3 a for obtaining according to method of the present invention
xthe correlation curve figure predicting the outcome with measured result;
The F of Fig. 3 b for obtaining according to method of the present invention
ythe correlation curve figure predicting the outcome with measured result;
The F of Fig. 3 c for obtaining according to method of the present invention
zthe correlation curve figure predicting the outcome with measured result.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.
As everyone knows, cutting force is one of important physical phenomena in metal cutting process, it directly affects workpiece quality, cutter life, lathe power consumption, is one of design lathe, cutter, the indispensable key element of fixture, is also in process, to affect the most important factor of processing characteristics.The evaluation index of cutting force generally has two kinds: average cutting force and maximum cutting force.Average cutting force refers to the mean value at cutting force cutting force in the cycle; Maximum cutting force refers in whole measurement range, the maximum of points on cutting force curve that obtains.Consider that maximum cutting force can be used as the most important factor that affects stability and cutter life in process, thereby in the present invention, mentioned Milling Force is all to insert the maximum Milling Force of milling as evaluation index.
Referring to Fig. 1, Fig. 1 is the method flow block scheme of inserting the maximum Milling Force of milling process for measuring difficult-to-machine material according to of the present invention.As shown in fig. 1, first, for realizing, insert maximum Milling Force prediction in milling process, should be difficult-to-machine material and set up a kind of forecast model of inserting maximum Milling Force in milling process that reflects.Those skilled in the art will readily understand, in Tool in Cutting process, the resistance that cutting force produces while being workpiece material opposing Tool in Cutting.1, overcome the drag of machined material to elastic deformation its source is following three aspects, that is:; 2, overcome the drag of machined material to plastic yield; 3, overcome smear metal to the friction force of rake face and cutter after knife face to the friction force between transitional surface and machined surface.Therefore, the cutting force in process can be decomposed into three orthogonal component F
x, F
yand F
z, F wherein
xthat the component of total cutting force F in X-direction is also the drag that cutter bears in X-direction, F
ythat the component of total cutting force F in Y direction is also the drag that cutter bears in Y direction, F
zit is also i.e. drag in cutting-in direction of the component of total cutting force F in Z-direction.Based on above analysis, general adoptable Milling Force exponential model is:
Wherein: F
xfor with respect to machining tool X-direction cutting force, F
yfor with respect to machining tool Y direction cutting force, F
zfor with respect to machining tool Z-direction cutting force,
with
represent respectively by material and the determined correction factor of machining condition, a
pexpression is laterally cut wide, f
zrepresent per tooth feeding, v
crepresent cutting speed, a
1～a
3, b
1～b
3and c
1～c
3the index that represents respectively this forecast model.
Yet, in on-the-spot actual process, by comparing with the actual data value recording in scene by the drawn predicted value of above-mentioned exponential model, there is larger error between the two.By further experiment and data analysis, find, the introducing of side direction step pitch is very large for the impact of inserting the maximum Milling Force of milling, more obvious to the action effect of X, the maximum cutting force of Y-axis; Especially by a large amount of, insert the slotting milling Milling Force data that millings experiment obtains and also demonstrate after treatment, side direction step pitch this in inserting milling distinctive parameter for slotting milling process in maximum Milling Force there is important meaning.
Analysis based on above, for the singularity of Milling Force in inserting milling process, by changing parameter format and content of parameter, the reflection of setting up for difficult-to-machine material in the present invention is inserted the maximum Milling Force exponential model of milling and is:
F wherein
xexpression is with respect to the Milling Force of machining tool X-direction, F
yexpression is with respect to the Milling Force of machining tool Y direction, F
zexpression is with respect to the Milling Force of machining tool Z-direction, a
pexpression is laterally cut wide, f
zrepresent per tooth feeding, v
crepresent cutting speed, s represents side direction step pitch,
with
represent respectively by material and the determined correction factor of machining condition, a
1～a
4, b
1～b
4and c
1～c
4the index that represents respectively this forecast model.
Then, set up the new exponential model of the maximum Milling Force of its slotting milling of reflection for difficult-to-machine material after, can pass through contrived experiment scheme, carry out such as be the slotting milling experiment of the difficult-to-machine material of nickel base superalloy, and gather the Milling Force data and curves of its slotting milling process.Show after deliberation, adopt orthogonal experiment method to design and insert milling experimental program, can reduce largely experiment number, and obtain higher experiment precision.For example, in the present embodiment, can design the slotting milling orthogonal experiment group of nickel base superalloy, wherein select mountain high (SECO) to plug in milling cutter, knife bar model is MM06-12070.3-0005, and blade model is MM06-06004-R10-PL-MD02F30M, and carries out orthogonal test as shown in table 1 below on MIKRON UCP800 lathe, the Milling Force data and curves of the slotting milling process of reflection correspondingly, gathering as shown in Figure 2.
| Rotating speed (r/min) | Feeding (mm/min) | Cut wide (mm) | Side direction step pitch (mm) |
| 800 | 80 | 0.5 | 1 |
| 800 | 100 | 1 | 2 |
| 800 | 120 | 1.5 | 3 |
| 1000 | 80 | 1 | 3 |
| 1000 | 100 | 1.5 | 1 |
| 1000 | 120 | 0.5 | 2 |
| 1250 | 80 | 1.5 | 2 |
| 1250 | 100 | 0.5 | 3 |
| 1250 | 120 | 1 | 2 |
Table 1
By the data and curves that above step is gathered, carry out filtering and get extreme value and process, obtaining thus the experimental data that the maximum Milling Force of milling is inserted in reflection, as shown in Table 2 below:
Table 2
Under existing mathematics manipulation condition, can use mathematical methods such as matching, interpolation to process the data of the maximum Milling Force of the reflection in above table.In the present embodiment, adopted the method for multiple linear regression to process above-mentioned data, calculated each correction factor and index in described forecast model, be identified for thus predicting the expression formula of the maximum Milling Force of the slotting milling of difficult-to-machine material, its detailed process is:
First, the slotting milling Milling Force exponential model that the first step is set up is carried out and is asked logarithm process, that is:
With F
xfor example:
If Y=lgF
x,
, X
1=lga
p, X
2=lgf
z, X
3=lgv
c, X
4=lgs
Available linearization is
Y=A+a
1X
1+a
2X
2+a
3X
3+a
4X
4
After linear regression analysis:
Y=2.306+0.120X
1+0.018X
2-0.209X
3+0.956X
4
By converting, can draw:
In like manner can obtain respectively F
y, F
zfor:
More than be determined for predicting the expression formula of the maximum Milling Force of the slotting milling of difficult-to-machine material.After being identified for predicting that difficult-to-machine material is inserted the expression formula of the maximum Milling Force of milling, can also carry out related experiment checking with corresponding exponential model, with the accuracy of testing model.The confirmatory experiment group that for example, can design is as shown in the following Table 3 checked the determined slotting milling Milling Force Model of above step:
| Group number | Rotating speed (r/min) | Feeding (mm/min) | Cut wide (mm) | Side direction step pitch (mm) |
| 1 | 800 | 120 | 1.5 | 3 |
| 2 | 800 | 140 | 1.5 | 3 |
| 3 | 800 | 100 | 2 | 4 |
| 4 | 1000 | 140 | 1.5 | 3 |
| 5 | 1200 | 120 | 2 | 2 |
| 6 | 1200 | 130 | 1.5 | 3 |
| 7 | 1200 | 120 | 2 | 3 |
| 8 | 1200 | 120 | 3 | 4 |
Table 3
Then, by the above measured value of inserting milling experimental group is compared with the predicted value calculating according to slotting milling Milling Force Model, its comparative result is respectively as shown in Fig. 3 a, 3b, 3c.
By to the measured data shown in 3a, 3b, 3c and predicted value to recently seeing, according to model of the present invention due to the side direction step pitch of having considered in slotting milling process, approach more truly the milling process of inserting, therefore can improve accuracy and the reliability of inserting the prediction of milling Milling Force, and reflect preferably the especially slotting milling power Changing Pattern of nickel base superalloy in inserting milling process of difficult-to-machine material.
Finally, use the slotting milling Milling Force exponential model after check, carry out the prediction of difficult-to-machine material being inserted to maximum Milling Force value in milling process, complete thus whole forecasting process.
By to same material, for example nickel-base high-temperature alloy material repeatedly repeats the above-mentioned steps according to Forecasting Methodology of the present invention, result shows, the related coefficient obtaining and index rationally fluctuate substantially in a very little scope, have verified thus rationality and the accuracy of predictive index model of the present invention.
In addition, can also be according to above-mentioned forecasting process of the present invention to corresponding its exponential sum correction factor of trying to achieve of the difficult-to-machine material of other types, and determine its predictive index model.Like this, in the situation that choosing slotting milling machined parameters, can insert milling Milling Force to the maximum in process prediction is provided, simultaneously also can be in the situation that setting suitable maximum slotting milling power, oppositely determine and select rational machined parameters, can preferably provide guidance for the parameter optimization in actual process and cutter thus.
Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention; not in order to limit the present invention, all any modifications of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.
Claims (4)
1. for predicting a method for the maximum Milling Force of the slotting milling of nickel-base high-temperature alloy material, the method comprises the following steps:
(1), for nickel-base high-temperature alloy material is set up the forecast model that the maximum Milling Force of milling is inserted in reflection, this forecast model is used the side direction step pitch of inserting in milling process, laterally cuts wide, per tooth feeding and these parameters of cutting speed as predictor as follows:
Wherein, F
xexpression is with respect to the Milling Force of machining tool X-direction, F
yexpression is with respect to the Milling Force of machining tool Y direction, F
zexpression is with respect to the Milling Force of machining tool Z-direction, a
pexpression is laterally cut wide, f
zrepresent per tooth feeding, v
crepresent cutting speed, s represents side direction step pitch,
with
represent respectively by material and the determined correction factor of machining condition, a
1～a
4, b
1～b
4and c
1～c
4the index that represents respectively this forecast model;
(2) design and carry out the slotting milling machining experiment of metal difficult-to-machine material, and gather the Milling Force data and curves in its slotting milling process;
(3) by the data and curves to gathering in step (2), carry out filtering and get extreme value and process to obtain the experimental data that the maximum Milling Force of milling is inserted in reflection, and these data acquisitions are processed to calculate each correction factor and the index in described forecast model with matching or interpolation method, be identified for thus predicting the exponential model of the maximum Milling Force of the slotting milling of metal difficult-to-machine material; And
(4) use the determined exponential model of step (3), carry out the forecasting process of metal difficult-to-machine material being inserted to maximum Milling Force value in milling process.
2. the method for claim 1, is characterized in that, described slotting milling machining experiment adopts orthogonal experiment method and designs, and adopts the mode of multiple linear regression to calculate described correction factor and index.
3. method as claimed in claim 1 or 2, is characterized in that, in described step (3) afterwards, design verification experimental group is checked determined slotting milling Milling Force Model.
4. method as claimed in claim 3, is characterized in that, the mode of described slotting milling is that milling is inserted in numerical control.
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| CN104625182A (en) * | 2015-01-01 | 2015-05-20 | 东莞市东兴铝业有限公司 | Improved aluminum alloy sectional material cutting machining method and aluminum alloy sectional material |
| CN105538050B (en) * | 2016-01-28 | 2017-08-29 | 南京理工大学 | The prediction of Turning Force with Artificial method of fragile material ultrasonic vibration side grinding |
| CN105701323A (en) * | 2016-03-23 | 2016-06-22 | 华中科技大学 | Plunge milling machining cutting force predicting and modeling method |
| CN106424969B (en) * | 2016-09-09 | 2018-11-23 | 武汉理工大学 | A kind of slotting accurate prediction technique of milling dynamic cutting force considering cutter deflection |
| CN106971078B (en) * | 2017-04-11 | 2020-01-14 | 重庆大学 | Grinding force accurate prediction method considering motion parameters in screw grinding process |
| CN107991995A (en) * | 2017-12-01 | 2018-05-04 | 长春设备工艺研究所 | Titanium alloy NC Milling Technology parameter optimization method based on engineer testing data model |
| CN108733878B (en) * | 2018-04-11 | 2022-09-27 | 兰州城市学院 | Method for establishing cutting force prediction model for high-speed machining of high-manganese steel material |
| CN111061218B (en) * | 2019-12-31 | 2021-07-27 | 华中科技大学 | Complex curved surface machining cutting force prediction method and device based on ACIS |
| CN112231931B (en) * | 2020-11-04 | 2024-02-02 | 中国人民解放军陆军装甲兵学院 | Engine life prediction method by establishing PFR model |
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