CN107330197A - A kind of optimization method of the lower cutting high temperature alloy Predictive Model of Cutting Force of high pressure cooling - Google Patents
A kind of optimization method of the lower cutting high temperature alloy Predictive Model of Cutting Force of high pressure cooling Download PDFInfo
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Abstract
A kind of optimization method of the lower cutting high temperature alloy Predictive Model of Cutting Force of high pressure cooling, belongs to metal cutting process field.Choose vc、f、apScope, different cutting force F are gathered under high pressure cooling in cutting high temperature alloy experimental basisx、Fy、FzSignal；Based on described single factor experiment and orthogonal test, Predictive Model of Cutting Force is built；Based on described Predictive Model of Cutting Force and residual error diagnostic method, residual error is asked for；According to the statistical property of the cutting force data of collection, standard deviation is calculated；Threshold value is set to judge and reject cutting force exceptional value according to cutting environment and residual error rate of change, threshold size is 0.1.The present invention is based on residual error diagnostic analysis, global optimum from model parameter space, consider the cutting environmental complexity that the factor such as vibration and impact is caused, it is effective to reject cutting force exceptional value and correspondingly reduce error, meet test value and checking guarantee forecasting accuracy is carried out with optimal value best fit degree and in trial stretch.
Description
Technical field
The present invention relates to a kind of optimization method of Predictive Model of Cutting Force, belong to metal cutting process field.
Background technology
In recent years, high temperature alloy development was very fast, because it possesses what is added in good physical and mechanical property, material
The elements such as cobalt, tungsten improve the stability of alloy；More metallic compound make high temperature alloy in 800 DEG C of hot environment still
Very high intensity can be kept, tensile strength is up to 800Mpa；Firm atomic structure makes high temperature alloy plasticity preferable.High temperature alloy
Widely apply in fields such as aeronautical manufacture, Marine engineering, nuclear reactors, it is right at present with the development of China's mechanical processing industry
The technique processing of high temperature alloy proposes higher requirement.
High temperature alloy processing problems are that the hardening constituent of Dispersed precipitate in manufacturing industry difficult point, material causes serious processing hard
Change, constitutionally stable atom requirement very big cutting force, almost the 2~3 of steel times, conventional cutting can not be completed well
High temperature alloy processing request, high pressure cooling processing can but be effectively ensured the Tool in Cutting time and significantly improve processing efficiency, still
The factor non-linear effects such as the lower cutting high temperature alloy vibration of high pressure cooling and impact are still larger, direct interference to Cutting Force Signal
Collection, and existing cutting force measurement device can't overcome drawbacks described above.
Cutting force is the major reason for causing cutting heat, and causes tool wear and destruction, eventually affects workpiece and has added
Work surface quality；Cutting force is to calculate cutting power, design and use lathe, the foundation of fixture again in actual production.Therefore such as
What effectively reduces the effective way that the cutting force-induced error collected is researching high-temperature alloy machining, lower to high pressure cooling to cut
Power, which carries out research, can probe into high temperature alloy cutting lay characteristic and surface formation mechenism etc., finally be carried for highly-efficient processing high temperature alloy
For reference and use for reference, Predictive Model of Cutting Force is analyzed and optimization will be helpful to analysis cutting scheme and raising actual production
Efficiency.
The content of the invention
It is an object of the invention in view of the above-mentioned problems, proposing a kind of high pressure cooling, cutting high temperature alloy cutting force is pre- down
Survey the optimization method of model, based on residual error diagnostic analysis, the global optimum from model parameter space, it is considered to vibration and impact
The cutting environmental complexity caused etc. factor, effectively rejects and cutting force exceptional value and correspondingly reduces error, meet test value and
Optimal value best fit degree and the progress checking guarantee forecasting accuracy in trial stretch, are the processing of high-efficient cutting difficult-to-machine material
Technique provides reference, is that certain basis is established in the lower high temperature alloy cutting scheme research of high pressure cooling.
To achieve the above object, the technical scheme is that：
A kind of optimization method of the lower cutting high temperature alloy Predictive Model of Cutting Force of high pressure cooling, described optimization method includes
Following steps：
Step one：Rational choice vc、f、apScope, gathers different under high pressure cooling in cutting high temperature alloy experimental basis
Cutting force Fx、Fy、FzSignal；Described cutting force Fx、Fy、FzRespectively feed drag, cutting-in drag, main cutting force；
Wherein, described vc、f、apRespectively cutting speed, the amount of feeding, cutting depth, with reference to High Speed Cutting Technique
With cutting of hardworking material feature, selection range must meet following relations：
The lower cutting high temperature alloy experiment of described high pressure cooling includes single factor experiment and orthogonal test two parts are (described
Single factor experiment probes into PCBN cutters chamfered edge influences situation to cutting force, understands it and acts on notable scope, on this basis preferably
Optimal chamfered edge width, angle, carry out multifactor multilevel orthogonal test), v in described single factor experiment and orthogonal testc、
f、apFollowing relations are met respectively：
Wherein, viFor vcSpecific value condition, vi+1With viRelation represents with above formula, N*For positive integer, fi+1With f relations,
ai+1With apRelation similarly, vc、f、apUnit is m/min, mm, mm/r respectively；
Described feeding drag Fx, cutting-in drag Fy, main cutting force FzFollowing condition is met with cutting force F relations：
Step 2：Based on described single factor experiment and orthogonal test, Predictive Model of Cutting Force is built；
Described cutting force F forecast models select optimum combination jointly to predict or estimate cutting force by regression analysis,
Meet following condition：
Wherein, CFProcessing coefficient is represented, its value depends on processing conditions；vcRepresent cutting speed, m/min；apExpression is cut
Cut depth, mm；F represents amount of feeding mm/r；xF、yF、zFCutting speed, the amount of feeding, the index of cutting depth are represented respectively；
Cutting force F (the F that described experiment is obtained1, F2... F16) specific manifestation form meets following condition：
Wherein, y=lgF, y include y1~y16；x1=lgvc, x1Including x0101~x0116, x2=lgap, x2Including x0201~
x0216；x3=lgf, x3Including x0301~x0316；b0=lgCp；b1=xF；b2=yF；b3=zF；
Step 3：Based on described Predictive Model of Cutting Force and residual error diagnostic method, residual error is asked for；
The residual error eiMeet following relational expression：
Wherein, yiThe cutting force data that the orthogonal test is collected, and i ∈ [1,16] are represented,Represent prediction of Turning Force with Artificial
The optimal value of model,
Step 4：According to the statistical property of the cutting force data of collection, standard deviation is calculated；
The standard deviation calculation formula of i-th described of residual error is：
Wherein, hiiThe leverage of i-th of cutting force observation is represented, n is the orthogonal test sample size, yiTo be orthogonal
I-th of observation station cutting force data is tested,For all cutting force mean of observations,Represent returning for Predictive Model of Cutting Force
Return standard deviation；It is the standard deviation of i-th of residual error；
Step 5：Threshold value is set to judge and reject cutting force exceptional value according to cutting environment and residual error rate of change,
Described threshold size is 0.1.
The present invention is relative to the beneficial effect of prior art：
The present invention is based on residual error diagnostic analysis, the global optimum from model parameter space, it is considered to vibration and impact etc.
The cutting environmental complexity that factor is caused, effectively reject cutting force exceptional value simultaneously correspondingly reduce error, meet test value with it is excellent
Change value best fit degree and the progress checking guarantee forecasting accuracy in trial stretch, above method are that high-efficient cutting hardly possible processes material
Expect that processing technology provides reference, be that certain basis is established in the research of high temperature alloy cutting scheme.
Brief description of the drawings
Fig. 1 is cutting force measurement and harvester structure chart in embodiments of the invention 1；
Fig. 2 is the residual error lever diagram of forecast model in embodiments of the invention 1；
Fig. 3 is forecast model optimum results figure in embodiments of the invention 1.
Embodiment
Below in conjunction with accompanying drawing to the lower cutting high temperature alloy Predictive Model of Cutting Force of a kind of high pressure cooling shown in the present invention
Optimization method is elaborated, but the present invention is not limited only to following embodiment.
Embodiment one：Present embodiment discloses a kind of lower cutting high temperature alloy prediction of Turning Force with Artificial mould of high pressure cooling
The optimization method of type, described optimization method comprises the following steps：
Step one：Rational choice vc、f、apScope, the cutting high temperature (1000-1500 under high pressure (70-110bar) cooling
DEG C) different cutting force F are gathered in alloy experimental basisx、Fy、FzSignal；Described cutting force Fx、Fy、FzRespectively feeding drag,
Cutting-in drag, main cutting force；
Wherein, described vc、f、apRespectively cutting speed, the amount of feeding, cutting depth, with reference to High Speed Cutting Technique
With cutting of hardworking material feature, selection range must meet following relations：
The lower cutting high temperature alloy experiment of described high pressure cooling includes single factor experiment and orthogonal test two parts are (described
Single factor experiment probes into PCBN cutters chamfered edge influences situation to cutting force, understands it and acts on notable scope, on this basis preferably
Optimal chamfered edge width, angle, carry out multifactor multilevel orthogonal test), v in described single factor experiment and orthogonal testc、
f、apFollowing relations are met respectively：
Wherein, viFor vcSpecific value condition, vi+1With viRelation represents with above formula, N*For positive integer, fi+1With f relations,
ai+1With apRelation similarly, vc、f、apUnit is m/min, mm, mm/r respectively；
Described feeding drag Fx, cutting-in drag Fy, main cutting force FzFollowing condition is met with cutting force F relations：
Step 2：Based on described single factor experiment and orthogonal test, Predictive Model of Cutting Force is built；
Described cutting force F forecast models select optimum combination jointly to predict or estimate cutting force by regression analysis,
Meet following condition：
Wherein, CFProcessing coefficient is represented, its value depends on processing conditions；vcRepresent cutting speed, m/min；apExpression is cut
Cut depth, mm；F represents amount of feeding mm/r；xF、yF、zFCutting speed, the amount of feeding, the index of cutting depth are represented respectively；
Cutting force F (the F that described experiment is obtained1, F2... F16) specific manifestation form meets following condition：
Wherein, y=lgF, y include y1~y16；x1=lgvc, x1Including x0101~x0116, x2=lgap, x2Including x0201~
x0216；x3=lgf, x3Including x0301~x0316；b0=lgCp；b1=xF；b2=yF；b3=zF；
Step 3：Based on described Predictive Model of Cutting Force and residual error diagnostic method, residual error is asked for；
The residual error eiMeet following relational expression：
Wherein, yiThe cutting force data that the orthogonal test is collected, and i ∈ [1,16] are represented,Represent prediction of Turning Force with Artificial
The optimal value of model,
Step 4：According to the statistical property of the cutting force data of collection, standard deviation is calculated；
The standard deviation calculation formula of i-th described of residual error is：
Wherein, hiiThe leverage of i-th of cutting force observation is represented, n is the orthogonal test sample size, yiTo be orthogonal
I-th of observation station cutting force data is tested,For all cutting force mean of observations,Represent returning for Predictive Model of Cutting Force
Return standard deviation；It is the standard deviation of i-th of residual error；
Step 5：Threshold value is set to judge and reject cutting force exceptional value according to cutting environment and residual error rate of change,
Described threshold size is 0.1.
Embodiment 1：
It is described this embodiment discloses herein a kind of optimization method of the lower cutting high temperature alloy Predictive Model of Cutting Force of high pressure cooling
Optimization method comprise the following steps：
Step one：
Rational choice vc、f、apScope, collection difference is cut in the cutting experimental basis of superalloy specimens 6 under high pressure cooling
Cut power Fx、Fy、FzSignal；
As shown in figure 1, described cutting force Fx、Fy、FzSignal acquisition is by the dynamometer 2 and the electric charge that are fixed on blade 1
Amplifier 3, data actuation 4 and computer 5 are realized；
Described vc、f、apRespectively cutting speed, the amount of feeding, cutting depth, add with reference to High Speed Cutting Technique and hardly possible
Work material cutting characteristic, selection range must meet following relations：
The lower cutting high temperature alloy experiment of described high pressure cooling includes single factor experiment and orthogonal test two parts, maximum pressure
Power is up to 110bar, and single factor experiment probes into PCBN cutters chamfered edge influences situation to cutting force, understands it and acts on notable scope,
Preferably optimal chamfered edge width, angle on the basis of this, carry out multifactor multilevel orthogonal test, described single factor experiment and just
Hand over v in experimentc、f、apFollowing relations are met respectively：
Wherein, viFor vcSpecific value condition, vi+1With viRelation represents with above formula, N*For positive integer, fi+1With f relations,
ai+1With apRelation similarly, vc、f、apUnit is m/min, mm, mm/r respectively；
Described cutting force Fx、Fy、FzRespectively feeding drag, cutting-in drag, main cutting force, meet with cutting force F relations
Following condition：
As shown in table 1, described single factor experiment includes 1~15 group, vcChange successively, respectively V1、V2、V3、V4、V5；f
Change successively, respectively f1、f2、f3、f4、f5；apChange successively, respectively a1、a2、a3、a4、a5；As shown in table 2, it is described just
Experiment is handed over to include 1~16 group, vc、f、apCorrespondence value condition has been listed, and the single factor experiment probes into PCBN cutter chamfered edges pair
Cutting force influences situation, understands it and acts on notable scope, preferably optimal chamfered edge width, angle are carried out multifactor on this basis
V in multilevel orthogonal test, described single factor experiment and orthogonal testc、f、apFollowing relations are met respectively, it is necessary to illustrate
Be that following relations are only a kind of currently preferred situation：
Through dynamometer 2 (three-dimensional), charge amplifier 3, it is converted into digital quantity and preserves, handles, prints in computer 5, institute
The feeding drag F statedx, cutting-in drag Fy, main cutting force FzFollowing condition is met with cutting force F relations：
Step 2：Based on described single factor experiment and orthogonal test, Predictive Model of Cutting Force is built, how in cutting examination
Determine that described forecast model is that by the key technology of model optimization on the basis of testing；
Described cutting force F forecast models select optimum combination jointly to predict or estimate cutting force by regression analysis,
Meet following condition：
Wherein, CFProcessing coefficient is represented, its value depends on processing conditions, vcRepresent cutting speed m/min；apExpression is cut
Cut depth, mm；F represents the amount of feeding, mm/r；xF、yF、zFCutting speed, the amount of feeding, the index of cutting depth are represented respectively；
Cutting force F (the F that described experiment is obtained1, F2... F16) specific manifestation form meets following condition：
Wherein, y=lgF, y include y1~y16；x1=lgvc, x1Including x0101~x0116, x2=lgap, x2Including x0201~
x0216；x3=lgf, x3Including x0301~x0316；b0=lgCp；b1=xF；b2=yF；b3=zF；
Step 3：Based on described Predictive Model of Cutting Force and residual error diagnostic method, residual error is asked for；
Described residual error eiMeet following relational expression：
Wherein, yiThe orthogonal test cutting force data, and i ∈ [1,16] are represented,Represent Predictive Model of Cutting Force optimization
Value；
Step 4：According to the statistical property of the cutting force data of collection, standard deviation is calculated；
The standard deviation calculation formula of i-th described of residual error is：
Wherein, hiiThe leverage of i-th of cutting force observation is represented, n is the cutting force sample size of orthogonal test, yiFor
Described i-th of observation station cutting force data of orthogonal test,For the average value of all cutting force observations,Cut described in representing
The recurrence standard deviation of power forecast model is cut,It is the standard deviation of i-th of residual error；
Step 5：Threshold value is set to judge and reject cutting force exceptional value according to cutting environment and residual error rate of change,
As shown in Fig. 2 the 6th point data is more than threshold value, it is necessary to reject in the cutting force observation, other points meet the requirements；Rejecting is shaken
The exceptional value that the disturbing factors such as dynamic and impact are caused cuts the optimization of high temperature alloy Predictive Model of Cutting Force to realize that high pressure cooling is lower
The threshold value is set as 0.1；
Step 6：In order to be more intuitively indicated to described forecast model, to cutting force test value, model value, excellent
Change value carries out contrast verification, as shown in figure 3, integrally smaller than test value error for optimal value relative model value, effect of optimization
Preferably.
The single factor experiment scheme of table 1
The orthogonal test scheme of table 2
Claims (1)
1. a kind of optimization method of the lower cutting high temperature alloy Predictive Model of Cutting Force of high pressure cooling, it is characterised in that：Described is excellent
Change method comprises the following steps：
Step one：Rational choice vc、f、apScope, different cuttings are gathered under high pressure cooling in cutting high temperature alloy experimental basis
Power Fx、Fy、FzSignal；Described cutting force Fx、Fy、FzRespectively feed drag, cutting-in drag, main cutting force；
Wherein, described vc、f、apRespectively cutting speed, the amount of feeding, cutting depth, with reference to High Speed Cutting Technique and difficulty
Rapidoprint cutting characteristic, selection range must meet following relations：
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The lower cutting high temperature alloy experiment of described high pressure cooling includes single factor experiment and orthogonal test two parts (described Dan Yin
PCBN cutters chamfered edge is probed into element experiment influences situation to cutting force, understands it and acts on notable scope, preferably optimal on this basis
Chamfered edge width, angle, carry out multifactor multilevel orthogonal test), v in described single factor experiment and orthogonal testc、f、ap
Following relations are met respectively：
Wherein, viFor vcSpecific value condition, vi+1With viRelation represents with above formula, N*For positive integer, fi+1With f relations, ai+1With
apRelation similarly, vc、f、apUnit is m/min, mm, mm/r respectively；
Described feeding drag Fx, cutting-in drag Fy, main cutting force FzFollowing condition is met with cutting force F relations：
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Step 2：Based on described single factor experiment and orthogonal test, Predictive Model of Cutting Force is built；
Described cutting force F forecast models select optimum combination jointly to predict or estimate cutting force by regression analysis, meet
Following condition：
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Wherein, CFProcessing coefficient is represented, its value depends on processing conditions；vcRepresent cutting speed, m/min；apRepresent that cutting is deep
Degree, mm；F represents amount of feeding mm/r；xF、yF、zFCutting speed, the amount of feeding, the index of cutting depth are represented respectively；
Cutting force F (the F that described experiment is obtained1, F2... F16) specific manifestation form meets following condition：
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Wherein, y=lgF, y include y1~y16；x1=lgvc, x1Including x0101~x0116, x2=lgap, x2Including x0201~x0216；
x3=lgf, x3Including x0301~x0316；b0=lgCp；b1=xF；b2=yF；b3=zF；
Step 3：Based on described Predictive Model of Cutting Force and residual error diagnostic method, residual error is asked for；
The residual error eiMeet following relational expression：
Wherein, yiThe cutting force data that the orthogonal test is collected, and i ∈ [1,16] are represented,Represent Predictive Model of Cutting Force
Optimal value,
Step 4：According to the statistical property of the cutting force data of collection, standard deviation is calculated；
The standard deviation calculation formula of i-th described of residual error is：
Wherein, hiiThe leverage of i-th of cutting force observation is represented, n is the orthogonal test sample size, yiFor orthogonal test
I-th of observation station cuts force data,For all cutting force mean of observations,Represent the recurrence mark of Predictive Model of Cutting Force
It is accurate poor；It is the standard deviation of i-th of residual error；
Step 5：Threshold value is set to judge and reject cutting force exceptional value according to cutting environment and residual error rate of change, it is described
Threshold size be 0.1.
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| CN108717494A (en) * | 2018-05-15 | 2018-10-30 | 哈尔滨理工大学 | Titanium alloy blisk Flank machining cutting parameter optimization method towards multiple target |
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