CN103198186B - Aircraft structural part cutting parameter optimization method based on characteristics - Google Patents
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Abstract
The invention discloses an aircraft structural part cutting parameter optimization method based on characteristics. The method provides a cutting parameter optimization model based on characteristics and gives full consideration to problems of clamping, feed strategies, characteristic types, characteristic structures and cutting strategies. The method uses a machine tool, a workpiece material and a cutter as restraints, uses efficiency, processing quality and cost as optimization goals, and uses rotating speed, feed, cutting depth and cutting width as optimization factors to build the model, and cutting parameter optimization is achieved through a genetic algorithm. The method can solve the problem that aircraft structural part processing parameters at present are too conservative, can greatly improve efficiency, and simultaneously can avoid the problem that deformation occurs easily during processing, the position of a thin wall vibrates easily, and the cutter can be bounced easily at the position of a corner.
Description
Technical field
The present invention relates to a kind of optimization method of machining parameter, especially a kind of optimization method of cutting parameter, specifically an aircraft structure cutting parameter optimization method for feature based, relates to the problem such as genetic algorithm and neural network prediction, still belongs to CNC processing technology field.
Background technology
It is large that aircraft structure has size, is rich in thin-wall construction, dimensional accuracy and surface quality requirements high, is also the difficult point of processing.Different features is because of its size, rigidity difference, and the change of its cutting output is uneven.For preventing processing problems, NC technology personnel often select the cutting parameter comparatively guarded, but still there will be in some features, because cutting parameter is improper, quality problems occur, and have a strong impact on working (machining) efficiency, therefore cutting parameter has become the key factor affecting Flight Structures NC Machining working (machining) efficiency and quality.Consult prior art and document, domestic and international research institution has carried out large quantifier elimination from aspects such as theory, experiments.
There is Jitender K.Rai abroad at academic journal " International Journal of ProductionResearch " 2011, the Optimization of Milling Parameters system delivered on 49 (10): 3045 ~ 3068 based on genetic algorithm carrys out the cutting parameter of prediction optimization, by providing existing process program, generate the combination of the technological parameter of the optimization of Feature Oriented, and defer to function and the technological constraint of machine tool and part.
Domestic have Chen Zhitong, Zhang Baoguo is at academic journal " mechanical engineering " 2009,45 (5): 230 ~ 236. deliver the cutting parameter Optimized model towards unit working angles, to utilize volume value coefficient, area values coefficient, volumetric ablation rate and area resection rate to set up be objective function with unit interval rate of profit or unit interval profit Optimized model, to solve the difficult problems such as crudy, difficulty of processing, working (machining) efficiency, weight between processing cost and processing profit.
But mostly the optimization of cutting parameter is based on the experiment of material and the simulation optimization based on part level, machining parameters optimization for feature level just provides the cutting parameter of various feature at present, and do not consider the design feature of feature in process, geometric parameter and the rigidity of feature, the rigidity of process system, be difficult to adapt to the cutting parameter optimization that aircraft structure adds man-hour.
For above problem, this patent proposes a kind of aircraft structure cutting parameter optimization method of feature based, this method propose the cutting parameter Optimized model of feature based, based on the limit cutting force of neural network prediction machining feature, as the constraint condition that cutting parameter is optimized, by genetic algorithm, cutting parameter is optimized, the problem that current aircraft structure machined parameters is too guarded can be improved, increase substantially efficiency, avoid processing yielding, thin-walled place that flutter, corner easily occur and easily occur to play the problems such as cutter.
Summary of the invention:
The object of the invention is to be mostly based on the experiment of material and the simulation optimization based on part level for the optimization of existing cutting parameter, thus there is poor universality, especially the problem that aircraft structure cutting parameter is optimized is not suitable for, invent a kind of aircraft structure cutting parameter optimization method of feature based, which propose the cutting parameter Optimized model of feature based, based on the limit cutting force of neural network prediction machining feature, as the constraint condition that cutting parameter is optimized, by genetic algorithm, cutting parameter is optimized, the problem that current aircraft structure machined parameters is too guarded can be improved, increase substantially efficiency, avoid processing yielding, easily there is flutter in thin-walled place, corner easily occurs to play the problems such as cutter.
Technical scheme of the present invention is:
An aircraft structure cutting parameter optimization method for feature based, is characterized in that it comprises the following steps:
Step 1, extract the influence factor of feature rigidity, i.e. the structure of feature and size attribute;
Step 2, select characteristic feature and typical structure thereof and typical sizes, the method respectively by finite element analogy and experimental verification draws the limit cutting force of sample characteristics;
Step 3, structure neural network, using the rigidity factor of characteristic feature as input, using the limit cutting force of feature as output, utilize sample characteristics neural network training, and then predict the limit cutting force of feature of cutting parameter to be optimized, and limit of utilization cutting force the constraint condition optimized as cutting parameter, the direction of different characteristic limit cutting force is different;
Step 4, determine optimized variable, constraint function and optimization aim;
Step 5, utilize genetic algorithm as optimized algorithm Optimal Parameters;
Step 6, cutting parameter export.
Described characteristic feature comprises cavity feature web, cavity feature inner mold, muscle feature and contour feature, wherein the rigidity effects factor of cavity feature web comprises its Kekelé structure count, web area, web thickness, whether unsettled, whether apertures, the rigidity effects factor of cavity feature inner mold is height and thickness, the rigidity effects factor of muscle feature is the high and thickness of muscle, and the rigidity effects factor of contour feature is height.
Described neural network is using selected characteristic feature as sample, using the rigidity effects factor of feature as input, predict that the limit cutting force of each feature is as output by the method for finite element analogy, for often kind of feature construction network predicts cutting force binding occurrence.
Described optimized variable comprises speed of mainshaft n, cutting speed V
c, feed engagement fz, feed of every rotation V
f, speed of feed V
f, axial cutting-in ap and the wide ae of radial cut.
Described constraint condition comprises processing stability constraint, cutting force constraint, surface quality constraint, machine power constraint, the constraint of machine torque, the constraint of cutter rigidity, feed engagement constraint and the wide constraint of radial cut.
Described optimization aim is largest production efficiency and minimum production cost.
Described optimized algorithm utilizes genetic algorithm to be optimized multiple goal, namely parameter to be optimized is changed into the chromosome of specific system through programming, different chromosome is utilized to form an initial population, and then produce initial population at random, and using the initial solution of initial population as problem, through copying, the repetitive operation of these 3 kinds of operators of crossover and mutation, iterative evolution, can optimum solution be obtained, and then realize parameter optimization.
Beneficial effect of the present invention:
The present invention has taken into full account the design feature of feature in aircraft structure process, geometric parameter and the rigidity of feature, the rigidity of process system.According to the cutting parameter under the different size of different characteristic, difformity and this feature of rigid line inclusions, effectively improve aircraft structure and process yielding problem, improve working (machining) efficiency, and effectively inhibit thin-walled place that flutter, corner easily occur easily to occur to play the problem such as cutter.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the aircraft structure cutting parameter optimization method of feature based of the present invention.
Fig. 2 is test part of the present invention, and in figure, (1 ~ 13) identifies all groove web features of front processing.
Fig. 3 is the process flow diagram utilizing genetic algorithm optimization parameter.
Fig. 4 is Comparative result before and after optimizing.
Embodiment:
The present invention is further illustrated for Structure Figure and embodiment below.
As Figure 1-4.
Fig. 1 is the aircraft structure cutting parameter optimization method process flow diagram of feature based of the present invention.As shown in Figure 1, the aircraft structure cutting parameter optimization method of feature based of the present invention, comprises the following steps:
1, the influence factor of feature rigidity is extracted.
The feature of part mainly comprises groove web, groove type, muscle feature, contour feature, corner characteristic sum hole characteristic as shown in Figure 2, can obtain the dimensional parameters of web feature, muscle feature, inner mold characteristic sum contour feature by identifying, and corner characteristic sum hole characteristic is usually placed in web and inner mold feature and considers;
2, prediction of Turning Force with Artificial.
According to the structure of feature, constraint and dimensional parameters, build finite element model, the constitutive equation of setting material, construction feature finite element model, then builds cutter finite element model.In the process of finite element analogy, increase the cutting force of cutter gradually, when the maximum deformation quantity of finite element model reaches certain value, record corresponding cutting force, as limit cutting force.The direction of different feature cutting force is different, and web characteristic sum muscle is characterized as Z-direction cutting force, and inner mold is X or Y-direction cutting force.When carrying out finite element analogy to different features, consider the process redundancy of feature, according to processing technology knowledge and experience, this patent gives the chipping allowance of 2mm when simulating, and setting maximum deformation quantity is that 0.2mm is to carry out the limit cutting force that finite element analysis obtains each feature.Obtained the sample of feature cutting force ultimate value by finite element analogy, build neural network, the limit cutting force under predicted characteristics various sizes.For web, neural network structure and Forecasting Methodology are the inputs using the thickness of web, area and Kekelé structure count as neural network, and wherein Kekelé structure count carries out the process that quantizes, and closed assignment is 0, and opening assignment is 1, and opening both sides assignment is 2.Neural network adopts two-layer BP network, and node in hidden layer is 6, exports as single output, i.e. limit cutting force.The structure of neural network can utilize sample to carry out neural metwork training after determining, obtains corresponding weights, can predict its limit cutting force after neural metwork training completes to web feature various sizes.
Contrived experiment carries out respectively verifying to the finite element simulation of cutting force and neural network prediction and corrects, and utilizes three points of dynamometers to measure the cutting force in process, utilizes thickness gauge measure the thickness after processing and then calculate deflection.For finite element analogy, when the error that limit cutting force and the experiment of finite element analogy obtain limit cutting force is greater than setting value, adjust stress and strain model and the boundary condition of characteristic sum cutter in finite element model, until the error that the limit cutting force of finite element analogy and experiment obtain limit cutting force is less than setting value; For neural network prediction, if when the error that the limit cutting force of neural network prediction and experiment obtain limit cutting force exceeds setting value, the structure of adjustment neural network, comprise the middle number of plies, hidden layer node etc., until the limit cutting force of neural network prediction with test the error of limit cutting force that obtains within setting value.
3, variable, constraint function and objective function is determined
1) variable is determined
For cut, cutting parameter comprises speed of mainshaft n, cutting speed V
c, feed engagement f
z, feed of every rotation V
f, speed of feed V
f, axial cutting-in a
p, and the wide a of radial cut
e.This patent mainly considers the parameter larger on objective function impact, therefore selects speed of mainshaft n, feed engagement f
z, axial cutting-in a
p, and the wide a of radial cut
eas the design variable that cutting parameter is optimized, and meet following requirement between parameter
2) constraint function is determined
In conjunction with the processing features of aircraft structure and the influence factor of cutting parameter, below the constraint of cutting parameter optimization is mainly considered:
A, cutting force retrain
In process, cutting force has material impact for the crudy of workpiece, cutter, if especially for the aircaft configuration thin-wall part of rigidity, requires higher to cutting force, equally for large outstanding long weak rigid blade arbor, cutting force is also important consideration factor stable in process.The cutting force Fz of the x, y, z three-dimensional of feature F is obtained by experimental formula
f, Fy
f, Fx
fmeet the following conditions:
Fx
Fmin≤Fx
F≤Fx
Fmax
Fy
Fmin≤Fy
F≤Fy
Fmax
Fz
Fmin≤Fz
F≤Fz
Fmax
In formula, Fx
fminrepresentation feature cutter at the minimum cutting force in x direction, Fx
fmaxrepresentation feature cutter is at the maximum cutting force in x direction.Fy
fminrepresentation feature cutter at the minimum cutting force in y direction, Fy
fmaxrepresentation feature cutter is at the maximum cutting force in y direction.Fz
fminrepresentation feature cutter at the minimum cutting force in z direction, Fz
fmaxrepresentation feature cutter is at the maximum cutting force in z direction.
B, machine power retrain
Use P
erepresent the rated power of lathe, P
qrepresent the real power of lathe, η represents the power efficiency of lathe, then the constraint expression of power is:
P
q≤P
eη
Wherein, the calculation expression of machine power is:
In above formula, Ft represents tangential force suffered by main shaft, is tried to achieve by cutting force experimental formula, can utilization index experimental formula, and d represents major axis diameter.
The constraint of c, main-shaft torque
Use T
erepresent the nominal torque of lathe, T
qrepresent the main-shaft torque of milling cutter, the expression formula of moment of torsion constraint is:
T
q≤T
e
Wherein the main-shaft torque calculation expression of milling cutter is:
T
q=F
t×d
D, cutter rigid constraint
If cutting force is excessive in process, can there is yield deformation in cutter, therefore needs to retrain cutter rigidity, uses δ
maxrepresent the Allowable deflection of cutter, δ represents cutter distortion amount, and its deformation constrain is expressed as:
δ≤δ
max
The computing method of cutter distortion amount are:
Wherein F
rrepresent cutter force in radial, tried to achieve by cutting force experimental formula, can utilization index experimental formula, l represents that cutter is outstanding long, and EI represents the bending strength of knife bar.
E, corner speed of feed retrain
Be subject to the restriction of lathe and digital control system performance, when processing corner, speed of feed needs within certain value, and represent with Acc the peak acceleration that lathe allows when turning, Rc represents the knuckle radius of corner, the speed V of corner
cornerconstraint expression formula be:
F, feed engagement retrain
Use f
zmin, f
zmaxrepresent the minimum and maximum feed engagement determined by lathe and processing technology respectively, then the constraint expression of feed engagement is:
f
zmin≤f
z≤f
zmax
The wide constraint of g, radial cut
Use a
eminand a
emaxrepresent respectively determined by processing technology minimum and maximum cut wide, the diameter as cutter limits, and in the wide constraint expression formula of radial cut is:
a
emin≤a
e≤a
emax
H, surface quality retrain
Different machining feature has different surface quality requirements, and surfaceness weighs the leading indicator of surface quality, sets up roughness constraint function as follows:
R(X)=C
v xf
ya
p z
In above formula, C
vrepresent coefficient, f represents feed engagement, and x, y, z represent that needs are by testing the parameter determined.
I, processing stability retrain
For weak rigidity process system, in working angles, very easily there is flutter.The speed of mainshaft reflected by chatter stability lobes leaf lobe figure and the relation of axial cutting-in obtain stable cutting scope, in cutting stable region, select the speed of mainshaft and cutting-in.In leaf lobe figure, have multiple stable region, select best value according to optimized algorithm.
In leaf lobe figure, be stable region with S, for the coordinate be made up of the speed of mainshaft and cutting-in, meet:
（n,a
p）∈S
3) objective function is determined
Optimization aim described in the present invention comprises largest production efficiency and minimum production cost, and what therefore need to determine is a multiple objective function.Represent largest production efficiency with M (X), C (X) represents minimum production cost, β
1, β
2represent production efficiency and the weight of production cost in objective function, then objective function MF(X) can be expressed as:
MF(X)=β
1M(X)+β
2C(X)
Wherein production efficiency was determined by the process time of processing object, and depended on material remove rate the process time of part, used a
pfor axial cutting-in, a
efor radial cutting-in, n represents the speed of mainshaft (r/min), and N represents the cutter number of teeth, then the objective function of material removing rate is expressed as:
M(X)=a
p·a
e·f
z·N
Use C
mrepresent the cost of raw material, C
mrepresent lathe this with regard to cost minimization production cost, C
trepresent the cost of charp tool, C
hrepresent labor cost, C
prepresent power cost, the objective function of minimum production cost can be obtained:
C(X)=C
M+C
m+C
t+C
h+C
p
The expression formula of often kind of cost is:
C
m=e
m(t
m+t
r)
C
t=e
tt
m=c
tt
m/T
C
h=e
h(t
m+t
r)
C
p=e
p(t
m+t
r)
Wherein e
mrepresent lathe cost depreciation rate, e
trepresent the nonrecoverable cost rate of cutter, e
hrepresent workman's time cost rate, e
prepresent power cost rate, t
mrepresent process time, t
rrepresent the secondary process time, T represents the life-span allowable of cutter.E
m, e
h, e
t, e
pby following formulae discovery:
T
mobtain by numerical control program emulation, or utilize neural network algorithm to obtain, t
rempirical evaluation can be passed through according to accessory size, process program etc.
4, genetic algorithm is built
The present invention selects genetic algorithm as multi-objective optimization algorithm, and optimizing process as shown in Figure 3.Parameter to be optimized is changed into specific chromosome through programming, utilizes different chromosome to form an initial population, and as the initial solution of problem, through copying, intersecting, make a variation the repetitive operation of these three kinds of operators, iterative evolution, and then obtain optimum solution, export the parameter optimized.
Because the present invention selects largest production efficiency and minimum production cost to be the multiple objective function of target, therefore objective function is mapped to the form of maximizing and guarantees fitness function value non-negative by fitness function, and method is as follows:
M (x) represents production efficiency value during certain generation iteration, the value of production cost when c (x) represents certain generation iteration.MF
minit can be certain generation
minimum value, also can be the value (generally choosing between-10 ~ 10) that manually input is suitable, both prevent fitness value excessive, and also can eliminate individual chromosomes.There will be in order to avoid single operator intersects the problem reached unanimity simultaneously, and adopt many operators to intersect, strengthen operating effect.If the i-th group chromosome form in r generation is X
i(r)=[a
1, a
2..., a
n], then utilize existing two chromosomes:
X
i(r)=[0.51,0.35 ..., 0.48], X
j(r)=[0.37,0.54 ..., 0.58] and (i ≠ j), obtaining similarity is:
e=[|0.51-0.37|+|0.35-0.54|+…+|0.48-0.58|]/n
When e>0.2 time, think that similarity is too low, then directly carry out next group hybridization, many operators crossing formula of hybridization is as follows:
X
i(r+1)=lX
i(r)+(1-l)X
j(r)
X
j(r+1)=lX
j(r)+(1-l)X
i(r)
L is the number of stochastic generation [0,1].In order to finally obtain optimum solution, in the every generation population selected, all retaining a best individuality do not hybridized.
5, cutting parameter optimization
First material properties is determined, i.e. cutter material, workpiece material, and known conditions is input in optimization system, known conditions comprises mill type, sense of rotation, milling cutter diameter, milling cutter ' s helix angle, point of a knife radius of circle, cutter tooth number, axial cutting depth, the radial cutting degree of depth, tool feeding rate, after determining constraint condition, carry out cutting parameter optimization.The parameter that can optimize comprises the speed of mainshaft, speed of feed, feed engagement, feed of every rotation, cutting speed, axial cutting-in and radial cutting-in.This is to provide three kinds of prioritization schemes, is the minimum cost prioritization scheme focusing on cost respectively, and top efficiency prioritization scheme and the integration objective of focusing on efficiency calculate.For the part shown in Fig. 2, utilize integration objective to calculate and carry out cutting parameter optimization, the parameters input after optimization is processed same feature again to lathe and carries out result comparison, comparison result is as Fig. 4.
The part that the present invention does not relate to prior art that maybe can adopt same as the prior art is realized.
Claims (6)
1. an aircraft structure cutting parameter optimization method for feature based, is characterized in that it comprises the following steps:
Step 1, extract the influence factor of feature rigidity, i.e. the structure of feature and size attribute;
Step 2, select characteristic feature, the method respectively by finite element analogy and experimental verification draws the limit cutting force of sample characteristics;
Step 3, structure neural network, using the rigidity factor of characteristic feature as input, using the limit cutting force of feature as output, utilize sample characteristics neural network training, and then predict the limit cutting force of feature of cutting parameter to be optimized, and the constraint condition that limit of utilization cutting force is optimized as cutting parameter, the direction of different characteristic limit cutting force is different;
Step 4, determine optimized variable, constraint function and optimization aim;
Step 5, utilize genetic algorithm as optimized algorithm Optimal Parameters;
Step 6, cutting parameter export.
2. method according to claim 1, it is characterized in that described characteristic feature comprises cavity feature web, cavity feature inner mold, muscle feature and contour feature, wherein the rigidity effects factor of cavity feature web comprises its Kekelé structure count, web area, web thickness, whether unsettled, whether apertures, the rigidity effects factor of cavity feature inner mold is height and thickness, the rigidity effects factor of muscle feature is the high and thickness of muscle, and the rigidity effects factor of contour feature is height.
3. method according to claim 1, it is characterized in that described neural network is using selected characteristic feature as sample, using the rigidity effects factor of feature as input, predict that the limit cutting force of each feature is as output by the method for finite element analogy, for often kind of feature construction network predicts cutting force binding occurrence.
4. method according to claim 1, is characterized in that described optimized variable comprises speed of mainshaft n, cutting speed V
c, feed engagement fz, feed of every rotation V
f, speed of feed V
f, axial cutting-in ap and the wide ae of radial cut.
5. method according to claim 1, is characterized in that described optimization aim is largest production efficiency and minimum production cost.
6. method according to claim 1, it is characterized in that described optimized algorithm utilizes genetic algorithm to be optimized multiple goal, namely parameter to be optimized is changed into the chromosome of specific system through programming, utilize different chromosome to form an initial population, and then produce initial population at random, and using the initial solution of initial population as problem, through copying, the repetitive operation of these 3 kinds of operators of crossover and mutation, iterative evolution, can obtain optimum solution, and then realize parameter optimization.
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Application publication date: 20130710 Assignee: Jiangsu Mai Lin aviation Polytron Technologies Inc Assignor: Nanjing University of Aeronautics and Astronautics Contract record no.: 2018320000038 Denomination of invention: Aircraft structural part cutting parameter optimization method based on characteristics Granted publication date: 20150708 License type: Exclusive License Record date: 20180306 |