CN104636563B - High-speed blanking press entablature reliability design approach - Google Patents
High-speed blanking press entablature reliability design approach Download PDFInfo
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Abstract
The invention discloses a kind of high-speed blanking press entablature reliability design approach.Comprise the following steps：Reliability requirement selection design variable, establishes the high-speed blanking press entablature reliability design model that uncertain factor is described with section in being designed according to actual entablature；Sample point needed for fitting Kriging is obtained using LHS in experimental design, by the object function corresponding to each sample point of collaborative simulation technical limit spacing and constraint functional value, and Kriging models are built with this；Reliability constraint value in reliability design model is calculated based on uniform section dominance；Meet the optimization design scheme of reliability requirement using the double-layer nested genetic algorithm search based on Operations of Interva Constraint violation degree.The present invention calculates reliability index value using uniform section dominance in reliability design, can easily obtain the high-speed blanking press entablature design for meeting reliability requirement according to high-speed blanking press entablature achieved reliability demand.
Description
Technical field
The invention belongs to high-speed blanking press design field, more particularly to a kind of high-speed blanking press entablature reliability design side
Method.
Technical background
The performance of high-speed blanking press directly affects the precision, performance and production efficiency of stamping products, and entablature
As the important component of high-speed blanking press, its rigidity and intensity have to the integral working and machining accuracy of forcing press
Direct and important influence.In order to improve the machining accuracy of forcing press and operating efficiency, production cost and energy resource consumption are reduced, should
Improving entablature rigidity as much as possible and mitigating the weight of entablature while intensity, therefore, it is necessary to it is being carried out with Gao Gang
Degree light weight turns to the reliability design of target.
In the manufacture of high-speed blanking press actual design, there is certain uncertainty in the stressing conditions and material properties of entablature
Fluctuation so that its performance has certain variability, if being optimized still according to certain problem solution throughway, not only finally
Result it is not necessarily optimal, in some instances it may even be possible to larger deviation be present, available accuracy demand can not be met.Therefore, in order to ensure to set
The reliability of result is counted, the uncertainty of these objective realities must be taken into full account in design process, to horizontal on high-speed blanking press
Beam carries out reliability design, could obtain real reliable optimization design scheme.
Optimization design based on probabilistic reliability is one of probabilistic effective way of processing, on methods and applications all
Existing more sufficiently research.Probability and reliability analysis needs substantial amounts of sample data to obtain on the accurate general of Uncertainty
Rate distributed intelligence, but very limited amount of sample data is often can only obtain in engineering, also, probabilistic reliability is to random parameter
Distributed intelligence be probably sensitive, i.e. the small error of probabilistic model parameter can cause the larger error that structural reliability calculates.
In actual decision-making, though the probability distribution of Uncertainty is not easy accurately to obtain, its boundary being deteriorated then is easy to determine, therefore,
The border of Uncertainty can be described using interval mathematical theory, structure is determined in the gamut of Uncertainty
Reliability, this has actually get more structurally sound structural system.In the Multidisciplinary systems design based on section, a change
Measure value and reflect the uncertainty of the variable-value for interval number, thus the size of interval number compares should be able to reflect change
The uncertainty of value is measured, the magnitude relationship of interval number should not generally be thought in absolute terms, and a section should be provided
Count the degree greater than, equal to or less than another interval number.Therefore the entablature reliability design of uncertain variables is described in section
, it is necessary to calculate the reliability index of constraint with a kind of more generally applicable, objective, comprehensive interval number comparative approach in model solution
Value.
The content of the invention
To solve multiple uncertainty present in Practical Project high speed press crown high rigidity light-weight design
Problem, object of the present invention is to provide a kind of high-speed blanking press entablature reliability design approach, according to actual entablature
Reliability requirement selection design variable in design, establish the high-speed blanking press entablature of uncertain factor is described with section can
Designed a model by property, the reliability constraint value in reliability design model is calculated based on uniform section dominance, using double-deck embedding
The genetic algorithm of set and high-precision Kriging agent models, which are combined, directly finds reliability design model optimal solution.The party
Method can obtain high reliability and high-precision press crown design in the case where multiple uncertainty be present.
The present invention is achieved by the following technical solutions：A kind of high-speed blanking press entablature reliability design approach, bag
Include following steps：
(1) the high-speed blanking press entablature reliability design model that uncertain factor is described with section is established：According to reality
Border design requirement, determine optimization aim in high-speed blanking press entablature reliability design and constraints, design variable and its
Uncertain factor and its waving interval considered is needed in span, design, establishes the entablature based on interval variable as follows
Reliability design model：
X=(x1,x2,…,xn)∈Rn；
U=(U1,U2,…,Uq)∈Iq；
Wherein, x is that n ties up design vector, and U is that q ties up interval vector, and f (x, U) is the target letter of entablature reliability design
Number, gi(x, U) is i-th of mechanical performance index that need to consider reliability, Bi、RgiAnd RsiRespectively allow variation zone corresponding to it
Between, achieved reliability and given reliability constraint value,Respectively BiLower bound and the upper bound；hj(x, U) need not for j-th
Consider the mechanical performance index of reliability, CjAllow constant interval corresponding to it,Respectively CjLower bound and the upper bound；
(2) using Latin Hypercube Sampling method (Latin Hypercube Sampling, LHS) in input variable space
Experimental design is carried out, obtains fitting sample point；
In experimental design, according to design vector x and interval vector U fluctuation range, become in the input being made up of x and U
It is sampled in quantity space using LHS, S input variable, the Latin hypercube experimental design of n times test run are expressed as value
Scope is N × S rank matrixes of [0,1], obtains with the uniform property in space and project the sample point group of uniformity, then by its anti-normalizing
Change into the input variable space of x and U compositions, complete the initial samples to design vector x and interval vector U；
(3) parameterized model is established, power in target corresponding to sample point and constraint function is obtained by collaborative simulation technology
Learn the response of performance indications；
Using 3 d modeling software, using design vector x as independent control parameter, high-speed blanking press entablature parametrization is established
Threedimensional model；The real time bidirectional transmission of parameter between modeling software and finite element analysis software is realized by interfacing；Pass through association
With emulation, call the parametrization threedimensional model of dynamic renewal to carry out finite element analysis computation, obtain the target corresponding to each sample
With the response of mechanical performance index in constraint function；
(4) complete input-output sample points evidence is utilized, using entablature design variable and uncertain factor as input
Parameter, using the response of entablature mechanical performance index as output parameter, establish Kriging response surface models；
Kriging models approximate expression is a probability distribution function and a multinomial sum, is shown below：
Y (x)=f (x) β+z (x)
In formula, y (x) is unknown Kriging models, and f (x) is the known function on x, there is provided in design space
Global approximate simulation, β is regression function undetermined coefficient, and its value is estimated to obtain by known response；Z (x) is one random
Process, be created on the basis of global simulate be desired for 0, variance σ2Partial deviations, its covariance matrix cov [z
(xi),z(xj)] be expressed as
cov[z(xi),z(xj)]=σ2R[R(xi,xj)]
In formula, R is correlation matrix；R(xi,xj) represent any two sample point xi,xjCorrelation function, select Gaussian function
Number is used as correlation function, and its expression formula is：
Wherein, n' is the number of sample point, according to condition of unbiasedness and variance minimal condition, with reference to method of Lagrange multipliers
Sum Maximum Likelihood Estimate method, try to achieve parameter beta, R and θkValue, and then obtain required Kriging models；
(5) the Calculation of Reliability criterion based on uniform section dominance is established；
According to interval mathematical theory, section A=[aL,aR] relative to interval B=[bL,bR] dominance P(A>B)Calculating
Method：
(a) a is worked asL≥bRWhen, P(A>B)=1；
(b) b is worked asL≤aL≤bR≤aRWhen,
(c) a is worked asL≤bL≤bR≤aRWhen,
(d) a is worked asL≤bL≤aR≤bRWhen,
(e) b is worked asL≤aL≤aR≤bRWhen,
(f) a is worked asL≤aR≤bL≤bRWhen, P(A>B)=0；
The section that each design constraint performance of high-speed blanking press entablature is calculated using above-mentioned section dominance computational methods can
By property index Rgi[gi(x,U)≤Bi]；
(6) entablature reliability design model is solved using double-layer nested genetic algorithm, it is current to outer layer genetic optimization
For all individuals in population, its institute is calculated using the Kriging models established in internal layer single objective genetic algorithm and step 4
The bound f of mechanical performance index interval value in corresponding object function and constraint functionR(x), fL(x), And obtain wherein object function and non-reliability constraint function interval value midpoint and
Radius fC(x), fW(x), In conjunction with the Calculation of Reliability criterion of uniform section dominance in step 5, obtain can
By property binding occurrence Rgi[gi(x,U)≤Bi]；Wherein, subscript R, L, C, W represents the section upper bound, section lower bound, interval midpoint respectively
With section radius；
To reliability constraint Rgi[gi(x,U)≤Bi]≥RsiFor, the calculation of its constraint violation degree is：
If (a) Rgi[gi(x,U)≤Bi]≥Rsi, then its constraint violation degree Vi(x)=0；
If (b) Rgi[gi(x,U)≤Bi]<Rsi, then its constraint violation degree is Vi(x)=Rsi-Rgi[gi(x,U)≤Bi]；
To non-reliability constraint hj(x,U)≤CjFor, the calculation of its constraint violation degree is:
(c) whenWhen, constraint violation degree Vj(x)=<0,0>；
(d) whenWhen, ifThen Vj(x)=<0,0>IfThen
(e) whenWhen, constraint violation degree is
It is possible thereby to calculate when all individual total constraint violation degree of former generation populationp
For constraint number total in entablature reliability design model, then VT(x) solution=0 is feasible solution, is otherwise infeasible solution；
By fC(x), fW(x), VT(x) after result of calculation is delivered to each sample point of outer layer optimization by internal layer optimization, it is based on
All individuals that the major relation criterion of Operations of Interva Constraint violation degree optimizes to outer layer in population carry out trap queuing, determine that it is good and bad
Tagmeme, work as all individual fitness in former generation population so as to calculate to obtain, determine design vector x1With x2The mode of good and bad relation
For：
If (a) x1For feasible solution, x2For infeasible solution, then there is x all the time1Better than x2；
If (b) x1With x2It is feasible solution, then both relative superior or inferiors is judged with object function interval value, work as fC(x1)<fC
(x2) when, or fC(x1)=fC(x2) and fW(x1)<fW(x2) when, x1Better than x2；
If (c) x1With x2It is infeasible solution, then judges that it is good and bad according to constraint violation degree, if VT(x1)<VT(x2), then
x1Better than x2, otherwise, x2Better than x1；
If outer layer genetic algorithm evolutionary generation reaches given maximum or reaches convergence requirement, outer layer heredity is terminated
Algorithm evolution process, individual of the output with maximum adaptation angle value as optimum individual, using the design vector corresponding to it as
Optimal design vector, it is met the high-speed blanking press entablature design of reliability requirement；Otherwise, population of new generation is generated
Individual, evolutionary generation add 1, continue outer layer genetic evolution process.
The invention has the advantages that：
1) 3 d modeling software and finite element software collaborative simulation are used, can be square for large complicated assembly structure
Just parameterized model is established, realizes the bi-directional of parameter and the dynamic renewal of model.
2) consider uncertain factor existing for objectivity in Practical Project problem, retouched in the form of interval number
State, establish more objective and real Multidisciplinary systems Optimized model, uniform section dominance is applied to reliability index
Calculating, the constraint reliability index value of each sample point can be effectively obtained in the case of uncertain variables Probability Distributed Unknown.
3) Kriging model of the structure with good global statistics, avoids adjusting repeatedly in reliability design solution procedure
Mechanical performance index value of the given entablature design under uncertain factor effect is calculated with finite element analysis software, can
Ensure good computational accuracy and robustness while solution efficiency is improved.
4) entablature section is carried out using based on the double-layer nested genetic algorithm of constraint violation degree and uniform section dominance
The direct solution of reliability design model, determination need to be first converted into by avoiding the existing reliability design approach based on section
Property the model subjectivity that parameter selects during the loss and model conversion of unascertained information when being solved again it is random.
Brief description of the drawings
Fig. 1 is high-speed blanking press entablature reliability design flow chart；
Fig. 2 is the model graphics of high-speed blanking press entablature 1/4；
Fig. 3 be high-speed blanking press entablature the constrained and schematic diagram of load；
Fig. 4 is high-speed blanking press entablature cross-sectional view and each key dimension plan.
Embodiment
Below in conjunction with drawings and examples, the invention will be further described.
As shown in figure 1, a kind of high-speed blanking press entablature reliability design approach of the present invention, comprises the following steps：
(1) the high-speed blanking press entablature reliability design model that uncertain factor is described with section is established：
It is research object to select certain model high-speed blanking press entablature, is illustrated in figure 2 its 1/4 model, according to actually setting
Meter demand, with the sectional dimension h of entablature in Fig. 41、h2、l1、l2And l3For size design variable, with external force P shown in Fig. 31、P2、
P3With the uncertain factor that density p is section description, using deflection as optimization design target, the maximum permissible stress of entablature
For [60,61] MPa, reliability requirement is 0.98, and high rigidity lightweight reliability design is carried out to entablature.
Defined function function δ (x, U) is the maximum equivalent of entablature, then its Reliability Constraint is Rg[δ(x,U)≤
[60,61]MPa]≥Rs=0.98, i.e.,
Rg[δ (x, U)=δ (h1,h2,l1,l2,l3,P1,P2,P3,ρ)≤[60,61]MPa]≥Rs=0.98
Defined function w (x, ρ) is the weight of entablature, as the constraint function to design a model, i.e.,
W (x, ρ)=w (h1,h2,l1,l2,l3,ρ)≤5500kg
Then the entablature reliability design model based on section can be expressed as：
s.t.Rg[δ (x, U)=δ (h1,h2,l1,l2,l3,P1,P2,P3,ρ)≤[60,61]MPa]≥Rs=0.98；
W (x, ρ)=w (h1,h2,l1,l2,l3,ρ)≤5500kg；
200mm≤h1≤260mm,250mm≤h2≤300mm；
80mm≤l1≤120mm,20mm≤l2≤60mm,330mm≤l3≤400mm；
P1=[P1 L,P1 R]=[2.44 × 105kN,2.56×105kN]；
P2=[P2 L,P2 R]=[4.90 × 105kN,5.10×105kN]；
P3=[P3 L,P3 R]=[7.80 × 105kN,8.20×105kN]；
ρ=[ρL,ρR]=[7290kgm-2,7310kg·m-2]。
Wherein, design vector x=(h1,h2,l1,l2,l3), the uncertain vectorial U=(P of section description1,P2,P3, ρ), d
(x, U) be characterize entablature maximum deformation quantity object function, Rg[δ (x, U)≤[60,61] MPa] is maximum to characterize entablature
The reliability constraint of equivalent stress, reliability requirement are that 0.98, w (x, ρ) is the non-reliability constraint for characterizing entablature weight.
(2) fitting sample point is obtained in input variable space using LHS.
In the case of design vector x and uncertain factor vector U spans are fixed, in the input being made up of x and U
It is the square of [0,1] using the Latin Hypercube Sampling method generation span based on minimax Optimality Criteria in the variable space
Battle array, 70 sample points with the uniform property in space are obtained, and by its renormalization into input vector space, completed to design
The initial samples of vector sum uncertainty interval vector；
(3) the parametrization threedimensional model of high-speed blanking press entablature is established, sample point pair is obtained by collaborative simulation technology
The response of each mechanical performance index in the target and constraint function answered；
Using 3 d modeling software, using design vector x as independent control parameter, establish horizontal on target model high-speed blanking press
Beam parameterized model；It is secondary input parameter that uncertain factor vector U is added in finite element software；Realized by interfacing
The bi-directional of parameter between 3 d modeling software and finite element analysis software；By collaborative simulation, three-dimensional parametric modeling is called
Finite element analysis computation is carried out, obtains the response of mechanical performance index in target corresponding to 70 samples and constraint function；
(4) using sample points according to the Kriging models for establishing each mechanical performance index in target and constraint function；
According to the sample points comprising complete input-output information according to establishing mechanical performance index in target and constraint function
Kriging models, in fit procedure, it be basic function to select Gaussian function, selectes second order regression function and is fitted, and right
Fitting result verified, ensures its fitting precision and generalization ability meets actual demand, the Kriging models of foundation are as follows：
(5) the Kriging models of reliability design model, full sample point data and fitting are updated to double-layer nested
In genetic algorithm, the maximum evolutionary generation for giving ectonexine genetic algorithm is respectively the 200 and 400, kind of ectonexine genetic algorithm
Group's scale is respectively that the 100 and 200, crossover probability of ectonexine genetic algorithm is respectively 0.95 and 0.90, ectonexine genetic algorithm
Mutation probability be respectively 0.01 and 0.05.When outer layer genetic algorithm reaches end condition, output result scheme is h1=
229.7mm,h2=264.2mm, l1=119.6mm, l2=55.1mm, l3=337.5mm, its result and initial scheme are carried out
Contrast, its object function interval midpoint valueAnd meeting
Weight is reduced in the case of reliability requirement, meets high-speed blanking press entablature high rigidity lightweight reliability design requirement.
Claims (3)
1. a kind of high-speed blanking press entablature reliability design approach, it is characterised in that this method comprises the following steps：
(1) the high-speed blanking press entablature reliability design model that uncertain factor is described with section is established：According to actually setting
Meter demand, determine optimization aim and constraints, design variable and its value in high-speed blanking press entablature reliability design
Uncertain factor and its waving interval considered is needed in scope, design, it is reliable to establish the entablature based on interval variable as follows
Property designs a model：
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X=(x1,x2,…,xn)∈Rn；
U=(U1,U2,…,Uq)∈Iq；
Wherein, x be n tie up design vector, U be q tie up interval vector, f (x, U) be entablature reliability design object function, gi
(x, U) is i-th of mechanical performance index that need to consider reliability, Bi、RgiAnd RsiRespectively allow constant interval, reality corresponding to it
Border reliability and given reliability constraint value,Respectively BiLower bound and the upper bound；hj(x, U) is j-th without considering
The mechanical performance index of reliability, CjAllow constant interval corresponding to it,Respectively CjLower bound and the upper bound；
(2) experimental design is carried out in input variable space using Latin Hypercube Sampling method LHS, obtains fitting sample point：
In experimental design, according to design vector x and interval vector U fluctuation range, adopted in the input variable space being made up of x and U
Be sampled with LHS, S input variable, the Latin hypercube experimental design of n times test run be expressed as span for [0,
1] N × S rank matrixes, the sample point group of uniformity is obtained with the uniform property in space and projects, then by its renormalization to x and U
In the input variable space of composition, the initial samples to design vector x and interval vector U are completed；
(3) parameterized model is established, mechanical property in target corresponding to sample point and constraint function is obtained by collaborative simulation technology
The response of energy index：Using 3 d modeling software, using design vector x as independent control parameter, establish horizontal on high-speed blanking press
Beam parameterizes threedimensional model；The real time bidirectional for realizing parameter between modeling software and finite element analysis software by interfacing passes
Pass；By collaborative simulation, call the parametrization threedimensional model of dynamic renewal to carry out finite element analysis computation, it is right to obtain each sample institute
The response of mechanical performance index in the target and constraint function answered；
(4) complete input-output sample points evidence is utilized, is joined using entablature design variable and uncertain factor as input
Number, using the response of entablature mechanical performance index as output parameter, establishes Kriging response surface models；
Kriging models approximate expression is a probability distribution function and a multinomial sum, is shown below：
Y (x)=f (x) β+z (x)
In formula, y (x) is unknown Kriging models, and f (x) is the known function on x, there is provided complete in design space
Office's approximate simulation, β is regression function undetermined coefficient, and its value is estimated to obtain by known response；Z (x) is a random process,
Be created on the basis of global simulate be desired for 0, variance σ2Partial deviations, its covariance matrix cov [z (xi),z
(xj)] be expressed as
cov[z(xi),z(xj)]=σ2R[R(xi,xj)]
In formula, R is correlation matrix；R(xi,xj) represent any two sample point xi,xjCorrelation function, select Gaussian function conduct
Correlation function, its expression formula are：
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Wherein, according to condition of unbiasedness and variance minimal condition, with reference to method of Lagrange multipliers Sum Maximum Likelihood Estimate method, try to achieve
Parameter beta, R and θkValue, and then obtain required Kriging models；
(5) the Calculation of Reliability criterion based on uniform section dominance is established；
According to interval mathematical theory, section A=[aL,aR] relative to interval B=[bL,bR] dominance P(A>B)Computational methods：
(a) a is worked asL≥bRWhen, P(A>B)=1；
(b) b is worked asL≤aL≤bR≤aRWhen,
(c) a is worked asL≤bL≤bR≤aRWhen,
(d) a is worked asL≤bL≤aR≤bRWhen,
(e) b is worked asL≤aL≤aR≤bRWhen,
(f) a is worked asL≤aR≤bL≤bRWhen, P(A>B)=0；
The section reliability of each design constraint performance of high-speed blanking press entablature is calculated using above-mentioned section dominance computational methods
Index Rgi[gi(x,U)≤Bi]；
(6) entablature reliability design model is solved using double-layer nested genetic algorithm, former generation population is worked as to outer layer genetic optimization
In all individuals, calculated using the Kriging models established in internal layer single objective genetic algorithm and step (4) corresponding to it
The bound f of mechanical performance index interval value in object function and constraint functionR(x), fL(x), And obtain midpoint and the radius f of wherein object function and non-reliability constraint function interval valueC(x), fW(x), Reliability constraint value R is obtained in conjunction with the Calculation of Reliability criterion of uniform section dominance in step (5)gi[gi(x,U)
≤Bi]；Wherein, subscript R, L, C, W represents the section upper bound, section lower bound, interval midpoint and section radius respectively；
To reliability constraint Rgi[gi(x,U)≤Bi]≥RsiFor, the calculation of its constraint violation degree is：
If (a) Rgi[gi(x,U)≤Bi]≥Rsi, then its constraint violation degree Vi(x)=0；
If (b) Rgi[gi(x,U)≤Bi]<Rsi, then its constraint violation degree is Vi(x)=Rsi-Rgi[gi(x,U)≤Bi]；
To non-reliability constraint hj(x,U)≤CjFor, the calculation of its constraint violation degree is:
(c) whenWhen, constraint violation degree Vj(x)=<0,0>；
(d) whenWhen, ifThen Vj(x)=<0,0>IfThen
(e) whenWhen, constraint violation degree is
It is possible thereby to calculate when all individual total constraint violation degree of former generation populationP is upper
Total constraint number in crossbeam reliability design model, then VT(x) solution=0 is feasible solution, is otherwise infeasible solution；
By fC(x), fW(x), VT(x) after result of calculation is delivered to each sample point of outer layer optimization by internal layer optimization, based on section
All individuals that the major relation criterion of constraint violation degree optimizes to outer layer in population carry out trap queuing, determine its good and bad sequence
Position, work as all individual fitness in former generation population so as to calculate to obtain, determine design vector x1With x2The mode of good and bad relation
For：
If (a) x1For feasible solution, x2For infeasible solution, then there is x all the time1Better than x2；
If (b) x1With x2It is feasible solution, then both relative superior or inferiors is judged with object function interval value, work as fC(x1)<fC(x2)
When, or fC(x1)=fC(x2) and fW(x1)<fW(x2) when, x1Better than x2；
If (c) x1With x2It is infeasible solution, then judges that it is good and bad according to constraint violation degree, if VT(x1)<VT(x2), then x1It is excellent
In x2, otherwise, x2Better than x1；
If outer layer genetic algorithm evolutionary generation reaches given maximum or reaches convergence requirement, outer layer genetic algorithm is terminated
Evolutionary process, individual of the output with maximum adaptation angle value is as optimum individual, using the design vector corresponding to it as optimal
Design vector, it is met the high-speed blanking press entablature design of reliability requirement；Otherwise, population of new generation is generated
Body, evolutionary generation add 1, continue outer layer genetic evolution process.
A kind of 2. high-speed blanking press entablature reliability design approach according to claim 1, it is characterised in that：The step
Suddenly in (5), the reliability index value of entablature organization plan corresponding to each design vector is calculated using uniform section dominance, from
And the violation degree of each constraints in reliability design model is calculated, and thus calculate total constraint violation degree.
A kind of 3. high-speed blanking press entablature reliability design approach according to claim 1, it is characterised in that：The step
Suddenly in (6), realized using the double-layer nested genetic algorithm based on constraint violation degree and uniform section dominance horizontal on forcing press
The direct solution of beam reliability design model, wherein, internal layer genetic algorithm calculates reliability design mesh using Kriging models
Each entablature mechanical performance index value in mark and constraint, outer layer genetic algorithm are calculating each constraint bar using uniform section dominance
On the basis of the total constraint violation degree of violation degree and design of part, determine whether design feasible, to feasible program according to
Its object function section response carries out trap queuing, and to infeasible scheme, trap queuing is carried out according to its constraint violation degree.
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| CN105930562A (en) * | 2016-04-13 | 2016-09-07 | 浙江大学 | Structural performance optimum design method under non-probability conditions |
| CN105956368B (en) * | 2016-04-21 | 2018-07-03 | 北京航空航天大学 | A kind of non-probability dynamic reliability appraisal procedure of composite laminated structures |
| CN106096127B (en) * | 2016-06-07 | 2019-11-19 | 浙江大学 | The robust error estimator method of the uncertainty structure containing interval parameter |
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| CN107016173B (en) * | 2017-03-22 | 2020-04-17 | 浙江大学 | Reliability design method for dynamic characteristics of high-speed press base |
| CN107391804B (en) * | 2017-06-27 | 2020-12-22 | 广东省建筑设计研究院有限公司 | High-rise building structure anti-seismic performance optimization method based on comprehensive construction cost method |
| CN107563046A (en) * | 2017-08-30 | 2018-01-09 | 国家电网公司 | Failure risk rate computational methods and device based on dam invalidation functions function model |
| CN108427832B (en) * | 2018-02-11 | 2020-04-14 | 浙江大学 | Structural performance robust optimization method based on interval constraint three-dimensional violation vector |
| CN109992848B (en) * | 2019-03-14 | 2020-11-13 | 浙江大学 | Press machine upper crossbeam robust optimization design method based on negative ideal solution approach distance |
| CN110847894B (en) * | 2019-10-30 | 2022-11-04 | 中国石油天然气股份有限公司 | Method for determining flow pressure of underground throttling gas well |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102867083A (en) * | 2012-08-30 | 2013-01-09 | 浙江大学 | High-rigidity and light-weight design method considering uncertainty of slide block mechanism of press machine |
| CN103699720A (en) * | 2013-12-09 | 2014-04-02 | 浙江大学 | High-speed press slider mechanism size optimization method based on interval constraint violation degrees |
| CN103942375A (en) * | 2014-04-11 | 2014-07-23 | 浙江大学 | High-speed press sliding block dimension robust design method based on interval |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8001515B2 (en) * | 2007-12-21 | 2011-08-16 | National Semiconductor Corporation | Simultaneous optimization of analog design parameters using a cost function of responses |
-
2015
- 2015-02-14 CN CN201510079808.8A patent/CN104636563B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102867083A (en) * | 2012-08-30 | 2013-01-09 | 浙江大学 | High-rigidity and light-weight design method considering uncertainty of slide block mechanism of press machine |
| CN103699720A (en) * | 2013-12-09 | 2014-04-02 | 浙江大学 | High-speed press slider mechanism size optimization method based on interval constraint violation degrees |
| CN103942375A (en) * | 2014-04-11 | 2014-07-23 | 浙江大学 | High-speed press sliding block dimension robust design method based on interval |
Non-Patent Citations (1)
| Title |
|---|
| "基于双层更新Kriging模型的机械结构动态特性稳健优化设计";李小刚等;《机械工程学报》;20141231;第50卷(第3期);165-173 * |
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