CN109976261A - The method for solving of surplus Optimized model towards processing positioning - Google Patents
The method for solving of surplus Optimized model towards processing positioning Download PDFInfo
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- CN109976261A CN109976261A CN201910333799.9A CN201910333799A CN109976261A CN 109976261 A CN109976261 A CN 109976261A CN 201910333799 A CN201910333799 A CN 201910333799A CN 109976261 A CN109976261 A CN 109976261A
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- surplus
- optimized model
- blank
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- machining allowance
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/4097—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by using design data to control NC machines, e.g. CAD/CAM
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/35—Nc in input of data, input till input file format
- G05B2219/35215—Generate optimal nc program variant as function of cost, time, surface, energy
Abstract
The invention discloses a kind of method for solving of surplus Optimized model towards processing positioning, cannot achieve the technical issues of machining allowance is uniformly distributed for solving minimax optimization model disclosed in existing method.Technical solution is to comprehensively consider the minimum margin and maximum surplus of blank, establishes the surplus Optimized model of the two optimization simultaneously；And surplus Optimized model is solved using particle swarm algorithm；Under the premise of blank qualification, being uniformly distributed for machining allowance is realized while guaranteeing that CAD digital-to-analogue machined surface has sufficient machining allowance based on the positioning result that the surplus Optimized model solves.
Description
Technical field
The present invention relates to a kind of method for solving of surplus Optimized model towards processing positioning.
Background technique
Intricate casting blank in domestic air mail industry will determine benchmark by traditional artificial hatched manner, can not quantitatively evaluating
Geometrical dimensions, homogenizing machining allowance, process need to repair benchmark repeatedly, repeatedly adjust processing program, lead to processing week
The problems such as phase is long, processing quality is unstable.To remove this technical process crossed by hand, the general side using digitlization registration
Formula, steps are as follows: (1) obtaining blank surface using three coordinate measuring machine and measure point set；(2) the surplus optimization of CAD digital-to-analogue is established
Model；(3) surplus Optimized model is solved to be aligned blank surface measurement point set and CAD digital-to-analogue, guarantees that all machined surfaces have foot
Enough machining allowance；(4) if calculated result meets machining allowance requirement, the processing of blank is carried out；Otherwise determine that blank does not conform to
Lattice.
Document " An unconstrained approach to blank localization with allowance
Assurance for machining complex parts, international journal of advanced
Manufacturing technology, 2014, Vol73, pp647-658 " discloses a kind of unconfined surplus Optimized model
With method for solving.This method constructs unconfined minimax optimization model
max min[di(x)] i=1 ..., n
D in formulai(x) machining allowance at ith measurement point is indicated, while excellent to minimax using the method for entropy optimization
Change model and carries out the positioning result for solving to obtain blank；It ensure that CAD digital-to-analogue machined surface has under the premise of blank qualification
Sufficient machining allowance.Document the method only considered the minimum margin optimization problem of blank, cannot achieve machining allowance
It is uniformly distributed.
Summary of the invention
In order to overcome minimax optimization model disclosed in existing method to cannot achieve the equally distributed deficiency of machining allowance,
The present invention provides a kind of method for solving of surplus Optimized model towards processing positioning.This method comprehensively considers more than the minimum of blank
Amount and maximum surplus, establish the surplus Optimized model of the two optimization simultaneously；And using particle swarm algorithm to surplus Optimized model into
Row solves；Under the premise of blank qualification, added based on the positioning result that the surplus Optimized model solves in guarantee CAD digital-to-analogue
Being uniformly distributed for machining allowance may be implemented while having sufficient machining allowance in work face.
A kind of the technical solution adopted by the present invention to solve the technical problems: surplus Optimized model towards processing positioning
Method for solving, its main feature is that the following steps are included:
The first step, by blank with any attitude clamping on numerically controlled machine, obtain hair using three coordinate measuring machine
Base surface measurement point set.
Second step measures point set and CAD digital-to-analogue progress rough registration to blank surface using three-point fix principle, makes the two phase
To being closely located to, obtain blank surface measure point set to CAD digital-to-analogue rough registration transformation matrix.
Third step establishes minimum margin and maximum surplus while the surplus Optimized model of optimizationFormula
Middle di(x) machining allowance at ith measurement point is indicated.
4th step, on the basis of rough registration, surplus Optimized model is solved using particle swarm algorithm, is obtained final
Positional parameter；Terminate if positioning result is met the requirements at this time；Determine that blank does not conform to if positioning result is unable to satisfy requirement
Lattice.
The beneficial effects of the present invention are: this method comprehensively considers the minimum margin and maximum surplus of blank, it is same to establish the two
The surplus Optimized model of Shi Youhua；And surplus Optimized model is solved using particle swarm algorithm；In the premise of blank qualification
Under, sufficient machining allowance is had in guarantee CAD digital-to-analogue machined surface based on the positioning result that the surplus Optimized model solves
Being uniformly distributed for machining allowance is realized simultaneously.
It elaborates with reference to the accompanying drawings and detailed description to the present invention.
Detailed description of the invention
Fig. 1 is the flow chart of the method for solving of surplus Optimized model of the present invention towards processing positioning.
Fig. 2 is the allowance balance comparison diagram of the method for the present invention example positioning result.
Specific embodiment
Referring to Fig.1-2.Specific step is as follows for the method for solving of surplus Optimized model of the present invention towards processing positioning:
Step 1 obtains blank measurement point set.
By blade blank with any attitude clamping on numerically controlled machine, blank table is obtained using three coordinate measuring machine
Planar survey point set.
Step 2, the rough registration for carrying out blank surface measurement point set and CAD digital-to-analogue.
Rough registration is carried out to blank surface measurement point set and CAD digital-to-analogue using three-point fix principle, makes the two relative position
It is close, it obtains blank surface and measures point set to CAD digital-to-analogue rough registration transformation matrix.
Step 3 establishes surplus Optimized model.
Establish minimum margin and maximum surplus while the surplus Optimized model of optimizationD in formulai(x)
Indicate the machining allowance at ith measurement point.
Step 4 solves surplus Optimized model using particle swarm algorithm.
On the basis of rough registration, surplus Optimized model is solved using particle swarm algorithm, particle swarm algorithm is set
In each parameter value: using the search capability and efficiency of algorithm for guaranteeing algorithm as principle, take population scale M=40, take greatest iteration
Times N=50；In the speed formula of kth step iterationIn, i is particle sequence
Number, j is particle dimension, pijFor the personal best particle that each particle search arrives, pgjThe global optimum position searched for population
It sets, r1, r2It is the random number in [0,1], inertial factor w is tactful using LDW (Linearly Decreasing Weight),
InTake wmin=0.4, wmax=0.9, autognosis factor c1With the group cognition factor
c2Using PSO-TVAC (PSO with Time Varying Acceleration Coefficients) method, whereinTake c1i=2.5, c1f=0.5, c2i=0.5, c2f=2.5；With
Machine initializes M coordinate transform vector (including along X, the translational movement of Y-direction and rotation amount about the z axis) as primary, if
Position fixing converts the value range [x of three components in vector xmin, xmax], wherein translation measures [- 5mm, 5mm], rotation is measured
[- 5 °, 5 °], the maximum value of particle rapidity absolute value takes vmax=xmax-x min；By the objective function of surplus Optimized modelAs the fitness function of algorithm, group's optimal particle p is calculatedijWith group optimal particle pgj, to calculate
The speed of each particleAccording toPosition of each particle in following iteration step is calculated until iterative steps
Reach maximum number of iterations N；Gained group optimal particle is the positional parameter of blank；At this time if positioning result is met the requirements
Terminate；Otherwise determine that blank is unqualified and be unable to satisfy requirement.
The implementation result of the present embodiment is as shown in Fig. 2, table 1:
Table 1: positioning result comparison
Table 1 is of the invention statistics indicate that the minimum margin that solves of minimax optimization model and Optimized model of the present invention is close
The maximum surplus and surplus variance that Optimized model solves all are significantly less than minimax optimization model solution.Fig. 2 shows this hair
The allowance balance figure that bright method obtains has lower wave crest and higher trough.Illustrate in the positioning result that the present invention solves
It ensure that blade has sufficient machining allowance, and allowance balance is more more uniform than the positioning result that Min-max search algorithm solves.
Claims (1)
1. a kind of method for solving of the surplus Optimized model towards processing positioning, it is characterised in that the following steps are included:
The first step, by blank with any attitude clamping on numerically controlled machine, obtain blank table using three coordinate measuring machine
Planar survey point set；
Second step measures point set and CAD digital-to-analogue progress rough registration to blank surface using three-point fix principle, makes the opposite position of the two
Set it is close, obtain blank surface measure point set to CAD digital-to-analogue rough registration transformation matrix；
Third step establishes minimum margin and maximum surplus while the surplus Optimized model of optimizationD in formulai
(x) machining allowance at ith measurement point is indicated；
4th step, on the basis of rough registration, surplus Optimized model is solved using particle swarm algorithm, obtains and final determines
Position parameter；Terminate if positioning result is met the requirements at this time；Determine that blank is unqualified if positioning result is unable to satisfy requirement.
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| CN201910333799.9A CN109976261B (en) | 2019-04-24 | 2019-04-24 | Solving method of margin optimization model facing to machining positioning |
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| CN201910333799.9A CN109976261B (en) | 2019-04-24 | 2019-04-24 | Solving method of margin optimization model facing to machining positioning |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113536488A (en) * | 2021-08-07 | 2021-10-22 | 西北工业大学 | Blank quality containment analysis and allowance optimization method based on registration algorithm |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113536488A (en) * | 2021-08-07 | 2021-10-22 | 西北工业大学 | Blank quality containment analysis and allowance optimization method based on registration algorithm |
| CN113536488B (en) * | 2021-08-07 | 2023-01-24 | 西北工业大学 | Blank quality containment analysis and allowance optimization method based on registration algorithm |
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| CN109976261B (en) | 2021-10-01 |
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