CN109976261B - Solving method of margin optimization model facing to machining positioning - Google Patents

Solving method of margin optimization model facing to machining positioning Download PDF

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CN109976261B
CN109976261B CN201910333799.9A CN201910333799A CN109976261B CN 109976261 B CN109976261 B CN 109976261B CN 201910333799 A CN201910333799 A CN 201910333799A CN 109976261 B CN109976261 B CN 109976261B
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optimization model
allowance
margin
blank
machining
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CN109976261A (en
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张定华
刘广鑫
张莹
吴宝海
吴晓峰
胡思嘉
种磊
刘志军
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Northwestern Polytechnical University
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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
    • 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

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Abstract

The invention discloses a solving method of a margin optimization model facing to machining positioning, which is used for solving the technical problem that the minimum-maximum optimization model disclosed by the existing method cannot realize uniform distribution of machining margins. The technical scheme is that the minimum allowance and the maximum allowance of a blank are comprehensively considered, and an allowance optimization model for optimizing the minimum allowance and the maximum allowance simultaneously is established; solving the margin optimization model by adopting a particle swarm algorithm; on the premise that the blank is qualified, the positioning result obtained by solving based on the allowance optimization model guarantees that the CAD digital-analog machining surface has sufficient machining allowance and meanwhile achieves uniform distribution of the machining allowance.

Description

Solving method of margin optimization model facing to machining positioning
Technical Field
The invention relates to a solving method of a margin optimization model facing to machining positioning.
Background
The complex casting blank in the domestic aviation industry needs to be subjected to benchmark setting by a traditional manual marking mode, the geometric dimension of the blank and the homogenization machining allowance cannot be evaluated quantitatively, the benchmark needs to be repaired repeatedly in the machining process, the machining program needs to be adjusted for multiple times, and the problems of long machining period, unstable machining quality and the like are caused. To eliminate the manual scribing process, a digital registration method is generally adopted, and the steps are as follows: (1) obtaining a blank surface measuring point set by using a three-coordinate measuring machine; (2) establishing a margin optimization model of the CAD digital model; (3) solving a margin optimization model to align the blank surface measurement point set and the CAD digital analogy, and ensuring that all processing surfaces have enough processing margin; (4) if the calculation result meets the requirement of machining allowance, machining the blank; otherwise, judging that the blank is unqualified.
The documents "An unconfined approach to blank localization with overall analysis for making complex parts, and international journal of advanced manufacturing technology, 2014, Vol73, pp 647-658" disclose a margin optimization model without constraints and a solution method. The method constructs an unconstrained maximum and minimum optimization model
max min[di(x)]i＝1,…,n
In the formula di(x) Representing the machining allowance at the ith measuring point, and solving the maximum and minimum optimization model by adopting an entropy optimization method so as to obtain a positioning result of the blank; and on the premise that the blank is qualified, the sufficient machining allowance of the CAD digital-analog machining surface is ensured. The method disclosed by the literature only considers the problem of minimum allowance optimization of the blank, and cannot realize uniform distribution of the machining allowance.
Disclosure of Invention
In order to overcome the defect that the minimum-maximum optimization model disclosed by the existing method cannot realize uniform distribution of machining allowance, the invention provides a method for solving the allowance optimization model facing to machining positioning. The method comprehensively considers the minimum allowance and the maximum allowance of a blank and establishes an allowance optimization model for optimizing the minimum allowance and the maximum allowance simultaneously; solving the margin optimization model by adopting a particle swarm algorithm; on the premise that the blank is qualified, the positioning result obtained by solving based on the allowance optimization model can ensure that the CAD digital-analog processing surface has sufficient processing allowance and can realize uniform distribution of the processing allowance.
The technical scheme adopted by the invention for solving the technical problems is as follows: a solution method of a margin optimization model facing to machining positioning is characterized by comprising the following steps:
firstly, clamping a blank on a numerical control machine tool workbench in any posture, and obtaining a blank surface measuring point set by using a three-coordinate measuring machine.
And secondly, performing coarse registration on the blank surface measurement point set and the CAD digital analogy by adopting a three-point positioning principle to enable the relative positions of the blank surface measurement point set and the CAD digital analogy to be close to each other, and obtaining a coarse registration transformation matrix from the blank surface measurement point set to the CAD digital analogy.
Thirdly, establishing a margin optimization model for simultaneously optimizing the minimum margin and the maximum margin
Figure BDA0002038515720000021
In the formula di(x) Indicating the machining allowance at the ith measurement point.
Fourthly, solving the margin optimization model by adopting a particle swarm algorithm on the basis of the coarse registration to obtain final positioning parameters; if the positioning result meets the requirement, ending the process; and if the positioning result cannot meet the requirement, judging that the blank is unqualified.
The invention has the beneficial effects that: the method comprehensively considers the minimum allowance and the maximum allowance of a blank and establishes an allowance optimization model for optimizing the minimum allowance and the maximum allowance simultaneously; solving the margin optimization model by adopting a particle swarm algorithm; on the premise that the blank is qualified, the positioning result obtained by solving based on the allowance optimization model guarantees that the CAD digital-analog machining surface has sufficient machining allowance and meanwhile achieves uniform distribution of the machining allowance.
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Drawings
FIG. 1 is a flow chart of a solution method of the machining-oriented margin optimization model of the invention.
FIG. 2 is a comparison graph of the residual distribution of the positioning result of the embodiment of the method of the present invention.
Detailed Description
Reference is made to fig. 1-2. The invention relates to a solving method of a margin optimization model for processing positioning, which comprises the following specific steps:
step 1, obtaining a blank measuring point set.
And clamping the blade blank on a numerical control machine tool workbench in any posture, and obtaining a blank surface measurement point set by using a three-coordinate measuring machine.
And 2, carrying out rough registration of the blank surface measurement point set and the CAD digital model.
And carrying out rough registration on the blank surface measurement point set and the CAD digital-analog by adopting a three-point positioning principle to enable the relative positions of the blank surface measurement point set and the CAD digital-analog to be close to each other, and obtaining a rough registration transformation matrix from the blank surface measurement point set to the CAD digital-analog.
And 3, establishing a margin optimization model.
Establishing a margin optimization model for simultaneously optimizing minimum margin and maximum margin
Figure BDA0002038515720000022
In the formula di(x) Indicating the machining allowance at the ith measurement point.
And 4, solving the margin optimization model by adopting a particle swarm algorithm.
On the basis of coarse registration, solving the margin optimization model by adopting a particle swarm algorithm, and setting parameter values in the particle swarm algorithm: on the basis of ensuring the searching capability and the algorithm efficiency of the algorithm, the particle swarm size M is 40, and the maximum iteration number N is 50; velocity formula iterated at step k
Figure BDA0002038515720000031
In which i is the number of the particle, j is the dimension of the particle, pijFor each individual optimum position, p, searched forgjGlobal optimum position, r, searched for a particle swarm1，r2Is [0,1 ]]The random number in (I) and the inertia factor w adopt an LDW (Linear creating weight) strategy, wherein
Figure BDA0002038515720000032
Get wmin＝0.4，wmax0.9 self-cognition factor c1And a population cognition factor c2Adopting a PSO-TVAC (PSO with Time Varying addition coefficients) method, wherein
Figure BDA0002038515720000033
Get c1i＝2.5，c1f＝0.5，c2i＝0.5，c2f2.5; m coordinate transformation vectors (including translation amounts in X, Y directions and rotation amounts around Z axis) are randomly initialized as initial particles, and settings are madeValue ranges [ x ] of three components in coordinate transformation vector xmin，xmax]Wherein the translation amount is [ -5mm, 5mm]The rotation is measured to [ -5 DEG, 5 DEG ]]The maximum value of the absolute value of the particle velocity is taken as vmax＝xmax-x min(ii) a Optimizing the objective function of the model by margin
Figure BDA0002038515720000034
Calculating the population optimal particle p as the fitness function of the algorithmijAnd population-optimal particle pgjThereby calculating the velocity of each particle
Figure BDA0002038515720000035
According to
Figure BDA0002038515720000036
Calculating the position of each particle in the next iteration step until the number of iteration steps reaches the maximum iteration number N; the obtained group optimal particles are the positioning parameters of the blank; if the positioning result meets the requirement, ending the process; otherwise, judging that the blank is unqualified and cannot meet the requirement.
The implementation effect of this example is shown in fig. 2 and table 1:
table 1: comparison of positioning results
Figure BDA0002038515720000037
The data in table 1 indicate that the minimum margin of the maximum minimum optimization model is close to the minimum margin of the optimization model of the invention, and the maximum margin and the margin variance of the optimization model of the invention are obviously smaller than those of the maximum minimum optimization model. Figure 2 shows that the process of the invention gives a margin profile with lower peaks and higher troughs. The invention guarantees that the blade has sufficient machining allowance in the solved positioning result, and the allowance distribution is more uniform than the positioning result solved by the maximum and minimum model.

Claims (1)

1. A solution method of a margin optimization model facing to machining positioning is characterized by comprising the following steps:
firstly, clamping a blank on a numerical control machine tool workbench in any posture, and obtaining a blank surface measuring point set by using a three-coordinate measuring machine;
secondly, carrying out coarse registration on the blank surface measurement point set and the CAD digital analog by adopting a three-point positioning principle to enable the relative positions of the blank surface measurement point set and the CAD digital analog to be close to each other, and obtaining a coarse registration transformation matrix from the blank surface measurement point set to the CAD digital analog;
thirdly, establishing a margin optimization model for simultaneously optimizing the minimum margin and the maximum margin
Figure FDA0002038515710000011
In the formula di(x) Indicating the machining allowance at the ith measuring point;
fourthly, solving the margin optimization model by adopting a particle swarm algorithm on the basis of the coarse registration to obtain final positioning parameters; if the positioning result meets the requirement, ending the process; and if the positioning result cannot meet the requirement, judging that the blank is unqualified.
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CN106001720A (en) * 2016-06-12 2016-10-12 西北工业大学 Thin-walled vane nine-point control variable-allowance milling method based on Newton interpolation
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