CN106774162A - A kind of digital control processing parameter Multipurpose Optimal Method - Google Patents
A kind of digital control processing parameter Multipurpose Optimal Method Download PDFInfo
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- 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
- G05B19/408—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 data handling or data format, e.g. reading, buffering or conversion of data
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Abstract
The present invention relates to a kind of digital control processing parameter Multipurpose Optimal Method, first according to NC Machining Process, processing request is analyzed, the multiple-objection optimization Mathematical Modeling of the relation set up between the reflection design variable for determining and the multiple target variable to be optimized；Multiple-objection optimization Mathematical Modeling is solved with Pareto genetic algorithms again, the Pareto optimal solution sets of design variable and multiple target variable are obtained；Relation between multiple target variable parameter in analysis Pareto optimal solution sets, the contradiction matrix table between target variable is set up according to TRIZ theories, then according to TRIZ theory inventive principle problem analyses, one group of optimal machined parameters is determined from above-mentioned Pareto optimal solution sets according to analysis result.The pattern of decision-making, overcomes the subjectivity drawback that decision-making is carried out based on preference or experience after the perfect digital control processing parameter multiple-objection optimization elder generation optimizing of the present invention, it is real realize and perfect first optimizing after decision-making Solution model, improve effect of optimization.
Description
Technical field
The invention belongs to digital control processing parameter optimization technique field, and in particular to a kind of digital control processing parameter multiple-objection optimization
Method.
Background technology
Crudy of the choice relation of digital control processing parameter to the productivity ratio, production cost and product of system of processing.
At present, system of processing optimization model is mostly set up using optimization method, and optimal adding is obtained by certain optimized algorithm
Work parameter, realizes the purpose of process optimization.From the point of view of optimized algorithm development, develop to intelligent direction, for example：Heredity is calculated
The intelligent algorithms such as method, ant group algorithm and particle cluster algorithm.It is many based on digital control processing optimization aim for Optimized model, and target
Between exist collide with each other, therefore at present mostly is use with improve processing efficiency, reduce processing cost, obtain high quality of products
It is the Multipurpose Optimal Method of target.
At present, parameter Multipurpose Optimal Method has following several:1. weighting factor method is utilized, multiple target is converted into monocular
Mark；2. rule of thumb subjectivity determines the importance of target, and multiple target is converted into single goal；3. intelligent algorithm is used, for example
Pareto genetic algorithms, obtain optimal solution set, and decision-making is carried out based on experience and knowledge, determine one group of optimal solution.First two method
All it is the first artificial decision-making when beginning is optimized, the third method is then decision-making after first optimizing, but the decision-making based on experience, all deposit
In subjective sex chromosome mosaicism, resulting optimal solution not optimized parameter, so as to influence effect of optimization.
The content of the invention
There is provided a kind of based on Pareto genetic algorithms and TRIZ it is an object of the invention to solve above-mentioned technical problem
Theoretical digital control processing parameter Multipurpose Optimal Method, the method can be according to TRIZ theoretical inventive principle to Pareto optimal solutions
The optimal solution of concentration carries out rational decision making, obtains reasonably optimal machined parameters, improves effect of optimization.
To achieve the above object, the present invention is adopted the following technical scheme that：
A kind of digital control processing parameter Multipurpose Optimal Method, comprises the following steps：
According to NC Machining Process, processing request is analyzed, set up design variable and the multiple target to be optimized that reflection determines
The multiple-objection optimization Mathematical Modeling of the relation between variable；
Multiple-objection optimization Mathematical Modeling is solved with Pareto genetic algorithms, is designed using MATLAB softwares
The Pareto optimal solution sets of variable and multiple target variable；
Relation between multiple target variable parameter in analysis Pareto optimal solution sets, between setting up target variable according to TRIZ theories
Contradiction matrix table, then according to TRIZ theory inventive principle problem analyses, according to analysis result from above-mentioned Pareto optimal solution sets
In determine one group of optimal machined parameters.
The present invention carries out optimizing based on Pareto genetic algorithms to numerical control machined parameters Model for Multi-Objective Optimization, and utilizes
MATLAB softwares obtain relation between Pareto optimal solution sets, analytic solution lumped parameter, are selected further according to TRIZ theory summaries and formation
The principles of science of optimal solution is selected, and then rational decision making goes out optimal solution, after perfect digital control processing parameter multiple-objection optimization elder generation optimizing
The pattern of decision-making, overcomes the subjectivity drawback that decision-making is carried out based on preference or experience, it is real realize and perfect first optimizing after
The Solution model of decision-making, improves effect of optimization.
Brief description of the drawings
Fig. 1 is the FB(flow block) of digital control processing parameter Multipurpose Optimal Method of the invention；
Fig. 2 is the principle schematic of helical milling digital control processing；
Fig. 3 is the Pareto optimal solution sets obtained based on Pareto genetic algorithms；
Fig. 4 is based on the theoretical principle schematics to the analysis and decision of optimal solution in Pareto optimal solution sets of TRIZ.
Specific embodiment
Below, substantive distinguishing features of the invention and advantage are further described with reference to example, but the present invention not office
It is limited to listed embodiment.
Below, implementation process of the invention is described in detail by taking helical milling as an example.In helical milling NC Machining Process,
Screw is carried out in working surface milling on the surface of work piece 2 by cutter 1, it is shown in Figure 2.
Shown in Figure 1, a kind of digital control processing parameter Multipurpose Optimal Method comprises the following steps：
S101, according to NC Machining Process, analyzes processing request, and the design variable for setting up reflection determination is more with what is optimized
The multiple-objection optimization Mathematical Modeling of the relation between target；
1) determines the design variable of the influence multiple target variable to be optimized
After work piece, cutter and lathe relevant parameter are determined, cutting speed, the amount of feeding and cutting depth are influences
Produce and process the principal element of efficiency.By taking helical milling as an example, related processing parameters have cutting speed, per tooth to feed to the present invention
Amount, axial cutting depth, tool diameter and the number of teeth, in view of influence of the cutting depth to tool life is smaller, are typically regarded
Be constant, and work as cutter it is selected after, tool diameter and the number of teeth are also set value, accordingly, it is determined that design variable is change to be optimized
Measuring parameter X is：Cutting speed v and feed engagement f, its expression formula is X=(x1,x2)=(v, f)；
2) determines to calculate the multi-goal optimizing function of the multiple target variable to be optimized according to above-mentioned design variable
The target of machining parameters optimization is reduces cost, improves processing efficiency, while ensure crudy, wherein, cutter
Abrasion and failure maximum is influenceed on processing cost.It is thus determined that processing efficiency E and tool life T are used as optimization aim, plus
Work efficiency rate E is expressed as the inverse of process time, as follows：
In formula：tmIt it is the cutting time, unit is second s；D is diameter of work, and unit is millimeter mm；V is cutting speed, m/
min；L is workpiece length of cut, and unit is millimeter mm；F is feed engagement, mm/ teeth；Z is the cutter number of teeth.
Theoretical according to metal cutting, tool life T (v, f) is expressed as：
In formula：Durability coefficient CzIt is by determinations such as workpiece material, cutter material and other machining conditions；apIt is cutting
Depth, α, β, γ represent v, f, a respectivelypInfluence the index of tool life degree；
3) determines the constraints of multi-goal optimizing function, sets up multiple-objection optimization Mathematical Modeling；
In NC Machining Process, constraints is typically cut by the influence such as cutting force, workpiece surface roughness and machine power
Cut the factors composition of consumption selection.Active constraint condition has：Cutting force constraint, machine power constraint, Cutting Parameter constraint；
In the present invention, selection constraints is workpiece surface roughness, the cutting speed and feed engagement of cutter；
Set up with cutting speed and feed engagement as design variable, tool life (processing cost) and machining are imitated
Rate is the multiple-objection optimization Mathematical Modeling of optimization aim；It is as follows,
Design variable is：x1(v), x2(f),
Constraints is：
Cutting speed 50m/min≤x1≤100m/min
Feed engagement 0.05mm/ teeth≤x2≤ 0.08mm/ teeth
3 μm≤Ra≤8 μm of surface roughness
S102, solves with Pareto genetic algorithms to multiple-objection optimization Mathematical Modeling, is obtained using MATLAB softwares
To design variable and the Pareto optimal solution sets of multiple target variable：
1) determines that multiple-objection optimization Mathematical Modeling calculates machined parameters and processing phase relation used according to processing conditions
Number；
For example, obtaining tool life coefficient based on cutting data handbook：Cz=2118, α=1.5, β=0.5, γ=
0.5；Empirically determined ap=0.15mm；Screw-on cutter parameter：Tooth number Z=4, tool diameter d=8mm；Processing part length L
=152mm.
2) sets multi-objective genetic algorithm calculating parameter.
Determine that calculating parameter is as follows according to multi-objective genetic algorithm：Iterations 200, initial population size 60, variation
Probability 0.05, crossover probability 0.8；
3) is calculated multiple-objection optimization Mathematical Modeling with MATLAB Multi-objective genetic algorithms tool box, is obtained
To design variable and the Pareto optimal solution sets of multiple target variable；As shown in figure 3, abscissa represents tool life, ordinate
Processing efficiency is represented, each black color dots is a solution.
Two target variables of processing efficiency and tool life are such as taken into account, rule of thumb with both knowledge-chosens median,
Determine four groups of optimization solutions, as shown in table 1 the machining parameters optimization value based on experience selection.
Table 1
S103, target variable parametric relationship in analysis Pareto optimal solution sets, target variable is set up according to TRIZ theories
Between contradiction matrix table, it is optimal from above-mentioned Pareto according to analysis result then according to TRIZ theory inventive principle problem analyses
Solution concentration determines one group of optimal machined parameters；
1) target variable parametric relationship in analyses Pareto optimal solution sets.
Four groups of machined parameters according to above-mentioned table 1, it is found that processing efficiency is minimum when tool life is maximum；Processing
During efficiency highest, tool life is minimum；It is inversely prroportional relationship between i.e. two target variable parameters；
2) sets up the contradiction matrix table between target variable according to TRIZ theories.
It is theoretical based on TRIZ, two above target variable parametric relationship is changed into technical contradiction parameter, to be improved
Target variable is " tool life ", and the target variable of deterioration is " processing efficiency "；
According to TRIZ 39 general engineering parameters of theory, by " tool life " and " processing efficiency " respectively with " using thing
The action time of body " and " productivity ratio " are represented, and then it is as shown in table 2 below to set up contradiction matrix table.
Table 2
3) is with the inventive principle problem analysis in contradiction matrix table, and determines optimal machined parameters according to analysis result.
According to TRIZ theories, by tool life, the two target components change into technical contradiction with processing efficiency, and set up
After contradiction matrix table, solution principle is therefrom found, then TRIZ working solutions are obtained according to TRIZ theoretical principal content, then will
TRIZ working solutions are converted into specific parameter selection specific method, and rationally being determined further according to parameter selection specific method can be same
When meet the cutting speed and the optimum combination value of feed engagement of tool life and two target components of processing efficiency, realize
Rational decision making goes out optimal solution from Pareto optimal solution sets, that is, obtain the optimal value of machined parameters.
Specifically, in contradiction matrix table inventive principle 14,17,19,35 further problem analyses, as shown in figure 4,
Result display improves productivity ratio and overweights raising tool life, therefore, to being based in empirical four groups of machined parameters in table 1
Lateral comparison is carried out, with high production rate, to be taken into account and carry out decision-making and selection according to tool life higher, determine the 9th group of parameter
It is optimal machined parameters, so as to realize carrying out Pareto disaggregation based on TRIZ theories the rational choice and decision-making of optimal solution, reaches
The multiple-objection optimization of the digital control processing parameter being combined to Pareto genetic algorithms and TRIZ theories.
The present invention carries out the analysis and decision of optimal solution with TRIZ theories to Pareto optimal solution sets, for digital control processing
The multiple-objection optimization of parameter, improves effect of optimization and numerical control processing characteristics.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (3)
1. a kind of digital control processing parameter Multipurpose Optimal Method, it is characterised in that comprise the following steps：
According to NC Machining Process, processing request is analyzed, set up design variable and the multiple target variable to be optimized that reflection determines
Between relation multiple-objection optimization Mathematical Modeling；
Multiple-objection optimization Mathematical Modeling is solved with Pareto genetic algorithms, design variable is obtained using MATLAB softwares
With the Pareto optimal solution sets of multiple target variable；
Relation between multiple target variable parameter, the contradiction between target variable is set up according to TRIZ theories in analysis Pareto optimal solution sets
Matrix table, it is true from above-mentioned Pareto optimal solution sets according to analysis result then according to TRIZ theory inventive principle problem analyses
Make one group of optimal machined parameters.
2. digital control processing parameter Multipurpose Optimal Method according to claim 1, it is characterised in that the foundation reflection determines
Design variable and the multiple target variable to be optimized between relation multiple-objection optimization Mathematical Modeling the step of it is as follows：
It is determined that the design variable of the influence multiple target variable to be optimized；
Determined to calculate the multi-goal optimizing function of the multiple target variable to be optimized according to above-mentioned design variable；
Determine the constraints of multi-goal optimizing function, set up design variable and the multiple target variable to be optimized that reflection determines it
Between relation multiple-objection optimization Mathematical Modeling.
3. digital control processing parameter Multipurpose Optimal Method according to claim 2, it is characterised in that the utilization Pareto loses
Propagation algorithm is solved to objective optimization Mathematical Modeling, and design variable is obtained with multiple target variable using MATLAB softwares
The step of Pareto optimal solution sets, is as follows：
According to processing conditions, determine that multiple-objection optimization Mathematical Modeling calculates machined parameters and processing coefficient used；
Multi-objective genetic algorithm calculating parameter is set；
Multiple-objection optimization Mathematical Modeling is calculated with MATLAB Multi-objective genetic algorithms tool box, using MATLAB
Software obtains the Pareto optimal solution sets of design variable and multiple target variable.
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| CN107391809A (en) * | 2017-06-30 | 2017-11-24 | 山东省产品质量检验研究院 | A kind of method of key parameter in equilibrium assignment industrial products |
| CN108921344A (en) * | 2018-06-27 | 2018-11-30 | 浙江智海化工设备工程有限公司 | It is a kind of with ideality method to the optimum design method of self-cleaning air filter resistance |
| CN109033617A (en) * | 2018-07-23 | 2018-12-18 | 五邑大学 | The multi-target parameter optimizing method of direct current permanent magnetic brushless motor based on genetic algorithm |
| CN109901514A (en) * | 2019-03-21 | 2019-06-18 | 西北工业大学 | The complex parts NC technology that process oriented reuses optimizes and revises method |
| CN110716494A (en) * | 2019-11-13 | 2020-01-21 | 中国航发动力股份有限公司 | Tool parameter identification method and cycloid machining parameter optimization method based on tool parameters |
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107391809A (en) * | 2017-06-30 | 2017-11-24 | 山东省产品质量检验研究院 | A kind of method of key parameter in equilibrium assignment industrial products |
| CN108921344A (en) * | 2018-06-27 | 2018-11-30 | 浙江智海化工设备工程有限公司 | It is a kind of with ideality method to the optimum design method of self-cleaning air filter resistance |
| CN109033617A (en) * | 2018-07-23 | 2018-12-18 | 五邑大学 | The multi-target parameter optimizing method of direct current permanent magnetic brushless motor based on genetic algorithm |
| CN109033617B (en) * | 2018-07-23 | 2023-02-24 | 五邑大学 | Multi-target parameter optimization method of direct-current permanent magnet brushless motor based on genetic algorithm |
| CN109901514A (en) * | 2019-03-21 | 2019-06-18 | 西北工业大学 | The complex parts NC technology that process oriented reuses optimizes and revises method |
| CN109901514B (en) * | 2019-03-21 | 2021-09-17 | 西北工业大学 | Complex part numerical control process optimization and adjustment method oriented to process reuse |
| CN110716494A (en) * | 2019-11-13 | 2020-01-21 | 中国航发动力股份有限公司 | Tool parameter identification method and cycloid machining parameter optimization method based on tool parameters |
| CN116484655A (en) * | 2023-06-21 | 2023-07-25 | 宁波力劲科技有限公司 | Multi-objective optimization design method for die clamping mechanism of extrusion casting equipment |
| CN116484655B (en) * | 2023-06-21 | 2023-08-25 | 宁波力劲科技有限公司 | Multi-objective optimization design method for die clamping mechanism of extrusion casting equipment |
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