CN106094722A - Intelligent lathe control method - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 34
- 239000002245 particle Substances 0.000 claims abstract description 40
- 238000005457 optimization Methods 0.000 claims abstract description 33
- 238000012545 processing Methods 0.000 claims abstract description 19
- 230000008569 process Effects 0.000 claims abstract description 13
- 238000003754 machining Methods 0.000 claims abstract description 8
- 238000013528 artificial neural network Methods 0.000 claims description 24
- 230000002068 genetic effect Effects 0.000 claims description 20
- 239000002699 waste material Substances 0.000 claims description 9
- 230000007935 neutral effect Effects 0.000 claims description 7
- 230000003044 adaptive effect Effects 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 5
- 230000001133 acceleration Effects 0.000 claims description 3
- 230000006978 adaptation Effects 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 230000035772 mutation Effects 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
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- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
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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
- G05B19/401—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 control arrangements for measuring, e.g. calibration and initialisation, measuring workpiece for machining purposes
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Abstract
The invention discloses an intelligent lathe control method, which comprises the following steps: the method comprises the following steps: in the processing process, the current of the main motor and the deviation value returned by the grating ruler are detected in real time so as to obtainGiven speed afChange of (a)fAs a system adjustment quantity, closed loop feedback learning control of the machining process is realized; step two: in the process of processing the workpiece, the vibration condition of the workpiece of the machine tool is taken as an input value, and the parameter of the servo driver is set by adopting a particle swarm optimization algorithm, so that the system is more stable in operation. The invention can realize the parameter self-tuning of the numerical control driving device, and the numerical control system can acquire the shape and position error information of the workpiece in time, thereby facilitating the subsequent process parameter adjustment.
Description
Technical field
The present invention relates to a kind of Lathe control method, particularly relate to a kind of Intelligent lathe control method.
Background technology
Digital control system is the key control unit of Digit Control Machine Tool, realizes controlling to machine tool motion and the course of processing comprehensively.And
There is following functions: control the number of axle and number of motion axes；Interpolation function；Feed function；Main shaft function；Tool function；Cutter compensation；
Machine error compensates；Operating function；Program management function；Character graphics display function；Aided programming function；Automatically diagnostic alarms
Function；Communication function.For a nc program, if there is logical error, system automatic diagnostic alarms function can carry
Show amendment, but for the unreasonable selection of machined parameters in program, automatic diagnostic alarms function is the most helpless.Therefore this is performed
One machined parameters of sample selects irrational program, its result or reduce the processing of lathe because machining dosage selects conservative
Efficiency；Or because machining dosage selects excessive damage cutter, make workpiece scrap even and damage machining tool, after causing seriously
Really.
Along with modern mechanical processing, the requirement of complication, precise treatment, maximization and automatization is improved constantly, one simultaneously
A little high-grade accurate digital control process equipments are used widely day by day.These equipment play key or even core to crudy and efficiency
Heart effect, often cost is fairly expensive；Even some product processed, due to complexity or the spy such as elaboration or maximization
Levying, its single-piece cost or processing cost are the most surprising.In the case, process equipment damages or product rejection is only even
The reduction of working (machining) efficiency is all likely to result in huge loss.
Setting for machined parameters is based on the experience of people or reference books is carried out traditionally, and for abecedarian or
Even the operation that person is the most skilled is also to be difficult to provide preferable machined parameters, simultaneously because the setting of machined parameters relates to people
Operation, it is possible to there will be hands wait by mistake and to the machined parameters even endangering lathe, cutter and workpiece made mistake, and
These decode error detection for system and all cannot detect.At Application No. " 200810153139.4 ", patent name
Chinese patent for " there is the intelligent numerical control method of three-stage process self-optimization function " proposes the mode using fuzzy control,
Owing to fuzzy control sets up fuzzy table and control rule according further to veteran personnel, or exist many uncertain
Property；Therefore this patent proposes, according to processing work pieces process degree of jitter, to use particle swarm optimization algorithm to carry out real time modifying parameter, make
System is more stable.
Summary of the invention
The technical problem to be solved is to provide a kind of Intelligent lathe control method, and it is capable of numerical control and drives
Device parameter Self-tuning System, digital control system can obtain workpiece Form and position error information in time, it is simple to subsequent technique parameter adjustment.
The present invention provides a kind of Intelligent lathe control method, and it comprises the following steps:
Step one: in the course of processing, the deviation value that the electric current of detection mair motor returns with grating scale in real time, with feeding speed
Degree afChanges delta afAs system call interception amount, it is achieved the closed loop feedback study of the course of processing controls；
Step 2: during processing workpiece, with the Vibration Condition of Machinetool workpiece as input value, use particle group optimizing
Servo-driver parameter is adjusted by algorithm, makes system run more stable.
Preferably, described step one comprises the following steps: to set up metal-cutting waste formula and cutter life and cutting factor
Relational expression,
Metal-cutting waste formula such as following formula: Qz=aeapafzn；
Cutter life and the relational expression such as following formula of cutting factor:
In two formulas, QzRepresentation unit time metal-cutting waste；T represents cutter life；
aeRepresent working engagement of the cutting edge；apRepresent cutting depth；afRepresent each amount of feeding；
Z represents the cutter number of teeth；N represents the speed of mainshaft (r/min)；d0Represent tool diameter (mm)；
V represents cutting speed；CvRepresent the coefficient relevant with machining condition；
kvRepresent correction factor；qv、xv、yv、uv、pv, m represent index of correlation parameter, xv≤yv＜ 1.
Preferably, described particle swarm optimization algorithm comprises the following steps:
Step 2 11: process of optimization step, particle swarm optimization algorithm produces population, by the grain in this population
Son is assigned to parameter K of PID controller successivelyp、Ki、Kd, then operation control system model, detects through grating scale, obtains correspondence
Performance indications, then be delivered in particle swarm optimization algorithm, time adjustment pid parameter, until end of run；
Step 2 12: particle swarm optimization algorithm realizes step, in the ultimate principle of particle swarm optimization algorithm, further
Speed in search volume, ground and position, determine according to following two formula:
vt+1=ω vt+c1r1(Pt-xt)+c2r2(Gt-xt)
xt+1=xt+vt+1
Wherein: x represents the position of particle；Vx represents the speed of particle；ω x represents inertial factor；c1、c2X represents acceleration
Constant；r1、r2X represents the random number that [0,1] is interval；PtX represents the optimal location that particle searches up to now；GtX represents whole
The optimal location that individual population searches up to now.
Preferably, described step one determines neutral net.
Preferably, described neutral net uses neural network and genetic algorithm, is specifically divided into BP neural network structure and determines, loses
Propagation algorithm optimizes and three parts of BP neural network prediction；Wherein, BP neural network structure determines that part is defeated according to fitting function
Enter output parameter number and determine BP neural network structure, and then determine genetic algorithm individual lengths；Genetic algorithm optimization uses to be lost
The weights of propagation algorithm Optimized BP Neural Network and threshold value, each individuality in population contains a network ownership value and threshold
Value, calculates ideal adaptation angle value individual by fitness function, and genetic algorithm finds optimum by selection, intersection and mutation operation
Adaptive value correspondence is individual；BP neural network prediction genetic algorithm obtains optimum individual to network initial weight and threshold value assignment,
The trained rear anticipation function of network exports.
The most progressive effect of the present invention is: the present invention is capable of numerical control driving equipment parameter self-tuning, numerical control system
System can obtain workpiece Form and position error information in time, it is simple to subsequent technique parameter adjustment.
Accompanying drawing explanation
Fig. 1 is the flow chart of this paper particle swarm optimization algorithm of Intelligent lathe control method of the present invention.
Fig. 2 is the flow chart of the neural network and genetic algorithm of Intelligent lathe control method of the present invention.
Detailed description of the invention
Provide present pre-ferred embodiments below in conjunction with the accompanying drawings, to describe technical scheme in detail.
Intelligent lathe control method of the present invention mainly comprises the steps:
Step one: in the course of processing, the deviation value that in real time electric current of detection mair motor returns with grating scale, have 2 inputs,
1 output, i.e. determines that BP neural network structure is 251, with feed speed afChanges delta afAs system call interception amount, it is achieved
The closed loop feedback study of the course of processing controls；
Step 2: during processing workpiece, be defeated with the Vibration Condition (i.e. grating scale fluctuation situation) of Machinetool workpiece
Enter value, use particle swarm optimization algorithm (Particle Swarm Optimization, PSO) that servo-driver parameter is carried out
Adjust, make system run more stable.
Described step one comprises the following steps:
Set up the relational expression of metal-cutting waste formula and cutter life and cutting factor,
Metal-cutting waste formula such as following formula (1):
Qz=aeapafzn......(1)
Cutter life and the relational expression such as following formula (2) of cutting factor:
In two formulas, QzRepresentation unit time metal-cutting waste；T represents cutter life；
aeRepresent working engagement of the cutting edge；apRepresent cutting depth；afRepresent each amount of feeding；
Z represents the cutter number of teeth；N represents the speed of mainshaft (r/min)；d0Represent tool diameter (mm)；
V represents cutting speed；CvRepresent the coefficient relevant with machining condition；
kvRepresent correction factor；qv、xv、yv、uv、pv, m represent index of correlation parameter, xv≤yv＜ 1.
Owing to lathe is in processing workpiece, all can produce vibration, the different speed of mainshaft, feed speed all can give processing work
Part causes impact in various degree, and this patent, by grating scale fluctuation deviation value, controls servo with particle swarm optimization algorithm and drives
Dynamic systematic parameter, main its pid parameter of amendment.
The optimization problem of PID controller just determines that one group of suitable parameter Kp、Ki、KdSo that index reaches optimum.This
The index that patent uses, is defined as formula (3):
The Digit Control Machine Tool controlled device typically chosen is five rank time-dependent systems.Described particle swarm optimization algorithm includes following
Step:
Step 2 11: process of optimization step, optimizes process as shown in Figure 1: PSO produces population, by this particle
Particle in Qun is assigned to parameter K of PID controller successivelyp、Ki、Kd, then operation control system model, permissible through grating scale
Detection, obtains the performance indications of correspondence, then is delivered in PSO, time adjustment pid parameter, until end of run.
Step 2 12: particle swarm optimization algorithm realizes step, in the ultimate principle of particle swarm optimization algorithm, further
Ground, the speed in search volume and position, determine according to below equation (4) and (5):
vt+1=ω vt+c1r1(Pt-xt)+c2r2(Gt-xt)......(4)
xt+1=xt+vt+1......(5)
Wherein: x represents the position of particle；Vx represents the speed of particle；ω x represents inertial factor；c1、c2X represents acceleration
Constant；r1、r2X represents the random number that [0,1] is interval；PtX represents the optimal location that particle searches up to now；GtX represents whole
The optimal location that individual population searches up to now.
The flow process of PSO is as follows:
Step 3 11: initialize population, randomly generate speed and the position of all particles, and determine the P of particletWith
Gt；
Step 3 12: to each particle, the optimal location P that its adaptive value and this particle are lived throughtAdaptive value
Compare, if preferably, then as current Pt；
Step 3 13: to each particle, the optimal location G that its adaptive value and this whole population are lived throught's
Adaptive value compares, if preferably, then as current Gt；
Step 3 14: by formula (4) and the speed of formula (5) more new particle and position；
Step 3 15: without meeting end condition (this patent is set to process finishing, and motor is out of service), then
Return step 3 12；Otherwise, exit algorithm, obtain optimal solution.
As in figure 2 it is shown, described step one determines neutral net.Neutral net uses neural network and genetic algorithm, specifically divides
Determine for BP neural network structure, genetic algorithm optimization and 3 parts of BP neural network prediction.Wherein, BP neural network structure
Determine that part determines BP neural network structure according to fitting function input/output argument number, and then determine that genetic algorithm individuality is long
Degree.Genetic algorithm optimization uses weights and the threshold value of genetic algorithm optimization BP neural network, and each individuality in population comprises
One network ownership value and threshold value, calculate ideal adaptation angle value individual by fitness function, genetic algorithm by selecting,
Intersect and find with mutation operation adaptive optimal control value corresponding individual.BP neural network prediction genetic algorithm obtains optimum individual to net
Network initial weight and threshold value assignment, the trained rear anticipation function of network exports.
In the present invention, owing to having 2 inputs parameter, output parameters, so the BP neural network structure arranged is
251, i.e. input layer has 2 nodes, and hidden layer has 5 nodes, and output layer has 1 node, has 2 × 5+5 × 1=15
Weights, 5+1=6 threshold value, so a length of 16+5=21 of genetic algorithm individual UVR exposure.
The present invention is to have the intelligent control method of dicyclo optimized algorithm: internal ring is system stability control, according to lathe
In processing workpiece, the extent of vibration of lathe, the undulating value that grating scale returns, use particle swarm optimization algorithm to optimize servo and amplify
Parameter in device；Outer shroud is that system feed compensation controls, and according to grating scale return value and current value, uses neural network and genetic algorithm
Adjust feed speed.The present invention is capable of numerical control driving equipment parameter self-tuning, and digital control system can obtain workpiece morpheme in time
Control information, it is simple to subsequent technique parameter adjustment.
Particular embodiments described above, solves the technical problem that the present invention, technical scheme and beneficial effect are carried out
Further describe, be it should be understood that the specific embodiment that the foregoing is only the present invention, be not limited to
The present invention, all within the spirit and principles in the present invention, any modification, equivalent substitution and improvement etc. done, should be included in this
Within the protection domain of invention.
Claims (5)
1. an Intelligent lathe control method, it is characterised in that it comprises the following steps:
Step one: in the course of processing, the deviation value that the electric current of detection mair motor returns with grating scale in real time, with feed speed af
Changes delta afAs system call interception amount, it is achieved the closed loop feedback study of the course of processing controls；
Step 2: during processing workpiece, with the Vibration Condition of Machinetool workpiece as input value, use particle swarm optimization algorithm
Servo-driver parameter is adjusted, makes system run more stable.
2. Intelligent lathe control method as claimed in claim 1, it is characterised in that described step one comprises the following steps: to build
Found the relational expression of metal-cutting waste formula and cutter life and cutting factor,
Metal-cutting waste formula such as following formula: Qz=aeapafzn；
Cutter life and the relational expression such as following formula of cutting factor:
In two formulas, QzRepresentation unit time metal-cutting waste；T represents cutter life；
aeRepresent working engagement of the cutting edge；apRepresent cutting depth；afRepresent each amount of feeding；
Z represents the cutter number of teeth；N represents the speed of mainshaft (r/min)；d0Represent tool diameter (mm)；
V represents cutting speed；CvRepresent the coefficient relevant with machining condition；
kvRepresent correction factor；qv、xv、yv、uv、pv, m represent index of correlation parameter, xv≤yv＜ 1.
3. Intelligent lathe control method as claimed in claim 1, it is characterised in that described particle swarm optimization algorithm includes following
Step:
Step 2 11: process of optimization step, particle swarm optimization algorithm produces population, is depended on by the particle in this population
Secondary parameter K being assigned to PID controllerp、Ki、Kd, then operation control system model, detects through grating scale, obtains the property of correspondence
Energy index, then be delivered in particle swarm optimization algorithm, time adjustment pid parameter, until end of run；
Step 2 12: particle swarm optimization algorithm realizes step, in the ultimate principle of particle swarm optimization algorithm, searches further
Speed in rope space and position, determine according to following two formula:
vt+1=ω vt+c1r1(Pt-xt)+c2r2(Gt-xt)
xt+1=xt+vt+1
Wherein: x represents the position of particle；Vx represents the speed of particle；ω x represents inertial factor；c1、c2X represents that acceleration is normal
Number；r1、r2X represents the random number that [0,1] is interval；PtX represents the optimal location that particle searches up to now；GtX represents whole
The optimal location that population searches up to now.
4. Intelligent lathe control method as claimed in claim 1, it is characterised in that described step one determines neutral net.
5. Intelligent lathe control method as claimed in claim 4, it is characterised in that described neutral net uses neutral net to lose
Propagation algorithm, be specifically divided into BP neural network structure determine, genetic algorithm optimization and three parts of BP neural network prediction；Wherein,
BP neural network structure determines that part determines BP neural network structure according to fitting function input/output argument number, and then determines
Genetic algorithm individual lengths；Genetic algorithm optimization uses weights and the threshold value of genetic algorithm optimization BP neural network, in population
Each individuality contains a network ownership value and threshold value, calculates ideal adaptation angle value individual by fitness function, loses
Propagation algorithm finds adaptive optimal control value corresponding individual by selecting, intersecting with mutation operation；BP neural network prediction genetic algorithm
Obtaining optimum individual to network initial weight and threshold value assignment, the trained rear anticipation function of network exports.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109901383A (en) * | 2019-03-01 | 2019-06-18 | 江苏理工学院 | A kind of AC servo machinery driving device control method |
| CN110488754A (en) * | 2019-08-09 | 2019-11-22 | 大连理工大学 | A kind of lathe self-adaptation control method based on GA-BP neural network algorithm |
| CN117300184A (en) * | 2023-11-29 | 2023-12-29 | 山东正祥工矿设备股份有限公司 | Control system for processing lathe for copper casting production |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090319077A1 (en) * | 2006-07-24 | 2009-12-24 | Rolls-Royce Plc. | Control of a machining operation |
| CN102609591A (en) * | 2012-02-16 | 2012-07-25 | 华中科技大学 | Optimization method of cutting parameters of heavy machine tool |
| CN103809506A (en) * | 2014-01-26 | 2014-05-21 | 西安理工大学 | Method for obtaining optimal dispatching scheme of part machining based on one-dimensional particle swarm algorithm |
-
2016
- 2016-07-18 CN CN201610567946.5A patent/CN106094722B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090319077A1 (en) * | 2006-07-24 | 2009-12-24 | Rolls-Royce Plc. | Control of a machining operation |
| CN102609591A (en) * | 2012-02-16 | 2012-07-25 | 华中科技大学 | Optimization method of cutting parameters of heavy machine tool |
| CN103809506A (en) * | 2014-01-26 | 2014-05-21 | 西安理工大学 | Method for obtaining optimal dispatching scheme of part machining based on one-dimensional particle swarm algorithm |
Non-Patent Citations (3)
| Title |
|---|
| 丁庆新 等: "精密复合数控磨床伺服进给***优化设计", 《制造技术与机床》 * |
| 李大胜 等: "遗传神经网络在数控机床刀具监测与控制***中的应用", 《湖南工业大学学报》 * |
| 陈爽: "PSO 算法在数控机床交流伺服*** PID 参数优化中的应用", 《微计算机信息（测控自动化）》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109901383A (en) * | 2019-03-01 | 2019-06-18 | 江苏理工学院 | A kind of AC servo machinery driving device control method |
| CN110488754A (en) * | 2019-08-09 | 2019-11-22 | 大连理工大学 | A kind of lathe self-adaptation control method based on GA-BP neural network algorithm |
| CN117300184A (en) * | 2023-11-29 | 2023-12-29 | 山东正祥工矿设备股份有限公司 | Control system for processing lathe for copper casting production |
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