CN106094722B - Intelligent lathe control method - Google Patents
Intelligent lathe control method Download PDFInfo
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- CN106094722B CN106094722B CN201610567946.5A CN201610567946A CN106094722B CN 106094722 B CN106094722 B CN 106094722B CN 201610567946 A CN201610567946 A CN 201610567946A CN 106094722 B CN106094722 B CN 106094722B
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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
Abstract
The invention discloses a kind of Intelligent lathe control methods comprising following steps: step 1: in process, the deviation that the electric current and grating scale of real-time detection main motor return, with feed speed afChanges delta afAs system call interception amount, the closed loop feedback study control of process is realized；Step 2: during workpieces processing, using the Vibration Condition of Machinetool workpiece as input value, adjusting servo-driver parameter using particle swarm optimization algorithm, keeps system operation more stable.The present invention can be realized numerical control driving equipment parameter self-tuning, and digital control system can obtain workpiece Form and position error information in time, adjust convenient for subsequent technique parameter.
Description
Technical field
The present invention relates to a kind of Lathe control methods, more particularly to a kind of Intelligent lathe control method.
Background technique
Digital control system is the key control unit of numerically-controlled machine tool, realizes control comprehensively to machine tool motion and process.And
It is with the following functions: the control number of axle and number of motion axes；Interpolation function；Feed function；Main shaft function；Tool function；Cutter compensation；
Machine error compensation；Operating function；Program management function；Character graphics display function；Aided programming function；Automatic diagnostic alarms
Function；Communication function.For a nc program, if there is logic error, the automatic diagnostic alarms function of system can be mentioned
Show modification, but the unreasonable selection for machined parameters in program, automatic diagnostic alarms function is then helpless.Therefore this is executed
One machined parameters of sample select unreasonable program, result or the processing for guarding and reducing lathe because of machining dosage selection
Efficiency；Or because machining dosage selects excessive damage cutter, workpiece is set to scrap even damage machining tool, after causing seriously
Fruit.
It processes the requirement to complication, precise treatment, enlargement and automation with modern mechanical simultaneously to be continuously improved, one
A little top grade accurate digital control process equipments are increasingly used widely.These equipment play crucial or even core to processing quality and efficiency
Heart effect, often cost is fairly expensive；Even certain products processed, due to spies such as complexity or accuracy or enlargements
Sign, single-piece cost or processing cost are also quite surprising.In the case, process equipment damage or scrap of the product are even only
The reduction of processing efficiency all may cause huge loss.
Be for the setting of machined parameters traditionally carried out according to the experience or reference books of people, and for beginner or
Even the very skilled operation of person is also to be difficult to provide preferable machined parameters, simultaneously because the setting of machined parameters is related to people
Operation, it is possible to will appear hand accidentally etc. and to the machined parameters for even endangering lathe, cutter and workpiece that make mistake, and
These can not all detected the decoding error detection of system.Application No. is " 200810153139.4 ", patent name
It is proposed by the way of fuzzy control in Chinese patent for " intelligent numerical control method with three-stage process self-optimization function ",
Since fuzzy table and control rule are established in fuzzy control according further to veteran personnel, still exist many uncertain
Property；Therefore this patent proposes that process degree of jitter according to workpieces processing is made using particle swarm optimization algorithm come real time modifying parameter
System is more stable.
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of Intelligent lathe control methods, can be realized numerical control driving
Device parameter Self-tuning System, digital control system can obtain workpiece Form and position error information in time, adjust convenient for subsequent technique parameter.
The present invention provides a kind of Intelligent lathe control method comprising following steps:
Step 1: in process, the deviation that the electric current and grating scale of real-time detection main motor return, to feed speed
Spend afChanges delta afAs system call interception amount, the closed loop feedback study control of process is realized；
Step 2: during workpieces processing, using the Vibration Condition of Machinetool workpiece as input value, using particle group optimizing
Algorithm adjusts servo-driver parameter, keeps system operation more stable.
Preferably, the step 1 includes the following steps: to establish 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 of cutting factor such as following formula:
In two formulas, QzIndicate unit time metal-cutting waste；T indicates cutter life；
aeIndicate working engagement of the cutting edge；apIndicate cutting depth；afIndicate each amount of feeding；
Z indicates the cutter number of teeth；N indicates the speed of mainshaft (r/min)；d0It indicates cutter diameter (mm)；
V indicates cutting speed；CvIndicate coefficient related with machining condition；
kvIndicate correction factor；qv、xv、yv、uv、pv, m indicate index of correlation parameter, xv≤yv< 1.
Preferably, the particle swarm optimization algorithm the following steps are included:
Step 2 11: process of optimization step, particle swarm optimization algorithm generates population, by the grain in the population
Son is successively assigned to the parameter K of PID controllerp、Ki、Kd, then operation control system model, is detected through grating scale, is corresponded to
Performance indicator, then be transmitted in particle swarm optimization algorithm, time adjustment pid parameter, until end of run；
Step 2 12: particle swarm optimization algorithm realizes step, in the basic principle of particle swarm optimization algorithm, further
Speed and position in ground search space are determined according to following two formula:
vt+1=ω vt+c1r1(Pt-xt)+c2r2(Gt-xt)
xt+1=xt+vt+1
Wherein: the position of x expression particle；The speed of vx expression particle；ω x indicates inertial factor；c1、c2X indicates acceleration
Constant；r1、r2The random number in x expression [0,1] section；PtX indicates the optimal location that particle searches so far；GtX indicates whole
The optimal location that a population searches so far.
Preferably, the step 1 determines neural network.
Preferably, the neural network uses neural network and genetic algorithm, is specifically divided into BP neural network structure determination, loses
Propagation algorithm optimization and BP neural network predict three parts；Wherein, BP neural network structure determination part is defeated according to fitting function
Enter output parameter number and determine BP neural network structure, and then determines genetic algorithm individual lengths；Genetic algorithm optimization uses something lost
The weight and threshold value of propagation algorithm Optimized BP Neural Network, each of population individual contain a network ownership value and threshold
Value, individual calculate ideal adaptation angle value by fitness function, and genetic algorithm is found optimal by selection, intersection and mutation operation
The corresponding individual of adaptive value；BP neural network prediction obtains optimum individual to network initial weight and threshold value assignment with genetic algorithm,
Anticipation function output after network is trained.
The positive effect of the present invention is that: the present invention can be realized numerical control driving equipment parameter self-tuning, numerical control system
System can obtain workpiece Form and position error information in time, adjust convenient for subsequent technique parameter.
Detailed description of the invention
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.
Specific embodiment
Present pre-ferred embodiments are provided with reference to the accompanying drawing, in order to explain the technical scheme of the invention in detail.
Intelligent lathe control method of the present invention mainly includes the following steps:
Step 1: in process, the deviation that the electric current of real-time detection main motor and grating scale return, have 2 inputs,
1 output determines that BP neural network structure is 2-5-1, with feed speed afChanges delta afAs system call interception amount, realize
The closed loop feedback of process learns control；
Step 2: being defeated with the Vibration Condition (i.e. grating scale fluctuation situation) of Machinetool workpiece during workpieces processing
Enter value, servo-driver parameter is carried out using particle swarm optimization algorithm (Particle Swarm Optimization, PSO)
Adjusting keeps system operation more stable.
The step 1 includes the following steps:
The relational expression of metal-cutting waste formula and cutter life and cutting factor is established,
Metal-cutting waste formula such as following formula (1):
Qz=aeapafzn......(1)
Cutter life and the relational expression of cutting factor such as following formula (2):
In two formulas, QzIndicate unit time metal-cutting waste；T indicates cutter life；
aeIndicate working engagement of the cutting edge；apIndicate cutting depth；afIndicate each amount of feeding；
Z indicates the cutter number of teeth；N indicates the speed of mainshaft (r/min)；d0It indicates cutter diameter (mm)；
V indicates cutting speed；CvIndicate coefficient related with machining condition；
kvIndicate correction factor；qv、xv、yv、uv、pv, m indicate index of correlation parameter, xv≤yv< 1.
Since lathe is in workpieces processing, vibration can be all generated, the different speed of mainshaft, feed speed can all give processing work
Part causes different degrees of influence, and this patent fluctuates deviation by grating scale, is driven with particle swarm optimization algorithm to control servo
Dynamic system parameter, mainly modifies its pid parameter.
The optimization problem of PID controller is exactly to determine one group of suitable parameter Kp、Ki、Kd, so that index is optimal.This
The index that patent uses, is defined as formula (3):
The numerically-controlled machine tool controlled device generally chosen is five rank time-dependent systems.The particle swarm optimization algorithm includes following
Step:
Step 2 11: process of optimization step, PSO generates population to optimization process as shown in Figure 1:, by the particle
Particle in group is successively assigned to the parameter K of PID controllerp、Ki、Kd, then operation control system model can be with through grating scale
Detection, obtains corresponding performance indicator, then be transmitted in PSO, time adjustment pid parameter, until end of run.
Step 2 12: particle swarm optimization algorithm realizes step, in the basic principle of particle swarm optimization algorithm, further
Ground, speed and position in search space, (4) and (5) determine according to the following formula:
vt+1=ω vt+c1r1(Pt-xt)+c2r2(Gt-xt)......(4)
xt+1=xt+vt+1......(5)
Wherein: the position of x expression particle；The speed of vx expression particle；ω x indicates inertial factor；c1、c2X indicates acceleration
Constant；r1、r2The random number in x expression [0,1] section；PtX indicates the optimal location that particle searches so far；GtX indicates whole
The optimal location that a population searches so far.
The process of PSO is as follows:
Step 3 11: initialization population is randomly generated speed and the position of all particles, and determines the P of particletWith
Gt；
Step 3 12: to each particle, the optimal location P that its adaptive value and the particle are lived throughtAdaptive value
It is compared, if preferably, as current Pt；
Step 3 13: to each particle, the optimal location G that its adaptive value and the entire population are lived throught's
Adaptive value is compared, if preferably, as current Gt；
Step 3 14: speed and position by formula (4) and formula (5) more new particle；
Step 3 15: if not meeting termination condition (this patent is set as process finishing, and motor is out of service), then
Return step 32；Otherwise, algorithm is exited, optimal solution is obtained.
As shown in Fig. 2, the step 1 determines neural network.Neural network uses neural network and genetic algorithm, specific to divide
3 parts are predicted for BP neural network structure determination, genetic algorithm optimization and BP neural network.Wherein, BP neural network structure
It determines that part determines BP neural network structure according to fitting function input/output argument number, and then determines that genetic algorithm individual is long
Degree.Genetic algorithm optimization uses the weight and threshold value of genetic algorithm optimization BP neural network, and each of population individual includes
One network ownership value and threshold value, individual calculate ideal adaptation angle value by fitness function, genetic algorithm by selecting,
Intersect and mutation operation to find adaptive optimal control value corresponding individual.BP neural network prediction obtains optimum individual to net with genetic algorithm
Network initial weight and threshold value assignment, anticipation function output after network is trained.
In the present invention, due to having 2 input parameters, an output parameters, so the BP neural network structure of setting is
2-5-1, i.e. input layer has 2 nodes, and hidden layer has 5 nodes, and output layer has 1 node, shares 2 × 5+5 × 1=15
Weight, 5+1=6 threshold value, so genetic algorithm individual UVR exposure length is 16+5=21.
The present invention is the intelligent control method with bicyclic optimization algorithm: inner ring is system stability control, according to lathe
In workpieces processing, the extent of vibration of lathe, the undulating value that grating scale returns, using particle swarm optimization algorithm optimization servo amplification
Parameter in device；Outer ring is the control of system feed compensation, according to grating scale return value and current value, uses neural network and genetic algorithm
Adjust feed speed.The present invention can be realized numerical control driving equipment parameter self-tuning, and digital control system can obtain workpiece morpheme in time
Control information adjusts convenient for subsequent technique parameter.
Particular embodiments described above, the technical issues of to solution of the invention, technical scheme and beneficial effects carry out
It is further described, it should be understood that the above is only a specific embodiment of the present invention, is not limited to
The present invention, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should be included in this
Within the protection scope of invention.
Claims (3)
1. a kind of Intelligent lathe control method, which is characterized in that itself the following steps are included:
Step 1: in process, the deviation that the electric current and grating scale of real-time detection main motor return, with feed speed af
Changes delta afAs system call interception amount, the closed loop feedback study control of process is realized；
Step 2: during workpieces processing, using the Vibration Condition of Machinetool workpiece as input value, using particle swarm optimization algorithm
Servo-driver parameter is adjusted, keeps system operation more stable；Wherein, servo-driver parameter includes pid parameter, PID
Parameter is adjusted by PID controller；
The step 1 includes the following steps: the relational expression for establishing 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 of cutting factor such as following formula:
In two formulas, QzIndicate unit time metal-cutting waste；T indicates cutter life；
aeIndicate working engagement of the cutting edge；apIndicate cutting depth；afIndicate each amount of feeding；
Z indicates the cutter number of teeth；N indicates the speed of mainshaft (r/min)；d0It indicates cutter diameter (mm)；
V indicates cutting speed；CvIndicate coefficient related with machining condition；
kvIndicate correction factor；qv、xv、yv、uv、pv, m indicate index of correlation parameter, xv≤yv< 1；
PID controller is for determining one group of suitable parameter Kp、Ki、Kd, so that index is optimal；Index definition is such as following formula:
The particle swarm optimization algorithm the following steps are included:
Step 2 11: process of optimization step, particle swarm optimization algorithm generate population, by the particle in the population according to
The secondary parameter K for being assigned to PID controllerp、Ki、Kd, then operation control system model, is detected through grating scale, obtains corresponding property
Energy index, then be transmitted in particle swarm optimization algorithm, time adjustment pid parameter, until end of run；
Step 2 12: particle swarm optimization algorithm realizes that step is further searched in the basic principle of particle swarm optimization algorithm
Speed and position in rope space are determined according to following two formula:
First formula: vt+1=ω vt+c1r1(Pt-xt)+c2r2(Gt-xt)
Second formula: xt+1=xt+vt+1
Wherein: the position of x expression particle；The speed of vx expression particle；ω x indicates inertial factor；c1、c2X indicates that acceleration is normal
Number；r1、r2The random number in x expression [0,1] section；PtX indicates the optimal location that particle searches so far；GtX indicates entire
The optimal location that population searches so far；
The process of particle swarm optimization algorithm is as follows:
Step 3 11: initialization population is randomly generated speed and the position of all particles, and determines the P of particletAnd Gt；
Step 3 12: to each particle, the optimal location P that its adaptive value and the particle are lived throughtAdaptive value compared
Compared with, if preferably, as current Pt；
Step 3 13: to each particle, the optimal location G that its adaptive value and the entire population are lived throughtAdaptive value
It is compared, if preferably, as current Gt；
Step 3 14: by the speed and position of the first formula and the second formula more new particle；
Step 3 15: if not meeting termination condition, return step 32；Otherwise, algorithm is exited, is obtained optimal
Solution.
2. Intelligent lathe control method as described in claim 1, which is characterized in that the step 1 determines neural network.
3. Intelligent lathe control method as claimed in claim 2, which is characterized in that the neural network is lost using neural network
Propagation algorithm is specifically divided into BP neural network structure determination, genetic algorithm optimization and BP neural network and predicts three parts；Wherein,
BP neural network structure determination 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 the weight and threshold value of genetic algorithm optimization BP neural network, in population
Each individual contains a network ownership value and threshold value, and individual calculates ideal adaptation angle value by fitness function, loses
Propagation algorithm finds the corresponding individual of adaptive optimal control value by selection, intersection and mutation operation；Genetic algorithm is used in BP neural network prediction
Optimum individual is obtained to network initial weight and threshold value assignment, anticipation function exports after network is trained.
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| CN109901383A (en) * | 2019-03-01 | 2019-06-18 | 江苏理工学院 | A kind of AC servo machinery driving device control method |
| CN110488754B (en) * | 2019-08-09 | 2020-07-14 | 大连理工大学 | Machine tool self-adaptive 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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| 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 |
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