CN101477351B - Intelligent numerical control method with three-stage process self-optimization function - Google Patents

Abstract

The invention belongs to the technical field of digital control of electromechanical integration, and relates to an intelligent control method with the three-stage processing self-optimization function. The intelligent control method comprises the following steps: firstly, optimizing and an executing processing program according to optimum processing parameters; secondly, detecting the current and the power supply voltage of a main motor in real time during processing, taking the current as a decision-making value, the voltage as an supplementary means and the variation delta af of the feeding speed af as a system adjustment value, and realizing closed-loop feedback fuzzy control of the processing process; and thirdly, taking a trigger signal of a three-dimensional measuring head as an input mark value after completing the procedure of detection of geometrical information of workpieces, reading the actual coordinate of a current measuring point after detecting the input mark value of the measuring head, estimating errors of processed workpieces after measuring all the measuring points, and performing secondary self-adaptive optimization and adjustment on the processing program. The intelligent control method can realize self-optimization of a digital control program; and a digital control system can acquire shape and position error information of the workpieces in time, so that parameters in subsequent technology are convenient to adjust.

Description

Intelligent numerical control method with three-stage process self-optimization function

Technical field

The invention belongs to the fields of numeric control technique of electromechanical integration, specifically, relate to the intelligent numerical control system that a kind of status monitoring based on the machine tooling scene and real-time optimization and fuzzy control are merged mutually.

Background technology

Digital control system is the key control unit of numerically-controlled machine, and machine tool motion and process are realized control comprehensively.And has a following function: the number of axes and the interlock number of axle; Interpolation function; Feed function; The main shaft function; Tool function; Cutter compensation; The machine error compensation; Operating function; The program management function; The character graphics Presentation Function; The aided programming function; The automatic diagnosis warning function; Communication function.For a nc program, if logic error, system's automatic diagnosis warning function can be pointed out modification, but selects for use for the unreasonable of machined parameters in the program, and the automatic diagnosis warning function is then powerless.Therefore carrying out such machined parameters selects irrational program, its result or the working (machining) efficiency reduction lathe because machining dosage selects conservative; Perhaps, workpiece is scrapped even damaged machining tool, cause serious consequence because machining dosage is selected excessive damage cutter.

Along with the requirement of modern mechanical processing to complicated, precise treatment, maximization and robotization improves constantly, some high-grade accurate digital control process equipments are used widely day by day simultaneously.These equipment play key and even central role to crudy and efficient, and often cost is quite expensive; Even some product that processes, because complicacy or features such as accuracy or maximization, its single-piece cost or processing cost are also quite surprising.In the case, process equipment damage or product rejection even only be that the reduction of working (machining) efficiency all may cause tremendous loss.

Be that experience or relevant handbook according to the people carries out for the setting of machined parameters traditionally, even and also be to be difficult to provide machined parameters preferably for beginner or very skilled operation, simultaneously because the setting of machined parameters relates to people's operation, hand mistake etc. just might occur and give that make mistake or even machined parameters harm lathe, cutter and workpiece, and these decoding error detections for system all can't detect.

Summary of the invention

The objective of the invention is, overcome the above-mentioned deficiency of prior art, provide a kind of and carry out the machined parameters self-optimizing for numerical control program, and the intelligence control method of three-stage process self-optimization function that carries out the secondary self-optimizing of parameter real-time optimization and numerical control program based on the status monitoring at machine tooling scene, realize with processing optimize, the high-level efficiency of processing and the intelligent solution that process safety is target.For this reason, the present invention adopts following technical scheme:

A kind of intelligence control method with three-stage process self-optimization function, obtaining spindle motor of machine tool, cutter and processing work material information, and optimization job sequence, after obtaining the processing cutting depth amount of every section job sequence, whether its cutting depth is judged in safe range, if just directly do not stopping optimization process and reporting to the police,, realize the Based Intelligent Control of self-optimization function if in the reasonable scope then according to the following step:

(5) set up the mathematical model of lathe-cutter-workpiece system, utilize the Adaptive Genetic algorithm computation to go out optimum machined parameters;

(6), optimize and the execution job sequence according to optimum machined parameters;

(7) in process, detect the electric current and the supply voltage of main motor in real time, with the electric current decision content, voltage is supplementary means, with speed of feed a _fChanges delta a _fAs system's adjustment amount, realize the close-loop feedback fuzzy control of process;

(8) after finishing the operation that need detect to the workpiece geological information, with the trigger pip of three dimensional probe, 3-D probe as input sign amount, after detecting gauge head input sign amount, read current measurement point actual coordinate, after all measurement points measure, carry out the error evaluation of processing work,, job sequence is carried out the adjustment of secondary adaptive optimization with the information source basis that the error evaluation data are adjusted as subsequent technique.

As preferred implementation, the intelligence control method with three-stage process self-optimization function of the present invention, step wherein (1) comprises the following steps:

1.1: set up metal resection rate formula Q _z=a _ea _pa _fThe relational expression of zn and cutter life and cutting factor $T={\left(\frac{C_v{d}_0^{q_v}}{v{a}_p^{x_v}{a}_f^{y_v}{a}_e^{u_v}{z}^{p_v}}{k}_v\right)}^{\frac{1}{m}}$ , in two formulas, Q _z, T, a _e, a _p, a _f, z, n, d ₀, v is respectively unit interval metal resection rate (mm ³/ min), cutter life (min), working engagement of the cutting edge (mm), cutting depth (mm), each amount of feeding (mm/g), the cutter number of teeth, the speed of mainshaft (r/min), tool diameter (mm) and cutting speed (m/min); C _vBe the coefficient relevant with machining condition; k _vBe correction factor; q _v, x _v, y _v, u _v, p _v, m is respectively index of correlation parameter, x _v≤ y _v＜1;

1.2: consideration is that the metal resection rate of main constraint maximizes as optimization aim with the cutter life, and objective function is got f (x)=min (f _Max-Q _z), wherein, f _MaxFor making f _x〉=0 one enough big on the occasion of, a _e, a _pAs determining amount, v=π d ₀N/1000, target variable is taken as a _f, n;

1.3: set up the constraint condition that comprises the speed of mainshaft, speed of feed, cutting force and cutting power, be respectively: T 〉=T _RefN≤n _Maxa _fZn≤v _Fmax $\frac{C_F{a}_p^{x_F}{a}_f^{y_F}{a}_e^{u_F}}{d_0^{q_F}{n}^{w_F}}{k}_{F_c}\≤F_{\max };$ ?F _cv/(6×10 ⁴×η)≤P _max；

1.4: according to following formula, adopt self-adapted genetic algorithm, optimize machined parameters, termination rules is 10 for the fitness value error ^-7Or the genetic iteration number of times was 100 generations:

$P_C=\left\{\begin{array}{cc}0.9-\frac{0.3(f\&prime;-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}},& f\&prime;\&GreaterEqual;f_{\mathrm{avg}}\\ 0.9,& f\&prime;<f_{\mathrm{avg}}\end{array}\right.,$ $P_m=\left\{\begin{array}{cc}0.1-\frac{0.009(f_{\max }-f)}{f_{\max }-f_{\mathrm{avg}}},& f\&GreaterEqual;f_{\mathrm{avg}}\\ 0.1,& f<f_{\mathrm{avg}}\end{array}\right.$

In the formula, P _c, P _m, f _Max, f _Avg, f ', f be respectively fitness value maximum in crossover probability, variation probability, the colony, the average fitness value of per generation colony, the fitness value of bigger fitness value in two individualities that will intersect, the individuality that will make a variation.

Intelligent numerical control method with three-stage process self-optimization function of the present invention, step wherein (3) realizes in the following manner: fuzzy control input language variable is taken as current deviation E _IAnd deviation variation rate EC _I, the output language variable is that speed of feed changes

, make the fuzzy set domain of these three linguistic variables quantize shelves number n _fAll get identical value 6, quantizing factor k _e, k _EcAnd scale factor k _uAccording to controlling the basic domain that requires variation and deciding, subordinate function is a triangular function, sets up fuzzy control rule according to getting in touch of decision content and system's adjustment amount, for the fuzzy set on the given fuzzy control input language variable domain

With

, utilize the reasoning composition rule to finish fuzzy reasoning to the fuzzy set on the output language variable domain, finish the output fuzzy set by the mapping of fuzzy set by method of weighted mean to ordinary set, obtain the input regulated quantity of controlled system of processing.

The present invention utilizes FUZZY ALGORITHMS FOR CONTROL to optimize the result with two kinds and merges mutually at first based on the parameter optimization method and the method for setting up the mathematical model of lathe-cutter-workpiece system of database, and nc program is carried out self-optimizing; Then in program operation process, according to the monitoring of machine tooling state, obtain the lathe real-time running state, and carry out real-time adaptive machining parameter adjustment on this basis; Evaluate, analyze by various type bit errors at last,, ask numerical control program is counter once more, obtain the program code that part appears in mismachining tolerance, handle by its self-optimizing of carrying out job sequence again based on this information to the machine tooling part.After three grades processing optimization process, guaranteed the optimization of machined parameters on the one hand, by the real-time parameter adjustment in the process, lathe can be changed according to the service condition of reality on the other hand, make system have certain adaptivity and intelligent.And traditional machined parameters not only can not carry out oneself's modification, and is not possess adaptive control ability.

The intelligent numerical control system of the fusion three-stage process self-optimization function that foundation intelligence control method of the present invention makes up, adopt multiple physical states classification monitoring system for handling, by integrated fast parallel monitoring and the adaptive optimization adjustment of finishing multiple physical state information in the digital control processing process of digital control system.Wherein at the workpiece processing geological information can reduce the workpiece number of times that is installed in the machine quality testing, and realize the self-optimizing of numerical control program, digital control system can in time be obtained workpiece morpheme control information, is convenient to the subsequent technique parameter adjustment, helps the rate of reducing the number of rejects and seconds.Digital control system has made full use of the software and hardware technology advantage of high speed development, can integrated realization to the motion control of lathe and the monitoring and the optimization of on-the-spot multiple physical states, strengthened level of integrated system.

Description of drawings

Fig. 1 is based on the digital control processing adaptive model based control of fuzzy logic.

The processing flow chart of the machined parameters self-optimizing of Fig. 2 numerical control program of the present invention.

Fig. 3: based on the processing self-optimizing process flow diagram of the status monitoring at machine tooling scene.

Embodiment

Intelligent numerical control system with the fusion three-stage process self-optimization function that makes up according to intelligence control method of the present invention is an example below, and intelligence control method of the present invention is done detailed description.

Digital control system of the present invention mainly is divided into main control module, motion-control module, communication module, numerical control program machined parameters self-optimizing module, processing operation real-time optimization module, at machine testing module and numerical control program secondary self-optimizing module several sections.Adopting high-speed dsp respectively except that motion-control module and collection and analytic unit is the individual cores processor, and remainder and main control module are shared high-performance microprocessor.Each intermodule is realized funcall and data transmission through 1 link of internal system bus.The synthetic operation of each module can realize to the motion control of lathe and based on three grades of processing self-optimizings of machine tooling presence states monitoring.The processing main-process stream of machined parameters self-optimizing of the present invention as shown in Figure 2.

At first introduce three basic modules of digital control system of the present invention below: main control module, motion-control module, communication module.It is specifically composed as follows:

(1) main control module

This module mainly is to carry out entire system control and coordination.Comprise tasks such as system initialization, parameter management, global data management, overall tasks coordination, man-machine interaction management, motor program error detection, lathe adjustment, User Defined functional development, system help, and the fault handling measure of responsive state monitoring modular.

This module includes a central high-performance microprocessor, three data storage chips, a data managing chip, and 16 RISC single-chip microcomputer is formed.And comprise various numerical control operation panels, LCD, handwheel, alarm, switch and corresponding interface circuits, power supply clock circuit.And each chip is connected to integral body by fieldbus.

1, central high-performance microprocessor: under the working environment of supporting with it embedded real-time operating system, realize control and the coordination of main control module to entire system.But and can under the cooperation of the coprocessor that makes up by the ARM chip of high-speed dsp digital signal processing chip autonomous operation etc., finish the big and high complicated control task of real-time of some calculated amount.

2, pin-saving chip mainly comprises: 1, the FLASH-ROM chip-stored PLC interpretation software, PLC application program, graphics display control software etc. 2, the S-RAM chip-stored systematic parameter, job sequence, user's macroprogram, PLC parameter, cutter compensation and workpiece coordinate offset data, The compensation of pitch error data, 3, D-RAM chip, as working storage, about in service buffer memory of system.

3, data management chip: be used to other chips in the digital control system to transmit various data messages, also be used for receiving and store the various data messages that other chips of digital control system transmit, the resource sharing that increases system works.

4,16 RISC single-chip microcomputers: utilize the PLC interpretive routine and the application program of storing in the FLASH-ROM chip, finish the switch of cutting fluid, air pump etc. in the lathe, the startup of the various motors/control that stops, the input of the signal of limit switch.And interrelate by internal system bus and central processing unit, with and the command execution corresponding function.

5, system bus: adopt 32 bit data bus, 24 bit address buses and 30 control buss to form, mainly be responsible for the connection between each functional chip, interface, storage chip and the central processing unit, transmit data, address and control signal.

(2) motion-control module

Motion-control module is core with the high-speed dsp, by the motor program and the configuration parameter that download in the S-RAM chip memory, can independently finish the relevant motion control functions of lathe action such as motor program decoding, cutter compensation, pitch compensation, interpolation, servocontrol.Carry out the task allotment by main control module, drive the operation of lathe performance element, and will be correlated with and carry out information feedback to main control module.

(3) communication module

Communication module contains RS232 interface, USB interface and Ethernet interface.Be used to handle digital control system and the data message of other digital control systems, workshop numerical control network, Intranet Intranet and Internet Internet and the transmission of control information.The realization system is shared with extraneous communication and resource information.

The characteristic of invention is mainly reflected in the machined parameters self-optimizing system of numerical control program, based on the processing operation real-time optimization system of the status monitoring at machine tooling scene, based on the numerical control program secondary self-optimizing system of the status monitoring at machine tooling scene and the integrated mechanism of digital control system that merges above-mentioned system.The present invention is on the basis that the basic framework of constructing system and function realize, integrated use numerical control, observing and controlling and artificial intelligence technology, with high-performance microprocessor and DSP is core, the machined parameters self-optimizing of structure job sequence reaches the intelligent numerical control system based on the three-stage process self-optimization function of the secondary self-optimizing of the processing operation real-time optimization of the status monitoring at machine tooling scene and job sequence, realizes digital control processing control, status monitoring and optimization coordination parallel running.

(1), the machined parameters self-optimizing system of numerical control program is achieved in that

The machined parameters self-optimizing of numerical control program is to be handled by the numerical control program machined parameters self-optimizing module of digital control system.This module contains a slice FLASH-ROM storage chip, and with the shared central high speed microprocessor of the main control module of digital control system, by the task scheduling of main control module, carry out the response of function.This module mainly is the realization of the software function of optimized Algorithm.The FLASH-ROM storage chip is used to store relevantly optimizes the data database data and based on the parameter optimization algorithm of model, chip links to each other with central high speed microprocessor and other chip of internal system by the bus of internal system.When by central high speed microprocessor the content in the storage chip being carried out read-write operation after the mission-enabling, and the combination algorithm operation of being correlated with calculates, and realizes optimizational function.

In this module, its work is as follows:

At first be initialization operation, the machined parameters of optimization can be provided for the lathe of different model spindle motor of machine tool, cutter and processing work material information.

By to after the handling of job sequence according to the required form of optimisation strategy, obtain the processing cutting depth amount of every section job sequence, and whether its cutting depth judged in safe range, if just directly do not stopping optimization process and carrying out alarm indication, if be reference then in the reasonable scope with spindle motor, cutter and processing work material, cutting depth, guaranteeing that crudy is under the prerequisite of safety, with the working (machining) efficiency is purpose, is optimized processing according to the optimisation strategy of inside modules for machined parameters.

This inside modules adopts two kinds of strategies that optimization method merges mutually: a kind of optimization of parameter choice method that is based on database promptly makes up database according to the data that various publications and cutter manufacturer provide, as the support of parameter optimization; Another kind is based on the parameter optimization method of model, promptly by setting up the mathematical model of lathe-cutter-workpiece system, utilizes neural network, genetic algorithm to calculate optimum machined parameters.Utilize FUZZY ALGORITHMS FOR CONTROL to obtain machined parameters then.

The intelligent algorithm of taking among the present invention based on mathematical model:

By metal resection rate formula: Q _z=a _ea _pa _fZn (1)

As can be known, increase that any one can be boosted productivity in several cutting factors.Yet the increase of cutting factor may cause the quick wearing and tearing of cutter, so that the frequent tool changing of work in-process needs, not only increases cost but also will weaken owing to improving the jump that resection rate brings.Again by the relational expression of cutter life and cutting factor

$T={\left(\frac{C_v{d}_0^{q_v}}{v{a}_p^{x_v}{a}_f^{y_v}{a}_e^{u_v}{z}^{p_v}}{k}_v\right)}^{\frac{1}{m}}---\left(2\right)$

Above in two formulas, Q _z, T, a _e, a _p, a _f, z, n, d ₀, v is respectively unit interval metal resection rate (mm ³/ min), cutter life (min), working engagement of the cutting edge (mm), cutting depth (mm), each amount of feeding (mm/g), the cutter number of teeth, the speed of mainshaft (r/min), tool diameter (mm) and cutting speed (m/min); C _vBe the coefficient relevant with machining condition; k _vBe correction factor; q _v, x _v, y _v, u _v, p _v, m is respectively index of correlation parameter, x usually _v≤ y _v＜1.

As can be seen, cutting factor is different to the influence of cutter life.With regard to v, a _f, a _pThe three, its influence degree is successively decreased successively.By formula

v＝πd ₀n/1000 (3)

V and n are simple linear proportional relation as can be known.Therefore, under the prerequisite that guarantees resection rate, also need rationally to determine each cutting factor in the formula (1), cross quick-wearing to prevent cutter.

Here we ignore roughness and elastic deformation factor, and consideration is that the metal resection rate of main constraint maximizes as optimization aim with the cutter life, and objective function is got

f(x)＝min(f _max-Q _z) (4)

Wherein, f _MaxBe one enough big on the occasion of, make f _x〉=0.

a _e, a _pBecause of influenced by actual processing technology, therefore as determining amount, target variable is taken as a _f, n.

Constraint condition is as follows:

T≥T _ref (5)

n≤n _max (6)

a _fzn≤v _f?max (7)

$\frac{C_F{a}_p^{x_F}{a}_f^{y_F}{a}_e^{u_F}}{d_0^{q_F}{n}^{w_F}}{k}_{F_c}\&le;F_{\max }$ (8)

F _cv/(6×10 ⁴×η)≤P _max (9)

Formula (5)-(9) are respectively the constraint condition of cutter life, the speed of mainshaft, speed of feed, cutting force and cutting power.When practical application, can increase constraints such as knife bar and blade strength according to rig-site utilization environment needs.

For avoiding basic genetic algorithmic in searching process, easily to be absorbed in local minimum point and precocious defective, intersect and the variation probability select for use adaptive algorithm (Adaptive GA, AGA), as follows respectively:

$P_C=\left\{\begin{array}{cc}0.9-\frac{0.3(f\&prime;-f_{\mathrm{avg}})}{f_{\max }-f_{\mathrm{avg}}},& f\&prime;\&GreaterEqual;f_{\mathrm{avg}}\\ 0.9,& f\&prime;<f_{\mathrm{avg}}\end{array}\right.---\left(10\right)$

$P_m=\left\{\begin{array}{cc}0.1-\frac{0.009(f_{\max }-f)}{f_{\max }-f_{\mathrm{avg}}},& f\&GreaterEqual;f_{\mathrm{avg}}\\ 0.1,& f<f_{\mathrm{avg}}\end{array}\right.---\left(11\right)$

In the formula, P _c, P _m, f _Max, f _Avg, f ', f be respectively fitness value maximum in crossover probability, variation probability, the colony, the average fitness value of per generation colony, the fitness value of bigger fitness value in two individualities that will intersect, the individuality that will make a variation.It is 10 that termination rules is got the fitness value error ^-7Or the genetic iteration number of times was 100 generations.

(2), the processing operation real-time optimization system based on the status monitoring at machine tooling scene is achieved in that

Monitoring state information is divided into numerically controlled processing equipment (comprising numerically-controlled machine and digital control system) running state information and workpiece to be machined geological information two big classes.

The numerically controlled processing equipment running state information is handled by processing operation real-time optimization module.In this module, process equipment that equipment running status information is obtained by sensor measurement and the performance of the signal form of process physical state variable.With DSP is that core is equipped with high capacity D-RAM and the FLASH-ROM chip makes up signals collecting, analysis and machined parameters optimization unit.D-RAM moves the required memory space in order to keep the unit internal program, and can be used as the buffer area of each passage image data.The system intelligence policy library that the FLASH-ROM chip-stored can repeatedly be downloaded.Signal gathering unit can be configured to 2 to 32 passages on demand, is equipped with the continuous acquisition that front end various kinds of sensors array can be finished machining state signals such as vibration, cutting force, acoustic emission, temperature, current of electric, line voltage.Signal analysis unit is finished the conventional Characteristic Extraction of signal by the special chip that is solidified with the classical signals analysis means.Machined parameters is optimized the unit and is finished signal condition identification and parameters optimization calculating by expert knowledge library that FLASH-ROM stores and fuzzy policy library.The synchronous signal analytic unit, comprises the physical quantity state of original sampling data, signal characteristic quantity, signal representative and optimizes the result to the main control module data of reporting the result by the digital control system internal bus.

The process real-time optimization algorithm of taking in the invention:

The processing site physical state is taken as the electric current and the supply voltage of main motor in the present invention, obtains the monitor signal data by current sensor and voltage sensor.As decision content, voltage is supplementary means with electric current.And constitute the close-loop feedback control of system of processing with this.Because current signal is used for replacing force signal analysis among the present invention, at the cutting force formula

$F_c=\frac{C_F{a}_p^{x_F}{a}_f^{y_F}{a}_e^{u_F}}{d_0^{q_F}{n}^{w_F}}{k}_{F_c}$

In, index parameters w _FMuch smaller than y _F, in most cases its value is 0, so the influence that speed of mainshaft n applies cutting force is much smaller than amount of feeding a _f, and because rotating speed is relevant with concrete technology, so be unsuitable for doing the self-optimizing adjustment.Simultaneously, working engagement of the cutting edge a _eWith cutting depth a _pRetrained by factors such as processing technology, cutter, process redundancy, therefore here only with speed of feed a _fChanges delta a _fAs system's adjustment amount.

For digital control processing, the controlled device that is made of numerically-controlled machine, cutter, workpiece is difficult to set up its precise math model, therefore adopts fuzzy control scheme.

Fuzzy control model input language variable is taken as current deviation E _IAnd deviation variation rate EC _I, the output language variable is that speed of feed changes

Make the fuzzy set domain of these three linguistic variables quantize shelves number n _fAll get identical value 6.Quantizing factor k _e, k _EcAnd scale factor k _uAccording to controlling the basic domain that requires variation and deciding.The linguistic variable value is taken as respectively

(1) to E _I: NB, NM, NS, NO, PO, PS, PM, PB;

(2) to EC _I: NB, NM, NS, ZO, PS, PM, PB;

(3) right

: NB, NM, NS, ZO, PS, PM, PB.

Accelerate response speed for reducing calculated amount, subordinate function is all represented with triangular function.Thus, set up 56 fuzzy control rules according to getting in touch of decision content and controlled object.

If X ₁, X ₂, Y corresponding linguistic variable E respectively _I, EC _I, Domain, then above-mentioned each bar fuzzy rule can be expressed as a product space (X ₁* X ₂A fuzzy implication on the) * Y ${{\tilde{E}}_I}^i{{\widetilde{\mathrm{EC}}}_I}^i{\&RightArrow;\tilde{U}}_{a_f}^i$ , wherein

Be respectively X ₁, X ₂, the fuzzy set on the Y, corresponding to each linguistic variable value.Fuzzy implication adopts the minimum value rule in the native system.Because this fuzzy system is the dual input single output system, therefore, every rule determines a ternary relation again, promptly

${\tilde{R}}^i=({{\tilde{E}}_I}^i\&times;{{\widetilde{\mathrm{EC}}}_I}^i)\&times;{\tilde{U}}_{a_f}^i$ 1≤i≤56

Thus, total fuzzy relation that can obtain this fuzzy system control law is

For the fuzzy set on the given Fuzzy control system input language variable domain With

, utilize the reasoning composition rule can finish fuzzy reasoning to the fuzzy set on the output language variable domain:

The fuzzy judgment process of system is finished the output fuzzy set by the mapping of fuzzy set to ordinary set by method of weighted mean, obtains the input regulated quantity of controlled system of processing

$\mathrm{\&Delta;}{a}_f=\frac{\underset{i}{\overset{13}{\mathrm{\&Sigma;}}}{x}_i{\mathrm{\&mu;}}_{{\tilde{u}}_{a_f}^L}\left(x_i\right)}{\underset{i}{\overset{13}{\mathrm{\&Sigma;}}}{\mathrm{\&mu;}}_{{\tilde{u}}_{a_f}^L}\left(x_i\right)}$

X in the formula _i∈ Y,

Subordinate function for the output fuzzy set.

Utilize above-mentioned fuzzy logic controller to set up and adjust controlling models as shown in Figure 1 based on the digital control processing process amount of feeding self-optimizing of main current of electric and voltage signal monitoring.Among the figure, FLC is a fuzzy controller, and CNC_Mach is controlled numerically-controlled machine, and Δ U is the voltage and the normal voltage difference of on-line monitoring, and I is an actual current signal, I _RefBe given current reference value.The amount of feeding that is input to CNC_Mach in arbitrary sampling instant is

$a_f^{i+1}=a_f^i+\mathrm{\&Delta;}{a_f}^{i+1}$ i＝0，1，2，...，n

Wherein, $a_f^0=a_f^{\mathrm{Init}},$ Be the initial given amount of feeding.Fuzzy controller is by adjusting Δ a in real time _fRealization is to the self-optimizing adjustment of the amount of feeding.

(3) the numerical control program secondary self-optimizing system based on the status monitoring at machine tooling scene is achieved in that

The detection of workpiece to be machined geological information is realized in the machine testing module by digital control system.In this module, constitute, and link to each other with central high speed microprocessor and other chip of internal system by the bus of internal system by an ARM chip and a D-RAM chip, a S-RAM chip.Carry out the response of function by the task scheduling of main control module and the input of outside activation signal.The ARM chip mainly is that the signal that gauge head sends is discerned, and according to the analytical algorithm of solidifying in the S-RAM chip, carries out the scheduling of overall task in the processing of data and the unit.D-RAM mainly stores measuring point coordinate data and analysis result, and provides memory headroom for the program run in the unit.

In the machine testing module with the trigger pip of outside three dimensional probe, 3-D probe as input sign amount.Gauge head can be selected for use homemade or foreign brand name ruby contact-type 3 D gauge head.Parameters such as gauge head specification, chaining pin length and survey bulb diameter are selected according to bed piece and workpiece size.In the digital control processing process, after finishing a certain certain working procedure, begin to enter workpiece geological information testing process, be mainly used in and analyze this operation and finish the geometric accuracy of back workpiece and provide information source for the self-optimizing adjustment of follow-up job sequence.After the machine testing module detects gauge head input sign amount, obtain the current measurement point actual coordinate of feedback from motion-control module through system bus.All measurement points measure the back measurement data will be admitted to the error evaluation unit.All measurement point measurement data and error evaluation element analysis result will be admitted to numerical control program secondary self-optimizing module by the digital control system internal bus.

In this module, by job sequence is read, obtain the three-dimensional stereo model of processing work, then the three-dimensional model with the measuring point data structure carries out the data comparison, and utilize the error evaluation result, obtain the data difference between the desirable achievement of actual processing effect and program, and then obtain error program segment and relevant cutting data and put bit data, serve as that intelligent algorithms such as basis utilization expert knowledge library, module controls carry out automatic adaptive optimization adjustment to job sequence then, make it meet the crudy requirement with these data.All measurement point measurement data and error evaluation element analysis result are kept in the machine testing memory block with the daily record form simultaneously, as the information source of subsequent technique adjustment.Based on the processing self-optimizing flow process of the status monitoring at machine tooling scene as shown in Figure 3.

(4) the integrated mechanism of digital control system of fusion three-stage process self-optimization function is achieved in that

Described digital control system has been expanded three grades of self-optimization functions of processing except that finishing the motion control function to lathe that traditional digital control system has.Digital control system mainly is divided into main control module, motion-control module, communication module, numerical control program machined parameters self-optimizing module, processing operation real-time optimization module, in machine testing module and numerical control program secondary self-optimizing module 7 major parts.Main control module is responsible for the management and the allotment of entire system task, finishes the basic function realization of system under the support of motion-control module, Tongxu module by the internal system bus.

Numerical control program machined parameters self-optimizing module, processing operation real-time optimization module, embodied then that multiple physical state classification monitoring that digital control system self possessed is handled and function is independently planned the architectural feature of processing in machine testing module and numerical control program secondary self-optimizing module.User command is sent by main control module, and except that the order that main control module self can respond, all the other orders are automatically forwarded to corresponding module through the internal system bus and handle.The synthetic operation of each module has realized merging the integrated mechanism of digital control system of three grades of machined parameters self-optimization functions.

Claims (2)

1. intelligence control method with three-stage process self-optimization function, obtaining spindle motor of machine tool, cutter and processing work material information, and optimization job sequence, after obtaining the processing cutting depth amount of every section job sequence, whether its cutting depth is judged in safe range, if just directly do not stopping optimization process and reporting to the police,, realize the Based Intelligent Control of self-optimization function if in the reasonable scope then according to the following step:

(1) sets up the mathematical model of lathe-cutter-workpiece system according to the following step, utilize the Adaptive Genetic algorithm computation to go out optimum machined parameters;

1.1: set up metal resection rate formula Q _z=a _ea _pa _fThe relational expression of zn and cutter life and cutting factor

In two formulas, Q _z, T, a _e, a _p, a _f, z, n, d ₀, v is respectively unit interval metal resection rate (mm ³/ min), cutter life (min), working engagement of the cutting edge (mm), cutting depth (mm), speed of feed (mm/g), the cutter number of teeth, the speed of mainshaft (r/min), tool diameter (mm) and cutting speed (m/min); C _vBe the coefficient relevant with machining condition; k _vBe correction factor; q _v, x _v, y _v, u _v, p _v, m is respectively index of correlation parameter, x _v≤ y _v＜1;

1.2: consideration is that the metal resection rate of main constraint maximizes as optimization aim with the cutter life, and objective function is got f (x)=min (f _Max-Q _z), wherein, f _MaxFor making f _x〉=0 one enough big on the occasion of, a _e, a _pAs determining amount, v=π d ₀N/1000, target variable is taken as a _f, n;

1.3: set up the constraint condition that comprises the speed of mainshaft, speed of feed, cutting force and cutting power, be respectively: T 〉=T _RefN≤n _Maxa _fZn≤v _Fmax

F _cV/ (6 * 10 ⁴* η)≤P _Max

1.4: according to following formula, adopt self-adapted genetic algorithm, optimize machined parameters, termination rules is 10 for the fitness value error ^-7Or the genetic iteration number of times was 100 generations:

In the formula, P _c, P _m, f _Max, f _Avg, f ', f be respectively fitness value maximum in crossover probability, variation probability, the colony, the average fitness value of per generation colony, the fitness value of bigger fitness value in two individualities that will intersect, the individuality that will make a variation;

(2), optimize and the execution job sequence according to optimum machined parameters;

(3) in process, detect the electric current and the supply voltage of spindle motor of machine tool in real time, with the electric current decision content, voltage is supplementary means, with speed of feed a _fChanges delta a _fAs system's adjustment amount, realize the close-loop feedback fuzzy control of process;

(4) after finishing the operation that need detect to the workpiece geological information, with the trigger pip of three dimensional probe, 3-D probe as input sign amount, after detecting gauge head input sign amount, read current measurement point actual coordinate, after all measurement points measure, carry out the error evaluation of processing work,, job sequence is carried out the adjustment of secondary adaptive optimization with the information source basis that the error evaluation data are adjusted as subsequent technique.

2. the intelligence control method with three-stage process self-optimization function according to claim 1 is characterized in that, step wherein (3) realizes in the following manner: fuzzy control input language variable is taken as current deviation E _IAnd deviation variation rate EC _I, the output language variable is that speed of feed changes , make the fuzzy set domain of these three linguistic variables quantize shelves number n _fAll get identical value 6, quantizing factor k _e, k _EcAnd scale factor k _uAccording to controlling the basic domain that requires variation and deciding, subordinate function is a triangular function, sets up fuzzy control rule according to getting in touch of decision content and system's adjustment amount, for the fuzzy set on the given fuzzy control input language variable domain

With

Utilize the reasoning composition rule to finish fuzzy reasoning, finish the output fuzzy set by the mapping of fuzzy set, obtain system's adjustment amount of controlled system of processing to ordinary set by method of weighted mean to the fuzzy set on the output language variable domain.

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