CN101477351B - Intelligent numerical control method with three-stage process self-optimization function - Google Patents
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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
Technical Field
The invention belongs to the technical field of electromechanical integrated numerical control, and particularly relates to an intelligent numerical control system based on the fusion of state monitoring, real-time optimization and fuzzy control of a machine tool machining site.
Background
The numerical control system is a core control unit of the numerical control machine tool and realizes comprehensive control on the movement and the processing process of the machine tool. And has the following functions: controlling the number of shafts and the number of linkage shafts; an interpolation function; a feeding function; a spindle function; a cutter function; tool compensation; compensating mechanical errors; an operating function; a program management function; a character graphic display function; an auxiliary programming function; an automatic diagnosis and alarm function; and (4) communication function. For a numerical control machining program, if a logic error occurs, the automatic diagnosis alarm function of the system can prompt modification, but for the unreasonable selection of machining parameters in the program, the automatic diagnosis alarm function cannot be used. Therefore, a program in which the selection of machining parameters is not reasonable is executed, and as a result, the machining efficiency of the machine tool is reduced because the selection of machining amount is conservative; or the cutter is damaged due to the selection of too large machining dosage, so that the workpiece is scrapped and even the machining tool is damaged, and serious consequences are caused.
Meanwhile, with the continuous improvement of the requirements of modern mechanical processing on complication, precision, large-scale and automation, some high-grade precise numerical control processing equipment is increasingly widely applied. These devices play a key or even core role in processing quality and efficiency, and are often quite expensive to manufacture; even some of the finished products are quite expensive in their individual parts or tooling costs due to their complexity, precision, or size. In this case, damage to the processing equipment or rejection of the product, even a mere reduction in the processing efficiency, can cause a significant loss.
Conventionally, the setting of the processing parameters is performed according to human experience or a related manual, and it is difficult for beginners or even very skilled operations to give better processing parameters, and since the setting of the processing parameters involves human operations, hand errors and the like may occur to give wrong processing parameters which even harm the machine tool, the tool and the workpiece, and the decoding and error detection of the system cannot be detected.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provide an intelligent control method with a three-level machining self-optimization function of performing machining parameter self-optimization on a numerical control program, performing parameter real-time optimization and secondary self-optimization of the numerical control program based on state monitoring of a machine tool machining field, and realize an intelligent solution aiming at machining optimization, high efficiency of machining and machining safety. Therefore, the invention adopts the following technical scheme:
an intelligent control method with three-stage processing self-optimization function is characterized in that after material information of a machine tool spindle motor, a cutter and a processed workpiece is obtained, a processing program is optimized, and the processing cutting depth of each processing program is obtained, whether the cutting depth is in a safe range is judged, if not, optimization processing is directly stopped and an alarm is given, and if the cutting depth is in a reasonable range, the intelligent control of the self-optimization function is realized according to the following steps:
(5) establishing a mathematical model of a machine tool-cutter-workpiece system, and calculating optimal processing parameters by using a self-adaptive genetic algorithm;
(6) optimizing and executing the machining program according to the optimal machining parameters;
(7) in the processing process, the current and the power supply voltage of a main motor are detected in real time, the current is taken as a decision quantity, the voltage is taken as an auxiliary means, and the feeding speed a is takenfChange of (a)fAs a system adjustment quantity, closed-loop feedback fuzzy control of the machining process is realized;
(8) after the process of detecting geometric information of the workpiece is finished, a trigger signal of the three-dimensional measuring head is used as an input mark quantity, the actual coordinates of the current measuring point are read after the input mark quantity of the measuring head is detected, error evaluation of the machined workpiece is carried out after all measuring points are measured, and secondary self-adaptive optimization adjustment is carried out on the machining program by taking error evaluation data as an information source basis of subsequent process adjustment.
As a preferred embodiment, the intelligent control method with three-stage processing self-optimization function of the present invention, wherein the step (1), comprises the following steps:
1.1: establishing a formula Q for metal removal ratez＝aeapafzn and relation between tool life and cutting element In the two formulas, Qz、T、ae、ap、af、z、n、d0V is the metal removal rate per unit time (mm)3/min), tool life (min), side bite (mm), depth of cut (mm), feed per pass (mm/g), number of tool teeth, spindle speed (r/min), tool diameter (mm), and cutting speed (m/min); cvIs a coefficient related to cutting conditions; k is a radical ofvIs a correction factor; q. q.sv、xv、yv、uv、pvM is a related index parameter, xv≤yv<1；
1.2: considering the maximization of the metal removal rate with the tool life as the main constraint as an optimization target, the objective function is f (x) min (f)max-Qz) Wherein f ismaxSo that f isxA sufficiently large positive value of > 0, ae，apAs the determined amount, v ═ pi d0n/1000, the target variable is af，n；
1.3: establishing including main shaft rotation speed and advanceThe given speed, the cutting force and the cutting power are respectively as follows: t is more than or equal to Tref；n≤nmax；afzn≤vfmax； <math> <mrow> <mfrac> <mrow> <msub> <mi>C</mi> <mi>F</mi> </msub> <msubsup> <mi>a</mi> <mi>p</mi> <msub> <mi>x</mi> <mi>F</mi> </msub> </msubsup> <msubsup> <mi>a</mi> <mi>f</mi> <msub> <mi>y</mi> <mi>F</mi> </msub> </msubsup> <msubsup> <mi>a</mi> <mi>e</mi> <msub> <mi>u</mi> <mi>F</mi> </msub> </msubsup> </mrow> <mrow> <msubsup> <mi>d</mi> <mn>0</mn> <msub> <mi>q</mi> <mi>F</mi> </msub> </msubsup> <msup> <mi>n</mi> <msub> <mi>w</mi> <mi>F</mi> </msub> </msup> </mrow> </mfrac> <msub> <mi>k</mi> <msub> <mi>F</mi> <mi>c</mi> </msub> </msub> <mo>≤</mo> <msub> <mi>F</mi> <mi>max</mi> </msub> <mo>;</mo> </mrow></math> Fcv/(6×104×η)≤Pmax；
1.4: optimizing the processing parameters by adopting an adaptive genetic algorithm according to the following formula, wherein the termination rule is that the error of the fitness value is 10-7Or the number of genetic iterations is 100 generations:
in the formula, Pc、Pm、fmax、favgF' and f are respectively the crossing probability, the mutation probability, the maximum fitness value in the group, the average fitness value of each generation of group, the larger fitness value of two individuals to be crossed and the fitness value of the individual to be mutated.
The invention discloses an intelligent numerical control method with a three-level processing self-optimization function, wherein the step (3) is realized in the following way: fuzzy control input linguistic variables are taken as current deviations EIAnd rate of change of deviation ECIThe output language variable being a change in feed rate
The fuzzy set domain quantization step number n of the three linguistic variablesfAll take the same value 6, quantization factor ke、kecAnd a scale factor kuThe membership function is a triangular function according to the basic domain of discourse of the control requirement variation, a fuzzy control rule is established according to the relation between the decision quantity and the system adjustment quantity, and a fuzzy set on the domain of the given fuzzy control input linguistic variable is subjected to
And
and completing fuzzy reasoning on the fuzzy set on the output linguistic variable domain by using a reasoning and synthesizing rule, and completing mapping of the output fuzzy set from the fuzzy set to a common set according to a weighted average method to obtain the input regulating quantity of the controlled processing system.
The method comprises the steps of firstly, fusing two optimization results by utilizing a fuzzy control algorithm based on a parameter optimization method of a database and a method for establishing a mathematical model of a machine tool-cutter-workpiece system, and performing self-optimization on a numerical control machining program; then, in the program running process, the real-time running state of the machine tool is obtained according to the monitoring of the machine tool machining state, and the real-time self-adaptive machining parameter adjustment is carried out according to the real-time running state; and finally, evaluating and analyzing various type position errors of the machine tool machining part, reversely solving the numerical control program on the basis of the information to obtain a program code of a part with the machining error, and performing self-optimization processing on the machining program by the program code. After three-level machining optimization treatment, on one hand, optimization of machining parameters is guaranteed, and on the other hand, a machine tool can be changed according to actual running conditions through real-time parameter adjustment in the machining process, so that the system has certain adaptivity and intelligence. The traditional processing parameters are not only not self-modified, but also have no self-adaptive control capability.
According to the intelligent numerical control system which is constructed by the intelligent control method and integrates the three-level machining self-optimization function, a multi-physical-state classification monitoring and processing system is adopted, and the numerical control system is integrated to complete rapid parallel monitoring and self-adaptive optimization adjustment of various physical state information in the numerical control machining process. The on-machine quality detection aiming at the geometric information of the workpiece processing can reduce the clamping times of the workpiece, realize the self-optimization of a numerical control program, and a numerical control system can timely obtain the form and position error information of the workpiece, thereby facilitating the adjustment of subsequent process parameters and being beneficial to reducing the rejection rate. The numerical control system fully utilizes the technical advantages of software and hardware which are developed at a high speed, can integrally realize the motion control of the machine tool and the monitoring and optimization of on-site multiple physical states, and enhances the integration level of the system.
Drawings
FIG. 1 is a numerical control processing self-adaptive control model based on fuzzy logic.
FIG. 2 is a flow chart of the process for the numerical control program of the present invention for the self-optimization of machining parameters.
FIG. 3: and (3) a machining self-optimization flow chart based on state monitoring of the machine tool machining site.
Detailed Description
The intelligent control method of the present invention is described in detail below by taking an example of an intelligent numerical control system that integrates a three-level processing self-optimization function and is constructed according to the intelligent control method of the present invention.
The numerical control system is mainly divided into a main control module, a motion control module, a communication module, a numerical control program processing parameter self-optimization module, a processing operation real-time optimization module, an on-machine detection module and a numerical control program secondary self-optimization module. Except that the motion control module and the acquisition and analysis unit respectively adopt a high-speed DSP as an independent core processor, the rest parts share a high-performance microprocessor with the main control module. All modules are linked through a system internal bus 1 to realize function calling and data transmission. The cooperative operation of all modules can realize the motion control of the machine tool and the three-level machining self-optimization based on the monitoring of the machining site state of the machine tool. The general process flow for the self-optimization of process parameters of the present invention is shown in fig. 2.
The following first introduces three basic modules of the numerical control system of the present invention: the device comprises a main control module, a motion control module and a communication module. The concrete composition is as follows:
(1) main control module
The module mainly performs overall control and coordination of the system. The system comprises tasks such as system initialization, parameter management, global data management, overall task coordination, man-machine interaction management, motion program error detection, machine tool adjustment, user-defined function development, system assistance and the like, and responds to fault processing measures of a state monitoring module.
The module comprises a central high-performance microprocessor, three data storage chips, a data management chip and a 16-bit RISC singlechip. And comprises various numerical control operation panels, a liquid crystal display, a hand wheel, an alarm, a switch, a corresponding interface circuit and a power supply clock circuit. And the chips are connected into a whole through a field bus.
1. Central high-performance microprocessor: under the working environment of the embedded real-time operating system matched with the main control module, the main control module can control and coordinate the whole system. And can complete some complex control tasks with large calculation amount and high real-time performance under the coordination of a coprocessor which is constructed by a high-speed DSP digital signal processing chip, an ARM chip capable of running autonomously and the like.
2. The data storage chip mainly comprises: 1. the FLASH-ROM chip stores 2 PLC interpretation software, PLC application program, graphic display control software and the like, the S-RAM chip stores system parameters, processing programs, user macro programs, PLC parameters, tool compensation and workpiece coordinate compensation data and pitch error compensation data, and the 3D-RAM chip is used as a working memory and is cached in the system operation process.
3. A data management chip: the chip is used for transmitting various data information for other chips in the numerical control system and also used for receiving and storing various data information transmitted by other chips in the numerical control system, so that the resource sharing of the system work is increased.
4. 16-bit RISC singlechip: the PLC interpreter and the application program stored in the FLASH-ROM chip are utilized to complete the switching of cutting fluid, an air pump and the like in the machine tool, the starting/stopping control of various motors and the signal input of a limit switch. And is connected with central processing unit by means of internal bus of system and its command can implement correspondent function.
5. A system bus: the system is composed of a 32-bit data bus, a 24-bit address bus and 30 control buses, and is mainly responsible for connection among functional chips, interfaces, memory chips and a central processing unit and transmission of data, addresses and control signals.
(2) Motion control module
The motion control module takes a high-speed DSP as a core, and can independently complete motion control functions related to machine tool actions such as motion program decoding, cutter compensation, pitch compensation, interpolation, servo control and the like through a motion program and configuration parameters downloaded into an S-RAM chip memory. And the main control module is used for allocating tasks, driving the machine tool execution unit to operate and feeding back relevant execution information to the main control module.
(3) Communication module
The communication module comprises an RS232 interface, a USB interface and an Ethernet interface. The system is used for processing the transmission of data information and control information of a numerical control system and other numerical control systems, a workshop numerical control network, an enterprise internal network and the Internet. And the communication and resource information sharing between the system and the outside are realized.
The invention is characterized in that the invention is mainly embodied in a machining parameter self-optimization system of a numerical control program, a machining operation real-time optimization system based on state monitoring of a machine tool machining site, a numerical control program secondary self-optimization system based on state monitoring of the machine tool machining site and a numerical control system integration mechanism fusing the systems. On the basis of basic architecture and function realization of a construction system, numerical control, measurement and control and artificial intelligence technologies are comprehensively utilized, a high-performance microprocessor and a DSP are taken as cores, an intelligent numerical control system with a three-level machining self-optimization function is constructed, machining parameter self-optimization of a machining program, machining operation real-time optimization based on state monitoring of a machine tool machining site and secondary self-optimization of the machining program are realized, and numerical control machining control, state monitoring and optimization coordination parallel operation are realized.
(1) The machining parameter self-optimization system of the numerical control program is realized as follows:
the numerical control program processing parameter self-optimization module of the numerical control system is used for processing the processing parameters of the numerical control program. The module comprises a FLASH-ROM storage chip, shares a central high-speed microprocessor with a main control module of the numerical control system, and responds to functions through task scheduling of the main control module. The module is mainly used for realizing the software function of the optimization algorithm. The FLASH-ROM storage chip is used for storing relevant optimization data database data and a model-based parameter optimization algorithm, and is connected with the central high-speed microprocessor and other chips in the system through a bus in the system. After the task is activated, the central high-speed microprocessor performs read-write operation on the content in the storage chip, and performs related operation calculation by combining an algorithm to realize an optimization function.
In this module, it works as follows:
firstly, initializing the information of a spindle motor, a cutter and a workpiece to be machined of the machine tool, and providing optimized machining parameters for machine tools of different models.
The machining depth of each section of machining program is obtained after the machining program is processed according to the format required by the optimization strategy, whether the cutting depth is in a safe range is judged, if not, the optimization is directly stopped and alarm display is carried out, if the cutting depth is in a reasonable range, the material and the cutting depth of a spindle motor, a cutter and a machining workpiece are taken as references, and on the premise that the machining quality is ensured to be safe, the machining efficiency is taken as the aim, and the machining parameters are optimized according to the optimization strategy in the module.
The module adopts a strategy of fusing two optimization methods: one is a parameter optimization selection method based on a database, namely, a database is constructed according to various publications and data provided by a tool manufacturer to serve as a support for parameter optimization; the other method is a parameter optimization method based on a model, namely, the optimal machining parameters are calculated by establishing a mathematical model of a machine tool-cutter-workpiece system and utilizing a neural network and a genetic algorithm. And then processing parameters are obtained by using a fuzzy control algorithm.
The artificial intelligence algorithm based on the mathematical model adopted in the invention is as follows:
from the formula of metal removal rate: qz＝aeapafzn (1)
It is known that increasing any one of several cutting elements can improve productivity. However, the increase in cutting elements may result in rapid wear of the tool, requiring frequent tool changes in the process, both increasing costs and diminishing the time advantage of increased cutting rates. And from the relationship between tool life and cutting element
In the above two formulae, Qz、T、ae、ap、af、z、n、d0V is the metal removal rate per unit time (mm)3/min), tool life (min), side bite (mm), depth of cut (mm), feed per pass (mm/g), number of tool teeth, spindle speed (r/min), tool diameter (mm), and cutting speed (m/min); cvIs a coefficient related to cutting conditions; k is a radical ofvIs a correction factor; q. q.sv、xv、yv、uv、pvM are respectively related index parameters, usually xv≤yv<1。
It can be seen that the effect of the cutting elements on tool life is different. V, af，apFor the three, the influence degree decreases in turn. By the formula
v＝πd0n/1000 (3)
It is known that v is simply linearly proportional to n. Therefore, on the premise of ensuring the cutting rate, the cutting elements in the formula (1) need to be reasonably determined so as to prevent the tool from being worn too fast.
Here we neglect the roughness and elastic deformation factor, consider the maximum metal removal rate with the tool life as the main constraint as the optimization target, the objective function is taken
f(x)＝min(fmax-Qz) (4)
Wherein f ismaxIs a positive value large enough that fx≥0。
ae，apThe target variable is a as a definite amount due to the influence of the actual processing techniquef，n。
The constraints are as follows:
T≥Tref (5)
n≤nmax (6)
afzn≤vf max (7)
Fcv/(6×104×η)≤Pmax (9)
the equations (5) - (9) are the constraint conditions of the tool life, the spindle rotation speed, the feed speed, the cutting force and the cutting power, respectively. In practical application, constraints such as the strength of the cutter bar and the cutter blade can be increased according to the requirements of field application environments.
In order to avoid the basic genetic algorithm from being easily trapped into local minimum points and early maturing defects in the optimization process, Adaptive algorithms (Adaptive GA, AGA) are used for cross and variation probabilities, and are respectively shown as follows:
in the formula, Pc、Pm、fmax、favgF' and f are respectively the crossing probability, the mutation probability, the maximum fitness value in the group, the average fitness value of each generation of group, the larger fitness value of two individuals to be crossed and the fitness value of the individual to be mutated. The termination rule takes the fitness value error as 10-7Or the number of genetic iterations is 100.
(2) The real-time optimization system for the machining operation based on the state monitoring of the machine tool machining site is realized as follows:
the monitoring state information is divided into two categories, namely running state information of numerical control machining equipment (including numerical control machines and numerical control systems) and geometric information of machined workpieces.
The running state information of the numerical control machining equipment is processed by a machining running real-time optimization module. In this module, the device operating state information is represented in the form of signals of physical state variables of the processing device and the processing process measured by the sensors. And a signal acquisition, analysis and processing parameter optimization unit is constructed by taking the DSP as a core and matching a high-capacity D-RAM and a FLASH-ROM chip. The D-RAM is used for maintaining the memory space required by the running of the programs in the unit and can be used as a cache region for collecting data of each channel. The FLASH-ROM chip stores a system intelligent strategy library which can be downloaded for many times. The signal acquisition unit can be configured into 2 to 32 channels as required, and can be matched with various front-end sensor arrays to finish continuous acquisition of processing state signals such as vibration, cutting force, acoustic emission, temperature, motor current, power grid voltage and the like. The signal analysis unit completes the conventional characteristic quantity extraction of the signal through a special chip solidified with a classical signal analysis means. The processing parameter optimization unit completes signal state identification and optimization parameter calculation through an expert knowledge base and a fuzzy strategy base stored in the FLASH-ROM. Meanwhile, the signal analysis unit reports result data including original sampling data, signal characteristic quantity, physical quantity state represented by the signal and an optimization result to the main control module through the internal bus of the numerical control system.
The processing process real-time optimization algorithm adopted in the invention is as follows:
in the invention, the physical state of the processing site is taken as the current and the power supply voltage of the main motor, and the monitoring signal data is obtained through the current sensor and the voltage sensor. The current is used as decision quantity, and the voltage is used as an auxiliary means. And thus, the closed-loop feedback control of the processing system is formed. Since the current signal is analyzed instead of the force signal in the present invention, the cutting force formula
Middle, exponential parameter wFIs much less than yFIn most cases, the value is 0, so that the influence of the spindle speed n on the cutting force is much smaller than the feed afAnd is not suitable for self-optimizing adjustments since the speed of rotation is process specific. At the same time, the side cutting amount aeAnd depth of cut apConstrained by the machining process, the tool, the machining allowance and other factors, so that the feeding speed a is only usedfChange of (a)fAs a system adjustment amount.
For numerical control machining, a controlled object consisting of a numerical control machine tool, a cutter and a workpiece is difficult to establish an accurate mathematical model, so a fuzzy control scheme is adopted.
The fuzzy control model input language variable is measured as a current deviation EIAnd rate of change of deviation ECIThe output language variable being a change in feed rate
. Fuzzy set domain quantization step number n of three linguistic variablesfThe same value 6 is taken for each. Quantization factor ke、kecAnd a scale factor kuDepending on the fundamental domain of discourse in which the control requirements vary. Values of language variables are respectively taken as
(1) To EI：NB，NM，NS，NO，PO，PS，PM，PB；
(2) To ECI：NB，NM，NS，ZO，PS，PM，PB；
(3) To pair
：NB，NM，NS，ZO，PS，PM，PB。
In order to reduce the calculation amount and increase the response speed, the membership functions are all expressed by triangular functions. Thus, 56 fuzzy control rules are established based on the relationship between the decision-making quantity and the controlled target.
Let X1、X2Y respectively correspond to linguistic variables EI、ECI、Each of the above fuzzy rules may be expressed as a product space (X)1×X2) One fuzzy implication on x Y <math> <mrow> <msup> <msub> <mover> <mi>E</mi> <mo>~</mo> </mover> <mi>I</mi> </msub> <mi>i</mi> </msup> <msup> <msub> <mover> <mi>EC</mi> <mo>~</mo> </mover> <mi>I</mi> </msub> <mi>i</mi> </msup> <msubsup> <mrow> <mo>→</mo> <mover> <mi>U</mi> <mo>~</mo> </mover> </mrow> <msub> <mi>a</mi> <mi>f</mi> </msub> <mi>i</mi> </msubsup> </mrow></math> Wherein 
Are each X1、X2And the fuzzy sets on Y correspond to the values of the respective linguistic variables. Minimum rules are adopted for fuzzy implications in the system. Since the fuzzy system is a dual-input single-output system, each rule determines a ternary relationship, i.e.
From this, the overall fuzzy relation of the fuzzy system control rule can be obtained as
Fuzzy sets on input linguistic variables for a given fuzzy control systemAnd
fuzzy reasoning on the fuzzy set on the output linguistic variable domain can be completed by using the reasoning synthesis rule:
the fuzzy decision process of the system completes the mapping of the output fuzzy set from the fuzzy set to the common set according to a weighted average method to obtain the input regulating quantity of the controlled processing system
In the formula xi∈Y，
Is the membership function of the output fuzzy set.
The fuzzy logic controller is utilized to establish a numerical control machining process feed amount self-optimization adjustment control model based on the monitoring of main motor current and voltage signals, which is shown in figure 1. In the figure, FLC is a fuzzy controller, CNC _ Mach is a controlled numerical control machine tool, delta U is the difference value between the voltage monitored on line and a standard voltage, I is an actual current signal, I isrefFor a given current reference value. The feed amount input to the CNC _ Mach at any sampling time is
Wherein, is initially given the feed amount. Fuzzy controller adjusts delta a in real timefAnd the self-optimization adjustment of the feeding amount is realized.
(3) The numerical control program secondary self-optimization system based on the state monitoring of the machine tool machining site is realized as follows:
the detection of the geometric information of the processed workpiece is realized by the on-machine detection module of the numerical control system. The module consists of an ARM chip, a D-RAM chip and an S-RAM chip and is connected with a central high-speed microprocessor and other chips in the system through a bus in the system. And responding the function through the task scheduling of the main control module and the input of an external activation signal. The ARM chip is mainly used for identifying signals sent by the measuring head and carrying out data processing and scheduling of whole tasks in the unit according to an analysis algorithm solidified in the S-RAM chip. The D-RAM is mainly used for storing coordinate data of the measuring points and analysis results and providing a memory space for program operation in the unit.
And the trigger signal of the external three-dimensional measuring head is used as an input mark quantity by the on-machine detection module. The measuring head can be a ruby contact type three-dimensional measuring head made in China or a foreign brand. The parameters of the measuring head specification, the length of the measuring needle, the diameter of the measuring ball and the like are selected according to the size of the machine tool body and the size of a workpiece. In the numerical control machining process, after a certain specific procedure is finished, a workpiece geometric information detection flow is started, and the numerical control machining process is mainly used for analyzing the geometric precision of the workpiece after the procedure is finished and providing an information source for the self-optimization adjustment of a subsequent machining program. And after the on-machine detection module detects the input mark quantity of the measuring head, the fed back actual coordinates of the current measuring point are obtained from the motion control module through the system bus. After all the measuring points have been measured, the measuring data are fed to an error evaluation unit. All the measurement data of the measurement points and the analysis result of the error evaluation unit are sent to the numerical control program secondary self-optimization module through the internal bus of the numerical control system.
In the module, a three-dimensional model of a processed workpiece is obtained by reading a processing program, then data comparison is carried out on the three-dimensional model constructed by measuring point data, a data difference between an actual processing effect and a program ideal result is obtained by utilizing an error evaluation result, an error program segment, related cutting data and point position data are further obtained, and then automatic self-adaptive optimization adjustment is carried out on the processing program by using an expert knowledge base, module control and other artificial intelligence algorithms on the basis of the data so as to enable the processing program to meet the processing quality requirement. Meanwhile, the measurement data of all the measurement points and the analysis results of the error evaluation unit are stored in an on-machine detection storage area in a log mode and are used as information sources for subsequent process adjustment. The process self-optimization flow based on the state monitoring of the machine tool machining site is shown in fig. 3.
(4) The numerical control system integration mechanism integrating the three-level processing self-optimization function is realized as follows:
the numerical control system not only completes the motion control function of the traditional numerical control system on the machine tool, but also expands the three-level self-optimization function of processing. The numerical control system is mainly divided into a main control module, a motion control module, a communication module, a numerical control program processing parameter self-optimization module, a processing operation real-time optimization module, an on-machine detection module and a numerical control program secondary self-optimization module 7. The main control module is responsible for managing and allocating the whole tasks of the system, and the basic functions of the system are realized through the internal bus of the system under the support of the motion control module and the permission module.
The numerical control program processing parameter self-optimization module, the processing operation real-time optimization module, the on-machine detection module and the numerical control program secondary self-optimization module embody the system characteristics of various physical state classification monitoring processing and function autonomous planning processing of the numerical control system. The user command is sent by the main control module, and except the command that the main control module can respond to, other commands are automatically forwarded to the corresponding modules for processing through the internal bus of the system. The cooperative operation of all the modules realizes an integrated mechanism of the numerical control system with the function of integrating three-level processing parameter self-optimization.
Claims (2)
1. An intelligent control method with three-stage processing self-optimization function is characterized in that after material information of a machine tool spindle motor, a cutter and a processed workpiece is obtained, a processing program is optimized, and the processing cutting depth of each processing program is obtained, whether the cutting depth is in a safe range is judged, if not, optimization processing is directly stopped and an alarm is given, and if the cutting depth is in a reasonable range, the intelligent control of the self-optimization function is realized according to the following steps:
(1) establishing a mathematical model of a machine tool-cutter-workpiece system according to the following steps, and calculating optimal processing parameters by using a self-adaptive genetic algorithm;
1.1: establishing a formula Q for metal removal ratez＝aeapafzn and relation between tool life and cutting element
In the two formulas, Qz、T、ae、ap、af、z、n、d0V is the metal removal rate per unit time (mm)3/min), tool life (min), side bite (mm), depth of cut (mm), feed speed (mm/g), number of tool teeth, spindle speed (r/min), tool diameter (mm), and cutting speed (m/min); cvIs a coefficient related to cutting conditions; k is a radical ofvIs a correction factor; q. q.sv、xv、yv、uv、pvM is a related index parameter, xv≤yv＜1；
1.2: considering the maximization of the metal removal rate with the tool life as the main constraint as an optimization target, the objective function is f (x) min (f)max-Qz) Wherein f ismaxSo that f isxA sufficiently large positive value of > 0, ae，apAs the determined amount, v ═ pi d0n/1000, the target variable is af，n；
1.3: establishing constraint conditions including the rotating speed of a main shaft, the feeding speed, the cutting force and the cutting power, wherein the constraint conditions are as follows: t is more than or equal to Tref；n≤nmax；afzn≤vfmax；
Fcv/(6×104×η)≤Pmax；
1.4: optimizing the processing parameters by adopting an adaptive genetic algorithm according to the following formula, wherein the termination rule is that the error of the fitness value is 10-7Or the number of genetic iterations is 100 generations:
in the formula, Pc、Pm、fmax、favgF' and f are respectively the crossing probability, the variation probability, the maximum fitness value in the group, the average fitness value of each generation of group, the larger fitness value of two individuals to be crossed and the fitness value of the individual to be varied;
(2) optimizing and executing the machining program according to the optimal machining parameters;
(3) in the machining process, the current and the power supply voltage of a machine tool spindle motor are detected in real time, the current is used as a decision quantity, the voltage is used as an auxiliary means, and the feeding speed a is usedfChange of (a)fAs a system adjustment quantity, closed-loop feedback fuzzy control of the machining process is realized;
(4) after the process of detecting geometric information of the workpiece is finished, a trigger signal of the three-dimensional measuring head is used as an input mark quantity, the actual coordinates of the current measuring point are read after the input mark quantity of the measuring head is detected, error evaluation of the machined workpiece is carried out after all measuring points are measured, and secondary self-adaptive optimization adjustment is carried out on the machining program by taking error evaluation data as an information source basis of subsequent process adjustment.
2. The intelligent control method with three-stage processing self-optimization function according to claim 1, wherein the step (3) is implemented as follows: fuzzy control input linguistic variables are taken as current deviations EIAnd rate of change of deviation ECIThe output language variable being a change in feed rateThe fuzzy set domain quantization step number n of the three linguistic variablesfAll take the same value 6, quantization factor ke、kecAnd a scale factor kuDependent on the fundamental domain of variation of the control demand, the membership function being threeAn angle function, establishing fuzzy control rules according to the relation between decision quantity and system regulation quantity, and inputting fuzzy sets on the discourse domain of linguistic variables for given fuzzy control
And
and completing fuzzy reasoning on the fuzzy set on the output linguistic variable domain by using a reasoning synthesis rule, and completing mapping of the output fuzzy set from the fuzzy set to a common set according to a weighted average method to obtain the system adjustment quantity of the controlled processing system.
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