CN108008695A - A kind of numerical-control processing method and control system of intelligent die manufacture - Google Patents
A kind of numerical-control processing method and control system of intelligent die manufacture Download PDFInfo
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- CN108008695A CN108008695A CN201711271339.5A CN201711271339A CN108008695A CN 108008695 A CN108008695 A CN 108008695A CN 201711271339 A CN201711271339 A CN 201711271339A CN 108008695 A CN108008695 A CN 108008695A
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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 belongs to mould manufacturing field, discloses a kind of numerical-control processing method and control system of intelligent die manufacture, is made of 7 modules, each module relation situation is：Process monitoring blocks, setup parameter input module connect main control module by circuit line respectively；Main control module is connected computing module by circuit line, checks module, correcting module, independent navigation haul module respectively.Each module has the function of as follows：Storage is carried out to data message and makes mould with handling analysis, and according to setup parameter；Calculating numerically controlled data is carried out to mould processing technology process；Whether check has process corrections and supplement part in die face；The feature of die face change is modified numerically controlled data；Autonomous transport operation control is carried out to raw material and finished work-piece.The present invention improves the type face precision of mould and the dimensional accuracy and stability of final stamping parts；It can realize zero defect, efficient, intelligence programming, so as to improve work efficiency, reduce processing cost.
Description
Technical field
The invention belongs to mould manufacturing field, more particularly to a kind of numerical-control processing method of intelligent die manufacture and control system
System.
Background technology
Mould, in industrial production being molded, being blow molded, extruding, die casting or the methods of forging forming, smelting, punching press obtain
The various moulds and instrument of required product.In brief, mould is the instrument for making formed article, and this instrument is by various
Part is formed, and different moulds is made of different parts.It is mainly realized by the change of institute's moulding material physical state
The processing of article shape.However, being corrected by hand when existing mold manufacturing technology later stage mould is rectified and improved due to existing, processing is lacked
Data, when secondary rectification, can be very time-consuming and laborious, is also unfavorable for ensureing die quality；The mould numeral that client provides at the same time is set
Model is counted there may be defect, it is necessary to spend a large amount of inspections and reparation for being manually designed defect；Existing NC Machining Program
The processing route planning efficiency of system is not high, and the machining feature that can not be directed to mold component carries out efficient machining path planning,
Cause mold component NC Machining Program process very time-consuming.
In conclusion problem existing in the prior art is：Existing mold manufacturing technology later stage mould has lacked when rectifying and improving to be added
The data of work, when secondary rectification, can be very time-consuming and laborious, are also unfavorable for ensureing die quality；Existing NC Machining Program system
Processing route planning efficiency is not high.
The content of the invention
In view of the problems of the existing technology, the numerical-control processing method the present invention provides a kind of manufacture of intelligent die and control
System processed.
Digital control processing control system of the present invention based on a kind of manufacture of intelligent die includes：
Process monitoring blocks, are connected with main control module, for the sensor by being connected on making apparatus, real time monitoring
Operation process, records relevant statistics, and carries out quality monitoring；
Setup parameter input module, is connected with main control module, and the parameter for being made to mold design carries out input operation；
Main control module, with process monitoring blocks, setup parameter input module, computing module, check module, correcting module,
Independent navigation haul module connect, for by the data message that process monitoring blocks, setup parameter input module are transmitted into
Row storage is analyzed with processing, and makes mould according to setup parameter；
The main control module recognition methods includes：
Step 1, docking collection of letters s (t) carry out nonlinear transformation, carry out as follows：
WhereinA represents the amplitude of signal, and a (m) represents letter
Number symbol, p (t) represent shaping function, fcRepresent the carrier frequency of signal,The phase of signal is represented, by this
It can obtain after nonlinear transformation：
Step 2, calculates the broad sense single order cyclic cumulants for receiving signal s (t)With broad sense second-order cyclic cumulantThe characteristic parameter of signal s (t) is received by calculatingClassify with using least mean-square error
Device, identifies 2FSK signals；Calculate the Generalized Cyclic cumulant for receiving signalCarry out as follows：
WithIt is Generalized Cyclic square, is defined as：
Wherein s (t) is signal, and n is wide
The exponent number of adopted Cyclic Moment, conjugation item are m；
Receive the characteristic parameter M of signal s (t)1Theoretical valueSpecific calculating process is such as
Lower progress：
It is computed understanding, for 2FSK signals, the signalFor 1, and for MSK, BPSK, QPSK, 8PSK,
16QAM and 64QAM signalsIt is 0, it is possible thereby to be gone out 2FSK signal identifications by least mean-square error grader
Come, the expression-form of the grader is：
In formulaIt is characterized parameter M1Actual value；
Step 3, calculates the broad sense second-order cyclic cumulant for receiving signal s (t)Signal s (t) is received by calculating
Characteristic parameterWith utilize least mean-square error grader, and by detecting Generalized Cyclic cumulant
Amplitude spectrumSpectral peak number identify bpsk signal and msk signal；The broad sense second order for calculating reception signal s (t) follows
Ring cumulantCarry out as follows：
Receive the characteristic parameter M of signal s (t)2Theoretical valueSpecific formula for calculation is：
Understood by calculating, bpsk signal and msk signalIt is 1, QPSK, 8PSK, 16QAM and 64QAM signal
'sBe 0, it is possible thereby to least mean-square error grader by BPSK, msk signal and QPSK, 8PSK, 16QAM,
64QAM signals separate；For bpsk signal, in Generalized Cyclic cumulant amplitude spectrumOn only in carrier frequency position
There are an obvious spectral peak, and msk signal respectively has an obvious spectral peak at two frequencies, thus can pass through characteristic parameter M2With
Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number bpsk signal and msk signal are identified；
Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number specific method it is as follows：
Generalized Cyclic cumulant amplitude spectrum is searched for firstMaximum Max and its position correspondence circulation frequency
Rate α0, by its small neighbourhood [α0-δ0,α0+δ0] interior zero setting, wherein δ0For a positive number, if | α0-fc|/fc＜ σ0, wherein δ0For one
Close to 0 positive number, fcFor the carrier frequency of signal, then judge that this signal type for bpsk signal, otherwise continues search for second largest value
The cycle frequency α of Max1 and its position correspondence1；If | Max-Max1 |/Max ＜ σ0, and | (α0+α1)/2-fc|/fc＜ σ0, then
Judge this signal type for msk signal；
Step 4, calculates the broad sense quadravalence cyclic cumulants for receiving signal s (t)Signal s (t) is received by calculating
Characteristic parameterWith using least mean-square error grader, QPSK signals, 8PSK letters are identified
Number, 16QAM signals and 64QAM signals；Calculate the broad sense second-order cyclic cumulant for receiving signal s (t)As follows
Carry out：
Receive the characteristic parameter M of signal s (t)3Theoretical valueSpecific calculating process is such as
Under：
Understood by calculating, QPSK signalsFor 1,8PSK signalsFor 0,16QAM signals
For 0.5747,64QAM signalsFor 0.3580, from there through least mean-square error grader by QPSK, 8PSK,
16QAM and 64QAM signal identifications come out；
Computing module, is connected with main control module, for calculating numerically controlled data according to mould processing technology process；
Check module, be connected with main control module, for checking whether there is process corrections and supplement portion in the die face
Point；
Correcting module, is connected with main control module, and the feature for being changed according to die face is modified numerically controlled data；
Independent navigation haul module, is connected with main control module, for controlling the transport operation of raw material and finished work-piece；
The sensor node energy consumption of the independent navigation haul module is divided into transmitting data energy consumption, receives data energy consumption and gather
Data energy consumption is closed, the distance of node to receiving point is less than threshold value d0, then using free space model, otherwise, declined using multipath
Subtract model, so that it is as follows for the energy expenditure of receiving point to distance to launch bit data：
Wherein EelecFor radiating circuit energy expenditure, εfsFor energy, ε needed for power amplification circuit under free space modelmp
For energy needed for power amplification circuit under multipath attenuation model, bit data energy consumption is received：
ERx(l)=l × Eelec；
It polymerize the energy expenditure of bit data：
EA=l × EDA；
Wherein EDARepresent the energy expenditure of 1 bit data of polymerization.
Another object of the present invention is to provide a kind of numerical-control processing method of intelligent die manufacture to comprise the following steps：
Step 1, the data message that process monitoring blocks, setup parameter input module are transmitted to by main control module into
Row storage is analyzed with processing, and makes mould according to setup parameter；
The step of data aggregation method of the data message, is as follows：
(1) in the deployment region that area is S=LL, the wireless sensor node of the N number of isomorphism of random distribution, sink nodes
Outside deployment region, the data that are collected into the whole wireless sensor network of node processing；
(2) non-homogeneous cluster
Sink nodes are located at the top of deployment region；Deployment region X-axis is divided into S swimming lane first, and all swimming lanes have phase
Same width w, and each length of swimming lane and the equal length of deployment region；By the use of the ID from 1 to s as swimming lane, high order end
The ID of swimming lane be 1, then each swimming lane is divided into multiple rectangular mesh along y-axis, each grid in each swimming lane by
A level is defined, the level of the lowermost grid is 1, and each grid and each swimming lane have identical width w；In each swimming lane
Distance dependent of number, length and the swimming lane of grid to sink；The size of grid is adjusted by setting the length of grid；For
Different swimming lanes, the lattice number that swimming lane more remote distance sink contains are smaller；For same swimming lane, net more remote distance sink
The length of lattice is bigger；Assuming that contain S element, the number of k-th of element representation grid in k-th of swimming lane in A；Each grid
ID is used as with an array (i, j), represents that i-th of swimming lane has horizontal j；Define the length of S array representation grid, v-th of number
Group HvRepresent the length of grid in v-th of swimming lane, and HvW-th of element hvwRepresent the length of grid (v, w)；Grid (i,
J) border is：
O_x+ (i-1) × w ＜ x≤o_x+i × w
Non-uniform grid carries out the cluster stage after dividing；Algorithm, which is divided into many wheels, to carry out, and chooses in each round each
The node of dump energy maximum is as cluster head node in grid, remaining node adds cluster according to nearby principle, then again into line number
According to polymerization；
(3) Grubbs pre-process
Sensor node needs to pre-process the data of collection, then transmits data to cluster head node again；Using lattice
The data that this pre- criterion of granny rag collects sensor node carry out pretreatment and assume that some cluster head node contains a sensor
Node, the data that sensor node is collected into are x1,x2,…,xn, Normal Distribution, and set：
According to order statistics principle, Grubbs statistic is calculated：
After given significance (α=0.05), measured value meets gi≤g0(n, α), then it is assumed that measured value is effective, surveys
Value participates in the data aggregate of next level；It is on the contrary, then it is assumed that measured value is invalid, it is therefore desirable to reject, that is, be not involved in down
The data aggregate of one level；
(4) adaptive aggregating algorithm
The unbiased estimator of each node measurement data is obtained by iteration, asks for the measurement data of each sensor node
Euclidean distance between value and estimate, adaptive weighted warm weights are used as using normalized Euclidean distance；Select in cluster
The average value of the maxima and minimas of data that collects of sensor node as centre data；
There is a sensor node in some cluster, with dimensional vector D=(d1,d2,…,dn) represent respective nodes measured value,
Euclidean distance by calculating each node data and centre data reacts the deviation between different node datas and centre data
Size, wherein liCalculation formula be：
According to the corresponding weights size of Euclidean distance adaptive setting, the bigger weights of distance are smaller, got over apart from smaller weights
Greatly；
WhereinwiFor corresponding weights；
Step 2, calculating numerically controlled data is carried out by computing module to mould processing technology process；By checking that module is examined
Whether look into the die face has process corrections and supplement part；The feature logarithm changed by correcting module to die face
Control data are modified；
Raw material and finished work-piece are carried out autonomous transport operation control by step 3 by independent navigation haul module.
Further, the correcting module modification method is as follows：
First, Design of Moulds is read in；
Then, geometric properties identification is carried out to mold component；In mould numeral designs a model, containing largely there is mould
Have the geometry of feature, it is necessary to being identified, retrieving to these typical mould features in designing a model, arranging with classifying,
Data preparation is carried out for follow-up design defect diagnosis；
Finally, calculate and correct.
Computing module of the present invention controls the type face quality of mould by the machining accuracy of lathe, so as to improve the type of mould
The dimensional accuracy and stability of face precision and final stamping parts；Die industry is built by correcting module and programs expert knowledge library,
Realize zero defect, efficient, intelligence programming, improve work efficiency, reduce processing cost.
Brief description of the drawings
Fig. 1 is the digital control processing control system architecture schematic diagram that the intelligent die that the present invention implements to provide manufactures；
Fig. 2 is the numerical-control processing method flow chart that the intelligent die that the present invention implements to provide manufactures；
In Fig. 1：1st, process monitoring blocks；2nd, setup parameter input module；3rd, main control module；4th, computing module；5th, check
Module；6th, correcting module；7th, independent navigation haul module.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, below in conjunction with case study on implementation, to this hair
It is bright to be further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and do not have to
It is of the invention in limiting.
The application principle of the present invention is explained in detail below in conjunction with the accompanying drawings.
As shown in Figure 1, the digital control processing control system for the intelligent die manufacture that case study on implementation of the present invention provides includes：Process
Monitoring module 1, setup parameter input module 2, main control module 3, computing module 4, inspection module 5, correcting module 6, independent navigation
Haul module 7.
Process monitoring blocks 1, are connected with main control module 3, for the sensor by being connected in making apparatus, real time monitoring
Operation process, records relevant statistics, and carries out quality monitoring；
Setup parameter input module 2, is connected with main control module 3, and the parameter for being made to mold design carries out input behaviour
Make；
Main control module 3, with process monitoring blocks 1, setup parameter input module 2, computing module 4, inspection module 5, amendment
Module 6, independent navigation haul module 7 connect, for the number for being transmitted to process monitoring blocks 1, setup parameter input module 2
It is believed that breath carries out storage makes mould with handling analysis, and according to setup parameter；
Computing module 4, is connected with main control module 3, for calculating numerically controlled data according to mould processing technology process；
Check module 5, be connected with main control module 3, for checking in the die face whether there is process corrections and supplement
Part；
Correcting module 6, is connected with main control module 3, and the feature for being changed according to die face repaiies numerically controlled data
Just；
Independent navigation haul module 7, is connected with main control module 3, for controlling the transport operation of raw material and finished work-piece.
The main control module recognition methods includes：
Step 1, docking collection of letters s (t) carry out nonlinear transformation；Dock collection of letters s (t) and carry out nonlinear transformation, by such as
Lower formula carries out：
WhereinA represents the amplitude of signal, and a (m) represents letter
Number symbol, p (t) represent shaping function, fcRepresent the carrier frequency of signal,The phase of signal is represented, by this
It can obtain after nonlinear transformation：
Step 2, calculates the broad sense single order cyclic cumulants for receiving signal s (t)With broad sense second-order cyclic cumulantThe characteristic parameter of signal s (t) is received by calculatingClassify with using least mean-square error
Device, identifies 2FSK signals；Calculate the Generalized Cyclic cumulant for receiving signalCarry out as follows：
WithIt is Generalized Cyclic square, is defined as：
Wherein s (t) is signal, and n is wide
The exponent number of adopted Cyclic Moment, conjugation item are m；
Receive the characteristic parameter M of signal s (t)1Theoretical valueSpecific calculating process is as follows
Carry out：
It is computed understanding, for 2FSK signals, the signalFor 1, and for MSK, BPSK, QPSK, 8PSK,
16QAM and 64QAM signalsIt is 0, it is possible thereby to be gone out 2FSK signal identifications by least mean-square error grader
Come, the expression-form of the grader is：
In formulaIt is characterized parameter M1Actual value；
Step 3, calculates the broad sense second-order cyclic cumulant for receiving signal s (t)Signal s (t) is received by calculating
Characteristic parameterWith utilize least mean-square error grader, and by detecting Generalized Cyclic cumulant
Amplitude spectrumSpectral peak number identify bpsk signal and msk signal；The broad sense second order for calculating reception signal s (t) follows
Ring cumulantCarry out as follows：
Receive the characteristic parameter M of signal s (t)2Theoretical valueSpecific formula for calculation is：
Understood by calculating, bpsk signal and msk signalIt is 1, QPSK, 8PSK, 16QAM and 64QAM signal
'sBe 0, it is possible thereby to least mean-square error grader by BPSK, msk signal and QPSK, 8PSK, 16QAM,
64QAM signals separate；For bpsk signal, in Generalized Cyclic cumulant amplitude spectrumOn only in carrier frequency position
There are an obvious spectral peak, and msk signal respectively has an obvious spectral peak at two frequencies, thus can pass through characteristic parameter M2With
Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number bpsk signal and msk signal are identified；
Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number specific method it is as follows：
Generalized Cyclic cumulant amplitude spectrum is searched for firstMaximum Max and its position correspondence circulation frequency
Rate α0, by its small neighbourhood [α0-δ0,α0+δ0] interior zero setting, wherein δ0For a positive number, if | α0-fc|/fc＜ σ0, wherein δ0For one
Close to 0 positive number, fcFor the carrier frequency of signal, then judge that this signal type for bpsk signal, otherwise continues search for second largest value
The cycle frequency α of Max1 and its position correspondence1；If | Max-Max1 |/Max ＜ σ0, and | (α0+α1)/2-fc|/fc＜ σ0, then
Judge this signal type for msk signal；
Step 4, calculates the broad sense quadravalence cyclic cumulants for receiving signal s (t)Signal s (t) is received by calculating
Characteristic parameterWith using least mean-square error grader, QPSK signals, 8PSK letters are identified
Number, 16QAM signals and 64QAM signals；Calculate the broad sense second-order cyclic cumulant for receiving signal s (t)As follows
Carry out：
Receive the characteristic parameter M of signal s (t)3Theoretical valueSpecific calculating process is such as
Under：
Understood by calculating, QPSK signalsFor 1,8PSK signalsFor 0,16QAM signals
For 0.5747,64QAM signalsFor 0.3580, from there through least mean-square error grader by QPSK, 8PSK, 16QAM
Come out with 64QAM signal identifications；
The sensor node energy consumption of the independent navigation haul module is divided into transmitting data energy consumption, receives data energy consumption and gather
Data energy consumption is closed, the distance of node to receiving point is less than threshold value d0, then using free space model, otherwise, declined using multipath
Subtract model, so that it is as follows for the energy expenditure of receiving point to distance to launch bit data：
Wherein EelecFor radiating circuit energy expenditure, εfsFor energy, ε needed for power amplification circuit under free space modelmp
For energy needed for power amplification circuit under multipath attenuation model, bit data energy consumption is received：
ERx(l)=l × Eelec；
It polymerize the energy expenditure of bit data：
EA=l × EDA；
Wherein EDARepresent the energy expenditure of 1 bit data of polymerization.
As shown in Fig. 2, the numerical-control processing method of intelligent die manufacture provided in an embodiment of the present invention comprises the following steps：
S101, the data message that process monitoring blocks, setup parameter input module are transmitted to are carried out by main control module
Storage is analyzed with processing, and makes mould according to setup parameter；
S102, calculating numerically controlled data is carried out by computing module to mould processing technology process；By checking module check
Whether process corrections and supplement part are had in the die face；The feature changed by correcting module to die face is to numerical control
Data are modified；
Raw material and finished work-piece are carried out autonomous transport operation by S103 by independent navigation haul module.
The step of data aggregation method of the data message, is as follows：
(1) in the deployment region that area is S=LL, the wireless sensor node of the N number of isomorphism of random distribution, sink nodes
Outside deployment region, the data that are collected into the whole wireless sensor network of node processing；
(2) non-homogeneous cluster
Sink nodes are located at the top of deployment region；Deployment region X-axis is divided into S swimming lane first, and all swimming lanes have phase
Same width w, and each length of swimming lane and the equal length of deployment region；By the use of the ID from 1 to s as swimming lane, high order end
The ID of swimming lane be 1, then each swimming lane is divided into multiple rectangular mesh along y-axis, each grid in each swimming lane by
A level is defined, the level of the lowermost grid is 1, and each grid and each swimming lane have identical width w；In each swimming lane
Distance dependent of number, length and the swimming lane of grid to sink；The size of grid is adjusted by setting the length of grid；For
Different swimming lanes, the lattice number that swimming lane more remote distance sink contains are smaller；For same swimming lane, net more remote distance sink
The length of lattice is bigger；Assuming that contain S element, the number of k-th of element representation grid in k-th of swimming lane in A；Each grid
ID is used as with an array (i, j), represents that i-th of swimming lane has horizontal j；Define the length of S array representation grid, v-th of number
Group HvRepresent the length of grid in v-th of swimming lane, and HvW-th of element hvwRepresent the length of grid (v, w)；Grid (i,
J) border is：
O_x+ (i-1) × w ＜ x≤o_x+i × w
Non-uniform grid carries out the cluster stage after dividing；Algorithm, which is divided into many wheels, to carry out, and chooses in each round each
The node of dump energy maximum is as cluster head node in grid, remaining node adds cluster according to nearby principle, then again into line number
According to polymerization；
(3) Grubbs pre-process
Sensor node needs to pre-process the data of collection, then transmits data to cluster head node again；Using lattice
The data that this pre- criterion of granny rag collects sensor node carry out pretreatment and assume that some cluster head node contains a sensor
Node, the data that sensor node is collected into are x1,x2,…,xn, Normal Distribution, and set：
According to order statistics principle, Grubbs statistic is calculated：
After given significance (α=0.05), measured value meets gi≤g0(n, α), then it is assumed that measured value is effective, surveys
Value participates in the data aggregate of next level；It is on the contrary, then it is assumed that measured value is invalid, it is therefore desirable to reject, that is, be not involved in down
The data aggregate of one level；
(4) adaptive aggregating algorithm
The unbiased estimator of each node measurement data is obtained by iteration, asks for the measurement data of each sensor node
Euclidean distance between value and estimate, adaptive weighted warm weights are used as using normalized Euclidean distance；Select in cluster
The average value of the maxima and minimas of data that collects of sensor node as centre data；
There is a sensor node in some cluster, with dimensional vector D=(d1,d2,…,dn) represent respective nodes measured value,
Euclidean distance by calculating each node data and centre data reacts the deviation between different node datas and centre data
Size, wherein liCalculation formula be：
According to the corresponding weights size of Euclidean distance adaptive setting, the bigger weights of distance are smaller, got over apart from smaller weights
Greatly；
WhereinwiFor corresponding weights；
Correcting module modification method provided by the invention is as follows：
First, Design of Moulds is read in；
Then, geometric properties identification is carried out to mold component；In mould numeral designs a model, containing largely there is mould
Have the geometry of feature, it is necessary to being identified, retrieving to these typical mould features in designing a model, arranging with classifying,
Data preparation is carried out for follow-up design defect diagnosis；
Finally, calculate and correct.
Establish mold design knowledge base, a large amount of expertises of storage management, rule, the fact, concept, and with this to designing mould
Type carries out comprehensive quick diagnosis, list there are the problem of and propose optimal modification, to facilitate repairing for engineers and technicians
Referendum plan；According to the diagnostic result listed, to design a model there are the problem of interact the automatic of formula and repair；After reparation
Design a model middle extraction machining feature, and the support in data is provided for follow-up high efficiency smart NC Machining Program；According to mould
Parts information coordinates numerical control processing technology knowledge and the numerical control resource information of numerical control resources bank in numerical control processing technology knowledge base, real
The decision support of existing part by numerical control processing technology, including the selection of numerical-control processing method, determines that fixture, cutter, numerical control add
Work parameter and numerically controlled processing equipment；Machining Path optimizes.
The foregoing is merely the preferable case study on implementation of the present invention, it is not intended to limit the invention, it is all the present invention's
All any modification, equivalent and improvement made within spirit and principle etc., should all be included in the protection scope of the present invention.
Claims (3)
1. a kind of digital control processing control system of intelligent die manufacture, it is characterised in that the numerical control of the intelligent die manufacture adds
Work control system includes：
Process monitoring blocks, are connected with main control module, for the sensor by being connected on making apparatus, monitor operation in real time
Process, records relevant statistics, and carries out quality monitoring；
Setup parameter input module, is connected with main control module, and the parameter for being made to mold design carries out input operation；
Main control module, with process monitoring blocks, setup parameter input module, computing module, check module, correcting module, autonomous
Haul of navigating module connection, the data message for process monitoring blocks, setup parameter input module to be transmitted to are deposited
Storage is analyzed with processing, and makes mould according to setup parameter；
The main control module recognition methods includes：
Step 1, docking collection of letters s (t) carries out nonlinear transformation, and carries out as follows：
<mrow>
<mi>f</mi>
<mo>&lsqb;</mo>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>=</mo>
<mfrac>
<mrow>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>*</mo>
<mi>l</mi>
<mi>n</mi>
<mo>|</mo>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
</mrow>
<mrow>
<mo>|</mo>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
</mrow>
</mfrac>
<mo>=</mo>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mi>c</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
</mrow>
WhereinA represents the amplitude of signal, and a (m) represents signal
Symbol, p (t) represent shaping function, fcRepresent the carrier frequency of signal,Represent the phase of signal, it is non-thread by this
Property conversion after can obtain：
<mrow>
<mi>f</mi>
<mo>&lsqb;</mo>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>=</mo>
<mi>s</mi>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mfrac>
<mrow>
<mi>l</mi>
<mi>n</mi>
<mo>|</mo>
<mi>A</mi>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
</mrow>
<mrow>
<mo>|</mo>
<mi>A</mi>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>m</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
</mrow>
</mfrac>
<mo>;</mo>
</mrow>
Step 2, calculates the broad sense single order cyclic cumulants for receiving signal s (t)With broad sense second-order cyclic cumulantThe characteristic parameter of signal s (t) is received by calculatingClassify with using least mean-square error
Device, identifies 2FSK signals；Calculate the Generalized Cyclic cumulant for receiving signalCarry out as follows：
<mrow>
<msubsup>
<mi>GC</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>10</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>GM</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>10</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>;</mo>
</mrow>
<mrow>
<msubsup>
<mi>GC</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>21</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>GM</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>21</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>;</mo>
</mrow>
WithIt is Generalized Cyclic square, is defined as：
Wherein s (t) is signal, and n follows for broad sense
The exponent number of ring square, conjugation item are m；
Receive the characteristic parameter M of signal s (t)1Theoretical valueSpecific calculating process as follows into
OK：
<mrow>
<msubsup>
<mi>GC</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>10</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mi>l</mi>
<mi>n</mi>
<mo>|</mo>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>|</mo>
</mrow>
<mrow>
<msubsup>
<mi>GC</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>21</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<msup>
<mi>a</mi>
<mo>*</mo>
</msup>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<msup>
<mrow>
<mo>|</mo>
<mi>l</mi>
<mi>n</mi>
<mo>|</mo>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>|</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
It is computed understanding, for 2FSK signals, the signalFor 1, and for MSK, BPSK, QPSK, 8PSK, 16QAM and
64QAM signalsIt is 0, it is possible thereby to be come out 2FSK signal identifications by least mean-square error grader, the classification
The expression-form of device is：
<mrow>
<msub>
<mi>E</mi>
<mn>1</mn>
</msub>
<mo>=</mo>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>M</mi>
<mrow>
<mi>t</mi>
<mi>h</mi>
<mi>e</mi>
<mi>o</mi>
<mi>r</mi>
<mi>y</mi>
</mrow>
<mn>1</mn>
</msubsup>
<mo>-</mo>
<msubsup>
<mi>M</mi>
<mrow>
<mi>a</mi>
<mi>c</mi>
<mi>t</mi>
<mi>u</mi>
<mi>a</mi>
<mi>l</mi>
</mrow>
<mn>1</mn>
</msubsup>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>;</mo>
</mrow>
In formulaIt is characterized parameter M1Actual value；
Step 3, calculates the broad sense second-order cyclic cumulant for receiving signal s (t)The spy of signal s (t) is received by calculating
Levy parameterDischarge amplitude is accumulated with using least mean-square error grader, and by detecting Generalized Cyclic
SpectrumSpectral peak number identify bpsk signal and msk signal；The broad sense second-order cyclic for calculating reception signal s (t) is tired out
Accumulated amountCarry out as follows：
<mrow>
<msubsup>
<mi>GC</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>20</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>GM</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>20</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>;</mo>
</mrow>
Receive the characteristic parameter M of signal s (t)2Theoretical valueSpecific formula for calculation is：
<mrow>
<msubsup>
<mi>GC</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>20</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mi>l</mi>
<mi>n</mi>
<mo>|</mo>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
</mrow>
Understood by calculating, bpsk signal and msk signalIt is 1, QPSK, 8PSK, 16QAM and 64QAM signalIt is 0, it is possible thereby to least mean-square error grader by BPSK, msk signal and QPSK, 8PSK, 16QAM, 64QAM
Signal separates；For bpsk signal, in Generalized Cyclic cumulant amplitude spectrumOn only in carrier frequency position, there are one
A obvious spectral peak, and msk signal respectively has an obvious spectral peak at two frequencies, thus can pass through characteristic parameter M2It is wide with detection
Adopted cyclic cumulants amplitude spectrumSpectral peak number bpsk signal and msk signal are identified；
Detect Generalized Cyclic cumulant amplitude spectrumSpectral peak number specific method it is as follows：
Generalized Cyclic cumulant amplitude spectrum is searched for firstMaximum Max and its position correspondence cycle frequency α0,
By its small neighbourhood [α0-δ0,α0+δ0] interior zero setting, wherein δ0For a positive number, if | α0-fc|/fc＜ σ0, wherein δ0It is close for one
0 positive number, fcFor the carrier frequency of signal, then judge that this signal type for bpsk signal, otherwise continues search for second largest value Max1
And its cycle frequency α of position correspondence1；If | Max-Max1 |/Max ＜ σ0, and | (α0+α1)/2-fc|/fc＜ σ0, then judge
This signal type is msk signal；
Step 4, calculates the broad sense quadravalence cyclic cumulants for receiving signal s (t)The spy of signal s (t) is received by calculating
Levy parameterWith utilize least mean-square error grader, identify QPSK signals, 8PSK signals,
16QAM signals and 64QAM signals；Calculate the broad sense second-order cyclic cumulant for receiving signal s (t)As follows into
OK：
<mrow>
<msubsup>
<mi>GC</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>40</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>=</mo>
<msubsup>
<mi>GM</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>40</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>-</mo>
<mn>3</mn>
<msup>
<mrow>
<mo>(</mo>
<msubsup>
<mi>GM</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>20</mn>
</mrow>
<mrow>
<mi>&beta;</mi>
<mo>/</mo>
<mn>2</mn>
</mrow>
</msubsup>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>;</mo>
</mrow>
Receive the characteristic parameter M of signal s (t)3Theoretical valueSpecific calculating process is as follows：
<mrow>
<msubsup>
<mi>GC</mi>
<mrow>
<mi>s</mi>
<mo>,</mo>
<mn>40</mn>
</mrow>
<mi>&beta;</mi>
</msubsup>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msup>
<mrow>
<mo>&lsqb;</mo>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mn>4</mn>
</msup>
<msup>
<mrow>
<mo>|</mo>
<mi>l</mi>
<mi>n</mi>
<mo>|</mo>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>|</mo>
</mrow>
<mn>4</mn>
</msup>
<mo>-</mo>
<mn>3</mn>
<msup>
<mrow>
<mo>&lsqb;</mo>
<mfrac>
<mn>1</mn>
<mi>N</mi>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>N</mi>
</munderover>
<msup>
<mrow>
<mo>&lsqb;</mo>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
</mrow>
<mn>2</mn>
</msup>
<msup>
<mrow>
<mo>|</mo>
<mi>l</mi>
<mi>n</mi>
<mo>|</mo>
<mi>a</mi>
<mrow>
<mo>(</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>|</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>&rsqb;</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
Understood by calculating, QPSK signalsFor 1,8PSK signalsFor 0,16QAM signalsFor
0.5747,64QAM signalFor 0.3580, from there through least mean-square error grader by QPSK, 8PSK, 16QAM and
64QAM signal identifications come out；
Computing module, is connected with main control module, for calculating numerically controlled data according to mould processing technology process；
Check module, be connected with main control module, for checking in the die face whether there is process corrections and supplement part；
Correcting module, is connected with main control module, and the feature for being changed according to die face is modified numerically controlled data；
Independent navigation haul module, is connected with main control module, for controlling the transport operation of raw material and finished work-piece；
The sensor node energy consumption of the independent navigation haul module is divided into transmitting data energy consumption, receives data energy consumption and aggregate number
According to energy consumption, the distance of node to receiving point is less than threshold value d0, then using free space model, otherwise, using multipath attenuation mould
Type, transmitting bit data to distance are as follows for the energy expenditure of receiving point：
<mrow>
<msub>
<mi>E</mi>
<mrow>
<mi>T</mi>
<mi>X</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>l</mi>
<mo>,</mo>
<mi>d</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<mi>l</mi>
<mo>&times;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>e</mi>
<mi>l</mi>
<mi>e</mi>
<mi>c</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>l</mi>
<mo>&times;</mo>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mi>f</mi>
<mi>s</mi>
</mrow>
</msub>
<mo>&times;</mo>
<msub>
<mi>d</mi>
<mn>2</mn>
</msub>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>d</mi>
<mo><</mo>
<msub>
<mi>d</mi>
<mn>0</mn>
</msub>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<mi>l</mi>
<mo>&times;</mo>
<msub>
<mi>E</mi>
<mrow>
<mi>e</mi>
<mi>l</mi>
<mi>e</mi>
<mi>c</mi>
</mrow>
</msub>
<mo>+</mo>
<mi>l</mi>
<mo>&times;</mo>
<msub>
<mi>&epsiv;</mi>
<mrow>
<mi>m</mi>
<mi>p</mi>
</mrow>
</msub>
<mo>&times;</mo>
<msub>
<mi>d</mi>
<mn>4</mn>
</msub>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>d</mi>
<mo>&GreaterEqual;</mo>
<msub>
<mi>d</mi>
<mn>0</mn>
</msub>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
<mo>;</mo>
</mrow>
Wherein EelecFor radiating circuit energy expenditure, εfsFor energy, ε needed for power amplification circuit under free space modelmpTo be more
Energy needed for power amplification circuit under path attenuation model, receives bit data energy consumption：
ERx(l)=l × Eelec；
It polymerize the energy expenditure of bit data：
EA=l × EDA；
Wherein EDARepresent the energy expenditure of 1 bit data of polymerization.
A kind of 2. numerical-control processing method of the manufacture of the intelligent die as described in the claims 1, it is characterised in that the intelligence mould
The numerical-control processing method of tool manufacture comprises the following steps：
Step 1, data message carry out storage and make mould with handling analysis, and according to setup parameter；
The step of data aggregation method of the data message, is as follows：
(1) in the deployment region that area is S=LL, the wireless sensor node of the N number of isomorphism of random distribution, sink nodes are located at
Outside deployment region, the data that are collected into the whole wireless sensor network of node processing；
(2) non-homogeneous cluster
Sink nodes are located at the top of deployment region；Deployment region X-axis is divided into S swimming lane first, and all swimming lanes have identical
Width w, and each length of swimming lane and the equal length of deployment region；By the use of the ID from 1 to s as swimming lane, the swimming of high order end
The ID in road is 1, and then each swimming lane is divided into multiple rectangular mesh along y-axis, and each grid in each swimming lane is defined
One level, the level of the lowermost grid is 1, and each grid and each swimming lane have identical width w；Grid in each swimming lane
Number, length and swimming lane to sink distance dependent；The size of grid is adjusted by setting the length of grid；For difference
Swimming lane, the lattice number that swimming lane more remote distance sink contains is smaller；For same swimming lane, grid more remote distance sink
Length is bigger；Assuming that contain S element, the number of k-th of element representation grid in k-th of swimming lane in A；Each grid is with one
A array (i, j) is used as ID, represents that i-th of swimming lane has horizontal j；Define the length of S array representation grid, v-th of array Hv
Represent the length of grid in v-th of swimming lane, and HvW-th of element hvwRepresent the length of grid (v, w)；Grid (i, j)
Border is：
O_x+ (i-1) × w ＜ x≤o_x+i × w
<mrow>
<mi>o</mi>
<mo>_</mo>
<mi>y</mi>
<mo>+</mo>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>k</mi>
<mo>&le;</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<msub>
<mi>h</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
</msub>
<mo><</mo>
<mi>y</mi>
<mo>&le;</mo>
<mi>o</mi>
<mo>_</mo>
<mi>y</mi>
<mo>+</mo>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>k</mi>
<mo>&le;</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<msub>
<mi>h</mi>
<mrow>
<mi>i</mi>
<mi>k</mi>
</mrow>
</msub>
</mrow>
Non-uniform grid carries out the cluster stage after dividing；Algorithm, which is divided into many wheels, to carry out, and chooses each grid in each round
The node of middle dump energy maximum adds cluster according to nearby principle, then carries out data again and gather as cluster head node, remaining node
Close；
(3) Grubbs pre-process
Sensor node needs to pre-process the data of collection, then transmits data to cluster head node again；Using Ge Labu
The data that this pre- criterion collects sensor node carry out pretreatment and assume that some cluster head node contains a sensor node,
The data that sensor node is collected into are x1,x2,…,xn, Normal Distribution, and set：
<mrow>
<msub>
<mi>x</mi>
<mn>0</mn>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mi>n</mi>
</mfrac>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</msubsup>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>x</mi>
<mn>0</mn>
</msub>
<mo>,</mo>
<mi>&delta;</mi>
<mo>=</mo>
<msqrt>
<mrow>
<mfrac>
<mn>1</mn>
<mi>n</mi>
</mfrac>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</msubsup>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
</mrow>
</msqrt>
<mo>;</mo>
</mrow>
According to order statistics principle, Grubbs statistic is calculated：
<mrow>
<msub>
<mi>g</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>x</mi>
<mn>0</mn>
</msub>
</mrow>
<mi>&delta;</mi>
</mfrac>
<mo>;</mo>
</mrow>
After given significance (α=0.05), measured value meets gi≤g0(n, α), then it is assumed that measured value is effective, measured value
Participate in the data aggregate of next level；It is on the contrary, then it is assumed that measured value is invalid, it is therefore desirable to reject, that is, be not involved in next layer
Secondary data aggregate；
(4) adaptive aggregating algorithm
The unbiased estimator of each node measurement data is obtained by iteration, ask for the measured data values of each sensor node with
Euclidean distance between estimate, adaptive weighted warm weights are used as using normalized Euclidean distance；Select the biography in cluster
The average value of the maxima and minima for the data that sensor node collects is as centre data；
There is a sensor node in some cluster, with dimensional vector D=(d1,d2,…,dn) represent respective nodes measured value, pass through
The deviation size between the different node datas of Euclidean distance reaction of each node data and centre data and centre data is calculated,
Wherein liCalculation formula be：
<mrow>
<msub>
<mi>l</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<msqrt>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>d</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<mi>T</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</msqrt>
<mo>;</mo>
</mrow>
According to the corresponding weights size of Euclidean distance adaptive setting, the bigger weights of distance are smaller, bigger apart from smaller weights；
<mrow>
<msub>
<mi>w</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mn>1</mn>
<mo>/</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>l</mi>
<mi>i</mi>
</msub>
<mo>/</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</munderover>
<mn>1</mn>
<mo>/</mo>
<msub>
<mi>l</mi>
<mi>i</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>;</mo>
</mrow>
WhereinwiFor corresponding weights；
Step 2, calculating numerically controlled data is carried out to mould processing technology process；Check die face in whether have process corrections and
Supplement part；The feature of die face change is modified numerically controlled data；
Raw material and finished work-piece are carried out autonomous transport operation control by step 3.
3. the numerical-control processing method of the intelligent die manufacture as described in the claims 2, it is characterised in that the correcting module
Modification method is as follows：
First, Design of Moulds is read in；
Then, geometric properties identification is carried out to mold component；In mould numeral designs a model, containing largely having, mould is special
The geometry of sign is, it is necessary to being identified these typical mould features in designing a model, retrieving, arranging with classifying, after being
Data preparation is carried out in continuous design defect diagnosis；
Finally, calculate and correct.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711271339.5A CN108008695A (en) | 2017-12-05 | 2017-12-05 | A kind of numerical-control processing method and control system of intelligent die manufacture |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711271339.5A CN108008695A (en) | 2017-12-05 | 2017-12-05 | A kind of numerical-control processing method and control system of intelligent die manufacture |
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