CN104914718B - Injection machine quick die sinking self-regulation control method and the injection machine realizing the method - Google Patents
Injection machine quick die sinking self-regulation control method and the injection machine realizing the method Download PDFInfo
- Publication number
- CN104914718B CN104914718B CN201510174644.7A CN201510174644A CN104914718B CN 104914718 B CN104914718 B CN 104914718B CN 201510174644 A CN201510174644 A CN 201510174644A CN 104914718 B CN104914718 B CN 104914718B
- Authority
- CN
- China
- Prior art keywords
- die sinking
- controling parameters
- injection machine
- optimization
- maximum
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Injection Moulding Of Plastics Or The Like (AREA)
Abstract
The invention discloses a kind of injection machine quick die sinking self-regulation control method, comprise the following steps: after the mould 1) more renewed at injection machine, according to machine parameter and die parameters, obtain initial die sinking controling parameters: 2) carry out primary injection moulding machine mould open action, obtain primary die sinking result data; 3) die sinking position L is judged whether in the deviation range of setting, to determine whether carry out position optimization; 4) judge whether maximum noise D is less than the critical value of setting under current die sinking controling parameters; To determine whether carry out noise optimization.The present invention is when without the need to increasing extras, by the analysis optimization process to injection moulding machine mould open controling parameters and result data, can after the mould more renewed Lookup protocol die sinking controling parameters, eliminate complicated artificial setting, best die sinking controling parameters can be obtained simultaneously, both improve the speed of die sinking, in turn ensure that the stationarity of die sinking process.
Description
Technical field
The invention belongs to injection machine field, more specifically, relate to a kind of injection machine quick die sinking self-regulation control method.
Background technology
Plastic products are produced mainly through injection molding process, and injection machine is main production equipment.Typical injection molding process comprises following six steps: matched moulds, injection, pressurize, cooling, die sinking, eject, wherein die sinking refers to that plastic products are cooled to a certain degree in mold cavity, mould is opened according to certain speed and pressure, carry out next step mechanical arm pickup again, or carry out the operations such as in-mold label.
What the current control system for shot machine overwhelming majority adopted is action open loop hierarchical control method, and generally the die sinking action of injection machine can be divided into level Four action: die sinking one is slow, and die sinking one is fast, and die sinking two is fast, and die sinking two is slow.Following problem is mainly there is in injection moulding die sinking process under current control mode:
1) positioning error: under open loop classification action control mode, when die sinking action arrives setting stop position, owing to there is the moderating process of a pressure, flow, add the response time lag of hydraulic system, thus at the end of causing die sinking, produce larger positioning error.
2) machine vibration: machine is in the process of die sinking, and especially in the position started and terminate, there are larger vibrations, large vibrations can bring impact to the physical construction of machine and the life-span of mould, and large vibrations simultaneously can bring the pollution of noise.
3) opening speed is slower: in order to avoid producing large vibrations in die sinking process, then must reduce the setting speed of each section in die sinking process, especially the speed that die sinking one is fast with two soon, overall opening speed then can be caused thus slower, long open time, thus affect production efficiency.
Summary of the invention
For above defect or the Improvement requirement of prior art, the invention provides a kind of injection machine quick die sinking self-regulation control method, can make the positioning error of injection machine when die sinking little, vibrate little, speed is fast.
For achieving the above object, according to one aspect of the present invention, provide a kind of injection machine quick die sinking self-regulation control method, comprise the following steps:
1), after the mould more renewed at injection machine, according to machine parameter and die parameters, from instance database, initial die sinking controling parameters is obtained by similarity search:
2) carry out primary injection moulding machine mould open action according to initial die sinking controling parameters, obtain primary die sinking result data, described die sinking result data comprises the maximum noise D in die sinking position L and die sinking process;
3) judge die sinking position L whether in the deviation range of setting, if so, then forward step 4 to); If not, then split cavity controling parameters carries out position optimization according to the priority level of setting, until the die sinking position carrying out die sinking action acquisition according to the die sinking controling parameters optimized is in the deviation range of setting, then forwards step 4 to);
4) judge whether maximum noise D is less than the critical value of setting under current die sinking controling parameters; If so, then current die sinking controling parameters is preserved; If not, then split cavity controling parameters carries out noise optimization to reduce the vibrations in die sinking process.
Preferably, step 1) described in machine parameter comprise the maximum clamp force of the maximum (top) speed of electric motor of injection machine, the maximum pressure of Hydraulic System for Injection Moulding Machine, the maximum die sinking position of injection machine and injection machine, described die parameters comprises the length, width and height size of mould and the weight of mould; Described die sinking controling parameters comprises the length n of die sinking segments N (s), each section
rthe speed v of (s), each section
rthe pressure p of (s), each section
r(s) and the slope k between section and section
t(s), wherein, r=1,2 ..., N (s), t=1,2 ..., N (s)+1, s is die sinking number of times, s=0,1,2,3, Described length n
rs () is the number of control cycle in each section; Described speed v
rs number percent that () is motor speed; Described pressure p
rs pressure that () is hydraulic system; Described slope k
ts () is the number of control cycle between section and section; Described instance database comprises some records, and every bar record comprises the maximum (top) speed of electric motor of injection machine, the maximum pressure of Hydraulic System for Injection Moulding Machine, the maximum die sinking position of injection machine, the maximum clamp force of injection machine, the length, width and height size of mould, the weight of mould and corresponding N (s), n
r(s), v
r(s), p
r(s) and k
tthe value of (s).
Preferably, step 3) in adopt fuzzy reasoning method to be optimized according to the priority level of setting the die sinking controling parameters needing to optimize, the priority level of each die sinking controling parameters is as follows: n
r(s) >v
r(s) >k
t(s) >N (s) >p
r(s).
Preferably, one in each position optimization split cavity controling parameters is optimized.
Preferably, step 3) in be to the maximum length n of value
rs () carries out position optimization, Optimization Steps is as follows:
3.1) obfuscation: by the die sinking controling parameters needing to optimize and the die sinking position obfuscation carrying out die sinking action acquisition under this die sinking controling parameters, the domain arranging die sinking position deviation e is [-40,40], this domain is set up five fuzzy subsets, and its corresponding linguistic variable is: negative large NB, negative little NS, zero O, just little PS, honest PB; Above-mentioned length n is set
rs the domain of () is [-8,8], and this domain is set up five fuzzy subsets, and its corresponding linguistic variable is: negative large NB, negative little NS, zero O, just little PS, honest PB;
Described die sinking position deviation e=L-L
0, wherein L
0for the die sinking position of setting, L is actual die sinking position, if | e|=|L-L
0| >E
lim, then think that current die sinking position needs to optimize, otherwise think that current die sinking position does not need to optimize, wherein E
limfor the critical value of setting;
3.2) fuzzy rule is set:
3.2.1) if input e is NB, then exporting n is PB;
3.2.2) if input e is NS, then exporting n is PS;
3.2.3) if input e is O, then exporting n is O;
3.2.4) if input e is PS, then exporting n is NS;
3.2.5) if input e is PB, then exporting n is NB;
3.3) fuzzy reasoning: adopt fuzzy reasoning graphical method to carry out fuzzy reasoning; The membership function of input e and the fuzzy set A exported corresponding to n and B gets trigonometric function, the result obtained is got " and ", obtain the fuzzy set membership function of n;
3.4) ambiguity solution: adopt gravity model appoach ambiguity solution, expression formula is as follows:
N* (s)=∫ μ
b(n) ndn/ ∫ μ
b(n) dn, wherein μ
bfor the value of domain;
Then obtain die sinking controling parameters n (s+1)=n (the s)+n* (s) after optimizing.
Preferably, step 4) in be that noise optimization is carried out to the length of the slope between second from the bottom section and last section and final stage, namely optimize k
r(s) and n
rthe value of (s); k
r(s) and n
rs () meets following equation:
Wherein, T
0for unit cycle length, k
rs () is the slope between second from the bottom section and last section before optimization, v
r(s-1) be the speed of second from the bottom section, v
rs speed that () is final stage, n
rs length that () is final stage,
for the slope between second from the bottom section and last section after optimization,
for the final stage length after optimization;
Repeat this step until the maximum noise D measured is less than the critical value D of setting
lim, optimize and terminate, and preserve the die sinking controling parameters after optimizing.
Realize an injection machine for said method, comprise injection machine main frame, sensor, storer, processor and servo controller, wherein, storer is used for the result data that storing machine parameter, die parameters, die sinking controling parameters and optimization obtain;
Processor is used for calculating and optimizing die sinking controling parameters, and described die sinking controling parameters is transferred to injection machine main frame;
Sensor is for the noise that detects in die sinking position and die sinking process and pass to injection machine main frame;
Result data, for accepting the result data of sensor, is sent to processor and accepts the die sinking controling parameters that processor sends, and die sinking controling parameters being sent to servo controller by injection machine main frame;
Servo controller, for receiving the die sinking controling parameters that injection machine main frame sends, and controls the rotation of servomotor according to the controling parameters received.
In general, the above technical scheme conceived by the present invention compared with prior art, can obtain following beneficial effect:
1) the present invention is when without the need to increasing extras, by the analysis optimization process to injection moulding machine mould open controling parameters and result data, can after the mould more renewed Lookup protocol die sinking controling parameters, eliminate complicated artificial setting, best die sinking controling parameters can be obtained simultaneously, both ensure that fast opening speed, turn improve the positioning precision of die sinking;
2) the vibrations noise of split cavity process is monitored, automatically relevant die sinking controling parameters can be adjusted when producing large vibrations in process of production, effectively can avoid the damage that large vibrations bring to machine and mould, extend machine and mold use life-span, significantly can reduce the noise pollution in production run simultaneously.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of injection machine of the present invention quick die sinking self-regulation control method;
Fig. 2 is the domain of position deviation e and the membership function of fuzzy set A;
Fig. 3 is the domain of controlled quentity controlled variable n and the membership function of fuzzy set B;
Fig. 4 is position optimization e
1initial condition;
Fig. 5 be position optimization obtained by rule fuzzy control quantity " and " result;
Fig. 6 is noise optimization e
2initial condition;
Fig. 7 be noise optimization obtained by rule fuzzy control quantity " and " result.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.In addition, if below in described each embodiment of the present invention involved technical characteristic do not form conflict each other and just can mutually combine.
As shown in Fig. 1 ~ Fig. 7, a kind of injection machine provided by the present invention quick die sinking self-regulation control method, can position optimization and noise optimization be carried out, mainly comprise the following steps:
1), after the mould more renewed at injection machine, according to machine parameter and die parameters, from instance database, initial die sinking controling parameters is obtained by similarity search:
Described machine parameter comprises the maximum clamp force of the maximum (top) speed of electric motor of injection machine, the maximum pressure of Hydraulic System for Injection Moulding Machine, the maximum die sinking position of injection machine and injection machine, and described die parameters comprises the length, width and height size of mould and the weight of mould;
Described die sinking controling parameters comprises the length n of die sinking segments N (s), each section
rthe speed v of (s), each section
rthe pressure p of (s), each section
r(s) and the slope k between section and section
t(s); Wherein, r=1,2 ..., N (s), t=1,2 ..., N (s)+1, s=0,1,2,3 ..., s represents die sinking number of times.
Described length n
rs () is the number of control cycle in each section, each control cycle is 2.4ms; Described speed v
rs number percent that () is motor speed; Described pressure p
rs pressure that () is hydraulic system, unit is bar; Described slope k
ts change speed that () is speed between section and section, the specifically number of control cycle between the section of being expressed as and section;
Described instance database comprises some records, every bar record comprises the weight of the maximum (top) speed of electric motor of injection machine, the maximum pressure of Hydraulic System for Injection Moulding Machine, the maximum die sinking position of injection machine, the maximum clamp force of injection machine, the length, width and height size of mould and mould, and corresponding N (s), n
r(s), v
r(s), p
r(s), k
tthe value of (s), each bar record derives from the successful case in injection mo(u)lding manufacturer actual production process;
2) by 1) obtain die sinking controling parameters N (0), the n of initial die sinking
r(0), v
r(0), p
r(0), k
t(0) after, carry out primary injection moulding machine mould open action, obtain the result data of first time die sinking;
Die sinking result data comprises the maximum noise D in die sinking position L and die sinking process, can also comprise open time T, and the present invention mainly studies for the maximum noise D in die sinking position L and die sinking process.
3) according to step 2) initial die sinking controling parameters N (0), n
r(0), v
r(0), p
r(0), k
t(0) die sinking action is carried out, obtain the result data of first time die sinking, judge whether this die sinking position L meets in the deviation range of setting, if, then carry out next step noise optimization, if not, then the method for fuzzy reasoning is adopted to carry out position optimization to the die sinking controling parameters needing to optimize successively according to the priority level of setting;
The priority level of die sinking controling parameters refers to when optimizing a certain die sinking controling parameters, for 1) described in 5 die sinking controling parameters according to presetting priority level, by order from high to low, each die sinking controling parameters is optimized successively, every suboptimization is not be all optimized 5 die sinking controling parameters, but only optimizes wherein 1 die sinking controling parameters; The priority level of each die sinking controling parameters is as follows: n
r(s) >v
r(s) >k
t(s) >N (s) >p
r(s), namely preferential to n
rs () is optimized.Generally optimize the requirement that the first two or three parameters just can meet setting; For example at every turn can only to n in the front action of die sinking several times
rs () is optimized, repeatedly to n
rs when () optimization still cannot make die sinking controling parameters meet setting requirement, then continue to optimize die sinking controling parameters v below
r(s) ...
Because maximum one section of the value of length n (s) produces large impact to final result, be therefore optimized the length n (s) that value is maximum, fuzzy reasoning method comprises following process:
(3.1) obfuscation, by needing the die sinking position of optimization and corresponding die sinking controling parameters obfuscation, if the domain of die sinking position deviation e is [-40,40], this domain is set up five fuzzy subset A
1, A
2, A
3, A
4, A
5, its corresponding linguistic variable is: negative large (NB), negative little (NS), zero (O), just little (PS), honest (PB), and its membership function as shown in Figure 2; If the length n of each section
rs the domain of () is [-8,8], and this domain is set up five fuzzy subset B
1, B
2, B
3, B
4, B
5, its corresponding linguistic variable is: negative large (NB), negative little (NS), zero (O), just little (PS), honest (PB), and its membership function as shown in Figure 3;
Described die sinking position deviation e=L-L
0, wherein L
0for the die sinking position of setting, L is actual die sinking position, if | e|=|L-L
0| >E
lim, then think that current location parameter needs to optimize, otherwise, then think that current location parameter does not need to optimize, continue next step noise optimization; Wherein E
limfor the critical value of setting;
(3.2) fuzzy rule, according to expertise, designs following fuzzy rule:
3.2.1) if input e is NB, then exporting n is PB;
3.2.2) if input e is NS, then exporting n is PS;
3.2.3) if input e is O, then exporting n is O;
3.2.4) if input e is PS, then exporting n is NS;
3.2.5) if input e is PB, then exporting n is NB;
(3.3) fuzzy reasoning, adopts fuzzy reasoning graphical method to carry out fuzzy reasoning; The membership function of input e and the fuzzy set A exported corresponding to n and B gets trigonometric function; The result obtained by rule-based reasoning is got " and ", obtain the fuzzy set membership function of n.
(3.4) ambiguity solution, adopt gravity model appoach ambiguity solution, mathematic(al) representation is as follows:
N* (s)=∫ μ
b(n) ndn/ ∫ μ
b(n) dn, wherein μ
bfor the value of domain;
Die sinking controling parameters n (s+1)=n (s)+n* (s) after then can optimizing;
If the value of N (s) is 3, then second segment said n after segmentation
rs the optimization of () is optimized second segment, because the length n of general second segment
rs () is maximum, produce large impact to final result.
4) according to step 3) die sinking controling parameters N (1), n after the primary position optimization that obtains
r(1), v
r(1), p
rand k (1)
t(1) (wherein r=1,2 ..., N (1), t=1,2 ..., N (1)+1) carry out secondary injection moulding machine mould open action again, obtain second time die sinking result data; If the die sinking position L of second time die sinking result data reaches setting requirement, then stop optimizing, and preserve current die sinking controling parameters, then carry out next step noise optimization; If do not arrive setting requirement, then using optimize after die sinking controling parameters together with the die sinking result data obtained as reference data, jump to step 3) and continue to optimize corresponding die sinking controling parameters by priority level, until carry out the die sinking position L after die sinking action according to the die sinking controling parameters optimized to meet setting requirement, after this carry out next step noise optimization again;
5) judge, under current die sinking controling parameters, in injection moulding machine mould open process, whether to there are larger vibrations, whether steady to judge in die sinking process; Described vibrations measure the maximum decibel value D of sound in die sinking process by decibelmeter, and with the critical value D of setting
limcontrast, if the maximum decibel value measured is less than the critical value of setting, then think to there is not big bang in die sinking process, each die sinking controling parameters meets the requirements, and preserves current die sinking controling parameters; If the maximum decibel value measured is greater than the critical value of setting, then thinks to there are large vibrations, and carry out noise optimization;
By 4) die sinking controling parameters after the position optimization that obtains, proceed noise optimization to reduce the vibrations in die sinking process.
Described noise optimization on the basis of position optimization, for 5) in exist large seismism split cavity controling parameters be optimized further; Slope between the mainly die sinking process reciprocal second segment that injection moulding machine mould open process stationarity is had the greatest impact and last section, the k namely between second from the bottom section and last section
rthe value of (s); It is therefore main that what optimize is slope k between second from the bottom section and last section
r(s) and final stage n
rthe length of (s).
In order to the noise reduced in die sinking process needs to reduce k
rs the value of (), simultaneously in order to ensure that die sinking position does not change, then needs to increase n
rs the value of (), is specifically expressed as k
r(s) and n
rs () need meet following equation:
Wherein, T
0for unit cycle length, k
rs () is the slope between second from the bottom section and last section before optimization, v
r(s-1) be the speed of second from the bottom section, v
rs speed that () is final stage, n
rs () is final stage length,
for the slope between second from the bottom section and last section after optimization,
for the final stage length after optimization;
Repeat this step until the noise D measured is less than the critical value D of setting
lim, optimize and terminate, and preserve the die sinking controling parameters after optimizing.
If the value of N (s) is 3, then the n of above-mentioned optimization
rs () is the length of the 3rd section, with the length n of the second segment of position optimization
rs () is different, position optimization and noise optimization can not influence each other.
Below in conjunction with concrete parameter in detail control method of the present invention is described in detail:
1) first time die sinking controling parameters is determined:
After new mould changed by injection machine, according to the die sinking parameter of the parameter of known injection machine, the parameter of mould and setting, from instance database, obtain initial die sinking controling parameters by similarity search;
Experiment injection machine: the N60 type injection machine of Ningbo En Ruide company, the maximum die sinking position 280.0mm of machine, motor maximum (top) speed is 1800rpm, maximum clamp force is 600KN, system maximum pressure is 140bar, and mold weight is 105kg, and mould length, width and height are of a size of 250mm*250mm*225mm;
The die sinking position L of setting
0for 250mm, the critical value E of site error
limfor 0.2mm, maximum permission noise D
limfor 70db;
According to the parameter of machine and mould, from instance database, obtain initial die sinking controling parameters by similarity search, institute's initial die sinking parameter that obtains is in table 1;
The initial die sinking controling parameters of table 1
| N(0) | k 1(0) | n 1(0) | v 1(0) | p 1(0) | k 2(0) | n 2(0) |
| 3 | 4 | 4 | 15 | 35 | 8 | 55 |
| v 2(0) | p 2(0) | k 3(0) | n 3(0) | v 3(0) | p 3(0) | k 4(0) |
| 80 | 80 | 12 | 10 | 10 | 30 | 9 |
2) first time die sinking and result feedback;
According to 1) the initial die sinking controling parameters determined, carries out primary injection moulding machine mould open action, obtains primary die sinking result data: L
1=252.8mm, | e
1|=| L
1-L
0|=2.8mm>E
lim, D
1=65db<D
lim, so location parameter needs to optimize, noise parameter does not need to optimize;
3) parameter position is optimized;
The method of fuzzy reasoning is adopted to optimize location parameter, the e in described domain
1for actual e
1be multiplied by 10, i.e. e
1=28, solve to obtain μ by Fig. 4
a(e
1)=0.4 and 0.6, the rule according to providing has:
If input e is PS, then exporting n is NS;
If input e is PB, then exporting n is NB;
In Fig. 5 dotted portion represent fuzzy output that diagram obtains " and " result, adopt gravity model appoach ambiguity solution to obtain exporting controlled quentity controlled variable n
2(0) *:
Round
by
obtain the die sinking controling parameters after primary position optimization, in table 2;
Die sinking controling parameters after the primary position optimization of table 2
| N(1) | k 1(1) | n 1(1) | v 1(1) | p 1(1) | k 2(1) | n 2(1) |
| 3 | 4 | 4 | 15 | 35 | 8 | 51 |
| v 2(1) | p 2(1) | k 3(1) | n 3(1) | v 3(1) | p 3(1) | k 4(1) |
| 80 | 80 | 12 | 10 | 10 | 30 | 9 |
Carry out the second time die sinking action of injection machine according to the die sinking controling parameters after position optimization, obtain result data, L
2=249.5mm, | e
2|=| L
2-L
0|=0.5mm>E
lim, D
2=67db<D
lim, so location parameter needs to optimize; μ is solved to obtain by Fig. 6
a(e
2)=0.25 and 0.75, the rule according to providing has:
If input e is NS, then exporting n is PS;
If input e is O, then exporting n is O;
In Fig. 7 dotted portion represent fuzzy output that diagram obtains " and " result, adopt gravity model appoach ambiguity solution to obtain exporting controlled quentity controlled variable n
2(1) *=1.1579, round to obtain n
2(1) *=1, then n
2(2)=n
2(1)+n
2(1) *=52, obtain the die sinking controling parameters after secondary position optimization, in table 3;
Die sinking controling parameters after the secondary position optimization of table 3
| N(2) | k 1(2) | n 1(2) | v 1(2) | p 1(2) | k 2(2) | n 2(2) |
| 3 | 4 | 4 | 15 | 35 | 8 | 51 |
| v 2(2) | p 2(2) | k 3(2) | n 3(2) | v 3(2) | p 3(2) | k 4(2) |
| 80 | 80 | 12 | 10 | 10 | 30 | 9 |
Carry out the third time die sinking action of injection machine according to the die sinking controling parameters after noise optimization, obtain die sinking result data, L
3=250.1mm, | e
3|=| L
3-L
0|=0.1mm<E
lim, D
3=68db<D
lim, each result data all meets setting requirement, optimizes and terminates, preserve current die sinking controling parameters.
Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention; not in order to limit the present invention, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.
Claims (6)
1. an injection machine quick die sinking self-regulation control method, is characterized in that: comprise the following steps:
1), after the mould more renewed at injection machine, according to machine parameter and die parameters, from instance database, initial die sinking controling parameters is obtained by similarity search; Described machine parameter comprises the maximum clamp force of the maximum (top) speed of electric motor of injection machine, the maximum pressure of Hydraulic System for Injection Moulding Machine, the maximum die sinking position of injection machine and injection machine, and described die parameters comprises the length, width and height size of mould and the weight of mould; Described die sinking controling parameters comprises the length n of die sinking segments N (s), each section
rthe speed v of (s), each section
rthe pressure p of (s), each section
r(s) and the slope k between section and section
t(s), wherein, r=1,2 ..., N (s), t=1,2 ..., N (s)+1, s is die sinking number of times, s=0,1,2,3, Described length n
rs () is the number of control cycle in each section; Described speed v
rs number percent that () is motor speed; Described pressure p
rs pressure that () is hydraulic system; Described slope k
ts () is the number of control cycle between section and section; Described instance database comprises some records, and every bar record comprises the maximum (top) speed of electric motor of injection machine, the maximum pressure of Hydraulic System for Injection Moulding Machine, the maximum die sinking position of injection machine, the maximum clamp force of injection machine, the length, width and height size of mould, the weight of mould and corresponding N (s), n
r(s), v
r(s), p
r(s) and k
tthe value of (s);
2) carry out primary injection moulding machine mould open action according to initial die sinking controling parameters, obtain primary die sinking result data, described die sinking result data comprises the maximum noise D in die sinking position L and die sinking process;
3) judge die sinking position L whether in the deviation range of setting, if so, then forward step 4 to); If not, then split cavity controling parameters carries out position optimization according to the priority level of setting, until the die sinking position carrying out die sinking action acquisition according to the die sinking controling parameters optimized is in the deviation range of setting, then forwards step 4 to);
4) judge whether maximum noise D is less than the critical value of setting under current die sinking controling parameters; If so, then current die sinking controling parameters is preserved; If not, then split cavity controling parameters carries out noise optimization to reduce the vibrations in die sinking process.
2. a kind of injection machine according to claim 1 quick die sinking self-regulation control method, it is characterized in that: step 3) in adopt fuzzy reasoning method to optimize needs die sinking controling parameters be optimized according to the priority level set, the priority level of each die sinking controling parameters is as follows: n
r(s) >v
r(s) >k
t(s) >N (s) >p
r(s).
3. a kind of injection machine according to claim 2 quick die sinking self-regulation control method, is characterized in that: one in each position optimization split cavity controling parameters is optimized.
4. a kind of injection machine according to claim 1 quick die sinking self-regulation control method, is characterized in that: step 3) in be to the maximum length n of value
rs () carries out position optimization, Optimization Steps is as follows:
3.1) obfuscation: by the die sinking controling parameters needing to optimize and the die sinking position obfuscation carrying out die sinking action acquisition under this die sinking controling parameters, the domain arranging die sinking position deviation e is [-40,40], this domain is set up five fuzzy subsets, and its corresponding linguistic variable is: negative large NB, negative little NS, zero O, just little PS, honest PB; Above-mentioned length n is set
rs the domain of () is [-8,8], and this domain is set up five fuzzy subsets, and its corresponding linguistic variable is: negative large NB, negative little NS, zero O, just little PS, honest PB;
Described die sinking position deviation e=L-L
0, wherein L
0for the die sinking position of setting, L is actual die sinking position, if | e|=|L-L
0| >E
lim, then think that current die sinking position needs to optimize, otherwise think that current die sinking position does not need to optimize, wherein E
limfor the critical value of setting;
3.2) fuzzy rule is set:
3.2.1) if input e is NB, then exporting n is PB;
3.2.2) if input e is NS, then exporting n is PS;
3.2.3) if input e is O, then exporting n is O;
3.2.4) if input e is PS, then exporting n is NS;
3.2.5) if input e is PB, then exporting n is NB;
3.3) fuzzy reasoning: adopt fuzzy reasoning graphical method to carry out fuzzy reasoning; The membership function of input e and the fuzzy set A exported corresponding to n and B gets trigonometric function, the result obtained is got " and ", obtain the fuzzy set membership function of n;
3.4) ambiguity solution: adopt gravity model appoach ambiguity solution, expression formula is as follows:
Then obtain the die sinking controling parameters n after optimizing
r(s+1)=n
r(s)+n
r* (s).
5. a kind of injection machine according to claim 1 quick die sinking self-regulation control method, is characterized in that: step 4) in be that noise optimization is carried out to the length of the slope between second from the bottom section and last section and final stage, namely optimize k
r(s) and n
rthe value of (s); k
r(s) and n
rs () meets following equation:
Wherein, T
0for unit cycle length, k
rs () is the slope between second from the bottom section and last section before optimization, v
r(s-1) be the speed of second from the bottom section, v
rs speed that () is final stage, n
rs length that () is final stage,
for the slope between second from the bottom section and last section after optimization,
for the final stage length after optimization;
Repeat this step until the maximum noise D measured is less than the critical value D of setting
lim, optimize and terminate, and preserve the die sinking controling parameters after optimizing.
6. realize an injection machine for arbitrary described method in Claims 1 to 5, it is characterized in that: comprise injection machine main frame, sensor, storer, processor and servo controller, wherein,
Storer is used for the result data that storing machine parameter, die parameters, die sinking controling parameters and optimization obtain;
Processor is used for calculating and optimizing die sinking controling parameters, and described die sinking controling parameters is transferred to injection machine main frame;
Sensor is for the noise that detects in die sinking position and die sinking process and pass to injection machine main frame;
Result data, for accepting the result data of sensor, is sent to processor and the die sinking controling parameters of receiving processor transmission, and die sinking controling parameters is sent to servo controller by injection machine main frame;
Servo controller, for receiving the die sinking controling parameters that injection machine main frame sends, and controls the rotation of servomotor according to the controling parameters received.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510174644.7A CN104914718B (en) | 2015-04-14 | 2015-04-14 | Injection machine quick die sinking self-regulation control method and the injection machine realizing the method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510174644.7A CN104914718B (en) | 2015-04-14 | 2015-04-14 | Injection machine quick die sinking self-regulation control method and the injection machine realizing the method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104914718A CN104914718A (en) | 2015-09-16 |
| CN104914718B true CN104914718B (en) | 2015-12-30 |
Family
ID=54083889
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510174644.7A Active CN104914718B (en) | 2015-04-14 | 2015-04-14 | Injection machine quick die sinking self-regulation control method and the injection machine realizing the method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104914718B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105643891B (en) * | 2016-01-21 | 2018-02-16 | 华中科技大学 | A kind of injection moulding machine mould open method for optimizing route |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR950005721B1 (en) * | 1989-01-27 | 1995-05-29 | 나까야마 고교 가부시끼가이샤 | Method and apparatus for controlling injection molding |
| CN101327641B (en) * | 2008-07-29 | 2011-08-10 | 夏善海 | Accurate control method of die sinking position of injection moulding machine |
| CN102019677A (en) * | 2009-09-18 | 2011-04-20 | 广州市香港科大霍英东研究院 | Method for quickly and accurately positioning die in injection molding process |
-
2015
- 2015-04-14 CN CN201510174644.7A patent/CN104914718B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN104914718A (en) | 2015-09-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105608218A (en) | Intelligent question answering knowledge base establishment method, establishment device and establishment system | |
| CN104932263A (en) | Minimum operation time control method of multistage intermittent process | |
| CN1752984A (en) | Determination method of plastic injection technological parameter and injection moulding machine | |
| CN104914718B (en) | Injection machine quick die sinking self-regulation control method and the injection machine realizing the method | |
| CN105407158A (en) | Method and device for building model and pushing message | |
| CN107765639A (en) | A kind of rounding error compensating method of S curve acceleration and deceleration | |
| CN114676522B (en) | Pneumatic shape optimization design method, system and equipment integrating GAN and migration learning | |
| CN108415253A (en) | Control method of flywheel battery magnetic bearing system based on Markov jump theory | |
| CN107310117B (en) | A kind of middle sub-control system of injection (mo(u)lding) machine | |
| CN108615069A (en) | A kind of optimized calculation method based on improved adaptable quanta particle swarm optimization | |
| CN110442935B (en) | Production beat design and optimization method for automatic equipment | |
| CN105643891B (en) | A kind of injection moulding machine mould open method for optimizing route | |
| CN117227121A (en) | Forming control method and system for bus duct plastic parts | |
| CN110861264A (en) | Reinforced (rfd) injection mold of compound die | |
| Cao et al. | Design of an automobile injection mould based on automation technology | |
| CN113703433B (en) | Speed planning method and device for motion trail of robot | |
| EP1366887A1 (en) | Method and device for design of preform | |
| CN114137833B (en) | Optimal feedback control method and controller for injection filling process | |
| CN114311574B (en) | Injection speed optimization control method, system and device of injection molding machine | |
| CN201436407U (en) | Multi-pump hydraulic control device | |
| Du et al. | The trajectory optimization of mobile crossbeam in composite hydraulic press based on modified multi-island genetic algorithm | |
| CN110362580A (en) | BIM (building information modeling) construction engineering data retrieval optimization classification method and system thereof | |
| CN104669569A (en) | Hydraulic system of five-point toggle die closing mechanism | |
| CN207156384U (en) | Injection machine mould condition monitoring system | |
| CN117874081B (en) | Mold database management method and system based on artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |