CN114115380A - Temperature control method and system for 3D glass hot bending die - Google Patents
Temperature control method and system for 3D glass hot bending die Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
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- C—CHEMISTRY; METALLURGY
- C03—GLASS; MINERAL OR SLAG WOOL
- C03B—MANUFACTURE, SHAPING, OR SUPPLEMENTARY PROCESSES
- C03B23/00—Re-forming shaped glass
- C03B23/02—Re-forming glass sheets
- C03B23/023—Re-forming glass sheets by bending
- C03B23/03—Re-forming glass sheets by bending by press-bending between shaping moulds
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
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Abstract
A 3D glass hot bend mold temperature control method and system, the method comprising: acquiring a temperature rise curve of each temperature measurement point of the 3D glass hot bending mould, carrying out segmentation and segmented discrete control to obtain theoretical temperature SUi (tn) of the ith temperature measurement point at the future time of a time region [ tm, tn ], and detecting instantaneous temperature Vui (t); establishing an LSTM prediction model, inputting a plurality of instantaneous feature vectors into the trained LSTM prediction model, and obtaining the predicted temperature Wui (tn) of the ith temperature measurement point at the future time of a time region [ tm, tn ]; calculating the difference between the theoretical temperature SUi (tn) and the instantaneous temperature Vui (t) to obtain an instantaneous temperature difference value, and calculating to obtain a main control quantity corresponding to the ith temperature measurement point by utilizing a PID control algorithm; and calculating the power adjustment rate corresponding to the ith temperature measurement point by adopting a fuzzy control method, and finely adjusting the main control quantity by utilizing the power adjustment rate. The invention realizes the temperature cooperative control in time and space of the die in the die temperature control process, and improves the temperature regulation precision.
Description
Technical Field
The invention belongs to the technical field of molds, and particularly relates to a temperature control method and system for a 3D glass hot bending mold.
Background
With the development of electronic information technology, the application of transparent glass components in the industry is continuously increased, and particularly with the rapid advance of 5G technology, there is a great industrial demand for the wide application of wireless charging technology and flexible OLEDs. This requires that the terminal cover glass (e.g., front and back cover glass of a mobile phone, front cover glass of a smart watch, etc.) is designed to be a curved surface shape, also called 3D cover glass. From the current market application, 3D cover glass is well evaluated, and its demand is increasing. Compared with the traditional processing technology, the hot bending forming technology has the advantages of low cost and batch production, and is particularly suitable for the production of 3D cover plate glass. However, since the glass and other materials are amorphous materials, if the temperature of the hot bending is too low, the 3D glass member is easily broken or the size is not satisfactory; on the contrary, if the temperature of the hot bending is too high, the 3D glass component is easy to have the defects of scald, water ripple and the like; in addition, the 3D glass component is in a curved surface shape, so that heat transfer of the mold is uneven, and the hot bending yield of the 3D glass product is further reduced.
Generally speaking, the performance of the hot bending mold material requires that the material should have the characteristics of fine crystal grains, compact and uniform structure, high thermal stability, easy processing, good thermal conductivity, small thermal expansibility and the like. Alternative materials include: alloys, ceramics, graphite, and the like. Because of the excellent heat transfer performance and the precise processing characteristic of the graphite, the graphite better meets the requirements of a 3D cover plate glass hot bending forming die. At present, graphite is mostly used as a raw material of a hot bending die in the industry, and in order to improve the technological performance of products, graphite dies are generally used in pairs, namely, concave-convex dies are matched for use.
For the 3D glass hot bending forming process, the process parameters (temperature and pressure) have very important influence on the forming quality, particularly the temperature parameter. The traditional upper and lower die temperature control adopts a single PID (proportion integration differentiation) control method based on the deviation between the temperature of the current time and the expected temperature, and the temperature of the whole upper die or the whole lower die is regulated and controlled.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a temperature control method and a temperature control system for a 3D glass hot bending die, solves the problem of low precision in the die temperature control process, and improves the temperature regulation and control precision by realizing the temperature cooperative control in time and die space.
The invention firstly provides a temperature control method for a 3D glass hot bending die, which comprises the following steps:
step S1, obtaining a temperature rise curve of each temperature measurement point of the 3D glass hot bending mould, segmenting the temperature rise curve according to time intervals, sequentially carrying out segmented discrete control on each obtained time region, obtaining theoretical temperature SUi (tn) of the ith temperature measurement point at the future time of the time region [ tm, tn ] for the time region [ tm, tn ] with the time tn as the future time, and detecting the instant temperature Vui (t) corresponding to the heated ith temperature measurement point at the time region [ tm, tn ];
step S2, establishing an LSTM prediction model of the mold temperature, collecting an instantaneous feature vector of the ith temperature measurement point of the 3D glass hot bending mold in a time region [ tm, tn ], inputting the instantaneous feature vector into the trained LSTM prediction model, and obtaining the predicted temperature Wui (tn) of the ith temperature measurement point at the future time of the time region [ tm, tn ];
step S3, calculating the difference between the theoretical temperature SUi (tn) and the instantaneous temperature Vui (t) to obtain an instantaneous temperature difference value, and calculating to obtain a main control quantity corresponding to the ith temperature measurement point by using a PID control algorithm;
step S4, using the predicted temperature wui (tn) and the theoretical temperature sui (tn) at the future time tn of the ith temperature measurement point as inputs, calculating the power adjustment rate corresponding to the ith temperature measurement point by using a fuzzy control method, and fine-tuning the main control quantity by using the power adjustment rate.
Further, the temperature rise curve and the LSTM prediction model are obtained through simulation experiments and/or processing experiments.
Further, the 3D glass hot bending mould comprises an upper mould and a lower mould which are matched with each other.
Further, in step S2, the instantaneous feature vector includes a current position temperature, a current position first neighboring point temperature, a current position second neighboring point temperature, a current position third neighboring point temperature, a thermal bender internal cavity temperature, a thermal bender external temperature, a mold working time, and a mold type.
Further, in step S3, the calculation formula of the instantaneous temperature difference value eui (t) of the ith temperature measurement point at the future time of the time zone [ tm, tn ] is:
eUi(t)＝SUi(tn)-VUi(t)。
further, in step S3, the relationship between the ith temperature measurement point input eui (t) and the output xui (t) is:
in the formula, eUi(t)、
Respectively, the error integral and the error differential of the corresponding ith temperature measuring pointTerm, kiUP,kiUI,kiUDRespectively, a proportionality coefficient, an integral coefficient and a differential coefficient corresponding to the ith temperature measurement point.
Further, in step S4, when the desired temperature SUi (tn) ∈ (0 ℃ -300 ℃) of the ith temperature measurement point at the future time tn and ABS (SUi (tn) — WUi (tn)) ≦ a ℃, the power adjustment rate is (100+ a)%, if SUi (tn) is greater than WUi (tn), and (100-a)%, if SUi (tn) is less than WUi (tn).
Further, after step S4, the method further includes training the LSTM prediction model in real time, and replacing the current LSTM prediction model when the training of the LSTM prediction model is completed.
The invention also provides a system of the temperature control method for the 3D glass hot bending die, which comprises the following steps:
the theoretical model module is used for obtaining a temperature rise curve of each temperature measurement point of the 3D glass hot bending mould, segmenting the temperature rise curve according to time intervals, sequentially carrying out segmented discrete control on each obtained time region, and obtaining theoretical temperature SUi (tn) of the ith temperature measurement point at the future time of the time region [ tm, tn ] for the time region [ tm, tn ] with the time tn as the future time;
the instantaneous detection module is used for detecting the instantaneous temperature Vui (t) corresponding to the heated ith temperature measurement point in the time region [ tm, tn ];
the prediction model module is used for establishing an LSTM prediction model of the mold temperature, acquiring an instantaneous feature vector of the ith temperature measurement point of the 3D glass hot bending mold in a time region [ tm, tn ], inputting the instantaneous feature vector into the trained LSTM prediction model, and obtaining the predicted temperature Wui (tn) of the ith temperature measurement point at the future time of the time region [ tm, tn ];
the main control module is used for calculating the difference between the theoretical temperature SUi (tn) and the instantaneous temperature Vui (t) to obtain an instantaneous temperature difference value, and calculating a main control quantity corresponding to the ith temperature measurement point by utilizing a PID control algorithm;
and the auxiliary control module is used for taking the predicted temperature Wui (tn) of the future time tn of the ith temperature measurement point and the theoretical temperature SUi (tn) as input, calculating the power adjustment rate corresponding to the ith temperature measurement point by adopting a fuzzy control method, and finely adjusting the main control quantity by utilizing the power adjustment rate.
Furthermore, the main control module comprises a PID controller and a heating device, the auxiliary control module comprises a fuzzy controller, and the output ends of the PID controller and the fuzzy controller are respectively connected with the input end of the heating device.
The temperature control method and the system for the 3D glass hot bending die provided by the invention have the following beneficial effects:
1) the problem that the precision of a control method only aiming at the temperature deviation at the current moment is low in the process of controlling the temperature of the die is solved, a time cooperative control method of the temperature of the die integrating the temperature deviation at the current moment and the temperature deviation at the future moment is provided, and the temperature regulation and control precision is improved;
2) because the space structure of the die is complex, the temperature acquired by a single temperature sensor is not enough to represent the heat conduction rule in the die heating process, and the data of a plurality of adjacent temperature sensors are acquired in space, so that accurate data are provided for predicting the temperature value at the future moment;
3) the plurality of independent temperature regulation and control modules are arranged in the die, so that different areas can be independently controlled and the temperature can be regulated and controlled, and the precise temperature control, namely the spatial cooperative control, can be realized for the specific area to be thermally bent of the 3C glass component by fusing PID control and fuzzy control according to the thermal bending process requirement of the 3C glass component.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a 3D glass hot bending mold temperature control method according to an embodiment of the invention;
FIG. 2 is a schematic connection diagram of a temperature control system for a 3D glass hot bending mold according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the distribution of mold temperature measurement points for an embodiment of the present invention;
FIG. 4 is a sample vector diagram of a prediction model module according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without any inventive step, are within the scope of the present invention.
Furthermore, the following description of the various embodiments refers to the accompanying drawings, which illustrate specific embodiments in which the invention may be practiced. Directional phrases used in this disclosure, such as, for example, "upper," "lower," "front," "rear," "left," "right," "inner," "outer," "side," and the like, refer only to the orientation of the appended drawings and are, therefore, used herein for better and clearer illustration and understanding of the invention, and do not indicate or imply that the device or element so referred to must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
The embodiment of the invention discloses a temperature control method for a 3D glass hot bending die, which comprises the following steps of referring to the attached drawing 1:
step S110, obtaining a temperature rise curve of each temperature measurement point of the 3D glass hot bending mould, segmenting the temperature rise curve according to time intervals, sequentially carrying out segmented discrete control on each obtained time region, regarding the time region [ tm, tn ], and regarding the tn moment as the future moment, obtaining the theoretical temperature SUi (tn) of the ith temperature measurement point at the future moment of the time region [ tm, tn ];
step S120, detecting the instant temperature Vui (t) corresponding to the heated ith temperature measuring point in a time region [ tm, tn ];
step S130, establishing an LSTM prediction model of the mold temperature, collecting an instantaneous characteristic vector of the ith temperature measurement point of the 3D glass hot bending mold in a time region [ tm, tn ], inputting the instantaneous characteristic vector into the trained LSTM prediction model, and obtaining the predicted temperature Wui (tn) of the ith temperature measurement point at the future time of the time region [ tm, tn ];
step S140, calculating the difference between the theoretical temperature SUi (tn) and the instantaneous temperature Vui (t) to obtain an instantaneous temperature difference value, and calculating to obtain a main control quantity corresponding to the ith temperature measurement point by utilizing a PID control algorithm;
step S150, the predicted temperature Wui (tn) of the future time tn of the ith temperature measurement point and the theoretical temperature SUi (tn) are used as input, a fuzzy control method is adopted to calculate the power adjustment rate corresponding to the ith temperature measurement point, and the power adjustment rate is used for fine adjustment of the main control quantity.
According to the temperature control method of the 3D glass hot bending mold, the difference is made between the predicted temperature of the ith temperature measuring point of the mold at the next future moment of a time region [ tm, tn ] and the theoretical temperature, then the obtained temperature deviation signal is input into a mold sub-controller and used as a power compensation signal of a mold heating wire to adjust the power of a heating rod in real time, and therefore the main controller of the mold is finely adjusted; therefore, the control precision of the system is improved, and the dynamic performance of the temperature control process of the system is also improved.
The temperature rise curve and the LSTM prediction model are obtained through simulation experiments and/or processing experiments. In step S110, after a simulation experiment and a machining experiment, an optimal temperature rise curve of the mold is determined, and the temperature rise curve is segmented at intervals of 1 to 3 seconds. Taking the first measurement point of the mold as an example, the temperature rise curve after cutting is recorded as { SU1t1,SU1t2,…,SU1tnFor each time zone (e.g. [ t1, t2 ] in turn],…,[tn-1,tn]) Implementing piecewise discrete control for [ ti, tj]For temperature control in the time period of (d), the time tj is the future time corresponding to the temperature of the optimal mold temperature rise curve (SU 1)tj) Is the theoretical temperature at the next future instant in time for that time period.
In an embodiment of the present invention, the 3D glass hot bending mold includes an upper mold 100 and a lower mold 200 that are fitted to each other.
In step S130, the instantaneous feature vector includes a current position temperature, a current position first neighboring point temperature, a current position second neighboring point temperature, a current position third neighboring point temperature, a thermal bender internal cavity temperature, a thermal bender external temperature, a mold working time, and a mold type. The upper die LSTM prediction module collects multiple groups of characteristic vectors, wherein the multiple groups of characteristic vectors comprise the temperature of the current position of an upper die, the temperature of a first adjacent point of the current position of the upper die, the temperature of a second adjacent point of the current position of the upper die, the temperature of a third adjacent point of the current position of the upper die, the temperature of an inner cavity of a hot bending machine, the temperature of the outer part of the hot bending machine, the working time of the die and the type of the die. As shown in fig. 3, for the UD1 temperature measurement point of the upper die 100, the upper die current position first adjacent point, the upper die current position second adjacent point and the upper die current position third adjacent point are an upper die UD2 temperature measurement point, a lower die DD1 temperature measurement point and a lower die DD2 temperature measurement point, respectively; for the UD2 temperature measurement point of the upper die 100, the first adjacent point of the current position of the upper die, the second adjacent point of the current position of the upper die and the third adjacent point of the current position of the upper die are respectively an UD1 temperature measurement point of the upper die, a DD3 temperature measurement point of the upper die and a DD2 temperature measurement point of the lower die. Inputting the instantaneous feature vector collected in the [ tm, tn ] time zone of the ith temperature measurement point into the trained upper model LSTM prediction module to obtain the temperature output of the measurement point at the future tn moment, and marking the temperature output as Wui (tn).
In step S140, the calculation formula of the instantaneous temperature difference value eui (t) of the ith temperature measurement point at the future time of the time zone [ tm, tn ] is:
eUi(t)＝SUi(tn)-VUi(t)。
for the upper die, after the eui (t) is input through upper die PID control, the main module control quantity XUi (t) of the ith temperature measurement point of the upper die is obtained.
In step S140, the main control quantity xui (t) for the ith temperature measurement point is controlled by the PID controller and calculated by using the PID control algorithm, taking the above mold as an example. The PID controller consists of a proportional unit (P), an integral unit (I) and a differential unit (D), and the relationship between the input eUi (t) and the output XUi (t) of the ith temperature measuring point is as follows:
in the formula, eUi(t)、
Respectively, the error integral and the error differential term, k, of the corresponding first temperature measuring pointiUP,kiUI,kiUDRespectively, a proportionality coefficient, an integral coefficient and a differential coefficient corresponding to the ith temperature measurement point.
For the following mold as an example, the control quantity XDi (t) of the ith temperature measurement point is also controlled by a PID controller and calculated by adopting a PID control algorithm: the PID controller consists of a proportional unit (P), an integral unit (I) and a differential unit (D), and the relation between the ith temperature measuring point input eDi (t) and the output XDi (t) is as follows:
in the formula, eDi(t)，
Respectively an error, an error integral and an error differential term; k is a radical ofiDP,kiDI,kiDDThe proportional coefficient, the integral coefficient and the differential coefficient of the ith temperature measuring point of the lower die are respectively.
In step S150, the predicted temperature wui (tn) at the time tn in the future of the ith temperature measurement point and the desired temperature sui (tn) at the time tn in the future are input, the power adjustment rate of the upper mold heating rod corresponding to the temperature measurement point is controlled by a fuzzy control method, and the heat generation amount of the upper mold heating rod is controlled after merging with the upper mold main control amount xui (t). The control method of the lower die heating rod is similar and is not repeated.
In step S150, when the desired temperature SUi (tn) epsilon (0-300 ℃ C.) of the ith temperature measurement point at the future time tn and ABS (SUi (tn) -WUi (tn)) ≦ a ℃ C., if SUi (tn) is greater than WUi (tn), the power adjustment rate is (100+ a)%, and if SUi (tn) is less than WUi (tn), the power adjustment rate is (100-a)%.
Wherein, for the upper die, the theoretical temperature and the predicted temperature at the time tn in the future are used as input, and the power adjustment rate (increase or decrease) of the upper die heating rod is used as output, as shown in table 1; taking one example of the above, when the expected temperature sui (tn) epsilon (0-300 ℃) at the time tn in the future and ABS (sui (tn) -wui (tn)) ≦ 1 ℃, if sui (tn) is greater than wui (tn), the power adjustment rate of the upper mold heating rod is increased by 1%, which is 101% of the standard power (preferably 800W/heating rod), and if sui (tn) is less than wui (tn), the power adjustment rate of the upper mold heating rod is decreased by 1%, which is 99% of the standard power (preferably 800W/heating rod).
Fuzzy control method for die power adjustment rate in table 1
For the lower die, the desired temperature and the predicted temperature at the time tn in the future are used as input, and the power adjustment rate (increase or decrease) of the upper die heating rod is used as output, as shown in table 2; taking one example of the above, when the expected temperature SDi (tn) epsilon (0-300 ℃) at the time tn in the future and ABS (SDi (tn) -WDi (tn)) ≦ 3 ℃, if SDi (tn) is greater than WDi (tn), the power adjustment rate of the upper mold heating rod is increased by 3%, which is 103% of the standard power (preferably 800W/heating rod), and if SDi (tn) is less than Wui (tn), the power adjustment rate of the upper mold heating rod is decreased by 3%, which is 97% of the standard power (preferably 800W/heating rod).
TABLE 2 fuzzy control method for power regulation rate of lower die
Further, after step S150, the method further includes training the LSTM prediction model in real time, and replacing the current LSTM prediction model after the training of the LSTM prediction model is completed.
The training of the upper model LSTM prediction model comprises a pre-training part and a post-training part, wherein samples of the pre-training part and the post-training part are shown in FIG. 4 and comprise duration, characteristics and batches; the time length takes the sampling period of 40-100ms as an interval, discretization processing is carried out on the whole heating time, and quantized data on a time scale are obtained; the characteristics comprise an upper die characteristic and a lower die characteristic, wherein the upper die characteristic comprises an upper die current position temperature, an upper die current position first adjacent point temperature, an upper die current position second adjacent point temperature, an upper die current position third adjacent point temperature, a hot bending machine inner cavity temperature, a hot bending machine outer temperature, a die working time and a die type; the batches were 32 batches, with 16 pre-training batches and 16 post-training batches. The pre-training data of the upper model LSTM prediction model is from simulation or experimental data; when the post-training data batch of the upper mould LSTM prediction model is less than 16 batches, the feature vector at the current moment is randomly extracted from the pre-training 16 batches of data to replace, so that the total batch is 32 batches, and the upper mould LSTM prediction model can be normally trained. The post-training 16 batches of data of the upper model LSTM prediction model are divided into 3 updating areas which are respectively an optimal prediction area, a worst prediction area and a latest prediction area, the optimal prediction area is 6 batches of data, the worst prediction area is 6 batches of data, and the latest prediction area is 4 batches of data. And after one round of 3D glass hot bending forming is finished, the upper mold LSTM prediction model evaluates the prediction temperature of each discrete time point, calculates the prediction error of the temperature, and stores the prediction error in the temperature. The optimal prediction area is a batch with the best prediction error at the current time after the past hot bending forming, the worst prediction area is a batch with the worst prediction error at the current time after the past hot bending forming, and the latest prediction area is the last latest batch. The upper module LSTM prediction model is realized by an ARM processor and a related data memory, and sample data of pre-training and post-training and the prediction model thereof are stored. And the upper mold LSTM prediction model trains the prediction model in real time at the background, and after the prediction model is trained, the current prediction model is replaced when the next round of 3D glass hot bending forming is finished. The lower LSTM prediction model is similar to the upper LSTM prediction model and is not described again.
By the above, the output of the control quantity of the upper die main loop and the output of the control quantity of the lower die main loop can be obtained, the conventional error of the upper die main loop and the conventional error of the lower die main loop are adjusted, meanwhile, the auxiliary loop is utilized, the temperature deviation of the upper die heating rod and the lower die heating rod at the future moment is adjusted, and therefore the problem that temperature regulation is not accurate due to the fact that the heat flow density is not uniform under the influence of the die space structure can be effectively solved.
In addition, in order to avoid steady-state oscillation of the system, the system adopts a control strategy of automatic switching of the PID parameters in a partition area, and in order to improve the sensitivity and the precision of control, the optimal sampling period of the system is 40-100 ms. PID parameters of each sub-region are determined in advance by simulation or experiment and are stored in a control chip (preferably, the realization chip is a low-energy ARM processor) in the upper die main loop control quantity output and the lower die main loop.
The invention also provides a system of the temperature control method for the 3D glass hot bending mold, referring to fig. 2, including:
the theoretical model module 10 is configured to obtain a temperature rise curve of each temperature measurement point of the 3D glass hot bending mold, segment the temperature rise curve according to a time interval, sequentially perform segmented discrete control on each obtained time region, and obtain a theoretical temperature sui (tn) of an ith temperature measurement point at a future time of the time region [ tm, tn ] for the time region [ tm, tn ] with the time tn as the future time;
an instantaneous detection module 20, configured to detect an instantaneous temperature vui (t) corresponding to the heated ith temperature measurement point in the time region [ tm, tn ];
the prediction model module 30 is used for establishing an LSTM prediction model of the mold temperature, acquiring an instantaneous feature vector of the ith temperature measurement point of the 3D glass hot bending mold in a time region [ tm, tn ], inputting the instantaneous feature vector into the trained LSTM prediction model, and obtaining a predicted temperature Wui (tn) of the ith temperature measurement point at the future time of the time region [ tm, tn ];
the main control module 40 is configured to calculate a difference between the theoretical temperature sui (tn) and the instantaneous temperature vui (t) to obtain an instantaneous temperature difference value, and calculate a main control quantity corresponding to the ith temperature measurement point by using a PID control algorithm;
and the auxiliary control module 50 is configured to use the predicted temperature wui (tn) and the theoretical temperature sui (tn) of the ith temperature measurement point at the future time tn as inputs, calculate a power adjustment rate corresponding to the ith temperature measurement point by using a fuzzy control method, and finely adjust the main control quantity by using the power adjustment rate, so as to control the power of the mold heating rod 60.
In practical application of the temperature control system for the 3D glass hot bending die provided by the embodiment of the invention, for small and medium 3C glass components (such as a 4-6 inch smart phone glass cover plate), the number of the preferable upper die heating rods (built-in resistance wires) is 4, the corresponding positions in the middle of the upper die are respectively provided with one temperature sensor, the number of the lower die heating rods (built-in resistance wires) is 5, and the corresponding positions in the middle of the lower die are also respectively provided with one temperature sensor.
The prediction model module comprises an upper module LSTM prediction module and a lower module LSTM prediction module. The upper mould LSTM prediction module has a temperature prediction function and predicts the temperature of the position where each sensor of the upper mould is arranged, and a plurality of groups (the preferred group number is 80-120) of feature vectors (the current time and the historical time) input by the upper mould LSTM prediction module comprise: the temperature of the current position of the upper die, the temperature of a first adjacent point of the current position of the upper die, the temperature of a second adjacent point of the current position of the upper die, the temperature of a third adjacent point of the current position of the upper die, the temperature of an inner cavity of a hot bending machine, the temperature of the outside of the hot bending machine, the working time of the die and the type of the die. The lower die LSTM prediction module also has a temperature prediction function and predicts the temperature of the position where each sensor of the lower die is arranged, and the lower die LSTM prediction module inputs a plurality of groups (the preferred group number is 80-120) of feature vectors (the current time and the historical time) including: the temperature of the current position of the lower die, the temperature of a first adjacent point of the current position of the lower die, the temperature of a second adjacent point of the current position of the lower die, the temperature of a third adjacent point of the current position of the lower die, the temperature of an inner cavity of a hot bending machine, the temperature of the outside of the hot bending machine, the working time of the die and the type of the die.
The main control module comprises a PID controller and a heating device, the auxiliary control module comprises a fuzzy controller, and the output ends of the PID controller and the fuzzy controller are respectively connected with the input end of the heating device. Specifically, it has last mould main control unit and last mould heating rod to go up mould main control module, it has last mould sub-control unit to go up mould sub-control module, will go up mould main control unit and last mould sub-control unit's output and be connected with the input of last mould heating rod respectively, and it needs to carry out independent control to last mould heating rod to go up mould main control module very much. The lower mould main control module is provided with a lower mould main controller and a lower mould heating rod, the lower mould auxiliary control module is provided with a lower mould auxiliary controller, the output ends of the lower mould main controller and the lower mould auxiliary controller are respectively connected with the input end of the lower mould heating rod, and particularly, the lower mould main control module needs to independently control the lower mould heating rod. The upper die main controller and the lower die main controller are PID controllers; and the upper die sub-controller and the lower die sub-controller are fuzzy controllers.
The above is not limited to the embodiments of the present invention, the above description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention, and the above specific embodiments are only schematic and are not limiting. Any person skilled in the art can substitute or change the technical scheme and the inventive concept of the present invention equally within the scope of the present invention.
Claims (10)
1. A temperature control method for a 3D glass hot bending die is characterized by comprising the following steps:
step S1, obtaining a temperature rise curve of each temperature measurement point of the 3D glass hot bending mould, segmenting the temperature rise curve according to time intervals, sequentially carrying out segmented discrete control on each obtained time region, obtaining theoretical temperature SUi (tn) of the ith temperature measurement point at the future time of the time region [ tm, tn ] for the time region [ tm, tn ] with the time tn as the future time, and detecting the instant temperature Vui (t) corresponding to the heated ith temperature measurement point at the time region [ tm, tn ];
step S2, establishing an LSTM prediction model of the mold temperature, collecting an instantaneous feature vector of the ith temperature measurement point of the 3D glass hot bending mold in a time region [ tm, tn ], inputting the instantaneous feature vector into the trained LSTM prediction model, and obtaining the predicted temperature Wui (tn) of the ith temperature measurement point at the future time of the time region [ tm, tn ];
step S3, calculating the difference between the theoretical temperature SUi (tn) and the instantaneous temperature Vui (t) to obtain an instantaneous temperature difference value, and calculating to obtain a main control quantity corresponding to the ith temperature measurement point by using a PID control algorithm;
step S4, using the predicted temperature wui (tn) and the theoretical temperature sui (tn) at the future time tn of the ith temperature measurement point as inputs, calculating the power adjustment rate corresponding to the ith temperature measurement point by using a fuzzy control method, and fine-tuning the main control quantity by using the power adjustment rate.
2. The 3D glass hot-bending mold temperature control method according to claim 1, wherein: the temperature rise curve and the LSTM prediction model are obtained through simulation experiments and/or processing experiments.
3. The 3D glass hot-bending mold temperature control method according to claim 1, wherein the 3D glass hot-bending mold comprises an upper mold and a lower mold which are matched with each other.
4. The method for controlling the temperature of a 3D glass hot bending mold according to claim 1, wherein in step S2, the instantaneous feature vector includes a current position temperature, a current position first neighboring point temperature, a current position second neighboring point temperature, a current position third neighboring point temperature, a hot bending machine inner cavity temperature, a hot bending machine outer temperature, a mold working time and a mold type.
5. The method for controlling the temperature of a 3D glass hot-bending mold according to claim 1, wherein in step S3, the calculation formula of the instantaneous temperature difference value eUi (t) of the ith temperature measuring point at the future time of the time zone [ tm, tn ] is:
eUi(t)＝SUi(tn)-VUi(t)。
6. the method of claim 5, wherein in step S3, the i-th temperature measurement point input eUi (t) is related to the output XUi (t) by:
in the formula, eUi(t)、
Respectively, an error integral and an error differential term, k, corresponding to the ith temperature measurement pointiUP,kiUI,kiUDRespectively, a proportionality coefficient, an integral coefficient and a differential coefficient corresponding to the ith temperature measurement point.
7. The method for controlling the temperature of a 3D glass hot-bending mold according to claim 1, wherein in step S4, when SUi (tn) epsilon (0 ℃ -300 ℃) which is the expected temperature of the ith temperature measurement point at the future time tn and ABS (SUi (tn) -WUi (tn)) ≦ a ℃, if SUi (tn) is greater than WUi (tn), the power adjustment rate is (100+ a)%, and if SUi (tn) is less than WUi (tn), the power adjustment rate is (100-a)%.
8. The 3D glass hot-bending mold temperature control method according to claim 1, further comprising training the LSTM prediction model in real time after step S4, and replacing the current LSTM prediction model when the LSTM prediction model training is completed.
9. A system for applying the temperature control method for the 3D glass hot bending mold according to any one of claims 1 to 8, comprising:
the theoretical model module is used for obtaining a temperature rise curve of each temperature measurement point of the 3D glass hot bending mould, segmenting the temperature rise curve according to time intervals, sequentially carrying out segmented discrete control on each obtained time region, and obtaining theoretical temperature SUi (tn) of the ith temperature measurement point at the future time of the time region [ tm, tn ] for the time region [ tm, tn ] with the time tn as the future time;
the instantaneous detection module is used for detecting the instantaneous temperature Vui (t) corresponding to the heated ith temperature measurement point in the time region [ tm, tn ];
the prediction model module is used for establishing an LSTM prediction model of the mold temperature, acquiring an instantaneous feature vector of the ith temperature measurement point of the 3D glass hot bending mold in a time region [ tm, tn ], inputting the instantaneous feature vector into the trained LSTM prediction model, and obtaining the predicted temperature Wui (tn) of the ith temperature measurement point at the future time of the time region [ tm, tn ];
the main control module is used for calculating the difference between the theoretical temperature SUi (tn) and the instantaneous temperature Vui (t) to obtain an instantaneous temperature difference value, and calculating a main control quantity corresponding to the ith temperature measurement point by utilizing a PID control algorithm;
and the auxiliary control module is used for taking the predicted temperature Wui (tn) of the future time tn of the ith temperature measurement point and the theoretical temperature SUi (tn) as input, calculating the power adjustment rate corresponding to the ith temperature measurement point by adopting a fuzzy control method, and finely adjusting the main control quantity by utilizing the power adjustment rate.
10. The 3D glass hot-bending mold temperature control system according to claim 9, wherein the main control module comprises a PID controller and a heating device, the secondary control module comprises a fuzzy controller, and output ends of the PID controller and the fuzzy controller are respectively connected with an input end of the heating device.
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