CN112915594A - Artificial intelligence-based vacuum stirring, defoaming and material preparation method and system - Google Patents
Artificial intelligence-based vacuum stirring, defoaming and material preparation method and system Download PDFInfo
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Abstract
The invention discloses a vacuum stirring, defoaming and material preparing method and system based on artificial intelligence, wherein the method comprises the following steps: obtaining a first incident position; obtaining a first emergent position; obtaining a standard emergent position of the first glue material according to the refractive index information of the first glue material; obtaining first angle information; obtaining second angle information; inputting the first angle information and the second angle information into a vacuum prejudgment model to obtain first vacuum grade information of the first glue material; judging whether the first vacuum grade information of the first glue material meets the preset grade threshold value or not, and obtaining a first judgment result; determining whether a first defoaming instruction is obtained or not according to the first judgment result; and according to the first defoaming instruction, defoaming the first glue material. The technical problem that in the prior art, air bubbles exist in glue during the glue dispensing process, so that empty spots are caused to affect the product quality is solved.
Description
Technical Field
The invention relates to the field of material deaeration, in particular to a vacuum stirring deaeration material preparation method and system based on artificial intelligence.
Background
The defoaming and stirring device is mainly applied to the field of mixing and stirring materials of products in high, sharp and fine fields such as electronic components, nano powder materials, fine chemical materials, printed electronic materials, electronic packaging materials, new energy materials and the like, such as fluorescent powder, silica gel, silver paste, adhesives, printing ink and the like, and can be used for stirring liquid, solid, liquid and liquid substances and solid substances.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
in the prior art, air bubbles exist in glue during the glue dispensing process, so that the technical problem that the product quality is influenced by the empty points is caused.
Disclosure of Invention
The embodiment of the application provides a vacuum stirring defoaming material preparation method and system based on artificial intelligence, solves the technical problem that in the prior art, when glue is in a dispensing process, air bubbles exist, so that the air bubbles affect the product quality, achieves the technical effects of more professional and accurate bubble detection, improves the material defoaming efficiency, and further satisfies the product process quality.
In view of the above problems, the present application provides a method and a system for preparing materials for vacuum stirring and defoaming based on artificial intelligence.
In a first aspect, an embodiment of the present application provides a vacuum stirring, defoaming and material preparing method based on artificial intelligence, where the method includes: the first linear light source irradiates a first glue material in the glue dispensing device to obtain a first incident position; obtaining a first emergent position; obtaining refractive index information of the first glue material; obtaining a standard emergent position of the first glue material according to the refractive index information of the first glue material; obtaining first angle information according to the first incident position and the first emergent position; obtaining second angle information according to the first input position and the standard emergent position; inputting the first angle information and the second angle information into a vacuum prejudgment model to obtain first vacuum grade information of the first glue material; obtaining a predetermined grade threshold; judging whether the first vacuum grade information of the first glue material meets the preset grade threshold value or not, and obtaining a first judgment result; determining whether a first defoaming instruction is obtained or not according to the first judgment result; and according to the first defoaming instruction, defoaming the first glue material.
On the other hand, this application still provides a vacuum mixing deaeration system of prepareeing material based on artificial intelligence, the system includes: the first obtaining unit is used for irradiating a first glue material in the gluing device by a first linear light source to obtain a first incident position; a second obtaining unit for obtaining a first exit position; a third obtaining unit, configured to obtain refractive index information of the first glue material; a fourth obtaining unit, configured to obtain a standard exit position of the first glue material according to refractive index information of the first glue material; a fifth obtaining unit configured to obtain first angle information according to the first incident position and the first exit position; a sixth obtaining unit, configured to obtain second angle information according to the first incident position and the standard exit position; a seventh obtaining unit, configured to input the first angle information and the second angle information into a vacuum prejudgment model, and obtain first vacuum level information of the first glue material; an eighth obtaining unit configured to obtain a predetermined level threshold; the first judging unit is used for judging whether the first vacuum grade information of the first glue material meets the preset grade threshold value or not to obtain a first judging result; a first determining unit, configured to determine whether to obtain a first defoaming instruction according to the first determination result; and the first processing unit is used for carrying out defoaming processing on the first glue material according to the first defoaming instruction.
In a third aspect, the present invention provides an artificial intelligence-based vacuum stirring debubbling stock preparation system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the first incident position and the first emergent position are obtained; obtaining a standard emergent position of the first glue material according to the refractive index information of the first glue material; obtaining first angle information and second angle information; inputting the first angle information and the second angle information into a vacuum prejudgment model to obtain first vacuum grade information of the first glue material; judging whether the first vacuum grade information of the first glue material meets the preset grade threshold value or not, and obtaining a first judgment result; determining whether a first defoaming instruction is obtained or not according to the first judgment result; according to the first defoaming instruction, defoaming treatment is carried out on the first glue material, so that the technical effects of being more professional and accurate in bubble detection, improving material defoaming efficiency and further meeting product process quality are achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart of a method for preparing materials for vacuum stirring and defoaming based on artificial intelligence in an embodiment of the present application;
fig. 2 is a schematic structural diagram of a vacuum stirring defoaming material preparation system based on artificial intelligence in an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a seventh obtaining unit 17, an eighth obtaining unit 18, a first judging unit 19, a first determining unit 20, a first processing unit 21, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
The embodiment of the application provides a vacuum stirring defoaming material preparation method and system based on artificial intelligence, solves the technical problem that in the prior art, when glue is in a dispensing process, air bubbles exist, so that the air bubbles affect the product quality, achieves the technical effects of more professional and accurate bubble detection, improves the material defoaming efficiency, and further satisfies the product process quality. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
The defoaming and stirring device is mainly applied to the field of mixing and stirring materials of products in high, sharp and fine fields such as electronic components, nano powder materials, fine chemical materials, printed electronic materials, electronic packaging materials, new energy materials and the like, such as fluorescent powder, silica gel, silver paste, adhesives, printing ink and the like, and can be used for stirring liquid, solid, liquid and liquid substances and solid substances. However, in the prior art, air bubbles exist in the glue dispensing process, so that the technical problem that the product quality is influenced by the empty spots is caused.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the embodiment of the application provides a vacuum stirring, defoaming and material preparing method based on artificial intelligence, which comprises the following steps: the first linear light source irradiates a first glue material in the glue dispensing device to obtain a first incident position; obtaining a first emergent position; obtaining refractive index information of the first glue material; obtaining a standard emergent position of the first glue material according to the refractive index information of the first glue material; obtaining first angle information according to the first incident position and the first emergent position; obtaining second angle information according to the first input position and the standard emergent position; inputting the first angle information and the second angle information into a vacuum prejudgment model to obtain first vacuum grade information of the first glue material; obtaining a predetermined grade threshold; judging whether the first vacuum grade information of the first glue material meets the preset grade threshold value or not, and obtaining a first judgment result; determining whether a first defoaming instruction is obtained or not according to the first judgment result; and according to the first defoaming instruction, defoaming the first glue material.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a vacuum stirring, defoaming and material preparing method based on artificial intelligence, where the method includes:
step S100: the first linear light source irradiates a first glue material in the glue dispensing device to obtain a first incident position;
step S200: obtaining a first emergent position;
specifically, the first linear light source is a point-like light emitting source capable of emitting continuous visible light, light of the point-like light emitting source is linearly transmitted in one direction only in space, the glue dispensing device is machine equipment used for dispensing, coating, spraying, filling and sprinkling glue in industrial application, the glue dispensing device is widely applied to any working procedures related to glue dispensing technology and fluid control in industrial production, and glue dispensers are divided into three major categories: single component dispensing machines such as dispensing controllers, desktop dispensing machines and floor-standing dispensing machines; two-component dispensing machines, such as semi-automatic two-component dispensing machines and full-automatic two-component dispensing machines; a non-standard dispenser. The first glue material is a material of intermediate glue for connecting two materials, is mostly a water aqua, belongs to the fine chemical industry, and is various in variety, and common instant glue such as 1203 instant glue-ethyl cyanoacrylate strong instant adhesive, epoxy resin bonding, anaerobic glue, UV glue such as ultraviolet light curing, hot melt glue, pressure sensitive glue, latex glue and the like. The first incidence position is a position where the first linear light source irradiates a first glue material in the glue dispensing device and comprises an included angle between incident light and a normal line of an incidence surface, a reflection angle is equal to an incidence angle, the first emergent position is a position where the first linear light source emits light and comprises an included angle between emergent light and a normal line of an emergent surface, an angle formed between the emergent light and the normal line of the emergent surface is an emergent angle, and the emergent light comprises reflected light and refracted light.
Step S300: obtaining refractive index information of the first glue material;
specifically, the refractive index information of the first glue material is the ratio of the propagation speed of light in vacuum to the propagation speed of light in the medium, i.e. the first glue material, the higher the refractive index of the material is, the stronger the ability of incident light to refract is, the refractive index is also related to frequency, so called dispersion phenomenon, light is emitted from a relatively optically dense medium to a relatively optically sparse medium, the refractive index of the medium is usually determined by experiments, there are various measuring methods, the minimum deviation angle method or the self-collimation method is commonly used, and the value of the refractive index of a certain object, for example, water is 1.33, which means that the refractive index of sodium yellow light (wavelength is 5893 × 10-10 m).
Step S400: obtaining a standard emergent position of the first glue material according to the refractive index information of the first glue material;
specifically, the standard exit position is an exit position calculated according to the refractive index information of the first glue material, for example, the first linear light source irradiates the first glue material, that is, when light enters the medium from the air of the medium and is refracted in the first glue material, a ratio of a sine of an incident angle to a sine of a refraction angle is called a refractive index of the first glue material, so that a refraction exit angle, that is, the standard exit position of the first glue material, is calculated according to the refractive index information.
Step S500: obtaining first angle information according to the first incident position and the first emergent position;
step S600: obtaining second angle information according to the first incident position and the standard emergent position;
specifically, the first angle is refraction angle information obtained from the measured first incident position and the first exit position, the angle formed with the normal of the exit surface is an exit refraction angle, the second angle is refraction angle information obtained by calculation from the first input position and the standard exit position, and the refraction angle is calculated according to the ratio of the incidence angle and the sine of the refraction angle, namely the refraction index of the first glue material.
Step S700: inputting the first angle information and the second angle information into a vacuum prejudgment model to obtain first vacuum grade information of the first glue material;
further, in a step S700 of the embodiment of the present application, the inputting the first angle information and the second angle information into a vacuum prejudgment model to obtain first vacuum level information of the first glue material further includes:
step S710: inputting the first angle information and the second angle information as input information into a vacuum prejudgment model for training, wherein the vacuum prejudgment model is obtained by training a plurality of groups of training data, and each group of data in the plurality of groups of training data comprises: said first and second angle information and identification information for identifying a first vacuum level of a first glue material;
step S720: and obtaining output information of the vacuum prejudging model, wherein the output information comprises first vacuum grade information of the first glue material.
Specifically, the first training model is a Neural network model, which is a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely connecting a large number of simple processing units (called neurons), which reflects many basic features of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (ANN), are a description of the first-order characteristics of the human brain system. Briefly, it is a mathematical model. And inputting the first angle information and the second angle information into a neural network model through training of a large amount of training data, and outputting first vacuum level information of the first glue material.
More specifically, the training process is essentially a supervised learning process, each group of supervised data includes the first angle information, the second angle information and identification information for identifying a first vacuum level of the first glue material, the first angle information and the second angle information are input into a neural network model, the neural network model performs continuous self-correction and adjustment according to the identification information for identifying the first vacuum level of the first glue material, and the group of data supervised learning is ended until the obtained output information is consistent with the identification information, and the next group of data supervised learning is performed; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through the supervised learning of the neural network model, the neural network model can process the input information more accurately, so that the output first vacuum level information is more reasonable and accurate, and the technical effect of enabling the establishment of the vacuum model to be finer and more accurate by combining the refraction angle information of the glue material is achieved.
Step S800: obtaining a predetermined grade threshold;
step S900: judging whether the first vacuum grade information of the first glue material meets the preset grade threshold value or not, and obtaining a first judgment result;
specifically, the predetermined level threshold is a preset material vacuum level range, that is, a level range of the material bubbles allowed in the preset range, and the first determination result is that whether the first vacuum level information of the first glue material meets the predetermined level threshold is determined by comparing the output first vacuum level information of the first glue material with the predetermined level threshold.
Step S1000: determining whether a first defoaming instruction is obtained or not according to the first judgment result;
further, in step S1000 in this embodiment of the present application, determining whether to obtain a first defoaming instruction according to the first determination result, further includes:
step S1010: if the first judgment result is that the first vacuum grade information of the first glue material meets the preset grade threshold, determining that the first glue material does not have first bubbles, and forbidding obtaining of the first defoaming instruction;
step S1020: and if the first judgment result shows that the first vacuum grade information of the first glue material does not meet the preset grade threshold, determining that the first glue material has first bubbles, and obtaining the first defoaming instruction.
Specifically, if the first determination result is that the first vacuum level information of the first glue material satisfies the predetermined level threshold, the first glue material does not have the first bubble, that is, the first glue material is fully and uniformly mixed without bubbles, the obtaining of the first defoaming instruction is prohibited, and if the first determination result is that the first vacuum level information of the first glue material does not satisfy the predetermined level threshold, it is determined that the first glue material has the first bubble, that is, the first glue material does not have bubbles sufficiently and uniformly mixed, defoaming processing needs to be performed, the first defoaming instruction is obtained, and a technical effect of determining whether the material has bubbles by determining the vacuum level so that the detection of the material bubbles is more professional and accurate is achieved.
Step S1100: and according to the first defoaming instruction, defoaming the first glue material.
Further, in step S1100 in this embodiment of the present application, the defoaming the first glue material according to the first defoaming instruction further includes:
step S1110: acquiring the first bubble position information according to the first defoaming instruction;
step S1120: obtaining a first defoaming scheme according to the first bubble position information;
step S1130: and according to the first defoaming scheme, defoaming the first glue material.
Specifically, the first bubble position information is position information of the first bubble in the first glue material, the first defoaming scheme is a scheme for defoaming, i.e., removing the bubble, the first glue material is defoamed according to the first defoaming scheme, and a common defoaming method includes: heating and defoaming: the bubbles float to the surface and are broken by utilizing the temperature rise and the viscosity reduction; vacuum defoaming: the pressure outside is reduced, the volume of the bubbles is increased, and the bubbles float to the surface and are broken; single-shaft centrifugal defoaming: the bubbles with light specific gravity are enabled to run to the liquid surface by utilizing centrifugal force, but the filling material with high specific gravity is doubtful to be precipitated to the bottom; biaxial centrifugal defoaming (planetary defoaming): as with the uniaxial centrifugation principle, however, the filler having a large specific gravity is redispersed in the resin by the component force of revolution without precipitation; biaxial centrifugation and vacuum defoaming: the defoaming method with the best effect can lead the bubbles to quickly run to the surface to be broken; ultrasonic defoaming: the bubbles are separated from the adhered object by micro-amplitude rapid vibration, and the micro-amplitude rapid vibration is usually only suitable for liquid with low viscosity.
Further, in step S1110 of this embodiment, where the first bubble position information is obtained according to the first defoaming instruction, the method further includes:
step S1111: obtaining a first incident angle of the first linear light source;
step S1112: obtaining a first exit angle of the first linear light source;
step S1113: obtaining first input information according to the first incidence position and the first incidence angle;
step S1114: obtaining second input information according to the first emergent position and the first emergent angle;
step S1115: and inputting the first input information and the second input information into a bubble positioning model to obtain first bubble position information, wherein the first bubble position information is position information of bubbles in the first glue material.
Specifically, the first incident angle of the first linear light source is the degree of the included angle between the incident light of the first linear light source and the normal of the incident surface, the first emergent angle of the first linear light source is the included angle between the emergent ray of the first linear light source and the surface normal, the bubble positioning model is a neural network model, inputting the first input information and the second input information into a neural network model through training of a large amount of training data, outputting first bubble position information, and performing supervised learning on the neural network model, thereby the input information processed by the neural network model is more accurate, the output first bubble position information is more reasonable and accurate, and further, the technical effect of combining the incident angle information and the emergent angle information of the linear light source to enable the obtained bubble position information to be more accurate is achieved.
Further, step S1130 in the embodiment of the present application further includes:
step S1131: obtaining standard density information of the first glue material;
step S1132: obtaining first weight information and first volume information of the first glue material in the glue dispensing device;
step S1133: obtaining standard volume information of the first glue material according to the standard density information;
step S1134: obtaining volume difference information according to the first volume information and the standard volume information;
step S1135: and obtaining first volume information of the first bubble according to the volume difference information.
Specifically, the standard density information of the first glue material is the mass of the first glue material with a volume of 1 cubic meter at 20 ℃ under the standard atmospheric pressure, that is, the standard material density of the material, the first weight information is the weight information of the first glue material in the glue dispensing device, the first volume information is the volume information of the first glue material in the glue dispensing device, that is, the size of the space occupied by the glue material, the standard volume information of the first glue material is the standard volume calculated according to the standard density information and the first weight information, the volume difference information is the volume difference between the first volume information of the first glue material and the standard volume information, and the first volume information of the first air bubbles is the volume difference information of the first glue material, the technical effect of determining the size of the bubbles by calculating the volume difference of the materials so as to better perform defoaming treatment is achieved.
Further, step S1135 in this embodiment of the present application further includes:
step S11351: obtaining a second defoaming scheme according to the first volume information of the first bubbles and the first bubble position information;
step S11352: and according to the second defoaming scheme, defoaming the first glue material.
Specifically, the second defoaming scheme is to perform vacuum defoaming treatment on the first air bubbles in the first glue material according to the first volume information of the first air bubbles and the first air bubble position information, so as to perform defoaming treatment on the material according to the positions and volumes of the air bubbles, so that the defoaming effect is more accurate, and the technical effect of material defoaming efficiency is improved.
Further, obtaining the predetermined level threshold, step S800 in this embodiment of the present application further includes:
step S810: obtaining a first production product quality requirement;
step S820: obtaining a first predetermined level threshold according to the first production product quality requirement;
step S830: obtaining a first production temperature and a first production humidity;
step S840: inputting the first production temperature and the first production humidity as input information into a bubble level threshold model to obtain a bubble level correction parameter;
step S850: correcting the preset grade threshold according to the bubble grade correction parameter;
specifically, the first production product quality requirement is requirement information that satisfies the production product quality, the first predetermined level threshold is a reasonable range of bubbles that can be allowed according to the first production product quality requirement, the first production temperature and the first production humidity are ambient temperature and humidity for producing the first production product, the bubble level comparison model is a neural network model, the first production temperature and the first production humidity are input into the neural network model through training of a large amount of training data, the bubble level correction parameter is output, the neural network model processes the input information more accurately through supervised learning of the neural network model, the output bubble level correction parameter is more reasonable and accurate, and the predetermined level threshold after correction is obtained according to the bubble level correction parameter, and further, the technical effect that the bubble preset grade threshold value is more accurate by combining the product quality requirement and the production environment is achieved.
To sum up, the vacuum stirring, defoaming and material preparation method and system based on artificial intelligence provided by the embodiment of the application have the following technical effects:
1. the first incident position and the first emergent position are obtained; obtaining a standard emergent position of the first glue material according to the refractive index information of the first glue material; obtaining first angle information and second angle information; inputting the first angle information and the second angle information into a vacuum prejudgment model to obtain first vacuum grade information of the first glue material; judging whether the first vacuum grade information of the first glue material meets the preset grade threshold value or not, and obtaining a first judgment result; determining whether a first defoaming instruction is obtained or not according to the first judgment result; according to the first defoaming instruction, defoaming treatment is carried out on the first glue material, so that the technical effects of being more professional and accurate in bubble detection, improving material defoaming efficiency and further meeting product process quality are achieved.
2. Because the mode of inputting the first angle information and the second angle information into the neural network model is adopted, the output first vacuum grade information is more reasonable and accurate, and the technical effect of enabling the establishment of the vacuum model to be finer and more accurate by combining the refraction angle information of the glue material is achieved.
3. The technical effects of determining the vacuum level, judging whether the material has bubbles or not, and defoaming the material according to the scheme of combining the position and the volume of the bubbles are achieved, so that the detection of the material bubbles is more professional and accurate, the defoaming effect is more accurate, and the defoaming efficiency of the material is improved.
Example two
Based on the same inventive concept as the vacuum stirring, defoaming and material preparing method based on artificial intelligence in the foregoing embodiment, the present invention further provides a vacuum stirring, defoaming and material preparing system based on artificial intelligence, as shown in fig. 2, the system includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to irradiate a first glue material in a gluing device with a first linear light source to obtain a first incident position;
a second obtaining unit 12, the second obtaining unit 12 being configured to obtain a first exit position;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain refractive index information of the first glue material;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to obtain a standard exit position of the first glue material according to the refractive index information of the first glue material;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to obtain first angle information according to the first incident position and the first exit position;
a sixth obtaining unit 16, where the sixth obtaining unit 16 is configured to obtain second angle information according to the first incident position and the standard exit position;
a seventh obtaining unit 17, where the seventh obtaining unit 17 is configured to input the first angle information and the second angle information into a vacuum prejudgment model, so as to obtain first vacuum level information of the first glue material;
an eighth obtaining unit 18, wherein the eighth obtaining unit 18 is configured to obtain a predetermined level threshold;
the first judging unit 19 is configured to judge whether the first vacuum level information of the first glue material meets the predetermined level threshold, and obtain a first judgment result;
a first determining unit 20, where the first determining unit 20 is configured to determine whether to obtain a first defoaming instruction according to the first determination result;
the first processing unit 21 is configured to perform defoaming processing on the first glue material according to the first defoaming instruction.
Further, the system further comprises:
a second determining unit, configured to determine that the first glue material does not have the first bubble if the first determination result is that the first vacuum level information of the first glue material satisfies the predetermined level threshold, and prohibit obtaining the first defoaming instruction;
a third determining unit, configured to determine that the first glue material has first bubbles if the first determination result is that the first vacuum level information of the first glue material does not meet the predetermined level threshold, and obtain the first defoaming instruction.
Further, the system further comprises:
a first input unit, configured to input the first angle information and the second angle information as input information into a vacuum prejudgment model for training, where the vacuum prejudgment model is obtained by training multiple sets of training data, and each set of data in the multiple sets of training data includes: said first and second angle information and identification information for identifying a first vacuum level of a first glue material;
a ninth obtaining unit, configured to obtain output information of the vacuum prejudgment model, where the output information includes first vacuum level information of the first glue material.
Further, the system further comprises:
a tenth obtaining unit, configured to obtain the first bubble position information according to the first defoaming instruction;
an eleventh obtaining unit, configured to obtain a first defoaming scheme according to the first bubble position information;
and the second processing unit is used for carrying out defoaming treatment on the first glue material according to the first defoaming scheme.
Further, the system further comprises:
a twelfth obtaining unit for obtaining a first incident angle of the first linear light source;
a thirteenth obtaining unit for obtaining a first exit angle of the first linear light source;
a fourteenth obtaining unit configured to obtain first input information from the first incident position and the first incident angle;
a fifteenth obtaining unit, configured to obtain second input information according to the first exit position and the first exit angle;
a sixteenth obtaining unit, configured to input the first input information and the second input information into a bubble positioning model, and obtain first bubble position information, where the first bubble position information is position information of bubbles in the first glue material.
Further, the system further comprises:
a seventeenth obtaining unit, configured to obtain standard density information of the first glue material;
an eighteenth obtaining unit, configured to obtain first weight information and first volume information of the first glue material in the glue dispensing device;
a nineteenth obtaining unit, configured to obtain standard volume information of the first glue material according to the standard density information;
a twentieth obtaining unit configured to obtain volume difference information from the first volume information and the standard volume information;
a twenty-first obtaining unit, configured to obtain first volume information of the first bubble according to the volume difference information.
Further, the system further comprises:
a twenty-second obtaining unit, configured to obtain a second defoaming scheme according to the first volume information of the first bubble and the first bubble position information;
and the third processing unit is used for carrying out defoaming treatment on the first glue material according to the second defoaming scheme.
Various changes and specific examples of the artificial intelligence based vacuum stirring debubbling stock preparation method in the first embodiment of fig. 1 are also applicable to the artificial intelligence based vacuum stirring debubbling stock preparation system of this embodiment, and through the foregoing detailed description of the artificial intelligence based vacuum stirring debubbling stock preparation method, those skilled in the art can clearly know that an implementation method of the artificial intelligence based vacuum stirring debubbling stock preparation system in this embodiment is not described in detail herein for the sake of brevity of the description.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of the artificial intelligence-based vacuum stirring, defoaming and material preparing method in the foregoing embodiments, the present invention further provides an artificial intelligence-based vacuum stirring, defoaming and material preparing system, on which a computer program is stored, and when the program is executed by a processor, the computer program implements the steps of any one of the artificial intelligence-based vacuum stirring, defoaming and material preparing methods.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
The embodiment of the invention provides a vacuum stirring, defoaming and material preparing method based on artificial intelligence, which comprises the following steps: the first linear light source irradiates a first glue material in the glue dispensing device to obtain a first incident position; obtaining a first emergent position; obtaining refractive index information of the first glue material; obtaining a standard emergent position of the first glue material according to the refractive index information of the first glue material; obtaining first angle information according to the first incident position and the first emergent position; obtaining second angle information according to the first input position and the standard emergent position; inputting the first angle information and the second angle information into a vacuum prejudgment model to obtain first vacuum grade information of the first glue material; obtaining a predetermined grade threshold; judging whether the first vacuum grade information of the first glue material meets the preset grade threshold value or not, and obtaining a first judgment result; determining whether a first defoaming instruction is obtained or not according to the first judgment result; and according to the first defoaming instruction, defoaming the first glue material. The technical problem that in the prior art, the product quality is affected by the empty points due to the fact that bubbles exist in glue in the glue dispensing process is solved, the technical effects that the bubble detection is more professional and accurate, the material deaeration efficiency is improved, and the product process quality is further met
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A vacuum stirring defoaming material preparation method based on artificial intelligence is applied to a vacuum stirring defoaming material preparation system based on artificial intelligence, the system is applied to a dispensing device, the system comprises a first linear light source, and the method comprises the following steps:
the first linear light source irradiates a first glue material in the glue dispensing device to obtain a first incident position;
obtaining a first emergent position;
obtaining refractive index information of the first glue material;
obtaining a standard emergent position of the first glue material according to the refractive index information of the first glue material;
obtaining first angle information according to the first incident position and the first emergent position;
obtaining second angle information according to the first incident position and the standard emergent position;
inputting the first angle information and the second angle information into a vacuum prejudgment model to obtain first vacuum grade information of the first glue material;
obtaining a predetermined grade threshold;
judging whether the first vacuum grade information of the first glue material meets the preset grade threshold value or not, and obtaining a first judgment result;
determining whether a first defoaming instruction is obtained or not according to the first judgment result;
and according to the first defoaming instruction, defoaming the first glue material.
2. The method of claim 1, wherein the determining whether to obtain a first deaeration instruction according to the first determination result comprises:
if the first judgment result is that the first vacuum grade information of the first glue material meets the preset grade threshold, determining that the first glue material does not have first bubbles, and forbidding obtaining of the first defoaming instruction;
and if the first judgment result shows that the first vacuum grade information of the first glue material does not meet the preset grade threshold, determining that the first glue material has first bubbles, and obtaining the first defoaming instruction.
3. The method of claim 1, wherein said inputting said first angle information and said second angle information into a vacuum anticipation model to obtain first vacuum level information of said first glue material comprises:
inputting the first angle information and the second angle information as input information into a vacuum prejudgment model for training, wherein the vacuum prejudgment model is obtained by training a plurality of groups of training data, and each group of data in the plurality of groups of training data comprises: said first and second angle information and identification information for identifying a first vacuum level of a first glue material;
and obtaining output information of the vacuum prejudging model, wherein the output information comprises first vacuum grade information of the first glue material.
4. The method of claim 2, wherein said de-bubbling the first glue material according to the first de-bubbling instruction comprises:
acquiring the first bubble position information according to the first defoaming instruction;
obtaining a first defoaming scheme according to the first bubble position information;
and according to the first defoaming scheme, defoaming the first glue material.
5. The method of claim 4, wherein the obtaining the first bubble location information according to the first debubbling instruction comprises:
obtaining a first incident angle of the first linear light source;
obtaining a first exit angle of the first linear light source;
obtaining first input information according to the first incidence position and the first incidence angle;
obtaining second input information according to the first emergent position and the first emergent angle;
and inputting the first input information and the second input information into a bubble positioning model to obtain first bubble position information, wherein the first bubble position information is position information of bubbles in the first glue material.
6. The method of claim 4, wherein the method comprises:
obtaining standard density information of the first glue material;
obtaining first weight information and first volume information of the first glue material in the glue dispensing device;
obtaining standard volume information of the first glue material according to the standard density information;
obtaining volume difference information according to the first volume information and the standard volume information;
and obtaining first volume information of the first bubble according to the volume difference information.
7. The method of claim 6, wherein the method comprises:
obtaining a second defoaming scheme according to the first volume information of the first bubbles and the first bubble position information;
and according to the second defoaming scheme, defoaming the first glue material.
8. The utility model provides a vacuum mixing deaeration system of prepareeing material based on artificial intelligence, wherein, the system includes:
the first obtaining unit is used for irradiating a first glue material in the gluing device by a first linear light source to obtain a first incident position;
a second obtaining unit for obtaining a first exit position;
a third obtaining unit, configured to obtain refractive index information of the first glue material;
a fourth obtaining unit, configured to obtain a standard exit position of the first glue material according to refractive index information of the first glue material;
a fifth obtaining unit configured to obtain first angle information according to the first incident position and the first exit position;
a sixth obtaining unit, configured to obtain second angle information according to the first incident position and the standard exit position;
a seventh obtaining unit, configured to input the first angle information and the second angle information into a vacuum prejudgment model, and obtain first vacuum level information of the first glue material;
an eighth obtaining unit configured to obtain a predetermined level threshold;
the first judging unit is used for judging whether the first vacuum grade information of the first glue material meets the preset grade threshold value or not to obtain a first judging result;
a first determining unit, configured to determine whether to obtain a first defoaming instruction according to the first determination result;
and the first processing unit is used for carrying out defoaming processing on the first glue material according to the first defoaming instruction.
9. An artificial intelligence based vacuum mixing debubbling preparation system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the program.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114103031A (en) * | 2021-11-22 | 2022-03-01 | 兴宇伟业（天津）科技有限公司 | Silica gel shaping and processing device and processing method thereof |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105118909A (en) * | 2015-07-31 | 2015-12-02 | 苏州南光电子科技有限公司 | Vacuum defoaming method during direct insert type LED lamp gluing packaging process |
| CN105940293A (en) * | 2014-02-07 | 2016-09-14 | 株式会社岛津制作所 | Measurement method using differential refractometer, differential refractometer using said measurement method, and liquid chromatograph |
| CN208612937U (en) * | 2018-06-20 | 2019-03-19 | 深圳市鑫路远电子设备有限公司 | A kind of defoaming device of vacuum glue pouring machine |
-
2021
- 2021-01-22 CN CN202110088524.0A patent/CN112915594B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105940293A (en) * | 2014-02-07 | 2016-09-14 | 株式会社岛津制作所 | Measurement method using differential refractometer, differential refractometer using said measurement method, and liquid chromatograph |
| CN105118909A (en) * | 2015-07-31 | 2015-12-02 | 苏州南光电子科技有限公司 | Vacuum defoaming method during direct insert type LED lamp gluing packaging process |
| CN208612937U (en) * | 2018-06-20 | 2019-03-19 | 深圳市鑫路远电子设备有限公司 | A kind of defoaming device of vacuum glue pouring machine |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114103031A (en) * | 2021-11-22 | 2022-03-01 | 兴宇伟业（天津）科技有限公司 | Silica gel shaping and processing device and processing method thereof |
| CN114103031B (en) * | 2021-11-22 | 2023-11-21 | 兴宇伟业（天津）科技有限公司 | Silica gel shaping processing device and processing method thereof |
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