CN118331198B - Curing process parameter regulation and control method and system for LED production - Google Patents
Curing process parameter regulation and control method and system for LED production Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
- B05D—PROCESSES FOR APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
- B05D3/00—Pretreatment of surfaces to which liquids or other fluent materials are to be applied; After-treatment of applied coatings, e.g. intermediate treating of an applied coating preparatory to subsequent applications of liquids or other fluent materials
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- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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Abstract
The application provides a curing process parameter regulation and control method and a curing process parameter regulation and control system for LED production, which relate to the technical field of intelligent regulation and control of optical elements, and the method comprises the following steps: reading the requirements of a curing process, and determining matching index characteristics based on the coating material; performing interaction and cooperative association analysis on the matched index features to determine a parameter-control cooperative relationship; combining the matched index features with the parameter control cooperative relationship to train a solidification decision model; carrying out working condition control decision and optimizing correction by combining the curing decision model, and determining dynamic curing parameters to carry out optical parameter control; and synchronously performing curing monitoring and performing feedback regulation and control by combining an emergency plan library. The application can solve the technical problems of poor curing quality and efficiency of the LED and the coating material caused by lower setting accuracy and precision of the curing parameters and incapability of timely and pertinently adjusting the curing parameters, and can improve the setting accuracy and precision of the curing parameters and achieve the effect of improving the curing quality and efficiency.
Description
Technical Field
The application relates to the technical field of intelligent regulation and control of optical elements, in particular to a curing process parameter regulation and control method and system for LED production.
Background
LED curing production is one of the key links in LED manufacturing processes, mainly involving the curing process of the encapsulating coating material, for example: the quality of the LED curing quality determines the stability and reliability of the performance of the LED product to a great extent by packaging materials such as glue, fluorescent powder and the like.
At present, when the existing curing process is used for LED curing production, fixed curing parameters are generally adopted for equipment control in the whole curing period, and as the states of the packaging coating materials in the whole curing period are different, the degree of matching between the fixed curing parameters and the states of the materials is low, meanwhile, the existing process cannot timely adjust the original curing parameters according to the curing states, so that the setting precision and accuracy of the curing parameters are low, and the curing quality and efficiency of the LEDs and the coating materials are affected.
In summary, the existing LED curing process has the technical problems that the setting accuracy and precision of the curing parameters are low, and meanwhile, the curing parameters cannot be accurately and pointedly adjusted according to the real-time curing state in time, so that the curing quality and efficiency of the LED and the coating material are poor.
Disclosure of Invention
The application aims to provide a method and a system for regulating and controlling curing process parameters for LED production, which are used for solving the technical problems that the existing LED curing process has lower setting accuracy and precision of the curing parameters, and simultaneously the curing parameters cannot be accurately regulated in time according to a real-time curing state, so that the curing quality and efficiency of an LED and a coating material are poor.
In view of the above problems, the present application provides a method and a system for adjusting and controlling curing process parameters for LED production.
In a first aspect, the present application provides a method for regulating and controlling a curing process parameter for LED production, the method being implemented by a curing process parameter regulating and controlling system for LED production, wherein the method comprises: reading the curing process requirement based on a production work order, and determining the matching index characteristic based on the packaging coating material, wherein the matching index characteristic is marked with an index constraint value, and the index constraint value is set based on a material distortion boundary value; performing interaction and cooperative association analysis on the matched index features to determine a parameter-control cooperative relationship; the bottom layer operation control logic of the interactive curing equipment is combined with the matched index characteristics and the parameter control cooperative relationship to train a curing decision model, and communication connection between the curing decision model and the photo-curing control system is established; aiming at the curing process requirement, working condition control decision and optimization verification are carried out by combining the curing decision model, and dynamic curing parameters based on a curing period are determined, wherein the optimization mode comprises monotone optimization and joint optimization; transmitting the dynamic curing parameters to the photo-curing control system, and controlling the photo-parameters of the LED curing processing of the curing equipment; and synchronously carrying out LED curing monitoring, and carrying out feedback regulation and control on LED curing processing by combining an emergency plan library.
In a second aspect, the present application also provides a curing process parameter tuning system for LED production, for performing a curing process parameter tuning method for LED production according to the first aspect, the system being communicatively connected to a photo-curing control system, wherein the system comprises: the system comprises a matching index feature determining module, a packaging coating material processing module and a packaging coating material processing module, wherein the matching index feature determining module is used for reading the curing process requirement based on a production work order, determining the matching index feature based on the packaging coating material, and identifying an index constraint value, wherein the index constraint value is set based on a material distortion limit value; the parameter control cooperative relation determining module is used for carrying out interaction and cooperative association analysis on the matched index characteristics to determine a parameter control cooperative relation; the curing decision model training module is used for interacting bottom layer operation control logic of the curing equipment, combining the matched index features with the reference control cooperative relationship, training a curing decision model and establishing communication connection between the curing decision model and the photo-curing control system; the dynamic curing parameter determining module is used for carrying out working condition control decision and optimization verification by combining the curing decision model according to the curing process requirement to determine the dynamic curing parameter based on the curing period, wherein the optimization mode comprises monotone optimization and joint debugging optimization; the optical parameter control module is used for transmitting the dynamic curing parameters to the optical curing control system and controlling the optical parameters of the LED curing processing of the curing equipment; and the feedback control module is used for synchronously carrying out LED solidification monitoring and carrying out feedback control of LED solidification processing by combining an emergency plan library.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
1. Determining a matching index feature based on the packaging coating material by reading the curing process requirement based on the production work order, wherein the matching index feature is marked with an index constraint value, and the index constraint value is set based on a material distortion boundary value; performing interaction and cooperative association analysis on the matched index features to determine a parameter-control cooperative relationship; the bottom layer operation control logic of the interactive curing equipment is combined with the matched index characteristics and the parameter control cooperative relationship to train a curing decision model, and communication connection between the curing decision model and the photo-curing control system is established; aiming at the curing process requirement, working condition control decision and optimization verification are carried out by combining the curing decision model, and dynamic curing parameters based on a curing period are determined, wherein the optimization mode comprises monotone optimization and joint optimization; transmitting the dynamic curing parameters to the photo-curing control system, and controlling the photo-parameters of the LED curing processing of the curing equipment; and synchronously carrying out LED curing monitoring, and carrying out feedback regulation and control on LED curing processing by combining an emergency plan library. That is, by setting the dynamic curing parameters in the curing period, the precision and accuracy of setting the curing parameters can be improved, the curing process of the LED can be synchronously monitored, the curing parameters can be timely fed back and regulated according to the monitoring result, the adaptation degree of the curing parameters and the curing state can be further improved, and the technical effects of improving the curing quality and efficiency of the LED and the coating material are achieved.
2. By training the curing decision model according to the matched index characteristics and the parameter control cooperative relation and carrying out working condition control decision and optimizing correction based on the curing decision model, the dynamic curing parameters based on the curing period are obtained, and the adaptation degree of the dynamic curing parameters, the packaging coating material and the curing period can be improved, so that the curing quality and the curing efficiency of the LED and the coating material are improved.
3. By synchronously carrying out LED curing monitoring and timely carrying out feedback regulation and control on curing parameters in the LED curing processing process according to monitoring results, potential quality problems in the LED curing process can be timely repaired, and thus the curing quality and efficiency of the LEDs and the coating materials are further improved.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent. It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the application or the technical solutions of the prior art, the following brief description will be given of the drawings used in the description of the embodiments or the prior art, it being obvious that the drawings in the description below are only exemplary and that other drawings can be obtained from the drawings provided without the inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for regulating and controlling parameters of a curing process for LED production according to the present application.
Fig. 2 is a schematic flow chart of determining matching index characteristics based on a packaging coating material in a curing process parameter adjusting method for LED production according to the present application.
Fig. 3 is a schematic structural diagram of a curing process parameter control system for LED production according to the present application.
Reference numerals illustrate:
the system comprises a matching index feature determining module 11, a parameter control cooperative relation determining module 12, a curing decision model training module 13, a dynamic curing parameter determining module 14, an optical parameter control module 15 and a feedback regulation and control module 16.
Detailed Description
The application provides a method and a system for regulating and controlling curing process parameters for LED production, which solve the technical problems that the existing LED curing process has lower setting accuracy and precision of the curing parameters, and the curing parameters cannot be accurately regulated in time according to the real-time curing state, so that the curing quality and efficiency of LEDs and coating materials are poor. By setting dynamic curing parameters in the curing period, the precision and accuracy of setting the curing parameters can be improved, the LED curing process is synchronously monitored, the curing parameters are timely fed back and regulated according to the monitoring result, the adaptation degree of the curing parameters and the curing state can be further improved, and the technical effects of improving the curing quality and efficiency of the LEDs and the coating materials are achieved.
In the following, the technical solutions of the present application will be clearly and completely described with reference to the accompanying drawings, and it should be understood that the described embodiments are only some embodiments of the present application, but not all embodiments of the present application, and that the present application is not limited by the exemplary embodiments described herein. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present application are shown.
Example 1
Referring to fig. 1, the present application provides a method for adjusting and controlling curing process parameters for LED production, wherein the method is applied to a curing process parameter adjusting and controlling system for LED production, the system is in communication connection with a photo-curing control system, and the method specifically comprises the following steps:
Step one: reading the curing process requirement based on a production work order, and determining the matching index characteristic based on the packaging coating material, wherein the matching index characteristic is marked with an index constraint value, and the index constraint value is set based on a material distortion boundary value;
Specifically, the method provided by the application is used for optimizing the curing process parameters of the LED production to achieve the aim of improving the precision and accuracy of setting the curing parameters, so as to achieve the technical effect of improving the curing quality and efficiency of the LED and the coating material, and the method is specifically implemented in a curing process parameter regulation system for the LED production, wherein the system is in communication connection with a light curing control system, and the light curing control system is used for controlling light source curing equipment in the curing process, such as: UVLED curing machines, and the like.
Firstly, an LED production work order is obtained, wherein the LED production work order comprises information such as product types, product production requirements and the like, and then the curing process requirements of the LED products are read based on the LED production work order, wherein the curing process requirements can be set according to actual conditions and comprise information such as curing time, curing uniformity and curing durability.
The encapsulating coating material is determined based on the LED production worksheet, and can be set according to actual conditions, for example: packaging glue, fluorescent powder, canning glue and the like. And determining a matching index feature based on the packaging coating material, wherein the matching index feature refers to a curing processing feature adapted to the packaging coating material and comprises parameters such as light source wavelength, illumination intensity, temperature and the like, wherein the matching index feature is marked with an index constraint value, the index constraint value is the maximum allowed by the index parameter, the index constraint value is set based on a material distortion limit value, and the material distortion limit value is a critical value of material distortion caused by the index parameter. By determining curing process requirements and matching index features, raw data support is provided for subsequent curing parameter analysis.
Step two: performing interaction and cooperative association analysis on the matched index features to determine a parameter-control cooperative relationship;
Specifically, the matching index features are further subjected to interaction and collaborative association analysis, wherein the interaction performs parameter interaction analysis on index parameters in the matching index features, for example: the influence of illumination intensity on temperature, the influence of light source wavelength on illumination intensity, etc.; collaborative association analysis refers to analysis of collaborative relationships between index parameters, such as: when other external conditions are unchanged, the temperature is increased due to the increase of the illumination intensity, and the like, and the parameter control cooperative relationship is determined according to the interaction and cooperative association analysis result. By determining the reference control cooperative relationship, support is provided for the next step of curing decision model training, and meanwhile, the accuracy of curing decision model training can be improved.
Step three: the bottom layer operation control logic of the interactive curing equipment is combined with the matched index characteristics and the parameter control cooperative relationship to train a curing decision model, and communication connection between the curing decision model and the photo-curing control system is established;
specifically, the bottom layer operation control logic of the interactive curing device refers to the operation flow and control logic of the curing device, and includes information such as device operation parameters. And constructing a curing decision model, wherein the curing decision model comprises a parameter control decision layer and an optimizing calibration layer and is used for optimizing curing parameters. And training the curing decision model according to the matching index characteristics and the reference control cooperative relation to obtain a curing decision model conforming to expected training indexes, and simultaneously establishing communication connection between the curing decision model and the photocuring control system, wherein the communication connection means that data interaction can be performed in a signal transmission mode. By constructing a curing decision model, support is provided for optimizing the curing parameters of the next step and obtaining dynamic curing parameters based on the curing period.
Step four: aiming at the curing process requirement, working condition control decision and optimization verification are carried out by combining the curing decision model, and dynamic curing parameters based on a curing period are determined, wherein the optimization mode comprises monotone optimization and joint optimization;
Specifically, aiming at meeting the requirements of the curing process, a working condition control decision is made through a parameter control decision layer of the curing decision model to obtain curing parameters, and the curing parameters are subjected to optimizing and correcting through a optimizing and correcting layer to obtain dynamic curing parameters based on a curing period, wherein the optimizing mode in the optimizing and correcting layer comprises monotone optimizing and joint optimizing, monotone optimizing means that only a single index parameter is optimized, other parameters are unchanged, and joint optimizing means that a plurality of index parameters are comprehensively optimized.
Step five: transmitting the dynamic curing parameters to the photo-curing control system, and controlling the photo-parameters of the LED curing processing of the curing equipment;
specifically, the dynamic curing parameters are transmitted to the photo-curing control system, and in the LED curing process, the photo-parameters of the curing equipment are controlled according to the dynamic curing parameters.
Step six: and synchronously carrying out LED curing monitoring, and carrying out feedback regulation and control on LED curing processing by combining an emergency plan library.
Specifically, in the process of curing the LED, the real-time curing state of the LED and the packaging coating material is synchronously monitored, and the real-time curing state is compared with the standard curing state under the same time node in the curing period, so that the curing state deviation under the node is determined. And then inputting the deviation of the solidification state into a pre-constructed emergency plan library for matching to obtain an emergency remedy scheme, and carrying out feedback regulation and control according to the emergency remedy scheme.
By timely performing feedback regulation and control according to the deviation of the curing state in the curing process of the LED, the potential quality problem in the curing process of the LED can be timely repaired, and therefore the curing quality and efficiency of the LED and the coating material are further improved.
The method for regulating and controlling the curing process parameters for LED production is applied to a system for regulating and controlling the curing process parameters for LED production, and can solve the technical problems that the existing LED curing process is low in curing parameter setting accuracy and precision, and meanwhile, the curing parameters cannot be accurately regulated in time according to the real-time curing state, so that the curing quality and efficiency of LEDs and coating materials are poor. Firstly, reading the curing process requirement based on a production work order, and determining the matching index characteristic based on the packaging coating material, wherein the matching index characteristic is marked with an index constraint value, and the index constraint value is set based on a material distortion boundary value; then, carrying out interaction and cooperative association analysis on the matched index features to determine a parameter-control cooperative relationship; then, a bottom layer operation control logic of the interactive curing equipment is combined with the matched index characteristics and the parameter control cooperative relation to train a curing decision model, and communication connection between the curing decision model and the photo-curing control system is established; next, aiming at the curing process requirement, carrying out working condition control decision and optimization correction by combining the curing decision model, and determining a dynamic curing parameter based on a curing period, wherein the optimization mode comprises monotone optimization and joint optimization; in addition, the dynamic curing parameters are transmitted to the photo-curing control system, and the curing equipment is subjected to photo-parameter control of LED curing processing; and finally, synchronously carrying out LED curing monitoring, and carrying out feedback regulation and control on LED curing processing by combining an emergency plan library. By setting dynamic curing parameters in the curing period, the precision and accuracy of setting the curing parameters can be improved, the LED curing process is synchronously monitored, the curing parameters are timely fed back and regulated according to the monitoring result, the adaptation degree of the curing parameters and the curing state can be further improved, and the technical effects of improving the curing quality and efficiency of the LEDs and the coating materials are achieved.
Further, the matching index features at least include a light source wavelength, a light intensity, and a temperature, and the determining is based on the matching index features of the encapsulating coating material, as shown in fig. 2, a first step of the present application includes:
Checking the absorption characteristics of the packaging coating material and the multi-wavelength light, and screening and matching the wavelength of the light source;
searching industrial big data aiming at the packaging coating material, and determining a material curing record;
digging a light intensity constraint value of a material distortion limit value based on a light intensity index based on the material curing record, wherein the light intensity is positively correlated with the curing speed and the curing quality;
digging a temperature constraint value of a material distortion limit value based on a temperature index based on the material solidification record;
And adding the matching light source wavelength, the light intensity constraint value and the temperature constraint value into the matching index feature.
Specifically, the matching index features at least include light source wavelength, illumination intensity and temperature, and the person skilled in the art can increase the matching index features according to actual situations. When determining the matching index characteristics based on the packaging coating material, firstly, verifying the absorption characteristics of the packaging coating material under the condition of a plurality of different light source wavelengths, wherein the absorption characteristics of the different packaging coating materials under the condition of different light source wavelengths are different due to different structures and properties, determining the absorption characteristics under the condition of the plurality of different light source wavelengths, and selecting the light source wavelength with the absorption characteristics larger than a preset absorption threshold as the matching light source wavelength, namely selecting the light source wavelength with stronger absorption as the matching light source wavelength.
The industrial big data technology is a series of technology and method for mining and showing the value contained in industrial big data, and comprises the technical means of data planning, data acquisition, analysis mining and the like, and then information retrieval is carried out by taking the packaging coating material as a constraint based on the industrial big data technology to obtain a material curing record, wherein the material curing record comprises a plurality of material curing historical data. And then, based on the material curing record, carrying out mining analysis on a light intensity constraint value of a material distortion limit value based on an illumination intensity index, wherein the light intensity constraint value refers to a minimum illumination intensity value when the illumination intensity is too high and causes irreversible mass change of the packaging coating material, for example, when the illumination intensity is too high, a critical value when the surface of the packaging coating material is burnt or discolored, namely, the minimum illumination intensity in the irreversible mass change state is caused, wherein the illumination intensity is positively related to the curing speed and the curing quality, namely, when the illumination intensity is smaller than the light intensity constraint value, the higher the illumination intensity is, the faster the curing speed is and the better the curing quality is.
And excavating a temperature constraint value of a material distortion limit value based on a temperature index based on the material curing record, wherein the temperature constraint value is a minimum temperature value which causes irreversible mass change of the surface of the packaging coating material when the temperature is too high, and the temperature constraint value is obtained. And finally, adding the wavelength of the matched light source, the light intensity constraint value and the temperature constraint value into a matched index feature to obtain the matched index feature.
Further, the solidification decision model comprises a parameter control decision layer and an optimizing calibration layer, and the fourth step of the application comprises the following steps:
Determining a curing control influence factor, and calibrating the curing decision model, wherein the curing control influence factor is determined based on factor dimensions of stability, light source uniformity and additive characteristics of curing equipment;
Based on the curing process requirement, making a decision at the parameter control decision layer to determine initial curing parameters;
predicting whether the initial curing parameters meet production quality standards, and if not, activating the optimizing calibration layer;
and based on the optimizing calibration layer, combining the index constraint value, executing expansion optimizing and optimal direction screening based on the initial curing parameters, and iteratively determining the dynamic curing parameters meeting the production quality standard, wherein the dynamic curing parameters are time zone parameters of a complete curing period.
Specifically, the curing decision model includes a reference decision layer and an optimizing calibration layer, and first, curing control influencing factors are determined, wherein the curing control influencing factors are determined based on factor dimensions of curing equipment stability, light source uniformity and additive characteristics, for example: factors affecting the stability of the curing apparatus include temperature, illumination intensity, material characteristics, etc., and factors affecting the uniformity of the light source include illumination position, shape of the light source, etc. And then based on an incremental learning principle, calibrating the curing decision model according to the curing control influence factors, namely, improving the fit degree of the curing decision model and an actual application scene, so as to improve the accuracy of the curing decision model, wherein the incremental learning means that the model can continuously learn new knowledge and modes from new data in the training process, and simultaneously keeps the memory of the previously learned knowledge, which is beneficial to the model to adapt to the continuously changed environment and data distribution and continuously improve the performance of the model.
And taking the purpose of meeting the curing process requirement as an aim, making a decision at the parameter control decision layer, namely performing curing parameter simulation at the parameter control decision layer to obtain initial curing parameters meeting the curing process requirement, wherein the parameter control decision layer is constructed based on a BP neural network, takes curing equipment as equipment constraint conditions, and performs supervision training through sample data for a neural network model which can be subjected to iterative optimization in machine learning. The input data of the parameter control decision layer is curing process requirements, and the output data is curing parameters.
And then inputting the initial solidification parameters into a quality prediction channel for production quality prediction, wherein the quality prediction channel is also constructed based on a BP neural network, and can be used for carrying out iterative optimization, wherein input data of the quality prediction channel are solidification parameters, output data are production quality prediction results, information retrieval can be carried out based on industrial big data technology, and sample training data are obtained for carrying out supervision training on the quality prediction channel. And obtaining a production quality prediction result corresponding to the initial curing parameter, further judging whether the production quality prediction result meets a production quality standard, setting the production quality standard based on actual conditions, and activating the optimizing calibration layer when the initial curing parameter does not meet the production quality standard.
And further based on the optimizing calibration layer, carrying out expansion optimizing and optimizing direction screening on the initial curing parameters by taking the index constraint value as constraint condition, judging the curing parameters obtained by optimizing according to the production quality standard after optimizing each time, and when the optimizing result cannot meet the production quality standard, carrying out iterative optimizing until the dynamic curing parameters meeting the production quality standard are obtained, wherein the dynamic curing parameters are time zone parameters of a complete curing period, namely a plurality of curing time zones exist in the complete curing period, each curing time zone corresponds to one curing parameter, and the curing parameters meet the production quality standard corresponding to the time zone.
The parameter control decision layer is constructed based on the BP neural network, the initial curing parameters are generated based on the parameter control decision layer, and then under the condition that the initial curing parameters do not meet the production quality standard, the initial curing parameters are optimized based on the optimizing calibration layer, so that the dynamic curing parameters meeting the production quality standard are obtained, the accuracy and efficiency of obtaining the dynamic curing parameters can be improved, and the curing processing quality and efficiency of the LED and the packaging coating material are improved.
Further, the method performs expansion optimizing and optimizing screening based on the initial curing parameters, and the method further comprises the following steps:
the monotonic optimization has a pattern priority;
Activating the optimizing calibration layer, and determining a first calibration parameter by taking the parameter core degree and the parameter deviation degree as references;
optimizing and determining the calibration parameters of the optimal amplitude modulation, replacing the initial curing parameters, and judging whether the production quality standard is met;
If the dynamic curing parameters do not meet the production quality standards, determining second calibration parameters, carrying out joint optimization decision by combining the first calibration parameters, carrying out iterative calibration, and determining the dynamic curing parameters meeting the production quality standards.
Specifically, the optimizing mode of the optimizing and calibrating layer comprises monotone optimizing and joint debugging optimizing, wherein the optimizing has mode priority, the mode priority of the monotone optimizing is larger than that of the joint debugging, and in the monotone optimizing, if only single index parameters are regulated and optimized and production quality standard can be met, multi-index parameter joint analysis is not needed. Meanwhile, the calculation force can be reduced through monotone optimizing, so that the optimizing efficiency of the curing parameters is improved.
When the expansion optimizing and optimizing screening based on the initial curing parameters is executed, firstly, the optimizing calibration layer is activated, the parameter core degree and the parameter deviation degree are used as the reference, namely, index parameters with larger parameter core degree and larger parameter deviation degree are used as the first calibration parameters, the core degree and the deviation degree can be evaluated by a plurality of index parameters, weight coefficients are respectively set for the core degree and the deviation degree, then the evaluation coefficients are obtained through weighting calculation according to the evaluation results, and the index parameter with the largest evaluation coefficient is selected as the first calibration parameter. The accuracy and the rationality of the first calibration parameter setting can be improved by comprehensively evaluating the index parameters to determine the first calibration parameters.
The method comprises the steps of obtaining a preset optimizing step length, wherein the preset optimizing step length refers to an adjusting step length of the first calibration parameter and can be set according to optimizing requirements, and the higher the optimizing requirement accuracy is, the smaller the preset optimizing step length is. And then adjusting the first calibration parameters according to the preset optimizing step length to obtain first adjustment calibration parameters, replacing the first calibration parameters in the initial curing parameters according to the first adjustment calibration parameters to obtain first adjustment curing parameters, further judging whether the first adjustment curing parameters meet the production quality standard or not, when the first adjustment curing parameters do not meet the production quality standard, carrying out iterative optimizing to obtain optimal adjustment curing parameters, judging whether the optimal adjustment curing parameters meet the production quality standard or not, and when the optimal adjustment curing parameters do not meet the production quality standard, characterizing that monotone optimizing at the moment cannot meet the production quality standard, carrying out joint optimizing, then determining second calibration parameters, wherein the second calibration parameters refer to index parameters with evaluation coefficients only smaller than the first calibration parameters, carrying out joint optimizing decision based on the preset optimizing step length, carrying out iterative calibrating continuously until dynamic curing parameters meeting the production quality standard are obtained, and stopping optimizing to obtain the dynamic curing parameters.
Further, after determining the dynamic curing parameters meeting the production quality criteria, the present application further includes the steps of:
determining rigidity control requirements of curing uniformity and curing depth, performing light focusing adjustment, and determining first coordination data;
Based on the first coordination data, regulating and controlling a light source focusing system of the curing equipment;
and carrying out same-frequency mapping identification on the dynamic curing parameters and the first coordination data.
Specifically, after the dynamic curing parameters meeting the production quality standard are determined, first, a rigidity control requirement of curing uniformity and curing depth is determined, and then light focusing adjustment is performed based on the rigidity control requirement, wherein the light focusing adjustment means that a light source is focused into a curing area of the rigidity control requirement, and first coordination data is obtained, and the first coordination data means that the light source is focused in the curing process.
And then regulating and controlling a light source focusing system of the curing equipment according to the first coordination data so as to meet the light source focusing requirement of the first coordination data, and carrying out same-frequency mapping identification on the dynamic curing parameters and the first coordination data. By performing light focusing adjustment based on the rigidity control requirement in the curing process, the light energy can be ensured to be intensively irradiated to the area needing curing, the light energy loss is reduced, and the light source utilization rate is improved.
Further, the sixth step of the present application includes:
returning the curing monitoring data, and determining control abnormal characteristics and exogenous abnormal characteristics;
Identifying the exogenous abnormal characteristics, traversing the emergency plan library to match a target plan, and performing processing emergency regulation;
and identifying the control abnormal characteristics, and performing abnormal tracing and feedback regulation analysis based on the solidification decision model.
Specifically, curing monitoring data is obtained, wherein the curing monitoring data refers to a real-time curing state of an LED and a coating material in a curing process, and the curing monitoring data can be obtained by data feedback under a preset time node, wherein the preset node can be set according to actual conditions, for example: monitoring data was returned every 1 minute. And comparing the curing state deviation based on the curing monitoring data to obtain curing state deviation, and determining abnormal control characteristics and exogenous abnormal characteristics based on the curing state deviation, wherein the abnormal control characteristics refer to non-abnormal control parameters in the curing process, and the exogenous abnormal characteristics refer to external factor interference, such as: ultraviolet rays, high temperature and the like in the external environment cause deviation of the curing state.
And then identifying the exogenous abnormal characteristics, inputting the exogenous abnormal characteristics into the emergency plan library for plan matching to obtain a target plan, wherein the target plan refers to a solution scheme aiming at the exogenous abnormal characteristics, and the emergency plan library is stored with a plurality of emergency plans corresponding to the exogenous abnormal characteristics and can be constructed according to historical solidification processing data analysis. And further identifying the control abnormal characteristics, carrying out abnormal tracing based on the control abnormal characteristics, and carrying out feedback regulation analysis on an abnormal tracing result through the solidification decision model.
Further, the application also comprises the following steps:
entity adjustable judgment is carried out on the abnormal tracing result, and adjustability information is determined;
if the adjustability information is yes, regulating and controlling the exogenous entity based on the abnormal tracing result;
And if the adjustability information is negative, performing parameter control calibration based on the abnormal traceability result by combining the curing decision model.
Specifically, entity adjustable judgment is performed on the abnormal tracing result, wherein the entity adjustable judgment refers to judging whether the abnormal tracing result can be regulated and optimized through external factor adjustment, for example: when the equipment mode and the environmental temperature influence, the equipment mode can be adjusted, or the equipment can adjust the environmental temperature to eliminate the abnormal tracing result, so as to determine the adjustability information. And if the adjustability information is yes, carrying out targeted regulation and control on the abnormal tracing result through an exogenous entity, and if the adjustability information is no, carrying out targeted parameter control calibration on the abnormal tracing result by combining the solidification decision model.
By carrying out entity adjustable judgment on the abnormal tracing result and selecting an adaptive regulation and control scheme according to the judgment result, the accuracy and efficiency of regulation and control of the abnormal tracing result can be improved, and thus potential problems in the curing process can be accurately treated in time.
Further, the application also comprises the following steps:
identifying the solidification monitoring data, carrying out identical-frequency parameter consistency judgment of control response, and determining response consistency, wherein the response consistency identifies a parameter response difference frequency time zone;
and based on the response consistency, performing feedback regulation and control on the LED curing processing.
Specifically, identifying the curing monitoring data, and carrying out identical frequency parameter consistency judgment of control response on the curing monitoring data, wherein identical frequency parameters refer to control parameters under a time node, namely, calculating response time deviation of the identical frequency parameters, judging the response time deviation based on a parameter response difference frequency time zone, and representing that the response time deviation is identical to the response time deviation when the response time deviation is smaller than or equal to the parameter response difference frequency time zone; and when the response time deviation is larger than the parameter response difference frequency time zone, characterizing that the response of the two is inconsistent, and obtaining the response consistency. And then performing feedback regulation and control on the LED curing processing based on the response consistency. For example: if the same-frequency parameter responses are inconsistent, parameter control time calibration is needed, namely parameters of the later responses are controlled in advance, so that synchronous control of the same-frequency parameters is achieved.
By synchronously controlling the same-frequency parameters, parameter control errors can be reduced, and the control precision of the curing parameters is improved, so that the curing quality of the LED and the coating material is further improved.
In summary, the method for regulating and controlling the curing process parameters for LED production provided by the application has the following technical effects:
1. by setting dynamic curing parameters in the curing period, the precision and accuracy of setting the curing parameters can be improved, the LED curing process is synchronously monitored, the curing parameters are timely fed back and regulated according to the monitoring result, the adaptation degree of the curing parameters and the curing state can be further improved, and the technical effects of improving the curing quality and efficiency of the LEDs and the coating materials are achieved.
2. The parameter control decision layer is constructed based on the BP neural network, the initial curing parameters are generated based on the parameter control decision layer, and then under the condition that the initial curing parameters do not meet the production quality standard, the initial curing parameters are optimized based on the optimizing calibration layer, so that the dynamic curing parameters meeting the production quality standard are obtained, the accuracy and efficiency of obtaining the dynamic curing parameters can be improved, and the curing processing quality and efficiency of the LED and the packaging coating material are improved.
3. By timely performing feedback regulation and control according to the deviation of the curing state in the curing process of the LED, the potential quality problem in the curing process of the LED can be timely repaired, and therefore the curing quality and efficiency of the LED and the coating material are further improved.
4. By synchronously controlling the same-frequency parameters, parameter control errors can be reduced, and the control precision of the curing parameters is improved, so that the curing quality of the LED and the coating material is further improved.
Example two
Based on the same concept as the curing process parameter adjusting and controlling method for LED production in the foregoing embodiment, the present application further provides a curing process parameter adjusting and controlling system for LED production, which is communicatively connected to a photo-curing control system, referring to fig. 3, and the system includes:
The matching index feature determining module 11 is used for reading the curing process requirement based on the production work order, determining the matching index feature based on the packaging coating material, and identifying an index constraint value, wherein the index constraint value is set based on a material distortion boundary value;
The parameter control cooperative relation determining module 12 is used for performing mutual influence and cooperative association analysis on the matching index characteristics by the parameter control cooperative relation determining module 12 to determine a parameter control cooperative relation;
the curing decision model training module 13 is used for interacting bottom layer operation control logic of the curing equipment, combining the matching index features with the parameter control cooperative relationship, training a curing decision model, and establishing communication connection between the curing decision model and the photocuring control system;
The dynamic curing parameter determining module 14 is configured to determine a dynamic curing parameter based on a curing period by combining a working condition control decision and a optimizing calibration with the curing decision model according to the curing process requirement, wherein the optimizing mode includes monotone optimizing and joint debugging optimizing;
the optical parameter control module 15 is used for transmitting the dynamic curing parameters to the optical curing control system, and controlling the optical parameters of the curing equipment for LED curing processing;
the feedback control module 16, the feedback control module 16 is used for synchronously carrying out LED solidification monitoring, and combining with an emergency plan library, carrying out feedback control of LED solidification processing.
Further, the matching index feature determination module 11 in the system is further configured to:
Checking the absorption characteristics of the packaging coating material and the multi-wavelength light, and screening and matching the wavelength of the light source;
searching industrial big data aiming at the packaging coating material, and determining a material curing record;
digging a light intensity constraint value of a material distortion limit value based on a light intensity index based on the material curing record, wherein the light intensity is positively correlated with the curing speed and the curing quality;
digging a temperature constraint value of a material distortion limit value based on a temperature index based on the material solidification record;
And adding the matching light source wavelength, the light intensity constraint value and the temperature constraint value into the matching index feature.
Further, the dynamic cure parameter determination module 14 in the system is also configured to:
Determining a curing control influence factor, and calibrating the curing decision model, wherein the curing control influence factor is determined based on factor dimensions of stability, light source uniformity and additive characteristics of curing equipment;
Based on the curing process requirement, making a decision at the parameter control decision layer to determine initial curing parameters;
predicting whether the initial curing parameters meet production quality standards, and if not, activating the optimizing calibration layer;
and based on the optimizing calibration layer, combining the index constraint value, executing expansion optimizing and optimal direction screening based on the initial curing parameters, and iteratively determining the dynamic curing parameters meeting the production quality standard, wherein the dynamic curing parameters are time zone parameters of a complete curing period.
Further, the dynamic cure parameter determination module 14 in the system is also configured to:
the monotonic optimization has a pattern priority;
Activating the optimizing calibration layer, and determining a first calibration parameter by taking the parameter core degree and the parameter deviation degree as references;
optimizing and determining the calibration parameters of the optimal amplitude modulation, replacing the initial curing parameters, and judging whether the production quality standard is met;
If the dynamic curing parameters do not meet the production quality standards, determining second calibration parameters, carrying out joint optimization decision by combining the first calibration parameters, carrying out iterative calibration, and determining the dynamic curing parameters meeting the production quality standards.
Further, the dynamic cure parameter determination module 14 in the system is also configured to:
determining rigidity control requirements of curing uniformity and curing depth, performing light focusing adjustment, and determining first coordination data;
Based on the first coordination data, regulating and controlling a light source focusing system of the curing equipment;
and carrying out same-frequency mapping identification on the dynamic curing parameters and the first coordination data.
Further, the feedback regulation module 16 in the system is also configured to:
returning the curing monitoring data, and determining control abnormal characteristics and exogenous abnormal characteristics;
Identifying the exogenous abnormal characteristics, traversing the emergency plan library to match a target plan, and performing processing emergency regulation;
and identifying the control abnormal characteristics, and performing abnormal tracing and feedback regulation analysis based on the solidification decision model.
Further, the feedback regulation module 16 in the system is also configured to:
entity adjustable judgment is carried out on the abnormal tracing result, and adjustability information is determined;
if the adjustability information is yes, regulating and controlling the exogenous entity based on the abnormal tracing result;
And if the adjustability information is negative, performing parameter control calibration based on the abnormal traceability result by combining the curing decision model.
Further, the feedback regulation module 16 in the system is also configured to:
identifying the solidification monitoring data, carrying out identical-frequency parameter consistency judgment of control response, and determining response consistency, wherein the response consistency identifies a parameter response difference frequency time zone;
and based on the response consistency, performing feedback regulation and control on the LED curing processing.
The embodiments in this specification are described in a progressive manner, and each embodiment focuses on the difference from the other embodiments, so that a curing process parameter adjusting method and a specific example for LED production in the first embodiment are equally applicable to a curing process parameter adjusting system for LED production in this embodiment, and a curing process parameter adjusting system for LED production in this embodiment is not described in detail herein for brevity of description, since a person skilled in the art will clearly know about the foregoing detailed description of a curing process parameter adjusting method for LED production. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalent techniques thereof, the present application is also intended to include such modifications and variations.
Claims (5)
1. The method for regulating and controlling the curing process parameters for LED production is characterized by being applied to a photocuring control system and comprising the following steps of:
Reading the curing process requirement based on a production work order, and determining the matching index characteristic based on the packaging coating material, wherein the matching index characteristic is marked with an index constraint value, and the index constraint value is set based on a material distortion boundary value;
Performing interaction and cooperative association analysis on the matched index features to determine a parameter-control cooperative relationship;
the bottom layer operation control logic of the interactive curing equipment is combined with the matched index characteristics and the parameter control cooperative relationship to train a curing decision model, and communication connection between the curing decision model and the photo-curing control system is established;
Aiming at the curing process requirement, working condition control decision and optimization verification are carried out by combining the curing decision model, and dynamic curing parameters based on a curing period are determined, wherein the optimization mode comprises monotone optimization and joint optimization;
transmitting the dynamic curing parameters to the photo-curing control system, and controlling the photo-parameters of the LED curing processing of the curing equipment;
Synchronously carrying out LED curing monitoring, and carrying out feedback regulation and control on LED curing processing by combining an emergency plan library;
Wherein, the matching index features at least comprise light source wavelength, light intensity and temperature, the determining is based on the matching index features of the packaging coating material, and the method comprises the following steps:
Checking the absorption characteristics of the packaging coating material and the multi-wavelength light, and screening and matching the wavelength of the light source;
searching industrial big data aiming at the packaging coating material, and determining a material curing record;
digging a light intensity constraint value of a material distortion limit value based on a light intensity index based on the material curing record, wherein the light intensity is positively correlated with the curing speed and the curing quality;
digging a temperature constraint value of a material distortion limit value based on a temperature index based on the material solidification record;
Adding the matching light source wavelength, the light intensity constraint value and the temperature constraint value into the matching index feature;
the curing decision model comprises a parameter control decision layer and an optimizing calibration layer, and comprises the following components:
Determining a curing control influence factor, and calibrating the curing decision model, wherein the curing control influence factor is determined based on factor dimensions of stability, light source uniformity and additive characteristics of curing equipment;
Based on the curing process requirement, making a decision at the parameter control decision layer to determine initial curing parameters;
predicting whether the initial curing parameters meet production quality standards, and if not, activating the optimizing calibration layer;
Based on the optimizing calibration layer, combining the index constraint value, performing extended optimizing and optimal direction screening based on the initial curing parameters, and iteratively determining the dynamic curing parameters meeting the production quality standard, wherein the dynamic curing parameters are time zone parameters of a complete curing period;
wherein the performing of the extended optimizing and optimizing screening based on the initial curing parameters includes:
the monotonic optimization has a pattern priority;
Activating the optimizing calibration layer, and determining a first calibration parameter by taking the parameter core degree and the parameter deviation degree as references;
optimizing and determining the calibration parameters of the optimal amplitude modulation, replacing the initial curing parameters, and judging whether the production quality standard is met;
If the dynamic curing parameters do not meet the production quality standard, determining a second calibration parameter, carrying out joint optimization decision by combining the first calibration parameter, carrying out iterative calibration, and determining the dynamic curing parameters meeting the production quality standard;
Wherein after determining the dynamic curing parameters meeting the production quality criteria, the method comprises:
determining rigidity control requirements of curing uniformity and curing depth, performing light focusing adjustment, and determining first coordination data;
Based on the first coordination data, regulating and controlling a light source focusing system of the curing equipment;
and carrying out same-frequency mapping identification on the dynamic curing parameters and the first coordination data.
2. The method of claim 1, wherein the method further comprises:
returning the curing monitoring data, and determining control abnormal characteristics and exogenous abnormal characteristics;
Identifying the exogenous abnormal characteristics, traversing the emergency plan library to match a target plan, and performing processing emergency regulation;
and identifying the control abnormal characteristics, and performing abnormal tracing and feedback regulation analysis based on the solidification decision model.
3. The method of claim 2, wherein the method further comprises:
entity adjustable judgment is carried out on the abnormal tracing result, and adjustability information is determined;
if the adjustability information is yes, regulating and controlling the exogenous entity based on the abnormal tracing result;
And if the adjustability information is negative, performing parameter control calibration based on the abnormal traceability result by combining the curing decision model.
4. The method of claim 2, wherein the method further comprises:
identifying the solidification monitoring data, carrying out identical-frequency parameter consistency judgment of control response, and determining response consistency, wherein the response consistency identifies a parameter response difference frequency time zone;
and based on the response consistency, performing feedback regulation and control on the LED curing processing.
5. A curing process parameter tuning system for LED production, characterized by the steps for implementing the method of any one of claims 1 to 4, said system being communicatively connected to a light curing control system, comprising:
the system comprises a matching index feature determining module, a packaging coating material processing module and a packaging coating material processing module, wherein the matching index feature determining module is used for reading the curing process requirement based on a production work order, determining the matching index feature based on the packaging coating material, and identifying an index constraint value, wherein the index constraint value is set based on a material distortion limit value;
the parameter control cooperative relation determining module is used for carrying out interaction and cooperative association analysis on the matched index characteristics to determine a parameter control cooperative relation;
The curing decision model training module is used for interacting bottom layer operation control logic of the curing equipment, combining the matched index features with the reference control cooperative relationship, training a curing decision model and establishing communication connection between the curing decision model and the photo-curing control system;
The dynamic curing parameter determining module is used for carrying out working condition control decision and optimization verification by combining the curing decision model according to the curing process requirement to determine the dynamic curing parameter based on the curing period, wherein the optimization mode comprises monotone optimization and joint debugging optimization;
The optical parameter control module is used for transmitting the dynamic curing parameters to the optical curing control system and controlling the optical parameters of the LED curing processing of the curing equipment;
And the feedback control module is used for synchronously carrying out LED solidification monitoring and carrying out feedback control of LED solidification processing by combining an emergency plan library.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111354312A (en) * | 2019-12-27 | 2020-06-30 | 深圳市华星光电半导体显示技术有限公司 | OLED efficiency attenuation compensation method, device and system for display panel |
| WO2023165147A1 (en) * | 2022-03-03 | 2023-09-07 | 芯体素(杭州)科技发展有限公司 | Printing apparatus and method for led dam of display panel |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113283213B (en) * | 2021-07-19 | 2021-11-09 | 苏州贝克微电子有限公司 | Circuit design system and method based on machine learning |
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| WO2023165147A1 (en) * | 2022-03-03 | 2023-09-07 | 芯体素(杭州)科技发展有限公司 | Printing apparatus and method for led dam of display panel |
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