CN117193178B - Production process optimization control method and system for glass fiber composite board - Google Patents
Production process optimization control method and system for glass fiber composite board Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention provides a production process optimization control method and a production process optimization control system for a glass fiber composite board, which relate to the technical field of data processing, wherein resin glue solution is prepared by matching resin glue solution formula information based on preparation requirement information, and a sample bonding sheet is processed based on bonding sheet processing equipment; and (3) obtaining a sample bonding sheet performance detection result, comparing preparation requirement information, and controlling, optimizing and adjusting bonding sheet processing equipment according to the comparison result so as to carry out mass production and multi-layer pressing of the bonding sheets to obtain the glass fiber composite board. The technical problem that the intelligent degree of the production process parameter adjustment control of the glass fiber composite board is low in the prior art, so that the obtained glass fiber composite board is low in performance and user demand adaptation degree is solved. The technical effects of automatically, intelligently, optimally and automatically adjusting the production process of the resin glue solution and the adhesive sheet according to the requirements of users and improving the product performance of the glass fiber composite board and the adaptation degree of the requirements of the users are achieved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a production process optimization control method and system of a glass fiber composite board.
Background
In the current production of glass fiber composite boards, the production process for resin glue solution and bonding sheets needs to involve a great deal of parameters and technical details, and is difficult to realize fully automatic adjustment and control.
In the production process of the resin glue solution, a plurality of complex technical requirements, such as a plurality of parameters of solution concentration, mixing proportion, dipping time and the like, need to be mastered, and the parameters have a mutual influence relationship. In addition, the stability of the resin glue solution is also affected by various factors such as ambient temperature, humidity and the like, so that the production difficulty of the product is further increased.
In the prior art, the intelligent degree of the production process parameter adjustment control of the glass fiber composite board is low, so that the obtained glass fiber composite board has low performance and user demand adaptation degree.
Disclosure of Invention
The application provides a production process optimization control method and system of a glass fiber composite board, which are used for solving the technical problems in the prior art that the degree of intellectualization of adjusting and controlling production process parameters of the glass fiber composite board is low, so that the performance of the obtained glass fiber composite board is low in adaptation degree with the requirements of users.
In view of the above problems, the present application provides a method and a system for optimizing and controlling a production process of a glass fiber composite board.
In a first aspect of the present application, there is provided a method for optimizing control of a production process of a glass fiber composite panel, the method comprising: receiving preparation requirement information, wherein the preparation requirement information comprises size constraint information and performance constraint information; inputting first sub-performance constraint information of the performance constraint information into a formula library to match resin glue solution formula information, and according to the resin glue solution formula information, preparing raw materials to control resin glue solution preparation equipment to obtain resin glue solution; conveying the resin glue solution to adhesive sheet processing equipment to obtain a sample adhesive sheet; acquiring size characteristic information of the sample bonding sheet through an image acquisition device, and sampling and detecting the sample bonding sheet through sampling equipment to acquire a performance detection result; when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information or/and the size characteristic information does not meet the size constraint information, controlling, optimizing and adjusting the adhesive sheet processing equipment; the optimized and adjusted adhesive sheet processing equipment is controlled to process the resin glue solution to generate a batch of adhesive sheets; and conveying the batch of bonding sheets to a press processing device for pressing to obtain the glass fiber composite board.
Further, the method further comprises:
the first sub-performance constraint information comprises a solid content expected interval and a first gel expected duration interval; randomly generating M pieces of initial resin glue solution formula information according to the formula library, wherein the M pieces of initial resin glue solution formula information comprise M component proportions and M configuration control processes; taking a solid content detection result as a first retrieval task, taking gel time length as a second retrieval task, and carrying out production data mining based on the M component proportions and the M configuration control processes to obtain resin glue solution production log information; and acquiring the resin glue solution formula information meeting the solid content expected interval and meeting the first gel expected duration interval according to the resin glue solution production log information.
Further, the method further comprises:
acquiring a first component ratio of the M component ratios, traversing the M configuration control processes for combination, and generating a first extended formula information set; obtaining an Mth component ratio of the M component ratios, traversing the M configuration control processes for combination, and generating an Mth expansion formula information set; and traversing the first extended formula information set until the Mth extended formula information and M initial resin glue solution formula information are subjected to production data mining by taking a solid content detection result as a first retrieval task and gel duration as a second retrieval task, and obtaining the resin glue solution production log information.
Further, the method further comprises:
collecting sample image information of the sample adhesive sheet through the image collecting device; constructing an adhesive sheet conceptual model according to the preparation demand information, and performing size deviation calculation with the sample image information to generate the size characteristic information; the second sub-performance constraint information includes a desired resin content, a desired second gel duration interval, a desired resin flow, and a desired volatiles content; traversing the expected resin content, the expected second gel duration interval, the expected resin flow degree and the expected volatile content, and matching sampling process parameters and detection process parameters; and controlling a sampling mechanical arm of the performance detection equipment to sample according to the sampling process parameters, conveying the sample to a performance detection area of the performance detection equipment, and performing detection control based on the detection process parameters to obtain the performance detection result.
Further, the method further comprises:
when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information, defect index attribute information and index performance deviation values are obtained; matching the associated control parameters according to the defect index attribute information; inputting the index performance deviation value and the associated control parameter into a control parameter adjustment model, and outputting an associated control parameter optimization result; when the size characteristic information does not meet the size constraint information, acquiring size deviation distribution information, wherein the size deviation distribution information comprises a size deviation position and a size deviation value; adjusting the shearing control parameters of the adhesive sheet according to the size deviation position and the size deviation value to generate a shearing control parameter optimization result; and adjusting the adhesive sheet preparation control module of the adhesive sheet processing equipment according to the associated control parameter optimization result, and adjusting the adhesive sheet shearing control module of the adhesive sheet processing equipment according to the shearing control parameter optimization result.
Further, the method further comprises:
carrying out relevance analysis according to preset attributes of the defect indexes to obtain relevant control parameter types; taking the type of the related control parameter as a guiding variable, taking an index performance deviation characteristic value of the defect index preset attribute as a following variable, and performing data mining to generate related control parameter record data and index performance deviation record data; and taking the associated control parameter type and the index performance deviation record data as input data, taking the associated control parameter record data as output identification data, generating a control parameter adjustment model construction data set, and training a production process optimization control system of which the control parameter adjustment model is embedded in the glass fiber composite board.
Further, the method further comprises:
acquiring an adhesive sheet preparation control parameter type set according to the adhesive sheet processing equipment; taking the kth control parameter type of the bonding sheet preparation control parameter type set as a variable type, taking other control parameters of the bonding sheet preparation control parameter type set as quantitative types, and collecting bonding sheet preparation record data; the bonding sheet preparation record data comprises kth control parameter record data and index detection record data of the defect index preset attribute; carrying out pearson correlation analysis according to the kth control parameter record data and the index detection record data to obtain a correlation analysis result; and when the correlation analysis result meets a correlation threshold interval, adding the kth control parameter type into the associated control parameter type.
In a second aspect of the present application, there is provided a production process optimization control system for a glass fiber composite board, the system comprising: the preparation demand receiving module is used for receiving preparation demand information, wherein the preparation demand information comprises size constraint information and performance constraint information; the resin glue solution acquisition module is used for inputting the first sub-performance constraint information of the performance constraint information into a formula library to match the resin glue solution formula information, and according to the resin glue solution formula information, raw materials are called to control resin glue solution preparation equipment to acquire resin glue solution; the conveying processing execution module is used for conveying the resin glue solution to the adhesive sheet processing equipment to obtain a sample adhesive sheet; the performance detection execution module is used for acquiring the size characteristic information of the sample adhesive sheet through the image acquisition device, and carrying out sampling detection on the sample adhesive sheet through the sampling equipment to acquire a performance detection result; the control optimization adjustment module is used for performing control optimization adjustment on the bonding sheet processing equipment when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information or/and the size characteristic information does not meet the size constraint information; the resin glue solution processing module is used for controlling the optimized and adjusted adhesive sheet processing equipment to process the resin glue solution to generate batch adhesive sheets; and the press processing execution module is used for conveying the batch of adhesive sheets to press processing equipment for pressing to obtain the glass fiber composite board.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method provided by the embodiment of the application comprises the steps of receiving preparation requirement information, wherein the preparation requirement information comprises size constraint information and performance constraint information; inputting first sub-performance constraint information of the performance constraint information into a formula library to match resin glue formula information, and providing reliable resin glue for obtaining glass fiber composite boards meeting preparation requirement information for subsequent production by obtaining the resin glue formula information; according to the formula information of the resin glue solution, raw materials are prepared to control resin glue solution preparation equipment, and the resin glue solution is obtained; conveying the resin glue solution to adhesive sheet processing equipment to obtain a sample adhesive sheet; acquiring size characteristic information of the sample bonding sheet through an image acquisition device, sampling and detecting the sample bonding sheet through a sampling device to acquire a performance detection result, judging whether the sample bonding sheet meets the requirement of a purchasing party or not to provide scientific reference data for subsequent combination of second sub-performance constraint information, and simultaneously, performing control optimization adjustment on bonding sheet processing equipment to enable the bonding sheet processing equipment to produce effective reference information for equipment control optimization adjustment of bonding sheet production meeting the requirement; when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information or/and the size characteristic information does not meet the size constraint information, controlling, optimizing and adjusting the adhesive sheet processing equipment; the optimized and adjusted adhesive sheet processing equipment is controlled to process the resin glue solution to generate a batch of adhesive sheets; and conveying the batch of bonding sheets to a press processing device for pressing to obtain the glass fiber composite board. The technical effects of automatically, intelligently, optimally and automatically adjusting the production process of the resin glue solution and the adhesive sheet according to the requirements of users and improving the product performance of the glass fiber composite board and the adaptation degree of the requirements of the users are achieved.
Drawings
Fig. 1 is a schematic flow chart of a production process optimization control method of a glass fiber composite board provided by the application;
fig. 2 is a schematic flow chart of obtaining resin glue formulation information in the method for optimizing and controlling the production process of the glass fiber composite board provided by the application;
FIG. 3 is a schematic flow chart of obtaining performance detection results in the method for optimizing and controlling the production process of the glass fiber composite board;
fig. 4 is a schematic structural diagram of a production process optimization control system for a glass fiber composite board provided by the application.
Reference numerals illustrate: the device comprises a preparation demand receiving module 1, a resin glue solution obtaining module 2, a conveying processing executing module 3, a performance detecting executing module 4, a control optimizing and adjusting module 5, a resin glue solution processing module 6 and a pressing processing executing module 7.
Detailed Description
The application provides a production process optimization control method and system of a glass fiber composite board, which are used for solving the technical problems in the prior art that the degree of intellectualization of adjusting and controlling production process parameters of the glass fiber composite board is low, so that the performance of the obtained glass fiber composite board is low in adaptation degree with the requirements of users. The technical effects of automatically, intelligently, optimally and automatically adjusting the production process of the resin glue solution and the adhesive sheet according to the requirements of users and improving the product performance of the glass fiber composite board and the adaptation degree of the requirements of the users are achieved.
The technical scheme of the invention accords with related regulations on data acquisition, storage, use, processing and the like.
In the following, the technical solutions of the present invention 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 invention, but not all embodiments of the present invention, and that the present invention 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 invention without making any inventive effort, are intended to be within the scope of the invention. It should be further noted that, for convenience of description, only some, but not all of the drawings related to the present invention are shown.
Embodiment one: as shown in fig. 1, the present application provides a production process optimization control method of a glass fiber composite board, which is applied to a production process optimization control system of a glass fiber composite board, wherein the system is in communication connection with a resin glue solution preparation device, a bonding sheet processing device and a press processing device, and includes:
s100, receiving preparation requirement information, wherein the preparation requirement information comprises size constraint information and performance constraint information;
It should be understood that the glass fiber composite board is a composite material, uses resin (such as epoxy resin, polyester resin and the like) as an adhesive, and uses glass fiber cloth as a reinforcing material, and has the characteristics of light weight, high strength, difficult deformation, water resistance, corrosion resistance and the like, so that the glass fiber composite board is widely applied to the fields of aviation, aerospace, automobiles, ships and the like.
In this embodiment, the optimized control method of the production process of the glass fiber composite board performs targeted optimization adjustment on the resin glue formulation, the bonding sheet processing technology and the pressing processing technology of the glass fiber composite board, and correspondingly, the optimized control system of the production process of the glass fiber composite board, which applies the optimized control method of the production process of the glass fiber composite board, is correspondingly in communication connection with the resin glue preparation equipment, the bonding sheet processing equipment and the pressing processing equipment, so as to implement targeted adjustment on control parameters of the resin glue preparation equipment, the bonding sheet processing equipment and the pressing processing equipment based on centralized high efficiency of the system.
It should be understood that the logic for preparing the glass fiber composite board is to generate bonding sheets based on resin glue solution and glass fiber cloth, and the multi-layer bonding sheets are pressed and combined by processing equipment to generate the glass fiber composite board.
In this embodiment, the preparation requirement information is the requirement of the unspecified glass fiber composite board purchasing party on the performance and size aspects of the glass fiber composite board planned to be purchased. The preparation requirement information is important reference information for preparing the glass fiber composite board and is a standard for measuring whether the glass fiber composite board is qualified or not.
And the production process optimization control system of the glass fiber composite board receives and obtains the preparation demand information given by the purchasing party through letters or other reserved contact ways, wherein the preparation demand information comprises size constraint information and performance constraint information.
The size constraint information is the length, width and thickness size requirements of the purchasing side on the single-layer adhesive sheet and a size deviation range allowed interval; the performance constraint information is an expected value interval of the purchasing party on various performances of the glass fiber composite board, the various performances of the glass fiber composite board are in a corresponding expected value interval range, and the performances of the product can still be accepted.
S200, inputting first sub-performance constraint information of the performance constraint information into a formula library to match resin glue solution formula information, and calling raw materials according to the resin glue solution formula information to control resin glue solution preparation equipment to obtain resin glue solution;
In one embodiment, as shown in fig. 2, the method step S200 provided in the present application further includes:
s210, the first sub-performance constraint information comprises a solid content expected interval and a first gel expected duration interval;
s220, randomly generating M pieces of initial resin glue solution formula information according to the formula library, wherein the M pieces of initial resin glue solution formula information comprise M component proportions and M configuration control processes;
s230, taking a solid content detection result as a first retrieval task, taking gel time length as a second retrieval task, and carrying out production data mining based on the M component proportions and the M configuration control processes to obtain resin glue solution production log information;
s240, acquiring the resin glue solution formula information meeting the solid content expected interval and meeting the first gel expected duration interval according to the resin glue solution production log information.
In one embodiment, the method further includes the step of performing production data mining based on the M component ratios and the M configuration control processes to obtain production log information of the resin glue solution, where the first search task is a solid content detection result, and the second search task is a gel duration, and the step S230 of the method further includes:
S231, acquiring a first component ratio of the M component ratios, traversing the M configuration control processes to combine, and generating a first extended formula information set;
s232, obtaining an Mth component ratio of the M component ratios, traversing the M configuration control processes for combination, and generating an Mth expansion formula information set;
s233, taking a solid content detection result as a first retrieval task, taking a gel time length as a second retrieval task, traversing the first extended formula information set until the Mth extended formula information and M initial resin glue solution formula information are subjected to production data mining, and obtaining the resin glue solution production log information.
Specifically, in this embodiment, the first sub-performance constraint information is obtained based on the performance constraint information extraction, where the constraint object of the first sub-performance constraint information is a resin glue solution obtained by blending based on a certain resin glue solution formula, and is intended for glass fiber composite board production. The first sub-performance constraint information comprises a solid content expected interval and a first gel expected duration interval, wherein the solid content expected interval refers to a proportion interval of the mass of solid components in the resin glue solution to the total mass, and the first gel expected duration interval is a time span interval from the beginning of mixing of the resin glue solution to the changing of the resin glue solution into a semi-solid state (such as the beginning of gelation).
In this embodiment, based on the formula library used by the glass fiber composite board manufacturer to record the resin glue solution formula information, M pieces of initial resin glue solution formula information are obtained by random extraction, where the M pieces of initial resin glue solution formula information include M component proportions and M configuration control processes, and the configuration control processes are methods for controlling each link of selection, mixing, processing, quality control, and the like of raw materials included in the component proportions.
And randomly extracting the M component proportions in a mode of taking out and not replacing to obtain a first component proportion, combining the first component proportion by traversing the M configuration control processes to generate a first extended formula information set, wherein the first extended formula information set is M extended formulas with resin glue liquid formulas fixed to be the first component proportion and changed in the resin glue liquid configuration control process.
And adopting the same method for obtaining the first extended formula information set, randomly extracting the first extended formula information set from the M component ratios in a taking-out and non-returning mode to obtain a second component ratio, traversing the M configuration control processes to combine the first extended formula information set, and the like until the M component ratios of the M component ratios are obtained, traversing the M configuration control processes to combine the second extended formula information set, and generating the M extended formula information set.
Obtaining M initial resin glue solution formula information, and the first to the M extended formula information sets, wherein the production quality detection information of all the resin glue solution formulas comprises multi-dimensional resin glue solution production quality detection.
According to the embodiment, a solid content detection result is taken as a first search task, a gel duration is taken as a second search task, the production quality detection information is traversed to carry out production data mining, the resin glue solution production log information is obtained, the resin glue solution production log information only comprises M initial resin glue solution formula information, a first expansion formula information set and an M expansion formula information set, and resin gel production quality detection data of two dimensions of solid content detection result information and gel duration detection result information of all resin glue solution formulas.
And acquiring the resin glue solution formula information meeting the solid content expected interval and meeting the first gel expected duration interval according to the resin glue solution production log information.
If the solid content expected interval is met and the resin glue solution formulas meeting the first gel expected duration interval are K (K is a positive integer greater than 1), further obtaining the production cost of the K resin glue solution formulas, serializing the K production costs, and extracting the resin glue solution formulas corresponding to the minimum value of the production cost as the resin glue solution formula information.
The technical effect of improving the probability of obtaining the resin glue solution formula information conforming to the first sub-performance constraint information is achieved by expanding the resin glue solution formula, and the technical effect of providing reliable resin glue solution for obtaining the glass fiber composite board conforming to the preparation requirement information for subsequent production is achieved by obtaining the resin glue solution formula information.
S300, conveying the resin glue solution to adhesive sheet processing equipment to obtain a sample adhesive sheet;
specifically, in this embodiment, after the resin glue formulation information is obtained based on step S200, raw materials are prepared based on the component proportions in the numerical glue formulation information, and the resin glue preparation device is controlled according to the configuration control process in the numerical glue formulation information to obtain the resin glue.
The automatic processing equipment of bonding sheet processing equipment for at least integrated guide roll, gluey groove, crowded rubber roll, oven, cutting equipment is based on cutting control module and is carried out bonding sheet shearing control parameter's adjustment, cutting equipment's initial bonding sheet shearing control parameter is based on preparation demand information includes size constraint information setting, guide roll, gluey groove, crowded rubber roll, oven's control parameter is based on bonding sheet preparation control module adjusts.
The resin glue solution is conveyed and injected into a glue tank of the bonding sheet processing equipment, in the bonding sheet processing equipment, the glass fiber cloth is uncoiled and then enters the glue tank for glue dipping through a guide roller, the resin content of the glass fiber cloth is adjusted through a glue extrusion roller after glue dipping, and then volatile matters such as solvents in the resin glue solution are removed through an oven, so that the resin glue solution is in a semi-solidified state.
And after the glass fiber composite board is discharged out of the oven, the size requirement of the glass fiber composite board is extracted according to the preparation requirement information, and the cutting equipment is controlled to automatically cut the adhesive sheet, so that the sample adhesive sheet is obtained.
S400, acquiring size characteristic information of the sample adhesive sheet through an image acquisition device, and sampling and detecting the sample adhesive sheet through sampling equipment to acquire a performance detection result;
in one embodiment, as shown in fig. 3, the production process optimization control system applied to the glass fiber composite board is in communication connection with a performance detection device, the size characteristic information of the sample bonding sheet is collected through an image collecting device, the sample bonding sheet is sampled and detected through a sampling device, and a performance detection result is obtained, and a method step S400 provided by the application further includes:
S410, collecting sample image information of the sample adhesive sheet through the image collecting device;
s420, constructing an adhesive sheet conceptual model according to the preparation demand information, and performing size deviation calculation with the sample image information to generate the size characteristic information;
s430, the second sub-performance constraint information comprises a resin expected content, a second gel expected duration interval, a resin flow expected degree and a volatile expected content;
s440, traversing the expected resin content, the expected second gel duration interval, the expected resin flow degree and the expected volatile content, and matching sampling process parameters and detection process parameters;
s450, controlling a sampling mechanical arm of the performance detection equipment to sample according to the sampling process parameters, conveying the sample to a performance detection area of the performance detection equipment, and performing detection control based on the detection process parameters to obtain the performance detection result.
Specifically, in this embodiment, the performance detecting device is a comprehensive device integrating an image capturing device and a sampling device, where the image capturing device is preferably an industrial camera, and based on the image capturing device, the size characteristic information of the sample adhesive sheet can be captured.
In this embodiment, the performance detection device is in communication connection with the production process optimization control system of the glass fiber composite board, so as to transfer the data information obtained by the performance detection device.
And carrying out omnibearing multi-angle image acquisition on the sample adhesive sheet through the image acquisition device to obtain sample image information, wherein the sample image information marks the length, width and thickness dimension data of the sample adhesive sheet.
Obtaining the length, width and thickness dimension requirements of the purchasing side on the single-layer adhesive sheet according to the preparation requirement information, and constructing the adhesive sheet conceptual model for representing the adhesive sheet dimension frame by adopting CAD software.
And intuitively carrying out size deviation calculation on the sample image information and the size constraint information based on the adhesive sheet conceptual model, and generating the size characteristic information, wherein the size characteristic information consists of a length characteristic deviation value, a width characteristic deviation value and a thickness characteristic deviation value.
And extracting and obtaining second sub-performance constraint information based on the performance constraint information, wherein a constraint object of the second sub-performance constraint information is resin glue solution in the sample bonding sheet. The second sub-performance constraint information comprises a resin expected content, a second gel expected duration interval, a resin flowing expected degree and a volatile expected content, wherein the numerical expected content is the resin content in an adhesive sheet produced by the adhesive sheet processing equipment, the second gel expected duration interval is a time-consuming interval for solidifying a resin glue solution to a semi-solidified state in an oven of the adhesive sheet processing equipment, the resin flowing expected degree is expected for the flowing capability of the resin glue solution in the semi-solidified state in the adhesive sheet produced by the adhesive sheet processing equipment, and the volatile expected content is the content expected value of the volatile in the adhesive sheet after the oven of the adhesive sheet processing equipment is processed.
The sampling process parameters are sampling position and sampling size requirement information for representative sampling of the sample adhesive sheet, and the detection process parameters are detection flow information for obtaining scientific and credible sample adhesive sheet detection results.
Traversing the expected resin content, the expected second gel duration interval, the expected resin flow degree and the expected volatile content one by one through big data, and obtaining the sampling process parameters and the detection process parameters based on big data retrieval and matching.
Illustratively, using the desired resin content as search information, in a browser/expertise website (e.g., web), search matching to obtain disclosed other user histories as follows for sampling process parameters and detection process parameters of the produced adhesive sheet design based on the desired resin content:
sampling process parameters: at least 20mm of the edge of the adhesive sheet, four points were obtained in the width direction, and 4 samples were cut at the four points with sample sizes of 100mm×100 mm.
Detecting technological parameters: adhesive sheet Zhang Chenchong (M) 1 ) Accurate to 0.001g. And (4) placing the 4 samples in a muffle furnace at 550-620 ℃ and burning until all carbide is removed. The 4 samples were transferred to a desiccator and cooled to room temperature. For the surplus adhesive sheet Zhang Chenchong (M 2 ) Accurate to 0.001g. Resin content = [ (M) 1 -M 2 )/M 1 ]×100％。
The method for detecting the content of the resin comprises the steps of obtaining the sampling process parameters and the detection process parameters of the gel duration, the resin fluidity and the volatile content by adopting the same method for obtaining the sampling process parameters and the detection process parameters for detecting the content of the resin.
And controlling a sampling mechanical arm of the performance detection equipment to sample according to the sampling process parameters, conveying the sample to a performance detection area of the performance detection equipment, and detecting and controlling based on the detection process parameters to obtain the performance detection result, wherein the performance detection result comprises a resin content detection result, a gel duration detection result, a resin fluidity detection result and a volatile content detection result.
According to the embodiment, the performance detection result representing the performance of the sample bonding sheet is obtained by performing performance detection on the sample bonding sheet, scientific reference data is provided for judging whether the sample bonding sheet meets the demand of a purchasing party or not by combining the second sub-performance constraint information subsequently, and meanwhile, the control optimization adjustment is performed on the bonding sheet processing equipment subsequently, so that the bonding sheet processing equipment produces effective reference information for the control optimization adjustment of the equipment, which meets the requirements, of the bonding sheet providing equipment.
S500, when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information or/and the size characteristic information does not meet the size constraint information, controlling, optimizing and adjusting the adhesive sheet processing equipment;
in one embodiment, when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information, or/and the size feature information does not meet the size constraint information, the control optimization adjustment is performed on the adhesive sheet processing device, and the method step S500 provided in the application further includes:
s510, when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information, obtaining defect index attribute information and index performance deviation values;
s520, matching the associated control parameters according to the defect index attribute information;
s530, inputting the index performance deviation value and the associated control parameter into a control parameter adjustment model, and outputting an associated control parameter optimization result;
s540, when the size characteristic information does not meet the size constraint information, acquiring size deviation distribution information, wherein the size deviation distribution information comprises a size deviation position and a size deviation value;
S550, adjusting the shearing control parameters of the adhesive sheet according to the size deviation position and the size deviation value to generate a shearing control parameter optimization result;
and S560, adjusting the adhesive sheet preparation control module of the adhesive sheet processing equipment according to the associated control parameter optimization result, and adjusting the adhesive sheet shearing control module of the adhesive sheet processing equipment according to the shearing control parameter optimization result.
In one embodiment, the method step S530 further includes:
s531, carrying out relevance analysis according to preset attributes of defect indexes to obtain relevant control parameter types;
s532, performing data mining by taking the type of the associated control parameter as a guiding variable and taking an index performance deviation characteristic value of the preset attribute of the defect index as a following variable to generate associated control parameter record data and index performance deviation record data;
and S533, taking the associated control parameter type and the index performance deviation record data as input data, taking the associated control parameter record data as output identification data, generating a control parameter adjustment model construction data set, and training a production process optimization control system of which the control parameter adjustment model is embedded in the glass fiber composite board.
In one embodiment, the correlation analysis is performed according to the preset attribute of the defect index, so as to obtain the type of the correlation control parameter, and the method provided in the application step S531 further includes:
s531-1, acquiring an adhesive sheet preparation control parameter type set according to the adhesive sheet processing equipment;
s531-2, collecting adhesive sheet preparation record data by taking the kth control parameter type of the adhesive sheet preparation control parameter type set as a variable type and taking other control parameters of the adhesive sheet preparation control parameter type set as quantitative types;
s531-3, the adhesive sheet preparation record data comprises kth control parameter record data and index detection record data of the defect index preset attribute;
s531-4, carrying out Pearson correlation analysis according to the kth control parameter record data and the index detection record data to obtain a correlation analysis result;
and S531-5, adding the kth control parameter type into the associated control parameter type when the correlation analysis result meets a correlation threshold interval.
Specifically, in this embodiment, the performance detection result obtained in step S400 is compared with the second sub-performance constraint information one by one according to a corresponding relationship, and when a performance detection index item of the second sub-performance constraint information satisfying the performance constraint information exists in the performance detection result, defect index attribute information and index performance deviation values are obtained.
For example, if the gel duration detection result does not meet (deviate from) the second gel expected duration interval, calculating a deviation value of duration data of the gel duration detection result, which is greater than the maximum value of the second gel expected duration interval or less than the minimum value of the second gel expected duration interval, as the index performance deviation value, and taking the gel duration as the defect index attribute information.
And matching associated control parameters according to the defect index attribute information, wherein the associated control parameters are adjustment values of a plurality of control parameters of the adhesive sheet processing equipment which have an association relationship with the defect index attribute information.
The optimal method for matching and determining the associated control parameters is as follows:
in this embodiment, based on a control panel of the adhesive sheet processing apparatus, all control parameters adjustable in the operation process of the adhesive sheet processing apparatus are obtained to form the adhesive sheet preparation control parameter type set.
And taking the kth control parameter type of the bonding sheet preparation control parameter type set as a variable type and taking other control parameters of the bonding sheet preparation control parameter type set as quantitative types. And acquiring historical bonding sheet production data of the bonding sheet processing equipment, extracting and acquiring bonding sheet preparation record data based on the historical bonding sheet production data of the bonding sheet processing equipment, wherein the bonding sheet preparation record data comprises kth control parameter record data and index detection record data of the defect index preset attribute, for example, the kth control parameter record data is 50 pieces of drying temperature data, and the index detection record data of the defect index preset attribute is 50 pieces of gel duration data.
And carrying out pearson correlation analysis according to the k control parameter record data and the index detection record data to obtain a correlation analysis result, wherein the correlation analysis result is pearson correlation coefficient data of indexes corresponding to the defect index attribute information and the k control parameters.
A correlation threshold interval is preset, for example, the pearson correlation coefficient is between 0.6 and 1.0. When the correlation analysis result meets a correlation threshold interval, adding the kth control parameter type into the correlation control parameter type, traversing by adopting the same method to obtain the correlation analysis result of all control parameters in the adhesive sheet preparation control parameter type set, comparing the correlation analysis result with the correlation threshold interval, and filling the correlation control parameter type based on the comparison result.
And acquiring historical bonding sheet production data of the bonding sheet processing equipment, performing data mining on the historical bonding sheet production data by taking the type of the associated control parameter as a guiding variable and taking an index performance deviation characteristic value of the defect index preset attribute as a following variable, and generating associated control parameter record data and index performance deviation record data based on the mapping relation of the bonding sheet.
And constructing the control parameter adjustment model based on the BP neural network, wherein the input data of the control parameter adjustment model is an index performance deviation value and the associated control parameter, the output result is an associated control parameter optimization result, and the associated control parameter optimization result is an adjustment value of the associated control parameter.
And taking the associated control parameter type and the index performance deviation record data as input data, taking the associated control parameter record data as output identification data, generating a control parameter adjustment model construction data set, and dividing the construction data set identification into a training set, a test set and a verification set. And training and testing the control parameter adjustment model based on the training set and the testing set, verifying the model output accuracy based on the verification set until the model output accuracy is higher than 97%, stopping model training, and embedding the control parameter adjustment model into the production process optimization control system of the glass fiber composite board. And inputting the index performance deviation value and the associated control parameter into a control parameter adjustment model, and outputting an associated control parameter optimization result.
And judging whether the size characteristic information meets the size constraint information, and acquiring size deviation distribution information when the size characteristic information does not meet the size constraint information, wherein the size deviation distribution information comprises a size deviation position and a size deviation value, for example, the size deviation position is left width deviation, and the size deviation value is width exceeding the size constraint information by 0.3mm. And adjusting the shearing control parameters of the initial adhesive sheet according to the size deviation position and the size deviation value to generate the shearing control parameter optimization result.
Based on the step S300, in this embodiment, the cutting device integrated in the adhesive sheet processing device adjusts the control parameters of the guide roller, the adhesive groove, the glue extruding roller, and the oven integrated with the adjustment of the adhesive sheet cutting control parameters based on the cutting control module, and adjusts the control parameters based on the adhesive sheet preparation control module.
Therefore, in this embodiment, the adhesive sheet preparation control module of the adhesive sheet processing apparatus is adjusted according to the result of optimizing the associated control parameter, and the adhesive sheet shearing control module of the adhesive sheet processing apparatus is adjusted according to the result of optimizing the shearing control parameter, so as to complete the control optimization adjustment of the adhesive sheet processing apparatus.
The embodiment realizes the accurate and intelligent adjustment of the adhesive sheet production control parameters according to the preparation requirements of users so as to produce and obtain the technical effect of the single-layer adhesive sheet meeting the preparation requirements of the users.
S600, controlling the optimized and adjusted adhesive sheet processing equipment to process the resin glue solution to generate batch adhesive sheets;
and S700, conveying the batch of bonding sheets to a press processing device for pressing to obtain the glass fiber composite board.
Specifically, in this embodiment, the operation of the bonding sheet processing device that is optimally adjusted is controlled, and mass production of the bonding sheets is performed by using the resin glue solution and the glass fiber cloth as raw materials, so as to obtain the mass bonding sheets, where the number of the mass bonding sheets meets the requirement for the number of bonding sheet layers of the glass fiber composite board in the preparation requirement information.
And conveying the batch of adhesive sheets to a press processing device for pressing through a conveying device. In the pressing process, the resin glue solution in the adhesive sheet is activated and forms a connecting material through a plurality of process steps of heating, applying pressure and the like. Finally, the bonding sheets are tightly compacted by the action of the press processing equipment and form a tough, durable glass fiber composite panel.
The embodiment realizes the automatic intelligent optimization and adjustment of the production process of the resin glue solution and the bonding sheet according to the user requirements, and the production of the glass fiber composite board meeting the user performance requirements has the technical effect.
Embodiment two: based on the same inventive concept as the production process optimization control method of a glass fiber composite board in the foregoing embodiment, as shown in fig. 4, the present application provides a production process optimization control system of a glass fiber composite board, wherein the system includes:
a preparation demand receiving module 1, configured to receive preparation demand information, where the preparation demand information includes size constraint information and performance constraint information;
the resin glue solution acquisition module 2 is used for inputting first sub-performance constraint information of the performance constraint information into a formula library to match resin glue solution formula information, and according to the resin glue solution formula information, raw materials are called to control resin glue solution preparation equipment to acquire resin glue solution;
The conveying and processing execution module 3 is used for conveying the resin glue solution to adhesive sheet processing equipment to obtain a sample adhesive sheet;
the performance detection execution module 4 is used for acquiring the size characteristic information of the sample adhesive sheet through the image acquisition device, and carrying out sampling detection on the sample adhesive sheet through the sampling equipment to acquire a performance detection result;
a control optimization adjustment module 5, configured to perform control optimization adjustment on the adhesive sheet processing apparatus when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information, or/and the size characteristic information does not meet the size constraint information;
the resin glue solution processing module 6 is used for controlling the optimized and adjusted adhesive sheet processing equipment to process the resin glue solution to generate batch adhesive sheets;
and the press processing execution module 7 is used for conveying the batch of adhesive sheets to press processing equipment for pressing to obtain the glass fiber composite board.
In one embodiment, the system further comprises:
a desired interval obtaining unit, configured to obtain, according to the first sub-performance constraint information, a desired solid content interval and a desired first gel duration interval;
the formula information obtaining unit is used for randomly generating M pieces of initial resin glue solution formula information according to the formula library, wherein the M pieces of initial resin glue solution formula information comprise M component proportions and M configuration control processes;
The production log obtaining unit is used for carrying out production data mining based on the M component proportions and the M configuration control processes by taking a solid content detection result as a first retrieval task and a gel duration as a second retrieval task to obtain resin glue solution production log information;
the glue solution formula obtaining unit is used for obtaining the resin glue solution formula information meeting the solid content expected interval and meeting the first gel expected duration interval according to the resin glue solution production log information.
In one embodiment, the system further comprises:
an extended recipe obtaining unit, configured to obtain a first component ratio of the M component ratios, traverse the M configuration control processes, and combine the first component ratios to generate a first extended recipe information set;
an extended formula obtaining unit, configured to obtain an mth component ratio of the M component ratios, traverse the M configuration control processes, and combine the M configuration control processes to generate an mth extended formula information set;
and the production data mining unit is used for taking a solid content detection result as a first retrieval task, taking a gel duration as a second retrieval task, traversing the first extended formula information set until the M-th extended formula information and M initial resin glue solution formula information are subjected to production data mining, and obtaining the resin glue solution production log information.
In one embodiment, the system further comprises:
the sample image acquisition unit is used for acquiring sample image information of the sample adhesive sheet through the image acquisition device;
the dimension characteristic calculation unit is used for constructing an adhesive sheet conceptual model according to the preparation requirement information, calculating dimension deviation with the sample image information and generating dimension characteristic information;
a constraint information obtaining unit, configured to obtain second sub-performance constraint information including a desired resin content, a desired second gel duration interval, a desired resin flow degree, and a desired volatile content;
the traversal matching execution unit is used for traversing the expected resin content, the expected second gel duration interval, the expected resin flow degree and the expected volatile content, and matching sampling process parameters and detection process parameters;
and the detection control execution unit is used for controlling the sampling mechanical arm of the performance detection equipment to sample according to the sampling process parameters, conveying the sample to the performance detection area of the performance detection equipment, and carrying out detection control based on the detection process parameters to obtain the performance detection result.
In one embodiment, the system further comprises:
A defect deviation obtaining unit, configured to obtain defect index attribute information and an index performance deviation value when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information;
a control parameter matching unit, configured to match associated control parameters according to the defect indicator attribute information;
the control parameter optimization unit is used for inputting the index performance deviation value and the associated control parameter into a control parameter adjustment model and outputting an associated control parameter optimization result;
a deviation distribution obtaining unit configured to obtain size deviation distribution information when the size characteristic information does not satisfy the size constraint information, wherein the size deviation distribution information includes a size deviation position and a size deviation value;
the control parameter optimizing unit is used for adjusting the shearing control parameters of the adhesive sheet according to the size deviation position and the size deviation value to generate a shearing control parameter optimizing result;
and the control module adjusting unit is used for adjusting the adhesive sheet preparation control module of the adhesive sheet processing equipment according to the associated control parameter optimizing result and adjusting the adhesive sheet shearing control module of the adhesive sheet processing equipment according to the shearing control parameter optimizing result.
In one embodiment, the system further comprises:
the association analysis execution unit is used for carrying out association analysis according to the preset attribute of the defect index to obtain an association control parameter type;
the record data obtaining unit is used for carrying out data mining by taking the type of the associated control parameter as a guiding variable and taking an index performance deviation characteristic value of the preset attribute of the defect index as a following variable to generate associated control parameter record data and index performance deviation record data;
the model training execution unit is used for taking the associated control parameter type and the index performance deviation record data as input data, taking the associated control parameter record data as output identification data, generating a control parameter adjustment model construction data set, and training the control parameter adjustment model to be embedded in the production process optimization control system of the glass fiber composite board.
In one embodiment, the system further comprises:
the control parameter acquisition unit is used for acquiring a control parameter type set for preparing the adhesive sheet according to the adhesive sheet processing equipment;
the recording data acquisition unit is used for acquiring the adhesive sheet preparation recording data by taking the kth control parameter type of the adhesive sheet preparation control parameter type set as a variable type and taking other control parameters of the adhesive sheet preparation control parameter type set as a quantitative type;
The record data expansion unit is used for preparing record data of the bonding sheet, wherein the record data comprises kth control parameter record data and index detection record data of the defect index preset attribute;
the correlation analysis unit is used for carrying out pearson correlation analysis according to the kth control parameter record data and the index detection record data to obtain a correlation analysis result;
and the control parameter adding unit is used for adding the kth control parameter type into the associated control parameter type when the correlation analysis result meets a correlation threshold interval.
Any of the methods or steps described above may be stored as computer instructions or programs in various non-limiting types of computer memories, and identified by various non-limiting types of computer processors, thereby implementing any of the methods or steps described above.
Based on the above-mentioned embodiments of the present invention, any improvements and modifications to the present invention without departing from the principles of the present invention should fall within the scope of the present invention.
Claims (6)
1. The production process optimization control method of the glass fiber composite board is characterized by being applied to a production process optimization control system of the glass fiber composite board, wherein the system is in communication connection with resin glue solution preparation equipment, bonding sheet processing equipment and pressing processing equipment, and comprises the following steps:
Receiving preparation requirement information, wherein the preparation requirement information comprises size constraint information and performance constraint information;
inputting first sub-performance constraint information of the performance constraint information into a formula library to match resin glue solution formula information, and according to the resin glue solution formula information, preparing raw materials, controlling resin glue solution preparation equipment to obtain resin glue solution;
conveying the resin glue solution to adhesive sheet processing equipment to obtain a sample adhesive sheet;
acquiring size characteristic information of the sample bonding sheet through an image acquisition device, and sampling and detecting the sample bonding sheet through sampling equipment to acquire a performance detection result;
when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information or/and the size characteristic information does not meet the size constraint information, controlling, optimizing and adjusting the adhesive sheet processing equipment;
the optimized and adjusted adhesive sheet processing equipment is controlled to process the resin glue solution to generate a batch of adhesive sheets;
conveying the batch of bonding sheets to a press processing device for pressing to obtain a glass fiber composite board;
inputting the first sub-performance constraint information of the performance constraint information into a formula library to match resin glue solution formula information, wherein the method comprises the following steps:
The first sub-performance constraint information comprises a solid content expected interval and a first gel expected duration interval;
randomly generating M pieces of initial resin glue solution formula information according to the formula library, wherein the M pieces of initial resin glue solution formula information comprise M component proportions and M configuration control processes;
taking a solid content detection result as a first retrieval task, taking gel time length as a second retrieval task, and carrying out production data mining based on the M component proportions and the M configuration control processes to obtain resin glue solution production log information;
acquiring the resin glue solution formula information meeting the solid content expected interval and meeting the first gel expected duration interval according to the resin glue solution production log information;
be applied to glass fiber composite sheet's production technology optimization control system, system and performance detection equipment communication connection gather through image acquisition device the size characteristic information of sample bonding piece, through sampling equipment with sample bonding piece carries out the sample detection, acquires the performance testing result, includes:
collecting sample image information of the sample adhesive sheet through the image collecting device;
Constructing an adhesive sheet conceptual model according to the preparation demand information, and performing size deviation calculation with the sample image information to generate the size characteristic information;
the second sub-performance constraint information includes a desired resin content, a desired second gel duration interval, a desired resin flow, and a desired volatiles content;
traversing the expected resin content, the expected second gel duration interval, the expected resin flow degree and the expected volatile content, and matching sampling process parameters and detection process parameters;
and controlling a sampling mechanical arm of the performance detection equipment to sample according to the sampling process parameters, conveying the sample to a performance detection area of the performance detection equipment, and performing detection control based on the detection process parameters to obtain the performance detection result.
2. The method of claim 1, wherein the step of performing production data mining based on the M component ratios and the M configuration control processes with the solid content detection result as a first search task and the gel duration as a second search task to obtain the resin glue production log information comprises:
acquiring a first component ratio of the M component ratios, traversing the M configuration control processes for combination, and generating a first extended formula information set;
Obtaining an Mth component ratio of the M component ratios, traversing the M configuration control processes for combination, and generating an Mth expansion formula information set;
and traversing the first extended formula information set until the M-th extended formula information set and M initial resin glue solution formula information are subjected to production data mining by taking a solid content detection result as a first retrieval task and gel duration as a second retrieval task, and obtaining the resin glue solution production log information.
3. The method according to claim 1, wherein when the performance detection result does not satisfy the second sub-performance constraint information of the performance constraint information, or/and the size feature information does not satisfy the size constraint information, performing control optimization adjustment on the adhesive sheet processing apparatus includes:
when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information, defect index attribute information and index performance deviation values are obtained;
matching the associated control parameters according to the defect index attribute information;
inputting the index performance deviation value and the associated control parameter into a control parameter adjustment model, and outputting an associated control parameter optimization result;
When the size characteristic information does not meet the size constraint information, acquiring size deviation distribution information, wherein the size deviation distribution information comprises a size deviation position and a size deviation value;
adjusting the shearing control parameters of the adhesive sheet according to the size deviation position and the size deviation value to generate a shearing control parameter optimization result;
and adjusting the adhesive sheet preparation control module of the adhesive sheet processing equipment according to the associated control parameter optimization result, and adjusting the adhesive sheet shearing control module of the adhesive sheet processing equipment according to the shearing control parameter optimization result.
4. The method of claim 3, wherein inputting the index performance bias value and the associated control parameter into a control parameter adjustment model, outputting an associated control parameter optimization result, comprises:
carrying out relevance analysis according to preset attributes of the defect indexes to obtain relevant control parameter types;
taking the type of the related control parameter as a guiding variable, taking an index performance deviation characteristic value of the defect index preset attribute as a following variable, and performing data mining to generate related control parameter record data and index performance deviation record data;
And taking the associated control parameter type and the index performance deviation record data as input data, taking the associated control parameter record data as output identification data, generating a control parameter adjustment model construction data set, and training a production process optimization control system of which the control parameter adjustment model is embedded in the glass fiber composite board.
5. The method of claim 4, wherein performing the correlation analysis according to the defect indicator preset attribute to obtain the type of the correlation control parameter comprises:
acquiring an adhesive sheet preparation control parameter type set according to the adhesive sheet processing equipment;
taking the kth control parameter type of the bonding sheet preparation control parameter type set as a variable type, taking other control parameters of the bonding sheet preparation control parameter type set as quantitative types, and collecting bonding sheet preparation record data;
the bonding sheet preparation record data comprises kth control parameter record data and index detection record data of the defect index preset attribute;
carrying out pearson correlation analysis according to the kth control parameter record data and the index detection record data to obtain a correlation analysis result;
and when the correlation analysis result meets a correlation threshold interval, adding the kth control parameter type into the associated control parameter type.
6. A system for optimizing control of a production process of a glass fiber composite board, the system comprising:
the preparation demand receiving module is used for receiving preparation demand information, wherein the preparation demand information comprises size constraint information and performance constraint information;
the resin glue solution acquisition module is used for inputting the first sub-performance constraint information of the performance constraint information into a formula library to match the resin glue solution formula information, and according to the resin glue solution formula information, raw materials are prepared, and resin glue solution preparation equipment is controlled to obtain resin glue solution;
the conveying processing execution module is used for conveying the resin glue solution to the adhesive sheet processing equipment to obtain a sample adhesive sheet;
the performance detection execution module is used for acquiring the size characteristic information of the sample adhesive sheet through the image acquisition device, and carrying out sampling detection on the sample adhesive sheet through the sampling equipment to acquire a performance detection result;
the control optimization adjustment module is used for performing control optimization adjustment on the bonding sheet processing equipment when the performance detection result does not meet the second sub-performance constraint information of the performance constraint information or/and the size characteristic information does not meet the size constraint information;
The resin glue solution processing module is used for controlling the optimized and adjusted adhesive sheet processing equipment to process the resin glue solution to generate batch adhesive sheets;
the pressing execution module is used for conveying the batch of adhesive sheets to pressing equipment for pressing to obtain a glass fiber composite board;
the system further comprises:
a desired interval obtaining unit, configured to obtain, according to the first sub-performance constraint information, a desired solid content interval and a desired first gel duration interval;
the formula information obtaining unit is used for randomly generating M pieces of initial resin glue solution formula information according to the formula library, wherein the M pieces of initial resin glue solution formula information comprise M component proportions and M configuration control processes;
the production log obtaining unit is used for carrying out production data mining based on the M component proportions and the M configuration control processes by taking a solid content detection result as a first retrieval task and a gel duration as a second retrieval task to obtain resin glue solution production log information;
the glue solution formula obtaining unit is used for obtaining the resin glue solution formula information meeting the solid content expected interval and meeting the first gel expected duration interval according to the resin glue solution production log information;
The sample image acquisition unit is used for acquiring sample image information of the sample adhesive sheet through the image acquisition device;
the dimension characteristic calculation unit is used for constructing an adhesive sheet conceptual model according to the preparation requirement information, calculating dimension deviation with the sample image information and generating dimension characteristic information;
a constraint information obtaining unit, configured to obtain second sub-performance constraint information including a desired resin content, a desired second gel duration interval, a desired resin flow degree, and a desired volatile content;
the traversal matching execution unit is used for traversing the expected resin content, the expected second gel duration interval, the expected resin flow degree and the expected volatile content, and matching sampling process parameters and detection process parameters;
and the detection control execution unit is used for controlling the sampling mechanical arm of the performance detection equipment to sample according to the sampling process parameters, conveying the sample to the performance detection area of the performance detection equipment, and carrying out detection control based on the detection process parameters to obtain the performance detection result.
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