CN114202262B - Prepreg process improvement method and system based on neural network and storage medium - Google Patents
Prepreg process improvement method and system based on neural network and storage medium Download PDFInfo
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Abstract
The invention discloses a prepreg process improvement method, a prepreg process improvement system and a storage medium based on a neural network, and belongs to the field of prepreg processing, wherein the method comprises the following steps: acquiring user order information to match a raw material sheet, and generating an experiment data sheet and a processing data sheet based on the raw material sheet; extracting the experimental data list and outputting a training experiment table to a user; and extracting corresponding parameter values of the trained neural network model, and combining the processing data sheet to generate a processing production table to be output to the user side. The invention sets two categories of training experiment and processing production, utilizes the neural network model to carry out the training experiment to obtain each parameter up to the standard, further carries out specific processing production based on different parameters, can effectively guarantee the yield, and can carry out adaptation according to the requirements of different users.
Description
Technical Field
The invention relates to the technical field of prepreg processing, in particular to a prepreg process improvement method and system based on a neural network and a storage medium.
Background
The prepreg is an intermediate material for manufacturing a composite material, wherein resin is combined with fibers in advance before being cured, the service life of the prepreg is kept within a certain range, rows of layers can be laid and molded at any time within the service life range, and the prepreg is particularly used for manufacturing the composite material.
The prepreg is classified into a unidirectional prepreg, a unidirectional fabric prepreg, and a fabric prepreg according to physical state; the prepreg is divided into thermosetting resin prepreg and thermoplastic resin prepreg according to different resin matrixes; according to different reinforcing materials, the prepreg is divided into carbon fiber (fabric) prepreg, glass fiber (fabric) prepreg and aramid fiber (fabric) prepreg; according to different fiber lengths, the prepreg is divided into short fiber prepreg, long fiber prepreg and continuous fiber prepreg; according to the curing temperature difference, divide into medium temperature solidification preimpregnation material, high temperature solidification preimpregnation material etc. and the different processing production standard of purchasing demand correspondence of user difference, but the majority is the manual differentiation among the prior art, and the defective percentage is difficult to can be ensured.
Disclosure of Invention
The invention aims to provide a prepreg process improvement method, a prepreg process improvement system and a storage medium based on a neural network, wherein a neural network model is used for training experiments to obtain various parameters reaching the standard, and further, specific processing production is carried out based on different parameters, so that the yield can be effectively guaranteed, and adaptation can be carried out according to the requirements of different users.
The invention provides a prepreg process improvement method based on a neural network, which comprises the following steps:
acquiring user order information to match a raw material sheet, and generating an experiment data sheet and a processing data sheet based on the raw material sheet;
extracting the experimental data list and outputting a training experiment table to a user side, wherein feedback data of the user side are obtained, and stress, temperature and curing parameters are changed by using a preset neural network model so as to update the training experiment table;
and after the output result of the training experiment reaches a preset standard, extracting the corresponding parameter values of the trained neural network model, and combining the processing data sheet to generate a processing production table to output the processing production table to the user side.
In this scheme, obtaining user order information and matching a raw material sheet, generating an experimental data sheet and a processing data sheet based on the raw material sheet, specifically include:
matching the user order information based on a preset raw material database and outputting the raw material list;
identifying the quantity grades of the raw materials of the current raw material list, wherein different quantity grades are matched with corresponding classification proportions;
and classifying the current raw material sheet according to the classification proportion to obtain the corresponding experimental data sheet and the corresponding processing data sheet.
In this scheme, the different quantity grades correspond to different classification proportions, and specifically include:
obtaining the raw material quantity grades based on the raw material type, the raw material specific gravity and the raw material weighing of the current raw material sheet, wherein the raw material quantity grades are divided into I grade, II grade and III grade;
the classification proportion corresponding to the raw material quantity grade I and the raw material quantity grade II is a first proportion, and the classification proportion corresponding to the raw material quantity grade III is a second proportion.
In this scheme, the extracting the experimental data list and outputting a training test table to a user side, wherein feedback data of the user side is obtained, and a preset neural network model is used to change stress, temperature and curing parameters, so as to update the training test table, and the method specifically includes:
outputting the corresponding training experiment table to the user side based on the experiment data sheet so as to carry out a prenatal experiment;
in the experimental process, the stress, the temperature and the curing parameters are updated by utilizing the neural network model through acquiring the feedback data of the user side, so that the training experiment table is continuously updated to be used by the user side for carrying out the prenatal experiment;
and stopping the prenatal experiment after the experiment result of the prenatal experiment is recognized to reach the preset standard, and recording the current parameter values of the neural network model.
In this scheme, after the output result of the experiment to be trained reaches the preset standard, extracting the corresponding parameter values of the trained neural network model, and generating a processing production table by combining the processing data sheet to output to the user side, specifically including:
when the antenatal experiment is ended, extracting each parameter value of the current neural network model as a first data packet;
and generating the processing production table based on the first data packet and the processing data sheet so as to output the processing production table to the user side for the user side to perform processing production.
In this scheme, the method further includes extracting the first data packet for visual display.
The second aspect of the present invention also provides a prepreg process improvement system based on a neural network, which includes a memory and a processor, wherein the memory includes a prepreg process improvement method program based on the neural network, and when executed by the processor, the prepreg process improvement method program based on the neural network realizes the following steps:
acquiring user order information to match a raw material sheet, and generating an experiment data sheet and a processing data sheet based on the raw material sheet;
extracting the experimental data list and outputting a training experiment table to a user side, wherein feedback data of the user side are obtained, and stress, temperature and curing parameters are changed by using a preset neural network model so as to update the training experiment table;
and after the output result of the training experiment reaches a preset standard, extracting the corresponding parameter values of the trained neural network model, and combining the processing data sheet to generate a processing production table to output the processing production table to the user side.
In this scheme, obtaining user order information and matching a raw material sheet, generating an experimental data sheet and a processing data sheet based on the raw material sheet, specifically include:
matching the user order information based on a preset raw material database and outputting the raw material list;
identifying the quantity grades of the raw materials of the current raw material list, wherein different quantity grades are matched with corresponding classification proportions;
and classifying the current raw material sheet according to the classification proportion to obtain the corresponding experimental data sheet and the corresponding processing data sheet.
In this scheme, the different quantity grades correspond to different classification proportions, and specifically include:
obtaining the raw material quantity grades based on the raw material type, the raw material specific gravity and the raw material weighing of the current raw material sheet, wherein the raw material quantity grades are divided into I grade, II grade and III grade;
the classification proportion corresponding to the raw material quantity grade I and the raw material quantity grade II is a first proportion, and the classification proportion corresponding to the raw material quantity grade III is a second proportion.
In this scheme, the extracting the experimental data list and outputting a training test table to a user side, wherein feedback data of the user side is obtained, and a preset neural network model is used to change stress, temperature and curing parameters, so as to update the training test table, and the method specifically includes:
outputting the corresponding training experiment table to the user side based on the experiment data sheet so as to carry out a prenatal experiment;
in the experimental process, the stress, the temperature and the curing parameters are updated by utilizing the neural network model through acquiring the feedback data of the user side, so that the training experiment table is continuously updated to be used by the user side for carrying out the prenatal experiment;
and stopping the prenatal experiment after the experiment result of the prenatal experiment is recognized to reach the preset standard, and recording the current parameter values of the neural network model.
In this scheme, after the output result of the experiment to be trained reaches the preset standard, extract the corresponding parameter values of the trained neural network model, combine the processing data sheet to generate the processing production table to output to the user side, specifically include:
when the antenatal experiment is ended, extracting each parameter value of the current neural network model as a first data packet;
and generating the processing production table based on the first data packet and the processing data sheet so as to output the processing production table to the user side for the user side to perform processing production.
In this scheme, the method further comprises extracting the first data packet for visual display.
A third aspect of the invention provides a computer readable storage medium including a program of a neural network based prepreg process improvement method of a machine, which when executed by a processor implements the steps of a neural network based prepreg process improvement method as claimed in any one of the preceding claims.
The prepreg process improving method, the prepreg process improving system and the storage medium based on the neural network can automatically analyze raw material data based on user order information, classify the raw material data based on the raw material data, set two categories of a training experiment and a processing production, perform the training experiment by using the neural network model to obtain each parameter which reaches the standard, further perform the specific processing production based on different parameters, effectively guarantee the yield, and can be adapted according to the requirements of different users.
Drawings
FIG. 1 shows a flow diagram of a neural network based prepreg process modification method of the present invention;
fig. 2 shows a block diagram of a prepreg process improvement system based on a neural network according to the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as specifically described herein and, therefore, the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of a prepreg process improvement method based on a neural network according to the present application.
As shown in fig. 1, the present application discloses a prepreg process improvement method based on a neural network, comprising the following steps:
s102, obtaining user order information to match a raw material sheet, and generating an experiment data sheet and a processing data sheet based on the raw material sheet;
s104, extracting the experimental data sheet and outputting a training experiment table to a user side, wherein feedback data of the user side are obtained, and stress, temperature and curing parameters are changed by using a preset neural network model so as to update the training experiment table;
and S106, after the output result of the training experiment reaches a preset standard, extracting corresponding parameter values of the trained neural network model, and combining the processing data sheet to generate a processing production table to be output to the user side.
According to the embodiment of the invention, the obtaining of the user order information matches with a raw material sheet, and the generating of the experimental data sheet and the processing data sheet based on the raw material sheet specifically comprises:
matching the user order information based on a preset raw material database and outputting the raw material list;
identifying the quantity grades of the raw materials of the current raw material list, wherein different quantity grades are matched with corresponding classification proportions;
and classifying the current raw material sheet according to the classification proportion to obtain the corresponding experimental data sheet and the corresponding processing data sheet.
It should be noted that after the user order information is obtained, the preset raw material database is used to match the order information to output the raw material sheet, the raw material sheet is made of, for example, glass fiber, carbon fiber or aramid fiber, and the corresponding classification proportion is matched based on the raw material quantity grade corresponding to the raw material sheet, wherein after the raw material sheet is classified based on the classification proportion, the experimental data sheet and the processing data sheet can be obtained by differentiation.
According to the embodiment of the present invention, the different quantity grades correspond to different classification proportions, and specifically include:
obtaining the raw material quantity grades based on the raw material type, the raw material specific gravity and the raw material weighing of the current raw material sheet, wherein the raw material quantity grades are divided into I grade, II grade and III grade;
the classification proportion corresponding to the raw material quantity grade I and the raw material quantity grade II is a first proportion, and the classification proportion corresponding to the raw material quantity grade III is a second proportion.
It should be noted that the raw material quantity grade may be specifically divided into the raw material type, the raw material specific gravity and the raw material weighing, and may be divided into a grade I, a grade II and a grade III based on different raw material quantity grades, where the classification ratio corresponding to the raw material quantity grade I and the raw material quantity grade II is a first ratio, for example, the ratio of the experimental raw material sheet to the processing raw material sheet is "1: 1000", and the classification ratio corresponding to the raw material quantity grade III is a second ratio, for example, the ratio of the experimental raw material sheet to the processing raw material sheet is "1: 800".
According to the embodiment of the invention, the extracting the experimental data list and outputting the training experiment table to a user side, wherein feedback data of the user side is obtained, and stress, temperature and curing parameters are changed by using a preset neural network model to update the training experiment table, and the method specifically comprises the following steps:
outputting the corresponding training experiment table to the user side based on the experiment data sheet so as to carry out a prenatal experiment;
in the experimental process, the stress, the temperature and the curing parameters are updated by utilizing the neural network model through acquiring the feedback data of the user side, so that the training experiment table is continuously updated to be used by the user side for carrying out the prenatal experiment;
and stopping the prenatal experiment after the experiment result of the prenatal experiment is recognized to reach the preset standard, and recording the current parameter values of the neural network model.
It should be noted that, during the prenatal experiment, the neural network model may be used to update various parameters in the experiment, including the stress, temperature and curing parameters, for example, the resin material is internally adjusted at 36-37 ℃ in summer and 30-32 ℃ in winter, the curing time does not exceed 90min, the results of the prenatal experiment are different by continuously adjusting parameters, then obtaining the feedback data of the user side updated each time for continuous updating until the experimental result of the prenatal experiment reaches the preset standard, the preset standard may be that the yield is higher than "98%", and therefore, when it is recognized that the yield in the experimental result of the pre-production experiment is higher than "98%", the pre-production experiment is stopped, and the current parameter values of the neural network model are recorded.
According to the embodiment of the present invention, after the output result of the experiment to be trained reaches the preset standard, extracting the corresponding parameter values of the trained neural network model, and generating a processing production table by combining the processing data sheet to output to the user side, specifically includes:
when the prenatal experiment is ended, extracting various parameter values of the current neural network model as a first data packet;
and generating the processing production table based on the first data packet and the processing data sheet so as to output the processing production table to the user side for the user side to perform processing production.
It should be noted that, in the prenatal experiment process, since each parameter value is to be adjusted after the feedback data is acquired, each parameter value is dynamically changed, and therefore, each parameter value corresponding to the termination of the prenatal experiment is a processing parameter that can match the requirement of the current user order information, and therefore, each parameter value of the current neural network model can be extracted and output as the first data packet, so that the user terminal performs processing and production based on the first data packet.
It is worth mentioning that the method further comprises extracting the first data packet for visual display.
It should be noted that, when the first data packet is output to the user side, it may also be visually displayed, and preferably, when the corresponding parameter is adjusted based on the neural network model, each parameter may also be visually displayed.
It should be noted that the method further includes identifying the processing response value in the user order information before the raw material sheet is matched with the user order information.
It should be noted that each factory distinguishes processing and preparation methods, wherein, the preparation method of the prepreg includes a dry method and a wet method, and therefore, before obtaining the user order information and matching the raw material sheet, a processing response value in the user order information needs to be identified, for example, a certain manufacturer a only has the capability of dry preparation and processing, but identifies that the corresponding processing response value is "dry + wet combination", so that the manufacturer a does not satisfy the processing and preparation conditions, that is, does not perform matching and output the corresponding raw material sheet.
It is worth mentioning that the method further comprises adjusting the curing parameters based on the user order information.
It should be noted that, when adjusting the curing parameter, not only the feedback data of the user terminal may be obtained to perform adjustment based on the neural network model, but also initial adjustment may be performed based on the user order information, for example: the prepreg required by a user needs to have a certain storage period at room temperature, so that a latent curing agent needs to be used as the curing agent corresponding to the curing parameter, so that the curing agent does not react with the resin at normal temperature and normal pressure, but under a special temperature and pressure, the resin is promoted to generate a crosslinking curing reaction so as to be beneficial to the storage of the prepreg at normal temperature, and further, the curing agent is usually a dispersion type curing agent, namely, the curing agent is solid at normal temperature, cannot be dissolved in the epoxy resin, but can be mixed with the epoxy resin when being heated to the vicinity of the melting point of the curing agent, so that the curing reaction can be rapidly generated.
Fig. 2 shows a block diagram of a prepreg process improvement system based on a neural network of the present invention.
As shown in fig. 2, the present invention discloses a prepreg process improvement system based on a neural network, which includes a memory and a processor, wherein the memory includes a prepreg process improvement method program based on the neural network, and when executed by the processor, the prepreg process improvement method program based on the neural network implements the following steps:
acquiring user order information to match a raw material sheet, and generating an experimental data sheet and a processing data sheet based on the raw material sheet;
extracting the experimental data list and outputting a training experiment table to a user side, wherein feedback data of the user side are obtained, and stress, temperature and curing parameters are changed by using a preset neural network model so as to update the training experiment table;
and after the output result of the training experiment reaches a preset standard, extracting the corresponding parameter values of the trained neural network model, and combining the processing data sheet to generate a processing production table to output the processing production table to the user side.
According to the embodiment of the invention, the obtaining of the user order information matches with a raw material sheet, and the generating of the experimental data sheet and the processing data sheet based on the raw material sheet specifically comprises:
matching the user order information based on a preset raw material database and outputting the raw material list;
identifying the quantity grades of the raw materials of the current raw material list, wherein different quantity grades are matched with corresponding classification proportions;
and classifying the current raw material sheet according to the classification proportion to obtain the corresponding experimental data sheet and the corresponding processing data sheet.
It should be noted that after the user order information is obtained, the preset raw material database is used to match the order information to output the raw material sheet, the raw material sheet is made of, for example, glass fiber, carbon fiber or aramid fiber, and the corresponding classification proportion is matched based on the raw material quantity grade corresponding to the raw material sheet, wherein after the raw material sheet is classified based on the classification proportion, the experimental data sheet and the processing data sheet can be obtained by differentiation.
According to the embodiment of the present invention, the different quantity grades correspond to different classification proportions, and specifically include:
obtaining the quantity grade of the raw materials based on the type, the specific gravity and the weighing of the raw materials of the current raw material sheet, wherein the quantity grade of the raw materials is divided into I grade, II grade and III grade;
the classification proportion corresponding to the raw material quantity grade I and the raw material quantity grade II is a first proportion, and the classification proportion corresponding to the raw material quantity grade III is a second proportion.
It should be noted that the raw material quantity grade may be specifically divided into the raw material type, the raw material specific gravity and the raw material weighing, and may be divided into a grade I, a grade II and a grade III based on different raw material quantity grades, where the classification ratio corresponding to the raw material quantity grade I and the raw material quantity grade II is a first ratio, for example, the ratio of the experimental raw material sheet to the processing raw material sheet is "1: 1000", and the classification ratio corresponding to the raw material quantity grade III is a second ratio, for example, the ratio of the experimental raw material sheet to the processing raw material sheet is "1: 800".
According to the embodiment of the invention, the extracting the experimental data list and outputting the training experiment table to a user side, wherein feedback data of the user side is obtained, and stress, temperature and curing parameters are changed by using a preset neural network model to update the training experiment table, and the method specifically comprises the following steps:
outputting the corresponding training experiment table to the user side based on the experiment data sheet so as to carry out a prenatal experiment;
in the experiment process, the stress, the temperature and the curing parameters are updated by utilizing the neural network model through acquiring the feedback data of the user side, so that the training experiment table is continuously updated to be used for the user side to carry out the prenatal experiment;
and stopping the prenatal experiment after the experiment result of the prenatal experiment is recognized to reach the preset standard, and recording the current parameter values of the neural network model.
It should be noted that, during the prenatal experiment, the neural network model may be used to update various parameters in the experiment, including the stress, temperature and curing parameters, for example, the resin material is internally adjusted at 36-37 ℃ in summer and 30-32 ℃ in winter, the curing time does not exceed 90min, the results of the prenatal experiment are different through continuously adjusting parameters, then obtaining the feedback data of the user side updated each time for continuous updating until the experimental result of the prenatal experiment reaches the preset standard, the preset standard may be that the yield is higher than "98%", and therefore, when it is recognized that the yield in the experimental result of the pre-production experiment is higher than "98%", the pre-production experiment is stopped, and the current parameter values of the neural network model are recorded.
According to the embodiment of the present invention, after the output result of the experiment to be trained reaches the preset standard, extracting the corresponding parameter values of the trained neural network model, and generating a processing production table by combining the processing data sheet to output to the user side, specifically includes:
when the antenatal experiment is ended, extracting each parameter value of the current neural network model as a first data packet;
and generating the processing production table based on the first data packet and the processing data sheet so as to output the processing production table to the user side for the user side to perform processing production.
It should be noted that, in the prenatal experiment process, since each parameter value is to be adjusted after the feedback data is acquired, each parameter value is dynamically changed, and therefore, each parameter value corresponding to the termination of the prenatal experiment is a processing parameter that can match the requirement of the current user order information, and therefore, each parameter value of the current neural network model can be extracted and output as the first data packet, so that the user terminal performs processing and production based on the first data packet.
It is worth mentioning that the method further comprises extracting the first data packet for visual display.
It should be noted that, when the first data packet is output to the user side, it may also be visually displayed, and preferably, when the corresponding parameter is adjusted based on the neural network model, each parameter may also be visually displayed.
It should be noted that the method further includes identifying the processing response value in the user order information before the user order information is obtained and matched to the raw material sheet.
It should be noted that each factory distinguishes processing and preparation methods, wherein, the preparation method of the prepreg includes a dry method and a wet method, and therefore, before obtaining the user order information and matching the raw material sheet, a processing response value in the user order information needs to be identified, for example, a certain manufacturer a only has the capability of dry preparation and processing, but identifies that the corresponding processing response value is "dry + wet combination", so that the manufacturer a does not satisfy the processing and preparation conditions, that is, does not perform matching and output the corresponding raw material sheet.
It is worth mentioning that the method further comprises adjusting the curing parameters based on the user order information.
It should be noted that, when adjusting the curing parameters, not only the feedback data of the user terminal may be obtained to perform adjustment based on the neural network model, but also initial adjustment may be performed based on the user order information, for example: the prepreg required by a user needs to have a certain storage period at room temperature, so that a latent curing agent needs to be used as the curing agent corresponding to the curing parameter, so that the curing agent does not react with the resin at normal temperature and normal pressure, but under a special temperature and pressure, the resin is promoted to generate a crosslinking curing reaction so as to be beneficial to the storage of the prepreg at normal temperature, and further, the curing agent is usually a dispersion type curing agent, namely, the curing agent is solid at normal temperature, cannot be dissolved in the epoxy resin, but can be mixed with the epoxy resin when being heated to the vicinity of the melting point of the curing agent, so that the curing reaction can be rapidly generated.
A third aspect of the invention provides a computer readable storage medium, which includes a program of a method for improving a prepreg process based on a neural network, and when the program of the method is executed by a processor, the steps of the method for improving a prepreg process based on a neural network are realized.
The prepreg process improvement method, the prepreg process improvement system and the storage medium based on the neural network can automatically analyze raw material data based on user order information, classify based on the raw material data, set two categories of a training experiment and processing production, perform the training experiment by using the neural network model to obtain each parameter reaching the standard, further perform specific processing production based on different parameters, effectively guarantee the yield, and can be adapted according to the requirements of different users.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
Claims (4)
1. A prepreg process improvement method based on a neural network is characterized by comprising the following steps:
acquiring user order information to match a raw material sheet, and generating an experiment data sheet and a processing data sheet based on the raw material sheet;
extracting the experimental data list and outputting a training experiment table to a user side, wherein feedback data of the user side are obtained, and stress, temperature and curing parameters are changed by using a preset neural network model so as to update the training experiment table;
after the output result of the training experiment reaches a preset standard, extracting the corresponding parameter values of the trained neural network model, and combining the processing data sheet to generate a processing production table to output the processing production table to the user side;
the method for obtaining the user order information and matching the user order information to obtain the raw material sheet, and generating the experimental data sheet and the processing data sheet based on the raw material sheet specifically comprises the following steps:
matching the user order information based on a preset raw material database and outputting the raw material list;
identifying the quantity grades of the raw materials of the current raw material list, wherein different quantity grades are matched with corresponding classification proportions;
classifying the current raw material sheet according to the classification proportion to obtain the corresponding experimental data sheet and the corresponding processing data sheet;
the different quantity grades correspond to different classification proportions, and specifically include:
obtaining the raw material quantity grades based on the raw material type, the raw material specific gravity and the raw material weighing of the current raw material sheet, wherein the raw material quantity grades are divided into I grade, II grade and III grade;
the classification proportion corresponding to the raw material quantity grade I and the raw material quantity grade II is a first proportion, and the classification proportion corresponding to the raw material quantity grade III is a second proportion;
the extracting of the experimental data sheet and the outputting of the training experiment table to a user side are performed, wherein feedback data of the user side are obtained, and stress, temperature and curing parameters are changed by using a preset neural network model so as to update the training experiment table, and the method specifically comprises the following steps:
outputting the corresponding training experiment table to the user side based on the experiment data sheet so as to carry out a prenatal experiment;
in the experimental process, the stress, the temperature and the curing parameters are updated by utilizing the neural network model through acquiring the feedback data of the user side, so that the training experiment table is continuously updated to be used by the user side for carrying out the prenatal experiment;
stopping the prenatal experiment after the experiment result of the prenatal experiment is recognized to reach the preset standard, and recording the current parameter values of the neural network model;
after the output result of the experiment to be trained reaches a preset standard, extracting corresponding parameter values of the trained neural network model, and combining the processing data sheet to generate a processing production table to output to the user side, specifically comprising:
when the antenatal experiment is ended, extracting each parameter value of the current neural network model as a first data packet;
and generating the processing production table based on the first data packet and the processing data sheet so as to output the processing production table to the user side for the user side to perform processing production.
2. The improved prepreg process based on neural network according to claim 1, further comprising extracting the first data packet for visual display.
3. A prepreg process improvement system based on a neural network is characterized by comprising a memory and a processor, wherein the memory comprises a prepreg process improvement method program based on the neural network, and the prepreg process improvement method program based on the neural network realizes the following steps when being executed by the processor:
acquiring user order information to match a raw material sheet, and generating an experiment data sheet and a processing data sheet based on the raw material sheet;
extracting the experimental data list and outputting a training experiment table to a user side, wherein feedback data of the user side are obtained, and stress, temperature and curing parameters are changed by using a preset neural network model so as to update the training experiment table;
after the output result of the training experiment reaches a preset standard, extracting the corresponding parameter values of the trained neural network model, and combining the processing data sheet to generate a processing production table to output the processing production table to the user side;
the method for obtaining the user order information and matching the user order information to obtain the raw material sheet, and generating the experimental data sheet and the processing data sheet based on the raw material sheet specifically comprises the following steps:
matching the user order information based on a preset raw material database and outputting the raw material list;
identifying the quantity grades of the raw materials of the current raw material list, wherein different quantity grades are matched with corresponding classification proportions;
classifying the current raw material sheet according to the classification proportion to obtain the corresponding experimental data sheet and the corresponding processing data sheet;
the different quantity grades correspond to different classification proportions, and specifically include:
obtaining the raw material quantity grades based on the raw material type, the raw material specific gravity and the raw material weighing of the current raw material sheet, wherein the raw material quantity grades are divided into I grade, II grade and III grade;
the classification proportion corresponding to the raw material quantity grade I and the raw material quantity grade II is a first proportion, and the classification proportion corresponding to the raw material quantity grade III is a second proportion;
the extracting of the experimental data list and outputting of the training experiment table to a user side are specifically performed, wherein feedback data of the user side are obtained, and stress, temperature and curing parameters are changed by using a preset neural network model so as to update the training experiment table, and the extracting of the experimental data list and the outputting of the training experiment table specifically include:
outputting the corresponding training experiment table to the user side based on the experiment data sheet so as to carry out a prenatal experiment;
in the experimental process, the stress, the temperature and the curing parameters are updated by utilizing the neural network model through acquiring the feedback data of the user side, so that the training experiment table is continuously updated to be used by the user side for carrying out the prenatal experiment;
stopping the prenatal experiment after the experiment result of the prenatal experiment is recognized to reach the preset standard, and recording the current parameter values of the neural network model;
after the output result of the experiment to be trained reaches a preset standard, extracting corresponding parameter values of the trained neural network model, and combining the processing data sheet to generate a processing production table to output to the user side, specifically comprising:
when the antenatal experiment is ended, extracting each parameter value of the current neural network model as a first data packet;
and generating the processing production table based on the first data packet and the processing data sheet so as to output the processing production table to the user side for the user side to perform processing production.
4. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a neural network-based prepreg process improvement method program, which when executed by a processor, implements the steps of a neural network-based prepreg process improvement method as claimed in any one of claims 1 to 2.
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