A Method for Monitoring the Curing State of a Black Phosphorene-Carbon Nanotube Epoxy Composite Material

Abstract

The present invention relates to the technical field of data analysis state monitoring, and particularly relates to a method for monitoring the curing state of a black phosphorus nanotube epoxy resin composite material. The method includes: during the preparation process of the black phosphorus nanotube epoxy resin composite material, collecting and processing curing temperature data, and obtaining a curing rate performance progress coefficient according to the temperature data during stirring and defoaming and curing; obtaining a composite material curing process trend coefficient according to the distribution of the curing rate performance progress coefficient; obtaining a composite material curing process state offset coefficient according to the composite material curing process trend coefficient, and judging the curing state; cooling and demolding: after curing is completed, cooling the material in the mold to room temperature, and obtaining the black phosphorus nanotube epoxy resin composite material through demolding. Thus, the preparation of the black phosphorus nanotube epoxy resin composite material is realized.

Description

Technical Field

[0001] This application relates to the technical field of data analysis state monitoring, and specifically relates to a method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material. Background Art

[0002] Currently, the main encapsulation materials for electronic devices are epoxy resins, followed by silicone resins, polyimides, and liquid crystal polymers. Therefore, improving the comprehensive performance of epoxy resins has important practical significance for the development of new-generation encapsulation materials.

[0003] Epoxy resins have excellent comprehensive performance and are widely used in the fields of fiber-reinforced composite materials, coatings, adhesives, and electronic packaging. However, epoxy resins are flammable and have poor thermal conductivity, which limits their application in fields with high flame retardancy requirements. In recent years, with the increasing global call for environmental protection, traditional halogen-containing flame retardants will be gradually phased out, posing new requirements and challenges for epoxy resin flame retardancy. Therefore, it is of great practical significance to prepare an epoxy resin system with flame retardancy without reducing its mechanical properties. Starting from the perspective of molecular design, this application introduces two-dimensional black phosphorus and one-dimensional carbon nanotubes into the epoxy resin system to prepare a new type of flame-retardant epoxy resin system, and systematically studies the influence laws of the black phosphorus-carbon nanotube heterogeneous system on the mechanical properties, flame retardancy, and curing process of epoxy resins.

[0004] The preparation process of black phosphorus-carbon nanotube epoxy resin includes preparing a black phosphorus-carbon nanotube heterostructure hybrid system, adding epoxy resin and curing agent, stirring and degassing, and curing and demolding. Among them, during the curing and demolding process of epoxy resin, the judgment of the curing state has a great impact on the preparation quality of black phosphorus-carbon nanotube epoxy resin composites. Therefore, during the preparation process of black phosphorus-carbon nanotube epoxy resin composites, it is crucial to judge and monitor the curing state. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material to solve the existing problems.

[0006] The method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material of the present invention adopts the following technical solution:

[0007] An embodiment of the present invention provides a method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material, and the method includes the following steps:

[0008] Obtain temperature data during the stirring and degassing and curing processes in the preparation of multiple black phosphorus-carbon nanotube epoxy resin composite materials;

[0009] The curing rate performance progress coefficient is obtained based on the analysis of the temperature data during the stirring and degassing and curing processes; the composite material curing process trend coefficient is determined based on the distribution of the curing rate performance progress coefficient;

[0010] Based on the composite material curing process trend coefficient, the composite material curing process state offset coefficient is determined to judge the curing state;

[0011] After curing is completed, cooling and demolding are carried out to obtain the black phosphorus-carbon nanotube epoxy composite material.

[0012] Furthermore, the curing rate performance progress coefficient includes:

[0013] Each black phosphorus-carbon nanotube epoxy composite material is recorded as a sample, the temperatures of multiple samples during the stirring and degassing and curing processes are collected, and the temperature data is arranged in ascending order of the collection time to form the composite material curing data sequence of each sample;

[0014] A sliding window with a preset size is set in the composite material curing data sequence, the sliding step length is a preset value, and the temperatures within each sliding window are arranged in ascending order of time to form a curing short-term process data sequence;

[0015] The short-term process trend data sequence and the short-term process cycle data sequence of each curing short-term process data sequence are constructed respectively;

[0016] Based on the differences between the maximum and minimum values in any two adjacent curing short-term process data sequences, the curing process advancement coefficient between any two adjacent curing short-term process data sequences is determined;

[0017] The distances between the short-term process trend data sequences of any two adjacent curing short-term process data sequences and the distances between the short-term process cycle data sequences are calculated respectively, and the similarity degree between any two adjacent curing short-term process data sequences is calculated;

[0018] Based on the distance, the similarity degree, and the curing process advancement coefficient, the curing rate performance progress coefficient between any two adjacent curing short-term process data sequences is determined; wherein, the curing rate performance progress coefficient is positively correlated with the curing process advancement coefficient and the distance respectively, and negatively correlated with the similarity degree.

[0019] Furthermore, the construction processes of the short-term process trend data sequence and the short-term process cycle data sequence of each curing short-term process data sequence are as follows:

[0020] For each solidification short-term process data sequence, the STL algorithm is used to obtain the trend component and the periodic component of each element in the solidification short-term process data sequence. The trend components of all elements in the solidification short-term process data sequence and the periodic components respectively form the short-term process trend data sequence and the short-term process periodic data sequence of the solidification short-term process data sequence.

[0021] Further, the method for determining the solidification process advancement coefficient between any two adjacent solidification short-term process data sequences is as follows:

[0022] The maximum values in the solidification short-term process data sequence are arranged in descending order to form a maximum value sequence, and a minimum value sequence is obtained by using the method for obtaining the maximum value sequence;

[0023] Calculate the difference between each data in the maximum value sequence of each solidification short-term process data sequence and the data at the same position in the minimum value sequence, and denote it as the extreme value deviation;

[0024] Based on the extreme value deviation calculated from the maximum value sequence and the minimum value sequence of any two adjacent solidification short-term process data sequences, determine the solidification process advancement coefficient of the any two adjacent solidification short-term process data sequences.

[0025] Further, the specific calculation process of the solidification process advancement coefficient is as follows:

[0026] Calculate the ratio of the extreme value deviation calculated from the latter solidification short-term process data sequence to the extreme value deviation calculated from the former solidification short-term process data sequence among any two adjacent solidification short-term process data sequences, and denote it as the deviation ratio;

[0027] Determine the mean value of all the deviation ratios calculated from the maximum value sequence and the minimum value sequence of any two adjacent solidification short-term process data sequences as the solidification process advancement coefficient of the any two adjacent solidification short-term process data sequences.

[0028] Further, obtaining the composite material solidification process trend coefficient according to the distribution of the solidification rate performance process coefficient includes:

[0029] Construct a solidification rate performance process coefficient sequence for each sample;

[0030] Take the distance between the solidification rate performance process coefficient sequences of different samples as the metric distance of the clustering algorithm to cluster all samples; use the trend test algorithm to obtain the trend statistic and the trend verification value of the solidification rate performance process coefficient sequence of each sample;

[0031] Obtain the ratio of the trend statistic to the trend verification value of each sample in the clustering cluster, and denote it as the trend ratio;

[0032] Determine the composite material curing process trend coefficient of each clustering cluster based on the differences in the trend ratios of the samples in the clustering cluster and the distances of the curing rate performance process coefficient sequences.

[0033] Further, the construction process of the curing rate performance process coefficient sequence of each sample is as follows: The curing rate performance process coefficients between all adjacent curing short-time process data sequences are arranged in the order of the adjacent curing short-time process data sequences in the sliding direction of the sliding window to form the curing rate performance process coefficient sequence of each sample.

[0034] Further, the determining of the composite material curing process trend coefficient of each clustering cluster based on the differences in the trend ratios of the samples in the clustering cluster and the distances of the curing rate performance process coefficient sequences includes:

[0035] Obtain the distance between the curing rate performance process coefficient sequences of any two samples in the clustering cluster, denoted as the curing rate deviation, and calculate the cumulative sum of the products of the differences in the trend ratios of all pairs of samples in the clustering cluster and the curing rate deviation;

[0036] Take the ratio of the mean of the trend statistics of all samples in the clustering cluster to the cumulative sum as the composite material curing process trend coefficient of the clustering cluster.

[0037] Further, the determining method of the composite material curing process state offset coefficient is:

[0038] Obtain the difference between the composite material curing process trend coefficients of each clustering cluster and other clustering clusters, denoted as the trend difference, calculate the ratio of the trend difference to the composite material curing process trend coefficient of each clustering cluster, denoted as the difference coefficient ratio, and take the mean of the difference coefficient ratios calculated for each clustering cluster and all other clustering clusters as the composite material curing process state offset coefficient of each clustering cluster.

[0039] Further, the judging and monitoring of the curing state include:

[0040] Form a vector by arranging the composite material curing process state offset coefficients of all clustering clusters in ascending order as the curing state analysis vector of the black phosphorus-carbon nanotube-epoxy resin composite material, take the curing state vector as the input of the convolutional neural network model, and the convolutional neural network outputs the curing offset monitoring result, where the curing offset monitoring result includes curing state offset and no curing state offset.

[0041] The present invention has at least the following beneficial effects:

[0042] The black phosphorus used in the present invention has a large surface area and good thermal conductivity. The carbon nanotubes have a large aspect ratio, and the black phosphorus-carbon nanotube heterostructure has the advantage of being able to construct a good thermal conduction path in epoxy resin. At the same time, during the preparation process, by analyzing the change characteristics of the curing process at different time periods during the curing of the black phosphorus-carbon nanotube-epoxy resin composite material, the curing rate performance process coefficient is calculated, and the change difference of the photocuring state during the preparation process of the composite material is reflected by the curing rate performance process coefficient; further, the present invention constructs a composite material curing process trend coefficient according to the difference in the curing state of different black phosphorus-carbon nanotube-epoxy resin composite materials during the preparation process, and reflects the trend characteristics of the curing state during the preparation process of the black phosphorus-carbon nanotube-epoxy resin composite material through the composite material curing process trend coefficient. Based on the composite material curing process trend coefficient, the curing process state offset coefficient is calculated, and the curing condition during the preparation process of the black phosphorus-carbon nanotube-epoxy resin composite material is monitored through the curing process state offset coefficient, so as to improve the accuracy of monitoring the curing state offset during the preparation process, and further improve the curing efficiency during the curing process and the quality of the prepared black phosphorus-carbon nanotube-epoxy resin composite material. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0044] Figure 1 It is a flowchart of the steps of a method for monitoring the curing state of a black phosphorus-carbon nanotube-epoxy resin composite material provided by the present invention;

[0045] Figure 2 It is a schematic diagram of a black phosphorus-carbon nanotube heterostructure hybridization system;

[0046] Figure 3 It is a flowchart for obtaining the curing process advancement coefficient between any two adjacent curing short-time process data sequences;

[0047] Figure 4 It is a flowchart for obtaining the curing rate performance process coefficient between any two adjacent curing short-time process data sequences;

[0048] Figure 5 It is a flowchart for obtaining the composite material curing process trend coefficient of each clustering cluster;

[0049] Figure 6 It is a flowchart for obtaining the composite material curing process state offset coefficient of each clustering cluster. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, in conjunction with the accompanying drawings and preferred embodiments, a method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material proposed according to the present invention, including its specific implementation manner, structure, characteristics, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0052] The following specifically describes in conjunction with the accompanying drawings the specific solution of a method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material provided by the present invention.

[0053] A method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material provided by an embodiment of the present invention specifically provides the following method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material. Please refer to Figure 1 , the method includes the following steps:

[0054] Step S1, prepare a black phosphorus-carbon nanotube heterostructure hybrid system.

[0055] Put black phosphorus and carbon nanotubes into ethyl acetate to obtain a mixed solution of black phosphorus and carbon nanotubes. The mass ratio of black phosphorus to carbon nanotubes is 1 to 100. In this embodiment, the mass ratio of black phosphorus to carbon nanotubes is 1:1. Ultrasonic composite the mixed solution of black phosphorus and carbon nanotubes by ultrasonic method. After ultrasonic composite, obtain a black phosphorus-carbon nanotube heterostructure hybrid system. The schematic diagram of the black phosphorus-carbon nanotube heterostructure hybrid system is as Figure 2 shown.

[0056] Step S2, add epoxy resin and curing agent.

[0057] The obtained black phosphorus-carbon nanotube heterostructure hybrid system was introduced into bisphenol A epoxy resin by the solution mixing method at mass ratios of 1 wt%, 3 wt%, 5 wt%, and 7 wt% respectively. The solvent was removed by rotary evaporation, and finally an equivalent amount of linear phenolic resin was added. Among them, the epoxy resin is not limited to bisphenol A epoxy resin, and the epoxy resin is one or more of bisphenol A epoxy resin, biphenyl epoxy resin, o-cresol novolac epoxy resin, resorcinol formaldehyde type epoxy resin, and triphenylolmethane type epoxy resin; the curing agent is one or more of linear phenolic resin, methylhexahydrophthalic anhydride, and DDM (4,4'-diaminodiphenylmethane). In this example, linear phenolic resin is used, and implementers in different examples can select it by themselves.

[0058] Step S3, stirring, defoaming and curing.

[0059] The mixed material after adding the epoxy resin and the curing agent was stirred evenly and degassed under vacuum. After the vacuum degassing was completed, the mixed material was poured into a pre-prepared mold and cured at a temperature of 180°C for 3 hours. Among them, the curing parameters were adaptively adjusted according to the curing state bromination characteristics in the preparation of the black phosphorus-carbon nanotube epoxy resin composite material. The specific process schematic diagram is as Figure 3 shown, and the specific adjustment steps are as follows:

[0060] The first step: Collect the temperature data during the curing process of the black phosphorus-carbon nanotube epoxy resin composite material.

[0061] On the production line of the black phosphorus-carbon nanotube epoxy resin composite material, obtain temperature data during the stirring, defoaming and curing process of the black phosphorus-carbon nanotube epoxy resin composite material. Specifically, during the stirring, defoaming and curing process of each black phosphorus-carbon nanotube epoxy resin composite material preparation, use a temperature sensor to collect the temperature data during the curing process of the black phosphorus-carbon nanotube epoxy resin. The time interval for collecting the temperature data is , and in this example, the value is 0.01 s. The sequence formed by arranging the collected temperature data in ascending order of the collection time is used as the composite material curing data sequence of each black phosphorus-carbon nanotube epoxy resin composite material.

[0062] So far, the composite material curing data sequence has been obtained.

[0063] The second step: Construct a curing rate performance process coefficient according to the influence characteristics of the temperature change of the black phosphorus-carbon nanotube composite material on the reaction rate and the curing degree in different time periods during the curing process, and calculate the composite material curing process trend coefficient according to the sustainable stable trend change characteristics of the curing rate performance process coefficient.

[0064] During the preparation process of black phosphorus nanotube composite materials, the curing temperature and time have different effects on different composite materials. In the actual production process, due to the complex environment of the production workshop, during the curing process of black phosphorus nanotube composite materials, the unstable change of temperature may lead to instability characteristics in the curing degree, network structure and performance of the epoxy resin matrix during the preparation of black phosphorus nanotube composite materials, affecting the quality of the prepared black phosphorus nanotube composite materials.

[0065] Specifically, within a certain curing time, increasing the curing temperature can accelerate the reaction rate, promote the improvement of the curing degree and the formation of the network structure, thereby enhancing the mechanical properties of the composite material. Generally, the higher the curing temperature, the higher the mechanical properties such as hardness, elastic modulus and strength of the composite material. Further, the curing temperature and time also affect the thermal stability of the composite material. A higher curing temperature and a long curing process can improve the thermal stability of the composite material, reduce its thermal decomposition temperature and thermal weight loss rate. This helps to improve the high-temperature resistance performance of the composite material and is suitable for applications in high-temperature environments; at the same time, the curing temperature and time also affect the electrical conductivity of the composite material. Appropriate curing temperature and time can ensure that carbon nanotubes and black phosphorus are fully dispersed and form a good conductive network with the epoxy resin matrix, thereby improving the electrical conductivity of the composite material.

[0066] Further, if the high-temperature or low-temperature change characteristics of the curing temperature during the preparation of black phosphorus nanotube epoxy composite materials have a greater impact on the quality of the composite material preparation, and at the same time, due to the unstable change characteristics of the temperature, the curing efficiency may be reduced or increased, and then there may be problems of too long or too short curing time within the set curing time, reducing the production quality of black phosphorus nanotube epoxy composite materials. Therefore, by collecting the temperature data during the production process of black phosphorus nanotube epoxy composite materials, the change characteristics of the curing process in different time periods are analyzed. Specifically, for each composite material curing data sequence of black phosphorus nanotube epoxy composite materials, a sliding window of size is set in the composite material curing data sequence, where the value in this embodiment is 500, and the sliding step of the sliding window is 100, which can be set by the implementer himself. The sequence formed by arranging the data within the sliding window in ascending order of time is used as a curing short-term process data sequence.

[0067] Further, taking each solidified short-term process data sequence as an input, the STL (Seasonal and Trend decomposition using Loess) algorithm is used to obtain the trend component and the periodic component of each element in each solidified short-term process data sequence. The sequence composed of the trend components of all elements in each solidified short-term process data sequence is used as the short-term process trend data sequence The sequence composed of the periodic components of all elements in each solidified short-term process data sequence is used as the short-term process periodic data sequence The specific calculation process of the STL algorithm is a well-known technology and will not be elaborated here; the maximum value and the minimum value in each solidified short-term process data sequence are obtained, and the sequences composed of the maximum value and the minimum value in descending order are used as the maximum value sequence and the minimum value sequence respectively, where the minimum value of the number of maximum values and the number of minimum values is .

[0068] The solidification rate performance process coefficient is calculated according to the data change characteristics of two adjacent solidified short-term process data sequences. Taking two adjacent solidified short-term process data sequences: the x-th and the y-th solidified short-term process data sequences as an example, where the y-th solidified short-term process data sequence is the next set of data of the x-th solidified short-term process data sequence on the sliding window, that is, the adjacent solidified short-term process data sequence of the x-th solidified short-term process data sequence. The specific acquisition process of the solidification rate performance process coefficient is as follows:

[0069] First, based on the extreme value deviation calculated from the maximum value sequence and the minimum value sequence of any two adjacent solidified short-term process data sequences, the solidification process advancement coefficient of the any two adjacent solidified short-term process data sequences is determined: calculate the ratio of the extreme value deviation calculated from the latter solidified short-term process data sequence to the extreme value deviation calculated from the former solidified short-term process data sequence among the any two adjacent solidified short-term process data sequences, denoted as the deviation ratio; the mean value of all the deviation ratios calculated from the any two adjacent solidified short-term process data sequences is determined as the solidification process advancement coefficient of the any two adjacent solidified short-term process data sequences

[0070] Specifically, for the flowchart of obtaining the solidification process advancement coefficient between any two adjacent solidified short-term process data sequences, please refer to Figure 3 .

[0071] Further, calculate the distances between the short-term process trend data sequences and the distances between the short-term process cycle data sequences of any two adjacent cured short-term process data sequences respectively, and calculate the similarity degree between any two adjacent cured short-term process data sequences; determine the curing rate performance process coefficient between any two adjacent cured short-term process data sequences based on the distance, the similarity degree, and the curing process advancement coefficient; wherein, the curing rate performance process coefficient is positively correlated with the curing process advancement coefficient and the distance respectively, and negatively correlated with the similarity degree.

[0072] Preferably, as an embodiment, the calculation formula for the curing process advancement coefficient between the x-th and y-th cured short-term process data sequences can be: ; wherein, represents the curing process advancement coefficient between the x-th and y-th cured short-term process data sequences; represents the curing rate performance process coefficient between the x-th and y-th cured short-term process data sequences; and respectively represent the short-term process trend data sequences of the x-th and y-th cured short-term process data sequences, represents the Manhattan distance, and the implementer can also select other distance measurement methods to analyze the distance between sequences; and respectively represent the short-term process cycle data sequences of the x-th and y-th cured short-term process data sequences; and represent the x-th and y-th cured short-term process data sequences respectively, represents the Jaccard coefficient, and the implementer can also select other calculation methods for the similarity degree between sequences to measure the similarity degree between sequences. It should be noted that the larger the Jaccard coefficient, the higher the similarity degree between sequences.

[0073] It should be understood that if the change in the extreme value of the y-th cured short-term process data sequence is significantly different from the change in the extreme value of the x-th cured short-term process data sequence, then the calculated value is larger, that is, the curing process advancement coefficient value between the x-th and y-th cured short-term process data sequences is larger, indicating that the possibility of a progressive growth trend in the curing process of the black phosphorus-carbon nanotube epoxy composite material is greater; at the same time, the greater the difference in the trend change and cycle change between the x-th and y-th cured short-term process data sequences, the larger the calculated value; and the greater the difference in the updated data between the x-th and y-th cured short-term process data sequences, the calculated The smaller the value is; that is, the curing rate performance process coefficient between the x-th and y-th cured short-term process data sequences obtained by calculation The larger the value is, the greater the possibility that the curing process of the black phosphorus nanotube epoxy resin composite material accelerates during the curing process.

[0074] Specifically, for the flowchart of obtaining the curing rate performance process coefficient between any two adjacent cured short-term process data sequences, please refer to Figure 4 。

[0075] Furthermore, due to the environmental dynamic influence characteristics during the curing process on the production line of the black phosphorus nanotube epoxy resin composite material, the state change characteristics of the curing process are different during the preparation of different black phosphorus nanotube epoxy resin composite materials. Therefore, by analyzing the state change characteristics of the curing process of each black phosphorus nanotube epoxy resin composite material, cluster analysis is performed on the black phosphorus nanotube epoxy resin composite materials on the production line, and the curing state change during the preparation of the black phosphorus nanotube epoxy resin composite material is reflected through the results of the cluster analysis.

[0076] Specifically, the curing rate performance process coefficients between all adjacent cured short-term process data sequences are used to form a sequence according to the arrangement order of the adjacent cured short-term process data sequences in the sliding direction of the sliding window, as the curing rate performance process coefficient sequence of each black phosphorus nanotube epoxy resin composite material. Each black phosphorus nanotube epoxy resin composite material is used as a sample, and the distance between the curing rate performance process coefficient sequences of different samples is used as the metric distance of the clustering algorithm to cluster all samples to obtain each cluster.

[0077] After that, a trend test algorithm is used to obtain the trend statistic and trend verification value of the curing rate performance process coefficient sequence of each sample; obtain the ratio of the trend statistic to the trend verification value of each sample in the cluster, denoted as the trend ratio; obtain the distance between the curing rate performance process coefficient sequences of any two samples in the cluster, denoted as the curing rate deviation, and calculate the cumulative sum of the product of the difference of the trend ratios of all any two samples in the cluster and the curing rate deviation; use the ratio of the mean value of the trend statistics of all samples in the cluster to the cumulative sum as the composite material curing process trend coefficient of the cluster.

[0078] Preferably, as an embodiment of the present application, during the clustering process, the DTW distance between the curing rate performance process coefficient sequences of different samples is used as the distance measurement result between the samples. The clustering algorithm uses the agglomerative hierarchical clustering algorithm, and the implementer can select other clustering algorithms by himself; the Mann-Kendall trend test algorithm is used to obtain the trend statistic and trend verification value of the curing rate performance process coefficient sequence corresponding to the sample. The specific implementation process of the Mann-Kendall trend test algorithm is a well-known technology and will not be elaborated here; the DTW distance can be used to calculate the curing rate deviation; the difference between the trend ratios of two samples can be analyzed by the absolute value of the difference between the two trend ratios.

[0079] Thus, the trend coefficient of the composite material curing process is obtained.

[0080] Specifically, for the flowchart of obtaining the trend coefficient of the composite material curing process of each clustering cluster, please refer to Figure 5 .

[0081] Step 3: Construct the state offset coefficient of the composite material curing process to judge and monitor the curing state of the black phosphorus-carbon nanotube.

[0082] Since the dynamic change characteristics of the environment on the production line of the black phosphorus-carbon nanotube epoxy composite material will affect the quality of the production of the black phosphorus-carbon nanotube epoxy composite material, and the change characteristics of the environmental dynamic influence have a greater impact on the curing process of the black phosphorus-carbon nanotube epoxy composite material. Therefore, by analyzing the trend change characteristics of the process parameters of the curing process of different black phosphorus-carbon nanotube epoxy composite materials on the production line, the trend coefficient of the composite material curing process is obtained. If the trend coefficient of the composite material curing process is large and the difference in the trend of the composite material curing process reflected by different black phosphorus-carbon nanotube epoxy composite materials is large, it indicates that the parameters of the curing process in production are offset.

[0083] Furthermore, the state offset coefficient of the composite material curing process is calculated through the trend coefficient of the composite material curing process, and the offset degree of the production state of different black phosphorus-carbon nanotube epoxy composite materials affected by the environment is reflected through the state offset coefficient of the composite material curing process. The process of obtaining the state offset coefficient of the composite material curing process is as follows:

[0084] Obtain the difference between the trend coefficients of the composite material curing process of each clustering cluster and other clustering clusters, denoted as the trend difference. Among them, the trend difference can be obtained by taking the absolute value of the difference between the trend coefficients of the composite material curing process of two clustering clusters; calculate the ratio of the trend difference to the trend coefficient of the composite material curing process of each clustering cluster, denoted as the difference coefficient ratio, and take the mean value of the difference coefficient ratios calculated for each clustering cluster and all other clustering clusters as the state offset coefficient of the composite material curing process of each clustering cluster.

[0085] It should be understood that if in the few-layer black phosphorus-carbon nanotube epoxy resin composite material, through cluster analysis, the trend characteristics of the curing state of the few-layer black phosphorus-carbon nanotube epoxy resin composite material in the cluster are significantly different from those in other clusters, the larger the calculated trend ratio is, that is, the larger the curing process state offset coefficient of the few-layer black phosphorus-carbon nanotube epoxy resin composite material in the

[0086] cluster is, it indicates that the curing state of the corresponding few-layer black phosphorus-carbon nanotube epoxy resin composite material in the Figure 6 cluster may deviate.

[0087] Specifically, for the flow chart of obtaining the curing process state offset coefficient of the composite material in each cluster, please refer to Figure 6 .

[0087] Furthermore, the vector composed of the curing process state offset coefficients of all clusters arranged in ascending order is used as the obtained curing state analysis vector of the few-layer black phosphorus-carbon nanotube epoxy resin composite material. Therefore, the and sets of composite material samples on the production line of the few-layer black phosphorus-carbon nanotube epoxy resin composite material are obtained respectively. and In this embodiment, the values are 500 and 200 respectively. Each set of composite material samples contains few-layer black phosphorus-carbon nanotube epoxy resin composite materials and corresponding data of the curing process. The curing state analysis vectors of the sets of composite material samples are used as the training set, and the curing state analysis vectors of the sets of composite material samples are used as the test set. The convolutional neural network model is used to monitor the curing state through training. The specific training process of the convolutional neural network model is a well-known technology and will not be elaborated here.

[0088] Furthermore, on the production line of the few-layer black phosphorus-carbon nanotube epoxy resin composite material, as the production progresses, the data of the curing process of the currently produced few-layer black phosphorus-carbon nanotube epoxy resin composite material are collected, and the curing state analysis vector corresponding to the currently produced few-layer black phosphorus-carbon nanotube epoxy resin composite material is obtained. Using the curing state analysis vector as the input, the curing offset monitoring result of the production of the few-layer black phosphorus-carbon nanotube epoxy resin composite material is obtained by using the above-mentioned convolutional neural network model. The curing offset monitoring result includes two states: curing state offset and non-offset curing state.

[0089] After obtaining the curing state monitoring result, the curing process can be adjusted based on the curing state monitoring result.

[0090] Preferably, as an embodiment of the present application, if the monitoring result of the current production shows an offset in the curing state, the curing parameters in the production process are adjusted; if the monitoring result of the current production shows no offset in the curing state, the production continues. It should be noted that when the curing state is offset, the specific implementer of the curing parameter adjustment can decide according to the actual situation. The main purpose of the embodiments of the present invention is to monitor the curing status during the sample curing process to achieve the effect of timely warning, ensure timely prompting the relevant operator to adjust the curing parameters during the curing process, and avoid the problem of poor composite material preparation effect caused by the curing state deviating for a long time. There is no special limitation on the specific adjustment in this embodiment.

[0091] Step S4, cooling and demolding to obtain the required composite material.

[0092] It is worth noting that the positive correlation and negative correlation in the present application are used to characterize the change trend between variables. Positive correlation means that the dependent variable increases (decreases) as the independent variable increases (decreases), which can be an additive relationship or a multiplicative relationship, specifically determined according to the actual scenario; negative correlation means that the dependent variable decreases (increases) as the independent variable increases (decreases), which can be a negative correlation relationship such as a reciprocal relationship. There is no special limitation in the present application, and the implementer sets it by himself during the actual application process.

[0093] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the advantages or disadvantages of the embodiments. And the above specific embodiments of the present specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0094] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0095] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; modifying the technical solutions recorded in the foregoing embodiments, or equivalently replacing some of the technical features, does not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of each embodiment of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material, characterized in that The method includes the following steps: Obtain the temperature data during the stirring and degassing and curing processes in the preparation of multiple black phosphorus nanotube epoxy resin composites; Obtain the curing rate performance progress coefficient based on the analysis of the temperature data during the stirring and degassing and curing processes; determine the composite material curing process trend coefficient based on the distribution of the curing rate performance progress coefficient; Determine the composite material curing process state offset coefficient based on the composite material curing process trend coefficient, and judge and monitor the curing state; After curing is completed, cool and demold to obtain the black phosphorus nanotube epoxy resin composite; The curing rate performance progress coefficient includes: Record each black phosphorus nanotube epoxy resin composite as a sample, collect the temperatures of multiple samples during the stirring and degassing and curing processes, and arrange the temperature data in ascending order of the collection time to form the composite material curing data sequence of each sample; Set a sliding window with a preset size in the composite material curing data sequence, the sliding step is a preset value, and the temperatures within each sliding window are arranged in ascending order of time to form a curing short-term process data sequence; Construct the short-term process trend data sequence and the short-term process cycle data sequence of each curing short-term process data sequence respectively; Based on the difference between the maximum value and the minimum value in any two adjacent curing short-term process data sequences, determine the curing process advancement coefficient between the two adjacent curing short-term process data sequences; Calculate the distances between the short-term process trend data sequences of any two adjacent curing short-term process data sequences and the distances between the short-term process cycle data sequences respectively, and calculate the similarity between the two adjacent curing short-term process data sequences; Determine the curing rate performance progress coefficient between any two adjacent curing short-term process data sequences based on the distance, the similarity, and the curing process advancement coefficient; wherein, the curing rate performance progress coefficient is positively correlated with the curing process advancement coefficient and the distance respectively, and negatively correlated with the similarity.

2. A method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material according to claim 1, characterized in that, The construction process of the short-term process trend data sequence and the short-term process cycle data sequence of each curing short-term process data sequence is: For each curing short-term process data sequence, use the STL algorithm to obtain the trend component and the cycle component of each element in the curing short-term process data sequence, and the trend components and cycle components of all elements in the curing short-term process data sequence form the short-term process trend data sequence and the short-term process cycle data sequence of the curing short-term process data sequence respectively.

3. A method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material as described in claim 1, characterized in that, The method for determining the curing process advancement coefficient between any two adjacent curing short-term process data sequences is: Arrange the maximum values in the curing short-term process data sequence in descending order to form a maximum value sequence, and use the method for obtaining the maximum value sequence to obtain a minimum value sequence; Calculate the difference between each data in the maximum value sequence of each curing short-term process data sequence and the data at the same position in the minimum value sequence, and record it as the extreme value deviation; Based on the extreme value deviation calculated from the maximum value sequence and the minimum value sequence of any two adjacent curing short-term process data sequences, determine the curing process advancement coefficient between the two adjacent curing short-term process data sequences.

4. The method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material according to claim 3, characterized in that, The calculation process of the curing process advancement coefficient is specifically as follows: Calculate the ratio of the calculated extreme value deviation of the latter curing short-time process data sequence to the calculated extreme value deviation of the former curing short-time process data sequence in any two adjacent curing short-time process data sequences, and denote it as the deviation ratio; Determine the mean value of all the deviation ratios calculated from the maximum value sequence and the minimum value sequence of any two adjacent curing short-time process data sequences as the curing process advancement coefficient of any two adjacent curing short-time process data sequences.

5. A method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material as described in claim 4, characterized in that, Determining the composite material curing process trend coefficient based on the distribution of the curing rate performance process coefficients includes: Construct a curing rate performance process coefficient sequence for each sample; Take the distance between the curing rate performance process coefficient sequences of different samples as the metric distance of the clustering algorithm to cluster all samples; use the trend test algorithm to obtain the trend statistic and trend verification value of the curing rate performance process coefficient sequence of each sample; Obtain the ratio of the trend statistic to the trend verification value of each sample in the clustering cluster, and denote it as the trend ratio; Determine the composite material curing process trend coefficient of each clustering cluster based on the difference in the trend ratios of each sample in the clustering cluster and the distance of the curing rate performance process coefficient sequence.

6. A method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material according to claim 5, characterized in that The construction process of the curing rate performance process coefficient sequence of each sample is as follows: The curing rate performance process coefficients between all adjacent curing short-time process data sequences are arranged in the order of the sliding direction of the adjacent curing short-time process data sequences in the sliding window to form the curing rate performance process coefficient sequence of each sample.

7. A method for monitoring the curing state of a black phosphorene carbon nanotube epoxy resin composite material according to claim 6, characterized in that, Determining the composite material curing process trend coefficient of each clustering cluster based on the difference in the trend ratios of each sample in the clustering cluster and the distance of the curing rate performance process coefficient sequence includes: Obtain the distance between the curing rate performance process coefficient sequences of any two samples in the clustering cluster, denoted as the curing rate deviation, and calculate the sum of the products of the differences in the trend ratios of all pairs of samples in the clustering cluster and the curing rate deviation; Take the ratio of the mean value of the trend statistics of all samples in the clustering cluster to the sum as the composite material curing process trend coefficient of the clustering cluster.

8. A method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material as claimed in claim 7, characterized in that, The determination method of the composite material curing process state offset coefficient is: Obtain the difference in the composite material curing process trend coefficients between each clustering cluster and other clustering clusters, denoted as the trend difference, calculate the ratio of the trend difference to the composite material curing process trend coefficient of each clustering cluster, denoted as the difference coefficient ratio, and take the mean value of the difference coefficient ratios calculated for each clustering cluster and all other clustering clusters as the composite material curing process state offset coefficient of each clustering cluster.

9. A method for monitoring the curing state of a black phosphorene-carbon nanotube epoxy resin composite material as described in claim 8, characterized in that, Judging and monitoring the curing state includes: Form a vector by arranging the composite material curing process state offset coefficients of all clustering clusters in ascending order as the curing state analysis vector of the black phosphorus-carbon nanotube-epoxy resin composite material, take the curing state vector as the input of the convolutional neural network model, and the convolutional neural network outputs the curing offset monitoring result, where the curing offset monitoring result includes curing state offset and no curing state offset.

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