Method for monitoring curing state of black phosphorus alkene carbon nanotube epoxy resin composite material

Abstract

The invention relates to the technical field of data analysis state monitoring, in particular to a method for monitoring the curing state of a black phosphorene carbon nanotube epoxy resin composite material, which comprises the following steps: in the preparation process of the black phosphorene carbon nanotube epoxy resin composite material, collecting curing temperature data, processing and analyzing; obtaining a curing rate performance process coefficient according to the temperature data in the stirring, defoaming and curing processes; obtaining a composite material curing process trend coefficient according to the distribution condition of the curing rate performance process coefficient; obtaining a composite material curing process state offset coefficient according to the composite material curing process trend coefficient, and judging the curing state; and cooling and demolding: after curing is completed, cooling the material in the mold to room temperature, and demolding to obtain the black phosphorus alkene carbon nanotube epoxy resin composite material. Therefore, the preparation of the black phosphorus alkene carbon nanotube epoxy resin composite material is realized.

Description

Technical Field

[0001] This application relates to the technical field of data analysis and status monitoring, and specifically relates to a method for monitoring the curing status 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 fields such as 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 a curing agent, stirring and defoaming, 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] In order to solve the above technical problems, the present invention provides a method for monitoring the curing status of a black phosphorus-carbon nanotube epoxy resin composite material to solve the existing problems.

[0006] The method for monitoring the curing status of a black phosphorus-carbon nanotube epoxy resin composite material of the present invention adopts the following technical solution: An embodiment of the present invention provides a method for monitoring the curing status of a black phosphorus-carbon nanotube epoxy resin composite material, and the method includes the following steps: Obtain temperature data during the stirring and defoaming and curing processes in the preparation process of multiple black phosphorus-carbon nanotube epoxy resin composite materials; The curing rate performance progress coefficient is obtained based on the analysis of 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; Based on the composite material curing process trend coefficient, the composite material curing process state offset coefficient is determined to judge the curing state; After curing is completed, cooling and demolding are carried out to obtain the black phosphorus-carbon nanotube epoxy resin composite material.

[0007] Further, the curing rate performance progress coefficient includes: Each black phosphorus-carbon nanotube epoxy resin 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 are arranged in ascending order of the collection time to form the composite material curing data sequence of each sample; A sliding window with a preset size is set in the composite material curing data sequence, the sliding step size 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; The short-term process trend data sequence and the short-term process cycle data sequence of each curing short-term process data sequence are respectively constructed; Based on the differences between the maximum value and the minimum value in any two adjacent curing short-term process data sequences, the curing process advancement coefficient between the any two adjacent curing short-term process data sequences is determined; 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 curing short-term process data sequences are respectively calculated, and the similarity degree between the any two adjacent curing short-term process data sequences is calculated; 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 is negatively correlated with the similarity degree.

[0008] Further, 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: For each curing short-term process data sequence, the STL algorithm is used 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 the cycle components of all elements in the curing short-term process data sequence respectively form the short-term process trend data sequence and the short-term process cycle data sequence of the curing short-term process data sequence.

[0009] Further, 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 solidification short-time process data sequence in descending order to form a maximum value sequence, and obtain a minimum value sequence using the method for obtaining the maximum value sequence; Calculate the difference between each data in the maximum value sequence of each solidification short-time process data sequence and the data at the same position in the minimum value sequence, and denote 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 solidification short-time process data sequences, determine the solidification process advancement coefficient of the any two adjacent solidification short-time process data sequences.

[0010] Further, the calculation process of the solidification process advancement coefficient is specifically as follows: Calculate the ratio of the extreme value deviation calculated for the latter solidification short-time process data sequence to the extreme value deviation calculated for the former solidification short-time process data sequence among the any two adjacent solidification 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 the any two adjacent solidification short-time process data sequences as the solidification process advancement coefficient of the any two adjacent solidification short-time process data sequences.

[0011] Further, obtaining the composite material solidification process trend coefficient according to the distribution of the solidification rate performance process coefficient includes: Construct a solidification rate performance process coefficient sequence for each sample; 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 trend verification value of the solidification 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; Based on the difference in the trend ratios of each sample in the clustering cluster and the distance of the solidification rate performance process coefficient sequence, determine the composite material solidification process trend coefficient of each clustering cluster.

[0012] Further, the construction process of the solidification rate performance process coefficient sequence of each sample is as follows: Arrange the solidification rate performance process coefficients between all adjacent solidification short-time process data sequences in the arrangement order of the adjacent solidification short-time process data sequences in the sliding direction of the sliding window to form the solidification rate performance process coefficient sequence of each sample.

[0013] Further, the determining the composite material solidification 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 solidification 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 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.

[0014] Furthermore, the method for determining the composite material curing process state offset coefficient is as follows: 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 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.

[0015] Furthermore, the judgment and monitoring of the curing state include: Take the vector composed of 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 composite material, use 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.

[0016] The present invention has at least the following beneficial effects: 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 can construct a good thermal conduction path in the epoxy resin. At the same time, during the preparation process, by analyzing the curing process change characteristics of the black phosphorus-carbon nanotube epoxy composite material at different time periods, the curing rate performance process coefficient is calculated, and the change difference of the photocuring state during the composite material preparation process is reflected through the curing rate performance process coefficient; further, the present invention constructs the composite material curing process trend coefficient according to the differences in the curing states of different black phosphorus-carbon nanotube epoxy composite materials during the preparation process, and reflects the curing state trend characteristics during the preparation process of the black phosphorus-carbon nanotube epoxy 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 composite material is monitored through the curing process state offset coefficient, improving the accuracy of monitoring the curing state offset during the preparation process, thereby improving the curing efficiency during the curing process and improving the quality of the prepared black phosphorus-carbon nanotube epoxy composite material. Description of the Drawings

[0017] 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 be obtained based on these drawings.

[0018] 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; Figure 2 It is a schematic diagram of a black phosphorus-carbon nanotube heterostructure hybrid system; Figure 3 It is a flowchart for obtaining the curing process advancement coefficient between any two adjacent short-term curing process data sequences; Figure 4 It is a flowchart for obtaining the curing rate performance process coefficient between any two adjacent short-term curing process data sequences; Figure 5 It is a flowchart for obtaining the curing process trend coefficient of the composite material for each clustering cluster; Figure 6 It is a flowchart for obtaining the curing process state offset coefficient of the composite material for each clustering cluster. Specific Embodiments

[0019] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in combination with the accompanying drawings and preferred embodiments, detail the specific embodiments, structures, features, and effects of a method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material proposed according to the present invention. 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.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0021] The following will specifically describe 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 with reference to the accompanying drawings.

[0022] 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, the following method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material is provided. Please refer to Figure 1 , and the method includes the following steps: Step S1, prepare a black phosphorus-carbon nanotube heterostructure hybrid system.

[0023] 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 1-100. In this embodiment, the mass ratio of black phosphorus to carbon nanotubes is 1:1. Ultrasonically compound the mixed solution of black phosphorus and carbon nanotubes by ultrasonic method. After ultrasonic compounding, a black phosphorus-carbon nanotube heterostructure hybrid system is obtained. The schematic diagram of the black phosphorus-carbon nanotube heterostructure hybrid system is as Figure 2 shown.

[0024] Step S2, add epoxy resin and curing agent.

[0025] Introduce the obtained black phosphorus-carbon nanotube heterostructure hybrid system into bisphenol A epoxy resin by solution mixing method at mass ratios of 1wt%, 3wt%, 5wt% and 7wt% respectively, remove the solvent by rotary evaporation, and finally add an equivalent amount of linear phenolic resin. Among them, the epoxy resin is not limited to bisphenol A epoxy resin. The epoxy resin is one or several 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 several of linear phenolic resin, methylhexahydrophthalic anhydride, DDM (4,4'-diaminodiphenylmethane). In this embodiment, linear phenolic resin is used, and implementers in different embodiments can select by themselves.

[0026] Step S3, stir, defoam and cure.

[0027] Stir the mixed material after adding epoxy resin and curing agent evenly and defoam it under vacuum. After vacuum defoaming is completed, pour the mixed material into a pre-prepared mold and cure it at a temperature of 180°C for 3 hours. Among them, the curing parameters are adaptively adjusted according to the curing state bromination characteristics in the preparation of black phosphorus-carbon nanotube epoxy composite material. The specific process schematic diagram is as Figure 3 shown, and the specific adjustment steps are as follows: The first step: collect the temperature data during the curing process of the black phosphorus-carbon nanotube epoxy composite material.

[0028] On the production line of the black phosphorus-carbon nanotube epoxy composite material, obtain temperature data during the stirring, defoaming and curing process of the black phosphorus-carbon nanotube epoxy composite material. Specifically, during the stirring, defoaming and curing process of each black phosphorus-carbon nanotube epoxy 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 temperature data is , in this embodiment, 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 composite material.

[0029] Thus, the composite material curing data sequence is obtained.

[0030] Second step: Construct a curing rate performance progress coefficient according to the influence characteristics of the temperature change on the reaction rate and curing degree of the black phosphorus-carbon nanotube composite material during different time periods in 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 progress coefficient.

[0031] During the preparation process of the black phosphorus-carbon nanotube composite material, the curing temperature and time have different effects on different composite materials. In the actual production process, due to the complex environment in the production workshop, during the curing process of the black phosphorus-carbon nanotube composite material, the unstable change of temperature may cause the curing degree, network structure and performance of the epoxy resin matrix in the preparation process of the black phosphorus-carbon nanotube composite material to show instability characteristics, affecting the quality of the prepared black phosphorus-carbon nanotube composite material.

[0032] 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 the 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 the 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.

[0033] Furthermore, if the high or low temperature change characteristics shown during the curing process of the black phosphorus nanotube epoxy resin composite have a significant impact on the quality of the composite preparation, and the unstable temperature change characteristics may lead to a decrease or increase in the curing efficiency, resulting in problems such as too long or too short curing time within the set curing time, reducing the production quality of the black phosphorus nanotube epoxy resin composite. Therefore, by collecting the temperature data during the production process of the black phosphorus nanotube epoxy resin composite, the change characteristics of the curing process in different time periods are analyzed. Specifically, for each composite curing data sequence of the black phosphorus nanotube epoxy resin composite, a sliding window of size is set in the composite 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. The sequence formed by arranging the data within the sliding window in ascending order of time is used as a short-term curing process data sequence.

[0034] Furthermore, each short-term curing process data sequence is used as an input, and the STL (Seasonal and Trend decomposition using Loess) algorithm is used to obtain the trend component and periodic component of each element in each short-term curing process data sequence. The sequence formed by the trend components of all elements in each short-term curing process data sequence is used as the short-term process trend data sequence and the sequence formed by the periodic components of all elements in each short-term curing 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 and minimum values in each short-term curing process data sequence are obtained, and the sequences formed by arranging the maximum and minimum values 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 .

[0035] The curing rate performance process coefficient is calculated according to the data change characteristics of two adjacent short-term curing process data sequences. Taking two adjacent short-term curing process data sequences: the x-th and the y-th short-term curing process data sequences as an example, where the y-th short-term curing process data sequence is the next set of data of the x-th short-term curing process data sequence on the sliding window, that is, the adjacent short-term curing process data sequence of the x-th short-term curing process data sequence. The specific obtaining process of the curing rate performance process coefficient is as follows: First, determine the curing process advancement coefficient of any two adjacent cured short-time process data sequences based on the extreme value deviation calculated from the maximum value sequence and the minimum value sequence of any two adjacent cured short-time process data sequences: Calculate the ratio of the extreme value deviation calculated from the latter cured short-time process data sequence to the extreme value deviation calculated from the former cured short-time process data sequence among any two adjacent cured short-time process data sequences, and denote it as the deviation ratio; Determine the mean value of all the deviation ratios calculated from any two adjacent cured short-time process data sequences as the curing process advancement coefficient of any two adjacent cured short-time process data sequences.

[0036] Specifically, for the flowchart of obtaining the curing process advancement coefficient between any two adjacent cured short-time process data sequences, please refer to Figure 3 .

[0037] Furthermore, calculate the distances between the short-time process trend data sequences and the distances between the short-time process period data sequences of any two adjacent cured short-time process data sequences respectively, and calculate the similarity degree between any two adjacent cured short-time process data sequences; Determine the curing rate performance process coefficient between any two adjacent cured short-time process data sequences based on the distance, the similarity degree, and the curing process advancement coefficient; Among them, 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.

[0038] Preferably, as an embodiment, the calculation formula for the curing process advancement coefficient between the x-th and y-th cured short-time process data sequences can be: ; where represents the curing process advancement coefficient between the x-th and y-th cured short-time process data sequences; represents the curing rate performance process coefficient between the x-th and y-th cured short-time process data sequences; and represent the short-time process trend data sequences of the x-th and y-th cured short-time process data sequences respectively, represents the Manhattan distance, and the implementer can also select other distance measurement methods to analyze the distance between sequences; and represent the short-time process period data sequences of the x-th and y-th cured short-time process data sequences respectively; and represent the x-th and y-th cured short-time 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.

[0039] 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, the calculated value is larger, that is, the curing process advancement coefficient between the x-th and y-th cured short-term process data sequences is larger, indicating a greater possibility that the curing process shows a progressive growth trend during the curing of the black phosphorus nanotube epoxy composite; at the same time, the greater the difference in trend change and periodic 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 updated data between the x-th and y-th cured short-term process data sequences, the smaller the calculated value; that is, the larger the calculated curing rate performance process coefficient between the x-th and y-th cured short-term process data sequences, the greater the possibility that the curing process accelerates during the curing of the black phosphorus nanotube epoxy composite.

[0040] 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 .

[0041] Furthermore, due to the characteristics of the dynamic environmental impact during the curing process on the production line of the black phosphorus nanotube epoxy composite, the state change characteristics of the curing process during the preparation of different black phosphorus nanotube epoxy composites are different. Therefore, by analyzing the state change characteristics of the curing process of each black phosphorus nanotube epoxy composite, cluster analysis is performed on the black phosphorus nanotube epoxy composites on the production line, and the curing state change during the preparation of the black phosphorus nanotube epoxy composite is reflected through the results of the cluster analysis.

[0042] 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, which is used as the curing rate performance process coefficient sequence of each black phosphorus nanotube epoxy composite. Each black phosphorus nanotube epoxy composite is used as a sample, and the distance between the curing rate performance process coefficient sequences of different samples is used as the measurement distance of the clustering algorithm to cluster all samples to obtain each cluster.

[0043] 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; the ratio of the trend statistic to the trend verification value of each sample in the clustering cluster is obtained, denoted as the trend ratio; the distance between the curing rate performance process coefficient sequences of any two samples in the clustering cluster is obtained, denoted as the curing rate deviation, and the sum of the products of the differences between the trend ratios of all pairs of samples in the clustering cluster and the curing rate deviation is calculated; the ratio of the mean value of the trend statistics of all samples in the clustering cluster to the sum is used as the trend coefficient of the composite material curing process in the clustering cluster.

[0044] 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 samples, and the clustering algorithm uses the agglomerative hierarchical clustering algorithm. Implementers can select other clustering algorithms by themselves; 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 for the calculation of 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.

[0045] Thus, the trend coefficient of the composite material curing process is obtained.

[0046] Specifically, for the flowchart of obtaining the trend coefficient of the composite material curing process of each clustering cluster, please refer to Figure 5 .

[0047] 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.

[0048] 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 curing process parameters 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 trend differences of the composite material curing processes reflected by different black phosphorus-carbon nanotube epoxy composite materials are large, it indicates that the parameters of the curing process in production deviate.

[0049] Further, the curing process state offset coefficient of the composite material is calculated through the curing process trend coefficient of the composite material, and the offset degree of the production state of different black phosphorus nanotube epoxy resin composites affected by the environment is reflected by the curing process state offset coefficient of the composite material. The process of obtaining the curing process state offset coefficient of the composite material is as follows: Obtain the difference between the curing process trend coefficients 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 curing process trend coefficients of two clustering clusters; calculate the ratio of the trend difference to the 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 curing process state offset coefficient of each clustering cluster.

[0050] It should be understood that if black phosphorus nanotube epoxy resin composites, and through cluster analysis, the th clustering cluster has a significant difference in the trend characteristics of the curing state from the black phosphorus nanotube epoxy resin composites in other clustering clusters, then the larger the calculated trend ratio, that is, the th clustering cluster has a larger curing process state offset coefficient of the composite material, indicating that the curing state of the corresponding black phosphorus nanotube epoxy resin composite material in the th clustering cluster may deviate.

[0051] Specifically, for the flowchart of obtaining the curing process state offset coefficient of each clustering cluster, please refer to Figure 6 .

[0052] Further, the vector composed of the curing process state offset coefficients of all clustering clusters in ascending order is used as the obtained black phosphorus nanotube epoxy resin composite material curing state analysis vector. Therefore, on the production line of the black phosphorus nanotube epoxy resin composite material, and groups of composite material samples are respectively obtained. and In this embodiment, the values are 500 and 200 respectively. Each group of composite material samples contains black phosphorus nanotube epoxy resin composite materials and corresponding curing process data. The curing state analysis vectors of groups of composite material samples are used as the training set, and the curing state analysis vectors of groups of composite material samples are used as the test set. A 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.

[0053] Further, on the production line of the black phosphorus nanotube epoxy resin composite material, as the production progresses, collect the data of the curing process of the black phosphorus nanotube epoxy resin composite materials currently produced, and obtain the curing state analysis vectors corresponding to the black phosphorus nanotube epoxy resin composite materials currently produced. Using the curing state analysis vectors as inputs, utilize the above convolutional neural network model to obtain the curing offset monitoring results of the production of the black phosphorus nanotube epoxy resin composite material. The curing offset monitoring results include two states: curing state offset and non-offset curing state.

[0054] After obtaining the curing state monitoring results, the curing process can be adjusted based on the curing state monitoring results.

[0055] Preferably, as an embodiment of the present application, if the monitoring result of the current production is a curing state offset, then adjust the curing parameters during the production process; if the monitoring result of the current production is a non-offset curing state, then continue the production. It should be noted that when the curing state is offset, the specific implementer of adjusting the curing parameters can decide according to the actual situation. The main purpose of the embodiments of the present invention is to monitor the curing condition during the sample curing process to achieve the effect of timely warning, ensure that the relevant operator is timely prompted to adjust the curing parameters of the curing process, and avoid the problem that the preparation effect of the composite material is poor due to the curing state deviating for a long time. The specific adjustment is not specially limited in this embodiment.

[0056] Step S4, cooling and demolding to obtain the required composite material.

[0057] It should be noted that the positive correlation and negative correlation in the present application are used to characterize the change trends 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, etc. The present application does not make special restrictions, and the implementer sets it by himself during the actual application process.

[0058] 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 and disadvantages of the embodiments. And the above describes specific embodiments of this specification. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0059] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the differences between each embodiment and other embodiments are emphasized.

[0060] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; any modification to the technical solutions described in the foregoing embodiments, or any equivalent replacement of some of the technical features, does not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of each embodiment of the present application, and should all be included within 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 temperature data during the stirring and degassing and curing processes in the preparation of multiple black phosphorus nanotube epoxy resin composites; Obtain a curing rate performance process coefficient based on the analysis of the temperature data during the stirring and degassing and curing processes; determine a composite material curing process trend coefficient based on the distribution of the curing rate performance process coefficient; Determine a 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 a black phosphorus nanotube epoxy resin composite.

2. The curing state monitoring method of a black phosphorene-carbon nanotube epoxy resin composite material according to claim 1, characterized in that, The curing rate performance process 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 a composite material curing data sequence for each sample; Set a sliding window with a preset size in the composite material curing data sequence, the sliding step size 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 a short-term process trend data sequence and a short-term process cycle data sequence for each curing short-term process data sequence respectively; Determine a curing process advancement coefficient between any two adjacent curing short-term process data sequences based on the differences between the maximum and minimum values in any two adjacent curing short-term process data sequences; 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 curing short-term process data sequences respectively, and calculate the similarity degree between any two adjacent curing short-term process data sequences; Determine the curing rate performance process coefficient between any two adjacent curing 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.

3. A method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material according to claim 2, characterized in that, 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: 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.

4. The curing state monitoring method of a black phosphorene-carbon nanotube epoxy resin composite material according to claim 2, characterized in that The method for determining the curing process advancement coefficient between any two adjacent curing short-term process data sequences is as follows: 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; Determine the curing process advancement coefficient between any two adjacent curing short-term process data sequences 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.

5. The method for monitoring the curing state of a black phosphorene-carbon nanotube epoxy resin composite material according to claim 4, wherein, 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.

6. The method for monitoring the curing state of a black phosphorene-carbon nanotube epoxy resin composite material according to claim 5, wherein, Obtaining the composite material curing process trend coefficient according to the distribution of the curing rate performance process coefficient 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 measurement distance of the clustering algorithm, and 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; Based on the differences in the trend ratios of the samples in the clustering cluster and the distance of the curing rate performance process coefficient sequences, determine the composite material curing process trend coefficient of each clustering cluster.

7. A method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material according to claim 6, 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.

8. The curing state monitoring method of a black phosphorus-carbon nanotube epoxy resin composite material according to claim 7, characterized in that Determining 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 distance of the curing rate performance process coefficient sequences 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 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; Take the ratio of the mean value 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.

9. A method for monitoring the curing state of a black phosphorene-carbon nanotube epoxy resin composite material according to claim 8, characterized in that, The method for determining the composite material curing process state offset coefficient is: 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 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.

10. A method for monitoring the curing state of a black phosphorus-carbon nanotube epoxy resin composite material according to claim 9, characterized in that, The judgment and monitoring of the curing state include: Take the vector formed 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, and take the curing state vector as the input of the convolutional neural network model. 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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