Prediction method for curing temperature of black phosphorene epoxy plastic package composite material

Abstract

The invention relates to the technical field of data processing prediction, in particular to a black phosphorene epoxy plastic package composite material curing temperature prediction method, which comprises the following steps: selecting raw materials, mixing and stirring to obtain a mixed solution; after vacuum defoaming, pouring into a mold and curing at a set temperature; in the curing process, obtaining a curing dynamic collinearity characteristic difference coefficient according to the temperature data difference of different acquisition points in the curing process; and obtaining a local collinear feature division coefficient according to the curing dynamic collinear feature difference coefficient, obtaining a curing temperature ridge regression estimator according to the local collinear feature division coefficient, carrying out adaptive adjustment on the curing temperature, and carrying out cooling demolding after curing to obtain the epoxy molding compound nano composite material. According to the invention, accurate prediction of the temperature in the preparation process of the black phosphorene epoxy plastic package composite material can be realized.

Description

Technical Field

[0001] This application relates to the technical field of data processing prediction, and particularly relates to a method for predicting the curing temperature of a black phosphorus epoxy encapsulation composite material. Background Art

[0002] As the frequency, power, and heat generation of electronic devices are getting higher and higher, higher requirements are put forward for the performance of electronic packaging materials and electronic substrate materials. Developing a new generation of high thermal conductivity packaging materials has important practical significance. Currently, the most effective method is to add thermal conductive fillers to the plastic encapsulation materials, but the modification effect on their thermal conductivity is not obvious. One-dimensional materials such as carbon nanotubes with high thermal conductivity and large specific surface area and classical two-dimensional material graphene have also been applied to the field of electronic packaging. Some scholars have tried to compound graphene or carbon nanotubes with thermal conductive fillers to prepare G / GO / C thermal conductive filler composite materials, combining the advantages of the two materials to prepare composite materials with good thermal conductivity. Black phosphorus, a new type of two-dimensional material, has higher thermal conductivity compared to the classical two-dimensional material graphene. Compared with graphene or carbon nanotubes, black phosphorus can be used as a thermal conductive filler.

[0003] The thermal conductive fillers applied to plastic encapsulation materials are mainly nanoscale fillers such as silica, alumina, aluminum nitride, and boron nitride. However, due to the small size, high surface activity, and easy agglomeration of pure nanoparticles, they cannot be in full contact with the medium, which reduces the thermal conductivity activity and is not conducive to improving the thermal conductivity of plastic encapsulation materials, resulting in the loss of many excellent properties of nanomaterials. Compounding single nanomaterials into nanocomposites can solve the agglomeration problem of single nanoparticles, and the components of nanocomposite energetic materials can achieve nanoscale contact. Such composite materials not only have the quantum size effect, small size effect, surface and interface effect, and macroscopic quantum tunneling effect of single nanoparticles, but also have a synergistic effect, giving full play to the various excellent characteristics of single nanoparticles, making nanocomposite energetic materials have practical significance, meeting the needs of different occasions, and at the same time providing an effective method for the manufacture of new composite energetic materials.

[0004] The preparation process of the epoxy encapsulant composite material based on black phosphorus has realized an automated production process. The specific preparation process includes the preparation of a black phosphorus and epoxy resin mixed system, the mixing of a curing accelerator, a curing agent, and a thermal conductive filler, vacuum degassing, casting and curing, and cooling and demolding. During the casting and curing process, the temperature change directly affects the quality of the cured product. For example, uniform temperature control during the curing process can prepare high-quality epoxy encapsulant composite materials. However, due to problems such as many influencing factors, complex correlation relationships, and difficult-to-precisely-set boundary conditions in the curing process, the temperature changes unevenly during the preparation process of the epoxy encapsulant composite material, reducing the quality of the epoxy encapsulant composite material prepared by black phosphorus. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a method for predicting the curing temperature of a black phosphorus epoxy encapsulation composite material to solve the existing problems.

[0006] The method for predicting the curing temperature of a black phosphorus epoxy encapsulation composite material of the present invention adopts the following technical solutions: An embodiment of the present invention provides a method for predicting the curing temperature of a black phosphorus epoxy encapsulation composite material, and the method includes the following steps: Select the raw materials for preparing the black phosphorus epoxy encapsulation nano-composite material, and predict the curing temperature during the preparation process, specifically including: Obtain the temperature at each data acquisition point at each acquisition moment during the curing process, and based on the temperature data differences at different acquisition points during the curing process, combined with cluster analysis, obtain the curing dynamic collinearity characteristic difference coefficient; Obtain the local collinearity characteristic division coefficient based on the curing dynamic collinearity characteristic difference coefficient; obtain the ridge regression estimator of the curing temperature based on the local collinearity characteristic division coefficient, and predict the curing temperature; After the curing process is completed, the epoxy encapsulation nano-composite material is obtained by cooling and demolding.

[0007] In some embodiments, the preparation raw materials are black phosphorus, epoxy resin, curing agent, curing accelerator, and heat-conducting filler.

[0008] In some embodiments, the method for obtaining the curing dynamic collinearity characteristic difference coefficient is as follows: Based on the collected temperature data, construct a composite material curing difference analysis sequence for each data acquisition point; Calculate the range and coefficient of variation of each composite material curing difference analysis sequence respectively, take the range and coefficient of variation as the abscissa value and ordinate value respectively, and the coordinates corresponding to each composite material curing difference analysis sequence are used as a sample, and all samples are clustered to obtain each clustering cluster; Based on the correlation between the composite material curing difference analysis sequences of any two samples in any two clustering clusters, determine the curing dynamic collinearity characteristic difference coefficient between the any two clustering clusters.

[0009] In some embodiments, the method for constructing the composite material curing difference analysis sequence is: the temperature data of each data acquisition point at different acquisition moments constitute the composite material curing difference analysis sequence of each data acquisition point.

[0010] In some embodiments, the method for determining the curing dynamic collinearity characteristic difference coefficient between any two clustering clusters is as follows: Analyze the distance between any two samples in the any two clustering clusters; Analyze the correlation coefficient between the composite material curing difference analysis sequences corresponding to any two samples in any two clustering clusters; Among them, the curing dynamic collinearity feature difference coefficient between any two clustering clusters is positively correlated with the distance between all any two samples in any two clustering clusters, and negatively correlated with the correlation coefficient.

[0011] In some embodiments, the method for obtaining the local collinearity feature division coefficient is as follows: Statistically analyze the maximum value of the curing dynamic collinearity feature difference coefficients between all any two clustering clusters, and analyze the ratio of each curing dynamic collinearity feature difference coefficient to the maximum value; Determine the local collinearity feature division coefficient based on the ratio, where the local collinearity feature division coefficient is positively correlated with the ratio obtained by analyzing all curing dynamic collinearity feature difference coefficients.

[0012] In some embodiments, the method for obtaining the curing temperature ridge regression estimator is as follows: Construct each sub-data sequence of each data acquisition point based on the local collinearity feature division coefficient and the composite material curing difference analysis sequence; Analyze the variance inflation factor of the sub-data sequences of all data acquisition points in each time interval; Construct a curing temperature monitoring matrix, and use the ridge trace graph method to obtain the initial ridge regression estimator of the curing temperature monitoring matrix; Determine the curing temperature ridge regression estimator based on the difference in variance inflation factors of different time intervals and the initial ridge regression estimator, where the curing temperature ridge regression estimator is positively correlated with the difference in variance inflation factors of all any two time intervals and the initial ridge regression estimator respectively.

[0013] In some embodiments, the method for constructing the sub-data sequence is as follows: evenly divide the composite material curing difference analysis sequence of each data acquisition point into w sub-data sequences, where the value of w is the local collinearity feature division coefficient.

[0014] In some embodiments, each row of the curing temperature monitoring matrix consists of temperature data of each data acquisition point at different acquisition times.

[0015] In some embodiments, predicting the curing temperature includes: Based on the curing temperature ridge regression estimator and the curing temperature monitoring matrix, obtain the predicted value of the curing temperature through the ridge regression algorithm.

[0016] The present invention has at least the following beneficial effects: The present invention prepares an epoxy molding compound composite using black phosphorus, enabling the black phosphorus epoxy molding compound composite to possess good thermal conductivity, mechanical properties, and flame retardancy. Further, by analyzing the dynamic change difference characteristics of the curing temperature during the preparation of the black phosphorus epoxy molding compound, the curing dynamic collinearity characteristic difference coefficient is calculated. The curing dynamic collinearity characteristic difference coefficient reflects the dynamic change difference of the curing temperature at different acquisition moments during the curing process. Based on the analysis of the dynamic change difference, a local collinearity characteristic division coefficient is constructed. The time interval and sub-data sequence are divided by the local collinearity characteristic division coefficient. The curing temperature ridge regression estimator is obtained through the dynamic collinearity characteristic difference of the subsequences in different time intervals. Based on the curing temperature ridge regression estimator, the prediction value of the curing temperature is obtained using the ridge regression estimation algorithm. The beneficial effect is that it considers the dynamic change difference of the curing temperature at different positions during the curing process of the black phosphorus epoxy molding compound composite to divide the time interval, determines the parameters of the ridge regression algorithm through the collinearity characteristic difference of different time intervals in the divided time interval, improves the accuracy of predicting the curing temperature using the ridge regression algorithm, and further precisely adjusts the temperature during the curing process, thereby improving the performance and quality of the black phosphorus epoxy molding compound prepared based on black phosphorus. 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 accompanying drawings required for the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying 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.

[0018] Figure 1 It is a flowchart of the steps of a method for predicting the curing temperature of a black phosphorus epoxy molding composite provided by the present invention; Figure 2 It is an SEM image of black phosphorus; Figure 3 It is a schematic diagram of the temperature-time curve of the curing temperature monitoring data sequence cured at 100°C for 3 hours; Figure 4 It is a schematic diagram of the temperature-time curve of the curing temperature monitoring data sequence cured at 180°C for 3 hours; Figure 5 It is a schematic diagram of the clustering result. Detailed Embodiments

[0019] 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 predicting the curing temperature of a black phosphorus epoxy encapsulation 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, 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 skilled in the technical field to which this invention belongs.

[0021] The following specifically describes the specific solution of a method for predicting the curing temperature of a black phosphorus epoxy encapsulation composite material provided by the present invention in conjunction with the accompanying drawings.

[0022] Example 1 provides a method for predicting the curing temperature of a black phosphorus epoxy encapsulation composite material. For the preparation flow chart, please refer to Figure 1 , and the specific preparation process is as follows: (1) Prepare a black phosphorus-epoxy resin mixed system: Add 0.018 - 0.18 g of PNs (black phosphorus) to 10 g of EP (epoxy resin). The epoxy resin includes one or several of biphenyl epoxy resin, o-cresol novolac epoxy resin, bisphenol A epoxy resin, resorcinol formaldehyde epoxy resin, and triphenylolmethane epoxy resin. In this embodiment, o-cresol novolac epoxy resin is preferably used. Manually stir for 10 min, then put it into an ultrasonic cleaner and ultrasonicate for 5 min. The ultrasonic power is 95% and the temperature is room temperature. The SEM image of the black phosphorus used is as Figure 2 shown.

[0023] (2) Adding a curing agent, a curing accelerator, and a thermal conductive filler: After ultrasonically dispersing the above-mentioned black phosphorus and epoxy resin, a black phosphorus-epoxy resin mixed system is obtained. 0.9 g of a curing agent is added to the black phosphorus-epoxy resin mixed system. The curing agent is one or more of linear phenolic resin, methylhexahydrophthalic anhydride, and DDM (diaminodiphenylmethane). In this embodiment, linear phenolic resin is preferably used; the curing accelerator is 2,4,6-tris(dimethylaminomethyl)phenol (DMP-30); one or more of imidazole accelerators such as 2-methylimidazole (2-MZ). In this embodiment, DMP-30 (2,4,6-tris(dimethylaminomethyl)phenol) is preferably used, and a thermal conductive filler is added. The thermal conductive filler includes one or more of silicon dioxide (SiO2), aluminum oxide (Al2O3), aluminum nitride (AlN), boron nitride (BN), silicon carbide (SiC), silver (Ag), gold (Au), and copper (Cu). In this embodiment, silicon dioxide is preferably used, with a mass of 13.5 g. Manually stir for 10 min to obtain a uniformly mixed liquid.

[0024] (3) Vacuum degassing and curing: Add 8 g of linear phenolic resin to the above-mentioned mixed liquid, manually stir for 10 min, mix evenly and perform vacuum degassing, then pour it into a pre-prepared mold and cure it at 100 °C for 3 h and 180 °C for 3 h respectively. During the curing process, the curing temperature is predicted and analyzed to complete the curing process. The specific temperature prediction steps are as follows: Step S001, collect the temperature monitoring data during the curing process of the epoxy molding compound, and construct a curing temperature monitoring matrix based on the collected temperature monitoring data.

[0025] In the process of preparing the epoxy molding compound composite using black phosphorus, the curing process refers to pouring the mixed raw materials into a mold and curing them in a set temperature environment. Further, a temperature probe sensor is used to collect temperature data at different data collection points during the curing process of the composite material. The time interval for collecting data is t (0.1 s in this embodiment), and the number of positions of the set data collection points is (10 in this embodiment). The sequence formed by arranging the data collected at each data collection point in ascending order of time is used as the curing temperature monitoring data sequence. The specific curing process is divided into two stages, namely curing at 100 °C for 3 hours and curing at 180 °C for 3 hours. The temperature-time curves of the curing temperature monitoring data sequences obtained by collecting curing data in the two stages are as Figure 3 and Figure 4 shown. In the figure, the abscissa is time, with the unit of s, and the ordinate is temperature, with the unit of °C.

[0026] Further, each solidification temperature monitoring data sequence is used as a row of data in a matrix, and the matrix composed of the corresponding solidification temperature data sequences of all acquisition points is used as the solidification temperature monitoring matrix, where each element in the solidification temperature monitoring matrix represents the temperature data corresponding to one acquisition moment at one of the data acquisition points. For example, the element at the i th row and the j th column in the solidification temperature monitoring matrix indicates the temperature data obtained at the i th data acquisition point at the j th acquisition moment.

[0027] Thus, the solidification temperature monitoring matrix during the solidification process of the black phosphorus-epoxy encapsulant composite material is obtained.

[0028] Step S002: Construct a solidification dynamic collinearity feature difference coefficient based on the dynamic change difference features between different data acquisition points in the solidification temperature monitoring matrix, construct a local collinear feature division coefficient according to the solidification dynamic collinearity feature difference coefficient, and calculate the solidification temperature ridge regression estimator based on the local collinear feature division coefficient.

[0029] During the preparation process of the black phosphorus-epoxy encapsulant composite material, due to the difference in solidification temperature control, uneven temperature changes occur during the solidification process, causing deviations in the structure of the black phosphorus-epoxy encapsulant composite material and reducing the quality of the preparation of the black phosphorus-epoxy encapsulant composite material. Specifically, as heating progresses, the surface temperature difference of the composite material is rapidly enlarged. An excessive temperature difference may cause the maximum heating temperature to be reached, thereby triggering the condition for stopping heating. Further, it may cause a decrease in the reaction rate between the materials during the solidification process, affecting the structure of the black phosphorus-epoxy encapsulant composite material, and further affecting the thermal conductivity in the black phosphorus-epoxy encapsulant.

[0030] Further, the uneven temperature change characteristics during the solidification process lead to unstable change characteristics in the solidification reaction rate of the black phosphorus-epoxy encapsulant composite material, resulting in deviations in the internal structure of the prepared black phosphorus-epoxy encapsulant composite material, and further reducing the thermal conductivity of the composite material. Among them, the influence of environmental changes on the change of solidification temperature during the preparation process is a randomly changing process, and the degree of influence on the change of solidification temperature reflected by different data acquisition points during the solidification process may be different. The greater the difference in the degree of influence, the more significant the solidification temperature deviation characteristics of the composite material. Therefore, in this embodiment, the dynamic change characteristics difference of the solidification temperature will be analyzed.

[0031] Specifically, the change moments of the data acquisition points are divided according to the temperature change amplitude differences of each data acquisition point at different acquisition moments during the curing process of the composite material. According to the division results, the local characteristics of the curing process change are analyzed. Among them, the sequence formed by arranging the data in each row of the curing temperature monitoring matrix from top to bottom is used as the composite material curing difference analysis sequence of each data acquisition point, that is, the temperature data of each data acquisition point at different acquisition moments are used to form the composite material curing difference analysis sequence of each data acquisition point. Then, the range and coefficient of variation of each composite material curing difference analysis sequence are calculated. The range of the data in each composite material curing difference analysis sequence is used as the abscissa value, and the coefficient of variation of the composite material curing difference analysis sequence is used as the ordinate value. The coordinates corresponding to each composite material curing difference analysis sequence are used as a sample, and the coordinates corresponding to all the samples in the curing temperature monitoring matrix are used to form a sample set. Among them, the calculation method of the coefficient of variation is a prior art and will not be elaborated in this embodiment. The density peak clustering algorithm is used to cluster and divide the sample set to obtain each clustering cluster. The schematic diagram of the specific clustering result is as shown in Figure 5 shown. In this embodiment, the Manhattan distance between samples is used as the measurement result of the similarity between samples. The specific calculation process of the density peak clustering algorithm is a well-known technology and will not be elaborated.

[0032] Furthermore, the clustering result of the sample set reveals the degree of interference of the curing process among different data acquisition points from the perspective of the temperature change differences of each data acquisition point at different acquisition moments. Then, the curing dynamic collinearity characteristic difference coefficient is calculated based on the differences between different clustering clusters in the clustering result of the sample set. The curing dynamic collinearity characteristic difference coefficient reflects the uneven change degree of the process during the curing process of the black phosphorus-epoxy encapsulant composite material. Based on the correlation between the composite material curing difference analysis sequences of each sample in any two clustering clusters, the curing dynamic collinearity characteristic difference coefficient between the any two clustering clusters is determined: analyze the distance between any two samples in the any two clustering clusters; analyze the correlation coefficient between the composite material curing difference analysis sequences corresponding to any two samples in the any two clustering clusters; among them, the curing dynamic collinearity characteristic difference coefficient between the any two clustering clusters is positively correlated with the distances of all any two samples in the any two clustering clusters and negatively correlated with the correlation coefficient.

[0033] Preferably, as an embodiment of the present application, the specific calculation formula of the curing dynamic collinearity characteristic difference coefficient can be: ; where represents the curing dynamic collinearity characteristic difference coefficient between the b th clustering cluster and the c th clustering cluster; represents the bThe first x samples, Indicates c The first y samples, represents the Euclidean distance, express and The Euclidean distance between Indicates b The first x The composite material curing difference analysis sequence corresponding to the samples, Indicates c The first y The composite material curing difference analysis sequence corresponding to the samples, represents the Pearson correlation coefficient, express and The specific calculation process of the Pearson correlation coefficient is a well-known technology and will not be described in detail; X and Y Respectively represent b Clusters and c The number of sequences in a cluster.

[0034] It should be noted that, in this embodiment, the Pearson correlation coefficient is used as the correlation coefficient between sequences to evaluate the correlation between sequences. As other implementation methods, the implementer may also use other correlation measurement methods to analyze the correlation between sequences, and this application does not impose any special restrictions.

[0035] It should be understood that if b Clusters and c The greater the difference in the corresponding data changes of the samples between the clusters, that is, the greater the difference in the range and coefficient of variation of the data between different samples, the calculated The larger the value, the b Clusters and c The greater the difference in the correlation between the data of different samples in the clusters, the The smaller the value of The larger the value of b Clusters and c The number of cluster samples is significantly different, so the calculated The larger the value of b Clusters and c The coefficient of difference of the solidified dynamic collinearity characteristics between clusters The larger the value is, the more significant the dynamic difference characteristics of the curing temperature of the black phosphorene-epoxy molding compound composite material are.

[0036] Further, determine the number of data interval divisions during the curing process of the black phosphorus-epoxy encapsulant composite based on the curing dynamic collinearity characteristic difference coefficient, and perform local dynamic collinearity characteristic analysis on the temperature data during the curing process through the number of divided data intervals; use the set composed of the curing dynamic collinearity characteristic difference coefficients between all different clustering clusters as the local collinearity characteristic analysis set Q , calculate the local collinearity characteristic division coefficient according to the local collinearity characteristic analysis set: count the Q maximum value of, and analyze the ratio of each curing dynamic collinearity characteristic difference coefficient to the maximum value; determine the local collinearity characteristic division coefficient based on the ratio, where the local collinearity characteristic division coefficient is positively correlated with the ratio obtained from the analysis of all curing dynamic collinearity characteristic difference coefficients

[0037] Preferably, as an embodiment of the present application, the specific calculation formula of the local collinearity characteristic division coefficient can be: ; where represents the local collinearity characteristic division coefficient; represents the b th clustering cluster and the c th clustering cluster between the curing dynamic collinearity characteristic difference coefficient; Q represents the local collinearity characteristic analysis set, represents Q the maximum value in; T represents the number of clustering clusters; and are both preset positive constant coefficients, which function as coefficients to map the curing dynamic collinearity characteristic difference coefficient to the appropriate number of intervals, and the empirical values are 5 and 10 respectively; represents the ceiling function

[0038] It should be understood that if the curing dynamic collinearity characteristic difference coefficient between the clustering clusters in the clustering result of the sample set is large, the calculated value is larger, that is, the calculated local collinearity characteristic division coefficient value is larger, indicating that the temperature change amplitude difference of different data acquisition points during the curing process of the composite material is large, and it is necessary to divide smaller data intervals to analyze the local state difference

[0039] Further, the data collected at each data acquisition point is evenly divided according to the local collinearity feature division coefficient. For example, if the value of the local collinearity feature division coefficient is 8, the composite material curing difference analysis sequence at each data acquisition point is evenly divided into 8 sub-data sequences. Since the data acquisition between different data acquisition points is carried out synchronously, the divided data sequences between different data acquisition points correspond to each other, that is, within each time interval, there are sub-data sequences. According to the variance inflation factor between the sub-data sequences of different data acquisition points corresponding to each time interval, the curing temperature ridge regression estimator is calculated.

[0040] Specifically, first, the variance inflation factor method is used to obtain the variance inflation factor of each time interval, that is, the degree represented by the correlation of the temperature data changes of different data acquisition points.

[0041] After that, the initial ridge regression estimator is calculated through the variance inflation factors of different time intervals. The input is the curing temperature monitoring matrix, and the initial ridge regression estimator of the curing temperature monitoring matrix is obtained by using the ridge trace method. The specific calculation processes of the variance inflation factor method and the ridge trace method are well-known technologies and will not be elaborated here.

[0042] Based on the differences in the variance inflation factors of different time intervals and the initial ridge regression estimator, the curing temperature ridge regression estimator is determined, where the curing temperature ridge regression estimator is positively correlated with the differences in the variance inflation factors of all any two time intervals and the initial ridge regression estimator respectively.

[0043] Preferably, as an embodiment of the present application, the calculation formula of the curing temperature ridge regression estimator can be: ; where L represents the curing temperature ridge regression estimator; represents the initial ridge regression estimator; and respectively represent the th and the th variance inflation factors of the time intervals.

[0044] It should be understood that if the correlation change characteristics between the sub-data sequences of different data acquisition points within the divided time intervals are significant, the calculated value is larger, and if the correlation change characteristics between different time intervals are significantly different, the calculated value is larger, that is, the calculated value is smaller, then the calculated curing temperature ridge regression estimator LThe smaller the value is, it indicates that the influence of the collinearity characteristics reflected by the difference in the local change collinearity characteristics analyzed by dividing the time interval is greater. Then, the initial ridge regression estimator is reduced to increase the regularization intensity and improve the accuracy of the curing temperature change prediction.

[0045] Thus, the ridge regression estimator of the curing temperature is obtained.

[0046] Step S003: Based on the ridge regression estimator of the curing temperature, use the ridge regression algorithm to obtain the predicted value of the curing temperature of the epoxy molding compound composite material. According to the predicted value of the curing temperature, adjust the curing temperature during the preparation process of the epoxy molding compound composite material to complete the curing process of the epoxy molding compound composite material.

[0047] Through the above steps, the ridge regression estimator of the curing temperature can be obtained. Taking the ridge regression estimator of the curing temperature and the curing temperature monitoring matrix as inputs, use the ridge regression algorithm to obtain the predicted value of the curing temperature. In this embodiment, the specific process of dividing the feature variables and the target variable is as follows: Calculate the coefficient of variation of each row of data in the curing temperature monitoring matrix, and take the row of data in the curing temperature monitoring matrix corresponding to the minimum value among all the coefficients of variation as the target variable, and take the remaining rows of data in the curing temperature monitoring matrix as the feature variables; The specific calculation process of the ridge regression algorithm is a well-known technology and will not be elaborated here.

[0048] Furthermore, compensate the temperature of the curing process of the black phosphorus-epoxy molding compound composite material according to the predicted value of the curing temperature.

[0049] Preferably, as an embodiment of the present application, if the predicted value of the curing temperature is less than or greater than the set temperature value, the curing temperature is adaptively adjusted through the temperature control system in the production process of the black phosphorus-epoxy molding compound composite material to ensure the suitability of the temperature during the curing process and complete the curing process.

[0050] (4) Cooling and demolding: Slowly cool to room temperature after curing, and a black EP / PNs nanocomposite material is obtained after demolding.

[0051] It should be noted that: The above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification is given. 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.

[0052] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

[0053] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; modifying the technical solutions described in the foregoing embodiments, or equivalently replacing some of the technical features therein, does not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A method for predicting the curing temperature of a black phosphorus epoxy encapsulation composite material, characterized in that, The method includes the following steps: Select raw materials for preparing the black phosphorus epoxy encapsulant nanocomposite material, and predict the curing temperature during the preparation process, specifically including: Obtain the temperature at each data acquisition point at each acquisition moment during the curing process. Based on the temperature data differences of different acquisition points during the curing process, and combining cluster analysis, obtain the curing dynamic collinearity characteristic difference coefficient; Obtain the local collinearity characteristic division coefficient based on the curing dynamic collinearity characteristic difference coefficient; obtain the ridge regression estimator of the curing temperature based on the local collinearity characteristic division coefficient, and predict the curing temperature; After the curing process is completed, cool and demold to obtain the epoxy encapsulant nanocomposite material.

2. The curing temperature prediction method of the black phosphorene epoxy encapsulation composite material according to claim 1, wherein The preparation raw materials are black phosphorus, epoxy resin, curing agent, curing accelerator, and thermal conductive filler.

3. The curing temperature prediction method of a black phosphorene epoxy encapsulation composite material according to claim 1, characterized in that The method for obtaining the curing dynamic collinearity characteristic difference coefficient is as follows: Construct a composite material curing difference analysis sequence for each data acquisition point based on the collected temperature data; Calculate the range and coefficient of variation of each composite material curing difference analysis sequence respectively. Take the range and coefficient of variation as the abscissa value and ordinate value respectively. The coordinates corresponding to each composite material curing difference analysis sequence are used as a sample, and cluster all samples to obtain each cluster; Determine the curing dynamic collinearity characteristic difference coefficient between any two clusters based on the correlation between the composite material curing difference analysis sequences of the samples in any two clusters.

4. The curing temperature prediction method of a black phosphorus epoxy encapsulation composite material according to claim 3, wherein, The construction method of the composite material curing difference analysis sequence is: the temperature data at each data acquisition point at different acquisition moments form the composite material curing difference analysis sequence of each data acquisition point.

5. A method for predicting the curing temperature of a black phosphorene epoxy encapsulation composite material according to claim 3, characterized in that, The method for determining the curing dynamic collinearity characteristic difference coefficient between any two clusters is as follows: Analyze the distance between any two samples in any two clusters; Analyze the correlation coefficient between the composite material curing difference analysis sequences corresponding to any two samples in any two clusters; Among them, the curing dynamic collinearity characteristic difference coefficient between any two clusters is positively correlated with the distances of all any two samples in any two clusters, and negatively correlated with the correlation coefficient.

6. The curing temperature prediction method of a black phosphorene epoxy encapsulation composite material according to claim 1, characterized in that, The method for obtaining the local collinearity characteristic division coefficient is as follows: Statistical maximum value of the curing dynamic collinearity characteristic difference coefficient between all any two clusters, and analyze the ratio of each curing dynamic collinearity characteristic difference coefficient to the maximum value; Determine the local collinearity characteristic division coefficient based on the ratio, where the local collinearity characteristic division coefficient is positively correlated with the ratio obtained by analyzing all curing dynamic collinearity characteristic difference coefficients.

7. A method for predicting the curing temperature of a black phosphorus epoxy encapsulation composite material according to claim 1, characterized in that, The method for obtaining the ridge regression estimator of the curing temperature is as follows: Construct each sub-data sequence of each data acquisition point based on the local collinearity characteristic division coefficient and the composite material curing difference analysis sequence; Analyze the variance inflation factor of the sub-data sequences of all data acquisition points in each time interval; Construct a curing temperature monitoring matrix, and use the ridge trace method to obtain the initial ridge regression estimator of the curing temperature monitoring matrix; Determine the curing temperature ridge regression estimator based on the differences in variance inflation factors for different time intervals and the initial ridge regression estimator, where the curing temperature ridge regression estimator is positively correlated with the differences in variance inflation factors for any two time intervals and the initial ridge regression estimator, respectively.

8. A method for predicting the curing temperature of a black phosphorene epoxy encapsulation composite material according to claim 7, characterized in that, The method for constructing the sub-data sequences is as follows: evenly divide the composite material curing difference analysis sequence at each data acquisition point into w sub-data sequences, where the value of w is the local collinearity feature division coefficient.

9. A method for predicting the curing temperature of a black phosphorus epoxy encapsulation composite material according to claim 7, characterized in that, Each row of the curing temperature monitoring matrix consists of the temperature data of each data acquisition point at different acquisition times.

10. The curing temperature prediction method of a black phosphorene epoxy encapsulation composite material according to claim 1, wherein, The prediction of the curing temperature includes: Based on the curing temperature ridge regression estimator and the curing temperature monitoring matrix, obtain the predicted value of the curing temperature through the ridge regression algorithm.

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