Interface optimization method of hollow micron silicon oxide sphere-aluminum nitride whisker composite material

Abstract

The invention relates to the technical field of interface optimization of composite materials, and discloses an interface optimization method of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material, which is applied to a material compounding system comprising a dispersity sensor and an interface regulation and control system in a hot pressing mold. The method comprises the following steps: acquiring spherocrystal dispersion data and interface stress data through a sensor to generate a data set, constructing a dispersion matrix by a regulation and control system, determining material interface combination characteristics, namely processing the matrix, performing thermal field distribution modeling, calculating thermal field distribution gradient and combination prediction information, constructing a coupling regulation and control model, outputting topological distribution characteristics and updating the data set. And finally dynamically regulating and controlling the composite parameters according to the combined characteristics. The method realizes real-time monitoring and accurate regulation and control of the composite material interface, improves the interface bonding quality and the material performance, and is suitable for interface optimization in the composite material preparation process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of composite material interface optimization, in particular to an interface optimization method of hollow micron silicon oxide ball-aluminum nitride whisker composite material. BACKGROUND

[0002] In the research and application of advanced composites, hollow micron silicon oxide ball-aluminum nitride whisker composite material has broad application prospects in the fields of electronic packaging and high-temperature structural parts due to its combination of high-temperature resistance, insulation properties of silicon oxide and high thermal conductivity, mechanical properties of aluminum nitride. However, this type of composite material faces serious interface problems during preparation, which seriously hinders the improvement of material comprehensive performance and the promotion of practical application.

[0003] From the characteristics of the material itself, there are significant differences in the physical and chemical properties of hollow micron silicon oxide balls and aluminum nitride whiskers. The thermal expansion coefficients of the two do not match, and during the preparation and use of the composite material, temperature changes will cause large thermal stress at the interface, easily causing interface debonding, crack propagation and other problems. At the same time, their surface energies are different, making it difficult to disperse uniformly during the compounding process, and easily causing agglomeration, thereby affecting the uniformity of the mechanical properties and thermal conductivity of the material.

[0004] Existing composite material interface optimization methods have many limitations in dealing with such problems. Traditional interface modification methods, such as surface coating treatment, can improve the interface bonding to some extent, but it is often difficult to accurately control the coating thickness and uniformity, and for complex structure composite materials, the effect of coating treatment is not ideal. In terms of composite material preparation process, hot pressing is a commonly used method, but the traditional hot pressing process is difficult to monitor and control the material state in the composite area in real time. Due to the lack of effective real-time monitoring means, it is difficult to accurately obtain the data of crystal dispersion and interface stress, and it is also difficult to dynamically adjust the process parameters according to the actual situation, resulting in unstable interface bonding strength and large material performance dispersion.

[0005] In addition, there are also deficiencies in the interface analysis and optimization model construction of composite materials. Existing models are mostly based on simplified assumptions and do not fully consider the complex physical and chemical changes of materials during the compounding process, such as the influence of thermal field distribution gradient, whisker orientation and other factors on interface bonding. This makes the prediction accuracy of the model low, and it is difficult to provide accurate guidance for interface optimization. Moreover, the traditional method often detects and repairs defects after the fact, which not only increases production costs, but also makes it difficult to ensure the reliability and consistency of the material.

[0006] With the increasing demand for high-performance composite materials in the fields of electronics, aerospace, etc., there is an urgent need for an optimization method that can monitor and accurately control the interfacial bonding state of composite materials in real time. This method needs to solve the problems of insufficient interfacial bonding strength, uneven material performance, lack of real-time process control, etc. in the prior art, in order to improve the comprehensive performance and practical application value of hollow micron silica sphere-aluminum nitride whisker composite materials. SUMMARY

[0007] The purpose of the present application is to provide an interface optimization method for hollow micron silica sphere-aluminum nitride whisker composite materials to solve the problems raised in the background art.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solution: an interface optimization method for hollow micron silica sphere-aluminum nitride whisker composite materials, the method comprising:

[0009] Collecting sphere crystal dispersion data and interfacial stress data in the composite region by the dispersion degree sensor to generate a material state data set;

[0010] Receiving real-time material state data of multiple dispersion degree sensors by the interface control system to construct a regional dispersion degree matrix;

[0011] Determining material interfacial bonding characteristics according to the material state data set and real-time data of each dispersion degree sensor, wherein determining material interfacial bonding characteristics comprises: processing the dispersion degree matrix, extracting interfacial characteristics in combination with the material state data set, predicting interfacial bonding according to thermal field distribution gradient and whisker orientation information, outputting topological distribution characteristics of material interfacial bonding through a coupling control model, and updating the material state data set according to the topological distribution characteristics;

[0012] According to the bonding characteristics, dynamically regulating regional material composite parameters.

[0013] Preferably, the determination of material interfacial bonding characteristics comprises:

[0014] Processing the dispersion degree matrix to extract sphere crystal dispersion topology, interfacial stress characteristics and bonding strength trend;

[0015] According to the sphere crystal dispersion topology and interfacial stress characteristics, a thermal field distribution model is established for the dispersion degree matrix, the composite region is divided into multiple sub-units and labeled with unit identifiers, the sphere crystal dispersion degree of the sub-units is associated and matched with the material state data set, and the unit identifiers are labeled in the material state data set;

[0016] According to the thermal field distribution gradient calculated according to the dispersion degree sensor position, the interfacial bonding distribution is predicted according to the thermal field distribution gradient and the bonding strength trend, and the bonding prediction information of each sub-unit is calculated.

[0017] Constructing a coupling regulation model, using the binding prediction information as an input parameter of the coupling regulation model, performing spatial correlation modeling on the binding prediction information through the coupling regulation model, and outputting topological distribution characteristics of material interface binding;

[0018] The material state data set is updated according to the topological distribution characteristics to obtain the material interface bonding characteristics.

[0019] Preferably, the processing of the dispersion matrix includes:

[0020] Normalizing the dispersion matrix, intercepting the stress concentration area in the matrix through a sliding window, performing noise suppression on the concentration area, and calculating the bonding strength trend through a tensor decomposition algorithm;

[0021] Calculating the spatial correlation characteristics of the dispersion matrix, calculating the stress interference coefficient, whisker stability index and interface defectivity between units based on the spatial correlation characteristics, constructing a feature fusion network, and calculating the interface stress characteristics through the feature fusion network;

[0022] The time domain features and frequency domain features collected by each dispersion sensor are extracted, and the sensor combination feature vector is calculated based on the phase difference between the time domain features and the frequency domain features. The dispersion sensors at different positions are feature matched based on the combination feature vector, and the combination strength trend is calculated.

[0023] Preferably, the thermal field distribution modeling of the dispersion matrix includes:

[0024] According to the spherulite dispersion topology, spherulite dispersion sampling points are extracted from each frame of data, and the sampling points are correlated and mapped with the interface stress characteristics to generate a thermal field distribution map. The thermal field distribution maps collected by multiple sensors are spatially registered to calculate the thermal field distribution intensity of the region.

[0025] Set a defect threshold, locate the defect source based on the spherulite dispersion value of the multi-frame dispersion matrix, and calculate the defect intensity difference. If the defect intensity difference is greater than or equal to the defect threshold, it indicates that an interface defect exists in the unit. Perform thermal pressure parameter constraint compensation on the current unit, iteratively correct the thermal field distribution intensity of the current unit based on the heat conduction model corresponding to the current unit, and calculate the thermal field compensation value of the defect area based on the correction result.

[0026] The dispersion matrix is ​​used to perform thermal field distribution modeling according to the thermal field distribution intensity, and the regional thermal field model is stress-labeled according to the interface stress characteristics.

[0027] Preferably, the step of calculating the thermal field distribution gradient according to the position of the dispersion sensor includes:

[0028] According to the multi-group material composite data, a heat field intensity change point is extracted, and the change point is mapped to a unified thermodynamic coordinate system according to the deployment position of the sensor, fitting is performed on the change point through a heat flow vector interpolation algorithm, and a heat field distribution model of the region is generated;

[0029] Equal-interval sampling is performed along a heat transfer path of the heat field distribution model, heat attenuation rates, stress fluctuation indexes and heat field change slopes of the path are calculated according to sampling results, and a heat field change parameter is calculated according to the heat attenuation rates, the stress fluctuation indexes and the heat field change slopes;

[0030] According to the deployment parameters and the collection accuracy of the dispersion sensors, distribution characteristics of the interfacial bonding strength in each frame of data are projected to the heat field distribution model, the heat field distribution model is partitioned in a heat transfer direction according to the number of sensors, variation laws of the interfacial bonding strength in the partitions are analyzed, and bonding distribution characteristics are calculated according to the variation laws.

[0031] Preferably, based on a thermodynamic coordinate range from a first dispersion sensor to a last dispersion sensor, a heat field coordinate point is selected in a sensor deployment direction, a product of a heat field intensity characteristic weight value and a bonding distribution characteristic weight value in a spatial resolution range is cumulatively calculated, and an influence value of a sensor collection frequency on a heat field intensity change rate is superimposed.

[0032] Preferably, the calculation of the bonding prediction information of each subunit includes:

[0033] Taking a main heat transfer path of the heat field distribution model as a reference line and taking a peak position of the interfacial bonding strength in each frame of data as a reference point, a bonding offset is calculated, and a bonding distribution curve is drawn according to thermodynamic coordinates.

[0034] According to the heat field distribution gradient, a growth rate and a direction in a bonding strength trend are corrected.

[0035] Starting from a nearest bonding distribution point, the distribution curve is continuously drawn according to a correction result of the growth rate and the direction, a next time period bonding distribution point is generated, and until the distribution point covers the entire target region, the bonding prediction information is generated.

[0036] Preferably, the construction of the coupling regulation model includes:

[0037] An input layer is configured to organize the bonding prediction information into spatial distribution data and perform normalization processing.

[0038] A feature fusion layer is configured to extract regional correlation features of the interfacial bonding by processing the spatial distribution data, and construct a dependency relationship between material units.

[0039] A parameter regulation layer is configured to integrate the correlation relationship of the interfacial bonding on the spatial unit, and generate a material composite parameter regulation strategy.

[0040] Preferably, the material interface bonding feature includes:

[0041] According to the topological distribution feature of the material interface bonding output by the coupling regulation model, the identification of the sub-unit is corresponded to the topological distribution feature;

[0042] The unit data in the material state data set is reorganized according to the topological feature, and a unit distribution graph sorted by interface bonding strength is generated;

[0043] According to the reorganized unit distribution graph, the optimized interface bonding distribution feature is output.

[0044] Preferably, the dynamic regulation of the regional material composite parameters includes:

[0045] When the interface bonding in the target region reaches a preset strength threshold, a hot-pressing power promotion instruction of the adjacent region is triggered;

[0046] According to the whisker orientation strategy, the hot-pressing parameters are dynamically combined to generate a holding time vector;

[0047] Based on the holding time vector, the mold parameters of the hot-pressing node of the target region are adjusted.

[0048] Compared with the prior art, the beneficial effects of the present application are:

[0049] The spherulite dispersion data and interface stress data in the composite region are collected by the dispersity sensor and a material state data set is generated, the interface regulation system receives real-time data to construct a regional dispersity matrix, which can realize comprehensive and real-time monitoring of the interface state of the composite material and provide accurate data support for interface optimization. This real-time monitoring mechanism changes the passive situation of post-detection in the traditional method, so that interface problems can be found in time during the preparation of the composite material, and material waste and performance defects caused by untimely problem discovery are avoided.

[0050] In the process of determining the material interface bonding feature, the dispersity matrix is processed, the spherulite dispersion topology, interface stress feature and bonding strength trend are extracted, and the interface bonding is predicted in combination with the thermal field distribution gradient and whisker orientation information, and the topological distribution feature of the material interface bonding is output by the coupling regulation model. This series of operations fully considers various complex factors affecting the interface bonding and can accurately grasp the actual situation of the interface bonding. Unlike the traditional model based on simplified assumptions, the model constructed by this method is more consistent with the physical and chemical changes in the actual composite process, greatly improving the accuracy of the interface bonding state prediction and providing a reliable basis for subsequent interface optimization regulation.

[0051] According to the dynamic regulation of the region material composite parameters according to the bonding characteristics, when the interface bonding in the target region reaches the preset strength threshold, the hot pressing power of the adjacent region is triggered, the hot pressing parameters are dynamically combined according to the whisker orientation strategy to generate a holding time vector, and the mold parameters of the hot pressing node of the target region are adjusted based on the vector. This dynamic regulation method can accurately adjust the process parameters according to the interface state of different regions and different times during the composite process, and realizes the fine control of the interface bonding of the composite material. Compared with the traditional hot pressing process which is difficult to adjust the parameters in real time, this method effectively solves the problems of unstable interface bonding strength and large material performance dispersion, and significantly improves the uniformity and consistency of the performance of the composite material.

[0052] When processing the dispersion matrix, normalization, sliding window stress concentration area extraction, noise suppression, and tensor decomposition algorithm are used to calculate the bonding strength trend, and spatial correlation features are calculated to construct a feature fusion network to calculate the interface stress features. The time domain and frequency domain features of the sensor are extracted to calculate the bonding feature vector for feature matching. These processing methods can effectively remove noise interference in the data, extract features that better reflect the true state of the interface, and improve the accuracy and reliability of data processing, providing high-quality data support for subsequent interface analysis and optimization.

[0053] When modeling the thermal field distribution of the dispersion matrix, the spherical crystal dispersion sampling points are extracted and associated with the interface stress features to generate a thermal field distribution map. The spatial registration is calculated to calculate the regional thermal field distribution intensity, the defect threshold is set for defect source positioning and hot pressing parameter compensation, and the interface stress features are used to label the stress of the thermal field model. This series of thermal field distribution modeling operations realizes the accurate description of the thermal field distribution in the composite region and the effective positioning and compensation of defects, solves the problems of unclear thermal field distribution and passive defect processing in traditional methods, further optimizes the interface bonding state of the composite material, and improves the reliability and stability of the material.

[0054] When calculating the thermal field distribution gradient according to the dispersion sensor position, the thermal field intensity change points are extracted and mapped to a unified thermodynamic coordinate system, a thermal field distribution model is fitted and generated through a heat flow vector interpolation algorithm, the thermal field change parameters are calculated along the heat transfer path, the interface bonding strength variation law is analyzed, and the bonding distribution characteristics are calculated. Finally, the thermal field distribution gradient is calculated. This process fully considers the sensor deployment position and collection accuracy, accurately describes the thermal field distribution gradient and bonding distribution characteristics, provides more accurate parameters for bonding prediction, and makes the prediction of the interface bonding state more accurate, so that the interface optimization and regulation can be more targeted.

[0055] When calculating the bonding prediction information of each sub-unit, the bonding offset is calculated based on the heat field distribution model heat transfer main path as the reference line to draw the bonding distribution curve, the growth rate and direction of the bonding strength trend are corrected according to the heat field distribution gradient, and the bonding distribution point of the next period is generated until the target area is covered. This bonding prediction method can accurately predict the bonding distribution of the future period based on the current interface state and heat field distribution, provide forward-looking guidance for dynamic regulation and control, make the interface optimization and control more scientific and reasonable, and further improve the quality and performance of the composite material interface bonding.

[0056] When constructing the coupling regulation model, the bonding prediction information is organized as spatial distribution data and normalized by the input layer, the feature fusion layer and the parameter regulation layer, the regional correlation features of the interface bonding are extracted to construct the material unit dependency relationship, and the material composite parameter regulation strategy is generated. The model realizes effective integration and analysis of the spatial distribution characteristics of the interface bonding, can generate accurate regulation strategy according to the actual needs of the interface bonding, improves the efficiency and effect of the interface optimization and control, makes the interface bonding of the composite material more optimized, and significantly improves the comprehensive performance.

[0057] When obtaining the material interface bonding features, the sub-unit identifier is corresponded to the topological distribution features, the unit data in the material state data set is reorganized according to the topological features to generate a unit distribution map and output the optimized interface bonding distribution features. This process realizes clear presentation and orderly management of the interface bonding features, facilitates intuitive understanding of the overall condition and local details of the composite material interface bonding, and provides strong support for further optimization of the design and preparation process of the composite material. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 A working principle diagram of the interface optimization method of the hollow micron silicon oxide ball-aluminum nitride whisker composite material described in the present application;

[0059] Figure 2 A working principle diagram for dispersion matrix processing;

[0060] Figure 3 A working principle diagram for dispersion matrix heat field distribution modeling;

[0061] Figure 4 A working principle diagram for heat field distribution gradient calculation. DETAILED DESCRIPTION

[0062] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0063] Please refer to Figures 1-4 The present application relates to an interface optimization method for hollow micron-sized silicon oxide ball-aluminum nitride whisker composite materials, which comprises a plurality of dispersity sensors arranged in a hot-pressing mold and an interface regulation system connected to the dispersity sensors, and adjacent two dispersity sensors are spaced apart by a set distance. The specific implementation steps are as follows:

[0064] The dispersity sensors collect ball crystal dispersity data and interface stress data in the composite region to generate a material state data set. The dispersity sensors are uniformly distributed in the hot-pressing mold to monitor the changes of various parameters during the material compounding process in real time, so as to ensure that the collected data accurately reflect the actual state of the composite region. The interface regulation system receives real-time material state data from a plurality of dispersity sensors to construct a regional dispersity matrix. The interface regulation system processes and integrates the received data, and converts the data collected by each sensor into a matrix form for subsequent analysis.

[0065] According to the material state data set and the real-time data of each dispersity sensor, the material interface bonding characteristics are determined. Specifically, the dispersity matrix is processed, the interface characteristics are extracted in combination with the material state data set, and the interface bonding is predicted according to the thermal field distribution gradient and the whisker orientation information, and the topological distribution characteristics of the material interface bonding are output through the coupling regulation model, and the material state data set is updated according to the topological distribution characteristics.

[0066] According to the bonding characteristics, the regional material compounding parameters are dynamically regulated. Based on the determined interface bonding characteristics, it is analyzed whether the current material compounding parameters are appropriate, and if there is a deficiency, the parameters are adjusted to optimize the material interface bonding performance.

[0067] Embodiment 1:

[0068] In the material compounding system, the system comprises a plurality of dispersity sensors arranged in a hot-pressing mold, and adjacent two dispersity sensors are spaced apart by a set distance, and there is also an interface regulation system connected to the dispersity sensors.

[0069] The dispersion matrix is processed to extract the spherulite dispersion topology, interface stress characteristics, and bonding strength trends. In processing the dispersion matrix, the first step is to normalize it, which can make the data comparable and uniform, laying the foundation for subsequent analysis. After normalization, the stress concentration area in the matrix is intercepted by sliding window. The size and position of the sliding window can be adjusted according to the specific data characteristics and analysis requirements to accurately capture the stress concentration area. After intercepting the stress concentration area, noise suppression is performed on the area, because some noise will inevitably be introduced during data acquisition and transmission, which will affect the analysis and understanding of the data. Noise suppression can improve the quality and reliability of the data. After completing noise suppression, the tensor decomposition algorithm is used to calculate the bonding strength trend. Tensor decomposition algorithm is a powerful data analysis tool that can decompose complex tensor data into a combination of multiple low-dimensional tensors, extracting the potential features and trends in the data. Through this algorithm, the variation trend of the bonding strength can be accurately calculated.

[0070] The spatial correlation characteristics of the dispersion matrix are calculated. Spatial correlation characteristics reflect the distribution and association of data in space, and by calculating these characteristics, the mutual influence between different positions of the material during the composite process can be better understood. According to the calculated spatial correlation characteristics, the stress interference coefficient between units, the whisker stability index, and the interface defect degree are further calculated. The stress interference coefficient is used to measure the degree of mutual interference of stress between different units, the whisker stability index reflects the stability of the whisker during the composite process, and the interface defect degree represents the degree of interface defects. After obtaining these parameters, a feature fusion network is constructed, which can fuse and integrate multiple features, fully utilize the complementarity between features, and calculate the interface stress characteristics through the feature fusion network, thereby more comprehensively and accurately describing the stress state of the interface.

[0071] Time domain features and frequency domain features collected by each dispersion sensor are extracted. Time domain features describe the variation of signals in the time dimension, and frequency domain features reflect the distribution of signals in the frequency dimension. The bonding feature vector of the sensor is calculated according to the phase difference between the time domain features and the frequency domain features. The phase difference is an important parameter between the time domain features and the frequency domain features, which contains information such as the structure and properties of the signal. The bonding feature vector calculated by the phase difference can comprehensively reflect the bonding of the sensor at different positions. Then, the dispersion sensors at different positions are matched according to the bonding feature vector, and the association and correspondence between different sensors are determined through feature matching, and the bonding strength trend is calculated.

[0072] In the process of processing the dispersion matrix, each step is closely connected and mutually influenced. The normalization processing provides a good data basis for the subsequent stress concentration area interception, the noise suppression improves the data quality, and the tensor decomposition algorithm accurately extracts the bonding strength trend; the calculation of spatial correlation characteristics provides the basis for the calculation of each parameter between units, and the feature fusion network fully integrates each feature to obtain accurate interface stress characteristics; the extraction of time domain and frequency domain features and the calculation of combined feature vectors provide key information for feature matching and bonding strength trend calculation of different sensors.

[0073] Embodiment 2:

[0074] In the process of determining the material interface bonding characteristics, it is necessary to model the thermal field distribution of the dispersion matrix according to the spherulite dispersion topology and the interface stress characteristics, and the specific implementation is as follows:

[0075] According to the spherulite dispersion topology, the spherulite dispersion sampling points are extracted in each frame of data. The spherulite dispersion topology reflects the distribution form and structure of spherulites in the composite area, and through the analysis of each frame of data, the specific position of spherulite dispersion can be accurately located, so as to extract the corresponding sampling points. These sampling points contain the key information of spherulite distribution and are the basis for subsequent analysis. Then, according to the correlation mapping of these sampling points and the interface stress characteristics, the interface stress characteristics describe the distribution and change of the stress at the interface, and the correlation of the sampling points and the interface stress characteristics can establish the relationship between the spherulite distribution and the stress distribution, and then generate the thermal field distribution atlas. The thermal field distribution atlas directly shows the distribution state of the thermal field in the composite area. Then, the thermal field distribution atlases collected by multiple sensors are spatially registered. Due to the different positions and collection angles of different sensors, the thermal field distribution atlases collected may have spatial differences. Through spatial registration, these atlases can be unified to the same spatial coordinate system, ensuring the consistency and comparability of the data, and finally calculating the thermal field distribution intensity of the region. The intensity value can reflect the overall distribution of the thermal field in the entire composite area.

[0076] A defect threshold is set. This threshold should be determined based on the material's performance requirements and the actual application scenario. It is an important criterion for determining the presence of interface defects. Defect sources are located based on the spherulite dispersion values ​​of the multi-frame dispersion matrix. Multi-frame data provides more comprehensive information, and analysis of this data allows accurate determination of the defect source's location. The defect intensity difference is calculated, comparing the defect intensity of the current frame with the set defect threshold. If the defect intensity difference is greater than or equal to the defect threshold, this indicates the presence of an interface defect in that cell. If an interface defect is determined, thermal pressure parameter constraints are applied to the current cell. Thermal pressure parameters include pressure, temperature, and time. Adjusting these parameters can improve the interface defect. Based on the thermal conduction model corresponding to the current cell, which describes the heat conduction process and patterns within the cell, the thermal field distribution intensity of the current cell is iteratively corrected. Through continuous adjustment and optimization, the thermal field distribution intensity gradually approaches the ideal state. Finally, based on the correction results, a thermal field compensation value is calculated for the defect area. This compensation value is used to subsequently compensate for the thermal field in the defect area to improve the material's interfacial bonding performance.

[0077] Thermal field distribution modeling is performed on the dispersion matrix based on the thermal field distribution intensity. The thermal field distribution intensity is an important basis for modeling, as it reflects the impact of the thermal field on the dispersion matrix. Through thermal field distribution modeling, the distribution of the thermal field can be integrated into the dispersion matrix, allowing the dispersion matrix to more comprehensively reflect the composite state of the material. The regional thermal field model is stress-annotated using interface stress characteristics. Interface stress characteristics are closely related to the thermal field distribution. Annotating these interface stress characteristics on the thermal field model provides a more intuitive understanding of the relationship between the thermal field distribution and interface stress, providing more accurate information for subsequent analysis and control.

[0078] Throughout the thermal field distribution modeling process, each step is interrelated and mutually influential. Extracting spherulite dispersion sampling points and correlating and mapping interface stress characteristics is the basis for generating thermal field distribution maps. Spatial registration and calculating thermal field distribution intensity ensure the accuracy and reliability of thermal field distribution maps. Setting defect thresholds and locating defect sources are key to determining interface defects. Compensating for thermal pressure parameter constraints and iteratively correcting thermal field distribution intensity are important means of improving interface defects. Thermal field distribution modeling based on thermal field distribution intensity and stress annotation through interface stress characteristics integrate thermal field and stress information into the model, enabling the model to more comprehensively reflect the interface bonding state of the material.

[0079] Example 3:

[0080] When calculating the thermal field distribution gradient, a series of operations need to be performed according to the position of the dispersion sensor. The specific implementation is as follows:

[0081] According to the multiple sets of material composite data, the points of change in thermal field intensity are extracted. The multiple sets of material composite data contain thermal field information of materials under different conditions when they are compounded. Through analysis and processing of these data, the points of change in thermal field intensity can be accurately identified. Then, according to the deployment position of the sensor, these change points are mapped to a unified thermodynamic coordinate system. Since the sensor is deployed at different positions of the hot press mold, in order to make the change points at different positions comparable and unified, they need to be converted to the same thermodynamic coordinate system. Then, the change points are fitted by a heat flow vector interpolation algorithm. The heat flow vector interpolation algorithm can infer the thermal field intensity value at unknown positions according to the known change point data, thereby generating a thermal field distribution model of the region. The model directly shows the distribution of the thermal field in the entire region.

[0082] The heat transfer path along the thermal field distribution model is sampled at equal intervals. The heat transfer path is the main direction of heat transfer in the thermal field, and sampling at equal intervals on this path can obtain detailed information of the thermal field at different positions. According to the sampling results, the heat attenuation rate, stress fluctuation index and thermal field change slope of the path are calculated. The heat attenuation rate reflects the degree of attenuation of heat in the transfer process, the stress fluctuation index represents the fluctuation of stress on the heat transfer path, and the thermal field change slope reflects the speed of change of thermal field intensity with position. Then, the thermal field change parameters are calculated according to the heat attenuation rate, stress fluctuation index and thermal field change slope. These parameters comprehensively consider various change characteristics of the thermal field on the heat transfer path, and can more comprehensively describe the change of the thermal field.

[0083] According to the deployment parameters and collection accuracy of the dispersity sensor, the distribution characteristics of the interfacial bonding strength in each frame of data are projected to the thermal field distribution model. The deployment parameters of the dispersity sensor determine the position and range of the collected data, and the collection accuracy affects the accuracy of the data. By projecting the distribution characteristics of the interfacial bonding strength to the thermal field distribution model, the relationship between the interfacial bonding strength and the thermal field distribution can be established. According to the number of sensors, the heat transfer direction of the thermal field distribution model is partitioned. The number of sensors determines the number and range of partitions, and through partitioning, the change of interfacial bonding strength in different regions can be analyzed in more detail. The change law of interfacial bonding strength in each partition is analyzed. The interfacial bonding strength in each partition may exhibit different trends, and by analyzing these trends, the change law can be summarized. Finally, the bonding distribution characteristics are calculated according to the change law. The bonding distribution characteristics can reflect the distribution of the interfacial bonding strength in the entire thermal field distribution model.

[0084] According to the thermal field change parameter and the bonding distribution characteristic, the thermal field distribution gradient is calculated. The calculation process of the thermal field distribution gradient is as follows: based on the thermodynamic coordinate range from the first dispersion sensor to the last dispersion sensor, the thermal field coordinate points are selected in the sensor deployment direction. These coordinate points are used to represent different positions in the sensor deployment direction. The product of the thermal field intensity characteristic weight value and the bonding distribution characteristic weight value in the spatial resolution range is calculated cumulatively, the spatial resolution determines the accuracy and range of the calculation, the thermal field intensity characteristic weight value and the bonding distribution characteristic weight value respectively represent the importance of the thermal field intensity characteristic and the bonding distribution characteristic in the calculation, and the product thereof reflects the comprehensive influence of the two factors. The influence value of the sensor acquisition frequency on the thermal field intensity change rate is superimposed, the higher the sensor acquisition frequency, the more accurate the monitoring of the thermal field intensity change rate, and therefore the influence thereof needs to be taken into account in the calculation.

[0085] In the calculation of the thermal field distribution gradient, a formula is involved:

[0086]

[0087] Among them, represents the thermal field distribution gradient, which is the final calculation result and is used to describe the distribution change of the thermal field in space. represents the number of thermal field coordinate points selected in the sensor deployment direction, and the number of coordinate points determines the detail of the calculation. represents the weight value of the thermal field intensity characteristic at the th coordinate point, which is determined according to the importance of the thermal field intensity characteristic and reflects the influence degree of the thermal field intensity at the coordinate point. represents the thermal field intensity value at the th coordinate point, which is the actual thermal field intensity of the coordinate point obtained through the thermal field distribution model. represents the weight value of the bonding distribution characteristic at the th coordinate point, which is determined according to the importance of the bonding distribution characteristic and reflects the action size of the bonding distribution characteristic at the coordinate point. represents the bonding distribution characteristic value at the th coordinate point, which is the bonding distribution of the coordinate point obtained through the analysis of the interface bonding strength distribution characteristic. represents the acquisition frequency of the sensor, which reflects the speed of the sensor in collecting data. represents the thermal field intensity change rate, which describes the change of the thermal field intensity with time.

[0088] Example 4:

[0089] In calculating the bonding prediction information of each sub-unit, the heat field distribution model is used as the basis to predict the development trend of the interfacial bonding strength combined with the heat field distribution gradient. The specific implementation is as follows:

[0090] Take the heat transfer main path of the heat field distribution model as the reference line. The heat transfer main path is the main path of heat transfer in the heat field, and its direction and distribution reflect the core characteristics of the heat field. The peak position of the interfacial bonding strength in each frame of data is taken as the reference point, and the peak position represents the maximum of the interfacial bonding strength in the frame of data, which has important reference significance. By calculating the bonding offset, i.e. the position deviation of the current frame peak position from the heat transfer main path, the spatial distribution difference of the interfacial bonding strength is measured. For example, assuming that the heat transfer main path of the heat field distribution model is a straight line along the length direction of the mold, the peak of the interfacial bonding strength in a frame of data appears at a position deviating from the main path by 5 mm, then the bonding offset is 5 mm. Subsequently, the bonding distribution curve is drawn according to the thermodynamic coordinates, which comprehensively considers the factors of temperature and spatial position, and can accurately locate the position of each data point in the heat field, so as to convert the peak position and bonding offset of each frame into a curve, and intuitively show the distribution trend of the interfacial bonding strength in the heat field.

[0091] According to the heat field distribution gradient, the growth rate and direction in the bonding strength trend are corrected. The heat field distribution gradient reflects the change rate of the heat field strength in space, and has an important influence on the development of the interfacial bonding strength. For example, when the heat field distribution gradient is large, it means that the heat field strength changes significantly in a short distance, which may accelerate the growth of the interfacial bonding strength or change its growth direction. Assuming that the original bonding strength trend predicts a growth rate of 10% per unit time, and the direction is along the positive direction of the heat transfer main path, but due to the influence of the heat field distribution gradient, the actual growth rate may be adjusted to 12%, and the direction may be slightly biased to one side of the heat transfer main path. This correction needs to consider the size, direction of the heat field distribution gradient and the characteristics of the bonding strength itself, so as to ensure that the prediction result is more consistent with the actual situation.

[0092] From the latest combination distribution point, the distribution curve is drawn according to the modified growth rate and direction. The latest combination distribution point is based on the latest detection data, which can reflect the actual state of the current interface bonding strength. For example, if the latest combination distribution point is located at the thermodynamic coordinates (10, 20, 30), the bonding strength is 80 MPa, the modified growth rate is 12% per unit time, and the direction is 20 degrees along the positive direction of the main heat transfer path, then the combination distribution point at the next time point can be calculated as follows: on the basis of the original coordinates, move a certain distance along the modified direction, which is determined by the growth rate and the time interval. Continue this process to generate the next period of combination distribution points until the distribution points cover the entire target area, thereby generating complete combination prediction information. The target area may be the entire composite area of the hot press mold, or a specific sub-unit, depending on the actual application requirements.

[0093] During the entire calculation process, the accuracy of the thermal field distribution model is crucial. It needs to be generated based on multiple sets of material composite data and real-time data collected by sensors to ensure that it can truly reflect the distribution of the thermal field. For example, when fitting the thermal field intensity change points by the heat flow vector interpolation algorithm, the deployment position and collection accuracy of the sensor need to be fully considered to avoid model errors. At the same time, the modification of the combination strength trend needs to consider factors such as thermal field distribution gradient and whisker orientation. Whisker orientation can affect the mechanical properties and interface bonding characteristics of materials, and when the thermal field distribution gradient interacts with the whisker orientation, it may have a complex effect on the growth rate and direction of the bonding strength, so it needs to be fully considered in the modification process.

[0094] The collection frequency and accuracy of each frame of data also affect the accuracy of the combination prediction information. Higher collection frequency can capture rapid changes in thermal field and interface bonding strength, while high-precision collection data can ensure the reliability of the calculation results. For example, if the sensor's collection frequency is 10 times per second, it can timely detect sudden changes in the thermal field distribution gradient and adjust the bonding strength trend accordingly, thereby improving the real-time and accuracy of the prediction.

[0095] Example 5:

[0096] When building the coupling control model and obtaining the material interface bonding characteristics and dynamically regulating the regional material composite parameters, the optimization and regulation of the composite material interface need to be realized through multiple steps, and the specific implementation is as follows:

[0097] In constructing the coupling regulation model, the input layer is used to organize the binding prediction information into spatial distribution data and perform normalization processing. For example, assuming that the binding prediction information of a certain subunit at different positions is obtained through the previous steps, such as the binding strength prediction value at coordinates (10, 20, 30) being 75 MPa and at coordinates (15, 25, 35) being 80 MPa, the input layer will organize these coordinates and corresponding intensity values into spatial distribution data, and then through normalization processing, convert the intensity values of different magnitudes into values between 0 and 1 for subsequent processing.

[0098] The feature fusion layer is used to extract the regional correlation features of the interface binding by processing the spatial distribution data and construct the dependency relationship between the material units. For example, in a hot-pressing mold, the interface binding strength of adjacent subunits may have mutual influence, and the feature fusion layer will analyze the spatial distribution data of these subunits to find out the rules of the strength changes of adjacent subunits when the strength of a certain subunit increases, thereby constructing a dependency relationship model between the units. For example, when the binding strength of subunit A increases, the strength of the adjacent subunit B on the right side may increase by 0.8 times, and the feature fusion layer can capture this correlation feature.

[0099] The parameter regulation layer is used to integrate the correlation relationship of the interface binding on the spatial unit and generate a material composite parameter regulation strategy. For example, if the feature fusion layer finds that the binding strength of subunit C and subunit D has a positive correlation relationship, and the current binding strength of subunit C does not meet the expectation, the parameter regulation layer will generate a strategy to adjust the hot-pressing temperature and pressure in the region where subunits C and D are located, such as increasing the hot-pressing temperature of subunit C by 5°C and increasing the pressure of subunit D by 10 MPa, to promote the interface binding of the two.

[0100] In obtaining the material interface binding features, the identification of the subunit is corresponded to the topological distribution features output by the coupling regulation model. For example, the topological distribution features output by the coupling regulation model show a grid-shaped high-strength distribution in a certain region, and at this time, the features are corresponded to the subunit identified as "region A", and the interface binding state of the subunit is determined.

[0101] The unit data in the material state data set is reorganized according to the topological features to generate a unit distribution map sorted by interface binding strength. For example, the material state data set contains the binding strength data of multiple subunits, such as subunit 1 having a strength of 60 MPa, subunit 2 having a strength of 70 MPa, and subunit 3 having a strength of 85 MPa, etc. After reorganization according to the topological features, these subunits are arranged in order of strength from high to low to generate a unit distribution map, which visually displays the differences in binding strength of each subunit.

[0102] According to the recombined unit distribution map, the optimized interface bonding distribution characteristics are output. For example, it can be seen from the unit distribution map that the sub-unit bonding strength of the center region of the hot-pressing mold is generally high, and the edge region is low, and thus the optimized characteristics are that the interface bonding of the center region is good, and the edge region needs to be further regulated, thereby providing a basis for subsequent execution actions.

[0103] When the interface bonding in the target region reaches the preset strength threshold, the hot-pressing power of the adjacent region is triggered. For example, the interface bonding strength threshold of the target region is set to 80 MPa, and when the strength of a sub-unit in the region reaches 80 MPa, the hot-pressing power of the adjacent region is triggered to increase by 15%, so as to promote the synchronous improvement of the interface bonding of the adjacent region.

[0104] According to the whisker orientation strategy, the hot-pressing parameters are dynamically combined to generate a holding time vector. For example, the whisker orientation strategy requires that the whiskers are arranged more closely in a certain direction, and at this time, the hot-pressing temperature is dynamically combined to be 180℃, and the pressure is 15 MPa, and a holding time vector is generated according to these parameters, such as holding for 20 minutes at 180℃ and holding for 15 minutes at 15 MPa pressure, so as to ensure that the whiskers are arranged in the expected orientation.

[0105] Based on the holding time vector, the mold parameters of the hot-pressing node of the target region are adjusted. For example, the holding time vector shows that it needs to be held at 180℃ for 20 minutes, so the mold temperature parameter of the hot-pressing node of the target region is adjusted to 180℃, and the holding time is set to 20 minutes, and the pressure parameter is adjusted to 15 MPa, so that the mold operates according to the set parameters during the composite process, and realizes the accurate regulation of the interface bonding of the material.

[0106] It should be noted that in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0107] Although the embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the interface of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material, applied to a material composite system, wherein the system comprises a plurality of dispersion sensors deployed in a hot pressing mold and an interface control system connected to the dispersion sensors, wherein two adjacent dispersion sensors are spaced apart by a set distance, and wherein: The method comprises: The dispersion sensor collects spherulite dispersion data and interface stress data in the composite area to generate a material state data set; receiving real-time material status data from a plurality of dispersion sensors through the interface control system to construct a regional dispersion matrix; Determining the material interface bonding characteristics based on the material state data set and the real-time data of each dispersion sensor, wherein determining the material interface bonding characteristics includes: processing the dispersion matrix, extracting interface characteristics in combination with the material state data set, and predicting the interface bonding based on the thermal field distribution gradient and whisker orientation information, outputting the topological distribution characteristics of the material interface bonding through a coupled control model, and updating the material state data set according to the topological distribution characteristics; Based on the combination characteristics, the regional material composite parameters are dynamically regulated.

2. The interface optimization method of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material according to claim 1, characterized in that: Determining the material interface bonding characteristics includes: Processing the dispersion matrix to extract spherulite dispersion topology, interface stress characteristics, and bonding strength trends; Performing thermal field distribution modeling on the dispersion matrix according to the spherulite dispersion topology and interface stress characteristics, dividing the composite region into a plurality of subunits and marking the unit identifiers, correlating and matching the spherulite dispersions of the subunits with the material state data set, and marking the unit identifiers in the material state data set; Calculating a thermal field distribution gradient according to the position of the dispersion sensor, predicting the interface binding distribution according to the thermal field distribution gradient and the binding strength trend, and calculating binding prediction information for each subunit; Constructing a coupling regulation model, using the binding prediction information as an input parameter of the coupling regulation model, performing spatial correlation modeling on the binding prediction information through the coupling regulation model, and outputting topological distribution characteristics of material interface binding; The material state data set is updated according to the topological distribution characteristics to obtain the material interface bonding characteristics.

3. The interface optimization method of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material according to claim 2, characterized in that: The processing of the dispersion matrix includes: Normalizing the dispersion matrix, intercepting the stress concentration area in the matrix through a sliding window, performing noise suppression on the concentration area, and calculating the bonding strength trend through a tensor decomposition algorithm; Calculating the spatial correlation characteristics of the dispersion matrix, calculating the stress interference coefficient, whisker stability index and interface defectivity between units based on the spatial correlation characteristics, constructing a feature fusion network, and calculating the interface stress characteristics through the feature fusion network; The time domain features and frequency domain features collected by each dispersion sensor are extracted, and the sensor combination feature vector is calculated based on the phase difference between the time domain features and the frequency domain features. The dispersion sensors at different positions are feature matched based on the combination feature vector, and the combination strength trend is calculated.

4. The interface optimization method of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material according to claim 3, characterized in that: The thermal field distribution modeling of the dispersion matrix includes: According to the spherulite dispersion topology, spherulite dispersion sampling points are extracted from each frame of data, and the sampling points are correlated and mapped with the interface stress characteristics to generate a thermal field distribution map. The thermal field distribution maps collected by multiple sensors are spatially registered to calculate the thermal field distribution intensity of the region. Set a defect threshold, locate the defect source based on the spherulite dispersion value of the multi-frame dispersion matrix, and calculate the defect intensity difference. If the defect intensity difference is greater than or equal to the defect threshold, it indicates that an interface defect exists in the unit. Perform thermal pressure parameter constraint compensation on the current unit, iteratively correct the thermal field distribution intensity of the current unit based on the heat conduction model corresponding to the current unit, and calculate the thermal field compensation value of the defect area based on the correction result. The dispersion matrix is ​​used to perform thermal field distribution modeling according to the thermal field distribution intensity, and the regional thermal field model is stress-labeled according to the interface stress characteristics.

5. The interface optimization method of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material according to claim 4, characterized in that: The step of calculating the thermal field distribution gradient according to the position of the dispersion sensor includes: Based on multiple sets of material composite data, the thermal field intensity change points are extracted and mapped to a unified thermodynamic coordinate system based on the deployment location of the sensors. The change points are fitted using a heat flow vector interpolation algorithm to generate a regional thermal field distribution model. Performing equal-interval sampling along the heat transfer path of the thermal field distribution model, calculating the thermal attenuation rate, stress fluctuation index, and thermal field change slope of the path based on the sampling results, and calculating the thermal field change parameter based on the thermal attenuation rate, stress fluctuation index, and thermal field change slope; Based on the deployment parameters and acquisition accuracy of the dispersion sensor, the distribution characteristics of the interface bonding strength in each frame of data are projected onto the thermal field distribution model. The thermal field distribution model is partitioned along the heat transfer direction according to the number of sensors. The variation pattern of the interface bonding strength within the partition is analyzed, and the bonding distribution characteristics are calculated based on this variation pattern.

6. The interface optimization method of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material according to claim 5, characterized in that: The method of calculating the thermal field distribution gradient according to the position of the dispersion sensor further includes: selecting a thermal field coordinate point in the sensor deployment direction based on the thermodynamic coordinate range from the first dispersion sensor to the last dispersion sensor, cumulatively calculating the product of the thermal field intensity characteristic weight value and the combined distribution characteristic weight value within the spatial resolution range, and superimposing the influence value of the sensor acquisition frequency on the thermal field intensity change rate.

7. The interface optimization method of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material according to claim 5, characterized in that: The calculation of the binding prediction information of each subunit includes: Using the main heat transfer diameter of the thermal field distribution model as a baseline, the peak position of the interface bonding strength in each frame of data as a reference point, calculating the bonding offset, and drawing a bonding distribution curve according to thermodynamic coordinates; Correcting the growth rate and direction of the bonding strength trend according to the thermal field distribution gradient; Starting from the most recent combined distribution point, the distribution curve is continuously drawn according to the correction results of the growth rate and direction to generate the combined distribution points for the next period until the distribution points cover the entire target area and generate combined prediction information.

8. The interface optimization method of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material according to claim 2, characterized in that: The construction of the coupling control model includes: The input layer is used to organize the combined prediction information into spatial distribution data and perform normalization; The feature fusion layer is used to extract regional correlation features of interface bonding by processing spatial distribution data and construct dependency relationships between material units; The parameter control layer is used to integrate the relationship between interfaces and spatial units and generate a material composite parameter control strategy.

9. The interface optimization method of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material according to claim 2, characterized in that: The obtaining of material interface bonding characteristics comprises: According to the topological distribution characteristics of the material interface output by the coupling control model, the subunit identification is matched with the topological distribution characteristics; The unit data in the material state data set are reorganized according to the topological characteristics to generate a unit distribution map sorted by interface bonding strength; According to the reorganized unit distribution map, the optimized interface binding distribution characteristics are output.

10. The interface optimization method of a hollow micron silicon oxide sphere-aluminum nitride whisker composite material according to claim 1, characterized in that: The dynamic regulation of regional material composite parameters includes: When the interface bonding reaches a preset strength threshold in the target area, a thermal pressure power increase instruction is triggered in the adjacent area; Dynamically combine hot pressing parameters according to whisker orientation strategy to generate holding time vector; The mold parameters of the hot pressing node in the target area are adjusted based on the holding time vector.

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