Nano composite material of liquid silica gel and preparation equipment control system of nano composite material

Abstract

The invention discloses a nano composite material of liquid silica gel and a preparation equipment control system thereof. The nano composite material of liquid silica gel is prepared from the following materials: 50-85% of a silica gel matrix; 50 to 300 parts of nano filler, wherein the nano filler is prepared from graphene nanosheets, nano titanium dioxide and carbon nanotubes; 15-100 parts of a surface modifier, wherein the surface modifier is selected from a composite system containing a siloxane coupling agent and polyether amine; 10-50 parts of a functional auxiliary agent, wherein the functional auxiliary agent comprises a flame retardant and a heat conducting agent; the nanofiller is uniformly distributed in the silica gel matrix through an in-situ dispersion technology, and the surface of the nanofiller is subjected to plasma treatment to form a chemical bonding interface. According to the material design, the problems of dispersion and function collaboration of the nanofiller are solved, an intelligent control system achieves multi-layer optimization of technological parameters through deep fusion of an algorithm and hardware, the industrialization potential is achieved, and the method is particularly suitable for the high-end fields such as high-precision electronic packaging adhesives and flexible heat conduction interface materials.

Description

Technical Field

[0001] The present invention belongs to the technical field of composite materials, and more specifically, particularly relates to a nano-composite material of liquid silicone. At the same time, the present invention also relates to a control system for the preparation equipment of the nano-composite material of liquid silicone. Background Art

[0002] Due to its excellent flexibility, weather resistance and functionalization characteristics, the nano-composite material of liquid silicone is widely used in the fields of electronic packaging and medical devices. However, the nano-composite material of liquid silicone produced by the existing technology still has the following deficiencies in terms of material properties and the control of preparation equipment:

[0003] In traditional composite materials, nano-fillers are prone to agglomeration (such as graphene stacking), resulting in poor dispersion uniformity (dispersion deviation > 10%), weak interfacial bonding force, and the inability to synergistically improve thermal conductivity / flame retardancy performance;

[0004] The mixing method relying on mechanical stirring or static ultrasound has low efficiency (mixing time > 2 hours), and the bubble residue rate in the vacuum degassing link is high (> 0.1%), affecting the material density;

[0005] Most of the existing control systems adopt fixed PID parameters or single-sensor feedback, and it is difficult to dynamically respond to viscosity mutations (such as a 50% sudden increase in viscosity caused by filler moisture absorption) and dispersion fluctuations, resulting in poor process stability;

[0006] On the basis of the above, we propose a nano-composite material of liquid silicone and its preparation equipment control system, which effectively and specifically solves the problems existing in the prior art. Summary of the Invention

[0007] The purpose of the present invention is to solve the deficiencies existing in the prior art, and propose a nano-composite material of liquid silicone and its preparation equipment control system. The material design breaks through the problem of the synergy between nano-filler dispersion and function, and the intelligent control system realizes the multi-layer optimization of process parameters through the deep integration of algorithms and hardware, has the potential for industrialization, and is especially suitable for high-end fields such as high-precision electronic packaging adhesives and flexible thermal interface materials.

[0008] To achieve the above purpose, the present invention provides the following technical solutions:

[0009] A nano-composite material of liquid silicone, which is prepared from the following materials: silicone matrix: 50% - 85%;

[0010] Nano-fillers: 50 parts - 300 parts, and the nano-fillers are prepared from graphene nanosheets, nano-titanium dioxide, and carbon nanotubes;

[0011] Surface modifier: 15 parts - 100 parts, and the surface modifier is selected from a composite system containing a siloxane coupling agent and a polyetheramine;

[0012] Functional additives: 10 parts - 50 parts, and the functional additives include a flame retardant and a thermal conductive agent;

[0013] The nano-fillers are uniformly distributed in the silicone matrix through an in-situ dispersion technique, and a chemical bonding interface is formed on their surfaces through plasma treatment;

[0014] The preparation method of the nano-composite material of the liquid silicone is as follows:

[0015] Plasma pretreatment of the nano-fillers;

[0016] Mixing the matrix with the surface modifier and the functional additives in an ultrasonic high-speed shear coupling field;

[0017] Vacuum degassing and gradient temperature rise curing, wherein the curing temperature is gradually increased from 50 °C to 150 °C, and the heating rate in each stage ≤ 5 °C / min.

[0018] Preferably, the mass ratio of graphene nanosheets, nano-titanium dioxide, and carbon nanotubes in the nano-fillers is 2:1:0.3.

[0019] Preferably, in the composite system of the surface modifier, the molar ratio of the siloxane coupling agent to the polyetheramine is 1:0.2 - 1, and the contact angle of the modified nano-fillers ≤ 30°, and the static sedimentation rate is reduced by 60% compared with the unmodified material.

[0020] Preferably, the functional additive is a combination of a phosphorus-based flame retardant and boron nitride nanosheets, and the boron nitride nanosheets are vertically arranged in the silicone matrix through chemical vapor deposition to form a heat conduction path, and the thermal conductivity of the heat conduction path ≥ 0.8 W / (m·K).

[0021] A control system for a nano-composite material preparation device, which is used to control the preparation device for preparing the nano-composite material of the above liquid silicone, and is characterized by including:

[0022] Main control module: A coupling controller based on a neural network model and a fuzzy PID control algorithm;

[0023] Sensor array: A multi-source sensor for real-time monitoring of temperature, pressure, viscosity, and filler dispersion;

[0024] Actuator: A linkage device including an ultrasonic power oscillator, a high-speed shear paddle, and a vacuum degasser;

[0025] The control system of the nano-composite material preparation device realizes dynamic adjustment of process parameters through the main control module, the sensor array, and the actuator, and the expression is:

[0026]

[0027] Wherein, P(t) is the regulated output at the current moment, TT is the temperature change rate, ΔS is the deviation of the filler dispersion degree, D(τ) is the real-time feedback value of the viscosity, α, β, γ, λ, ∈ are weight coefficients, and α + β + γ = 1;

[0028] The sensor array includes a non-contact infrared spectroscopy sensor for on-line detection of the dispersion degree of nano-fillers, with a detection error ≤ 2% and a data sampling frequency ≥ 100 Hz.

[0029] Preferably, the control system of the nano-composite material preparation equipment further includes:

[0030] An edge computing module, deployed at the local end of the equipment, for correcting the fuzzy PID parameters, and the expression is:

[0031]

[0032] Wherein, K p , K i , K d are the initial PID parameters, η is the equipment operation aging factor, and t is the cumulative operation time.

[0033] 7. The control system of the nano-composite material preparation equipment according to claim 5, wherein the control logic of the vacuum degasser is based on a multi-objective optimization algorithm, and the objective function is:

[0034] min(w 1 ·t 脱泡 + w 2 ·E 能耗 ) s.t. the bubble residue rate ≤ 0.05%;

[0035] Wherein, ω1 and ω2 are dynamically adjusted weight factors, and ω1 + ω2 = 1.

[0036] Preferably, the coordinated control of the ultrasonic power oscillator, the high-speed shear paddle and the vacuum degasser adopts an adaptive fuzzy sliding mode control algorithm, which specifically includes the following steps:

[0037] 1) Obtain the real-time viscosity μ(t) and the filler dispersion degree S(t) through the sensor array;

[0038] 2) Define the sliding mode surface, and the expression is:

[0039]

[0040] Wherein, e(t) = S target-S(t): Represents the deviation between the actual dispersion S(t) of the current filler and the target dispersion S, directly reflecting the instantaneous control demand; target is the cumulative integral of historical errors, and the gain coefficient κ adjusts the influence weight of the integral to eliminate long-term cumulative errors and improve control accuracy; is the cumulative integral of historical errors, and the gain coefficient κ adjusts the influence weight of the integral to eliminate long-term cumulative errors and improve control accuracy;

[0041] 3) The adaptive law dynamically adjusts the ultrasonic power P 超声 and the shear rotational speed ω, and the expression is:

[0042]

[0043] In the formula, α, κ, K, β are adaptive parameters, which are updated online based on the Lyapunov stability theory.

[0044] Preferably, the sensor data is collaboratively analyzed through a multi-modal feature fusion model, including:

[0045] Construct a temperature-viscosity-pressure joint feature matrix \(\mathbf{F}=[T(t),\mu(t),P(t)]^T\);

[0046] Use kernel principal component analysis to reduce the dimensionality of the features and extract the principal component vector C;

[0047] Predict the filler dispersion deviation ΔS through a correlation function:

[0048]

[0049] where, ω i is the dynamic weight, and θ i are the parameters of the convolutional neural network obtained through training; the prediction error of the fused data ≤ 1.5%.

[0050] Technical effects and advantages of the present invention: A nano-composite material of liquid silicone and its preparation equipment control system provided by the present invention have the following advantages compared with the prior art:

[0051] The material properties are significantly improved:

[0052] The dispersion degree is improved, the nano-fillers are evenly distributed, the contact angle drops to, and the static sedimentation rate decreases;

[0053] The functions are coordinated, and the vertically aligned boron nitride makes the thermal conductivity ≥ 1.2 W / (m·K), and the limiting oxygen index (LOI) of the phosphorus-nitrogen flame retardant system > 35%;

[0054] The preparation efficiency and accuracy are optimized:

[0055] The mixing time is shortened, and the mixing efficiency is improved by the action of the ultrasonic-shear coupling field;

[0056] The defoaming effect is improved, and the multi-stage vacuum gradient control enables the bubble residue rate to reach the optical grade silicone standard;

[0057] The yield is increased. The adaptive algorithm expands the process fluctuation tolerance, resulting in an increase in the yield;

[0058] Intelligent equipment control:

[0059] Response speed. The LSTM prediction module anticipates parameter anomalies in advance;

[0060] The energy consumption is reduced. Through dynamic PID correction and particle swarm optimization, the energy consumption per unit output decreases;

[0061] The operation and maintenance cost is reduced. The fault self-check module reduces the downtime and extends the equipment life. Brief Description of the Drawings

[0062] Figure 1 It is a control system architecture diagram of the preparation equipment for the nano-composite material of liquid silicone of the present invention. Detailed Embodiments

[0063] In order to make the objectives, technical solutions and advantages of the present invention clearer, the following further describes the present invention in detail with reference to specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0064] The present invention provides a nano-composite material of liquid silicone and its preparation equipment control system. The material design breaks through the problems of nano-filler dispersion and functional synergy. The intelligent control system realizes multi-layer optimization of process parameters through the deep integration of algorithms and hardware, has the potential for industrialization, and is particularly suitable for high-end fields such as high-precision electronic packaging adhesives and flexible thermal interface materials.

[0065] The nano-composite material of liquid silicone is prepared from the following materials: silicone matrix: 50%-85%;

[0066] Nano-filler: 50 parts - 300 parts. The nano-filler is prepared from graphene nanosheets, nano-titanium dioxide, and carbon nanotubes; the mass ratio of graphene nanosheets, nano-titanium dioxide, and carbon nanotubes in the nano-filler is 2:1:0.3;

[0067] Surface modifier: 15 parts - 100 parts. The surface modifier is selected from a composite system containing a siloxane coupling agent and a polyetheramine; in the composite system of the surface modifier, the molar ratio of the siloxane coupling agent to the polyetheramine is 1:0.2 - 1, and the contact angle of the modified nano-filler ≤ 30°, and the static sedimentation speed is reduced by 60% compared with the unmodified material.

[0068] Functional additives: 10 parts - 50 parts, and the functional additives include flame retardants and thermal conductors;

[0069] The nano-fillers are uniformly distributed in the silicone matrix through in-situ dispersion technology, and their surfaces are treated by plasma to form a chemically bonded interface; the functional additives are a combination of a phosphorus-based flame retardant and boron nitride nanosheets, and the boron nitride nanosheets are vertically arranged in the silicone matrix by chemical vapor deposition to form a thermal conduction path, and the thermal conductivity of this thermal conduction path is ≥ 0.8 W / (m·K);

[0070] The preparation method of the nano-composite material of liquid silicone is as follows:

[0071] Plasma pretreatment of nano-fillers;

[0072] Mixing the matrix with surface modifiers and functional additives in an ultrasonic high-speed shear coupling field;

[0073] Vacuum degassing and gradient temperature rise curing, wherein the curing temperature is gradually increased from 50 °C to 150 °C, and the heating rate per stage is ≤ 5 °C / min.

[0074] This embodiment also proposes a control system for the nano-composite material preparation equipment, and this system is used to control the nano-composite material preparation equipment for preparing the above-mentioned liquid silicone, such as Figure 1 , including:

[0075] Main control module: A coupling controller based on a neural network model and a fuzzy PID control algorithm;

[0076] Sensor array: A multi-source sensor for real-time monitoring of temperature, pressure, viscosity and filler dispersion;

[0077] Actuator: A linkage device including an ultrasonic power oscillator, a high-speed shear paddle, and a vacuum degasser; the control logic of the vacuum degasser is based on a multi-objective optimization algorithm, and the objective function is:

[0078] min(w 1 ·t 脱泡 +w 2 ·E 能耗 ) s.t. The bubble residue rate ≤ 0.05%;

[0079] In the formula, ω1 and ω2 are dynamically adjusted weight factors, and ω1 + ω2 = 1.

[0080] Furthermore, the linkage control of the ultrasonic power oscillator, the high-speed shear paddle and the vacuum degasser adopts an adaptive fuzzy sliding mode control algorithm, which specifically includes the following steps:

[0081] 1) Obtain the real-time viscosity μ(t) and filler dispersion S(t) through the sensor array; the sensor data is collaboratively analyzed by the multi-modal feature fusion model, including:

[0082] Construct the temperature-viscosity-pressure joint feature matrix \(\mathbf{F} = [T(t),\mu(t),P(t)]^T\);

[0083] Adopt kernel principal component analysis for feature dimensionality reduction to extract the principal component vector \(\mathbf{C}\);

[0084] Predict the filler dispersion deviation \(\Delta S\) through the correlation function:

[0085]

[0086] where, \(\omega\) i is the dynamic weight, and \(\theta\) i are the parameters of the convolutional neural network obtained through training; the prediction error of the fused data \(\leq 1.5\%\).

[0087] 2) Define the sliding mode surface, and the expression is:

[0088]

[0089] In the formula, \(e(t) = S\) target - S(t): represents the deviation between the actual dispersion S(t) of the current filler and the target dispersion S target , which directly reflects the instantaneous control requirement; is the cumulative integral of the historical error, and the gain coefficient \(\kappa\) adjusts the integral influence weight to eliminate the long-term cumulative error and improve the control accuracy;

[0090] 3) Calculate the adaptive law to dynamically adjust the ultrasonic power \(P\) 超声 and the shear rotation speed \(\omega\), and the expression is:

[0091]

[0092] In the formula, \(\alpha,\kappa,K,\beta\) are adaptive parameters and are updated online based on the Lyapunov stability theory.

[0093] The control system of the nano-composite material preparation equipment realizes the dynamic adjustment of process parameters through the main control module, the sensor array and the actuator, and the expression is:

[0094]

[0095] In the formula, \(P(t)\) is the regulation output at the current moment, \(T_T\) is the temperature change rate, \(\Delta S\) is the filler dispersion deviation, \(D(\tau)\) is the real-time viscosity feedback value, and \(\alpha,\beta,\gamma,\lambda,\in\) are weight coefficients, and \(\alpha+\beta+\gamma = 1\);

[0096] ​The sensor array includes a non-contact infrared spectroscopy sensor for on-line detection of the dispersion degree of nano-fillers, with a detection error ≤ 2% and a data sampling frequency ≥ 100 Hz.

[0097] Furthermore, the control system of the nano-composite material preparation equipment further includes:

[0098] An edge computing module, deployed at the local end of the equipment, for correcting the fuzzy PID parameters, and the expression is:

[0099]

[0100] In the formula, K p , K i , K d are the initial PID parameters, η is the equipment operation aging factor, and t is the cumulative operation time.

[0101] According to the above material formula and using the control system of the preparation equipment to precisely control the preparation equipment, the nano-composite material of liquid silicone prepared is compared with the mature materials in the prior art as follows:

[0102] Index Traditional process This solution Improvement range Deviation of filler dispersion 8％-12％ ≤2％ 75％-83％ Thermal conductivity 0.3 - 0.5 W / (m·K) ≥0.8 W / (m·K) 60％-167％ Unit energy consumption 3.5 kWh / kg 1.9 kWh / kg 45.7％↓ Defoaming time 50 minutes 25 minutes 50％↓

[0103] Optionally, in this embodiment, a process parameter self-correction mechanism is introduced in the control of the actuator, specifically:

[0104] Establish a process parameter library: store the historical optimal parameter combinations and their corresponding performance indicators Q j ;

[0105] Calculate the similarity matching degree according to the current feature vector X, specifically:

[0106] In the formula, select the parameter combination with Sim j ≥ 0.8 as the initial value of the control quantity, and perform online optimization through the particle swarm algorithm, with a convergence time ≤ 10 s.

[0107] In summary, compared with the prior art, the present invention has the following advantages:

[0108] Significant improvement in material performance:

[0109] The dispersion degree is improved, the nano-fillers are evenly distributed, the contact angle drops to, and the static sedimentation rate decreases;

[0110] Function synergy, the vertical arrangement of boron nitride makes the thermal conductivity ≥ 1.2 W / (m·K), and the limiting oxygen index (LOI) of the phosphorus-nitrogen flame retardant system > 35%;

[0111] Optimization of preparation efficiency and precision:

[0112] The mixing time is shortened, and the ultrasonic-shear coupling field improves the mixing efficiency;

[0113] The defoaming effect is improved, and the multi-stage vacuum gradient control makes the bubble residue rate meet the optical grade silica gel standard;

[0114] The yield is increased. The adaptive algorithm expands the process fluctuation tolerance and improves the yield;

[0115] Intelligent equipment control:

[0116] Response speed. The LSTM prediction module anticipates parameter anomalies in advance;

[0117] The energy consumption is reduced. Through dynamic PID correction and particle swarm optimization, the energy consumption per unit output decreases;

[0118] The operation and maintenance cost is reduced. The fault self-checking module reduces the downtime and extends the equipment life

[0119] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A nanocomposite material of liquid silica gel, characterized in that: The nanocomposite material of liquid silica gel is prepared from the following materials: silica gel matrix: 50%-85%; Nano filler: 50-300 parts, the nano filler is made of graphene nano sheets, nano titanium dioxide, and carbon nano tubes; Surface modifier: 15-100 parts, the surface modifier is selected from a composite system containing a siloxane coupling agent and a polyetheramine; Functional additives: 10-50 parts, the functional additives include flame retardants and thermal conductive agents; The nanofiller is uniformly distributed in the silica gel matrix by an in-situ dispersion technique, and its surface is treated by plasma to form a chemical bonding interface; The preparation method of the nanocomposite material of liquid silica gel is as follows: Plasma pretreatment of nanofillers; Mixing the matrix with the surface modifier and the functional additive in an ultrasonic high-speed shear coupling field; Vacuum degassing and gradient temperature curing, wherein the curing temperature is gradually increased from 50°C to 150°C, and the heating rate in each stage is ≤5°C / min.

2. The nanocomposite material of liquid silica gel according to claim 1, characterized in that: The mass ratio of graphene nanosheets, nano-titanium dioxide and carbon nanotubes in the nano-filler is 2:1:0.

3.

3. The nanocomposite material of liquid silica gel according to claim 1, characterized in that: In the composite system of the surface modifier, the molar ratio of the siloxane coupling agent to the polyetheramine is 1:0.2-1, and the contact angle of the modified nanofiller is ≤30°, and the static sedimentation velocity is reduced by 60% compared with the unmodified material.

4. The nanocomposite material of liquid silica gel according to claim 1, characterized in that: The functional auxiliary agent is a combination of a phosphorus flame retardant and boron nitride nanosheets. The boron nitride nanosheets are vertically arranged in a silica gel matrix by chemical vapor deposition to form a heat conduction path. The thermal conductivity of the heat conduction path is ≥0.8W / (m·K).

5. A control system for a nanocomposite material preparation device, the system being used to control a device for preparing a nanocomposite material of liquid silicone rubber according to any one of claims 1 to 4, characterized in that: include: Main control module: coupling controller based on neural network model and fuzzy PID control algorithm; Sensor array: multi-source sensors for real-time monitoring of temperature, pressure, viscosity and filler dispersion; Actuator: including linkage device of ultrasonic power oscillator, high-speed shearing paddle and vacuum deaerator; The control system of the nanocomposite material preparation equipment realizes dynamic adjustment of process parameters through the main control module, sensor array and actuator, and the expression is: Where P(t) is the current control output, TT is the temperature change rate, ΔS is the filler dispersion deviation, D(τ) is the real-time feedback value of viscosity, α, β, γ, λ,∈ are weight coefficients, and α+β+γ=1; The sensor array includes a non-contact infrared spectrum sensor for online detection of the dispersion of nano-fillers, with a detection error of ≤2% and a data sampling frequency of ≥100 Hz.

6. The control system of the nanocomposite material preparation equipment according to claim 5, characterized in that: The control system of the nanocomposite material preparation equipment also includes: The edge computing module is deployed on the local side of the device and is used to correct the fuzzy PID parameters. The expression is: In the formula, K p ,K i ,K d is the initial PID parameter, η is the equipment operation aging factor, and t is the cumulative operating time.

7. The control system of the nanocomposite material preparation equipment according to claim 5, characterized in that: The control logic of the vacuum degasser is based on a multi-objective optimization algorithm, and the objective function is: min(w1·t 脱泡 +w2·E 能耗 )st bubble residual rate ≤ 0.05%; Wherein, ω1, ω2 are dynamically adjusted weight factors, and ω1+ω2=1.

8. The control system of the nanocomposite material preparation equipment according to claim 7, characterized in that: The linkage control of the ultrasonic power oscillator, the high-speed shearing paddle and the vacuum deaerator adopts an adaptive fuzzy sliding mode control algorithm, which specifically includes the following steps: 1) Obtain real-time viscosity μ(t) and filler dispersion S(t) through the sensor array; 2) Define the sliding surface, the expression is: Where, e(t) = S target -S(t): represents the actual dispersion S(t) and target dispersion S of the current filler target The deviation directly reflects the instantaneous control demand; It is the cumulative integral of historical errors. The gain coefficient κ adjusts the integral influence weight to eliminate long-term cumulative errors and improve control accuracy. 3) Adaptive law dynamically adjusts ultrasonic power P 超声 and shear speed ω, the expression is: Where α, κ, K, and β are adaptive parameters, which are updated online based on Lyapunov stability theory.

9. The control system of the nanocomposite material preparation equipment according to claim 8, characterized in that: Sensor data is collaboratively analyzed through a multimodal feature fusion model, including: Construct the temperature-viscosity-pressure joint feature matrix \F = [T(t), μ(t), P(t)]T; Kernel principal component analysis is used to reduce the feature dimension and extract the principal component vector C; Prediction of filler dispersion deviation ΔS by correlation function: Among them, ω i is the dynamic weight, θ i are the convolutional neural network parameters obtained through training; the prediction error of the fused data is ≤1.5%.

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