A method for synergistic optimization of performance in high-pressure polypropylene composites

Abstract

This invention relates to the field of material performance analysis, specifically a method for synergistic optimization of the performance of high-pressure polypropylene composites. The method includes: determining a combination of process parameters based on the gradient ratio of modified conductive carbon black and polar monomer-grafted polypropylene, combined with multi-scale control of agglomerate premixing and melt blending indices, and synergistic optimization of segmented temperature control parameters and gradient temperature-controlled crystallization parameters. This method solves the technical problem that adding conductive fillers and compatibility modifiers in a fixed ratio, coupled with a lack of synergistic linkage between process parameters in the premixing, melting, and crystallization stages, easily leads to uneven dispersion of modified components and insufficient consistency in the performance of the prepared composite materials. It achieves significant improvement in the process stability of high-pressure polypropylene composites by synergistically optimizing multi-scale process parameters such as agglomerate premixing, melt blending, and segmented temperature control to dynamically match the gradient distribution of modified components with the thermal-fluid-structural evolution process.

Description

Technical Field

[0001] This invention relates to the field of material performance analysis technology, specifically to a method for synergistic performance optimization of high-pressure polypropylene composite materials. Background Technology

[0002] High-voltage polypropylene composite materials are widely used in high-end electrical equipment fields such as cable accessories, power capacitors, and high-voltage connectors for new energy vehicles due to their excellent electrical insulation, mechanical strength, and processing performance. As equipment develops towards higher voltage and miniaturization, multiple performance requirements are put forward for materials, including low volume resistivity, high tensile strength, and high surface finish.

[0003] Existing polypropylene composite material preparation processes mostly employ fixed proportions and empirical process control, failing to establish a quantitative mapping relationship between the gradient ratio of modified components and key performance indicators. Process conditions such as temperature control and cooling rate are often set based on experience, without dynamic constraint optimization incorporating material thermal stability and melt rheological properties. This results in large batch-to-batch performance fluctuations and frequent defects such as surface protrusions, making it difficult to meet the requirements of high-voltage electrical applications for material consistency and reliability.

[0004] In summary, the existing technology has the technical problem that the conductive filler and compatibility modifier are added in a fixed ratio, and the process parameters of the premixing, melting and crystallization stages lack coordination, which easily leads to uneven dispersion of the modified components and insufficient consistency of the properties of the prepared composite materials. Summary of the Invention

[0005] This application provides a method for synergistic optimization of the performance of high-pressure polypropylene composites, aiming to solve the technical problem in the prior art where conductive fillers and compatibility modifiers are added in a fixed ratio, and the process parameters in the premixing, melting and crystallization stages lack synergistic linkage, which easily leads to uneven dispersion of modified components and insufficient performance consistency of the prepared composite materials.

[0006] In view of the above problems, the technical solution to achieve the present application is as follows: This application provides a method for synergistic performance optimization of high-pressure polypropylene composites. The method includes: adding modified conductive carbon black and polar monomer-grafted polypropylene in a gradient ratio according to the application performance requirements of the high-pressure polypropylene composite; setting a pellet premixing index and a melt blending index; using the pellet premixing index and melt blending index, drying the copolymerized polypropylene matrix and polyolefin elastomer under an inert atmosphere; then feeding the mixture into a twin-screw extruder according to a preset ratio; and setting segmented temperature control parameters. The method involves multi-scale control based on the gradient ratio of modified conductive carbon black and polar monomer-grafted polypropylene, combined with the pellet premixing index and melt blending index, and synergistic optimization of the segmented temperature control parameters and gradient temperature-controlled crystallization parameters to determine the combination of process parameters.

[0007] In one possible implementation, the melt-blended material is pumped to a granulation die by a metering pump and extruded to form composite material particles. During the cooling process of the composite material particles, a gradient temperature-controlled crystallization treatment is used to obtain a high-pressure polypropylene composite material product.

[0008] In possible implementations, the application performance requirements include volume resistivity, tensile strength, and the number of surface protrusions; a correlation database is established between the gradient ratios of modified conductive carbon black and polar monomer-grafted polypropylene and the application performance requirements, and multiple gradient ratio ranges are determined, with response surface optimization performed using the multiple gradient ratio ranges.

[0009] In possible implementations, the modified conductive carbon black filling amount and grafted polypropylene content are used as core variables. Within the boundary conditions of the associated database, effective variable intervals are selected. Based on the effective variable intervals, parameter synergy terms and quadratic terms are configured, and the material performance response surface is generated by comparing with the multi-gradient ratio intervals.

[0010] In possible implementations, the parameter synergy terms include conductive dispersion synergy terms and interfacial compatibility synergy terms; based on the modified conductive carbon black filling amount and the premixing speed of the agglomerate premixing index, the conductive dispersion synergy terms under the parameter coupling effect are determined; based on the grafted polypropylene content and the blending temperature of the melt blending index, the interfacial compatibility synergy terms under the parameter coupling effect are determined.

[0011] In a possible implementation, the synergistic effect term and the quadratic term of the parameters are substituted into the material performance response surface to construct a multivariate nonlinear model that includes the amount of modified conductive carbon black filling, the content of grafted polypropylene, the premixing speed, and the blending temperature. The multivariate nonlinear model is then globally optimized by iterating the predicted values ​​of volume resistivity, tensile strength, and surface finish under different combinations of variables through crossover and mutation operations. During the optimization process, an objective function is formulated based on the predicted values ​​of volume resistivity, tensile strength, and surface finish to determine the set of multi-scale control parameters.

[0012] In one possible implementation, the twin-screw extruder is linked to collect historical temperature data and corresponding melt pressure fluctuation signals for each heating section. A convolutional neural network is used to extract the temperature-melt uniformity feature mapping relationship. Based on the temperature-melt uniformity feature mapping relationship, combined with the thermal stability parameters of modified conductive carbon black and the thermal decomposition temperature of polar monomer-grafted polypropylene, temperature control constraints are constructed. Under the temperature control constraints, the segmented temperature control parameters are searched and determined.

[0013] In possible implementations, the crystallization orientation and stress-strain parameters of the composite material particles during the molding process are established, and the molding pressure range is determined in combination with the target application performance requirements of the high-pressure polypropylene composite material; the temperature gradient, aging time and cooling rate of thermo-oxidative aging are set as optimization variables, and an orthogonal test scenario is proposed; through the change rate of tensile strength, elongation at break and volume resistivity under the orthogonal test scenario, synergistic optimization is performed within the molding pressure range to determine the combination of process parameters.

[0014] In one possible implementation, a digital twin model is constructed under the correlation of performance optimization of high-pressure polypropylene composite materials, and the process parameters are combined and uploaded for simulation verification. At the same time, the evolution data of conductive network connectivity, interface compatibility and crystal morphology during the simulation are collected to identify key process parameters that affect the performance stability of the finished high-pressure polypropylene composite materials. A dynamic compensation mechanism is set up to adaptively adjust the key process parameters according to the fluctuation of raw material batches and equipment status.

[0015] In one possible implementation, a material surface defect detection unit under machine vision is established, and a YOLO model is trained to identify defects in high-pressure polypropylene composite materials, including surface protrusions and micropores. The defect identification information of high-pressure polypropylene composite materials is correlated with the key process parameters to construct a defect source fault tree. The defect source fault tree is then used to locate and mark the defects in the high-pressure polypropylene composite materials.

[0016] In summary, one or more technical solutions provided in this application achieve the technical effect of significantly improving the process stability of high-pressure polypropylene composite materials by synergistically optimizing multi-scale process parameters such as pellet premixing, melt blending and segmented temperature control, and dynamically matching the gradient distribution of modified components with the thermal-fluid-structural evolution process. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0018] Figure 1 This application provides a flowchart illustrating a method for synergistic performance optimization of high-pressure polypropylene composites. Detailed Implementation

[0019] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0020] The embodiments are described in detail below with reference to the accompanying drawings, such as... Figure 1 As shown, this application provides a method for synergistic performance optimization of high-pressure polypropylene composites, wherein the method includes: S1: Based on the application performance requirements of high-pressure polypropylene composite materials, modified conductive carbon black and polar monomer grafted polypropylene are added in a gradient ratio, and agglomerate premixing index and melt blending index are set; S2: Based on the agglomerate premixing index and melt blending index, the copolymerized polypropylene matrix and polyolefin elastomer are dried under an inert atmosphere, and then fed into a twin-screw extruder according to a preset ratio, and segmented temperature control parameters are set.

[0021] Specifically, modified conductive carbon black refers to conductive carbon black filler that has undergone surface oxidation, grafting treatment, or coating modification. Compared with unmodified carbon black, it has better dispersibility and interfacial compatibility with the polypropylene matrix. Its DBP oil absorption value is usually controlled at 120-180mL / 100g, its specific surface area is 50-150m² / g, and its surface oxygen-containing functional group content is increased to 2-5wt%, enabling it to form an effective conductive network at a low filler content. Polar monomer grafted polypropylene is a functionalized polymer that introduces polar monomers such as maleic anhydride and glycidyl methacrylate into the polypropylene molecular chain through melt grafting or solution grafting processes. The grafting rate is generally controlled at 0.5-3.0wt%, which can act as a compatibilizer to reduce the interfacial tension between carbon black and polypropylene and improve the dispersion state of the filler.

[0022] Gradient proportion mixing refers to adding ingredients according to target performance requirements, such as volume resistivity of 10. 2 -10 6 Different combinations of Ω·cm and tensile strength ≥25MPa were used to differentiate the formulation of modified conductive carbon black and polar monomer grafted polypropylene in multiple preset ratios, such as a gradient sequence of carbon black / grafted polypropylene mass ratios of 3:1, 4:1, and 5:1. Agglomeration premixing parameters included premixing speed (300rpm-800rpm), premixing temperature (60℃-90℃), premixing time (5min-15min), and filling coefficient (0.6-0.8) to control the initial dispersion state of solid powder in the agglomeration stage. Melt blending parameters included blending temperature (180℃-220℃), screw speed (100rpm-300rpm), melt residence time (2min-5min), and shear rate (100s). -1 -1000s -1 ), used to regulate dispersion and interfacial reactions in the molten state.

[0023] The copolymer polypropylene matrix refers to ethylene-propylene random copolymers or block copolymers, which serve as the matrix material to provide mechanical support and insulation properties. The polyolefin elastomer mainly refers to ethylene-octene copolymers or ethylene-butene copolymers, used to improve the low-temperature toughness and impact resistance of polypropylene. The inert atmosphere refers to a nitrogen or argon environment with an oxygen content controlled below 50 ppm to prevent thermo-oxidative degradation of the polypropylene matrix during high-temperature drying. The segmented temperature control parameters refer to the temperature of at least four heating sections set along the material conveying direction of the twin-screw extruder, such as 160℃-180℃ for the feeding section, 180℃-200℃ for the plasticizing section, 200℃-220℃ for the mixing section, and 190℃-210℃ for the homogenizing section. The temperature difference between each section is controlled within the range of 10℃-30℃ to achieve gradual plasticization and uniform mixing.

[0024] Execution steps: Based on the end-application scenario, determine the gradient ratio combination of modified conductive carbon black and polar monomer grafted polypropylene, and simultaneously set the key process windows for agglomerate premixing and melt blending. For example, when the carbon black addition is 8wt% and the grafted PP is 4wt%, if the premixing time is less than 8min, the carbon black agglomerate size is >50μm, resulting in uneven dispersion during subsequent extrusion and black spot defects on the product surface. When the premixing time reaches 12min and the speed is 500rpm, the agglomerate size is reduced to <10μm, significantly improving the dispersion uniformity. Subsequently, the copolymerized polypropylene matrix and polyolefin elastomer are dried at 80℃ for 4h under nitrogen protection to remove moisture and avoid hydrolysis or bubble generation during high-temperature extrusion. After being fed into the twin-screw extruder according to the preset ratio, a segmented temperature control strategy is adopted: the feeding section is 180℃ to prevent premature melting and bridging, the compression section is raised to 210–220℃ to ensure sufficient melting and shear dispersion, and the metering section is slightly lowered to 215℃ to reduce the risk of thermal degradation. Preferably, by designing gradient components and quantitatively setting premixing / blending indices, a material and process basis is provided for multi-scale synergistic regulation.

[0025] S3: The process parameter combination is determined by multi-scale control based on the gradient ratio of modified conductive carbon black and polar monomer grafted polypropylene, combined with the agglomerate premix index and melt blending index, and in conjunction with the synergistic optimization of the segmented temperature control parameters and gradient temperature-controlled crystallization parameters.

[0026] Specifically, multi-scale regulation refers to a multi-level synergistic control strategy at the nanoscale, microscale, and macroscale. By establishing quantitative correlations between structural parameters and process indicators at different scales, it achieves full-chain optimization from formulation design to processing. Correspondingly, the nanoscale and microscale involve the interfacial interaction between the grafted segments of polar monomer-grafted polypropylene and the functional groups on the surface of modified conductive carbon black, the dispersion state of primary carbon black aggregates in the matrix, and the distribution of secondary aggregates formed after particle breakage. The macroscale involves melt rheological behavior and extrusion molding quality. Gradient temperature-controlled crystallization parameters are crystallization control conditions with temperature gradient distribution set during the cooling stage after extrusion molding, including crystallization temperature range, temperature gradient rate, cooling rate, and isothermal crystallization time. These are used to regulate the folding arrangement of polypropylene molecular chains, spherulite size distribution, and perfection, thereby affecting the mechanical properties and dimensional stability of the material.

[0027] Synergistic optimization refers to incorporating the gradient ratio of modified components, agglomerate premixing and melt blending indices, segmented temperature control parameters, and gradient temperature-controlled crystallization parameters into a unified mathematical optimization framework. Response surface methodology, genetic algorithms, or multi-objective optimization algorithms are used to find the Pareto optimal combination of process parameters that achieves multiple objective functions such as volume resistivity, tensile strength, and surface finish. The process parameter combination refers to a complete set of process schemes with clearly defined numerical boundaries, determined after synergistic optimization. This includes the amount of modified conductive carbon black filler, the content of polar monomer-grafted polypropylene, premixing speed, blending temperature, temperature setpoints for each section of the extruder, and gradient temperature-controlled crystallization curves. This combination is solidified in the form of process specifications or digital control codes to guide mass production.

[0028] Execution steps: Based on the determined gradient ratio of modified conductive carbon black to polar monomer-grafted polypropylene, the ratio is back-mapped to agglomerate premixing and melt blending indices to ensure initial dispersion uniformity. The melt blending indices provide sufficient shear to break secondary agglomeration and are further dynamically matched with the temperature parameters of each section of the twin-screw extruder. For example, under high carbon black load, the compression section needs to be increased to 220°C to reduce melt viscosity and avoid dispersion defects due to insufficient shear. Simultaneously, gradient temperature-controlled crystallization is introduced immediately after extrusion. A composite cooling strategy of rapid cooling to suppress the formation of large spherulites followed by slow cooling to promote perfect crystallization can control the average spherulite size to 10μm–20μm, significantly improving surface finish and maintaining tensile strength ≥32MPa. The above multidimensional parameters are synergistically optimized using response surface methodology or machine learning models to select the optimal combination of process parameters that balances volume resistivity, tensile strength, and surface defect rate. Preferably, the previously set gradient components and process indices are transformed into a practically operable and mutually coupled processing path, significantly improving the reliability and consistency of the material in high-voltage electrical applications.

[0029] Furthermore, the method of this application includes: The melt-blended material is pumped to a granulation die by a metering pump and extruded to form composite material particles. During the cooling process of the composite material particles, a gradient temperature-controlled crystallization treatment is adopted to obtain a high-pressure polypropylene composite material product.

[0030] Specifically, a metering pump refers to a volumetric precision conveying device, typically employing a gear pump or melt pump structure. It possesses high-precision flow control capabilities and is used to deliver the high-temperature molten material output from a twin-screw extruder to the granulation die at a constant pressure and stable flow rate. This ensures the continuity of the extrusion process and the stability of the melt pressure, preventing uneven particle size or surface defects caused by pressure fluctuations. The granulation die is the terminal component of the extrusion molding system, internally equipped with a flow distribution system and a perforated plate structure. After the melt is extruded through the die orifice, it is cut into cylindrical or spherical particles by a cutter. The die temperature is typically controlled between 190℃ and 210℃ to match the melt temperature and prevent degradation or premature crystallization. Composite material particles refer to semi-finished particles obtained after extrusion granulation, with a particle size of 2mm-4mm and a length of 2mm-5mm. These particles contain a homogeneous mixture of copolymer polypropylene matrix, polyolefin elastomer, modified conductive carbon black, and polar monomer grafted polypropylene. The melt index is controlled between 1g / 10min and 5g / 10min, and the bulk density is 0.5g / cm³. 3 -0.6g / cm 3 It provides standardized raw materials for injection molding or extrusion molding.

[0031] Gradient temperature-controlled crystallization refers to a precision thermal management process with a spatial temperature gradient distribution implemented during the particle cooling and solidification stage. Through multi-stage temperature control devices, the particles sequentially pass through a high-temperature nucleation zone (125℃-135℃), a medium-temperature growth zone (110℃-125℃), and a low-temperature solidification zone (80℃-110℃). The temperature gradient in each zone is controlled at 3-8℃ / cm, and the cooling rate decreases from 5℃ / min to 20℃ / min. This regulates the folding arrangement of polypropylene molecular chains and the spherulite growth kinetics to obtain a uniform and highly perfect crystalline structure. High-pressure polypropylene composite materials refer to the final material obtained after a complete preparation process that meets the performance indicators for high-voltage electrical applications, with a volume resistivity ≤10 Ω·cm. 3 With a strength of Ω·cm, tensile strength ≥25MPa, elongation at break ≥300%, surface finish Ra≤1.0μm, and spherulite size distribution coefficient CV≤15%, it can be directly used for injection molding or extrusion molding of products such as cable shielding layers and high-voltage connectors.

[0032] Execution steps: The molten blend material is fed into the granulation die at a constant flow rate via a metering pump, extruding it into strip-shaped melts with a diameter of 2.0 mm. These strips then enter a three-stage gradient cooling system: the first stage, a high-temperature water bath, delays initial crystallization, preventing stress concentration and microcracks caused by sudden cooling; the middle stage, a medium-temperature zone, promotes orderly α-crystal growth and increases crystallinity; and the final stage, room-temperature water, rapidly sets the melt, inhibiting excessive spherulite growth in the later stages. Preferably, by precisely controlling the thermal history during the cooling process, active intervention in the polypropylene crystallization behavior is achieved, thereby solidifying the microstructural advantages constructed through prior multi-scale synergistic optimization.

[0033] Compared to traditional single-stage direct cooling with cold water, the gradient temperature control strategy can achieve a more uniform spherulite size distribution in composite materials, reduce surface roughness, and maintain tensile strength within the range of 31MPa–33MPa while stabilizing volume resistivity at 3×10⁻⁶. 5 Ω·cm–6×10 5 Furthermore, since the cooling process is highly coupled with the preceding melt dispersion state, the carbon black network structure is effectively locked, preventing it from migrating and agglomerating during slow cooling. Therefore, gradient temperature-controlled crystallization is not only a physical shaping method, but also a key process node to achieve the synergy between the stability of the conductive network and the mechanical-appearance properties, which significantly improves the batch consistency of the finished product and the reliability of downstream processing.

[0034] Furthermore, the method of this application also includes: The application performance requirements include volume resistivity, tensile strength, and the number of surface protrusions. A correlation database is established between the gradient ratios of modified conductive carbon black and polar monomer-grafted polypropylene and the application performance requirements to determine multiple gradient ratio ranges, and response surface optimization is performed using the multiple gradient ratio ranges.

[0035] Specifically, volume resistivity is a core electrical indicator characterizing the conductivity of a material. It is defined as the ratio of the resistance to current flowing through the material to the geometric dimensions of the sample, measured in Ω·cm. For high-pressure polypropylene composites, it is categorized into semi-conductive shielding layers and insulating layers depending on the application. For cable shielding applications, the target volume resistivity is controlled within the range of 5×10²Ω·cm to 5×10³Ω·cm. The four-probe method or galvanometer-DC amplifier method is used, with a sample thickness of 2±0.1mm, a test voltage of 500V, and an ambient temperature of 23±2℃. Tensile strength is a mechanical indicator measuring a material's resistance to tensile failure. It is defined as the maximum tensile stress the sample can withstand before fracture, measured in MPa. The test is conducted according to GB / T [standard missing]. Standard 1040.2-2006, "Determination of tensile properties of plastics - Part 2: Test conditions for molded and extruded plastics", requires the use of Type I dumbbell-shaped specimens and a tensile rate of 50 mm / min. For polypropylene composites used in high-voltage electrical applications, the tensile strength must be ≥25 MPa and the elongation at break must be ≥300% to ensure structural integrity under cable bending installation and long-term thermal cycling stress.

[0036] The number of surface protrusions is a key appearance indicator for evaluating the surface quality of materials. It refers to the number of protrusion defects that can be measured by instruments per unit area, measured in units per cm². High-voltage cable shielding layers require ≤10 surface protrusions per cm² to avoid partial discharge and insulation breakdown caused by electric field concentration. Detection methods include optical microscopy and laser confocal scanning. The association database refers to a structured dataset established through systematic experimental design, containing quantitative mapping relationships between different gradient ratios and corresponding performance indicators. It typically contains no fewer than 50 valid samples, covering single-factor, two-factor, and multi-level interactions. Furthermore, different gradient ratios include full-factor combinations of 15-25 wt% modified conductive carbon black and 3-8 wt% polar monomer-grafted polypropylene. Performance indicators include volume resistivity, tensile strength, and the number of surface protrusions.

[0037] Multi-gradient ratio intervals refer to statistically significant optimization subspaces determined through data mining and boundary analysis based on an associated database. Typically, 3-5 gradient levels are set, such as carbon black / grafted polypropylene = 15 / 3wt%, 18 / 4wt%, 20 / 5wt%, 22 / 6wt%, 25 / 8wt%, with each interval boundary constrained by the allowable fluctuation range of performance indicators. Response surface optimization refers to the use of mathematical and statistical methods such as Box-Behnken design and Central Composite Design (CCD) to establish a continuous functional relationship between factors and responses. By analyzing response surface curvature, contour distribution, and stationary point properties, it predicts the optimal factor combination and evaluates interaction effects.

[0038] Execution steps: Establish an experimental matrix around the target performance: For example, select 15–20 ratios within the range of 4–12 wt% carbon black and 2–8 wt% grafted PP. Prepare samples for each ratio and test volume resistivity, tensile strength, and the number of surface protrusions. Furthermore, based on this, establish a correlation database containing 300+ data points, and use RSM to construct a prediction model in the following form: Y = β0 + β1C + β2G + β 11 C 2 +β 22 G 2 +β 12 CG+ε, where Y is the target response variable, such as volume resistivity (logarithmically), tensile strength, or number of surface protrusions, C is the carbon black content, G is the grafted PP content, and β0 intercept term represents the baseline response value predicted by the model when C=0 and G=0. It should be noted that this point may have no physical meaning in actual formulations, but it is used mathematically to locate the response surface.

[0039] β1 is the first-order coefficient of carbon black, reflecting the linear main effect of carbon black content on response Y; β2 is the first-order coefficient of grafted PP, reflecting the linear main effect of grafted P content on response Y; β 11β is the quadratic coefficient of carbon black, characterizing the nonlinear effect of carbon black on the response Y; 22 β represents the coefficient of the quadratic term of the grafted PP, reflecting the nonlinear effect of the grafted PP; 12 The synergistic effect term quantifies the coupling effect between carbon black and grafted PP; ε is the random error term, representing the influence of random fluctuations, measurement noise, or variables not included in the model. Preferably, multi-objective optimization, such as weighted methods or Pareto front analysis, is used to determine multiple gradient ratio ranges, guiding the parameter settings for subsequent premixing and extrusion processes, thus shifting the entire process from experience-based trial and error to data-driven approaches.

[0040] Furthermore, in performing response surface optimization using the aforementioned multi-gradient ratio range, the method of this application also includes: Using the modified conductive carbon black filling amount and grafted polypropylene content as core variables, effective variable intervals are screened within the boundary conditions of the associated database; based on the effective variable intervals, parameter synergy terms and quadratic terms are configured, and the material performance response surface is generated by comparing with the multi-gradient ratio intervals.

[0041] Specifically, the core variables refer to the independent factors that have a dominant influence in the response surface optimization model and require priority control. These are the modified conductive carbon black filler content and the polar monomer grafted polypropylene content. Further, the modified conductive carbon black filler content is denoted as X1 (wt%), and the polar monomer grafted polypropylene content is denoted as X2 (wt%). These two factors interact to jointly determine the formation of the conductive network and the interfacial bonding state of the composite material, and are key formulation parameters affecting volume resistivity and tensile strength. Boundary conditions refer to the effective value range of each factor in the associated database, determined by physical feasibility (e.g., the maximum carbon black filler content is limited by melt viscosity, typically ≤30wt%), economic cost (e.g., the price of grafted polypropylene is approximately 3-5 times that of the matrix, requiring content control to ≤10wt%), and performance thresholds (e.g., volume resistivity must be <10 Ω). 4 (Ω·cm) joint constraint.

[0042] The effective variable interval refers to the sub-interval within the boundary conditions that has a statistically significant impact on the response variable, selected through analysis of variance and significance tests. This excludes insignificant regions caused by experimental errors or random fluctuations, ensuring that the optimization process focuses on a high signal-to-noise ratio data space. The parameter synergy term refers to a mathematical term reflecting the interaction effect of two or more factors, in the form of X. i X j Furthermore, i≠j, specifically referring to the conductive dispersion synergistic effect term and the interfacial compatibility synergistic effect term, used to quantify the synergistic contribution of formulation parameters and process parameters to performance. Further, the conductive dispersion synergistic effect term is X1×premixing speed, and the interfacial compatibility synergistic effect term is X2×blending temperature.

[0043] Quadratic terms refer to mathematical terms that reflect the nonlinear effects and curvature characteristics of factors, and are in the form of X. i 2 It is used to capture extreme points of the response variable, including maximum, minimum, or saddle point; it also includes X1. 2 and X2 2 X1 2 This refers to the saturation effect of carbon black filler content, X2 2 This refers to the oversaturation effect of grafted polypropylene content, avoiding prediction bias caused by simple linear models; the material performance response surface refers to a three-dimensional surface or contour plot of performance indicators described by a quadratic polynomial in a multidimensional factor space. It can intuitively show the mapping relationship between factor combinations and performance output, identify the optimal region and robustness interval, and is the core visualization tool of the response surface method.

[0044] Execution steps: Identify entities in the associated database that meet the following criteria, including volume resistivity ≤ 10. 6 Ω·cm, tensile strength ≥30MPa, protrusions ≤5 / cm 2 Of the sample points with all performance constraints, only about 45% of the data points in the original proportioning space were found to be valid. Further analysis revealed that when carbon black content was <6.5%, conductivity was insufficient, and when it was >9.5%, protrusions increased significantly. Grafted PP content <3% resulted in weak interfacial bonding leading to decreased strength, while >5.5% caused the excessive compatibilizer to soften the matrix and reduce the modulus. Based on this, the effective variable range was selected as: carbon black 7.0–9.0 wt%, grafted PP 3.5–5.0 wt%.

[0045] Within the effective variable range, a central composite design was used to supplement 10 sets of verification experiments, constructing a complete quadratic response surface model containing linear, quadratic, and interaction terms. The model shows that carbon black dominates conductivity, but there is a significant quadratic effect, indicating that the percolation behavior is nonlinear. Simultaneously, the interaction term reveals that appropriately increasing the grafted PP can delay carbon black agglomeration, shifting the percolation threshold to the left. Comparing this model with multiple gradient ratio ranges, a three-dimensional response surface plot was generated, clearly identifying the optimal sweet spot. This response surface guides precise formulation locking, providing input boundaries for the coordinated adjustment of process parameters such as shear strength and cooling rate, and providing a quantitative basis for process decisions.

[0046] Furthermore, the method of this application also includes: The parameter synergy includes conductive dispersion synergy and interfacial compatibility synergy; based on the modified conductive carbon black filling amount and the premixing speed of the agglomerate premixing index, the conductive dispersion synergy under the parameter coupling effect is determined; based on the grafted polypropylene content and the blending temperature of the melt blending index, the interfacial compatibility synergy under the parameter coupling effect is determined.

[0047] Specifically, the conductive dispersion synergistic effect term refers to the mathematical interaction term characterizing the coupling effect between the modified conductive carbon black filling amount X1 and the premixing speed X3 in the agglomerate premixing index, in the form of β. 13 X1X3, where β 13 The interaction coefficient is used to quantify the contribution of the high filler content-high shear synergy to the dispersion state of the conductive network. It reflects the nonlinear enhancement effect of increasing the premixing speed on improving the dispersion of the high carbon black content system, and is usually negative, with units of Ω·cm / (wt%·rpm). The interfacial compatibility synergy term refers to the mathematical interaction term characterizing the coupling effect between the grafted polypropylene content X2 and the blending temperature X4 in the melt blending index, and is in the form of β. 24 X2X4, where β 24 The interaction coefficient is used to quantify the contribution of the high grafting amount-thermal history synergy to the degree of interfacial reaction and compatibility. It reflects the balance effect between the reactivity of grafted polypropylene and the risk of thermal degradation when the blending temperature is controlled. It is usually bidirectional, with units of MPa / (wt%·℃).

[0048] Inter-parameter coupling effect refers to the non-additive phenomenon where two independent factors influence each other through physical or chemical mechanisms and jointly determine the response variable. Unlike the simple superposition of main effects, it is manifested by the interaction term coefficient being significantly non-zero. Specifically, it is shown to be the dependence of premixing speed on the filling amount of carbon black dispersion efficiency, and the dependence of blending temperature on the grafting reaction kinetics of grafting reaction. Premixing speed refers to the stirring speed of the high-speed mixer in the premixing stage of agglomerates, usually set at 300-800 rpm, which directly affects the shear strength and collision frequency of solid powder, and thus determines the degree of breakage and coating uniformity of primary carbon black agglomerates. Blending temperature refers to the melt temperature of the twin-screw extruder in the melt blending stage, usually set at 180-220℃, which directly affects the melt viscosity of polypropylene and the reactivity of the functional groups of grafted polypropylene, and thus determines the chemical bonding efficiency and thermal degradation rate between the interfacial compatibilizer and the carbon black surface.

[0049] Execution steps: The construction of the conductive dispersion synergistic effect term is based on the following mechanism: When the carbon black filling amount is high (>8wt%), if the premixing speed is too low (<400rpm), the shear force is insufficient to break the primary agglomerates, making it difficult to achieve nanoscale dispersion in the melting stage, and the measured volume resistivity fluctuation reaches one order of magnitude; however, when the premixing speed is increased to above 550rpm, even if the carbon black reaches 9wt%, its agglomerate size can be controlled to <5μm, forming a continuous conductive path. Furthermore, the volume resistivity is significantly negatively correlated with the premixing speed, indicating that high speed can effectively compensate for the dispersion difficulty caused by high filling.

[0050] Similarly, the interfacial compatibility synergistic effect term originates from the thermal activation characteristics of grafted PP: the maleic anhydride groups in grafted PP need to be at a sufficient temperature to react effectively with the functional groups on the carbon black surface or the PP chain ends; when the blending temperature is below 195℃, even if the grafted PP content reaches 5wt%, the interfacial bonding is still weak, and the tensile cross-section SEM shows obvious debonding; when the temperature rises to 210–215℃, the grafting efficiency is significantly improved, and the interfacial shear strength is increased by about 40%. Based on this, by introducing a process-material coupling term with clear physical meaning into the response surface model, the traditional response surface model based solely on the formulation is upgraded to an integrated prediction system of formulation and process, providing theoretical support for the intelligent manufacturing of high-pressure polypropylene composite materials.

[0051] Furthermore, based on the gradient ratio of modified conductive carbon black and polar monomer-grafted polypropylene, and combined with the aforementioned agglomerate premixing index and melt blending index, the method of this application includes multi-scale control: Substituting the synergistic and quadratic terms of the parameters into the material performance response surface, a multivariate nonlinear model is constructed, including the amount of modified conductive carbon black filling, the content of grafted polypropylene, the premixing speed, and the blending temperature. Global optimization is performed on the multivariate nonlinear model, iterating the predicted values ​​of volume resistivity, tensile strength, and surface finish under different variable combinations through crossover and mutation operations. During the optimization process, an objective function is formulated based on the predicted values ​​of volume resistivity, tensile strength, and surface finish, and a set of multi-scale control parameters is determined.

[0052] Specifically, the multivariate nonlinear model refers to a complete quadratic polynomial regression equation containing four independent variables: modified conductive carbon black filling amount X1, grafted polypropylene content X2, premixing speed X3, and blending temperature X4, capturing the nonlinear relationships and interaction effects among factors; global optimization refers to searching for the solution set that simultaneously optimizes multiple objective functions in a four-dimensional parameter space, unlike the defects of local gradient optimization that are prone to getting trapped in saddle points or local extrema, it uses a heuristic algorithm to traverse the entire feasible region; crossover operation is the core genetic operator in genetic algorithms, simulating the biological gene recombination process, randomly selecting two parent individuals from the current population, exchanging gene fragments at random positions with crossover probability to produce offspring individuals, realizing the inheritance and recombination of excellent parameter combinations.

[0053] The mutation operation is a genetic operator that maintains population diversity. It randomly perturbs individual genes with mutation probability to avoid premature convergence of the algorithm to local optima and ensure that the undersampled regions are explored. The objective function integrates multiple response indicators into a single optimization criterion, taking into account conductivity, mechanical properties and surface quality. The multi-scale control parameter set refers to the optimal combination of parameters determined after global optimization, covering the amount of modified conductive carbon black filling, grafted polypropylene content, premixing speed and blending temperature. It is output in the form of a four-dimensional vector as the input constraint for subsequent segmented temperature control and crystallization optimization.

[0054] Furthermore, by coordinating the optimization of the segmented temperature control parameters and the gradient temperature-controlled crystallization parameters to determine the combination of process parameters, the method of this application includes: The twin-screw extruder is linked, and historical temperature data and corresponding melt pressure fluctuation signals of each heating section are collected. A convolutional neural network is used to extract the temperature-melt uniformity feature mapping relationship. Based on the temperature-melt uniformity feature mapping relationship, combined with the thermal stability parameters of modified conductive carbon black and the thermal decomposition temperature of polar monomer-grafted polypropylene, temperature control constraints are constructed. Under the temperature control constraints, the segmented temperature control parameters are searched and determined.

[0055] Specifically, historical temperature data refers to the temperature time series collected in previous production cycles for each heating section of the twin-screw extruder (usually five sections: feeding section, plasticizing section, mixing section, homogenizing section, and die section). The sampling frequency is 10-50Hz, and it includes set values, measured values, and fluctuation amplitude (within ±2℃). The data length is no less than 1000 batches or 72 hours of continuous operation, used to establish the correlation between temperature control and material state. Melt pressure fluctuation signal refers to the dynamic pressure data collected by the melt pressure sensor (range 0-50MPa, accuracy ±0.5%FS) installed at the end of the extruder barrel or in front of the die. It reflects the stability of melt delivery. The standard deviation of pressure fluctuation σ < 0.3MPa is considered to be good uniformity, and σ > 0.5MPa indicates uneven plasticization or local degradation.

[0056] Convolutional neural networks (CNNs) extract local features of temperature data (such as the rate of change of temperature gradient and fluctuation period) through convolutional layers, reduce dimensionality through pooling layers, and establish a mapping with melt uniformity indices through fully connected layers. The input is a 5×T temperature matrix (5 segments ×T time steps), and the output is the uniformity level (1-5 levels) or the predicted value of σ. The model accuracy is usually >92%. The mapping relationship between temperature and melt uniformity features refers to the functional relationship obtained by training a CNN that characterizes the causal relationship between a specific temperature curve pattern and the melt quality state, such as the implicit rule of "rapid temperature rise in the plasticizing section (>5℃ / min) + temperature overshoot in the mixing section (>3℃ set value) → pressure fluctuation increases by 40%".

[0057] Thermal stability parameters refer to the ability of modified conductive carbon black to maintain its structure at high temperatures. Key indicators are the initial decomposition temperature and the 5% weight loss temperature. Exceeding these temperatures leads to the decomposition of functional groups on the carbon black surface, resulting in the destruction of the conductive network. Thermal decomposition temperature refers to the temperature threshold at which the grafted functional groups in polar monomer-grafted polypropylene undergo significant chemical decomposition. The decomposition temperature of the grafted groups in maleic anhydride-grafted polypropylene is determined by DSC / TGA. Exceeding this temperature, the grafting rate decreases by more than 20%, and the interfacial compatibility deteriorates significantly. Temperature control constraints refer to a set of multidimensional inequalities constructed based on the above thermal limits and CNN mapping relationships to ensure that the temperature setting meets processing requirements without causing thermal damage to the material. Search refers to finding the combination of temperature parameters that minimizes energy consumption, maximizes yield, or optimizes quality within the 5-dimensional feasible domain defined by the constraints, using optimization algorithms such as Bayesian optimization and particle swarm optimization.

[0058] Execution steps: Deploy a high sampling rate data acquisition system on the twin-screw extruder to synchronously record the temperature of each heating section (e.g., T1–T8) and the melt pressure P(t). Input multiple batches of data pairs under normal / abnormal operating conditions ([T1(t),…,T8(t)],P(t)) into a one-dimensional CNN model (structure: Conv1D×3→MaxPool→Flatten→Dense) to train it to predict the melt uniformity index MUI. This index is calibrated by the carbon black dispersion obtained from offline SEM image analysis. The CNN can accurately identify the coupling mode between high-frequency components of pressure fluctuations and overheating in the compression section, and its MUI prediction R² reaches 0.91.

[0059] Hard constraints were set based on the material's thermal properties: Since the thermal decomposition initiation temperature of grafted PP is 255℃ (determined by the TGA 5% weight loss method), the upper limit of the temperature for the melting and metering sections was set at 220℃ (with a 35℃ safety margin); although carbon black is resistant to high temperatures, excessively high temperatures will exacerbate the oxidation of the PP matrix, therefore the overall temperature must not exceed 230℃. Under these constraints, the optimal segmented temperature curve was searched within the feasible region with the goal of maximizing the MUI output by the CNN. Preferably, the segmented temperature control parameters determined by fusing process sensor data and deep learning not only ensured thermal safety but also achieved proactive optimization of melt uniformity through a data-driven approach.

[0060] Furthermore, the method of this application also includes: The crystallization orientation and stress-strain parameters of the composite material particles during the molding process are established. Combined with the target application performance requirements of the high-pressure polypropylene composite material, the molding pressure range is determined. The temperature gradient, aging time and cooling rate of thermo-oxidative aging are set as optimization variables, and an orthogonal test scenario is proposed. Through the change rate of tensile strength, elongation at break and volume resistivity under the orthogonal test scenario, the process parameter combination is determined by synergistic optimization within the molding pressure range.

[0061] Specifically, crystal orientation refers to the ordered phenomenon in which polypropylene molecular chains preferentially align along a specific direction (usually the flow direction or pressure direction) during the molding process. The orientation parameter F is determined by wide-angle X-ray diffraction (0≤F≤1, F=0 is isotropic, F=1 is fully oriented). High-voltage cable shielding layers require F≤0.3 to avoid anisotropy of mechanical properties and non-uniformity of electrical properties caused by high orientation. Stress-strain parameters refer to the mechanical state of composite material particles during the molding process, including molding pressure, holding time, and shear rate. These parameters affect the relaxation behavior of molecular chains and crystallization kinetics, thereby determining the final aggregated structure.

[0062] Molding pressure range refers to the pressure interval determined after analysis of crystallization orientation and stress-strain correlation, which can balance mold filling integrity and low residual stress, and is usually determined by rheological simulation; thermo-oxidative aging refers to a test method that accelerates the deterioration of material properties in an oxygen-containing environment, used to evaluate long-term service reliability, temperature gradient refers to the set temperature sequence of the aging test, aging time refers to the cumulative heat exposure duration, and cooling rate refers to the cooling rate after aging, all three together simulating the thermal history in actual working conditions; orthogonal test scenario refers to a multi-factor, multi-level experimental scheme arranged based on orthogonal experimental design, using L 18 (3 7 An orthogonal array was used, with temperature gradient, aging time, and cooling rate set as optimization variables. A total of 18 sets of experiments were conducted to obtain the maximum amount of information with the fewest number of experiments. The tensile strength change rate refers to the percentage of tensile strength retained relative to the initial value after aging, the elongation at break change rate refers to the percentage change in elongation at break after aging, and the volume resistivity change rate refers to the relative change in volume resistivity after aging. The three together characterize the thermo-oxidative aging stability of the material.

[0063] Furthermore, the method of this application includes: A digital twin model for performance optimization of high-pressure polypropylene composite materials is constructed, and the process parameters are combined and uploaded for simulation verification. Simultaneously, evolution data of conductive network connectivity, interface compatibility, and crystal morphology are collected during the simulation to identify key process parameters affecting the performance stability of the finished high-pressure polypropylene composite material. A dynamic compensation mechanism is set up to adaptively adjust the key process parameters according to raw material batch fluctuations and equipment status.

[0064] Specifically, a digital twin model refers to a virtual simulation system that is built based on physical mechanisms and data-driven approaches and mapped in real time to the actual production system. It adopts a multi-scale coupling framework: microscale (molecular dynamics simulation of the interface between grafted polypropylene / carbon black, time scale ns-μs), mesoscale (phase field method simulation of conductive network percolation and crystal growth, time scale ms-s), and macroscale (finite element method simulation of extrusion flow and temperature fields, time scale min-h). Cross-scale information transfer is achieved through a reduced-order model. Simulation verification refers to inputting the aforementioned determined process parameters into the digital twin model to predict the finished product performance, including volume resistivity, tensile strength, and surface quality, and comparing them with historical best values ​​to verify the feasibility of the process.

[0065] Conductive network connectivity refers to the completeness of the conductive pathways formed by modified conductive carbon black in the matrix. It is quantified by percolation theory parameters and a resistance network model. In the simulation, the number of contact points with a spacing of <10nm between carbon black aggregates is tracked to evaluate network stability. Interface compatibility refers to the bonding strength between grafted polypropylene and the carbon black surface and polypropylene matrix. It is characterized by interface layer thickness, interface shear strength, and debonding work. In the simulation, the change in hydrogen / ester bond density at the interface is monitored. Crystallization morphology refers to the size distribution, perfection, and orientation of polypropylene spherulites. The crystallization kinetics are described by Avrami equation parameters. In the simulation, the spherulite growth front and impingement process are tracked. Key process parameters refer to the core variables with a contribution rate >15% to the variation of finished product performance, selected from the 12-dimensional parameter space through sensitivity analysis. The dynamic compensation mechanism refers to a closed-loop control system that automatically adjusts key process parameters based on real-time monitoring data and model prediction errors through a digital twin reverse optimization algorithm to compensate for raw material batch fluctuations and equipment state drift.

[0066] Furthermore, the method of this application also includes: A material surface defect detection unit under machine vision is established. By training a YOLO model, defects in high-pressure polypropylene composite materials, including surface protrusions and micropores, are identified. The defect identification information of high-pressure polypropylene composite materials is correlated with the key process parameters to construct a defect source fault tree. The defect source fault tree is used to locate and mark the defects in high-pressure polypropylene composite materials.

[0067] Specifically, machine vision refers to an automated image acquisition system constructed using an industrial camera (resolution ≥ 5 million pixels, frame rate ≥ 30fps), a ring LED light source (color temperature 5600K, illuminance ≥ 5000 lux), and an optical lens (focal length 25mm, depth of field ± 2mm) to perform non-contact scanning imaging on the surface of high-pressure polypropylene composite materials. The detection accuracy can reach 10μm level defect recognition. It adopts the YOLOv8 architecture and directly regresses the defect bounding box coordinates (x, y, w, h) and category probability through a convolutional neural network. The detection speed is ≥ 60 frames / second, and the mAP (mean accuracy) target is ≥ 0.90. It supports real-time recognition of two types of defects: surface protrusions (protrusion height > 50μm, diameter > 100μm) and micropores (pore diameter 20-200μm).

[0068] Surface protrusions refer to localized raised defects on the material surface caused by filler agglomeration, bubble escape, or mold contamination. These defects can cause electric field concentration in the shielding layer of high-voltage cables, and are a major cause of partial discharge and insulation breakdown. Micropores refer to surface openings formed by insufficient gas discharge or uneven shrinkage within the material. Pores with a diameter <50μm are micropores, and those >50μm are shrinkage cavities. Although micropores have little impact on mechanical properties, they can reduce surface density and resistance to electrical tracking. Fault tree analysis uses the top event as the root node and decomposes it layer by layer to the bottom event through logic gates. For example, if a key process parameter is abnormal, a tree-like cause-effect diagram is formed, and the importance of each bottom event is quantitatively calculated to locate the root cause of the defect. Location marking refers to the spatial location and visual labeling of the defect source on a digital twin model or physical production line based on the fault tree analysis results. For example, marking the specific location of the temperature sensor in the third heating section of a twin-screw extruder or the second side feed port can generate a maintenance work order.

[0069] In summary, the beneficial effects of the embodiments of this application are: This application employs a method for synergistically optimizing the performance of high-pressure polypropylene composites. Modified conductive carbon black and polar monomer-grafted polypropylene are added in gradient proportions, with premixing and melt blending indices set. Based on these indices, the copolymerized polypropylene matrix and polyolefin elastomer are dried under an inert atmosphere. The resulting mixture is then fed into a twin-screw extruder according to a preset ratio, with segmented temperature control parameters. Multi-scale control is achieved by adjusting the gradient proportions of modified conductive carbon black and polar monomer-grafted polypropylene, combined with the premixing and melt blending indices. This, along with the synergistic optimization of segmented temperature control parameters and gradient temperature-controlled crystallization parameters, determines the optimal combination of process parameters. This application provides a method for synergistically optimizing the performance of high-pressure polypropylene composites. It achieves dynamic matching between the gradient distribution of modified components and the thermal-fluid-structural evolution process through synergistic optimization of multi-scale process parameters such as premixing, melt blending, and segmented temperature control, significantly improving the process stability of high-pressure polypropylene composites.

[0070] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0071] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for performance co-optimization of high pressure polypropylene composites, characterized in that, The method comprises: According to the application performance requirements of high-pressure polypropylene composite materials, the modified conductive carbon black and the polar monomer grafted polypropylene are mixed in gradient proportions, and the pellet premixing index and the melt blending index are set; Through the pellet premixing index and the melt blending index, the copolymerized polypropylene matrix and the polyolefin elastomer are dried in an inert atmosphere, and then fed into a twin-screw extruder according to the preset ratio, and the segmented temperature control parameters are set. Among them, according to the gradient proportion of modified conductive carbon black and polar monomer grafted polypropylene, combined with the pellet premixing index and the melt blending index, the segmented temperature control parameters and the gradient temperature control crystallization parameters are optimized, and the process parameter combination is determined.

2. The method for performance co-optimization of high pressure polypropylene composites of claim 1, wherein, The method comprises: The material after melt blending is conveyed to a granulation die by a metering pump, and composite material particles are extruded, and gradient temperature control crystallization treatment is adopted during the cooling process of the composite material particles to obtain high-pressure polypropylene composite material finished products.

3. The method for performance co-optimization of high pressure polypropylene composites of claim 2, wherein, The method comprises: The application performance requirements include volume resistivity, tensile strength, and surface protrusion number; A correlation database of the gradient proportion of modified conductive carbon black and polar monomer grafted polypropylene and the application performance requirements is established, and a multi-gradient ratio interval is determined, and response surface optimization is performed in the multi-gradient ratio interval.

4. The method for performance co-optimization of high pressure polypropylene composites of claim 3, wherein, The method comprises: The modified conductive carbon black filling amount and the grafted polypropylene content are taken as core variables, and the effective variable interval is screened within the boundary conditions of the correlation database; Based on the effective variable interval, the parameter synergistic action term and the quadratic term are configured, and the material performance response surface is generated by comparing the multi-gradient ratio interval.

5. The method for performance co-optimization of high pressure polypropylene composites of claim 4, wherein, The parameter synergistic action term includes an electrically conductive dispersion synergistic action term and an interface compatibility synergistic action term; Based on the modified conductive carbon black filling amount and the premixing speed of the pellet premixing index, the electrically conductive dispersion synergistic action term under the coupling effect of parameters is determined; Based on the grafted polypropylene content and the blending temperature of the melt blending index, the interface compatibility synergistic action term under the coupling effect of parameters is determined.

6. The method for performance co-optimization of high pressure polypropylene composites of claim 5, wherein, According to the gradient proportion of modified conductive carbon black and polar monomer grafted polypropylene, combined with the pellet premixing index and the melt blending index, the method comprises: The parameter synergistic action term and the quadratic term are substituted into the material performance response surface to construct a multi-variable nonlinear model containing the modified conductive carbon black filling amount, the grafted polypropylene content, the premixing speed, and the blending temperature; The multi-variable nonlinear model is globally optimized, and the predicted volume resistivity, tensile strength, and surface finish prediction values under different variable combinations are obtained through cross operation and mutation operation; In the optimization process, the target function is determined according to the predicted volume resistivity, tensile strength, and surface finish prediction values, and a multi-scale control parameter set is determined.

7. The method for performance co-optimization of high pressure polypropylene composites of claim 2, wherein, The method comprises: Link the twin-screw extruder, collect historical temperature data and corresponding melt pressure fluctuation signals of each heating section, and use convolutional neural network to extract temperature and melt uniformity feature mapping relationship; Based on the temperature and melt uniformity feature mapping relationship, combined with the thermal stability parameters of modified conductive carbon black and the thermal decomposition temperature of polar monomer grafted polypropylene, the temperature control constraint condition is constructed; Search under the temperature control constraint condition to determine the segmented temperature control parameters.

8. The method for performance co-optimization of high pressure polypropylene composites of claim 7, wherein, The method comprises: Establishing the crystalline orientation and stress-strain parameters of the composite particles during the molding process, and determining the molding pressure range combined with the target application performance requirements of high-pressure polypropylene composites; Setting the temperature gradient, aging time and cooling rate of thermal oxidative aging as optimization variables, and designing orthogonal test scenarios; Through the tensile strength, elongation at break and volume resistivity change rate under the orthogonal test scenarios, the process parameter combination is determined by synergistic optimization within the molding pressure range.

9. The method for performance syngistic optimization of high pressure polypropylene composites as claimed in claim 1 wherein, The method comprises: Constructing a digital twin model under the performance optimization correlation of high-pressure polypropylene composites, and uploading the process parameter combination for simulation verification; At the same time, collect the evolution data of conductive network connectivity, interface compatibility and crystalline morphology during simulation running, and identify the key process parameters affecting the performance stability of high-pressure polypropylene composite products; Set up a dynamic compensation mechanism, which is used to adjust the key process parameters according to the raw material batch fluctuation and equipment state.

10. The method for performance co-optimization of high pressure polypropylene composites of claim 9, wherein, The method comprises: Establishing a material surface defect detection unit under machine vision, and identifying high-pressure polypropylene composite product defects including surface protrusions and micropores by training YOLO model; Correlate and analyze the high-pressure polypropylene composite product defect identification information and the key process parameters to construct a defect traceability fault tree; Use the defect traceability fault tree to locate and mark the defects of high-pressure polypropylene composite products.

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