Process for the production of plastic particles and plastic particles produced thereby

Abstract

The application relates to the technical field of plastic particles, and provides a plastic particle preparation method and plastic particles prepared by the method. The plastic particle preparation method comprises the following steps: determining a predicted plasticizing state point of a melt at a current plasticizing moment based on material characteristic information of current feeding; determining a basic plasticizing state point based on melting plasticizing basic process data; determining an initial plasticizing deviation between the predicted plasticizing state point and the basic plasticizing state point; performing process correction on the initial plasticizing deviation based on a plasticizing section topology coupling relationship of a melting plasticizing unit and real-time working condition fluctuation to obtain a target plasticizing deviation; superimposing and fitting the basic plasticizing state point and the target plasticizing deviation to obtain a target plasticizing state point of the melt; and adjusting melting plasticizing process parameters based on the target plasticizing state point to melt and plasticize the material, so that plastic particles are obtained after extrusion, granulation and cooling. The method improves the plasticizing position control precision and makes the material melting and mixing process more uniform and stable.

Description

Technical Field

[0001] This application belongs to the field of plastic particle technology, and particularly relates to a method for preparing plastic particles and the plastic particles prepared therefrom. Background Technology

[0002] Polyetherketone (PEK) ketone-based high thermal conductivity specialty engineering plastic particles, with their comprehensive properties of high temperature resistance, high mechanical strength, high thermal conductivity, and excellent electrical insulation, have become key basic materials in high-end manufacturing fields such as aerospace equipment structural components, new energy thermal management components, and high-end electronic heat dissipation components. The performance stability and batch consistency of these products directly determine the operational reliability and service life of high-end equipment. In the industrial production of these specialty plastic particles, melt plasticizing is the core process that determines the performance of the final product. Currently, the industry generally adopts a conventional preparation route of twin-screw extrusion melt plasticizing, die-cutting, and water-cooling shaping. This relies on the pre-set process parameters of the extrusion equipment to complete the material melting, mixing, and molding processing to achieve continuous mass production.

[0003] However, in actual mass production, existing conventional preparation processes and control methods mean that the processing position of the melt in the plasticizing unit is determined only by the preset process parameters of the equipment. The state stability of multi-component compound materials is poor during the melting and plasticizing process. Under the same process conditions, different batches and materials with different properties have significant differences in plasticizing effect. It is difficult to continuously ensure the uniformity of the melt, and the basic properties of the finished particles fluctuate significantly. Summary of the Invention

[0004] The purpose of this application is to provide a method for preparing plastic particles and the plastic particles prepared therefrom, which can improve the problem of significant differences in plasticizing effect.

[0005] To achieve the above-mentioned objectives, the technical solution adopted in this application is as follows: In a first aspect, this application provides a method for preparing plastic particles, the method comprising: Based on the material characteristics information of the current feed, the predicted plasticizing state point of the melt at the current plasticizing moment is determined; wherein, the predicted plasticizing state point represents the ideal plasticizing position that the melt should be in based on its own material characteristics. Based on the basic process data of melt plasticization, the basic plasticization state point of the melt at the current plasticization moment is determined; wherein, the basic process data of melt plasticization includes the plasticization temperature cycle, screw operation cycle, melt plasticization start section position, melt plasticization end section position, and the basic plasticization state point represents the reference plasticization position of the melt when the equipment is running according to the preset process. Determine the initial plasticization deviation between the predicted plasticization state point and the basic plasticization state point; wherein, the initial plasticization deviation characterizes the original position difference between the ideal plasticization position and the reference plasticization position; Based on the topological coupling relationship of the plasticizing section of the melt plasticizing unit and the real-time operating condition fluctuations, the initial plasticizing deviation is corrected to obtain the target plasticizing deviation; The target plasticizing state point of the melt is obtained by superimposing and fitting the basic plasticizing state point and the target plasticizing deviation; wherein, the target plasticizing state point represents the optimal plasticizing position adapted to the current material and working conditions. Based on the target plasticization state point, the melt plasticization process parameters are adjusted to melt plasticize the material, and plastic particles are obtained after extrusion, granulation, and cooling. The melt plasticization process parameters include screw speed, barrel segment temperature, feeding rate, and plasticization time domain allocation strategy.

[0006] The plastic particle preparation method provided in this application determines the predicted plasticizing state point, representing the ideal plasticizing position of the melt based on its own material characteristics, based on the material characteristic information of the current feed. Then, based on the basic melting plasticizing process data, including the plasticizing temperature cycle, screw operation cycle, melt plasticizing start section position, and melt plasticizing end section position, it determines the basic plasticizing state point, representing the reference plasticizing position of the melt at the current plasticizing moment when the characterization equipment is running according to the preset process. Next, it determines the initial plasticizing deviation, the difference between the predicted plasticizing state point and the basic plasticizing state point, representing the original position difference between the ideal plasticizing position and the reference plasticizing position. Subsequently, based on the topological coupling relationship of the plasticizing section of the melting plasticizing unit and real-time operating condition fluctuations, it performs process correction on the initial plasticizing deviation to obtain the target plasticizing deviation. Finally, it compares the basic plasticizing state point with the target... The plasticizing deviations are superimposed and fitted to obtain the target plasticizing state point that is the optimal plasticizing position for the current material and working conditions. Finally, based on this target plasticizing state point, the screw speed, barrel segment temperature, feeding rate, and plasticizing time domain allocation strategy are adjusted to complete the material melting and plasticizing, and then extrusion, granulation, and cooling to obtain plastic particles. This method effectively eliminates the adverse effects of inter-equipment coupling interference and working condition fluctuations through point measurement, deviation correction, and parameter linkage control throughout the entire process. It accurately matches the processing characteristics of different materials and real-time production status, significantly improves the plasticizing position control accuracy, and makes the material melting and mixing process more uniform and stable. It effectively avoids problems such as insufficient plasticizing, material degradation, and filler agglomeration, and significantly improves the quality consistency, comprehensive performance, and stability and controllability of the finished plastic particles.

[0007] Secondly, this application provides a plastic particle prepared by the plastic particle preparation method described in the above embodiments. The formulation of the plastic particle includes the following components in parts by weight: Polyetherketoneketone, 58-68 parts; Polyarylene ether nitrile, 9-15 parts; One-dimensional aluminum nitride whiskers, 7–12 parts; Hydroxylated boron nitride nanosheets, 6–10 parts; Siloxane-aryl ether grafted polyether ketone ketone compatibilizer, 3-5 parts; High-temperature composite antioxidant for polyether ketone ketone, 0.3-0.7 parts; Fluorine-containing high-temperature lubricant, 0.4–0.8 parts.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic flowchart of the plastic particle preparation method provided in the embodiments of this application; Figure 2 This is a thermal diagram of the topological coupling interference intensity of the plasticizing segment provided in the embodiments of this application; Figure 3 This is a radar chart showing the real-time operating condition fluctuation amplitude provided in the embodiments of this application; Figure 4 This is a schematic diagram of the control device of the plastic particle preparation apparatus provided in the embodiments of this application. Detailed Implementation

[0011] To make the technical problems, technical solutions, and beneficial effects of this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. In the following description, specific details such as particular system structures and technologies are set forth for illustrative purposes rather than for limiting the scope of the application, in order to provide a thorough understanding of the embodiments. However, those skilled in the art should recognize that this application can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted to avoid unnecessary detail that could obscure the description of this application.

[0012] In this application, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. When used in this application specification and appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or a collection thereof.

[0013] References such as "in one possible implementation" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0014] It should be understood that in the various embodiments of this application, the order of the above processes does not imply the order of execution. Some or all steps may be executed in parallel or sequentially. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0015] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0016] The weights of the relevant components mentioned in the embodiments of this application can refer not only to the specific content of each component, but also to the proportional relationship between the weights of the components. Therefore, any scaling up or down of the content of the relevant components according to the embodiments of this application is within the scope disclosed in the embodiments of this application. Specifically, the mass described in the embodiments of this application can be a well-known unit of mass in the chemical industry, such as µg, mg, g, or kg.

[0017] In traditional manufacturing processes, the processing position of the melt within the plasticizing unit is determined solely by preset process parameters of the equipment. This fails to match the appropriate plasticizing position based on the material's own characteristics. Multi-component compound materials exhibit poor state stability during the melting and plasticizing process. Under the same process conditions, different batches and materials with different properties show significant differences in plasticizing effects. Furthermore, it is difficult to eliminate plasticizing position deviations caused by coupling interference in the plasticizing zone and fluctuations in real-time operating conditions. It is also impossible to coordinate and control the screw speed, barrel segment temperature, feeding rate, and plasticizing time domain allocation based on the precise melt plasticizing position. Ultimately, this leads to continuous loss of control over the melt plasticizing state, significant fluctuations in the plasticizing effect, difficulty in consistently ensuring melt uniformity, and significant fluctuations in the basic properties of the finished particles.

[0018] Based on this, in order to improve the problem of significant differences in plasticizing effect during the preparation of plastic particles in related technologies, this application provides a method for preparing plastic particles and the plastic particles prepared therefrom.

[0019] At least some steps of the plastic particle preparation method provided in this application embodiment can be applied to a plastic particle preparation device. In this case, the plastic particle preparation device is the main body for executing the plastic particle preparation method provided in this application embodiment. This application embodiment does not limit the specific type of plastic particle preparation device.

[0020] For example, a plastic pellet preparation device may include a feeding device, a plasticizing extrusion device, a pelletizing and cooling device, and a control device. The feeding device may include a metering feeder for accurately conveying the content of each material. The plasticizing extrusion device is the core execution unit, realizing the melting, plasticizing, and mixing of raw materials. For example, the plasticizing extrusion device may include a servo-driven screw, a segmented heating barrel, multi-segment pressure and temperature sensors, and a plasticizing chamber. The screw is precisely divided into three axial segments: a feeding segment, a compression segment, and a melt homogenization segment. The segmented heating barrel can independently adjust the temperature of each segment to form a stable plasticizing temperature cycle. The servo motor precisely controls the screw speed to form a fixed screw operating cycle. The multi-segment sensors can collect real-time data on barrel segment temperature, screw internal pressure, and speed fluctuations. The pelletizing and cooling device may include a die head, a pelletizer, and a cooling water tank for shaping, pelletizing, and cooling the plasticized melt. The control device monitors and controls the entire plasticizing process.

[0021] For example, the control device can be a microcontroller, PLC, mobile phone, tablet computer, laptop computer, ultra-mobile personal computer (UMPC), desktop computer, smart screen, computing device or other processing device connected to a wireless modem, Internet of Things terminal, computer, and / or other devices for communication over a wireless system, as well as next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved Public Land Mobile Networks (PLMNs).

[0022] To better understand the plastic particle preparation method provided in the embodiments of this application, the specific implementation process of the plastic particle preparation method provided in the embodiments of this application will be described by way of example below.

[0023] Please see Figure 1 The first aspect of this application provides a method for preparing plastic particles, comprising: S100, based on the material characteristic information of the current feed, determines the predicted plasticizing state point of the melt at the current plasticizing moment; wherein, the predicted plasticizing state point represents the ideal plasticizing position that the melt should be in based on its own material characteristics.

[0024] S200, based on the basic process data of melt plasticization, determines the basic plasticization state point of the melt at the current plasticization moment; wherein, the basic process data of melt plasticization includes the plasticization temperature cycle, screw operation cycle, melt plasticization start section position, melt plasticization end section position, and the basic plasticization state point characterizes the reference plasticization position of the melt when the equipment is running according to the preset process.

[0025] S300, determine the initial plasticization deviation between the predicted plasticization state point and the basic plasticization state point; wherein, the initial plasticization deviation represents the original position difference between the ideal plasticization position and the reference plasticization position.

[0026] S400, based on the topological coupling relationship of the plasticizing section of the melt plasticizing unit and the real-time operating condition fluctuations, performs process correction on the initial plasticizing deviation to obtain the target plasticizing deviation.

[0027] S500 superimposes and fits the basic plasticizing state point and the target plasticizing deviation to obtain the target plasticizing state point of the melt; whereby the target plasticizing state point represents the optimal plasticizing position that is suitable for the current material and working conditions.

[0028] The S600 adjusts the melt plasticizing process parameters based on the target plasticizing state point to melt and plasticize the material, and obtains plastic particles after extrusion, granulation, and cooling. The melt plasticizing process parameters include screw speed, barrel segment temperature, feeding rate, and plasticizing time domain allocation strategy.

[0029] Material characteristic information refers to the set of inherent physicochemical properties of the material currently entering the plasticizing unit, which can be detected online or offline, such as the material's viscosity-temperature sensitivity index, filler dispersion, and melt flow index. This information can be obtained through near-infrared spectroscopy, online rheometer detection, or offline analysis based on the previous production batch. The predicted plasticizing state point is a multi-dimensional coordinate quantity (containing at least the axial position coordinate Z_pred, and may also include the radial position coordinate R_pred). It describes, under ideal conditions ignoring equipment interference, the optimal melt front position or fully homogenized position that the material should reach within the plasticizing unit based on its own melting, mixing, and homogenization kinetics. For example, for materials with high viscosity and poor thermal conductivity, the predicted point will be significantly shifted towards the screw end in exchange for longer heat conduction time and stronger shear work input.

[0030] The basic process data for melt plasticizing is the theoretical or calibration parameter set of the equipment operating under standard conditions (such as no material fluctuations and constant environment). The plasticizing temperature cycle refers to the change in the temperature distribution curve along the screw axis over time; the screw operating cycle refers to the time window for one rotation of the screw or the completion of one plasticizing stroke; the starting position of the melt plasticizing stage usually corresponds to the end of the feeding stage and the beginning of the compression stage (axial coordinate Z_start), and the ending position of the melt plasticizing stage usually corresponds to the end of the melting homogenization stage and the position in front of the die head (axial coordinate Z_end). The basic plasticizing state point Z_base(t) can be calculated based on the ideal model that "plasticizing progress is proportional to time," for example, using linear interpolation: Z_base(t) = Z_start + (t / T_total) × (Z_end - Z_start), where T_total is the preset calibration time required for the melt to move from the start to the end. This point represents the position where the equipment believes the melt should be under indistinguishable conditions.

[0031] The initial plasticizing deviation Δ0 = Z_pred - Z_base (axial). This initial plasticizing deviation quantifies the mismatch between the material's inherent requirements and the equipment's preset output. If Δ0 is positive, it indicates that the material requires a longer plasticizing stroke than the equipment defaults (i.e., the material is more difficult to plasticize), necessitating an increase in screw speed or temperature. If Δ0 is negative, it indicates that the material is easier to plasticize than preset, requiring a slower processing speed or lower temperature to prevent over-plasticization and degradation. However, this initial deviation contains spurious components from inherent inter-stage coupling interference and real-time operating condition fluctuations. Direct use of this deviation may lead to over- or mis-control.

[0032] The topological coupling relationship in the plasticizing section refers to the complex network of interactions between the feeding section, compression section, and melt homogenization section within the plasticizing unit. For example, increased pressure in the compression section can affect the conveying efficiency of the feeding section through material backflow (pressure coupling); temperature fluctuations in the melt homogenization section can negatively impact the softening degree of the material in the compression section through heat conduction in the metal barrel (thermal coupling); and fluctuations in the feeding section's conveying rate can cause a shift in the time it takes for the material to reach the melt homogenization section (temporal coupling). Real-time operating fluctuations include temperature fluctuations in each section of the barrel around the set value, screw drive motor speed fluctuations, and die head pressure fluctuations.

[0033] Superposition fitting refers to the synthesis of spatial coordinates between the base plasticizing state point (equipment baseline) and the target plasticizing deviation (actual material requirements). Specifically, Z_target = Z_base + ΔZ_target, R_target = R_base + ΔR_target. It is important to note that the superposition result is not a simple addition; it also needs to be time-series fitted and calibrated using a low-pass filter or trend predictor to ensure a smooth and abrupt trajectory for the generated target point. The final target plasticizing state point is an optimal control objective that simultaneously considers the inherent characteristics of the material (represented by Δ_target) and the current operating baseline of the equipment (represented by Z_base).

[0034] The adjustment of melt plasticizing process parameters is based on closed-loop negative feedback between the target point and the actual detection point. For example, when Z_target is greater than the currently detected melt front position Z_actual, it indicates that plasticizing is lagging. The control strategy can be to increase the screw speed to increase the material conveying speed, while appropriately increasing the barrel temperature of the compression and melting homogenization sections to accelerate melting, and fine-tuning the feed rate to match the speed; conversely, the opposite operation is performed. The plasticizing time-domain allocation strategy refers to the rational distribution of limited heat and shear work to different stages within a single plasticizing cycle (e.g., allocating more shear during the feeding period and more heat during the melting period). By adjusting the target point, the duration of each stage can be indirectly controlled.

[0035] As can be seen from the above, the plastic particle preparation method provided in this application determines the predicted plasticizing state point, representing the ideal plasticizing position of the melt based on its own material characteristics, based on the material characteristic information of the current feed. Then, based on the basic process data of melt plasticizing, including the plasticizing temperature cycle, screw running cycle, melt plasticizing start section position, and melt plasticizing end section position, it determines the basic plasticizing state point, representing the reference plasticizing position of the melt at the current plasticizing moment when the characterization equipment of the melt is running according to the preset process. Next, it determines the initial plasticizing deviation, which is the difference between the original position of the ideal plasticizing position and the reference plasticizing position, between the predicted plasticizing state point and the basic plasticizing state point. Subsequently, it performs process correction on the initial plasticizing deviation based on the topological coupling relationship of the plasticizing section of the melt plasticizing unit and the real-time operating condition fluctuations to obtain the target plasticizing deviation. Finally, it sets the basic plasticizing state... By superimposing and fitting the target plasticization deviation with the location point, the optimal plasticization position for the current material and working conditions is obtained. Finally, based on this target plasticization position, the screw speed, barrel segment temperature, feeding rate, and plasticization time domain allocation strategy are adjusted to complete the material melting and plasticization, and then extrusion, granulation, and cooling to obtain plastic particles. This method effectively eliminates the adverse effects of inter-equipment coupling interference and working condition fluctuations through full-process location calculation, deviation correction, and parameter linkage control. It accurately matches the processing characteristics of different materials and real-time production status, significantly improves the plasticization position control accuracy, and makes the material melting and mixing process more uniform and stable. It effectively avoids problems such as insufficient plasticization, material degradation, and filler agglomeration, and significantly improves the quality consistency, comprehensive performance, and stability and controllability of the finished plastic particles.

[0036] In one possible implementation, the material characteristic information includes the material viscosity gradient and the material dispersion uniformity. In step S100, based on the material characteristic information of the current feed, the predicted plasticizing state point of the melt at the current plasticizing moment is determined, including: S110, determine the theoretical plasticizing section of the melt at the current plasticizing moment based on the material viscosity gradient in the material characteristic information of the current feed.

[0037] It is understood that the viscosity gradient of a material refers to the derivative of the material's viscosity with respect to temperature or shear rate, commonly represented by dη / dT or dη / dγ̇. The magnitude of the viscosity gradient directly determines the material's sensitivity to heat and shear. In this application, the focus is primarily on the overall compounded feed mixture, specifically a complete melt feed system formed by polyetherketoneketone (PEKK) as the base resin, compounded polyarylene ether nitrile, one-dimensional aluminum nitride whiskers, hydroxylated boron nitride nanosheets, siloxane-arylene ether-grafted polyetherketoneketone compatibilizer, high-temperature antioxidant, and fluorinated lubricant, rather than a single resin component. This viscosity gradient characterizes the rate at which the viscosity of the overall compounded feed mixture changes with temperature and shear rate under melt plasticizing conditions, directly determining the required plasticizing zone length and shear matching requirements of the melt. The viscosity gradient of a material can be obtained through a combination of real-time acquisition by an online rheometer and offline calibration by a laboratory capillary rheometer. For the overall compounded material currently being fed, multiple sets of viscosity-temperature and viscosity-shear rate data are collected within the standard operating temperature range and shear rate range of melting and plasticizing. The derivatives of the material viscosity with temperature (dη / dT) and with shear rate (dη / dγ̇) are obtained through differential calculation or linear fitting. The viscosity gradient is determined by comprehensive weighting. The weight allocation can be set according to the dominant factors of the melting and plasticizing conditions. For the high-temperature melting characteristics of the polyether ketone ketone-based compound material of this application, the weight of the derivative of viscosity with temperature (dη / dT) is set to 0.6~0.8, and the weight of the derivative of viscosity with shear rate (dη / dγ̇) is set to 0.2~0.4. Based on the magnitude of this viscosity gradient, the plasticizing difficulty of the melt can be accurately determined, thereby matching the theoretical plasticizing zone of the corresponding feeding section, compression section, or melting homogenization section.

[0038] A specific method for determining the theoretical plasticizing zone can be as follows: First, measure the material viscosity data at different temperatures and shear rates using offline rheological experiments or an online viscometer, and establish a mapping database between the viscosity gradient and the offset of the plasticizing zone. Then, during production, based on the online detected material viscosity gradient values, match or interpolate the corresponding theoretical plasticizing zone position from the database, for example, using the interval [Z_start_pred, Z_end_pred].

[0039] S120, determine the ideal plasticizing level of the melt at the current plasticizing moment based on the material dispersion uniformity in the material characteristic information of the current feed.

[0040] It can be understood that material dispersion uniformity refers to the degree of microscopic mixing uniformity of dispersed phase components such as one-dimensional aluminum nitride whiskers, hydroxylated boron nitride nanosheets, compatibilizers, and high-temperature antioxidants in the polyether ketone ketone resin matrix of the current overall compounded material. It is usually quantitatively characterized by the uniformity of particle size distribution of dispersed phase particles, the proportion of agglomerates, and the surface distribution uniformity coefficient. Material dispersion uniformity can be obtained by combining real-time monitoring with an online microscopic imaging system, offline laser particle size analyzer sieving and statistics, and scanning electron microscopy (SEM) + energy dispersive spectroscopy (EDS) surface distribution calibration: for molten melt sampling, online microscopic imaging is used to capture the microscopic dispersion state inside the melt, the particle size dispersion coefficient of dispersed phase particles is statistically analyzed with a laser particle size analyzer, and the surface distribution uniformity of filler elements is obtained by combining SEM and EDS. The material dispersion uniformity level is determined by combining the three test results. The higher the dispersion uniformity, the lower the required shear mixing strength of the melt, which can match a more precise plasticizing station with gentler shear action. The lower the dispersion uniformity, the more strong shear mixing station is required to improve the dispersion state. Materials with poor dispersion (e.g., containing a large number of unopened inorganic nanoagglomerates) require stronger shear mixing and longer residence time in the plasticizing unit to break up the agglomerates and distribute them evenly into the matrix. Therefore, the ideal plasticizing position, i.e., the screw axial position required to achieve the target dispersion (e.g., more than 90% of particles with a diameter of less than 1 μm), will be significantly further back, possibly even extending into the latter half of the melt homogenization section.

[0041] One method for determining the ideal plasticizing level is as follows: On a laboratory extruder, for samples of the same material with different initial dispersions, samples are taken at different screw positions using the "shutdown quenching" method. The dispersion is measured using image analysis or a particle size analyzer, and a "dispersion-plasticizing level" correspondence table is established. In actual production, the uniformity of the feed dispersion is evaluated in real time using online optical detection or regression based on historical data. Then, the corresponding ideal plasticizing level coordinates (single value, such as Z_ideal) are obtained by looking up the table.

[0042] S130, based on the theoretical plasticizing zone and the ideal plasticizing level, determines the predicted plasticizing state point of the melt at the current plasticizing moment.

[0043] It is understandable that when combining the theoretical plasticizing zone (an interval) and the ideal plasticizing level (a single point), a weighted average or intersection method can be used. For example, the predicted plasticizing state point Z_pred can be chosen as the weighted average of the midpoint of the theoretical plasticizing zone and the ideal plasticizing level, ensuring that Z_pred falls within the theoretical plasticizing zone. Even better, the ideal plasticizing level can be given a higher weight, as dispersion is a key indicator determining the mechanical properties of the final product. The radial coordinate R_pred can be used to estimate the melt pool depth based on the material dispersion uniformity. The worse the dispersion, the stronger the radial mixing required, and the ideal radial position should be closer to the screw thread gap (i.e., a smaller radial coordinate).

[0044] With this setup, the viscosity gradient primarily influences the melt kinetics under thermal and shear forces (macroscale), while dispersion uniformity mainly affects the mixing and distribution kinetics of the dispersed phase components (mesoscale / microscale). Decoupling and then integrating these two parameters allows for a more precise description of the personalized plasticizing requirements of multi-component, multi-phase materials. The resulting predicted plasticizing state point is no longer a vague, empirically guessed value, but a quantitative indicator with a clear physical origin, traceability, and interpretability. This significantly enhances the ability to proactively control the plasticizing behavior of different batches and formulations of materials, providing high-quality feedforward input for subsequent deviation calculations and process corrections.

[0045] In one possible implementation, in step S200, based on the basic melt plasticizing process data, the basic plasticizing state point of the melt at the current plasticizing moment is determined, including: Based on the synchronous timing of the plasticizing temperature cycle and the screw running cycle, and combined with the axial interval of the starting and ending points of the melt plasticizing, the basic plasticizing state point of the melt at the current plasticizing moment is obtained through time-series linear fitting calculation.

[0046] It is understandable that the plasticizing temperature cycle does not simply refer to the static set temperature of each section of the barrel, but rather to the periodic change of the temperature distribution curve along the screw axis over time under normal operating conditions. In reality, because the screw rotation continuously conveys the material forward, the barrel heating system needs dynamic adjustment to maintain stable temperatures in each section. However, macroscopically, the temperature distribution can be considered statistically steady-state; therefore, the temperature cycle can be viewed as a distribution function T(z) that is spatially related but time-independent. The screw operating cycle is related to the screw's rotational speed, typically referring to the time required for one complete rotation, or more broadly, the preset calibration time T_cycle for the material to travel from the inlet to the die head. Synchronization timing refers to aligning the spatial distribution of the temperature field with the screw's conveying time: assuming that at time t=0, the material enters from the melt plasticizing start section (Z_start), then after time t, the position the material should reach under ideal no-slip conditions is z=Z_start+(v×t), where v is the preset conveying speed. The starting position of the melt plasticizing stage, Z_start, is typically set at the junction of the end of the feeding stage and the beginning of the compression stage, where the material begins to melt significantly. The ending position of the melt plasticizing stage, Z_end, is set at the end of the melting and homogenizing stage (i.e., before the die head connector), where the material should have completed melting and homogenization and is ready to enter the mold. The core assumption of the time-series linear fitting calculation is that, under standard, undisturbed basic processes, the plasticizing progress of the material is linearly related to time, and the conveying speed is constant. Therefore, for the current plasticizing time t (0≤t≤T_cycle), the axial coordinate Z_base(t) of the basic plasticizing state point can be calculated using the linear interpolation formula: Z_base(t) = Z_start + (t / T_cycle) × (Z_end - Z_start). If the radial coordinate R_base(t) is required, the median value of the screw groove depth is usually taken (i.e., assuming the melt flows in the center of the screw groove).

[0047] This setup quantifies the abstract plasticizing progress into basic plasticizing state points and uses linear fitting of the synchronization between the plasticizing temperature cycle and the screw running cycle to establish a clear, definite, and calculable reference benchmark for subsequent deviation calculations. The basic plasticizing state points are entirely based on the equipment's standard process parameters (temperature distribution, screw speed, start / end position), without introducing any real-time measurement noise or delay, ensuring the stability and purity of the benchmark and reducing control jitter caused by using a noisy benchmark to measure deviations. Automatically linking the screw running cycle T_cycle with the feeding rate and screw channel volume allows the benchmark to automatically scale with changes in output, exhibiting strong adaptability and meeting the plasticizing control needs under different production loads.

[0048] In one possible implementation, in step S400, the initial plasticizing deviation is corrected based on the topological coupling relationship of the plasticizing section of the melt plasticizing unit and real-time operating condition fluctuations to obtain the target plasticizing deviation, including: S410 identifies the coupling interference intensity between the feeding section, compression section, and melting homogenization section in the melting plasticizing unit based on the topological coupling relationship of the plasticizing section in the melting plasticizing unit.

[0049] Understandable, such as Figure 2 As shown, the pressure increase in the compression section is transmitted to the feeding section through the backflow of the solid bed or melt pool, reducing the conveying efficiency of the feeding section; this is a pressure-conveyance coupling. The temperature increase in the melting and homogenizing section heats the compression section in the opposite direction through axial heat conduction from the barrel wall, causing premature softening of the material and potentially leading to bridging or circulation; this is a heat-phase change coupling. Fluctuations in the feed rate in the feeding section cause changes in the material bulk density in the compression section, which in turn affects the pressure fluctuations in the melting and homogenizing section; this is a time-pressure coupling. The coupling interference strength is a quantitative indicator characterizing the severity of this inter-section interaction. It can be represented by the dimensionless coupling coefficient K_couple, typically ranging from 0 to 2 (0 indicating no coupling, 2 indicating extremely strong coupling). Identification methods can include: based on the equipment's structural parameters (screw configuration, barrel slotting) and operating parameters, establishing a functional relationship between the coupling coefficient and operating conditions through finite element simulation or regression analysis. For example, at high screw speeds, the material is greatly affected by shear heat, significantly increasing the thermal coupling strength; at high feed rates, the pressure coupling strength increases.

[0050] For example, based on the topological coupling relationship of the plasticizing sections in the melting and plasticizing unit, the temperature conduction difference, pressure interference value, and material conveying timing misalignment between each plasticizing section can be obtained. Then, based on the temperature conduction difference, pressure interference value, and material conveying timing misalignment, the coupling interference level between the feeding section, compression section, and melting homogenization section in the melting and plasticizing unit can be identified, and the corresponding coupling interference intensity can be obtained based on the coupling interference level. Alternatively, real-time operating parameters of each plasticizing section can be collected synchronously, and the coupling interference intensity can be directly calculated through the correlation of fluctuations in parameters of adjacent sections. Specifically, real-time data collection of parameters from the feeding section, compression section, and melting homogenization section can be performed. The system calculates the fluctuation compatibility of corresponding parameters in adjacent sections based on temperature, pressure, and material conveying rate data. It then weights the temperature fluctuation compatibility, pressure fluctuation compatibility, and conveying rate fluctuation compatibility according to preset weights, directly outputting the real-time value of the coupling interference intensity. The preset weights can be determined based on the dominant influencing factors of inter-section coupling in the melt plasticizing process. For the high-temperature plasticizing characteristics of the polyether ketone ketone-based compound material in this application, for example, the weight of temperature fluctuation compatibility is set to 0.5~0.7, the weight of pressure fluctuation compatibility is set to 0.2~0.3, and the weight of conveying rate fluctuation compatibility is set to 0.1~0.2.

[0051] In a possible implementation, in step S410, based on the topological coupling relationship of the plasticizing sections of the melt plasticization unit, identifying the coupling interference intensity among the feeding section, the compression section and the melt homogenization section in the melt plasticization unit comprises: S411, obtaining a temperature conduction difference value, a pressure interference value and a material conveying timing错位量 among all plasticizing sections based on the topological coupling relationship of the plasticizing sections of the melt plasticization unit.

[0052] It can be understood that the temperature conduction difference value refers to an influence difference value of a temperature change of one plasticizing section on the temperature of an adjacent or nearby plasticizing section caused by heat conduction of the metal barrel. For example, the temperature set for the melt homogenization section (adjacent to the head) is generally the highest, such as 300°C, while the temperature of the feeding section (adjacent to the hopper) is the lowest, such as 200°C. However, when the temperature of the melt homogenization section rises by 10°C due to overload, this part of heat will be transferred to the compression section and even the feeding section through the continuous metal barrel, causing the actual temperature of these sections to deviate from the set value. The temperature conduction difference value can be obtained by solving the steady-state solution of a one-dimensional or two-dimensional heat conduction equation, or the transfer function coefficient can be obtained through experimental tests on the equipment. The pressure interference value refers to an influence amount of pressure fluctuation of one plasticizing section on the pressure of upstream and downstream sections. In the melt plasticization unit, the pressure is distributed in a gradient along the axial direction of the screw, the pressure in the compression section is the highest, and the pressures in the feeding section and the melt homogenization section are relatively low. When a pressure pulse ΔP_comp is generated in the compression section due to material accumulation, the pulse will travel to the upstream and downstream at the speed of sound (about 1000-1500m / s in the melt), causing the pressure in the feeding section to rise by k_fp×ΔP_comp and the pressure in the melt homogenization section to rise by k_mp×ΔP_comp, wherein k_fp and k_mp are pressure transfer coefficients (0<k<1). The pressure interference values are ΔP_comp×k_fp and ΔP_comp×k_mp. The material conveying timing offset refers to the offset between the actual time when the material arrives at a certain position and the time predicted by the ideal linear model caused by the mismatched conveying speed among all sections. For example, the increase of pressure in the compression section causes the delay of solid bed breaking, and the residence time of the material in this section is prolonged, which further causes the time when the material arrives at the melt homogenization section to be δt seconds later than the expected time. δt is the timing offset, which can be obtained by installing a plurality of pressure or temperature sensors on the screw and calculating the time delay of signals through cross-correlation analysis.

[0053] S412, identifying the coupling interference level among the feeding section, the compression section and the melt homogenization section in the melt plasticization unit according to the temperature conduction difference value, the pressure interference value and the material conveying timing offset, and obtaining the corresponding coupling interference intensity based on the coupling interference level.

[0054] Understandably, each indicator is first normalized: for example, the temperature conduction difference is normalized to ΔT_norm = ΔT / ΔT_max (ΔT_max is the maximum allowable conduction temperature difference), the pressure interference value is normalized to ΔP_norm = ΔP_interference / ΔP_max, and the timing misalignment is normalized to δt_norm = |δt| / δt_max. Then, the comprehensive coupling index C_index is calculated as C_index = w_T × ΔT_norm + w_P × ΔP_norm + w_t × δt_norm, with the weights summed to 1. Coupling interference levels are determined based on the range of C_index values: for example, 0 ≤ C_index < 0.3 is low level, 0.3 ≤ C_index < 0.7 is medium level, and 0.7 ≤ C_index ≤ 1 is high level. Each level corresponds to a preset coupling interference strength coefficient K_couple: low level corresponds to K_couple = 0.2, medium level corresponds to K_couple = 0.5, and high level corresponds to K_couple = 0.9. Alternatively, a continuous function mapping can be used: K_couple=C_index^γ, where γ is the exponent (γ>1 to suppress high coupling, γ<1 to emphasize high coupling).

[0055] In this configuration, temperature, pressure, and time are the three core dimensions describing the plasticizing dynamics, and the coupling between these three is the main source of nonlinearity in the plasticizing process. By quantifying these three couplings separately, it is possible to more accurately identify whether thermal coupling, pressure coupling, or time coupling is dominant, thus providing a more detailed basis for subsequent compensation calibration (for example, if time misalignment is found to be the main contributor, the matching of feed rate and rotation speed can be adjusted first, rather than blindly changing the temperature). Furthermore, by calculating the comprehensive coupling index and classifying it into levels, multidimensional information is integrated into an intuitive strength coefficient that is easy to use in the control algorithm.

[0056] S420 obtains the amplitude of operating condition fluctuations based on real-time operating condition fluctuations.

[0057] Real-time operating condition fluctuations can be understood as random or quasi-periodic fluctuations arising from uncertainties in equipment, environment, or materials, based on fundamental process data. Specifically, these include: dynamic deviations between the actual temperature and setpoint of each section of the barrel (usually caused by PID control overshoot, ambient temperature changes, or heater aging); instantaneous deviations between the actual screw speed and the set speed (caused by response lag in the motor speed control system or sudden load changes); and internal pressure fluctuations in the barrel (caused by uneven feeding, melt pump pulsation, or filter blockage). The amplitude of operating condition fluctuations is a scalar value resulting from the comprehensive quantification of these multi-source fluctuations.

[0058] For example, based on the real-time fluctuations of the barrel segment temperature, screw operating pressure, and screw speed, the deviation ratio between the real-time fluctuation difference of each operating parameter and the benchmark parameter can be calculated, and the fluctuation amplitude of the operating conditions within the current plasticizing cycle can be obtained by integration. Alternatively, a normalized weighted fusion method can be used to directly calculate the comprehensive fluctuation amplitude of the operating conditions. Specifically, the barrel segment temperature fluctuation, screw operating pressure fluctuation, and screw speed fluctuation are first normalized to a dimensionless range of 0 to 1 to eliminate the differences in dimensions and numerical magnitudes of different parameters. Then, weights are assigned based on the operating characteristics of the polyether ketone ketone-based material in this application at high temperatures. For example, the weight of temperature fluctuation can be set to 0.6, the weight of pressure fluctuation to 0.3, and the weight of speed fluctuation to 0.1. Finally, the normalized fluctuation values ​​are multiplied by their corresponding weights and summed to directly obtain the fluctuation amplitude of the operating conditions within the current plasticizing cycle.

[0059] In one possible implementation, such as Figure 3 As shown, in step S420, the amplitude of the operating condition fluctuation is obtained based on the real-time operating condition fluctuation, including: Based on the real-time fluctuations of barrel segment temperature, screw operating pressure, and screw speed, the deviation ratio between the real-time fluctuation difference of each operating parameter and the reference parameter is calculated, and the fluctuation amplitude of the operating condition within the current plasticizing cycle is obtained by integrating them.

[0060] It can be understood that the segmented temperature fluctuation value of the barrel refers to the instantaneous deviation between the actual measured temperature and the corresponding set temperature of each segment along the barrel axis (such as the feeding section, compression section, melting section, and homogenization section). Since the barrel is typically divided into 4-8 independent heating zones, each zone has a thermocouple for feedback. To quantify the overall temperature fluctuation, a simple average cannot be used because the influence of different segments on the plasticizing process has different weights: the temperature fluctuation in the melting section has the greatest impact on melt quality, while the impact in the feeding section is relatively smaller. Therefore, the percentage of temperature deviation for each segment can be calculated first: δT_i = (T_actual_i - T_set_i) / T_set_i (i = 1..n). Then, a weighted average or weighted root mean square is taken for δT_i of each segment. The weighting coefficient w_Ti is set according to the degree of influence of that segment on the plasticizing quality; for example, the weight is 0.1 for the feeding section, 0.2 for the compression section, 0.4 for the melting section, and 0.3 for the homogenization section. The final comprehensive temperature fluctuation index ΔT_total = sqrt(Σ(w_Ti × (δT_i)^2)) is obtained. The screw operating pressure fluctuation value refers to the fluctuation of measurements from multiple pressure sensors inside the barrel (usually located in the compression and melting sections). Pressure fluctuation is a key indicator reflecting feeding stability and melt delivery uniformity. First, the pressure deviation percentage of each pressure monitoring point is calculated: δP_j = (P_actual_j - P_base_j) / P_base_j, where P_base_j is the reference pressure at that point (obtainable through calibration). Then, a weighted integration is performed, with the weight w_Pj set according to the importance of the sensor location (pressure fluctuation in the compression section has the greatest impact on plasticization, so a weight of 0.6 can be set, and 0.4 for the melting section). The comprehensive pressure fluctuation index ΔP_total = sqrt(Σ(w_Pj × (δP_j)^2)) is obtained. The screw speed fluctuation value is directly taken from the encoder feedback of the drive motor. The speed deviation percentage is calculated: δN = (N_actual - N_set) / N_set. N_actual is the instantaneous rotational speed, and N_set is the set rotational speed. Since the plasticizing process is highly sensitive to rotational speed fluctuations (affecting shear heat and residence time), the weight of δN can be set to a relatively high value. Finally, the fluctuation range of the operating conditions within the current plasticizing cycle, F_disturb, is obtained by integration. The integration formula can use a weighted Euclidean norm: F_disturb = sqrt(W_T × (ΔT_total)^2 + W_P × (ΔP_total)^2 + W_N × (δN)^2). Where W_T, W_P, and W_N are weighting coefficients, and W_T + W_P + W_N = 1. These weighting coefficients reflect the sensitivity of different operating condition fluctuations to the plasticizing quality and can be obtained through Design of Experiments (DoE) or historical data analysis. For example, for heat-sensitive materials, W_T should be a larger value; for shear-sensitive materials, W_N should be a larger value. F_disturb is a dimensionless number between 0 and 1; a larger value indicates a more unstable current operating condition.

[0061] This setup, by calculating the real-time fluctuation difference between the three core operating parameters—cylinder segment temperature, screw operating pressure, and screw speed—and the deviation ratio of these parameters from the baseline parameters, and integrating these deviation ratios to obtain the operating condition fluctuation amplitude within the current plasticizing cycle, can comprehensively, accurately, and quantitatively reflect the actual fluctuation degree of the real-time operating conditions during the melting and plasticizing process. This allows deviation correction to effectively offset the plasticizing position shift caused by operating condition fluctuations, improving the calculation accuracy and control adaptability of the target plasticizing deviation.

[0062] S430, based on the coupling interference intensity and the fluctuation amplitude of the operating conditions, compensates and calibrates the initial plasticization deviation to obtain the target plasticization deviation after removing the interference.

[0063] It is understandable that the initial plasticizing deviation is the original difference obtained by directly subtracting the ideal plasticizing position of the material from the equipment's reference plasticizing position. This difference does not fully reflect the actual plasticizing position requirement of the material. It contains two types of interference components that cannot be eliminated through basic calculations. One type is the inherent topological coupling interference component between the feeding section, compression section, and melting homogenization section of the melt plasticizing unit. The other type is the interference component generated by the random fluctuations of real-time operating conditions such as barrel temperature, screw pressure, and screw speed. These two types of components will cause significant distortion and deviation in the initial plasticizing deviation. If it is directly used for subsequent point fitting, it will directly cause the problem of incorrect or over-controlled process parameters, which in turn leads to a series of quality defects such as insufficient melt plasticizing, over-degradation, and filler agglomeration. The core implementation process of compensation calibration is to use the coupling interference intensity identified in the early stage and the calculated operating condition fluctuation amplitude as dual correction basis. First, a corresponding mapping relationship between coupling interference and deviation compensation is established to offset the position deviation caused by the interaction between inherent equipment sections. Then, a dynamic correction relationship between operating condition fluctuation and deviation compensation is established to offset the position deviation caused by real-time operation fluctuation. Through two-step calibration, invalid and false components in the initial deviation are eliminated layer by layer, and only the effective deviation values ​​that can truly reflect the plasticization requirements of the material itself are retained. The final target plasticization deviation is a pure deviation value without equipment interference and operating condition fluctuation, which can be directly used as the core basis for the superposition and fitting of basic plasticization state points, and perfectly matches the actual plasticization position requirements of the material under the current production conditions.

[0064] For example, a fixed deviation compensation coefficient can be matched based on the coupling interference strength, and then the fixed deviation compensation coefficient can be finely adjusted based on the operating condition fluctuation amplitude to obtain a real-time compensation coefficient. Then, based on the real-time compensation coefficient, the initial plasticizing deviation can be compensated and calibrated to obtain the target plasticizing deviation after removing the interference. Alternatively, a real-time fusion compensation model adapted to the high-temperature plasticizing characteristics of polyether ketone ketone-based compound materials can be pre-constructed, using the coupling interference strength (including the three-dimensional quantification results of thermal coupling, pressure coupling, and temporal coupling) and the operating condition fluctuation amplitude (including the proportion of three-dimensional fluctuations in temperature, pressure, and speed) as the model's dual core input parameters. Based on the plasticizing process mechanism and a large amount of experimental calibration data, a nonlinear mapping relationship is set for the model with a coupling interference weight of 0.6 and a working condition fluctuation weight of 0.4. The model has a built-in deviation component stripping algorithm, which can simultaneously identify the static inherent deviation component caused by equipment topology coupling and the dynamic random deviation component caused by real-time working condition fluctuation in the initial plasticizing deviation. After the initial plasticizing deviation is directly input into the fusion model, the model does not need to perform step-by-step calculations and can complete the accurate stripping and elimination of the two types of false deviation components in one go, directly outputting only the target plasticizing deviation that retains the true plasticizing requirements of the material, and so on, but not limited to this.

[0065] This setup, through a dual compensation calibration mechanism that couples interference intensity and operating condition fluctuation amplitude, fundamentally eliminates the interference of inherent structural defects in the equipment and real-time production environment fluctuations on plasticizing deviation. It completely solves the technical problems of distorted deviation calculations and inaccurate control basis in traditional processes, significantly improving the accuracy and reliability of plasticizing deviation calculations. The calibrated target plasticizing deviation accurately and objectively reflects the true difference between the ideal plasticizing position of the material and the equipment's reference position, providing unique, reliable, and accurate core data support for the subsequent superposition and fitting of target plasticizing state points. This reduces the problems of misadjustment and arbitrary adjustment of process parameters caused by false deviations. Furthermore, this compensation calibration process does not require additional complex hardware; it can be achieved solely through data computation, adapting to the real-time requirements of continuous industrial production. It does not increase production cycle time or costs, and effectively reduces the defect rate caused by control deviations, improving production stability and finished product qualification rate. This lays a core foundation for precise control of the entire melting and plasticizing process.

[0066] In one possible implementation, in step S430, the initial plasticizing deviation is compensated and calibrated based on the coupling interference strength and the operating condition fluctuation amplitude to obtain the target plasticizing deviation after removing the interference, including: S431 is a fixed deviation compensation coefficient based on the matching of coupling interference strength.

[0067] It is understandable that the fixed deviation compensation coefficient is a constant value obtained in advance through extensive laboratory calibration and industrial mass production testing. It is used to counteract the inherent inter-segment topological coupling interference in the melting and plasticizing unit. There is a one-to-one quantitative mapping relationship between the fixed deviation compensation coefficient and the intensity of coupling interference. In the implementation process, the coupling interference has been divided into three levels: low, medium, and high, based on the temperature conduction difference, pressure interference value, and material conveying timing misalignment. Each level corresponds to a unique fixed deviation compensation coefficient. The greater the intensity of coupling interference, the larger the corresponding fixed deviation compensation coefficient value, and the stronger the compensation and offsetting effect. For example, low coupling interference intensity corresponds to a fixed compensation coefficient of 0.2, medium coupling interference intensity corresponds to 0.5, and high coupling interference intensity corresponds to 0.9.

[0068] S432, based on the fluctuation amplitude of the operating conditions, fine-tunes the fixed deviation compensation coefficient to obtain the real-time compensation coefficient.

[0069] It is understandable that a fixed deviation compensation coefficient can only offset the inherent coupling interference of the equipment and cannot adapt to the random fluctuations of real-time operating conditions such as barrel temperature, screw pressure, and screw speed. Since real-time operating condition fluctuations are dynamic and continuously generate new deviation interference components, the fixed deviation compensation coefficient is dynamically fine-tuned based on the amplitude of the operating condition fluctuations, transforming it from a fixed value into a dynamic real-time compensation coefficient that adapts to the current real-time operating conditions. The fine-tuning process is based on the amplitude of the operating condition fluctuations, which is a dimensionless value obtained by integrating the deviation proportions of three parameters: temperature, pressure, and speed. The larger the value, the more severe the operating condition fluctuations, and the larger the required fine-tuning amplitude. A proportional fine-tuning algorithm is used, weighting the operating condition fluctuation amplitude with the fixed compensation coefficient. Small-amplitude fine-tuning is performed when the fluctuation amplitude is in the range of 0 to 0.3, medium-amplitude fine-tuning is performed in the range of 0.3 to 0.7, and large-amplitude fine-tuning is performed in the range of 0.7 to 1. Throughout the fine-tuning process, the compensation coefficient is always kept within a reasonable range.

[0070] S433, based on the real-time compensation coefficient, compensates and calibrates the initial plasticizing deviation to obtain the target plasticizing deviation after eliminating interference.

[0071] The core process of compensation calibration is to perform standardized arithmetic operations on the initial plasticizing deviation and the real-time compensation coefficient. The calculation logic precisely matches the positive and negative directions of the deviation with the values ​​of the compensation coefficients. When the initial plasticizing deviation is positive, it means that the material requires a longer plasticizing stroke. The positive deviation component caused by coupling and operating condition interference is deducted by the real-time compensation coefficient. When the initial plasticizing deviation is negative, it means that the material is easier to plasticize. The negative deviation component caused by coupling and operating condition interference is deducted by the real-time compensation coefficient. During the calculation, the real-time compensation coefficient plays a dual role in offsetting coupling interference and operating condition fluctuations, eliminating all false and invalid components mixed in the initial deviation, and retaining only the true plasticizing position deviation that reflects the material's own viscosity and dispersion characteristics. The target plasticizing deviation obtained after calibration is a pure deviation value without equipment interference, operating condition fluctuations, or distortion offset. Its value and positive and negative directions completely correspond to the material's true plasticizing position requirements and can be directly superimposed and fitted to the basic plasticizing state points.

[0072] This setup first eliminates the systematic deviation caused by the inherent topological coupling between the feeding section, compression section, and melting homogenization section of the equipment by matching the corresponding fixed deviation compensation coefficient based on the coupling interference intensity. Then, it dynamically fine-tunes the fixed deviation compensation coefficient according to the fluctuation range of the operating conditions to adapt to the random deviation caused by the real-time changes in barrel temperature, screw pressure, and screw speed. This results in a real-time compensation coefficient that combines stability and adaptability. Finally, this real-time compensation coefficient is used to accurately compensate and calibrate the initial plasticizing deviation. This approach can comprehensively and reliably eliminate the false deviation components caused by equipment coupling interference and real-time operating condition fluctuations, obtaining the target plasticizing deviation that truly reflects the plasticizing requirements of the material itself, and significantly improving the accuracy of deviation calculation.

[0073] In one possible implementation, in step S500, the basic plasticizing state point and the target plasticizing deviation are superimposed and fitted to obtain the target plasticizing state point of the melt, including: S510 extracts the axial and radial coordinates of the basic plasticized state points.

[0074] It can be understood that the basic plasticizing state point is the reference plasticizing position under the preset process of the equipment. It is not a single axial value, but a two-dimensional spatial coordinate system composed of axial and radial position coordinates, which can completely characterize the precise spatial position of the melt within the melting and plasticizing unit. The axial position coordinate is the position value along the screw axis, corresponding to the range from the starting position of the melt plasticizing section (the junction of the feeding section and the compression section) to the ending position of the melt plasticizing section (the end of the melting homogenization section). It represents the reference position of the melt in the forward and backward direction of the screw and directly determines the plasticizing stroke and residence time of the melt. The radial position coordinate is the position value along the radial direction of the screw groove, corresponding to the range from the screw core to the inner wall of the barrel. It represents the reference position of the melt in the inward and outward direction of the screw groove and directly determines the shear mixing strength of the melt. The extraction process involves simultaneously retrieving the reference coordinate values ​​of the axial and radial directions from the time-series linear fitting calculation results. Both coordinates are dimensionless standardized values.

[0075] S520, superimpose the axial compensation amount corresponding to the target plasticization deviation with the axial position coordinate, and superimpose the axial compensation amount corresponding to the target plasticization deviation with the radial position coordinate to obtain the superimposed coordinate value.

[0076] It can be understood that the axial compensation amount is the compensation value for the target plasticizing deviation along the screw axis, used to correct the axial position of the base point to adapt to the material's plasticizing stroke and residence time requirements; the radial compensation amount is the compensation value for the target plasticizing deviation in the screw radial direction, used to correct the radial position of the base point to adapt to the material's shear mixing strength requirements. The superposition calculation uses standardized arithmetic addition: axial superposition coordinates = base axial coordinates + axial compensation amount; radial superposition coordinates = base radial coordinates + radial compensation amount.

[0077] S530, the superimposed coordinate values ​​are time-series fitted and calibrated to obtain the target plasticized state point of the melt.

[0078] It is understandable that the coordinate values ​​obtained from the superposition operation are instantaneous static values, which have slight numerical abrupt changes and jumps, and cannot be directly adapted to the continuous operation sequence of the melting and plasticizing unit. Therefore, time-series fitting calibration is required to transform the static superimposed coordinates into continuous, smooth, and dynamic target points that are adapted to the plasticizing cycle. The calibration process uses low-pass filtering and time-series linear smoothing algorithms. Taking the screw running cycle as the time reference, the superimposed coordinate values ​​are incorporated into the time axis of a single plasticizing cycle. Instantaneous abrupt changes and abnormal jumps in coordinate values ​​are eliminated, and continuous coordinate points are linearly smoothed to ensure that the coordinate trajectory changes smoothly with the plasticizing time without abrupt fluctuations. The calibrated target plasticizing state points are continuous, smooth, and directly executable dynamic spatial coordinates. Their trajectory is completely synchronized with the screw running cycle and the plasticizing temperature cycle, and can perfectly adapt to the operating rhythm of the equipment.

[0079] This setup, by extracting the axial and radial two-dimensional coordinates of the basic plasticizing state points, accurately superimposes the axial and radial compensation amounts corresponding to the target plasticizing deviations with the corresponding coordinates, and performs time-series fitting calibration on the superimposed coordinate values. This allows for the precise integration of the actual plasticizing requirements of the material into the equipment's reference position, achieving dual optimization matching of spatial position and time series. The resulting target plasticizing state points possess both equipment operational stability and material processing adaptability, with precise positioning, smooth trajectory, and no abrupt changes. They can be directly used as reliable control benchmarks, effectively improving the accuracy of melt plasticizing position control.

[0080] In one possible implementation, after step S500, where the basic plasticizing state point and the target plasticizing deviation are superimposed and fitted to obtain the target plasticizing state point of the melt, the method further includes: S540 limits the target plasticization state point to a preset range between the starting position and the ending position of melt plasticization.

[0081] It is understandable that the melt plasticizing initiation section is the boundary between the end of the feeding section and the beginning of the compression section, while the melt plasticizing endpoint section is the end of the melt homogenization section and the position in front of the die head. The interval between these two is the safe and effective plasticizing range of the melt plasticizing unit. It is the only range that, after equipment calibration and process verification, can achieve normal melting, mixing, and homogenization of materials. Exceeding this range will lead to the material failing to plasticize properly or equipment malfunction. The preset range values ​​are fixed ranges pre-calibrated based on screw configuration, processing technology, and material characteristics. They have strict boundary constraints, limiting the target plasticizing state point within this range. This is a prerequisite for ensuring the safety, effectiveness, and stability of the melt plasticizing process. The limiting process uses an interval boundary value comparison algorithm to compare the axial coordinates of the target point with the start and end coordinates of the interval in real time, ensuring that the point always remains within the interval.

[0082] S550: If the target plasticization state point is within the preset range, the point is determined to be valid and locked as the final control benchmark; if the target plasticization state point exceeds the preset range, the deviation is recalibrated based on the range boundary value, and the valid target plasticization state point is updated.

[0083] It is understandable that the target plasticizing state point is the optimal spatial position obtained by fitting the basic plasticizing state point and the target plasticizing deviation and then calibrating it over time. Theoretically, it can be directly used for process control. However, in actual continuous production, due to uncontrollable factors such as sudden changes in materials, drastic fluctuations in operating conditions, and instantaneous noise during data acquisition, the point value may exceed the reasonable range. Therefore, a secondary verification must be performed through an interval determination mechanism: when the target point falls within the preset safe interval between the starting and ending points of melt plasticization, it indicates that the point meets the requirements of equipment structure limitations, process operation rules, and material melting characteristics, and has no risk of runaway. In this case, the point is directly determined to be valid, and its coordinate values ​​are locked as the final control benchmark for this plasticization cycle, ensuring that subsequent adjustments to parameters such as screw speed, barrel segment temperature, feeding rate, and plasticization time domain allocation strategy are based on a unique, stable, and non-drifting basis. When the target point exceeds the preset interval, whether it is biased towards the starting or ending point, it means that the point has entered a dangerous area where normal plasticization cannot be achieved. If it continues to be used for control, it will directly lead to serious problems such as insufficient material plasticization, local overheating and degradation, packing breakage and agglomeration, and abnormal die head pressure. Therefore, the deviation recalibration process must be initiated immediately, using the interval boundary as a hard constraint condition, to correct the target plasticization deviation, and to perform superposition fitting and time-series calibration again until a valid target plasticization state point falling within the safe interval is generated.

[0084] This setup, by adding interval limits, validity judgment, and automatic recalibration mechanisms for the target plasticization state points after superposition and fitting, constructs a complete safety control closed loop from calculation, generation, verification to correction. It fundamentally avoids the risk of uncontrolled plasticization positions caused by data noise, sudden changes in operating conditions, or instantaneous model errors, and completely eliminates invalid or out-of-bounds points from entering the control process. By combining the optimal calculation point with the equipment's safety range, it retains the core advantages of this application in accurately matching material characteristics and eliminating operating condition interference in real time, while ensuring the reliability of equipment operation and the sufficiency of material plasticization through hard boundary constraints. This ensures that the final locked control benchmark simultaneously meets the three requirements of accuracy, safety, and stability.

[0085] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0086] The second aspect of this application provides a plastic particle prepared by the plastic particle preparation method described in any of the above embodiments. The formulation of the plastic particle includes the following components in parts by weight: 58-68 parts of polyetherketoneketone; 9-15 parts of polyaryletheronitrile; 7-12 parts of one-dimensional aluminum nitride whiskers; 6-10 parts of hydroxylated boron nitride nanosheets; 3-5 parts of siloxane-arylether grafted polyetherketoneketone compatibilizer; 0.3-0.7 parts of polyetherketoneketone-specific high-temperature composite antioxidant; and 0.4-0.8 parts of fluorine-containing high-temperature lubricant.

[0087] It can be understood that 58-68 parts of polyether ketone ketone refers to the weight of the unsaturated resin being 58-68 parts when the total weight of the raw materials is 100 parts. For example, it could be 58, 62, 68, etc., but it is not limited to this. Polyether ketone ketone-specific high-temperature composite antioxidants refer to high-temperature antioxidant systems composed of hindered phenols and phosphites. For example, it can be a composite system composed of pentaerythritol tetrakis[β-(3,5-di-tert-butyl-4-hydroxyphenyl)propionate] and tris(2,4-di-tert-butylphenyl) phosphite in a reasonable ratio, or a composite system composed of 2,2'-methylene bis(4-methyl-6-tert-butylphenol) and bis(2,4-di-tert-butylphenyl) pentaerythritol diphosphite, etc. Fluorinated high-temperature lubricants refer to perfluoropolyether-based additives, such as long-chain perfluoroalkyl polyether high-temperature lubricants, branched perfluoropolyether lubricant dispersants, etc.

[0088] This configuration uses 58-68 parts of polyetherketoneketone as the matrix resin, forming the core framework of the entire particle and providing excellent high-temperature resistance, mechanical strength, electrical insulation, and processing stability. 9-15 parts of polyaryletheronitrile synergistically enhance the high-temperature stability of the melt and reduce the risk of degradation during high-temperature processing. 7-12 parts of one-dimensional aluminum nitride whiskers and 6-10 parts of hydroxylated boron nitride nanosheets serve as nano-thermally conductive fillers, synergistically constructing continuous three-dimensional thermal conductivity pathways and significantly improving the thermal conductivity of the particles. 3-5 parts of a siloxane-arylether grafted polyetherketoneketone compatibilizer act as an interface modifier, improving the interaction between the inorganic nanofiller and the polyetherketoneketone resin. The interfacial bonding force of the ester matrix reduces filler agglomeration and improves dispersion uniformity; 0.3-0.7 parts of a high-temperature composite antioxidant for polyether ketone ketone is suitable for processing windows above 300℃ for PEKK, inhibiting high-temperature oxidative degradation of materials; 0.4-0.8 parts of a fluorinated high-temperature lubricant reduce the high-temperature friction coefficient between the melt and the screw and barrel, improving melt flowability. The resulting plastic particles have ultra-high heat resistance, high thermal conductivity, excellent mechanical properties and processing stability, with extremely low performance dispersion and excellent batch consistency, to meet the stringent requirements of high-end applications such as aerospace, new energy thermal management, and high-end electronic equipment.

[0089] The present application will be further described in detail below with reference to specific embodiments.

[0090] Example 1 Raw material formula (parts by weight) 62 parts of polyetherketoneketone, 12 parts of polyarylether nitrile, 9 parts of one-dimensional aluminum nitride whiskers, 8 parts of hydroxylated boron nitride nanosheets, 4 parts of siloxane-arylether grafted polyetherketoneketone compatibilizer, 0.5 parts of polyetherketoneketone-specific high-temperature composite antioxidant, and 0.6 parts of fluorine-containing high-temperature lubricant.

[0091] Preparation method (1) Determine the theoretical plasticizing zone of the melt based on the viscosity gradient of the current feed material, and determine the ideal plasticizing level based on the material dispersion uniformity to obtain the predicted plasticizing state point. (2) Based on the plasticizing temperature cycle, screw running cycle, melt plasticizing start section position, melt plasticizing end section position, time-series linear fitting calculation is used to obtain the basic plasticizing state points; (3) Calculate the initial plasticization deviation between the predicted plasticization state point and the basic plasticization state point; (4) Identify the temperature conduction difference, pressure interference value, and conveying timing misalignment of the feeding section, compression section, and melting homogenization section to determine the coupling interference intensity; collect the barrel temperature, screw pressure, and speed fluctuation values ​​to calculate the working condition fluctuation amplitude; match the fixed deviation compensation coefficient and fine-tune it to the real-time compensation coefficient to compensate and calibrate the initial plasticizing deviation to obtain the target plasticizing deviation; (5) Extract the axial and radial coordinates of the basic plasticized state points, superimpose them with the compensation amount of the target plasticized deviation, and perform time-series calibration to obtain the target plasticized state points; (6) Limit the target plasticization state point to the plasticization start and end interval, and lock it as the control benchmark after verification; (7) Based on the target plasticizing state point, adjust the screw speed, barrel segment temperature, feeding rate and plasticizing time domain allocation strategy, and obtain plastic particles after melting and plasticizing through extrusion, underwater pelletizing and cooling.

[0092] Example 2 Raw material formula (parts by weight) 58 parts of polyetherketoneketone, 9 parts of polyarylether nitrile, 7 parts of one-dimensional aluminum nitride whiskers, 6 parts of hydroxylated boron nitride nanosheets, 3 parts of siloxane-arylether grafted polyetherketoneketone compatibilizer, 0.3 parts of polyetherketoneketone-specific high-temperature composite antioxidant, and 0.4 parts of fluorine-containing high-temperature lubricant.

[0093] Preparation method: Same as in Example 1.

[0094] Example 3 Raw material formula (parts by weight) 68 parts of polyetherketoneketone, 15 parts of polyarylether ether nitrile, 12 parts of one-dimensional aluminum nitride whiskers, 10 parts of hydroxylated boron nitride nanosheets, 5 parts of siloxane-arylether grafted polyetherketoneketone compatibilizer, 0.7 parts of polyetherketoneketone special high-temperature composite antioxidant, and 0.8 parts of fluorine-containing high-temperature lubricant.

[0095] Preparation method: Same as in Example 1.

[0096] Comparative Example 1 The preparation was carried out using conventional fixed process parameters, without deviation correction or point verification. The remaining formulas and steps were the same as in Example 1.

[0097] Comparative Example 2 Only the screw speed and barrel temperature are adjusted, without multi-parameter coordinated scheduling; the rest of the formula and steps are the same as in Example 1.

[0098] Performance testing The plastic particles obtained in Examples 1-3 and Comparative Examples 1-2 were subjected to performance testing. The testing methods included: Thermal conductivity: The planar thermal conductivity of the particle-pressed standard sample was tested using a laser scintillation thermal conductivity tester according to ASTM E1464 standard.

[0099] Tensile strength: The particles are injection molded into standard tensile specimens, and the tensile strength is tested by a universal testing machine according to GB / T 1040 standard, and the tensile strength value is recorded.

[0100] Performance dispersion: For each sample, 10 sets of parallel samples were randomly selected to test the thermal conductivity. The ratio of the standard deviation to the mean of the 10 sets of thermal conductivity data was calculated as the performance dispersion. The lower the value, the better the consistency of the batch particle performance.

[0101] Packing material dispersion grade: The cross-section of the particles was photographed using a scanning electron microscope to observe their microscopic morphology. The size and number of inorganic packing material agglomerates were counted. The packing material was classified into 1 to 5 grades according to no agglomeration, trace small agglomerates, and large large agglomerates. Grade 1 is the best dispersion grade and grade 5 is the worst dispersion grade.

[0102] The specific values ​​are shown in Table 1 below: Table 1 As shown in Table 1, the plastic particles prepared using the complete plastic particle preparation method of this application in Examples 1, 2, and 3 exhibit significantly superior comprehensive performance compared to Comparative Examples 1 and 2. Regarding thermal conductivity, the thermal conductivity of the three examples ranges from a minimum of 3.15 W / (m·K) to a maximum of 4.36 W / (m·K), far exceeding that of Comparative Example 1, which relies solely on a fixed process, and Comparative Example 2, which only manually adjusts two process parameters. This is because this application accurately calculates and predicts the plasticizing point by considering the material viscosity gradient and dispersion uniformity, while simultaneously identifying topological coupling interference in the plasticizing section and completing deviation compensation calibration based on the fluctuation amplitude of the quantitative processing conditions. This generates the optimal target plasticizing state point, and relies on this point to coordinately control all plasticizing process parameters, enabling the inorganic aluminum nitride crystals to achieve optimal plasticizing state. Both types of thermally conductive fillers, namely shavings and hydroxylated boron nitride nanosheets, are fully mixed in the resin matrix without significant agglomeration, thus establishing a complete and continuous thermally conductive pathway and fully releasing the thermal conductivity potential of the fillers. In terms of mechanical properties, the tensile strength of all examples exceeds 160 MPa, while the tensile strength of the comparative example is only 134~141 MPa. This is because the comparative example cannot eliminate the plasticization deviation caused by inter-segment coupling interference and operating condition fluctuations. The melt is prone to local over-degradation and insufficient plasticization, resulting in damage to the resin molecular chains and an increase in interface defects between the filler and the matrix, which directly weakens the tensile mechanical properties of the material.

[0103] Analysis of performance dispersion indicators shows that the performance dispersion of the examples is only 1.8%~2.4%, which is extremely low, indicating that the differences in thermal conductivity and mechanical properties among plastic particles in the same batch are minimal, and the batch stability is excellent. In contrast, the dispersion of Comparative Example 1 is 9.7% and that of Comparative Example 2 is 7.3%, indicating severe batch performance fluctuations. The core issue is that the traditional fixed process and simple two-parameter adjustment scheme cannot offset the real-time feed fluctuations and the plasticizing position shift caused by the pressure and temperature coupling between barrel sections. The degree of melting and mixing of the melt is inconsistent at each moment, resulting in inconsistent particle performance in the final product. Regarding the filler dispersion level, all three examples reached the optimal level 1, with almost no filler agglomerates observed under an electron microscope. The comparative examples were levels 3 and 4, respectively, with a large number of micron-sized filler agglomerates. This confirms that the adaptive plasticizing control scheme of this application can match the corresponding plasticizing position according to the uniformity of feed dispersion, and, together with the deviation compensation mechanism, stably maintain the melt shear mixing strength, thus thoroughly improving the filler dispersion effect of multiphase compound materials from the process level.

[0104] The test results above demonstrate that the plastic particle preparation method provided in this application calculates the initial plasticization deviation by predicting the plasticization point and the basic plasticization point as dual benchmarks. Then, it completes double-layer deviation calibration by combining the coupling interference of the plasticization section and the real-time operating condition fluctuations. It superimposes coordinate fitting and sets a plasticization interval verification mechanism. Finally, it relies on the optimal target plasticization point to coordinately control multiple plasticization process parameters. This method can effectively avoid the defects of traditional fixed processes and simple parameter adjustment schemes, such as plasticization runaway, filler agglomeration, resin degradation, and large batch performance fluctuations. It can stably prepare polyetherketone ketone-based thermally conductive plastic particles with high thermal conductivity, high tensile strength, uniform filler dispersion, and good batch performance consistency, which are suitable for the stringent performance requirements of high-end application scenarios such as new energy thermal management, aerospace, and high-end electronic components.

[0105] This application also provides a plastic particle preparation apparatus, including a feeding device, a plasticizing extrusion device, a granulation and cooling device, and a control device, wherein the control device is electrically connected to the feeding device, the plasticizing extrusion device, and the granulation and cooling device. Figure 4 This is a schematic diagram of the structure of the control device 4 provided in one embodiment of this application. Figure 4 As shown, the control device 4 in this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown in the image), at least one memory 41 ( Figure 4 (Only one is shown in the image) and a computer program 42 stored in the at least one memory 41 and executable on the at least one processor 40, wherein when the processor 40 executes the computer program 42, it causes the control device 4 to perform the steps of the plasticizing process in the above-described plastic particle preparation method embodiment.

[0106] The control device 4 can be a computing device such as a PLC, microcontroller, desktop computer, laptop, handheld computer, or cloud server. The control device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that... Figure 4 This is merely an example of control device 4 and does not constitute a limitation on control device 4. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0107] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0108] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0109] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for preparing plastic particles, characterized in that, include: Based on the material characteristics information of the current feed, the predicted plasticizing state point of the melt at the current plasticizing moment is determined; wherein, the predicted plasticizing state point represents the ideal plasticizing position that the melt should be in based on its own material characteristics. Based on the basic process data of melt plasticization, the basic plasticization state point of the melt at the current plasticization moment is determined; wherein, the basic process data of melt plasticization includes the plasticization temperature cycle, screw operation cycle, melt plasticization start section position, melt plasticization end section position, and the basic plasticization state point represents the reference plasticization position of the melt when the equipment is running according to the preset process. Determine the initial plasticization deviation between the predicted plasticization state point and the basic plasticization state point; wherein, the initial plasticization deviation characterizes the original position difference between the ideal plasticization position and the reference plasticization position; Based on the topological coupling relationship of the plasticizing section of the melt plasticizing unit and the real-time operating condition fluctuations, the initial plasticizing deviation is corrected to obtain the target plasticizing deviation; The target plasticizing state point of the melt is obtained by superimposing and fitting the basic plasticizing state point and the target plasticizing deviation; wherein, the target plasticizing state point represents the optimal plasticizing position adapted to the current material and working conditions. Based on the target plasticization state point, the melt plasticization process parameters are adjusted to melt plasticize the material, and plastic particles are obtained after extrusion, granulation, and cooling. The melt plasticization process parameters include screw speed, barrel segment temperature, feeding rate, and plasticization time domain allocation strategy. The process correction of the initial plasticizing deviation based on the topological coupling relationship of the plasticizing section of the melt plasticizing unit and real-time operating condition fluctuations yields the target plasticizing deviation, including: Based on the topological coupling relationship of the plasticizing section in the melt plasticizing unit, the coupling interference intensity between the feeding section, compression section, and melt homogenization section in the melt plasticizing unit is identified; The amplitude of operating condition fluctuations is obtained based on real-time operating condition fluctuations. Based on the coupling interference intensity and the operating condition fluctuation amplitude, the initial plasticizing deviation is compensated and calibrated to obtain the target plasticizing deviation after removing the interference; The topological coupling relationship of the plasticizing section based on the melt plasticizing unit, identifying the coupling interference intensity between the feeding section, compression section, and melt homogenization section in the melt plasticizing unit, includes: Based on the topological coupling relationship of the plasticizing sections of the melt plasticizing unit, the temperature conduction difference, pressure interference value and material conveying timing misalignment between each plasticizing section are obtained; Based on the temperature conduction difference, the pressure interference value, and the material conveying timing misalignment, the coupling interference level between the feeding section, the compression section, and the melting homogenization section in the melting and plasticizing unit is identified, and the corresponding coupling interference intensity is obtained based on the coupling interference level. The method of obtaining the operating condition fluctuation amplitude based on real-time operating condition fluctuations includes: Based on the real-time fluctuations of barrel segment temperature, screw operating pressure, and screw speed, the deviation ratio between the real-time fluctuation difference of each operating parameter and the reference parameter is calculated, and the fluctuation amplitude of the operating condition within the current plasticizing cycle is obtained by integrating them.

2. The method for preparing plastic particles as described in claim 1, characterized in that, The material characteristic information includes the material viscosity gradient and the material dispersion uniformity. The step of determining the predicted plasticizing state point of the melt at the current plasticizing moment based on the material characteristic information of the current feed includes: The theoretical plasticizing zone of the melt at the current plasticizing moment is determined based on the material viscosity gradient in the material characteristic information of the current feed. The ideal plasticizing level of the melt at the current plasticizing moment is determined based on the material dispersion uniformity in the material characteristic information of the current feed. Based on the theoretical plasticizing zone and the ideal plasticizing level, the predicted plasticizing state point of the melt at the current plasticizing moment is determined.

3. The method for preparing plastic particles as described in claim 1, characterized in that, The determination of the basic plasticizing state point of the melt at the current plasticizing moment based on the basic melt plasticizing process data includes: Based on the synchronization of the plasticizing temperature cycle and the screw running cycle, and combined with the axial interval of the melt plasticizing start segment position and the melt plasticizing end segment position, the basic plasticizing state point of the melt at the current plasticizing moment is obtained through time-series linear fitting calculation.

4. The method for preparing plastic particles as described in claim 1, characterized in that, The initial plasticizing deviation is compensated and calibrated based on the coupling interference intensity and the operating condition fluctuation amplitude to obtain the target plasticizing deviation after removing the interference, including: Based on the fixed deviation compensation coefficient corresponding to the coupling interference strength matching; The fixed deviation compensation coefficient is finely adjusted based on the fluctuation range of the operating condition to obtain the real-time compensation coefficient. Based on the real-time compensation coefficient, the initial plasticizing deviation is compensated and calibrated to obtain the target plasticizing deviation after removing interference.

5. The method for preparing plastic particles as described in claim 1, characterized in that, The step of superimposing and fitting the basic plasticizing state points and the target plasticizing deviation to obtain the target plasticizing state points of the melt includes: Extract the axial and radial coordinates of the basic plasticized state points; The axial compensation amount corresponding to the target plasticization deviation is superimposed with the axial position coordinate, and the axial compensation amount corresponding to the target plasticization deviation is superimposed with the radial position coordinate to obtain the superimposed coordinate value; The superimposed coordinate values ​​are time-series fitted and calibrated to obtain the target plasticized state point of the melt.

6. The method for preparing plastic particles as described in claim 1, characterized in that, After superimposing and fitting the basic plasticizing state point and the target plasticizing deviation to obtain the target plasticizing state point of the melt, the method further includes: The target plasticization state point is limited to a preset range between the starting point of the melt plasticization and the ending point of the melt plasticization; If the target plasticized state point is within the preset range, the point is determined to be valid and locked as the final control benchmark; If the target plasticized state point exceeds the preset range, the deviation is recalibrated based on the range boundary value to update and obtain a valid target plasticized state point.

7. A type of plastic particle, characterized in that, The plastic particles are prepared by the method described in any one of claims 1 to 6, wherein the plastic particles comprise the following components in parts by weight: Polyetherketoneketone, 58-68 parts; Polyarylene ether nitrile, 9-15 parts; One-dimensional aluminum nitride whiskers, 7–12 parts; Hydroxylated boron nitride nanosheets, 6–10 parts; Siloxane-aryl ether grafted polyether ketone ketone compatibilizer, 3-5 parts; High-temperature composite antioxidant for polyether ketone ketone, 0.3-0.7 parts; Fluorine-containing high-temperature lubricant, 0.4–0.8 parts.

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