Glass fiber reinforced polypropylene structure strength optimization method for flow battery pole frame

Abstract

The invention discloses a glass fiber reinforced polypropylene structure strength optimization method for a flow battery pole frame, and particularly relates to the technical field of material processing quality control, and the method comprises the following steps: collecting multi-parameter information forming original processing data, constructing an input dimension group, and determining whether a structure optimization process determination condition is satisfied; after the conditions are met, an arrangement orientation index and a bonding activity index are synchronously constructed; on the basis of an index combination activation path shunting mechanism, selecting an optimal logic to generate an adjustment instruction set; the machining variables are adjusted in a linkage mode according to the instruction set; after the processing is completed, a staged cooling gradient is maintained through an isothermal heat field, and the curing process of the structure arrangement and the bonding state is completed; according to the method, a structure optimization process is started based on multi-parameter judgment, a double-index driving path shunting mechanism is constructed, and dynamic instruction adjustment and isothermal thermal control cooling are combined, so that synchronous optimization of fiber arrangement and interface bonding in the whole processing process is realized, and the structural stability and the bonding integrity are improved.

Description

Technical Field

[0001] This invention relates to the field of material processing quality control technology, and more specifically, to a method for optimizing the structural strength of glass fiber reinforced polypropylene for flow battery electrode frames. Background Technology

[0002] Glass fiber reinforced polypropylene used in flow battery frames is a high-performance engineering plastic material specifically used in the structural shell of flow batteries. Its basic component is polypropylene (PP), and it is physically reinforced by adding a certain proportion (usually 15% to 30%) of glass fiber (referred to as "glass fiber") to significantly improve the material's mechanical properties, thermal stability, and chemical corrosion resistance. This meets the comprehensive performance requirements of flow batteries for shell materials under harsh application conditions such as long-term operation, high-strength pressure resistance, sealing structure maintenance, and contact with chemical media.

[0003] First, polypropylene is a lightweight, high-toughness, corrosion-resistant, and easily processed thermoplastic polymer with good moldability and cost-effectiveness. However, pure polypropylene has certain limitations in terms of strength, rigidity, dimensional stability, and heat resistance, making it difficult to meet the high standards required for flow battery electrode frames in terms of pressure resistance, sealing, and chemical resistance. To overcome this shortcoming, glass fiber is introduced into the polypropylene matrix to form a "glass fiber reinforced polypropylene" composite material. As a reinforcing phase, glass fiber is uniformly distributed in the polypropylene matrix in the form of slender fibers, significantly improving the material's tensile strength, flexural modulus, dimensional stability, and creep resistance, enabling it to maintain controllable deformation and structural integrity under long-term internal fluid pressure and mechanical loads.

[0004] Secondly, in flow batteries, the casing not only undertakes key functions such as fluid containment, flow guidance, sealing, and structural support, but also needs to withstand the risk of chemical corrosion from long-term immersion in acidic electrolytes (such as sulfuric acid or fluorine-containing solutions). Therefore, in addition to mechanical properties, this type of material also has extremely high requirements for chemical stability. This material specifically requires the use of "virgin material" in its production and undergoes rigorous long-term immersion tests in sulfuric acid and electrolyte (365 days, 50°C, under different medium concentrations) to ensure the stability of the interfacial bonding between the glass fiber and polypropylene, preventing issues such as glass fiber precipitation, material brittleness, or a decrease in mechanical properties after immersion. Furthermore, to ensure the sealing and stacking assembly precision of the battery assembly, the injection-molded products of this material must also meet strict standards for deformation (≤5mm), surface contour (0.5mm), and appearance defects such as black spots / scratches / burrs, ensuring assembly consistency between casings, reliable gas-liquid sealing, and long-term durability.

[0005] Therefore, glass fiber reinforced polypropylene used in flow battery frames is a high-performance thermoplastic composite material that has undergone structural reinforcement and performance verification. Its core design lies in taking into account multiple performance indicators such as mechanical properties, chemical stability, injection molding quality and laser welding transmittance, providing solid material support for the modularization, lightweighting and high reliability of flow batteries.

[0006] Flow batteries have been widely used in large-scale energy storage applications in recent years due to their high scalability, good safety, and long cycle life. As a key structural component, the flow battery frame must withstand long-term electrolyte corrosion, thermal cycling, and mechanical load impacts, thus placing high demands on its structural strength, sealing performance, and long-term bonding stability.

[0007] Polypropylene, a commonly used matrix material for flow battery frames, boasts advantages such as light weight, good chemical resistance, and excellent processability. However, its bulk mechanical properties and interfacial bonding capabilities are limited, making it difficult to meet the demands of complex service conditions. To enhance its strength and stability, existing technologies often employ glass fiber reinforced polypropylene (GF-PP) as the molding material. However, the actual performance of glass fiber reinforced structures is influenced by a combination of factors, including glass fiber content, fiber orientation, interfacial coupling state, and injection molding process variables. These factors are not only interconnected but also exhibit nonlinear responses during the molding process.

[0008] Existing GF-PP injection molding methods generally rely on fixed process windows and lack a path response mechanism based on original processing data and dynamic adjustment of multiple variables. This easily leads to uneven fiber spatial distribution and insufficient interfacial bonding, ultimately causing stress concentration, structural delamination, or adhesive failure in localized areas. Furthermore, existing processing methods largely depend on empirical parameter tuning, lacking interpretable parameter adjustment logic, and cannot achieve stable and efficient structural performance optimization under complex boundary conditions. Therefore, this invention proposes a method for optimizing the structural strength of glass fiber reinforced polypropylene for flow battery electrode frames to address the aforementioned problems. Summary of the Invention

[0009] To achieve the above objectives, the present invention provides the following technical solution: A method for optimizing the strength of glass fiber reinforced polypropylene structures used in flow battery electrode frames includes the following steps: Raw processing data consisting of glass fiber content, fiber length distribution, polypropylene matrix melting temperature range, flow viscosity, interfacial coupling parameters and environmental process variables were collected. Input dimension groups were constructed and it was determined whether they met the criteria for entering the structural optimization process. After the judgment conditions are met, the data is processed by feature mapping to construct the arrangement orientation index representing the fiber arrangement state and the bonding activity index representing the molecular bonding trend. Based on the combined activation path diversion mechanism of arrangement orientation index and bonding activity index, the optimal path is selected by graph reasoning rules, boundary control logic or expert processing model to generate a dynamic adjustment instruction set for the current processing stage. This instruction set includes parameter adjustment category, position of action and execution order. According to the dynamic adjustment instruction set, the injection molding process is subjected to the linkage adjustment of preset multi-dimensional variables, which drives the material to undergo a synchronous evolution process of fiber spatial rearrangement and interface bonding behavior during processing. After processing, the operation control stage cooling gradient is maintained by the isothermal thermal field to carry out structural stress reconstruction and interface reaction stabilization, and complete the structural arrangement shaping and overall bonding state curing process.

[0010] In a preferred embodiment, the step of constructing the input dimension group includes converting glass fiber content, fiber length distribution, polypropylene matrix melting temperature zone, flow viscosity, interfacial coupling parameters and environmental process variables into calculable numerical variables, and constructing an input vector structure with six types of parameters as axes. Glass fiber content is converted to a percentage by weight ratio and melt volume percentage; The fiber length distribution was statistically analyzed using the standard optical particle size identification method, and the maximum, minimum and mode values ​​were calculated to form the length distribution interval. The melting temperature range of the polypropylene matrix was obtained by differential scanning calorimetry (DSC) to determine the initial melting temperature and the final melting temperature, and the center temperature of the representative range was calculated by linear interpolation. Within the set shear rate range, the flow viscosity is obtained by acquiring the shear-viscosity curve and taking the average value of the numerical integral at the corresponding temperature; The interface coupling parameter is based on measured data of the change in interface energy. The calculation formula is: the interface coupling parameter P is equal to the product of the coupling agent concentration and the treatment time divided by the unit change in interface energy, specifically P=(C×T) / Δγ, where C is the coupling agent concentration (in percentage concentration), T is the coupling treatment time (in seconds), and Δγ is the difference in interface energy before and after treatment (in millijoules per square meter). This parameter is used to evaluate the degree of interface bonding tendency. Environmental process variables include temperature, humidity, pressure, and time series.

[0011] In a preferred embodiment, the step of determining whether the conditions for entering the structural optimization process are met includes setting judgment threshold ranges for glass fiber content, fiber length distribution, polypropylene matrix melting temperature range, flow viscosity, interface coupling parameters, and environmental process variables based on the constructed input dimension group, and confirming whether the start criteria are met through a comprehensive matching judgment method. If any item exceeds the set range, it is marked as not meeting the judgment conditions, and the start of the structural optimization process is automatically blocked; if all are met, the process proceeds to the subsequent feature mapping step.

[0012] In a preferred embodiment, the construction of the arrangement orientation index specifically includes the following steps: First, the fiber length distribution is divided into five length segments according to a preset length interval, denoted as the first segment to the fifth segment; The angle data is obtained by measuring the angle between the fiber orientation angle and the load principal axis within each length segment. The segments are divided into five-degree intervals, and the proportion of fibers with an angle between zero and thirty degrees in each segment is counted to the total number of fibers in that length segment. This proportion is recorded as the orientation degree of that segment. Subsequently, the orientation degrees of the five length segments are weighted and combined according to the proportion of their number of segments, and then multiplied by the inverse ratio of the standard deviation of the overall fiber length distribution. The specific calculation formula is: the arrangement orientation index is equal to the sum of the orientation degree of each segment multiplied by the proportion of the fiber quantity in that segment, multiplied by one minus the standard deviation of the fiber length distribution divided by the maximum length. The value range of this index is from zero to one, accurate to three decimal places, and is used to express the degree of consistency of the overall arrangement of glass fibers in space in the main axis direction during the current processing stage.

[0013] In a preferred embodiment, the construction of the bonding activity index specifically includes the following steps: First, the ratio of coupling agent concentration percentage, reaction time in seconds and interfacial energy change value is measured, and then the interfacial coupling parameter P is calculated. Next, within the processing temperature range, the interface temperature and humidity are sampled every m seconds, and the maximum change and average value within m seconds are calculated respectively. Next, calculate the overlap rate between the melt holding time and the corresponding diffusion range of the coupling agent, that is, the ratio of diffusion time to melt state time, multiply by the interface coupling parameter, and use it as the result of the first step of the calculation. Meanwhile, the product of the temperature fluctuation rate (maximum change divided by average) and the humidity change rate is used as the second calculation factor. Finally, the two factors are standardized to the range of zero to one and then multiplied together. The result is the bonding activity index, which is used to measure the instantaneous reaction potential of the interfacial bonding state under the action of heat and humidity per unit time. The value range is set to zero to one and three decimal places are retained.

[0014] In a preferred embodiment, after the determination conditions are met, the construction of the bonding activity index and the construction of the arrangement orientation index are carried out simultaneously within the same time window.

[0015] In a preferred embodiment, the activation of the path diversion mechanism is triggered by the combination of the arrangement orientation index and the bonding activity index. This combination is input into the path selection engine in the form of a two-dimensional numerical coordinate. The path selection is divided into nine quadrants with the first value as the horizontal axis and the second value as the vertical axis. Each region corresponds to one of the following: graph inference rules, boundary control logic, or expert processing model. The specific selection rules are as follows: when both the arrangement orientation index and the bonding activity index are less than 0.5, the boundary control logic is invoked; when both are greater than 0.5, the expert processing model is invoked; and the graph inference rules are enabled for the remaining regions.

[0016] In a preferred embodiment, the graph reasoning rule is based on the prior path graph structure, calculates the shortest adjustment response sequence in the node path, and outputs the operation number; the boundary control logic constructs a gradient adjustment hierarchy table based on the deviation of the current index from the minimum threshold, and adjusts the variable parameters according to priority; the expert processing model directly generates adjustment actions based on the mapping of the established empirical rule table. All three paths output a dynamic adjustment instruction set, which consists of three items: the first item is the parameter adjustment category, selected from processing temperature, shear rate, cooling rhythm, and injection pressure; the second item is the action location, represented by the processing flow channel segment number; and the third item is the execution sequence, represented by the step execution number. The combination of these three items constitutes a single instruction set, which is executed within the current processing cycle.

[0017] In a preferred embodiment, the process of maintaining the operational control staged cooling gradient through an isothermal thermal field includes the following steps: A heat-sealed insulated chamber is set up, and the molded structure is placed in a temperature-controlled environment immediately after processing. The isothermal thermal field is composed of multi-point distributed heating units. Each unit maintains the target constant temperature range with a temperature difference of no more than three degrees Celsius. The constant temperature range is set between five degrees Celsius below the initial crystallization temperature of the polypropylene matrix and ten degrees Celsius above the glass transition temperature, and the control time is no less than twenty seconds. The phased cooling gradient is established by using zoned airflow control and time-series cooling methods. In the first phase, the temperature of the outer surface is controlled to decrease at a rate of one degree Celsius per second for fifteen seconds. Then, the heat exchange channel of the middle layer is switched, and the cooling rate is set to 0.5 degrees Celsius per second for another twenty seconds. Finally, the cooling process is switched to the core area until the temperature is reduced to the ambient value. The total cooling process takes more than fifty seconds.

[0018] The technical effects and advantages of this invention are as follows: This invention establishes a multi-parameter, multi-dimensional raw information set by collecting raw processing data consisting of glass fiber content, fiber length distribution, polypropylene matrix melting temperature range, flow viscosity, interfacial coupling parameters, and environmental process variables. Based on this, an input dimension group is constructed, and judgment conditions for entering the structural optimization process are set, ensuring that the triggering of the structural optimization process is based on real and complete processing environment and material state information. This process not only achieves simultaneous monitoring and judgment of multiple key parameters affecting structural performance but also provides a clear basis for subsequent process control. Through this mechanism, this invention effectively avoids the risk of blindly initiating the structural optimization process under conditions of unstable material state, fluctuating processing environment, or insufficient interfacial compatibility, improving the accuracy and reliability of overall processing decisions and providing a unified and representative parameter entry point for subsequent index construction and path adjustment.

[0019] This invention, under the premise of meeting the structural optimization process judgment conditions, constructs an arrangement orientation index to characterize the spatial arrangement behavior of fibers during processing and a bonding activity index to characterize the reaction trend of molecular interfaces by performing feature mapping on the collected data. The combination of these two indices is used to activate the path diversion mechanism. This mechanism maps the index combination into two-dimensional numerical coordinates to input the path selection engine, and then automatically selects graph inference rules, boundary control logic, or expert processing models to activate the response path based on this input. Unlike traditional process control processes that rely on fixed parameter curves or manual adjustments, this invention enables the structural adjustment logic to no longer rely on preset processes, but instead make adaptive path judgments and response operation selections based on the specific numerical characteristics of the current processing state. This path decision strategy significantly improves the ability to identify and respond to abnormal and transitional states during processing, making each round of process adjustment actions interpretable and precisely targeted, thereby continuously advancing the material structure towards the target state in dynamic processing scenarios.

[0020] After determining the adjustment path and generating a dynamic adjustment instruction set, this invention achieves multi-dimensional variable linkage adjustment of the injection molding process through a ternary instruction combination of parameter adjustment category, action location, and execution sequence. After the processing is completed, an isothermal thermal field is used to maintain the phased cooling gradient for operational control. This cooling control strategy differs from traditional single cooling or static heat preservation methods. It establishes an ordered cooling sequence from the outer layer to the core region and from high temperature to ambient temperature. Through a gradient adjustment mechanism of time and space, it achieves coordinated control of structural stress release and interfacial chemical reactions during thermal cooling. The entire process not only ensures balanced thermal stress distribution and sufficient material interfacial reaction in the later stages of processing but also guarantees the stable arrangement of glass fibers in the molded structure and the complete bonding between the polypropylene matrix and the reinforcing phase. This achieves overall optimization of the molded part's structural and interfacial performance, enhancing the material's application reliability and long-term stability under the service conditions of flow battery electrode frames. Attached Figure Description

[0021] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a schematic diagram of the strength optimization method for glass fiber reinforced polypropylene structure used in flow battery electrode frames according to the present invention.

[0022] Figure 2 This is a structural view of the injection-molded product of the flow battery electrode frame of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] Reference Figure 1 The following examples were obtained: Example 1: A method for optimizing the structural strength of glass fiber reinforced polypropylene for flow battery electrode frames, comprising the following steps: collecting raw processing data consisting of glass fiber content, fiber length distribution, polypropylene matrix melting temperature range, flow viscosity, interfacial coupling parameters, and environmental process variables; constructing an input dimension group and determining whether it meets the criteria for entering the structural optimization process; wherein, glass fiber content is collected in real time through raw material weighing and feed ratio setting; fiber length distribution is statistically analyzed by an online imaging recognition system; the polypropylene matrix melting temperature range is accurately provided by a melting point scanning detection device; flow viscosity is obtained in real time through an online viscometer during processing to show changes with temperature and shear rate; interfacial coupling parameters are calculated based on coupling agent concentration, action time, and interfacial energy measurement; environmental process variables include variable external conditions such as temperature, humidity, pressure, and vibration, all of which are continuously recorded by a sensor array. The above parameters constitute a six-dimensional data input, which is uniformly normalized to form the input dimension group. Based on the preset acceptable range and judgment logic model, each item is subjected to boundary comparison and tolerance analysis to determine whether the overall data status has entered the judgment area where structural optimization can be performed. If not, the process is interrupted and an exception is reported.

[0025] After meeting the criteria, the data undergoes feature mapping to construct an orientation index representing fiber arrangement and a bonding activity index representing molecular bonding trends. This step is initiated immediately after the data meets the optimization prerequisites, mapping the structural and interfacial chemical data from the original dimensional set to two independent but coordinated feature spaces. The orientation index is derived from statistical calculations of the fiber's directional distribution and length proportion in three-dimensional space, quantifying the regularity of its overall arrangement relative to the loading direction. The bonding activity index is constructed based on the rate of change of interfacial energy, diffusion coverage ratio, and the degree of fluctuation in the reaction environment during the coupling reaction, characterizing the probability of effective bonding reactions between the fiber and the matrix. Both indices are calculated within the same time window and serve as numerical inputs for subsequent path diversion mechanisms, exhibiting real-time responsiveness and quantifiable analytical characteristics.

[0026] Based on the combined activation path diversion mechanism of the arrangement orientation index and bonding activity index, the optimal path is selected by graph inference rules, boundary control logic, or expert processing model to generate a dynamic adjustment instruction set for the current processing stage. This instruction set includes parameter adjustment category, location of action, and execution order. After the two indices are calculated, they are located in a preset two-dimensional index map according to their combination results. Different combination intervals trigger different types of processing logic. The graph inference rules search for the solution with the shortest response path and the smallest adjustment amount under the current structural state by traversing the node connection methods in the historical path graph database. The boundary control logic establishes an adjustment gradient curve using the distance between the current index and the safety threshold as a variable and selects the adjustment order according to the priority strategy. The expert processing model directly calls a fixed adjustment scheme based on an experience knowledge base. After the path is determined, the generated dynamic adjustment instruction set consists of three components: parameter adjustment category, which specifies the type of variable to be adjusted; location of action, which is located in the physical area number of the processing system; and execution order, which indicates the actual execution order of the action within the current cycle. The instruction set can be executed once in a processing cycle or applied cyclically to multiple cycles.

[0027] Based on the dynamic adjustment instruction set, the injection molding process is subjected to coordinated adjustment of preset multi-dimensional variables, driving the synchronous evolution of fiber spatial rearrangement and interfacial bonding behavior during processing. This step, following the dynamic adjustment instruction set generated in the third step, coordinates the temperature control unit, runner guidance system, shearing module, and feed cycle control device of the injection molding equipment. Parameter adjustments include, but are not limited to, key variables such as injection pressure, screw speed, mold cavity zone temperature, and holding time, targeting specific position segments in the instruction set to achieve precise local spatial control. During continuous processing, the adjusted flow shearing behavior and thermal field distribution induce glass fiber rearrangement, gradually bringing it towards the target orientation on a macroscopic scale. Simultaneously, at the microscopic scale, in conjunction with the dynamic window of coupling reaction, the interfaces gradually complete effective bonding reactions. The above processes evolve synchronously at both the spatial and chemical structure levels, forming an integrated strengthening path.

[0028] After processing, a controlled cooling gradient is maintained in an isothermal thermal field to reconstruct structural stress and stabilize interfacial reactions, completing the structural arrangement and overall bonding process. Immediately after processing, the thermal field control stage begins. A stable temperature zone is formed within a closed environment using a multi-point temperature control device, with the temperature set between the material's crystallization initiation point and glass transition point, controlling the temperature difference to not exceed three degrees Celsius. Subsequently, the temperature is reduced in stages, applying a cooling gradient from the structural surface to the core region, allowing each layer to achieve thermal-mechanical equilibrium release sequentially. During this process, the fiber positions are spatially stabilized due to the gradual release of temperature differences, and the interfacial reaction terminates due to the delayed cooling, completing chemical bonding and maintaining a stable bonded state. The final structural morphology, internal stress, and interfacial state are fixed and stably encapsulated, providing a consistent and continuous structural strength foundation for the molded part.

[0029] The steps for constructing the input dimension set include converting glass fiber content, fiber length distribution, polypropylene matrix melting temperature range, flow viscosity, interfacial coupling parameters, and environmental process variables into calculable numerical variables, and constructing an input vector structure with six types of parameters as axes. Among these, glass fiber content refers to the proportion of glass fiber in a unit mass of material, which is obtained by converting the pre-feed weighing ratio with the melt density and volume during the melting stage to obtain its mass fraction percentage. For example, when the weighing value is 15 grams and the polypropylene is 85 grams, its mass fraction is 15%. The melt volume is corrected by comparing the density before and after melting to ensure that volume expansion during heating does not affect the proportional accuracy.

[0030] Fiber length distribution refers to the statistical characteristics of the length variation of glass fibers in composite materials. Particle size data is acquired by an online optical imaging particle size analysis system, classified and statistically analyzed according to preset intervals, and the maximum length, minimum length, and mode are calculated separately. For example, a laser particle size analyzer is used to slice fiber bundle samples before molding in real time, with a scanning frequency set to fifty frames per second and a length accuracy of ten micrometers. The output distribution histogram is used to form the interval structure. The polypropylene matrix melting temperature range represents the phase transition range of polypropylene from the solid to the molten state. This parameter is measured by differential scanning calorimetry (DSC), recording the endothermic changes at a constant heating rate. The initial melting temperature is defined as the starting point of a significant endothermic signal, and the termination temperature is the stable point after the endothermic peak. The midpoint of the interval between the two is used as the representative temperature of the interval. Linear interpolation is used to calculate the center temperature value to improve the accuracy of subsequent normalization processing.

[0031] Flow viscosity refers to the flow characteristics of polypropylene matrix at different shear rates, reflecting its processing flow performance. The shear rate-viscosity curve obtained by a capillary rheometer is integrated at the target processing temperature to obtain the average viscosity value at a given temperature point. The viscosity value is expressed in Pascal-seconds (Pa·s) and is logarithmically normalized in subsequent input vectors. The interfacial coupling parameter is a quantitative indicator reflecting the bonding trend between the coupling agent on the glass fiber surface and the polypropylene interface. The difference in surface free energy (in millijoules per square meter) before and after interfacial treatment is collected through previous experiments. Combined with the percentage concentration of the coupling agent and the application time, the calculation formula is: P = (C × T) / Δγ. Where C is the weight concentration of the coupling agent, expressed as a percentage; T is the duration of the coupling reaction, in seconds; and Δγ is the change in surface energy of the glass fiber before and after coupling. For example, when C is 1%, T is 30 seconds, and Δγ is 5 millijoules per square meter, then P is 6. This parameter is obtained through droplet angle testing and extended interfacial energy calculation methods, representing a quantitative expression of the degree of interfacial bonding.

[0032] Environmental process variables refer to external controllable factors in the environment that may affect material properties during the molding process, including temperature, humidity, pressure, and their time series. Temperature and humidity are collected in real time by an environmental monitoring module, pressure is recorded by a feedback pressure sensor from the injection molding equipment, and the time series refers to the arrangement of time points corresponding to each parameter during the collection process, used for synchronous comparison with actual processing behavior. All the above variables are standardized in units and ranges through a preprocessing module and directly participate in subsequent judgment conditions as input vectors of the six-dimensional structure. In the actual embodiment, after the input dimension group is formed, it is sent to the judgment logic program as the initial basis for triggering the structural optimization process, ensuring that the processing state entering the optimization process is representative, adjustable, and controllable.

[0033] The steps for determining whether the conditions for entering the structural optimization process are met include setting judgment threshold ranges for glass fiber content, fiber length distribution, polypropylene matrix melting temperature range, flow viscosity, interface coupling parameters, and environmental process variables based on the constructed input dimension group. A comprehensive matching judgment method is used to confirm whether the start-up criteria are met. If any item exceeds the set range, it is marked as not meeting the judgment conditions, and the structural optimization process is automatically blocked. If all conditions are met, the process proceeds to the subsequent feature mapping step. Specifically, the judgment threshold range for glass fiber content is set between 12% and 30% by mass. This range corresponds to the minimum reinforcement level and processing limit upper limit achievable under the current processing platform. For example, if the glass fiber content in the raw material is measured to be 11% during injection molding, the system directly judges it as deviating from the lower limit and does not proceed to subsequent processing. The fiber length distribution uses a main interval length judgment method, with a threshold range set between 400 micrometers and 1000 micrometers. Glass fibers within this range can form an effective supporting skeleton structure and maintain a stable distribution during the holding pressure stage. If the maximum detected length is less than 300 micrometers, it is judged as insufficient structural skeleton, and the process is blocked.

[0034] The melting temperature range of the polypropylene matrix is ​​determined based on the melting range obtained from differential scanning calorimetry (DSC), with a standard range set at 148 to 175 degrees Celsius. Melting too early or too late can lead to abnormal shear flow or fiber drift; therefore, exceeding this range is considered a failure to meet the requirements. Flow viscosity is determined based on the average viscosity range of 100 to 200 Pa·s at the target processing temperature. If the average value is below 100 Pa, sufficient melt tension cannot be formed, leading to disordered fiber arrangement; if it is above 200 Pa, it may affect fiber orientation and the interfacial reaction window.

[0035] The interface coupling parameters are calculated using the aforementioned formula, with thresholds set between three and twenty, reflecting that the coupling agent concentration and diffusion binding ability are within the medium-to-high efficiency binding range. For example, when the coupling agent concentration is 0.5%, the action time is 20 seconds, and the interfacial energy change is 5 millijoules per square meter, then P=2, which is less than the minimum threshold and is marked as not meeting the requirements. Among the environmental process variables, the temperature stability range is ±2 degrees Celsius, the humidity change rate must not exceed 1% per minute, and the pressure fluctuation range does not exceed 5%, judged based on the deviation between the sensor's 5-second average value and the reference value. The determination of the above six types of parameters is performed through single-item judgment and multi-item comprehensive matching: if any single item is not met, the whole system is marked as not meeting the judgment conditions; if all parameters meet the preset range, the system determines that the start-up criteria are met, automatically triggering the subsequent feature mapping steps and entering the construction stage of the arrangement orientation index and bonding activity index.

[0036] The construction of the fiber orientation index includes the following steps: First, the fiber length distribution is divided into five length segments according to a preset length range, denoted as segment one to segment five. Fiber length distribution refers to the statistical distribution of the length of glass fibers in the composite material before processing, which can be obtained through an online particle size imaging system. The length range is set to 200 micrometers to 1000 micrometers, and divided into five segments at equal intervals: segment one (200–360 micrometers), segment two (360–520 micrometers), segment three (520–680 micrometers), segment four (680–840 micrometers), and segment five (840–1000 micrometers). The number of fibers within each length range is counted, and independent orientation degree calculations are performed in subsequent steps. The angle between the fiber orientation angle and the principal axis of the load within each length segment is measured to obtain angle data. The segments are divided into five-degree intervals, and the proportion of fibers with an angle between 0 and 30 degrees in each segment is counted to represent the total number of fibers in that length segment, which is recorded as the orientation degree of that segment.

[0037] Fiber orientation angle refers to the angle in space between the fiber's principal axis and the load's principal axis, measured in degrees. Using a combination of two-dimensional projection and three-dimensional laser scanning, the orientation angle of each fiber is assigned to a five-degree interval, such as 0–5° or 5–10°. The percentage of fibers in all directions within the range of 0–30 degrees represents the orientation degree of that segment, reflecting whether the fibers are distributed along the primary load-bearing direction. The orientation degrees of the five length segments are then weighted according to their proportions and multiplied by the inverse standard deviation of the overall fiber length distribution. The specific formula is: the orientation index equals the sum of the orientation degrees of each segment multiplied by the percentage of fibers in that segment, multiplied by one minus the standard deviation of the fiber length distribution divided by the maximum length. This index ranges from zero to one, accurate to three decimal places, and is used to express the overall consistency of the glass fibers' spatial arrangement along the principal axis during the current processing stage. The formula is expressed as follows: Arrangement orientation index = (T1*R1+T2*R2+T3*R3+T4*R4+T5*R5)*(1-σ / Lmax); Wherein, T1 to T5: Orientation degree of the first to fifth segments (unit: dimensionless proportion); R1 to R5: Fiber quantity ratio of the first to fifth segments (unit: percentage); σ: Standard deviation of all fiber lengths (unit: micrometers), calculated by weighting the squared deviation of the length of each fiber in the five segments; Lmax: Maximum fiber length in the current sample (unit: micrometers); "1-σ / Lmax" is the inverse weight of the standard deviation, used to suppress the arrangement interference caused by excessive length dispersion.

[0038] For example, in a typical processing sample, the five orientation degrees are 0.82, 0.76, 0.71, 0.67, and 0.62, respectively, with corresponding proportions of 0.12, 0.20, 0.26, 0.24, and 0.18. The standard deviation of the fiber length distribution is 85 micrometers, and the maximum length is 1000 micrometers. Then, the arrangement orientation index = (0.82×0.12+0.76×0.20+0.71×0.26+0.67×0.24+0.62×0.18)×(1-85 / 1000)≈0.711×0.915=0.651. This value is between zero and one. The closer it is to one, the more concentrated the overall orientation of the glass fibers in the material is, and the more it tends to be consistent with the principal axis of the load. This is beneficial for path reasoning and variable matching in subsequent structural optimization behavior.

[0039] The construction of the bonding activity index includes the following steps: First, the ratio of the coupling agent concentration percentage, the reaction time (in seconds), and the interfacial energy change value is measured, and the interfacial coupling parameter P is calculated. The coupling agent concentration percentage refers to the percentage of the mass of the coupling agent applied to the glass fiber surface relative to the matrix material in the composite material, expressed as %; the reaction time (in seconds) refers to the total time the coupling agent maintains contact with the fiber surface and remains under reaction conditions, expressed as seconds; the interfacial energy change value is the difference in free energy per unit area of ​​the glass fiber interface before and after treatment, expressed as millijoules per square meter (mJ / m²), measured using the droplet method. The formula for calculating the interfacial coupling parameter P is: P = (C × T) / Δγ. For example, when C is 0.8%, T is 40 seconds, and Δγ is 6 mJ / m², then P = (0.8 × 40) / 6 = 5.33. Next, interface temperature and humidity are sampled every m seconds within the processing temperature range, and their maximum change and average value within m seconds are calculated. In this step, the processing temperature range refers to the temperature range during the entire processing cycle in which the polypropylene matrix remains in a molten state, typically ranging from 150℃ to 175℃; the sampling period m can be selected as 5 seconds. The interface temperature is the actual temperature near the fiber surface inside the mold cavity, collected by an embedded thermocouple; the interface humidity is the effective water activity in the microenvironment, detected by a miniature humidity sensor. Within each m-second cycle, the maximum value (T_max, H_max) and average value (T_avg, H_avg) are recorded for subsequent calculation of volatility and rate of change.

[0040] Next, the overlap rate between the melt holding time and the corresponding diffusion range of the coupling agent is calculated, i.e., the ratio of diffusion time to melt state time. This ratio is multiplied by the interfacial coupling parameter to obtain the result of the first step calculation. The melt holding time refers to the duration for which the polypropylene matrix remains in an effective flow state and maintains high reactivity, measured in seconds. The coupling agent diffusion time refers to the time required for the coupling agent to enter the polypropylene interfacial region and reach the reaction plateau, also measured in seconds. The ratio of these two values ​​forms the overlap rate, which is then multiplied by the interfacial coupling parameter P to constitute the first calculation factor F1 of the bonding activity index. F1 = (Diffusion time / Melt holding time) × P. For example, if the diffusion time is 20 seconds, the melt holding time is 30 seconds, and P is 5.33, then F1 = (20 / 30) × 5.33 ≈ 3.55. Simultaneously, the product of the temperature fluctuation rate (maximum change divided by the average value) and the humidity change rate is used as the second calculation factor. In this step, the temperature fluctuation rate W_T = (T_max - T_avg) / T_avg, and the humidity change rate W_H = (H_max - H_avg) / H_avg. The product of these two factors constitutes the second calculation factor F2: F2 = W_T × W_H. For example, if T_max = 170℃, T_avg = 165℃, H_max = 60%, and H_avg = 55%, then F2 = (5 / 165) × (5 / 55) ≈ 0.0033.

[0041] Finally, the two factors are standardized to the interval between zero and one and then multiplied. The result is the bonding activity index, which measures the instantaneous reaction potential of the interfacial bonding state under heat and humidity per unit time. The value range is set to zero to one, and three decimal places are retained. The standardization process uses the min-max normalization method, scaling F1 and F2 to the interval [0,1], denoted as F1′ and F2′ respectively. The final calculation result is: bonding activity index = F1 × F2′. For example, if F1 = 3.55, after normalization F1′ = 0.71; F2 = 0.0033, after normalization F2′ = 0.11, then the final index is 0.71 × 0.11 ≈ 0.078, and three decimal places are retained as 0.078. This value can be directly used as one of the input conditions of the path diversion mechanism, and together with the arrangement orientation index, it forms a two-dimensional index coordinate, serving as the basis for the subsequent adjustment command generation logic.

[0042] After the judgment conditions are met, the construction of the bonding activity index and the arrangement orientation index are carried out synchronously within the same time window. In the above, the "time window" refers to a unified time period used for data acquisition, status identification, and calculation during the processing. The start and end points of synchronization are set based on the system timestamp, with the unit being seconds. The start time of this time window is the beginning of the first sampling cycle of the structure optimization process, and the end time is the end point after all parameter sampling within this cycle is completed and the initial feature calculation is finished. A typical setting is ten seconds to ensure that the original data upon which the two indices depend are generated under completely consistent environmental parameters, thereby ensuring consistency, effectiveness, and comparability when their combined input to the path diversion mechanism. The construction of the bonding activity index depends on interfacial energy change, temperature and humidity change rate, and coupling diffusion behavior parameters, while the construction of the arrangement orientation index depends on fiber length distribution, orientation angle, and statistical distribution standard deviation. Both call different data processing models and acquisition algorithms, employing a parallel processing mechanism at the system level, synchronously calling their respective required data sources and entering the calculation process in real time. The processing flow employs parallel thread scheduling, using timestamp locking to ensure consistent sampling batches. This guarantees that within each ten-second window period, the values ​​obtained for the bonding activity index and the alignment orientation index are generated based on the same set of input data within that period. Therefore, when inputting the judgment engine in the subsequent path diversion mechanism using two-dimensional combined coordinates, data deviations caused by sampling time differences, processing delays, or external fluctuations are avoided. This ensures that the path activation judgment logic is based on the actual state and accurately reflects the processing behavior characteristics.

[0043] The activation of the path diversion mechanism is triggered by the combination of the orientation index and the bonding activity index. This combination is input into the path selection engine in the form of a two-dimensional numerical coordinate system. The path selection is divided into nine quadrants with the first value as the horizontal axis and the second value as the vertical axis. Each quadrant corresponds to one of the following: graph inference rules, boundary control logic, or expert processing model. The specific selection rules are as follows: when both the orientation index and the bonding activity index are less than 0.5, the boundary control logic is invoked; when both are greater than 0.5, the expert processing model is invoked; and for the remaining quadrants, graph inference rules are used. The orientation index and the bonding activity index are both real numbers ranging from 0 to 1, representing the consistency of the glass fibers' alignment towards the principal axis in the processing space and the response trend of the interface reaction system per unit time, respectively. In the two-dimensional coordinate system, the horizontal axis represents the orientation index, and the vertical axis represents the bonding activity index. The entire interval is divided into three equal parts into a 3×3 quadrant, with each quadrant corresponding to a path decision method, ensuring that the response results of the path diversion mechanism are interpretable and specific.

[0044] Graph reasoning rules are based on a priori path graph structure. They calculate the shortest adjustment response sequence in the node path and output the operation number. Specifically, the graph reasoning rules construct a directed graph containing preset processing state nodes and adjustment action nodes. Each node corresponds to a specific process state (such as fiber turbulence, interface bonding delay, etc.), and each directed edge represents the adjustment operation required to transition from one state to another. The initial node is mapped onto the graph using the two-dimensional coordinates corresponding to the current arrangement orientation index and bonding activity index. The shortest response path from the initial state to the target state (i.e., ideal arrangement-bonding state) is identified using a graph traversal algorithm (such as Dijkstra's shortest path algorithm), and the operation node number in the path is output as the basis for the subsequent dynamic adjustment instruction set. Processing variables and adjustment ranges are pre-stored in the nodes to achieve a closed loop from state recognition to specific operation mapping.

[0045] The boundary control logic constructs a gradient adjustment hierarchy table based on the deviation of the current indicator from the minimum threshold and adjusts variable parameters according to priority. This logic mode is suitable for regions where indicators are low and both indicators decrease simultaneously (i.e., both the orientation index and bonding activity index are below 0.5). First, the deviation values ​​ΔP and ΔK between the current value of each indicator and its minimum activation threshold (default is 0.4) are calculated. Then, the deviation values ​​are compared to construct a variable adjustment priority table, with larger deviation values ​​adjusted first. Based on the preset variable response curve, a corresponding gradient adjustment hierarchy table is generated, such as the processing temperature adjustment starting from +2℃ and increasing sequentially to +8℃ to achieve step-by-step activation of the response. The entire logic process does not rely on empirical templates but constructs a deviation-guided parameter evolution path in real time, suitable for basic adjustment scenarios that deviate from the ideal range and lack feature pattern support.

[0046] The expert processing model directly generates adjustment actions based on a predetermined empirical rule table. This model is suitable for situations where both the orientation index and bonding activity index are in the high range (i.e., greater than 0.5). The system assumes that the current processing stage already has a good orientation trend and reaction potential, and directly maps the optimal maintenance or fine-tuning parameters through the expert empirical rule table. The empirical rule table is trained from a large number of manually tuned samples and historical optimal parameter combinations, such as maintaining a stable shear rate in the later stages of the injection cycle and slightly increasing the injection pressure when the bonding trend is significant. Each rule consists of a condition item (dual index range, processing stage number) and an execution item (parameter category, adjustment range, execution order). No path calculation or deviation analysis is required; the adjustment command is directly output, achieving rapid response and experience-driven operation.

[0047] All three paths output dynamic adjustment instruction sets, which consist of three parts: the first part is the parameter adjustment category, selected from processing temperature, shear rate, cooling rhythm, and injection pressure; the second part is the application location, indicated by the processing runner segment number; and the third part is the execution sequence, indicated by the step execution number. The combination of these three parts constitutes a single instruction set, which is executed within the current processing cycle. Specifically, the parameter adjustment category is the type of adjustment operation, covering core thermal-fluid-pressure process variables; the application location refers to the injection runner segment to which the current adjustment command applies, with segment numbers sequentially according to the processing path direction; and the execution sequence is the actual execution order of the instruction within the current processing cycle, assigned in real-time by the system scheduling module. The dynamic adjustment instruction set is sequentially read by the process execution unit and applied in real-time during the processing, thereby achieving synchronous evolution control of fiber arrangement trends and interface reaction trends, supporting the achievement of structural optimization goals.

[0048] After processing, a phased cooling gradient is maintained through an isothermal thermal field to reconstruct structural stress and stabilize interfacial reactions, completing the structural arrangement and overall bonding process. The process of maintaining the phased cooling gradient through an isothermal thermal field includes the following steps: setting up a heat-sealed insulated chamber and immediately placing the molded structure into the temperature-controlled environment after processing. The isothermal thermal field consists of multiple distributed heating units, each maintaining a target constant temperature range with a temperature difference not exceeding three degrees Celsius. This constant temperature range is set between five degrees Celsius below the initial crystallization temperature of the polypropylene matrix and ten degrees Celsius above the glass transition temperature, with a control time of no less than twenty seconds. The heat-sealed insulated chamber is a thermally insulated and sealed operating container used to isolate it from external environmental disturbances. The temperature-controlled environment consists of multiple distributed heating units inside the sealed chamber. Each heating unit maintains its own temperature setpoint through an independent temperature control module and receives real-time temperature feedback signals. The maximum temperature difference between units does not exceed 3°C, ensuring a continuous isothermal thermal zone inside the chamber and avoiding localized rapid cooling. The target isothermal range is calculated and set based on the polypropylene crystallization initiation temperature (e.g., 120°C) and glass transition temperature (e.g., -10°C), typically set to 115°C to 0°C. The isothermal control phase lasts for at least 20 seconds to provide sufficient thermal stability conditions for interfacial reactions and internal stress relaxation.

[0049] The phased cooling gradient is established by sequentially employing zoned airflow control and time-series cooling. In the first phase, the external surface temperature decreases at a rate of one degree Celsius per second for fifteen seconds. Then, the intermediate heat exchange channel is switched, with a cooling rate set at 0.5 degrees Celsius per second for another twenty seconds. Finally, the cooling process switches to the core area until the temperature is reduced to ambient levels, with the total cooling time exceeding fifty seconds. Zoned airflow control utilizes a three-layer independent air channel system to achieve graded cooling rate control: in the first phase, the surface airflow channel is used to control the cooling air velocity and temperature, strictly limiting the surface temperature decrease rate to less than 1 degree Celsius per second; in the second phase, the intermediate heat exchange channel is activated, adjusting the cooling rate of the middle layer to 0.5 degrees Celsius per second through a pre-set cold air duct bypass channel, maintaining this for 20 seconds to ensure a smooth progression of the heat field from the outside in.

[0050] Finally, the cooling process switches to the core area, and the control process until the temperature is reduced to the ambient value includes: real-time monitoring of the temperature of the center of the formed structure using an embedded thermal probe; after the intermediate layer cooling maintenance is completed, the deep cooling air intake unit located at the bottom or center channel of the structure is activated; this air intake unit is a closed air chamber docking structure, and its outlet points directly to the center area of ​​the formed structure; the control unit sets the target temperature to ambient temperature ±2℃ and automatically adjusts the cooling air velocity to ensure that the cooling rate does not exceed 0.2℃ per second, preventing interface cracks or stress concentration caused by sudden cooling of the center area; the control logic continuously monitors the temperature change curve of the center area, and when the temperature curve remains within the set ambient temperature ±2℃ for 5 consecutive seconds, it is determined that the core area cooling is complete; the system enters the heat preservation static mode to form the final stable structure. This staged cooling mechanism achieves a thermal buffer transition process from the outside to the inside through hierarchical control in the dimensions of time, space, and temperature, preventing stress concentration or delamination defects caused by uneven cooling rate or interface embrittlement, and providing thermal foundation support for the long-term stability of the subsequent structure.

[0051] like Figure 2As shown, this diagram is a structural view of a flow battery electrode frame injection molded product, used to define the key functional surfaces of the injection molded product. Two important surface areas, "A-side" and "E-side," are marked. This diagram illustrates the definition of the key surfaces of the flow battery electrode frame injection molded product, indicating "A-side" (left image) from the front view and "E-side" (right image) from the back view. This type of housing has a plate-frame structure and is widely used in flow battery stacking modules, undertaking functions such as fluid guidance, sealing connection, and structural support. "A-side" is the front of the injection molded product and is one of the main functional interfaces, requiring strict standards for appearance and dimensions, including consistency, no burrs, and no white spots. "E-side" is the back of the product, typically containing key structures such as flow channels and injection grooves, and is the core control area for sealing performance and assembly precision. According to quality standards, both "A-side" and "E-side" are strictly controlled surfaces with the lowest defect tolerance, and defects affecting appearance or performance, such as black spots, scratches, white spots, burrs, and silver streaks, are not allowed. This diagram serves as one of the fundamental reference diagrams for incoming material inspection of injection molded parts and mold development, playing an important role in surface identification, structural positioning, and defect analysis.

[0052] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0053] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes 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.

[0054] 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.

[0055] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0056] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing the structural strength of glass fiber reinforced polypropylene for flow battery electrode frames, characterized in that, Includes the following steps: Raw processing data consisting of glass fiber content, fiber length distribution, polypropylene matrix melting temperature range, flow viscosity, interfacial coupling parameters and environmental process variables were collected. Input dimension groups were constructed and it was determined whether they met the criteria for entering the structural optimization process. After the judgment conditions are met, the data is processed by feature mapping to construct the arrangement orientation index representing the fiber arrangement state and the bonding activity index representing the molecular bonding trend. Based on the combined activation path diversion mechanism of arrangement orientation index and bonding activity index, the optimal path is selected by graph reasoning rules, boundary control logic or expert processing model to generate a dynamic adjustment instruction set for the current processing stage. This instruction set includes parameter adjustment category, position of action and execution order. The injection molding process is dynamically adjusted according to the set of instructions, and the multi-dimensional variables are linked and adjusted accordingly. After the process is completed, the cooling gradient of the operation control is maintained by the isothermal thermal field to complete the process of structural arrangement shaping and overall bonding state curing.

2. The method for optimizing the strength of glass fiber reinforced polypropylene structures for flow battery electrode frames according to claim 1, characterized in that, The steps for constructing the input dimension group include converting glass fiber content, fiber length distribution, polypropylene matrix melting temperature zone, flow viscosity, interfacial coupling parameters and environmental process variables into computable numerical variables, and constructing an input vector structure with six types of parameters as axes; Glass fiber content is converted to a percentage by weight ratio and melt volume percentage; The fiber length distribution was statistically analyzed using the standard optical particle size identification method, and the maximum, minimum and mode values ​​were calculated to form the length distribution interval. The melting temperature range of the polypropylene matrix was obtained by differential scanning calorimetry (DSC) to determine the initial melting temperature and the final melting temperature, and the center temperature of the representative range was calculated by linear interpolation. Within the set shear rate range, the flow viscosity is obtained by acquiring the shear-viscosity curve and taking the average value of the numerical integral at the corresponding temperature; The interface coupling parameter is based on the measured data of the change in interface energy. The calculation formula is: the interface coupling parameter P is equal to the product of the coupling agent concentration and the treatment time divided by the unit change in interface energy, specifically P=(C×T) / Δγ, where C is the coupling agent concentration, T is the coupling treatment time, and Δγ is the difference in interface energy before and after treatment. This parameter is used to evaluate the degree of interface bonding tendency. Environmental process variables include temperature, humidity, pressure, and time series.

3. The method for optimizing the strength of glass fiber reinforced polypropylene structures for flow battery electrode frames according to claim 2, characterized in that, The steps for determining whether the conditions for entering the structural optimization process are met include setting judgment threshold ranges for glass fiber content, fiber length distribution, polypropylene matrix melting temperature range, flow viscosity, interface coupling parameters, and environmental process variables based on the constructed input dimension group. A comprehensive matching judgment method is used to confirm whether the start criteria are met. If any item exceeds the set range, it is marked as not meeting the judgment conditions, and the start of the structural optimization process is automatically blocked; if all conditions are met, the process proceeds to the subsequent feature mapping step.

4. The method for optimizing the strength of glass fiber reinforced polypropylene structures for flow battery electrode frames according to claim 3, characterized in that, The construction of the arrangement orientation index includes the following steps: First, the fiber length distribution is divided into five length segments according to a preset length interval, denoted as the first segment to the fifth segment; The angle data is obtained by measuring the angle between the fiber orientation angle and the load principal axis within each length segment. The segments are divided into five-degree intervals, and the proportion of fibers with an angle between zero and thirty degrees in each segment is counted to the total number of fibers in that length segment. This proportion is recorded as the orientation degree of that segment. Then, the orientation degree of the five length segments is weighted and combined according to the proportion of their number of segments, and then multiplied by the inverse ratio of the standard deviation of the overall fiber length distribution. The specific calculation formula is: the arrangement orientation index is equal to the sum of the orientation degree of each segment multiplied by the proportion of the fiber quantity in that segment, and then multiplied by one minus the standard deviation of the fiber length distribution divided by the maximum length.

5. The method for optimizing the strength of glass fiber reinforced polypropylene structures for flow battery electrode frames according to claim 4, characterized in that, The construction of the bonding activity index includes the following steps: First, the ratio of coupling agent concentration percentage, reaction time in seconds and interfacial energy change value is measured, and then the interfacial coupling parameter P is calculated. Next, within the processing temperature range, the interface temperature and humidity are sampled every m seconds, and the maximum change and average value within m seconds are calculated respectively. Next, calculate the overlap rate between the melt holding time and the corresponding diffusion range of the coupling agent, that is, the ratio of diffusion time to melt state time, multiply by the interface coupling parameter, and use it as the result of the first step of the calculation. Meanwhile, the product of the temperature fluctuation rate (maximum change divided by average) and the humidity change rate is used as the second calculation factor; finally, the two factors are standardized to the range of zero to one and then multiplied together, and the result is the bonding activity index.

6. The method for optimizing the strength of glass fiber reinforced polypropylene structures for flow battery electrode frames according to claim 5, characterized in that, Once the criteria are met, the construction of the bonding activity index and the arrangement orientation index are carried out simultaneously within the same time window.

7. The method for optimizing the strength of glass fiber reinforced polypropylene structures for flow battery electrode frames according to claim 6, characterized in that, The activation of the path diversion mechanism is triggered by the combination of the arrangement orientation index and the bonding activity index. This combination is input into the path selection engine in the form of a two-dimensional numerical coordinate. The path selection is divided into nine regions with the first value as the horizontal axis and the second value as the vertical axis. Each region corresponds to one of the following: graph inference rules, boundary control logic, or expert processing model. The specific selection rules are as follows: when both the arrangement orientation index and the bonding activity index are less than 0.5, the boundary control logic is invoked; when both are greater than 0.5, the expert processing model is invoked; and the graph inference rules are used for the remaining regions.

8. The method for optimizing the strength of glass fiber reinforced polypropylene structures for flow battery electrode frames according to claim 7, characterized in that, The graph reasoning rules are based on the prior path graph structure, calculate the shortest adjustment response sequence in the node path, and output the operation number; the boundary control logic constructs a gradient adjustment hierarchy table based on the deviation of the current index from the minimum threshold, and adjusts the variable parameters according to priority; the expert processing model directly generates adjustment actions based on the mapping of the established empirical rule table. All three paths output a dynamic adjustment instruction set, which consists of three items: the first item is the parameter adjustment category, selected from processing temperature, shear rate, cooling rhythm, and injection pressure; the second item is the action location, represented by the processing flow channel segment number; and the third item is the execution sequence, represented by the step execution number. The combination of these three items constitutes a single instruction set, which is executed within the current processing cycle.

9. The method for optimizing the strength of glass fiber reinforced polypropylene structures for flow battery electrode frames according to claim 8, characterized in that, The process of maintaining the phased cooling gradient for operational control through an isothermal thermal field includes the following steps: A heat-sealed insulated chamber is set up, and the molded structure is placed in a temperature-controlled environment immediately after processing. The isothermal thermal field is composed of multi-point distributed heating units. Each unit maintains the target constant temperature range with a temperature difference of no more than three degrees Celsius. The constant temperature range is set between five degrees Celsius below the initial crystallization temperature of the polypropylene matrix and ten degrees Celsius above the glass transition temperature, and the control time is no less than twenty seconds. The phased cooling gradient is established by using zoned airflow control and time-series cooling methods. In the first phase, the temperature of the outer surface is controlled to decrease at a rate of one degree Celsius per second for fifteen seconds. Then, the heat exchange channel of the middle layer is switched, and the cooling rate is set to 0.5 degrees Celsius per second for another twenty seconds. Finally, the cooling process is switched to the core area until the temperature is reduced to the ambient value. The total cooling process takes more than fifty seconds.

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