Method and system for manufacturing high-performance steel fiber reinforced anti-crack concrete centrifugal electric pole

Abstract

The invention relates to the technical field of concrete electric pole manufacturing, and discloses a method and system for manufacturing a centrifugal electric pole based on high-performance steel fiber reinforced anti-crack concrete. The method comprises the following steps: collecting raw material performance index data including concrete rheological characteristics, steel fiber distribution and matrix strength data; an electric pole forming multi-scale coupling model is constructed through a material simulation platform; identifying fiber orientation characteristics according to steel fiber distribution data, comparing the fiber orientation characteristics with a fiber distribution-doping demand mapping table, judging whether to adjust a fiber doping strategy or not, and starting a manufacturing parameter decision process if the fiber doping strategy needs to be adjusted; stirring, centrifugal forming and maintenance parameters are extracted from preset rules in combination with performance index data, and details such as the fiber feeding time sequence, the rotating speed and the temperature and humidity gradient are determined; the model is used for simulating the whole manufacturing process, related indexes are output to evaluate the proportion, parameters and quality stability, an instruction optimization process is generated, and the manufacturing scientificity is improved.

Description

Technical Field

[0001] This invention relates to the field of concrete pole manufacturing technology, specifically to a method and system for manufacturing centrifugal poles based on high-performance steel fiber reinforced crack-resistant concrete. Background Technology

[0002] In the construction of infrastructure such as power transmission and communication transmission, centrifugal concrete poles have long held an important position due to their lightweight structure, moderate cost, and convenient installation. As infrastructure construction moves towards higher loads and more complex environments, higher demands are being placed on the mechanical properties of centrifugal poles, especially their crack resistance. Traditional centrifugal poles are mostly cast from ordinary concrete. During long-term service, they are prone to surface cracks due to factors such as temperature changes, load fluctuations, and environmental erosion. This not only affects the pole's appearance but also leads to corrosion of the internal reinforcing steel, shortening the pole's service life and increasing maintenance costs and safety hazards.

[0003] To improve the crack resistance of centrifugal poles, the industry has gradually explored incorporating steel fibers into the concrete matrix, utilizing the bridging effect of steel fibers to inhibit crack initiation and propagation. However, in actual manufacturing, the production of steel fiber reinforced crack-resistant concrete centrifugal poles still faces many technical challenges. First, the uniformity of steel fiber distribution in the concrete matrix is ​​difficult to control. Affected by mixing process parameters, fiber agglomeration or insufficient fiber content in certain areas can easily occur, leading to significant differences in crack resistance in different parts of the pole and failing to fully utilize the reinforcing effect of steel fibers. Second, during centrifugal molding, the setting of parameters such as rotation speed, acceleration, and mold vibration mode largely relies on accumulated experience and lacks precise matching with material performance indicators. This often results in insufficient density and excessively high internal porosity in the molded poles, further weakening their mechanical properties and crack resistance.

[0004] Temperature and humidity control during the curing stage is crucial for the development of concrete strength and the interfacial bonding strength between steel fibers and the matrix. Traditional curing methods often use fixed temperature and humidity conditions, failing to consider the hydration reaction characteristics of concrete with different mix proportions. This can easily lead to insufficient or excessive curing, affecting the final performance stability of the poles. Furthermore, existing manufacturing processes lack systematic simulation and evaluation methods for the entire process from mixing to curing. This makes it impossible to predict the rationality of material proportions and the suitability of process parameters in advance, often requiring multiple trial productions to adjust parameters. This not only increases production costs but also extends the production cycle, making it difficult to meet the demands of large-scale, high-quality production. These problems restrict the widespread application of steel fiber reinforced crack-resistant concrete centrifugal poles, necessitating a scientific and efficient manufacturing method to address them. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for manufacturing centrifugal poles based on high-performance steel fiber reinforced crack-resistant concrete, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a method for manufacturing centrifugal poles based on high-performance steel fiber reinforced crack-resistant concrete, the method comprising: Collect performance data of raw materials required for the manufacture of centrifugal poles, including concrete rheological properties data, steel fiber distribution data, and matrix strength data; Based on the aforementioned performance index data, a multi-scale coupled model of the corresponding pole forming process is constructed using a material simulation platform. Based on the steel fiber distribution data, identify the fiber orientation characteristics and compare them with the fiber distribution-incorporation demand mapping table to determine whether the fiber incorporation strategy needs to be adjusted. If adjustment is required, initiate the manufacturing parameter decision process. By combining performance index data, mixing process parameters, centrifugal molding parameters, and curing control parameters are extracted from the preset parameter decision rules. The mixing process parameters include fiber feeding sequence, mixing rate and duration; the centrifugal molding parameters include centrifugal speed, acceleration curve and mold vibration mode; and the curing control parameters include temperature and humidity gradient and duration. The multi-scale coupling model is used to simulate the entire manufacturing process from mixing to curing, outputting the steel fiber dispersion state, interfacial bonding strength and crack suppression effect. Based on this, the rationality of material ratio, the adaptability of process parameters and the stability of molding quality are evaluated, and parameter adjustment instructions are generated to optimize the manufacturing process.

[0007] Preferably, the specific construction process of the multi-scale coupling model is as follows: The rheological properties data of concrete, the distribution data of steel fibers, and the strength data of the matrix are normalized and converted into a standardized format that can be analyzed by the material constitutive model. A model of the interaction between steel fiber and concrete matrix was established using discrete element analysis, and the strength properties of the material were assigned based on the matrix strength data. A predictive model for material flow and fiber orientation during the pole forming process is constructed by combining centrifugal dynamics equations and fluid kinematics equations.

[0008] Preferably, the specific determination process for adjusting the fiber incorporation strategy includes: Fiber length distribution, aspect ratio, and spatial density indices are extracted from steel fiber distribution data. According to the preset fiber clustering rules, steel fibers with similar orientation characteristics are divided into the same orientation group; Calculate the average orientation angle and distribution dispersion of each orientation group, and compare them with the threshold in the fiber distribution-incorporation demand mapping table. If the dispersion of any orientation group exceeds the threshold or the average orientation angle deviates from the ideal range, it is determined that the manufacturing parameter decision process needs to be initiated.

[0009] Preferably, the specific content of the fiber clustering rule is as follows: Steel fibers that are spatially adjacent and whose orientation angle deviation is within the allowable range are grouped into the same orientation group; The density-weighted average of the fibers in each group is used as the representative orientation angle of that group, and the maximum dispersion within the group is used as the distribution dispersion index of that group.

[0010] Preferably, the preset parameter decision rules include stirring process parameter decision rules, centrifugal molding parameter decision rules, and curing control parameter decision rules, wherein the stirring process parameter decision rules are as follows: Based on the aspect ratio in the steel fiber distribution data and the viscosity coefficient in the matrix strength data, query the stirring rate correction coefficient corresponding to the fiber suspension characteristics in the database; The actual mixing rate is calculated based on the correction factor and the reference mixing rate, and the time interval for segmented feeding is determined according to the fiber length distribution. The initial setting time in the matrix strength data and the slump loss rate in the rheological properties were used as the basis for controlling the total stirring time.

[0011] Preferably, the specific content of the centrifugal molding parameter decision rule is as follows: Based on the spatial density distribution in the steel fiber distribution data and the yield stress in the rheological properties of concrete, the optimal centrifugal acceleration curve in the database is matched. The centrifugal force gradient is calculated based on the mold structure and pole dimensions, and the vibration frequency and amplitude of the mold are dynamically adjusted in combination with the fiber orientation characteristics.

[0012] Preferably, the evaluation method for the rationality of the material ratio includes: Compare the degree of deviation between the simulated steel fiber dispersion state and the ideal dispersion state; If the proportion of fiber agglomeration area exceeds the allowable threshold or the local density variation coefficient is greater than the critical value, the material ratio is deemed unreasonable. If the fiber distribution is uniform and the orientation angle meets the design requirements, then the material ratio is considered reasonable.

[0013] Preferably, the evaluation method for the adaptability of the process parameters includes: The theoretical critical anchorage length of the fiber is calculated based on the tensile strength in the matrix strength data and the simulated interface bonding strength. Compare the average fiber length in the actual fiber distribution data with the theoretical critical anchorage length; If the average fiber length reaches a preset multiple of the theoretical value and the interfacial bonding strength meets the design benchmark, then the process parameters are deemed to be compatible.

[0014] Preferably, the method for evaluating the stability of molding quality includes: Statistical simulation of crack suppression effect: number of cracks and propagation depth; If the width of the main crack is less than the allowable value and the crack path is effectively blocked by the fiber, the molding quality is considered stable. If a through crack or a crack width exceeds the limit, the molding quality is considered unstable.

[0015] Preferably, the present invention also includes a system for manufacturing centrifugal poles based on high-performance steel fiber reinforced crack-resistant concrete. The system includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the above-described method for manufacturing centrifugal poles based on steel fiber reinforced crack-resistant concrete.

[0016] Compared with the prior art, the beneficial effects of the present invention are: By collecting performance data of the raw materials required for centrifugal pole manufacturing, precise basic data support is provided for subsequent manufacturing processes, avoiding parameter setting deviations caused by insufficient understanding of material properties in traditional manufacturing. A multi-scale coupled model constructed based on performance data can comprehensively simulate the pole forming process, clearly presenting the dispersion state of steel fibers in the concrete matrix, interfacial bonding, and crack suppression effect. This makes key performance changes during manufacturing observable and analyzable, eliminating reliance on experience-based judgment and improving control over the manufacturing process.

[0017] In the steel fiber incorporation strategy adjustment stage, by identifying fiber orientation characteristics and comparing them with the fiber distribution-incorporation requirement mapping table, situations where fiber distribution does not meet requirements can be identified in a timely manner. This allows for the initiation of the manufacturing parameter decision-making process for adjustment, effectively solving the problem of uneven steel fiber distribution in traditional manufacturing. This ensures that the steel fibers are uniformly distributed in the matrix, fully leveraging their bridging role and guaranteeing the consistency of crack resistance across all parts of the pole. By combining performance index data with preset parameter decision rules, key process parameters for the mixing, centrifugal molding, and curing stages are extracted, achieving precise matching between process parameters and material properties, avoiding the limitations of traditional empirical parameter settings. Specifically, the clear definition of fiber feeding sequence, mixing rate, and duration in the mixing process parameters effectively prevents fiber agglomeration and further improves fiber dispersion uniformity. The optimization of rotation speed, acceleration curves, and mold vibration modes in the centrifugal molding parameters improves the pole's molding density, reduces internal porosity, and enhances the pole's mechanical properties. The precise setting of temperature and humidity gradients and duration in the curing control parameters adapts to the hydration reaction characteristics of concrete with different mix proportions, promoting stable concrete strength development and strengthening the interfacial bonding strength between the steel fibers and the matrix.

[0018] By simulating the entire manufacturing process using a multi-scale coupled model and outputting key performance indicators, the rationality of material proportions, the suitability of process parameters, and the stability of molding quality can be assessed in advance. Potential problems can be identified without multiple trial productions, and the manufacturing process can be optimized based on parameter adjustment instructions. This reduces the number of trial productions, lowers production costs, shortens the production cycle, and ensures the stability and consistency of product quality. Furthermore, the entire manufacturing method forms a complete closed loop from raw material performance analysis, parameter decision-making, process simulation to process optimization, improving the scientific rigor and efficiency of steel fiber reinforced crack-resistant concrete centrifugal pole manufacturing. This will help promote the widespread application of this type of pole in high-load, complex environments, meet the demand for high-quality poles in infrastructure construction, reduce subsequent maintenance costs, and extend the service life of the poles. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the working principle of the method for manufacturing centrifugal poles based on high-performance steel fiber reinforced crack-resistant concrete as described in this invention. Figure 2 Flowchart for constructing a multi-scale coupling model; Figure 3 A flowchart for determining stirring process parameters. Detailed Implementation

[0020] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 This invention provides a method for manufacturing centrifugal poles based on high-performance steel fiber reinforced crack-resistant concrete. The method includes: collecting raw material performance data required for the manufacturing process, specifically including rheological property data reflecting the workability of concrete, steel fiber distribution data describing the dispersion of steel fibers in the matrix, and matrix strength data characterizing the mechanical properties of hardened concrete. Based on the collected performance data, a multi-scale coupled model capable of simulating the entire process of pole fabrication from material feeding to molding is constructed using a professional material simulation platform. This model integrates microscopic fiber-matrix interactions with macroscopic centrifugal molding dynamics. The orientation characteristics of the fibers in space are identified based on the steel fiber distribution data, and the identified characteristics are compared with a preset fiber distribution-admixture requirement mapping table to determine whether the current fiber distribution meets the crack resistance requirements. If the comparison result exceeds the tolerance range, a manufacturing parameter decision process is triggered.

[0022] During the parameter decision-making phase, key manufacturing process parameters are extracted from a pre-set parameter decision rule library based on initial performance index data. These parameters cover three core stages: mixing, centrifugal molding, and curing. Mixing parameters include the timing of steel fiber addition, mixer speed setting, and total mixing time. Centrifugal molding parameters include the centrifuge speed curve, acceleration variation law, and mold vibration mode. Curing control parameters include the temperature and humidity gradients within the curing kiln and the duration of each stage. A pre-constructed multi-scale coupled model dynamically simulates the complete manufacturing process, from raw material mixing through centrifugal molding to final curing. Key intermediate results output by the model include the final dispersion state of steel fibers in concrete, the interfacial bonding strength between fibers and the concrete matrix, and the concrete's resistance to crack initiation and propagation (crack suppression effect). Based on these simulation outputs, the system automatically evaluates the rationality of the currently set material ratio, whether the adopted process parameters match the material properties, and whether the final pole molding quality is stable and controllable. Based on the evaluation conclusions, specific parameter adjustment instructions are generated and fed back to the manufacturing execution system to optimize the actual production process.

[0023] Example 1: See Figure 2The acquisition of raw performance data is the starting point of the entire process. Concrete rheological properties are measured using a rheometer to obtain parameters such as viscosity and yield stress of the slurry. Steel fiber distribution data is obtained through industrial CT scans or analysis of images from ready-mixed concrete samples, including the spatial coordinates, orientation angles, and length information of the fibers. Matrix strength data comes from the compressive and tensile strength values ​​obtained after standard tests on raw materials such as cement, aggregates, and admixtures. Because these raw data come from diverse sources, have different dimensions, and significantly different numerical ranges, they must undergo rigorous normalization before they can be effectively analyzed by the subsequent mathematical model. The normalization process linearly or non-linearly transforms each index data to the [0,1] interval or a format conforming to a standard normal distribution according to a preset algorithm. For example, it scales the huge viscosity values ​​measured by the rheometer to a decimal range acceptable to the model and converts the pixel coordinates obtained from CT scans into a unified physical coordinate system. This step eliminates the influence of dimensions and lays the data foundation for the accurate construction of the multi-scale model. After data standardization is completed, the construction of the multi-scale coupled model begins. The core of this model lies in connecting the microscopic fiber-matrix interaction with the macroscopic centrifugal forming process. At the microscale, a discrete element analysis (DEM) model of the interaction between steel fiber and concrete matrix is ​​established. The concrete matrix is ​​discretized into a collection of particles of different sizes, while the steel fiber is modeled as a collection of deformable or rigid cylindrical or chain-like particles. Material properties derived from normalized matrix strength data are assigned to these discrete elements, such as interparticle bond strength, friction coefficient, and elastic modulus. This model can simulate how the fiber collides, slips, and rotates with the surrounding slurry and aggregate particles under shear force, thereby predicting the initial motion trajectory and distribution trend of the fiber. At the macroscopic scale, it is necessary to construct a predictive model that reflects the overall flow of materials and the evolution of fiber orientation during centrifugal molding. This model combines centrifugal dynamics equations and fluid kinematics equations. Centrifugal dynamics equations describe the centrifugal acceleration generated by the mold rotation and the volume force effect of the Coriolis force on the material, while fluid kinematics equations describe the flow, spreading, and compaction process of non-Newtonian fluids such as concrete under the action of centrifugal force field. By coupling the local slurry rheological parameters calculated by the microscopic discrete element model with the macroscopic fluid equations, it is possible to predict how steel fibers move together with the concrete slurry and eventually form an orientation distribution under specific centrifugal rotation speed and acceleration curves.

[0024] Determining the need to adjust fiber incorporation strategies is a data-driven rule-based analysis process that runs concurrently with model building. The determination begins with deep feature extraction from steel fiber distribution data. From the raw fiber position and orientation data, the number of fibers within a unit volume is calculated to obtain a spatial density index. The length values ​​of all fibers are statistically analyzed to form a length distribution histogram, and the aspect ratio of each fiber is calculated. These feature indices quantify the basic geometric properties of the fiber population. Subsequently, based on pre-defined fiber clustering rules, the massive amount of individual fiber data is grouped and classified. The clustering rules define "spatial proximity" as fibers with a distance less than a set threshold in three-dimensional Euclidean space, and "orientation angle deviation within the allowable range" as the cosine of the angle between fiber orientation vectors being greater than a certain critical value. Fibers meeting these two conditions are grouped into the same orientation group. This simplifies tens of thousands of individual fibers into several statistically representative fiber groups, significantly reducing the complexity of subsequent calculations.

[0025] For each fiber orientation group formed through clustering, it is necessary to calculate the average orientation angle characterizing the overall fiber orientation direction within the group and the distribution dispersion reflecting the consistency of fiber orientation within the group. The average orientation angle is usually taken as the vector sum of all fiber orientation vectors within the group, or as the arithmetic mean or density-weighted average of the fiber orientation angles. The distribution dispersion is usually represented by the standard deviation or variance of the orientation angles. These calculated statistics need to be compared with a pre-established fiber distribution-incorporation requirement mapping table. This mapping table is a knowledge base that stores the ideal fiber distribution parameters and their allowable fluctuation thresholds for different pole types and different stress requirements. For example, for poles that mainly bear bending moments, the ideal fiber orientation angle in the tension zone may need to be as parallel to the axis as possible, and the dispersion needs to be controlled within a small range. The system logically compares the average orientation angle and distribution dispersion of each orientation group with the threshold of the corresponding item in the mapping table. If any fiber dispersion exceeds the upper limit of the threshold, or its average orientation angle deviates from the ideal range and the deviation is greater than the tolerance, a flag is triggered, indicating that the current fiber distribution does not meet the crack resistance design expectations. The manufacturing parameter decision process needs to be initiated to adjust the fiber incorporation strategy, such as changing the fiber feeding method or adjusting the centrifugation process parameters. If the parameters of all orientation groups are within the threshold, the current strategy is deemed feasible and no adjustment is needed. The implementation process demonstrates the tight integration of data preprocessing, physical modeling, and rule judgment. Normalization ensures the consistency of multi-source heterogeneous data in the model. The coupled model of discrete element method and continuum mechanics enables cross-scale simulation from microscopic interactions to macroscopic forming effects. The fiber incorporation strategy judgment based on clustering and threshold comparison directly links simulation prediction with production control decisions, forming a closed-loop feedback system from data to model to decision. The effective operation of this system depends on the precise execution and seamless connection of each link.

[0026] Example 2: See Figure 3 The refined execution of fiber clustering rules and the specific application of stirring process parameter decision rules are crucial. The precise definition of fiber clustering rules forms the basis for subsequent fiber distribution feature extraction and process decision-making. The rules first clarify the quantitative standard of "spatial proximity." This standard is not a simple straight-line distance judgment, but rather comprehensively considers the potential local flow field influences of fibers in the rheological slurry. Typically, the criterion is whether the Euclidean distance between the fiber centroids is less than a threshold dynamically adjusted based on the average fiber length and the stirring tank size. Simultaneously, "orientation angle deviation within the allowable range" means that the angle between the direction vectors of two fibers is less than a preset angular tolerance. This tolerance range is usually determined based on the crack resistance requirements of the pole product. For example, for areas requiring fibers to be mainly arranged in a one-dimensional orientation, this tolerance angle will be set smaller. When a batch of fiber data simultaneously meets both spatial proximity and directional similarity conditions, they are grouped into the same orientation group. This grouping method effectively distinguishes fiber communities with different orientation characteristics caused by flow field vortices or uneven feeding. After initial fiber clustering, representative statistical parameters need to be calculated for each orientation group. The density-weighted average of fibers in each group is used as the calculation method for the representative orientation angle of that group. This fully considers the impact of uneven fiber spatial distribution on the overall orientation. Instead of simply averaging the orientation angles of all fibers within a group, the calculation multiplies the orientation angle of each fiber by the fiber density weighting factor of its local region before weighting. This approach ensures that the orientation characteristics of densely fiberd areas contribute more significantly to the group's average, better reflecting the dominant orientation in that region. The dispersion of fiber distribution within a group is characterized by the standard deviation of the orientation angles of all fibers within the group or the magnitude of the calculated orientation vector. The closer the magnitude is to 1, the more concentrated the fiber orientations and the smaller the dispersion. This index quantifies the consistency of fiber orientations within a group and is an important basis for judging the uniformity of fiber distribution.

[0027] The implementation of the mixing process parameter decision rule is a dynamic process based on material property query and calculation. The core of this rule lies in linking the geometric characteristics of steel fibers, the rheological properties of the concrete matrix, and the operating parameters of the mixing equipment. The decision-making process begins with extracting the aspect ratio index from the steel fiber distribution data and obtaining the viscosity coefficient reflecting the slurry viscosity from the matrix strength data. The system uses these two key parameters to query the built-in material property database, which stores a table of correspondences between fiber suspension characteristics and mixing rate correction coefficients established through numerous process experiments. For example, for high aspect ratio fibers in high-viscosity slurries, a higher shear rate is required to prevent fiber settling and agglomeration; therefore, the obtained correction coefficient may be greater than 1. The system multiplies the obtained correction coefficient by a preset benchmark mixing rate based on pole specifications and production capacity requirements to calculate the actual mixer speed to be used under the current material combination. This speed must generate sufficient shear force to ensure uniform fiber dispersion, but it cannot be too high to avoid fiber breakage or slurry segregation.

[0028] The determination of fiber feeding sequence is closely related to fiber length distribution data. The decision-making rule usually follows the principle of batch feeding, setting different feeding time points for fibers of different lengths. Longer fibers, because they are more likely to entangle or form spherical agglomerates, are usually fed after the dry materials (such as aggregates and cement) have been added and pre-mixed for a period of time, but before all the mixing water has been added. This allows for initial dispersion using the relatively dry mixture. Shorter fibers, on the other hand, can be fed after most of the mixing water has been added, during the middle of the mixing process, to disperse them using the already fluidized slurry. The time interval for segmented feeding is set based on the median and standard deviation of the fiber length distribution. The feeding interval between long and short fibers must ensure that the previous batch of fibers has been initially dispersed, thereby avoiding interference between fibers of different specifications.

[0029] Controlling the total mixing time is a comprehensive decision that requires balancing the requirements of fiber dispersion uniformity and maintaining concrete workability. The decision is mainly based on the initial setting time of cement from the matrix strength data and the slump loss rate measured from the concrete rheological properties. The total mixing time must be set with sufficient safety margin before the cement paste initially sets, while also taking into account the process interval between the end of mixing and the pouring of concrete into the centrifuge mold. On the other hand, excessive mixing time will exacerbate the slump loss of concrete, which may lead to insufficient fluidity of the paste during centrifugation and affect the compaction effect. Therefore, the system predicts the slump value at the time of discharge under different mixing times based on the slump loss rate model to ensure that it meets the centrifugation process requirements. The final determined total mixing time is the optimal solution under the combined constraints of initial setting time and workability requirements.

[0030] Example 3: The system reads the fiber spatial density distribution map generated from the steel fiber distribution data. This map, presented as a three-dimensional point cloud or voxel mesh, visually reflects the degree of fiber aggregation and distribution density in different regions of the mold space. Simultaneously, it retrieves the slurry yield stress value, precisely measured through rheological experiments, from the concrete rheological property data. This value characterizes the minimum shear stress required for the concrete mixture to begin flowing. These two core parameters—the spatial distribution morphology of the fibers and the deformation resistance of the base slurry—constitute the main basis for selecting centrifugal dynamics parameters. The system combines their values ​​and matches them with a large number of cases pre-stored in the process database. This database accumulates validated optimal centrifugal acceleration curves for different formulations and mold sizes. Each curve precisely defines the functional relationship between the centrifuge spindle speed and time, including the duration of the initial low-speed paving stage, the slope of acceleration to rated speed, the stable speed value of the high-speed compaction stage, and the curve shape of the final deceleration stage.

[0031] After matching the most suitable baseline centrifugal acceleration curve, a more refined centrifugal force field analysis is required. This analysis is based on the specific structural parameters of the mold, such as the cylinder length, end diameters, presence of taper, and wall thickness design requirements. By combining the angular velocity value at any moment in the acceleration curve, the centrifugal acceleration experienced by any point within the mold during rotation can be calculated. This acceleration is not uniformly distributed but is proportional to the radial distance from the point to the axis of rotation, thus forming a centrifugal force gradient from the inner wall to the outer wall and from the small diameter end to the large diameter end. This calculated force gradient distribution information, combined with fiber orientation characteristics extracted from fiber distribution data, is used to dynamically control the operating parameters of the vibration system installed on the mold. For example, when the system detects that the deviation of the fiber orientation from the ideal direction in a certain area of ​​the mold exceeds a threshold during a specific centrifugal stage, it generates a control command to adjust the vibration frequency and amplitude acting on the mold during that time period. The adjustment amount is guided by the following relationship:

[0032]

[0033] Where: symbol The vector representing the adjustment amount of the vibration parameters (including frequency and amplitude) that need to be applied, with the symbol... It is a coupling coefficient related to the specific characteristics of the vibrating equipment and the properties of the concrete mixture. This coefficient is determined through prior experiments and stored in the system. The symbol is... This represents the actual average orientation angle of the fiber at the current moment, identified from the simulation data, with the sign... This indicates the ideal orientation angle that the fibers are expected to achieve during this centrifugation stage and at the current position of the mold. This ideal value is derived from the product's crack resistance design model. (Symbol: ...) This represents the centrifugal force gradient vector at that location, calculated previously. This relationship demonstrates how the mechanical environment of the fiber can be locally altered by introducing controlled external mechanical vibrations, thereby correcting its trajectory and compensating for an ideal orientation distribution that may not be achievable solely through centrifugal force.

[0034] The assessment of material proportioning rationality is a separate analytical process initiated after the centrifugal molding simulation cycle. This assessment aims to determine whether the current combination and proportion of raw materials can support the formation of an ideal fiber-reinforced concrete microstructure. The core of the assessment is to compare the final three-dimensional model output from the multi-scale coupled model, reflecting the spatial distribution of steel fibers in the hardened concrete matrix, with a pre-stored digital benchmark in the database, set for the ideal dispersion state and mechanical performance requirements of this type of pole. This digital benchmark is not a single image but rather a set of quantitative indicators regarding fiber spacing, allowable fluctuations in local fiber density, and the preferred range of fiber orientation angles in different regions.

[0035] The evaluation algorithm automatically calculates two key quantitative indicators: the proportion of fiber agglomeration area and the local fiber density variation coefficient. In the simulated 3D dispersion map, based on a preset fiber concentration threshold, the algorithm automatically identifies volumetric unit clusters with significantly higher fiber counts than the surrounding areas, defining these clusters as fiber agglomeration regions. It then calculates the ratio of the total projected area of ​​all these agglomeration regions on a specified evaluation section (usually a critical stress section) to the total area of ​​that section. Simultaneously, the entire 3D model space is divided into numerous small volumetric units, and the number of fibers contained in each unit is counted to obtain the unit fiber density. The ratio of the standard deviation to the average density of all units is then calculated, i.e., the local fiber density variation coefficient. If the calculated proportion of fiber agglomeration area exceeds the maximum allowable threshold set according to product safety level and performance requirements, or if the local fiber density variation coefficient is greater than a critical value, the evaluation logic will determine that the current material mix is ​​unreasonable. This is because such uneven dispersion may cause stress concentration in the concrete under load, significantly reducing the reinforcing and toughening effect of the fibers. At this point, the system will generate detailed adjustment suggestions, which may involve changing the gradation or maximum particle size of the coarse aggregate to improve workability, adjusting the dosage of admixtures such as water-reducing agents to change the slurry viscosity, or reconsidering the fiber type, aspect ratio, and volumetric content. Conversely, if the simulation results show that the fibers are spatially distributed continuously in the matrix without large-scale obvious agglomeration or sparse areas, and statistical analysis shows that the overall orientation distribution of the fibers meets the design expectations, then the current material ratio is considered reasonable from the perspective of fiber dispersion, and can ensure the final crack resistance of the pole.

[0036] Example 4: Evaluation of process parameter suitability. This evaluation aims to determine whether the adopted process parameters, such as stirring and centrifugation, can effectively activate the reinforcing effect of the fibers. Its core lies in analyzing the interfacial bonding between the fibers and the concrete matrix. The evaluation process begins with the calculation of key mechanical parameters. The system reads the tensile strength values ​​of concrete obtained through standard tests from the matrix strength data, and simultaneously acquires the fiber-concrete matrix interfacial bonding strength distribution cloud map output from the multi-scale coupled model simulation. Representative statistical values ​​(such as average values ​​or specific quantile values) are extracted from this cloud map. Based on these strength data, the system uses classical fiber-reinforced mechanics theory to calculate the theoretical critical anchorage length of the fibers. This length refers to the minimum embedding length required for the fibers to be fully bonded in the concrete matrix, thereby allowing their tensile strength to be fully realized. Its calculation depends on parameters such as the matrix tensile strength, interfacial bonding strength, and the ultimate tensile strength of the fibers themselves.

[0037] After the theoretical critical anchorage length of the fiber is calculated, the evaluation logic compares it with the actual average fiber length obtained from the steel fiber distribution data. This average fiber length is the statistical result of the final state of all fibers in the concrete matrix after the simulation process. The purpose of the comparison is to verify whether the process parameters are sufficient to ensure that the fibers maintain a sufficient effective length after molding to play a role in bridging cracks. The evaluation rules stipulate that the actual average fiber length must reach a certain preset multiple of the theoretical critical anchorage length in order to be considered as having an "effective" fiber length.

[0038] To illustrate this evaluation process more concretely, consider a simulation analysis example for a specific type of utility pole. Assume that after the simulation is completed, the system extracts a set of key data, as shown in Table 1.

[0039] Table 1: Key data from a process parameter compatibility assessment

[0040] Based on the data in the table, the system performs logical judgments. First, it calculates the minimum effective fiber length threshold that meets the requirements: the theoretical critical anchorage length of the fiber (28.1 mm) multiplied by a preset safety factor (1.15) yields a threshold of approximately 32.3 mm. Then, it compares the actual average fiber length (32.5 mm) obtained after simulation with this threshold. 32.5 mm > 32.3 mm, thus meeting the length requirement. Next, the system checks the interface bond strength condition, comparing the average simulated interface bond strength (3.2 MPa) with the design benchmark value determined according to the mix design and specification requirements (e.g., not less than 3.0 MPa). 3.2 MPa > 3.0 MPa, thus meeting the strength requirement.

[0041] Since the data in the above examples simultaneously met both conditions—average fiber length reaching the threshold and interfacial bonding strength exceeding the design baseline—the system determined that the currently set mixing process parameters and centrifugal molding parameters were compatible with the material properties used. This determination means that the current process parameters successfully protected the fibers during manufacturing, preventing excessive breakage and maintaining sufficient effective length. Simultaneously, the process also fostered a good fiber-matrix interface microstructure, resulting in sufficient interfacial bonding strength. Such process parameter settings are considered effective, ensuring that the fibers are effectively pulled out rather than slipped when concrete cracks, thus fully utilizing their crack-resistant and reinforcing functions. If the simulation results show that the average fiber length is lower than the calculated threshold, it indicates a potential problem with the process parameters. For example, excessively high mixing speed or time may cause excessive fiber breakage by the mixing blades, or severe collisions between the fibers and the mold or aggregate during centrifugation may cause damage. In this case, the system will generate parameter adjustment instructions, suggesting reducing the mixing speed, shortening the mixing time, or optimizing the centrifugal acceleration curve to reduce mechanical damage to the fibers. Conversely, if the interfacial bonding strength is lower than the design baseline, it may indicate that the process parameters have failed to promote good interfacial formation. For example, improper water-cement ratio, uneven mixing leading to weak areas, or an inappropriate curing regime causing microcracks in the interfacial area may be contributing factors. The system will then suggest adjusting the water content in the mix design, optimizing the mixing process to ensure uniformity, or modifying the temperature and humidity control parameters for curing.

[0042] The assessment of process parameter adaptability is a crucial step in optimizing the entire manufacturing process. It links abstract process parameter settings with specific microscopic mechanical properties, and the evaluation criteria directly target two core elements for the effective functioning of fibers: sufficient length and strong bond. Through this quantitative assessment based on mechanical principles and simulation data, the system can transcend the traditional extensive control method that relies on the strength of macroscopic test blocks, achieving precise diagnosis and targeted optimization of the effects of process parameters. The implementation of this embodiment ensures that each key process parameter is not set in isolation, but closely serves the core performance of the final product: crack resistance, forming an intelligent closed loop from process setting to performance prediction to parameter feedback. This evaluation mechanism enables the manufacturing process to have the ability to continuously self-optimize, dynamically adjusting process parameters to address fluctuations in raw material batches or different product specification requirements, thereby stably producing high-performance steel fiber reinforced crack-resistant concrete centrifugal poles.

[0043] Example 5: The simulation process applies mechanical loads equivalent to actual service conditions, such as simulating the stress state of a utility pole under bending moment, wind load, or conductor tension. The system then scans the simulation results, identifying and recording all microcracks that initiate and propagate in the concrete matrix. The evaluation algorithm accurately counts the total number of cracks appearing at a specified load level and measures the straight-line distance or curved distance along the crack path of each identifiable crack as its propagation depth, while also recording the maximum opening width of all cracks under load. The evaluation logic first checks for the presence of penetrating cracks, which typically refer to cracks whose path has penetrated the entire cross-sectional wall thickness of the pole or connect multiple main cracks to form a continuous weak surface. In the 3D simulation results, the algorithm detects whether crack surfaces extend from an inner surface to an outer surface, or whether cracks extending in different directions intersect in space to form a penetrating defect network. If any such through-crack is detected, regardless of its width or size, the system will immediately determine that the molding quality is unstable, because through-cracks will severely damage the integrity of the structure and greatly reduce its load-bearing capacity and durability.

[0044] Assuming no through-cracks are found, the evaluation shifts to a detailed analysis of the width of major cracks, typically defined as those exceeding a set threshold depth (e.g., more than one-tenth of the wall thickness) or located in critical stress areas. The system measures the opening width of each major crack when the simulated load reaches its maximum value and compares each measurement to the allowable limit specified in product design standards or relevant specifications. This allowable limit is pre-set based on the pole's operating environment and durability requirements; for example, it might require a crack width not exceeding 0.1 mm. If any major crack is found to exceed this limit, a determination of unstable forming quality is triggered, as excessively wide cracks may indicate an increased risk of rebar corrosion or a decrease in structural stiffness. Beyond the crack geometry, the evaluation delves into the interaction between crack propagation behavior and steel fibers, crucial for determining fiber effectiveness. The algorithm tracks the propagation path of each major crack, examining the fiber distribution in front of and to the sides of the crack tip. Effective crack suppression manifests as the crack encountering steel fibers across its path during propagation. The fibers, through bridging, generate a closing stress at the crack tip, thereby preventing or significantly delaying further propagation. Simulation results will show whether the crack path is deflected, bypassed, or terminated due to the presence of fibers. If the simulation shows that most major crack paths are effectively blocked by fibers—that is, the crack stops propagating upon encountering the fibers or requires a significantly increased load to bypass them—then this indicates that the toughening mechanism of the fibers is fully utilized.

[0045] If the simulation results show no through-cracks, all major crack widths are less than the allowable value, and the crack propagation paths clearly show signs of being effectively blocked or significantly deflected by the fibers, then the system determines that the forming quality of the pole is stable under the combined material ratio and process parameters, and its crack resistance meets the expected requirements. For example, in a simulation, the system may count five microcracks under standard bending load, with the deepest crack extending 15 mm. However, the maximum width of all cracks does not exceed the limit of 0.08 mm, and the simulation animation clearly shows that a crack that might have extended longitudinally is effectively limited to a local area after encountering three fibers distributed at different angles, failing to form a continuous failure path. In this case, the forming quality stability assessment will pass. Conversely, if the simulation results show cracks penetrating the cross-sectional wall thickness, or if the width of a major crack reaches 0.15 mm, far exceeding the limit of 0.1 mm, even if the fiber distribution appears uniform, the system will determine that the forming quality is unstable. This determination implies a defect in the current material composition or manufacturing process, failing to effectively control crack development. An unstable determination triggers the system's optimization instruction generation process. Instructions may direct adjustments to the material mix, such as increasing fiber content or selecting a higher-toughness concrete matrix; or they may direct optimization of process parameters, such as adjusting the centrifugation speed to improve fiber orientation, making it more perpendicular to the principal tensile stress direction; or modifying the curing regime to reduce the generation of initial microcracks.

[0046] The assessment of molding quality stability is the final acceptance step in the manufacturing process simulation. It directly links all previous microscopic and mesoscopic analyses of material proportions, fiber distribution, and interfacial bonding with macroscopic structural performance. This assessment is no longer limited to isolated material parameters or process steps, but comprehensively examines the overall behavior of the final product under simulated stress conditions. Its assessment criteria directly correspond to one of the most critical performance characteristics of the pole in actual use: crack resistance. Through this assessment based on physical simulation and rigorous criteria, the quality level of the product can be predicted before physical production, and areas requiring improvement can be precisely identified, thereby achieving feedforward control of the manufacturing process and early mitigation of quality risks. The implementation of this assessment mechanism ensures that only parameter combinations that demonstrate excellent crack resistance in simulation are applied to actual production, fundamentally improving product reliability and consistency.

[0047] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for manufacturing centrifugal poles based on high-performance steel fiber reinforced crack-resistant concrete, the method comprising the following steps: Collect performance data of raw materials required for the manufacture of centrifugal poles, including concrete rheological properties data, steel fiber distribution data, and matrix strength data; Based on the aforementioned performance index data, a multi-scale coupled model of the corresponding pole forming process is constructed using a material simulation platform. Based on the steel fiber distribution data, identify the fiber orientation characteristics and compare them with the fiber distribution-incorporation demand mapping table to determine whether the fiber incorporation strategy needs to be adjusted. If adjustment is required, initiate the manufacturing parameter decision process. By combining performance index data, mixing process parameters, centrifugal molding parameters, and curing control parameters are extracted from the preset parameter decision rules. The mixing process parameters include fiber feeding sequence, mixing rate and duration; the centrifugal molding parameters include centrifugal speed, acceleration curve and mold vibration mode; and the curing control parameters include temperature and humidity gradient and duration. The multi-scale coupling model is used to simulate the entire manufacturing process from mixing to curing, outputting the steel fiber dispersion state, interfacial bonding strength and crack suppression effect. Based on this, the rationality of material ratio, the adaptability of process parameters and the stability of molding quality are evaluated, and parameter adjustment instructions are generated to optimize the manufacturing process.

2. The method for manufacturing a centrifugal pole based on high-performance steel fiber reinforced crack-resistant concrete according to claim 1, characterized in that, The specific construction process of the multi-scale coupling model is as follows: The rheological properties data of concrete, the distribution data of steel fibers, and the strength data of the matrix are normalized and converted into a standardized format that can be analyzed by the material constitutive model. A model of the interaction between steel fiber and concrete matrix was established using discrete element analysis, and the strength properties of the material were assigned based on the matrix strength data. A predictive model for material flow and fiber orientation during the pole forming process is constructed by combining centrifugal dynamics equations and fluid kinematics equations.

3. The method for manufacturing a centrifugal pole based on high-performance steel fiber reinforced crack-resistant concrete according to claim 1, characterized in that, The specific judgment process for adjusting the fiber incorporation strategy requirements includes: Fiber length distribution, aspect ratio, and spatial density indices are extracted from steel fiber distribution data. According to the preset fiber clustering rules, steel fibers with similar orientation characteristics are divided into the same orientation group; Calculate the average orientation angle and distribution dispersion of each orientation group, and compare them with the threshold in the fiber distribution-incorporation demand mapping table. If the dispersion of any orientation group exceeds the threshold or the average orientation angle deviates from the ideal range, it is determined that the manufacturing parameter decision process needs to be initiated.

4. The method for manufacturing a centrifugal pole based on high-performance steel fiber reinforced crack-resistant concrete according to claim 3, characterized in that, The specific content of the fiber clustering rules is as follows: Steel fibers that are spatially adjacent and whose orientation angle deviation is within the allowable range are grouped into the same orientation group; The density-weighted average of the fibers in each group is used as the representative orientation angle of that group, and the maximum dispersion within the group is used as the distribution dispersion index of that group.

5. The method for manufacturing a centrifugal pole based on high-performance steel fiber reinforced crack-resistant concrete according to claim 3, characterized in that, The preset parameter decision rules include stirring process parameter decision rules, centrifugal molding parameter decision rules, and curing control parameter decision rules, wherein the stirring process parameter decision rules are as follows: Based on the aspect ratio in the steel fiber distribution data and the viscosity coefficient in the matrix strength data, query the stirring rate correction coefficient corresponding to the fiber suspension characteristics in the database; The actual mixing rate is calculated based on the correction factor and the reference mixing rate, and the time interval for segmented feeding is determined according to the fiber length distribution. The initial setting time in the matrix strength data and the slump loss rate in the rheological properties were used as the basis for controlling the total stirring time.

6. The method for manufacturing a centrifugal pole based on high-performance steel fiber reinforced crack-resistant concrete according to claim 5, characterized in that, The specific content of the centrifugal molding parameter decision rule is as follows: Based on the spatial density distribution in the steel fiber distribution data and the yield stress in the rheological properties of concrete, the optimal centrifugal acceleration curve in the database is matched. The centrifugal force gradient is calculated based on the mold structure and pole dimensions, and the vibration frequency and amplitude of the mold are dynamically adjusted in combination with the fiber orientation characteristics.

7. The method for manufacturing a centrifugal pole based on high-performance steel fiber reinforced crack-resistant concrete according to claim 1, characterized in that, The evaluation methods for the rationality of the material ratio include: Compare the degree of deviation between the simulated steel fiber dispersion state and the ideal dispersion state; If the proportion of fiber agglomeration area exceeds the allowable threshold or the local density variation coefficient is greater than the critical value, the material ratio is deemed unreasonable. If the fiber distribution is uniform and the orientation angle meets the design requirements, then the material ratio is considered reasonable.

8. The method for manufacturing a centrifugal pole based on high-performance steel fiber reinforced crack-resistant concrete according to claim 1, characterized in that, The evaluation methods for the process parameter adaptability include: The theoretical critical anchorage length of the fiber is calculated based on the tensile strength in the matrix strength data and the simulated interface bonding strength. Compare the average fiber length in the actual fiber distribution data with the theoretical critical anchorage length; If the average fiber length reaches a preset multiple of the theoretical value and the interfacial bonding strength meets the design benchmark, then the process parameters are deemed to be compatible.

9. The method for manufacturing a centrifugal pole based on high-performance steel fiber reinforced crack-resistant concrete according to claim 1, characterized in that, The methods for evaluating the stability of molding quality include: Statistical simulation of crack suppression effect: number of cracks and propagation depth; If the width of the main crack is less than the allowable value and the crack path is effectively blocked by the fiber, the molding quality is considered stable. If a through crack or a crack width exceeds the limit, the molding quality is considered unstable.

10. A system for manufacturing centrifugal poles based on high-performance steel fiber reinforced crack-resistant concrete, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for manufacturing centrifugal poles based on high-performance steel fiber reinforced crack-resistant concrete as described in any one of claims 1 to 9.

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