A method and system for optimizing carbon fiber composite core pultrusion speed

Abstract

The present application relates to product forming technology field, disclose a kind of carbon fiber composite core pultrusion speed optimization method and system, method includes: the inner wall temperature and inner wall resin pressure of the inner wall of the corresponding mould of the preheating section starting end, gel section center position and solidification section end of mould are acquired in real time, while the surface layer temperature of target object when out of mould is acquired in real time at mould outlet, and the diameter of target object is acquired;By collecting the inner wall temperature and inner wall resin pressure of preheating section starting end, gel section center position, solidification section end and analyzing, speed optimization mark is generated, and the speed optimization strategy is determined in combination with the real-time current and rated current of traction motor, the present scheme can make pultrusion speed can dynamically adapt to resin heat release change, to ensure the stable pultrusion forming of large-diameter carbon fiber composite core.

Description

Technical Field

[0001] This invention relates to the field of product molding technology, specifically to a method and system for optimizing the pultrusion speed of carbon fiber composite cores. Background Technology

[0002] Currently, in the carbon fiber composite core pultrusion process, a constant speed control mode is generally adopted to ensure the pultrusion molding of the carbon fiber composite core. That is, the operator sets a fixed pultrusion speed value according to the characteristics of the resin and the length distribution of the mold heating zone, so that the carbon fiber bundle after impregnation can be fully softened in the preheating zone, crosslinked in the gel zone, and fully formed in the curing zone when passing through the segmented heating mold.

[0003] However, the above-mentioned pultrusion method still has the following drawbacks: When pultruding carbon fiber composite cores with large diameters, such as ≥12mm, the carbon fiber composite core is a poor conductor of heat, and there is a significant lag effect in the heat transfer of the core. When the resin undergoes a cross-linking reaction inside the mold, it will release heat, and the rate of heat release is strongly correlated with the pultrusion speed. For example, the faster the speed, the more resin participates in the reaction per unit time, and the more intense the instantaneous heat release. However, the constant pultrusion speed in the existing technology cannot be dynamically adjusted according to the real-time changes in the heat release of the reaction, which affects the pultrusion molding effect of the carbon fiber composite core. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for optimizing the pultrusion speed of carbon fiber composite cores, thus solving the aforementioned problems.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution: A method for optimizing the pultrusion speed of carbon fiber composite cores includes: Step S1: Real-time acquisition of the inner wall temperature and inner wall resin pressure of the mold corresponding to the start end of the preheating section, the center position of the gel section, and the end end of the curing section. At the same time, real-time acquisition of the surface temperature of the target object when it exits the mold and the diameter of the target object. Step S2: Analyze the inner wall resin pressure at the center of the gel segment and the beginning of the preheating segment to generate the in-mold pressure fluctuation coefficient, which represents the influence of the mold front pressure on the stability of the gel segment. Calculate the inner wall resin pressure at the end of the curing segment and the inner wall resin pressure at the center of the gel segment to obtain the curing resistance coefficient, which represents the demolding resistance state of the target object at the end of the mold. Step S3: Calculate the surface temperature, diameter, and inner wall temperature at the center of the gel segment to obtain the curing interface temperature difference value, which reflects the degree of matching between the curing progress of the surface and interior of the target object. Step S4: Perform a collaborative analysis of the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference to generate a speed optimization indicator representing the direction of speed adjustment. Step S5: Obtain the real-time current value and rated current of the traction motor in the pultrusion equipment of the target object, and determine the speed optimization strategy based on the speed optimization identifier, real-time current value and rated current.

[0006] Furthermore, the mold and the target object include: The mold is a metal mold used for molding carbon fiber composite cores; The target object is a carbon fiber composite core.

[0007] Furthermore, the resin pressure on the inner wall at the center of the gel segment and the beginning of the preheating segment was analyzed to generate an in-mold pressure fluctuation coefficient representing the influence of the mold front-end pressure on the stability of the gel segment, including: The fluctuation amplitude and frequency of the resin pressure on the inner wall at the beginning of the preheating section are analyzed to generate a pressure fluctuation base value that reflects the degree of dynamic pressure change. The pressure fluctuation baseline value is correlated with the inner wall resin pressure at the center of the gel segment. The transmission and attenuation of the fluctuation energy are analyzed to generate the in-mold pressure fluctuation coefficient, which represents the influence of the mold front pressure on the stability of the gel segment.

[0008] Furthermore, the resin pressure on the inner wall at the end of the curing section and the resin pressure on the inner wall at the center of the gel section are calculated to obtain the curing resistance coefficient, which represents the demolding resistance state of the target object at the end of the mold, including: The resin pressure on the inner wall at the end of the curing section and the resin pressure on the inner wall at the center of the gel section are analyzed. The deviation of pressure distribution and attenuation is analyzed to obtain the curing resistance coefficient, which represents the demolding resistance state of the target object at the end of the mold.

[0009] Furthermore, the surface temperature, diameter, and inner wall temperature at the center of the gel segment are calculated to obtain the curing interface temperature difference value, which reflects the degree of matching between the curing progress of the target object's surface and interior, including: A correlation analysis was performed on the inner wall temperature and surface temperature at the center of the gel segment. Combined with the diameter of the target object, the radial distribution of temperature differences was analyzed, and radial temperature characteristic values ​​reflecting the degree of drastic temperature changes in the radial direction inside the target object were generated.

[0010] Furthermore, the surface temperature, diameter, and inner wall temperature at the center of the gel segment are calculated to obtain the curing interface temperature difference value, which reflects the degree of matching between the curing progress of the target object's surface and interior. This also includes: Based on the radial temperature characteristic value, the influence and disturbance of the surface temperature on the radial temperature distribution are analyzed, and the curing interface temperature difference value reflecting the degree of matching between the curing progress of the surface and the interior of the target object is obtained.

[0011] Furthermore, a collaborative analysis is performed on the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference to generate a speed optimization indicator representing the direction of speed adjustment, including: The fluctuation synchronicity and trend consistency among the in-mold pressure fluctuation coefficient, curing resistance coefficient and curing interface temperature difference are analyzed to generate a process fluctuation correlation degree that reflects the degree of coordination and matching between the pressure field, resistance field and temperature field under the current process state. By analyzing the correlation of process fluctuations, the deviations of the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference are traced back to obtain the main control factors that represent the core sources of process state changes.

[0012] Furthermore, a collaborative analysis of the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference is performed to generate a speed optimization indicator representing the direction of speed adjustment. This also includes: Based on the deviation from the main control factor, determine the direction of speed adjustment and generate a speed optimization label indicating the direction of speed adjustment.

[0013] Furthermore, based on the speed optimization identifier, real-time current value, and rated current, a speed optimization strategy is determined, including: If the speed optimization flag is 1 and the real-time current value is less than the rated current, then the speed optimization strategy is to allow speed increase. If the speed optimization flag is 2, then the speed optimization strategy is to allow speed reduction; Otherwise, all will be processed according to the speed optimization flag being 3. If the speed optimization flag is 3, The speed optimization strategy is to maintain the current speed.

[0014] Furthermore, a carbon fiber composite core pultrusion speed optimization system, applied to the above optimization method, includes: The data acquisition unit is used to acquire in real time the inner wall temperature and inner wall resin pressure of the mold corresponding to the start end of the preheating section, the center position of the gel section, and the end end of the curing section. At the same time, it can acquire the surface temperature of the target object when it exits the mold and the diameter of the target object in real time at the mold outlet. The influence analysis unit is used to analyze the inner wall resin pressure at the center of the gel segment and the beginning of the preheating segment, generate the in-mold pressure fluctuation coefficient representing the influence of the mold front pressure on the stability of the gel segment, and calculate the inner wall resin pressure at the end of the curing segment and the inner wall resin pressure at the center of the gel segment to obtain the curing resistance coefficient representing the demolding resistance state of the target object at the end of the mold. The temperature analysis unit is used to calculate the surface temperature, diameter, and inner wall temperature at the center of the gel segment to obtain the curing interface temperature difference value, which reflects the degree of matching between the curing progress of the surface and the interior of the target object. The speed optimization unit is used to perform collaborative analysis on the in-mold pressure fluctuation coefficient, curing resistance coefficient and curing interface temperature difference value, and generate a speed optimization label indicating the direction of speed adjustment. The speed adjustment unit is used to obtain the real-time current value and rated current of the traction motor in the pultrusion equipment of the target object, and determine the speed optimization strategy based on the speed optimization identifier, real-time current value and rated current.

[0015] In summary, the present invention has the following main beneficial effects: By analyzing the fluctuation of resin pressure on the inner wall at the beginning of the preheating section and the center of the gel section, an in-mold pressure fluctuation coefficient is generated, reflecting the degree of disturbance of the front-end pressure to the stability of the gel section. By analyzing the pressure distribution deviation of resin pressure on the inner wall at the end of the curing section and the center of the gel section, a curing resistance coefficient is generated, accurately identifying the nature and intensity of the end demolding resistance. By analyzing the radial temperature difference between the inner wall temperature, surface temperature and diameter at the center of the gel section, a curing interface temperature difference value is generated, reflecting the matching degree of core and surface curing progress in real time. At the same time, the in-mold pressure fluctuation coefficient, curing resistance coefficient and curing interface temperature difference value are analyzed synergistically to calculate the correlation of process fluctuations to evaluate the synergistic matching state of the pressure field, resistance field and temperature field. By tracing the deviation of the main control factor through energy proportion and change intensity, the core source of the current process state change is accurately identified. By generating a deceleration indicator when the pressure field dominates to suppress front-end pressure fluctuations and ensure sufficient resin impregnation, generating an acceleration indicator when the resistance field dominates to improve demolding force and avoid adhesion or extrusion, and generating a speed-maintaining indicator when the temperature field dominates to avoid generating new thermal disturbances, a new speed optimization strategy is formed when multiple fields dominate. This solution achieves dynamic adaptive adjustment of the pultrusion speed of large-diameter carbon fiber composite cores to the changes in resin curing exothermic heat, which can further improve the molding quality of carbon fiber composite cores. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of a carbon fiber composite core pultrusion speed optimization method according to the present invention; Figure 2 This is a schematic diagram of a carbon fiber composite core pultrusion speed optimization system according to the present invention. Detailed Implementation

[0017] 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.

[0018] refer to Figure 1 and Figure 2A method for optimizing the pultrusion speed of carbon fiber composite cores, comprising: Step S1: Real-time acquisition of the inner wall temperature and inner wall resin pressure of the mold corresponding to the start end of the preheating section, the center position of the gel section, and the end end of the curing section. At the same time, real-time acquisition of the surface temperature of the target object when it exits the mold and the diameter of the target object. Among them, the beginning of the preheating section is the mold inlet, the center of the gel section is the center of the region where the resin crosslinking reaction is most intense, and the end of the curing section is the mold outlet. Step S2: Analyze the inner wall resin pressure at the center of the gel segment and the beginning of the preheating segment to generate the in-mold pressure fluctuation coefficient, which represents the influence of the mold front pressure on the stability of the gel segment. Calculate the inner wall resin pressure at the end of the curing segment and the inner wall resin pressure at the center of the gel segment to obtain the curing resistance coefficient, which represents the demolding resistance state of the target object at the end of the mold. Step S3: Calculate the surface temperature, diameter, and inner wall temperature at the center of the gel segment to obtain the curing interface temperature difference value, which reflects the degree of matching between the curing progress of the surface and interior of the target object. Step S4: Perform a collaborative analysis of the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference to generate a speed optimization indicator representing the direction of speed adjustment. Step S5: Obtain the real-time current value and rated current of the traction motor in the pultrusion equipment of the target object, and determine the speed optimization strategy based on the speed optimization identifier, real-time current value and rated current.

[0019] In one embodiment, the mold and the target object include: The mold is a metal mold used for molding carbon fiber composite cores; The target object is a carbon fiber composite core.

[0020] In one embodiment, the inner wall resin pressure at the center of the gel segment and the beginning of the preheating segment is analyzed to generate an in-mold pressure fluctuation coefficient representing the influence of the mold front pressure on the stability of the gel segment, including: The fluctuation amplitude and frequency of the resin pressure on the inner wall at the beginning of the preheating section were analyzed to generate a pressure fluctuation baseline value reflecting the degree of dynamic pressure change. Specifically, this involved: continuously collecting the resin pressure on the inner wall at the beginning of the preheating section to construct a preliminary sequence; preprocessing the preliminary sequence to obtain a pressure time series; identifying all local maxima and minima in the pressure time series within a 60-second sliding time window, which were used as pressure peaks and valleys, respectively; calculating the pressure difference between each pressure peak and its immediate successor, and between each pressure valley and its immediate successor, in chronological order, to obtain a set of pressure differences; and then performing root mean square calculation on this set of pressure differences to obtain the effective fluctuating pressure value, in MPa. A fast Fourier transform is performed on the pressure time series within the same sliding time window to obtain a spectrum. The frequency component with the largest amplitude in the spectrum is extracted as the main disturbance frequency, in Hertz. The main disturbance frequency is divided by 1 Hertz to obtain the dimensionless main disturbance frequency value. The effective fluctuating pressure value is divided by 1 MPa to obtain the dimensionless effective fluctuating pressure value. The dimensionless effective fluctuating pressure value is multiplied by the main disturbance frequency value to obtain the pressure fluctuation base value reflecting the degree of pressure dynamic change.

[0021] The pressure fluctuation baseline value is correlated with the inner wall resin pressure at the center of the gel segment to analyze the transmission and attenuation of fluctuation energy, and generate an in-mold pressure fluctuation coefficient that represents the influence of the mold front pressure on the stability of the gel segment. Specifically, this includes: aligning the pressure fluctuation baseline value with the inner wall resin pressure value at the center of the gel segment in time, obtaining the average value of the inner wall resin pressure at the center of the gel segment within the same 60-second sliding time window, and using it as the gel segment reference pressure value in megapascals; dividing the gel segment reference pressure value by 1 megapascal to obtain the dimensionless gel segment reference pressure coefficient. Obtain the physical distance along the mold direction from the starting end of the preheating section to the center of the gel section, in meters. Divide the physical distance by 1 meter to obtain the dimensionless transmission distance coefficient. Calculate the distance attenuation factor with the natural constant e as the base and the negative transmission distance coefficient as the exponent. Multiply the reciprocal of the main disturbance frequency value by the distance attenuation factor to obtain the comprehensive attenuation coefficient. Multiply the pressure fluctuation base value by the comprehensive attenuation coefficient to obtain the equivalent fluctuation influence value. Add the equivalent fluctuation influence value to the gel segment reference pressure coefficient to generate the in-mold pressure fluctuation coefficient, which represents the influence of the mold front pressure on the stability of the gel segment. The larger the in-mold pressure fluctuation coefficient, the stronger the disturbance of the front fluctuation on the stability of the gel segment.

[0022] By continuously collecting and constructing a time series of resin pressure on the inner wall of the preheating section's starting end, using a sliding window to identify extreme points and calculate the root mean square effective fluctuation pressure value, and combining this with a fast Fourier transform to extract the main disturbance frequency, the dynamic change of pressure is accurately reflected. Then, the correlation between the pressure fluctuation baseline value and the reference pressure at the center of the gel section is analyzed to find the transmission and attenuation law of fluctuation energy. The resulting in-mold pressure fluctuation coefficient can directly reflect the strength of the disturbance of the mold front-end pressure on the stability of the gel section, achieving precise control of the pultrusion process and solving the molding quality problems of large-diameter carbon fiber composite cores caused by thermal hysteresis and exothermic effects.

[0023] In one embodiment, the resin pressure on the inner wall at the end of the cured section and the resin pressure on the inner wall at the center of the gel section are calculated to obtain a curing resistance coefficient representing the demolding resistance state of the target object at the end of the mold, including: The inner wall resin pressure at the end of the curing section and the inner wall resin pressure at the center of the gel section are analyzed to analyze the deviation of pressure distribution and attenuation, and to obtain the curing resistance coefficient, which represents the demolding resistance state of the target object at the end of the mold. Specifically, the inner wall resin pressure collected in real time at the end of the curing section and the inner wall resin pressure collected in real time at the center of the gel section are time-aligned, and the average pressure values ​​at the end of the curing section and the center of the gel section within the same 60-second sliding time window are obtained respectively, which are recorded as the average pressure at the end of the curing section and the average pressure at the center of the gel section, both in megapascals. The physical distance from the center of the gel segment to the end of the cured segment along the mold direction is obtained in meters. This physical distance is divided into ten consecutive characteristic segments, each corresponding to a spatial position. A pressure decay curve is formed with the average pressure at the center of the gel segment as the starting point and the average pressure at the end of the cured segment as the ending point, and the physical distance as the independent variable. The pressure value of the inner wall resin at the corresponding spatial position of each feature segment is collected in real time to obtain the actual pressure distribution curve. The area difference between the actual pressure distribution curve and the pressure decay curve is calculated. That is, the pressure difference between the two curves on the ten feature segments is integrated and accumulated to obtain the cumulative pressure deviation, which is in megapascals. The cumulative pressure deviation is divided by the physical distance to obtain the average pressure deviation intensity, which is in megapascals. The real-time inner wall temperature at the end of the curing section is obtained. The real-time inner wall temperature is preprocessed to obtain a dimensionless end temperature coefficient. The average pressure deviation intensity is preprocessed to obtain a dimensionless average pressure deviation coefficient. The end temperature coefficient and the average pressure deviation coefficient are multiplied to obtain the curing resistance coefficient, which represents the demolding resistance state of the target object at the end of the mold. The magnitude of the absolute value of the curing resistance coefficient represents the intensity of the demolding resistance, and the positive or negative sign of the curing resistance coefficient represents the direction of the resistance.

[0024] By analyzing the inner wall resin pressure at the end of the curing section and the center of the gel section, the distribution characteristics of the pressure inside the mold are accurately captured. By dividing the characteristic sections and calculating the cumulative pressure deviation and the average pressure deviation intensity, the pressure attenuation deviation can be accurately represented. The final curing resistance coefficient can accurately reflect the intensity and direction of the demolding resistance at the end of the mold, providing a precise basis for the dynamic adjustment of the pultrusion speed, effectively mitigating the effects of core thermal hysteresis and uneven resin exothermics, and improving the molding quality and stability of large-diameter carbon fiber composite cores.

[0025] In one embodiment, the surface temperature, diameter, and inner wall temperature at the center of the gel segment are calculated to obtain a curing interface temperature difference value reflecting the degree of matching between the curing progress of the target object's surface and interior, including: A correlation analysis was performed on the inner wall temperature and surface temperature at the center of the gel segment. Combined with the diameter of the target object, the radial distribution of temperature differences was analyzed to generate radial temperature characteristic values ​​that reflect the intensity of temperature changes along the radial direction inside the target object. Specifically, this included: aligning the inner wall temperature at the center of the gel segment with the surface temperature at the mold exit in time, calculating the difference between the inner wall temperature and the surface temperature to obtain the radial temperature difference in degrees Celsius; dividing the diameter of the target object by 2 to obtain the radius in meters; and dividing the radial temperature difference by the radius to obtain the average radial temperature gradient in degrees Celsius per meter. Divide the radial temperature difference by the inner wall temperature at the center of the gel segment to obtain the relative temperature difference coefficient. Multiply the average radial temperature gradient by the relative temperature difference coefficient, multiply by the diameter of the target object, and divide by 1 degree Celsius to generate a dimensionless radial temperature characteristic value that reflects the degree of temperature change in the radial direction inside the target object. The larger the value of the radial temperature characteristic value, the more drastic the temperature change in the radial direction inside the target object, that is, the more significant the difference in curing progress between the surface and the interior.

[0026] In one embodiment, the surface temperature, diameter, and inner wall temperature at the center of the gel segment are calculated to obtain a curing interface temperature difference value reflecting the degree of matching between the curing progress of the target object's surface and interior. This also includes: Based on the radial temperature characteristic value, the influence and disturbance of surface temperature on radial temperature distribution are analyzed to obtain the curing interface temperature difference value reflecting the matching degree of curing progress between the surface and interior of the target object. Specifically, this includes: acquiring the surface temperature of the target object collected in real time at the mold exit, constructing a surface temperature time series within a 60-second sliding time window, calculating the mean of the surface temperature time series, and obtaining the average surface temperature in degrees Celsius; simultaneously acquiring the mean inner wall temperature at the center of the gel segment within the sliding time window, and obtaining the average core temperature in degrees Celsius; then calculating the difference between the average surface temperature and the average core temperature to obtain the basic radial temperature difference in degrees Celsius. Subtract the mean core temperature from each surface temperature in the surface temperature time series to obtain a set of instantaneous deviation values. Calculate the root mean square of this set of instantaneous deviation values ​​to obtain the surface temperature fluctuation intensity, in degrees Celsius. The surface temperature fluctuation intensity is used to reflect the overall fluctuation range of the surface temperature relative to the core reference temperature. Among them, the radial temperature characteristic value reflects the degree of drastic temperature change along the radial direction under ideal conditions, while the surface temperature fluctuation intensity reflects the dynamic deviation of the actual surface temperature. Multiplying the preprocessed surface temperature fluctuation intensity with the radial temperature characteristic value yields the perturbation coupling coefficient, which reflects the degree of superposition perturbation of the radial temperature distribution by the actual surface temperature fluctuation. The difference between the surface temperature and the core temperature at the current moment is calculated to obtain the instantaneous radial temperature difference in degrees Celsius. The instantaneous radial temperature difference is divided by the radial temperature baseline difference to obtain the instantaneous temperature difference relative coefficient. Finally, the disturbance coupling coefficient is multiplied by the instantaneous temperature difference relative coefficient, and then multiplied by the radial temperature baseline difference to obtain the curing interface temperature difference value, which reflects the degree of matching between the curing progress of the surface and the interior of the target object. The curing interface temperature difference value comprehensively reflects the degree of deviation of the actual temperature difference between the surface and the core at the current moment from the baseline state under the surface temperature fluctuation disturbance. The larger the value, the worse the degree of matching between the curing progress of the surface and the interior.

[0027] By calculating the inner wall temperature and surface temperature at the center of the gel segment, as well as the diameter of the target object, parameters such as radial temperature difference and average radial temperature gradient are obtained. These parameters can reflect the degree of drastic radial temperature changes. Within the sliding time window, by combining the average surface temperature, the average core temperature, and the intensity of surface temperature fluctuations, the disturbance coupling coefficient and the temperature difference at the curing interface are obtained. This can accurately reflect the matching degree of curing progress between the surface and the interior, effectively alleviate the effects of thermal hysteresis, and improve the molding consistency and quality stability of large-diameter carbon fiber composite cores.

[0028] In one embodiment, a collaborative analysis is performed on the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference to generate a speed optimization identifier indicating the direction of speed adjustment, including: The synchronicity and trend consistency of fluctuations among the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference are analyzed to generate a process fluctuation correlation degree that reflects the degree of coordination and matching between the pressure field, resistance field, and temperature field under the current process state. Specifically, within the same sliding time window, the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference are obtained respectively, and three sequences are formed. Linear fitting is performed on each sequence to obtain the slope of change of each within the sliding time window, which are the pressure trend slope, resistance trend slope, and temperature difference trend slope, respectively. Divide each of the three slopes by its absolute value to obtain a sign value. A positive sign value is 1, and a negative sign value is -1. Add the three sign values ​​to obtain a sign sum. When all three slopes are positive or all three are negative, the absolute value of the sign sum is 3, and the trend consistency coefficient is 1, indicating that the three fields are completely coordinated in their directions of change. When two of the three slopes are in the same direction and one is in the opposite direction, the absolute value of the sign sum is 1, and the trend consistency coefficient is 0.3, indicating that the three fields are partially coordinated in their directions of change. When two of the three slopes are positive and one is negative, or two are negative and one is positive, and the sign sum is 0, it indicates that the three fields are completely inconsistent in their directions of change, and the trend consistency coefficient is 0. The trend consistency coefficient is used to reflect the degree of coordination of the trends of change of the three fields. Calculate the mean of each sequence within the current sliding time window, then subtract the mean from the value at each time step in the sequence to obtain three new zero-mean fluctuation sequences. Calculate the covariance of each of these three fluctuation sequences pairwise to obtain three covariance values. Simultaneously, calculate the standard deviation of each fluctuation sequence to obtain three standard deviations. Divide the average of the three covariance values ​​by the average of the products of the three standard deviations to obtain the average correlation coefficient. Use the absolute value of the average correlation coefficient as the volatility coefficient, which is between 0 and 1. The larger the volatility coefficient, the higher the degree of synchronous change in the volatility of the three coefficients over time. Multiplying the trend consistency coefficient by the fluctuation amplitude coefficient and normalizing the result to the 0-1 range yields the process fluctuation correlation degree, which reflects the degree of coordination and matching between the pressure field, resistance field, and temperature field under the current process state. The larger the value of the process fluctuation correlation degree, the more synchronous and consistent the fluctuations of the pressure field, resistance field, and temperature field under the current process state are, that is, the higher the degree of coordination and matching of the three fields. Conversely, it indicates that there are uncoordinated fluctuations between the three fields, indicating the instability of the process state.

[0029] The correlation of process fluctuations is analyzed, and the deviations of the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference are traced in reverse to obtain the main control factors of deviation representing the core source of process state changes. Specifically, this includes: within the same sliding time window, for the three sequences formed by the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference, the variance of the sequence to which the in-mold pressure fluctuation coefficient belongs is calculated to obtain the total energy of pressure disturbance; the covariance of the sequences to which the in-mold pressure fluctuation coefficient and curing resistance coefficient belong is calculated as the coupling energy of pressure disturbance transmitted to the resistance field; the covariance of the sequences to which the curing interface temperature difference value and the in-mold pressure fluctuation coefficient belong is calculated as the coupling energy of pressure disturbance transmitted to the temperature field. Subtracting the sum of the two coupled energies from the total energy of the pressure disturbance yields the residual energy maintained by the pressure field itself. Dividing each of the three energy values ​​by the sum of the two coupled energies and the total energy of the pressure disturbance yields the energy proportions of the pressure field itself, the drag field coupled energy, and the temperature field coupled energy. The sum of these three proportions is 1, representing the energy share of the pressure field's own fluctuations, the influence of pressure on drag, and the influence of pressure on temperature in the current process fluctuations. Obtain the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference value at the current moment. Subtract the mean of the corresponding sequence in the previous sliding time window from each of these three parameters to obtain three instantaneous changes. Divide each of the three instantaneous changes by the standard deviation of the corresponding sequence in the previous sliding time window to obtain three dimensionless change intensity coefficients. Multiplying the three energy percentages by their corresponding change intensity coefficients yields the dominance of the pressure field, the dominance of the resistance field, and the dominance of the temperature field. These three dominance values ​​represent the degree of dominance of each field in process fluctuations. The maximum value among the dominance of the pressure field, the dominance of the drag field, and the dominance of the temperature field is taken as the deviation from the dominance factor. The deviation from the dominance factor is marked as the field corresponding to the maximum value and is then dominated. If two of the dominance degrees of the pressure field, drag field, and temperature field are equal and both are at their maximum values, then the deviation dominance factor is clearly identified as the two fields with equal dominance degrees that dominate. If the values ​​of the dominance of the pressure field, the dominance of the resistance field, and the dominance of the temperature field are all the same, then the deviation from the dominance factor is clearly identified as the three dominant fields of the pressure field, the resistance field, and the temperature field; thus, the deviation from the dominance factor representing the core source of the change in the process state can be obtained.

[0030] In one embodiment, the method further includes performing a collaborative analysis of the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference to generate a speed optimization indicator representing the speed adjustment direction. Based on the deviation from the main control factor, the direction of speed adjustment is determined, and a speed optimization indicator representing the direction of speed adjustment is generated. Specifically, if the deviation from the main control factor is dominated by the pressure field alone, the corresponding pultrusion speed needs to be reduced because excessive pressure fluctuation at the front end indicates that the resin injection is too fast or the impregnation is unstable. Reducing the pultrusion speed can prolong the flow time of the resin in the preheating section, thereby suppressing pressure fluctuation. In this case, the speed optimization indicator is 2, indicating that the pultrusion speed needs to be reduced. If the deviation from the main control factor is dominated by the resistance field alone, then the corresponding pultrusion speed needs to be increased. This is because abnormal end demolding resistance indicates that the solidification shrinkage and the inner wall of the mold are excessively adhered or squeezed. Increasing the pultrusion speed can change the stress state at the moment of demolding and help overcome the resistance. At this time, the speed optimization indicator is 1, indicating that the pultrusion speed needs to be increased. If the deviation from the main control factor is solely dominated by the temperature field, then the corresponding pultrusion speed remains unchanged. This is because radial temperature difference is usually mainly affected by heating. Adjusting the pultrusion speed will cause new thermal disturbances. The current speed should be maintained and the problem investigated. In this case, the speed optimization indicator is 3, indicating that the pultrusion speed needs to remain unchanged. If the deviation from the master control factor is dominated by multiple fields, regardless of whether it is dominated by two or three fields, as long as any one of the dominant fields satisfies the deceleration logic, the speed optimization is marked as 2; if none of them satisfy the deceleration logic but there is an acceleration logic, the speed optimization is marked as 1; otherwise, the speed optimization is marked as 3. Among them, pressure field dominance is considered to satisfy the deceleration logic; resistance field dominance is considered to satisfy the acceleration logic; temperature field dominance satisfies neither the acceleration logic nor the deceleration logic.

[0031] By calculating the correlation of process fluctuations and tracing the deviation from the main control factor, the core source of the current process state change can be accurately identified, and a speed optimization indicator can be generated. When the pressure field is identified as dominant, the pultrusion speed is reduced to suppress front-end pressure fluctuations and ensure sufficient resin impregnation. When the resistance field is identified as dominant, the pultrusion speed is increased to improve the demolding stress state. When the temperature field is identified as dominant, the speed is maintained to avoid causing new thermal disturbances. This solution enables the pultrusion speed to dynamically adapt to the real-time changes in the curing reaction, effectively solving the problem of uneven curing caused by heat lag in large-diameter carbon fiber composite cores, reducing the surface defect rate, and realizing high-quality and stable pultrusion molding of large-diameter carbon fiber composite cores.

[0032] In one embodiment, a speed optimization strategy is determined based on the speed optimization identifier, the real-time current value, and the rated current, including: If the speed optimization flag is 1 and the real-time current value is less than the rated current, then the speed optimization strategy is to allow speed increase. If the speed optimization flag is 2, then the speed optimization strategy is to allow speed reduction; Otherwise, all will be processed according to the speed optimization flag being 3. If the speed optimization flag is 3, The speed optimization strategy is to maintain the current speed.

[0033] In one embodiment, a carbon fiber composite core pultrusion speed optimization system is applied to the above-described optimization method, comprising: The data acquisition unit is used to acquire in real time the inner wall temperature and inner wall resin pressure of the mold corresponding to the start end of the preheating section, the center position of the gel section, and the end end of the curing section. At the same time, it can acquire the surface temperature of the target object when it exits the mold and the diameter of the target object in real time at the mold outlet. The influence analysis unit is used to analyze the inner wall resin pressure at the center of the gel segment and the beginning of the preheating segment, generate the in-mold pressure fluctuation coefficient representing the influence of the mold front pressure on the stability of the gel segment, and calculate the inner wall resin pressure at the end of the curing segment and the inner wall resin pressure at the center of the gel segment to obtain the curing resistance coefficient representing the demolding resistance state of the target object at the end of the mold. The temperature analysis unit is used to calculate the surface temperature, diameter, and inner wall temperature at the center of the gel segment to obtain the curing interface temperature difference value, which reflects the degree of matching between the curing progress of the surface and the interior of the target object. The speed optimization unit is used to perform collaborative analysis on the in-mold pressure fluctuation coefficient, curing resistance coefficient and curing interface temperature difference value, and generate a speed optimization label indicating the direction of speed adjustment. The speed adjustment unit is used to obtain the real-time current value and rated current of the traction motor in the pultrusion equipment of the target object, and determine the speed optimization strategy based on the speed optimization identifier, real-time current value and rated current.

[0034] In one aspect of this embodiment, to verify the effectiveness of the carbon fiber composite core pultrusion speed optimization method proposed in this embodiment, a comparative experiment was conducted using the same type of pultrusion die, the same carbon fiber composite core rod and molding process, under the same ambient temperature conditions. The effects of the pultrusion process under three different speed strategies were compared and analyzed.

[0035] I. Experimental Setup The experimental subject was a carbon fiber composite mandrel with a designed diameter of 12 mm; The comparison method is as follows: Method A (Control 1): The pulling speed was fixed at 0.35 m / min and was not adjusted throughout the entire process, serving as the baseline control group.

[0036] Method B (Control 2): ​​Only the inner wall resin pressure at the center of the gel segment was monitored. When the pressure exceeded 0.8 MPa, the rate of decrease was 0.02 m / min, and when the pressure was below 0.4 MPa, the rate of increase was 0.02 m / min, simulating the traditional empirical control method.

[0037] Method C (Experimental Group): The analysis was performed according to the method of the present invention, and the speed was optimized to finally determine the speed optimization strategy; Each experimental method was run continuously for 4 hours, with samples taken every 30 minutes to test the diameter and surface quality of the composite core.

[0038] II. The Role of Evaluation Indicators Among them, pressure fluctuation amplitude is mainly used to reflect the stability of front-end pressure, demolding resistance is mainly used to reflect the magnitude of demolding resistance, surface temperature fluctuation is mainly used to reflect temperature stability, and product diameter deviation is used to reflect the change in the diameter of the target object. Surface defects indicate the number of defects such as cracks, burrs, and white spots that appear on the surface of the composite core per 100 meters of length. Traction motor current is used to reflect the load condition, and the number of speed adjustments indicates the total number of times the pultrusion speed is adjusted within 4 hours.

[0039] Table 1 below shows a comparison of the pultrusion process effects under different control strategies.

[0040] The experimental results in Table 1 show that: Regarding process stability, the pressure fluctuation amplitude of method C was 0.13 MPa, which was 53.6% lower than that of method A and 38.1% lower than that of method B. This is because method C assesses the impact of the front-end pressure on the gel segment in real time through the in-mold pressure fluctuation coefficient. When fluctuation energy transfer is detected, the speed is adjusted in advance to avoid further amplification of pressure fluctuation. In addition, the surface temperature fluctuation was reduced to 2.3℃, which is only 44.2% of that of method A. This indicates that method C effectively maintains the matching of the curing progress between the surface and the interior of the composite core by calculating the temperature difference at the curing interface, and reduces temperature fluctuations caused by excessive temperature differences. Regarding demolding resistance, the demolding resistance of method C is 0.26 MPa, which is 36.6% lower than that of method A and 25.7% lower than that of method B. This is because the curing resistance coefficient can accurately identify the nature of the demolding resistance. When extrusion resistance or adhesive resistance is detected, the pultrusion speed is adjusted in time through the speed optimization indicator so that the composite core can be released from the mold at the best time. In terms of product quality, the diameter deviation of the product in method C is controlled within ±0.04mm, which is better than ±0.12mm in method A and ±0.09mm in method B; the surface defects are reduced to 1.2 per 100 meters, which is much lower than 5.3 per 100 meters in method A. This shows that through the synergistic optimization of pressure field, resistance field and temperature field, the curing of composite core is more uniform and the molding quality is significantly improved. In terms of energy consumption load, the average current of the traction motor in method C is 15.8A, which is 15.1% lower than that in method A and 8.1% lower than that in method B. This is because the optimized pultrusion speed avoids the increase in traction load caused by excessive demolding resistance. At the same time, the constraint of motor current ensures that the speed adjustment is carried out within the safe range of the equipment, thus avoiding the risk of overload. In terms of control strategy, method C adjusts the speed 6 times in 4 hours, which is less than the frequency of 12 times for method B. This is because method C does not simply make reactive adjustments based on a single-point threshold, but identifies the real source of process state changes through the synergistic analysis of pressure, resistance and temperature, thus avoiding ineffective adjustments caused by local fluctuations.

[0041] III. Summary of the advantages of this speed optimization scheme The carbon fiber composite core pultrusion speed optimization method proposed in this embodiment can accurately identify the core sources of process state changes through the synergistic analysis of the in-mold pressure fluctuation coefficient, curing resistance coefficient and curing interface temperature difference, generate reasonable speed optimization indicators, and determine the final speed optimization strategy in combination with motor current constraints.

[0042] Experimental data show that, compared with traditional constant speed control and single-parameter threshold control, this method can effectively reduce pressure fluctuation amplitude by 53.6%, reduce demolding resistance by 36.6%, stabilize surface temperature fluctuation, control the diameter deviation of 12mm carbon fiber composite mandrel within ±0.04mm, and reduce the surface defect rate to 1.2 defects per 100 meters. At the same time, the traction motor load is reduced by 15.1%, and the speed adjustment frequency is also lower than that of traditional control methods. In summary, this method can effectively improve the product quality of carbon fiber composite mandrels and reduce equipment load by optimizing the pultrusion speed of the target material while maintaining process stability, and has significant practical application value.

[0043] 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 optimizing the pultrusion speed of carbon fiber composite cores, characterized in that, include: Step S1: Real-time acquisition of the inner wall temperature and inner wall resin pressure of the mold corresponding to the start end of the preheating section, the center position of the gel section, and the end end of the curing section. At the same time, real-time acquisition of the surface temperature of the target object when it exits the mold and the diameter of the target object. Step S2: Analyze the inner wall resin pressure at the center of the gel segment and the beginning of the preheating segment to generate the in-mold pressure fluctuation coefficient, which represents the influence of the mold front pressure on the stability of the gel segment. Calculate the inner wall resin pressure at the end of the curing segment and the inner wall resin pressure at the center of the gel segment to obtain the curing resistance coefficient, which represents the demolding resistance state of the target object at the end of the mold. Step S3: Calculate the surface temperature, diameter, and inner wall temperature at the center of the gel segment to obtain the curing interface temperature difference value, which reflects the degree of matching between the curing progress of the surface and interior of the target object. Step S4 involves a collaborative analysis of the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference to generate a speed optimization indicator representing the direction of speed adjustment, including: The synchronicity and trend consistency of fluctuations among the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference are analyzed to generate a process fluctuation correlation degree that reflects the degree of coordination and matching between the pressure field, resistance field, and temperature field under the current process state. Specifically, within the same sliding time window, the in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference are obtained respectively, and three sequences are formed. Linear fitting is performed on each sequence to obtain the pressure trend slope, resistance trend slope, and temperature difference trend slope. Divide the three slopes by their respective absolute values ​​to obtain sign values, and determine the sign sum and trend consistency coefficient based on the sign values. Calculate the mean of each sequence within the current sliding time window, and then subtract the mean from the value at each moment in the sequence to obtain three new zero-mean fluctuation sequences. Calculate the covariance of each of these three fluctuation sequences pairwise to obtain three covariance values. Simultaneously, calculate the standard deviation of each fluctuation sequence to obtain three standard deviations. Divide the average of the three covariance values ​​by the average of the products of the three standard deviations to obtain the average correlation coefficient. Use the absolute value of the average correlation coefficient as the fluctuation amplitude coefficient. Multiply the trend consistency coefficient by the fluctuation amplitude coefficient to obtain the process fluctuation correlation degree, which reflects the degree of coordination and matching between the pressure field, resistance field, and temperature field under the current process conditions. The correlation of process fluctuations is analyzed, and the deviation of the in-mold pressure fluctuation coefficient, curing resistance coefficient and curing interface temperature difference is traced in reverse to obtain the deviation control factor that represents the core source of process state change. Specifically, within the same sliding time window, for the three sequences formed by the in-mold pressure fluctuation coefficient, curing resistance coefficient and curing interface temperature difference, the variance of the in-mold pressure fluctuation coefficient sequence is calculated as the total energy of pressure disturbance, and the covariance of the in-mold pressure fluctuation coefficient sequence with the other two sequences is calculated. The covariance is used as the coupling energy of pressure disturbance to resistance field and temperature field. Subtract the sum of the two coupled energies from the total energy of the pressure disturbance to obtain the residual energy maintained by the pressure field itself. Divide the three energy values ​​by the sum of the two coupled energies and the total energy of the pressure disturbance to obtain the energy ratio of the pressure field itself, the energy ratio of the drag field coupled energy, and the energy ratio of the temperature field coupled energy. For the current moment's in-mold pressure fluctuation coefficient, curing resistance coefficient, and curing interface temperature difference, subtract the mean of their respective sequences within the previous sliding time window to obtain three instantaneous changes. Divide each of the three instantaneous changes by the standard deviation of their respective sequences within the previous sliding time window to obtain the change intensity coefficient. Multiply the three energy proportions by their corresponding change intensity coefficients to obtain the pressure field dominance, resistance field dominance, and temperature field dominance. The maximum value of the three controllability degrees is used as the deviation controllability factor, and its corresponding dominant field is marked. If two controllability degrees are equal and are the maximum values, they are marked as the two fields that dominate. If the three controllability degrees are the same, they are marked as the three fields that dominate. In this way, the deviation controllability factor of the core source of process state change is obtained. Based on the deviation from the main control factor, the direction of speed adjustment is determined, and a speed optimization indicator representing the direction of speed adjustment is generated. Specifically, when the deviation from the main control factor is solely dominated by the pressure field, the pultrusion speed decreases, and the speed optimization indicator is 2; when the deviation from the main control factor is solely dominated by the resistance field, the pultrusion speed increases, and the speed optimization indicator is 1; when the deviation from the main control factor is solely dominated by the temperature field, the pultrusion speed remains unchanged, and the speed optimization indicator is 3. If the deviation from the master control factor is dominated by multiple fields, regardless of whether it is dominated by two or three fields, as long as any one of the dominant fields satisfies the deceleration logic, the speed optimization is marked as 2; if none of them satisfy the deceleration logic but there is an acceleration logic, the speed optimization is marked as 1; otherwise, the speed optimization is marked as 3. Among them, pressure field dominance is considered to satisfy the deceleration logic; resistance field dominance is considered to satisfy the acceleration logic; temperature field dominance satisfies neither the acceleration logic nor the deceleration logic. Step S5: Obtain the real-time current value and rated current of the traction motor in the pultrusion equipment of the target object, and determine the speed optimization strategy based on the speed optimization identifier, real-time current value and rated current.

2. The method for optimizing the pultrusion speed of a carbon fiber composite core according to claim 1, characterized in that, The mold and the target object include: The mold is a metal mold used for molding carbon fiber composite cores; The target object is a carbon fiber composite core.

3. The method for optimizing the pultrusion speed of a carbon fiber composite core according to claim 1, characterized in that, The resin pressure on the inner wall at the center of the gel segment and the beginning of the preheating segment was analyzed to generate an in-mold pressure fluctuation coefficient representing the influence of the mold front pressure on the stability of the gel segment, including: The fluctuation amplitude and frequency of the resin pressure on the inner wall at the beginning of the preheating section are analyzed to generate a pressure fluctuation baseline value reflecting the degree of dynamic pressure change. Specifically, this includes: continuously collecting the resin pressure on the inner wall at the beginning of the preheating section and obtaining a pressure time series after preprocessing; identifying all local maxima and minima in the pressure time series as pressure peaks and valleys, respectively; calculating the pressure difference between adjacent pressure peaks and valleys, and performing root mean square calculation on the pressure difference to obtain the effective fluctuating pressure value; performing a fast Fourier transform on the pressure time series to obtain a spectrum, extracting the frequency component with the largest amplitude in the spectrum as the main perturbation frequency, and multiplying the processed main perturbation frequency with the effective fluctuating pressure value to obtain the pressure fluctuation baseline value reflecting the degree of dynamic pressure change. The pressure fluctuation baseline value is correlated with the inner wall resin pressure at the center of the gel segment. The transmission and attenuation of fluctuation energy are analyzed to generate the in-mold pressure fluctuation coefficient, which represents the influence of the mold front pressure on the stability of the gel segment. Specifically, for the pressure fluctuation baseline value and the inner wall resin pressure value, the average value of the inner wall resin pressure at the center of the gel segment is processed within the same sliding time window to obtain the gel segment baseline pressure coefficient. The physical distance from the beginning of the preheating section to the center of the gel section along the mold direction is obtained. After processing the physical distance, the transmission distance coefficient is obtained. The distance attenuation factor is calculated with the natural constant as the base and the negative transmission distance coefficient as the exponent. Multiply the reciprocal of the main disturbance frequency value by the distance attenuation factor to obtain the comprehensive attenuation coefficient. Multiply the pressure fluctuation base value by the comprehensive attenuation coefficient to obtain the equivalent fluctuation influence value. Add the equivalent fluctuation influence value to the gel segment reference pressure coefficient to generate the in-mold pressure fluctuation coefficient, which represents the influence of the mold front pressure on the stability of the gel segment.

4. The method for optimizing the pultrusion speed of a carbon fiber composite core according to claim 1, characterized in that, The resin pressure on the inner wall at the end of the cured section and the resin pressure on the inner wall at the center of the gel section are calculated to obtain the curing resistance coefficient, which represents the demolding resistance state of the target object at the end of the mold. This coefficient includes: The inner wall resin pressure at the end of the curing section and the inner wall resin pressure at the center of the gel section are analyzed to analyze the deviation of pressure distribution and attenuation, and to obtain the curing resistance coefficient representing the demolding resistance state of the target object at the end of the mold. Specifically, for the inner wall resin pressure at the end of the curing section and the inner wall resin pressure at the center of the gel section, the average pressure of the two is calculated within the same sliding time window and recorded as the average pressure at the end of the curing section and the average pressure at the center of the gel section. The physical distance from the center of the gel segment to the end of the cured segment along the mold direction is obtained. The physical distance is divided into continuous characteristic segments. The pressure decay curve is constructed with the average pressure at the center of the gel segment as the starting point, the average pressure at the end of the cured segment as the ending point, and the physical distance as the independent variable. Real-time acquisition of the inner wall resin pressure value at the corresponding spatial position of each feature segment to obtain the actual pressure distribution curve, calculation of the area difference between the actual pressure distribution curve and the pressure decay curve to obtain the cumulative pressure deviation, and division of the cumulative pressure deviation by the physical distance to obtain the average pressure deviation intensity. The real-time inner wall temperature at the end of the curing section is obtained and pre-processed to obtain the end temperature coefficient. The average pressure deviation intensity is pre-processed to obtain the average pressure deviation coefficient. The end temperature coefficient and the average pressure deviation coefficient are multiplied to obtain the curing resistance coefficient, which represents the demolding resistance state of the target object at the end of the mold.

5. The method for optimizing the pultrusion speed of a carbon fiber composite core according to claim 1, characterized in that, The surface temperature, diameter, and inner wall temperature at the center of the gel segment are calculated to obtain the curing interface temperature difference value, which reflects the degree of matching between the curing progress of the target object's surface and interior. This includes: A correlation analysis was performed on the inner wall temperature and surface temperature at the center of the gel segment. Combined with the diameter of the target object, the radial distribution of temperature differences was analyzed to generate a radial temperature feature value reflecting the degree of drastic temperature change within the target object. Specifically, this involved: calculating the difference between the inner wall temperature and the surface temperature to obtain the radial temperature difference; dividing the radial temperature difference by half the diameter of the target object to obtain the average radial temperature gradient; dividing the radial temperature difference by the inner wall temperature at the center of the gel segment, then multiplying it sequentially by the average radial temperature gradient, the relative temperature difference coefficient, and the diameter of the target object, and dividing by 1 degree Celsius to generate a radial temperature feature value reflecting the degree of drastic temperature change within the target object.

6. The method for optimizing the pultrusion speed of a carbon fiber composite core according to claim 5, characterized in that, The surface temperature, diameter, and inner wall temperature at the center of the gel segment are calculated to obtain the curing interface temperature difference value, which reflects the degree of matching between the curing progress of the target object's surface and interior. This also includes: Based on the radial temperature characteristic value, the influence and disturbance of surface temperature on radial temperature distribution are analyzed to obtain the curing interface temperature difference value reflecting the matching degree of curing progress between the surface and interior of the target object. Specifically, this includes: acquiring the surface temperature of the target object collected in real time at the mold exit, constructing a surface temperature time series within the same sliding time window, calculating the mean of the surface temperature time series, and obtaining the average surface temperature; at the same time, acquiring the mean of the inner wall temperature at the center of the gel segment within the sliding time window to obtain the average core temperature, and then calculating the difference between the average surface temperature and the average core temperature to obtain the basic radial temperature difference. Calculate the instantaneous deviation between the average surface temperature and the mean core temperature in the surface temperature time series, and take the root mean square of the instantaneous deviation to obtain the surface temperature fluctuation intensity. The perturbation coupling coefficient is obtained by multiplying the pretreated surface temperature fluctuation intensity with the radial temperature characteristic value; the difference between the surface temperature value and the core temperature value at the current moment is calculated to obtain the instantaneous radial temperature difference; the instantaneous radial temperature difference is divided by the radial temperature baseline difference to obtain the instantaneous temperature difference relative coefficient; the perturbation coupling coefficient, the instantaneous temperature difference relative coefficient and the radial temperature baseline difference are multiplied in sequence to obtain the solidification interface temperature difference value.

7. The method for optimizing the pultrusion speed of a carbon fiber composite core according to claim 1, characterized in that, Based on the speed optimization identifier, real-time current value, and rated current, a speed optimization strategy is determined, including: If the speed optimization flag is 1 and the real-time current value is less than the rated current, then the speed optimization strategy is to allow speed increase. If the speed optimization flag is 2, then the speed optimization strategy is to allow speed reduction; If the speed optimization flag is 3, the speed optimization strategy is to maintain the current speed; Among them, the speed optimization indicator is 1, which means that the current process state is dominated by a single resistance field or by multiple fields, with only the resistance field dominating and no pressure field dominating. The speed optimization indicator is 2, which means that the current process state is dominated by a single pressure field or by multiple fields, including the pressure field. The speed optimization indicator is 3, which means that the current process state is dominated by the temperature field alone or by multiple fields, including only the temperature field and not the pressure field or the resistance field.

8. A carbon fiber composite core pultrusion speed optimization system, applied in the optimization method as described in any one of claims 1-7, characterized in that, include: The data acquisition unit is used to acquire in real time the inner wall temperature and inner wall resin pressure of the mold corresponding to the start end of the preheating section, the center position of the gel section, and the end end of the curing section. At the same time, it can acquire the surface temperature of the target object when it exits the mold and the diameter of the target object in real time at the mold outlet. The influence analysis unit is used to analyze the inner wall resin pressure at the center of the gel segment and the beginning of the preheating segment, generate the in-mold pressure fluctuation coefficient representing the influence of the mold front pressure on the stability of the gel segment, and calculate the inner wall resin pressure at the end of the curing segment and the inner wall resin pressure at the center of the gel segment to obtain the curing resistance coefficient representing the demolding resistance state of the target object at the end of the mold. The temperature analysis unit is used to calculate the surface temperature, diameter, and inner wall temperature at the center of the gel segment to obtain the curing interface temperature difference value, which reflects the degree of matching between the curing progress of the surface and the interior of the target object. The speed optimization unit is used to perform collaborative analysis on the in-mold pressure fluctuation coefficient, curing resistance coefficient and curing interface temperature difference value, and generate a speed optimization label indicating the direction of speed adjustment. The speed adjustment unit is used to obtain the real-time current value and rated current of the traction motor in the pultrusion equipment of the target object, and determine the speed optimization strategy based on the speed optimization identifier, real-time current value and rated current.

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