Method and system for optimizing slitting parameters of carbon fiber prepreg

Abstract

The invention provides a carbon fiber prepreg slitting parameter optimization method and system, and relates to the field of prepreg processing.The method comprises the steps that local thermal performance difference information of a carbon fiber prepreg about to enter a slitting station is obtained, and the corresponding relation between the information and the physical position of the prepreg is established; judging the local thermal state of the prepreg according to the local thermal performance difference information, and generating a thermal regulation instruction according to the local thermal state; carrying out local thermal regulation on the prepreg according to the thermal regulation instruction between the upstream of the slitting station and a slitting knife group; through the local thermal regulation, the thermal performance of the prepreg is homogenized, and the method has the advantages that the problem of unstable notch quality caused by non-uniform local thermal performance of the carbon fiber prepreg in the slitting process can be effectively solved, and the edge quality and subsequent forming efficiency of a slitting material belt are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of prepreg processing, and in particular to a method and system for optimizing carbon fiber prepreg slitting parameters. Background Art

[0002] In the manufacturing of composite components, precisely slitting wide carbon fiber prepreg into multiple narrow strips is a fundamental and critical preparatory step for subsequent automated molding. The edge quality of these strips directly determines the mechanical properties and reliability of the final component. In a multi-head parallel slitting production model, ensuring highly consistent, excellent cut quality across each strip is a key technical focus in this field.

[0003] Specifically, during the slitting process of carbon fiber prepreg, due to the dense arrangement of the blades, heat tends to accumulate in the center of the blade assembly and is difficult to dissipate, while the edges dissipate heat more quickly. This results in a non-uniform thermal field across the width of the prepreg, with a high temperature in the center and low temperatures on both sides. The epoxy resin matrix in carbon fiber prepreg is extremely sensitive to temperature changes, and rising temperatures significantly reduce its viscosity. This temperature difference directly leads to inconsistent slitting quality: the strips slit from the center of the equipment often experience resin overflow and stickiness at the edges of the cuts, while the strips slit from the side areas have relatively dry and clean cuts.

[0004] There are also small, continuous fluctuations in the conveying tension of prepreg. Instantaneous changes in tension will slightly change the effective thickness and internal stress of the prepreg, thereby affecting its mechanical response when being cut. The above-mentioned types of disturbances originating from materials and equipment, acting on microscopic scales and instantaneous processes, are intertwined, causing the local thermal properties of the prepreg to show significant differences and heterogeneity when entering the slitting station. Any set of fixed global process parameters cannot effectively cope with this complex and dynamically changing material state, which ultimately leads to a random and difficult-to-reproduce degradation of the edge quality of the entire batch of material strips, seriously affecting the subsequent precision placement molding quality. In response to the above problems, the existing technology urgently needs to be improved. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for optimizing carbon fiber prepreg slitting parameters, which can effectively solve the problem of unstable cut quality caused by local thermal property heterogeneity of carbon fiber prepreg during slitting, and significantly improve the edge quality of slit material strips and subsequent molding efficiency.

[0006] The present application provides a method for optimizing carbon fiber prepreg slitting parameters, comprising the following steps: Obtain local thermal performance difference information of carbon fiber prepregs about to enter the slitting station and establish a corresponding relationship between this information and the physical location of the prepregs; Determine the local thermal state of the prepreg based on the local thermal performance difference information, and generate thermal adjustment instructions based on the local thermal state; Between the upstream of the slitting station and the slitting knife group, the prepreg is locally thermally regulated according to the thermal regulation instructions; The thermal properties of the prepreg are homogenized through local thermal regulation.

[0007] Through the above scheme, the thermal properties of the prepreg are uniformed by local thermal regulation, thereby effectively solving the problem of inconsistent slitting quality caused by local thermal property differences of the prepreg in the prior art and improving the incision quality.

[0008] To further solve the problem, the present application also proposes a carbon fiber prepreg slitting parameter optimization system, which includes: An information acquisition module is used to obtain local thermal performance difference information of the carbon fiber prepreg that is about to enter the slitting station and establish a corresponding relationship between the information and the physical position of the prepreg; A state determination and instruction generation module is used to determine the local thermal state of the prepreg based on the local thermal performance difference information and generate thermal adjustment instructions based on the local thermal state; The local thermal regulation module is used to perform local thermal regulation on the prepreg upstream of the slitting station and between the slitting knife group according to the thermal regulation instructions; A homogenization module is used to homogenize the thermal properties of the prepreg through the local thermal regulation.

[0009] Through the above solution, a system for implementing the above optimization method is provided, which provides hardware foundation and functional module support for the practical application of the method.

[0010] In summary, the present application provides a method and system for optimizing the slitting parameters of carbon fiber prepregs. By obtaining information on the local thermal performance differences of the prepregs and performing local thermal adjustments, the thermal properties of the prepregs are homogenized, effectively solving the problem of unstable slitting quality in the prior art. This has the advantage of being able to effectively solve the problem of unstable cut quality caused by the uneven local thermal properties of the carbon fiber prepregs during the slitting process, and significantly improve the edge quality of the slitting strips and the subsequent molding efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of a carbon fiber prepreg slitting parameter optimization method provided in this application.

[0012] Figure 2 A schematic diagram of a carbon fiber prepreg slitting parameter optimization system provided in this application. DETAILED DESCRIPTION

[0013] The technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. The components of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for which protection is claimed, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0014] This application first considers the real-time adjustment of the parameters of the slitting knife group, such as dynamically changing the downward pressure or rotation speed of the cutter head according to the local cutting conditions. However, this passive adjustment made at the moment of cutting is difficult to effectively deal with the randomly distributed microscopic defects and instantaneous thermal responses inside the prepreg, and the fine local adjustment of the cutter head parameters has limitations in response speed and accuracy in actual operation, making it difficult to fundamentally solve the problem. In this regard, the application further considers that since the root of the problem lies in the uneven thermal properties of the material itself and the non-uniformity of the thermal field of the processing environment, is it possible to actively intervene in and homogenize the thermal properties of the material before it enters the slitting knife group?

[0015] This application envisions that if it is possible to perceive in advance the difference information of the thermal properties of the prepreg at different physical positions, and establish a correspondence between this information and the physical position of the prepreg, it can provide a basis for subsequent precise intervention. Based on this perception, the local thermal state of the prepreg can be determined, such as which areas have high temperatures and which areas have abnormal thermal conductivity, and targeted thermal adjustment instructions can be generated accordingly. Subsequently, before the prepreg reaches the slitting knife group, that is, between the upstream of the slitting station and the slitting knife group, the prepreg is locally thermally adjusted according to the thermal adjustment instruction, for example, by local heating or cooling, so that its thermal properties are as uniform as possible when it enters the knife group. This active intervention before cutting can reduce the uncertainty in the cutting process from the source, thereby avoiding incision quality problems caused by differences in the thermal properties of the material.

[0016] Reference Figure 1 , shows a schematic diagram of an embodiment of a method for optimizing carbon fiber prepreg slitting parameters according to an embodiment of the present invention, which may specifically include the following steps: S101, obtaining local thermal property difference information of the carbon fiber prepreg about to enter the slitting station, and establishing a corresponding relationship between the information and the physical position of the prepreg; S102, determining the local thermal state of the prepreg according to the local thermal performance difference information, and generating a thermal adjustment instruction according to the local thermal state; S103, performing local thermal adjustment on the prepreg between the upstream of the slitting station and the slitting knife group according to the thermal adjustment instruction; S104, homogenizing the thermal properties of the prepreg through local thermal regulation.

[0017] Among them, local thermal performance difference information refers to the non-uniform distribution of thermal properties (such as temperature, thermal conductivity, specific heat capacity, etc.) of carbon fiber prepreg in the width direction or length direction. It can be obtained by non-contact thermal imaging technology, infrared scanning sensors or distributed thermocouple arrays. Its main purpose is to identify which areas of the prepreg have deviations in thermal performance before entering the slitting station, and provide a data basis for subsequent precise intervention. Local thermal state refers to the evaluation or classification of the current thermal conditions of a specific area of ​​the prepreg based on the obtained local thermal performance difference information. It can be determined based on a preset thermal model, empirical threshold or machine learning algorithm. Its main purpose is to convert the original thermal performance data into a state description with clear physical meaning that can be used for decision-making, thereby guiding the generation of thermal regulation instructions. Thermal regulation instructions refer to specific commands issued by the control system to guide the thermal regulation unit to perform heating or cooling operations based on the determined local thermal state. They may include parameters such as adjusting the target temperature, heating / cooling power, action time or adjustment area. Their main purpose is to convert the intention to intervene in the thermal properties of the prepreg into an executable physical operation to ensure the accuracy and effectiveness of the regulation. Local thermal regulation refers to the application of targeted heating or cooling effects to specific areas of the prepreg before slitting to change the thermal properties of the area. It can be achieved by using semiconductor refrigeration sheets, hot air nozzles, infrared heaters or coolant circulation devices. Its main purpose is to actively correct the thermal unevenness of the prepreg so that the thermal properties of each area tend to be consistent when it enters the slitting knife group, thereby improving the slitting conditions.

[0018] The core innovation of this application lies in introducing real-time acquisition and analysis of local thermal performance difference information of prepregs upstream of the carbon fiber prepreg slitting station, and generating precise thermal adjustment instructions based on this, thereby performing local thermal adjustment on the prepregs, making the thermal properties of the prepregs uniform, and solving the problem of unstable incision quality caused by defects in the prepregs themselves and uneven processing environment from the source, thereby achieving the effect of improving the consistency and stability of slitting quality.

[0019] The solution of this application achieves proactive optimization of the slitting quality of carbon fiber prepreg through a series of collaborative steps. First, before the prepreg enters the slitting station, the system obtains information on its local thermal performance differences and simultaneously establishes a correspondence between this information and the prepreg in physical space. This step is the foundation of the entire solution, ensuring that all subsequent interventions can be targeted and act on the areas that need adjustment. It is precisely because of the perception of the prepreg's thermal properties that subsequent decision-making is possible. Based on this, the system determines the local thermal state of the prepreg based on the acquired local thermal performance difference information. This determination process converts raw data into a practical physical state, such as determining whether a certain area is overheated, undercooled, resin-rich, or resin-poor. The system then generates corresponding thermal adjustment instructions based on the determined local thermal state. These instructions are customized for specific areas and specific thermal states, and are intended to guide subsequent thermal adjustment operations. Next, at a specific location upstream of the slitting station and between the slitting blade assembly, the system performs local thermal conditioning on the prepreg based on previously generated thermal conditioning instructions. This step, the core intervention in the solution, alters the thermal properties of the prepreg by applying heating or cooling to a localized area of ​​the prepreg. This conditioning occurs before the slitting action and therefore influences the physical state of the prepreg at the moment of cutting, thereby reducing uncertainty during the cutting process. Ultimately, through this local thermal conditioning, the thermal properties of the prepreg are homogenized. By the time the prepreg reaches the slitting blade assembly, the thermal properties (such as temperature and viscosity) of its different regions will converge, thereby reducing fluctuations in cut quality caused by material inhomogeneities or differences in the processing environment. This holistic thermal homogenization ensures consistent cut quality for each slit strip, resolving the issues of resin overflow, stickiness, or burrs mentioned in the background art.

[0020] As a specific implementation, an infrared thermal imaging camera array can be deployed along the carbon fiber prepreg conveyor path to continuously scan the prepreg before it enters the slitting station, thereby acquiring real-time temperature distribution data across the prepreg web. Furthermore, by coordinating with an encoder or visual positioning system on the prepreg conveyor mechanism, a correlation can be established between this temperature data and the physical location of the prepreg. For example, each temperature pixel can be mapped to a specific transverse and longitudinal coordinate on the prepreg. The acquired temperature distribution data is then transmitted to an industrial computer, which runs a pre-defined thermal model and control algorithm. Based on the real-time temperature data, the computer determines the local thermal state of the prepreg. For example, if the temperature of a particular area is significantly above or below the preset slitting temperature range, or if the temperature gradient is excessive, it is considered an abnormal thermal state. Based on this determination, the computer generates appropriate thermal control instructions. For example, for overheated areas, a cooling instruction to reduce the temperature is generated; for undercooled areas, a heating instruction to increase the temperature is generated, specifying the target area and duration. Subsequently, a localized thermal conditioning device consisting of multiple independently controlled condensers or an array of hot / cold air nozzles can be deployed upstream of the slitting station and at a distance from the slitting blade assembly. Each condenser or nozzle corresponds to a specific area on the prepreg web. Upon receiving a thermal conditioning command from the computer, the corresponding condenser will heat or cool the prepreg web accordingly, or the corresponding nozzle will spray hot or cold air at a set temperature and flow rate, targeting the designated area of ​​the prepreg web. For example, if a command calls for cooling an overheated area, the corresponding condenser will activate cooling mode, reducing the temperature of that area to the target range. This localized thermal conditioning homogenizes the thermal properties of the prepreg web, particularly the temperature and, consequently, the resin viscosity, before it enters the slitting blade assembly. This ensures that the slitting blade assembly cuts a thermally more stable material, reducing defects such as resin overflow, stickiness, or fiber burrs caused by thermal inhomogeneity, thereby ensuring consistent slitting quality.

[0021] Through the above technical solution, the present application can solve the problem of unstable cut quality of carbon fiber prepregs during the slitting process due to the uneven thermal properties of the material itself and the difference in the thermal field of the processing environment. By actively and locally regulating the prepreg thermally before slitting, its thermal properties are homogenized, thereby reducing the uncertainty in the cutting process. This improves the edge quality of the slit narrow strips, reduces the occurrence of defects such as resin overflow, stickiness or fiber breakage, and thus ensures the quality of raw materials for subsequent automatic filament placement or automatic tape placement molding processes, and improves the mechanical properties and reliability of the final composite material components.

[0022] In some of the aforementioned embodiments of this application, it is proposed to determine the local thermal state of the prepreg based on local thermal property difference information, and to generate thermal adjustment instructions based on the local thermal state, so as to perform local thermal adjustment on the prepreg and homogenize the thermal properties of the prepreg. However, in actual applications, relying solely on local thermal property difference information to determine the thermal state may not be accurate enough, because environmental parameters (such as ambient temperature and humidity) and the state of the thermal adjustment unit itself (such as heating power and cooling intensity) can affect the instantaneous thermal response of the prepreg, thereby affecting the accuracy of the thermal state determination, resulting in deviations in the thermal adjustment instructions, and ultimately affecting the slitting quality.

[0023] In this regard, the present application further proposes that the steps of determining the local thermal state of the prepreg based on the local thermal performance difference information and generating a thermal adjustment instruction based on the local thermal state include: Obtaining environmental parameters or thermal regulation unit state parameters that affect transient thermal response parameters; Calibrate the transient thermal response parameters according to the environmental parameters or the state parameters of the thermal regulation unit; Compare the calibrated transient thermal response parameters with the preset characteristic parameters to determine the local thermal state of the prepreg; Generate thermal adjustment instructions based on local thermal conditions.

[0024] Among them, the transient thermal response parameter refers to the change or rate of change of the thermal characteristics of the prepreg in a short period of time after being subjected to transient heat, such as the instantaneous value of the temperature change rate, thermal diffusivity or thermal conductivity, which can be measured by devices such as thermocouples, infrared sensors or heat flow sensors, and its purpose is to reflect the local thermal characteristics of the prepreg at a specific moment. Among them, the environmental parameter refers to the external environmental factors that affect the thermal behavior of the prepreg, specifically the ambient temperature, ambient humidity or air flow velocity, etc., which can be obtained by devices such as temperature sensors, humidity sensors or anemometers, and its purpose is to quantify the impact of the external environment on the thermal properties of the prepreg. Among them, the thermal regulation unit state parameter refers to the working state information of the equipment used to perform local thermal regulation on the prepreg, specifically heating power, cooling intensity, fan speed or coolant flow, etc., which can be obtained by power meters, flow meters or internal sensors of the equipment, and its purpose is to reflect the intensity of the thermal action applied by the thermal regulation unit to the prepreg.

[0025] Among them, calibration refers to the process of correcting or adjusting the originally obtained transient thermal response parameters based on environmental parameters or thermal regulation unit state parameters. Specifically, it can be achieved by applying a preset mathematical model, calibration curve or lookup table. Its purpose is to eliminate or reduce the influence of external interference factors on the accuracy of transient thermal response parameter measurement, so that it can more realistically reflect the actual thermal state of the prepreg. Among them, the preset characteristic parameters refer to a set of predetermined reference values ​​or ranges for evaluating or classifying the local thermal state of the prepreg, specifically temperature thresholds, slope ranges of thermal response curves, or ideal intervals of thermal diffusivity, etc., which can be determined based on material properties, process requirements or historical data analysis. Its purpose is to provide an objective basis for determining the local thermal state of the prepreg. Among them, the local thermal state refers to the comprehensive performance state of thermal properties such as temperature, viscosity or degree of curing of a specific area of ​​the prepreg at the current moment, specifically overheating, undercooling or normal, etc., and its purpose is to classify the local thermal characteristics of the prepreg so that corresponding adjustment measures can be taken. Among them, the thermal regulation instruction refers to the specific operation command issued to the thermal regulation unit based on the determined local thermal state, specifically increasing the heating power, reducing the cooling intensity or maintaining the current state, etc., through the thermal regulation unit to perform precise local thermal intervention on the prepreg.

[0026] The solution of the present application improves the accuracy of the determination of the local thermal state of the prepreg by obtaining environmental parameters or thermal regulation unit state parameters that affect the transient thermal response parameters and calibrating the transient thermal response parameters based on them. Specifically, before determining the local thermal state of the prepreg, the system first obtains external environmental factors that may affect the accuracy of the transient thermal response parameters and the working state of the thermal regulation unit itself. These parameters directly affect the actual thermal response of the prepreg. For example, an increase in ambient temperature may cause the surface temperature reading of the prepreg to be too high, and the fluctuation of the heating power of the thermal regulation unit will directly change the heat applied by it.

[0027] The system then calibrates the original transient thermal response parameters based on these acquired environmental parameters or thermal regulation unit status parameters. This calibration process is crucial, as it effectively compensates for or eliminates interference from these external and internal factors on the measurement results, ensuring that the calibrated transient thermal response parameters more accurately reflect the true local thermal properties of the prepreg. For example, if the ambient temperature is above the standard value, the calibration process can appropriately adjust the measured transient thermal response parameters downward to offset the influence of ambient heat on the measurement. The calibrated transient thermal response parameters are then compared with preset characteristic parameters. These characteristic parameters are pre-set thresholds or ranges based on ideal process conditions and are used to distinguish different thermal states of the prepreg. By comparing these more accurate calibrated parameters, the system can more reliably determine whether the prepreg is currently overheated, undercooled, or in the ideal local thermal state. Ultimately, based on this accurately determined local thermal state, the system generates corresponding thermal regulation instructions.

[0028] For example, if it is determined to be overheated, the instruction may require reducing the heating power or increasing the cooling intensity; if it is determined to be overcooled, the instruction may require increasing the heating power or reducing the cooling intensity. The synergistic effect of this series of steps makes the determination of the local thermal state of the prepreg no longer rely solely on a single instantaneous thermal response data, but takes into account a variety of influencing factors, thereby greatly improving the accuracy and reliability of the determination. This accurate determination can then generate more precise thermal regulation instructions to ensure that the local thermal regulation of the prepreg is more accurate and effective. In this way, the solution of the present application can more finely control the homogenization process of the thermal properties of the prepreg, effectively solve the problem of thermal regulation deviation caused by inaccurate thermal state determination, thereby ensuring the consistency and stability of the material strip cut quality from the source during the carbon fiber prepreg slitting process, and significantly improving the slitting quality.

[0029] In some preferred embodiments, the present application is implemented as follows: On a carbon fiber prepreg slitting production line, to accurately determine the local thermal state of the prepreg and generate thermal adjustment instructions, the system first acquires various auxiliary parameters that influence the transient thermal response parameters. For example, temperature and humidity sensors can be deployed near the slitting station to acquire the ambient temperature and humidity in real time as environmental parameters. Simultaneously, by reading data from the internal controller of a thermal adjustment unit (e.g., a local heater or cooling fan), the current heating power or cooling fan speed is acquired as the thermal adjustment unit state parameter. Next, the system calibrates the transient thermal response parameters of the prepreg based on these acquired environmental or thermal adjustment unit state parameters. Specifically, a multivariate calibration model can be pre-established. This model takes as input parameters such as ambient temperature, humidity, and heating power, and outputs a calibration coefficient or correction factor. Once the transient thermal response parameters are acquired in real time, the system uses this model to calculate the corresponding calibration coefficients and applies them to the original transient thermal response parameters, for example, through multiplication or addition, to obtain the calibrated transient thermal response parameters. For example, if the ambient temperature is high, the calibration model calculates a negative correction, ensuring that the calibrated transient thermal response parameters eliminate the effects of ambient heat on the measurement. The calibrated transient thermal response parameters are then compared with pre-set characteristic parameters to determine the prepreg's local thermal state. For example, three characteristic parameter intervals can be preset: when the calibrated transient thermal response parameters fall within the "ideal temperature range," the prepreg is considered to be in a "normal" thermal state; when they are above the "overheating threshold," the prepreg is considered to be in an "overheating" state; and when they are below the "undercooling threshold," the prepreg is considered to be in an "undercooling" state. Finally, based on the determined local thermal state, the system generates corresponding thermal regulation instructions. For example, if the system determines the state to be "overheated," it generates a "reduce heating power" or "increase cooling intensity" instruction and sends it to the thermal regulation unit; if the system determines the state to be "undercooling," it generates a "increase heating power" or "reduce cooling intensity" instruction; and if the system determines the state to be "normal," it generates a "maintain current state" instruction. In this way, the thermal regulation unit can receive more accurate instructions, thereby performing precise local thermal regulation on the prepreg.

[0030] The above technical solution solves the problem that relying solely on local thermal performance difference information to determine the thermal state may not be accurate enough. By obtaining and using environmental parameters or thermal regulation unit state parameters to calibrate the transient thermal response parameters, the determination of the local thermal state of the prepreg is made more accurate and reliable. This accurate determination can generate more precise thermal regulation instructions, thereby effectively avoiding improper thermal regulation caused by deviations in thermal state determination, ultimately ensuring the homogenization of thermal properties during the slitting process of carbon fiber prepreg, and improving the stability and consistency of slitting quality.

[0031] In this regard, the present application further proposes that the method also includes: when the upstream detection station is separated from the downstream thermal adjustment station and during local thermal adjustment, a detection heat pulse is applied to the prepreg through the thermal adjustment unit; and the instantaneous thermal response parameters of the thermal adjustment unit are collected.

[0032] A detection heat pulse refers to a transient heat input of specific energy and duration applied to the prepreg by the thermal conditioning unit. Specifically, it can be a short, high-intensity laser pulse, a rapid temperature rise pulse generated by a resistance heater, or transient thermal radiation emitted by an infrared radiation source. Its purpose is to stimulate the local thermal response of the prepreg without significantly changing the overall thermal state of the prepreg, thereby facilitating rapid, non-invasive detection of its current thermal properties. Transient thermal response parameters refer to physical quantities related to the thermal behavior of the prepreg, captured by the thermal conditioning unit or its nearby sensors within a very short period of time after the detection heat pulse is applied. Specifically, they can be the instantaneous temperature change curve, heat flux density change rate, temperature change rate, thermal diffusivity, or thermal conductivity of the measured area. The purpose is to reflect the dynamic characteristics of the prepreg's heat absorption, conduction, and dissipation through the changing trends and characteristics of these parameters, thereby indirectly revealing the prepreg's current local thermal state, such as its resin viscosity, degree of cure, or internal defect distribution.

[0033] The solution of the present application realizes the dynamic tracking and evaluation of the local thermal state of the prepreg by applying a detection heat pulse to the prepreg at a critical moment and collecting transient thermal response parameters. Specifically, when the upstream detection station is physically separated from the downstream thermal regulation station, or when the local thermal regulation process itself is in progress, the thermal state of the prepreg may deviate due to ambient temperature fluctuations, heat loss during transportation, or the heat transfer effect of the regulation unit itself. In order to cope with this dynamic change, the thermal regulation unit is designed to not only apply regulation heat, but also serve as a detection tool. By applying a known detection heat pulse to the prepreg, the prepreg will produce a specific transient thermal response according to its current thermal state. For example, if the resin viscosity of the prepreg decreases due to temperature increase, its absorption and conduction characteristics of the heat pulse will change. The thermal regulation unit then collects these transient thermal response parameters, such as the transient rate of temperature rise or the transient change in heat flow.

[0034] In some preferred embodiments, when the upstream inspection station is separated from the downstream thermal conditioning station, for example, when the prepreg is being moved between the two stations via a conveyor belt, or when a local thermal conditioning unit is applying heat to the prepreg for conditioning, the thermal conditioning unit can periodically or under specific triggering conditions apply a probing heat pulse to the prepreg. Specifically, the thermal conditioning unit can integrate a microlaser capable of emitting a laser pulse of extremely short duration (e.g., 10 to 100 milliseconds) with controllable energy, which serves as the probing heat pulse. This laser pulse is focused onto a localized area of ​​the prepreg, causing its surface temperature to rise momentarily by several degrees Celsius. Subsequently, a highly sensitive infrared temperature sensor or thermocouple array can be positioned within or immediately adjacent to the thermal conditioning unit to capture the instantaneous temperature response curve of the prepreg in the detected area in real time. For example, the sensor can record the rise and subsequent decay of the prepreg surface temperature after the laser pulse is applied, at a rate of hundreds or even thousands of times per second. These transient temperature data, including peak temperature, temperature rise rate, and temperature decay rate, are collected as transient thermal response parameters. By analyzing these parameters, thermal properties such as the prepreg's current thermal conductivity, thermal diffusivity, or surface thermal resistance can be inferred, and its current local thermal state can be evaluated, such as whether the resin's viscosity or degree of cure has changed.

[0035] In some of the above-mentioned embodiments of the present application, it is proposed to calibrate the transient thermal response parameters according to the environmental parameters or the state parameters of the thermal regulation unit. The calibration of the transient thermal response parameters according to the environmental parameters or the state parameters of the thermal regulation unit can be specifically carried out by pre-establishing a fixed mapping table between the ambient temperature and the thermal response parameters, or by correcting the transient thermal response parameters according to the power setting value of the thermal regulation unit and the empirical formula of the thermal response parameters to eliminate the influence of the external environment or the equipment state on the measurement results. This can improve the accuracy of thermal performance detection and thus optimize the cutting parameters. However, in its implementation process, the environmental parameters and the state parameters of the thermal regulation unit may drift over time, resulting in a deviation in the calibration relationship itself. If there is a deviation in the calibration relationship, the calibration accuracy of the transient thermal response parameters will be reduced, thereby affecting the accurate judgment of the local thermal state of the prepreg, and ultimately leading to inaccurate thermal regulation and the inability to effectively homogenize the thermal properties of the prepreg.

[0036] In this regard, the present application further proposes that the steps of calibrating the transient thermal response parameters according to the environmental parameters or the state parameters of the thermal regulation unit include: When the evaluation results indicate that there is a deviation in the calibration relationship, continuously obtain the evaluation results and accumulate the evaluation results; Compare the accumulated assessment results with the preset deviation persistence threshold to determine whether the deviation persists; When the deviation persists and its magnitude exceeds a preset adjustment initiation threshold, determining an adjustment amount for the calibration relationship based on the magnitude of the deviation; According to the adjustment amount, the calibration relationship is incrementally adjusted until the deviation meets the preset convergence condition; The transient thermal response parameters are calibrated according to the adjusted calibration relationship and the environmental parameters or the state parameters of the thermal regulation unit.

[0037] Among them, the evaluation result refers to a quantitative indicator used to indicate the accuracy of the calibration relationship, which can be a calibration error, residual or degree of deviation from the standard value, and its purpose is to quantify the current state of the calibration relationship; the calibration relationship refers to a mathematical model, lookup table or algorithm used to correct the transient thermal response parameters to true or standard values, and its purpose is to provide a basis for correcting the transient thermal response parameters; the deviation persistence threshold refers to a preset standard used to determine whether the calibration relationship deviation is persistent rather than a random fluctuation, which can be multiple consecutive evaluation results exceeding a certain range, or the average value of the evaluation results over a period of time exceeding a certain range, and its purpose is to avoid frequent triggering of adjustments due to transient fluctuations; the adjustment start threshold refers to the threshold used to trigger The lower limit of the deviation amplitude of the calibration relationship adjustment operation can be that the absolute value of the deviation exceeds a certain percentage or a fixed value, and its purpose is to prevent excessive adjustment of small deviations; the adjustment amount refers to the specific value or parameter change for correcting the existing calibration relationship, and its purpose is to provide a specific basis for correcting the calibration relationship; incremental adjustment refers to correcting the calibration relationship in a small step and gradual approximation manner to ensure the stability and convergence of the adjustment process; the convergence condition refers to the standard for judging whether the calibration relationship adjustment process reaches the expected stable state or accuracy requirement, which can be that the deviation is reduced to within the preset range, or the deviation change rate is lower than a certain value after multiple consecutive adjustments, and its purpose is to ensure that the calibration relationship is adjusted to the ideal state.

[0038] In some preferred embodiments, the present application is implemented as follows. Assume that there is a sensor for detecting the transient thermal response parameters of prepregs, and its calibration relationship may be affected by factors such as ambient temperature or sensor aging. To ensure calibration accuracy, the system can periodically or under specific conditions obtain evaluation results by measuring standard materials with known thermal properties. For example, when the system detects a difference between the transient thermal response parameters of the standard material and the theoretical value, this difference value is the evaluation result. The system continuously obtains these evaluation results and accumulates them in a data buffer. For example, the most recent N evaluation results can be stored. Subsequently, a processing unit compares these accumulated evaluation results with a preset deviation persistence threshold to determine whether the deviation persists. For example, if M of the most recent N evaluation results (M is less than N) indicate that the deviation direction is consistent and exceeds a certain small range, then the deviation is considered to persist. If the deviation persists and its cumulative magnitude exceeds a preset adjustment threshold—for example, when the absolute value of the cumulative deviation exceeds a preset percentage—the system determines the amount of adjustment to the calibration relationship based on the magnitude of the deviation using a pre-set adjustment algorithm, such as a proportional-integral-derivative (PID) controller. This adjustment can be a small increment or decrement of the calibration coefficient. The system then incrementally adjusts the current calibration relationship based on this adjustment, for example, by updating the slope or intercept parameters in the calibration equation. This incremental adjustment process continues until the deviation meets a preset convergence condition, for example, when the absolute value of the evaluation result falls within a preset minimum error range for multiple consecutive times. Finally, the system uses this adaptively adjusted calibration relationship, combined with real-time environmental parameters or thermal conditioning unit state parameters, to calibrate the actual transient thermal response parameters, thereby obtaining more accurate prepreg thermal state data.

[0039] Through the above technical solution, the present application can effectively deal with the problem of deviation in the calibration relationship caused by the drift of environmental parameters and thermal regulation unit state parameters over time. By continuously monitoring and adaptively adjusting the calibration relationship, the long-term accuracy and reliability of the calibration relationship are ensured. This enables the calibration accuracy of the transient thermal response parameters to be maintained at a high precision level, thereby improving the accuracy of the judgment of the local thermal state of the prepreg. Furthermore, more accurate thermal regulation instructions can be generated to achieve effective homogenization of the local thermal properties of the prepreg, ultimately improving the quality of carbon fiber prepreg slitting.

[0040] In some embodiments of the present application described above, it is proposed to calibrate the transient thermal response parameters by environmental parameters or thermal regulation unit state parameters to improve the accuracy of the determination of the local thermal state of the prepreg. The calibration can specifically be carried out by combining the instantaneous thermal response parameters obtained in real time with the current environmental temperature, humidity or the power and working mode of the thermal regulation unit through a pre-established mathematical model or lookup table, so as to obtain thermal response data that is closer to the true value, which can make the determination of the local thermal state of the prepreg more accurate. However, in its implementation process, the accuracy of the calibration relationship itself will deviate with the change of time and production conditions. If the calibration relationship is inaccurate, the transient thermal response parameters cannot be accurately calibrated, and thus the local thermal state of the prepreg cannot be accurately determined. Therefore, relying solely on a preset calibration relationship or periodic manual calibration may not be able to timely detect and correct the drift of the calibration relationship, thereby affecting the reliability of the overall determination.

[0041] In this regard, the present application further proposes a method for optimizing carbon fiber prepreg slitting parameters, which includes: Obtaining instantaneous thermal response parameters of standard materials and corresponding environmental parameters or thermal regulation unit state parameters; The accuracy of the calibration relationship used to calibrate the transient thermal response parameters is evaluated based on the known thermal properties of the standard material, and an evaluation result is obtained.

[0042] Among them, the standard material refers to a reference material with stable, repeatable and known thermal properties, which can be achieved by, for example, a polytetrafluoroethylene plate of a specific thickness, a metal block with a known thermal conductivity, or a strictly calibrated composite material sample. The transient thermal response parameters can be collected in real time using sensors such as infrared thermal imagers, thermocouple arrays, or thermistors. Environmental parameters or thermal regulation unit state parameters refer to external environmental factors that affect thermal measurements, such as ambient temperature, humidity, air flow velocity, and the working state of the thermal regulation unit, such as heating power, cooling flow, working mode or distance, etc., which can be obtained using devices such as temperature sensors, humidity sensors, flow meters or power meters. The calibration relationship refers to a mathematical model, algorithm or lookup table used to correct the transient thermal response parameters, which can be achieved using a linear regression model, a polynomial fitting model, a neural network model or a preset calibration curve. Evaluating the accuracy of a calibration relationship used to calibrate transient thermal response parameters involves comparing the calibration relationship's prediction of the thermal response of a reference material with the reference material's known thermal properties to determine whether the relationship effectively reflects the actual thermal behavior. This can be achieved using calculated error, percentage deviation, or statistical indicators. The evaluation result is a quantitative or qualitative assessment of the accuracy of the calibration relationship, which can be presented in the form of an error value, deviation rate, etc.

[0043] This solution systematically evaluates and ensures the accuracy of the calibration relationship of transient thermal response parameters by introducing standard materials as references. Specifically, first, a standard material with stable and known thermal properties is placed under conditions similar to the actual production environment, and its transient thermal response parameters under these conditions and the corresponding environmental parameters or thermal regulation unit state parameters are obtained. These acquired parameters are the basic data for evaluating the calibration relationship, and they reflect the actual thermal response of the standard material under the action of a specific environment and thermal regulation unit. Given that the thermal properties of the standard material are predetermined, its theoretical thermal response under specific conditions or the ideal thermal response after calibration can be inferred. Therefore, this solution further compares the actually acquired transient thermal response parameters with the theoretically calculated values ​​based on the known thermal properties of the standard material, thereby reversely evaluating the accuracy of the calibration relationship currently used to calibrate the transient thermal response parameters of prepregs.

[0044] On this basis, when the evaluation results indicate that there is a deviation in the calibration relationship, these evaluation results can be continuously obtained and accumulated to determine whether the deviation persists. When the deviation persists and its amplitude exceeds the preset adjustment start threshold, the adjustment amount of the calibration relationship can be determined based on the deviation amplitude, and the calibration relationship can be incrementally adjusted until the deviation meets the preset convergence condition. Finally, the transient thermal response parameters are calibrated based on the adjusted calibration relationship and the environmental parameters or thermal regulation unit state parameters. This mechanism ensures the dynamic adaptability of the calibration relationship, enabling it to self-correct as time and production conditions change, thereby ensuring the reliability of the transient thermal response parameter calibration, thereby improving the accuracy of the local thermal state determination of the prepreg, and effectively solving the problem of inaccurate determination caused by calibration relationship drift.

[0045] In some preferred embodiments, this solution is implemented as follows. A standard polymer sheet with uniform thickness and known thermal conductivity can be selected as the standard material, for example, a polymethyl methacrylate (PMMA) sheet measuring 100 mm x 100 mm x 2 mm. This PMMA sheet is placed upstream of the same slitting station as the carbon fiber prepreg, allowing it to be subjected to a detection heat pulse applied by a thermal conditioning unit similar to the prepreg. While applying the detection heat pulse, a high-precision infrared thermal imager can be used to collect real-time instantaneous temperature distribution data on the PMMA sheet's surface as a transient thermal response parameter. Simultaneously, an ambient temperature sensor can be used to obtain the current ambient temperature, and the thermal conditioning unit's built-in sensors can be used to obtain its heating power and working distance as environmental parameters or thermal conditioning unit state parameters. Once these data are obtained, the transient temperature response curve that should theoretically be achieved on the PMMA sheet's surface under the current environmental parameters and thermal conditioning unit state parameters can be calculated using finite element analysis or analytical heat transfer models based on the known thermal properties of the PMMA sheet, such as its specific heat capacity, density, and thermal conductivity, combined with the input energy and action time of the thermal conditioning unit. The actual transient thermal response parameters collected are then compared point by point with the theoretically calculated temperature response curve, and the root mean square error (RMSE) or maximum deviation between the two is calculated. This RMSE or maximum deviation is the evaluation result. If the evaluation result exceeds a preset threshold, such as 0.5°C, it indicates that the calibration relationship used to calibrate the transient thermal response parameters is deviating and requires adjustment. In this way, the calibration relationship can be verified periodically or before the start of a specific production batch to ensure its accuracy in real-world applications.

[0046] Through the above technical solution, this solution provides a reliable mechanism for evaluating the accuracy of the calibration relationship. By using the known thermal properties of the standard material as a reference, the accuracy of the local thermal state determination of the prepreg is improved, avoiding misjudgments caused by inaccurate calibration relationships, and thus ensuring the stability of slitting quality.

[0047] In some of the aforementioned embodiments of the present application, when a deviation exists in the calibration relationship and its magnitude exceeds a preset threshold, an adjustment amount for the calibration relationship is determined based on the magnitude of the deviation. Specifically, determining the adjustment amount based on the magnitude of the deviation can be performed by presetting a fixed adjustment coefficient or by searching a single adjustment curve based on the magnitude of the deviation to calculate the adjustment amount. For example, when the magnitude of the deviation is X, the adjustment amount is fixed to Y, or obtained from a preset curve by linear interpolation. This allows for automated adjustment of the calibration relationship. However, in this implementation, determining the adjustment amount based solely on the magnitude of the deviation lacks consideration of actual production conditions, which may result in a single and inadequate adjustment strategy. This strategy cannot fully address the differentiated requirements for calibration accuracy and efficiency in different production scenarios, resulting in unsatisfactory calibration results. In particular, under production conditions with high precision requirements, even small deviations may require detailed adjustments, while under production conditions with low precision requirements, larger deviations may only require rough adjustments. Therefore, how to more accurately determine the adjustment amount for the calibration relationship based on different production conditions is a problem that needs to be solved.

[0048] In this regard, the present application further proposes that the steps of determining the adjustment amount of the calibration relationship include: determining an adjustment method based on the deviation amplitude according to production condition parameters; According to the deviation magnitude, matching adjustment method; According to the matching adjustment method, the adjustment amount of the calibration relationship is determined.

[0049] Among them, production condition parameters refer to various production process-related data or indicators that affect the selection of calibration relationship adjustment strategies, which may include but are not limited to parameters such as material batch characteristics, equipment operating status, environmental factors or product quality requirements, providing more comprehensive background information for the adjustment of the calibration relationship to achieve more adaptive adjustments. The adjustment method based on deviation amplitude refers to a specific method of determining the calibration relationship adjustment amount based on the calibration deviation amplitude of the transient thermal response parameter. It can be implemented in the form of lookup tables, piecewise functions, fuzzy logic rules or machine learning models to provide differentiated adjustment guidance for different deviation amplitudes. The matching adjustment method refers to selecting one or a group of adjustment strategies that best suit the current situation from a variety of preset or dynamically generated adjustment methods based on the current deviation amplitude. It can be implemented by conditional judgment, threshold comparison, pattern recognition or adaptive recommendation. Its purpose is to ensure that the selected adjustment strategy is consistent with the actual deviation situation, thereby improving the accuracy and effectiveness of the adjustment.

[0050] The solution of the present application overcomes the limitation of relying solely on the deviation amplitude for adjustment by introducing production condition parameters as the determining factors of the calibration relationship adjustment amount. Specifically, first, the system will obtain the current production condition parameters, which reflect multiple dimensions such as the production environment, material characteristics or product requirements. Based on these production condition parameters, the system can dynamically or predetermine a set or a deviation amplitude-based adjustment method suitable for the current production scenario. The accuracy and stability of the instantaneous thermal response parameter calibration are improved, ensuring that the thermal properties of the prepreg can be accurately and uniformly processed under various production conditions, thereby ensuring the slitting quality.

[0051] In some preferred embodiments, the present application is implemented as follows: On a carbon fiber prepreg slitting production line, to more accurately calibrate the transient thermal response parameters, the system first obtains the current production condition parameters. For example, these parameters may include the material properties of the current production batch, the operating mode of the slitting equipment, and the product quality requirements of the current order. The system internally presets multiple sets of adjustment methods based on deviation amplitude, each corresponding to a different range or combination of production condition parameters. For example, for production conditions of "high-precision products" and "high-speed mode," the system selects a more sensitive and detailed adjustment method, which may require small incremental adjustments even for slight deviation amplitudes. For production conditions of "standard products" and "low-speed mode," the system may select a more relaxed adjustment method, allowing for relatively rough adjustments even with larger deviation amplitudes. When the system detects a deviation in the calibration relationship of the transient thermal response parameters, and this deviation persists and exceeds a preset adjustment trigger threshold, the system will adjust the adjustment method based on the current deviation amplitude and the previously determined adjustment method. For example, if the current deviation is large, the matched adjustment method may indicate a larger adjustment step size; if the deviation is small, a smaller adjustment step size may be used. Ultimately, the system calculates the specific adjustment amount for the calibration relationship based on the matched adjustment method and makes incremental adjustments to the calibration relationship until the deviation meets the preset convergence criteria. In this way, the adjustment of the calibration relationship can fully consider the actual production situation, avoiding a "one-size-fits-all" adjustment strategy, making the calibration process more adaptive and effective.

[0052] Through the above technical solution, the present application can more accurately determine the adjustment amount of the calibration relationship according to different production conditions. This allows the calibration process to adapt to changes in the production environment and differences in precision requirements, avoiding the limitations of a single adjustment strategy. Therefore, even under variable production conditions, accurate calibration of the transient thermal response parameters can be achieved, thereby ensuring the continuity and uniformity of the carbon fiber prepreg slitting quality.

[0053] In some of the above-mentioned embodiments of the present application, it is proposed to determine the adjustment amount of the calibration relationship based on the deviation amplitude. The adjustment amount of the calibration relationship determined based on the deviation amplitude can be specifically determined by presetting a fixed adjustment coefficient or a lookup table to directly map the amplitude of the current calibration deviation to an adjustment amount. For example, when the deviation amplitude is X, the adjustment amount is fixed to Y. In this way, the calibration relationship can be initially corrected. However, in its implementation process, relying solely on the deviation amplitude to adjust the calibration relationship may not be accurate enough. Under different production conditions, the same deviation amplitude may require different adjustment strategies. For example, when the material batch is unstable, an adjustment strategy with a smaller adjustment amplitude and a more stable adjustment strategy may be required, while when the equipment is in good operating condition, an adjustment strategy with a faster response speed can be adopted. Therefore, how to more accurately determine the adjustment method based on the deviation amplitude according to different production condition parameters is the problem to be solved by the present application.

[0054] In this regard, the present application further proposes that the steps of determining an adjustment method based on the deviation amplitude according to production condition parameters include: Preset multiple sets of adjustment methods or rule parameters based on deviation amplitude; Establishing mapping relationships between production condition parameters and multiple groups of adjustment methods or rule parameters; According to the obtained production condition parameters, the corresponding adjustment method or rule parameter based on the deviation amplitude is searched or selected from the mapping relationship.

[0055] Presetting multiple sets of deviation-based adjustment methods or rule parameters refers to presetting and storing a variety of different strategies or algorithms to guide how to calculate the adjustment amount based on the magnitude of the calibration deviation. These adjustment methods can include different adjustment steps, adjustment frequencies, adjustment curve types, or specific control algorithms, such as proportional-integral-derivative (PID) control parameter sets. The purpose is to build a library of optional adjustment strategies to meet different calibration requirements.

[0056] Among them, the mapping relationship refers to the logical structure that associates one or more production condition parameters with preset adjustment methods or rule parameters. It can be implemented in the form of a lookup table, decision tree, rule engine or machine learning model. Its purpose is to quickly and accurately locate the appropriate adjustment strategy based on real-time production conditions.

[0057] Among them, searching or selecting refers to matching or reasoning in the established mapping relationship based on the currently obtained production condition parameters to determine the adjustment method or rule parameters that are in line with the current production conditions. Its purpose is to achieve dynamic adaptive adjustment of the calibration strategy.

[0058] The solution of this application dynamically selects the adjustment method for the calibration relationship by incorporating production condition parameters, thereby improving calibration accuracy and adaptability. Specifically, multiple sets of deviation-based adjustment methods or rule parameters are pre-set. This is equivalent to building a library of various adjustment strategies, each targeting a specific deviation or adjustment target. Next, a mapping relationship is established between the production condition parameters and these multiple sets of adjustment methods or rule parameters. This step is the core of adaptive adjustment. Through this mapping, the system understands which adjustment strategy is optimal under specific production conditions. For example, when a material batch is unstable, a more stable adjustment method with a smaller adjustment range may be required to avoid overcorrection; while when the equipment is operating well, a more responsive adjustment method can be used to accelerate calibration convergence. Finally, based on the real-time production condition parameters, the system searches or selects the corresponding deviation-based adjustment method or rule parameter from the pre-established mapping relationship. This dynamic selection mechanism ensures that the calibration process can flexibly adapt to changes in the actual production environment, no longer relying solely on a single deviation range.

[0059] This mechanism of dynamically determining the adjustment method based on the production condition parameters is closely combined with the step of determining the adjustment amount of the calibration relationship based on the deviation amplitude in the previous scheme, forming a more complete calibration system. In the previous scheme, when there is a deviation in the calibration relationship and it persists and the amplitude exceeds the threshold, it is necessary to determine the adjustment amount. On this basis, the present application no longer simply determines the adjustment amount directly based on the deviation amplitude, but first selects an adjustment method suitable for the current situation based on the current production condition parameters. For example, when the material batches fluctuate greatly, even if the deviation amplitude is the same, the system will choose an adjustment method with a smaller adjustment amplitude and a more stable adjustment method to avoid misjudgment and over-adjustment due to changes in material properties; when the equipment is running stably and the environment is constant, a faster response adjustment method can be selected to quickly eliminate the deviation. This dynamic, contextual adjustment method enables the calibration process to better adapt to the complex and changeable characteristics of the production site, significantly improving the accuracy and robustness of the transient thermal response parameter calibration, thereby ensuring the accuracy of the local thermal state judgment of the prepreg, and ultimately improving the effectiveness of the thermal regulation instructions, thereby making the thermal properties of the prepreg uniform, and effectively solving the problem of inconsistent incision quality caused by the complexity of the material and processing environment during the multi-head slitting process.

[0060] In some embodiments, the present application is specifically implemented as follows. On the carbon fiber prepreg slitting production line, multiple groups of strategies for adjusting the calibration relationship can be preset. For example, three groups of adjustment methods can be preset: the first group is a "conservative adjustment mode", which has a small adjustment step size, slow convergence speed but high stability, and is suitable for situations where material batches fluctuate greatly or the equipment state is unstable; the second group is a "standard adjustment mode", which has a moderate adjustment step size and convergence speed, and is suitable for conventional production conditions; the third group is an "aggressive adjustment mode", which has a large adjustment step size and fast convergence speed, and is suitable for scenarios where equipment operation is stable and production efficiency requirements are high. These adjustment methods can be specifically expressed as different PID control parameter sets, or different adjustment amount calculation formulas.

[0061] At the same time, a mapping relationship can be established between production condition parameters and these adjustment methods. For example, a lookup table can be constructed containing discrete or continuous ranges for production condition parameters such as "material batch stability level," "equipment vibration level," and "ambient temperature," with a corresponding adjustment mode assigned to each parameter combination. For example, when the material batch stability level is "low" and the equipment vibration level is "high," the mapping relationship could point to "conservative adjustment mode"; when the material batch stability level is "high" and the equipment vibration level is "low," the mapping relationship could point to "aggressive adjustment mode."

[0062] During actual production, the system can acquire current production condition parameters in real time, such as material batch information, equipment operating status sensor data, and ambient temperature and humidity sensor data, all monitored through sensors. The system can then search pre-established mapping relationships based on the acquired production condition parameters. For example, if the currently acquired material batch stability level is "medium," the equipment vibration level is "medium," and the ambient temperature is within the normal range, the system can select "standard adjustment mode" from the mapping relationship. Once the corresponding adjustment method is determined, subsequent calibration relationship adjustments can be calculated based on this selected adjustment method and the current deviation amplitude, thereby achieving dynamic optimization of the calibration process.

[0063] Through the above technical solution, the present application can more accurately determine the adjustment method based on the deviation amplitude according to different production condition parameters. This makes the adjustment of the calibration relationship no longer a single, fixed mode, but can be dynamically adapted according to actual production conditions such as material batch characteristics, equipment operating status, ambient temperature and humidity. Therefore, even in the case of complex and changeable production environments, the accuracy and adaptability of the calibration process can be ensured, effectively avoiding the problem of inaccurate calibration or over-adjustment that may result from relying solely on the deviation amplitude for adjustment, thereby improving the control accuracy and stability of the thermal performance homogenization during the slitting process of carbon fiber prepregs.

[0064] In some of the above-mentioned embodiments of the present application, it is proposed to determine an adjustment method based on the deviation amplitude according to the production condition parameters. The determination method can be specifically to directly obtain the instantaneous value of the production condition parameter and accurately match it with a preset single parameter range, so as to select a corresponding adjustment method. For example, when the production line temperature instantaneously reaches a certain specific value, a preset specific adjustment strategy is immediately enabled, so that a rapid response to changes in production conditions can be achieved. However, in its implementation process, the production condition parameters may fluctuate or be noisy. Directly using the instantaneous value may cause the adjustment method to be frequently switched, affecting the stability of the calibration relationship. Moreover, simple parameter matching may not fully utilize the potential information of multiple adjustment methods or rule parameters, resulting in limited adjustment accuracy.

[0065] In this regard, the present application further proposes that the steps of searching or selecting corresponding adjustment methods or rule parameters based on deviation amplitude from a mapping relationship according to the obtained production condition parameters include: Obtain real-time values ​​of production condition parameters; Smoothing the real-time values ​​of the production condition parameters to obtain smoothed production condition parameters; Comparing the smoothed production condition parameters with the preset parameter range in the mapping relationship, and identifying multiple adjustment methods or rule parameters whose matching degree meets the preset threshold; According to the matching degree of the multiple adjustment methods or rule parameters, the corresponding adjustment method or rule parameter based on the deviation amplitude is determined.

[0066] Among them, smoothing refers to eliminating random noise or short-term fluctuations in data through algorithms to reveal potential trends or patterns in the data. It can be achieved by using methods such as moving average, exponential smoothing or Kalman filtering, with the aim of improving the stability of production condition parameters and avoiding misjudgments due to instantaneous fluctuations; mapping relationship refers to the corresponding rule set established between production condition parameters and preset adjustment methods or rule parameters, which can be achieved in the form of lookup tables, decision tree models or neural network models, with the aim of providing the system with a basis for selecting appropriate adjustment strategies according to current production conditions; preset parameter range refers to the numerical range corresponding to the production condition parameters set for different adjustment methods or rule parameters in the mapping relationship. It can be implemented in the form of discrete intervals, continuous intervals or fuzzy sets, with the aim of defining the applicable conditions for each adjustment method or rule parameter; matching degree refers to the degree of fit or similarity quantitative index between the smoothed production condition parameters and a preset parameter range in the mapping relationship, which can be calculated using distance measurement, similarity coefficient or membership function, with the aim of evaluating the applicability of different adjustment methods or rule parameters to current production conditions; the preset threshold refers to the minimum matching standard for screening adjustment methods or rule parameters with qualified matching degrees, which can be set in the form of fixed values, dynamic adjustment values ​​or percentages, with the aim of ensuring that the identified adjustment methods or rule parameters have sufficient applicability.

[0067] The solution of this application obtains real-time values ​​of production condition parameters to provide foundational data for subsequent decision-making. Given the inherent volatility of parameters in actual production environments, these real-time values ​​are smoothed to produce more stable and reliable smoothed production condition parameters. This process effectively filters out transient noise and short-term fluctuations, ensuring the accuracy and stability of subsequent judgments. Furthermore, the smoothed production condition parameters are compared with a preset parameter range in a pre-established mapping relationship. Rather than being limited to a single optimal match, multiple adjustment methods or rule parameters are identified whose matching degree meets a preset threshold. This multi-recognition mechanism enables the system to more comprehensively consider a variety of potential applicable strategies under current production conditions. Furthermore, based on the matching degree of each of these identified adjustment methods or rule parameters, the system can conduct a refined evaluation and selection, ultimately determining the adjustment method or rule parameter based on the deviation amplitude that best suits the current production conditions. This comprehensive consideration of multiple options and decision-making based on matching degree avoids the limitations of a single matching method and significantly improves the accuracy and robustness of adjustment method selection. Thanks to this smoothing and multi-matching screening mechanism, this solution overcomes the challenges posed by fluctuating production parameters when determining the adjustment amount for the calibration relationship. It not only ensures the stability of the selected adjustment method and avoids frequent switching, but also, by comprehensively leveraging the potential information from multiple adjustment methods or rule parameters, the final adjustment method can more accurately adapt to complex production environments. This improves the accuracy and reliability of transient thermal response parameter calibration overall, thereby ensuring the precise determination of the local thermal state of the prepreg. Ultimately, this helps achieve uniform thermal properties of the prepreg and improves slitting quality.

[0068] In some preferred embodiments, the present application is implemented as follows. When obtaining real-time values ​​of production condition parameters, for example, sensors can be used to collect data such as ambient temperature, humidity, equipment speed, and material tension on the production line. To smooth these real-time values, a sliding average filter can be used. For example, temperature data from the last 10 seconds is collected and its average is calculated as the smoothed temperature parameter. Alternatively, an exponentially weighted moving average method can be used to assign higher weight to recent data. When comparing the smoothed production condition parameters with preset parameter ranges in a mapping relationship, the mapping relationship can be a lookup table stored in a database, where each row records a production condition parameter range (e.g., temperature between 20-25 degrees Celsius and humidity between 50-60%) and the corresponding recommended adjustment method or rule parameter set. The preset parameter range can be defined as a specific numerical interval. For example, the temperature parameter range can be set to [20°C, 25°C], [25°C, 30°C], etc. When the smoothed temperature is 26°C, it may fall within the range [25°C, 30°C] and also overlap with the broader range [20°C, 27°C]. When identifying multiple adjustment methods or rule parameters whose matching degree meets a preset threshold, the matching degree can be calculated based on the distance between the parameter value and the center value of the preset parameter range, with closer distances indicating higher matching degree. Alternatively, the matching degree can be calculated using a fuzzy logic membership function, which represents the degree to which the parameter value falls within a certain range. The preset threshold can be set as a percentage; for example, only adjustment methods with a matching degree exceeding 80% are considered to meet the criteria. For example, if the smoothed temperature is 26°C, three adjustment methods A, B, and C may be identified, associated with the temperature ranges [25°C, 30°C], [20°C, 27°C], and [24°C, 28°C], respectively, and their matching degrees all exceed the preset threshold. When determining the corresponding adjustment method or rule parameter based on the deviation amplitude according to the matching degree of multiple adjustment methods or rule parameters, the weighted average method can be adopted, that is, the matching degree of each identified adjustment method is used as a weight to perform weighted averaging on the corresponding rule parameters to obtain the final adjustment rule parameters; or a priority sorting method can be adopted to set the priority according to the matching degree and select the adjustment method with the highest matching degree; or an expert system rule can be adopted to combine the characteristics of multiple matching methods and make a comprehensive judgment through preset decision rules. For example, if the adjustment method with the highest temperature matching degree is A, but the adjustment method with the highest humidity matching degree is B, the system can determine the final adjustment method according to the preset priority rules or combination rules.

[0069] Through the above technical solution, the present application can effectively cope with fluctuations and noise interference in production condition parameters, avoid frequent switching of adjustment methods due to instantaneous parameter changes, and thus significantly improve the stability of the calibration relationship. At the same time, by identifying multiple adjustment methods or rule parameters whose matching degrees meet the preset threshold and making a comprehensive judgment based on their matching degrees, the final adjustment method can be more accurately adapted to the current production conditions, making full use of the potential multiple adjustment strategy information, thereby improving the accuracy and robustness of the adjustment.

[0070] In some of the above-mentioned embodiments of the present application, it is proposed to determine an adjustment method based on the deviation amplitude according to production condition parameters. This determination method can be specifically determined by presetting a set of fixed adjustment rules. The rules only select the adjustment method based on conventional environmental parameters such as temperature and humidity on the production line. This can simplify the adjustment logic and quickly respond to changes in the production environment. However, during its implementation, the production condition parameters themselves may have inaccuracies, such as differences in material batches, changes in equipment status, or adjustments to product quality requirements. These factors may affect the accuracy of the adjustment rules, resulting in deviations in the calibration relationship adjustment, and ultimately affecting the slitting quality. Therefore, it is necessary to consider the production condition parameters that affect the accuracy of the adjustment rules to improve the accuracy and reliability of the calibration relationship adjustment.

[0071] In this regard, the present application further proposes a method comprising: Obtain production condition parameters that affect the accuracy of the adjustment rules; production condition parameters include material batch characteristic parameters, equipment operating status parameters or product quality requirement parameters.

[0072] Among them, material batch characteristic parameters refer to the difference indicators in the physical and chemical properties of different batches of carbon fiber prepregs, which can be resin content, fiber volume fraction, prepreg thickness uniformity or surface roughness, etc. Its purpose is to reflect the impact of the inherent properties of the raw materials on the slitting performance; Among them, equipment operating status parameters refer to the real-time operating status indicators of key components of the slitting equipment during operation. Specifically, they can be tool wear, tool pressure, equipment vibration frequency, tool temperature or conveyor belt tension, etc. Its purpose is to reflect the impact of the equipment's own status on the slitting accuracy; Among them, the product quality requirement parameters refer to the specific provisions on the quality standards of the final slit products, which can be the burr level of the slit edge, the width deviation range, the cut flatness or the resin overflow, etc. Its purpose is to adjust the slitting strategy according to different quality standards to meet specific needs.

[0073] The solution of the present application optimizes the adjustment method of the calibration relationship by obtaining the production condition parameters that affect the accuracy of the adjustment rules. During the slitting process of carbon fiber prepregs, the calibration relationship needs to be dynamically adjusted according to the instantaneous thermal response parameters to ensure the accuracy of thermal regulation. However, determining the adjustment method based solely on the deviation amplitude may not be able to fully cope with the changing working conditions in actual production. It is precisely because of the dynamic changes in factors such as material batch characteristics, equipment operating status, and product quality requirements that will directly affect the thermal response and mechanical behavior during the slitting process, which in turn leads to deviations in the original adjustment rules.

[0074] Therefore, before determining the calibration relationship adjustment, this solution proactively captures production condition parameters that influence the accuracy of the adjustment rules. These parameters include material batch characteristics, such as the resin content or fiber distribution of the prepreg; equipment operating status parameters, such as tool wear or vibration; and product quality requirements, such as specific requirements for slitting edge burrs or width deviation. By incorporating these multi-dimensional production condition parameters, the system can more comprehensively assess the current slitting environment and material characteristics.

[0075] Based on these more precise production condition parameters, the system can more effectively determine the appropriate adjustment method based on the magnitude of the deviation. For example, when a specific material batch is detected to have a high resin content, the system can automatically select a more conservative adjustment method to avoid resin overflow caused by excessive thermal adjustment. When tool wear reaches a certain level, the system can switch to a more aggressive adjustment method to compensate for the impact of reduced tool performance. This selection of adjustment methods based on multi-dimensional production condition parameters means that the adjustment of the calibration relationship is no longer a single-dimensional response, but can be refined and adaptively optimized according to actual working conditions.

[0076] In this way, this solution overcomes the limitations of relying solely on deviation amplitude for adjustment, improving the accuracy and reliability of calibration relationship adjustments. This not only ensures more accurate calibration of transient thermal response parameters, but also enables subsequent thermal regulation instructions to be more precisely applied to the prepreg, thus ensuring uniform thermal properties during the carbon fiber prepreg slitting process from the source, ultimately improving slitting quality.

[0077] In some preferred embodiments, to capture production parameters that influence the accuracy of adjustment rules, a variety of sensors and data interfaces can be used for data collection. For example, for material batch characteristic parameters, a material information input interface can be provided, allowing operators to manually enter information such as the resin content, fiber type, and thickness tolerance of the current batch when changing material batches, or automatically synchronize information through an interface with the material supplier's database. Furthermore, near-infrared spectroscopy sensors or X-ray transilluminators can be integrated into the production line to monitor the resin content and fiber distribution uniformity of the prepreg in real time.

[0078] To monitor equipment operating status, vibration sensors can be deployed near the slitting blade assembly to monitor the blade's vibration frequency and amplitude in real time, assessing tool wear and equipment stability. Furthermore, current sensors or power meters can be integrated into the tool drive motor to indirectly assess the tool's cutting status by monitoring changes in motor load. Pressure sensors can measure the tool's downward force in real time. Data from these sensors can be periodically collected and transmitted to a central control unit.

[0079] For product quality requirement parameters, different product quality level profiles can be preset, such as "High Precision Mode," "Standard Mode," or "Economy Mode." Each mode corresponds to different allowable ranges for slitting edge burrs, width deviation, and resin overflow. The operator can select the corresponding product quality requirement mode before production begins, or the system can automatically load it based on order information. Access to these parameters enables the system to dynamically adjust the calibration relationship strategy based on actual production needs and operating conditions, thereby ensuring slitting quality.

[0080] Through this technical solution, the system can capture production condition parameters that influence the accuracy of adjustment rules, including material batch characteristic parameters, equipment operating status parameters, or product quality requirement parameters. This allows calibration relationship adjustments to no longer rely solely on deviation amplitude, but instead comprehensively consider multiple factors such as materials, equipment, and product requirements. As a result, calibration relationship adjustments are more precise and reliable, avoiding adjustment deviations caused by inaccurate production condition parameters, thereby improving the quality stability of carbon fiber prepreg slitting.

[0081] In some of the above-mentioned embodiments of the present application, it is proposed to obtain the local thermal performance difference information of the carbon fiber prepreg that is about to enter the slitting station, and establish a corresponding relationship between the information and the physical position of the prepreg, determine the local thermal state of the prepreg based on the local thermal performance difference information, and generate thermal adjustment instructions. Finally, the prepreg is locally thermally adjusted between the upstream of the slitting station and the slitting knife group. This method can carry out targeted intervention according to the actual thermal state of the prepreg, thereby achieving uniformity of thermal properties and improving slitting quality. However, in its implementation process, how to ensure that the three key links of information acquisition, state determination and instruction generation, and local thermal adjustment can work together efficiently and accurately to ensure the accuracy and reliability of the optimization of carbon fiber prepreg slitting parameters is a problem that needs to be further solved.

[0082] Secondly, refer to Figure 2 , the present application further proposes a carbon fiber prepreg slitting parameter optimization system, the system comprising: The information acquisition module 201 is used to obtain local thermal performance difference information of the carbon fiber prepreg that is about to enter the slitting station and establish a corresponding relationship between the information and the physical position of the prepreg; The state determination and instruction generation module 202 is used to determine the local thermal state of the prepreg according to the local thermal performance difference information and generate a thermal adjustment instruction according to the local thermal state; The local thermal regulation module 203 is used to perform local thermal regulation on the prepreg upstream of the slitting station and between the slitting knife group according to the thermal regulation instruction; The homogenization module 204 is configured to homogenize the thermal properties of the prepreg through the local thermal regulation.

[0083] Through the above technical solution, a carbon fiber prepreg slitting parameter optimization system is provided. The system serves as a specific implementation carrier of the carbon fiber prepreg slitting parameter optimization method, enabling the method to be effectively executed and applied.

[0084] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for optimizing carbon fiber prepreg slitting parameters, characterized in that: The following steps are involved: Obtaining local thermal property difference information of the carbon fiber prepreg about to enter the slitting station, and establishing a corresponding relationship between the information and the physical position of the prepreg; Determining a local thermal state of the prepreg according to the local thermal performance difference information, and generating a thermal adjustment instruction according to the local thermal state; performing local thermal regulation on the prepreg according to the thermal regulation instruction between the upstream of the slitting station and the slitting knife group; The thermal properties of the prepreg are homogenized through the local thermal regulation.

2. The method for optimizing carbon fiber prepreg slitting parameters according to claim 1, wherein: The step of determining the local thermal state of the prepreg according to the local thermal performance difference information and generating a thermal adjustment instruction according to the local thermal state includes: Obtaining environmental parameters or thermal regulation unit state parameters that affect transient thermal response parameters; calibrating the transient thermal response parameter according to the environmental parameter or the state parameter of the thermal regulation unit; Comparing the calibrated transient thermal response parameters with preset characteristic parameters to determine the local thermal state of the prepreg; The thermal adjustment instruction is generated according to the local thermal state.

3. The method for optimizing carbon fiber prepreg slitting parameters according to claim 2, wherein: The method further comprises: When the upstream detection station is separated from the downstream thermal adjustment station and during the local thermal adjustment, a detection heat pulse is applied to the prepreg through the thermal adjustment unit; and the instantaneous thermal response parameters of the thermal adjustment unit are collected.

4. The method for optimizing carbon fiber prepreg slitting parameters according to claim 2, wherein: The step of calibrating the transient thermal response parameter according to the environmental parameter or the state parameter of the thermal regulation unit includes: When the evaluation result indicates that there is a deviation in the calibration relationship, continuously obtaining the evaluation result and accumulating the evaluation result; Comparing the accumulated evaluation results with a preset deviation persistence threshold to determine whether the deviation persists; When the deviation persists and its magnitude exceeds a preset adjustment start threshold, determining an adjustment amount for the calibration relationship according to the deviation magnitude; Incrementally adjusting the calibration relationship according to the adjustment amount until the deviation satisfies a preset convergence condition; The transient thermal response parameter is calibrated according to the adjusted calibration relationship and the environmental parameter or the state parameter of the thermal regulation unit.

5. The method for optimizing carbon fiber prepreg slitting parameters according to claim 4, wherein: The method comprises: Obtaining instantaneous thermal response parameters of standard materials and corresponding environmental parameters or thermal regulation unit state parameters; The accuracy of the calibration relationship used to calibrate the transient thermal response parameter is evaluated based on the known thermal properties of the standard material to obtain an evaluation result.

6. The method for optimizing carbon fiber prepreg slitting parameters according to claim 4, wherein: The step of determining the adjustment amount of the calibration relationship according to the deviation amplitude includes: Determine the adjustment method based on the deviation range according to the production condition parameters; According to the deviation magnitude, matching the adjustment method; An adjustment amount of the calibration relationship is determined according to the matching adjustment manner.

7. The method for optimizing carbon fiber prepreg slitting parameters according to claim 6, characterized in that: The step of determining the adjustment method based on the deviation amplitude according to the production condition parameters includes: Presetting multiple sets of adjustment methods or rule parameters based on the deviation amplitude; Establishing a mapping relationship between the production condition parameters and multiple groups of adjustment methods or rule parameters; According to the obtained production condition parameters, the corresponding adjustment method or rule parameters based on the deviation amplitude are searched or selected from the mapping relationship.

8. The method for optimizing carbon fiber prepreg slitting parameters according to claim 7, wherein: The step of searching or selecting a corresponding adjustment method or rule parameter based on the deviation amplitude from the mapping relationship according to the obtained production condition parameter includes: Obtaining real-time values ​​of the production condition parameters; Smoothing the real-time value of the production condition parameter to obtain a smoothed production condition parameter; Comparing the smoothed production condition parameters with the preset parameter range in the mapping relationship, and identifying multiple adjustment methods or rule parameters whose matching degrees meet a preset threshold; According to the matching degree of the plurality of adjustment methods or rule parameters, the corresponding adjustment method or rule parameter based on the deviation amplitude is determined.

9. The method for optimizing carbon fiber prepreg slitting parameters according to claim 6, wherein: The method comprises: Acquire production condition parameters that affect the accuracy of the adjustment rules; the production condition parameters include material batch characteristic parameters, equipment operating status parameters, or product quality requirement parameters.

10. A carbon fiber prepreg slitting parameter optimization system, characterized in that: The system includes: An information acquisition module is used to obtain local thermal performance difference information of the carbon fiber prepreg that is about to enter the slitting station and establish a corresponding relationship between the information and the physical position of the prepreg; a state determination and instruction generation module, configured to determine the local thermal state of the prepreg according to the local thermal performance difference information, and generate a thermal adjustment instruction according to the local thermal state; A local thermal regulation module, configured to perform local thermal regulation on the prepreg between the upstream of the slitting station and the slitting knife group according to the thermal regulation instruction; A homogenization module is used to homogenize the thermal properties of the prepreg through the local thermal regulation.

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