A method and system for optimizing carbon fiber prepreg slitting parameters

Abstract

The application provides a carbon fiber prepreg slitting parameter optimization method and system, relates to the field of prepreg processing, and comprises the following steps: obtaining local thermal performance difference information of carbon fiber prepreg about to enter a slitting station, and establishing a corresponding relationship between the information and a 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 regulation instruction according to the local thermal state; performing local thermal regulation on the prepreg according to the thermal regulation instruction between a slitting cutter group and the slitting station upstream; and homogenizing the thermal performance of the prepreg through the local thermal regulation, which can effectively solve the problem of unstable cut quality of carbon fiber prepreg in the slitting process caused by uneven local thermal performance, and significantly improve the edge quality of the slitting tape and the subsequent forming efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of prepreg processing, in particular, to a carbon fiber prepreg slitting parameter optimization method and system. BACKGROUND

[0002] In the field of composite component manufacturing, accurately slitting wide carbon fiber prepreg into multiple narrow strips is a key preparation process for subsequent automated molding process. The edge quality of these narrow strips directly determines the mechanical properties and reliability of the final component. In the production mode of parallel slitting with multiple knives, how to ensure that each strip has highly consistent excellent cutting quality from beginning to end is the technical focus of this field.

[0003] Specifically, in the slitting process of carbon fiber prepreg, due to the dense arrangement of the knife heads, the heat in the middle of the knife group is easy to accumulate and difficult to dissipate, while the heat on both sides dissipates faster, which leads to a non-uniform thermal field in the width direction of the prepreg, with high temperature in the middle and low temperature on both sides. The epoxy resin matrix in the carbon fiber prepreg is extremely sensitive to temperature changes, and an increase in temperature will significantly reduce its viscosity. This temperature difference directly leads to inconsistency in slitting quality: the strips cut from the center area of the equipment often have problems of resin overflow and stickiness; while the strips produced from the two side areas have relatively clean cuts.

[0004] There are also slight and continuous fluctuations in the tension of the prepreg. The instantaneous change 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 several types of disturbances originating from materials and equipment, acting on microscale and transient processes, interweave with each other, making the local thermal properties of the prepreg entering the slitting station present significant differences and non-uniformity. Any fixed global process parameters cannot effectively cope with this complex and dynamically changing material state, ultimately leading to a random and difficult-to-reproduce degradation of the edge quality of the entire batch of strips, seriously affecting the molding quality of subsequent precision laying. In view of the above problems, the prior art needs to be improved. SUMMARY

[0005] The purpose of the present application is to provide a carbon fiber prepreg slitting parameter optimization method and system, which can effectively solve the problem of unstable cutting quality caused by non-uniform local thermal properties of carbon fiber prepreg during slitting, and significantly improve the edge quality of slitted strips and subsequent molding efficiency.

[0006] The present application provides a carbon fiber prepreg slitting parameter optimization method, comprising the following steps:

[0007] Obtain the local thermal property difference information of the carbon fiber prepreg about to enter the slitting station, and establish the correspondence between the information and the physical position of the prepreg;

[0008] According to the local thermal performance difference information, the local thermal state of the prepreg is determined, and a thermal regulation instruction is generated according to the local thermal state;

[0009] According to the thermal regulation instruction, the local thermal regulation is performed on the prepreg between the slitting cutter group upstream of the slitting station;

[0010] Through the local thermal regulation, the thermal performance of the prepreg is homogenized.

[0011] Through the above scheme, the thermal performance of the prepreg is homogenized by performing local thermal regulation, thereby effectively solving the inconsistent slitting quality problem caused by the local thermal performance difference of the prepreg in the prior art, and improving the cut quality.

[0012] To further solve the problem, the application also provides a carbon fiber prepreg slitting parameter optimization system, which comprises:

[0013] An information acquisition module is configured to acquire local thermal performance difference information of the carbon fiber prepreg about to enter the slitting station, and establish a correspondence between the information and the physical position of the prepreg;

[0014] A state determination and instruction generation module is configured to determine the local thermal state of the prepreg according to the local thermal performance difference information, and generate a thermal regulation instruction according to the local thermal state;

[0015] A local thermal regulation module is configured to perform local thermal regulation on the prepreg according to the thermal regulation instruction between the slitting cutter group upstream of the slitting station;

[0016] A homogenization module is configured to homogenize the thermal performance of the prepreg through the local thermal regulation.

[0017] Through the above scheme, a system for implementing the above optimization method is provided, which provides hardware foundation and functional module support for the actual application of the method.

[0018] In summary, the application provides a carbon fiber prepreg slitting parameter optimization method and system, which realizes the homogenization of the thermal performance of the prepreg by acquiring the local thermal performance difference information of the prepreg and performing local thermal regulation, effectively solves the problem of unstable slitting quality in the prior art, and has the advantages of effectively solving the problem of unstable cut quality caused by the non-uniform local thermal performance of the carbon fiber prepreg during the slitting process, and significantly improving the edge quality of the slitting tape and the subsequent molding efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A schematic diagram of a carbon fiber prepreg slitting parameter optimization method provided by the application.

[0020] Figure 2 A schematic diagram of a carbon fiber prepreg slitting parameter optimization system provided by the present application is shown. DETAILED DESCRIPTION

[0021] The technical solutions in the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. The components of the present application described and shown in the drawings herein 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 claimed present application, but only 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 creative work fall within the scope of the present application.

[0022] The present application first considers real-time adjustment of the parameters of the slitting cutter group, such as dynamically changing the cutter head pressing force or rotating speed according to the local cutting condition. However, such passive adjustment at the moment of cutting is difficult to effectively respond to the random distribution of microscopic defects inside the prepreg and the transient thermal response, and the fine local adjustment of the cutter head parameters has limitations in response speed and accuracy in actual operation, which is difficult to fundamentally solve the problem. In view of this, the present application further thinks that if the root cause of the problem is the uneven thermal performance of the material itself and the non-uniformity of the thermal field of the processing environment, can the thermal performance of the material be actively intervened and homogenized before it enters the slitting cutter group.

[0023] The present application conceives that if the thermal performance difference information of the prepreg at different physical positions can be perceived in advance, and the correspondence between the information and the physical position of the prepreg is established, it can provide a basis for subsequent accurate intervention. Based on such perception, the local thermal state of the prepreg can be determined, such as which areas have high temperature and which areas have abnormal thermal conductivity, and targeted thermal regulation instructions can be generated accordingly. Subsequently, before the prepreg reaches the slitting cutter group, that is, between the slitting station upstream of the slitting cutter group, the local thermal regulation of the prepreg is carried out according to the thermal regulation instructions, such as local heating or cooling, so that the thermal performance of the prepreg is as uniform as possible when it enters the cutter group. Such active intervention before cutting can reduce the uncertainty in the cutting process from the source, thereby avoiding the cutting quality problems caused by the difference in thermal performance of the material.

[0024] REFERENCE Figure 1 A schematic diagram of an embodiment of a carbon fiber prepreg slitting parameter optimization method of the present application is shown, which can specifically include the following steps:

[0025] S101, obtain local thermal performance difference information of carbon fiber prepreg about to enter the slitting station, and establish a correspondence between the information and the physical location of the prepreg;

[0026] S102, determine the local thermal state of the prepreg according to the local thermal performance difference information, and generate thermal regulation instructions according to the local thermal state;

[0027] S103, according to the thermal regulation instructions, perform local thermal regulation on the prepreg between the slitting cutter group upstream of the slitting station;

[0028] S104, through local thermal regulation, the thermal performance of the prepreg is homogenized.

[0029] Wherein, the local thermal performance difference information refers to the non-uniform distribution of the thermal properties (such as temperature, thermal conductivity, specific heat capacity, etc.) of the carbon fiber prepreg in the width direction or length direction, which can be obtained by non-contact thermal imaging technology, infrared scanning sensor or distributed thermocouple array, etc. It is mainly to identify the deviation of the prepreg in the thermal performance before entering the slitting station, and to provide data basis for subsequent accurate intervention. The local thermal state refers to the evaluation or classification of the current thermal conditions of the specific area of the prepreg according to the obtained local thermal performance difference information, which can be determined based on the preset thermal model, experience threshold or machine learning algorithm. It is mainly to convert the original thermal performance data into a state description with clear physical meaning for decision-making, so as to guide the generation of thermal regulation instructions. The thermal regulation instruction refers to the specific command issued by the control system for guiding the heating or cooling operation of the thermal regulation unit according to the determined local thermal state, which can include regulation target temperature, heating / cooling power, action time or regulation area, etc. It is mainly to convert the intention of intervention on the thermal performance of the prepreg into executable physical operation to ensure the accuracy and effectiveness of the regulation. Local thermal regulation refers to the application of targeted heating or cooling to specific areas of the prepreg before slitting to change the thermal performance of the area, which can be achieved by using semiconductor refrigeration sheet, hot air nozzle, infrared heater or cooling liquid circulation device, etc. It is mainly to actively correct the thermal unevenness of the prepreg, so that the thermal performance of each area tends to be consistent when it enters the slitting cutter group, thereby improving the slitting conditions.

[0030] The core innovation of the present application is that the real-time acquisition and analysis of the local thermal performance difference information of the carbon fiber prepreg is introduced upstream of the carbon fiber prepreg slitting station, and accurate thermal regulation instructions are generated based on this, so as to locally regulate the heat of the prepreg, and the thermal performance of the prepreg is homogenized, thus solving the problem of unstable cut quality caused by defects of the prepreg itself and uneven processing environment from the source, and achieving the effect of improving the consistency and stability of the slitting quality.

[0031] The scheme of the present application realizes the active optimization of the carbon fiber prepreg slitting quality through a series of steps working in coordination. First, before the prepreg enters the slitting station, the system will acquire the local thermal performance difference information of the prepreg, and at the same time establish the correspondence between these information and the prepreg in the physical space. This link is the basis of the whole scheme, ensuring that all subsequent intervention measures can be targeted and act on the areas that need to be adjusted. It is precisely because of the perception of the thermal properties of the prepreg that the subsequent decision becomes possible. On this basis, the system will determine the local thermal state of the prepreg according to the acquired local thermal performance difference information. This determination process is to convert raw data into physical states with practical significance, such as determining whether a certain area is overheated, overcooled, rich in resin or poor in resin, etc. Subsequently, the system will generate corresponding thermal regulation instructions according to the determined local thermal state. These instructions are customized for specific areas and specific thermal states, aiming to guide the subsequent thermal regulation operation. Then, at a specific position between the slitting station and the slitting knife group upstream, the system will adjust the local heat of the prepreg according to the previously generated thermal regulation instructions. This step is the core intervention link of the scheme, which changes the thermal properties of the prepreg by applying heating or cooling to its local area. This adjustment occurs before the cutting action, so it can affect the physical state of the prepreg at the moment of cutting, thereby reducing the uncertainty in the cutting process. Finally, through the above local thermal regulation, the thermal performance of the prepreg is homogenized. When the prepreg reaches the slitting knife group, the thermal properties (such as temperature, viscosity, etc.) of different areas of the prepreg will tend to be consistent, thereby reducing the fluctuation of cut quality caused by uneven material itself or differences in processing environment. This overall thermal homogenization ensures that each slitting tape can obtain consistent cut quality, solving the problems of resin overflow, stickiness or burr mentioned in the background art.

[0032] As a specific implementation, an array of infrared thermal imagers can be deployed on the carbon fiber prepreg conveying path to continuously scan the prepreg before it enters the slitting station, thereby obtaining real-time temperature distribution data on the prepreg web. At the same time, by cooperating with the encoders or visual positioning systems on the prepreg conveying mechanism, a correspondence between these temperature data and the physical position of the prepreg can be established, for example, mapping each temperature pixel point to a specific transverse and longitudinal coordinate of the prepreg. The obtained temperature distribution data will be transmitted to an industrial computer, which internally runs a pre-set thermal model and control algorithm to determine the local thermal state of the prepreg based on real-time temperature data. For example, if the temperature of a certain area is significantly higher or lower than the pre-set slitting temperature range, or its temperature gradient is too large, it is determined to be an abnormal thermal state. Based on this determination, the computer generates corresponding thermal regulation instructions. For example, for the overheated area, generate a "lower temperature" cooling instruction; for the over-cooled area, generate a "raise temperature" heating instruction, and specify the action area and duration. Subsequently, a local thermal regulation device composed of multiple independently controlled semiconductor refrigeration plates or hot / cold air nozzle arrays can be deployed at a position upstream of the slitting station, a certain distance from the slitting knife group. Each semiconductor refrigeration plate or nozzle corresponds to a specific area on the prepreg web. When receiving the thermal regulation instructions issued by the computer, the corresponding semiconductor refrigeration plate will heat or cool according to the instructions, or the corresponding nozzle will blow hot or cold air of a set temperature and flow rate to the specified local area of the prepreg. For example, when the instruction requires cooling of an overheated area, the corresponding semiconductor refrigeration plate will start the refrigeration mode to lower the temperature of the area to the target range. Through this local thermal regulation, the thermal performance of the entire prepreg web, especially the temperature and the resin viscosity affected thereby, will be uniformized before it enters the slitting knife group. This makes the slitting knife group face a material with more stable thermal properties when cutting, thereby reducing defects such as resin overflow, stickiness or fiber edge caused by thermal unevenness, ensuring the consistency of slitting quality.

[0033] Through the above technical solution, the present application can solve the problem of unstable cutting quality of carbon fiber prepreg caused by uneven thermal performance of the material itself and differences in processing environment thermal field during slitting. By actively and locally regulating the thermal performance of the prepreg before slitting, the thermal performance is uniformized, thereby reducing the uncertainty in the cutting process. This improves the edge quality of the narrow tape, reduces the occurrence of defects such as resin overflow, stickiness or fiber breakage, thereby ensuring the quality of the raw material for subsequent automatic fiber laying or automatic tape laying molding process, and improving the mechanical properties and reliability of the final composite material component.

[0034] In some embodiments of the present application, the local thermal state of the prepreg is determined according to the local thermal performance difference information, and the thermal regulation instruction is generated according to the local thermal state, so as to perform local thermal regulation on the prepreg and homogenize the thermal performance of the prepreg. However, in actual application, it may not be accurate enough to determine the thermal state only by relying on the local thermal performance difference information, because the environmental parameters (such as environmental temperature, humidity) and the state of the thermal regulation unit itself (such as heating power, cooling intensity) will affect the instantaneous thermal response of the prepreg, and further affect the accuracy of the determination of the thermal state, resulting in deviation of the thermal regulation instruction, and finally affecting the slitting quality.

[0035] To this end, the present application further proposes that the step of determining the local thermal state of the prepreg according to the local thermal performance difference information and generating the thermal regulation instruction according to the local thermal state comprises:

[0036] obtaining an environmental parameter or a thermal regulation unit state parameter affecting the instantaneous thermal response parameter;

[0037] calibrating the instantaneous thermal response parameter according to the environmental parameter or the thermal regulation unit state parameter;

[0038] comparing the calibrated instantaneous thermal response parameter with a preset characteristic parameter to determine the local thermal state of the prepreg;

[0039] generating the thermal regulation instruction according to the local thermal state.

[0040] The instantaneous thermal response parameter refers to the change amount or rate of the thermal characteristics of the prepreg within a short time after being subjected to instantaneous heat, such as the temperature change rate, the instantaneous value of the thermal diffusivity or the thermal conductivity, which can be measured by devices such as thermocouples, infrared sensors or heat flow sensors, and the purpose is to reflect the local thermal characteristics of the prepreg at a specific time. The environmental parameter refers to the external environmental factors affecting the thermal behavior of the prepreg, specifically the environmental temperature, humidity or air flow speed, which can be obtained by devices such as temperature sensors, humidity sensors or anemometers, and the purpose is to quantify the influence of the external environment on the thermal performance of the prepreg. The thermal regulation unit state parameter refers to the working state information of the device for local thermal regulation of the prepreg, specifically the heating power, cooling intensity, fan speed or cooling liquid flow, which can be obtained by power meters, flow meters or internal sensors of the device, and the purpose is to reflect the intensity of the thermal action of the thermal regulation unit on the prepreg.

[0041] The calibration refers to the process of correcting or adjusting the original acquired instantaneous thermal response parameters according to the environmental parameters or the state parameters of the thermal regulation unit, which can be specifically realized by applying a preset mathematical model, a calibration curve or a lookup table, and the purpose is to eliminate or reduce the influence of external interference factors on the measurement accuracy of the instantaneous thermal response parameters, so that it more truly reflects the actual thermal state of the prepreg. The preset characteristic parameters refer to a set of reference values or ranges for evaluating or classifying the local thermal state of the prepreg, which are specific temperature thresholds, slope ranges of thermal response curves or ideal intervals of thermal diffusivity, etc., which can be determined according to material properties, process requirements or historical data analysis, and the purpose is to provide objective basis for determining the local thermal state of the prepreg. The local thermal state refers to the comprehensive performance state of the temperature, viscosity or curing degree of the specific area of the prepreg at the current time, which is specific to overheating, overcooling or normal, etc., and the purpose is to classify the local thermal properties of the prepreg in order to take corresponding adjustment measures. The thermal regulation instruction refers to the specific operation command issued to the thermal regulation unit according to the determined local thermal state, which is specific to increasing heating power, reducing cooling intensity or maintaining the current state, etc., and the thermal regulation unit accurately intervenes in the local thermal of the prepreg.

[0042] The scheme of the present application obtains the environmental parameters or the state parameters of the thermal regulation unit that affect the instantaneous thermal response parameters, and calibrates the instantaneous thermal response parameters based on this, thereby improving the accuracy of the determination of the local thermal state of the prepreg. Specifically, before determining the local thermal state of the prepreg, the system first obtains the external environmental factors that may affect the accuracy of the instantaneous 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, the increase of the environmental 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.

[0043] Subsequently, the system calibrates the original instantaneous thermal response parameter according to these acquired environmental parameters or thermoregulation unit state parameters. This calibration process is critical, as it effectively compensates or eliminates the interference of these external and internal factors on the measurement results, so that the calibrated instantaneous thermal response parameter can more accurately reflect the real local thermal characteristics of the prepreg. For example, if the ambient temperature is higher than the standard value, the calibration process can appropriately down-regulate the measured instantaneous thermal response parameter to offset the impact of environmental heat on the measurement. Then, the calibrated instantaneous thermal response parameter is compared with the preset characteristic parameters. These characteristic parameters are thresholds or ranges set in advance according to the ideal process state, used to distinguish different thermal states of the prepreg. By comparing with the more accurate parameter after calibration, the system can more reliably determine whether the prepreg is currently in an overheated, undercooled, or ideal local thermal state. Finally, according to this accurate determination of the local thermal state, the system generates corresponding thermoregulation instructions.

[0044] For example, if it is determined to be overheated, the instructions may require reducing the heating power or increasing the cooling intensity; if it is determined to be undercooled, the instructions 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 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 in turn can generate more accurate thermoregulation instructions, ensuring more precise and effective local thermoregulation of the prepreg. In this way, the scheme of the present application can more finely control the thermal performance uniformization process of the prepreg, effectively solve the problem of thermoregulation deviation caused by inaccurate thermal state determination, and thereby ensure the consistency and stability of the cut quality of the tape in the carbon fiber prepreg slitting process from the source, significantly improving the slitting quality.

[0045] In some preferred embodiments, the present application is implemented as follows: in order to accurately determine the local thermal state of the prepreg and generate thermal regulation instructions on the carbon fiber prepreg slitting production line, first, the system will obtain a plurality of auxiliary parameters that affect the instantaneous thermal response parameters. For example, by deploying temperature sensors and humidity sensors near the slitting station, the ambient temperature and humidity can be obtained in real time as environmental parameters. At the same time, by reading the internal controller data of the thermal regulation unit (for example, a local heater or cooling fan), the current heating power or cooling fan speed can be obtained as the thermal regulation unit state parameter. Then, the system will calibrate the instantaneous thermal response parameters of the prepreg according to these obtained environmental parameters or thermal regulation unit state parameters. Specifically, a multivariate calibration model can be established in advance, which inputs environmental temperature, environmental humidity, heating power, etc. parameters, and outputs a calibration coefficient or correction amount. When the instantaneous thermal response parameters are obtained in real time, the system will calculate the corresponding calibration coefficient using the model, and apply it to the original instantaneous thermal response parameters, for example, by multiplication or addition operation for correction, to obtain the calibrated instantaneous thermal response parameters. For example, if the ambient temperature is high, the calibration model will calculate a negative correction amount, so that the calibrated instantaneous thermal response parameters can exclude the influence of environmental heat on the measurement. Then, the calibrated instantaneous thermal response parameters are compared with the preset characteristic parameters to determine the local thermal state of the prepreg. For example, three characteristic parameter intervals can be preset: when the calibrated instantaneous thermal response parameters fall within the "ideal temperature range", it is determined that the prepreg is in a "normal" thermal state; when it is higher than the "overheating threshold", it is determined to be in an "overheating" state; when it is lower than the "overcooling threshold", it is determined to be in an "overcooling" state. Finally, according to the determined local thermal state, the system will generate corresponding thermal regulation instructions. For example, if it is determined to be in an "overheating" state, the system will generate instructions to "reduce heating power" or "increase cooling intensity" and send them to the thermal regulation unit; if it is determined to be in an "overcooling" state, it will generate instructions to "increase heating power" or "reduce cooling intensity"; if it is determined to be in a "normal" state, it will generate instructions to "maintain the current state". In this way, the thermal regulation unit can receive more accurate instructions, thereby accurately regulating the local heat of the prepreg.

[0046] By the above technical solution, the problem that the determination of the thermal state may not be accurate enough by relying only on local thermal performance difference information is solved. By obtaining and utilizing environmental parameters or thermal regulation unit state parameters to calibrate the instantaneous thermal response parameters, the determination of the local thermal state of the prepreg is more accurate and reliable. This accurate determination can generate more accurate thermal regulation instructions, thereby effectively avoiding improper thermal regulation due to determination deviation of the thermal state, and ultimately ensuring the uniformity of the thermal performance during the slitting process of the carbon fiber prepreg, and improving the stability and consistency of the slitting quality.

[0047] To this end, the application further proposes that the method further comprises: when the upstream detection station is separated from the downstream thermal conditioning station and during the local thermal conditioning, applying a probing heat pulse to the prepreg by the thermal conditioning unit; and collecting an instantaneous thermal response parameter of the thermal conditioning unit.

[0048] The probing heat pulse refers to a transient heat input with a specific energy and duration applied to the prepreg by the thermal conditioning unit, which can be a short high-intensity laser pulse, a rapid heating pulse generated by a resistive heater, or an instantaneous heat radiation emitted by an infrared radiation source. The purpose is to stimulate the local thermal response of the prepreg without significantly changing the overall thermal state of the prepreg, so as to quickly and non-invasively detect the current thermal performance of the prepreg. The instantaneous thermal response parameter refers to the physical quantity related to the thermal behavior of the prepreg captured by the thermal conditioning unit or its nearby sensors in a very short time after the probing heat pulse is applied, which can be the instantaneous temperature change curve, the heat flux density change rate, the temperature change rate, the thermal diffusivity or the instantaneous value of the thermal conductivity of the measured region. The purpose is to reflect the dynamic characteristics of the prepreg in absorbing, conducting and dissipating heat through the change trend and characteristics of these parameters, thereby indirectly revealing the current local thermal state of the prepreg, such as its resin viscosity, curing degree or internal defect distribution.

[0049] The scheme of the application realizes dynamic tracking and evaluation of the local thermal state of the prepreg by applying a probing heat pulse to the prepreg at a critical moment and collecting an instantaneous thermal response parameter. Specifically, when there is physical separation between the upstream detection station and the downstream thermal conditioning station, or during the local thermal conditioning process itself, the thermal state of the prepreg may deviate due to environmental temperature fluctuations, heat dissipation during transportation or the heat transfer effect of the conditioning unit itself. To cope with this dynamic change, the thermal conditioning unit is designed not only to apply conditioning heat, but also to serve as a detection tool. By applying a known probing heat pulse to the prepreg, the prepreg will produce a specific instantaneous thermal response according to its current thermal state. For example, if the resin viscosity of the prepreg decreases due to temperature rise, its absorption and conduction characteristics of the heat pulse will change. The thermal conditioning unit then collects these instantaneous thermal response parameters, such as the instantaneous rise rate of temperature or the instantaneous change amount of heat flow.

[0050] In some preferred embodiments, when the upstream detection station is separated from the downstream thermal conditioning station, for example, the prepreg is moved between the two stations by a conveyor belt, or when the local thermal conditioning unit is adjusting the heat applied to the prepreg, the thermal conditioning unit can periodically or under certain triggering conditions apply a probing heat pulse to the prepreg. Specifically, the thermal conditioning unit can integrate a miniature laser that can emit a laser pulse with extremely short duration (e.g., 10-100 milliseconds) and controllable energy as the probing heat pulse. The laser pulse is focused on a local area of the prepreg, causing its surface temperature to rise by a few degrees Celsius in an instant. Subsequently, the thermal conditioning unit or its immediate vicinity can be equipped with a high-sensitivity infrared temperature sensor or thermocouple array to collect the instantaneous temperature response curve of the probed area of the prepreg 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 frequency of several hundred or even thousands of times per second. These instantaneous temperature data, including the peak temperature, temperature rise rate, and temperature decay rate, are collected as instantaneous thermal response parameters. By analyzing these parameters, the current thermal conductivity, thermal diffusivity, or surface thermal resistance of the prepreg can be inferred, and thus its current local thermal state, such as changes in resin viscosity or degree of cure, can be assessed.

[0051] In some embodiments of the present application described above, the instantaneous thermal response parameters are calibrated according to environmental parameters or thermal conditioning unit state parameters. This calibration of instantaneous thermal response parameters according to environmental parameters or thermal conditioning unit state parameters can be performed by a pre-established fixed mapping table between environmental temperature and thermal response parameters, or an empirical formula relating the power setting value of the thermal conditioning unit to the thermal response parameters, to correct the instantaneous thermal response parameters and eliminate the influence of external environment or equipment state on the measurement results, thereby improving the accuracy of thermal performance detection and optimizing the slitting parameters. However, during implementation, the environmental parameters and thermal conditioning unit state parameters can drift over time, causing the calibration relationship itself to deviate. If the calibration relationship deviates, the calibration accuracy of the instantaneous thermal response parameters will be reduced, which will affect the accurate judgment of the local thermal state of the prepreg, and ultimately result in inaccurate thermal conditioning and ineffective uniformization of the thermal performance of the prepreg.

[0052] To this end, the present application further proposes that the step of calibrating the instantaneous thermal response parameters according to the environmental parameters or the thermal conditioning unit state parameters comprises:

[0053] When the evaluation result indicates that the calibration relationship deviates, the evaluation result is continuously obtained and accumulated;

[0054] The accumulated evaluation result is compared with a pre-set deviation persistence threshold to determine whether the deviation persists.

[0055] When the deviation persists and its magnitude exceeds a preset adjustment starting threshold, an adjustment amount of the calibration relationship is determined according to the deviation magnitude;

[0056] According to the adjustment amount, the calibration relationship is incrementally adjusted until the deviation meets a preset convergence condition;

[0057] According to the adjusted calibration relationship and the environmental parameter or the thermal regulation unit state parameter, the instantaneous thermal response parameter is calibrated.

[0058] Wherein, the evaluation result refers to a quantitative indicator for indicating the accuracy of the calibration relationship, which can be a calibration error, a residual error or a deviation from a standard value, and its purpose is to quantify the current state of the calibration relationship; the calibration relationship refers to a mathematical model, a lookup table or an algorithm for correcting the instantaneous thermal response parameter to a real or standard value, and its purpose is to provide a basis for correcting the instantaneous thermal response parameter; the deviation persistence threshold refers to a preset standard for determining whether the calibration relationship deviation is persistent or accidental fluctuation, which can be that the evaluation result exceeds a certain range for multiple times in succession, or that the average value of the evaluation result exceeds a certain range within a period of time, and its purpose is to avoid frequent triggering of adjustment due to instantaneous fluctuation; the adjustment starting threshold refers to a lower limit of the deviation magnitude for triggering the calibration relationship adjustment operation, which 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 minor deviations; the adjustment amount refers to a specific value or parameter change amount for modifying the existing calibration relationship, and its purpose is to provide a specific basis for modifying the calibration relationship; the incremental adjustment refers to a step-by-step approach to modifying the calibration relationship in small steps to ensure the stability and convergence of the adjustment process; the convergence condition refers to a standard for determining whether the calibration relationship adjustment process reaches the expected stable state or precision requirement, which can be that the deviation is reduced to within a preset range, or that the deviation change rate is lower than a certain value after multiple adjustments, and its purpose is to ensure that the calibration relationship is adjusted to an ideal state.

[0059] In some preferred embodiments, the application is implemented as follows. Assuming there is a sensor for detecting the instantaneous thermal response parameter of the prepreg, the calibration relationship thereof can be affected by factors such as ambient temperature or sensor aging. To ensure calibration accuracy, the system can periodically or under certain conditions obtain evaluation results by measuring standard materials with known thermal properties. For example, when the system detects a difference between the instantaneous thermal response parameter of the standard material and the theoretical value, the difference value is the evaluation result. The system continuously obtains these evaluation results and accumulates them in a data buffer, for example, the last 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 last N evaluation results (M is less than N) indicate consistent deviation direction and exceed a certain small range, it is considered that the deviation persists. When it is determined that the deviation persists and its cumulative amplitude exceeds a preset adjustment start threshold, for example, when the absolute value of the cumulative deviation exceeds a certain preset percentage, the system determines the adjustment amount of the calibration relationship according to the amplitude of the deviation through a preset adjustment algorithm, for example, a proportional-integral-derivative (PID) controller. The adjustment amount can be a small increment or decrement of a calibration coefficient. Then, the system adjusts the current calibration relationship incrementally according to the adjustment amount, for example, updates the slope or intercept parameters in the calibration equation. This incremental adjustment process continues until the deviation meets the preset convergence condition, for example, when the absolute value of the evaluation result falls within a preset small error range for multiple times in a row, the adjustment process stops. Finally, the system uses the calibration relationship adjusted adaptively to calibrate the actual collected instantaneous thermal response parameter, thereby obtaining more accurate prepreg thermal state data in combination with real-time environmental parameters or thermal regulation unit state parameters.

[0060] Through the above technical solutions, the application can effectively deal with the problem of deviation of 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 instantaneous thermal response parameter to be maintained at a high precision level, thereby improving the accuracy of the judgment of the local thermal state of the prepreg. Further, more accurate thermal regulation instructions can be generated to effectively uniformize the local thermal performance of the prepreg, ultimately improving the quality of carbon fiber prepreg slitting.

[0061] In some embodiments of the present application, a calibration of the instantaneous thermal response parameter by environmental parameters or HVAC unit state parameters is proposed to improve the accuracy of the local thermal state determination of the prepreg. The calibration can be specifically a correction of the real-time acquired instantaneous thermal response parameter combined with the current environmental temperature, humidity, or HVAC unit power, working mode, and other parameters through a pre-established mathematical model or lookup table, so as to obtain thermal response data closer to the true value, which can make the determination of the local thermal state of the prepreg more accurate. However, in the implementation process, the accuracy of the calibration relationship itself will deviate with the change of time and production conditions. If the calibration relationship is not accurate, the instantaneous thermal response parameter cannot be accurately calibrated, and the local thermal state of the prepreg cannot be accurately determined. Therefore, relying only on the preset calibration relationship or periodic manual calibration may not be able to timely discover and correct the drift of the calibration relationship, thereby affecting the reliability of the overall determination.

[0062] To this end, the present application further proposes a carbon fiber prepreg slitting parameter optimization method, which comprises:

[0063] acquiring the instantaneous thermal response parameter of the standard material and the corresponding environmental parameter or HVAC unit state parameter;

[0064] evaluating the accuracy of the calibration relationship for calibrating the instantaneous thermal response parameter according to the known thermal characteristics of the standard material, to obtain an evaluation result.

[0065] The standard material refers to a reference substance with stable, repeatable, and known thermal properties, which can be realized by, for example, a specific thickness of polytetrafluoroethylene plate, a metal block with known thermal conductivity, or a strictly calibrated composite material sample. The instantaneous thermal response parameter can be acquired in real time by sensors such as infrared thermal imagers, thermocouple arrays, or thermistors. The environmental parameter or HVAC unit state parameter refers to external environmental factors that affect thermal measurement, such as environmental temperature, humidity, air flow speed, and working state of the HVAC unit, such as heating power, cooling flow, working mode, or distance, which can be acquired by 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 instantaneous thermal response parameter, which can be realized by a linear regression model, a polynomial fitting model, a neural network model, or a preset calibration curve. Evaluating the accuracy of the calibration relationship for calibrating the instantaneous thermal response parameter refers to judging whether the calibration relationship effectively reflects the true thermal behavior by comparing the difference between the predicted value of the calibration relationship for the standard material thermal response and the actual known thermal characteristics of the standard material, which can be realized by calculating the error, percentage of deviation, or statistical indicators. The evaluation result refers to the output of the quantitative or qualitative judgment of the accuracy of the calibration relationship, which can be presented in the form of error value, deviation rate, etc.

[0066] The present scheme systematically evaluates and ensures the accuracy of the calibration relationship of the instantaneous thermal response parameters by introducing a standard material as a reference. Specifically, first, a standard material with stable and known thermal properties is placed in conditions similar to the actual production environment, and its instantaneous thermal response parameters and corresponding environmental parameters or thermal regulation unit state parameters under these conditions are obtained. These obtained parameters are the basic data for evaluating the calibration relationship, which reflect the actual thermal response of the standard material under the action of the specific environment and thermal regulation unit. Since the thermal properties of the standard material are predetermined, its theoretical thermal response or ideal thermal response after calibration under specific conditions can be calculated. Therefore, the present scheme further compares the actually obtained instantaneous thermal response parameters with the theoretically calculated values according to the known thermal properties of the standard material, thereby inversely evaluating the accuracy of the calibration relationship currently used to calibrate the instantaneous thermal response parameters of the prepreg.

[0067] On this basis, when the evaluation result indicates that the calibration relationship has a deviation, the evaluation results can be continuously obtained and accumulated, and then it is judged whether the deviation persists. When the deviation persists and its amplitude exceeds the preset adjustment starting threshold, the adjustment amount of the calibration relationship can be determined according to the deviation amplitude, and the calibration relationship can be incrementally adjusted until the deviation meets the preset convergence condition. Finally, the instantaneous thermal response parameters are calibrated according to the adjusted calibration relationship and the environmental parameters or thermal regulation unit state parameters. This mechanism ensures the dynamic adaptability of the calibration relationship, which can self-correct with the changes of time and production conditions, thereby ensuring the reliability of the calibration of the instantaneous thermal response parameters, and further improving the accuracy of the determination of the local thermal state of the prepreg, effectively solving the problem of inaccurate determination caused by the drift of the calibration relationship.

[0068] In some preferred embodiments, the present solution is implemented as follows. A standard polymer plate with uniform thickness and known thermal conductivity can be selected as a standard material, such as a polymethyl methacrylate (PMMA) plate with a size of 100 mm x 100 mm x 2 mm. The PMMA plate is placed upstream of the same slitting station as the carbon fiber prepreg, so that it can be subjected to a detection heat pulse applied by the thermal conditioning unit similar to the prepreg. At the same time of applying the detection heat pulse, the instantaneous temperature distribution data of the PMMA plate surface can be collected in real time using a high-precision infrared thermal imager as the instantaneous thermal response parameter. At the same time, the current environmental temperature can be obtained using an environmental temperature sensor, and the heating power and working distance of the thermal conditioning unit can be obtained using the built-in sensor of the thermal conditioning unit as the environmental parameter or the thermal conditioning unit state parameter. After obtaining these data, the known thermal properties of the PMMA plate, such as its specific heat capacity, density, and thermal conductivity, can be combined with the input energy and action time of the thermal conditioning unit to calculate the theoretical instantaneous temperature response curve that the PMMA plate surface should theoretically reach under the current environmental parameters and thermal conditioning unit state parameters through finite element analysis or analytical heat transfer model. Subsequently, the actual collected instantaneous thermal response parameter is compared point by point with the theoretically calculated temperature response curve, and the root mean square error (RMSE) or maximum deviation value between the two is calculated. This root mean square error or maximum deviation value is the evaluation result. If the evaluation result exceeds a predetermined threshold, such as 0.5°C, it indicates that the calibration relationship used to calibrate the instantaneous thermal response parameter has a deviation and needs to be adjusted. In this way, the calibration relationship can be verified periodically or before the start of a specific production batch to ensure its accuracy in actual application.

[0069] Through the above technical solution, the present solution provides a reliable calibration relationship accuracy evaluation mechanism. By using the known thermal properties of the standard material as a reference, the accuracy of the prepreg local thermal state determination is improved, and misjudgment caused by inaccurate calibration relationship is avoided, thereby ensuring the stability of the slitting quality.

[0070] In some embodiments of the present application, when the calibration relationship deviates and the magnitude of the deviation exceeds a preset threshold, the adjustment amount of the calibration relationship is determined according to the magnitude of the deviation. The determination of the adjustment amount according to the magnitude of the deviation can be achieved by presetting a fixed adjustment coefficient or looking up a single adjustment curve based on the magnitude of the deviation, for example, when the magnitude of the deviation is X, the adjustment amount is fixed as Y, or the adjustment amount is obtained by linear interpolation from the preset curve. This can realize the automatic adjustment of the calibration relationship. However, in the implementation process, only the adjustment amount is determined according to the magnitude of the deviation, and the actual production conditions are not considered, which may lead to the single and insufficient adaptability of the adjustment strategy, and cannot fully meet the differentiated requirements of calibration accuracy and efficiency in different production scenarios, resulting in that the final calibration effect is not as expected, especially in the production conditions with high precision requirements, even if the magnitude of the deviation is small, detailed adjustment is also needed; and in the production conditions with low precision requirements, a larger magnitude of the deviation may only need rough adjustment. Therefore, how to more accurately determine the adjustment amount of the calibration relationship according to different production conditions is a problem to be solved.

[0071] To this end, the present application further provides a step of determining the adjustment amount of the calibration relationship, including: determining an adjustment mode based on the magnitude of the deviation according to a production condition parameter.

[0072] Matching the adjustment mode according to the magnitude of the deviation;

[0073] Determining the adjustment amount of the calibration relationship according to the matched adjustment mode.

[0074] The production condition parameter refers to various production process related data or indicators that affect the selection of the calibration relationship adjustment strategy, which can include but is not limited to material batch characteristics, equipment operating state, environmental factors or product quality requirements, etc. The production condition parameter provides more comprehensive background information for the adjustment of the calibration relationship to realize more adaptive adjustment. The adjustment mode based on the magnitude of the deviation refers to a specific way of determining the adjustment amount of the calibration relationship according to the magnitude of the deviation of the instantaneous thermal response parameter, which can be realized in the form of lookup table, piecewise function, fuzzy logic rule or machine learning model, etc. to provide differentiated adjustment guidance for different magnitudes of deviation. The matching adjustment mode refers to selecting one or a group of adjustment strategies that are most suitable for the current situation from a plurality of preset or dynamically generated adjustment modes according to the current magnitude of the deviation, which can be realized by condition judgment, threshold comparison, pattern recognition or adaptive recommendation, etc. The purpose is to ensure that the selected adjustment strategy is consistent with the actual deviation, thereby improving the accuracy and effectiveness of the adjustment.

[0075] The technical scheme of the present application overcomes the limitation of adjusting only by the deviation amplitude by introducing the production condition parameters as the determining factor of the adjustment amount of the calibration relationship. Specifically, first, the system obtains the current production condition parameters, which reflect various dimensions such as production environment, material characteristics or product requirements. Based on these production condition parameters, the system can dynamically or in advance determine a set or a kind of adjustment mode based on the deviation amplitude suitable for the current production scene. The accuracy and stability of the instantaneous thermal response parameter calibration are improved, ensuring accurate and uniform treatment of the thermal performance of the prepreg under various production conditions, thereby ensuring the cutting quality.

[0076] In some preferred embodiments, the present application is implemented as follows: in the carbon fiber prepreg cutting production line, in order to more accurately calibrate the instantaneous thermal response parameter, the system first obtains the current production condition parameters. For example, these parameters can include the material characteristics of the current production batch, the running mode of the cutting equipment, and the product quality requirements of the current order. The system is internally preset with multiple sets of adjustment modes based on the deviation amplitude, and each set of adjustment mode corresponds to different production condition parameter ranges or combinations. For example, for the production condition of "high-precision product" and "high-speed mode", the system will select a set of more sensitive and detailed adjustment mode, which may stipulate that even a small deviation amplitude needs to be adjusted in small steps; for the production condition of "standard product" and "low-speed mode", the system may select a set of more relaxed adjustment mode, allowing relatively rough adjustment under a larger deviation amplitude. When the system detects that the calibration relationship of the instantaneous thermal response parameter has a deviation, and the deviation persists and the amplitude exceeds the preset adjustment starting threshold, the system will match according to the current deviation amplitude and the previously determined adjustment mode. For example, if the current deviation amplitude is large, the matched adjustment mode may indicate a large adjustment step; if the deviation amplitude is small, a small adjustment step may be indicated. Finally, the system calculates the specific adjustment amount of the calibration relationship according to the matched adjustment mode, and makes incremental adjustment to the calibration relationship until the deviation meets the preset convergence condition. In this way, the adjustment of the calibration relationship can fully consider the actual situation of production, avoiding the "one-size-fits-all" adjustment strategy, making the calibration process more adaptive and effective.

[0077] Through the above technical scheme, the present application can more accurately determine the adjustment amount of the calibration relationship according to different production conditions. This makes the calibration process adapt to changes in the production environment and differences in precision requirements, avoiding the limitations of a single adjustment strategy. Therefore, even under varying production conditions, accurate calibration of the instantaneous thermal response parameter can be achieved, thereby ensuring the consistency and uniformity of the carbon fiber prepreg cutting quality.

[0078] In some embodiments of the present application, the adjustment amount of the calibration relationship is determined according to the deviation amplitude. The adjustment amount of the calibration relationship determined according to the deviation amplitude can be directly mapped to an adjustment amount by presetting a fixed adjustment coefficient or a lookup table, for example, when the deviation amplitude is X, the adjustment amount is fixed as Y. In this way, the calibration relationship can be preliminarily corrected. However, in the implementation process, only relying 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, a smaller adjustment amplitude and a more stable adjustment strategy may be required, while when the equipment is in good operating condition, a faster adjustment strategy can be adopted. Therefore, how to more accurately determine the adjustment method based on the deviation amplitude according to different production condition parameters is a problem to be solved by the present application.

[0079] To this end, the present application further proposes a step of determining the adjustment method based on the deviation amplitude according to the production condition parameter, which comprises:

[0080] presetting a plurality of adjustment methods or rule parameters based on the deviation amplitude;

[0081] establishing a mapping relationship between the production condition parameter and the plurality of adjustment methods or rule parameters based on the deviation amplitude;

[0082] According to the obtained production condition parameter, the corresponding adjustment method or rule parameter based on the deviation amplitude is found or selected from the mapping relationship.

[0083] Among them, the plurality of adjustment methods or rule parameters based on the deviation amplitude means that a plurality of different strategies or algorithms are preset and stored, which are used to guide how to calculate the adjustment amount according to the amplitude 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 set, the purpose of which is to build a selection of adjustment strategy library to meet different calibration needs.

[0084] Among them, the mapping relationship means that one or more production condition parameters are associated with the preset adjustment method or rule parameter, which can be realized in the form of lookup table, decision tree, rule engine or machine learning model, and the purpose is to quickly and accurately locate the appropriate adjustment strategy according to the real-time production condition.

[0085] Among them, the finding or selection means that according to the current obtained production condition parameter, the mapping relationship established is matched or reasoned to determine the adjustment method or rule parameter that meets the current production condition, and the purpose is to realize the dynamic adaptive adjustment of the calibration strategy.

[0086] The scheme of the present application dynamically selects the adjustment mode of the calibration relationship by introducing the production condition parameter, thereby improving the accuracy and adaptability of calibration. Specifically, first, a plurality of adjustment modes or rule parameters based on deviation amplitude are preset, which is equivalent to constructing a library containing multiple adjustment strategies, each of which is targeted at a specific deviation amplitude or adjustment target. Subsequently, a mapping relationship between the production condition parameter and the plurality of adjustment modes or rule parameters is established, which is the core step of realizing adaptive adjustment. Through this mapping, the system can understand which adjustment strategy is the best under specific production conditions. For example, when the material batch is unstable, a smaller adjustment amplitude and a more stable adjustment mode may be needed to avoid excessive correction; when the equipment is in good operating condition, a faster response speed adjustment mode can be used to speed up the calibration convergence speed. Finally, the system selects the corresponding adjustment mode or rule parameter based on the deviation amplitude according to the real-time acquisition of the production condition parameter from the pre-established mapping relationship. This dynamic selection mechanism ensures that the calibration process can be flexibly adjusted according to the changes in the actual production environment, and no longer relies solely on a single deviation amplitude.

[0087] This mechanism of dynamically determining the adjustment mode according to the production condition parameter is closely combined with the step of determining the adjustment amount of the calibration relationship according to the deviation amplitude in the previous scheme, forming a more complete calibration system. In the previous scheme, when the calibration relationship has a deviation and the deviation persists and the amplitude exceeds a threshold, the adjustment amount needs to be determined. Based on this, the present application no longer simply determines the adjustment amount directly according to the deviation amplitude, but first selects an adjustment mode suitable for the current situation according to the current production condition parameter. For example, when the material batch fluctuates greatly, even if the deviation amplitude is the same, the system will select a smaller adjustment amplitude and a more stable adjustment mode to avoid misjudgment and excessive adjustment caused by changes in material properties; in the case of stable equipment operation and constant environment, a faster response speed adjustment mode can be selected to quickly eliminate the deviation. This dynamic and situational adjustment mode selection enables the calibration process to better adapt to the complex and variable characteristics of the production site, significantly improving the accuracy and robustness of the instantaneous thermal response parameter calibration, thereby ensuring the accuracy of the pre-impregnated material local thermal state determination, and ultimately improving the effectiveness of the thermal regulation instruction, thereby homogenizing the thermal performance of the pre-impregnated material and effectively solving the problem of inconsistent cut quality in the multi-knife cutting process due to the complexity of the material and processing environment.

[0088] In some embodiments, the application is implemented as follows. On a carbon fiber prepreg slitting production line, a plurality of sets of strategies for adjusting the calibration relationship can be preset. For example, three sets of adjustment modes can be preset: the first set is a "conservative adjustment mode", which has a small adjustment step, slow convergence speed but high stability, and is suitable for cases where the material batch fluctuates greatly or the equipment state is unstable; the second set is a "standard adjustment mode", which has a moderate adjustment step and convergence speed, and is suitable for normal production conditions; the third set is an "aggressive adjustment mode", which has a larger adjustment step and faster convergence speed, and is suitable for scenarios where the equipment is running stably and the production efficiency is required to be high. These adjustment modes can be specifically manifested as different sets of PID control parameters or different adjustment amount calculation formulas.

[0089] At the same time, a mapping relationship between the production condition parameters and these adjustment modes can be established. For example, a lookup table can be constructed, which contains discrete or continuous ranges of production condition parameters such as "material batch stability level", "equipment vibration level", "environmental temperature", etc., and specifies a corresponding adjustment mode for each parameter combination. For example, when the material batch stability level is "low" and the equipment vibration level is "high", the mapping relationship can point to the "conservative adjustment mode"; when the material batch stability level is "high" and the equipment vibration level is "low", it points to the "aggressive adjustment mode".

[0090] In actual production process, the system can obtain the current production condition parameters in real time, such as monitoring the material batch information, equipment running state sensor data and environmental temperature and humidity sensor data through sensors. Then, the system can look up in the pre-established mapping relationship according to the obtained production condition parameters. For example, if the current obtained material batch stability level is "medium", the equipment vibration level is "medium", and the environmental temperature is within the normal range, the system can select the "standard adjustment mode" from the mapping relationship. Once the corresponding adjustment mode is determined, the subsequent calibration relationship adjustment amount can be calculated according to the selected adjustment mode combined with the current deviation amplitude, so as to realize the dynamic optimization of the calibration process.

[0091] Through the above technical solution, the application can more accurately determine the adjustment mode based on the deviation amplitude according to different production condition parameters. This makes the adjustment of the calibration relationship no longer a single and fixed mode, but can be dynamically adapted according to the actual production conditions such as material batch characteristics, equipment running state, environmental temperature and humidity, etc. Therefore, even in the case of complex and variable production environment, the accuracy and adaptability of the calibration process can be ensured, effectively avoiding the problems of inaccurate calibration or over-adjustment caused by relying only on the deviation amplitude for adjustment, thereby improving the control precision and stability of the thermal performance uniformization in the carbon fiber prepreg slitting process.

[0092] In some embodiments of the present application, a determination method of the adjustment mode based on the deviation amplitude according to the production condition parameter is proposed. The determination method can be specifically that the instantaneous value of the production condition parameter is directly obtained, and is accurately matched with a preset single parameter range, so as to select a corresponding adjustment mode. For example, when the instantaneous value of the production line temperature reaches a certain specific value, a preset specific adjustment strategy is immediately enabled. In this way, the change of the production condition can be quickly responded. However, in the implementation process, the production condition parameter may fluctuate or have noise. Directly using the instantaneous value may cause the adjustment mode to frequently switch, and affect the stability of the calibration relationship. In addition, the simple parameter matching may not fully utilize the potential information of the multiple adjustment modes or rule parameters, and the adjustment precision is limited.

[0093] To this end, the present application further proposes that the step of searching or selecting the corresponding adjustment mode based on the deviation amplitude or the rule parameter of the production condition parameter from the mapping relationship comprises:

[0094] obtaining a real-time value of the production condition parameter;

[0095] smoothing the real-time value of the production condition parameter to obtain a smoothed production condition parameter;

[0096] comparing the smoothed production condition parameter with a preset parameter range in the mapping relationship, and identifying a plurality of adjustment modes or rule parameters whose matching degrees satisfy a preset threshold;

[0097] determining the corresponding adjustment mode based on the deviation amplitude or the rule parameter according to the matching degrees of the plurality of adjustment modes or rule parameters.

[0098] The smoothing processing refers to eliminating random noise or short-term fluctuations in the data through an algorithm to reveal the potential trend or pattern of the data, which can be implemented by methods such as moving average, exponential smoothing or Kalman filtering, and the purpose is to improve the stability of the production condition parameters and avoid misjudgment caused by instantaneous fluctuations. The mapping relationship refers to the corresponding rule set established between the production condition parameters and the preset adjustment mode or rule parameters, which can be implemented in the form of a lookup table, a decision tree model or a neural network model, and the purpose is to provide a basis for the system to select an appropriate adjustment strategy according to the current production conditions. The preset parameter range refers to the numerical interval corresponding to the production condition parameters set for different adjustment modes or rule parameters in the mapping relationship, which can be implemented in the form of a discrete interval, a continuous interval or a fuzzy set, and the purpose is to define the applicable conditions of each adjustment mode or rule parameter. The matching degree refers to the degree of agreement or similarity quantification index of the smoothed production condition parameters and a certain preset parameter range in the mapping relationship, which can be calculated by methods such as distance measurement, similarity coefficient or membership function, and the purpose is to evaluate the applicability of different adjustment modes or rule parameters to the current production conditions. The preset threshold refers to the minimum matching degree standard for screening adjustment modes or rule parameters with qualified matching degrees, which can be set in the form of a fixed value, a dynamically adjusted value or a percentage, and the purpose is to ensure that the identified adjustment mode or rule parameter has sufficient applicability.

[0099] The scheme of the present application obtains real-time values of production condition parameters, providing basic data for subsequent decision-making. Given the inherent volatility of parameters in actual production environment, these real-time values are smoothed to obtain more stable and reliable smoothed production condition parameters. This processing effectively filters out transient noise and short-term fluctuations, ensuring the accuracy and stability of subsequent judgments. On this basis, the smoothed production condition parameters are compared with the preset parameter range in the pre-established mapping relationship, no longer limited to a single best match, but identifying multiple adjustment methods or rule parameters that meet the preset threshold. This multiple identification mechanism enables the system to more comprehensively consider multiple potential applicable strategies under current production conditions. Further, according to the matching degrees of these identified multiple adjustment methods or rule parameters, the system can perform fine evaluation and selection to ultimately determine the adjustment method or rule parameter based on the deviation amplitude that is most suitable for the current production condition. This way of considering multiple options and making decisions based on matching degrees avoids the limitations that may be brought about by a single match, significantly improving the accuracy and robustness of adjustment method selection. It is precisely due to this smoothing and multiple matching screening mechanism that the present scheme can overcome the challenges brought about by the fluctuations of production condition parameters when determining the adjustment amount of the calibration relationship. It not only ensures the stability of adjustment method selection and avoids frequent switching, but also makes the ultimately determined adjustment method more accurately adapt to complex production environments by comprehensively utilizing the potential information of multiple adjustment methods or rule parameters, thereby improving the accuracy and reliability of instantaneous thermal response parameter calibration as a whole, ensuring the accuracy of pre-preg local thermal state determination, and ultimately helping to achieve the uniformity of pre-preg thermal performance and improve the cutting quality.

[0100] In some preferred embodiments, the application is implemented as follows. When acquiring real-time values of production condition parameters, data such as environmental temperature, humidity, equipment running speed, material tension, etc. on the production line can be collected in real time, for example, by sensors. In order to smooth these real-time values, a moving average filter can be used, for example, collecting temperature data in the last 10 seconds and calculating its average value as the smoothed temperature parameter, or using an exponentially weighted moving average method, giving higher weight to recent data. When comparing the smoothed production condition parameters with the preset parameter ranges in the mapping relationship, the mapping relationship can be a lookup table stored in a database, where each row records a production condition parameter range (for example, temperature is 20-25 degrees Celsius, humidity is 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℃, 25℃], [25℃, 30℃], etc. When the smoothed temperature is 26℃, it may fall within the range of [25℃, 30℃] and also have some overlap with the wider range of [20℃, 27℃]. When multiple adjustment methods or rule parameters that meet the preset threshold are identified, the matching degree can be calculated based on the distance between the parameter value and the center value of the preset parameter range, the closer the distance, the higher the matching degree; or use the membership function of fuzzy logic to calculate, representing the degree to which the parameter value belongs to a certain range. The preset threshold can be set as a percentage, for example, adjustment methods with a matching degree higher than 80% are considered to meet the conditions. For example, if the smoothed temperature is 26℃, three adjustment methods A, B, C may be identified, which are associated with temperature ranges [25℃, 30℃], [20℃, 27℃], [24℃, 28℃] respectively, and their matching degrees are all higher than the preset threshold. When determining the corresponding adjustment method or rule parameter based on the deviation amplitude based on the matching degrees of multiple adjustment methods or rule parameters, a weighted average method can be used, that is, according to the matching degree of each identified adjustment method as the weight, the weighted average of its corresponding rule parameter is obtained. The final adjustment rule parameter; or use the priority sorting method, set the priority according to the matching degree, select the adjustment method with the highest matching degree; or use the expert system rule, combine the characteristics of multiple matching methods, and make a comprehensive judgment through the preset decision rule, 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 rule or combination rule.

[0101] By the technical solution, the application can effectively cope with the fluctuation of production condition parameters and noise interference, avoid frequent switching of adjustment modes caused by instantaneous parameter changes, and significantly improve the stability of the calibration relationship. At the same time, by identifying multiple adjustment modes or rule parameters that meet the preset threshold, and comprehensively judging them according to their matching degrees, the finally determined adjustment mode can more accurately adapt to the current production conditions, fully utilize the potential multiple adjustment strategy information, and further improve the accuracy and robustness of the adjustment.

[0102] In some embodiments of the application, the adjustment mode based on the deviation amplitude is determined according to the production condition parameters. The determination method can be to preset a set of fixed adjustment rules, which only select the adjustment mode based on the conventional temperature, humidity and other environmental parameters on the production line. This can simplify the adjustment logic and quickly respond to changes in the production environment. However, during implementation, the production condition parameters themselves may not be accurate, such as material batch differences, equipment state changes or product quality requirement adjustments, etc. These factors can affect the accuracy of the adjustment rule, cause deviation in the calibration relationship adjustment, and ultimately affect the slitting quality. Therefore, the production condition parameters that affect the accuracy of the adjustment rule need to be considered to improve the accuracy and reliability of the calibration relationship adjustment.

[0103] To this end, the application further provides a method comprising:

[0104] Obtaining production condition parameters that affect the accuracy of the adjustment rule; the production condition parameters include material batch characteristic parameters, equipment operating state parameters or product quality requirement parameters.

[0105] The material batch characteristic parameters refer to the difference indexes of different batches of carbon fiber prepreg in physical and chemical properties, which can be resin content, fiber volume fraction, prepreg thickness uniformity or surface roughness, etc. The purpose is to reflect the influence of raw material inherent properties on the slitting performance.

[0106] The equipment operating state parameters refer to the real-time operating condition indexes of the key components of the slitting equipment during operation, which can be tool wear degree, tool pressing force, equipment vibration frequency, tool temperature or conveyor belt tension, etc. The purpose is to reflect the influence of the equipment state on the slitting accuracy.

[0107] The product quality requirement parameters refer to specific provisions for the quality standards of the final slitting product, which can be slitting edge burr grade, width deviation range, cut flatness or resin overflow, etc. The purpose is to adjust the slitting strategy according to different quality standards to meet specific needs.

[0108] The scheme of the present application optimizes the adjustment mode of the calibration relationship by obtaining production condition parameters that affect the accuracy of the adjustment rule. In the process of cutting carbon fiber prepreg, the calibration relationship needs to be dynamically adjusted according to the instantaneous thermal response parameter to ensure the accuracy of thermal regulation. However, relying solely on the deviation amplitude to determine the adjustment mode may not be able to fully cope with the changing working conditions in actual production. Due to the dynamic changes of factors such as material batch characteristics, equipment operating state and product quality requirements, the thermal response and mechanical behavior in the cutting process will be directly affected, which will cause the original adjustment rule to deviate.

[0109] Therefore, before determining the adjustment amount of the calibration relationship, the present scheme actively obtains these production condition parameters that affect the accuracy of the adjustment rule. These parameters include material batch characteristic parameters such as the resin content or fiber distribution of the prepreg, equipment operating state parameters such as the degree of tool wear or the vibration condition of the equipment, and product quality requirement parameters such as specific requirements for cutting edge burrs or width deviation. By taking these multi-dimensional production condition parameters into account, the system can more comprehensively evaluate the current cutting environment and material characteristics.

[0110] Based on these more accurate production condition parameters, the system can more effectively determine the adjustment mode based on the deviation amplitude. For example, when it is detected that a particular material batch has a higher resin content, the system can automatically select a more conservative adjustment mode to avoid excessive thermal regulation leading to resin overflow; when it is detected that the tool wear reaches a certain degree, the system can switch to a more aggressive adjustment mode to compensate for the impact of tool performance degradation. This adjustment mode selection based on multi-dimensional production condition parameters makes the adjustment of the calibration relationship no longer a single-dimensional response, but can be finely and adaptively optimized according to the actual working conditions.

[0111] In this way, the present scheme can compensate for the limitations of relying solely on deviation amplitude for adjustment, improving the accuracy and reliability of the calibration relationship adjustment. This not only ensures that the calibration of the instantaneous thermal response parameter is more accurate, but also enables the subsequent thermal regulation instructions to act more accurately on the prepreg, thereby ensuring the thermal performance homogenization in the process of cutting carbon fiber prepreg from the source, and ultimately improving the cutting quality.

[0112] In some preferred embodiments, in order to obtain the production condition parameters affecting the accuracy of the adjustment rule, various sensors and data interfaces can be used for data acquisition. For example, for the material batch characteristic parameters, a material information input interface can be provided to allow the operator to manually input the resin content, fiber type, thickness tolerance, etc. of the current batch when changing the material batch, or to automatically synchronize with the database of the material supplier. In addition, a near-infrared spectroscopy sensor or an X-ray transmission instrument can also be integrated on the production line to detect the resin content and fiber distribution uniformity of the prepreg in real time.

[0113] For the equipment operating state parameters, a vibration sensor can be deployed near the slitting knife group to monitor the vibration frequency and amplitude of the knife in real time to evaluate the wear degree of the knife and the stability of the equipment. At the same time, a current sensor or a power meter can be integrated into the knife driving motor to indirectly judge the cutting state of the knife by monitoring the load change of the motor. The downforce of the knife can be measured in real time by a pressure sensor. The data of these sensors can be periodically collected and transmitted to the central control unit.

[0114] For the product quality requirement parameters, different product quality level configuration files can be preset, such as "high precision mode", "standard mode" or "economic mode", each mode corresponding to different allowable ranges of slitting edge burr, width deviation and resin overflow. The operator can select the corresponding product quality requirement mode before production starts, or the system can automatically load according to the order information. The acquisition of these parameters enables the system to dynamically adjust the adjustment strategy of the calibration relationship according to the actual production requirements and working conditions, thereby ensuring the slitting quality.

[0115] Through the above technical solutions, the system can obtain production condition parameters affecting the accuracy of the adjustment rule, including material batch characteristic parameters, equipment operating state parameters or product quality requirement parameters. This makes the adjustment of the calibration relationship no longer rely solely on the deviation amplitude, but can consider various factors such as material, equipment and product requirements. Therefore, the adjustment of the calibration relationship is more accurate and reliable, avoiding the adjustment deviation caused by inaccurate production condition parameters, thereby improving the quality stability of carbon fiber prepreg slitting.

[0116] In some embodiments of the present application, the local thermal performance difference information of the carbon fiber prepreg about to enter the slitting station is obtained, and the correspondence between the information and the physical position of the prepreg is established, the local thermal state of the prepreg is determined according to the local thermal performance difference information, the thermal regulation instruction is generated, and finally the local thermal regulation of the prepreg is carried out between the slitting cutter group and the slitting station upstream. The method can intervene according to the actual thermal state of the prepreg, so as to realize the uniformization of the thermal performance and improve the slitting quality. However, in the implementation process, how to ensure that the three key links of information acquisition, state determination and instruction generation, and local thermal regulation can work efficiently and accurately to ensure the accuracy and reliability of the carbon fiber prepreg slitting parameter optimization is a problem that needs to be further solved.

[0117] In a second aspect, referring to Figure 2 The present application further provides a carbon fiber prepreg slitting parameter optimization system, which comprises:

[0118] The information acquisition module 201 is configured to acquire the local thermal performance difference information of the carbon fiber prepreg about to enter the slitting station, and establish the correspondence between the information and the physical position of the prepreg.

[0119] The state determination and instruction generation module 202 is configured to determine the local thermal state of the prepreg according to the local thermal performance difference information, and generate the thermal regulation instruction according to the local thermal state.

[0120] The local thermal regulation module 203 is configured to regulate the local thermal performance of the prepreg according to the thermal regulation instruction between the slitting cutter group and the slitting station upstream.

[0121] The uniformization module 204 is configured to uniformize the thermal performance of the prepreg by the local thermal regulation.

[0122] Through the above technical solution, a carbon fiber prepreg slitting parameter optimization system is provided, which serves as a specific implementation carrier of the carbon fiber prepreg slitting parameter optimization method, so that the method can be effectively executed and applied.

[0123] The above only describes the embodiments of the present application and does not limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing a carbon fiber prepreg slitting parameter, characterized by, The method comprises the following steps: acquiring local thermal performance difference information of carbon fiber prepreg entering a slitting station, and establishing a correspondence between the information and a 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 regulation instruction according to the local thermal state; performing local thermal regulation on the prepreg according to the thermal regulation instruction between a slitting cutter group and the slitting station upstream of the slitting cutter group; homogenizing thermal performance of the prepreg through the local thermal regulation; The step of determining a 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 comprises: acquiring environmental parameters or thermal regulation unit state parameters affecting instantaneous thermal response parameters; calibrating the instantaneous thermal response parameters according to the environmental parameters or the thermal regulation unit state parameters; comparing the calibrated instantaneous thermal response parameters with preset characteristic parameters to determine the local thermal state of the prepreg; generating the thermal regulation instruction according to the local thermal state; The method further comprises: when the upstream detection station and the downstream thermal regulation station are separated and during the local thermal regulation, applying a detection heat pulse to the prepreg through the thermal regulation unit; and collecting instantaneous thermal response parameters of the thermal regulation unit; The step of calibrating the instantaneous thermal response parameters according to the environmental parameters or the thermal regulation unit state parameters comprises: when the evaluation result indicates that there is a deviation in the calibration relationship, continuously acquiring the evaluation result and accumulating the evaluation result; comparing the accumulated evaluation result with a preset deviation persistence threshold to determine whether the deviation persists; when the deviation persists and its amplitude exceeds a preset adjustment start threshold, determining an adjustment amount of the calibration relationship according to the deviation amplitude; incrementally adjusting the calibration relationship according to the adjustment amount until the deviation meets a preset convergence condition; calibrating the instantaneous thermal response parameters according to the adjusted calibration relationship and the environmental parameters or the thermal regulation unit state parameters.

2. The method according to claim 1, wherein, The method comprises: acquiring instantaneous thermal response parameters of a standard material and corresponding environmental parameters or thermal regulation unit state parameters; evaluating accuracy of a calibration relationship for calibrating the instantaneous thermal response parameters according to known thermal characteristics of the standard material, to obtain an evaluation result.

3. The method of claim 1, wherein the cutting parameters are optimized by using a cutting force model. The step of determining an adjustment amount of the calibration relationship according to the deviation amplitude comprises: determining an adjustment mode based on the deviation amplitude according to a production condition parameter; matching the adjustment mode according to the deviation amplitude; determining the adjustment amount of the calibration relationship according to the matched adjustment mode.

4. The method according to claim 3, wherein the cutting parameters are optimized by using a cutting machine. The step of determining an adjustment mode based on the deviation amplitude according to a production condition parameter comprises: presetting multiple groups of adjustment modes or rule parameters based on the deviation amplitude; establishing a mapping relationship between the production condition parameter and the multiple groups of adjustment modes or rule parameters; and According to the obtained production condition parameter, a corresponding adjustment mode or rule parameter based on the deviation amplitude is found or selected from the mapping relationship.

5. The method of claim 4, wherein the cutting parameters are optimized by using a cutting force model. The step of finding or selecting the corresponding adjustment mode or rule parameter based on the deviation amplitude from the mapping relationship according to the obtained production condition parameter comprises: obtaining a real-time value of the production condition parameter; performing smoothing processing on the real-time value of the production condition parameter to obtain a smoothed production condition parameter; comparing the smoothed production condition parameter with a preset parameter range in the mapping relationship to identify a plurality of adjustment modes or rule parameters whose matching degrees satisfy a preset threshold; determining the corresponding adjustment mode or rule parameter based on the deviation amplitude according to the matching degrees of the plurality of adjustment modes or rule parameters.

6. The method of claim 3, wherein the cutting parameters are optimized by using a cutting force model. The method comprises: obtaining a production condition parameter affecting the accuracy of the adjustment rule; the production condition parameter comprises a material batch characteristic parameter, an equipment operation state parameter or a product quality requirement parameter.

7. A carbon fiber prepreg slitting parameter optimization system, characterized by, The system comprises: an information acquisition module configured to acquire local thermal performance difference information of carbon fiber prepreg about to enter a slitting station and establish a correspondence between the information and a physical position of the prepreg; a state determination and instruction generation module configured to determine a local thermal state of the prepreg according to the local thermal performance difference information and generate a thermal regulation instruction according to the local thermal state; a local thermal regulation module configured to perform local thermal regulation on the prepreg according to the thermal regulation instruction between an upstream position of the slitting station and a slitting knife group; a uniformization module configured to homogenize the thermal performance of the prepreg through the local thermal regulation; the state determination and instruction generation module is further configured to acquire an environmental parameter or a thermal regulation unit state parameter affecting an instantaneous thermal response parameter; calibrate the instantaneous thermal response parameter according to the environmental parameter or the thermal regulation unit state parameter; compare the calibrated instantaneous thermal response parameter with a preset characteristic parameter to determine the local thermal state of the prepreg; generate the thermal regulation instruction according to the local thermal state; the local thermal regulation module is further configured to, when the upstream detection station and the downstream thermal regulation station are separated and during the local thermal regulation, apply a detection thermal pulse to the prepreg through the thermal regulation unit and collect an instantaneous thermal response parameter of the thermal regulation unit; the state determination and instruction generation module is further configured to, when an evaluation result indicates that there is a deviation in the calibration relationship, continuously acquire the evaluation result and accumulate the evaluation result; compare the accumulated evaluation result with a preset deviation persistence threshold to determine whether the deviation persists; when the deviation persists and its amplitude exceeds a preset adjustment start threshold, determine an adjustment amount of the calibration relationship according to the deviation amplitude; perform incremental adjustment on the calibration relationship according to the adjustment amount until the deviation satisfies a preset convergence condition; calibrate the instantaneous thermal response parameter according to the adjusted calibration relationship and the environmental parameter or the thermal regulation unit state parameter.

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