Enameled wire production process quality on-line control closed-loop management system

Abstract

The invention discloses an enameled wire production process quality on-line control closed-loop management system, and relates to the technical field of intelligent manufacturing, the system collects original physical data in the enameled wire production process in real time by arranging a sensor network, and after data processing, a coating feature data set and a process state data set are obtained; performing process quality analysis according to the coating characteristic data set, generating coating quality evaluation, performing production efficiency analysis through the process state data set when the coating quality meets requirements, performing process coupling steady-state analysis in combination with a process quality analysis result, and generating coupling state evaluation; according to the method, production data and evaluation results of all batches are filed, behavior offset in the production process is traced, a causal path is extracted to generate a control offset event set, and self-adaptive strategy backtracking and process correction are carried out. According to the system, closed-loop control of the production process is realized, the production quality and efficiency are improved, the production fluctuation is reduced, and the intellectualization and self-adaptive capability of the production process are enhanced.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, specifically to a closed-loop management system for online quality control in the production process of enameled wire. Background Technology

[0002] Enameled wire is widely used in motors, transformers, and electrical equipment, serving as a crucial conductor material. The quality of enameled wire directly impacts the performance and safety of electrical equipment. The uniformity, thickness, and stability of its coating, as well as the control of the manufacturing process, are key factors determining its quality. During production, the uniformity and thickness of the coating directly affect the insulation performance and durability of the electrical equipment. Processing this industrial data is crucial for ensuring production quality and improving efficiency, such as the handling of process parameters. Therefore, the demand for quality control in enameled wire production is increasing, particularly in ensuring a balance between coating quality and process stability. Consequently, more and more production lines are relying on advanced online closed-loop management systems to achieve quality monitoring, efficiency analysis, and adaptive correction, driving the refinement and intelligentization of enameled wire production.

[0003] Existing enameled wire production processes largely rely on manual intervention or traditional process monitoring methods, which have significant shortcomings in terms of real-time performance, accuracy, and adaptability. For example, traditional coating quality assessment methods typically depend on manual inspection or small-scale sampling, failing to provide real-time feedback on actual coating quality changes. This can easily lead to uneven coating or significant thickness deviations during production. Furthermore, changes in parameters such as process temperature and production speed, if not adjusted promptly, can also result in substandard coating quality or low production efficiency, affecting overall output and product stability. Current systems lack closed-loop feedback mechanisms, hindering effective linkage and correction between quality assessment and production efficiency analysis, thus preventing adaptive adjustments during production. There is also no robust traceability and correction mechanism for quality issues caused by abnormal process parameters, making it difficult to quickly and effectively restore production when system failures occur. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an online quality control closed-loop management system for enameled wire production, which solves the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an online quality control closed-loop management system for enameled wire production, comprising a data processing module, a process analysis module, an efficiency analysis module, an optimization decision-making module, and a feedback learning module; The data processing module is used to collect the raw physical data of the enameled wire based on the deployed sensor network, and obtain the coating feature data group and process status data group after data processing. The process analysis module is used to perform process quality analysis based on the coating feature data set, and generate a coating quality assessment based on the analysis results. The efficiency analysis module is used to perform production efficiency analysis on the enameled line when the coating quality is assessed as meeting the requirements, based on the process status data group. The optimization decision module is used to fit the process quality analysis results with the production efficiency analysis results, and combine the process temperature T to perform a process coupling steady-state analysis of the enameled wire production process, and generate a coupling state assessment. The feedback learning module is used to archive and trace the analysis and evaluation results in multiple dimensions, extract the causal path of abnormal offsets, and generate a set of control offset events for adaptive strategy backtracking and process correction.

[0006] Preferably, the data processing module includes a data acquisition unit and a data processing unit; The data acquisition unit is used to acquire raw physical data on the film formation quality and process stability of the enameling line based on the sensor network deployed at each node of the enameling production line. The sensor network includes optical scanning sensors, laser thickness sensors, infrared temperature probes, coded velocimeters, and pressure transmitters. The optical scanning sensor is installed between the end of the spraying section and the inlet of the cooling section to scan the surface image of the wire that has just been sprayed but has not yet fully cured, and to acquire a set of surface images. The laser thickness sensor is installed at the outlet of the cooling section to perform non-contact thickness measurement on the wire after the temperature has stabilized, and to obtain the coating thickness th. The infrared temperature probes are respectively installed on the top of the drying zone and the inner wall of the spraying section to monitor the process temperature T of the wire. The coded speed measuring device is installed on the back of the take-up end guide wheel shaft or tension guide wheel to record the wire running speed and obtain the production speed V; The pressure transmitter is installed in the paint supply branch of the paint nozzle to collect the spraying pressure P in real time during the production process of the enameled wire.

[0007] Preferably, the data processing unit is used to establish a data transmission channel between the sensor group and the control closed-loop management system based on the wireless network, and transmit the original physical data to the online control closed-loop management system in real time for data processing to obtain coating feature data group and process status data group. The data processing is used to perform image processing on the original physical data, followed by denoising, data time synchronization, outlier removal, and data normalization. The image processing is used to acquire image frames of a surface image set based on a linear CCD array. Each frame contains a two-dimensional grayscale value matrix I(x,y,t) of the wire surface within a time window. Each image frame is divided into m×n pixel block regions R. i,j And calculate the R region for each pixel block. i,j gray standard deviation σ i,j Then calculate the relative average deviation of the gray-level variance in the entire image. The surface uniformity U of the coating is calculated. The denoising process uses wavelet denoising technology to preserve the transient structural features of the original physical data and remove high-frequency noise. Data time synchronization uses a streaming processing framework to align the original physical data with the same timestamp based on the timestamps collected by the sensors. Outlier removal involves sequentially applying moving average filtering to the synchronously collected original physical data to smooth instantaneous fluctuations, followed by amplitude limiting filtering to remove abnormal peak values ​​and obtain a continuous data sequence. Data normalization uses the Max-Min method to remove the dimensional influence of the original physical data. The coating feature data set includes coating surface uniformity U and coating thickness th; The process status data set includes production speed V and process temperature T.

[0008] Preferably, the process analysis module includes a process analysis unit and a process feedback unit; The process analysis unit is used to perform process quality analysis on the coating in the enameled wire production process based on the coating feature data set, and to construct a coating structure quality score Tzl to quantify the non-static coupling relationship between coating thickness and surface uniformity. By jointly analyzing the real-time thickness deviation and surface uniformity of the coating, the stability of the physical forming process of the enameled wire surface coating is judged.

[0009] Preferably, the process feedback unit includes a process evaluation unit and a process correction unit; The process evaluation unit is used to collect coating quality scores Tzl when the coating quality meets the requirements within one year, and calculate the mean value through statistical methods. The mean value is preset as the coating quality standard threshold Ty, and the coating quality is evaluated by comparing it with the coating quality score Tzl acquired in real time. The specific evaluation scheme is as follows. When the coating structure quality score Tzl is less than the coating quality standard threshold Ty, it means that the coating quality does not meet the requirements. At this time, the quality non-compliance information is generated and transmitted to the control closed-loop management system. When the coating structure quality score Tzl is greater than or equal to the coating quality standard threshold Ty, it means that the coating quality meets the requirements. Maintain the current production settings and perform a production efficiency analysis. The process correction unit is used to increase the coating pressure by 15% through the online control closed-loop management system when the system receives information about non-conforming quality. It also performs iterative analysis through the process analysis unit. If the quality is still not met after three consecutive adjustments, the unit sends a shutdown command to the control closed-loop management system and generates manual intervention information to notify maintenance personnel to perform maintenance and adjustments.

[0010] Preferably, the efficiency analysis module is used to perform production efficiency analysis on the enameled wire when the coating quality assessment is that the coating quality meets the requirements, based on the process status data group, and to construct a thermodynamic synergistic capacity efficiency score Tkc, which is used to quantify the bivariate synergistic relationship between thermal parameters and line speed parameters, and to quantify the thermal stability and dynamic capacity balance of the production process.

[0011] Preferably, the optimization decision module includes a comprehensive steady-state analysis unit and a steady-state evaluation unit; The comprehensive steady-state analysis unit is used to fit the coating structure quality score Tzl and the thermodynamic synergistic production efficiency score Tkc, and then introduce the real-time process temperature T as a temperature penalty term to perform process coupling steady-state analysis on the enameled wire production process, and construct the process coupling steady-state score ZHY, which represents the comprehensive performance score of the current enameled wire production process in terms of quality and efficiency, and is used to quantify the comprehensive steady-state relationship between process efficiency and coating structure quality.

[0012] Preferably, the steady-state evaluation unit includes a steady-state equilibrium evaluation unit and a linkage correction unit; The steady-state equilibrium assessment unit is used to collect the process coupling steady-state score ZHY when the production efficiency and quality reach a balanced state within one year, and calculate the mean value through statistical methods. The mean value is preset as the critical threshold Zy of process thermal coupling, and the coupling state is assessed with the real-time acquired process coupling steady-state score ZHY. The specific assessment scheme is as follows: When the steady-state score of process coupling ZHY < the critical threshold of process thermal coupling Zy, it indicates that production efficiency and quality are not in balance. At this time, imbalance information is generated and transmitted to the control closed-loop management system. When the steady-state score of process coupling ZHY is greater than or equal to the critical threshold of process thermal coupling Zy, it indicates that production efficiency and quality have reached a balance and entered a stable production state. At this time, no adjustment is needed and the current production instruction can be maintained.

[0013] Preferably, the linkage correction unit is used to calculate the average target speed when production efficiency and quality reach a balance state when the closed-loop management system receives information about non-conforming quality. Target process temperature and target spraying pressure The production of enameled wire is modified by taking into account the maximum acceptable coating thickness deviation range Δth, process temperature deviation range ΔT, and spraying pressure deviation range ΔP. If |V(t)− When |> ΔV, execute speed correction and increase production speed by 10%; If |T(t)− When |>ΔT, perform temperature control self-calibration and increase the process temperature by 10%; If |P(t)− When |>ΔP, pressure regulation is performed, increasing the coating pressure by 15%.

[0014] Preferably, the feedback learning module includes a data archiving unit and a behavior tracing unit; The data archiving unit is used to classify, store and structure the production data and evaluation results generated during each batch of production. The production data includes coating feature data group, process status data group, coating structure quality score Tzl, thermal-dynamic synergistic capacity efficiency score Tkc, process coupling steady-state score ZHY, production timestamp, batch number, equipment operation number and correction instructions. The archived data will be classified and indexed according to the two dimensions of time window and operating condition characteristics, and multi-channel data tags will be generated. The behavior tracing unit is used to periodically traverse the archived data. When it detects a deviation in the evaluation result or an inconsistency between the control response and the evaluation result, it automatically calls the process parameter records in the historical archived data that are similar to the current environmental conditions and are operating stably. It extracts the corresponding control instruction snapshots as reference samples, performs structured analysis on the changes in control logic in each period of the current production cycle, records all operation information including operation type, adjustment range, and response delay, constructs a time-ordered chain control path sequence, and generates a behavior tracing graph containing three types of nodes: operation events, response parameters, and evaluation changes, based on the chain control path sequence. With the help of the relationship analysis of the associated edges in the graph structure, it automatically extracts the causal path and influencing nodes that cause the deviation from the initial stable state to the deviation abnormal state, as well as the causal chain formed, and generates a structured control deviation event set for adaptive strategy backtracking and control logic correction under abnormal operating conditions.

[0015] This invention provides a closed-loop management system for online quality control in the enameled wire production process. It offers the following advantages: (1) The system's data processing module collects raw physical data on the film quality and process stability of the enameled wire in real time through sensors precisely deployed at each key node in the enameled wire production process. After data processing, the data is transmitted to the online control closed-loop management system via wireless network to form coating feature data group and process status data group, ensuring the accuracy and reliability of the data. This not only guarantees the integrity of the data but also provides a stable and accurate foundation for the real-time monitoring and control system.

[0016] (2) The system's process analysis module performs process quality analysis on the enameled wire production process based on the coating characteristic data set. Through joint analysis of coating thickness and surface uniformity, the coating structure quality score Tzl is quantified, reflecting the quality fluctuations and stability of the coating at different time points. The coating quality standard threshold Ty is used to assess whether the coating quality meets the standard, providing an important basis for process adjustments during production. When the coating quality meets the standard, the efficiency analysis module analyzes production efficiency using the process state data set, generating a thermal-dynamic synergistic capacity efficiency score Tkc. By quantifying the synergistic relationship between production speed and process temperature, it provides a scientific basis for the thermal stability and dynamic capacity balance of the production process. The application of the thermal-dynamic synergistic capacity efficiency score Tkc helps to identify and optimize potential bottlenecks in production, improve production efficiency, and ensure that coating quality is not affected.

[0017] (3) The system's optimization decision module fits the coating quality analysis results and production efficiency analysis results, and combines them with the real-time process temperature T to perform a steady-state analysis of the enameled wire production process. By generating a process coupling steady-state score ZHY, the comprehensive steady-state relationship between production quality and efficiency is quantified, providing a precise adjustment plan for the production process. By evaluating the coupling state with the process thermal coupling critical threshold Zy, it is determined whether the production is in a stable state and whether further optimization of production parameters is needed. The feedback learning module provides a closed-loop management function on this basis. Through data archiving and behavior tracing, it analyzes historical production data, identifies the causal path of abnormal deviations, automatically generates a set of control deviation events, and uses these data to trace back the adjustment strategy in the production process. The core of behavior tracing is to compare historical data with current production data, find the key nodes affecting production deviations through structured analysis, and provide data support and theoretical basis for subsequent process correction. The feedback learning module enhances the system's adaptive capability, enabling it to respond and correct quickly during the production process, ensuring that production is always in a highly efficient and stable state, and improving the overall control capability and self-optimization function of the system. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the online quality control closed-loop management system for enameled wire production process of the present invention; Figure 2 This is a schematic diagram illustrating the operating principle of the closed-loop management system for online quality control in the enameled wire production process of this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 Please see Figure 1 This invention provides an online quality control closed-loop management system for enameled wire production. To achieve the above objectives, this invention is implemented through the following technical solutions: including a data processing module, a process analysis module, an efficiency analysis module, an optimization decision-making module, and a feedback learning module. The data processing module is used to collect the raw physical data of the enameled wire based on the deployed sensor network, and obtain the coating feature data group and process status data group after data processing. The process analysis module is used to perform process quality analysis based on the coating feature data set, and generate a coating quality assessment based on the analysis results. The efficiency analysis module is used to perform production efficiency analysis on the enameled line when the coating quality is assessed as meeting the requirements, based on the process status data group. The optimization decision module is used to fit the process quality analysis results with the production efficiency analysis results, and combine the process temperature T to perform a process coupling steady-state analysis of the enameled wire production process, and generate a coupling state assessment. The feedback learning module is used to archive and trace the analysis and evaluation results in multiple dimensions, extract the causal path of abnormal offsets, and generate a set of control offset events for adaptive strategy backtracking and process correction.

[0021] In this embodiment, the data processing module collects raw physical data of the enameled wire in real time through a sensor network, and generates coating feature data sets and process status data sets after data processing, providing a reliable basis for subsequent process analysis and production efficiency evaluation. Compared with existing technologies, this data processing method ensures the accuracy and real-time performance of data through multi-dimensional processing such as image processing, noise reduction, time synchronization, outlier removal, and data normalization, providing precise and comprehensive monitoring and analysis support for the production process. The process analysis module and efficiency analysis module complement each other, analyzing the production efficiency of the enameled wire under the premise that the coating quality meets the requirements. The process analysis module performs process quality analysis based on the coating feature data sets and generates a coating quality assessment based on the analysis results, quantifying the physical forming stability of the coating. When the coating quality meets the requirements, the efficiency analysis module performs production efficiency analysis on the enameled wire when the process is qualified based on the process status data sets, quantifying the thermal stability and dynamic capacity balance state during the production process. By combining process status data for multi-dimensional analysis, compared with traditional single quality or efficiency analysis, potential problems in the production process are revealed more comprehensively, ensuring the simultaneous improvement of quality and efficiency. The optimization decision-making module and feedback learning module provide adaptive adjustment and closed-loop management capabilities. The optimization decision-making module combines process quality analysis results with production efficiency analysis results, and incorporates real-time process temperature T for coupled steady-state analysis of the process, generating a coupled state assessment to optimize the stability of the production process. The feedback learning module automatically extracts the causal paths of abnormal deviations through multi-dimensional archiving and behavior tracing, providing intelligent support for process correction and adaptive strategy backtracking. This feedback closed-loop mechanism not only improves the system's responsiveness to production deviations but also effectively reduces the need for human intervention through continuous learning and optimization. Compared with existing technologies, this system can track and adjust the production process in real time, reducing unnecessary downtime and production losses, and improving production efficiency and quality control levels.

[0022] Example 2 Please refer to Figure 2 Specifically: the data processing module includes a data acquisition unit and a data processing unit; The data acquisition unit is used to acquire raw physical data on the film formation quality and process stability of the enameling line based on the sensor network deployed at each node of the enameling production line. The sensor network includes optical scanning sensors, laser thickness sensors, infrared temperature probes, coded velocimeters, and pressure transmitters. The optical scanning sensor is installed between the end of the spraying section and the inlet of the cooling section to scan the surface image of the wire that has just been sprayed but has not yet fully cured, and to acquire a set of surface images. The laser thickness sensor is installed at the outlet of the cooling section to perform non-contact thickness measurement on the wire after the temperature has stabilized, and to obtain the coating thickness th. The infrared temperature probes are respectively installed on the top of the drying zone and the inner wall of the spraying section to monitor the process temperature T of the wire. The coded speed measuring device is installed on the back of the take-up end guide wheel shaft or tension guide wheel to record the wire running speed and obtain the production speed V; The pressure transmitter is installed in the paint supply branch of the paint nozzle to collect the spraying pressure P in real time during the production process of the enameled wire.

[0023] The data processing unit is used to establish a data transmission channel between the sensor group and the control closed-loop management system based on the wireless network, and transmit the original physical data to the online control closed-loop management system for data processing in real time to obtain coating feature data group and process status data group. The data processing is used to perform image processing on the original physical data, followed by denoising, data time synchronization, outlier removal, and data normalization. The image processing is used to acquire image frames of a surface image set based on a linear CCD array. Each frame contains a two-dimensional grayscale value matrix I(x,y,t) of the wire surface within a time window. Each image frame is divided into m×n pixel block regions R. i,j And calculate the R region for each pixel block. i,j gray standard deviation σ i,j Then calculate the relative average deviation of the gray-level variance in the entire image. The surface uniformity U of the coating is calculated as follows: ; The denoising process uses wavelet denoising technology to preserve the transient structural features of the original physical data and remove high-frequency noise. Data time synchronization uses a streaming processing framework to align the original physical data with the same timestamp based on the timestamps collected by the sensors. Outlier removal involves sequentially applying moving average filtering to the synchronously collected original physical data to smooth instantaneous fluctuations, followed by amplitude limiting filtering to remove abnormal peak values ​​and obtain a continuous data sequence. Data normalization uses the Max-Min method to remove the dimensional influence of the original physical data. The coating feature data set includes coating surface uniformity U and coating thickness th; The process status data set includes production speed V and process temperature T.

[0024] In this embodiment, the data acquisition unit relies on a sensor network deployed at each production node to accurately acquire raw physical data, including coating surface image sets, coating surface uniformity U, coating thickness th, production speed V, process temperature T, and spraying pressure P. The data processing unit performs image processing on the raw physical data, followed by noise reduction, data time synchronization, outlier removal, and data normalization. This transforms the raw data into coating feature data sets and process status data sets, providing a scientific basis for subsequent process quality assessment and production efficiency analysis. Through this series of technical means, the system can acquire detailed data of the production process in real time. By analyzing coating uniformity and thickness and evaluating process status, it achieves efficient monitoring and precise control of the production process, significantly improving the stability, quality consistency, and production efficiency of the enameled wire production process. This reduces the need for human intervention, optimizes production decision-making and control strategies, and ensures the automation and accuracy of production.

[0025] Example 3 Please refer to Figure 2 Specifically: the process analysis module includes a process analysis unit and a process feedback unit; The process analysis unit is used to perform process quality analysis on the coating in the enameled wire production process based on the coating characteristic data set, and to construct a coating structure quality score Tzl to quantify the non-static coupling relationship between coating thickness and surface uniformity. By jointly analyzing the real-time thickness deviation and surface uniformity of the coating, the stability of the physical forming process of the enameled wire surface coating is determined, specifically: In the formula, Tzl(t) represents the coating quality score at time t, U(t) and th(t) represent the coating surface uniformity and coating thickness at time t, respectively. min This indicates the minimum allowable enameled wire coating thickness under standard conditions, th m This indicates the target coating thickness under standard conditions.

[0026] The process feedback unit includes a process evaluation unit and a process correction unit; The process evaluation unit is used to collect coating quality scores Tzl when the coating quality meets the requirements within one year, and calculate the mean value through statistical methods. The mean value is preset as the coating quality standard threshold Ty, and the coating quality is evaluated by comparing it with the coating quality score Tzl acquired in real time. The specific evaluation scheme is as follows. When the coating structure quality score Tzl is less than the coating quality standard threshold Ty, it means that the coating quality does not meet the requirements. At this time, the quality non-compliance information is generated and transmitted to the control closed-loop management system. When the coating structure quality score Tzl is greater than or equal to the coating quality standard threshold Ty, it means that the coating quality meets the requirements. Maintain the current production settings and perform a production efficiency analysis. The process correction unit is used to increase the coating pressure by 15% through the online control closed-loop management system when the system receives information about non-conforming quality. It also performs iterative analysis through the process analysis unit. If the quality is still not met after three consecutive adjustments, the unit sends a shutdown command to the control closed-loop management system and generates manual intervention information to notify maintenance personnel to perform maintenance and adjustments.

[0027] In this embodiment, the process analysis unit performs process quality analysis on the coating in the enameled wire production process based on the coating characteristic data set, and constructs a coating structure quality score Tzl to quantify the non-static coupling relationship between coating thickness and surface uniformity. Through joint analysis of real-time coating thickness deviation and surface uniformity changes, the system can evaluate the stability of the coating physical forming process.

[0028] The Tzl formula for coating structure quality scoring originates from the coating structure quality assessment process. Its purpose is to determine the stability of the physical forming process of the enameled wire coating through a joint analysis of coating thickness and surface uniformity. Coating quality is closely related to coating thickness and surface uniformity; coating thickness directly affects its mechanical strength and electrical properties. Therefore, the commonly used standardized scoring formula is... In physics, coating quality assessment requires comprehensive consideration of the non-static coupling relationship between coating thickness and uniformity. This formula employs a weighted model, with the weights determined by the coating surface uniformity U(t). U(t) represents the uniformity of the coating surface and is a dimensionless value; its function is to adjust the weights of the coating quality score based on the actual condition of the coating surface. The thickness deviation is represented by the difference (th(t)−th). min The value is expressed as th, representing the difference between the actual coating thickness and the minimum standard thickness. In the formula, th... min Represents the minimum standard coating thickness, th mThis refers to the target coating thickness under standard conditions. Therefore, by combining the above, a coating quality score Tzl(t) is constructed, which considers both coating thickness deviation and coating uniformity, and is normalized within a standard range to ensure the consistency and reliability of the evaluation results under different production conditions. The process evaluation unit evaluates the coating quality using a preset coating quality standard threshold Ty and the real-time acquired coating quality score Tzl. When the coating quality score Tzl is less than the coating quality standard threshold Ty, it indicates that the coating quality does not meet the requirements. The system generates a quality non-compliance message and transmits it to the closed-loop management system, triggering subsequent adjustments. If the coating quality score Tzl is greater than or equal to the coating quality standard threshold Ty, it indicates that the coating quality meets the standard. The system maintains the current production settings and performs production efficiency analysis. The process correction unit adjusts the coating by increasing the coating pressure by 15% upon receiving the quality non-compliance message. If the standard is still not met after three consecutive adjustments, the system automatically sends a shutdown command and notifies maintenance personnel for maintenance. Through this closed-loop management mechanism, the system ensures that the coating quality meets the standards, while improving the stability and efficiency of the production process, reducing production deviations, and improving the quality consistency and automation level of enameled wire.

[0029] Example 4 Please refer to Figure 2 Specifically: The efficiency analysis module is used to analyze the production efficiency of the enameled wire when the coating quality assessment indicates that the coating quality meets the requirements, based on the process status data group, and to construct a thermodynamic synergistic capacity efficiency score Tkc, which is used to quantify the bivariate synergistic relationship between thermal parameters and line speed parameters, and to quantitatively analyze the thermal stability and dynamic capacity balance of the production process. In the formula, Tkc(t) represents the thermodynamic synergistic capacity efficiency score at time t, V(t) and T(t) represent the production speed and process temperature at time t, respectively, and T m T represents the target process temperature under standard conditions. max and T min These represent the maximum and minimum permissible process temperatures, respectively.

[0030] In this embodiment, when the coating quality assessment is satisfactory, the efficiency analysis module conducts a detailed analysis of the production process by combining process status data sets, constructing a thermodynamic synergistic capacity efficiency score Tkc, and quantifying the bivariate synergistic relationship between production speed and process temperature. This analysis not only reveals the mutual influence between thermal stability and dynamic capacity balance, but also assesses the impact of temperature fluctuations on efficiency in real time. By accurately monitoring and evaluating production speed V(t) and process temperature T(t), it ensures that the process temperature is always kept within the allowable range, optimizing the balance between production efficiency and quality. This implementation improves the thermal stability of the production process, avoids the negative impact of temperature fluctuations on capacity, and makes the entire production process more efficient and stable.

[0031] This formula is derived based on classical thermodynamic and kinetic models. In classical thermodynamics, the relationship between production efficiency and temperature and speed is usually based on fundamental thermodynamic formulas, such as specific heat capacity, energy transfer, and temperature gradient. The thermodynamic model can be expressed as Q = mcΔT, where Q is the amount of heat transferred, m is the mass of the object, c is the specific heat capacity, and ΔT is the temperature change. Classical production efficiency models typically consider the impact of production speed and process temperature on capacity. A simplified production efficiency formula is... P actual It is the current actual production efficiency, P max It represents maximum production efficiency. Considering the synergistic relationship between thermal stability and production speed, the classic production efficiency formula typically includes the effect of temperature. A production efficiency model based on the thermodynamic synergistic effect can be expressed as: Where f(T(t)) is a temperature function, usually expressed as some form of temperature, such as the difference or deviation between the temperature and the standard temperature, ultimately leading to the thermodynamic synergistic production efficiency score Tkc. In the formula, It represents the ratio of the current production speed to the maximum production speed, and is a quantification of the production speed; This represents the deviation between the current process temperature and the target process temperature. In this formula, by combining temperature and production speed, a synergistic effect is constructed. Based on the thermodynamic coupling model, it is understood that under given production conditions, changes in temperature and speed jointly determine production efficiency, rather than being considered independently.

[0032] Example 5 Please refer to Figure 2 Specifically: the optimization decision module includes a comprehensive steady-state analysis unit and a steady-state evaluation unit; The comprehensive steady-state analysis unit is used to fit the coating structure quality score Tzl and the thermodynamic synergistic production efficiency score Tkc, and then introduces the real-time process temperature T as a temperature penalty term to perform process coupling steady-state analysis on the enameled wire production process, and constructs a process coupling steady-state score ZHY, which represents the comprehensive performance score of the current enameled wire production process in terms of quality and efficiency. This score is used to quantify the comprehensive steady-state relationship between process efficiency and coating structure quality. Specifically: In the formula, T m This indicates the target process temperature under standard conditions.

[0033] The steady-state evaluation unit includes a steady-state equilibrium evaluation unit and a linkage correction unit; The steady-state equilibrium assessment unit is used to collect the process coupling steady-state score ZHY when the production efficiency and quality reach a balanced state within one year, and calculate the mean value through statistical methods. The mean value is preset as the critical threshold Zy of process thermal coupling, and the coupling state is assessed with the real-time acquired process coupling steady-state score ZHY. The specific assessment scheme is as follows: When the steady-state score of process coupling ZHY < the critical threshold of process thermal coupling Zy, it indicates that production efficiency and quality are not in balance. At this time, imbalance information is generated and transmitted to the control closed-loop management system. When the steady-state score of process coupling ZHY is greater than or equal to the critical threshold of process thermal coupling Zy, it indicates that production efficiency and quality have reached a balance and entered a stable production state. At this time, no adjustment is needed and the current production instruction can be maintained.

[0034] The linkage correction unit is used to calculate the average target speed when production efficiency and quality reach a balanced state when the closed-loop management system receives information about quality non-compliance. Target process temperature and target spraying pressure The production of enameled wire is modified by taking into account the maximum acceptable coating thickness deviation range Δth, process temperature deviation range ΔT, and spraying pressure deviation range ΔP. If |V(t)− When |> ΔV, execute speed correction and increase production speed by 10%; If |T(t)− When |>ΔT, perform temperature control self-calibration and increase the process temperature by 10%; If |P(t)− When |>ΔP, pressure regulation is performed, increasing the coating pressure by 15%.

[0035] In this embodiment, the integrated steady-state analysis unit fits the coating structure quality score Tzl and the thermodynamic synergistic production efficiency score Tkc, and introduces the real-time process temperature T to perform process coupling steady-state analysis, constructing a process coupling steady-state score ZHY to quantify the comprehensive steady-state relationship between quality and efficiency. In classical thermodynamics, temperature has a significant impact on the physical properties of matter. During coating production, temperature deviations affect quality parameters such as coating uniformity and thickness. Typically, a temperature correction term is introduced to account for the inhibitory effect of temperature on the production process; the formula is as follows: Where Y represents the inhibition factor of temperature deviation on the overall score, and T(t) is the real-time process temperature. m The target process temperature is denoted as T(t), and |T(t)−Tm| represents the deviation of the process temperature. Coating quality and production efficiency are typically non-linearly related, and their basic relationship can be expressed through a product. The product of Tzl(t) and Tkc(t) represents the combined performance of coating quality and production efficiency, ultimately constructing a process coupling steady-state score ZHY. The steady-state evaluation unit compares the real-time acquired ZHY with a preset process thermal coupling critical threshold Zy to determine whether production has reached a balanced state. When production efficiency and quality are not balanced, the system automatically generates imbalance information and feeds it back to the control closed-loop management system, triggering adjustment operations. Upon receiving information about non-conforming quality, the linkage correction unit automatically adjusts the production speed, process temperature, and coating pressure based on the deviation ranges of coating thickness, process temperature, and spraying pressure to ensure optimization and correction of the production process. Through real-time data evaluation and intelligent correction, this system not only improves the stability and quality consistency of the production process but also reduces manual intervention and production downtime, thereby enhancing overall production efficiency and quality control.

[0036] Example 6 Please refer to Figure 1 Specifically: the feedback learning module includes a data archiving unit and a behavior tracing unit; The data archiving unit is used to classify, store and structure the production data and evaluation results generated during each batch of production. The production data includes coating feature data group, process status data group, coating structure quality score Tzl, thermal-dynamic synergistic capacity efficiency score Tkc, process coupling steady-state score ZHY, production timestamp, batch number, equipment operation number and correction instructions. The archived data will be classified and indexed according to the two dimensions of time window and operating condition characteristics, and multi-channel data tags will be generated. The behavior tracing unit is used to periodically traverse the archived data. When it detects a deviation in the evaluation result or an inconsistency between the control response and the evaluation result, it automatically calls the process parameter records in the historical archived data that are similar to the current environmental conditions and are operating stably. It extracts the corresponding control instruction snapshots as reference samples, performs structured analysis on the changes in control logic in each period of the current production cycle, records all operation information including operation type, adjustment range, and response delay, constructs a time-ordered chain control path sequence, and generates a behavior tracing graph containing three types of nodes: operation events, response parameters, and evaluation changes, based on the chain control path sequence. With the help of the relationship analysis of the associated edges in the graph structure, it automatically extracts the causal path and influencing nodes that cause the deviation from the initial stable state to the deviation abnormal state, as well as the causal chain formed, and generates a structured control deviation event set for adaptive strategy backtracking and control logic correction under abnormal operating conditions.

[0037] In this embodiment, the data archiving unit classifies and stores coating feature data groups, process status data groups, coating structure quality score Tzl, thermodynamic synergy capacity efficiency score Tkc, process coupling steady-state score ZHY, production timestamps, batch numbers, equipment operation numbers, and correction instructions. It also generates multi-channel data tags based on time windows and operating condition characteristics, providing a complete historical data foundation for subsequent analysis. The behavior tracing unit periodically traverses the archived data, automatically identifying deviations in evaluation results and inconsistencies in control responses. It extracts similar stable process parameter records from historical data for structured analysis. This module can not only construct a time-ordered chained control path sequence but also automatically extract and generate control deviation event sets, analyze influencing nodes and their causal chains, providing a basis for adaptive strategy backtracking and control logic correction under abnormal operating conditions. In this way, the system can provide real-time feedback and adjust for anomalies occurring during production, reducing human intervention, improving the automation level and accuracy of the production process, and achieving continuous optimization of production efficiency and quality.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A closed-loop management system for online quality control in the production process of enameled wire, characterized by: It includes a data processing module, a process analysis module, an efficiency analysis module, an optimization decision-making module, and a feedback learning module; The data processing module is used to collect the raw physical data of the enameled wire based on the deployed sensor network, and obtain the coating feature data group and process status data group after data processing. The process analysis module is used to perform process quality analysis based on the coating feature data set, and generate a coating quality assessment based on the analysis results. The efficiency analysis module is used to perform production efficiency analysis on the enameled line when the coating quality is assessed as meeting the requirements, based on the process status data group. The optimization decision module is used to fit the process quality analysis results with the production efficiency analysis results, and combine the process temperature T to perform a process coupling steady-state analysis of the enameled wire production process, and generate a coupling state assessment. The feedback learning module is used to archive and trace the analysis and evaluation results in multiple dimensions, extract the causal path of abnormal offsets, and generate a set of control offset events for adaptive strategy backtracking and process correction.

2. The online quality control closed-loop management system for enameled wire production process according to claim 1, characterized in that: The data processing module includes a data acquisition unit and a data processing unit; The data acquisition unit is used to acquire raw physical data on the film formation quality and process stability of the enameling line based on the sensor network deployed at each node of the enameling production line. The sensor network includes optical scanning sensors, laser thickness sensors, infrared temperature probes, coded velocimeters, and pressure transmitters. The optical scanning sensor is installed between the end of the spraying section and the inlet of the cooling section to scan the surface image of the wire that has just been sprayed but has not yet fully cured, and to acquire a set of surface images. The laser thickness sensor is installed at the outlet of the cooling section to perform non-contact thickness measurement on the wire after the temperature has stabilized, and to obtain the coating thickness th. The infrared temperature probes are respectively installed on the top of the drying zone and the inner wall of the spraying section to monitor the process temperature T of the wire. The coded speed measuring device is installed on the back of the take-up end guide wheel shaft or tension guide wheel to record the wire running speed and obtain the production speed V; The pressure transmitter is installed in the paint supply branch of the paint nozzle to collect the spraying pressure P in real time during the production process of the enameled wire.

3. The online quality control closed-loop management system for enameled wire production process according to claim 2, characterized in that: The data processing unit is used to establish a data transmission channel between the sensor group and the control closed-loop management system based on the wireless network, and transmit the original physical data to the online control closed-loop management system for data processing in real time to obtain coating feature data group and process status data group. The data processing is used to perform image processing on the original physical data, followed by denoising, data time synchronization, outlier removal, and data normalization. The image processing is used to acquire image frames of a surface image set based on a linear CCD array. Each frame contains a two-dimensional grayscale value matrix I(x,y,t) of the wire surface within a time window. Each image frame is divided into m×n pixel block regions R. i,j And calculate the R region for each pixel block. i,j gray standard deviation σ i,j Then calculate the relative average deviation of the gray-level variance in the entire image. The surface uniformity U of the coating is calculated. The denoising process uses wavelet denoising technology to preserve the transient structural features of the original physical data and remove high-frequency noise from the original physical data. Data time synchronization is based on the timestamps collected by the sensors, and the original physical data with the same timestamps are aligned online through a streaming processing framework. Outlier removal involves sequentially applying moving average filtering to the synchronously acquired raw physical data to smooth out instantaneous fluctuations, followed by amplitude limiting filtering to remove abnormal peak values ​​and obtain a continuous data sequence; data normalization is performed using the Max-Min method to remove the dimensional influence of the raw physical data. The coating feature data set includes coating surface uniformity U and coating thickness th; The process status data set includes production speed V and process temperature T.

4. The online quality control closed-loop management system for enameled wire production process according to claim 3, characterized in that: The process analysis module includes a process analysis unit and a process feedback unit; The process analysis unit is used to perform process quality analysis on the coating in the enameled wire production process based on the coating feature data set, and to construct a coating structure quality score Tzl to quantify the non-static coupling relationship between coating thickness and surface uniformity. By jointly analyzing the real-time thickness deviation and surface uniformity of the coating, the stability of the physical forming process of the enameled wire surface coating is judged.

5. The online quality control closed-loop management system for enameled wire production process according to claim 4, characterized in that: The process feedback unit includes a process evaluation unit and a process correction unit; The process evaluation unit is used to collect coating quality scores Tzl when the coating quality meets the requirements within one year, and calculate the mean value through statistical methods. The mean value is preset as the coating quality standard threshold Ty, and the coating quality is evaluated by comparing it with the coating quality score Tzl acquired in real time. The specific evaluation scheme is as follows. When the coating structure quality score Tzl is less than the coating quality standard threshold Ty, it means that the coating quality does not meet the requirements. At this time, the quality non-compliance information is generated and transmitted to the control closed-loop management system. When the coating structure quality score Tzl is greater than or equal to the coating quality standard threshold Ty, it means that the coating quality meets the requirements. Maintain the current production settings and perform a production efficiency analysis. The process correction unit is used to increase the coating pressure by 15% through the online control closed-loop management system when the system receives information about non-conforming quality. It also performs iterative analysis through the process analysis unit. If the quality is still not met after three consecutive adjustments, the unit sends a shutdown command to the control closed-loop management system and generates manual intervention information to notify maintenance personnel to perform maintenance and adjustments.

6. The online quality control closed-loop management system for enameled wire production process according to claim 5, characterized in that: The efficiency analysis module is used to analyze the production efficiency of the enameled wire when the coating quality assessment indicates that the coating quality meets the requirements, based on the process status data group, and to construct a thermodynamic synergistic capacity efficiency score Tkc, which is used to quantify the bivariate synergistic relationship between thermal parameters and line speed parameters, and to quantify the thermal stability and dynamic capacity balance of the production process.

7. The online quality control closed-loop management system for enameled wire production process according to claim 6, characterized in that: The optimization decision-making module includes a comprehensive steady-state analysis unit and a steady-state evaluation unit; The comprehensive steady-state analysis unit is used to fit the coating structure quality score Tzl and the thermodynamic synergistic production efficiency score Tkc, and then introduce the real-time process temperature T as a temperature penalty term to perform process coupling steady-state analysis on the enameled wire production process, and construct the process coupling steady-state score ZHY, which represents the comprehensive performance score of the current enameled wire production process in terms of quality and efficiency, and is used to quantify the comprehensive steady-state relationship between process efficiency and coating structure quality.

8. The online quality control closed-loop management system for enameled wire production process according to claim 7, characterized in that: The steady-state evaluation unit includes a steady-state equilibrium evaluation unit and a linkage correction unit; The steady-state equilibrium assessment unit is used to collect the process coupling steady-state score ZHY when the production efficiency and quality reach a balanced state within one year, and calculate the mean value through statistical methods. The mean value is preset as the critical threshold Zy of process thermal coupling, and the coupling state is assessed with the real-time acquired process coupling steady-state score ZHY. The specific assessment scheme is as follows: When the steady-state score of process coupling ZHY < the critical threshold of process thermal coupling Zy, it indicates that production efficiency and quality are not in balance. At this time, imbalance information is generated and transmitted to the control closed-loop management system. When the steady-state score of process coupling ZHY is greater than or equal to the critical threshold of process thermal coupling Zy, it indicates that production efficiency and quality have reached a balance and entered a stable production state. At this time, no adjustment is needed and the current production instruction can be maintained.

9. The online quality control closed-loop management system for enameled wire production process according to claim 8, characterized in that: The linkage correction unit is used to calculate the average target speed when production efficiency and quality reach a balanced state when the closed-loop management system receives information about quality non-compliance. Target process temperature and target spraying pressure The production of enameled wire is modified by taking into account the maximum acceptable coating thickness deviation range Δth, process temperature deviation range ΔT, and spraying pressure deviation range ΔP. If |V(t)− When |> ΔV, execute speed correction and increase production speed by 10%; If |T(t)− When |>ΔT, perform temperature control self-calibration and increase the process temperature by 10%; If |P(t)− When |>ΔP, pressure regulation is performed, increasing the coating pressure by 15%.

10. The online quality control closed-loop management system for enameled wire production process according to claim 1, characterized in that: The feedback learning module includes a data archiving unit and a behavior tracing unit; The data archiving unit is used to classify, store and structure the production data and evaluation results generated during each batch of production. The production data includes coating feature data group, process status data group, coating structure quality score Tzl, thermal-dynamic synergistic capacity efficiency score Tkc, process coupling steady-state score ZHY, production timestamp, batch number, equipment operation number and correction instructions. The archived data will be classified and indexed according to the two dimensions of time window and operating condition characteristics, and multi-channel data tags will be generated. The behavior tracing unit is used to periodically traverse the archived data. When it detects a deviation in the evaluation result or an inconsistency between the control response and the evaluation result, it automatically calls the process parameter records in the historical archived data that are similar to the current environmental conditions and are operating stably. It extracts the corresponding control instruction snapshots as reference samples, performs structured analysis on the changes in control logic in each period of the current production cycle, records all operation information including operation type, adjustment range, and response delay, constructs a time-ordered chain control path sequence, and generates a behavior tracing graph containing three types of nodes: operation events, response parameters, and evaluation changes, based on the chain control path sequence. With the help of the relationship analysis of the associated edges in the graph structure, it automatically extracts the causal path and influencing nodes that cause the deviation from the initial stable state to the deviation abnormal state, as well as the causal chain formed, and generates a structured control deviation event set for adaptive strategy backtracking and control logic correction under abnormal operating conditions.

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