Intelligent quality control method and system for PE wire extrusion molding

Abstract

The application discloses a kind of intelligent quality control method and system for PE line extrusion molding, belong to intelligent control field, wherein method includes: PE line raw materials are extruded by extrusion molding device, and real-time PE line product is obtained;Detection is carried out, and real-time PE line feature is obtained, compared with target PE line feature, and real-time difference feature is output, sent to extrusion automatic control unit, and the parameter optimization of initial state extrusion parameter is carried out, and optimized extrusion parameter is obtained, and extrusion molding device is carried out optimized extrusion molding control;Synchronous monitoring PE line product and extrusion parameter, and dynamic quality control management is carried out.The present application solves the technical problems that the quality control mechanism of PE line extrusion molding process in the prior art lacks intelligent and dynamic, leading to low PE line production efficiency and unstable quality, achieves the technical effect that the whole process intelligent quality control and active optimization mechanism are realized by accurate detection and parameter optimization, so as to improve the production efficiency and quality stability of PE line.

Description

Technical Field

[0001] This invention relates to the field of intelligent control, and more specifically to an intelligent quality control method and system for PE line extrusion molding. Background Technology

[0002] With the rapid development of the social economy, the demand for PE lines is increasing day by day. However, in the existing technology, PE line extrusion molding adopts an open-loop control mode with fixed empirical parameters, which cannot monitor and evaluate the key quality indicators of PE lines in real time. At the same time, there is a lack of a dynamic parameter optimization mechanism based on quality feedback in the production process, which leads to the common problems of low production efficiency and difficulty in guaranteeing product quality in the PE line extrusion molding production process. Summary of the Invention

[0003] This application provides an intelligent quality control method and system for PE line extrusion molding, aiming to solve the technical problem of low production efficiency and unstable quality of PE lines due to the lack of intelligent and dynamic quality control mechanisms in the existing PE line extrusion molding process.

[0004] In view of the above problems, this application provides an intelligent quality control method and system for PE line extrusion molding.

[0005] The first aspect of this application discloses an intelligent quality control method for PE line extrusion molding. The method includes: an interactive extrusion molding device; a front-end sensor array deployed at the extrusion port of the extrusion molding device and connected to a feature analysis unit; a rear-end sensor array deployed at the extrusion port of the extrusion molding device and connected to an extrusion automatic control unit, wherein the extrusion automatic control unit is communicatively connected to the feature analysis unit; starting the extrusion molding device and extruding PE line raw materials through the extrusion molding device according to initial extrusion parameters to obtain real-time PE line products; detecting the real-time PE line products at the extrusion port of the extrusion molding device based on the front-end sensor array to obtain real-time PE line characteristics; comparing the real-time PE line characteristics with target PE line characteristics based on the feature analysis unit and outputting real-time difference characteristics; sending the real-time difference characteristics to the extrusion automatic control unit and optimizing the initial extrusion parameters based on the real-time difference characteristics to obtain optimized extrusion parameters; optimizing the extrusion molding device using the optimized extrusion parameters; and simultaneously monitoring and optimizing the PE line products based on the front-end sensor array and the extrusion parameters based on the rear-end sensor array to perform dynamic quality control management of the extrusion molding control.

[0006] Another aspect of this application discloses an intelligent quality control system for PE line extrusion molding. The system includes: a front-end sensor deployment module for interacting with the extrusion molding device, deploying a front-end sensor array at the extrusion port of the extrusion molding device, and connecting the front-end sensor array to a feature analysis unit; a back-end sensor deployment module for deploying a back-end sensor array at the extrusion port of the extrusion molding device, and connecting the back-end sensor array to an extrusion automatic control unit, wherein the extrusion automatic control unit is communicatively connected to the feature analysis unit; a PE line extrusion molding module for starting the extrusion molding device, extruding PE line raw materials through the extrusion molding device according to initial extrusion parameters, and obtaining real-time PE line products; and a PE line feature extraction module for extracting features based on the front-end sensors. The system comprises four modules: an array for real-time detection of PE line products at the extrusion port of the extrusion molding device, acquiring real-time PE line characteristics; a feature difference comparison module for comparing real-time PE line characteristics with target PE line characteristics based on the feature analysis unit, outputting real-time difference characteristics; an extrusion parameter optimization module for sending real-time difference characteristics to the extrusion automatic control unit, optimizing the initial extrusion parameters based on the real-time difference characteristics, and obtaining optimized extrusion parameters; an optimized extrusion control module for optimizing the extrusion molding device using the optimized extrusion parameters; and a dynamic control management module for simultaneously monitoring and optimizing PE line products based on the front-end sensor array and optimizing extrusion parameters based on the back-end sensor array, performing dynamic quality control management of the extrusion molding control.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] This technical solution employs a front-end sensor array at the extrusion port for comprehensive quantitative detection of the extruded PE thread, acquiring sufficient thread quality characteristics; a rear-end sensor array at the extrusion port for detecting extrusion process parameters, enabling parameter monitoring; a feature analysis unit for analyzing thread quality characteristics and calculating real-time differences from set targets, achieving closed-loop quality control; and an extrusion automatic control unit for outputting optimized extrusion parameters based on real-time differences, achieving proactive parameter optimization. Based on the optimized extrusion parameters, extrusion control is adjusted to correct quality deviations, achieving intelligent quality control. This solution, which cyclically monitors PE thread characteristics and extrusion parameters to achieve dynamic detection and control throughout the entire process, solves the technical problem of low production efficiency and unstable quality in existing PE thread extrusion molding processes due to the lack of intelligent and dynamic quality control mechanisms. It achieves the technical effect of improving PE thread production efficiency and quality stability by realizing intelligent quality control and proactive optimization mechanisms throughout the entire process through precise detection and parameter optimization.

[0009] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0010] Figure 1 This application provides a schematic flowchart of an intelligent quality control method for PE line extrusion molding.

[0011] Figure 2 This application provides a schematic flowchart of a smart quality control method for obtaining optimized extrusion parameters in PE line extrusion molding.

[0012] Figure 3 This application provides a schematic diagram of a smart quality control system for PE line extrusion molding.

[0013] Explanation of reference numerals in the attached diagram: Front-end sensor deployment module 11, Back-end sensor deployment module 12, PE line extrusion molding module 13, PE line feature extraction module 14, Feature difference comparison module 15, Extrusion parameter optimization module 16, Optimized extrusion control module 17, Dynamic control and management module 18. Detailed Implementation

[0014] The overall concept of the technical solution provided in this application is as follows:

[0015] This application provides an intelligent quality control method and system for PE line extrusion molding. By setting up front and rear sensor arrays, the system can achieve overall measurement of extrusion parameters and quality characteristics during the PE line production process and obtain real-time difference characteristics. This allows for proactive optimization of extrusion parameters, thereby controlling and correcting the PE line extrusion process and improving the quality of the PE line.

[0016] Specifically, a front-end and back-end sensor array is set up to measure the wire quality characteristics and corresponding process parameters during the extrusion process, forming a detection link to collect overall feature data; a feature analysis unit is established to compare the real-time PE line characteristics with the target PE line characteristics and provide real-time difference features; this drives the extrusion automatic control unit to find optimized extrusion parameters based on the extrusion parameter optimization model to guide the control and correction of the extrusion process, thereby realizing intelligent control of the PE line extrusion quality.

[0017] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0018] Example 1:

[0019] like Figure 1As shown in the embodiment of this application, an intelligent quality control method for PE line extrusion molding is provided, the method comprising:

[0020] An interactive extrusion molding apparatus, wherein a front-end sensor array is arranged at the extrusion port of the extrusion molding apparatus, and the front-end sensor array is connected to a feature analysis unit;

[0021] In this embodiment, the extrusion molding apparatus is a device used to extrude and shape plastic granules such as polyethylene raw materials. Interacting with the extrusion molding apparatus, a front-end sensor array is installed at the extrusion port. This front-end sensor array includes various sensors used to detect various characteristics of the real-time PE line product extruded from the extrusion port, such as dimensions and surface quality. The front-end sensor array is connected to a feature analysis unit for comparing and analyzing the detected features of the real-time PE line product with the features of a target PE line.

[0022] By deploying a front-end sensor array at the extrusion port of the extrusion molding device, the characteristics of PE line products can be detected in real time, laying the foundation for intelligent quality control of the PE line in the future.

[0023] A rear-end sensor array is installed at the extrusion inlet of the extrusion molding device, and the rear-end sensor array is connected to the extrusion automatic control unit, wherein the extrusion automatic control unit is communicatively connected to the feature analysis unit;

[0024] In this embodiment, a back-end sensor array is further arranged at the extrusion inlet of the extrusion molding apparatus. This back-end sensor array includes temperature sensors, pressure sensors, etc., used to detect various extrusion parameters during the extrusion process, such as temperature and pressure data. The extrusion control unit communicates with the aforementioned feature analysis unit to receive real-time difference features and optimize the extrusion parameters of the extrusion molding apparatus based on these real-time difference features.

[0025] By installing a rear-end sensor array at the extrusion inlet of the extrusion molding unit, dual closed-loop optimization of extrusion parameters and product quality is achieved, laying the foundation for intelligent quality control of the PE line.

[0026] Start the extrusion molding device and extrude the PE line raw material through the extrusion molding device according to the initial extrusion parameters to obtain the real-time PE line product;

[0027] In this embodiment, after the front-end and back-end sensor arrays are deployed and connected, the extrusion molding device is started to extrude the PE raw material, such as polyethylene. A set of initial extrusion parameters is preset, defining initial process conditions such as extrusion temperature and extrusion rate, i.e., initial-state extrusion parameters. Then, the extrusion process of the extrusion molding device is started according to the set of initial-state extrusion parameters, and the PE raw material is extruded, heated, and mixed, finally extruded from the extruder to obtain a real-time polyethylene product, i.e., a real-time PE product.

[0028] Based on the aforementioned front-end sensor array, the real-time PE line product is detected at the extrusion port of the extrusion molding device to obtain real-time PE line characteristics.

[0029] In this embodiment, after the real-time PE line product exits the extrusion port of the extrusion molding device, it directly enters the detection area where the front-end sensor array is located. The front-end sensor array integrates multiple sensors, such as video cameras and laser sensors, which together form a sensor network. Then, based on this front-end sensor array, the freshly extruded real-time PE line product is comprehensively inspected to obtain characteristic information such as the product's dimensions, surface quality, and color, i.e., real-time PE line characteristics.

[0030] The obtained real-time PE line characteristics accurately reflect the current PE line extrusion process, providing crucial input for on-demand control of extrusion process parameters. The detection process of the front-end sensor array does not interfere with the extrusion process, enabling the entire extrusion molding and detection process to be carried out continuously online.

[0031] Based on the feature analysis unit, the real-time PE line features are compared with the target PE line features, and the real-time difference features are output.

[0032] In this embodiment, after obtaining the real-time PE line characteristics, these characteristics are transmitted to the feature analysis unit. The feature analysis unit pre-stores the desired characteristics of the PE line product, i.e., the target PE line characteristics, which are set by the user according to actual application requirements. Then, the feature analysis unit compares the real-time PE line characteristics with the target PE line characteristics item by item to obtain the real-time difference characteristics between the two. For example, for the wire diameter characteristic, if the real-time PE line characteristic value is 1.5mm and the target PE line characteristic value is 1.4mm, then the real-time difference characteristic is +0.1mm.

[0033] By extracting real-time difference features, the quality deviation between the current PE line products and the ideal target was clarified, providing a quantitative optimization direction and target for subsequent extrusion parameter optimization.

[0034] The real-time difference features are sent to the extrusion control unit, and the initial extrusion parameters are optimized based on the real-time difference features to obtain the optimized extrusion parameters.

[0035] In this embodiment, after acquiring the real-time difference features, the feature analysis unit sends the data to the extrusion control unit. The extrusion control unit, based on minimizing the real-time difference features as the optimization objective, searches and solves under defined constraints to determine a new set of extrusion parameters, i.e., optimized extrusion parameters. For example, if the real-time difference features indicate that the wire diameter is too large and the surface quality is poor, the extrusion control unit will automatically adjust parameters such as extrusion temperature and extrusion rate to optimize them towards reducing the wire diameter and improving surface quality, thereby obtaining a new set of optimized extrusion parameters to obtain a PE wire product that more closely approximates the target PE wire characteristics.

[0036] The extrusion molding apparatus is optimized and the extrusion molding process is controlled using the optimized extrusion parameters.

[0037] In this embodiment, after obtaining a set of optimized extrusion parameters, the extrusion control unit sends these parameters to the control system of the extrusion molding device to reset and adjust the extrusion process. The optimized extrusion parameters include new temperature setpoints, pressure setpoints, extrusion rates, etc., achieving closed-loop control of the process parameters. After loading the newly set optimized extrusion parameters, the extrusion molding device uses these parameters to start a new round of extrusion molding production. Compared to the initial extrusion parameters, these optimized parameters make the extruded PE line product characteristics closer to the target PE line characteristics, achieving automatic optimization and adjustment of extrusion process parameters based on real-time product quality differences, completing optimized extrusion molding control, and realizing quality control of PE line extrusion molding.

[0038] Simultaneously, the PE line products are monitored and optimized based on the front-end sensor array, and the extrusion parameters are monitored and optimized based on the back-end sensor array to achieve dynamic quality control management of extrusion molding.

[0039] In the embodiments of this application, during the extrusion molding process using optimized extrusion parameters in the extrusion molding apparatus, on the one hand, the front-end sensor array continues to be used to detect the newly generated optimized PE line products, obtain their various PE line characteristics, and based on the new PE line characteristics, new real-time difference characteristics are obtained, enabling the extrusion control unit to continue optimizing the extrusion parameters and control the extrusion molding apparatus with the new optimized extrusion parameters; on the other hand, the rear-end sensor array is used to monitor in real time whether the execution process of the optimized extrusion parameters meets the settings of the optimized extrusion parameters, such as actual temperature and pressure, to ensure the execution process of the optimized extrusion parameters.

[0040] By employing a dual closed-loop monitoring approach—simultaneous detection and optimization of PE line products and extrusion parameters—dynamic quality control is achieved throughout the entire extrusion molding process. Based on real-time differences, a new round of extrusion parameter optimization is rapidly initiated, enabling continuous improvement and quality control in extrusion molding.

[0041] Furthermore, embodiments of this application also include:

[0042] A human-machine interface connected to the extrusion molding device is used to receive PE line extrusion tasks.

[0043] Analyze the PE line extrusion task to obtain the characteristics of the target PE line;

[0044] The target PE line features are stored in the feature analysis unit and the extrusion control unit.

[0045] In one feasible implementation, a human-machine interface (HMI) is connected to the extrusion molding device via a wired or wireless communication interface. Through this HMI, the user can set and issue extrusion molding tasks for the PE line, i.e., PE line extrusion tasks. These tasks include the user's requirements for various PE line product quality indicators, such as PE wire diameter and surface quality. After receiving the user-defined PE line extrusion task, the extrusion molding device parses the data, extracts the user-set target product quality characteristic parameters, such as wire diameter and surface roughness, and converts and organizes them into a data format representing the target characteristics, i.e., target PE line features. This achieves automatic mapping and conversion between user-defined product quality requirements and target characteristic parameters, reflecting the user's personalized product quality settings. Subsequently, the parsed target PE line features are sent to the feature analysis unit and the extrusion control unit and stored in the storage devices of these two units, completing the configuration and distribution of the target PE line features. The feature analysis unit will conduct real-time feature comparison analysis of the target PE line features based on the saved target PE line features; the extrusion automatic control unit will determine the optimization direction and target of the extrusion parameters based on the received target PE line features, and complete the extrusion optimization control.

[0046] Furthermore, embodiments of this application also include:

[0047] Based on the target PE line features, PE line feature indicators are extracted, and threshold ranges for the PE line feature indicators are set according to the target PE line features to obtain feature indicator constraints.

[0048] Based on the PE line characteristic indicators and the constraints of the characteristic indicators, historical PE line extrusion data is searched and obtained, wherein the historical PE line extrusion data includes multiple extrusion parameter indicators.

[0049] For each extrusion parameter, a box plot of the extrusion parameters is constructed based on the historical PE line extrusion data;

[0050] Based on the boxes in the extrusion parameter box plot, the extrusion parameter constraints for each extrusion parameter index are obtained, and the extrusion solution space is obtained.

[0051] The extrusion unpacking space is stored in the extrusion automatic control unit.

[0052] In a preferred embodiment, after obtaining the target PE line characteristics, relevant feature indicators, such as wire diameter and surface roughness, are extracted sequentially based on these characteristics. Simultaneously, according to actual technical conditions and permissible quality levels, a tolerance threshold range is determined for each extracted PE line feature indicator, reflecting the acceptable constraint range for that indicator. For example, the tolerance range for wire diameter can be set to + / - 0.05 mm, forming a set of "feature indicator - tolerance threshold" constraint pairs. This constitutes the PE line quality feature constraint rules, i.e., feature indicator constraints, providing boundary conditions for optimizing extrusion parameters. Then, using the target PE line characteristics and feature indicator constraints as the query basis, the corresponding historical PE line extrusion data is searched and retrieved from the historical database. The historical database stores historical production data for PE line extrusion, including the product feature parameters and corresponding extrusion process parameters at that time.

[0053] Subsequently, for each extrusion parameter, all historical data for that parameter are extracted from the historical PE line extrusion data, such as the set of temperature parameter values ​​and the set of rate parameter values. The value sets for each extrusion parameter are then sorted and numbered, and the maximum, minimum, upper quartile, and lower quartile of each parameter set are calculated. Based on the quartile values, a box plot for each extrusion parameter is determined, providing a basis for determining the parameter constraint space. Next, based on the constructed box plots, the box segments for each extrusion parameter are extracted, i.e., the interval between the lower and upper quartiles of each extrusion parameter distribution, serving as the upper and lower constraints for that extrusion parameter. Then, the lower and upper constraints for each extrusion parameter are summed to obtain the extrusion parameter constraints for each parameter. Finally, these constraints are summed to form a multi-dimensional extrusion parameter constraint space, i.e., the extrusion solution set space, which defines the lower and upper limits of parameters such as temperature, pressure, and rate that are allowed to be optimized during extrusion parameter optimization calculations. Then, the determined extrusion solution space is sent to the extrusion automatic control unit and stored in the unit's storage unit. When the extrusion automatic control unit performs parameter optimization calculations, it directly calls and applies the pre-configured extrusion solution space to avoid the parameters from exceeding the range and ensure the safety of parameter control.

[0054] Furthermore, embodiments of this application also include:

[0055] Traverse the PE line characteristic indicators to obtain the first PE line characteristic indicator;

[0056] The first target PE line characteristics and the first characteristic index constraints are determined based on the first PE line characteristic index.

[0057] The first front-end sensor is deployed based on the characteristics of the first target PE line and the constraints of the first characteristic index.

[0058] A front-end sensor array is deployed based on the first front-end sensor.

[0059] In one feasible implementation, firstly, each characteristic index among the PE line characteristic indices is extracted sequentially, such as wire diameter, surface roughness, tensile strength, etc., and one characteristic index is selected as the first PE line characteristic index. Then, for this first PE line characteristic index, the corresponding target parameter value is extracted to constitute the first target PE line feature, such as a target wire diameter of 1.5 mm. Simultaneously, the allowable fluctuation range of the first PE line characteristic index is obtained as a constraint on the first characteristic index. Subsequently, based on the first target PE line feature and the first characteristic index constraint, a suitable sensor that meets the detection accuracy and range requirements is selected as the first front-end sensor. For example, a laser scanning sensor with a diameter measurement resolution of 0.01 mm is used as the front-end sensor for the wire diameter index. Next, according to the detection frequency and acquisition range requirements, the parameters of the first front-end sensor are configured to achieve online detection of the first PE line characteristic index, providing the basic unit for constructing the overall front-end sensor array. Next, based on the determined first front-end sensor, and according to the detection principle and location requirements of each PE line characteristic index, the first front-end sensor is planned and installed to form a front-end sensor array, which is fixedly installed at the extrusion port to achieve accurate and efficient online monitoring of PE line products.

[0060] Furthermore, such as Figure 2 As shown, embodiments of this application also include:

[0061] Based on the real-time difference features, obtain the difference feature vectors of each PE line feature index;

[0062] An extrusion parameter optimization model is pre-built and deployed in the extrusion automatic control unit;

[0063] The difference feature vector is input into the extrusion parameter optimization model. The target PE line feature is used as the optimization target, the extrusion solution space is used as the optimization space, and the difference feature vector is used as the optimization direction to optimize the initial extrusion parameters and obtain the optimized extrusion parameters.

[0064] In a preferred embodiment, after obtaining the real-time difference features between the real-time PE line characteristics and the corresponding target PE line characteristics for each PE line feature index, the actual difference value of each PE line feature index is extracted based on the real-time difference features, such as wire diameter difference, surface roughness difference, tensile strength difference, etc. Then, a vectorization representation method is used to construct a multi-dimensional difference feature vector from the difference values ​​in the real-time difference features, reflecting the magnitude of the deviation of each PE line feature index from the target requirements, reflecting the deviation distribution and main deviation direction of the real-time PE line product, and providing driving input for automatic parameter optimization.

[0065] To achieve adaptive parameter optimization calculation based on real-time product quality differences on the PE line, an extrusion parameter optimization model was pre-constructed. After the model was built, debugged, and verified, it was deployed to the extrusion control unit via configuration distribution, laying the foundation for automatic parameter optimization and updating based on difference feature vectors. Subsequently, the obtained difference feature vectors of the current PE line products were input into the extrusion parameter optimization model deployed in the extrusion control unit. This model uses the preset target PE line features as the final optimization target, the constructed extrusion solution space as the optimization constraint interval, and the magnitude and direction of the deviation represented by the difference feature vectors as the optimization direction. Based on the model's internal mapping mechanism, the initial extrusion parameters were optimized, and a new set of extrusion process parameters, i.e., the optimized extrusion parameters, were output.

[0066] The updated and optimized extrusion parameters can be directly applied to extrusion molding equipment to narrow the gap between the extruded PE line products and the target requirements, thereby achieving adaptive control of PE line quality.

[0067] Furthermore, embodiments of this application also include:

[0068] Based on the historical PE line extrusion data, historical PE line characteristic data are collected according to PE line characteristic indicators.

[0069] Data alignment is performed between the historical PE line extrusion data and the historical PE line feature data to obtain training samples for the mapping model.

[0070] Based on the training samples of the mapping model, a mapping model between extrusion data and PE line features is established, resulting in an extrusion parameter optimization model.

[0071] In one feasible implementation, characteristic data of historical PE lines during the extrusion production process are obtained through online quality inspection, forming a comprehensive historical PE line characteristic database. This database contains characteristic data of each PE line in the historical PE line extrusion data. First, according to the PE line characteristic indicators, the specific values ​​of these indicators on the corresponding historical products in the historical PE line extrusion data are retrieved and output from the historical PE line characteristic database, collected as historical PE line characteristic data. Then, the historical PE line extrusion data and historical PE line characteristic data are matched and mapped using product timestamps, sequence numbers, etc. For example, the characteristic data of a certain PE line's historical product is combined with the extrusion parameters used during processing to form a set of mapping samples, obtaining a large number of modeling samples that can directly reflect the mapping relationship between the two, i.e., mapping model training samples, laying the data foundation for building an extrusion parameter optimization model. Subsequently, based on the mapping model training samples, machine learning algorithms or neural networks are used, using the characteristic data in the mapping model training samples and the target PE characteristics at that time as sample inputs, and the extrusion parameter data as outputs, to train the model. After repeated iterations, an extrusion parameter optimization model is obtained, providing automated support for parameter optimization control.

[0072] Furthermore, embodiments of this application also include:

[0073] Based on the aforementioned front-end sensor array, the optimized product characteristics of the PE line product are identified and optimized.

[0074] Statistical optimization of the number and magnitude of features that break through feature index constraints in product features;

[0075] When the number of the breaking features does not meet the preset breaking number, or the amplitude of the breaking features does not meet the preset breaking threshold, the extrusion molding device is shut down for optimization.

[0076] In one feasible implementation, PE line products extruded according to optimized extrusion parameters are referred to as optimized PE line products. A deployed front-end sensor array continuously monitors the newly extruded optimized PE line products, acquiring various characteristic data to form optimized product features, reflecting the product status after parameter optimization. Then, each characteristic data in the optimized product features is compared one by one with the set characteristic index constraints, counting the number of PE line features exceeding the characteristic index constraints, and accumulating them to obtain the number of breakthrough features. Simultaneously, the exceedance values ​​of the PE line features exceeding the characteristic index constraints are counted, reflecting the magnitude of the constraint breach, and obtaining the breakthrough feature amplitude. Finally, the counted number of breakthrough features and the breakthrough feature amplitude are compared with a pre-set allowable breakthrough level. Specifically, the number of bursting features is compared with the maximum permissible preset bursting number. At the same time, the relationship between the bursting feature amplitude and the preset bursting threshold set according to the quality requirements of the PE line is compared. When either of the two statistical parameters fails to meet the requirements, that is, when the number of bursting features is greater than or equal to the preset bursting number, or the bursting feature amplitude is greater than or equal to the preset bursting threshold, a shutdown command for the extrusion molding device is triggered. The extrusion molding process is analyzed to find the cause of the quality deviation. Production can only be restarted after the extrusion molding device is shut down and optimized, so as to ensure the continuous and stable operation of the PE line extrusion molding process and the controllability of product quality.

[0077] In summary, the intelligent quality control method for PE line extrusion molding provided in this application has the following technical effects:

[0078] An interactive extrusion molding device employs a front-end sensor array at the extrusion port, connected to a feature analysis unit for real-time monitoring of the extruded PE line characteristics. A rear-end sensor array is also installed at the extrusion inlet, connected to an extrusion control unit. This control unit communicates with the feature analysis unit to monitor extrusion parameters in real-time. The extrusion molding device is started, and PE line raw materials are extruded according to initial extrusion parameters, yielding real-time PE line products and providing operational data for PE line production optimization. Based on the front-end sensor array, the real-time PE line products are detected at the extrusion port, acquiring real-time PE line characteristics to provide data support for obtaining real-time difference features and achieving intelligent quality control. The feature analysis unit compares the real-time PE line characteristics with target PE line characteristics, outputting real-time difference features to provide features for extrusion parameter optimization. These real-time difference features are sent to the extrusion control unit, which optimizes the initial extrusion parameters to obtain preferred parameters for intelligent quality control. By optimizing extrusion parameters, the extrusion molding unit is controlled to improve extrusion molding efficiency and thus control the quality of the PE line. Simultaneously, the product quality is optimized based on front-end sensor array monitoring, and the extrusion parameters are optimized based on back-end sensor array monitoring. This enables dynamic quality control management of the extrusion molding process, real-time monitoring of products and parameters to ensure optimization effectiveness, and continuous dynamic quality control management throughout the entire process, thereby improving the production efficiency and quality stability of the PE line.

[0079] Example 2:

[0080] Based on the same inventive concept as the intelligent quality control method for PE linear extrusion molding described in the foregoing embodiments, such as Figure 3 As shown in the figure, this application provides an intelligent quality control system for PE line extrusion molding, the system comprising:

[0081] The front-end sensor deployment module 11 is used for the interactive extrusion molding device, deploying a front-end sensor array at the extrusion port of the extrusion molding device, and connecting the front-end sensor array to the feature analysis unit.

[0082] The back-end sensor deployment module 12 is used to deploy a back-end sensor array at the extrusion inlet of the extrusion molding device and connect the back-end sensor array to the extrusion automatic control unit, wherein the extrusion automatic control unit is communicatively connected to the feature analysis unit.

[0083] PE line extrusion molding module 13 is used to start the extrusion molding device, and extrude the PE line raw material through the extrusion molding device according to the initial extrusion parameters to obtain real-time PE line products.

[0084] The PE line feature extraction module 14 is used to detect the real-time PE line product at the extrusion port of the extrusion molding device based on the front-end sensor array, and obtain the real-time PE line features.

[0085] The feature difference comparison module 15 is used to compare the real-time PE line features with the target PE line features based on the feature analysis unit, and output the real-time difference features.

[0086] The extrusion parameter optimization module 16 is used to send the real-time difference features to the extrusion automatic control unit, optimize the initial extrusion parameters based on the real-time difference features, and obtain optimized extrusion parameters.

[0087] The optimized extrusion control module 17 is used to optimize the extrusion molding control of the extrusion molding device with the optimized extrusion parameters.

[0088] The dynamic control and management module 18 is used to synchronously monitor and optimize PE line products based on the front-end sensor array and optimize extrusion parameters based on the back-end sensor array, and to perform dynamic quality control management of extrusion molding control.

[0089] Furthermore, embodiments of this application include a target feature acquisition module, which includes the following execution steps:

[0090] A human-machine interface connected to the extrusion molding device is used to receive PE line extrusion tasks.

[0091] Analyze the PE line extrusion task to obtain the characteristics of the target PE line;

[0092] The target PE line features are stored in the feature analysis unit and the extrusion control unit.

[0093] Furthermore, embodiments of this application include a solution space acquisition module, which includes the following execution steps:

[0094] Based on the target PE line features, PE line feature indicators are extracted, and threshold ranges for the PE line feature indicators are set according to the target PE line features to obtain feature indicator constraints.

[0095] Based on the PE line characteristic indicators and the constraints of the characteristic indicators, historical PE line extrusion data is searched and obtained, wherein the historical PE line extrusion data includes multiple extrusion parameter indicators.

[0096] For each extrusion parameter, a box plot of the extrusion parameters is constructed based on the historical PE line extrusion data;

[0097] Based on the boxes in the extrusion parameter box plot, the extrusion parameter constraints for each extrusion parameter index are obtained, and the extrusion solution space is obtained.

[0098] The extrusion unpacking space is stored in the extrusion automatic control unit.

[0099] Furthermore, the front-end sensor deployment module 11 includes the following execution steps:

[0100] Traverse the PE line characteristic indicators to obtain the first PE line characteristic indicator;

[0101] The first target PE line characteristics and the first characteristic index constraints are determined based on the first PE line characteristic index.

[0102] The first front-end sensor is deployed based on the characteristics of the first target PE line and the constraints of the first characteristic index.

[0103] A front-end sensor array is deployed based on the first front-end sensor.

[0104] Furthermore, the extrusion parameter optimization module 16 includes the following execution steps:

[0105] Based on the real-time difference features, obtain the difference feature vectors of each PE line feature index;

[0106] An extrusion parameter optimization model is pre-built and deployed in the extrusion automatic control unit;

[0107] The difference feature vector is input into the extrusion parameter optimization model. The target PE line feature is used as the optimization target, the extrusion solution space is used as the optimization space, and the difference feature vector is used as the optimization direction to optimize the initial extrusion parameters and obtain the optimized extrusion parameters.

[0108] Furthermore, the extrusion parameter optimization module 16 also includes the following execution steps:

[0109] Based on the historical PE line extrusion data, historical PE line characteristic data are collected according to PE line characteristic indicators.

[0110] Data alignment is performed between the historical PE line extrusion data and the historical PE line feature data to obtain training samples for the mapping model.

[0111] Based on the training samples of the mapping model, a mapping model between extrusion data and PE line features is established, resulting in an extrusion parameter optimization model.

[0112] Furthermore, embodiments of this application also include a shutdown optimization module, which includes the following execution steps:

[0113] Based on the aforementioned front-end sensor array, the optimized product characteristics of the PE line product are identified and optimized.

[0114] Statistical optimization of the number and magnitude of features that break through feature index constraints in product features;

[0115] When the number of the breaking features does not meet the preset breaking number, or the amplitude of the breaking features does not meet the preset breaking threshold, the extrusion molding device is shut down for optimization.

[0116] In summary, any step of the method described above can be stored as a computer instruction or program in an unrestricted computer memory, and can be called and identified by an unrestricted computer processor to implement any method in the embodiments of this application, without any additional restrictions.

[0117] Furthermore, the "first" or "second" mentioned above may not only represent a sequential relationship, but may also represent a specific concept, and / or refer to the individual or collective selection of multiple elements. Clearly, those skilled in the art can make various modifications and variations to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A smart quality control method for PE line extrusion molding, characterized in that, The method includes: An interactive extrusion molding apparatus, wherein a front-end sensor array is arranged at the extrusion port of the extrusion molding apparatus, and the front-end sensor array is connected to a feature analysis unit; A rear-end sensor array is installed at the extrusion inlet of the extrusion molding device, and the rear-end sensor array is connected to the extrusion automatic control unit, wherein the extrusion automatic control unit is communicatively connected to the feature analysis unit; Start the extrusion molding device and extrude the PE line raw material through the extrusion molding device according to the initial extrusion parameters to obtain the real-time PE line product; Based on the aforementioned front-end sensor array, the real-time PE line product is detected at the extrusion port of the extrusion molding device to obtain real-time PE line characteristics. Based on the feature analysis unit, the real-time PE line features are compared with the target PE line features, and the real-time difference features are output. The real-time difference features are sent to the extrusion control unit, and the initial extrusion parameters are optimized based on the real-time difference features to obtain the optimized extrusion parameters. The extrusion molding apparatus is optimized and the extrusion molding process is controlled using the optimized extrusion parameters. Simultaneously, the PE line products are monitored and optimized based on the front-end sensor array, and the extrusion parameters are monitored and optimized based on the back-end sensor array to achieve dynamic quality control management of extrusion molding control; The method includes: Based on the target PE line features, PE line feature indicators are extracted, and threshold ranges for the PE line feature indicators are set according to the target PE line features to obtain feature indicator constraints. Based on the PE line characteristic indicators and the constraints of the characteristic indicators, historical PE line extrusion data is searched and obtained, wherein the historical PE line extrusion data includes multiple extrusion parameter indicators. For each extrusion parameter, a box plot of the extrusion parameters is constructed based on the historical PE line extrusion data; Based on the boxes in the extrusion parameter box plot, the extrusion parameter constraints for each extrusion parameter index are obtained, and the extrusion solution space is obtained. The extrusion unpacking space is stored in the extrusion automatic control unit.

2. The method according to claim 1, characterized in that, The method further includes: A human-machine interface connected to the extrusion molding device is used to receive PE line extrusion tasks. Analyze the PE line extrusion task to obtain the characteristics of the target PE line; The target PE line features are stored in the feature analysis unit and the extrusion control unit.

3. The method according to claim 1, characterized in that, The method further includes: Traverse the PE line characteristic indicators to obtain the first PE line characteristic indicator; The first target PE line characteristics and the first characteristic index constraints are determined based on the first PE line characteristic index. The first front-end sensor is deployed based on the characteristics of the first target PE line and the constraints of the first characteristic index. A front-end sensor array is deployed based on the first front-end sensor.

4. The method according to claim 1, characterized in that, The real-time difference characteristics are sent to the extrusion control unit, and the initial extrusion parameters are optimized based on the real-time difference characteristics to obtain optimized extrusion parameters, including: Based on the real-time difference features, obtain the difference feature vectors of each PE line feature index; An extrusion parameter optimization model is pre-built and deployed in the extrusion automatic control unit; The difference feature vector is input into the extrusion parameter optimization model. The target PE line feature is used as the optimization target, the extrusion solution space is used as the optimization space, and the difference feature vector is used as the optimization direction to optimize the initial extrusion parameters and obtain the optimized extrusion parameters.

5. The method according to claim 4, characterized in that, Pre-build an extrusion parameter optimization model, including: Based on the historical PE line extrusion data, historical PE line characteristic data are collected according to PE line characteristic indicators. Data alignment is performed between the historical PE line extrusion data and the historical PE line feature data to obtain training samples for the mapping model. Based on the training samples of the mapping model, a mapping model between extrusion data and PE line features is established, resulting in an extrusion parameter optimization model.

6. The method according to claim 1, characterized in that, The method further includes: Based on the aforementioned front-end sensor array, the optimized product characteristics of the PE line product are identified and optimized. Statistical optimization of the number and magnitude of features that break through feature index constraints in product features; When the number of the breaking features does not meet the preset breaking number, or the amplitude of the breaking features does not meet the preset breaking threshold, the extrusion molding device is shut down for optimization.

7. An intelligent quality control system for PE line extrusion molding, characterized in that, The system is used to implement the intelligent quality control method for PE line extrusion molding according to any one of claims 1-6, the system comprising: A front-end sensor deployment module is used in an interactive extrusion molding device. A front-end sensor array is deployed at the extrusion port of the extrusion molding device, and the front-end sensor array is connected to a feature analysis unit. A back-end sensor deployment module is used to deploy a back-end sensor array at the extrusion inlet of the extrusion molding device and connect the back-end sensor array to the extrusion automatic control unit, wherein the extrusion automatic control unit is communicatively connected to the feature analysis unit. PE line extrusion molding module, the PE line extrusion molding module is used to start the extrusion molding device, and extrude the PE line raw material through the extrusion molding device according to the initial extrusion parameters to obtain real-time PE line products; A PE line feature extraction module is used to detect the real-time PE line product at the extrusion port of the extrusion molding device based on the front-end sensor array, and obtain the real-time PE line features. The feature difference comparison module is used to compare the real-time PE line features with the target PE line features based on the feature analysis unit, and output the real-time difference features. An extrusion parameter optimization module is used to send the real-time difference characteristics to the extrusion automatic control unit, optimize the initial extrusion parameters based on the real-time difference characteristics, and obtain optimized extrusion parameters. An optimized extrusion control module is used to optimize the extrusion molding device with the optimized extrusion parameters; The dynamic control and management module is used to synchronously monitor and optimize PE line products based on the front-end sensor array and optimize extrusion parameters based on the back-end sensor array, and to perform dynamic quality control management of extrusion molding control.

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