Laminated paper cup forming control method and system

Abstract

The invention discloses a lamination paper cup forming control method and system, and relates to the related field of automatic production control, and the method comprises the following steps: carrying out flaw detection on a raw material by using optical scanning detection equipment; uV printing ink is used for printing treatment, a laser film cutting machine is called for die cutting, and the input result is mapped to synchronize the die cutting result; controlling the automatic spraying and humidifying equipment to humidify the raw materials; forming optimization parameters of cup body forming are obtained, the synchronization result and the time sequence humidification parameters are called based on the production raw material forming position, and forming optimization parameter compensation is conducted; and controlling the servo motor to drive the forming device by using the forming compensation parameters, and sterilizing and packaging after forming is completed. The technical problems that existing lamination paper cup forming control lacks real-time parameter adjustment and optimization, the optimal forming effect is difficult to achieve, and the product percent of pass is affected are solved, and the technical effects of achieving the optimal forming effect and improving the product percent of pass through forming parameter optimization compensation are achieved.

Description

Technical Field

[0001] The present application relates to the field of automated production control, and in particular to a method and system for controlling the forming of a coated paper cup. Background Art

[0002] In the modern packaging industry, paper cups are a common food packaging container, and their production efficiency and product quality are crucial to meeting market demand and consumer safety. In existing paper cup production methods, molding parameters are often based on fixed, static parameter settings, which cannot adapt to the needs of different raw materials, different production conditions or different product specifications, resulting in poor molding effects. In addition, due to the lack of a real-time parameter adjustment mechanism, it is impossible to quickly respond and adjust according to actual production conditions.

[0003] In the current related technologies, there is a lack of real-time parameter adjustment and optimization in the molding control of coated paper cups, which makes it difficult to achieve the best molding effect and affects the product qualification rate. Summary of the invention

[0004] The present application provides a method and system for controlling the forming of coated paper cups, utilizes optical scanning detection equipment to perform high-precision defect detection on production raw materials, maps and enters the detection results with the production raw materials, uses UV ink for printing, and synchronously calls a laser film cutting machine for die-cutting, controls automatic spray humidification equipment to humidify the production raw materials after die-cutting, and records the timing humidification parameters, obtains the forming optimization parameters of the cup body, and calls the synchronization results and timing humidification parameters for compensation based on the forming position of the production raw materials, establishes forming compensation parameters, utilizes the forming compensation parameters to control the servo motor driven forming device, and performs sterilization and packaging after the forming is completed, thereby achieving the technical effect of achieving the best forming effect and improving the product qualification rate through the optimal compensation of the forming parameters.

[0005] The present application provides a method for controlling the forming of a coated paper cup, comprising:

[0006] After receiving the production raw materials, use optical scanning detection equipment to detect defects in the production raw materials, and map the defect detection results with the production raw materials for entry; use UV ink to print the production raw materials, and synchronously call the laser film cutting machine to die-cut the production raw materials after printing, and synchronize the die-cutting results with the mapping entry results to establish synchronization results; control the automatic spray humidification equipment to humidify the production raw materials after die-cutting, and record the timing humidification parameters; obtain the molding optimization parameters for cup body molding, and record the molding position of the production raw materials after humidification in real time, call the synchronization results and timing humidification parameters based on the molding position of the production raw materials, compensate for the molding optimization parameters according to the calling results, and establish molding compensation parameters; use the molding compensation parameters to control the servo motor driven molding device, and sterilize and package after molding is completed.

[0007] In a possible implementation, the forming optimization parameters of the cup body are obtained by performing the following processing:

[0008] The incoming raw materials for production are counted and the paper thickness distribution of the raw materials for production is established; thickness clustering of the paper thickness distribution is performed and a calibrated thickness interval is configured according to the thickness clustering result; a forming fitness function is established and the calibrated humidification uniformity and the calibrated thickness interval are used as basic parameters to perform parameter optimization of the cup body forming control parameters and establish the forming optimization parameters according to the parameter optimization result.

[0009] In a possible implementation, the parameter optimization of the cup body forming control parameters is performed by performing the following processing:

[0010] The historical control information of the servo motor driven forming device is obtained, and a parameter limit range is established based on the historical control information; a control similarity call is performed based on the historical control information to establish an initial parameter set; an initial solution space is established according to the initial parameter set and random parameters, and a forming fitness function is used to perform fitness calculation on each individual in the initial solution space to establish a fitness calculation result; iterative constraints of the solution are configured, and iterative optimization is performed within the parameter limit range based on the iterative constraints and fitness analysis results to establish forming optimization parameters.

[0011] In a possible implementation, the iterative constraints of the configuration solution are iteratively optimized within the parameter limit interval based on the iterative constraints and the fitness analysis results, and the following processing is performed:

[0012] The distribution of solutions in the initial solution space is analyzed to establish distribution clustering; collision constraints are established based on the distribution clustering; the individual with the highest fitness is located according to the fitness calculation result, and the individual following direction is determined based on the individual with the highest fitness; random weights are configured using distribution clustering and the number of iterations, collision constraints, random weights, and individual following directions are used as iteration constraints, and collision constraints are used to make update decisions for all individuals in the initial solution space according to random weights and individual following directions, and iterative optimization is performed.

[0013] In a possible implementation, the synchronization result and the sequential humidification parameter are called based on the production raw material molding position, and molding optimization parameter compensation is performed according to the calling result, and the following processing is performed:

[0014] Thickness deviation analysis is performed based on the synchronization results and the calibrated thickness range, and the thickness deviation analysis results are established. Humidification fitting is performed based on the time-series humidification parameters, and the humidification effect deviation is established with the humidification fitting results and the calibrated humidification uniformity. Joint impact analysis of molding control is performed based on the thickness deviation analysis results and the humidification effect deviation, and molding optimization parameter compensation is completed based on the joint impact analysis results.

[0015] In a possible implementation, the optical scanning detection device is used to detect defects in the production raw materials, and the following processing is performed:

[0016] After the production raw materials are transferred to the capture area of ​​the optical scanning detection equipment, image acquisition is performed by the optical scanning detection equipment under the illumination of the light source, image acquisition results are established, and image coordinates are established; after the image acquisition results are enhanced, pixel difference recognition is performed through a multi-level pixel frame, and defect detection is completed based on the pixel difference recognition results.

[0017] In a possible implementation, the pixel difference recognition is performed through the multi-level pixel frame, and the defect detection is completed based on the pixel difference recognition result, and the following processing is performed:

[0018] Read the historical defect size, and set the maximum value of the multi-level pixel frame based on the maximum size of the historical defect size; obtain the detection accuracy of the defect detection, and configure the minimum value of the multi-level pixel frame based on the detection accuracy; distribute the multi-level pixel frame according to the maximum value and minimum value to complete pixel difference recognition.

[0019] The present application also provides a laminated paper cup forming control system, comprising:

[0020] A defect detection module, which is used to use optical scanning detection equipment to perform defect detection on the production raw materials after receiving the production raw materials, and map the defect detection results with the production raw materials for entry; a die-cutting module, which is used to use UV ink to print the production raw materials, and synchronously call the laser film cutting machine to die-cut the production raw materials after printing, and synchronize the die-cutting results with the mapping entry results to establish synchronization results; a humidification treatment module, which is used to control the automatic spray humidification equipment to humidify the production raw materials after die-cutting, and record the timing humidification parameters; a molding optimization parameter compensation module, which is used to obtain the molding optimization parameters for cup body molding, and record the molding position of the production raw materials after humidification in real time, call the synchronization results and timing humidification parameters based on the molding position of the production raw materials, perform molding optimization parameter compensation according to the call results, and establish molding compensation parameters; a molding sterilization module, which is used to use the molding compensation parameters to control the servo motor driven molding device, and sterilize and package after molding is completed.

[0021] A coated paper cup forming control method and system proposed in this application, first, after receiving the production raw materials, use optical scanning detection equipment to detect defects in the production raw materials, and map the defect detection results with the production raw materials for entry, then use UV ink to print the production raw materials, and synchronously call the laser film cutting machine to die-cut the production raw materials after printing, and synchronize the die-cutting results with the mapping entry results to establish a synchronization result, and then control the automatic spray humidification equipment to humidify the production raw materials after die-cutting, and record the timing humidification parameters, and then obtain the molding optimization parameters for cup body molding, and record the molding position of the production raw materials after humidification in real time, call the synchronization results and timing humidification parameters based on the molding position of the production raw materials, compensate for the molding optimization parameters according to the calling results, establish molding compensation parameters, and finally use the molding compensation parameters to control the servo motor drive molding device, and sterilize and package after molding, so as to achieve the best molding effect and improve the product qualification rate through the optimization compensation of molding parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0023] Figure 1A schematic flow chart of a method for controlling the forming of a coated paper cup provided in an embodiment of the present application.

[0024] Figure 2 A schematic diagram of the structure of a laminated paper cup forming control system provided in an embodiment of the present application.

[0025] Explanation of the reference numerals: defect detection module 10 , die-cutting module 20 , humidification treatment module 30 , molding optimization parameter compensation module 40 , molding sterilization module 50 . DETAILED DESCRIPTION

[0026] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0027] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0028] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0029] The present application embodiment provides a method for controlling the forming of a coated paper cup. Figure 1 As shown, the method includes:

[0030] Step S100, after receiving the raw materials for production, use optical scanning detection equipment to detect defects on the raw materials for production, and map the defect detection results with the raw materials for production and enter them. Specifically, after receiving the raw materials (laminated paper) for producing laminated paper cups, first use optical scanning detection equipment (a device that uses optical principles to scan the surface of an object to obtain its surface image information) to fully scan the raw materials to detect whether there are defects on its surface (such as stains, damage, color difference, etc.). After the detection is completed, the detected defect information is mapped with the corresponding raw materials, that is, the location, size and other information of the defects are associated with the specific batch or number of the raw materials, and entered into the production management system. At the same time, the inspected raw materials are stored for use in subsequent processes.

[0031] In a possible implementation, the optical scanning detection device is used to detect defects in production raw materials, and step S100 further includes step S110, after the production raw materials are transferred to the capture area of ​​the optical scanning detection device, the optical scanning detection device is used to collect images under the irradiation of the light source, establish image collection results, and establish image coordinates. Specifically, after the production raw materials are transferred to the capture area of ​​the optical scanning detection device (the area where the optical scanning detection device can perform effective image collection), the device starts the light source to irradiate the raw materials. Under the irradiation of the light source, the optical scanning detection device uses its internal camera or image sensor to collect images of the raw materials and obtain image information on the surface of the raw materials. At the same time, the device records the position information of the raw materials during image collection and establishes image coordinates so as to accurately locate the defects in the image later. Step S120, after enhancing the image collection results, pixel difference recognition is performed through a multi-level pixel frame, and defect detection is completed based on the pixel difference recognition results. Specifically, after the original image is obtained, due to the influence of various factors (such as uneven lighting, reflection on the surface of the raw materials, etc.), the image quality is not ideal. Therefore, it is necessary to enhance the image to improve the contrast and clarity of the image. The enhanced image is sent to the multi-level pixel frame for pixel difference recognition. The multi-level pixel frame is an image processing technology that traverses the image by setting pixel frames (i.e. windows) of different sizes and compares the differences in pixel values ​​in each pixel frame. When the difference exceeds the preset threshold, it is considered that there is a defect in the pixel frame. Based on the results of pixel difference recognition, the defect detection of raw materials is completed. This implementation method uses optical scanning detection equipment to detect defects in production raw materials, and uses technical means such as image acquisition, image enhancement and pixel difference recognition to improve the accuracy and efficiency of defect detection.

[0032] In a possible implementation, the pixel difference recognition is performed through a multi-level pixel frame, and the defect detection is completed based on the pixel difference recognition result. Step S120 further includes step S121, reading the historical defect size, and setting the maximum value of the multi-level pixel frame based on the maximum size of the historical defect size. Specifically, during the production process, the system will maintain a database of historical defect sizes, which records the size information of various defects detected previously. When a new defect detection is performed, the historical defect size information in this database is first read, and then, based on the maximum size of these historical defect sizes, the maximum value of the multi-level pixel frame is set. This maximum value ensures that even in the face of the largest historical defect, the multi-level pixel frame can be fully covered and identified. Step S122, obtain the detection accuracy of the defect detection, and configure the minimum value of the multi-level pixel frame based on the detection accuracy. Specifically, the detection accuracy refers to the minimum defect size that the defect detection system can accurately identify, and this accuracy is jointly determined by the hardware performance and software algorithm of the system. When setting the minimum value of the multi-level pixel frame, the minimum size in the multi-level pixel frame is configured based on the detection accuracy to ensure that all defects greater than or equal to the minimum value can be identified. Step S123, the multi-level pixel frame is distributed according to the maximum value and the minimum value to complete the pixel difference recognition. Specifically, after the maximum value and the minimum value of the multi-level pixel frame are set, a plurality of pixel frames of different sizes are distributed between the two values ​​according to certain rules (such as arithmetic progression, geometric progression, etc.). These pixel frames traverse and check the image in a preset order, calculate the difference of pixel values ​​in each pixel frame, and judge whether there is a defect based on this difference. If the difference of pixel values ​​in a certain pixel frame exceeds a preset threshold, it is considered that there is a defect in this pixel frame. This implementation method ensures that the defect detection system can accurately identify defects of various sizes by setting the maximum value and the minimum value of the multi-level pixel frame according to the historical defect size and the detection accuracy, thereby improving the accuracy of recognition.

[0033] Step S200, use UV ink to print the production raw materials, and synchronously call the laser film cutter to die-cut the production raw materials after printing, and synchronize the die-cutting results with the mapping input results to establish a synchronization result. Specifically, use UV ink (ink that can be quickly cured under ultraviolet light) to print the raw materials to add patterns, text and other information. While printing, call the laser film cutter (a device that uses laser technology for precise cutting) to accurately die-cut the printed raw materials to form the basic shape of the paper cup. In this process, the previously entered defect information is synchronized with the die-cutting results, that is, to ensure that known defect areas are avoided during the die-cutting process to ensure the quality of the finished product.

[0034] Step S300, control the automatic spray humidification equipment to humidify the production raw materials after die-cutting, and record the time-series humidification parameters. Specifically, the die-cut raw materials are humidified with pure water through the automatic spray humidification equipment (equipment that can automatically adjust the humidification amount and humidification time, used for humidification of raw materials) to improve their molding performance. During the humidification process, the humidification time-series parameters, such as humidification time, humidity changes, etc., are recorded for subsequent analysis and optimization.

[0035] Step S400, obtain the molding optimization parameters for cup body molding, and record the molding position of the production raw materials after humidification in real time, call the synchronization results and sequential humidification parameters based on the molding position of the production raw materials, perform molding optimization parameter compensation according to the call results, and establish molding compensation parameters. Specifically, first, through a series of tests and analyses, obtain the optimal control parameters (i.e., molding optimization parameters) for cup body molding. Then, when the raw materials after humidification are molded, record their molding positions in real time, and compensate for the molding optimization parameters in combination with the synchronization results in step S200 and the sequential humidification parameters in step S300 to further optimize the molding effect.

[0036] In a possible implementation, the step S400 of obtaining the optimal parameters for forming the cup body further includes a step S410 of performing material statistics on the raw materials for production and establishing the paper thickness distribution of the raw materials for production. Specifically, the paper thickness of the raw materials for production is received and counted, wherein the paper thickness is obtained by using a high-precision measuring device to measure the thickness at multiple positions of each batch or roll of raw materials, and the measurement results are recorded and summarized to form a database or data set about the paper thickness distribution. This data set contains the thickness values ​​at different positions of the raw materials, reflecting the overall thickness variation of the raw materials. Step S420, performing thickness clustering of the paper thickness distribution, and configuring a calibrated thickness interval according to the thickness clustering results. Specifically, after obtaining the paper thickness distribution, a clustering algorithm (such as K-means, hierarchical clustering, etc.) is used to process the thickness data, and similar thickness values ​​are classified into one category, thereby identifying regions or layers of different thicknesses in the raw materials. According to the clustering results, a calibrated thickness interval is configured, and the calibrated thickness interval represents the common thickness range in the raw materials. Step S430, establish a forming fitness function, use the calibrated humidification uniformity and the calibrated thickness interval as basic parameters, perform parameter optimization of the cup body forming control parameters, and establish forming optimization parameters according to the parameter optimization results. Specifically, establish a forming fitness function, which is used to evaluate the forming quality of the cup body under different forming control parameters. Using the calibrated humidification uniformity (ideal humidification uniformity) and the calibrated thickness interval as basic parameters, use an optimization algorithm to search for the optimal combination of forming control parameters in a predetermined parameter space, and these parameter combinations can make the forming fitness function reach the optimal value. According to the results of the parameter optimization, establish forming optimization parameters, which are used for subsequent cup body forming control. This implementation method can adapt to raw materials from different batches or sources by considering key factors such as the paper thickness distribution and humidification uniformity of the production raw materials, thereby improving the flexibility and adaptability of forming optimization.

[0037] In a possible implementation, the parameter optimization of the cup body forming control parameters is performed, and step S430 further includes step S431, obtaining historical control information of the servo motor driven forming device, and establishing a parameter limit interval based on the historical control information. Specifically, the control information of the servo motor driven forming device (equipment that accurately controls the forming process through the servo motor) in the past period of time is collected, including historical data of parameters such as the motor speed, torque, and position. These data are stored in a database and can be accessed through a data analysis tool. Based on these historical data, the maximum and minimum values ​​of each control parameter are calculated to establish a parameter limit interval. This interval defines the range of values ​​that each control parameter can take in the subsequent parameter optimization process. Step S432, based on the historical control information, a control similarity call is made to establish an initial parameter set. Specifically, the historical control information is analyzed to find cases similar to the current control requirements. These similar cases refer to cases in which forming operations are performed under the same or similar production conditions, using similar raw materials and control parameters, and good forming results are obtained. Control parameters are extracted from these similar cases and an initial parameter set is established. This initial parameter set is based on the extraction and arrangement of historical successful cases, and contains multiple possible control parameter combinations, which are used as the starting point for subsequent parameter optimization. Step S433, establish an initial solution space based on the initial parameter set and random parameters, and use the molding fitness function to calculate the fitness of each individual in the initial solution space, and establish the fitness calculation result. Specifically, an initial solution space containing multiple possible solutions is established by combining the initial parameter set and the randomly generated parameter values. Using the established molding fitness function, the fitness of each solution in the initial solution space (i.e., each control parameter combination) is calculated. The result of the fitness calculation reflects the degree of excellence of each solution in the molding process, and these results are used to guide the subsequent iterative optimization process. Step S434, configure the iterative constraints of the solution, iteratively optimize within the parameter limit range based on the iterative constraints and the fitness analysis results, and establish the molding optimization parameters. Specifically, configure some iterative constraints, and within the parameter limit range, continuously iterate and update the solutions in the solution space according to the fitness analysis results and iterative constraints. Each iteration will retain a part of the excellent solutions (i.e., solutions with higher fitness) and eliminate a part of the poorer solutions. After multiple iterations, a set of optimal solutions, namely molding optimization parameters, are finally obtained. These parameters are used as control parameters for the subsequent molding process to achieve higher molding quality and production efficiency. This implementation method collects and analyzes the historical control information of the servo motor-driven molding device, fully utilizes the existing successful cases and experience, provides valuable references for subsequent parameter optimization, and narrows the search scope, improves optimization efficiency, and can find the optimal solution faster.

[0038] In a possible implementation, the iterative constraints of the configuration solution are iteratively optimized within the parameter limit interval based on the iterative constraints and the fitness analysis results. Step S434 further includes step S4341, analyzing the distribution of the solutions in the initial solution space and establishing distribution clustering. Specifically, all solutions in the initial solution space (i.e., different control parameter combinations) are subjected to distribution analysis, i.e., the position, density, and relationship between each solution in the parameter space are analyzed. Through distribution analysis, it is identified which solutions are closely clustered together (i.e., forming clusters) and which solutions exist in isolation. Based on this distribution information, distribution clustering is established to classify similar solutions. Step S4342, collision constraints are established according to distribution clustering. Specifically, collision constraints refer to limiting the relative positions between solutions in order to avoid solutions being too close (i.e., "collision") and falling into a local optimal solution during the iterative optimization process. By setting collision constraints, it is ensured that each solution can move freely within a certain spatial range during the iteration process, while avoiding excessive overlap or conflict with other solutions. Step S4343, locate the individual with the highest fitness according to the fitness calculation result, and determine the individual following direction based on the individual with the highest fitness. Specifically, locate the individual with the highest fitness according to the fitness calculation result, that is, the solution with the best performance in the current solution space. Determine the individual following direction based on this individual with the highest fitness, which refers to the direction in which other solutions should move during the iteration process in order to approach or reach the position of the individual with the highest fitness. Step S4344, configure random weights using distribution clustering and the number of iterations, use collision constraints, random weights, and individual following directions as iteration constraints, use collision constraints to make update decisions for all individuals in the initial solution space according to random weights and individual following directions, and perform iterative optimization. Specifically, use distribution clustering and the number of iterations to configure random weights, and set random weights to introduce a certain amount of randomness in the iteration process in order to better explore the solution space and avoid falling into a local optimal solution. Use collision constraints, random weights, and individual following directions as iteration constraints, and use these conditions to update all individuals in the initial solution space. The update decision includes fine-tuning or reallocating the position of each solution so as to gradually approach the position of the individual with the highest fitness while maintaining the relative position relationship between the solutions. Through multiple iterative updates, a set of optimal solutions (i.e., molding optimization parameters) are finally found, which can achieve the best effect in the cup body molding process. This implementation method sets iterative constraints to avoid solutions being too close to each other and falling into the local optimal solution, which helps to better explore the entire solution space and find the global optimal solution.

[0039] In a possible implementation, the synchronization result and the sequential humidification parameter are called based on the molding position of the raw material, and the molding optimization parameter compensation is performed according to the call result. Step S400 further includes step S440, which performs thickness deviation analysis based on the synchronization result and the calibrated thickness interval to establish the thickness deviation analysis result. Specifically, the thickness of the raw material at each position is compared with the calibrated thickness interval, and its deviation value is calculated. The deviation value can be a positive value (indicating too thick) or a negative value (indicating too thin), or it can be zero (indicating that the thickness is within the calibration range). The calculated thickness deviation values ​​of all positions are sorted to form a thickness deviation analysis result. This result is a two-dimensional mapping that shows the thickness deviation of each position on the raw material. Step S450, humidification fitting is performed based on the sequential humidification parameters, and the humidification effect deviation is established with the humidification fitting result and the calibrated humidification uniformity. Specifically, the sequential humidification parameters are fitted using a mathematical model (such as polynomial regression, etc.) to obtain a mathematical model of the humidification process. This model can predict the humidification effect of the raw material under different humidification parameters. The humidification fitting result is compared with the calibrated humidification uniformity to calculate the humidification effect deviation. The calibrated humidification uniformity is a pre-set standard that indicates the ideal humidification uniformity. The calculated humidification effect deviation is sorted to form a humidification effect deviation analysis result, which reflects the degree of deviation of the humidification uniformity of the raw material during the humidification process. Step S460, based on the thickness deviation analysis result and the humidification effect deviation, a joint impact analysis of the molding control is performed, and the molding optimization parameter compensation is completed based on the joint impact analysis result. Specifically, the thickness deviation analysis result and the humidification effect deviation analysis result are jointly analyzed to analyze the influence of the two on the molding control, including analyzing how the thickness deviation and the humidification effect deviation act together on the molding process of the raw material, and the degree of their influence on the molding quality. According to the joint impact analysis result, the molding optimization parameters are compensated, and the molding parameters are adjusted by compensation to offset the adverse effects of the thickness deviation and the humidification effect deviation on the molding quality. The compensation method can be to adjust parameters such as molding pressure and molding temperature. The compensated molding parameters are sorted to form molding compensation parameters, which are used for the subsequent servo motor drive molding device control to ensure the molding quality. This implementation method accurately identifies the key factors affecting molding quality through thickness deviation analysis and humidification effect deviation analysis, and compensates for them, reducing the adverse effects of uneven raw material thickness and uneven humidification on molding quality and improving the overall quality of the product.

[0040] Step S500, use the molding compensation parameters to control the servo motor driven molding device, and sterilize and package after the molding is completed. Specifically, use the servo motor to drive the molding device, and perform the molding process of the paper cup according to the obtained molding compensation parameters. After the molding is completed, the paper cup is sterilized by ultraviolet sterilization or other methods. After the sterilization is completed, the paper cup is cooled and dried to remove moisture and humidity on its surface to prevent mildew or odor during the packaging process. Finally, the cooled and dried paper cups are placed in PE bags or PE films for packaging. PE bags / films have good sealing and moisture resistance, and can protect paper cups from external contamination and damage. The PE bags / films containing paper cups are placed in cartons for packaging, and the packaged cartons are placed in warehouses for storage, waiting for factory delivery. The embodiment of the present application utilizes optical scanning detection equipment to perform high-precision defect detection on production raw materials, maps and enters the detection results with the production raw materials, uses UV ink for printing, and synchronously calls a laser film cutting machine for die-cutting, controls the automatic spray humidification equipment to humidify the production raw materials after die-cutting, and records the timing humidification parameters, obtains the molding optimization parameters for cup body molding, and calls the synchronization results and timing humidification parameters for compensation based on the molding position of the production raw materials, establishes molding compensation parameters, and uses the molding compensation parameters to control the servo motor driven molding device, and performs sterilization and packaging after molding is completed. Technical means such as achieving the best molding effect and improving the product qualification rate through optimal compensation of molding parameters are achieved.

[0041] In the above, refer to Figure 1 A method for controlling the forming of a coated paper cup according to an embodiment of the present invention is described in detail. Figure 2 A laminated paper cup forming control system according to an embodiment of the present invention is described.

[0042] A laminated paper cup forming control system according to an embodiment of the present invention is used to solve the technical problems of the existing laminated paper cup forming control system, such as the lack of real-time parameter adjustment and optimization, the difficulty in achieving the best forming effect, and the impact on the product qualification rate, so as to achieve the technical effect of achieving the best forming effect and improving the product qualification rate through the optimization compensation of the forming parameters. A laminated paper cup forming control system includes: a defect detection module 10, a die-cutting module 20, a humidification treatment module 30, a forming optimization parameter compensation module 40, and a forming sterilization module 50.

[0043] The defect detection module 10 is used to perform defect detection on the production raw materials using optical scanning detection equipment after receiving the production raw materials, and map and enter the defect detection results with the production raw materials; the die-cutting module 20 is used to use UV ink to print the production raw materials, and synchronously call the laser film cutting machine to die-cut the production raw materials after printing, and synchronize the die-cutting results with the mapping entry results to establish synchronization results; the humidification treatment module 30 is used to control the automatic spray humidification equipment to humidify the production raw materials after die-cutting, and record the timing humidification parameters; the molding optimization parameter compensation module 40 is used to obtain the molding optimization parameters for cup body molding, and record the molding position of the production raw materials after humidification in real time, call the synchronization results and timing humidification parameters based on the molding position of the production raw materials, perform molding optimization parameter compensation according to the call results, and establish molding compensation parameters; the molding sterilization module 50 is used to use the molding compensation parameters to control the servo motor driven molding device, and sterilize and package after molding is completed.

[0044] The specific configuration of the molding optimization parameter compensation module 40 will be described in detail below. As described above, the molding optimization parameters of the cup body molding are obtained, and the molding optimization parameter compensation module 40 can further include: a paper thickness distribution establishment unit for performing incoming material statistics on the production raw materials and establishing the paper thickness distribution of the production raw materials; a calibrated thickness interval configuration unit for performing thickness clustering of the paper thickness distribution and configuring the calibrated thickness interval according to the thickness clustering result; a molding optimization parameter establishment unit for establishing a molding fitness function, using the calibrated humidification uniformity and the calibrated thickness interval as basic parameters, performing parameter optimization of the cup body molding control parameters, and establishing molding optimization parameters according to the parameter optimization result.

[0045] Among them, the parameter optimization of the cup body molding control parameters is performed, and the molding optimization parameter establishment unit can further include: a parameter limit interval establishment subunit is used to obtain the historical control information of the servo motor driving the molding device, and establish the parameter limit interval based on the historical control information; an initial parameter set establishment subunit is used to perform control similarity calls based on the historical control information and establish an initial parameter set; a fitness calculation subunit is used to establish an initial solution space according to the initial parameter set and random parameters, and use the molding fitness function to perform fitness calculation on each individual in the initial solution space to establish a fitness calculation result; an iterative optimization subunit is used to configure the iterative constraints of the solution, iteratively optimize within the parameter limit interval based on the iterative constraints and fitness analysis results, and establish molding optimization parameters.

[0046] Among them, the iterative constraints of the configuration solution are iteratively optimized within the parameter limit range based on the iterative constraints and fitness analysis results. The iterative optimization subunit can further include: a distribution clustering establishment microunit for analyzing the distribution of solutions in the initial solution space and establishing distribution clusters; a collision constraint establishment microunit for establishing collision constraints based on distribution clusters; an individual following direction determination microunit for locating the individual with the highest fitness based on the fitness calculation results, and determining the individual following direction based on the individual with the highest fitness; an update decision subunit is used to configure random weights using distribution clusters and the number of iterations, and uses collision constraints, random weights, and individual following directions as iterative constraints. The collision constraints are used to make update decisions for all individuals in the initial solution space according to random weights and individual following directions, and perform iterative optimization.

[0047] Among them, the synchronization result and the sequential humidification parameter are called based on the molding position of the production raw materials, and the molding optimization parameter compensation is performed according to the calling result. The molding optimization parameter compensation module 40 may further include: a thickness deviation analysis unit is used to perform thickness deviation analysis based on the synchronization result and the calibrated thickness interval, and establish a thickness deviation analysis result; a humidification effect deviation establishment unit is used to perform humidification fitting based on the sequential humidification parameters, and establish a humidification effect deviation with the humidification fitting result and the calibrated humidification uniformity; a joint impact analysis unit is used to perform a joint impact analysis of molding control based on the thickness deviation analysis result and the humidification effect deviation, and complete the molding optimization parameter compensation based on the joint impact analysis result.

[0048] The specific configuration of the defect detection module 10 will be described in detail below. As described above, the optical scanning detection device is used to perform defect detection on the production raw materials, and the defect detection module 10 may further include: an image acquisition unit for performing image acquisition through the optical scanning detection device under the irradiation of a light source after the production raw materials are transferred to the capture area of ​​the optical scanning detection device, establishing an image acquisition result, and establishing image coordinates; a pixel difference recognition unit for performing pixel difference recognition through a multi-level pixel frame after enhancing the image acquisition result, and completing defect detection based on the pixel difference recognition result.

[0049] Among them, the pixel difference recognition is performed through the multi-level pixel frame, and the defect detection is completed based on the pixel difference recognition result. The pixel difference recognition unit can further include: a multi-level pixel frame maximum value setting subunit is used to read the historical defect size, and set the maximum value of the multi-level pixel frame based on the maximum size of the historical defect size; a multi-level pixel frame minimum value configuration subunit is used to obtain the detection accuracy of the defect detection, and configure the minimum value of the multi-level pixel frame based on the detection accuracy; the multi-level pixel frame distribution subunit is used to distribute the multi-level pixel frames according to the maximum value and minimum value to complete the pixel difference recognition.

[0050] A laminated paper cup forming control system provided in an embodiment of the present invention can execute a laminated paper cup forming control method provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0051] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0052] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for controlling the forming of a coated paper cup, characterized in that: The method comprises: After receiving the production raw materials, use optical scanning detection equipment to detect defects on the production raw materials, and map the defect detection results with the production raw materials and enter them; Use UV ink to print the raw materials, and call the laser film cutting machine to die-cut the raw materials after printing. Synchronize the die-cutting results with the mapping input results to establish synchronization results. Control the automatic spray humidification equipment to humidify the production raw materials after die-cutting, and record the time sequence humidification parameters; Acquire the optimal molding parameters for cup body molding, and record the molding position of the production raw materials after humidification in real time, call the synchronization results and the sequential humidification parameters based on the molding position of the production raw materials, perform molding optimal parameter compensation according to the call results, and establish molding compensation parameters; The molding compensation parameters are used to control the servo motor driven molding device, and sterilization and packaging are performed after molding is completed.

2. A method for controlling the forming of a coated paper cup according to claim 1, characterized in that: The obtaining of optimal parameters for cup body molding also includes: Carry out material statistics for production raw materials and establish paper thickness distribution of production raw materials; Perform thickness clustering of paper thickness distribution and configure calibration thickness intervals according to the thickness clustering results; A molding fitness function is established, and the calibrated humidification uniformity and the calibrated thickness range are used as basic parameters to perform parameter optimization of cup body molding control parameters, and molding optimization parameters are established according to the parameter optimization results.

3. A method for controlling the forming of a coated paper cup according to claim 2, characterized in that: The method of performing parameter optimization of cup body forming control parameters also includes: Acquire historical control information of a servo motor driven forming device, and establish a parameter limit range based on the historical control information; Perform control similarity calls based on the historical control information to establish an initial parameter set; An initial solution space is established according to the initial parameter set and the random parameters, and a fitness calculation is performed on each individual in the initial solution space using a shaping fitness function to establish a fitness calculation result; Iterative constraints of the configuration solution are used to iteratively optimize within the parameter limit range based on the iterative constraints and fitness analysis results to establish the molding optimization parameters.

4. A method for controlling the forming of a coated paper cup according to claim 3, characterized in that: The iterative constraints of the configuration solution, iterative optimization within the parameter limit interval based on the iterative constraints and the fitness analysis results, also include: Analyzing the distribution of solutions in the initial solution space and establishing distribution clustering; Establish collision constraints based on distribution clustering; Locate the individual with the highest fitness based on the fitness calculation results, and determine the individual following direction based on the individual with the highest fitness; Random weights are configured using distribution clustering and the number of iterations. Collision constraints, random weights, and individual following directions are used as iterative constraints. Collision constraints are used to make update decisions for all individuals in the initial solution space based on random weights and individual following directions, and iterative optimization is performed.

5. A method for controlling the forming of a coated paper cup according to claim 2, characterized in that: The method of calling the synchronization result and the time sequence humidification parameter based on the molding position of the production raw material and performing molding optimization parameter compensation according to the calling result also includes: Perform thickness deviation analysis based on synchronization results and calibrated thickness intervals, and establish thickness deviation analysis results; Humidification fitting is performed based on the time-series humidification parameters, and the humidification effect deviation is established based on the humidification fitting results and the calibrated humidification uniformity; Based on the thickness deviation analysis results and the humidification effect deviation, a joint impact analysis of molding control is performed, and based on the joint impact analysis results, molding optimization parameter compensation is completed.

6. A method for controlling the forming of a coated paper cup according to claim 1, characterized in that: The method of using optical scanning detection equipment to detect defects in production raw materials also includes: After the production raw materials are transferred to the capture area of ​​the optical scanning detection device, the optical scanning detection device is used to collect images under the illumination of the light source, the image collection results are established, and the image coordinates are established; After the image acquisition result is enhanced, pixel difference recognition is performed through a multi-level pixel frame, and defect detection is completed based on the pixel difference recognition result.

7. A method for controlling the forming of a coated paper cup according to claim 6, characterized in that: The method of performing pixel difference recognition through the multi-level pixel frame and completing defect detection based on the pixel difference recognition result also includes: Read the historical defect size, and set the maximum value of the multi-level pixel frame based on the maximum size of the historical defect size; Obtain the detection accuracy of defect detection, and configure the minimum value of the multi-level pixel frame based on the detection accuracy; Multi-level pixel frames are distributed according to the maximum and minimum values ​​to complete pixel difference recognition.

8. A laminated paper cup forming control system, characterized in that: The system is used to implement a laminated paper cup forming control method according to any one of claims 1 to 7, and the system comprises: A defect detection module, which is used to perform defect detection on the production raw materials using an optical scanning detection device after receiving the production raw materials, and map and enter the defect detection results with the production raw materials; A die-cutting module, which is used to use UV ink to print production raw materials, and synchronously call a laser film cutting machine to die-cut the production raw materials after printing, and synchronize the die-cutting results with the mapping input results to establish synchronization results; A humidification processing module, which is used to control the automatic spray humidification equipment to humidify the production raw materials after die-cutting, and record the time-series humidification parameters; A molding optimization parameter compensation module, which is used to obtain molding optimization parameters for cup body molding, and to record in real time the molding position of the production raw materials after humidification treatment, and to call the synchronization result and the sequential humidification parameter based on the molding position of the production raw materials, and to perform molding optimization parameter compensation according to the call result, and to establish molding compensation parameters; The molding and sterilization module is used to use molding compensation parameters to control the servo motor driven molding device, and sterilize and package after molding is completed.

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