Efficient auxiliary material production method and system based on roll-to-roll die cutting process

Abstract

The invention relates to the technical field of auxiliary material production, in particular to an efficient auxiliary material production method and system based on a roll-to-roll die cutting process. The method comprises the following steps: mounting a roll-shaped blue film plastic film raw material on a roll material unwinding device, and realizing continuous and automatic feeding of the plastic film through the unwinding device; the plastic film is controlled to pass through a high-precision tension control module in the conveying process, the tension of the plastic film is dynamically adjusted in real time, and it is ensured that the plastic film is flat, free of wrinkles and even and stable in tension in the conveying process; a plastic film is controlled to enter a die cutting unit, one-time die cutting forming action is carried out through a precision die in the die cutting unit, appearance cutting and internal hole site forming of the auxiliary materials are synchronously completed in the single-time die cutting process, the continuous roll-to-roll die cutting process is achieved, and the plastic film auxiliary materials obtained after die cutting forming are obtained.

Description

Technical Field

[0001] This application relates to the field of auxiliary material production technology, and in particular to an efficient auxiliary material production method and system based on roll-to-roll die-cutting process. Background Technology

[0002] In the field of auxiliary material production, the traditional single-sheet punching process generally adopts a discrete production flow of "material cutting-packing-drilling-punching". This process has the following significant drawbacks: First, the process chain is lengthy, requiring manual completion of multiple independent steps such as material cutting, packaging, and drilling, resulting in low production efficiency and high labor costs; Second, the drilling process is prone to material blockage, affecting product yield; Third, the single-sheet intermittent production mode is difficult to automate continuous operation, resulting in low equipment utilization and significant losses in production process connections.

[0003] Although there are some improved die-cutting processes in existing technologies, most of them have failed to break through the inherent model of "single sheet feeding + multi-stage processing", and cannot fundamentally solve the problems of process fragmentation and insufficient production continuity. Summary of the Invention

[0004] This application provides an efficient production method and system for auxiliary materials based on roll-to-roll die-cutting process. It aims to solve the problem that while there are some die-cutting process improvement schemes in the prior art, most of them fail to break through the inherent mode of "single sheet feeding + multi-stage processing", and cannot fundamentally solve the problems of process fragmentation and insufficient production continuity.

[0005] In a first aspect, embodiments of this application provide a method for efficient production of auxiliary materials based on roll-to-roll die-cutting technology, the method comprising: Rolled blue film plastic film raw material is installed on a roll unwinding device, and the continuous automated feeding of plastic film is achieved through the unwinding device; During the transmission process, the tension of the plastic film is dynamically adjusted in real time by a high-precision tension control module to ensure that the plastic film is flat and wrinkle-free and has uniform and stable tension. The plastic film is controlled to enter the die-cutting unit, and is die-cut in one operation through the precision mold in the die-cutting unit. During a single die-cutting process, the outer shape cutting and internal hole forming of the auxiliary material are completed simultaneously, realizing a continuous roll-to-roll die-cutting process to obtain the die-cut plastic film auxiliary material.

[0006] In some embodiments, the continuous automated feeding of plastic film via the unwinding device includes: driving the roll to rotate via a servo motor of the unwinding device, collecting the feed length data of the roll in real time using an encoder, and transmitting the data to a system controller. The controller controls the speed and number of rotations of the servo motor via pulse signals according to a preset feed speed threshold and length parameters, thereby achieving constant-length, constant-speed continuous feeding of the plastic film.

[0007] In some embodiments, controlling the plastic film during transmission via a high-precision tension control module includes: setting a tension sensor on the plastic film transmission path; the sensor collects the film tension value in real time and converts it into an electrical signal; the electrical signal is transmitted to the system controller via an analog-to-digital converter; and the controller triggers a magnetic powder brake or tension adjusting roller in the tension control module according to a preset tension threshold range to dynamically adjust the film tension in real time.

[0008] In some embodiments, the real-time dynamic adjustment of the tension of the plastic film to ensure that the plastic film is flat and wrinkle-free and has uniform and stable tension during transmission includes: based on a preset PID control algorithm, calculating and outputting a control signal to the tension adjustment mechanism according to the deviation between the real-time feedback data of the tension sensor and the target tension value, and adjusting the matching degree between the unwinding speed and the feed speed of the die-cutting unit to achieve tension fluctuation error control within ±5N.

[0009] In some embodiments, controlling the plastic film to enter the die-cutting unit includes: setting a visual positioning camera at the entrance of the die-cutting unit, the camera acquiring real-time images of the edge position of the plastic film, calculating the film offset through an image recognition algorithm, and the system controller driving a lateral adjustment cylinder according to the offset data to adjust the film transmission path and ensure that the film is accurately aligned with the processing area of ​​the die-cutting mold.

[0010] In some embodiments, the die-cutting action performed by the precision mold in the die-cutting unit, which simultaneously completes the outer shape cutting and internal hole forming of the auxiliary material during a single die-cutting process, includes: the die-cutting drive mechanism adopts a servo electric cylinder or hydraulic cylinder, and drives the mold to press down at a preset pressing speed through the die-cutting pressure curve and stroke parameters preset by the system controller; the outer shape punching blade and the hole forming component of the mold are designed with a stepped structure to achieve the synchronous completion of outer shape cutting and hole forming, and the single die-cutting cycle is controlled within 0.5-1.2 seconds.

[0011] In some embodiments, the method further includes: establishing a historical production database to store process data such as tension parameters, die-cutting pressure, and feed speed corresponding to blue film plastic films of different thicknesses and materials; training the historical data using machine learning algorithms to generate a process parameter prediction model; and when a new batch of raw materials is fed into the machine, the process parameter prediction model outputs the optimal tension value and die-cutting pressure value based on the input raw material parameters to achieve intelligent matching of process parameters.

[0012] In some embodiments, the method further includes: setting an online detection module at the outlet of the die-cutting unit, wherein the module takes real-time pictures of the die-cutting auxiliary materials using a linear CCD camera, analyzes the images using a convolutional neural network algorithm, and identifies whether there are defects such as unbroken die-cutting, hole misalignment, or waste residue; when a defect is detected, the system controller automatically triggers an alarm and adjusts the pressing depth of the die-cutting mold or the cleaning and waste removal mechanism according to the defect type.

[0013] In some embodiments, the method further includes: real-time acquisition of key data during the production process; key data includes tension fluctuation value, number of die-cuts and auxiliary material yield; establishing a process parameter optimization model through a fuzzy control algorithm; when the auxiliary material yield is continuously detected to be lower than a preset threshold, the process parameter optimization model automatically performs multi-factor optimization calculations on tension control parameters, die-cutting speed and mold temperature, generates new control parameter combinations and sends them to each actuator to achieve adaptive optimization of the production process.

[0014] Secondly, this application provides an efficient auxiliary material production system based on roll-to-roll die-cutting technology, the system comprising: A continuous feeding unit is used to load rolls of blue film plastic film raw material onto a roll unwinding device, thereby achieving continuous and automated feeding of the plastic film through the unwinding device. The tension adjustment unit is used to control the tension of the plastic film in real time and dynamically adjust it through the high-precision tension control module during the transmission process, so as to ensure that the plastic film is flat and wrinkle-free and has uniform and stable tension during the transmission process. The film entry unit is used to control the plastic film to enter the die-cutting unit. The film is die-cut in one operation by the precision mold in the die-cutting unit. During a single die-cutting process, the outer shape cutting and internal hole forming of the auxiliary material are completed simultaneously, realizing a continuous roll-to-roll die-cutting process to obtain the die-cut plastic film auxiliary material.

[0015] This application simplifies the production process from five steps to three (roll material loading - die-cutting - next process) by eliminating four traditional steps: material cutting, packaging, drilling, and separate punching. Through a continuous roll-to-roll operation mode, it achieves automated continuous material supply and processing, eliminating intermittent losses in traditional processes and improving production efficiency. It reduces manual intervention and equipment turnover losses, achieving cost savings per square meter and avoiding material waste caused by hole blockage. By using high-precision molds to complete the outer shape punching and hole processing in one step, it avoids the precision deviations of traditional step-by-step processing, significantly improving product consistency. It integrates fully automated unwinding, tension control, die-cutting, and rewinding devices, reducing manual labor intensity and adapting to the needs of large-scale industrial production.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart illustrating the steps of an efficient auxiliary material production method based on roll-to-roll die-cutting process provided in one embodiment of this application; Figure 2 This is a schematic block diagram of the structure of an efficient auxiliary material production system based on roll-to-roll die-cutting process provided in one embodiment of this application; Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0022] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and the terms "first" and "second" are not necessarily different.

[0023] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0024] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] In the field of auxiliary material production, the traditional single-sheet punching process generally adopts a discrete production flow of "material cutting-packing-drilling-punching". This process has the following significant drawbacks: First, the process chain is lengthy, requiring manual completion of multiple independent steps such as material cutting, packaging, and drilling, resulting in low production efficiency and high labor costs; Second, the drilling process is prone to material blockage, affecting product yield; Third, the single-sheet intermittent production mode is difficult to automate continuous operation, resulting in low equipment utilization and significant losses in production process connections.

[0026] Although there are some improved die-cutting processes in existing technologies, most of them have failed to break through the inherent model of "single sheet feeding + multi-stage processing", and cannot fundamentally solve the problems of process fragmentation and insufficient production continuity.

[0027] To solve the above problem, please refer to Figure 1 This application provides an efficient auxiliary material production method based on roll-to-roll die-cutting technology, applicable to computer equipment. The computer equipment can be deployed on a single server or server cluster. It can also be deployed on handheld terminals, laptops, wearable devices, or robots, etc. It should be noted that all information involved in the method provided in this application is extracted with the authorization of the relevant user and in accordance with relevant regulations, and will not infringe on user privacy.

[0028] The provided efficient production method for auxiliary materials based on roll-to-roll die-cutting technology includes steps S101 to S103. Details are as follows: Step S101. The roll of blue film plastic film raw material is installed on the roll unwinding device, and the continuous automated feeding of the plastic film is achieved through the unwinding device.

[0029] Specifically, the material supply method is innovated: abandoning the traditional single-sheet cutting mode, it adopts roll-shaped blue film plastic film raw materials directly onto the machine, achieving continuous and automated material supply through the roll unwinding device, eliminating discrete links such as manual cutting and packaging. This establishes the foundation for continuous production, avoiding the intermittent nature of single-sheet feeding and improving production efficiency. Roll installation involves fixing the roll-shaped blue film raw material to the unwinding device's shaft, ensuring the shaft is aligned with the raw material's central axis to avoid transmission deviation caused by eccentricity. The end of the raw material is guided to the subsequent tension control module by guide rollers, forming a stable transmission path. Automated feeding is achieved through the unwinding device equipped with a servo motor or stepper motor, with the feed speed (such as constant linear speed) set by the control system, realizing continuous and uniform material conveying. An optional automatic correction device (such as photoelectric sensors + correction rollers) can be added to correct roll deviation in real time, ensuring transmission direction accuracy.

[0030] Step S102. During the transmission process, the tension of the plastic film is dynamically adjusted in real time by a high-precision tension control module to ensure that the plastic film is flat and wrinkle-free and has uniform and stable tension during the transmission process.

[0031] Specifically, dynamic tension adjustment monitors and adjusts material tension in real time during roll material transport using a high-precision tension control module. This prevents wrinkles, stretching, or slack caused by uneven tension, ensuring a flat and stable material during die-cutting. The core objective is to guarantee die-cutting accuracy and prevent product defects (such as hole misalignment or cutting dimension deviation) caused by material deformation.

[0032] Tension monitoring involves placing tension sensors (such as strain gauges or magnetic powder brakes) along the transmission path to detect the tension value of the rolled material in real time and feed it back to the control system. The control system (such as a PLC or industrial computer) then dynamically balances the tension by adjusting the speed of the unwinding motor or the torque of the winding motor based on a preset tension threshold (e.g., using a PID control algorithm).

[0033] The mechanical structure assists by using multiple sets of guide roller devices (such as floating rollers and tension rollers) to distribute tension and reduce local stress concentration; the surface of the guide rollers is treated with anti-slip treatment (such as rubber coating) to prevent the rolled material from slipping.

[0034] Step S103. Control the plastic film to enter the die-cutting unit, and perform a die-cutting action through the precision mold in the die-cutting unit. In a single die-cutting process, the outer shape cutting and internal hole forming of the auxiliary material are completed simultaneously, realizing a continuous roll-to-roll die-cutting process, and obtaining the die-cut plastic film auxiliary material.

[0035] Specifically, the multi-process integration uses a precision die-cutting unit to simultaneously complete the outer shape cutting and internal hole forming of auxiliary materials in a single die-cutting action, replacing the traditional staged processing of "drilling + punching" and completely eliminating the independent drilling process.

[0036] The mold must have high flatness and dimensional accuracy to ensure smooth die-cut edges and burr-free holes, avoiding the risk of clogging.

[0037] The die structure employs a punch-die combination. The punch is responsible for external shape cutting, while the die has a built-in punching structure, achieving simultaneous forming of the external shape and holes in a single stamping operation. The die material must be a high-hardness alloy (such as SKD11), and the surface must be ground and polished to ensure a flatness ≤0.01mm. The die-cutting unit is driven by a servo motor or hydraulic system, providing stable stamping pressure (e.g., 5-10 tons), which can be adjusted in real time via the control system.

[0038] The die-cutting process is controlled by tension control as the rolled material enters the die-cutting station. When the die closes, positioning pins or a vision alignment system ensure that the material is aligned with the die position to avoid misalignment. After die-cutting, waste material is automatically removed by a waste removal device (such as a waste rewinding roller or vacuum adsorption), and the finished rolled material is directly rewound or conveyed to the next process.

[0039] Equipped with online inspection devices (such as vision cameras), it scans the die-cut auxiliary materials in real time to detect the integrity of the holes, deviations in shape and size, etc., and automatically marks or rejects unqualified products.

[0040] In some embodiments, the continuous automated feeding of plastic film via the unwinding device includes: driving the roll to rotate via a servo motor of the unwinding device, collecting the feed length data of the roll in real time using an encoder, and transmitting the data to a system controller. The controller controls the speed and number of rotations of the servo motor via pulse signals according to a preset feed speed threshold and length parameters, thereby achieving constant-length, constant-speed continuous feeding of the plastic film.

[0041] Servo motor drive control: The servo motor precisely controls the unwinding speed of the roll material, and combined with encoder feedback, it achieves constant length and constant speed feeding, solving the problems of intermittency and insufficient precision of traditional manual feeding.

[0042] Data closed-loop control: The encoder collects feed length data, and the system controller adjusts the motor speed in real time to ensure the stability and accuracy of roll material transmission.

[0043] The unwinding device is equipped with a servo motor, with the motor shaft rigidly connected to the roll of material, providing stable torque output. An incremental encoder (resolution ≥1000 lines / revolution) is installed at the end of the motor to collect the number of rotations of the roll in real time, converting it into the feed length (e.g., 0.1mm feed per pulse). The system controller (e.g., Siemens PLC) presets the feed speed threshold (e.g., 5m / min) and the single feed length (e.g., 200mm of raw material per die-cutting unit). The encoder feeds the length data back to the controller as a pulse signal. The controller adjusts the servo motor speed through pulse width modulation (PWM) or pulse frequency control to ensure that the actual feed amount deviates from the preset value by ≤±0.5mm.

[0044] It is suitable for auxiliary materials with high feeding accuracy requirements (such as microporous auxiliary materials) to avoid die-cutting misalignment caused by feeding errors.

[0045] In some embodiments, controlling the plastic film during transmission via a high-precision tension control module includes: setting a tension sensor on the plastic film transmission path; the sensor collects the film tension value in real time and converts it into an electrical signal; the electrical signal is transmitted to the system controller via an analog-to-digital converter; and the controller triggers a magnetic powder brake or tension adjusting roller in the tension control module according to a preset tension threshold range to dynamically adjust the film tension in real time.

[0046] Real-time tension monitoring uses a tension sensor to collect the tension value of the film, converts it into an electrical signal, and transmits it to the controller to achieve digital feedback of tension data.

[0047] The magnetic powder brake / tension regulating roller is based on feedback signals to trigger the actuator to adjust the tension, forming a closed loop of "detection-control-execution", which replaces the traditional manual adjustment.

[0048] The tension sensor is a strain gauge tension sensor (range 0-50N, accuracy ±0.1N), which is installed on the guide roller support of the transmission path to detect the tension of the rolled material in real time.

[0049] The magnetic powder brake is connected to the rear end of the unwinding motor. The braking torque is adjusted by the current signal output by the controller (e.g., current 0-2A corresponds to torque 0-20N·m) to suppress the inertial slippage of the coil.

[0050] The tension adjustment roller is a guide roller that can float up and down, driven by a cylinder. The tension is adjusted by changing the wrap angle of the roll (e.g., increasing the wrap angle by 15° can increase the tension by 3N).

[0051] The signal processing flow involves the sensor outputting a 4-20mA analog signal, which is then converted into a digital signal by an analog-to-digital converter (ADC) and input to the controller. The controller has a preset tension threshold range (e.g., 20±2N). When the measured value exceeds the range, it automatically triggers the magnetic powder brake to increase or decrease the braking force or adjusts the wrap angle of the adjusting roller until the tension returns to within the threshold.

[0052] In some embodiments, the real-time dynamic adjustment of the tension of the plastic film to ensure that the plastic film is flat and wrinkle-free and has uniform and stable tension during transmission includes: based on a preset PID control algorithm, calculating and outputting a control signal to the tension adjustment mechanism according to the deviation between the real-time feedback data of the tension sensor and the target tension value, and adjusting the matching degree between the unwinding speed and the feed speed of the die-cutting unit to achieve tension fluctuation error control within ±5N.

[0053] Based on the proportional-integral-derivative (PID) algorithm, dynamic compensation is performed for tension fluctuations, solving the problem of adjustment lag caused by fixed parameters in traditional PID.

[0054] By adjusting the matching degree between the unwinding speed and the feed speed of the die-cutting unit, the tension error can be precisely controlled (within ±5N).

[0055] The controller has a built-in incremental PID algorithm with parameters including proportional coefficient (Kp=0.5), integral coefficient (Ki=0.1 / s), and derivative coefficient (Kd=5N·s). It calculates the control quantity based on the real-time tension deviation (e(t)=target value-measured value): u(t)=Kp*e(t)+Ki*∫e(t)dt+Kd*de(t) / dt; the output signal drives the unwinding motor speed adjustment (e.g., when the deviation is +3N, the motor speed is reduced by 5% to increase the tension).

[0056] The collaborative control mechanism ensures that the feed speed of the die-cutting unit is synchronously controlled by the servo motor and fed back to the tension control system in real time through the encoder, so as to ensure that the difference between the unwinding and die-cutting speeds is ≤±0.2m / min and avoid sudden tension changes due to speed mismatch.

[0057] In some embodiments, controlling the plastic film to enter the die-cutting unit includes: setting a visual positioning camera at the entrance of the die-cutting unit, the camera acquiring real-time images of the edge position of the plastic film, calculating the film offset through an image recognition algorithm, and the system controller driving a lateral adjustment cylinder according to the offset data to adjust the film transmission path and ensure that the film is accurately aligned with the processing area of ​​the die-cutting mold.

[0058] Visual positioning and correction uses a visual camera to acquire images of the film edge in real time, calculates the offset through an image recognition algorithm, and drives a cylinder to adjust the transmission path, thus solving the problems of lag and insufficient accuracy in traditional mechanical positioning.

[0059] Dynamic correction response enables real-time detection and adjustment of millimeter-level offsets, ensuring the alignment accuracy of the film and the mold processing area (deviation ≤ ±0.1mm).

[0060] The hardware architecture includes: a visual positioning camera: a linear CCD camera (resolution ≥ 2048 pixels, frame rate ≥ 100fps), installed above the entrance of the die-cutting unit, with the shooting direction perpendicular to the roll material conveying direction. A lateral adjustment cylinder: stroke ±20mm, response time ≤ 50ms, connected to the guide rollers via a ball screw mechanism to achieve lateral position adjustment of the roll material.

[0061] The image processing flow includes: the camera acquires images of the roll material edges, extracts the contour using an edge detection algorithm (such as the Canny operator), and calculates the offset (Δx) between the actual edge position and the preset baseline. The controller outputs a PWM signal based on the Δx value to control the cylinder extension and retraction (e.g., when Δx = +0.5mm, the cylinder moves 0.5mm to the left), and updates the adjustment command every 50ms.

[0062] In some embodiments, the die-cutting action performed by the precision mold in the die-cutting unit, which simultaneously completes the outer shape cutting and internal hole forming of the auxiliary material during a single die-cutting process, includes: the die-cutting drive mechanism adopts a servo electric cylinder or hydraulic cylinder, and drives the mold to press down at a preset pressing speed through the die-cutting pressure curve and stroke parameters preset by the system controller; the outer shape punching blade and the hole forming component of the mold are designed with a stepped structure to achieve the synchronous completion of outer shape cutting and hole forming, and the single die-cutting cycle is controlled within 0.5-1.2 seconds.

[0063] Servo electric cylinder / hydraulic drive: A high-precision drive mechanism is used to achieve precise control of the die pressing speed and pressure, replacing the problem of uncontrollable impact force in traditional mechanical stamping. Stepped die design: The outer shape punching and hole forming components are arranged in layers to achieve synchronous processing and shorten the die cutting cycle (0.5-1.2 seconds / cycle).

[0064] Drive mechanism selection includes: Servo electric cylinder: repeatability ±0.01mm, speed adjustable from 0-200mm / s, suitable for high-precision requirements (such as micro-hole die-cutting with a 0.1mm diameter). Hydraulic cylinder: provides constant pressure (such as 10MPa), speed adjustable from 0-50mm / s, suitable for heavy-duty punching of thick film materials (thickness ≥0.5mm).

[0065] Mold structure design: The mold is divided into two layers: the upper layer is the outer shape punching blade (edge ​​roughness Ra≤0.8μm), and the lower layer is the hole forming component (including punching punch and die). The height difference between the two is 0.3-0.5mm, which ensures that the outer shape cutting is completed 0.1 seconds before the hole forming, thus avoiding material tearing.

[0066] Process parameter settings: Pressing speed: set to 50mm / s in servo electric cylinder mode and 20mm / s in hydraulic mode; Die-cutting pressure curve preset to "slow acceleration-constant pressure-slow deceleration" mode, peak pressure maintenance time ≥0.3 seconds.

[0067] In some embodiments, the method further includes: establishing a historical production database to store process data such as tension parameters, die-cutting pressure, and feed speed corresponding to blue film plastic films of different thicknesses and materials; training the historical data using machine learning algorithms to generate a process parameter prediction model; and when a new batch of raw materials is fed into the machine, the process parameter prediction model outputs the optimal tension value and die-cutting pressure value based on the input raw material parameters to achieve intelligent matching of process parameters.

[0068] A production database is established to store multi-dimensional process data. A parameter prediction model is trained using machine learning algorithms (such as regression analysis and random forest) to automatically match process parameters when new materials are introduced. This replaces the traditional manual trial-and-error parameter tuning method, reducing setup time (estimated to be reduced by 70%) and improving process consistency.

[0069] Data Acquisition and Storage: Database fields include: raw material thickness (0.05-0.3mm), material (PET / PE / PP), tension value (10-30N), die-cutting pressure (5-15 tons), feed speed (3-8m / min), yield (%), etc. Historical data is stored using a MySQL database, indexed by batch, supporting real-time querying and retrieval.

[0070] The machine learning model training included: input features: raw material thickness, material type, and ambient temperature; output labels: optimal tension value and die-cutting pressure. The Gradient Boosting Tree (GBDT) algorithm was used to train the model, with a learning rate of 0.1, a tree depth of 5, and 100 iterations. The model prediction error was ≤3%.

[0071] When new raw materials are fed into the machine, the operator inputs parameters such as thickness and material, and the model automatically outputs recommended parameters. The controller sends these parameters to each actuator with one click, eliminating the need for manual adjustments.

[0072] In some embodiments, the method further includes: setting an online detection module at the outlet of the die-cutting unit, wherein the module takes real-time pictures of the die-cutting auxiliary materials using a linear CCD camera, analyzes the images using a convolutional neural network algorithm, and identifies whether there are defects such as unbroken die-cutting, hole misalignment, or waste residue; when a defect is detected, the system controller automatically triggers an alarm and adjusts the pressing depth of the die-cutting mold or the cleaning and waste removal mechanism according to the defect type.

[0073] Machine vision defect detection uses a linear CCD camera to take real-time pictures of the die-cut finished product and uses a convolutional neural network (CNN) algorithm to identify defects such as continuous die-cutting and hole misalignment, replacing the traditional manual sampling inspection mode.

[0074] When a defect is detected, an alarm is automatically triggered and process parameters are adjusted, achieving full automation of the defect "detection-analysis-repair" process.

[0075] The detection hardware deployment includes: a linear CCD camera with a resolution ≥ 4096 pixels, installed above the die-cutting machine's exit, and equipped with a bar light source (brightness ≥ 5000 lux) to eliminate glare interference; and an image processing server equipped with a GPU (such as NVIDIA Jetson) to support real-time image analysis with a processing speed ≥ 20 frames per second.

[0076] The defect identification algorithm uses YOLOv5 model pre-trained weights and performs transfer learning on auxiliary material defects (such as uncut, hole deviation, and waste residue). The training set contains 100,000 labeled images, and the detection accuracy mAP@0.5≥95%.

[0077] When a die-cutting failure is detected, the controller automatically increases the die-cutting pressure by 5%; when a hole position offset is detected, the vision positioning system is triggered to recalibrate the film position; when the same type of defect is detected three times in a row, the machine is automatically stopped and an alarm is pushed to the mobile phone of the maintenance personnel.

[0078] In some embodiments, the method further includes: real-time acquisition of key data during the production process; key data includes tension fluctuation value, number of die-cuts and auxiliary material yield; establishing a process parameter optimization model through a fuzzy control algorithm; when the auxiliary material yield is continuously detected to be lower than a preset threshold, the process parameter optimization model automatically performs multi-factor optimization calculations on tension control parameters, die-cutting speed and mold temperature, generates new control parameter combinations and sends them to each actuator to achieve adaptive optimization of the production process.

[0079] Multi-factor data acquisition: Real-time acquisition of key data such as tension fluctuation, die-cutting times, and yield to establish a process parameter optimization model.

[0080] Fuzzy control algorithm application: When the yield is lower than the threshold, parameters such as tension, die-cutting speed, and mold temperature are automatically adjusted through fuzzy logic to achieve dynamic adaptive optimization of the production process.

[0081] The data acquisition system includes: a sensor network consisting of a tension sensor (accuracy ±0.1N), an encoder (recording the number of die-cuts), and a photoelectric counter (counting the number of good products). Data frequency: Key data is collected once per second and stored in a real-time database (such as InfluxDB), supporting trend analysis and anomaly warnings.

[0082] The fuzzy control model is constructed as follows: Input variables: yield deviation (E) and deviation change rate (EC), quantized into seven levels: {-3, -2, -1, 0, 1, 2, 3}. Output variables: tension adjustment (ΔT), die-cutting speed adjustment (ΔV), and die temperature adjustment (Δθ), defined using triangular membership functions to define fuzzy sets. Control rules: such as "if E is negative and EC is negative, then ΔT increases by 3 levels, and ΔV decreases by 2 levels", a total of 49 control rules are established.

[0083] The optimized execution process includes triggering a fuzzy control model when the yield is below 95% for 30 consecutive minutes, calculating the optimal parameter combination based on the current data (such as adjusting the tension from 20N to 22N and reducing the die-cutting speed from 5m / min to 4.5m / min), continuously monitoring yield changes after adjustment, and initiating a second optimization if there is no improvement.

[0084] In some embodiments, real-time laser thickness measurement combined with a machine learning prediction model enables dynamic sensing of raw material thickness fluctuations and automatic adjustment of process parameters (tension, die-cutting pressure), ensuring stable yield under raw material variations. The core logic is: increased raw material thickness → greater tension is needed to suppress wrinkles → greater die-cutting pressure is needed to ensure cutting effectiveness.

[0085] The hardware deployment involves installing a laser thickness gauge (such as the German DIAS DTS 100, with an accuracy of ±0.001mm and a sampling frequency ≥100Hz) between the roll unwinding device and the tension control module to collect the roll thickness data in real time (e.g., once every 10mm). The thickness gauge transmits the data to the system controller (such as a Siemens S7-1500 PLC) via Ethernet, and the controller associates and stores the thickness data with the production batch and timestamp.

[0086] Machine learning model training includes: input features: real-time thickness value (h), thickness change rate (Δh / Δt), raw material (e.g., PET / PE); output labels: optimal tension adjustment amount (ΔT), die-cutting pressure adjustment amount (ΔP); model selection: Long Short-Term Memory (LSTM) network is used, and historical production data (e.g., thickness-parameter-yield data of 1000 batches) is used for training to learn the nonlinear relationship between thickness fluctuation and parameter adjustment (e.g., thickness increase of 0.02mm → tension increase of 2N, die-cutting pressure increase of 1 ton).

[0087] The dynamic adjustment process includes: when the laser thickness gauge detects that the thickness exceeds the preset threshold (e.g., target thickness 0.1mm, fluctuation ±0.01mm), the controller triggers the LSTM model; the model outputs the adjusted tension value (e.g., from 20N to 22N) and the die-cutting pressure value (e.g., from 10 tons to 11 tons); the controller sends the new parameters to the tension control module (magnetic powder brake / adjusting roller) and the die-cutting drive mechanism (servo electric cylinder / hydraulic cylinder), with an adjustment time ≤0.5 seconds; after adjustment, the online detection module (Example 7) provides real-time feedback on the yield rate, and if the yield rate does not meet the standard (e.g., <95%), the model performs a second iteration optimization.

[0088] In some embodiments, to address issues such as continuous die-cutting and burrs at hole positions caused by mold wear (traditional periodic maintenance easily leads to excessive replacement or sudden failures), a die-cutting pressure sensor collects pressure changes and combines this with a machine learning wear prediction model to monitor the mold wear status in real time, providing early warnings of replacement time and avoiding unplanned downtime. The core logic is: mold wear → increased die-cutting resistance → increased peak die-cutting pressure → prediction of wear degree through pressure change rate.

[0089] The hardware deployment involves installing a high-precision pressure sensor (such as the Swiss HBM P3MB, accuracy ±0.1%FS, range 0-20 tons) at the pressure output end of the die-cutting drive mechanism (servo electric cylinder or hydraulic cylinder) to collect the peak pressure (P_peak) and pressure curve (such as rise time and fall time) for each die-cut in real time. The sensor transmits the data to the controller via analog signals (4-20mA), and the controller stores the pressure data and the number of die-cuts for each cut (counted by an encoder).

[0090] Wear prediction model training: Input features: peak pressure change rate (ΔP_peak / ΔN, N is the number of die cuts), rise time of pressure curve (t_rise), number of die cuts (N); Output labels: mold wear level (0-5, 0 for new mold, 5 for severe wear); Model selection: Random Forest algorithm is used, trained using historical mold wear data (such as the full life cycle data of 50 sets of molds), and the model accuracy is ≥92%.

[0091] Predictive maintenance process: The controller calculates the pressure change rate and rise time every hour and inputs them into a random forest model; when the model outputs a wear level ≥ 4 (e.g., peak pressure increases by 20% compared to a new mold, rise time is extended by 30%), an early warning mechanism is triggered: an alarm is pushed to the mobile phones of maintenance personnel (e.g., "Mold wear level 4, remaining lifespan approximately 2000 die cuts"); the wear trend curve is displayed on the monitoring interface (e.g., it will reach level 5 within the next 8 hours); maintenance personnel prepare spare molds in advance based on the early warning and replace them during planned downtime to avoid production interruptions caused by sudden failures (estimated to reduce downtime by 60%).

[0092] In some embodiments, to address the issue of long changeover times in multi-variety, small-batch production (traditional changeovers require manual trial-and-error parameter tuning, taking 1-2 hours), a digital twin model of the roll-to-roll die-cutting equipment is established. Through virtual simulation, the process parameters (tension, die-cutting pressure, feed speed) of new products are optimized in advance, enabling rapid changeover from "virtual debugging to actual application" and reducing on-site setup time. The core logic is: the digital twin simulates the equipment status and product processing, predicts optimal parameters, and replaces on-site trial and error.

[0093] The construction of a digital twin model includes: Physical layer: collecting real-time data from the equipment (such as servo motor speed, tension value, and die-cutting pressure) and transmitting it to a digital twin platform (such as Siemens MindSphere) via the OPC UA protocol; Virtual layer: using SolidWorks to build a three-dimensional geometric model of the equipment, using MATLAB / Simulink to build a dynamic model (such as a tension transmission model, a model showing the relationship between die-cutting pressure and cutting effect), and using Ansys to build a finite element model (such as a mold stress distribution model); Data layer: integrating historical process data, raw material parameters, and product design data (such as external dimensions and hole coordinates) to form the "knowledge foundation" of the digital twin.

[0094] Rapid changeover process: When a new product (such as a mobile phone accessory, 120x60mm in shape, 1.5mm in hole diameter, PE material) is put into operation, the operator inputs the product design parameters (CAD drawing) and raw material parameters (thickness 0.08mm, PE material) into the digital twin platform; the digital twin model optimizes the process parameters based on the genetic algorithm: objective function: minimize changeover time (number of machine adjustments) + maximize yield (≥98%); constraints: tension range 10-30N, die-cutting pressure 5-15 tons, feed speed 3-8m / min; The model outputs the optimal parameter combination (such as tension 18N, die-cutting pressure 12 tons, and feed speed 5m / min), and verifies it through virtual simulation (such as simulating the die-cutting process, showing that the hole offset is ≤0.05mm and the shape cutting rate is 100%).

[0095] In some embodiments, to address the issue of residual waste after die-cutting (traditional waste removal mechanisms use fixed pressure, which are prone to incomplete waste removal due to raw material stickiness and mold wear, requiring manual cleaning), a visual inspection combined with a fuzzy control algorithm is used to identify residual waste in real time (such as incomplete removal of waste edges). The pressure or speed of the waste removal mechanism is then dynamically adjusted to achieve closed-loop optimization of the waste removal effect. The core logic is: increased residual waste rate → greater waste removal pressure required → fuzzy control outputs pressure adjustment.

[0096] The hardware deployment involves installing a high-speed vision camera (such as Basler's acA2500-14gm, resolution 2592x1944, frame rate 14fps) between the die-cutting unit outlet and the winding unit to capture images of the auxiliary material after waste removal (e.g., once every 20mm); installing a pressure sensor (such as Omega's PX409, accuracy ±0.5%FS) at the waste removal roller (a rubber roller used to peel off waste edges) to collect the waste removal pressure (F) in real time; the waste removal roller is driven by a servo motor, and its pressure is adjusted by a controller (e.g., by pushing the roller closer to the auxiliary material with a cylinder, pressure range 0-500N).

[0097] Images captured by a vision camera are processed by a convolutional neural network (CNN) (such as using the YOLOv8 model) to identify waste residue (such as waste edge area ≥ 0.1 mm², or residue position deviating from the preset area); the CNN outputs the waste residue rate (R_waste, such as 5% indicating that 5% of the auxiliary material has residue), which is transmitted to the controller.

[0098] The fuzzy control adjustment includes: input variables including waste discharge residual rate (R_waste, quantized as {0,1,2,3,4}, corresponding to 0%, 2.5%, 5%, 7.5%, 10%) and residual rate change rate (ΔR_waste / Δt, quantized as {-2,-1,0,1,2}); output variables including waste discharge pressure adjustment amount (ΔF, quantized as {-2,-1,0,1,2}, corresponding to -50N, -25N, 0, +25N, +50N); the control rules adopt the "if-then" rule (e.g., "if R_waste is 3 and ΔR_waste is 1, then ΔF is 2", that is, residual rate is 5% and on an upward trend → pressure increases by 50N), a total of 25 rules are designed; defuzzification uses the centroid method to convert the fuzzy output into the actual pressure adjustment amount (e.g., ΔF=2 → pressure increases by 50N).

[0099] The closed-loop control process includes the following steps: when the CNN detects that R_waste ≥ 5%, the controller triggers the fuzzy control algorithm; the algorithm outputs ΔF (e.g., +50N), and the controller drives the cylinder to increase the pressure of the waste discharge roller (e.g., from 300N to 350N); after adjustment, the vision camera checks the waste discharge effect again. If R_waste drops to ≤ 2.5%, the current pressure is maintained; if there is no improvement, the adjustment is repeated (up to 3 times); if the standard is still not met, an alarm is triggered (e.g., "Severe waste residue, please check the mold").

[0100] Please see Figure 2 As shown, Figure 2This is a schematic diagram of the structure of an efficient auxiliary material production system 200 based on roll-to-roll die-cutting technology provided in this application embodiment. This efficient auxiliary material production system 200 based on roll-to-roll die-cutting technology is used to execute the steps of the efficient auxiliary material production method based on roll-to-roll die-cutting technology shown in the above embodiments. The efficient auxiliary material production system 200 based on roll-to-roll die-cutting technology can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, laptop computer, wearable device, or robot.

[0101] like Figure 2 As shown, the high-efficiency auxiliary material production system 200 based on roll-to-roll die-cutting technology includes: The continuous feeding unit 201 is used to load the roll of blue film plastic film raw material onto the roll unwinding device, thereby realizing the continuous and automated feeding of the plastic film through the unwinding device; The tension adjustment unit 202 is used to control the tension of the plastic film in real time and dynamically adjust it through the high-precision tension control module during the transmission process, so as to ensure that the plastic film is flat and wrinkle-free and has uniform and stable tension during the transmission process. The film entry unit 203 is used to control the plastic film to enter the die-cutting unit. The film is die-cut in one operation by the precision mold in the die-cutting unit. During the single die-cutting process, the outer shape cutting and internal hole forming of the auxiliary material are completed simultaneously, realizing continuous roll-to-roll die-cutting process and obtaining the die-cut plastic film auxiliary material.

[0102] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the auxiliary material high-efficiency production system and each module based on roll-to-roll die-cutting technology described above can be referred to the corresponding contents in the various embodiments of the auxiliary material high-efficiency production method based on roll-to-roll die-cutting technology, and will not be repeated here.

[0103] The aforementioned efficient production method for auxiliary materials based on roll-to-roll die-cutting technology can be implemented as a computer program, which can be used in, for example... Figure 2 It runs on the device shown.

[0104] Please see Figure 3 , Figure 3 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application. The computer device includes a processor, a memory, and a network interface connected via a device bus, wherein the memory may include a storage medium and internal memory.

[0105] The storage medium can store operating devices and computer programs. The computer program includes program instructions that, when executed, cause the processor to perform any efficient auxiliary material production method based on roll-to-roll die-cutting technology.

[0106] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0107] Internal memory provides an environment for the execution of computer programs in non-volatile storage media. When executed by a processor, the computer program enables the processor to perform any efficient auxiliary material production method based on roll-to-roll die-cutting technology.

[0108] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0109] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0110] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps: Rolled blue film plastic film raw material is installed on a roll unwinding device, and the continuous automated feeding of plastic film is achieved through the unwinding device; During the transmission process, the tension of the plastic film is dynamically adjusted in real time by a high-precision tension control module to ensure that the plastic film is flat and wrinkle-free and has uniform and stable tension. The plastic film is controlled to enter the die-cutting unit, and is die-cut in one operation through the precision mold in the die-cutting unit. During a single die-cutting process, the outer shape cutting and internal hole forming of the auxiliary material are completed simultaneously, realizing a continuous roll-to-roll die-cutting process to obtain the die-cut plastic film auxiliary material.

[0111] In some embodiments, the continuous automated feeding of plastic film via the unwinding device includes: driving the roll to rotate via a servo motor of the unwinding device, collecting the feed length data of the roll in real time using an encoder, and transmitting the data to a system controller. The controller controls the speed and number of rotations of the servo motor via pulse signals according to a preset feed speed threshold and length parameters, thereby achieving constant-length, constant-speed continuous feeding of the plastic film.

[0112] In some embodiments, controlling the plastic film during transmission via a high-precision tension control module includes: setting a tension sensor on the plastic film transmission path; the sensor collects the film tension value in real time and converts it into an electrical signal; the electrical signal is transmitted to the system controller via an analog-to-digital converter; and the controller triggers a magnetic powder brake or tension adjusting roller in the tension control module according to a preset tension threshold range to dynamically adjust the film tension in real time.

[0113] In some embodiments, the real-time dynamic adjustment of the tension of the plastic film to ensure that the plastic film is flat and wrinkle-free and has uniform and stable tension during transmission includes: based on a preset PID control algorithm, calculating and outputting a control signal to the tension adjustment mechanism according to the deviation between the real-time feedback data of the tension sensor and the target tension value, and adjusting the matching degree between the unwinding speed and the feed speed of the die-cutting unit to achieve tension fluctuation error control within ±5N.

[0114] In some embodiments, controlling the plastic film to enter the die-cutting unit includes: setting a visual positioning camera at the entrance of the die-cutting unit, the camera acquiring real-time images of the edge position of the plastic film, calculating the film offset through an image recognition algorithm, and the system controller driving a lateral adjustment cylinder according to the offset data to adjust the film transmission path and ensure that the film is accurately aligned with the processing area of ​​the die-cutting mold.

[0115] In some embodiments, the die-cutting action performed by the precision mold in the die-cutting unit, which simultaneously completes the outer shape cutting and internal hole forming of the auxiliary material during a single die-cutting process, includes: the die-cutting drive mechanism adopts a servo electric cylinder or hydraulic cylinder, and drives the mold to press down at a preset pressing speed through the die-cutting pressure curve and stroke parameters preset by the system controller; the outer shape punching blade and the hole forming component of the mold are designed with a stepped structure to achieve the synchronous completion of outer shape cutting and hole forming, and the single die-cutting cycle is controlled within 0.5-1.2 seconds.

[0116] In some embodiments, the method further includes: establishing a historical production database to store process data such as tension parameters, die-cutting pressure, and feed speed corresponding to blue film plastic films of different thicknesses and materials; training the historical data using machine learning algorithms to generate a process parameter prediction model; and when a new batch of raw materials is fed into the machine, the process parameter prediction model outputs the optimal tension value and die-cutting pressure value based on the input raw material parameters to achieve intelligent matching of process parameters.

[0117] In some embodiments, the method further includes: setting an online detection module at the outlet of the die-cutting unit, wherein the module takes real-time pictures of the die-cutting auxiliary materials using a linear CCD camera, analyzes the images using a convolutional neural network algorithm, and identifies whether there are defects such as unbroken die-cutting, hole misalignment, or waste residue; when a defect is detected, the system controller automatically triggers an alarm and adjusts the pressing depth of the die-cutting mold or the cleaning and waste removal mechanism according to the defect type.

[0118] In some embodiments, the method further includes: real-time acquisition of key data during the production process; key data includes tension fluctuation value, number of die-cuts and auxiliary material yield; establishing a process parameter optimization model through a fuzzy control algorithm; when the auxiliary material yield is continuously detected to be lower than a preset threshold, the process parameter optimization model automatically performs multi-factor optimization calculations on tension control parameters, die-cutting speed and mold temperature, generates new control parameter combinations and sends them to each actuator to achieve adaptive optimization of the production process.

[0119] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to implement the steps of the efficient auxiliary material production method based on roll-to-roll die-cutting process provided in any embodiment of this application.

[0120] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0121] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A high efficiency production method of a sub-material based on a roll-to-roll die cutting process, characterized by, The application relates to a continuous roll-to-roll die cutting method and device for plastic film auxiliary materials. The application comprises the following steps: The continuous automatic feeding of the plastic film is realized through the unwinding device. The high-precision tension control module is used to control the plastic film in the transmission process.

2. The method of claim 1, wherein, The tension of the plastic film is dynamically adjusted in real time. The plastic film is controlled to enter the die cutting unit.

3. The method of claim 1, wherein, The plastic film is cut and formed through the precise die in the die cutting unit. The die cutting driving mechanism adopts a servo electric cylinder or a hydraulic cylinder.

4. The method of claim 1, wherein, The historical production database is established. The application further comprises the following steps:

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8. The method of claim 1, wherein, The method further comprises: An online detection module is arranged at the outlet of the die cutting unit, which takes real-time photos of the die-cut auxiliary materials through a line array CCD camera, analyzes the images using a convolutional neural network algorithm, and identifies whether there are defects such as die cutting failure, hole position deviation, or waste residue; When defects are detected, the system controller automatically triggers an alarm and adjusts the die cutting die pressing depth or cleans the waste removal mechanism according to the defect type.

9. The method of claim 1, wherein, The method further comprises: Real-time acquisition of key data in the production process; key data includes tension fluctuation value, die cutting frequency, and auxiliary material yield; Through a fuzzy control algorithm, a process parameter optimization model is established, which automatically performs multi-factor optimization calculation on tension control parameters, die cutting speed, and die temperature when the auxiliary material yield is continuously detected to be lower than the preset threshold, generates a new control parameter combination, and issues it to each execution mechanism, realizing self-adaptive optimization of the production process.

10. A high efficiency production system of a substate based on a roll-to-roll die cutting process, characterized by, Applied to the method of any one of claims 1-9, comprising: A continuous feeding unit for installing the roll-shaped blue film plastic film raw material on a roll material unwinding device, realizing continuous automatic feeding of the plastic film through the unwinding device; A tension adjusting unit for controlling the plastic film to pass through a high-precision tension control module during transmission to dynamically adjust the tension of the plastic film in real time, ensuring that the plastic film is flat and wrinkle-free and the tension is uniform and stable during transmission; A film entering unit for controlling the plastic film to enter the die cutting unit, performing a one-time die cutting forming action through the precise die in the die cutting unit, synchronously completing the shape cutting and internal hole forming of the auxiliary material in a single die cutting process, realizing continuous roll-to-roll die cutting process, and obtaining the die-cut plastic film auxiliary material.

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