Feeding device of uncoiling machine
By using the triangular traction zone of the magnetic roller and the matching roller and the intelligent control system, the problem of low control accuracy of traditional unwinding devices is solved, and high-precision material tension and stress adjustment is achieved, improving the stability and intelligence level of the unwinding process.
Patent Information
- Application Number
- CN202511345775.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-19
- Publication Date
- 2026-01-02
AI Technical Summary
Traditional unwinding devices suffer from low control precision and slow response during the processing of roll materials such as films, metal foils, and nonwoven fabrics. This causes the material to be subjected to tensile impacts during startup, speed changes, or roll changes, resulting in wrinkling, deviation, or even breakage, which affects product quality and production stability.
The system employs a triangular traction zone composed of a magnetic roller and a cooperating roller. Combined with a high-frequency sensor data, a material mechanics model, and a fuzzy PID algorithm, the intelligent control system generates uniform friction between the magnetic roller and the material surface. This, along with adjustable reverse pressure, enables high-precision, adaptive adjustment of material tension and stress.
It effectively eliminates material deviation, wrinkling, and loosening, improves the stability of the unwinding process and the quality of material processing, and enhances the intelligence level of the equipment.
Smart Images
Figure CN121247532A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application relates to the technical field of material unwinding, and in particular to a feeding device of a material unwinding machine. BACKGROUND
[0002] In the processing of materials such as films, metal foils and non-woven fabrics, unwinding is a primary and key process. Traditional unwinding devices usually adopt mechanical or pneumatic brakes combined with simple roller groups to control tension, which has the problems of low control precision and slow response, and is prone to cause the material to bear tension impact during start, speed change or roll change, and further cause the material to wrinkle, deviate or even break, thereby affecting product quality, causing production interruption and raw material waste, and being difficult to meet the needs of modern high-precision and high-efficiency production for stability and intelligence. SUMMARY
[0003] In order to solve the above problems, the application provides a feeding device of a material unwinding machine.
[0004] The feeding device of the material unwinding machine provided by the application adopts the following technical scheme: A feeding device of a material unwinding machine, comprising a bottom plate, a material placing frame is fixedly connected to the top of the bottom plate, the material placing frame is used for placing the material to be unwound, a working frame is fixedly connected to the top of the bottom plate, mounting seats are fixedly connected to the left and right sides of the top of the working frame, a matching roller is rotatably connected to the inner wall of the mounting seat, a magnetic roller is rotatably connected to the inner wall of the mounting seat, a stress relief control box is arranged on the right side of the mounting seat on the right side, a driving motor is arranged in the stress relief control box, the output end of the driving motor is fixedly connected with the right end of the magnetic roller through a shaft coupling, a supporting frame is fixedly connected to the rear side of the top of the working frame, a matching plate is fixedly connected to the inner wall of the supporting frame, and the matching plate and the magnetic roller are located on the same horizontal line.
[0005] As a preferred technical scheme of the application, the number of the matching rollers is two, the two matching rollers are symmetrically distributed above and below, the magnetic roller is located between the two matching rollers, the magnetic roller and the two matching rollers are in a triangular distribution, a conveying roller is rotatably connected to the inner wall of the supporting frame, the number of the conveying rollers is four, the four conveying rollers are linearly arrayed, an installation frame is fixedly connected to the right side of the supporting frame, a rotating motor is fixedly connected to the top of the installation frame, and the output end of the rotating motor is fixedly connected with the right end of the last conveying roller through a shaft coupling.
[0006] As a preferred technical solution of the present application, the inside of the stress relief control box is integrated with a core processing module, the output end of the core processing module is electrically connected with a tension detection module, the output end of the tension detection module is electrically connected with a speed detection module, the output end of the speed detection module is electrically connected with a stress analysis module, the output end of the stress analysis module is electrically connected with a magnetic roller control module, the output end of the magnetic roller control module is electrically connected with a man-machine interaction module, the output end of the man-machine interaction module is electrically connected with a data storage module, the output end of the data storage module is electrically connected with an adaptive learning module, the core processing module is used for receiving sensor data and executing stress analysis algorithm and outputting control instruction, the tension detection module is used for detecting tension value of material in real time, the speed detection module is used for detecting feeding speed of material in real time, the stress analysis module calculates real-time stress distribution of material based on tension value and feeding speed, the magnetic roller control module dynamically adjusts rotating speed and torque of the magnetic roller according to the output of the stress analysis module, the man-machine interaction module is used for parameter setting, state display and alarm prompt, the data storage module is used for storing historical operation data and parameter configuration, the adaptive learning module is used for recording optimal control parameters of different materials under different working conditions and establishing prediction model based on machine learning algorithm, and the adaptive learning module can automatically call or fine-tune control parameters when encountering the same material subsequently.
[0007] As a preferred technical solution of the present application, the tension detection module comprises a tension sensor mounted on the matching plate, and the tension sensor collects tension signals of the material in real time at a sampling frequency of 100 Hz or more. The speed detection module comprises an encoder coaxially connected with any of the conveying rollers, and the encoder is used for accurately measuring linear speed of the material.
[0008] As a preferred technical solution of the present application, the stress analysis module is built-in with a stress calculation algorithm based on a material mechanics model, the stress calculation algorithm takes tension value, material elastic modulus, cross-section geometric parameters and speed change rate as input, and the stress calculation algorithm is used for outputting real-time stress value and stress distribution cloud diagram of the material. The magnetic roller control module is electrically connected with the driving motor in the stress relief control box through a frequency converter, the magnetic roller control module adopts a fuzzy PID control algorithm, and the magnetic roller control module adjusts rotating speed and output torque of the driving motor in real time according to the deviation between the stress value output by the stress analysis module and the set target value.
[0009] As a preferred technical solution of the present application, the machine learning algorithm adopted by the adaptive learning module is a reinforcement learning algorithm, and the reinforcement learning algorithm takes stability of stress control and quality of material processing as reward function and continuously optimizes control strategy.
[0010] As the preferred technical solution of the present application, the output end of the self-adaptive learning module is electrically connected with a remote communication module, the remote communication module supports 4G / 5G or industrial Ethernet protocol, and the remote communication module is used for uploading system running data and alarm information to a cloud server or a remote monitoring center and receiving instructions and parameter updates from the remote.
[0011] As the preferred technical solution of the present application, the output end of the remote communication module is electrically connected with a safety protection module, the safety protection module is used for continuously monitoring stress value, motor current and equipment temperature, the safety protection module will immediately trigger an audible and light alarm and perform an emergency shutdown or speed reduction operation when detecting that any parameter exceeds a safety threshold, and the safety protection module is used for protecting equipment and material safety.
[0012] To sum up, the present application includes at least one of the following beneficial technical effects of the feeding device of the unwinding machine: The present application realizes high-precision and self-adaptive adjustment of material tension and stress through the cooperation of the triangular traction area formed by the magnetic roller and the matching roller and the intelligent control system based on high-frequency sensing data, material mechanics model and fuzzy PID algorithm, thereby effectively preventing deviation, wrinkling and relaxation, and improving the stability of the unwinding process, material processing quality and equipment intelligence level. BRIEF DESCRIPTION OF DRAWINGS
[0013] Figure 1 is a schematic diagram of the overall structure of the present application; Figure 2 is a side view of the overall structure of the present application; Figure 1 Figure 3 is a control system distribution diagram of the stress control box in the present application. Figure 1
[0014] BRIEF DESCRIPTION OF DRAWINGS: 1, bottom plate; 2, support frame; 3, workbench; 4, material placing frame; 5, stress relief control box; 6, conveying roller; 7, mounting bracket; 8, rotating motor; 9, matching plate; 10, mounting seat; 11, matching roller; 12, magnetic roller. DETAILED DESCRIPTION
[0015] The present application will be further described in detail below in combination with the accompanying drawings. Figures 1-3
[0016] Reference is made to Figures 1-3 The utility model provides a kind of feed device of uncoiler, including bottom plate 1, the top of bottom plate 1 is fixedly connected with material frame 4, material frame 4 is used to place the material needing to be uncoiled, the top of bottom plate 1 is fixedly connected with working stand 3, the left and right sides of working stand 3 top are all fixedly connected with mounting seat 10, the inner wall of mounting seat 10 is rotatably connected with cooperation roller 11, the inner wall of mounting seat 10 is rotatably connected with magnetic roller 12, the right side of right side of mounting seat 10 is equipped with stress relief control box 5, the inside of stress relief control box 5 is equipped with driving motor, the output of driving motor is fixedly connected with the right end of magnetic roller 12 by shaft coupling, the rear side of working stand 3 top is fixedly connected with support frame 2, the inner wall of support frame 2 is fixedly connected with cooperation plate 9, cooperation plate 9 is located on the same horizontal line with magnetic roller 12;The number of cooperation roller 11 is two, two cooperation roller 11 is symmetrically distributed, magnetic roller 12 is located between two cooperation roller 11, magnetic roller 12 and two cooperation roller 11 are triangularly distributed, the inner wall of support frame 2 is rotatably connected with conveying roller 6, the number of conveying roller 6 is four, four conveying roller 6 is linear array distribution, the right side of support frame 2 is fixedly connected with mounting frame 7, the top of mounting frame 7 is fixedly connected with rotating motor 8, the output of rotating motor 8 is fixedly connected with the right end of last side conveying roller 6 by shaft coupling; Operator places roll material in material frame 4, material opening enters the triangular traction area formed by magnetic roller 12 and two cooperation roller 11, triangular layout ensures that material is uniformly constrained in three directions, effectively prevents material from running deviation or wrinkling phenomenon during uncoiling, magnetic roller 12 is driven to rotate as driving roller under the driving of driving motor in stress relief control box 5, generates uniform friction force with material surface through its special magnetic adsorption characteristics, while cooperation roller 11 distributed up and down provides adjustable reverse pressure under the action of air pressure device (air nozzle is arranged on cooperation roller 11, the output of air pressure device is communicated with the inside of cooperation roller 11), forms stable clamping force field, this kind of multi-roller collaborative working mode not only ensures that material runs smoothly in tensioned state, but also effectively eliminates the relaxation phenomenon that material can generate when initially uncoiling, the whole feeding process adopts gradual tension establishment principle, avoids sudden tension impact to cause damage to material.
[0017] The stress relief control box 5 integrates a core processing module. The output of the core processing module is electrically connected to a tension detection module, which in turn is electrically connected to a speed detection module. The speed detection module's output is electrically connected to a stress analysis module, which in turn is electrically connected to a magnetic roller control module. The magnetic roller control module's output is electrically connected to a human-machine interface module, which in turn is electrically connected to a data storage module. The data storage module's output is electrically connected to an adaptive learning module. The core processing module receives sensor data, executes stress analysis algorithms, and outputs control commands. The tension detection module... The system is designed for real-time detection of material tension values. The speed detection module is used to detect the material feeding speed in real time. The stress analysis module calculates the real-time stress distribution of the material based on the tension value and feeding speed. The magnetic roller control module dynamically adjusts the rotation speed and torque of the magnetic roller 12 according to the output of the stress analysis module. The human-machine interaction module is used for parameter setting, status display and alarm prompts. The data storage module is used to store historical operating data and parameter configurations. The adaptive learning module is used to record the optimal control parameters of different materials under different working conditions and build a prediction model based on machine learning algorithms. The adaptive learning module can automatically call or fine-tune the control parameters when encountering the same material in the future. The tension detection module includes a tension sensor mounted on the mating plate 9, which collects the tension signal of the material in real time at a sampling frequency of over 100Hz. The speed detection module includes an encoder coaxially connected to any of the conveying rollers 6, used to accurately measure the linear velocity of the material. The stress analysis module has a built-in stress calculation algorithm based on a material mechanics model. This algorithm takes the tension value, material elastic modulus, cross-sectional geometric parameters, and velocity change rate as input, and outputs the real-time stress value and stress distribution cloud map of the material. The magnetic roller control module is electrically connected to the drive motor in the stress relief control box 5 via a frequency converter. The magnetic roller control module uses a fuzzy PID control algorithm, adjusting the drive motor in real time based on the deviation between the stress value output by the stress analysis module and the set target value. The adaptive learning module uses a reinforcement learning algorithm as its machine learning algorithm, which uses the stability of stress control and the quality of material processing as reward functions to continuously optimize the control strategy. The output of the adaptive learning module is electrically connected to a remote communication module, which supports 4G / 5G or industrial Ethernet protocols. This module uploads system operating data and alarm information to a cloud server or remote monitoring center and receives instructions and parameter updates from a remote location. The output of the remote communication module is also electrically connected to a safety protection module, which continuously monitors stress values, motor current, and equipment temperature. When any parameter exceeds a safety threshold, the safety protection module immediately triggers an audible and visual alarm and performs an emergency stop or speed reduction operation to protect the equipment and materials. The high-precision tension sensor mounted on the mating plate uses strain gauge or fiber optic sensing technology to collect real-time micro-tension changes of the material as it passes through at a sampling frequency exceeding 100Hz. The measurement accuracy of the high-precision tension sensor is within ±0.5%. An incremental encoder coaxially connected to the main conveyor roller detects the linear velocity of the material in real time, ensuring the accuracy of the velocity measurement. The real-time detection signal is transmitted to the core processing module in the stress relief control box through a shielded cable. The signal first undergoes analog-to-digital conversion and digital filtering to eliminate the noise caused by electromagnetic interference and mechanical vibration on site. The core processing module uses an industrial-grade multi-core processor, which can process multiple sensor signals in parallel, perform real-time data fusion analysis on tension and velocity values, and establish a digital twin model of the material's operating state. This application has a signal self-diagnosis function, which can automatically identify sensor faults or signal abnormalities, ensuring the reliability of input data and providing an accurate data foundation for subsequent stress analysis. After obtaining the preprocessed sensor data, the core processing module immediately calls the preset material mechanics model in the stress analysis module for calculation. The model is based on the basic principles of material mechanics, combined with the material's inherent parameters such as elastic modulus, Poisson's ratio, and moment of inertia of the cross section, as well as the real-time collected tension values and velocity change rate. It calculates the stress distribution of the material under complex stress state through finite element analysis. The algorithm first establishes a discretized model of the material, dividing the material into several micro-elements. Then, based on the macroscopic tension value measured by the tension sensor and the velocity gradient change detected by the encoder, it uses an iterative calculation method to solve the stress state of each micro-element. The calculation process considers the anisotropic characteristics of the material, temperature influence factors, and strain rate effect. Finally, it outputs complete stress data including axial stress, shear stress, and equivalent stress, and visualizes it in the form of a color cloud map. This application can accurately identify stress concentration areas in the material and predict the location of possible defects, providing a quantitative basis for precise control. After receiving the real-time stress value output by the stress analysis module, the magnetic roller control module compares it with the preset target stress range and generates control commands using a fuzzy PID control algorithm. This algorithm combines the adaptability of fuzzy logic with the precision of PID control. First, the fuzzy inference system dynamically adjusts the PID parameters according to the magnitude and trend of the stress deviation. Then, the precise PID calculation outputs the control quantity. The control signal is transmitted to the frequency converter of the drive motor through a high-speed communication bus to adjust the motor speed and output torque. When the detected stress value is higher than the set upper limit, the control system gradually reduces the magnetic roller speed and output torque to release the material tension. When the stress value is lower than the set lower limit, the traction force and speed are appropriately increased to maintain the stable tension of the material. The entire control process adopts a multi-variable coordinated control strategy to ensure that no overshoot or oscillation occurs during the adjustment process, achieving a smooth transition of stress control. This application has a feedforward compensation function, which can pre-adjust the control parameters according to the changes in material properties to improve the response speed. The human-machine interface module provides an intuitive operating interface through a high-resolution touchscreen. Operators can set process parameters such as material type, target stress value, maximum allowable stress, and operating speed. The main interface displays key parameters such as material stress curve, operating speed, and current tension value in real time, and uses color changes to indicate the equipment's operating status. The secondary interface provides a historical data query function, allowing users to review stress control curves and alarm records from the past 24 hours. The tertiary interface is an expert setting interface, allowing authorized engineers to adjust control algorithm parameters and system configurations. All operating data, parameter settings, and alarm information are automatically stored in the data storage module, which uses industrial-grade solid-state storage to ensure the reliability and integrity of data storage. The stored data includes stress sampling values per minute, motor control command records, and equipment operating status logs. This data is organized using timestamps as an index and supports rapid retrieval and analysis by date, material batch, and other conditions, providing complete data support for product quality traceability. The adaptive learning module adopts a deep reinforcement learning-based algorithm architecture, using the stability of stress control and the quality of material processing as reward functions. Through continuous learning, it continuously optimizes the control strategy. This application first establishes a multi-dimensional state space including material type, initial stress state, running speed, and environmental conditions. Then, it uses the control parameters of the magnetic roller as the action space and trains the neural network model with a large amount of actual operating data. This application records the stress control effect and material surface quality evaluation results under different control strategies, and gradually establishes the mapping relationship between material properties and control parameters. When processing the same type of material again, it can automatically call the historical optimal control parameters from the knowledge base and make fine adjustments according to the current actual working conditions. This application can continuously expand its knowledge base and gradually improve its adaptability to new materials. The remote communication module transmits real-time operating data, alarm information, and learning model updates to the cloud server or remote monitoring center via 4G / 5G or industrial Ethernet. It can also receive remote commands and parameter optimization settings to realize network management and cross-regional monitoring of the equipment. The safety protection module continuously monitors key system parameters, including stress exceeding limits, abnormal motor current, or excessive equipment temperature. Once an abnormality is detected, it immediately triggers an audible and visual alarm and automatically performs emergency shutdown or speed reduction operations according to the severity, effectively ensuring the safety of equipment and materials.
[0018] This application achieves high-precision, adaptive adjustment of material tension and stress by working in concert with the triangular traction zone formed by the magnetic roller 12 and the cooperating roller 11, combined with an intelligent control system based on high-frequency sensor data, material mechanics model and fuzzy PID algorithm. This effectively eliminates deviation, wrinkling and loosening, and improves the stability of the unwinding process, the quality of material processing and the level of equipment intelligence.
[0019] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.
Claims
1. A feeding device for an unwinding machine, characterized in that: The system includes a base plate (1), a material placement frame (4) fixedly connected to the top of the base plate (1), the material placement frame (4) being used to place the material to be unwound, a work frame (3) fixedly connected to the top of the base plate (1), mounting seats (10) fixedly connected to the left and right sides of the top of the work frame (3), a mating roller (11) rotatably connected to the inner wall of the mounting seat (10), a magnetic roller (12) rotatably connected to the inner wall of the mounting seat (10), a stress relief control box (5) is provided on the right side of the mounting seat (10), a drive motor is provided inside the stress relief control box (5), the output end of the drive motor is fixedly connected to the right end of the magnetic roller (12) through a coupling, a support frame (2) is fixedly connected to the rear side of the top of the work frame (3), a mating plate (9) is fixedly connected to the inner wall of the support frame (2), and the mating plate (9) and the magnetic roller (12) are located on the same horizontal line.
2. The feeding device for an unwinding machine according to claim 1, characterized in that: There are two mating rollers (11), which are symmetrically distributed vertically. The magnetic roller (12) is located between the two mating rollers (11), and the magnetic roller (12) and the two mating rollers (11) are arranged in a triangle. The inner wall of the support frame (2) is rotatably connected to a conveying roller (6), which is four in number and arranged in a linear array. The right side of the support frame (2) is fixedly connected to a mounting frame (7), and the top of the mounting frame (7) is fixedly connected to a rotating motor (8). The output end of the rotating motor (8) is fixedly connected to the right end of the last conveying roller (6) through a coupling.
3. The feeding device for an unwinding machine according to claim 2, characterized in that: The stress relief control box (5) integrates a core processing module. The output of the core processing module is electrically connected to a tension detection module, the output of the tension detection module is electrically connected to a speed detection module, the output of the speed detection module is electrically connected to a stress analysis module, the output of the stress analysis module is electrically connected to a magnetic roller control module, the output of the magnetic roller control module is electrically connected to a human-machine interface module, the output of the human-machine interface module is electrically connected to a data storage module, and the output of the data storage module is electrically connected to an adaptive learning module. The core processing module receives sensor data, executes stress analysis algorithms, and outputs control commands. The tension detection module... The speed detection module is used to detect the material's feeding speed in real time. The stress analysis module calculates the material's real-time stress distribution based on the tension value and feeding speed. The magnetic roller control module dynamically adjusts the rotational speed and torque of the magnetic roller (12) according to the output of the stress analysis module. The human-machine interaction module is used for parameter setting, status display, and alarm prompts. The data storage module is used to store historical operating data and parameter configurations. The adaptive learning module is used to record the optimal control parameters of different materials under different working conditions and establish a prediction model based on machine learning algorithms. The adaptive learning module can automatically call or fine-tune the control parameters when encountering the same material in the future.
4. The feeding device for an unwinding machine according to claim 3, characterized in that: The tension detection module includes a tension sensor installed on the mating plate (9), and the tension sensor collects the tension signal of the material passing through in real time at a sampling frequency of more than 100Hz; The speed detection module includes an encoder, which is coaxially connected to any of the conveying rollers (6), and the encoder is used to accurately measure the linear velocity of the material.
5. The feeding device for an unwinding machine according to claim 4, characterized in that: The stress analysis module has a built-in stress calculation algorithm based on a material mechanics model. The stress calculation algorithm takes tension value, material elastic modulus, cross-sectional geometric parameters and velocity change rate as input, and outputs the real-time stress value and stress distribution cloud map of the material. The magnetic roller control module is electrically connected to the drive motor in the stress relief control box (5) through a frequency converter. The magnetic roller control module adopts a fuzzy PID control algorithm. The magnetic roller control module adjusts the speed and output torque of the drive motor in real time according to the deviation between the stress value output by the stress analysis module and the set target value.
6. The feeding device for an unwinding machine according to claim 5, characterized in that: The adaptive learning module employs a reinforcement learning algorithm, which uses the stability of stress control and the quality of material processing as reward functions to continuously optimize the control strategy.
7. The feeding device for an unwinding machine according to claim 6, characterized in that: The output of the adaptive learning module is electrically connected to a remote communication module. The remote communication module supports 4G / 5G or industrial Ethernet protocols. The remote communication module is used to upload system operation data and alarm information to a cloud server or remote monitoring center and to receive instructions and parameter updates from a remote location.
8. The feeding device for an unwinding machine according to claim 7, characterized in that: The output of the remote communication module is electrically connected to a safety protection module. The safety protection module is used to continuously monitor stress values, motor current, and equipment temperature. When the safety protection module detects that any parameter exceeds the safety threshold, it will immediately trigger an audible and visual alarm and perform an emergency stop or speed reduction operation. The safety protection module is used to protect the safety of equipment and materials.