High efficiency garment trademark printing dot positioning and hole cutting process

Abstract

This invention discloses a high-efficiency dot positioning and hole-cutting process for printing garment labels, relating to the field of garment processing technology. It is implemented based on a garment label processing system, which includes: a dot positioning module: employing image recognition technology, capturing image information of the area to be printed via a camera, and calculating the optimal printing position using an image processing algorithm; an intelligent path planning module: after hole positioning, the system automatically calculates the optimal printing and cutting path; this module utilizes path planning optimization based on a genetic algorithm to reduce the movement distance and time of the robotic arm, thereby improving overall efficiency; and a printing execution module: the printing head prints according to preset parameters; a dynamic adjustment algorithm is used to adapt to garment materials of different materials and thicknesses. This invention achieves positioning efficiency through the cooperation of the positioning mold and the hole, preventing material slippage, improving processing accuracy, and ensuring high reliability.

Description

Technical Field

[0001] This invention relates to the field of garment processing technology, and in particular to a high-efficiency garment label printing dot positioning and hole cutting process. Background Technology

[0002] In traditional garment production, reliable positioning of trademarks is required during cutting. Traditional positioning usually involves printing marks and then cutting according to the marks, which can easily lead to misalignment, resulting in low production efficiency and high costs.

[0003] A search revealed Chinese patent application number 202310534370.2, which discloses a garment cutting fabric positioning system, relating to the field of garment cutting fabric positioning technology. The system includes a cutting table, a pressing block for pressing the fabric above the cutting table, a guide structure above the pressing block for vertical lifting and lowering of the pressing block, and a lifting control mechanism below the cutting table for controlling the lifting and lowering of the pressing block. The positioning system in the aforementioned document has the following shortcomings: low positioning efficiency, and the possibility of fabric slippage during the positioning process, leading to defective products. Therefore, further improvements are needed. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a highly efficient process for positioning and cutting the printing dots of clothing trademarks.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A high-efficiency process for positioning and cutting printed dots on garment labels, based on a garment label processing system, the system comprising:

[0007] Dot positioning module: It uses image recognition technology to capture image information of the area to be printed through a camera and calculates the optimal printing position through image processing algorithms;

[0008] Intelligent path planning module: After the hole positioning is completed, the system automatically calculates the optimal printing and cutting path; this module uses path planning optimization based on genetic algorithm to reduce the moving distance and time of the robotic arm, thereby improving overall efficiency;

[0009] Printing execution module: The print head will print according to preset parameters; a dynamic adjustment algorithm is adopted to adapt to clothing materials of different materials and thicknesses to ensure printing quality; a deep learning algorithm is introduced to monitor printing quality in real time, and by analyzing historical printing data, printing parameters are predicted and adjusted to optimize printing quality;

[0010] Cutting execution module: After printing is completed, the system automatically switches to cutting mode and completes the precise cutting of the trademark according to the preset cutting position and parameters;

[0011] Intelligent control module: Responsible for the coordination and control of the entire system. Based on a central processing unit, it connects with various sub-modules through a high-speed data bus to realize real-time data exchange and processing. It introduces reinforcement learning algorithms to continuously learn and optimize decisions in the production process to improve the overall intelligence level and production efficiency of the system.

[0012] As a preferred embodiment of the present invention: in the positioning sleeve cutting process, a positioning device is used to position the product to be cut, the positioning device comprising:

[0013] A positioning mold, with a cutting groove on the top;

[0014] Positioning posts are set on the outer corner of the cutting groove to position the material to be cut; positioning marks that match the positioning posts are opened on the material to be cut.

[0015] The positioning mold is used in conjunction with a cutting blade, which includes a blade holder and a cutting blade. The cutting blade is mounted on one side of the blade holder; the shape of the cutting blade is adapted to the cutting groove; the other side of the blade holder is connected to the robotic arm.

[0016] In addition, the material to be cut is printed with multiple positioning marks so that the system can process the sleeve holes according to the positioning marks.

[0017] As a preferred embodiment of the present invention: the positioning mold has a connecting groove on its side, and a slot is provided in the connecting groove. The same connecting plate is detachably installed in the connecting groove of the two positioning molds. A pin is fixed on one side of the connecting plate. The pin is adapted to the slot. When the connecting plate is installed in the connecting groove of the two positioning molds, the two pins are respectively inserted into the slots of the two positioning molds to realize the splicing connection of the two positioning molds.

[0018] As a preferred embodiment of the present invention, the positioning device further includes a mounting base with multiple mounting slots. Positioning blocks are detachably installed in the mounting slots. A pull ring is integrally provided on one outer wall of the connecting plate. The shape of the pull ring is adapted to the positioning block. When the positioning mold is installed on the mounting base, the positioning blocks on both sides are respectively engaged in the pull rings on both sides of the positioning mold.

[0019] As a preferred embodiment of the present invention, the dot positioning module includes:

[0020] Image preprocessing unit: responsible for enhancing sharpness, removing noise, and correcting color in the captured image;

[0021] Feature extraction unit: uses edge detection and morphological operations to identify key features in the image;

[0022] Dot Recognition Unit: Employs a convolutional neural network from deep learning to accurately identify dot markers in images;

[0023] Positioning calculation unit: Combining template matching technology and optimization algorithms, it calculates the optimal printing point.

[0024] As a preferred embodiment of the present invention, the feature extraction unit uses the Canny edge detection algorithm, specifically the formula:

[0025]

[0026] Among them, G x and G y These are the edge gradients of the image in the x and y directions, respectively.

[0027] As a preferred embodiment of the present invention: the dot recognition unit employs a convolutional neural network in deep learning to accurately identify dot markers in an image. In the convolutional layer, the feature map is calculated using the following formula:

[0028]

[0029] Among them, F i,j It is the output feature map, w m,n It is the convolution kernel weight, I i+m,j+n σ is the input image region, b is the bias term, and σ is the activation function.

[0030] As a preferred embodiment of the present invention, the intelligent path planning module includes:

[0031] Dynamic analysis unit: assesses the current production line status and garment material characteristics to provide real-time data for path planning;

[0032] Path optimization unit: Using a genetic algorithm, the optimal movement path is calculated based on the kinematic characteristics of the robotic arm;

[0033] Predictive adjustment unit: Uses machine learning models to predict and avoid potential mechanical disturbances or obstacles.

[0034] As a preferred embodiment of the present invention, the printing execution module includes:

[0035] Parameter adjustment unit: Automatically adjusts the pressure, speed, and ink volume of the printing head based on the type and thickness of the garment material;

[0036] Quality monitoring unit: Monitors printing quality in real time through high-precision sensors and feeds back to the central control system;

[0037] Anomaly Handling Unit: When a printing defect is detected, the repair program is automatically initiated or the operator is notified to intervene.

[0038] As a preferred embodiment of the present invention, the trimming execution module includes:

[0039] Cutting path planning unit: Calculates the most reasonable cutting path based on the geometric characteristics of the printed pattern;

[0040] Laser control unit: precisely adjusts the power, focal length, and scanning speed of the laser cutter;

[0041] The intelligent control module includes:

[0042] Task scheduling unit: responsible for coordinating the work of various modules to ensure the smooth and synchronized production process;

[0043] Data Analysis Unit: Collects production process data and optimizes production processes and improves product quality through big data analysis.

[0044] The beneficial effects of this invention are as follows:

[0045] 1. This invention achieves positioning efficiency by using the cooperation of positioning mold and sleeve hole, preventing material slippage, improving processing accuracy, and ensuring high reliability.

[0046] 2. By setting up a positioning mold that can be spliced ​​and connected, the present invention can be disassembled and assembled according to actual needs, thereby improving processing efficiency.

[0047] 3. By setting up structures such as mounting bases and positioning blocks, this invention enables the positioning blocks to pass through the gap between the pull ring and the connecting plate and then be inserted into the mounting groove when multiple positioning molds are spliced ​​together, thereby achieving auxiliary fixation of each positioning mold and the connecting plate.

[0048] 4. By integrating image recognition, intelligent path planning, precise printing and cutting, this invention achieves a fully automated process from image capture to finished product cutting, significantly improving production efficiency. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the positioning device in the high-efficiency garment trademark printing dot positioning and hole cutting process proposed in this invention.

[0050] Figure 2 This is a schematic diagram of the structure of the positioning mold being removed from the mounting base in a high-efficiency garment trademark printing dot positioning sleeve cutting process proposed in this invention.

[0051] Figure 3 This is a schematic diagram of the structure of the connecting plate being removed from the positioning mold in a high-efficiency clothing trademark printing dot positioning hole cutting process proposed in this invention.

[0052] In the diagram: 1. Positioning mold, 2. Mounting base, 3. Positioning mark, 4. Material to be cut, 5. Sleeve hole, 6. Cutting blade, 7. Blade holder, 8. Positioning post, 9. Mounting groove, 10. Positioning block, 11. Pull ring, 12. Connecting plate, 13. Insert post, 14. Cutting groove, 15. Connecting groove, 16. Slot. Detailed Implementation

[0053] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0054] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0055] Example 1:

[0056] A high-efficiency process for positioning and cutting printed dots on garment labels, based on a garment label processing system, the system comprising:

[0057] Dot positioning module: It uses image recognition technology to capture image information of the area to be printed through a camera and calculates the optimal printing position through image processing algorithms;

[0058] Intelligent path planning module: After the hole positioning is completed, the system automatically calculates the optimal printing and cutting path; this module uses path planning optimization based on genetic algorithm to reduce the moving distance and time of the robotic arm, thereby improving overall efficiency;

[0059] Printing execution module: The print head will print according to preset parameters; a dynamic adjustment algorithm is adopted to adapt to clothing materials of different materials and thicknesses to ensure printing quality; a deep learning algorithm is introduced to monitor printing quality in real time, and by analyzing historical printing data, printing parameters are predicted and adjusted to optimize printing quality;

[0060] Cutting execution module: After printing is completed, the system automatically switches to cutting mode and completes the precise cutting of the trademark according to the preset cutting position and parameters;

[0061] Intelligent control module: Responsible for the coordination and control of the entire system. Based on a central processing unit, it connects with various sub-modules through a high-speed data bus to realize real-time data exchange and processing. It introduces reinforcement learning algorithms to continuously learn and optimize decisions in the production process to improve the overall intelligence level and production efficiency of the system.

[0062] In the positioning sleeve cutting process, a positioning device is used to position the product to be cut. The positioning device includes:

[0063] Positioning mold 1, with a cutting groove 14 on the top;

[0064] Positioning post 8 is located on the outer corner of the cutting groove 14 and is used to position the material to be cut 4; positioning mark 3 adapted to the positioning post 8 is provided on the material to be cut 4.

[0065] The positioning mold 1 is used in conjunction with a cutting blade, which includes a blade holder 7 and a cutting blade 6. The cutting blade 6 is installed on one side of the blade holder 7. The shape of the cutting blade 6 is adapted to the cutting groove 14. The other side of the blade holder 7 is connected to the robotic arm.

[0066] In addition, the material to be cut 4 is printed with multiple positioning marks 3 so that the system can process the sleeve hole 5 according to the positioning marks 3.

[0067] To facilitate the assembly and use of multiple molds; such as Figure 1-3 As shown, the positioning mold 1 has a connecting groove 15 on its side, and a slot 16 is provided in the connecting groove 15. The same connecting plate 12 is detachably installed in the connecting groove 15 of the two positioning molds 1. A pin 13 is fixed on one side of the connecting plate 12. The pin 13 is adapted to the slot 16. When the connecting plate 12 is installed in the connecting groove 15 of the two positioning molds 1, the two pins 13 are respectively inserted into the slot 16 of the two positioning molds 1, so as to realize the splicing connection of the two positioning molds 1.

[0068] By setting up a positioning mold 1 that can be spliced ​​and connected, it is possible to disassemble and assemble according to actual needs, thereby improving processing efficiency.

[0069] To improve the reliability of splicing; such as Figure 1-3 As shown, the positioning device also includes a mounting base 2, which has multiple mounting slots 9. Positioning blocks 10 are detachably installed in the mounting slots 9. A pull ring 11 is integrally provided on one outer wall of the connecting plate 12. The shape of the pull ring 11 is adapted to the positioning block 10. When the positioning mold 1 is installed on the mounting base 2, the positioning blocks 10 on both sides are respectively inserted into the pull rings 11 on both sides of the positioning mold 1.

[0070] By setting up structures such as mounting base 2 and positioning block 10, when multiple positioning molds 1 are spliced ​​together, the positioning block 10 can be inserted into the mounting groove 9 after passing through the gap between the pull ring 11 and the connecting plate 12, thereby achieving auxiliary fixation of each positioning mold 1 and the connecting plate 12.

[0071] The dot positioning module includes:

[0072] Image preprocessing unit: responsible for enhancing sharpness, removing noise, and correcting color in the captured image;

[0073] Feature extraction unit: uses edge detection and morphological operations to identify key features in the image;

[0074] Dot Recognition Unit: Employs a convolutional neural network from deep learning to accurately identify dot markers in images;

[0075] Positioning calculation unit: Combining template matching technology and optimization algorithms, it calculates the optimal printing point.

[0076] The feature extraction unit uses the Canny edge detection algorithm, and the specific formula is as follows:

[0077]

[0078] Among them, G x and G y These are the edge gradients of the image in the x and y directions, respectively.

[0079] The dot recognition unit employs a convolutional neural network in deep learning to accurately identify dot markers in an image. In the convolutional layer, the feature map is calculated using the following formula:

[0080]

[0081] Among them, F i,j It is the output feature map, w m,n It is the convolution kernel weight, I i+m,j+n σ is the input image region, b is the bias term, and σ is the activation function.

[0082] The intelligent path planning module includes:

[0083] Dynamic analysis unit: assesses the current production line status and garment material characteristics to provide real-time data for path planning;

[0084] Path optimization unit: Using a genetic algorithm, the optimal movement path is calculated based on the kinematic characteristics of the robotic arm;

[0085] Predictive adjustment unit: Uses machine learning models to predict and avoid potential mechanical disturbances or obstacles.

[0086] The printing execution module includes:

[0087] Parameter adjustment unit: Automatically adjusts the pressure, speed, and ink volume of the printing head based on the type and thickness of the garment material;

[0088] Quality monitoring unit: Monitors printing quality in real time through high-precision sensors and feeds back to the central control system;

[0089] Anomaly Handling Unit: When a printing defect is detected, the repair program is automatically initiated or the operator is notified to intervene.

[0090] The trimming execution module includes:

[0091] Cutting path planning unit: Calculates the most reasonable cutting path based on the geometric characteristics of the printed pattern;

[0092] Laser control unit: Precisely adjusts the power, focal length, and scanning speed of the laser cutter.

[0093] The intelligent control module includes:

[0094] Task scheduling unit: responsible for coordinating the work of various modules to ensure the smooth and synchronized production process;

[0095] Data Analysis Unit: Collects production process data and optimizes production processes and improves product quality through big data analysis.

[0096] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A high efficiency garment brand printing dot positioning nest cutting process characterized by, This is implemented based on a clothing label processing system, which includes: Dot positioning module: It uses image recognition technology to capture image information of the area to be printed through a camera and calculates the optimal printing position through image processing algorithms; Intelligent path planning module: After the hole positioning is completed, the system automatically calculates the optimal printing and cutting path; this module uses path planning optimization based on genetic algorithm to reduce the moving distance and time of the robotic arm, thereby improving overall efficiency; Printing execution module: The print head will print according to preset parameters; a dynamic adjustment algorithm is adopted to adapt to clothing materials of different materials and thicknesses to ensure printing quality; a deep learning algorithm is introduced to monitor printing quality in real time, and by analyzing historical printing data, printing parameters are predicted and adjusted to optimize printing quality; Cutting execution module: After printing is completed, the system automatically switches to cutting mode and completes the precise cutting of the trademark according to the preset cutting position and parameters; Intelligent control module: Responsible for the coordination and control of the entire system. Based on a central processing unit, it connects with various sub-modules through a high-speed data bus to realize real-time data exchange and processing. It introduces reinforcement learning algorithms to continuously learn and optimize decisions in the production process in order to improve the overall intelligence level and production efficiency of the system. In the positioning sleeve cutting process, a positioning device is used to position the product to be cut. The positioning device includes: Positioning mold (1), the top of positioning mold (1) is provided with a cutting groove (14); Positioning post (8) is set on the outside of the corner of the cutting groove (14) to position the material to be cut (4); the material to be cut (4) is provided with a positioning mark (3) that matches the positioning post (8). The positioning mold (1) is used in conjunction with a cutting blade, which includes a blade holder (7) and a cutting blade (6). The cutting blade (6) is installed on one side of the blade holder (7). The shape of the cutting blade (6) is adapted to the cutting groove (14). The other side of the blade holder (7) is connected to the robotic arm. In addition, the material to be cut (4) is printed with multiple positioning marks (3) so that the system can process the sleeve hole (5) according to the positioning marks (3); The positioning mold (1) has a connecting groove (15) on its side and a slot (16) inside the connecting groove (15). The same connecting plate (12) is detachably installed in the connecting groove (15) of the two positioning molds (1). A pin (13) is fixed on one side of the connecting plate (12). The pin (13) is adapted to the slot (16). When the connecting plate (12) is installed in the connecting groove (15) of the two positioning molds (1), the two pins (13) are respectively inserted into the slots (16) of the two positioning molds (1) to realize the splicing connection of the two positioning molds (1). The positioning device also includes a mounting base (2), which has multiple mounting slots (9). A positioning block (10) is detachably installed in the mounting slot (9). A pull ring (11) is integrally provided on one side of the outer wall of the connecting plate (12). The shape of the pull ring (11) is adapted to the positioning block (10). When the positioning mold (1) is installed on the mounting base (2), the positioning blocks (10) on both sides are respectively inserted into the pull rings (11) on both sides of the positioning mold (1).

2. A high efficiency garment tag printing dot positioning die cutting process according to claim 1 wherein, The dot positioning module includes: Image preprocessing unit: responsible for enhancing sharpness, removing noise, and correcting color in the captured image; Feature extraction unit: uses edge detection and morphological operations to identify key features in the image; Dot Recognition Unit: Employs a convolutional neural network from deep learning to accurately identify dot markers in images; Positioning calculation unit: Combining template matching technology and optimization algorithms, it calculates the optimal printing point.

3. A high efficiency apparel brand printing dot positioning nest cutting process according to claim 2, wherein, The feature extraction unit uses the Canny edge detection algorithm, and the specific formula is as follows: wherein and are the edge gradients of the image in the x and y directions, respectively.

4. A high efficiency garment tag printing dot positioning die cutting process according to claim 3, wherein, The dot recognition unit uses a convolutional neural network in deep learning to accurately identify dot marks in an image.

5. A high efficiency garment tag printing dot positioning die cutting process according to claim 1 wherein, The intelligent path planning module includes: Dynamic analysis unit: assesses the current production line status and garment material characteristics to provide real-time data for path planning; Path optimization unit: Using a genetic algorithm, the optimal movement path is calculated based on the kinematic characteristics of the robotic arm; Predictive adjustment unit: Uses machine learning models to predict and avoid potential mechanical disturbances or obstacles.

6. A high efficiency apparel brand printing dot positioning die cutting process according to claim 1, wherein, The printing execution module includes: Parameter adjustment unit: Automatically adjusts the pressure, speed, and ink volume of the printing head based on the type and thickness of the garment material; Quality monitoring unit: Monitors printing quality in real time through high-precision sensors and feeds back to the central control system; Anomaly Handling Unit: When a printing defect is detected, the repair program is automatically initiated or the operator is notified to intervene.

7. The high-efficiency garment trademark printing dot positioning and hole cutting process according to claim 1, characterized in that, The trimming execution module includes: Cutting path planning unit: Calculates the most reasonable cutting path based on the geometric characteristics of the printed pattern; Laser control unit: precisely adjusts the power, focal length, and scanning speed of the laser cutter; The intelligent control module includes: Task scheduling unit: responsible for coordinating the work of various modules to ensure the smooth and synchronized production process; Data Analysis Unit: Collects production process data and optimizes production processes and improves product quality through big data analysis.

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