Multi-shaft luggage processing equipment and control method

Abstract

The invention discloses multi-axis luggage processing equipment which comprises a self-adaptive flexible fixing unit, a multi-axis processing unit and a control unit, and is characterized in that the self-adaptive flexible fixing unit is used for self-adaptively fixing a to-be-processed luggage; the modularized multi-process processing machine head is used for executing at least two processing procedures of cutting, punching, riveting and sewing on the fixed luggage; the distributed cooperative control system is electrically connected with the self-adaptive flexible fixing unit and the modular multi-process machining machine head and used for controlling the fixing action of the fixing unit and the machining action of the machining machine head; wherein the self-adaptive flexible fixing unit is integrated with a negative pressure adsorption module, a mechanical clamping module and a visual positioning module, the negative pressure adsorption module and the mechanical clamping module cooperatively act, and the visual positioning module is used for recognizing the outline of the luggage and guiding the negative pressure adsorption module and the mechanical clamping module to act; in addition, the invention further discloses a control method of the multi-shaft luggage processing equipment. The fixing adaptability is improved, the processing precision is improved, and the processing efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of luggage processing equipment technology, and in particular to a multi-axis luggage processing equipment and control method. Background Technology

[0002] Existing bag processing equipment generally suffers from the following technical defects: Poor adaptability of fixing methods: Traditional negative pressure adsorption relies on planar contact, which is ineffective for fixing non-planar materials (such as curved or textured surfaces) or flexible materials (such as soft leather), easily leading to slippage during processing; Limited processing functions: Most equipment only supports a single process (such as cutting or polishing), requiring multiple machines to be used for processing, resulting in high process switching costs; Insufficient control precision: Using preset programs + static compensation control, it is impossible to respond in real time to material deformation (such as heat deformation during cutting) or positioning errors during processing, and processing accuracy depends on initial positioning; High operating threshold: Professional programming is required, parameter adjustment is cumbersome during new product testing, and there is a lack of intelligent human-machine interaction support.

[0003] A high-efficiency CNC multi-axis, multi-station bag processing equipment, with application number CN202320056043.6, relates to the technical field of bag processing equipment. It includes a mounting frame, a bridging beam at the top of the mounting frame, and multiple multi-axis processing heads on the upper side of the bridging beam. The mounting frame has multiple sliding tables, and the multi-axis processing heads are rotatably mounted on the upper side of each sliding table. By mounting multiple sliding tables corresponding to multiple multi-axis processing heads on the mounting frame, multiple bags can be processed simultaneously, thereby improving processing efficiency and reducing cost and space requirements. The sliding tables are equipped with suction stations capable of negative pressure adsorption of bag molds. Each suction station has an adsorption head that can be inserted into the bottom of the bag mold, and the adsorption head can adsorb and secure the bag mold through negative pressure, making it simple and convenient to use. While this solves some problems in existing bag processing technologies, it often fails to meet the processing requirements for customized bag processing needs such as non-planar fixed designs, offering flexibility in processing multiple specifications and high processing precision.

[0004] To address the aforementioned issues, this invention proposes a multi-axis luggage processing equipment and control method, which achieves high-precision, high-flexibility, and integrated processing through adaptive fixing, multi-process integration, and dynamic compensation technologies. Summary of the Invention

[0005] To address the shortcomings of existing technologies, such as insufficient adaptability of fixed methods, limited processing functions, low control precision, and complex operation, a multi-axis bag processing equipment and control method are provided to improve the precision, efficiency, and flexibility of bag processing.

[0006] To solve the above-mentioned technical problems, the objective of this invention is achieved as follows: A multi-axis bag processing device includes: an adaptive flexible fixing unit for adaptively fixing the bag to be processed; a modular multi-process processing head for performing at least two processing steps among cutting, punching, riveting, and sewing on the fixed bag; and a distributed collaborative control system electrically connected to the adaptive flexible fixing unit and the modular multi-process processing head for controlling the fixing action of the fixing unit and the processing action of the processing head; wherein the adaptive flexible fixing unit integrates a negative pressure adsorption module, a mechanical clamping module, and a vision positioning module, the negative pressure adsorption module and the mechanical clamping module work together, and the vision positioning module is used to identify the outline of the bag and guide the action of the negative pressure adsorption module and the mechanical clamping module.

[0007] Based on the above scheme and as a preferred embodiment of the above scheme: the negative pressure adsorption module includes an array of liftable adsorption heads, the lifting stroke of the adsorption heads is 0-5mm, and each adsorption head is equipped with an independent solenoid valve to control the local negative pressure.

[0008] Based on the above solution and as a preferred embodiment of the above solution: the mechanical clamping module includes four sets of rotatable corner covers, the rotation angle range of the corner covers is 0-90°, and the corner covers have built-in pressure sensors for real-time feedback of clamping force.

[0009] Based on the above solution and as a preferred embodiment of the above solution: the visual positioning module includes an industrial camera and a laser contour sensor for acquiring images of the bag surface, and the laser contour sensor is used to acquire three-dimensional contour data of the bag.

[0010] Based on the above scheme and as a preferred embodiment of the above scheme: the modular multi-process machining head includes: a tool library with built-in cutting blades, drilling bits, riveting heads and sewing needles; an automatic tool changer for switching tools in the tool library; and a six-axis linkage drive assembly for driving the machining head to achieve X / Y / Z axis movement and A / B / C axis rotation.

[0011] Based on the above scheme and as a preferred embodiment of the above scheme: the distributed collaborative control system includes: a hardware layer, including a central controller, a multi-axis motion controller and a sensor group, wherein the sensor group includes at least a pressure sensor, a temperature sensor and a vision sensor; and a software layer, including a real-time operating system, a process database and a dynamic compensation algorithm module, wherein the process database pre-stores processing parameter templates for various bag materials.

[0012] Furthermore, this application discloses a control method for a multi-axis luggage processing equipment, comprising the following steps: S1: Adaptive Fixing Step: The visual positioning module identifies the outline and surface type of the bag to be processed, and controls the negative pressure adsorption module and mechanical clamping module of the adaptive flexible fixing unit to work together to achieve adaptive fixing of the bag. S2: Real-time dynamic compensation step: During the processing, the position data and material deformation of the bag are collected in real time by a vision sensor, the processing path compensation value is calculated based on a preset algorithm, and the modular multi-process processing head is controlled to adjust the movement trajectory. S3: Process self-optimization step: Based on the material type of the bag to be processed, the initial processing parameters are retrieved from the process database. During the processing, the parameters are continuously optimized based on the yield feedback until the preset processing efficiency and accuracy requirements are met.

[0013] Based on the above scheme and as a preferred embodiment of the above scheme: In step S1, the identification of the surface type of the bag to be processed specifically includes: determining whether the surface of the bag is a planar hard material, a non-planar hard material, or a flexible material through the visual positioning module; if it is a planar hard material, only the negative pressure adsorption module is activated; if it is a non-planar hard material or a flexible material, the negative pressure adsorption module and the mechanical clamping module are activated to fix it together.

[0014] Based on the above scheme and as a preferred option: In step S2, the preset algorithm is an LSTM neural network model. The input of the model includes the real-time position of the bag collected by the vision sensor and the temperature of the processing area collected by the temperature sensor. The output is the axis compensation value of the processing head.

[0015] Based on the above scheme and as a preferred option of the above scheme: In step S3, the parameter optimization based on yield rate feedback specifically includes: after processing a set number of bags, the processing defect rate is calculated. If the defect rate is >0.5%, the processing parameters are adjusted. The adjustment direction includes increasing the cutting speed or reducing the clamping force until the defect rate is ≤0.5% and the processing efficiency is improved by ≥15%.

[0016] The outstanding and beneficial technical effects of this invention compared to existing technologies are: 1. Improved fixation adaptability: Through the synergy of negative pressure, mechanical clamping, and visual positioning, it supports the fixation of flat, non-flat, hard, and flexible materials for bags and luggage, reducing the slippage rate from 3-5% in existing technologies to below 0.1%; 2. Improved processing accuracy: Multi-axis linkage combined with real-time dynamic compensation significantly reduces processing errors, meeting the precision processing requirements of high-end bags and luggage; 3. Improved processing efficiency: Modular multi-process integration avoids multiple equipment transfers, effectively shortening the processing time for a single piece. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall structure of the device of the present invention; Figure 2 This is a flowchart of the processing control method of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the given embodiments, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] In the description of this application, it should be understood that the terms "upper" and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0020] In the description of this application, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0021] See details Figure 1 As shown, this application discloses a multi-axis bag processing equipment, including: an adaptive flexible fixing unit for adaptively fixing the bag to be processed; a modular multi-process processing head for performing at least two processing steps among cutting, punching, riveting, and sewing on the fixed bag; and a distributed collaborative control system electrically connected to the adaptive flexible fixing unit and the modular multi-process processing head for controlling the fixing action of the fixing unit and the processing action of the processing head; wherein, the adaptive flexible fixing unit integrates a negative pressure adsorption module, a mechanical clamping module, and a visual positioning module, the negative pressure adsorption module and the mechanical clamping module work together, and the visual positioning module is used to identify the outline of the bag and guide the action of the negative pressure adsorption module and the mechanical clamping module.

[0022] Specifically, the negative pressure adsorption module includes an adsorption head array: a 12×12 matrix with 144 adsorption heads, each 8mm in diameter, made of nitrile rubber to avoid leather dents; a lifting drive: a micro servo motor, model: Sanyo Denki 103H7126-0440, with a step angle of 1.8°, an idle speed of 3000rpm, a stroke of 0-5mm, and a positioning accuracy of ±0.01mm; a negative pressure system: an oil-free vacuum pump with a pumping speed of 100m³ / h; and a solenoid valve assembly, model: SMC VQZ1000, with a response time ≤5ms and a negative pressure adjustment range of -0.04~-0.1MPa.

[0023] The mechanical clamping module includes four sets of rotatable corner covers: made of aluminum alloy, preferably with anodized surface treatment, and with a 0.5mm thick silicone pad on the clamping surface; Rotation drive: harmonic reducer with a reduction ratio of 50:1, rotation angle range of 0-90°, and positioning accuracy of ±0.1°; Pressure sensor: miniature tension and compression sensor, preferably with a range of 0-50N, accuracy of ±0.1N, and sampling frequency of 1kHz.

[0024] The vision positioning module includes an industrial camera with a preferred resolution of 4096×3000 pixels, a frame rate of 30fps, a lens focal length of 16mm, a working distance of 300mm, and a field of view of 25°×18°; a laser contour sensor, preferably the Keyence LK-G5000 model, with a scanning speed of 5000Hz, a linear resolution of 0.5μm, a measurement range of ±10mm, and an EtherCAT data output interface; and a ring LED light source, preferably with a color temperature of 5000K and adjustable brightness.

[0025] Tool library includes: Cutting tools: Carbide circular cutters, preferably 3mm in diameter, with a 30° cutting edge angle and a speed range of 5000-20000rpm; Drilling tools: High-speed steel drill bits, preferably 2mm in diameter; Riveting tools: Pneumatic rivet guns with a working air pressure of 0.6MPa, rivet diameter of 5mm, and a stroke of 15mm; Sewing tools: Industrial sewing machines. Lockstitch, with an adjustable stitch density of 5-15 stitches / cm.

[0026] Automatic tool changer drive method: adopts a servo motor and ball screw structure; tool change process: tool identification (such as RFID tag, reading time ≤10ms) → tool removal → rotating tool selection (6-station tool magazine) → tool insertion.

[0027] Six-axis linkage assembly: X / Y / Z axes: linear modules; A / B / C axes: rotary axes; drive system: servo drivers that support EtherCAT bus.

[0028] At the hardware level, the central controller uses Advantech IPC-610L (Intel Core i7-12700E processor, 16GB DDR4 memory, 512GB SSD, Windows 10 IoT Enterprise system); the multi-axis motion controller uses Beckhoff CX2040; and the sensor group includes temperature and vibration sensors in addition to the aforementioned pressure / vision sensors.

[0029] Software layer: Real-time operating system: RTX64; Process database: Based on SQL Server 2019, with 25 pre-stored material parameter templates (including ABS, PC, nylon, leather, canvas, etc.), supporting the import of user-defined parameters; Dynamic compensation algorithm: LSTM neural network model (TensorFlow 2.8 framework, network structure: 8 neurons in the input layer → 64 neurons × 2 layers in the LSTM layer → 16 neurons in the fully connected layer → 3 neurons in the output layer (Δx, Δy, Δz), training data: 100,000 sets of historical processing error samples, prediction accuracy ±0.005mm).

[0030] Detailed implementation process of processing steps (a) Preparation stage (pre-processing) Material loading and positioning: The leather bags to be processed are manually placed on the base positioning pins of the self-adaptive flexible fixing unit, and the positioning pins are matched with the pre-set positioning holes of the bags; System initialization: Start the distributed collaborative control system, the software layer automatically loads the "arc-shaped leather bag" process template, and the hardware layer completes sensor self-test.

[0031] (II) Adaptive Flexible Fixing Stage Visual scanning and surface recognition: An industrial camera captures images of the bag's surface, while a laser contour sensor acquires the three-dimensional contour data of the bag's shell. The software layer identifies the contour size through edge detection algorithms and determines the surface type as "non-planar flexible material" through grayscale gradient analysis.

[0032] Collaborative fixed execution: Negative pressure adsorption start-up: The system control array adsorption head adjusts its height according to the three-dimensional contour data, the vacuum pump pumps air to -0.075MPa (pressure holding time 2s), and the pressure sensor reports that the adsorption force meets the standard; Mechanical clamping start: The four corner covers rotate to 35° (fitting the curved edge of the bag), the servo motor drives the clamping arm to apply the initial force, the pressure sensor provides real-time feedback of the force value, and the clamping force is stabilized to the set value, such as 15±0.3N, through PID adjustment; Verification of fixation effect: The laser contour sensor performs a second scan to detect the displacement of the bag. If the requirements are met, the bag enters the processing stage.

[0033] (III) Multi-process processing execution stage (including real-time dynamic compensation) Overall machining process: cutting (outer contour) → drilling → riveting → sewing (edge ​​stitching). The parameters and compensation process for each process are as follows: Real-time dynamic compensation: A vision sensor acquires the cutting trajectory at a frequency of 100Hz, and a temperature sensor monitors the tool temperature. An LSTM model is input with the cutting position (X=150.2mm, Y=100.1mm), temperature (42℃), and leather deformation (measured by a laser contour sensor) to predict the deformation trend: Δx=+0.02mm, Δy=-0.01mm. A multi-axis motion controller adjusts the X / Y axis positions in real time (after compensation, X=150.22mm, Y=100.09mm), with a compensation response time of 42ms. The processing result is: 0.03mm of burr on the cut edge and a contour dimension deviation of ±0.08mm, which is considered acceptable.

[0034] Drilling process: Drilling is performed using a drill bit.

[0035] Real-time dynamic compensation: Visual positioning of the center point of the hole, for example, to drill four holes, the coordinates of each hole are: (50,50)mm, (50,170)mm, (270,50)mm, (270,170)mm; The model predicts that the hole offset Δz = +0.03 mm (Z-axis direction) caused by the elastic deformation of the leather, and the drilling depth after compensation is 4.03 mm; Processing results: The positional error of the four holes is ±0.02mm, and there are no tears in the hole walls (meeting the requirements for leather processing).

[0036] Riveting process (tool: rivet gun): Basic parameters: working air pressure 0.6MPa, rivet feed speed 20mm / s, pressure holding time 0.5s; Process control: Pressure sensors monitor riveting force, and vision sensors confirm the flatness of the rivet head.

[0037] Sewing process (tools: lockstitch sewing needle): Basic parameters: sewing speed 250mm / min, stitch density 12 stitches / cm, top thread tension 3.5N, bottom thread tension 2.8N; Real-time compensation: The visual sensor detects the stitch spacing and corrects the stitch deviation by adjusting the X-axis movement speed, with the final stitch deviation being ±0.015mm (meeting leather processing requirements).

[0038] (iv) Process self-optimization stage (batch processing verification) Experimental conditions: Continuously process a set number (e.g., 100 pieces) of the same model of leather bags, statistically analyze the processing defect rate and efficiency, and compare and optimize with the initial parameters.

[0039] Defect rate statistics (items 1-100): Cutting burr rate: 0.8% (0.06-0.07mm edge burrs on 8 pieces, exceeding the threshold by 0.05mm); Drilling position error: 0.5% (5 pieces, error 0.035-0.04mm, exceeding the threshold 0.03mm); Riveting / sewing: No defects (100% pass rate).

[0040] Parameter optimization iteration (2 rounds in total): First round of optimization (for cutting burrs): Reduce cutting speed and increase cooling airflow; Second round of optimization (addressing drilling errors): Reduce drill bit feed rate and increase pre-pressure time before drilling by 0.2s; Optimized results (items 101-200): Burr rate during cutting: 0.2%; Drilling position error: 0.1%; the optimized processing effect is significantly improved.

[0041] This embodiment, through specific equipment configuration, processing steps, and experimental data verification, can achieve high-precision, high-efficiency, and high-flexibility (non-planar / flexible material adaptability) processing of curved soft leather bags, solving the problems of poor fixation effect, scattered processes, and low precision in existing technologies.

[0042] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape, and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A multi-axis bag processing equipment, characterized in that, include: An adaptive flexible fixing unit is used to adaptively fix the bags to be processed. A modular multi-processing head is used to perform at least two processing steps among cutting, punching, riveting, and sewing on a fixed bag. A distributed collaborative control system is electrically connected to the adaptive flexible fixing unit and the modular multi-processing head, respectively, and is used to control the fixing action of the fixing unit and the processing action of the processing head. The adaptive flexible fixing unit integrates a negative pressure adsorption module, a mechanical clamping module, and a vision positioning module. The negative pressure adsorption module and the mechanical clamping module work together, and the vision positioning module is used to identify the outline of the bag and guide the action of the negative pressure adsorption module and the mechanical clamping module.

2. The device according to claim 1, characterized in that, The negative pressure adsorption module includes an array of liftable adsorption heads, the lifting stroke of which is 0-5mm, and each adsorption head is equipped with an independent solenoid valve to control the local negative pressure.

3. The device according to claim 1, characterized in that, The mechanical clamping module includes four sets of rotatable corner covers, the rotation angle of which is 0-90°, and the corner covers have built-in pressure sensors for real-time feedback of clamping force.

4. The device according to claim 1, characterized in that, The visual positioning module includes an industrial camera and a laser contour sensor, used to acquire images of the bag surface, and the laser contour sensor is used to acquire three-dimensional contour data of the bag.

5. The device according to claim 1, characterized in that, The modular multi-process machining head includes: a tool library with built-in cutting tools, drilling bits, riveting heads, and sewing needles; an automatic tool changer for switching tools in the tool library; and a six-axis linkage drive assembly for driving the machining head to achieve X / Y / Z axis movement and A / B / C axis rotation.

6. The device according to claim 1, characterized in that, The distributed collaborative control system includes: a hardware layer comprising a central controller, a multi-axis motion controller, and a sensor group, wherein the sensor group includes at least a pressure sensor, a temperature sensor, and a vision sensor; and a software layer comprising a real-time operating system, a process database, and a dynamic compensation algorithm module, wherein the process database pre-stores processing parameter templates for various bag materials.

7. A control method for the multi-axis luggage processing equipment according to claims 1-6, characterized in that, Includes the following steps: S1: Adaptive Fixing Step: The visual positioning module identifies the outline and surface type of the bag to be processed, and controls the negative pressure adsorption module and mechanical clamping module of the adaptive flexible fixing unit to work together to achieve adaptive fixing of the bag. S2: Real-time dynamic compensation step: During the processing, the position data and material deformation of the bag are collected in real time by a vision sensor, the processing path compensation value is calculated based on a preset algorithm, and the modular multi-process processing head is controlled to adjust the movement trajectory. S3: Process self-optimization step: Based on the material type of the bag to be processed, the initial processing parameters are retrieved from the process database. During the processing, the parameters are continuously optimized based on the yield feedback until the preset processing efficiency and accuracy requirements are met.

8. The control method for the multi-axis bag processing equipment according to claim 7, characterized in that, In step S1, identifying the surface type of the bag to be processed specifically includes: determining whether the surface of the bag is a planar hard material, a non-planar hard material, or a flexible material through the visual positioning module; if it is a planar hard material, only the negative pressure adsorption module is activated; if it is a non-planar hard or flexible material, the negative pressure adsorption module and the mechanical clamping module are activated to fix it together.

9. The control method for the multi-axis bag processing equipment according to claim 7, characterized in that, In step S2, the preset algorithm is an LSTM neural network model. The input of the model includes the real-time position of the bag collected by the vision sensor and the temperature of the processing area collected by the temperature sensor. The output is the axis compensation value of the processing head.

10. The control method for the multi-axis luggage processing equipment according to claim 7, characterized in that, In step S3, the continuous optimization of parameters based on yield rate feedback specifically includes: after processing a set number of bags, the processing defect rate is calculated. If the defect rate is >0.5%, the processing parameters are adjusted. The adjustment direction includes increasing the cutting speed or reducing the clamping force until the defect rate is ≤0.5% and the processing efficiency is improved by ≥15%.

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