A collaborative system and method for a fully automated laser cutting production line based on MES control.

Abstract

This application provides a collaborative system and method for a fully automated laser cutting production line based on MES control, belonging to the field of laser cutting production line control technology. The system includes: an MES subsystem that receives and parses order data to generate cutting parameters and equipment control commands, which are then sent to the production line execution equipment. Simultaneously, the system monitors the status of the production line execution equipment and sends updated equipment control commands to it. An edge collaboration controller connects the MES controller and the production line execution equipment, forwarding the cutting parameters and equipment control commands from the MES subsystem to the corresponding production line execution equipment, and collecting the status of the production line execution equipment and forwarding it to the MES subsystem. The production line execution equipment responds to and executes the equipment control commands, completing the corresponding operations of the automated laser cutting production line. This invention achieves collaborative equipment operation, reduces manual intervention, and improves production efficiency and product quality through real-time data interaction and dynamic adjustment. It also possesses intelligent optimization and conflict avoidance capabilities.

Description

Technical Field

[0001] This application belongs to the field of laser cutting production line control technology, specifically relating to a collaborative system and method for a fully automated laser cutting production line based on MES control. Background Technology

[0002] Laser cutting, as a high-precision and high-efficiency processing technology, has been widely used in industrial production. It can quickly and accurately cut various metal materials according to preset patterns, providing precise components for subsequent processing and assembly.

[0003] Currently, most laser cutting production lines employ centralized PLC control or loosely coupled independent systems. While centralized PLC control systems can achieve centralized control of production line equipment to a certain extent, their relatively fixed control logic makes them difficult to adapt to complex and ever-changing production demands. Loosely coupled independent systems, on the other hand, often result in isolation between devices, leading to low efficiency in collaborative operations and hindering efficient task allocation and process optimization. In actual production, both centralized PLC control and loosely coupled independent systems lack real-time data interaction capabilities between the MES (Manufacturing Execution System) and the equipment layer (laser cutting machine / AGV). Information transmission between production plans and equipment operations is untimely and inaccurate, causing a disconnect between equipment operating status and production plans, and making production scheduling difficult. On the one hand, the relatively independent operation of each device prevents timely sharing and integration of information during the production process, affecting the accuracy and timeliness of production decisions. On the other hand, equipment movement conflicts do occur, such as overlapping trajectories between robotic arms and AGVs, and coordination problems between laser cutting machines and loading equipment. This not only easily leads to equipment failures and production accidents but also results in low production efficiency and significant resource waste. Furthermore, due to the aforementioned problems, traditional laser cutting production lines often require extensive manual intervention to coordinate the production process, which not only increases labor costs but also easily leads to low production efficiency and unstable product quality due to human factors. Summary of the Invention

[0004] In a first aspect, embodiments of this application provide a collaborative system for a fully automated laser cutting production line based on MES control, including an MES central control subsystem, an edge collaborative controller, and production line execution equipment; The production line equipment includes laser cutting machines, automated feeding systems, sorting robotic arms, and AGV transport vehicles; The MES central control subsystem receives and parses order data to generate cutting parameters and equipment control instructions, and sends them to the corresponding production line execution equipment. At the same time, it monitors the status of the production line execution equipment in real time, dynamically adjusts the production line task queue, sends updated equipment control instructions to the corresponding production line execution equipment, simulates the spatiotemporal relationship of the movement of the production line execution equipment, and controls the corresponding production line execution equipment to avoid movement conflicts between production line execution equipment. The edge collaboration controller connects the MES controller and the production line execution equipment, forwarding the cutting parameters and equipment control commands of the MES central control subsystem to the corresponding production line execution equipment, and collecting the status of the production line execution equipment in real time and forwarding it to the MES central control subsystem. The production line executes equipment responses and control commands to complete the corresponding operations of the automated laser cutting production line.

[0005] Furthermore, the MES central control subsystem includes: The work order parsing unit breaks down order data into a sequence of work processes for executable work orders, generates equipment control instructions based on the sequence of work processes, and matches the order data with the process parameter library to determine the optimal combination of cutting parameters. The laser cutting command is generated based on the optimal combination of cutting parameters and then sent to the laser cutting machine. The equipment control commands include loading commands, sorting commands, and AGV transportation commands. The loading instructions are sent to the automated loading warehouse, the sorting instructions are sent to the sorting robotic arm, and the AGV transportation instructions are sent to the scheduling system of the AGV transport vehicle. The dynamic resource allocation unit monitors the status of the production line execution equipment in real time, dynamically adjusts the production line task queue according to the status of the production line execution equipment, and determines the target operation of the production line execution equipment according to the changes in the production line tasks, and issues updated equipment control instructions to the corresponding production line execution equipment. The production line monitoring unit constructs a digital twin model of the production line to simulate the spatiotemporal relationship between the laser cutting machine's working area, the robotic arm's motion trajectory, and the AGV's path. It uses the GJK algorithm to detect collisions between equipment within a target time period and sends avoidance commands to the corresponding production line execution equipment when a collision is detected.

[0006] Furthermore, the edge collaboration controller includes: The multi-protocol adapter unit sends laser cutting commands to the controller of the laser cutting machine via the EtherCAT protocol; The sorting instructions are sent to the controller of the sorting robot arm via the Modbus-TCP protocol; The AGV transportation instructions are sent to the AGV transportation vehicle's scheduling system via the CAN bus; The real-time data acquisition unit collects the working light intensity and temperature data of the laser cutting machine and provides them to the MES central control subsystem for thermal deformation compensation and updating the laser cutting instructions. It also collects the SLAM positioning coordinates of the AGV transport vehicle and provides them to the MES central control subsystem to generate updated AGV transport instructions by updating the dynamic path. In addition, it collects the gripper pressure data of the sorting robot arm and adjusts the gripping force to generate updated sorting instructions.

[0007] Furthermore, in the production line execution equipment, The working area of ​​the laser cutting machine is equipped with an infrared thermal imaging sensor, which is connected to the edge coordinating controller. The laser cutting machine receives and parses the cutting instructions forwarded by the edge coordinating controller to obtain the cutting power and cutting speed, and then performs laser cutting. The edge coordinating controller monitors the temperature of the working area of ​​the laser cutting machine in real time through an infrared thermal imaging sensor, and triggers the MES central control subsystem to perform dynamic power compensation when the temperature exceeds the threshold. The automatic feeding system parses the feeding instructions forwarded by the edge collaboration controller to determine the material parameters. Based on the material parameters, it extracts the target board and performs RFID verification. Then, it outputs the target board to the feed port of the laser cutting machine via a conveyor belt. The sorting robot arm analyzes the sorting instructions forwarded by the edge collaboration controller, determines the tolerance standards, classification rules, gripping coordinates and clamping force, measures the workpiece size based on its own vision system and compares it with the tolerance standards, determines the category according to the classification rules, and then classifies and places the workpieces into the corresponding category AGV carriers according to the gripping coordinates and clamping force. The AGV transport vehicle parses the AGV transport instructions forwarded by the edge system controller, determines the dynamic path, and dynamically plans the transport path through an improved A* algorithm.

[0008] Furthermore, the AGV transport vehicle is equipped with lidar and UWB positioning modules; The AGV transport vehicle detects dynamic obstacles in real time using LiDAR and UWB positioning modules. When the distance to the robotic arm or another AGV transport vehicle is less than a distance threshold, one of the following avoidance strategies is triggered: The AGV transport vehicle controls its deceleration to the lower speed limit threshold through its own scheduling system, and broadcasts its location information to all AGV transport vehicles through the MQTT component; The AGV transport vehicle requests a re-planning of its transport route from the MES central control subsystem through its own scheduling system.

[0009] Secondly, embodiments of this application also provide a collaborative method for a fully automated laser cutting production line based on MES control, comprising the following steps: The S1.MES central control subsystem receives and parses order data, matches it with the process parameter library to determine the optimal combination of cutting parameters, and breaks down the order into an executable work order sequence of material loading → cutting → sorting → transportation. Based on the process sequence, it generates equipment control instructions with time-dependent relationships. S2. The edge collaborative controller manages the execution order of device control commands through a priority task queue, and issues device control commands to the corresponding production line execution devices according to the execution order; S3. The automatic feeding warehouse, laser cutting machine, sorting robotic arm and AGV transport vehicle interact and coordinate to execute the process according to the received equipment control instructions, and complete the feeding, cutting, sorting and transport operations in sequence; The S4.MES central control subsystem records latency data at each stage and optimizes the timing of equipment control instructions for subsequent work orders through reinforcement learning algorithms, as well as updating the optimal cutting parameter combination in the process parameter library.

[0010] Furthermore, in step S1, the MES central control subsystem determines the material type, material thickness, and cutting pattern by parsing the order data; The optimal combination of cutting parameters is determined by matching the material type and thickness with the process parameter library. The optimal combination of cutting parameters includes cutting power, cutting speed and type of auxiliary gas. When there is no matching optimal parameter combination in the process parameter library, the random forest is called to predict the optimal parameter combination. The loading, cutting, sorting, and AGV transportation instructions are generated sequentially based on the process sequence of loading → cutting → sorting → transportation. Material loading instruction generation process: Determine the target station for cutting, and generate a material loading instruction based on the material type and the target station; Cutting instruction generation process: The cutting instruction is generated based on the optimal combination of cutting parameters and material thickness, and the triggering condition for the cutting instruction is set to the completion of the loading instruction and the passing of RFID verification; Sorting instruction generation process: Sorting instructions are generated based on the tolerance standards of the cut parts, classification rules, gripping coordinates, and clamping force. The triggering conditions for sorting instructions are set to the completion of the cutting instruction and the temperature detection meeting the standard. AGV transport instruction generation process: Generate AGV transport instructions based on target coordinates and target speed, and set the trigger conditions for AGV transport instructions to be the completion of sorting instructions and visual classification confirmation.

[0011] Furthermore, the specific steps of step S2 are as follows: S21. The edge collaborative controller constructs a priority task queue, puts equipment control commands into the priority task queue, and sets the priorities of loading commands, cutting commands, sorting commands, and AGV transportation commands from high to low. S22. The edge collaboration controller monitors the completion status of control commands for each device in real time, and when it receives the corresponding completion signal, it raises the priority of the next device control command.

[0012] Furthermore, the specific steps of step S3 are as follows: S31. The automatic feeding warehouse receives the pull-up command, extracts the board type and target station, obtains the target board and verifies whether the RFID code of the target board matches the board type. When matching, the target board is transported to the feed port of the laser cutting machine at the target station via the conveyor belt, and then returns the feeding completion signal to the edge collaboration controller. S32. The laser cutting machine receives the cutting command, analyzes the cutting parameters such as cutting power, cutting speed, and auxiliary gas type, as well as the material thickness, and adjusts the focal length of the laser cutting machine according to the material thickness and the cutting parameters to execute the cutting. During the cutting process, the edge co-controller monitors the temperature of the cutting area in real time through an infrared thermal imaging sensor, and adjusts the laser power when the temperature exceeds the threshold to compensate for the cutting path deviation caused by the thermal deformation of the material. After the cutting is completed, the laser cutting machine returns a cutting completion signal to the edge co-controller. S33. The sorting robot arm receives the sorting instruction, analyzes the tolerance standard, classification rules, gripping coordinates and clamping force, measures the workpiece size through its built-in vision system and compares it with the tolerance standard, determines the category according to the classification rules, and then classifies and places the workpieces into the corresponding category AGV carriers according to the gripping coordinates and clamping force, and then returns the sorting completion signal to the edge collaboration controller. S34. The AGV transport vehicle receives the AGV transport instructions, analyzes the target coordinates and target speed, and plans a path based on the improved A* algorithm. It controls its own movement according to the planned path and target speed. At the same time, it detects dynamic obstacles in real time through LiDAR and UWB positioning module. When the distance to the robotic arm or the distance to another AGV transport vehicle is less than the distance threshold, it triggers an avoidance strategy. When it reaches the target position, it returns a transport completion signal to the edge collaborative controller.

[0013] Furthermore, the specific steps of step S21 are as follows: The dependency relationship of equipment control instructions in the same work order is modeled using a directed acyclic graph, and nodes are used to represent instructions and edges are used to represent timing constraints. In the initialization state, the loading command is set to have the highest priority, followed by the cutting command, sorting command, and AGV transportation command in that order. Step S22 also includes the following steps: When an urgent order is inserted, the timing of equipment control commands is globally rearranged, and the priority of the AGV transport vehicle is allocated through the contract network protocol. The edge collaboration controller sends cutting instructions to the laser cutting machine via the EtherCAT protocol, sorting instructions to the sorting robot arm via the Modbus-TCP protocol, and AGV transport instructions to the AGV transport vehicle via the CAN bus. In step S32, the edge collaborative controller monitors the temperature of the cutting area in real time using an infrared thermal imaging sensor, and adjusts the laser power to compensate for the cutting path offset caused by material thermal deformation when the temperature exceeds the threshold. The specific steps are as follows: The edge co-controller monitors the temperature of the cutting area in real time through an infrared thermal imaging sensor. If the local temperature exceeds the document threshold, the laser power is dynamically adjusted. The distance ΔZ between the cutting head and the sheet metal of the laser cutting machine is measured in real time using a laser displacement meter. The Z-axis servo motor of the laser cutting machine is adjusted by a PID controller to compensate for the focal length shift caused by thermal deformation. The specific steps of the AGV transport vehicle planning the path based on the improved A* algorithm in step S34 are as follows: SLAM is used to build a workshop grid map, the grid resolution is set, and static obstacles are marked. Load the digital twin model of the sorting robot's work area issued by the MES central control subsystem, and pre-map the motion trajectory of the sorting robot to the grid map; Calculate the cost function for each grid cell: Calculate the actual cost from the starting grid to the current grid, and calculate the heuristic cost from the current grid to the ending grid; Initialize the dynamic weight coefficients, and use the dynamic weight coefficients, actual cost, and heuristic cost to calculate the cost function for each grid cell; The position of the robotic arm within the next T seconds is predicted by the spatiotemporal grid method. If the distance between a certain grid in the AGV path and the trajectory of the sorting robotic arm is less than a set threshold, the dynamic weight coefficient of that grid is increased. Starting from the starting grid, traverse each grid and select the grid with the minimum cost function until the ending grid is reached; Perform cubic B-spline fitting on the original path point sequence to ensure that the radius of curvature is greater than the radius of curvature threshold, thus eliminating right-angle turns; Dynamically set the AGV speed based on the path curvature; New obstacles are detected in real time using LiDAR and UWB positioning modules; If an unmapped obstacle is detected or the positioning error exceeds the error threshold, local path replanning is triggered.

[0014] As can be seen from the above technical solutions, this application has the following advantages: The MES-based fully automated laser cutting production line collaborative system and method provided in this application achieves orderly collaboration among various devices by tightly integrating the MES central control subsystem, edge collaborative controller, and production line execution equipment. This improves production efficiency, reduces manual intervention, and lowers labor costs and the risk of human error. The MES central control subsystem monitors equipment status in real time, dynamically adjusts task queues, and updates control instructions to ensure the connection between production plans and equipment operations, improves the flexibility and accuracy of production scheduling, and enhances adaptability to complex and ever-changing production demands. Furthermore, reinforcement learning algorithms optimize the instruction timing of subsequent work orders, update the optimal cutting parameter combination in the process parameter library, and utilize collision detection algorithms and avoidance strategies to reduce the risk of equipment action conflicts, ensure the safety of the production process, and improve the stability of product quality. Attached Figure Description

[0015] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the MES-controlled fully automated laser cutting production line collaborative system of the present invention.

[0017] Figure 2 This is a schematic diagram of the collaborative method for fully automated laser cutting production lines based on MES control according to the present invention. Detailed Implementation

[0018] The various embodiments of this disclosure will be described more fully in the following detailed description of a collaborative system for a fully automated laser cutting production line based on MES control. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0019] For example, laser cutting, as a high-precision and high-efficiency processing technology, has been widely used in industrial production. Laser cutting can quickly and accurately cut various metal materials according to preset patterns, providing precise parts for subsequent processing and assembly.

[0020] Currently, most laser cutting production lines employ centralized PLC control or loosely coupled independent systems. While centralized PLC control systems can achieve centralized control of production line equipment to a certain extent, their relatively fixed control logic makes them difficult to adapt to complex and ever-changing production demands. Loosely coupled independent systems, on the other hand, often result in isolation between devices, leading to low efficiency in collaborative operations and hindering efficient task allocation and process optimization. In actual production, both centralized PLC control and loosely coupled independent systems lack real-time data interaction capabilities between the MES (Manufacturing Execution System) and the equipment layer (laser cutting machine / AGV). Information transmission between production plans and equipment operations is untimely and inaccurate, causing a disconnect between equipment operating status and production plans, and making production scheduling difficult. On the one hand, the relatively independent operation of each device prevents timely sharing and integration of information during the production process, affecting the accuracy and timeliness of production decisions. On the other hand, equipment movement conflicts do occur, such as overlapping trajectories between robotic arms and AGVs, and coordination problems between laser cutting machines and loading equipment. This not only easily leads to equipment failures and production accidents but also results in low production efficiency and significant resource waste. Furthermore, in traditional laser cutting production lines, due to the aforementioned problems, a significant amount of manual intervention is often required to coordinate the production process. This not only increases labor costs but also easily leads to low production efficiency and unstable product quality due to human factors. In other words...

[0021] To address the aforementioned issues, this embodiment provides a collaborative system for a fully automated laser cutting production line based on MES control, which realizes automated and intelligent management of the entire laser cutting production line process, from order receiving, task decomposition, equipment control to production execution and monitoring optimization.

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 The diagram shown is a schematic of a fully automated laser cutting production line collaborative system based on MES control in a specific embodiment, including the MES central control subsystem, the edge collaborative controller, and the production line execution equipment. The production line equipment includes laser cutting machines, automated feeding systems, sorting robotic arms, and AGV transport vehicles; The MES central control subsystem receives and parses order data to generate cutting parameters and equipment control instructions, and sends them to the corresponding production line execution equipment. At the same time, it monitors the status of the production line execution equipment in real time, dynamically adjusts the production line task queue, sends updated equipment control instructions to the corresponding production line execution equipment, simulates the spatiotemporal relationship of the movement of the production line execution equipment, and controls the corresponding production line execution equipment to avoid movement conflicts between production line execution equipment. The MES central control subsystem can comprehensively manage the production process, from receiving order data to generating cutting parameters and equipment control instructions, and then to real-time monitoring of equipment status and dynamic adjustment of task queues, ensuring the orderly progress of the production process and improving production efficiency and product quality. The edge collaboration controller connects the MES controller and the production line execution equipment, forwarding the cutting parameters and equipment control commands of the MES central control subsystem to the corresponding production line execution equipment, and collecting the status of the production line execution equipment in real time and forwarding it to the MES central control subsystem. As the hub connecting the MES central control subsystem and the production line execution equipment, the edge collaborative controller can not only forward control commands in a timely and accurate manner, but also collect equipment status information in real time and feed it back to the MES central control subsystem, ensuring two-way information flow and interaction, and providing data support for production scheduling and optimization. The production line executes equipment responses and control commands to complete the corresponding operations of the automated laser cutting production line. The production line executes equipment responses and control commands to complete the corresponding operations of the laser cutting automated production line, thereby automating the production process, improving production efficiency and processing accuracy, and reducing the uncertainty caused by human intervention.

[0024] In this embodiment, the MES central control subsystem provides overall coordination, the edge collaborative controller realizes data transfer and communication adaptation, and the production line execution equipment completes the actual production operation. The three work together to ensure the automated operation of the production line and improve production efficiency and stability.

[0025] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process in this embodiment, another MES-based fully automated laser cutting production line collaborative system is provided. This system includes an MES central control subsystem, an edge collaborative controller, and production line execution equipment. The production line equipment includes laser cutting machines, automated feeding systems, sorting robotic arms, and AGV transport vehicles; The MES central control subsystem receives and parses order data to generate cutting parameters and equipment control instructions, and sends them to the corresponding production line execution equipment. At the same time, it monitors the status of the production line execution equipment in real time, dynamically adjusts the production line task queue, sends updated equipment control instructions to the corresponding production line execution equipment, simulates the spatiotemporal relationship of the movement of the production line execution equipment, and controls the corresponding production line execution equipment to avoid movement conflicts between production line execution equipment. The edge collaboration controller connects the MES controller and the production line execution equipment, forwarding the cutting parameters and equipment control commands of the MES central control subsystem to the corresponding production line execution equipment, and collecting the status of the production line execution equipment in real time and forwarding it to the MES central control subsystem. The production line executes equipment responses and control commands to complete the corresponding operations of the automated laser cutting production line. The MES central control subsystem includes: The work order parsing unit breaks down order data into a sequence of work processes for executable work orders, generates equipment control instructions based on the sequence of work processes, and matches the order data with the process parameter library to determine the optimal combination of cutting parameters. The laser cutting command is generated based on the optimal combination of cutting parameters and then sent to the laser cutting machine. The equipment control commands include loading commands, sorting commands, and AGV transportation commands. The loading instructions are sent to the automated loading warehouse, the sorting instructions are sent to the sorting robotic arm, and the AGV transportation instructions are sent to the scheduling system of the AGV transport vehicle. The dynamic resource allocation unit monitors the status of the production line execution equipment in real time, dynamically adjusts the production line task queue according to the status of the production line execution equipment, and determines the target operation of the production line execution equipment according to the changes in the production line tasks, and issues updated equipment control instructions to the corresponding production line execution equipment. The production line monitoring unit constructs a digital twin model of the production line to simulate the spatiotemporal relationship between the laser cutting machine's working area, the robotic arm's motion trajectory, and the AGV's path. It uses the GJK algorithm to detect collisions between equipment within the target time period and sends avoidance commands to the corresponding production line execution equipment when a collision is detected. The edge collaboration controller includes: The multi-protocol adapter unit sends laser cutting commands to the controller of the laser cutting machine via the EtherCAT protocol; The sorting instructions are sent to the controller of the sorting robot arm via the Modbus-TCP protocol; The AGV transportation instructions are sent to the AGV transportation vehicle's scheduling system via the CAN bus; The real-time data acquisition unit collects the working light intensity and temperature data of the laser cutting machine and provides them to the MES central control subsystem for thermal deformation compensation and updating the laser cutting instructions. It also collects the SLAM positioning coordinates of the AGV transport vehicle and provides them to the MES central control subsystem to generate updated AGV transport instructions by updating the dynamic path. In addition, it collects the gripper pressure data of the sorting robot arm, adjusts the gripping force, and generates updated sorting instructions. In the production line execution equipment, The working area of ​​the laser cutting machine is equipped with an infrared thermal imaging sensor, which is connected to the edge coordinating controller. The laser cutting machine receives and parses the cutting instructions forwarded by the edge co-controller to obtain the cutting power and cutting speed, and then performs laser cutting. The edge co-controller monitors the temperature of the working area of ​​the laser cutting machine in real time through an infrared thermal imaging sensor, and triggers the MES central control subsystem to perform dynamic power compensation when the temperature exceeds a threshold (e.g., exceeding 300º). The automatic feeding system parses the feeding instructions forwarded by the edge collaboration controller to determine the material parameters. Based on the material parameters, it extracts the target board and performs RFID verification. Then, it outputs the target board to the feed port of the laser cutting machine via a conveyor belt. The sorting robot arm analyzes the sorting instructions forwarded by the edge collaboration controller, determines the tolerance standards, classification rules, gripping coordinates and clamping force, measures the workpiece size based on its own vision system and compares it with the tolerance standards, determines the category according to the classification rules, and then classifies the workpieces according to the gripping coordinates and clamping force (for example, classifying them as qualified products and defective products) and places them into the corresponding category of AGV carriers. The AGV transport vehicle parses the AGV transport instructions forwarded by the edge system controller, determines the dynamic path, and dynamically plans the transport path through an improved A* algorithm; The AGV transport vehicle is equipped with a lidar and a UWB positioning module; The AGV transport vehicle detects dynamic obstacles in real time using LiDAR and UWB positioning modules. When the distance to the robotic arm or another AGV transport vehicle is less than a distance threshold (e.g., less than 0.3m), one of the following avoidance strategies is triggered: The AGV transport vehicle controls its deceleration to the lower speed limit threshold through its own scheduling system, and broadcasts its location information to all AGV transport vehicles through the MQTT component; The AGV transport vehicle requests a re-planning of its transport route from the MES central control subsystem through its own scheduling system.

[0026] like Figure 2As shown, the following are embodiments of the collaborative method for a fully automated laser cutting production line based on MES control provided in this disclosure. This method belongs to the same inventive concept as the collaborative method for a fully automated laser cutting production line based on MES control in the above embodiments. For details not described in detail in the embodiments of the collaborative method for a fully automated laser cutting production line based on MES control, please refer to the embodiments of the collaborative system for a fully automated laser cutting production line based on MES control described above.

[0027] The method includes the following steps: The S1.MES central control subsystem receives and parses order data, matches it with the process parameter library to determine the optimal combination of cutting parameters, and breaks down the order into an executable work order sequence of material loading → cutting → sorting → transportation. Based on the process sequence, it generates equipment control instructions with time-dependent relationships. It should be noted that the MES central control subsystem receives and parses order data, matches it with the process parameter library to determine the optimal combination of cutting parameters, and can provide accurate cutting parameters for the production process. At the same time, it breaks down the order into a process sequence and generates equipment control instructions with time-series dependencies to ensure the orderly progress of the production process. S2. The edge collaborative controller manages the execution order of device control commands through a priority task queue, and issues device control commands to the corresponding production line execution devices according to the execution order; It should be noted that the edge collaborative controller manages the execution order of device control commands through a priority task queue and issues commands according to the execution order, which can reasonably arrange the operation of each device and improve production efficiency and equipment utilization. S3. The automatic feeding warehouse, laser cutting machine, sorting robotic arm and AGV transport vehicle interact and coordinate to execute the process according to the received equipment control instructions, and complete the feeding, cutting, sorting and transport operations in sequence; It should be noted that the production line equipment executes the process in a coordinated manner according to the equipment control instructions, and completes the operations of feeding, cutting, sorting and transportation in sequence, so as to realize the automatic and efficient production process and reduce manual intervention. The S4.MES central control subsystem records delay data at each stage and optimizes the timing of equipment control instructions for subsequent work orders through reinforcement learning algorithms, as well as updating the optimal cutting parameter combination in the process parameter library. It should be noted that the MES central control subsystem records the delay data at each stage and optimizes the timing of equipment control instructions for subsequent work orders through reinforcement learning algorithms, as well as updating the optimal cutting parameter combination in the process parameter library, thereby continuously improving the efficiency and quality of the production process.

[0028] This embodiment forms a closed-loop management system from order parsing, instruction generation, equipment control to optimization feedback. It uses reinforcement learning algorithms to optimize instruction timing and process parameters, thereby improving production line efficiency and product quality.

[0029] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process in this embodiment, another collaborative method for a fully automated laser cutting production line based on MES control is provided. This method includes the following steps: The S1.MES central control subsystem receives and parses order data, matches it with the process parameter library to determine the optimal combination of cutting parameters, and breaks down the order into an executable work order sequence of material loading → cutting → sorting → transportation. Based on the process sequence, it generates equipment control instructions with time-dependent relationships. In step S1, the MES central control subsystem determines the material type, material thickness, and cutting pattern by parsing the order data; The optimal combination of cutting parameters is determined by matching the material type and thickness with the process parameter library. The optimal combination of cutting parameters includes cutting power, cutting speed and type of auxiliary gas. When there is no matching optimal parameter combination in the process parameter library, the random forest is called to predict the optimal parameter combination. For example, material, thickness, and surface roughness features can be input into a random forest model to predict the optimal parameter combination; The loading, cutting, sorting, and AGV transportation instructions are generated sequentially based on the process sequence of loading → cutting → sorting → transportation. Material loading instruction generation process: Determine the target station for cutting, and generate a material loading instruction based on the material type and the target station; Cutting instruction generation process: The cutting instruction is generated based on the optimal combination of cutting parameters and material thickness, and the triggering condition for the cutting instruction is set to the completion of the loading instruction and the passing of RFID verification; Sorting instruction generation process: Sorting instructions are generated based on the tolerance standards of the cut parts, classification rules, gripping coordinates, and clamping force. The triggering conditions for sorting instructions are set to the completion of the cutting instruction and the temperature detection meeting the standard. AGV transport instruction generation process: Generate AGV transport instructions based on target coordinates and target speed, and set the trigger conditions for AGV transport instructions to be the completion of sorting instructions and visual classification confirmation; S2. The edge collaborative controller manages the execution order of device control commands through a priority task queue, and issues device control commands to the corresponding production line execution devices according to the execution order; The specific steps of step S2 are as follows: S21. The edge collaborative controller constructs a priority task queue, puts equipment control commands into the priority task queue, and sets the priorities of loading commands, cutting commands, sorting commands, and AGV transportation commands from high to low. The specific steps of step S21 are as follows: The dependency relationship of equipment control instructions in the same work order is modeled using a directed acyclic graph, and nodes are used to represent instructions and edges are used to represent timing constraints. In the initialization state, the loading command is set to have the highest priority, followed by the cutting command, sorting command, and AGV transportation command in that order. S22. The edge collaboration controller monitors the completion status of control commands for each device in real time, and when it receives the corresponding completion signal, it raises the priority of the next device control command; Step S22 also includes the following steps: When an urgent order is inserted, the timing of equipment control commands is globally rearranged, and the priority of the AGV transport vehicle is allocated through the contract network protocol. The edge collaboration controller sends cutting instructions to the laser cutting machine via the EtherCAT protocol, sorting instructions to the sorting robot arm via the Modbus-TCP protocol, and AGV transport instructions to the AGV transport vehicle via the CAN bus. It should be noted that the cutting instruction contains PID control parameters, such as K_p=0.8, Ki=0.2, K_d=0.05; the sorting instruction contains Sobel operator thresholds, such as Sobel operator threshold ≥120; the AGV transportation instruction contains dynamic path weight coefficients, such as 5.0 for the conflict zone and 1.0 for the non-conflict zone. S3. The automatic feeding warehouse, laser cutting machine, sorting robotic arm and AGV transport vehicle interact and coordinate to execute the process according to the received equipment control instructions, and complete the feeding, cutting, sorting and transport operations in sequence; The specific steps of step S3 are as follows: S31. The automatic feeding warehouse receives the pull-up command, extracts the board type and target station, obtains the target board and verifies whether the RFID code of the target board matches the board type. When matching, the target board is transported to the feed port of the laser cutting machine at the target station via the conveyor belt, and then returns the feeding completion signal to the edge collaboration controller. S32. The laser cutting machine receives the cutting command, analyzes the cutting parameters such as cutting power, cutting speed, and auxiliary gas type, as well as the material thickness, and adjusts the focal length of the laser cutting machine according to the material thickness and the cutting parameters to execute the cutting. During the cutting process, the edge co-controller monitors the temperature of the cutting area in real time through an infrared thermal imaging sensor, and adjusts the laser power when the temperature exceeds the threshold to compensate for the cutting path deviation caused by the thermal deformation of the material. After the cutting is completed, the laser cutting machine returns a cutting completion signal to the edge co-controller. In step S32, the edge collaborative controller monitors the temperature of the cutting area in real time using an infrared thermal imaging sensor, and adjusts the laser power to compensate for the cutting path offset caused by material thermal deformation when the temperature exceeds the threshold. The specific steps are as follows: The edge co-controller monitors the temperature of the cutting area in real time through an infrared thermal imaging sensor. If the local temperature exceeds the document threshold, the laser power is dynamically adjusted. The distance ΔZ between the cutting head and the sheet metal of the laser cutting machine is measured in real time using a laser displacement meter. The Z-axis servo motor of the laser cutting machine is adjusted by a PID controller to compensate for the focal length shift caused by thermal deformation. S33. The sorting robot arm receives the sorting instruction, analyzes the tolerance standard, classification rules, gripping coordinates and clamping force, measures the workpiece size through its built-in vision system and compares it with the tolerance standard, determines the category according to the classification rules, and then classifies and places the workpieces into the corresponding category AGV carriers according to the gripping coordinates and clamping force, and then returns the sorting completion signal to the edge collaboration controller. For example, the sorting robot arm uses its built-in vision system to perform Gaussian filtering (kernel size 5×5, σ=1.5) to reduce noise in the workpiece image; Workpiece size is calculated based on subpixel edge detection; if the size exceeds the tolerance by ±0.1mm, the workpiece is marked as defective. S34. The AGV transport vehicle receives the AGV transport instructions, analyzes the target coordinates and target speed, and plans the path based on the improved A* algorithm. It controls its own movement according to the planned path and target speed. At the same time, it detects dynamic obstacles in real time through LiDAR and UWB positioning module. When the distance to the robotic arm or the distance to another AGV transport vehicle is less than the distance threshold, it triggers the avoidance strategy. When it reaches the target position, it returns a transport completion signal to the edge collaborative controller. The specific steps of the AGV transport vehicle planning the path based on the improved A* algorithm in step S34 are as follows: A grid map of the workshop was built based on SLAM, with a grid resolution of 50mm, and static obstacles (such as shelves and equipment bases) were marked. Load the digital twin model of the sorting robot's work area issued by the MES central control subsystem, and pre-map the motion trajectory of the sorting robot to the grid map; Calculate the cost function for each grid cell: Calculate the actual cost from the starting grid to the current grid, and calculate the heuristic cost from the current grid to the ending grid; Initialize the dynamic weight coefficients, and use the dynamic weight coefficients, actual cost, and heuristic cost to calculate the cost function for each grid cell;

[0030] Where g(n) represents the actual cost from the starting grid to the current grid, h(n) represents the heuristic cost from the current node to the endpoint (using Euclidean distance), and w_dynamic represents the dynamic weight coefficient (default 1.0, increased to 5.0 in the robotic arm conflict area). The position of the robotic arm within the next T seconds (e.g., 3 seconds) is predicted by the spatiotemporal grid method. If the distance between a certain grid in the AGV path and the trajectory of the sorting robotic arm is less than a set threshold (e.g., 0.3m), the dynamic weight coefficient of that grid is increased (e.g., from 1 to 5). Starting from the starting grid, traverse each grid and select the grid with the minimum cost function until the ending grid is reached; Perform cubic B-spline fitting on the original path point sequence (e.g., basis function k=3) to ensure that the radius of curvature is greater than the radius of curvature threshold and eliminate right-angle turns; The AGV speed is dynamically set according to the curvature of the path (e.g., 1.0 m / s for straight sections and 0.3 m / s for curves). New obstacles are detected in real time using LiDAR and UWB positioning modules; If an unmapped obstacle is detected or the localization error exceeds the error threshold, local path replanning is triggered. It should be noted that local path replanning only updates the affected graticules, which is quick; The S4.MES central control subsystem records delay data at each stage and optimizes the timing of equipment control instructions for subsequent work orders through reinforcement learning algorithms, as well as updating the optimal cutting parameter combination in the process parameter library. In step S4, the OEE data (such as equipment utilization rate and processing pass rate) for each processing is also recorded through the MES central control subsystem.

[0031] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0032] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A collaborative system for a fully automated laser cutting production line based on MES control, characterized in that, This includes the MES central control subsystem, edge collaboration controllers, and production line execution equipment; The production line equipment includes laser cutting machines, automated feeding systems, sorting robotic arms, and AGV transport vehicles; The MES central control subsystem receives and parses order data to generate cutting parameters and equipment control commands, which are then sent to the corresponding production line execution equipment. Simultaneously, it monitors the status of the production line execution equipment in real time, dynamically adjusts the production line task queue, sends updated equipment control commands to the corresponding production line execution equipment, simulates the spatiotemporal relationships of the production line execution equipment's movements, and controls the corresponding production line execution equipment to avoid movement conflicts when conflicts arise. The MES central control subsystem includes: The work order parsing unit breaks down order data into a sequence of work processes for executable work orders, generates equipment control instructions based on the sequence of work processes, and matches the order data with the process parameter library to determine the optimal combination of cutting parameters. The laser cutting command is generated based on the optimal combination of cutting parameters and then sent to the laser cutting machine. The equipment control commands include loading commands, sorting commands, and AGV transportation commands. The loading instructions are sent to the automated loading warehouse, the sorting instructions are sent to the sorting robotic arm, and the AGV transportation instructions are sent to the scheduling system of the AGV transport vehicle. The dynamic resource allocation unit monitors the status of the production line execution equipment in real time, dynamically adjusts the production line task queue according to the status of the production line execution equipment, and determines the target operation of the production line execution equipment according to the changes in the production line tasks, and issues updated equipment control instructions to the corresponding production line execution equipment. The production line monitoring unit constructs a digital twin model of the production line to simulate the spatiotemporal relationship between the laser cutting machine's working area, the robotic arm's motion trajectory, and the AGV's path. It uses the GJK algorithm to detect collisions between equipment within the target time period and sends avoidance commands to the corresponding production line execution equipment when a collision is detected. The edge collaboration controller connects the MES central control subsystem with the production line execution equipment, forwards the cutting parameters and equipment control commands of the MES central control subsystem to the corresponding production line execution equipment, and collects the status of the production line execution equipment in real time and forwards it to the MES central control subsystem. The edge collaboration controller includes: The multi-protocol adapter unit sends laser cutting commands to the controller of the laser cutting machine via the EtherCAT protocol; The sorting instructions are sent to the controller of the sorting robot arm via the Modbus-TCP protocol; The AGV transportation instructions are sent to the AGV transportation vehicle's scheduling system via the CAN bus; The real-time data acquisition unit collects the working light intensity and temperature data of the laser cutting machine and provides them to the MES central control subsystem for thermal deformation compensation and updating the laser cutting instructions. It also collects the SLAM positioning coordinates of the AGV transport vehicle and provides them to the MES central control subsystem to generate updated AGV transport instructions by updating the dynamic path. In addition, it collects the gripper pressure data of the sorting robot arm, adjusts the gripping force, and generates updated sorting instructions. The production line executes equipment responses and control commands to complete the corresponding operations of the automated laser cutting production line; within the production line's executing equipment... The working area of ​​the laser cutting machine is equipped with an infrared thermal imaging sensor, which is connected to the edge coordinating controller. The laser cutting machine receives and parses the cutting instructions forwarded by the edge coordinating controller to obtain the cutting power and cutting speed, and then performs laser cutting. The edge coordinating controller monitors the temperature of the working area of ​​the laser cutting machine in real time through an infrared thermal imaging sensor, and triggers the MES central control subsystem to perform dynamic power compensation when the temperature exceeds the threshold. The automatic feeding system parses the feeding instructions forwarded by the edge collaboration controller to determine the material parameters. Based on the material parameters, it extracts the target board and performs RFID verification. Then, it outputs the target board to the feed port of the laser cutting machine via a conveyor belt. The sorting robot arm analyzes the sorting instructions forwarded by the edge collaboration controller, determines the tolerance standards, classification rules, gripping coordinates and clamping force, measures the workpiece size based on its own vision system and compares it with the tolerance standards, determines the category according to the classification rules, and then classifies and places the workpieces into the corresponding category AGV carriers according to the gripping coordinates and clamping force. The AGV transport vehicle parses the AGV transport instructions forwarded by the edge collaborative controller, determines the dynamic path, and dynamically plans the transport path through an improved A* algorithm.

2. The MES-controlled fully automated laser cutting production line collaborative system according to claim 1, characterized in that, The AGV transport vehicle is equipped with a lidar and a UWB positioning module; The AGV transport vehicle detects dynamic obstacles in real time using LiDAR and UWB positioning modules. When the distance to the robotic arm or another AGV transport vehicle is less than a distance threshold, one of the following avoidance strategies is triggered: The AGV transport vehicle controls its deceleration to the lower speed limit threshold through its own scheduling system, and broadcasts its location information to all AGV transport vehicles through the MQTT component; The AGV transport vehicle requests a re-planning of its transport route from the MES central control subsystem through its own scheduling system.

3. A collaborative method for a fully automated laser cutting production line based on MES control, based on the system described in any one of claims 1-2, characterized in that, Includes the following steps: The S1.MES central control subsystem receives and parses order data, matches it with the process parameter library to determine the optimal combination of cutting parameters, and breaks down the order into an executable work order sequence of material loading → cutting → sorting → transportation. Based on the process sequence, it generates equipment control instructions with time-dependent relationships. S2. The edge collaborative controller manages the execution order of device control commands through a priority task queue, and issues device control commands to the corresponding production line execution devices according to the execution order; S3. The automatic feeding warehouse, laser cutting machine, sorting robotic arm and AGV transport vehicle interact and coordinate to execute the process according to the received equipment control instructions, and complete the feeding, cutting, sorting and transport operations in sequence; The S4.MES central control subsystem records latency data at each stage and optimizes the timing of equipment control instructions for subsequent work orders through reinforcement learning algorithms, as well as updating the optimal cutting parameter combination in the process parameter library.

4. The collaborative method for a fully automated laser cutting production line based on MES control according to claim 3, characterized in that, In step S1, the MES central control subsystem determines the material type, material thickness, and cutting pattern by parsing the order data; The optimal combination of cutting parameters is determined by matching the material type and thickness with the process parameter library. The optimal combination of cutting parameters includes cutting power, cutting speed and type of auxiliary gas. When there is no matching optimal parameter combination in the process parameter library, the random forest is called to predict the optimal parameter combination. The loading, cutting, sorting, and AGV transportation instructions are generated sequentially based on the process sequence of loading → cutting → sorting → transportation. Material loading instruction generation process: Determine the target station for cutting, and generate a material loading instruction based on the material type and the target station; Cutting instruction generation process: The cutting instruction is generated based on the optimal combination of cutting parameters and material thickness, and the triggering condition for the cutting instruction is set to the completion of the loading instruction and the passing of RFID verification; Sorting instruction generation process: Sorting instructions are generated based on the tolerance standards of the cut parts, classification rules, gripping coordinates, and clamping force. The triggering conditions for sorting instructions are set to the completion of the cutting instruction and the temperature detection meeting the standard. AGV transport instruction generation process: Generate AGV transport instructions based on target coordinates and target speed, and set the trigger conditions for AGV transport instructions to be the completion of sorting instructions and visual classification confirmation.

5. The collaborative method for a fully automated laser cutting production line based on MES control according to claim 4, characterized in that, The specific steps of step S2 are as follows: S21. The edge collaborative controller constructs a priority task queue, puts equipment control commands into the priority task queue, and sets the priorities of loading commands, cutting commands, sorting commands, and AGV transportation commands from high to low. S22. The edge collaboration controller monitors the completion status of control commands for each device in real time, and when it receives the corresponding completion signal, it raises the priority of the next device control command.

6. The collaborative method for a fully automated laser cutting production line based on MES control according to claim 5, characterized in that, The specific steps of step S3 are as follows: S31. The automatic feeding warehouse receives the feeding instruction, extracts the board type and target station, obtains the target board and verifies whether the RFID code of the target board matches the board type. When a match is found, the target board is transported to the feed port of the laser cutting machine at the target station via the conveyor belt, and then the feeding completion signal is returned to the edge collaboration controller. S32. The laser cutting machine receives the cutting command, analyzes the cutting parameters such as cutting power, cutting speed, and auxiliary gas type, as well as the material thickness, and adjusts the focal length of the laser cutting machine according to the material thickness and the cutting parameters to execute the cutting. During the cutting process, the edge co-controller monitors the temperature of the cutting area in real time through an infrared thermal imaging sensor, and adjusts the laser power when the temperature exceeds the threshold to compensate for the cutting path deviation caused by the thermal deformation of the material. After the cutting is completed, the laser cutting machine returns a cutting completion signal to the edge co-controller. S33. The sorting robot arm receives the sorting instruction, analyzes the tolerance standard, classification rules, gripping coordinates and clamping force, measures the workpiece size through its built-in vision system and compares it with the tolerance standard, determines the category according to the classification rules, and then classifies and places the workpieces into the corresponding category AGV carriers according to the gripping coordinates and clamping force, and then returns the sorting completion signal to the edge collaboration controller. S34. The AGV transport vehicle receives the AGV transport instructions, analyzes the target coordinates and target speed, and plans a path based on the improved A* algorithm. It controls its own movement according to the planned path and target speed. At the same time, it detects dynamic obstacles in real time through LiDAR and UWB positioning module. When the distance to the robotic arm or the distance to another AGV transport vehicle is less than the distance threshold, it triggers an avoidance strategy. When it reaches the target position, it returns a transport completion signal to the edge collaborative controller.

7. The collaborative method for a fully automated laser cutting production line based on MES control according to claim 6, characterized in that, The specific steps of step S21 are as follows: The dependency relationship of equipment control instructions in the same work order is modeled using a directed acyclic graph, and nodes are used to represent instructions and edges are used to represent timing constraints. In the initialization state, the loading command is set to have the highest priority, followed by the cutting command, sorting command, and AGV transportation command in that order. Step S22 also includes the following steps: When an urgent order is inserted, the timing of equipment control commands is globally rearranged, and the priority of the AGV transport vehicle is allocated through the contract network protocol. The edge collaboration controller sends cutting instructions to the laser cutting machine via the EtherCAT protocol, sorting instructions to the sorting robot arm via the Modbus-TCP protocol, and AGV transport instructions to the AGV transport vehicle via the CAN bus. In step S32, the edge collaborative controller monitors the temperature of the cutting area in real time using an infrared thermal imaging sensor, and adjusts the laser power to compensate for the cutting path offset caused by material thermal deformation when the temperature exceeds the threshold. The specific steps are as follows: The edge co-controller monitors the temperature of the cutting area in real time through an infrared thermal imaging sensor. If the local temperature exceeds the temperature threshold, the laser power is dynamically adjusted. The distance ΔZ between the cutting head and the sheet metal of the laser cutting machine is measured in real time using a laser displacement meter. The Z-axis servo motor of the laser cutting machine is adjusted by a PID controller to compensate for the focal length shift caused by thermal deformation. The specific steps of the AGV transport vehicle planning the path based on the improved A* algorithm in step S34 are as follows: SLAM is used to build a workshop grid map, the grid resolution is set, and static obstacles are marked. Load the digital twin model of the sorting robot's work area issued by the MES central control subsystem, and pre-map the motion trajectory of the sorting robot to the grid map; Calculate the cost function for each grid cell: Calculate the actual cost from the starting grid to the current grid, and calculate the heuristic cost from the current grid to the ending grid; Initialize the dynamic weight coefficients, and use the dynamic weight coefficients, actual cost, and heuristic cost to calculate the cost function for each grid cell; The position of the robotic arm within the next T seconds is predicted by the spatiotemporal grid method. If the distance between a certain grid in the AGV path and the trajectory of the sorting robotic arm is less than a set threshold, the dynamic weight coefficient of that grid is increased. Starting from the starting grid, traverse each grid and select the grid with the minimum cost function until the ending grid is reached; Perform cubic B-spline fitting on the original path point sequence to ensure that the radius of curvature is greater than the radius of curvature threshold, thus eliminating right-angle turns; Dynamically set the AGV speed based on the path curvature; New obstacles are detected in real time using LiDAR and UWB positioning modules; If an unmapped obstacle is detected or the positioning error exceeds the error threshold, local path replanning is triggered.

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