Automated bending control method and apparatus for automotive components

Abstract

The application provides an automatic bending control method and device for automobile components, and relates to the technical field of automatic control, which comprises the following steps: analyzing the bending requirements of a first automobile component to obtain bending requirement information; obtaining device parameter information of an interactive bending device, including control parameter constraints and multi-axis conversion constraints; performing bending analysis to determine a bending control mode and a bending control parameter interval; obtaining a plurality of bending nodes and a plurality of bending tasks; performing parallel timestamp constraints and neighborhood task docking to generate a full-cycle bending task; performing collision avoidance analysis and control parameter optimization based on node spatial positions to generate a bending control strategy for controlling the full cycle; and responding to a control system to perform automatic bending control. The application solves the technical problems of low precision, poor coordination and excessive manual intervention in task scheduling when performing multi-axis linkage bending control in the prior art, thereby reducing production efficiency and the degree of automation.

Description

Technical Field

[0001] This invention relates to the field of automation control technology, specifically to an automated bending control method and device for automotive components. Background Technology

[0002] In automobile manufacturing, a large amount of sheet metal and other materials need to be bent into various components, such as body frames, chassis components, doors, and hoods. These components require precise geometry and strict quality standards. However, some technical problems still exist in the existing technology, which limit production efficiency and product quality. On the one hand, in multi-axis linkage bending equipment, the coordinated movement and control of multiple axes is very complex. Existing technologies have difficulties in task scheduling, coordination, and control, resulting in low equipment efficiency or operational conflicts. On the other hand, when multi-axis linkage equipment performs bending tasks, collisions or interferences may occur between the axes. Existing technologies lack effective means to manage collisions, leading to equipment damage or production interruptions. Summary of the Invention

[0003] This application provides an automated bending control method and device for automotive components, aiming to solve the technical problems of low accuracy, poor coordination, and excessive manual intervention in task scheduling when performing multi-axis linkage bending control in the prior art, resulting in low production efficiency and low degree of automation.

[0004] The first aspect of this application discloses an automated bending control method for automotive components. The method includes: performing a bending requirement analysis on a first automotive component to obtain bending requirement information for the component, wherein the bending requirement information includes bending geometry information, bending accuracy requirements, and surface quality requirements; accessing equipment parameter information for an interactive bending device, wherein the bending device includes a single-axis braking bending mode and a multi-axis linkage bending mode, and the equipment parameter information includes control parameter constraints and multi-axis conversion constraints; and performing a bending analysis on the bending requirement information of the automotive component based on the material property information of the first automotive component and in conjunction with the control parameter constraints to determine the bending control method and bending control parameter range. In this process, the bending control method includes single-axis braking and multi-axis linkage. When multi-axis linkage is used, multiple bending nodes are acquired, and the bending demand information of the vehicle component is allocated to obtain multiple bending tasks, wherein each bending task corresponds to one or more bending nodes. Parallel timestamp constraints and neighborhood task docking are applied to the multiple bending tasks to generate a full-cycle bending task. Based on the full-cycle bending task and combined with the bending control parameter range, collision avoidance analysis and control parameter optimization based on node spatial position are performed to generate a bending control strategy that controls the entire cycle. The bending control strategy is responded to by the control system to control the bending equipment to perform automated bending control of the first vehicle component.

[0005] The second aspect of this application discloses an automated bending control device for automotive components. The device is used in the aforementioned automated bending control method for automotive components. The device includes: a bending demand analysis module for performing bending demand analysis on a first automotive component to obtain bending demand information for the automotive component, wherein the bending demand information includes bending geometry information, bending accuracy requirements, and surface quality requirements; an equipment parameter acquisition module for interacting with the equipment parameter information of a bending device, wherein the bending device includes a single-axis braking bending mode and a multi-axis linkage bending mode, and the equipment parameter information includes control parameter constraints and multi-axis conversion constraints; and a bending analysis module for performing bending analysis on the bending demand information of the automotive component based on the material property information of the first automotive component and in conjunction with the control parameter constraints, to determine the bending control method and bending control... The system comprises: a parameter range, wherein the bending control method includes single-axis braking and multi-axis linkage; a demand allocation module, which, when multi-axis linkage is used, acquires multiple bending nodes, allocates the bending demand information of the automotive component, and obtains multiple bending tasks, wherein each bending task corresponds to one or more bending nodes; a neighborhood task docking module, which performs parallel timestamp constraints and neighborhood task docking on the multiple bending tasks to generate a full-cycle bending task; a control parameter optimization module, which, based on the full-cycle bending task and combined with the bending control parameter range, performs collision avoidance analysis and control parameter optimization based on node spatial position to generate a bending control strategy for the entire cycle; and a bending control module, which responds to the bending control strategy in the control system and controls the bending equipment to perform automated bending control of the first automotive component.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] By conducting a detailed analysis of the bending requirements of automotive components, comprehensive data including bending geometry, precision requirements, and surface quality requirements are obtained, enabling the control strategy to more accurately meet design requirements. By determining the parameter information of the bending equipment, including control parameter constraints and multi-axis transformation constraints, as well as the bending control mode (single-axis braking or multi-axis linkage), a foundation is laid for subsequent analysis. Based on the comprehensive analysis and test results of the bending task, control parameters are optimized and adjusted to ensure that each parameter during the bending process is within the optimal range, improving the final bending effect. When using the multi-axis linkage mode, the bending requirements are distributed to multiple bending nodes and multiple... This system enables more effective management of complex bending tasks. By using parallel timestamp constraints and neighbor task docking, it achieves synchronous processing and optimized scheduling of multiple bending tasks, solving complex scheduling problems in multi-axis equipment operation. Collision avoidance analysis based on node spatial position ensures that there is no interference between nodes during bending, avoiding equipment collisions, improving operational safety, optimizing control parameters, and generating a bending control strategy for the entire control cycle, ensuring the consistency and accuracy of overall operation. The final bending control strategy is then fed into the control system to achieve automated operation, reduce manual intervention, and improve the automation level and production efficiency of the production line.

[0008] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0009] Figure 1 A schematic flowchart of an automated bending control method for automotive components provided in an embodiment of this application;

[0010] Figure 2 This is a schematic diagram of the structure of an automated bending control device for automotive components provided in an embodiment of this application.

[0011] Explanation of reference numerals in the attached diagram: Bending demand analysis module 10, equipment parameter acquisition module 20, bending analysis module 30, demand allocation module 40, neighboring task docking module 50, control parameter optimization module 60, bending control module 70. Detailed Implementation

[0012] This application provides an automated bending control method and device for automotive components, which solves the problems of low accuracy, poor coordination, and excessive manual intervention in task scheduling during multi-axis linkage bending control in the prior art, resulting in low production efficiency and low automation.

[0013] After introducing the basic principles of this application, various non-limiting embodiments of this application will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0014] Example 1, as Figure 1 As shown in the figure, this application provides an automated bending control method for automotive components, the method comprising:

[0015] A bending requirement analysis is performed on the first automotive component to obtain bending requirement information for the automotive component. The bending requirement information for the automotive component includes bending geometry information, bending accuracy requirements, and surface quality requirements.

[0016] The specific automotive component requiring bending is identified as the first component. This could be a body frame, chassis, or other bending parts. Bending geometry information is obtained, for example, extracted from design drawings or CAD models. This information includes the bending angle, radius, length, and position. Bending accuracy requirements are also determined, for example, from design specifications, manufacturing standards, and quality requirements. This includes tolerance ranges, shape accuracy, and dimensional accuracy. For instance, the bending accuracy requirements for body structural components are typically high to ensure the overall rigidity and safety of the vehicle. Surface quality requirements are also obtained, from design documents, including surface roughness and finish, to assess the impact of the bending process on surface quality. The obtained bending requirements provide crucial data support for subsequent automated bending control, ensuring that the accuracy and quality of the bending process meet the requirements.

[0017] The equipment parameter information of the interactive bending device includes a single-axis braking bending mode and a multi-axis linkage bending mode, and the equipment parameter information includes control parameter constraints and multi-axis conversion constraints.

[0018] The bending equipment is used to bend the first automotive component. Detailed parameters of the bending equipment are collected from equipment manuals and technical documents. The obtained equipment parameter information includes control parameter constraints and multi-axis transformation constraints.

[0019] Among them, the control parameter constraints include torque constraints, speed constraints, and angle constraints, which represent the maximum and minimum torque, speed, and angle that the equipment can achieve, ensuring that the parameters are within the working range of the equipment during the bending process and will not cause damage to the materials or equipment; the multi-axis conversion constraints include inter-axis synchronization, positional accuracy, and force distribution, which represent the collaborative work requirements between the axes in the multi-axis linkage bending mode.

[0020] Bending equipment includes single-axis braking bending mode and multi-axis linkage bending mode. Single-axis braking bending mode refers to the bending mode in which the bending equipment bends along a single axis, which is suitable for simple bending tasks. Multi-axis linkage bending mode refers to the bending mode in which the bending equipment can perform complex bending under multi-axis linkage, which is suitable for bending tasks with complex shapes.

[0021] Based on the material properties of the first automotive component and the constraints of the control parameters, a bending analysis is performed on the bending requirement information of the automotive component to determine the bending control method and the bending control parameter range. The bending control method includes single-axle braking and multi-axle linkage.

[0022] Material property information of the first automotive component is obtained, including material type, material strength, and material ductility. Bending analysis is then performed on the bending requirements of the automotive component. Specifically, based on the obtained bending geometry information, the bending path and steps are analyzed. Combining material properties and the precision requirements of the bending equipment, potential errors during the bending process are assessed, and a precision compensation scheme is determined. Based on surface quality requirements, potential surface damage during bending is assessed, and surface protection measures are determined. Based on the material properties and bending requirement analysis results, an initial bending control parameter range is preliminarily determined, including the ranges for bending torque, bending speed, and bending angle. Combined with the equipment's control parameter constraints, the initial bending control parameter range is limited to ensure that the parameter range is within the equipment's allowable range.

[0023] Determine the bending control method. Specifically, assess whether a single-axis braking bending mode can be used, which is suitable for simpler bending tasks with relatively simple bending paths and easy control; assess whether a multi-axis linkage bending mode is needed, which is suitable for bending tasks with complex shapes, diverse bending paths, and more complex control.

[0024] Furthermore, methods for determining the range of bending control parameters include:

[0025] Based on the bending requirement information of the automotive components, a set of bending effect evaluation indicators is determined; a bending database of automotive components is mined, and the bending database is trained with classification and labeling based on the set of bending effect evaluation indicators to construct a multi-dimensional bending effect evaluation network; based on the material property information of the first automotive component, the multi-dimensional bending effect evaluation network is updated through differential training to obtain an updated multi-dimensional bending effect evaluation network; based on the updated multi-dimensional bending effect evaluation network, bending control analysis is performed on the bending requirement information of the automotive components to obtain an initial bending control parameter range; based on the control parameter constraints, interval constraints are applied to the initial bending control parameter range to obtain the final bending control parameter range.

[0026] Bending requirements include bending geometry, bending accuracy requirements, and surface quality requirements. Based on these requirements, key influencing factors are identified. For example, bending geometry includes bending angle, radius, and length; bending accuracy requirements include bending angle error and position error; and surface quality requirements include surface smoothness, absence of cracks, and absence of wrinkles. This leads to the determination of a set of evaluation indicators for bending performance.

[0027] A large amount of bending process and result data is collected to form a bending database. This data includes input effects such as angular accuracy and surface quality, as well as control parameters such as bending force, speed, and angle. Based on a defined set of bending effect evaluation indicators, the bending database is trained with classification labels. Specifically, each data sample is labeled with a corresponding evaluation indicator; for example, bending angle accuracy is categorized into high, medium, and low levels. Key features are extracted from the input parameters and used as input to the training model. Classification algorithms, such as decision trees, support vector machines, and neural networks, are used to classify and train the data. Based on the results of the classification and label training, a multi-dimensional bending effect evaluation network is constructed. This network is used to predict control parameters based on multi-dimensional effect features.

[0028] Differential training refers to further training or adjusting an existing model using new data or feature information to improve its adaptability and accuracy. Material property information is incorporated into the training dataset; for example, adding sample data with material properties, such as bending results of different materials. Differential training algorithms, such as fine-tuning and transfer learning, are used to update the existing multi-dimensional evaluation network for bending effects. Specifically, the loss function is adjusted based on the new data to incorporate the influence of material properties, and the network weights and biases are fine-tuned to better adapt to the new data characteristics. The updated model is then validated and tested to ensure improved model performance and adaptation to new material properties. Through differential training, an updated multi-dimensional evaluation network for bending effects that more accurately reflects material properties is obtained.

[0029] The updated multidimensional evaluation network for bending effect is applied to bending demand information to make predictions. The network outputs the initial bending control parameter range under given material properties and demand conditions, including the control range of bending moment, bending speed, and bending angle.

[0030] Control parameter constraints are limitations imposed on bending equipment and processes. These constraints are applied to an initial control parameter range, which is then adjusted. For example, the initial bending moment range of 50N to 60N might be adjusted to 50N to 55N based on equipment capacity constraints. By considering all constraints, the final bending control parameter range is determined, ensuring that actual operation is feasible and meets requirements.

[0031] When multi-axis linkage is used, multiple bending nodes are obtained, and the bending requirement information of the automotive components is allocated to obtain multiple bending tasks, wherein each bending task corresponds to one or more bending nodes.

[0032] When the multi-axis linkage bending mode is selected, the specific locations on the automotive component that need to be bent are identified based on the bending geometry information. The entire bending path is decomposed into multiple nodes, which are the specific points where the bending equipment needs to operate at different locations.

[0033] Obtain the bending angle and bending radius information for each node, determine the bending accuracy requirements for each node to ensure that the bent component meets the design specifications, evaluate the surface quality requirements for each node, determine the surface protection measures to be taken, and group the bending nodes according to the bending geometry, bending accuracy requirements, and surface quality requirements. Each group of nodes should be geographically close and have similar bending characteristics to optimize the bending path and control. Assign the grouped nodes to different bending tasks, with each bending task corresponding to one or more bending nodes.

[0034] Furthermore, the method for obtaining multiple bending tasks includes:

[0035] Based on the bending geometry information, multiple bending positions and multiple bending angles are determined; based on the multiple bending positions, multiple bending nodes are determined; based on the multiple bending angles, geometric commonality identification is performed on the multiple bending nodes to determine a first task partitioning constraint; based on the bending accuracy requirements and surface quality requirements, requirement commonality identification is performed on the multiple bending nodes to determine a second task partitioning constraint; combining the first task partitioning constraint and the second task partitioning constraint, the multiple bending tasks are determined.

[0036] Based on the bending geometry information, the bending points on the component are determined, typically according to design requirements or geometric features. These locations can be critical or specified bending points, for example, extracted directly from design drawings or CAD models, identifying multiple bending locations. The bending angle is the required angle at each bending location, which can be determined according to design documents or specifications.

[0037] Bending nodes refer to points that require special attention during the bending process. These points are the foundation of the bending task. Based on the bending location, multiple bending nodes are determined at these locations.

[0038] Based on the bending angle of each node, determine which nodes have similar bending requirements. Combined with the spatial distribution of the nodes, identify common areas. Based on geometric commonalities, divide the bending task into different sub-tasks, with the bending angle and position of each sub-task within a similar range.

[0039] Among multiple bending nodes, identify those with similar accuracy requirements. For example, some nodes may require high angular accuracy, while others have lower accuracy requirements. Also identify nodes with similar surface quality requirements. For example, some nodes may require higher surface finish, while others have lower requirements. Based on the identification results and the commonalities in accuracy and surface quality, the bending task is divided into different sub-tasks, with each sub-task having similar accuracy and surface quality requirements.

[0040] The tasks based on geometric commonality are integrated with those based on accuracy and surface quality requirements to ensure that each task meets both geometric requirements and accuracy and surface quality standards. Bending nodes are then assigned to the final bending task to ensure that the requirements of each task are met.

[0041] Parallel timestamp constraints and neighboring task docking are applied to the multiple bending tasks to generate a full-cycle bending task.

[0042] The entire lifecycle bending task plan is divided into multiple phases, each containing a set of related bending tasks. The priority of tasks in each phase is determined, with critical tasks being processed first or simpler tasks being performed first to test equipment performance. The possibility of parallel execution of each task is evaluated, and it is determined which tasks can be performed simultaneously. Based on the multi-axis linkage capability of the bending equipment, tasks that can be performed in parallel are allocated to the same time window for execution. The tasks in each phase are decomposed into multiple concurrent sub-strategies to ensure that multiple bending operations can be performed at the same time.

[0043] Estimate the required runtime for each concurrent sub-strategy, including bending operation time and device movement time. Align the start times of multiple concurrent sub-strategies to ensure that parallel tasks start simultaneously within the same time window. Implement synchronous control for each concurrent sub-strategy to ensure that all parallel tasks start and complete on schedule. Perform neighboring task docking to ensure that the next related task starts immediately after the previous one is completed, reducing idle and waiting time between tasks. Integrate the bending tasks and concurrent sub-strategies of all stages into a complete full-cycle bending task, including the execution time, sequence, and parameter settings of each task.

[0044] Furthermore, the method for applying parallel timestamp constraints and connecting with neighboring tasks to generate a full-cycle bending task for the multiple bending tasks includes:

[0045] A full-cycle bending strategy is obtained, and multiple phased bending strategies are divided based on the multiple bending tasks. For the first phased bending strategy, concurrent strategy allocation is performed on the multiple bending nodes to determine multiple concurrent sub-strategies. The execution time of the multiple concurrent sub-strategies is obtained. The strategy start time of the multiple concurrent sub-strategies is aligned, and combined with the execution time of the multiple strategies, parallel timestamp constraints are implemented to determine the first phased linkage bending strategy. In this way, the multiple phased bending strategies are traversed in turn, and tasks are connected stage by stage to generate the full-cycle bending task.

[0046] A full-cycle bending strategy is developed based on bending requirements, equipment capabilities, and process requirements. This strategy refers to the overall planning of the entire bending process, including the arrangement of all tasks, time allocation, and resource utilization. The full-cycle bending strategy is divided into several stages, each containing several bending tasks. These tasks are grouped according to their execution order or similarity to form multiple staged bending strategies.

[0047] A random phased bending strategy is selected as the first phase bending strategy. Concurrent strategy allocation refers to the strategy effect of processing multiple bending nodes or tasks simultaneously within a phase. Concurrent processing improves efficiency and reduces overall production time. Multiple bending nodes in the first phase bending strategy are processed concurrently. Specifically, nodes are allocated according to concurrency capabilities to ensure simultaneous processing of multiple tasks. Concurrent tasks are rationally allocated based on equipment capabilities and resource availability. Based on concurrent processing, specific sub-strategies are formulated, including task execution order, timing, and resource allocation, to ensure time coordination of concurrent tasks and avoid conflicts.

[0048] The estimated runtime of each concurrent sub-strategy is determined based on historical data, experience, or simulation experiments, and the runtime of multiple strategies is obtained.

[0049] The start time of each sub-strategy is adjusted to allow them to run concurrently, avoiding conflicts and resource contention. A schedule is generated, displaying the start and end times of each sub-strategy. Timestamp constraints are set for each sub-strategy to ensure they execute within the predetermined timeframe. Furthermore, task scheduling is optimized based on the runtime and resource limitations of concurrent tasks to reduce waiting time and improve efficiency. Within the first phase, time alignment and resource allocation information are integrated into the first-phase strategy to ensure all concurrent tasks are coordinated to achieve the overall task objective.

[0050] Similarly, tasks for each stage are processed sequentially. Within each stage, the execution order and timing of tasks are coordinated to ensure a smooth transition between tasks. Specifically, time coordination is performed to ensure that the tasks of one stage can smoothly proceed to the next stage after completion. For example, after the tasks of one stage are completed, resources and equipment can be quickly transferred to the tasks of the next stage. Resource coordination is also performed to ensure that the resource and equipment requirements of each stage are met and to carry out effective resource scheduling.

[0051] The task arrangements, schedules, and resource allocation information for each stage are integrated into a complete full-cycle bending task plan to generate the full-cycle bending task, thereby optimizing the scheduling of the full-cycle bending plan and ensuring the efficiency and smoothness of the entire bending process.

[0052] Based on the full-cycle bending task and the bending control parameter range, collision avoidance analysis and control parameter optimization based on node spatial location are performed to generate a bending control strategy that controls the entire cycle.

[0053] A global coordinate system is established for the bending equipment and automotive components to ensure that the position of all nodes can be accurately represented. Based on the position and bending path of each bending node, an independent coordinate system is determined for each node to facilitate the analysis of the spatial relationship of each node. The independent coordinate system is transformed into the global coordinate system to obtain the position and orientation of all nodes in the global coordinate system. All nodes are meshed to detect the collision risks between nodes and between nodes and equipment. Based on the collision detection results, the bending path and node positions are adjusted to ensure that no collision occurs during the bending process.

[0054] Initial control parameters, including bending moment, speed, and angle, are randomly extracted within the bending control parameter range. Pre-bending control is performed based on these initial parameters to simulate the bending process and obtain bending test parameters. The bending effect is evaluated based on the pre-bending test parameters, including bending angle accuracy, surface quality, and material stress distribution. Based on the bending effect evaluation results, the control parameters are adjusted and optimized. Optimization algorithms, such as genetic algorithms and particle swarm optimization, are used to seek the optimal control parameters. Through multiple iterations and optimizations, the optimal bending control parameters are determined to meet the accuracy and quality requirements in the bending demand information.

[0055] By combining the full-cycle bending task, optimized control parameters, and collision avoidance analysis results, a complete full-cycle bending control strategy is generated to ensure that the entire process is completed efficiently, safely, and accurately.

[0056] Furthermore, the method for performing collision avoidance analysis based on node spatial location includes:

[0057] A global coordinate system for the bending device is determined; based on the multiple bending nodes, multiple independent coordinate systems centered on the first vehicle component are determined; coordinate transformation is performed on the global coordinate system and the multiple independent coordinate systems to obtain the coordinate transformation result; based on the coordinate transformation result, mesh collision detection is performed on the multiple bending nodes, and collision avoidance is achieved according to the detection result.

[0058] The global coordinate system is a reference coordinate system used to describe the entire operating space of the bending equipment. Based on the geometric dimensions and installation position of the bending equipment, the origin, axis direction, and unit length of the global coordinate system are set. For example, a fixed reference point of the equipment is selected as the origin of the coordinate system, the positive direction of the coordinate axes (including the X-axis, Y-axis, and Z-axis) is determined according to the working direction of the equipment, and the unit length is determined according to the size of the equipment.

[0059] Independent coordinate systems are local coordinate systems used to describe the position and orientation of each bending node. These coordinate systems are centered on the vehicle component and are used to determine the position of each bending point in local space. The center of the vehicle component is used as the reference position, and an independent coordinate system is defined for each bending node. Each coordinate system includes its origin position and coordinate axis direction, and the relative position of these coordinate systems with respect to the center of the vehicle component is determined.

[0060] Coordinate transformation refers to converting position data in one coordinate system to another for unified analysis. Specifically, a transformation matrix is ​​used to transform the position data of each bend node in its independent coordinate system to the global coordinate system. The transformation matrix typically includes a translation matrix and a rotation matrix to obtain the coordinate transformation result. The coordinate transformation result includes the representation of the position information of each bend node in the global coordinate system, including the transformed coordinate values.

[0061] The geometry of equipment and vehicle components is converted into a mesh model, which consists of multiple small geometric units. This mesh model is used for collision detection to check whether the position of bending nodes in the global coordinate system collides with other nodes or equipment parts. When a collision occurs, collision avoidance is performed. For example, the order or timing of bending tasks can be adjusted to avoid collisions within the same time period, or the bending path or the movement trajectory of the equipment can be adjusted to avoid the collision area.

[0062] These steps ensure that bending operations do not result in collisions within the equipment's operating space, thereby improving production efficiency and ensuring the safety of both the equipment and the workpiece.

[0063] Furthermore, methods for optimizing control parameters include:

[0064] Based on the multiple bending nodes after collision avoidance, a first bending control parameter is randomly extracted from the bending control parameter range; pre-bending control of the vehicle component is performed based on the first bending control parameter to obtain the first bending test parameter of the vehicle component; bending effect optimization is performed based on the first bending test parameter of the vehicle component, and the optimal bending control parameter is determined according to the optimization result to generate the bending control strategy.

[0065] A set of control parameters is selected from the control parameter range using a random number generation algorithm. For example, parameter values ​​are randomly generated using a uniform or normal distribution and used as the first bending control parameter for subsequent pre-bending control.

[0066] The bending equipment is set according to the first bending control parameters, and the actual bending operation is carried out. During the bending operation, the actual bending data of the automotive component is monitored in real time, including bending angle, surface quality, bending stress, etc., to obtain the first automotive component bending test parameters, which are used to evaluate the effect of the control parameters.

[0067] The deviations between the bending test parameters of the first automotive component and the target requirements are evaluated, including angle deviation, speed fluctuation, and surface quality issues. Appropriate optimization algorithms, such as genetic algorithms, particle swarm optimization, and simulated annealing, are selected. An objective function is defined, including bending angle error, speed error, and surface quality score. The optimization objective is to minimize these errors. The control parameters are adjusted using the optimization algorithm to find the optimal combination of control parameters. The adjusted control parameters are applied in a simulation environment or on actual equipment for further testing to verify their effectiveness. The optimal bending control parameters with the best fitness are obtained iteratively, generating the bending control strategy.

[0068] The bending control strategy is responded to by the control system, which controls the bending device to perform automated bending control of the first automotive component.

[0069] The generated bending control strategy is imported into the control system of the bending equipment, the bending equipment is calibrated, and the control program is started in the control system to control the bending equipment to execute the various tasks in the bending control strategy in sequence, so as to ensure that the bending process of the first automotive component is automated, efficient and precise.

[0070] Furthermore, the method also includes:

[0071] Based on the material property information of the first automotive component, the elastic modulus of the material is obtained; the bending control strategy is compensated based on the elastic modulus of the material to obtain a compensated bending control strategy; the compensated bending control strategy is responded to by the control system.

[0072] The elastic modulus of a material describes its ability to deform under stress. It is the ratio of stress to strain during tension or compression. A high elastic modulus means that the material is more difficult to deform. It can be obtained from the material's technical data, through tensile or compression tests, etc.

[0073] Since the elastic modulus of a material affects the strain and stress distribution during bending, the bending control strategy needs to be adjusted according to the actual elastic modulus of the material to compensate for its actual behavior. Specifically, a compensation model is established based on the material's elastic modulus and the actual bending situation. The model calculates how to adjust the control parameters to counteract the elastic effect of the material. The bending angle, pressure, and speed are adjusted to better suit the material's actual behavior. For example, for materials with a high elastic modulus, the bending pressure needs to be increased or the bending angle adjusted to achieve the desired final shape. The compensation bending control strategy, including the adjusted control parameters, is generated based on the adjustment results.

[0074] The compensated bending control strategy is input into the control system, and the control parameters of the equipment, including bending angle, speed and pressure, are adjusted according to the compensation strategy to ensure that the final product meets the design requirements.

[0075] In summary, the automated bending control method for automotive components provided in this application has the following technical effects:

[0076] By conducting a detailed analysis of the bending requirements of automotive components, comprehensive data including bending geometry, precision requirements, and surface quality requirements are obtained, enabling the control strategy to more accurately meet design requirements. By determining the parameter information of the bending equipment, including control parameter constraints and multi-axis transformation constraints, as well as the bending control mode (single-axis braking or multi-axis linkage), a foundation is laid for subsequent analysis. Based on the comprehensive analysis and test results of the bending task, control parameters are optimized and adjusted to ensure that each parameter during the bending process is within the optimal range, improving the final bending effect. When using the multi-axis linkage mode, the bending requirements are distributed to multiple bending nodes and multiple... This system enables more effective management of complex bending tasks. By using parallel timestamp constraints and neighbor task docking, it achieves synchronous processing and optimized scheduling of multiple bending tasks, solving complex scheduling problems in multi-axis equipment operation. Collision avoidance analysis based on node spatial position ensures that there is no interference between nodes during bending, avoiding equipment collisions, improving operational safety, optimizing control parameters, and generating a bending control strategy for the entire control cycle, ensuring the consistency and accuracy of overall operation. The final bending control strategy is then fed into the control system to achieve automated operation, reduce manual intervention, and improve the automation level and production efficiency of the production line.

[0077] Example 2, based on the same inventive concept as the automated bending control method for automotive components in the foregoing examples, such as... Figure 2 As shown in the figure, this application provides an automated bending control device for automotive components, the device comprising:

[0078] A bending requirement analysis module 10 is used to perform bending requirement analysis on the first automotive component and obtain bending requirement information for the automotive component, including bending geometry information, bending accuracy requirements, and surface quality requirements. A device parameter acquisition module 20 is used to exchange device parameter information for bending equipment, including single-axis braking bending mode and multi-axis linkage bending mode. The device parameter information includes control parameter constraints and multi-axis conversion constraints. A bending analysis module 30 is used to perform bending analysis on the bending requirement information of the automotive component based on the material property information of the first automotive component and in combination with the control parameter constraints, determining the bending control method and bending control parameter range, wherein the bending control method includes single-axis braking and multi-axis linkage. Demand allocation. Module 40, the demand allocation module 40, is used to acquire multiple bending nodes when multi-axis linkage is used, and to allocate the bending demand information of the automotive component to obtain multiple bending tasks, wherein each bending task corresponds to one or more bending nodes; Neighborhood task docking module 50, the neighborhood task docking module 50, is used to perform parallel timestamp constraints and neighborhood task docking on the multiple bending tasks to generate a full-cycle bending task; Control parameter optimization module 60, the control parameter optimization module 60, is used to perform collision avoidance analysis and control parameter optimization based on the node spatial position based on the full-cycle bending task and combined with the bending control parameter range, to generate a bending control strategy for controlling the entire cycle; Bending control module 70, the bending control module 70, is used to respond to the bending control strategy to the control system, and control the bending equipment to perform automated bending control of the first automotive component.

[0079] Furthermore, the device also includes a bending control parameter range determination module to perform the following operation steps:

[0080] Based on the bending requirement information of the automotive components, a set of bending effect evaluation indicators is determined; a bending database of automotive components is mined, and the bending database is trained with classification and labeling based on the set of bending effect evaluation indicators to construct a multi-dimensional bending effect evaluation network; based on the material property information of the first automotive component, the multi-dimensional bending effect evaluation network is updated through differential training to obtain an updated multi-dimensional bending effect evaluation network; based on the updated multi-dimensional bending effect evaluation network, bending control analysis is performed on the bending requirement information of the automotive components to obtain an initial bending control parameter range; based on the control parameter constraints, interval constraints are applied to the initial bending control parameter range to obtain the final bending control parameter range.

[0081] Furthermore, the device also includes multiple bending task determination modules to perform the following operational steps:

[0082] Based on the bending geometry information, multiple bending positions and multiple bending angles are determined; based on the multiple bending positions, multiple bending nodes are determined; based on the multiple bending angles, geometric commonality identification is performed on the multiple bending nodes to determine a first task partitioning constraint; based on the bending accuracy requirements and surface quality requirements, requirement commonality identification is performed on the multiple bending nodes to determine a second task partitioning constraint; combining the first task partitioning constraint and the second task partitioning constraint, the multiple bending tasks are determined.

[0083] Furthermore, the device also includes a full-cycle bending task determination module to perform the following operational steps:

[0084] A full-cycle bending strategy is obtained, and multiple phased bending strategies are divided based on the multiple bending tasks. For the first phased bending strategy, concurrent strategy allocation is performed on the multiple bending nodes to determine multiple concurrent sub-strategies. The execution time of the multiple concurrent sub-strategies is obtained. The strategy start time of the multiple concurrent sub-strategies is aligned, and combined with the execution time of the multiple strategies, parallel timestamp constraints are implemented to determine the first phased linkage bending strategy. In this way, the multiple phased bending strategies are traversed in turn, and tasks are connected stage by stage to generate the full-cycle bending task.

[0085] Furthermore, the device also includes a collision avoidance module to perform the following operational steps:

[0086] A global coordinate system for the bending device is determined; based on the multiple bending nodes, multiple independent coordinate systems centered on the first vehicle component are determined; coordinate transformation is performed on the global coordinate system and the multiple independent coordinate systems to obtain the coordinate transformation result; based on the coordinate transformation result, mesh collision detection is performed on the multiple bending nodes, and collision avoidance is achieved according to the detection result.

[0087] Furthermore, the device also includes a control parameter optimization module to perform the following operational steps:

[0088] Based on the multiple bending nodes after collision avoidance, a first bending control parameter is randomly extracted from the bending control parameter range; pre-bending control of the vehicle component is performed based on the first bending control parameter to obtain the first bending test parameter of the vehicle component; bending effect optimization is performed based on the first bending test parameter of the vehicle component, and the optimal bending control parameter is determined according to the optimization result to generate the bending control strategy.

[0089] Furthermore, the device also includes a control strategy response module to perform the following operational steps:

[0090] Based on the material property information of the first automotive component, the elastic modulus of the material is obtained; the bending control strategy is compensated based on the elastic modulus of the material to obtain a compensated bending control strategy; the compensated bending control strategy is responded to by the control system.

[0091] Through the foregoing detailed description of the automated bending control method for automotive components, those skilled in the art can clearly understand the automated bending control device for automotive components in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and relevant parts can be referred to in the method section.

[0092] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. 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 this application. Therefore, this application 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. An automated bending control method for automotive components, characterized in that, The method includes: A bending requirement analysis is performed on the first automotive component to obtain bending requirement information for the automotive component, wherein the bending requirement information for the automotive component includes bending geometry information, bending accuracy requirements, and surface quality requirements; The equipment parameter information of the interactive bending device, wherein the bending device includes a single-axis braking bending mode and a multi-axis linkage bending mode, and the equipment parameter information includes control parameter constraints and multi-axis conversion constraints; Based on the material properties of the first automotive component and combined with the control parameter constraints, a bending analysis is performed on the bending demand information of the automotive component to determine the bending control method and the bending control parameter range. The bending control method includes single-axle braking and multi-axle linkage. When multi-axis linkage is used, multiple bending nodes are obtained, and the bending requirement information of the automotive components is allocated to obtain multiple bending tasks, wherein each bending task corresponds to one or more bending nodes. Parallel timestamp constraints and neighboring task docking are applied to the multiple bending tasks to generate a full-cycle bending task. Based on the full-cycle bending task and the bending control parameter range, collision avoidance analysis and control parameter optimization based on node spatial position are performed to generate a bending control strategy that controls the entire cycle. The bending control strategy is responded to by the control system, which controls the bending device to perform automated bending control of the first automotive component.

2. The automated bending control method for automotive components as described in claim 1, characterized in that, Methods for determining the range of bending control parameters include: Based on the bending requirement information of the automotive components, a set of bending effect evaluation indicators is determined; A database of automotive component bending is mined, and a multi-dimensional evaluation network for bending effect is constructed by classifying and training the database of automotive component bending based on the set of bending effect evaluation indicators. Based on the material property information of the first automotive component, the bending effect multidimensional evaluation network is differentially trained and updated to obtain an updated bending effect multidimensional evaluation network. Based on the updated bending effect multidimensional evaluation network, bending control analysis is performed on the bending demand information of the automotive components to obtain the initial bending control parameter range; Based on the control parameter constraints, the initial bending control parameter range is constrained to obtain the bending control parameter range.

3. The automated bending control method for automotive components as described in claim 1, characterized in that, The method for obtaining multiple bending tasks includes: Based on the bending geometry information, multiple bending positions and multiple bending angles are determined; Based on the multiple bending locations, the multiple bending nodes are determined; Based on the multiple bending angles, the geometric commonalities of the multiple bending nodes are identified to determine the first task partitioning constraints; Based on the bending accuracy requirements and surface quality requirements, the commonalities of the multiple bending nodes are identified to determine the second task partitioning constraints; The multiple bending tasks are determined by combining the first task partitioning constraint and the second task partitioning constraint.

4. The automated bending control method for automotive components as described in claim 1, characterized in that, The method for applying parallel timestamp constraints and connecting with neighboring tasks to generate a full-cycle bending task includes: Obtain the full-cycle bending strategy and divide it into multiple staged bending strategies based on the multiple bending tasks; For the first-stage bending strategy, the concurrent strategy distribution of the multiple bending nodes is performed to determine multiple concurrent sub-strategies; Obtain the execution time of multiple concurrent sub-strategies; Align the start times of the multiple concurrent sub-strategies and combine them with the running times of the multiple strategies to implement parallel timestamp constraints and determine the first-stage linkage bending strategy. Similarly, the multiple phased bending strategies are traversed in sequence, and tasks are connected stage by stage to generate the full-cycle bending task.

5. The automated bending control method for automotive components as described in claim 1, characterized in that, The method for performing collision avoidance analysis based on node spatial location includes: Determine the global coordinate system of the bending equipment; Based on the multiple bending nodes, multiple independent coordinate systems are determined with the first vehicle component as the center. Perform coordinate transformation on the global coordinate system and the multiple independent coordinate systems, and obtain the coordinate transformation results; Based on the coordinate transformation results, mesh collision detection is performed on the multiple bending nodes, and collision avoidance is achieved according to the detection results.

6. The automated bending control method for automotive components as described in claim 5, characterized in that, Methods for optimizing control parameters include: Based on the multiple bending nodes after collision avoidance, the first bending control parameter is randomly extracted from the bending control parameter range; Based on the first bending control parameters, pre-bending control of automotive components is performed to obtain the first automotive component bending test parameters. Based on the bending test parameters of the first automotive component, the bending effect is optimized, and the optimal bending control parameters are determined according to the optimization results to generate the bending control strategy.

7. The automated bending control method for automotive components as described in claim 1, characterized in that, The method further includes: Based on the material property information of the first automotive component, the elastic modulus of the material is obtained; The bending control strategy is compensated based on the elastic modulus of the material to obtain a compensated bending control strategy. The compensated bending control strategy is responded to by the control system.

8. An automated bending control device for automotive components, characterized in that, An apparatus for implementing an automated bending control method for an automotive component according to any one of claims 1-7, the apparatus comprising: A bending requirement analysis module is used to perform bending requirement analysis on the first automotive component and obtain bending requirement information of the automotive component, wherein the bending requirement information of the automotive component includes bending geometry information, bending accuracy requirements and surface quality requirements. The equipment parameter acquisition module is used to interactively obtain equipment parameter information of the bending equipment. The bending equipment includes a single-axis braking bending mode and a multi-axis linkage bending mode. The equipment parameter information includes control parameter constraints and multi-axis conversion constraints. The bending analysis module is used to perform bending analysis on the bending demand information of the first automotive component based on the material property information of the first automotive component and in combination with the control parameter constraints, and to determine the bending control method and the bending control parameter range. The bending control method includes single-axle braking and multi-axle linkage. The demand allocation module is used to obtain multiple bending nodes when there is multi-axis linkage, allocate the bending demand information of the automotive component, and obtain multiple bending tasks, wherein each bending task corresponds to one or more bending nodes. The neighborhood task docking module is used to perform parallel timestamp constraints and neighborhood task docking on the multiple bending tasks to generate full-cycle bending tasks. The control parameter optimization module is used to perform collision avoidance analysis and control parameter optimization based on the node spatial position based on the full-cycle bending task and the bending control parameter range, and generate a bending control strategy that controls the entire cycle. A bending control module is provided, which responds to the bending control strategy in response to the control system and controls the bending device to perform automated bending control of the first automotive component.

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