A robot welding method and system based on dynamic energy optimization

Abstract

The application discloses a kind of robot welding methods and systems based on dynamic energy optimization, and the application belongs to welding technical field.The method comprises: obtaining the welding task information of workpiece to be welded and carrying out welding path planning, priority and heat sensitivity are marked to each welding point in path;Synchronously obtain the real-time working state of welding gun, end effector movement ability and local thermal state data of welding area, and construct welding execution state data set;The welding energy requirement of each welding point is calculated in combination with welding path and execution state data, and the corresponding welding energy parameter is generated;According to the welding energy parameter, form the welding energy distribution scheme, and according to this, control welding robot to sequentially output matching energy along the planned path to complete the welding task.The scheme can dynamically calculate the energy required for each welding point and reasonably distribute, so that the robot executes welding according to the plan, thereby improving welding quality and efficiency, and reducing defects caused by overheating or insufficient energy.

Description

Technical Field

[0001] This application belongs to the field of welding technology, specifically relating to a robotic welding method and system based on dynamic energy optimization. Background Technology

[0002] In the industrial manufacturing sector, welding, as a critical joining process, is widely used in numerous industries such as automotive, shipbuilding, and aerospace. With the increasing complexity of products and rising quality requirements, the demands for welding precision, efficiency, and stability are becoming increasingly stringent. Especially when dealing with workpieces with complex structures and dense weld points, scientifically planning welding paths and rationally allocating welding energy to ensure welding quality while improving production efficiency has become a crucial direction for the development of welding technology.

[0003] In traditional welding processes, for welding tasks on workpieces, the general welding sequence is usually determined manually based on experience, and then the weld points on the workpiece are simply marked without fully considering the priority differences and heat sensitivity distribution between weld points. During the welding execution phase, the welding robot mainly operates based on preset fixed parameters and can only obtain the basic working status of the welding torch and the basic motion information of the end effector.

[0004] Existing technologies lack task-based fine-grained path planning, resulting in unreasonable welding sequences and uneven heat accumulation. Furthermore, the lack of prioritization and heat sensitivity marking for weld points makes it difficult to implement differentiated welding strategies. Additionally, relying solely on limited data to formulate solutions makes it impossible to perceive changes in the local thermal state of the workpiece in real time, leading to insufficient precision in welding energy distribution, making it difficult to adapt to complex working conditions and affecting welding efficiency and forming quality. Summary of the Invention

[0005] To overcome the aforementioned shortcomings, this invention is proposed to provide solutions, or at least partially solutions, to the technical problems of existing technologies, such as the lack of task-based fine-grained path planning, unreasonable welding sequence, easy uneven heat accumulation, failure to mark priority and heat sensitivity of weld points, difficulty in implementing differentiated welding strategies, and reliance on limited data to formulate plans, which makes it impossible to perceive changes in the local thermal state of the workpiece in real time, resulting in insufficient precision in welding energy distribution, difficulty in adapting to complex working conditions, and affecting welding efficiency and forming quality.

[0006] In a first aspect, the present invention provides a robotic welding method based on dynamic energy optimization, the method comprising: Obtain welding task information of the workpiece to be welded, plan the welding path based on the welding task information, and mark the priority and heat sensitivity of each weld point in the welding path to obtain a welding path that includes the welding priority and heat sensitivity level of each weld point. The real-time working status parameters of the welding gun of the welding robot are obtained, as well as the motion capability parameters of the end effector of the welding robot and the local thermal state data of the corresponding welding area of ​​the workpiece to be welded. Based on the real-time working status parameters, motion capability parameters and local thermal state data, a welding execution status dataset is generated. Welding energy demand is calculated based on the welding path and welding execution status dataset to obtain welding energy parameters corresponding to each weld point. Based on the welding energy parameters, a welding energy allocation scheme is generated for each weld point in the welding path, and the welding robot is controlled to perform welding operations sequentially according to the welding path based on the welding energy allocation scheme until the welding task is completed.

[0007] In a second aspect, the present invention provides a robotic welding system based on dynamic energy optimization, the system comprising: The welding path generation module is used to obtain welding task information of the workpiece to be welded, plan the welding path based on the welding task information, and mark the priority and heat sensitivity of each weld point in the welding path to obtain a welding path containing the welding priority and heat sensitivity level of each weld point. The dataset generation module is used to obtain the real-time working status parameters of the welding gun of the welding robot, as well as the motion capability parameters of the end effector of the welding robot and the local thermal state data of the corresponding welding area of ​​the workpiece to be welded, and to generate a welding execution status dataset based on the real-time working status parameters, motion capability parameters and local thermal state data. The welding energy calculation module is used to calculate the welding energy demand based on the welding path and the welding execution status dataset, and obtain the welding energy parameters corresponding to each weld point. The welding execution module is used to generate a welding energy distribution scheme for each weld point in the welding path based on the welding energy parameters, and to control the welding robot to perform welding operations sequentially according to the welding path based on the welding energy distribution scheme until the welding task is completed.

[0008] In a third aspect, an electronic device is provided, comprising a processor, a memory, and a program or instructions stored in the memory and executable on the processor, the program or instructions being loaded and run by the processor to perform the steps of the aforementioned robotic welding method based on dynamic energy optimization.

[0009] In a fourth aspect, a computer-readable storage medium is provided, wherein a plurality of program codes are stored therein, the program codes being adapted to be loaded and run by a processor to perform the steps of the above-described robotic welding method based on dynamic energy optimization.

[0010] The above-described technical solutions of the present invention have at least one or more of the following beneficial effects: In implementing the technical solution of this invention, the energy required for each weld point can be dynamically calculated and rationally allocated according to the welding task and welding path, combined with the real-time status of the robot and the thermal condition of the welding area, so that the robot can perform welding according to the plan, thereby improving welding quality and efficiency and reducing defects caused by overheating or insufficient energy. Attached Figure Description

[0011] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. Furthermore, similar numbers in the drawings are used to denote similar components, wherein: Figure 1 This is a schematic diagram of the first main steps of a robotic welding method based on dynamic energy optimization according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the second main step of a robotic welding method based on dynamic energy optimization according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the main structure of a robotic welding system based on dynamic energy optimization according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0012] Some embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0013] In the description of this invention, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and may also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media include any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0014] See appendix Figure 1 , Figure 1 This is a schematic diagram of the first main steps of a robotic welding method based on dynamic energy optimization according to an embodiment of the present invention. Figure 1 As shown, a robot welding method based on dynamic energy optimization in an embodiment of the present invention mainly includes the following steps S101-S104.

[0015] Step S101: Obtain welding task information of the workpiece to be welded, plan the welding path based on the welding task information, and mark the priority and heat sensitivity of each weld point in the welding path to obtain a welding path that includes the welding priority and heat sensitivity level of each weld point.

[0016] The workpiece to be welded refers to various objects or parts that require welding processing, including their geometric shape, material information, and core features such as the welding area.

[0017] Welding task information consists of various specific task data related to the welding process, including the location of weld points or welds, weld length, welding sequence requirements, material type, and other process constraints.

[0018] A weld point is a specific location or unit where welding operations are performed during the welding process. It is generally a discrete point on the weld seam and is the basic unit for welding path planning and welding energy distribution.

[0019] Welding priority is the importance and order of welding execution determined for each weld point, used to guide the welding robot to prioritize critical weld points in path planning.

[0020] Heat sensitivity rating refers to the degree to which a weld joint is sensitive to changes in welding heat input. It is expressed in the form of a rating or label and is used to adjust energy input during the welding process.

[0021] The welding path is the trajectory of a welding robot moving sequentially along the weld points on a workpiece. It includes the sequence of weld points and their spatial position information, and is used to guide the welding robot to accurately perform welding operations.

[0022] The system acquires welding task information for the workpiece to be welded, including weld location, weld length, welding material type, welding sequence constraints, and various process requirements. During operation, the system can extract weld geometry information from the 3D design model or CAD file of the workpiece to be welded, and then combine it with welding process specifications and material performance data to generate complete welding task information such as the spatial coordinates of each weld point, weld dimensions, predetermined welding sequence, and material parameters.

[0023] Welding path planning is carried out based on the obtained welding task information. First, the weld seam is discretized into multiple discrete welding points, and a sequence constraint matrix between these points is established according to the spatial location and process dependencies of the weld seam. Then, combining the geometric features of the workpiece to be welded and the motion capabilities of the welding robot, including maximum acceleration, maximum load, and reachable workspace, the optimal movement trajectory between the welding points is calculated, thereby generating the initial welding path. This welding path must clearly define the execution order of each welding point and the motion trajectory of the robot's end effector to ensure no collisions occur during the welding process and to guarantee stable welding quality.

[0024] Each weld point in the welding path is marked with a welding priority. The welding priority is determined based on the weld point's technological importance, its dependence on the overall structural assembly, and the actual impact of the weld point's location on the welding sequence. In practice, the task information and spatial relationship of each weld point are read, and the corresponding weld is compared and analyzed with the marked key weld points. Combining the criticality of the structural connection and the urgency of the weld completion sequence, a corresponding welding priority level is assigned to each weld point, ultimately resulting in a welding path with welding priority markings.

[0025] A heat sensitivity level assessment is conducted on the weld points in the welding path. Based on the material type, weld thickness, spacing between weld points, and welding sequence, the temperature rise trend of each weld point under standard welding power is calculated. Simultaneously, considering potential heat accumulation during welding, the response strength of the weld points to changes in heat input is evaluated. Weld points with high response levels, prone to overheating or abnormal molten pools, are marked as high heat sensitivity level; weld points with moderate response levels are marked as medium heat sensitivity level; and weld points with low response levels and insignificant heat effects are marked as low heat sensitivity level. Finally, welding priority and heat sensitivity level information are integrated into the welding path to form a complete welding path that includes the execution sequence, spatial location, welding priority, and heat sensitivity level of each weld point.

[0026] Based on the above technical solution, optionally, welding path planning is performed based on the welding task information, and priority and heat sensitivity are marked for each weld point in the welding path to obtain a welding path that includes the welding priority and heat sensitivity level of each weld point, including: Based on the welding task information, the process dependencies and structural assembly relationships between welds are analyzed to obtain analysis results, and welding sequence constraint information is determined based on the analysis results. The workpiece geometry features to be welded are obtained, and the motion capability information of the welding robot is obtained. Based on the welding sequence constraint information, workpiece geometry features and motion capability information, welding path planning is performed to obtain the initial welding path. Obtain welding feature information of each weld point in the initial welding path, evaluate the importance of each weld point based on the welding feature information, mark the welding priority of each weld point, and obtain a welding path containing the welding priority of each weld point. Based on the welding feature information, thermal sensitivity analysis is performed on each weld point, and a thermal sensitivity level is marked for each weld point to obtain a welding path that includes the welding priority and thermal sensitivity level of each weld point.

[0027] In this scheme, the analysis results clarify the welding sequence requirements for various types of welds, the range of welds that cannot be welded simultaneously, and the weld judgment conclusions that will affect subsequent assembly and structural stability.

[0028] The welding sequence constraint information is a welding sequence restriction rule formed from the above analysis results, which clarifies that certain welds can only be welded after the specified welds have been completed.

[0029] The geometric features of a workpiece include its external dimensions, the spatial location of the weld, the length and direction of the weld, the spacing between adjacent welds, and information related to the curvature and angle of the workpiece surface.

[0030] Motion capability information includes the achievable range of motion, joint rotation limits, maximum speed, acceleration, and the robot's end effector's posture adjustment capability during actual operation.

[0031] The initial welding path is a basic trajectory scheme planned under the premise of satisfying welding sequence constraints, adapting to workpiece geometry and welding robot motion capabilities, in which the welding torch moves sequentially from the starting welding point to each welding point.

[0032] Welding characteristic information describes the difficulty and impact of welding on a single weld point, including the weld point location, weld type, weld length, heat input requirements, and the degree of impact of the weld point on the structural strength.

[0033] Welding priority is a welding sequence number assigned to each weld point after evaluation based on welding characteristic information. It is used to guide the execution order of each weld point in the welding path.

[0034] The heat sensitivity rating is a classification result that reflects the sensitivity of a solder joint to changes in heat input. It is used to distinguish solder joints that are prone to deformation, stress, or welding defects due to heat accumulation.

[0035] The process involves acquiring welding task information for the workpiece to be welded, including the location, length, type, and role of each weld in the overall structure. Based on this information, potential process dependencies and structural assembly relationships between welds are determined. For example, some welds must be welded first to provide support for subsequent structures, while welding others simultaneously could lead to excessive workpiece deformation. By calculating the relative positions, weld spacing, and material connection sequence of each weld, the dependencies of each weld are ranked and weighted, ultimately forming the analysis results. Based on these results, welding sequence constraints are generated, clarifying which welds must be completed in advance, which welds can be welded in parallel, and possible preferred welding paths.

[0036] The process involves acquiring the geometric features of the workpiece to be welded, including its size, shape, weld direction, weld spacing, and surface curvature. Simultaneously, it collects motion capability information of the welding robot, such as joint angle limitations, maximum end-effector velocity and acceleration, reachable space range, and end-effector attitude adjustment capabilities. Based on this, and combined with welding sequence constraints, the position of the weld seam in three-dimensional space is mapped to the robot's reachable working area. Preliminary planning of the welding torch's movement trajectory ensures that each weld seam is within the robot's operable range, while balancing the welding sequence and robot motion efficiency, ultimately yielding an initial welding path. This path includes the continuous trajectory of the welding torch moving from the starting point to each weld point, along with the corresponding welding sequence.

[0037] Based on the initial welding path, welding characteristic information for each weld point is collected, including weld point location, weld type, weld length, welding difficulty, local material properties, and the weld point's contribution to the overall structural strength. Based on these characteristics, the importance of each weld point is quantitatively assessed; for example, critical load-bearing weld points or weld points near the workpiece edge are assigned higher weights. According to the assessment results, a welding priority is assigned to each weld point, forming a welding path with priority labels to guide the actual welding sequence and resource scheduling.

[0038] By utilizing the heat input requirements, weld location, and material properties from welding feature information, the heat sensitivity of each weld point is calculated. For example, a heat conduction model is used to assess the temperature accumulation and deformation risk around the weld point. Based on the calculation results, weld points are classified into different heat sensitivity levels. High-sensitivity weld points are more prone to overheating or localized deformation during welding. Finally, the heat sensitivity level is combined with welding priority to form complete annotation information for each weld point. This solution allows for simultaneous consideration of weld sequence, machine movement capability, and weld heat sensitivity when planning the welding path, thereby improving welding efficiency and structural quality. Furthermore, by prioritizing and assigning heat sensitivity, welding energy can be precisely allocated, reducing the risk of welding defects caused by overheating or insufficient energy.

[0039] Step S102: Obtain the real-time working status parameters of the welding gun of the welding robot, and obtain the motion capability parameters of the end effector of the welding robot and the local thermal state data of the corresponding welding area of ​​the workpiece to be welded. Generate a welding execution status dataset based on the real-time working status parameters, motion capability parameters and local thermal state data.

[0040] A welding robot is an automated device used to perform welding operations. It can control welding motions, operate the welding torch, and control the end effector, and can complete various welding tasks according to a preset path.

[0041] The welding torch is the core execution tool at the end effector of a welding robot, used to generate a welding arc.

[0042] Real-time operating status parameters are various operating information of the welding torch during the welding process, including welding current, voltage, power, welding speed, and molten pool status.

[0043] An end effector is a mechanical component at the end of a welding robot arm, including a welding torch and its supporting device. It can perform welding actions and precisely control the position and orientation of the welding torch.

[0044] Motion capability parameters are various data used to describe the motion performance of the end effector of a welding robot, including maximum acceleration, maximum speed, workspace range, and attitude angle limitations.

[0045] The welding area is a specific area on the workpiece to be welded where the welding operation is planned to be performed, including the weld location and the surrounding area that will be affected by the welding heat.

[0046] Local thermal state data refers to temperature-related information of the welded area during the welding process or in the pre-welding state, including the temperature distribution and heat accumulation of the weld and surrounding materials.

[0047] The welding execution status dataset is a dataset that centrally records the real-time working status of the welding torch, the motion status of the end effector, and the local thermal status of the welding area during the welding process.

[0048] The system acquires real-time operating status parameters of the welding torch used with the welding robot. Specifically, during the welding operation, key data such as the welding torch's current, voltage, power, welding speed, and molten pool temperature are continuously collected. Based on this real-time data, the heat input and welding energy output of the welding torch at the current welding point are calculated, ultimately forming a set of real-time operating status parameters for the welding torch.

[0049] The motion capability parameters of the welding robot's end effector are acquired synchronously. In specific operations, information such as the end effector's maximum acceleration, maximum velocity, joint angle, and workspace range in the current posture are recorded. Based on these recorded data, a motion capability parameter dataset is generated that describes the weld points reachable by the end effector and the motion constraints, ensuring that the welding robot can meet the requirements of the welding path for spatial position and motion speed when performing welding tasks.

[0050] Acquire local thermal state data of the welding area corresponding to the workpiece to be welded. Specifically, this can be achieved through methods such as thermal imaging technology, temperature sensor monitoring, or finite element thermal analysis to collect information on the temperature distribution, heat accumulation state, and thermal gradient of the weld and its adjacent areas. Based on these collected or calculated temperature-related data, local thermal state data of the welding area is generated to assess the heat-affected zone and the thermal sensitivity of the material during the welding process.

[0051] Based on the real-time working status parameters of the welding torch, the motion capability parameters of the end effector, and the local thermal state data of the corresponding welding area of ​​the workpiece to be welded, the various status information of each welding point during the welding process are integrated and calculated, including the achievable welding power, speed range, and thermal response characteristics; based on these comprehensive data, a welding execution status dataset is generated.

[0052] Step S103: Calculate the welding energy requirement based on the welding path and welding execution status dataset to obtain the welding energy parameters corresponding to each weld point.

[0053] Welding energy parameters are a summary of the various values ​​of energy required to weld each weld point, clearly showing how much heat needs to be input to complete the welding task and how this heat should be distributed.

[0054] First, analyze the welding priority and heat sensitivity level of each weld point along the welding path. Combine this with information such as the spatial location of the path and the weld length to determine whether each weld point should be welded first or last, and roughly estimate the heat input required for each weld point. Then, retrieve the real-time operating status data of the welding robot's welding torch, such as actual output power, current, voltage, and welding speed. Next, obtain the motion performance parameters of the end effector, such as maximum acceleration, maximum speed, and working range. Combine these motion parameters with the positional changes of the weld point along the path to determine the actual energy transfer efficiency during welding and the constraints on the robot's achievable speed.

[0055] Referring to local thermal state data of the welding area of ​​the workpiece to be welded, such as the temperature distribution around the weld, the heat accumulation of the already welded portion, and information about the heat-affected zone during welding of adjacent weld points, a comprehensive assessment of the potential thermal interference to each weld point is taken into account. Based on these factors, the previously estimated energy requirements are adjusted, further refining the core basis for the welding energy parameters. After the energy requirements for each weld point are initially determined, the energy allocation is adjusted according to welding priority and heat sensitivity level: for example, for high-priority or heat-sensitive weld points, more heat is supplied or the heat input is reduced appropriately. This ensures a stable molten pool state while effectively controlling the excessive expansion of the heat-affected zone, ultimately forming welding energy parameters that closely match the actual welding requirements.

[0056] Based on the above technical solution, optionally, welding energy demand can be calculated based on the welding path and welding execution status dataset to obtain welding energy parameters corresponding to each weld point, including: Based on the welding priority and heat sensitivity level of each weld point in the welding path, the welding energy required for each weld point is analyzed, and the energy demand analysis results of each weld point are obtained. Based on the real-time working status parameters, operating capability parameters, and local thermal state data, the available power data and available speed data of each weld point during the welding process are analyzed, and welding execution constraints are generated based on the available power data and available speed data. Based on the energy demand analysis results and welding execution constraints, the power demand data and available power limit data of each weld point are analyzed, and the actual welding power of each weld point is generated based on the power demand data and available power limit data. Based on welding execution constraints and actual welding power, analyze the speed requirement data and speed limit data of each weld point, and generate the actual welding speed of each weld point based on the speed requirement data and speed limit data. Welding energy parameters corresponding to each weld point are generated based on the actual welding power and actual welding speed.

[0057] In this scheme, the energy demand analysis results are based on the priority and heat sensitivity level of each weld point in the welding path, and the energy required for each weld point during the welding process is calculated.

[0058] Available power data represents the maximum safe power value that a welding robot can provide for each weld point during the welding process.

[0059] Available speed data is the maximum welding speed allowed for each weld point during the welding process to avoid welding defects or a decrease in welding quality.

[0060] Welding execution constraints are constraint information derived from the combination of available power data and speed limit data, defining the power and speed operating range for each weld point during actual welding.

[0061] Power demand data is based on the energy demand analysis results of the weld joints, calculating the actual power required for welding each weld joint to match available power constraints.

[0062] Available power limit data represents the upper limit of power that can be used for each solder joint in actual welding, used to prevent overload or overheating of the welding area.

[0063] The actual welding power is the final actual welding power value determined for each weld point under the constraints of power demand data and available power limitation data.

[0064] The speed requirement data is a welding speed target calculated based on the weld point energy requirement and welding power, used to ensure welding quality and energy balance.

[0065] Speed ​​limit data represents the maximum welding speed allowed for each weld point, used to avoid welding defects and ensure weld consistency.

[0066] The actual welding speed is the final welding speed determined for each weld point under the constraints of speed requirement data and speed limit data.

[0067] After determining the welding path, the required welding energy is calculated point-by-point for each weld point along the path, taking into account the pre-marked welding priority and heat sensitivity level. Specifically, based on the welding priority of each weld point, its execution order in the entire welding process is determined. High-priority weld points are mostly located in areas of concentrated structural stress or with high assembly precision requirements, and sufficient fusion quality must be ensured. Simultaneously, the heat sensitivity level of each weld point is used to assess its tolerable range of heat input variations. Then, based on the plate thickness, weld type, and heat sensitivity level corresponding to each weld point, a welding heat input calculation model is used to match and verify the heat required per unit length with the allowable upper limit of heat input. This yields the target welding energy value for each weld point while ensuring good weld formation and avoiding overheating deformation, thus forming the energy requirement analysis results for each weld point.

[0068] By combining the actual operating status of the welding equipment and robot within the current task cycle, the constraint boundaries of the welding process are clearly defined. Specifically, this involves referencing the real-time operating parameters of the welding equipment, such as power load, welding torch temperature rise, and cooling conditions, to assess the range of power that the equipment can continuously output; simultaneously, combining the welding robot's operational capability parameters, such as joint speed limits, load capacity, and trajectory following accuracy, to determine the range of movement speeds the robot can achieve at different spatial positions; and further, combining local thermal state data around the weld point to assess the heat accumulation in the welding area, preventing continuous welding from causing a continuous rise in local temperature. Integrating these factors, the maximum allowable welding power and maximum welding speed are calculated for each weld point, forming the available power and speed data for that weld point. These data are used as unified constraints for welding execution, ultimately generating welding execution constraints.

[0069] The energy demand analysis results for each weld point are combined with the corresponding welding execution constraints for joint calculation. For each weld point, based on its target welding energy value and the effective welding length in the welding path, the theoretical power level required to complete the welding is derived, resulting in power demand data. Simultaneously, this power demand data is compared with the previously determined available power data to determine if it exceeds the equipment's capacity or thermal tolerance, thereby determining the available power limit data for each weld point. If the power demand data does not exceed the available power limit data, the power demand value is directly used as the actual welding power for that weld point; if the power demand exceeds the available power limit, the actual welding power is constrained and corrected using the available power limit data as the upper limit, ultimately determining the actual welding power for each weld point.

[0070] After determining the actual welding power, the welding speed is then calculated in detail. For each weld joint, based on the determined actual welding power and the target welding energy for that joint, the theoretical welding speed required to complete the welding is derived, resulting in speed requirement data. Simultaneously, this speed requirement data is compared with the previously determined available speed data to determine if the theoretical speed meets the robot's motion capabilities and thermal constraints. If the speed requirement data is within the range of available speed data, this speed is directly used as the actual welding speed for the weld joint. If the speed requirement exceeds the upper limit of available speed, the available speed data is used as the speed limit data, and the welding speed is adjusted downwards to generate the actual welding speed for each weld joint.

[0071] Based on the final determined actual welding power and actual welding speed for each weld point, the welding energy is uniformly parameterized. By combining and calculating the actual welding power and the corresponding actual welding speed, the welding energy parameters for each weld point under actual operating conditions are obtained.

[0072] This solution ensures the quality of critical weld joint formation while avoiding localized overheating and excessive energy input, making the welding process more stable and controllable.

[0073] Step S104: Generate a welding energy distribution scheme for each weld point in the welding path based on the welding energy parameters, and control the welding robot to perform welding operations sequentially according to the welding path based on the welding energy distribution scheme until the welding task is completed.

[0074] A welding energy allocation scheme is a specific energy input plan for each weld point along the welding path, clarifying the distribution relationship between welding power, welding speed, and welding time for each weld point. Based on welding energy parameters, this scheme comprehensively considers weld point priority, heat sensitivity, weld location, weld length, as well as the power limitations and motion constraints of the welding robot, to rationally determine the energy allocation ratio and input sequence between adjacent weld points.

[0075] First, the welding energy parameters corresponding to each weld point on the welding path are extracted. Then, the energy demand of each weld point is quantitatively calculated based on the spatial relationship between the weld points and the weld connection sequence. At the same time, the distribution of energy demand intensity along the entire welding path and the thermal coupling relationship between adjacent weld points are analyzed to identify the energy distribution characteristics of each weld point, which serves as a reference for subsequent energy allocation.

[0076] Based on the energy distribution characteristics of the weld joints, combined with the predetermined welding sequence and the robot's motion capabilities, the energy input timing of the welding process is analyzed. By adjusting the energy demand of adjacent weld joints over time, overheating or insufficient energy in local areas is avoided, ultimately determining the energy input timing of each weld joint in the welding path. Based on this energy input timing, the welding power and welding speed of all weld joints on the welding path are proportionally allocated, while weighting is adjusted according to the heat sensitivity and welding priority of the weld joints. This ensures that critical weld joints receive sufficient energy supply first, while also preventing overheating of non-critical weld joints due to excess energy. Through repeated mapping and adjustment, the final welding energy allocation result for each weld joint is obtained.

[0077] By integrating the welding power, welding speed, and energy input time of each weld point, a complete welding energy allocation scheme is formed, clearly defining the energy input and execution sequence for each weld point, providing a directly executable basis for the control of the welding robot. Based on the generated welding energy allocation scheme, the welding robot is controlled to complete the welding operations of each weld point sequentially according to the welding path. During the robot's operation, the weld point position and corresponding welding parameters are read in real time, and the welding torch power and moving speed are dynamically adjusted to accurately implement the various requirements of the welding energy allocation scheme.

[0078] Based on the above steps S101-S104, the energy required for each weld point can be dynamically calculated and rationally allocated according to the welding task and welding path, combined with the robot's real-time status and the thermal condition of the welding area, so that the robot can perform welding according to the plan, thereby improving welding quality and efficiency and reducing defects caused by overheating or insufficient energy.

[0079] Based on the above technical solution, optionally, after controlling the welding robot to perform welding operations sequentially according to the welding path based on the welding energy distribution scheme, the method further includes: Real-time acquisition of welding execution process status information, and determination of the current welding status based on the status information; If the current welding state is that the current weld point is overheated or the welding energy is insufficient, the welding power and / or welding speed are adjusted based on the welding energy distribution scheme and the state information corresponding to the current welding state.

[0080] In this solution, the status information is data collected in real time during the welding process, which is used to reflect the current welding conditions and on-site environment of the weld point, such as weld point temperature, molten pool state, weld formation effect, welding current and voltage fluctuations, etc.

[0081] The current welding status is a category of welding condition determined based on status information. It is used to describe the welding effectiveness of the current weld point, such as "overheating", "insufficient energy" or "normal", and will change dynamically as the welding process progresses.

[0082] Welding power is the rate at which the welding equipment outputs electrical energy when welding the current weld point. It directly determines the amount of heat input and can be adjusted according to needs to control the welding quality and the formation effect of the weld pool.

[0083] Welding speed is the speed at which the welding robot or welding torch moves along the weld seam. It affects the heat input distribution of the weld seam and the size of the molten pool. It can also be adjusted to optimize the weld seam shape and welding quality.

[0084] During the welding process, the welding robot continuously collects data such as the welding torch output current, voltage, welding arc temperature, molten pool morphology, and weld surface image. It also records the welding speed and the current position of the welding torch. After real-time processing and filtering, the collected data generates numerical or characteristic indicators that reflect the welding status of each weld point, including weld point temperature, molten pool size, weld width, and welding energy input. These features are integrated to form status information to describe the dynamic changes in the current welding conditions of the weld point.

[0085] Based on the real-time generated status information, the welding conditions of each weld point are compared and analyzed. If the molten pool temperature or weld width exceeds the preset safety range, the current weld point is determined to be in the "overheated" welding state. If the welding energy is lower than the target value specified in the welding energy distribution scheme, or if the welding current or voltage does not meet the design requirements, the current welding state is determined to be "insufficient welding energy". If all status indicators are within the safety and target range, the current welding state is determined to be "normal". This judgment process will be dynamically adjusted in combination with the welding path, weld point priority and heat sensitivity level to ensure that the judgment result can accurately reflect the actual welding situation.

[0086] When the system detects that the current welding state is overheated or the welding energy is insufficient, it will perform an adjustment operation based on the welding energy distribution scheme and the status information of the weld point. The system retrieves the target energy value of the current weld point and the corresponding welding power and welding speed settings from the welding energy distribution scheme, and then calculates the severity of overheating or insufficient energy based on the status information to form an adjustment reference value. If the overheating degree is lower than the preset threshold, only one of the welding power or welding speed is adjusted, and the specific adjustment range is determined through a preset mapping relationship. If the overheating degree is high, or both the welding power and welding speed deviate from the target value, both parameters are adjusted simultaneously to quickly restore the heat input to the target range. After the adjustment is completed, the current welding state is fed back in real time to confirm that the adjustment of welding power and welding speed has taken effect, and the system will determine whether further adjustment is needed based on the new status information.

[0087] This solution can continuously sense changes in the welding state during the welding process and dynamically adjust the welding power and welding speed according to the actual situation, so that the welding energy is always kept within a reasonable range, thereby reducing weld defects caused by overheating or insufficient energy and improving the stability and consistency of the welding process.

[0088] Based on the above technical solution, optionally, if the current welding state is that the current weld joint is overheated, the welding power and / or welding speed are adjusted based on the welding energy distribution scheme and the state information, including: If the current welding state is that the current weld point is overheated, obtain the first target power and the first welding speed corresponding to the current weld point in the welding energy distribution scheme, and generate the first welding scheme data based on the first target power and the first welding speed. Based on the current welding status information and the first welding scheme data, the overheating degree value of the current weld point is calculated. When the overheating degree value is within the preset slight overheating range, the first welding power adjustment amount or the first welding speed adjustment amount is determined based on the preset overheating adjustment mapping relationship, and the welding power and / or welding speed are adjusted based on the first welding power adjustment amount or the first welding speed adjustment amount. Accordingly, after calculating the overheating value of the current solder joint, the method further includes: When the overheating value exceeds the preset slight overheating range, the second welding power adjustment amount and the second welding speed adjustment amount are determined based on the preset overheating adjustment mapping relationship, and the welding power and / or welding speed are adjusted based on the second welding power adjustment amount and the second welding speed adjustment amount. Accordingly, if the current welding state is characterized by insufficient welding energy, the welding power and / or welding speed are adjusted based on the welding energy allocation scheme and the state information corresponding to the current welding state, including: If the current welding state is insufficient welding energy, obtain the second target power and the second welding speed corresponding to the current weld point in the welding energy distribution scheme, and generate second welding scheme data based on the second target power and the second welding speed. Based on the current welding status information and the second welding scheme data, the energy deficiency value of the current weld point is calculated. When the energy deficiency value is within the preset slight deficiency range, the third welding power adjustment amount or the third welding speed adjustment amount is determined based on the preset energy compensation mapping relationship, and the welding power and / or welding speed are adjusted based on the third welding power adjustment amount or the third welding speed adjustment amount. Accordingly, after calculating the energy deficiency level of the current solder joint, the method further includes: When the energy deficiency value exceeds the preset slight deficiency range, the fourth welding power adjustment amount and the fourth welding speed adjustment amount are determined based on the preset energy compensation mapping relationship, and the welding power and / or welding speed are adjusted based on the fourth welding power adjustment amount and the fourth welding speed adjustment amount.

[0089] In this scheme, the first target power is the standard welding power preset for the current weld point in the welding energy distribution scheme. When the weld point does not show any overheating abnormalities, this power is used as the reference power for the weld point.

[0090] The first welding speed is the standard welding speed preset for the current weld point in the welding energy distribution scheme. When no overheating abnormality occurs at the weld point, this speed is used as the reference speed for that weld point.

[0091] The first welding scheme data consists of welding parameters formed by the combination of the first target power and the first welding speed, which are used to describe the planned welding method of the current weld point under normal welding conditions.

[0092] The overheating value is a value calculated by combining the current welding state information with the data of the first welding scheme. It is used to quantify the degree of deviation of the actual heat input of the current weld point from the target state.

[0093] The preset slight overheating range is a threshold range used to distinguish the severity of solder joint overheating. When the overheating level is within this range, it means that the current overheating state of the solder joint can be corrected by adjusting the parameters slightly.

[0094] The preset overheating adjustment mapping relationship is a rule that converts the overheating value into the corresponding adjustment amount of welding power and welding speed, reflecting the relationship between the degree of weld overheating and the parameter adjustment range.

[0095] The first welding power adjustment is the welding power change value obtained based on the preset overheating adjustment mapping relationship when the overheating degree value is within the preset slight overheating range. It is used to make a small correction to the welding power of the current weld point.

[0096] The first welding speed adjustment is the welding speed change value obtained based on the preset overheating adjustment mapping relationship when the overheating degree value is within the preset slight overheating range. It is used to make a small correction to the welding speed of the current weld point.

[0097] The second welding power adjustment is the welding power change value obtained based on the preset overheating adjustment mapping relationship when the overheating degree value exceeds the preset slight overheating range. It is used to make a larger correction to the welding power of the current weld point.

[0098] The second welding speed adjustment is the welding speed change value obtained based on the preset overheating adjustment mapping relationship when the overheating degree value exceeds the preset slight overheating range. It is used to make a larger correction to the welding speed of the current weld point.

[0099] The second target power is the standard welding power preset for the current weld point in the welding energy distribution scheme. When no energy deficiency abnormality occurs at the weld point, this is used as the reference power for that weld point.

[0100] The second welding speed is the standard welding speed preset for the current weld point in the welding energy distribution scheme. When no energy deficiency abnormality occurs at the weld point, this speed is used as the reference speed for that weld point.

[0101] The second welding scheme data consists of welding parameters formed by the combination of the second target power and the second welding speed, used to describe the planned welding method for the current weld point under normal energy input conditions.

[0102] The energy deficiency value is a numerical value calculated by combining the state information corresponding to the current welding state with the data of the second welding scheme, to quantify the degree of deficiency of the actual heat input of the current weld point relative to the target state.

[0103] The preset slight deficiency range is a threshold range used to distinguish the severity of insufficient solder joint energy. When the energy deficiency value is within this range, it means that the current insufficient energy state of the solder joint can be corrected by small parameter compensation.

[0104] The preset energy compensation mapping relationship is a rule that converts the energy deficiency value into the corresponding welding power adjustment amount and welding speed adjustment amount, reflecting the relationship between the degree of insufficient weld energy and the parameter compensation range.

[0105] The third welding power adjustment is a welding power change value obtained based on the preset energy compensation mapping relationship when the energy deficiency value is within the preset slight deficiency range. It is used to make a small compensation for the welding power of the current weld point.

[0106] The third welding speed adjustment is a welding speed change value obtained based on the preset energy compensation mapping relationship when the energy deficiency value is within the preset slight deficiency range. It is used to make a small compensation for the welding speed of the current weld point.

[0107] The fourth welding power adjustment is a welding power change value obtained based on the preset energy compensation mapping relationship when the energy deficiency value exceeds the preset slight deficiency range. It is used to make a larger compensation for the welding power of the current weld point.

[0108] The fourth welding speed adjustment is a welding speed change value obtained based on the preset energy compensation mapping relationship when the energy deficiency value exceeds the preset slight deficiency range. It is used to make a larger compensation for the welding speed of the current weld point.

[0109] If the condition is determined to be overheated, the first target power and the first welding speed are retrieved from the welding energy distribution scheme according to the sequence number of the weld point in the welding path. The first target power represents the energy input level per unit time under normal welding conditions for the weld point, and the first welding speed represents the welding torch travel speed under normal conditions for the weld point. The two are combined as the first welding scheme data for the current weld point.

[0110] After obtaining the data for the first welding scheme, the difference between the actual temperature value of the welding area collected in real time and the target temperature value corresponding to the data for the first welding scheme is calculated. At the same time, the rise slope of the actual temperature within the preset time window is calculated. The temperature difference and the rise slope of the temperature are weighted according to the preset weight to obtain the overheating degree value that reflects the degree of heat input of the current weld point being too high. When the overheating value is within the preset slight overheating range, a single adjustment magnitude is matched from the preset overheating adjustment mapping relationship based on its specific value within the range. This magnitude is limited to either a power reduction ratio or a speed increase ratio, and a first welding power adjustment amount or a first welding speed adjustment amount is determined accordingly to correct the current welding output power or welding torch travel speed, so that the heat input per unit length of weld gradually decreases. When the overheating value exceeds the preset slight overheating range, a second welding power adjustment amount and a second welding speed adjustment amount are determined from the preset overheating adjustment mapping relationship according to its corresponding range level. These adjustments are applied synchronously to the welding power output power and welding torch travel speed within the same welding control cycle, thereby quickly suppressing weld overheating by reducing instantaneous heat input and shortening the heat action time.

[0111] During welding, if the weld pool size is smaller than expected, the weld surface is insufficiently wetted, or the effective values ​​of welding current and voltage are consistently lower than the target range, and the current weld point is determined to be in a state of insufficient welding energy, then the corresponding second target power and second welding speed are retrieved from the welding energy allocation scheme based on the weld point's sequence number in the welding path. These two are combined as the second welding scheme data for the current weld point. Subsequently, the real-time collected estimated weld penetration depth and weld pool length change are compared with the corresponding target penetration depth and target weld pool length in the second welding scheme data. By calculating the relative missing ratio between the actual and target values, combined with the deviation of the effective values ​​of welding current and voltage, the degree of energy deficiency, characterizing the current weld point's energy deficiency, is obtained. When the energy deficiency value is within the preset slight deficiency range, a single compensation amplitude is matched from the preset energy compensation mapping relationship according to its specific position within the range. This amplitude is limited to either a power increase ratio or a speed decrease ratio, and a third welding power adjustment amount or a third welding speed adjustment amount is determined accordingly to adjust the welding output power or the welding torch travel speed. When the energy deficiency value exceeds the preset slight deficiency range, a fourth welding power adjustment amount and a fourth welding speed adjustment amount are determined from the preset energy compensation mapping relationship, which are simultaneously applied to the welding power supply control and the welding torch motion control. By increasing the instantaneous heat input and extending the heat treatment time, the insufficient energy of the weld point is quickly compensated, ensuring the weld formation quality. For example, when the weld point temperature is detected to be higher than the target value but not exceeding the preset upper limit, the welding speed is increased while the welding power remains constant according to the preset overheat adjustment mapping relationship, such as increasing the welding speed by 5% for every 10°C increase in temperature; when the weld point temperature significantly exceeds the preset upper limit, the welding power is reduced and the welding speed is increased simultaneously according to the mapping relationship, such as decreasing the power by 10% and increasing the speed by 10%, to quickly suppress heat input; when the molten pool size is detected to be slightly smaller than the target value and in a state of slight energy deficiency, the welding power is increased while the welding speed remains constant according to the preset energy compensation mapping relationship, such as increasing the power by 5% for every 0.2mm decrease in molten pool width; when the molten pool size and molten depth are both below the target threshold and are determined to be in a state of severe energy deficiency, the welding power is increased and the welding speed is reduced simultaneously according to the energy compensation mapping relationship, such as increasing the power by 15% and reducing the speed by 10%, to significantly enhance the heat input per unit length and restore welding stability.

[0112] This solution enables graded and quantitative adjustment of welding power and welding speed based on the real-time thermal state of the weld joint, avoiding overheating and burn-through or insufficient energy leading to incomplete fusion, thereby improving the consistency and stability of the weld formation.

[0113] Based on the above technical solution, optionally, controlling the welding robot to perform welding operations sequentially according to the welding path based on the welding energy distribution scheme includes: Based on the welding energy distribution scheme, the welding robot is controlled to drive the welding torch to output corresponding energy and move the end effector sequentially along the welding path to complete the welding operation.

[0114] In this scheme, before performing the welding operation, the welding energy distribution scheme and the sequence of weld points in the welding path are mapped one by one to establish the correlation between the weld point index and the energy parameters and pose parameters. The welding energy parameters corresponding to each weld point are further broken down into the power target value, the duration of action, and the corresponding travel speed constraint, which serve as the joint control benchmark for the welding torch output and motion control.

[0115] During the welding process, the welding robot will retrieve the welding energy parameters corresponding to the current weld point in real time according to the sequential position of the weld point in the welding path. At the same time, combined with the current joint state, end-effector pose and welding torch working state of the robot, it will continuously adjust the output current, voltage or power of the welding torch power supply, so that the welding torch gradually transitions to the target energy output state before entering the weld point action area, avoiding sudden energy changes that affect the stability of the molten pool.

[0116] At the same time, the robot motion control system will, based on the energy input intensity and duration set for the weld point in the welding energy distribution scheme, reverse-constrain the movement speed and acceleration / deceleration curve of the end effector, so that the dwell time and travel speed of the welding torch in the weld point area are matched with the target welding energy, ensuring that the actual energy input per unit length or per unit time conforms to the preset distribution result.

[0117] As the welding torch transitions from one weld point to an adjacent weld point along the welding path, it adjusts the output power of the welding torch and the movement speed of the end effector synchronously and gradually according to the change in the energy distribution ratio between adjacent weld points. This ensures that the energy output and spatial position changes remain continuous, thereby reducing the risk of thermal shock and heat accumulation at welding path turning points and densely packed weld points.

[0118] Throughout the welding process, feedback data such as the welding torch output status, arc characteristics, and the actual movement speed of the end effector are continuously collected and compared in real time with the target parameters in the welding energy distribution scheme. Once the actual energy input deviates from the preset welding energy distribution result, the welding torch output power or the end effector travel speed is dynamically adjusted to gradually bring the energy output of subsequent weld points back to the energy range defined by the welding energy distribution scheme, until the welding torch reaches the end of the welding path and completes the welding operation of all weld points.

[0119] See appendix Figure 2 , Figure 2 This is a schematic diagram of the second main step of a robotic welding method based on dynamic energy optimization according to an embodiment of the present invention. Figure 2As shown, a robot welding method based on dynamic energy optimization in an embodiment of the present invention mainly includes the following steps S201-S208.

[0120] Step S201: Obtain welding task information of the workpiece to be welded, plan the welding path based on the welding task information, and mark the priority and heat sensitivity of each weld point in the welding path to obtain a welding path that includes the welding priority and heat sensitivity level of each weld point.

[0121] Step S202: Obtain the real-time working status parameters of the welding gun of the welding robot, and obtain the motion capability parameters of the end effector of the welding robot and the local thermal state data of the corresponding welding area of ​​the workpiece to be welded. Generate a welding execution status dataset based on the real-time working status parameters, motion capability parameters and local thermal state data.

[0122] Step S203: Calculate the welding energy requirement based on the welding path and welding execution status dataset to obtain the welding energy parameters corresponding to each weld point.

[0123] Step S204: Based on the welding energy parameters, analyze the energy demand and spatial distribution of each weld point in the welding path to obtain the energy distribution characteristics of each weld point.

[0124] Step S205: Based on the energy distribution characteristics, calculate the energy input timing of each solder joint to obtain the energy input timing result of each solder joint.

[0125] Step S206: Based on the energy input timing results, adjust the energy distribution ratio between adjacent solder joints to obtain the welding energy distribution results for each solder joint.

[0126] Step S207: Based on the welding energy distribution results, generate a welding energy distribution scheme corresponding to each weld point in the welding path.

[0127] Step S208: Based on the welding energy distribution scheme, control the welding robot to perform welding operations sequentially according to the welding path until the welding task is completed.

[0128] In this embodiment, the energy distribution characteristics are the spatial distribution and variation of the welding energy parameters corresponding to each weld point in the welding path. They are used to describe the energy differences, energy concentration areas, and energy gradient changes of different weld points along the path. For example, the energy gradually increases along the welding direction, and local energy peaks are formed at structural corners or high-heat-sensitive weld points.

[0129] The energy input timing result is obtained by determining the order and time interval of the welding energy input at each weld point according to the execution sequence of the welding path. It characterizes the input sequence, duration and energy switching rhythm between adjacent weld points on the time axis.

[0130] The welding energy allocation result is the result of the final allocation of welding energy to each weld point in the welding path, based on the energy demand of the weld point, the energy coupling of adjacent weld points, and the timing of energy input. It is used to clarify the actual energy proportion and energy level of each weld point in the overall welding process.

[0131] During the welding process, the pre-determined welding energy parameters for each weld point are used as the basic input. These parameters are then mapped to the spatial positions of each weld point along the welding path, and the relative position of each weld point in the path coordinate system is individually determined. Next, considering the spacing between weld points, the path direction, and changes in local structures, the energy magnitude of each weld point is compared and normalized point by point. This allows for the identification of areas with high energy demand, rapid energy changes, and relatively concentrated energy along the welding path. Based on this, the energy magnitude of each weld point and its spatial position are combined for characterization, forming an energy distribution feature that reflects the fluctuations in energy levels, changes in energy gradients, and local energy concentrations along the welding path. This clearly characterizes the overall spatial distribution of welding energy.

[0132] After obtaining the energy distribution characteristics, the energy input sequence of each weld point is sorted based on the execution order of the welding path and the movement direction of the welding head. The energy input time interval between adjacent weld points is calculated according to the spatial distance between them, the energy difference, and the allowable heat accumulation during welding. Simultaneously, the energy input time is appropriately extended for weld points with higher energy and shortened for weld points with lower energy. This creates an energy input rhythm that matches the energy distribution characteristics on the time axis, ultimately yielding an energy input timing result that reflects the sequential and continuous relationship of welding energy at each weld point over time.

[0133] Based on the determined energy input timing, the cumulative thermal effects of adjacent weld points during continuous welding are further considered, and the energy distribution ratio between weld points is dynamically adjusted. Specifically, based on the energy input level of the preceding weld point and its position in the timing sequence, the energy proportion of the following weld point is compensated for or reduced, making the energy changes between adjacent weld points smoother and avoiding sudden increases or decreases in energy in local areas. Through this adjustment, a welding energy distribution result is obtained that satisfies the energy requirements of individual weld points while also ensuring a balanced overall heat distribution.

[0134] Based on the welding energy distribution results, the energy magnitude of each weld point, its specific location in the welding path, and the execution sequence are integrated to form a welding energy distribution scheme that includes the weld point number, energy magnitude, and energy input sequence.

[0135] Based on the above steps S201-S208, welding energy can be finely distributed along the welding path according to spatial position and time sequence, avoiding overheating or thermal deformation caused by local energy concentration, while reducing the heat accumulation effect between adjacent weld points; under the premise of ensuring stable welding quality, the utilization rate of welding energy is improved, and the overall consistency and reliability of complex path welding process are enhanced.

[0136] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effects of the present invention, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of the present invention.

[0137] Furthermore, the present invention also provides a robotic welding system based on dynamic energy optimization.

[0138] See appendix Figure 3 , Figure 3 This is a main structural block diagram of a robotic welding system based on dynamic energy optimization according to an embodiment of the present invention. Figure 3 As shown, it specifically includes: The welding path generation module 301 is used to obtain welding task information of the workpiece to be welded, plan the welding path based on the welding task information, and mark the priority and heat sensitivity of each weld point in the welding path to obtain a welding path containing the welding priority and heat sensitivity level of each weld point. The dataset generation module 302 is used to acquire the real-time working status parameters of the welding gun of the welding robot, as well as the motion capability parameters of the end effector of the welding robot and the local thermal state data of the welding area corresponding to the workpiece to be welded, and to generate a welding execution status dataset based on the real-time working status parameters, motion capability parameters and local thermal state data. The welding energy calculation module 303 is used to calculate the welding energy demand based on the welding path and the welding execution status dataset, and obtain the welding energy parameters corresponding to each weld point. The welding execution module 304 is used to generate a welding energy distribution scheme for each weld point in the welding path based on the welding energy parameters, and to control the welding robot to perform welding operations sequentially according to the welding path based on the welding energy distribution scheme until the welding task is completed.

[0139] The robotic welding system based on dynamic energy optimization provided in this application embodiment can achieve... Figure 1 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0140] Those skilled in the art will understand that all or part of the processes in the method of the above embodiment of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0141] Furthermore, the present invention also provides an electronic device 400, including a processor 401, a memory 402, and a program or instructions stored in the memory 402 and executable on the processor 401. When the program or instructions are executed by the processor 401, they implement the various processes of the above-described embodiment of a robot welding method based on dynamic energy optimization and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0142] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0143] Furthermore, the present invention also provides a computer-readable storage medium. In one embodiment of the computer-readable storage medium according to the present invention, the computer-readable storage medium can be configured to store a program for executing a robotic welding method based on dynamic energy optimization as described in the above-described method embodiments. This program can be loaded and run by a processor to implement the aforementioned robotic welding method based on dynamic energy optimization. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a storage device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0144] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of software and hardware. Therefore, the number of modules shown in the figures is merely illustrative.

[0145] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principles of the present invention; therefore, the technical solutions after splitting or combining will fall within the protection scope of the present invention.

[0146] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A robotic welding method based on dynamic energy optimization, characterized in that, The method includes: Obtain welding task information of the workpiece to be welded, plan the welding path based on the welding task information, and mark the priority and heat sensitivity of each weld point in the welding path to obtain a welding path that includes the welding priority and heat sensitivity level of each weld point. The real-time working status parameters of the welding gun of the welding robot are obtained, as well as the motion capability parameters of the end effector of the welding robot and the local thermal state data of the corresponding welding area of ​​the workpiece to be welded. Based on the real-time working status parameters, motion capability parameters and local thermal state data, a welding execution status dataset is generated. Welding energy demand is calculated based on the welding path and welding execution status dataset to obtain welding energy parameters corresponding to each weld point. Based on the welding energy parameters, a welding energy allocation scheme is generated for each weld point in the welding path, and the welding robot is controlled to perform welding operations sequentially according to the welding path based on the welding energy allocation scheme until the welding task is completed.

2. The robotic welding method based on dynamic energy optimization according to claim 1, characterized in that, in, After controlling the welding robot to perform welding operations sequentially according to the welding path based on the welding energy distribution scheme, the method further includes: Real-time acquisition of welding execution process status information, and determination of the current welding status based on the status information; If the current welding state is that the current weld joint is overheated or the welding energy is insufficient, the welding power and / or welding speed are adjusted based on the welding energy distribution scheme and the state information corresponding to the current welding state.

3. The robotic welding method based on dynamic energy optimization according to claim 2, characterized in that, in, If the current welding state is that the current weld joint is overheated, adjust the welding power and / or welding speed based on the welding energy distribution scheme and state information, including: If the current welding state is that the current weld point is overheated, obtain the first target power and the first welding speed corresponding to the current weld point in the welding energy distribution scheme, and generate the first welding scheme data based on the first target power and the first welding speed. Based on the current welding status information and the first welding scheme data, the overheating degree value of the current weld point is calculated. When the overheating degree value is within the preset slight overheating range, the first welding power adjustment amount or the first welding speed adjustment amount is determined based on the preset overheating adjustment mapping relationship, and the welding power and / or welding speed are adjusted based on the first welding power adjustment amount or the first welding speed adjustment amount. Accordingly, after calculating the overheating value of the current solder joint, the method further includes: When the overheating value exceeds the preset slight overheating range, the second welding power adjustment amount and the second welding speed adjustment amount are determined based on the preset overheating adjustment mapping relationship, and the welding power and / or welding speed are adjusted based on the second welding power adjustment amount and the second welding speed adjustment amount. Accordingly, if the current welding state is characterized by insufficient welding energy, the welding power and / or welding speed are adjusted based on the welding energy allocation scheme and the state information corresponding to the current welding state, including: If the current welding state is insufficient welding energy, obtain the second target power and the second welding speed corresponding to the current weld point in the welding energy distribution scheme, and generate second welding scheme data based on the second target power and the second welding speed. Based on the current welding status information and the second welding scheme data, the energy deficiency value of the current weld point is calculated. When the energy deficiency value is within the preset slight deficiency range, the third welding power adjustment amount or the third welding speed adjustment amount is determined based on the preset energy compensation mapping relationship, and the welding power and / or welding speed are adjusted based on the third welding power adjustment amount or the third welding speed adjustment amount. Accordingly, after calculating the energy deficiency level of the current solder joint, the method further includes: When the energy deficiency value exceeds the preset slight deficiency range, the fourth welding power adjustment amount and the fourth welding speed adjustment amount are determined based on the preset energy compensation mapping relationship, and the welding power and / or welding speed are adjusted based on the fourth welding power adjustment amount and the fourth welding speed adjustment amount.

4. The robotic welding method based on dynamic energy optimization according to claim 1, characterized in that, in, Based on the welding task information, a welding path is planned, and each weld point in the welding path is marked with priority and heat sensitivity, resulting in a welding path that includes the welding priority and heat sensitivity level of each weld point, including: Based on the welding task information, the process dependencies and structural assembly relationships between welds are analyzed to obtain analysis results, and welding sequence constraint information is determined based on the analysis results. The workpiece geometry features to be welded are obtained, and the motion capability information of the welding robot is obtained. Based on the welding sequence constraint information, workpiece geometry features and motion capability information, welding path planning is performed to obtain the initial welding path. Obtain welding feature information of each weld point in the initial welding path, evaluate the importance of each weld point based on the welding feature information, mark the welding priority of each weld point, and obtain a welding path containing the welding priority of each weld point. Based on the welding feature information, thermal sensitivity analysis is performed on each weld point, and a thermal sensitivity level is marked for each weld point to obtain a welding path that includes the welding priority and thermal sensitivity level of each weld point.

5. The robotic welding method based on dynamic energy optimization according to claim 1, characterized in that, in, Welding energy requirements are calculated based on the welding path and welding execution status dataset to obtain welding energy parameters corresponding to each weld point, including: Based on the welding priority and heat sensitivity level of each weld point in the welding path, the welding energy required for each weld point is analyzed, and the energy demand analysis results of each weld point are obtained. Based on the real-time working status parameters, operating capability parameters, and local thermal state data, the available power data and available speed data of each weld point during the welding process are analyzed, and welding execution constraints are generated based on the available power data and available speed data. Based on the energy demand analysis results and welding execution constraints, the power demand data and available power limit data of each weld point are analyzed, and the actual welding power of each weld point is generated based on the power demand data and available power limit data. Based on welding execution constraints and actual welding power, analyze the speed requirement data and speed limit data of each weld point, and generate the actual welding speed of each weld point based on the speed requirement data and speed limit data. Welding energy parameters corresponding to each weld point are generated based on the actual welding power and actual welding speed.

6. The robotic welding method based on dynamic energy optimization according to claim 1, characterized in that, in, Based on the welding energy parameters, a welding energy distribution scheme is generated for each weld point in the welding path, including: Based on the welding energy parameters, the energy demand and spatial distribution of each weld point in the welding path are analyzed to obtain the energy distribution characteristics of each weld point. Based on the energy distribution characteristics, the energy input timing sequence of each solder joint is calculated to obtain the energy input timing sequence results of each solder joint. Based on the energy input timing results, the energy distribution ratio between adjacent weld points is adjusted to obtain the welding energy distribution results for each weld point; Based on the welding energy distribution results, a welding energy distribution scheme corresponding to each weld point in the welding path is generated.

7. The robotic welding method based on dynamic energy optimization according to claim 1, characterized in that, in, Based on the welding energy distribution scheme, the welding robot is controlled to perform welding operations sequentially according to the welding path, including: Based on the welding energy distribution scheme, the welding robot is controlled to drive the welding torch to output corresponding energy and move the end effector sequentially along the welding path to complete the welding operation.

8. A robotic welding system based on dynamic energy optimization, characterized in that, The system includes: The welding path generation module is used to obtain welding task information of the workpiece to be welded, plan the welding path based on the welding task information, and mark the priority and heat sensitivity of each weld point in the welding path to obtain a welding path containing the welding priority and heat sensitivity level of each weld point. The dataset generation module is used to obtain the real-time working status parameters of the welding gun of the welding robot, as well as the motion capability parameters of the end effector of the welding robot and the local thermal state data of the corresponding welding area of ​​the workpiece to be welded, and to generate a welding execution status dataset based on the real-time working status parameters, motion capability parameters and local thermal state data. The welding energy calculation module is used to calculate the welding energy demand based on the welding path and the welding execution status dataset, and obtain the welding energy parameters corresponding to each weld point. The welding execution module is used to generate a welding energy distribution scheme for each weld point in the welding path based on the welding energy parameters, and to control the welding robot to perform welding operations sequentially according to the welding path based on the welding energy distribution scheme until the welding task is completed.

9. An electronic device comprising a processor, a memory, and a program or instructions stored in the memory and executable on the processor, characterized in that, The program or instructions are adapted to be loaded and run by the processor to perform a robotic welding method based on dynamic energy optimization as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a plurality of program codes, characterized in that, The program code is adapted to be loaded and run by a processor to perform a robotic welding method based on dynamic energy optimization as described in any one of claims 1 to 7.

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