A synergic control heating method and system based on induction heating and laser compensation

Abstract

The application discloses a kind of based on induction heating and laser compensation's collaborative control heating method and system, it is related to metal precision heat treatment technical field, comprising: the regional temperature matrix of the workpiece to be processed after obtaining basic heating;Pretreatment is carried out based on regional temperature matrix, and all low-temperature regions are identified and the corresponding core feature parameters are extracted;Based on core feature parameters and related parameters, construct transient heat conduction numerical model, and carry out iterative solution, obtain global temperature field evolution matrix;Based on global temperature field evolution matrix, construct multi-constraint objective optimization function, and obtain optimal heating path and corresponding laser power parameters by solving;Based on optimal heating path, corresponding laser head is controlled to move, and laser output power is adjusted in real time according to laser power parameters, and low-temperature region is compensated and heated;The above supplementary heating process is recycled until the global temperature of the workpiece to be processed meets the requirements. High-precision uniform regulation and control of the global temperature of special-shaped metal workpiece are realized.

Description

Technical Field

[0001] This invention relates to the field of precision heat treatment technology for metals, and more specifically to a synergistic control heating method and system based on induction heating and laser compensation. Background Technology

[0002] In precision metal brazing and heat treatment processes, induction heating is widely used due to its advantages of fast heating speed, high efficiency, and ease of automation. However, it is prone to localized low-temperature zones due to the influence of irregular workpiece structures, uneven electromagnetic field distribution, and differences in material heat dissipation characteristics. This leads to problems such as insufficient melting of the brazing filler metal and uneven microstructure and properties, failing to meet the stringent process requirements of high-end fields such as aerospace. Laser heating offers the advantage of precise local temperature control. Combining induction heating with localized laser compensation is the core direction for solving the problem of uniform heating of irregularly shaped workpieces.

[0003] However, existing induction-laser hybrid heating solutions generally suffer from four common core technical defects: First, insufficient temperature field sensing accuracy. Existing solutions mostly use single-point thermocouples or regional temperature measurement methods, which cannot achieve high-precision sensing of the entire workpiece temperature field, making it difficult to identify small-scale local low-temperature areas and providing data support for accurate compensation. Second, insufficient depth of multi-heat source coordination. Most solutions only use open-loop timing control or simple energy superposition, lacking deep real-time linkage between induction heating and laser heating. They cannot synchronously adjust the laser compensation path and power according to dynamic changes in the temperature field, making it difficult to cope with the heating process. The problems include: 1) dynamic changes in operating conditions; 2) inherent lag in control methods, relying heavily on fixed parameters calibrated by manual experience or conventional single-loop PID control, lacking scientific prediction of temperature field changes, resulting in significant control lag and inability to avoid local underheating or overheating problems in advance; 3) lack of a complete closed-loop decision-making system, failing to construct a full-chain control logic of "real-time perception - feature extraction - trend prediction - multi-objective optimization - closed-loop execution", making it impossible to achieve high-precision, automated control of temperature uniformity across the entire range during the heating process of irregularly shaped workpieces, and failing to meet the stringent requirements of heat treatment processes in high-end equipment manufacturing.

[0004] Therefore, how to achieve high-precision and uniform temperature control across the entire range of irregularly shaped metal workpieces is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of the above problems, this invention is proposed to provide a synergistic control heating method and system based on induction heating and laser compensation to overcome or at least partially solve the above problems. Through the synergistic architecture of "induction full-domain basic heating + laser local precise compensation" and full closed-loop real-time control, high-precision uniform control of the temperature of irregularly shaped metal workpieces is achieved. At the same time, this invention is based on classical heat transfer theory and mature industrial control algorithms, which has strong engineering practicality and high robustness, and can be adapted to different workpieces and process scenarios without a lot of manual calibration.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, embodiments of the present invention provide a synergistic control heating method based on induction heating and laser compensation, comprising: Obtain the temperature matrix of the workpiece area after basic heating; Preprocessing is performed based on the regional temperature matrix to identify all low-temperature regions and extract the corresponding core feature parameters. A transient heat conduction numerical model is constructed based on the core characteristic parameters and related parameters, and iterative solutions are performed to obtain the global temperature field evolution matrix. Based on the global temperature field evolution matrix, a multi-constraint objective optimization function is constructed, and the optimal heating path and corresponding laser power parameters are obtained by solving the problem. Based on the optimal heating path, the corresponding laser head is moved, and the laser output power is adjusted in real time according to the laser power parameters to compensate for heating the low-temperature region. Repeat the above supplementary heating process until the temperature of the entire workpiece meets the requirements.

[0008] Preferably, the method for obtaining the regional temperature matrix is ​​as follows: After fixing the workpiece to be processed, the workpiece to be processed is subjected to full-area basic heating with a preset constant power until the overall average temperature of the workpiece to be processed rises to the preset temperature, and then enters the heat preservation compensation stage. During the thermal insulation compensation stage, the global temperature field of the workpiece to be processed is collected and then median filtering for noise reduction, emissivity calibration correction, and pixel coordinate to physical coordinate mapping are performed sequentially to obtain the preprocessed global temperature matrix.

[0009] Preferably, all low-temperature regions are identified, specifically including: Based on the global temperature matrix, adaptive threshold segmentation is performed using the maximum inter-class variance method, and the optimal low-temperature zone segmentation threshold is obtained by acquiring the inter-class variance. Based on the optimal low temperature zone segmentation threshold as the temperature boundary line distinguishing between the low temperature zone and the normal temperature zone, the temperature field binarization segmentation is completed to obtain a binarized image. Based on structuring elements of a preset size, morphological opening and closing operations are sequentially performed on the binarized image to remove isolated noise points and complete the contour breaks of the low-temperature region, thereby obtaining all the low-temperature regions.

[0010] Preferably, the corresponding core feature parameters are extracted, specifically including: Based on all the low-temperature regions, the location coordinates, contour features, area, average temperature, and maximum temperature deviation of the low-temperature region are extracted and used as the core feature parameters.

[0011] Preferably, a transient heat conduction numerical model is constructed, specifically including: The normalized area of ​​the low-temperature region is obtained by normalizing the area of ​​the low-temperature region. Based on the thermal properties of the workpiece to be processed, the heating parameters, the normalized area of ​​the low-temperature region, and the maximum temperature deviation of the low-temperature region, the transient heat conduction numerical model is constructed based on the Fourier partial differential equation of heat conduction.

[0012] Preferably, the global temperature field evolution matrix is ​​obtained, specifically including: The spatial range of the low-temperature region is obtained based on the location coordinates of the low-temperature region and the contour features of the low-temperature region. The initial conditions for solving the temperature field are constructed based on the spatial range of the low-temperature region, the average temperature of the low-temperature region, and the global temperature matrix. The heat exchange law between the workpiece surface and the environment is defined, and the heat dissipation characteristics of the irregular structure are realistically simulated by using differentiated convective heat transfer coefficients to construct differentiated convective heat transfer boundary conditions. By prioritizing differentiated convergence accuracy to ensure prediction accuracy in the low-temperature region, an iterative convergence criterion is constructed. Based on the initial conditions for solving the temperature field, the differentiated convection heat transfer boundary conditions, and the iterative convergence criterion, the transient heat conduction numerical model is iteratively solved to obtain the global temperature field evolution matrix for multiple prediction steps over the future prediction duration.

[0013] Preferably, a multi-constraint objective function is constructed, specifically including: The predicted region temperature is obtained based on the global temperature field evolution matrix. Based on the weighted sum of squared deviations between the predicted temperature and the target insulation temperature, the temperature deviation cost term is obtained; Based on the weighted sum of the total laser output energy and the fluctuation of induction heating power in the predicted time domain, the energy consumption cost term is obtained; Based on the sum of acceleration changes in the predicted robotic arm motion path within the time domain, the path smoothness cost term is obtained. Based on the temperature deviation cost term, the energy consumption cost term, the path smoothness cost term, and the corresponding preset weight coefficients, the multi-constraint objective optimization function is constructed.

[0014] Preferably, the optimal heating path and corresponding laser power parameters are obtained by solving the problem, specifically including: The temperature deviation is obtained based on the average temperature of the low-temperature region and the target insulation temperature; The heating priority of each low-temperature region is obtained based on the temperature deviation and the area of ​​the low-temperature region. The corresponding low-temperature regions are sorted from high to low based on the heating priority to obtain the sorted low-temperature regions; Based on the sorted low-temperature regions, with the multi-constraint objective optimization function as the objective, the optimal path and regional power parameters of each low-temperature region are obtained in the sorted order. Based on the sorting order, the optimal paths within each of the low-temperature regions are spliced ​​together, and the nearest neighbor algorithm is used to plan the travel paths between regions to obtain the optimal heating path with the shortest total travel. Based on the optimal heating path and the regional power parameters, a global constraint verification is performed, and the laser power parameters are output.

[0015] Preferably, compensating heating is applied to the low-temperature region, specifically including: The target robotic arm drives the target laser to move along the optimal heating path; During the movement, a feedforward-feedback cascaded PID controller is used to achieve closed-loop regulation of the laser power. The laser power parameter is used as the feedforward input, and the real-time temperature measurement of the workpiece to be processed is used as the feedback quantity to correct the laser output power of the target laser in real time. Power limiting is used to ensure safe operation and complete the compensation heating of all the low-temperature regions.

[0016] In a second aspect, embodiments of the present invention provide a coordinated control heating system based on induction heating and laser compensation, for executing a coordinated control heating method based on induction heating and laser compensation as described in any of the first aspects, comprising: a global temperature acquisition module, a core parameter acquisition module, a temperature evolution prediction module, an optimal path output module, and a compensation heating execution module; The global temperature acquisition module is used to acquire the regional temperature matrix of the workpiece to be processed after basic heating; The core parameter acquisition module is used to preprocess the region temperature matrix to identify all low-temperature regions and extract the corresponding core feature parameters. The temperature evolution prediction module is used to construct a transient heat conduction numerical model based on the core feature parameters and related parameters, and to perform iterative solutions to obtain the global temperature field evolution matrix. The optimal path output module is used to construct a multi-constraint objective optimization function based on the global temperature field evolution matrix, and solve for the optimal heating path and the corresponding laser power parameters. The compensation heating execution module is used to control the movement of the corresponding laser head based on the optimal heating path, and adjust the laser output power in real time according to the laser power parameters to compensate for the low temperature region; the above compensation heating process is repeated until the temperature of the entire workpiece meets the requirements.

[0017] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a synergistic control heating method and system based on induction heating and laser compensation, which has the following beneficial effects: 1. Significantly improved heating accuracy and uniformity: This invention, through the collaborative architecture of "induction full-domain basic heating + laser local dynamic compensation", combined with high-precision temperature field sensing and full closed-loop real-time control, can stably control the full-domain temperature deviation of irregular / variable cross-section workpieces within ±5℃, which is far superior to the control accuracy of ±20℃ or more of the existing technology. It fully meets the process requirements of high-end heat treatment and greatly improves the consistency of the microstructure and properties and the yield of the workpiece after heat treatment.

[0018] 2. Significant heating efficiency and energy saving: Compared with pure laser heating solutions, this invention achieves a full-area basic temperature rise through induction heating and only uses laser for local supplementary heating. Compared with traditional pure induction heating solutions, there is no need to repeatedly adjust the induction coil contour design, and the workpiece changeover and adjustment time is shortened by more than 90%, which greatly improves production efficiency and is suitable for the needs of industrial mass production.

[0019] 3. Strong engineering practicality and robustness: The present invention is a full-process solution built on classical heat transfer theory and mature industrial control algorithms. The core links are combined with artificial intelligence models, eliminating the need for extensive manual process calibration for new workpieces. It can adapt to workpieces with different geometries and materials, and can effectively compensate for the effects of workpiece clamping errors, material batch differences, and environmental temperature fluctuations. It has strong anti-interference capabilities and is suitable for complex working conditions in industrial sites.

[0020] 4. Excellent real-time control and scenario scalability: This invention adopts mature numerical solution algorithms and rolling optimization control logic. The total time for single-frame image processing and optimization solution is ≤30ms, and the closed-loop control response delay is ≤100ms, enabling dynamic real-time correction of the heating process. The system adopts a modular design, supports algorithm replacement and flexible parameter configuration, and can be adapted to various heat treatment process scenarios such as aerospace blade quenching, automotive crankshaft forging preheating, and engineering machinery structural component welding preheating, with a wide range of applications. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0022] Figure 1 This is a flowchart of a synergistic control heating method based on induction heating and laser compensation provided in an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of laser heating path planning provided in an embodiment of the present invention.

[0024] Figure 3 This is a schematic diagram of a synergistic control heating system based on induction heating and laser compensation provided in an embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1 like Figure 1 As shown, this embodiment of the invention discloses a synergistic control heating method based on induction heating and laser compensation, including the following steps. For ease of description, these steps are numbered S1 to S6, and these numbers are not intended to limit the sequential relationship between the various steps of this invention: S1 obtains the temperature matrix of the area of ​​the workpiece to be processed after basic heating.

[0027] Furthermore, the method for obtaining the regional temperature matrix is ​​as follows: After fixing the workpiece to be processed, the workpiece is heated in its entirety with a preset constant power until the overall average temperature of the workpiece rises to the preset temperature, and then enters the heat preservation and compensation stage. During the thermal compensation stage, the global temperature field of the workpiece to be processed is collected and then the median filtering noise reduction, emissivity calibration correction, and pixel coordinate to physical coordinate mapping are performed sequentially to obtain the preprocessed global temperature matrix.

[0028] Furthermore, in this embodiment, the induction heating unit performs full-area basic heating of the workpiece to be processed at a constant power of 20kW. When the overall average temperature of the workpiece rises to 1180℃, the heat preservation compensation stage is entered. Simultaneously, the infrared thermal imager is started to collect the full-area temperature field of the workpiece. The original temperature data is processed sequentially by median filtering for noise reduction, emissivity calibration correction, and pixel coordinate to physical coordinate mapping. Finally, the pre-processed full-area temperature matrix is ​​output as the input data for low-temperature zone identification.

[0029] Furthermore, the induction heating unit includes a medium-frequency / high-frequency induction heating power supply, an induction coil, a matching transformer, and a water-cooling component. The power adjustment range of the induction heating power supply is 0-100kW, and the frequency adjustment range is 1-150kHz, which can adapt to the heating needs of workpieces of different materials and sizes.

[0030] Furthermore, in this embodiment, the infrared thermal imager is a cooled infrared thermal imager with a temperature measurement range of 0~1500℃, a temperature measurement accuracy of ≤±2℃, and a frame rate of ≥30Hz. The thermal imager is equipped with a fixed bracket to ensure that the field of view completely covers the entire heating area of ​​the workpiece.

[0031] Furthermore, in this embodiment, the vacuum induction brazing of the fuel pipeline of an aerospace engine is carried out. The workpiece to be processed is 1Cr18Ni9Ti austenitic stainless steel, and the structure to be welded is a circumferential butt weld between a pipe joint with a 22.5° inclined flange lug and a Φ19.8mm straight pipe. The inner diameter of the pipe joint is 20mm and the wall thickness is 1mm. The brazing filler metal is 0.9mm diameter BCu68NiSiB wire brazing filler metal.

[0032] The oblique flange pipe joint to be welded is coaxially clamped with the straight pipe in a vacuum brazing fixture to ensure that the workpiece is fixed and without displacement throughout the process; two 70mm inner diameter circular induction coils are placed around the outer periphery of the circumferential weld to be welded, covering the weld and a 10mm area on each side, to complete the system connection and process initialization.

[0033] S2 preprocesses the regional temperature matrix to identify all low-temperature regions and extract the corresponding core feature parameters.

[0034] Furthermore, all low-temperature regions were identified, specifically including: Adaptive threshold segmentation is performed based on the global temperature matrix using the maximum inter-class variance method, and the optimal low-temperature zone segmentation threshold is obtained by acquiring the inter-class variance. Based on the optimal low temperature zone segmentation threshold as the temperature boundary line to distinguish between the low temperature zone and the normal temperature zone, the temperature field binarization segmentation is completed to obtain a binarized image. Based on the structuring elements of a preset size, morphological opening and closing operations are sequentially performed on the binarized image to remove isolated noise points and complete the contour breaks of the low-temperature region, thus obtaining all the low-temperature regions.

[0035] Furthermore, based on the global temperature matrix, the Otsu's method (maximum inter-class variance) is used for adaptive threshold segmentation. The automatic division between low-temperature and normal-temperature zones is achieved through inter-class variance calculation. The calculation formula is: ; in, and These represent the pixel percentage of the foreground in the low-temperature zone and the pixel percentage of the background in the normal-temperature zone, respectively. and These are the average pixel temperatures of the foreground and the background, respectively. T This represents the temperature threshold for segmenting the temperature field.

[0036] Furthermore, based on inter-class variance The optimal low-temperature partitioning threshold is obtained: ; in, This represents the optimal low-temperature zone segmentation threshold, which is the temperature boundary line automatically calculated by the system to distinguish between the low-temperature zone and the normal temperature zone. The temperature is below... The region is identified as a low-temperature region that needs reheating, and the temperature field is binarized and segmented to obtain a binarized image.

[0037] The solution obtained in this embodiment is This completes the binarization segmentation of the temperature field, and areas with temperatures below 1190℃ are identified as low-temperature areas requiring additional heating.

[0038] Furthermore, in this embodiment, morphological opening and closing operations are sequentially performed on the binarized image using a 5×5 square structuring element to remove isolated noise points and complete the contour breaks of the low-temperature region, thereby obtaining the complete low-temperature region recognition result, i.e., all low-temperature regions.

[0039] This embodiment identifies two independent low-temperature regions: the first is the low-temperature region at the 22.5° angled flange lug, and the second is the low-temperature region on the underside of the circumferential weld.

[0040] Furthermore, the corresponding core feature parameters are extracted, specifically including: Based on all the low-temperature regions, the location coordinates, contour features, area, average temperature, and maximum temperature deviation of the low-temperature region are extracted and used as core feature parameters. Among them: the location coordinates and contour features of the low-temperature region are used to delineate the spatial range of the low-temperature region; the area of ​​the low-temperature region is used to calculate the correction intensity; the average temperature of the low-temperature region is used to set the initial conditions; and the maximum temperature deviation of the low-temperature region is used to correct the heat transfer coefficient and convergence accuracy.

[0041] Furthermore, an image processing method combining Otsu's method (OTSU) adaptive threshold segmentation with morphological opening and closing operations is adopted to achieve accurate identification of low-temperature regions. For complex contoured and irregularly shaped workpieces, a lightweight deep learning network can be used as an auxiliary identification method to improve the robustness of identification under complex working conditions.

[0042] S3 constructs a transient heat conduction numerical model based on core characteristic parameters and related parameters, and performs iterative solutions to obtain the global temperature field evolution matrix.

[0043] Furthermore, a transient heat conduction numerical model is constructed, specifically including: The normalized area of ​​the low-temperature region is obtained by normalizing the area of ​​the low-temperature region. Based on the thermal properties of the workpiece to be processed, heating parameters, normalized area of ​​the low-temperature region, and maximum temperature deviation of the low-temperature region, a transient heat conduction numerical model is constructed based on the Fourier partial differential equation of heat conduction.

[0044] Furthermore, the two-dimensional transient heat conduction model is as follows: ; in, This indicates the density of the workpiece to be processed, in this implementation. The density of 1Cr18Ni9Ti stainless steel; c This indicates the high-temperature, constant-pressure specific heat capacity of the workpiece to be processed; t Indicates the time of the heating process; This represents the gradient operator, used to describe the variation trend of physical quantities within the workpiece spatial domain; It represents the temperature gradient, reflecting the direction and rate of temperature change within the workpiece in space; This represents the node-specific thermal conductivity, taken as 25 W / (m²) in the low-temperature region. K), within the normal range, take 28W / (m K); This indicates the intensity of the induction heating internal heat source that is adapted to the wall thickness of the workpiece to be processed. This represents the latent heat of fusion correction term for the solder, taken within the solder melting range of 1129~1160℃. 2.13×10⁹ W / m³, with 0 in the remaining intervals; This represents the regional labeling coefficient, which is 1 in the low-temperature region and 0 in the normal region. and These represent the maximum temperature deviation in the low-temperature region and the normalized area of ​​the low-temperature region, respectively.

[0045] Furthermore, the global temperature field evolution matrix is ​​obtained, specifically including: The spatial extent of the low-temperature region is obtained based on its location coordinates and contour features. The initial conditions for solving the temperature field are constructed based on the spatial range of the low-temperature region, the average temperature of the low-temperature region, and the global temperature matrix. The heat exchange law between the workpiece surface and the environment is defined, and the heat dissipation characteristics of the irregular structure are realistically simulated by using differentiated convective heat transfer coefficients to construct differentiated convective heat transfer boundary conditions. By prioritizing differentiated convergence accuracy to ensure prediction accuracy in the low-temperature region, an iterative convergence criterion is constructed. Based on the initial conditions of the temperature field solution, the differentiated convection heat transfer boundary conditions, and the iterative convergence criterion, the transient heat conduction numerical model is iteratively solved to obtain the global temperature field evolution matrix for multiple prediction steps in the future prediction time.

[0046] Furthermore, the finite difference method is used to discretize the above two-dimensional transient heat conduction model in space and time, setting differentiated solution step sizes: 50ms time step and 0.25mm spatial step in the low temperature region; and 100ms time step and 0.5mm spatial step in the normal temperature region. The temperature field solution must simultaneously satisfy three boundary conditions: initial conditions, boundary conditions, and convergence criteria. These three together constitute a complete iterative solution system.

[0047] Furthermore, the initial conditions for solving the temperature field are constructed: the initial temperature of all nodes at the start of the calculation is defined, characteristic parameter correction is used to eliminate temperature measurement noise in the low-temperature region, and the preprocessed temperature is directly used in the normal temperature region. The formula is as follows: ; in, express Time coordinates The temperature at that location This indicates the average temperature of the low-temperature region. This represents the linear interpolation correction amount in the low-temperature region. Indicates the spatial range of the low-temperature region. This is the region temperature matrix output by S1.

[0048] Furthermore, differentiated convective heat transfer boundary conditions are constructed: the heat exchange law between the workpiece surface and the environment is defined, and each iteration must strictly satisfy it. Differentiated convective heat transfer coefficients are used to realistically simulate the heat dissipation characteristics of irregular structures, and the formula is: ; in, n The normal unit vector representing the workpiece boundary; Indicates the differential convective heat transfer coefficient in the low-temperature region. Indicates the reference convective heat transfer coefficient. Indicates the maximum temperature deviation in the low-temperature region; The ambient temperature.

[0049] Furthermore, an iterative convergence criterion is constructed: The accuracy requirement for iteration is defined; if the temperature change is less than this threshold, the calculation result is considered sufficiently accurate. Differential convergence accuracy is adopted to prioritize the prediction accuracy in the low-temperature region. The formula is: ; in, This represents the coordinates of the nth prediction step. Predicted temperature field value at location ). This represents the (n+1)th prediction step, with coordinates ( The predicted temperature field value at ().

[0050] Furthermore, after the iteration meets the convergence requirement, this embodiment outputs the global temperature field evolution matrix for the next 5 seconds (50 prediction steps). ,in, This represents the coordinates of the nth prediction step. The predicted temperature field value at () provides data support for subsequent laser compensation optimization.

[0051] S4 constructs a multi-constraint objective optimization function based on the global temperature field evolution matrix, and solves for the optimal heating path and the corresponding laser power parameters.

[0052] Furthermore, a multi-constraint objective function is constructed, specifically including: The temperature of the predicted region is obtained based on the global temperature field evolution matrix; The temperature deviation cost term is obtained based on the weighted sum of squared deviations between the predicted regional temperature and the target insulation temperature. Based on the weighted sum of the total laser output energy and the fluctuation of induction heating power in the predicted time domain, the energy consumption cost term is obtained; Based on the sum of acceleration changes in the predicted robotic arm motion path within the time domain, the path smoothness cost term is obtained. Based on the temperature deviation cost term, energy consumption cost term, path smoothness cost term, and corresponding preset weight coefficients, a multi-constraint objective optimization function is constructed.

[0053] Furthermore, this invention adopts a two-level path planning architecture of "regional-level heat compensation priority sorting + single-region multi-objective optimization". First, the processing order of multiple low-temperature zones is determined, and then the optimal path and power within each zone are solved, taking into account both priority compensation for key areas and real-time control requirements.

[0054] With global temperature uniformity as the core objective, a multi-constraint, multi-objective optimization function is constructed. : ; in, All represent preset weighting coefficients, and satisfy the following conditions: Prioritizing the core control requirement of ensuring brazing temperature uniformity, this embodiment sets it as follows: ; The temperature deviation cost term is the weighted sum of squares of the temperature deviations between the predicted area and the target insulation temperature within the prediction time domain over the next 5 seconds, with the weighting coefficient for the low-temperature area being higher than that for the normal-temperature area. The energy consumption cost term is the weighted sum of the total laser output energy and the fluctuation of induction heating power in the predicted time domain. The path smoothness cost term represents the sum of acceleration changes along the robotic arm's motion path in the prediction time domain.

[0055] Furthermore, the temperature deviation cost term is constructed by combining the temperature field prediction results with the characteristic parameters of the low-temperature region, and the formula is as follows: ; Where M represents the total number of grids in the solution domain; This represents the coordinates of the nth prediction step. Predicted temperature field value at location ). , indicating the target insulation temperature for brazing; This represents the weighting coefficient for the spatiotemporally differentiated low-temperature region.

[0056] Weighting coefficient for low temperature region The calculation is adaptively performed based on regional characteristics, and the formula is as follows: .

[0057] Furthermore, the optimal heating path and corresponding laser power parameters are obtained by solving, specifically including: The temperature deviation is obtained based on the average temperature of the low-temperature region and the target insulation temperature. The heating priority for each low-temperature region is determined based on the temperature deviation and the area of ​​the low-temperature region. Based on the priority of heat replenishment, the corresponding low-temperature regions are sorted from high to low to obtain the sorted low-temperature regions; Based on the sorted low-temperature regions, with a multi-constraint objective function as the objective, the optimal path and regional power parameters of each low-temperature region are obtained according to the sorting order. Based on the sorting order, the optimal paths within each low-temperature region are spliced ​​together, and the nearest neighbor algorithm is used to plan the travel paths between regions to obtain the optimal heating path with the shortest total travel. Global constraint verification is performed based on the optimal heating path and regional power parameters, and the laser power parameters are output.

[0058] Furthermore, based on multi-constraint multi-objective optimization functions Set optimization constraints: upper and lower limits of laser power, maximum movement speed of the robotic arm end effector, maximum allowable temperature of workpiece material, and induction heating power adjustment rate constraints.

[0059] In this embodiment, the upper and lower limits of laser power are set to 0 and 2000W, respectively; the maximum movement speed of the robotic arm end effector is set to 10mm / s; the maximum allowable temperature of the workpiece material is ≤1250℃; and the induction heating power adjustment rate is ≤4kW / 100ms.

[0060] Furthermore, based on the temperature deviation and the area of ​​the low-temperature region, the heating priority of each low-temperature region is obtained, which determines the processing order of the robotic arm. The formula is as follows: ; in, This indicates the priority of reheating; the larger the value, the higher the priority. This represents the area of ​​the low-temperature region extracted in step S2. Priority is given to both temperature deviation and region area; regions with lower temperatures and larger areas are given priority for reheating to prevent further temperature drops and insufficient melting of the solder.

[0061] Calculations show that in this embodiment, the priority for heat replenishment in the low-temperature zone at the oblique flange lug is higher than that in the low-temperature zone below the circumferential weld, thus forming a regional processing queue.

[0062] Furthermore, for each sorted low-temperature region, the optimal heating path and power parameters within that region are solved using a multi-objective optimization function, resulting in the optimal path and power parameters for that region. Optimization variables include path type, scanning step size, coordinates of key points on the path, and laser output power. During the solution process, a temperature deviation cost term calculates the temperature deviation of the region within the next 5 seconds, an energy consumption cost term calculates the total laser output energy, and a path smoothness cost term ensures smooth movement of the robotic arm.

[0063] In this embodiment, the single-area scanning step size is 0.8 times the laser spot diameter (2mm), the oblique flange lug area uses contour offset scanning, and the lower side area of ​​the circumferential weld uses grating scanning.

[0064] Furthermore, following the regional processing queue order, the optimal paths of each region are concatenated, and the nearest neighbor algorithm is used to plan the travel paths between regions, ensuring the shortest total travel distance, thus obtaining the optimal heating path. A global constraint verification is performed on the optimal heating path and the corresponding regional power parameters. After confirming that the total heating time is less than 5 seconds, the motion parameters are compliant throughout, and there is no risk of local overheating, the optimal heating path is obtained. Figure 2 As shown, the final control parameters output in this embodiment are: laser power of 1200W in the oblique flange lug area and robotic arm movement speed of 10mm / s; laser power of 800W in the lower area of ​​the circumferential weld and robotic arm movement speed of 8mm / s.

[0065] Furthermore, it also includes generating single-region filling paths: for a single low-temperature region, based on its contour features, a filling heating path is generated using spiral scanning, raster scanning, or contour offset scanning, with the path scanning step size matching the laser spot diameter.

[0066] Furthermore, a sequential quadratic programming method is used to solve the multi-constraint, multi-objective optimization function to obtain the optimal heating scheme. Each acquisition of a thermal imaging frame completes a solution update, executing only the most recent 1-2 steps of control commands each time, and immediately initiating a new round of calculations after execution. The laser heating path is represented by a parametric smooth curve, and path updates are completed by adjusting the key points of the curve. Laser power is set segment by segment, with a constant power output for a single segment. The final output shows the coordinates of the key points of the path curve and the corresponding laser power values ​​for each segment.

[0067] S5 controls the movement of the corresponding laser head based on the optimal heating path and adjusts the laser output power in real time according to the laser power parameters to compensate for heating in the low-temperature region.

[0068] Furthermore, compensatory heating is applied to the low-temperature region, specifically including: The target robotic arm drives the target laser to move along the optimal heating path; During the movement, a feedforward-feedback cascaded PID controller is used to achieve closed-loop regulation of the laser power. The laser power parameter is used as the feedforward setpoint, and the real-time temperature measurement of the workpiece to be processed is used as the feedback quantity to correct the laser output power of the target laser in real time. Power limiting is used to ensure safe operation and complete the compensation heating of all low-temperature areas.

[0069] In this embodiment, precise heat replenishment is achieved in the low-temperature zone at the 22.5° angled flange lug and the low-temperature zone on the lower side of the circumferential weld, without any local overheating throughout the process.

[0070] Furthermore, the output power of the induction heating unit is controlled in real time by the collaborative optimization module: during the basic heating stage, the induction heating unit adopts a constant power output mode; during the compensation heating stage, the induction heating unit dynamically fine-tunes its power based on real-time temperature field data to suppress the overall temperature drift of the workpiece.

[0071] Furthermore, in this embodiment, the target robotic arm is a six-axis collaborative robotic arm with a repeatability accuracy of ≤±0.02mm and an end-effector load of ≥3kg; the target laser is a fiber laser with an output power adjustment range of 0~3000W and an adjustable laser spot diameter range of 0.5~10mm.

[0072] S6 repeats the above supplementary heating process until the temperature of the entire workpiece meets the requirements.

[0073] Furthermore, S1~S5 are executed cyclically with a control cycle of 100ms to monitor the overall temperature in real time, and heating is terminated when the following conditions are met: ; in, Indicates the maximum temperature deviation across the entire region; and These represent the real-time highest temperature and the real-time lowest temperature, respectively. This indicates the duration of continuous heat preservation after the temperature reaches the target level.

[0074] Once the workpiece reaches the required temperature across its entire surface area, it enters the heat preservation stage. After continuous heat preservation for 30 seconds and the temperature stabilizes and meets the requirements, the heating process is terminated, completing the entire brazing process.

[0075] Furthermore, it also includes a process parameter self-calibration step: By collecting temperature field data, control commands, and actual heating effects from multiple heating processes, a process parameter database is constructed. The induction heating control parameters, low-temperature zone identification parameters, temperature field solution parameters, and laser compensation control parameters are iteratively optimized using interpolation fitting methods to improve the system's adaptability to new materials and workpieces.

[0076] Example 2 like Figure 3 As shown, based on the same inventive concept, this embodiment of the invention also provides a collaborative control heating system based on induction heating and laser compensation, including: a global temperature acquisition module, a core parameter acquisition module, a temperature evolution prediction module, an optimal path output module, and a compensation heating execution module; The global temperature acquisition module is used to acquire the regional temperature matrix of the workpiece to be processed after basic heating; The core parameter acquisition module is used to preprocess based on the regional temperature matrix, identify all low-temperature regions and extract the corresponding core feature parameters. The temperature evolution prediction module is used to construct a transient heat conduction numerical model based on core feature parameters and related parameters, and to perform iterative solutions to obtain the global temperature field evolution matrix. The optimal path output module is used to construct a multi-constraint objective optimization function based on the global temperature field evolution matrix, and solve for the optimal heating path and the corresponding laser power parameters. The compensation heating execution module is used to control the movement of the corresponding laser head based on the optimal heating path and adjust the laser output power in real time according to the laser power parameters to compensate for the heating of the low temperature area; the above compensation heating process is repeated until the temperature of the entire workpiece meets the requirements.

[0077] Furthermore, in this embodiment, the functional implementation methods of each functional module correspond one-to-one with the methods described above, and will not be repeated here.

[0078] Furthermore, it also includes a human-machine interaction module for process parameter preset, real-time monitoring of the heating process, data storage and traceability, abnormal alarm and emergency shutdown control.

[0079] Furthermore, it also includes a workpiece 3D vision positioning module, which is used to acquire the 3D contour and clamping position of the workpiece, to achieve precise matching between the heating area and the workpiece contour, and to compensate for workpiece clamping errors.

[0080] Furthermore, all modules adopt a modular packaging design, and each module supports algorithm replacement and online parameter configuration to adapt to different heat treatment process scenarios; the system units communicate with each other using EtherCAT / Profinet industrial Ethernet bus, and the transmission delay of control commands and acquired data is ≤1ms, meeting the real-time requirements of closed-loop control.

[0081] Example 3 Based on the same inventive concept, the present invention also provides an electronic device, which includes a processor and a memory. The memory stores instructions, which are loaded and executed by the processor to implement a synergistic control heating method based on induction heating and laser compensation as in Embodiment 1.

[0082] Based on the same inventive concept, the present invention also provides a computer device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes a program stored in the memory, it is able to implement a synergistic control heating method based on induction heating and laser compensation, as shown in Example 1.

[0083] The electronic device may include a processor, a communications interface, a memory, and a communication bus, wherein the processor, communications interface, and memory communicate with each other via the communication bus. The processor can call logical instructions in the memory to execute a coordinated control heating method based on induction heating and laser compensation as described in Embodiment 1.

[0084] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0085] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0086] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A synergistic control heating method based on induction heating and laser compensation, characterized in that, include: Obtain the temperature matrix of the workpiece area after basic heating; Preprocessing is performed based on the regional temperature matrix to identify all low-temperature regions and extract the corresponding core feature parameters. A transient heat conduction numerical model is constructed based on the core characteristic parameters and related parameters, and iterative solutions are performed to obtain the global temperature field evolution matrix. Based on the global temperature field evolution matrix, a multi-constraint objective optimization function is constructed, and the optimal heating path and corresponding laser power parameters are obtained by solving the problem. Based on the optimal heating path, the corresponding laser head is moved, and the laser output power is adjusted in real time according to the laser power parameters to compensate for heating the low-temperature region. Repeat the above supplementary heating process until the temperature of the entire workpiece meets the requirements.

2. The synergistic control heating method based on induction heating and laser compensation as described in claim 1, characterized in that, The method for obtaining the regional temperature matrix is ​​as follows: After fixing the workpiece to be processed, the workpiece to be processed is subjected to full-area basic heating with a preset constant power until the overall average temperature of the workpiece to be processed rises to the preset temperature, and then enters the heat preservation compensation stage. During the thermal insulation compensation stage, the global temperature field of the workpiece to be processed is collected and then median filtering for noise reduction, emissivity calibration correction, and pixel coordinate to physical coordinate mapping are performed sequentially to obtain the preprocessed global temperature matrix.

3. The synergistic control heating method based on induction heating and laser compensation as described in claim 2, characterized in that, All low-temperature regions were identified, specifically including: Based on the global temperature matrix, adaptive threshold segmentation is performed using the maximum inter-class variance method, and the optimal low-temperature zone segmentation threshold is obtained by acquiring the inter-class variance. Based on the optimal low temperature zone segmentation threshold as the temperature boundary line distinguishing between the low temperature zone and the normal temperature zone, the temperature field binarization segmentation is completed to obtain a binarized image. Based on structuring elements of a preset size, morphological opening and closing operations are sequentially performed on the binarized image to remove isolated noise points and complete the contour breaks of the low-temperature region, thereby obtaining all the low-temperature regions.

4. The synergistic control heating method based on induction heating and laser compensation as described in claim 3, characterized in that, Extract the relevant core feature parameters, specifically including: Based on all the low-temperature regions, the location coordinates, contour features, area, average temperature, and maximum temperature deviation of the low-temperature region are extracted and used as the core feature parameters.

5. The synergistic control heating method based on induction heating and laser compensation as described in claim 4, characterized in that, Constructing a transient heat conduction numerical model, specifically including: The normalized area of ​​the low-temperature region is obtained by normalizing the area of ​​the low-temperature region. Based on the thermal properties of the workpiece to be processed, the heating parameters, the normalized area of ​​the low-temperature region, and the maximum temperature deviation of the low-temperature region, the transient heat conduction numerical model is constructed based on the Fourier partial differential equation of heat conduction.

6. The synergistic control heating method based on induction heating and laser compensation as described in claim 5, characterized in that, The global temperature field evolution matrix is ​​obtained, specifically including: The spatial range of the low-temperature region is obtained based on the location coordinates of the low-temperature region and the contour features of the low-temperature region. The initial conditions for solving the temperature field are constructed based on the spatial range of the low-temperature region, the average temperature of the low-temperature region, and the global temperature matrix. The heat exchange law between the workpiece surface and the environment is defined, and the heat dissipation characteristics of the irregular structure are realistically simulated by using differentiated convective heat transfer coefficients to construct differentiated convective heat transfer boundary conditions. By prioritizing differentiated convergence accuracy to ensure prediction accuracy in the low-temperature region, an iterative convergence criterion is constructed. Based on the initial conditions for solving the temperature field, the differentiated convection heat transfer boundary conditions, and the iterative convergence criterion, the transient heat conduction numerical model is iteratively solved to obtain the global temperature field evolution matrix for multiple prediction steps over the future prediction duration.

7. The synergistic control heating method based on induction heating and laser compensation as described in claim 6, characterized in that, Constructing a multi-constraint objective function specifically includes: The predicted region temperature is obtained based on the global temperature field evolution matrix. Based on the weighted sum of squared deviations between the predicted temperature and the target insulation temperature, the temperature deviation cost term is obtained; Based on the weighted sum of the total laser output energy and the fluctuation of induction heating power in the predicted time domain, the energy consumption cost term is obtained; Based on the sum of acceleration changes in the predicted robotic arm motion path within the time domain, the path smoothness cost term is obtained. Based on the temperature deviation cost term, the energy consumption cost term, the path smoothness cost term, and the corresponding preset weight coefficients, the multi-constraint objective optimization function is constructed.

8. The synergistic control heating method based on induction heating and laser compensation as described in claim 7, characterized in that, The optimal heating path and corresponding laser power parameters are obtained by solving the problem, specifically including: The temperature deviation is obtained based on the average temperature of the low-temperature region and the target insulation temperature; The heating priority of each low-temperature region is obtained based on the temperature deviation and the area of ​​the low-temperature region. The corresponding low-temperature regions are sorted from high to low based on the heating priority to obtain the sorted low-temperature regions; Based on the sorted low-temperature regions, with the multi-constraint objective optimization function as the objective, the optimal path and regional power parameters of each low-temperature region are obtained in the sorted order. Based on the sorting order, the optimal paths within each of the low-temperature regions are spliced ​​together, and the nearest neighbor algorithm is used to plan the travel paths between regions to obtain the optimal heating path with the shortest total travel. Based on the optimal heating path and the regional power parameters, a global constraint verification is performed, and the laser power parameters are output.

9. The synergistic control heating method based on induction heating and laser compensation as described in claim 8, characterized in that, Compensating for the low-temperature region with heating specifically includes: The target robotic arm drives the target laser to move along the optimal heating path; During the movement, a feedforward-feedback cascaded PID controller is used to achieve closed-loop regulation of the laser power. The laser power parameter is used as the feedforward input, and the real-time temperature measurement of the workpiece to be processed is used as the feedback quantity to correct the laser output power of the target laser in real time. Power limiting is used to ensure safe operation and complete the compensation heating of all the low-temperature regions.

10. A synergistic control heating system based on induction heating and laser compensation, used to execute the synergistic control heating method based on induction heating and laser compensation as described in any one of claims 1-9, characterized in that, include: The system includes a global temperature acquisition module, a core parameter acquisition module, a temperature evolution prediction module, an optimal path output module, and a compensation heating execution module. The global temperature acquisition module is used to acquire the regional temperature matrix of the workpiece to be processed after basic heating; The core parameter acquisition module is used to preprocess the region temperature matrix to identify all low-temperature regions and extract the corresponding core feature parameters. The temperature evolution prediction module is used to construct a transient heat conduction numerical model based on the core feature parameters and related parameters, and to perform iterative solutions to obtain the global temperature field evolution matrix. The optimal path output module is used to construct a multi-constraint objective optimization function based on the global temperature field evolution matrix, and solve for the optimal heating path and the corresponding laser power parameters. The compensation heating execution module is used to control the movement of the corresponding laser head based on the optimal heating path, and adjust the laser output power in real time according to the laser power parameters to compensate for the low temperature region; the above compensation heating process is repeated until the temperature of the entire workpiece meets the requirements.

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