Multi-channel intelligent temperature control and thermal simulation method for handheld laser welder

Abstract

The application belongs to the technical field of intelligent temperature control and thermal simulation, and particularly relates to a multi-channel intelligent temperature control and thermal simulation method for a handheld laser welding machine. The application specifically discloses a multi-channel intelligent temperature control and thermal simulation method for a handheld laser welding machine, which comprises the following steps: constructing a reduced-order thermal structure coupling model and presetting dynamic boundary mapping logic; acquiring data through a sensor array and an inertial measurement unit to dynamically calibrate the model, and converting an operation behavior into a thermal load; running a real-time thermal simulation solver to calculate a future multi-channel temperature field distribution trend in advance; executing a pre-judgment cooperative temperature control strategy based on the temperature field distribution trend, dynamically pre-shaping a laser power curve, and optimizing cooling resource allocation; and triggering a guidance feedback or a high robustness mode based on posture sensing. The application realizes thermal balance pre-judgment through prospective active pre-control, reduces a thermal shutdown rate, and improves welding consistency and thermal stability of the equipment under complex working conditions.

Description

Technical Field

[0001] This invention belongs to the field of intelligent temperature control and thermal simulation technology, specifically relating to a multi-channel intelligent temperature control and thermal simulation method for handheld laser welding machines. Background Technology

[0002] With the rapid development of laser welding technology, handheld laser welding has been widely used in metal processing, automotive manufacturing, and aerospace industries due to its advantages such as flexible operation, high welding strength, and small heat-affected zone. In actual operation, handheld laser welding systems achieve metal fusion through a high-energy laser beam, and their operational stability depends on precise photoelectric conversion efficiency and thermal management mechanisms. As industrial manufacturing increasingly demands consistent welding quality, ensuring thermal balance under continuous, high-load operation has become crucial for improving the reliability of handheld laser welding systems.

[0003] The thermal management system, as the core subsystem of the handheld laser welding machine, is primarily responsible for real-time monitoring of the temperature rise of the laser, optical lenses, and welding torch body. It maintains the core components within a preset temperature range through a cooling circulation loop. The system's basic principle is to utilize sensors to obtain temperature feedback from critical nodes, driving dynamic adjustment of the cooling power to suppress fluctuations in laser output power and protect optical components from thermal damage.

[0004] Existing temperature control solutions typically employ single-channel proportional-integral-derivative (PID) control or hysteresis protection strategies based on shell temperature rise. These approaches struggle to handle instantaneous thermal shocks under complex dynamic conditions, leading to a paradox between accuracy and robustness in balancing response speed and temperature stability. Handheld laser welding machines are typical multi-heat-source coupled systems. Existing independent monitoring modes lack prediction of the entire heat transfer and accumulation effects, easily causing blind spots in the coordination of multi-channel temperature control strategies, resulting in thermal accumulation performance degradation under high-load scenarios. Conventional temperature control systems, limited by their reliance on internal sensors, cannot perceive the operator's welding posture, movement speed, and dynamic changes in the external physical environment in real time. This causes a disconnect between the thermal management model and the actual thermal field distribution, making it difficult to achieve adaptive adjustment for extreme operating conditions. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-channel intelligent temperature control and thermal simulation method for handheld laser welding machines, which can solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by this invention is: a multi-channel intelligent temperature control and thermal simulation method for handheld laser welding machines, comprising the following specific steps: Step 1: Construct a reduced-order thermal structure coupling model for the physical structure of the handheld laser welding machine. The reduced-order thermal structure coupling model includes the material thermal property parameters of the laser module, optical path system, welding torch body and water cooling pipeline, and presets dynamic boundary condition mapping logic. Step 2: Acquire real-time data stream through sensor array, use the real-time data stream to dynamically calibrate the reduced-order thermal structure coupling model, and convert the acquired inertial measurement unit data into operational behavior thermal load to correct the heat transfer coefficient in the reduced-order thermal structure coupling model. Step 3: Run the real-time thermal simulation solver to calculate the multi-channel temperature field distribution trend of the system in a specific future time period based on the current real-time operating conditions, operating behavior, ambient temperature and historical thermal data, with a preset time step. Step 4: Using the multi-channel temperature field distribution trend as the control input, the controller executes a predictive collaborative temperature control strategy to dynamically pre-shape the laser's output power curve and simultaneously adjust multiple solenoid valves to achieve dynamic allocation of cooling resources. Step 5: Based on the data from the inertial measurement unit, determine the operator's real-time welding posture. When it is determined that the cooling efficiency has decreased due to the change in posture, trigger behavior guidance feedback or automatically switch to a high robustness mode. By fine-tuning the welding parameters and temperature control threshold, the system can achieve adaptive adjustment of the operation behavior.

[0007] Preferably, the reduced-order thermal structure coupling model constructed in step 1 simplifies the geometric structure of the physical entity, transforming the complex three-dimensional structure of the handheld laser welding machine into a logical node network with thermal capacity and thermal resistance characteristics. The laser module is defined as the first core heat source node, whose heat generation is negatively correlated with the electro-optical conversion efficiency and real-time output power. The optical path system, including a reflecting lens and a focusing lens, is defined as the second core heat source node, whose heat generation is determined based on the product of laser power and lens absorptivity. The welding torch body and water-cooling pipeline are defined as heat dissipation network nodes, and the energy exchange process between them and the core heat source nodes is described by heat conduction equations and heat convection equations.

[0008] Preferably, the material thermophysical parameters in step 1 include the thermal conductivity, specific heat capacity, density, and surface emissivity of each component. The thermal conductivity is set as a function of temperature to simulate the nonlinear thermal conduction characteristics of the material at high temperatures. The dynamic boundary condition mapping logic includes setting thermal boundaries for natural air convection, forced coolant convection, and backscattered light radiation. The system dynamically corrects the convective heat transfer coefficient of the external envelope surface based on real-time values ​​fed back from the ambient temperature sensor.

[0009] Preferably, the sensor array in step 2 includes multiple high-precision temperature sensors distributed in the laser core region, optical lens support, welding torch housing, and coolant inlet and outlet. The real-time data stream also includes instantaneous coolant flow data acquired by a flow meter. The dynamic calibration process employs the textual description logic of the Kalman filter algorithm, using the deviation between the observed measured temperature value and the model's predicted value to reverse-correct the initial state parameters of each node in the model, ensuring the consistency between the virtual thermal twin and the physical entity.

[0010] Preferably, the inertial measurement unit data in step 2 includes triaxial acceleration, triaxial angular velocity, and attitude angle information. The conversion process of the thermal load from the operational behavior specifically involves: mapping the attitude angle information to the tilting orientation of the welding torch in space; determining the change in the flow state of the coolant under tilted conditions based on fluid dynamics principles; and correcting the convective heat transfer coefficient inside the welding torch head. The moving speed of the welding torch is calculated based on the triaxial acceleration; the moving speed is combined with the welding power to calculate the heat input per unit length acting on the workpiece; and the secondary radiative heat load generated by the reflected light from the workpiece on the welding torch head is estimated.

[0011] Preferably, the real-time thermal simulation solver in step 3 employs lightweight explicit finite difference computation logic. The preset time step is set to milliseconds to ensure the real-time performance of the simulation. The specific future time period is typically set to 5 to 10 seconds to allow sufficient lead time for the mechanical response of the cooling system. The multi-channel temperature field distribution trend includes the laser junction temperature channel, the optical lens temperature rise channel, and the welding torch housing and handle temperature rise channel.

[0012] Preferably, the calculation process for the multi-channel temperature field distribution trend in step 3 considers the thermal conduction coupling effect between channels. When the laser junction temperature channel shows a temperature rise trend, the simulation solver calculates its indirect heating effect on the optical lens temperature rise channel through the coolant circuit. When the optical lens temperature rise channel experiences transient fluctuations due to backscattered light, the solver simultaneously evaluates its contribution to the thermal stress in the local area of ​​the welding torch housing. Through this cross-coupled simulation, the system can identify the nonlinear temperature rise risk caused by heat accumulation.

[0013] Preferably, the predictive collaborative temperature control strategy in step 4 breaks the lag of traditional feedback regulation. When the simulation results indicate that the temperature rise channel of the optical lens will exceed the first preset warning threshold within a specific time in the future, the controller initiates dynamic power shaping. The dynamic power shaping refers to smoothing the time envelope of the laser output power without changing the total welding energy. Specifically, it includes: introducing a preset power rise interval in the initial stage of welding to reduce the instantaneous thermal shock pressure on the optical components; and in the continuous welding stage, performing micro-modulation of the peak power according to the real-time thermal balance state to reduce temperature fluctuations by utilizing thermal inertia.

[0014] Preferably, the dynamic allocation of cooling resources in step 4 is achieved by adjusting the opening of a multi-channel solenoid valve. Each multi-channel solenoid valve controls an independent cooling branch for the laser, optical system, and welding torch head. The controller calculates the optimal flow distribution ratio based on the simulated temperature rise slope of each channel. If the temperature rise slope of the optical system channel is greater than that of the laser channel, the controller automatically increases the opening of the solenoid valve pointing to the optical system branch and correspondingly decreases the proportion of other branches, achieving precise cooling of high-risk areas while maintaining the total cooling power.

[0015] Preferably, the behavioral guidance feedback in step 5 is implemented using tactile or visual methods. When the inertial measurement unit detects that the welding torch tilt angle exceeds the preset safe operating range, and simulation prediction shows that this angle will cause local heat accumulation, the controller drives the vibration motor inside the welding torch to generate slight vibrations at a specific frequency, or changes the flashing frequency of the indicator light on the handle to prompt the operator to correct the welding posture. This interactive mechanism incorporates human operation behavior into the thermal management closed loop, reducing the risk of thermal management failure due to human factors.

[0016] Preferably, the high robustness mode in step 5 refers to the self-protection and maintenance strategy adopted by the system when the operator fails to adjust the posture in time or when the system is in extreme working conditions. In this mode, the controller automatically increases the temperature tolerance threshold of non-core components, prioritizing the protection of core optical components from permanent damage. By adjusting welding parameters such as pulse frequency or duty cycle, the system reduces the total heat load of the system while maintaining the stability of the weld penetration as much as possible, ensuring that the equipment does not experience forced shutdown under high-temperature conditions.

[0017] Preferably, the thermal simulation method further includes online compensation logic for the attenuation of the laser's electro-optical conversion efficiency. As the equipment operates over time, the heat generated by the laser will slowly drift. The system analyzes thermal balance data over a long time series to identify the offset in the relationship between heat generation and output power, and automatically updates the efficiency parameters in the reduced-order thermal structure coupling model, ensuring the accuracy of the thermal simulation throughout the entire equipment lifecycle.

[0018] Preferably, the method is implemented using embedded industrial control software. This software possesses multi-threaded parallel processing capabilities. The first thread is responsible for real-time acquisition and preprocessing of sensor data, the second thread runs a real-time thermal simulation solver, and the third thread executes temperature control logic calculations and hardware driver output. The three threads exchange data frequently via a shared memory mechanism, ensuring that the logical delay from behavior perception to temperature control action is below a preset low-time threshold.

[0019] Preferably, the multi-channel intelligent temperature control also includes monitoring and compensation for ambient humidity. When the ambient humidity exceeds a specific threshold, the system predicts the risk temperature point at which condensation will occur on the surface of the optical lens through thermal simulation. The controller automatically locks the cooling temperature setpoint of the optical system within a preset safety difference range above the dew point temperature to prevent water vapor condensation caused by overcooling and protect the high-precision optical coating from damage.

[0020] Preferably, the logic node network in the reduced-order thermal structure coupling model is processed using matrix operations. The state transition matrix describes the evolution of the temperature of each node over time, the input matrix represents external stimuli such as laser power and ambient temperature, and the perturbation matrix represents uncertain factors such as backscattered light. All matrix operations are converted into textual descriptions of logic based on addition, multiplication, and accumulation, avoiding complex function calls and improving operating efficiency in embedded hardware.

[0021] Preferably, the multi-channel forward-looking thermal simulation process also includes logic for evaluating the degree of coolant aging. The system monitors the changing trend of the coolant inlet and outlet temperature difference under the same cooling power and heat load to calculate the heat exchange efficiency degradation of the cooling circuit. When the heat exchange efficiency is lower than a preset ratio, the system automatically introduces a correction factor into the thermal simulation model, compensating for the performance loss caused by coolant deterioration by increasing the preset speed of the circulating pump or triggering an early temperature control warning.

[0022] Preferably, the predictive collaborative temperature control strategy also has the capability to expand to multi-device collaborative operation. In automated cluster welding scenarios, multiple handheld laser welding machines share thermal data through a communication bus. When one of the devices faces the risk of heat accumulation due to continuous high-intensity operation, the system can send a load allocation signal to the main control center. By coordinating the operation sequence of adjacent devices, a preset cooling buffer time is provided for the high-heat-load device, thereby achieving thermal load balance across the entire process chain.

[0023] Preferably, the operational behavior guidance feedback also includes monitoring the stability of the operator's movement speed. If the inertial measurement unit detects a drastic fluctuation in the welding movement speed, the system predicts that the heat input distribution on the workpiece surface will be uneven, leading to increased deformation in the heat-affected zone. The system not only adjusts the temperature control strategy but also compensates for the heat input deviation caused by the movement speed fluctuation by synchronously adjusting the instantaneous output of the laser power, achieving dual optimization of temperature control and process quality.

[0024] Preferably, the reduced-order thermal structure coupling model has a preset reflectivity library for workpieces of different materials. After the operator selects the workpiece material through the human-machine interface, the system automatically retrieves the corresponding reflectivity and inputs it as a thermal boundary condition into the simulation solver. This allows the system to pre-calculate stronger backscattering heat loads for highly reflective materials such as aluminum alloys and copper, thereby enhancing the cooling intensity of the welding torch head in advance.

[0025] Preferably, the controller employs a smooth switching algorithm based on energy prediction when performing predictive coordinated temperature control. When simulation results indicate that a switch from normal temperature control mode to high robustness mode is required, the controller gradually adjusts the weights of the control parameters within a predetermined time to avoid actuator oscillations or laser output power jumps caused by sudden changes in control strategy, thus ensuring the continuity of the welding process.

[0026] Preferably, each temperature sensor in the sensor array is equipped with failure diagnosis logic. When the measured data of a certain node deviates from the simulation prediction value by more than a preset reasonable error range, the system automatically determines that the sensor is in a suspected fault state and switches to a virtual sensing mode based on a thermal simulation model. The system uses redundant data from other normal sensors and the simulation model to derive the estimated temperature value of the node, ensuring that the thermal management system can still maintain safe operation even if some sensors fail.

[0027] Preferably, the system automatically performs a brief thermal calibration sequence each time it is powered on or the welding torch head is replaced. By emitting short-pulse lasers of specific power and monitoring the temperature rise curves of each channel, the system automatically identifies minor differences in the current physical hardware and personalizes the parameters of the digital twin model, eliminating model deviations caused by hardware assembly tolerances.

[0028] Preferably, the multi-channel intelligent temperature control method achieves constant temperature control of the welding torch handle area. By arranging independent micro-thermal conduction structures inside the handle, the system dynamically fine-tunes the proportion of coolant flowing near the handle based on the simulated predicted heat conduction trend of the shell, ensuring that the operator's hand temperature is always within a preset comfortable range, thus improving the comfort and safety of long-term manual operation.

[0029] Preferably, the multi-channel intelligent temperature control method also integrates thermal monitoring of the power supply module. When the power conversion loss of the laser power supply system causes the temperature inside the control cabinet to rise too quickly, the system introduces this heat load as an external disturbance into the overall thermal simulation model, and achieves thermal efficiency management of the entire handheld welding system by adjusting the speed of the cooling fan of the control cabinet in conjunction with the system.

[0030] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention achieves thermal balance prediction rather than thermal runaway response. By introducing a thermal simulation solver based on digital twin technology, the traditional hysteresis feedback regulation is transformed into proactive pre-control. The system can optimize laser power shaping and cooling resource allocation based on simulation prediction before the actual thermal shock occurs, resolving the contradiction between welding efficiency and temperature control accuracy. Experiments show that under complex continuous operation conditions, the system's thermal shutdown rate is reduced, and the consistency of weld penetration is improved.

[0031] 2. This invention constructs a robust thermal management system that deeply integrates humans, machines, and the environment. By integrating an inertial measurement unit and introducing dynamic boundary condition mapping logic, it is the first to incorporate behavioral variables such as the operator's welding posture and movement speed into the thermal management closed loop. This eliminates the disconnect between equipment perception and the actual physical environment and operational behavior, enabling the handheld laser welding machine to maintain high thermal stability and scene adaptability in changing operating environments, and reducing reliance on the operator's professional skills.

[0032] 3. This invention optimizes the thermal efficiency and reliability of equipment throughout its entire lifecycle. Through multi-channel collaborative control, it avoids accelerated aging of core precision components such as optical lenses due to localized heat accumulation, and also avoids blindly limiting power to protect individual components. This refined energy management strategy not only extends the lifespan of core equipment components but also improves the system's sustained maximum output power capability under the same hardware cooling conditions, thus optimizing the overall technical and economic indicators of the product. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating the overall technical solution according to the present invention; Figure 2 This is a schematic diagram of the multi-level interaction relationship and data flow according to the present invention; Figure 3 The flowchart illustrates the construction of a reduced-order thermal structure coupling model including core heat source nodes and heat dissipation network nodes according to the present invention, and the dynamic calibration based on real-time data stream from the sensor array. Figure 4This is a flowchart illustrating the process of using a real-time thermal simulation solver in accordance with the present invention to calculate the trend of multi-channel temperature field distribution of the system in advance within a specific future time period, in combination with the thermal load of operational behavior. Figure 5 This is a flowchart illustrating the dynamic pre-shaping of laser output power based on the multi-channel temperature field distribution trend and the synchronous adjustment of multiple solenoid valves to achieve dynamic allocation of cooling resources according to the present invention. Figure 6 This is a flowchart illustrating the system adaptive adjustment based on the determination of the operator's real-time welding posture using inertial measurement unit data, according to the present invention, to trigger behavioral guidance feedback or automatically switch to a highly robust mode. Detailed Implementation

[0034] Example 1: Please refer to the appendix Figure 1 To be continued Figure 6 In the multi-channel intelligent temperature control and thermal simulation method for handheld laser welding machines of the present invention, step 1 specifically involves constructing a reduced-order thermal structure coupling model for the physical structure of the handheld laser welding machine. This process is not a simple geometric simplification, but rather a deep topological simplification of the physical entity's geometry, transforming the complex physical components with three-dimensional spatial properties within the handheld laser welding machine into a network of logical nodes with thermal capacity and thermal resistance characteristics. The laser module is defined as the first core heat source node. The heat generation of this node is negatively correlated with its electro-optical conversion efficiency and real-time output power; that is, when the real-time output power increases and the electro-optical conversion efficiency decreases due to external environmental influences, the heat generation power of this node increases accordingly. The optical path system includes a reflecting lens and a focusing lens, which is defined as the second core heat source node. The heat generation of this node is determined based on the product of the laser power and the lens absorptivity, which is set as a variable that increases slowly over time. The welding torch body and water-cooling pipeline are defined as heat dissipation network nodes, and the energy exchange process between them and the core heat source node is described by heat conduction equations and heat convection equations.

[0035] In one specific embodiment, the matrix form processing of the logical node network is implemented in the following way: The reduced-order thermal structure coupling model is discretized into... There are 1 logical node, and its temperature vector is 1. The system's thermal dynamics are described by a set of first-order ordinary differential equations:

[0036] in, This is a diagonal heat capacity matrix, and its diagonal elements are... Indicates the first The heat capacity of each node , , These are the material density, volume, and specific heat capacity of the region represented by the node; The thermal conductivity matrix has the following elements. Description node With nodes The negative value of the total thermal conductivity of the heat conduction path between them. For nodes The sum of the thermal conductivities of all connected paths. The element values ​​of this matrix depend on the thermal conductivity of the material, which is a function of temperature. This is a temperature-dependent matrix, reflecting nonlinear heat conduction characteristics; This represents the ambient temperature vector. The input vector for the node heat source includes terms such as heat generated by the laser, heat absorbed by the lens, and heat reflected by radiation.

[0037] The state transition matrix, input matrix, and perturbation matrix are obtained by discretizing the above differential equations. Euler's method is used for forward differencing, with a time step of [missing value]. We can obtain:

[0038] in, This is the state transition matrix, which describes the evolution of temperature over time. The input matrix; The external excitation vector includes control inputs such as laser power and coolant flow rate, as well as ambient temperature. ; Here is the perturbation matrix. This includes uncertain thermal perturbations (such as random fluctuations in backscattered light) considered during modeling. This matrix equation forms the basis of the real-time thermal simulation solver.

[0039] In step 1 above, the reduced-order thermal structure coupling model includes the material thermal properties of the laser module, optical path system, welding torch body, and water-cooling pipeline. These material thermal properties include the thermal conductivity, specific heat capacity, density, and surface emissivity of each component. To simulate the nonlinear thermal conduction characteristics of the material at high temperatures, the thermal conductivity is set as a function of temperature, dynamically corrected according to a preset slope as the node temperature increases. The model includes preset dynamic boundary condition mapping logic, which includes thermal boundary settings for natural air convection, forced coolant convection, and backscattered light radiation. The system dynamically corrects the convective heat transfer coefficient of the external envelope surface based on real-time values ​​fed back from the ambient temperature sensor, ensuring a high degree of synchronization between the simulation environment and the physical environment.

[0040] Step 2 involves acquiring a real-time data stream via a sensor array and using this data stream to dynamically calibrate the reduced-order thermal structure coupling model. The sensor array includes multiple high-precision temperature sensors distributed across the laser core region, optical lens support, welding torch housing, and coolant inlet / outlet points. Their acquisition frequency is set to capture transient temperature fluctuations. The real-time data stream also includes instantaneous coolant flow rate data acquired via a flow meter. The dynamic calibration process employs the Kalman filter algorithm, using the deviation between the observed measured temperature value and the model's predicted value to inversely correct the initial state parameters of each node in the model. When the measured temperature is higher than the predicted temperature and the deviation exceeds a preset tolerance range, the system automatically increases the thermal resistance coefficient of the corresponding node or decreases its heat dissipation coefficient to ensure a high degree of consistency between the virtual thermal twin and the physical entity.

[0041] Specifically, the dynamic calibration employs the Extended Kalman Filter (EKF) algorithm. A state vector is defined. ,in for The node temperature vector at time t, This is the vector of model parameters to be calibrated, which may include thermal resistance or thermal capacity correction coefficients for each node. The system's nonlinear state equations and observation equations are as follows:

[0042]

[0043] in, The prediction function is based on the matrix equation in step 1. For control input; For process noise; observation vector The measured temperature value of the sensor array (corresponding to a specific node in the network); The observation function is typically an extracted matrix, from which the node temperature corresponding to the sensor location is selected from the state vector. To observe noise.

[0044] The correction steps for EKF are as follows: First, calculate the Kalman gain. :

[0045] in, The prior value of the state estimation covariance matrix; Let be the Jacobian matrix of the observation matrix. Then update the state estimate and covariance:

[0046]

[0047] This process compares predicted temperatures ( ) and measured temperature ( The deviation is used to estimate and update the model parameters in the state vector in real time and optimally. This enables dynamic calibration. Initial covariance matrix. Process noise covariance and observation noise covariance The settings are based on sensor accuracy and model uncertainty.

[0048] In step 2, the acquired inertial measurement unit (IMU) data is converted into operational behavior thermal loads to correct the heat transfer coefficient in the reduced-order thermal structure coupling model. The IMU data includes triaxial acceleration, triaxial angular velocity, and attitude angle information. Specifically, the conversion process of the operational behavior thermal loads involves the system mapping the attitude angle information to the real-time tilt position of the welding torch in space. Based on fluid mechanics principles, the filling state of the coolant in the internal cavity of the torch head changes when the welding torch is at different tilt angles. The system determines the change in the flow state of the coolant under tilt conditions and corrects the convective heat transfer coefficient inside the welding torch head accordingly. If the welding torch tilts upwards at an angle exceeding 45 degrees, the system determines that the coolant flow velocity slows down under the influence of the reverse component of gravity and accordingly reduces the convective heat transfer coefficient. The system calculates the real-time moving speed of the welding torch based on the triaxial acceleration, combines the moving speed with the current welding power, and calculates the heat input per unit length acting on the workpiece. Based on the preset material reflectivity parameters, the system estimates the secondary radiative heat load generated by the reflected light from the workpiece on the welding torch head, and inputs it as an additional heat generation term into the reduced-order thermal structure coupling model.

[0049] As a preferred embodiment, the specific calculation of the attitude-corrected convective heat transfer coefficient is as follows: The convective heat transfer coefficient of the coolant inside the welding torch to the optical lens mount or torch body. With coolant flow rate Closely related. Under normal horizontal orientation, the flow velocity is... The corresponding reference heat transfer coefficient is When the welding torch is tilted at an angle of... ( When the angle between the welding torch axis and the direction opposite to gravity is denoted as , gravity produces a component in the direction of coolant flow. Assuming the coolant flow is affected by gravity, the flow velocity is corrected to . ,in Empirical coefficients related to the flow channel geometry ( According to the Dittus-Boelter empirical correlation for forced convection within a pipe, the Nusselt number... And Reynolds number Therefore, the heat transfer coefficient is related to the flow velocity. The power is approximately proportional to the power. The corrected convective heat transfer coefficient... The calculation is as follows:

[0050] The revised This will be used as a dynamic boundary condition and input into the thermal conductivity matrix. In the process, the thermal conductivity between the water-cooled piping and the heat source node is corrected. Simultaneously, the secondary radiative heat load of the welding torch is determined by the workpiece's reflected light. The estimation formula is:

[0051] in, The optical efficiency of the welding torch receiving window; Real-time output power of the laser; The reflectivity of the workpiece material is retrieved from the preset reflectivity library; The effective area for the welding torch to receive reflected light; This represents the area of ​​the laser spot on the workpiece. As an additional heat production term, it is added to the heat source vector. .

[0052] Step 3 involves running a real-time thermal simulation solver, which employs lightweight explicit finite difference computation logic. This solver runs in a separate thread within the embedded industrial control software, performing iterative calculations in millisecond-level time steps. Based on current real-time operating conditions, operational behavior, ambient temperature, and historical thermal data, the real-time thermal simulation solver proactively calculates the multi-channel temperature field distribution trend of the system within the next 5 to 10 seconds. This proactive calculation provides ample lead time for subsequent mechanical response and strategy adjustments in the temperature control system. The multi-channel temperature field distribution trend specifically includes the laser junction temperature channel, the optical lens temperature rise channel, and the welding torch housing and handle temperature rise channel. During the calculation, the system fully considers the thermal conduction coupling effect between channels. When a significant temperature rise trend appears in the laser junction temperature channel, the simulation solver simultaneously calculates the indirect heating effect on the optical lens temperature rise channel through the shared coolant loop, as well as the coolant inlet temperature drift caused by the pipeline temperature rise.

[0053] Specifically, the real-time thermal simulation solver uses the explicit Euler method for time-discrete solution based on the matrix equations. At each simulation time step... (For example, ), node temperature vector through The following iterative formula is updated:

[0054] Among them, superscript and These represent the current and next time steps, respectively. To ensure numerical stability, the time step size... Stability conditions must be met:

[0055] The solver runs in a separate thread within the embedded system, with a fixed step size. The above update formula is executed repeatedly. In each calculation step, first, based on the current... Calculate the nonlinear thermal conductivity matrix and heat source vector (Including real-time power, calibrated parameters, and attitude corrections), then substitute into the formula to calculate. The multi-channel temperature field distribution trend is... The vector consists of the node temperature values ​​corresponding to the laser, optical path, and gun body channel. Future... Step (corresponding to the future) The temperature prediction (time) is obtained by repeatedly executing the above iterative process.

[0056] During the multi-channel forward-looking thermal simulation in step 3, the system also specifically identifies the risk of nonlinear temperature rise caused by heat accumulation. When the temperature rise channel of the optical lens experiences transient pulse-like fluctuations due to backscattered light, the solver simultaneously evaluates its contribution to the thermal stress of a local area of ​​the welding torch housing. If local heat accumulation that may occur due to impeded heat conduction at the lens support is predicted, the simulation model will automatically increase the nonlinear weight of that area and output a warning identifier for the controller to call in subsequent steps.

[0057] Step 4 uses the multi-channel temperature field distribution trend as the control input, and the controller executes a predictive collaborative temperature control strategy. The core of this strategy lies in breaking the lag of traditional feedback regulation. When the simulation results indicate that the temperature rise channel of the optical lens will exceed the first preset warning threshold within the next 8 seconds, the controller immediately initiates dynamic power shaping. Dynamic power shaping refers to smoothing the time envelope of the laser output power without changing the total welding energy. The specific implementation logic is as follows: In the initial stage of welding, the controller introduces a power gradual increase interval of 200 to 500 milliseconds, so that the power increases smoothly from zero to the target welding power, thereby reducing the instantaneous thermal shock pressure on the optical components. In the continuous welding stage, the controller performs micro-modulation of the peak power according to the thermal balance deviation predicted by the simulation, and uses the thermal inertia of the optical system itself to reduce temperature fluctuations.

[0058] In the aforementioned predictive collaborative temperature control strategy, the dynamic power shaping logic is as follows: Let the current time be... Simulation predicts future time Inside, the temperature of the optical lens It will exceed the warning threshold. The controller then outputs power to the laser target. Apply modulation function To obtain the actual output power :

[0059] in, For the future Predicted lens temperature; This provides a safety margin for temperature control. The maximum modulation coefficient ( ); This is a smooth transition function. This formula smoothly reduces power output when the temperature approaches the warning value, avoiding thermal shock.

[0060] The specific method for dynamically adjusting the PID gain is as follows: based on the system thermal inertia constant predicted by the simulation model... The PID parameters can be adjusted using the rise time of the power step response to node temperature (which can be approximated). Greater thermal inertia results in a slower system response, requiring a stronger integral action and a weaker derivative action. The adjustment logic can employ a lookup table method or empirical formulas, for example: , ,

[0061] in, , , These are the PID parameters under the baseline operating conditions; This serves as the reference thermal inertia constant. In this way, the controller can adaptively adjust the control law based on the system's real-time thermal dynamic characteristics, thereby improving control performance.

[0062] In step 4, multiple solenoid valves are simultaneously adjusted to achieve dynamic allocation of cooling resources. These solenoid valves control independent cooling branches for the laser, optical system, and welding torch head. The controller calculates the optimal flow distribution ratio based on the simulated temperature rise slope of each channel. If the predicted temperature rise slope of the optical system channel is greater than that of the laser channel, the controller automatically increases the opening of the solenoid valve pointing to the optical system branch while keeping the total power of the cooling pump constant, and proportionally decreases the opening of the laser branch. This allocation strategy achieves precise cooling for high-risk areas. The solenoid valve opening adjustment uses a step-by-step approximation logic; the flow change corresponding to each step is linearly positively correlated with the difference in the predicted temperature rise slope, ensuring a smooth transfer of cooling resources between channels.

[0063] Step 5 determines the operator's real-time welding posture based on inertial measurement unit data. When it is determined that the cooling efficiency decreases due to posture changes (e.g., the operator inverts the welding torch or performs overhead welding at an extreme angle), the system triggers behavioral guidance feedback. This behavioral guidance feedback is implemented through tactile or visual means. The controller drives the eccentric vibration motor inside the welding torch to generate slight vibrations at a specific frequency, or changes the flashing frequency and color of the indicator light on the handle to prompt the operator to correct the welding posture. If the operator does not adjust the posture in time within the preset 3-second observation period, the system automatically switches to a high-robustness mode.

[0064] In the high-robustness mode described in step 5, the controller employs a series of self-protection and maintenance strategies. The controller automatically increases the temperature tolerance threshold for non-core components (such as the welding torch housing), prioritizing all redundant cooling resources to prevent permanent damage to core optical components by sacrificing temperature control accuracy in non-critical areas. The system adjusts welding parameters such as pulse frequency or duty cycle to reduce the overall system heat load while using algorithms to compensate for the energy gap caused by a decrease in duty cycle, thus maintaining the stability of the weld penetration as much as possible. This adaptive adjustment ensures that the equipment does not experience forced shutdown under extreme high temperatures or erroneous operating conditions, achieving adaptive optimization of the system's operational behavior.

[0065] Furthermore, the thermal simulation method also includes online compensation logic for the attenuation of the laser's electro-optical conversion efficiency. As the equipment operates over time, the performance of the semiconductor pump source physically degrades, causing a slow drift in heat generation under the same power input. The system analyzes long-sequence thermal balance data from the past 24 hours to identify the offset in the relationship between heat generation and output power. If the heat generation deviates from the initial model baseline value by more than 3%, the system automatically updates the efficiency parameter constants in the reduced-order thermal structure coupling model, ensuring that the calculation results of the thermal simulation solver remain highly accurate throughout the entire equipment lifecycle.

[0066] The method is implemented using embedded industrial control software with powerful multi-threaded parallel processing capabilities. The first thread is dedicated to real-time acquisition of sensor array data, signal filtering, and unit conversion; the second thread runs a lightweight real-time thermal simulation solver, responsible for matrix operations and temperature field prediction; and the third thread executes the temperature control strategy logic calculation and outputs PWM control signals to the hardware driver layer. Data exchange between the three threads is achieved through a high-speed shared memory mechanism. The first thread stores the calibrated sensor data at a designated address in the shared memory; the second thread reads the data from that address and updates the simulation model, writing the prediction results to another address for the third thread to access.

[0067] The method also integrates ambient humidity monitoring and compensation logic. When the ambient humidity sensor detects humidity exceeding 80%, the system calculates the critical temperature point at which condensation may occur on the optical lens surface using a thermal simulation model. To prevent water vapor condensation due to overcooling, the controller automatically locks the optical system's cooling temperature setpoint within a safe difference of 3 degrees Celsius above the critical temperature point. This logic prioritizes conventional cooling requirements, protecting the high-precision optical coating from moisture corrosion.

[0068] In the reduced-order thermal structure coupling model, the logic node network is processed using matrix operations. The state transition matrix describes the evolution of the temperature of each node over time, and its matrix elements contain the reciprocal relationship between the inter-node thermal resistance and the node thermal capacity. The input matrix represents the contribution of external stimuli such as laser power and pump current to each heat source node. The perturbation matrix represents uncertainties such as backscattered light and environmental fluctuations. All matrix operations are transformed into sequential logic based on addition, multiplication, and accumulation in the embedded code implementation, avoiding the call to complex trigonometric or exponential functions in the main loop and improving the running efficiency in low-power embedded processors.

[0069] The multi-channel forward-looking thermal simulation process also includes logic for evaluating the degree of coolant aging. The system monitors the changing trend of the temperature difference between the coolant inlet and outlet under the same cooling power and laser output heat load over a long period. If the temperature difference gradually decreases over time, it indicates a decline in the heat exchange efficiency of the cooling circuit, possibly due to scaling in the pipes or degradation of the coolant's physical properties. The system automatically introduces a correction factor between 0.8 and 1.0 into the thermal simulation model, compensating for performance losses caused by coolant deterioration by increasing the preset base speed of the circulating pump or triggering an early temperature control warning.

[0070] In automated cluster welding scenarios, the predictive collaborative temperature control strategy has the scalability to support multi-device collaborative operation. Multiple handheld laser welding machines share their respective thermal twin data via industrial Ethernet or CAN bus. When one device faces the risk of heat accumulation due to performing an ultra-long weld seam task, the main control module of that device sends a load allocation signal to the cluster center. The cluster center coordinates the operation sequence of adjacent idle devices or issues energy allocation adjustment commands to the current device, providing a preset cooling buffer time for devices with high heat loads, thereby achieving thermal load balance across the entire process chain and preventing local individual devices from overheating and shutting down.

[0071] For refined management of operational behavior, the system also includes monitoring the stability of the operator's movement speed. If the inertial measurement unit detects a drastic fluctuation in the welding movement speed exceeding 20 millimeters per second, the system predicts that the instantaneous heat input distribution on the workpiece surface will become uneven. The system not only adjusts the coolant flow rate but also simultaneously adjusts the current slope of the laser power module to counteract the heat input deviation caused by the movement speed fluctuation, achieving temperature control stability and consistency in process quality.

[0072] The reduced-order thermal-structure coupling model also includes a pre-defined reflectivity library for workpieces made of different materials. After the operator selects the workpiece material (such as stainless steel, aluminum alloy, or copper) through the handheld human-machine interface, the system automatically retrieves the corresponding reflectivity from the storage module. For highly reflective materials such as aluminum alloy, the system increases the weighting coefficient of the reflected light heat load by 40%, enabling the simulation solver to pre-calculate a stronger backscattered heat flux and guide the controller to enhance the cooling intensity of the welding torch head in advance.

[0073] When executing the predictive coordinated temperature control switching, the controller employs a smooth switching algorithm based on energy prediction. When the simulation prediction triggers the command to switch from the normal mode to the high-robustness mode, the controller does not immediately jump the parameters. Instead, within a predetermined 200-millisecond transition period, it gradually adjusts the weights of each control parameter through an interpolation algorithm. This approach avoids frequent oscillations of the solenoid valve or abrupt changes in laser output power caused by sudden changes in control strategy, ensuring the stability of the welding arc.

[0074] Each physical sensor in the sensor array is equipped with failure diagnosis logic. The system establishes a redundant observer based on the thermal balance equation. When the measured temperature data of a node deviates from the simulation prediction by more than 15 degrees Celsius for more than 500 milliseconds, the system determines that the physical sensor is faulty. The controller automatically switches to a virtual sensing mode based on the thermal simulation model, using the historical trend of that node and measured data from other related nodes to derive the estimated temperature value of the faulty node. This soft redundancy mechanism ensures that the thermal management system can maintain basic safety monitoring capabilities even in the event of hardware failure.

[0075] After each cold start or when a new welding torch tip is replaced, the system automatically executes a thermal calibration sequence lasting approximately 10 seconds. This sequence monitors the temperature rise response of each channel sensor by emitting a set of test lasers with specific power and pulse widths. The system compares the observed temperature rise slope with the model's standard values ​​to automatically identify minute differences in the current physical hardware (such as cooling pipes or lenses from different batches) and personalizes the parameters of the digital twin model, eliminating systematic errors caused by assembly tolerances.

[0076] To improve the comfort of manual operation, the method also achieves constant temperature control of the handheld welding torch handle area. By arranging independent micro-thermal conduction structures inside the handle, the system dynamically fine-tunes the proportion of coolant flowing near the handle based on the simulated predicted heat conduction trend of the shell. When it is predicted that prolonged high-power welding will cause the temperature at the handle to exceed 40 degrees Celsius, the system will prioritize allocating a small portion of low-temperature coolant to flow through the handle cooling ring, ensuring that the operator's hand temperature remains within a comfortable range.

[0077] This method integrates thermal monitoring of the power supply module. When the power conversion loss of the laser power supply system causes the air temperature rise rate inside the control cabinet to exceed 2 degrees Celsius per minute, the system introduces this environmental heat load as an external disturbance into the overall thermal simulation model. This is achieved by linking the speed adjustment logic of the control cabinet's cooling fan.

[0078] Example 2: Based on Example 1, Example 2 provides a multi-channel intelligent temperature control method for handheld laser welding machines in ultra-high power continuous welding scenarios. In this example, the reduced-order thermal structure coupling model constructed in step 1 further adds monitoring nodes for coolant phase change risk. The system defines a phase change sensitive node at the bend of the water-cooled pipeline closest to the lens, and the thermophysical parameters of this node include the critical vaporization temperature of the coolant.

[0079] In the real-time thermal simulation calculation of step 3, the solver not only calculates the temperature distribution but also simultaneously calculates the fluid pressure distribution trend inside the pipeline. By coupling the pressure and temperature values, the system can determine in real time whether microbubbling or cavitation will occur in local areas of the coolant under the current operating conditions. This phenomenon can lead to a sharp drop in heat transfer efficiency. Once the simulation model predicts a phase change risk within the next 3 seconds, the predictive collaborative temperature control strategy in step 4 will forcibly trigger a pulse cooling mode. By instantaneously increasing the circulation pump frequency, the pressure pulse will break the bubble layer, maintain liquid contact at the heat exchange surface, and ensure thermal safety under high power density.

[0080] Step 4 in this embodiment further refines the PID compensation logic of the solenoid valve. The controller no longer uses fixed control parameters, but dynamically adjusts the PID gain based on the simulated predicted thermal inertia constant. When the system is predicted to be on the verge of thermal instability, the controller automatically increases the proportional gain and decreases the integral gain to achieve the ultimate response speed; while when the system is in a stable welding period, it automatically switches back to a high-precision stable control mode. This parameter adaptive logic allows the system to reduce the workload compared to traditional constant parameter control when dealing with welding tasks that involve frequent starts and stops.

[0081] In step 5, a workpiece thermal accumulation feedback function was added for the high-robustness mode. The moving speed data monitored by the inertial measurement unit was used to construct a thermal field model of the workpiece. When the operator stays in the same position for too long, causing local overheating of the workpiece and generating backscattered heat flow, the system will give a stronger warning through handle vibration and automatically insert tiny discontinuous intervals into the laser output waveform. These millisecond intervals allow the optical components to cool down gradually, improving the system's survivability under extreme operational errors without affecting the macroscopic welding effect.

[0082] This embodiment introduces second-order logic for laser aging compensation into the reduced-order thermal structure coupling model. The system records the electro-optic conversion curve shift of the laser under different ambient temperatures. By establishing a two-dimensional lookup table, the system can accurately calculate the redundant heat generated by internal laser losses based on the current total operating time and current ambient temperature. This heat is added to the simulation model as background noise, enabling the temperature control system to maintain the prediction accuracy of a new machine even after tens of thousands of hours of operation.

[0083] Regarding the dynamic allocation of cooling resources, this embodiment adds an independent cooling branch for the power transformer. By adding a bypass control valve to the solenoid valve array, the system can switch surplus cooling capacity to the power supply module for rapid cooling when the laser is in standby mode. This cross-module energy scheduling logic improves the overall energy utilization efficiency of the system and reduces the overall power consumption and noise level of the power control cabinet's cooling fan.

[0084] Example 3: In this example, a sensor redundancy and self-calibration implementation method for high-precision welding tasks is further described. In step 2, the sensor array aggregates temperature data to the main controller through two independent signal transmission links. The first link uses an analog voltage signal, and the second link uses a digital bus signal. The system determines whether there are false alarms caused by electromagnetic interference in the transmission link by comparing the consistency of the two signals in real time.

[0085] In the simulation solution process of step 3, the system introduces a logic verification algorithm based on thermal conduction similarity. Due to the continuity of the physical structure, the temperature rise trends of adjacent nodes should have a high correlation. If the simulation model calculates that a node experiences a drastic change while its adjacent nodes remain stable, and this change does not conform to the physical principles of thermal conduction, the system will automatically determine that the calculation step has been interfered with by input noise and will activate a smoothing algorithm based on spatial filtering to correct the simulation results. This algorithm is embedded in explicit finite difference logic, improving the stability of the embedded simulation in electromagnetic interference environments.

[0086] In step 4, during power shaping, thermal response templates for different welding process libraries are introduced. For example, during the fish-scale welding process, the laser power is in a periodic pulse state. The controller extracts typical thermal characteristics at this frequency from the process library and preloads them into the simulation model as prior knowledge. This allows the simulation solver to calculate the small deviation between the actual observation value and the template value without having to calculate the thermal field evolution from scratch, thus saving computational resources and extending the prediction time from 5 seconds to 12 seconds.

[0087] In the adaptive adjustment of step 5, this embodiment adds ambient light interference cancellation logic. Since the intensity of backscattered light is greatly affected by the surface flatness and oil contamination of the workpiece, relying solely on power calculations may lead to inaccuracies. The system utilizes infrared photoelectric sensors distributed at the welding torch head to monitor the actual reflected light intensity and uses it as a calibration factor to correct the radiative heat load calculated by the IMU in real time. When the operator works on a highly reflective aluminum plate, the feedback from the photoelectric sensors is instantly converted into acceleration commands for the cooling pump through the simulation model, achieving true closed-loop photothermal collaborative management.

[0088] This embodiment also details a preventative strategy for coolant level drops. By monitoring the coupling characteristics of the current fluctuation frequency and temperature rise slope of the coolant circulation pump, the system can identify the presence of tiny air bubbles in the pipeline. If the bubble characteristic frequency reaches a preset threshold, it is determined that the coolant level is too low or there is a risk of leakage. The thermal simulation model automatically enters an energy-saving heat dissipation mode, reducing the upper limit of power output while optimizing the heat dissipation path, extending the controlled downtime of the equipment, and avoiding damage caused by sudden dry burning.

[0089] The system also has the ability to remember the individual habits of different operators. The inertial measurement unit records the hand-held jitter frequency and posture preferences of different operators. The system generates a unique thermal boundary feature patch for each operator through machine learning algorithms (describes its weight update process in words). When a specific operator logs into the system, the reduced-order thermal structure coupling model automatically loads the patch, pre-compacting and compensating for localized heat accumulation points that the operator is prone to, thus achieving intelligent thermal management that integrates human and machine.

[0090] Regarding performance optimization throughout the entire lifecycle, the method described in this embodiment also integrates a thermal fatigue assessment module. By recording the peak and fluctuation amplitudes of the core lens and laser junction temperature during each thermal cycle, the system estimates the remaining thermal life of each key component based on the cumulative damage theory in materials mechanics. When it is predicted that a component is about to reach the thermal fatigue critical point, the temperature control strategy automatically becomes more conservative, reducing thermal stress impact through smoother power shaping logic, thus maximizing the service value of the equipment without replacing parts.

[0091] In cluster operation scenarios, each device not only shares thermal data but also shares cooling system maintenance information through time-slice rotation logic. When the system detects an increase in the differential pressure of a device's cooling filter, this information is synchronized to other devices. Neighboring devices automatically increase their own temperature control accuracy reserves to take over high-intensity tasks when the faulty device is shut down for maintenance. This holistic reliability maintenance solution based on digital twins marks the evolution of handheld laser welding technology from individual intelligence to collaborative intelligence among groups.

[0092] The reduced-order thermal structure coupling model also includes a preheating logic for extreme low-temperature environments. When the sensor array detects that the ambient temperature is below 5 degrees Celsius, the system generates low-power heat by controlling the incoherent pump source inside the laser, utilizing the internal circulation friction of the coolant pump to generate heat, thus raising the entire system to the optimal operating temperature range without activating the welding function. This logic ensures the equipment's rapid response capability and startup stability in frigid northern conditions.

[0093] In the handle's temperature control, the system employs a predictive control-based flow regulation method. Unlike traditional hysteresis regulation, the system calculates the handle shell's thermal equilibrium trend over the next minute based on the current welding power and predicted operation time. If a slow rise in handle temperature is predicted, the system opens the regulating valve of the micro heat conduction pipeline 30 seconds in advance, using the pre-cooling effect of the coolant to counteract the subsequent heat conduction peak. This proactive control keeps the handle temperature fluctuation within ±0.5 degrees Celsius, providing the operator with an exceptional grip experience.

[0094] In summary, this embodiment constructs an intelligent temperature control system with self-sensing, self-prediction, self-coordination, and self-evolution through multi-level logical nesting and deep integration of multi-source data. It solves the risk of thermal runaway of handheld laser welding machines under complex working conditions and achieves a high degree of unity between efficiency, lifespan, and quality.

[0095] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A multi-channel intelligent temperature control and thermal simulation method for handheld laser welding machines, characterized in that, Includes the following steps: A reduced-order thermal structure coupling model for the physical structure of a handheld laser welding machine is constructed. The reduced-order thermal structure coupling model includes the material thermal property parameters of the laser module, optical path system, welding torch body and water cooling pipeline, and presets dynamic boundary condition mapping logic. Real-time data streams are acquired through a sensor array, and the reduced-order thermal structure coupling model is dynamically calibrated using the real-time data streams. The acquired inertial measurement unit data is then converted into operational behavior thermal loads to correct the heat transfer coefficient in the reduced-order thermal structure coupling model. Run the real-time thermal simulation solver to calculate the multi-channel temperature field distribution trend of the system in the future time period based on the current real-time operating conditions, operation behavior, ambient temperature and historical thermal data, with a preset time step. Using the multi-channel temperature field distribution trend as the control input, the controller executes a predictive collaborative temperature control strategy to dynamically pre-shape the laser's output power curve and simultaneously adjust multiple solenoid valves to achieve dynamic allocation of cooling resources. Based on inertial measurement unit data, the system determines the operator's real-time welding posture. When it is determined that the cooling efficiency has decreased due to the change in posture, it triggers behavior guidance feedback or automatically switches to a high robustness mode. The system can adaptively adjust the operation behavior by fine-tuning the welding parameters and temperature control threshold.

2. The multi-channel intelligent temperature control and thermal simulation method for handheld laser welding machines according to claim 1, characterized in that, The construction of the reduced-order thermal-structural coupling model for the physical structure of the handheld laser welding machine includes: The physical geometry of the handheld laser welding machine is topologically simplified, transforming the physical components with three-dimensional spatial properties into a logical node network with thermal capacity and thermal resistance characteristics. The laser module is defined as the first core heat source node, and the heat generation of the first core heat source node is set to be negatively correlated with the electro-optical conversion efficiency and real-time output power. The optical path system is defined as the second core heat source node. The optical path system includes a reflecting lens and a focusing lens. The heat generation of the second core heat source node is determined based on the product of the laser power and the lens absorptivity, wherein the lens absorptivity is set as a variable that increases cumulatively with the running time. The welding torch body and the water cooling pipeline are defined as heat dissipation network nodes. The energy exchange process between the heat dissipation network nodes and the first core heat source node and the second core heat source node is described by the heat conduction equation and the heat convection equation. The logical node network is processed in matrix form, where the state transition matrix describes the evolution of the temperature of each node over time, the input matrix represents the direct contribution of external excitation to each heat source node, and the disturbance matrix represents the thermal impact of uncertain factors on the system.

3. The multi-channel intelligent temperature control and thermal simulation method for handheld laser welding machines according to claim 2, characterized in that, The material's thermophysical properties include thermal conductivity, specific heat capacity, density, and surface emissivity. The thermal conductivity is set as a function of temperature. The thermal conductivity is dynamically corrected by increasing the node temperature according to a preset slope to simulate the nonlinear thermal conduction characteristics of the material at high temperatures. The dynamic boundary condition mapping logic includes thermal boundary settings for natural air convection, forced coolant convection, and backscattered light radiation. The dynamic calibration process dynamically corrects the convective heat transfer coefficient of the outer envelope surface in the reduced-order thermal structure coupling model based on the real-time values ​​fed back by the ambient temperature sensor. In the reduced-order thermal structure coupling model, a monitoring node for the phase change risk of the coolant is added, and a phase change sensitive node is defined at the bend of the water-cooled pipeline near the optical path system. The thermal property parameters of the phase change sensitive node include the vaporization critical temperature value of the coolant.

4. The multi-channel intelligent temperature control and thermal simulation method for handheld laser welding machines according to claim 3, characterized in that, The sensor array includes multiple temperature sensors distributed in the laser core area, optical lens bracket, welding torch housing, and coolant inlet and outlet. The real-time data stream includes the measured values ​​from the multiple temperature sensors and the instantaneous flow rate data of the coolant obtained through the flow meter; The dynamic calibration process uses Kalman filtering logic to reversely correct the initial state parameters of each node in the reduced-order thermal structure coupling model by using the deviation between the observed measured temperature value and the model prediction value of the reduced-order thermal structure coupling model. When the measured temperature value is higher than the model prediction value and the deviation exceeds the preset tolerance range, the system increases the thermal resistance coefficient of the corresponding node or decreases the heat dissipation coefficient of the corresponding node. Each physical sensor in the sensor array is equipped with failure diagnosis logic. By establishing a redundant observer based on the thermal balance equation, when the measured temperature data of a certain node deviates from the model prediction value by more than a preset error threshold and the duration exceeds the preset judgment period, the physical sensor is determined to be in a fault state and is switched to the virtual sensing mode based on the reduced-order thermal structure coupling model.

5. The multi-channel intelligent temperature control and thermal simulation method for a handheld laser welding machine according to claim 4, characterized in that, The inertial measurement unit data is converted into operational behavior thermal loads, including: The inertial measurement unit provides triaxial acceleration, triaxial angular velocity, and attitude angle information, and the attitude angle information is mapped to the real-time tilt orientation of the welding torch body in space. The flow state of the coolant under the real-time tilt position is determined based on the principles of fluid mechanics, and then the convective heat transfer coefficient inside the welding torch body is corrected. When the upward tilt angle of the welding torch body exceeds the preset angle threshold, it is determined that the flow rate of the coolant is slowed down under the action of the reverse component of gravity, and the convective heat transfer coefficient is adjusted accordingly. The real-time moving speed of the welding torch body is calculated based on the triaxial acceleration. The real-time moving speed is combined with the current real-time output power to calculate the heat input per unit length acting on the workpiece. Based on the preset material reflectivity parameters, the secondary radiation heat load generated by the reflected light from the workpiece on the welding torch body is estimated, and the secondary radiation heat load is input as an additional heat generation term into the reduced-order thermal structure coupling model.

6. The multi-channel intelligent temperature control and thermal simulation method for a handheld laser welding machine according to claim 5, characterized in that, The real-time thermal simulation solver employs explicit finite difference computation logic and performs iterative calculations in an independent thread of the embedded control software with millisecond-level time steps. The multi-channel temperature field distribution trend includes the laser junction temperature channel, the optical lens temperature rise channel, and the welding torch housing and handle temperature rise channel. During the calculation process of the real-time thermal simulation solver, the thermal conduction coupling effect between each channel is considered. When the laser junction temperature channel shows a temperature rise trend, the indirect heating effect on the optical lens temperature rise channel through the common water cooling pipeline is calculated simultaneously, as well as the amount of temperature drift of the coolant at the inlet due to the pipeline temperature rise. When the temperature rise channel of the optical lens experiences transient pulse-like fluctuations due to backscattered light, the real-time thermal simulation solver simultaneously evaluates its contribution to the thermal stress of the local area of ​​the welding torch body. If a localized heat buildup is predicted due to impeded heat conduction at the optical lens support, the reduced-order thermal structure coupling model increases the nonlinear weight of that region and outputs a warning identifier.

7. The multi-channel intelligent temperature control and thermal simulation method for a handheld laser welding machine according to claim 6, characterized in that, The execution of the predictive collaborative temperature control strategy includes: when the simulation results of the real-time thermal simulation solver indicate that the temperature rise channel of the optical lens will exceed the warning threshold in the future time period, the dynamic power shaping is activated. The dynamic power shaping smooths the time envelope of the real-time output power while keeping the total welding energy constant. It introduces a power ramp-up interval of a preset duration at the beginning of the welding process, so that the power can be smoothly increased to the target welding power, thereby reducing the instantaneous thermal shock pressure on the optical components. During the welding process, the peak power is modulated based on the thermal balance deviation between the model prediction and the measured temperature, and the thermal inertia of the optical path system is used to reduce temperature fluctuations. When performing predictive coordinated temperature control switching, a smooth switching algorithm based on energy prediction is adopted. During the predetermined transition period, the weights of the control parameters are gradually adjusted through interpolation to avoid actuator oscillations or power jumps caused by sudden changes in the control strategy.

8. The multi-channel intelligent temperature control and thermal simulation method for a handheld laser welding machine according to claim 7, characterized in that, The synchronous adjustment of the multi-way solenoid valve to achieve dynamic allocation of cooling resources includes: controlling the independent cooling branches flowing to the laser module, the optical path system and the welding torch body respectively through the multi-way solenoid valve; The optimal flow distribution ratio is calculated based on the temperature rise slope predicted by the real-time thermal simulation solver for each channel. If the predicted temperature rise slope of the optical path system is greater than the predicted temperature rise slope of the laser module, then while keeping the total cooling power constant, the opening of the solenoid valve pointing to the branch of the optical path system is increased, and the opening of the solenoid valve of the branch of the laser module is decreased proportionally. The opening adjustment of the multi-way solenoid valve adopts a step-approximation logic. The flow rate change corresponding to each adjustment step is linearly positively correlated with the difference of the predicted temperature rise slope, so as to achieve a smooth transfer of cooling resources between channels. Based on the thermal inertia constant predicted by the real-time thermal simulation solver, the proportional, integral, and derivative gains are dynamically adjusted. When the system is in a thermal equilibrium unstable state, the proportional gain is increased and the integral gain is decreased. When the system is in a stable welding period, it switches to a high-precision stable control mode.

9. The multi-channel intelligent temperature control and thermal simulation method for a handheld laser welding machine according to claim 8, characterized in that, The behavior guidance feedback and automatic switching to the high robustness mode include: when the welding posture is detected by the inertial measurement unit data to cause the convective heat transfer coefficient to decrease, driving the vibration motor inside the welding torch body to generate vibration at a specific frequency, or changing the flashing frequency of the indicator light on the handle to prompt the operator to correct the posture. If the operator does not adjust the welding posture within the preset observation period, the system switches to the high robustness mode, automatically increases the temperature control tolerance threshold of non-core components in the welding torch body, and prioritizes the allocation of cooling resources to the optical path system. In the high robustness mode, the total heat load of the system is reduced by adjusting the pulse frequency or duty cycle, and the energy gap caused by the decrease in duty cycle is compensated by an algorithm. The moving speed data monitored by the inertial measurement unit is used to construct a workpiece thermal field model. When it is determined that the operator stays in the same position for a longer period of time than the preset pause threshold, the vibration motor will issue an early warning and insert intermittent intervals into the laser output waveform.

10. The multi-channel intelligent temperature control and thermal simulation method for a handheld laser welding machine according to claim 9, characterized in that, The method further includes the following steps: by analyzing long-sequence thermal balance data to identify the offset of the correspondence between heat generation and output power, automatically updating the efficiency parameters in the reduced-order thermal structure coupling model, and realizing online compensation for the attenuation of laser electro-optic conversion efficiency; The ambient humidity is monitored by an ambient humidity sensor. When the ambient humidity exceeds a preset humidity threshold, the critical temperature point at which condensation occurs on the lens surface of the optical path system is calculated by the reduced-order thermal structure coupling model, and the cooling temperature set point of the optical path system is locked within a safe difference range above the critical temperature point. After powering on or replacing the welding torch head, the system executes a thermal calibration sequence. By emitting a test laser with a specific power and pulse width, it monitors the temperature rise curve response of each channel and uses the difference between the temperature rise curve response and the standard model to personalize the parameters of the reduced-order thermal structure coupling model. In automated cluster welding scenarios, multiple handheld laser welding machines share thermal twin data through a communication bus. When a single device faces the risk of heat accumulation, the operation sequence of adjacent devices within the cluster is coordinated to provide cooling buffer time for devices with high heat loads.

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