A drill bit welding temperature self-adaptive control method and system

Abstract

This invention belongs to the field of temperature control technology and relates to an adaptive control method and system for brazing tool welding temperature. The method first acquires the feedback temperature and execution power of the brazing tool welding area in real time; then, it evaluates the thermal inertia saturation index based on the temperature square term and cumulative energy input to reflect the real-time energy storage state of the workpiece; by coupling the thermal inertia saturation index with the instantaneous temperature rise change, it identifies the phase change damping adjustment factor to identify the physical plateau period of the brazing filler metal melting; finally, by combining the thermal inertia saturation index and the phase change damping adjustment factor, it performs nonlinear correction on the base heating power and combines it with temperature difference compensation to determine the dynamic power output command. This invention achieves precise control of energy input in complex welding processes, effectively eliminates the temperature rise pulse after phase change, and improves the strength consistency and energy efficiency of brazing tool welding.

Description

Technical Field

[0001] This invention belongs to the field of temperature control technology, specifically relating to an adaptive control method and system for welding tool temperature. Background Technology

[0002] In the manufacturing process of mining drill bits and rock drilling tools, high-frequency induction brazing is a key technology to ensure a stable connection between the cemented carbide ball teeth and the drill rod steel body. Due to the significant differences in physical properties such as thermal conductivity, coefficient of linear expansion, and specific heat capacity between cemented carbide and the steel matrix, coupled with the fact that high-performance drill bits are usually designed with multiple densely distributed alloy teeth, the thermal field distribution in the welding area exhibits extremely high complexity and non-uniformity.

[0003] Most existing brazing temperature control technologies use traditional proportional-integral-derivative (PID) control logic. Their core mechanism is to adjust the output power of the induction power supply based on the deviation between the real-time surface temperature and the set target temperature. This error feedback-based control mode performs reasonably well in linear heating processes, but faces severe challenges during the unique solid-liquid phase transition stage of brazing. When the brazing filler metal reaches its melting point and begins to melt, a significant endothermic reaction occurs. A large amount of heat energy is used to overcome lattice constraints and is converted into latent heat in the liquid phase, causing a sharp drop in the temperature rise rate of the welded surface or even a temporary temperature plateau. At this time, traditional control algorithms cannot recognize this physical phase transition characteristic and will still misjudge the temperature lag as insufficient heating power. Consequently, under integral action, the control gain continues to accumulate, blindly increasing the output power of the induction power supply.

[0004] Once the brazing filler metal completes its phase transition and the latent heat absorption process ends, the energy that had previously accumulated excessively in the system is instantly converted into sensible heat, causing the temperature in the welding area to rise explosively in a very short time. This thermal inertial shock can easily lead to severe overshooting of the brazing temperature, causing overheating of the steel substrate surface, filler metal loss, or brittle cracks in the cemented carbide due to excessive thermal stress, severely weakening the impact resistance and fatigue life of the brazing tool. In addition, the induction heating process itself is accompanied by the physical characteristic that the magnetic permeability of the material decreases with increasing temperature. Especially when the temperature is close to the Curie point of the steel, the heating efficiency will undergo a nonlinear abrupt change, and the heat loss due to radiation at high temperatures will also increase exponentially. Existing constant parameter control logic lacks the ability to evaluate this dynamic thermal saturation state and energy dissipation mechanism in real time, making it difficult to achieve precise clamping of heat input in the later stages of welding, resulting in poor product consistency, high energy consumption, and difficulty in effectively controlling the scrap rate. Summary of the Invention

[0005] The purpose of this invention is to propose an adaptive control method and system for brazing temperature of brazing tools, in order to solve the technical problems caused by traditional control algorithms, such as temperature control runaway, workpiece overheating or cold welding, due to the phase change heat sink effect during the melting of the brazing filler metal, the lack of identification of the heat saturation state, and the difference in thermal conductivity of dissimilar materials.

[0006] To solve the above problems, the technical solution of the adaptive control method for welding temperature of the brazing tool proposed in this invention is as follows:

[0007] An adaptive control method for welding temperature of brazing tools includes:

[0008] The feedback temperature of the welding area of ​​the brazing tool and the execution power of the induction power supply are collected in real time, and the feedback temperature and execution power are aligned with the execution time according to the sampling period.

[0009] A thermal inertia saturation index is constructed based on the nonlinear mapping relationship between the square of the feedback temperature and the cumulative execution energy from the start of heating to the current sampling time, in order to evaluate the real-time energy storage state of the drill bit as a heated body and the dynamic decay of its heat absorption capacity.

[0010] The thermal inertia saturation index is coupled with the temperature rise difference between the feedback temperature and the adjacent sampling time to obtain the phase change damping adjustment factor, which is used to characterize the resistance strength of the energy absorption trap generated by the brazing filler metal in the melting phase change stage to the temperature rise trend.

[0011] The phase change damping adjustment factor is used to perform multiplicative gain correction on the preset base heating power, and the control weight is adjusted in combination with the logarithmic mapping result of the thermal inertia saturation index. Then, the preset proportional temperature difference compensation between the target welding temperature and the feedback temperature is superimposed to generate a dynamic predicted power command to drive the induction power supply to perform adaptive power output.

[0012] The temperature rise curve changes during the welding process are monitored in real time, and the second derivative of the temperature rise curve is calculated to identify the inductive coupling state. When the second derivative of the temperature rise curve exceeds the preset sudden change threshold at the current sampling time, it is determined that the inductive coupling is unstable and safety load reduction protection is implemented.

[0013] Beneficial effects: This invention can accurately establish the causal relationship between energy input and temperature rise response by real-time acquisition and feedback of temperature and power and time alignment; it uses the thermal inertia saturation index to evaluate the energy storage state of the workpiece and combines the phase change damping adjustment factor to identify the physical characteristics of the melting stage, thus achieving precise control of energy input during welding; this invention effectively eliminates the temperature overshoot phenomenon after the phase change of the brazing filler metal by combining nonlinear correction and temperature difference compensation, improves the strength consistency of brazing tools, and prevents workpiece overheating or equipment damage caused by coupling instability through the second derivative monitoring mechanism of inductive coupling state.

[0014] Furthermore, the formula for calculating the thermal inertia saturation index is as follows:

[0015]

[0016] in, Indicates the thermal inertia saturation index. This indicates the feedback temperature at the current sampling time. Indicates the sampling period. This indicates the total number of sampling periods from the start of heating to the current sampling time. This indicates the execution power at a historical moment. This represents the system bias constant.

[0017] Beneficial effects: This solution establishes a ratio between the square of the feedback temperature and the cumulative execution energy, constructing a physical index that reflects the dynamic changes in the workpiece's heat absorption efficiency. This formula can assess the degree of thermal saturation of the drill bit during the heating process. As the temperature rises and radiative heat dissipation increases, the thermal inertia saturation index can reflect the decline in the workpiece's heat absorption capacity in a timely manner, thus providing a reference for subsequent power decisions that conforms to physical laws and avoiding blindly increasing power input during the thermal saturation stage.

[0018] Furthermore, the formula for calculating the phase change damping adjustment factor is as follows:

[0019]

[0020] in, This represents the phase transition damping adjustment factor. This indicates the feedback temperature at the previous sampling time. This indicates the execution power at the current sampling time. This represents the power safety constant.

[0021] Beneficial effects: This scheme couples the thermal inertia saturation index with the instantaneous temperature rise difference, which can sensitively capture the endothermic plateau characteristics during the melting of the brazing filler metal; when the system enters the phase change stage and the temperature rise stagnates, the phase change damping adjustment factor value drops rapidly, thereby logically suppressing the power output, accurately identifying and responding to the obstruction of the temperature rise trend by the absorption of latent heat of melting, and preventing the temperature from rising suddenly after the phase change ends.

[0022] Furthermore, the calculation formula for the dynamic predicted power command is as follows:

[0023]

[0024] in, This indicates the final dynamically predicted power command issued. Indicates the basic heating power. This indicates the target welding temperature. This represents the proportional feedback adjustment coefficient.

[0025] Beneficial effects: This scheme uses a phase change damping adjustment factor to multiplicatively suppress the base heating power, and uses the thermal inertia saturation index to adjust the denominator of the control weight, thus achieving an organic combination of the feedforward physical model and feedback temperature difference compensation. This calculation method can forcibly reduce the power output during the phase change period to meet the latent heat absorption requirements, while smoothly adjusting the gain according to the thermal saturation state when approaching the target temperature, ensuring that the dynamically predicted power command can maintain efficient heating and achieve a smooth temperature transition.

[0026] Furthermore, the specific method for acquiring the feedback temperature is as follows: the surface radiation signal of the welding area of ​​the brazing tool is obtained by a dual-color infrared thermometer installed above the induction coil, and real-time temperature data unaffected by workpiece emissivity fluctuations is calculated based on the intensity ratio of the dual-channel wavelengths.

[0027] Furthermore, the cumulative execution energy is determined by summing the product of the execution power and the sampling period for each sampling cycle after heating is started, in order to reflect the total heat absorbed by the workpiece.

[0028] Furthermore, when calculating the phase change damping adjustment factor, a boundary constraint is also included on the temperature rise difference: if the feedback temperature at the current sampling time is less than the feedback temperature at the previous sampling time, then the temperature rise difference is recorded as 0.

[0029] Furthermore, the safety load reduction protection includes: resetting the thermal inertia saturation index to its initial state and forcibly reducing the execution power of the inductive power supply to below a safety threshold.

[0030] Beneficial effects: When inductive coupling instability is detected, measures such as resetting the thermal inertia saturation index and forcibly reducing the execution power can quickly cut off the abnormal energy input circuit. This safety protection mechanism can immediately restore the system to a safe state in case of an accident, effectively preventing severe oxidation of the workpiece surface or overheating and scrapping of the drill rod body caused by uncontrolled heating, and reducing quality risks in the production process.

[0031] Furthermore, the specific method of the execution time alignment process is as follows: the feedback temperature obtained at the current sampling time is associated with the execution power at the previous sampling time to match the physical causal logic between the power input and the temperature rise feedback.

[0032] The technical solution of the adaptive control system for welding tool temperature proposed in this invention is as follows:

[0033] The adaptive control system for welding tool temperature includes a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the adaptive control method for welding tool temperature described in any of the above technical solutions is implemented.

[0034] The beneficial effects of this invention are as follows: This invention effectively solves the problem of temperature control runaway caused by the phase change heat sink effect during induction brazing with brazing tools, avoiding temperature rise after the filler metal melts and undergoes phase change, thus preventing overheating of the workpiece surface and filler metal loss. This invention can adapt to the dynamic decay of the heat absorption capacity of the brazing tool as a heat-receiving body, ensuring precise matching between the execution power of the induction power supply and the real-time energy storage state of the workpiece, avoiding excessive energy input during the heat saturation stage. This invention improves the strength consistency of brazing with brazing tools, ensuring the quality stability of heterogeneous material connections. Furthermore, this invention enhances the safety of the welding process, enabling timely identification of inductive coupling instability and implementing safety load reduction protection, preventing equipment damage and workpiece scrapping caused by sudden changes in the physical field. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating the steps of the adaptive control method for welding temperature of the brazing tool in an embodiment of the present invention.

[0036] Figure 2 This is a comparison diagram of the two-dimensional feature space distribution formed by the execution power and the real-time feedback temperature in an embodiment of the present invention;

[0037] Figure 3 This is a diagram showing the identification intensity distribution of the phase change damping adjustment factor in the multidimensional parameter space composed of the instantaneous temperature rise rate and the output power of the induction power supply in an embodiment of the present invention. Detailed Implementation

[0038] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0039] Specific embodiments of the adaptive control method for welding temperature of the brazing tool proposed in this invention:

[0040] like Figure 1 As shown, the adaptive control method for welding temperature of the brazing tool includes the following steps:

[0041] S1. Real-time acquisition of feedback temperature in the brazing area and execution power of the induction power supply, and alignment processing of feedback temperature, execution power and execution time according to the sampling period.

[0042] In the specific implementation process, a high-frequency data sampling channel is established to accurately capture the complex thermodynamic state of the welding area. For obtaining the feedback temperature, this embodiment uses a dual-color infrared thermometer installed directly above the induction heating coil as the sensing device. Unlike single-color temperature measurement technology, which is greatly affected by the environment, dual-color temperature measurement technology uses its internal optical system to simultaneously detect the spectral radiance at two specific wavelengths and determines the true temperature of the object by calculating the ratio of the radiant intensities of these two wavelengths. This colorimetric temperature measurement method has significant anti-interference characteristics and can effectively offset the signal attenuation interference caused by the thickening of the oxide scale on the surface of the solder tool, the nonlinear fluctuation of the material emissivity with the increase of temperature during induction heating, and the obstruction of the optical path by welding fumes, water mist, or dust present on site, thereby ensuring that the collected temperature data can truly reflect the thermodynamic energy level of the workpiece surface.

[0043] Meanwhile, the power output data of the inductive power supply does not rely on external sensors, but is read directly through the analog output interface or digital communication interface connected to the inductive power supply controller. Since the raw signal output by the controller is usually a standard industrial electrical signal, the system has a linear conversion module that maps and converts the received analog voltage or current signal into the actual physical power value in real time, based on the rated power parameters of the inductive power supply.

[0044] To ensure the accuracy of subsequent control model calculations, strict time alignment is performed on the two data streams from different sources. Considering that induction heating is a typical thermal inertial system, the physical chain from current loading to generate a magnetic field, the magnetic field inducing eddy currents, the eddy currents generating Joule heating, and finally the heat being conducted to the surface and captured by the thermometer, inherently involves a time lag. Simply pairing the temperature sampled at the current sampling moment with the power delivered at the current sampling moment would fail to accurately reflect the causal relationship between the cumulative effect of energy input and the temperature rise response. Therefore, the system sets a fixed system sampling period, such as 20 milliseconds. At each sampling moment, the system records the current feedback temperature and simultaneously retrieves historical execution power data. Specifically, the time alignment method establishes an association index between the feedback temperature acquired at the current sampling moment and the execution power at the previous sampling moment, thereby matching the physical causal logic between power input and temperature rise feedback. This alignment process eliminates the impact of first-order lag on model identification, establishes a clear data causal chain, and lays a precise data foundation for subsequent calculations of the thermal inertia saturation index and identification of phase transition damping characteristics.

[0045] S2. Construct a thermal inertia saturation index based on the nonlinear mapping relationship between the square of the feedback temperature and the cumulative execution energy from the start of heating to the current sampling time, in order to evaluate the real-time energy storage state of the drill bit as a heated body and the dynamic decay of its heat absorption capacity.

[0046] During the heating process of induction brazing, the thermal response characteristics of the workpiece are not linearly constant, but exhibit dynamic nonlinear characteristics as the temperature increases. To reflect this physical state, this invention introduces the physical quantity of thermal inertia saturation index, which is used to evaluate the effective temperature rise efficiency per unit energy input.

[0047] In the initial stage of induction heating, the surface temperature of the drill bit is low, the temperature difference with the environment is small, and the heat loss from radiation and convection is minimal. At this time, most of the induced energy is effectively absorbed by the workpiece and converted into internal energy, which macroscopically manifests as a significant temperature rise, i.e., the heat absorption efficiency is high. However, as the heating process continues, the surface temperature of the workpiece rises continuously. According to the thermodynamic law of radiation, the radiative heat dissipation power of an object's surface is proportional to a high power of temperature, leading to a sharp increase in the radiative heat dissipation rate. Simultaneously, the electromagnetic properties of the metallic material, such as resistivity and permeability, change with increasing temperature, causing the workpiece's coupling absorption efficiency of alternating magnetic field energy to gradually decrease and enter a saturation or decay plateau period. Therefore, a simple temperature value cannot fully reflect the current heating efficiency. In this embodiment, a mathematical model is constructed to capture this physical evolution process from high-efficiency heat absorption to a thermal saturation state.

[0048] Specifically, using the square of the feedback temperature as the numerator to reflect the sensitivity of the high-temperature range to state changes, and using the accumulated energy since the heating started as the denominator, the following formula for calculating the thermal inertia saturation index is constructed:

[0049]

[0050] in, The thermal inertia saturation index is used to characterize the temperature rise response capability of a workpiece under energy input, in order to evaluate the real-time energy storage state of the drill bit as a heat receiver and the dynamic decay of its heat absorption capacity; the downward trend of this value directly reflects the deepening of thermal saturation.

[0051] Indicates the current sampling time The measured feedback temperature is squared to amplify the influence weight of temperature changes on the exponent in the high-temperature range, thereby improving the system's sensitivity to identifying heat saturation trends.

[0052] Indicates the sampling period. This indicates the total number of sampling periods from the start of heating to the current sampling time. This represents the execution power at a historical moment. The denominator contains... The term employs the mathematical concept of discrete integrals, representing the time from the start of heating. The total energy input up to the current sampling time is calculated by analyzing the execution power at each historical moment. With system sampling period The products are summed to calculate the cumulative heat work exerted by the system on the workpiece.

[0053] This represents the system bias constant, used to prevent computational singularities where the denominator is zero due to zero accumulated energy at the moment of heating start-up, and to adjust the numerical reference range of the exponent in the initial stage. In the preferred embodiment of parameter settings, to balance computational load and integration accuracy, the system sampling period is... The preferred setting is 0.02 seconds, and the system bias constant is... Set to 500.

[0054] This invention constructs a thermal inertia saturation index, which can compare the cumulative nonlinear relationship between historical energy input and current temperature response, and accurately assess the real-time energy storage state of the workpiece and the dynamic decay of its heat absorption capacity.

[0055] S3. The thermal inertia saturation index is coupled with the temperature rise difference between the feedback temperature and the adjacent sampling time to obtain the phase change damping adjustment factor, which is used to characterize the resistance strength of the energy absorption trap generated by the brazing filler metal in the melting phase change stage to the temperature rise trend.

[0056] In the physical process of induction brazing, when the temperature of the brazing filler metal reaches the solid-liquid phase transition point, the internal crystal lattice structure of the material rearranges. At this time, a large amount of the input electromagnetic thermal energy is converted into latent heat of fusion rather than sensible heat, resulting in a sudden drop or even stagnation in the temperature rise rate of the workpiece surface on a macroscopic scale, i.e., the formation of a temperature plateau period.

[0057] To capture this microscopic physical signal, this invention no longer relies solely on absolute temperature values, but focuses on the rate of temperature change. By calculating the difference between the feedback temperature at the current sampling moment and the previous sampling moment, the instantaneous temperature rise rate can be obtained. To eliminate calculation deviations caused by measurement noise or signal jitter, this embodiment also applies boundary constraints to the temperature rise difference when calculating the phase change damping adjustment factor: if the feedback temperature at the current sampling moment is less than the feedback temperature at the previous sampling moment, the temperature rise difference is recorded as 0 to prevent negative values ​​from interfering with subsequent damping calculations. When this temperature rise difference approaches 0, it indicates that the system has encountered a strong heat sink effect.

[0058] This step not only focuses on the instantaneous temperature rise, but also incorporates the thermal inertia saturation index, which reflects the overall energy storage state of the system and was constructed in step S2, into the calculation. Through the product effect of the two, the macroscopic thermal history state is coupled with the microscopic instantaneous changes. This coupling mechanism can effectively distinguish between the slow temperature rise caused by insufficient heating power and the stagnation of temperature rise caused by the absorption of latent heat of phase change, thereby accurately identifying the physical inflection point of the solder melting.

[0059] Based on the above physical logic, this invention constructs a calculation model for the phase change damping adjustment factor, and the specific calculation formula is as follows:

[0060]

[0061] This represents the phase change damping adjustment factor, used to characterize the strength of the resistance to the temperature rise trend caused by the energy absorption traps generated during the melting phase change stage of the solder. It is the thermal inertia saturation index calculated in step S2, representing the thermal saturation background of the system. This indicates the feedback temperature at the current sampling time. This indicates the feedback temperature at the previous sampling time. This indicates the real-time operating power of the sensing power supply at the current sampling moment. The preset power safety constant is set to 10 in the preferred embodiment to ensure that the denominator is always positive and to provide a smoothing effect in the low power range.

[0062] In the formula This represents the effective temperature rise response weighted based on thermal inertia. It can sensitively reflect the temperature stagnation characteristics during phase transitions. Once the phase transition region is entered, this difference decreases sharply, directly leading to a decrease in the numerator value. (The denominator in the formula...) This represents the current energy input intensity. The formula divides the effective temperature rise response by the current execution power, essentially calculating the temperature rise efficiency per unit power input. When a high power input corresponds to a very small temperature rise difference, the calculated... The value will decrease significantly, thus mathematically forming a damping signal, indicating that the control system is currently in a phase transition stage characterized by high energy consumption and low temperature rise.

[0063] S4. The preset base heating power is multiplicatively corrected using the phase change damping adjustment factor, and the control weight is adjusted in combination with the logarithmic mapping result of the thermal inertia saturation index. Then, the preset proportional temperature difference compensation between the target welding temperature and the feedback temperature is superimposed to generate a dynamic predicted power command to drive the induction power supply to perform adaptive power output.

[0064] This step is the core of the entire control logic, and its purpose is to transform the physical characteristic parameters obtained in the previous steps into specific electrical signal commands that the induced power supply can execute.

[0065] Specifically, the basic heating power preset for the current brazing tool specification in the process library is first retrieved as the benchmark value for energy output. Then, the phase change damping adjustment factor calculated in step S3 is used as the core correction coefficient to multiply the basic heating power. Since the phase change damping adjustment factor drops sharply during the phase change of the brazing filler metal due to the stagnation of temperature rise, this multiplicative correction mechanism can forcibly compress the benchmark amount of power output when the phase change occurs, thereby avoiding excessive heat input from the source. At the same time, in order to adapt to the difference in heat absorption efficiency of the workpiece under different thermal saturation states, the thermal inertia saturation index calculated in step S2 is introduced. Through nonlinear mapping of the logarithmic function, it is transformed into a smooth denominator weight term to adjust the intensity of the feedforward power. Finally, in order to eliminate model calculation errors and cope with environmental disturbances, a proportional feedback term based on the difference between the target temperature and the real-time feedback temperature is superimposed on the physical feedforward quantity. This linear compensation ensures that the final temperature can accurately converge to the set value.

[0066] Based on the above control logic, the calculation formula for the dynamic predictive power command constructed in this step is as follows:

[0067]

[0068] In the formula, This refers to the final calculated and generated dynamic predicted power command that is sent to the inductive power controller. This indicates the base heating power, which is preset according to the drill bit model and process requirements, providing a reference energy for control. This is the phase change damping adjustment factor calculated in S3. It is the thermal inertia saturation index calculated in step S2. This indicates the preset target welding temperature. This indicates the feedback temperature at the current sampling moment. This represents the proportional feedback adjustment coefficient, which is used to set the sensitivity of temperature difference compensation. This coefficient is usually tuned according to the system response speed.

[0069] In the formula For the physical feedforward control term, the numerator part Using phase change damping adjustment factor Basic heating power Dynamic modulation is implemented to ensure a rapid reduction in energy supply during the sensitive phase of solder melting and endothermic reaction, preventing temperature backflow after phase transformation; denominator part Using the natural logarithm function to calculate the thermal inertia saturation index Smooth scaling is achieved by introducing a constant 2 to ensure that the argument of the logarithmic function is always greater than 1, thus avoiding mathematical singularities such as zero or negative values ​​in the denominator. The role of this denominator term is to adjust the intensity of power output based on the thermal inertia state of the workpiece. As the thermal inertia saturation index changes, the control gain is dynamically adjusted to make the energy input more consistent with the current heat absorption characteristics of the workpiece.

[0070] In the formula This is a proportional feedback compensation term used to handle residual deviations between the set target and the actual state, ensuring the steady-state accuracy of the welding process.

[0071] S5. Monitor the temperature rise curve changes during the welding process in real time, calculate the second derivative of the temperature rise curve to identify the inductive coupling state, and determine that the inductive coupling is unstable and perform safety load reduction protection when the second derivative of the temperature rise curve exceeds the preset sudden change threshold at the current sampling time.

[0072] In actual induction brazing industrial settings, the coupling distance, relative position, and electromagnetic field distribution between the workpiece and the induction coil are often affected by external factors such as mechanical vibration, loose fixtures, or workpiece deformation. This interference can cause sudden changes in induction heating efficiency or non-physical jumps in the infrared temperature measurement signal.

[0073] To ensure the safety and adaptability of the control system, a monitoring logic based on physical constraints runs in parallel while power closed-loop control is executed. The core of this monitoring logic is to track the real-time evolution of the feedback temperature over time and to reflect the acceleration of temperature change by calculating the second derivative of the temperature rise curve. From a physics perspective, the heat conduction of a heated workpiece has inherent thermal inertia; the rate of change of its surface temperature is limited by the specific heat capacity and thermal conductivity of the material, and it cannot experience infinitely large acceleration in a very short time. Therefore, the rate of change of temperature rise, i.e., the second derivative, is a key physical indicator for determining the stability of the inductive coupling state. If an abnormal peak is detected in the second derivative, it means that a signal abrupt change exceeding the physical laws of heat conduction has been detected. Based on this, it can be determined that inductive coupling instability has occurred. This instability may originate from arcing of the induction coil, sensor detachment, or drastic displacement of the workpiece position.

[0074] In this embodiment, the existing three-point difference method is used to solve for the second derivative of the temperature rise curve. After calculating the second derivative of the temperature rise curve at the current sampling time, its absolute value is compared with a preset mutation threshold. Once the preset mutation threshold is exceeded, a safety load reduction protection mechanism is immediately triggered. This protection mechanism includes two synchronous actions: First, the thermal inertia saturation index constructed in step S2 is forcibly reset to its initial state, because the relationship between the historical energy integral and the temperature response has been disrupted, and the original thermal inertia saturation index can no longer accurately reflect the workpiece state; second, the execution power of the induction power supply is forcibly reduced to below a preset safety threshold, such as reducing it to 10% of the rated power or directly shutting down, to prevent overheating of the workpiece surface or equipment damage due to control instability, thereby ensuring the safety of the production process.

[0075] The following combination Figure 2 and Figure 3 The effects of the present invention will be further explained.

[0076] like Figure 2 As shown in the figure, this diagram compares the temperature control performance of the present invention with that of existing conventional control technologies in a two-dimensional feature space composed of execution power and real-time feedback temperature. In the figure, the data points using the present invention are closely distributed near the ideal welding target temperature baseline, exhibiting high convergence and smoothness. In contrast, the discrete data region in the figure, which deviates significantly from the ideal welding target temperature baseline due to the phase change heat sink effect, demonstrates the temperature control response of the conventional control method during the solder phase change stage. It clearly exhibits temperature overshoot and runaway phenomena caused by energy accumulation after latent heat absorption. This comparison intuitively proves that the present invention can effectively eliminate the temperature rise pulse after phase change, locking the welding temperature within the target process range and improving control accuracy.

[0077] like Figure 3 As shown in the figure, this diagram illustrates the intensity distribution of the phase change damping adjustment factor in a multidimensional parameter space comprised of the instantaneous temperature rise rate and the output power of the induction power supply. The figure visually reflects the characteristic intensity under different physical states through changes in the numerical gradient. The specific rectangular area within the low temperature rise rate range is the precisely identified physical inflection point of the solder melting phase change. This distribution diagram demonstrates that the solution of this invention can accurately extract the characteristic region representing phase change heat absorption from the dynamically changing power and temperature data stream. Regardless of the power level, as long as the temperature rise rate abnormally stagnates, the algorithm can lock onto the physical inflection point, thereby providing a reliable adaptive adjustment basis for the control system.

[0078] Specific embodiments of the adaptive control system for welding tool temperature proposed in this invention:

[0079] The adaptive control system for welding tool temperature includes a processor and a memory. The memory stores computer program instructions. When the computer program instructions are executed by the processor, the adaptive control method for welding tool temperature in the above embodiments is implemented.

[0080] The adaptive control system for welding tool temperature also includes other components well known to those skilled in the art, such as communication buses and communication interfaces. Their settings and functions are known in the art and will not be described in detail here.

[0081] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.

Claims

1. A method for adaptive control of welding temperature of brazing tools, characterized in that, include: The feedback temperature of the welding area of ​​the brazing tool and the execution power of the induction power supply are collected in real time, and the feedback temperature and execution power are aligned with the execution time according to the sampling period. A thermal inertia saturation index is constructed based on the nonlinear mapping relationship between the square of the feedback temperature and the cumulative execution energy from the start of heating to the current sampling time, in order to evaluate the real-time energy storage state of the drill bit as a heated body and the dynamic decay of its heat absorption capacity. The thermal inertia saturation index is coupled with the temperature rise difference between the feedback temperature and the adjacent sampling time to obtain the phase change damping adjustment factor, which is used to characterize the resistance strength of the energy absorption trap generated by the brazing filler metal in the melting phase change stage to the temperature rise trend. The phase change damping adjustment factor is used to perform multiplicative gain correction on the preset base heating power, and the control weight is adjusted in combination with the logarithmic mapping result of the thermal inertia saturation index. Then, the preset proportional temperature difference compensation between the target welding temperature and the feedback temperature is superimposed to generate a dynamic predicted power command to drive the induction power supply to perform adaptive power output. The temperature rise curve changes during the welding process are monitored in real time, and the second derivative of the temperature rise curve is calculated to identify the inductive coupling state. When the second derivative of the temperature rise curve exceeds the preset sudden change threshold at the current sampling time, it is determined that the inductive coupling is unstable and safety load reduction protection is implemented.

2. The adaptive control method for welding temperature of the brazing tool according to claim 1, characterized in that, The formula for calculating the thermal inertia saturation index is as follows: in, Indicates the thermal inertia saturation index. This indicates the feedback temperature at the current sampling time. Indicates the sampling period. This indicates the total number of sampling periods from the start of heating to the current sampling time. This indicates the execution power at a historical moment. This represents the system bias constant.

3. The adaptive control method for welding temperature of the brazing tool according to claim 2, characterized in that, The formula for calculating the phase change damping adjustment factor is as follows: in, This represents the phase change damping adjustment factor. This indicates the feedback temperature at the previous sampling time. This indicates the execution power at the current sampling time. This represents the power safety constant.

4. The adaptive control method for welding temperature of the brazing tool according to claim 3, characterized in that, The calculation formula for the dynamic prediction power command is as follows: in, This indicates the final dynamically predicted power command issued. Indicates the basic heating power. This indicates the target welding temperature. This represents the proportional feedback adjustment coefficient.

5. The adaptive control method for welding temperature of the brazing tool according to claim 1, characterized in that, The specific method for collecting the feedback temperature is as follows: the surface radiation signal of the welding area of ​​the brazing tool is obtained by a dual-color infrared thermometer installed above the induction coil, and the real-time temperature data that is not affected by the workpiece emissivity fluctuation is calculated based on the intensity ratio of the dual-channel wavelengths.

6. The adaptive control method for welding temperature of the brazing tool according to claim 1, characterized in that, The cumulative execution energy is determined by summing the product of the execution power and the sampling period for each sampling cycle after heating is started, in order to reflect the total heat absorbed by the workpiece.

7. The adaptive control method for welding temperature of the brazing tool according to claim 1, characterized in that, The calculation of the phase change damping adjustment factor also includes a boundary constraint on the temperature rise difference: if the feedback temperature at the current sampling time is less than the feedback temperature at the previous sampling time, the temperature rise difference is recorded as 0.

8. The adaptive control method for welding temperature of the brazing tool according to claim 1, characterized in that, The safety load reduction protection includes: resetting the thermal inertia saturation index to its initial state and forcibly reducing the execution power of the inductive power supply to below a safe threshold.

9. The adaptive control method for welding temperature of the brazing tool according to claim 1, characterized in that, The specific method of execution time alignment processing is as follows: the feedback temperature obtained at the current sampling time is associated with the execution power at the previous sampling time to match the physical causal logic between the power input and the temperature rise feedback.

10. An adaptive control system for welding temperature of brazing tools, characterized in that, It includes a processor and a memory, the memory storing computer program instructions, which, when executed by the processor, implement the adaptive control method for welding temperature of the brazing tool as described in any one of claims 1-9.

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