Intelligent optimization method and system for technological parameters of galvanized steel sheet

Abstract

The invention provides an intelligent optimization method and system for technological parameters of a galvanized steel sheet. The method comprises the following steps: performing thickness detection on different areas of a plating layer formed on the surface of a galvanized steel sheet to obtain final plating layer thickness distribution data. And according to the temperature distribution data and the final coating thickness distribution data, heat conduction efficiency correction parameters of all the heating areas of the molten zinc tank are generated. According to the heat conduction efficiency correction parameters of all the heating areas of the molten zinc tank, a pre-trained first process parameter intelligent optimization model is adopted to optimize galvanization time and temperature distribution data, and a pre-trained second process parameter intelligent optimization model is adopted to optimize electrode pressure parameters and resistance spot welding parameters in the welding stage; the mass estimation value of the galvanized steel sheet is greater than a preset mass threshold value. According to the technical scheme, the galvanizing process and the welding parameters are intelligently optimized through double models, the production line is dynamically adjusted based on real-time data, and it is ensured that the quality of the galvanized steel sheet stably reaches the standard.

Description

Technical Field

[0001] The present application relates to the technical field of galvanized steel sheet production, and in particular to an intelligent optimization method and system for process parameters of galvanized steel sheets. Background Art

[0002] In the industrial production of continuously galvanized steel sheets, the thermal conductivity efficiency of the zinc bath and the process parameters directly determine the uniformity of the coating and the quality of the weld. Current production lines require precise control of the galvanizing time, the temperature distribution in the heating zone, and the welding parameters to achieve dynamic stability in the coating thickness and consistency in the mechanical properties of the galvanized steel sheets. Because coating formation involves the coupling of multiple physical fields and the complex process windows during the welding phase, a multi-parameter collaborative optimization method is urgently needed to overcome the overall quality fluctuations caused by local parameter adjustments while reducing energy consumption and trial-and-error costs.

[0003] Existing technology establishes a dynamic model of zinc bath heat conduction to simulate the spatial impact of heating zone temperature on coating growth rate. It then employs a genetic algorithm to optimize the galvanizing time and target temperature range. The model uses historical production data to calibrate thermal boundary conditions and solves the temperature field distribution in real time using a finite difference method. Ultimately, it recommends adjustments to local temperature control parameters to improve coating uniformity.

[0004] Existing technologies suffer from low computational efficiency, making it difficult to meet the real-time decision-making requirements of continuous production lines. Their heat conduction models lack deep integration with the zinc melt flow state and element diffusion effects experienced in actual production, leading to cumulative temperature field prediction errors over time. Single-objective optimization strategies focus solely on coating consistency indicators, failing to form a closed-loop synergy with welding process parameters. Consequently, optimized temperature control schemes can degrade weld interface strength or introduce intergranular brittle phases, ultimately creating bottlenecks in improving overall quality. Summary of the Invention

[0005] The present application provides an intelligent optimization method and system for the process parameters of galvanized steel sheets, which are used to solve the problem of poor quality stability of galvanized steel sheets caused by low computing efficiency, lack of multi-physical field coupling and insufficient coordination of multiple process parameters in the prior art.

[0006] In a first aspect, the present application provides an intelligent optimization method for process parameters of a galvanized steel sheet, comprising: obtaining first galvanizing time and temperature distribution data of different heating zones in a zinc bath in a galvanized steel sheet production process;

[0007] Conduct thickness testing on different areas of the coating formed on the surface of the galvanized steel sheet to obtain the final coating thickness distribution data;

[0008] generating heat conduction efficiency correction parameters for each heating zone of the zinc bath according to the temperature distribution data and the final coating thickness distribution data;

[0009] According to the heat conduction efficiency correction parameters of all heating zones in the zinc liquid tank, a pre-trained first process parameter intelligent optimization model is used to optimize the galvanizing time and temperature distribution data, and a pre-trained second process parameter intelligent optimization model is used to optimize the electrode pressure parameters and resistance spot welding parameters in the welding stage, so that the quality estimation value of the galvanized steel sheet is greater than the preset quality threshold.

[0010] Optionally, the method of modifying the heat conduction efficiency parameters of all heating zones in the zinc bath and optimizing the galvanizing time and temperature distribution data using a pre-trained first process parameter intelligent optimization model includes:

[0011] According to the heat conduction efficiency correction parameters of all heating zones of the zinc liquid tank, temperature gradient parameters, heat conduction response time and dynamic heat balance index are calculated respectively to generate a first parameter set for characterizing the thermal field stability of the zinc liquid tank;

[0012] Inputting the first parameter set into the first process parameter intelligent optimization model, and generating a galvanizing time correction value and a temperature distribution compensation vector through a heat conduction dynamic response function predefined in the first process parameter intelligent optimization model;

[0013] The galvanizing time is optimized according to the galvanizing time correction amount to obtain a second galvanizing time, and the power distribution ratio of each heating zone of the zinc liquid tank is dynamically adjusted according to the temperature distribution compensation vector to optimize the temperature distribution data.

[0014] Optionally, the optimizing the electrode pressure parameters and the resistance spot welding parameters in the welding stage by using the pre-trained second process parameter intelligent optimization model so that the quality estimation value of the galvanized steel sheet is greater than a preset quality threshold comprises:

[0015] Determine the welding area of ​​the galvanized steel sheet, and select the heat conduction efficiency correction parameter of the target heating area from the heat conduction efficiency correction parameters of all heating areas of the zinc bath, wherein the target heating area is the area in the zinc bath corresponding to the welding area in the galvanized steel sheet production process;

[0016] Calculating the temperature fluctuation amplitude, electrode pressure deviation, and resistance spot welding fluctuation coefficient according to the heat conduction efficiency correction parameter of the corresponding heating zone to generate a second parameter set for characterizing the heat influence effect of the welding zone;

[0017] Inputting the second parameter set into the second process parameter intelligent optimization model, and generating an electrode pressure correction value and a resistance spot welding parameter compensation vector using a welding dynamic compensation function predefined in the second process parameter intelligent optimization model;

[0018] The electrode pressure parameters in the welding stage are optimized according to the electrode pressure correction amount, and the welding pulse duration and interval time are dynamically adjusted according to the resistance spot welding parameter compensation vector to optimize the resistance spot welding parameters so that the quality estimation value of the galvanized steel sheet is greater than the preset quality threshold.

[0019] Optionally, inputting the first parameter set into the first process parameter intelligent optimization model, and generating a galvanizing time correction value and a temperature distribution compensation vector through a heat conduction dynamic response function predefined in the first process parameter intelligent optimization model, includes:

[0020] Inputting the first parameter set into the first process parameter intelligent optimization model, the first process parameter intelligent optimization model uses the ratio of the axial component of the temperature gradient parameter in the first parameter set to the maximum allowable axial temperature gradient of the zinc liquid tank as a target ratio, combining the target ratio with the heat conduction response time to obtain an axial gradient weight factor, and calculating the radial offset attenuation coefficient using a first exponential attenuation model based on the dynamic thermal balance index and the temperature fluctuation coefficient in the first parameter set;

[0021] Constructing a dynamic thermal field characteristic vector composed of the axial gradient weight factor and the radial offset attenuation coefficient;

[0022] According to the dynamic thermal field characteristic vector and the galvanizing time fluctuation coefficient, the galvanizing time correction amount and the temperature distribution compensation vector are calculated through the heat conduction dynamic response function.

[0023] Optionally, inputting the second parameter set into the second process parameter intelligent optimization model, and generating an electrode pressure correction amount and a resistance spot welding parameter compensation vector using a welding dynamic compensation function predefined in the second process parameter intelligent optimization model, includes:

[0024] The second parameter set is input into the second process parameter intelligent optimization model. The second process parameter intelligent optimization model obtains an axial gradient dynamic weight factor using a weight factor calculation formula based on the temperature fluctuation amplitude and the axial component of the temperature gradient parameter in the second parameter set. The impedance change attenuation coefficient is calculated using a second exponential decay model based on the electrode pressure deviation and the resistance spot welding fluctuation coefficient in the second parameter set.

[0025] Constructing a welding heat affected characteristic vector composed of the axial gradient dynamic weight factor and the impedance change attenuation coefficient;

[0026] According to the welding heat influence characteristic vector and the resistance spot welding fluctuation amplitude, the electrode pressure correction amount and the resistance spot welding parameter compensation vector are calculated through the welding dynamic compensation function.

[0027] Optionally, the thickness detection of different areas of the coating formed on the surface of the galvanized steel sheet to obtain final coating thickness distribution data includes:

[0028] Divide the surface of the galvanized steel sheet into a number of inspection areas according to a preset process grid, wherein the surface of the galvanized steel sheet is the coating;

[0029] The spectrum acquisition module of each detection area is triggered by the multi-spectral sensor array to obtain the original data of the spectral reflection intensity of each detection area;

[0030] Based on the original data of the spectral reflection intensity of each detection area, dynamically calibrate the coating thickness of each detection area to generate initial coating thickness distribution data;

[0031] Interpolating and correcting thickness jump abnormal points in the initial coating thickness distribution data by using the reflection intensity difference gradient between adjacent detection areas to generate corrected coating thickness distribution data;

[0032] Performing a regional consistency check on the corrected coating thickness distribution data to obtain checked coating thickness data;

[0033] The verified coating thickness data is spatially superimposed with the original data of spectral reflection intensity of all detection areas to generate final coating thickness distribution data.

[0034] Optionally, generating a heat conduction efficiency correction parameter for each heating zone of the zinc bath according to the temperature distribution data and the final coating thickness distribution data includes:

[0035] Dividing the temperature distribution data into a temperature gradient feature set according to the axial and radial distributions of the zinc bath, and dividing the final coating thickness distribution data into a thickness difference feature set according to a preset process grid;

[0036] constructing a dynamic heat conduction feature vector consisting of an axial gradient weight factor and a thickness standard deviation compensation coefficient based on the axial temperature gradient mean corresponding to the temperature gradient feature set and the regional thickness standard deviation corresponding to the thickness difference feature set;

[0037] The dynamic heat conduction characteristic vector is input into a predefined heat conduction efficiency correction function, and a heat conduction efficiency correction parameter is generated through the exponential decay term of the radial temperature offset amplitude and the piecewise linear correction term of the thermal field fluctuation coefficient in the slot in the heat conduction efficiency correction function.

[0038] In a second aspect, the present application provides an intelligent optimization system for process parameters of galvanized steel sheets, comprising:

[0039] An acquisition module is used to obtain the first galvanizing time and the temperature distribution data of different heating zones in the zinc bath during the galvanized steel sheet production process;

[0040] The detection module is used to detect the thickness of different areas of the coating formed on the surface of the galvanized steel sheet to obtain the final coating thickness distribution data;

[0041] a calculation module, configured to generate a heat conduction efficiency correction parameter for each heating zone of the zinc bath according to the temperature distribution data and the final coating thickness distribution data;

[0042] An optimization module is used to correct the parameters according to the heat conduction efficiency of all heating zones of the zinc liquid tank, optimize the galvanizing time and the temperature distribution data using a pre-trained first process parameter intelligent optimization model, and optimize the electrode pressure parameters and resistance spot welding parameters in the welding stage using a pre-trained second process parameter intelligent optimization model, so that the quality estimate of the galvanized steel sheet is greater than a preset quality threshold.

[0043] In a third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an intelligent optimization method for galvanized steel sheet process parameters as described in any one of the first aspects.

[0044] In a fourth aspect, the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements an intelligent optimization method for process parameters of a galvanized steel sheet as described in any one of the first aspects.

[0045] In the present application, an intelligent optimization method for process parameters of galvanized steel sheets is provided, the method comprising: obtaining the first galvanizing time and the temperature distribution data of different heating zones in the zinc liquid tank in the galvanized steel sheet production process; performing thickness detection on different areas of the coating formed on the surface of the galvanized steel sheet to obtain final coating thickness distribution data; generating a heat conduction efficiency correction parameter for each heating zone of the zinc liquid tank based on the temperature distribution data and the final coating thickness distribution data; optimizing the galvanizing time and the temperature distribution data using a pre-trained first process parameter intelligent optimization model based on the heat conduction efficiency correction parameter of all heating zones of the zinc liquid tank, and optimizing the electrode pressure parameters and resistance spot welding parameters in the welding stage using a pre-trained second process parameter intelligent optimization model, so that the quality estimate of the galvanized steel sheet is greater than a preset quality threshold.

[0046] This application constructs a digital mapping of the entire production process by real-time collection of galvanizing time, temperature distribution data of multiple heating zones in the zinc bath, and coating thickness distribution data, realizes dynamic monitoring of process status and abnormal location, overcomes the lag and local defects of traditional manual sampling, and improves the spatiotemporal resolution and process transparency of data collection. Based on the temperature-coating thickness coupling relationship, the actual heat conduction efficiency of each heating zone is inferred, and dynamic correction parameters are generated to break through the limitations of traditional static heat conduction models. By integrating multiple physical field effects such as heat transfer, flow, and phase change, the temperature field prediction accuracy is improved, and the mismatch problem between the coating growth rate and temperature distribution is reduced. A dual intelligent optimization model is used to achieve cross-process segment parameter coordination to resolve the coupling conflict between coating uniformity and welding interface strength. Through the multi-objective optimization strategy of quality estimation, the coating adhesion, corrosion resistance and weld joint strength are simultaneously improved, so that the comprehensive quality index stably exceeds the preset threshold, while reducing zinc consumption and energy consumption. Based on data-driven heat conduction correction parameters and model optimization results, a "perception-analysis-decision-execution" closed-loop control is formed to dynamically adapt to disturbance factors such as zinc liquid composition fluctuations and equipment aging, reduce the frequency of manual intervention, and improve the stability of continuous operation of the production line and the robustness of the process.

[0047] Furthermore, based on the thermal conduction efficiency correction parameters of each heating zone in the zinc bath, the first model calculates the temperature gradient, thermal conduction response time, and dynamic thermal balance index, generating a thermal field stability parameter set. The thermal conduction dynamic response function is then used to optimize the galvanizing time and temperature distribution. Simultaneously, for the target heating zone associated with the welding zone, the second model analyzes temperature fluctuations, electrode pressure deviations, and resistance spot welding fluctuation coefficients to generate a thermal influence effect parameter set. This is combined with the welding dynamic compensation function to optimize electrode pressure and welding pulse timing, achieving joint regulation of coating and welding quality. Through the collaborative optimization of the dual models of thermal field stability and welding thermal effect, the galvanizing process parameters and welding parameters are dynamically corrected, improving the uniformity of zinc bath heat conduction and the accuracy of weld zone thermal balance. This ensures the consistency of the galvanized layer thickness and reduces welding defects, ensuring that the quality estimate consistently exceeds the preset threshold.

[0048] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1A flow chart of an intelligent optimization method for process parameters of galvanized steel sheets provided in an embodiment of the present application;

[0051] Figure 2 A schematic diagram of the structure of an intelligent optimization system for process parameters of galvanized steel sheets provided in an embodiment of the present application;

[0052] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0054] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 11, 12, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0055] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0056] In order to solve the problem of poor quality stability of galvanized steel sheets in the prior art due to low computing efficiency, lack of multi-physical field coupling and insufficient coordination of multiple process parameters, the embodiment of the present application provides an intelligent optimization method for process parameters of galvanized steel sheets. The overall concept of the method is as follows: by real-time collection of temperature spatiotemporal distribution data and coating thickness distribution data of multiple heating zones of the zinc liquid tank during the galvanizing stage, a mapping relationship between thermodynamic parameters and coating quality results is established; based on a data-driven method, the influence of temperature distribution on coating thickness uniformity is analyzed, and dynamic correction parameters of heat conduction efficiency of each heating zone are generated to compensate for process fluctuations; the corrected thermal parameters and galvanizing time are then input into the first process optimization model, and the optimal process parameter combination is iteratively solved; at the same time, the electrode pressure, current waveform and other parameters in the welding stage are collaboratively adjusted through the second process optimization model, ultimately achieving dual protection of coating quality and welding strength, and ensuring that quality evaluation indicators exceed preset thresholds.

[0057] Figure 1 A flow chart of an intelligent optimization method for process parameters of galvanized steel sheets provided in an embodiment of the present application is shown as follows: Figure 1 As shown, the method includes:

[0058] S11. In the galvanized steel sheet production process, obtain the first galvanizing time and the temperature distribution data of different heating zones in the zinc bath.

[0059] The galvanized steel sheet can be simply referred to as the steel sheet. The first galvanizing time is the initial duration the steel sheet is immersed in the zinc bath, forming the base coating. The zinc bath temperature distribution data is the real-time temperature values ​​of different heating zones within the zinc bath collected by sensors, used to characterize thermal field uniformity.

[0060] In the embodiment of the present application, the time parameter is automatically recorded by the timing module of the production control system. The time parameter starts from the trigger signal of the steel plate immersing in the zinc liquid tank and ends when the exit signal of the steel plate leaving the zinc liquid tank is cut off. The value is directly derived from the process timing database of the programmable logic controller or distributed control system; and the temperature distribution data of different heating zones in the zinc liquid tank are collected in real time by thermocouple arrays or infrared thermal imagers embedded in each partition of the tank body.

[0061] S12. Detect the thickness of different areas of the coating formed on the surface of the galvanized steel sheet to obtain final coating thickness distribution data.

[0062] The final coating thickness distribution data is the coating thickness value of each area on the surface of the galvanized steel sheet. In the embodiment of the present application, the thickness detection process is as described in the following steps 121 to 126, which will not be repeated here.

[0063] S13. Generate heat conduction efficiency correction parameters for each heating zone of the zinc bath based on the temperature distribution data and the final coating thickness distribution data.

[0064] Among them, the heat conduction efficiency correction parameter is a parameter calculated based on the temperature distribution data and the final coating thickness distribution data. The parameter can be a temperature gradient compensation coefficient, a thermal field balance factor, etc., which is used to adjust the heat output efficiency of the heating zone.

[0065] In the embodiment of the present application, according to the temperature distribution data and the final coating thickness distribution data, based on the axial temperature gradient mean and the regional thickness standard deviation, the heat conduction efficiency correction parameters of each heating zone of the zinc liquid tank are generated. The specific description is as described in steps 131 to 133 and will not be repeated here.

[0066] S14. According to the heat conduction efficiency correction parameters of all heating zones in the zinc liquid tank, a pre-trained first process parameter intelligent optimization model is used to optimize the galvanizing time and temperature distribution data, and a pre-trained second process parameter intelligent optimization model is used to optimize the electrode pressure parameters and resistance spot welding parameters in the welding stage, so that the quality estimation value of the galvanized steel sheet is greater than the preset quality threshold.

[0067] Among them, the first process parameter intelligent optimization model can be a zinc liquid flow-heat transfer coupling model, specifically a machine learning model based on the dynamic response function of heat conduction, which outputs the galvanizing time and temperature compensation values. The second process parameter intelligent optimization model can be an electrode-coating contact mechanics model, specifically a deep learning model based on the welding heat influence effect, which optimizes the electrode pressure parameters and spot welding pulse parameters. Specifically, the first process parameter intelligent optimization model is a time series prediction model based on neural network training, with the input layer being the temperature gradient parameter, heat conduction response time and dynamic thermal balance index, and the output layer being the galvanizing time correction value and temperature distribution compensation vector. The second process parameter intelligent optimization model is a decision model based on reinforcement learning, using the Q-learning algorithm, and the reward function is the difference between the quality estimate and the preset threshold.

[0068] In an embodiment of the present application, the process of optimizing the galvanizing process by the first process parameter intelligent optimization model is as described in steps a1 to a3, and the process of optimizing the welding parameters by the second process parameter intelligent optimization model is as described in steps b1 to b4, which will not be repeated here.

[0069] The following is a specific example: An automotive steel sheet manufacturer produces galvanized door panels and needs to address issues such as excessive edge coating thickness and poor welds. Initially, the initial galvanizing time is set to 22 seconds. Six temperature sensors deployed in the zinc bath collect real-time temperature distribution data for each heating zone. The temperature of the target heating zone (e.g., heating zone D) of 635°C is detected, lower than the preset value of 650°C. X-ray thickness measurement reveals a coating thickness of 25μm at the edges (target 20μm) and 18μm in the middle (target 20μm). This indicates insufficient heat conduction in zone D, leading to poor zinc fluidity. Based on the temperature distribution data and the final coating thickness distribution data, it is calculated that the thermal conductivity efficiency of zone D needs to be increased by 12%. A thermal conductivity efficiency correction parameter is generated to achieve a power improvement factor of 1.12. The first process parameter intelligent optimization model optimizes the galvanizing process. Specifically, after inputting the thermal conductivity efficiency correction parameter for heating zone D, the first process parameter intelligent optimization model calculates a galvanizing time correction of -3 seconds, reducing the galvanizing time from 22 seconds to 19 seconds, and dynamically adjusts the power allocation ratio for heating zone D. After execution, the coating thickness in the edge area was reduced, and the standard deviation was lowered. The second process parameter intelligent optimization model optimized the welding parameters. Specifically, it determined that the target heating zone corresponding to the welding area was heating zone D. Based on the corrected temperature distribution data, the second process parameter adjusted the electrode pressure from 400N to 430N and extended the welding pulse from 12ms to 15ms, improving weld strength and reducing the false weld rate.

[0070] By executing S11 to S14, the embodiment of the present application collects temperature and coating data in real time, dynamically generates heat conduction correction parameters, and uses dual intelligent models to collaboratively optimize the galvanizing process (time, temperature, power) and welding parameters, thereby achieving simultaneous improvement in coating thickness uniformity and welding strength, ensuring that the quality of galvanized steel sheets stably exceeds the preset threshold, and reducing rework rate and production costs.

[0071] In one possible embodiment, S14, according to the heat conduction efficiency correction parameters of all heating zones in the zinc bath, optimizes the galvanizing time and temperature distribution data using a pre-trained first process parameter intelligent optimization model, including:

[0072] Step a1: Calculate the temperature gradient parameter, heat conduction response time and dynamic heat balance index respectively according to the heat conduction efficiency correction parameters of all heating zones of the zinc liquid tank to generate a first parameter set for characterizing the thermal field stability of the zinc liquid tank.

[0073] The temperature gradient parameter can be the average temperature difference between adjacent heating zones, which is used to characterize the uniformity of lateral heat conduction within the zinc bath. The heat conduction response time is the average time it takes for the temperature in the heating zone to recover from a fluctuating state to the set value, reflecting the dynamic regulation capability of the heating system. The dynamic thermal balance index is a normalized indicator calculated based on the temperature fluctuation amplitude and recovery rate, indicating the thermal field's ability to resist disturbances. A higher value indicates a stronger resistance to disturbances.

[0074] In the embodiment of the present application, based on the heat conduction efficiency correction parameters of each heating zone of the zinc liquid tank, the temperature gradient parameters are calculated by finite element thermal field simulation or three-dimensional space interpolation algorithm to calculate the temperature change rate between adjacent heating zones. The formula is: Where ΔT is the temperature difference between adjacent heating zones, Δx is the distance between heating zones, Correction parameters for heat conduction efficiency; heat conduction response time is fitted by transfer function analysis or transient thermal response experimental data, and the unsteady heat conduction equation is solved. Extract the characteristic time constant of the system step response, where ρ is the density of zinc liquid, c p is the specific heat capacity, k is the thermal conductivity, and Q is the heat source term; the dynamic thermal balance index constructs a state space model based on the law of conservation of energy, and uses the Kalman filter or recursive least squares method to calculate the dynamic ratio of heating power to heat loss in real time. Finally, the first parameter set of the indicator is generated through multi-parameter fusion technology to quantify the thermal field stability.

[0075] Step a2: input the first parameter set into the first process parameter intelligent optimization model, and generate the galvanizing time correction value and the temperature distribution compensation vector through the heat conduction dynamic response function predefined in the first process parameter intelligent optimization model.

[0076] The heat conduction dynamic response function is a time series prediction model based on neural network training. Its input is the first parameter set, and its output is the galvanizing time correction value and the temperature compensation vector. The temperature distribution compensation vector adjusts the temperature set value of each heating zone and the power distribution weight.

[0077] And the heat conduction dynamic response function formula is:

[0078] f LSTM (v t , δ T )=[ΔT,Δτ]=Decoder(LSTM(x t )), where v t is the dynamic thermal field eigenvector, δ T is the galvanizing time fluctuation coefficient, ΔT is the galvanizing time correction, Δτ is the temperature distribution compensation vector, Decoder is the fully connected layer, LSTM(x t ) is hidden.

[0079] The implementation process of step a2 is as described in steps a21 to a23 below and will not be repeated here.

[0080] Step a3: Optimize the galvanizing time according to the galvanizing time correction amount to obtain a second galvanizing time, and dynamically adjust the power distribution ratio of each heating zone of the zinc bath according to the temperature distribution compensation vector to optimize the temperature distribution data.

[0081] The second galvanizing time is the actual process execution time after the correction, which is 16.8 seconds. The power allocation ratio is the percentage of the total system power in each heating zone of the zinc bath, which is dynamically allocated according to the compensation vector.

[0082] In the embodiment of the present application, for the optimization of the galvanizing time, a reinforcement learning algorithm or model predictive control based on timing constraints is adopted. By establishing a coating thickness-time response model, the heat conduction efficiency correction parameter is used as input, and the coating uniformity is taken as the optimization target. The shortest second galvanizing time that meets the quality threshold is iteratively solved. Its mathematical expression is: , where Δt is the time correction, f is the optimization function, t new is the optimized second galvanizing time, t initial is the initial first galvanizing time, is a multi-parameter coupling function, η is the dynamic thermal balance index; for dynamic adjustment of temperature distribution, the temperature distribution compensation vector is used to drive the distributed collaborative control algorithm, combined with adaptive sliding mode control technology, to calculate the power allocation weight of each heating zone in real time, and send it to the heater actuator through the industrial Internet of Things platform to achieve closed-loop balanced regulation of the temperature field of the entire zinc liquid tank.

[0083] Continuing with the above example, in the production scenario of galvanized door panels in an automotive steel plate plant, based on the correction parameter that the heat conduction efficiency of heating zone D needs to be improved by 12%, combined with the heat conduction data of the other five heating zones, the temperature gradient parameters are calculated through finite element thermal field simulation, the response time of heating zone D is fitted using the unsteady-state heat conduction equation, and the dynamic thermal balance index is calculated in real time through Kalman filtering to generate a first parameter set that characterizes the stability of the thermal field. The parameters are input into the first process optimization model. The model's built-in heat conduction dynamic response function is combined with the coating thickness deviation to output a galvanizing time correction of -3 seconds and a temperature distribution compensation vector. The galvanizing time is shortened from 22 seconds to 19 seconds. At the same time, the power of each heating zone is dynamically adjusted through a distributed collaborative control algorithm. After optimization, the standard deviation of the temperature distribution is reduced from 9.2°C to 4.5°C, and the edge coating thickness is reduced to 21μm. The second model is simultaneously triggered to adjust the welding parameters, ultimately achieving a synergistic improvement in coating uniformity and weld strength.

[0084] By executing steps a1 to a3, the embodiment of the present application realizes the coordinated optimization of the thermal field stability of the zinc liquid tank and the coating quality: the precise calculation of the temperature gradient parameters and the dynamic thermal balance index supports the scientific decision-making of the galvanizing time correction amount, the power distribution ratio is dynamically adjusted to reduce the excessive thickness of the edge coating, the galvanizing time is shortened to improve production efficiency, and ultimately ensures that the coating uniformity, adhesion and welding area quality of the galvanized steel plate stably reach the preset threshold.

[0085] In one possible embodiment, S14, using a pre-trained second process parameter intelligent optimization model to optimize electrode pressure parameters and resistance spot welding parameters in the welding stage so that the quality estimate of the galvanized steel sheet is greater than a preset quality threshold, includes:

[0086] Step b1, determine the welding area of ​​the galvanized steel sheet, and select the heat conduction efficiency correction parameter of the target heating area from the heat conduction efficiency correction parameters of all heating areas of the zinc liquid tank. The target heating area is the area corresponding to the welding area in the zinc liquid tank during the galvanized steel sheet production process.

[0087] The welding zone is the designated area of ​​the galvanized steel sheet where resistance spot welding is performed, such as the weld point on the edge of a car door panel. The target heating zone is the heating zone number corresponding to the welding zone when the welding zone is immersed in the zinc bath during the galvanizing stage.

[0088] In an embodiment of the present application, the welding area is determined by mapping the physical position of the galvanized steel plate. Specifically, the coordinates of the welding area on the steel plate are matched with the spatial distribution of the heating zone of the zinc liquid tank to select the target heating area. Then, the parameters of the target heating area are extracted from the thermal conductivity efficiency correction parameters of all heating zones of the zinc liquid tank. This process relies on the regional positioning module and data screening algorithm of the programmable logic controller system or distributed control system.

[0089] Step b2: Calculate the temperature fluctuation amplitude, electrode pressure deviation, and resistance spot welding fluctuation coefficient based on the corresponding heat conduction efficiency correction parameter of the heating zone to generate a second parameter set for characterizing the heat influence effect of the welding zone.

[0090] The temperature fluctuation amplitude is the standard deviation of the target heating zone's temperature during the galvanizing phase, reflecting the stability of heat conduction. The electrode pressure deviation is the percentage deviation between the actual electrode pressure during welding and the theoretical value. The resistance spot welding fluctuation coefficient is the fluctuation rate of the welding current / voltage, which is caused by uneven heat conduction. The second parameter set includes the weld zone temperature fluctuation amplitude, electrode pressure deviation, and resistance spot welding fluctuation coefficient.

[0091] In an embodiment of the present application, based on the thermal conduction efficiency correction parameter of the target heating zone, the temperature fluctuation amplitude is calculated by a sliding window algorithm, the electrode pressure deviation is calculated in combination with historical electrode pressure data, and the resistance spot welding fluctuation coefficient is calculated using real-time resistance spot welding data, and finally a second parameter set is generated by multi-source data fusion; wherein the temperature fluctuation amplitude and the resistance spot welding fluctuation coefficient are calculated using a time series analysis method, and the electrode pressure deviation is calculated using a multivariate regression model.

[0092] Step b3: input the second parameter set into the second process parameter intelligent optimization model, and generate the electrode pressure correction value and the resistance spot welding parameter compensation vector through the welding dynamic compensation function predefined in the second process parameter intelligent optimization model.

[0093] The welding dynamic compensation function is a decision model based on reinforcement learning training, whose input is the second parameter set and output is the electrode pressure correction value and the spot welding pulse timing compensation vector. Optionally, the welding dynamic compensation function formula is:

[0094] s=[σ T , ΔF dev , η weld ] T , where σ T is the temperature fluctuation amplitude, ΔF dev is the electrode pressure deviation, η weld In addition, the specific implementation of step b3 can refer to the following steps b31 to b33, which will not be repeated here.

[0095] Step b4: Optimize the electrode pressure parameters in the welding stage according to the electrode pressure correction amount, and dynamically adjust the welding pulse duration and interval time according to the resistance spot welding parameter compensation vector to optimize the resistance spot welding parameters so that the quality estimation value of the galvanized steel sheet is greater than the preset quality threshold.

[0096] Among them, the resistance spot welding parameter compensation vector is the adjustment amount of welding pulse duration and interval time.

[0097] In an embodiment of the present application, the electrode pressure parameters in the welding stage are adjusted according to the electrode pressure correction amount, and the duration and interval time of the welding pulse are dynamically adjusted through the timing control module according to the resistance spot welding parameter compensation vector; this process ensures that the welding energy input matches the coating characteristics after the heat conduction efficiency is corrected through real-time feedback control, and ultimately the quality estimate of the galvanized steel plate exceeds the preset threshold.

[0098] Continuing with the above example, in the production scenario of galvanized door panels in an automotive steel sheet plant, the target heating zone is determined to be heating zone D of the zinc bath based on the welding zone position mapping relationship. Based on the heat conduction efficiency correction parameter of zone D, the temperature fluctuation amplitude is calculated as (655°C - 640°C) ÷ 647°C ≈ 2.3%, the electrode pressure deviation is ÷ 430N ≈ -7%, and the resistance spot welding fluctuation coefficient is calculated as the welding current standard deviation 15A ÷ mean 120A = 12.5%. This generates a second parameter set. This second parameter set is input into the second process parameter intelligent optimization model, and the welding dynamic compensation function outputs an electrode pressure correction of +30N and a resistance spot welding parameter compensation vector. After optimization, the electrode pressure is adjusted from 400N to 430N, the welding pulse is extended from 12ms to 15ms, the weld strength is increased from 350MPa to 390MPa, and the cold weld rate is reduced from 5% to 1.5%. At the same time, the improved coating uniformity further reduces the volatilization of the zinc layer in the weld heat-affected zone, achieving full quality compliance of the galvanized door panels.

[0099] By executing steps b1 to b4, the embodiment of the present application realizes the coordinated optimization of welding parameters and galvanizing process: the thermal conductivity efficiency correction parameters of the target heating zone drive the dynamic compensation of welding, and the precise adjustment of the electrode pressure and welding pulse can reduce the cold weld rate, while the uniformity of the coating is improved, and ultimately the comprehensive quality indicators of the galvanized steel sheet are stably above the preset threshold.

[0100] In a possible embodiment, step a2, inputting the first parameter set into the first process parameter intelligent optimization model, and generating the galvanizing time correction value and the temperature distribution compensation vector through the heat conduction dynamic response function predefined in the first process parameter intelligent optimization model, includes:

[0101] Step a21. Input the first parameter set into the first process parameter intelligent optimization model. The first process parameter intelligent optimization model uses the ratio of the axial component of the temperature gradient parameter in the first parameter set to the maximum allowable axial temperature gradient of the zinc liquid tank as the target ratio, combines the target ratio with the heat conduction response time, and obtains the axial gradient weight factor. According to the dynamic thermal balance index and temperature fluctuation coefficient in the first parameter set, the radial offset attenuation coefficient is calculated using the first exponential attenuation model.

[0102] Among them, the axial component of the temperature gradient parameter is the temperature gradient value of the adjacent heating zone along the length direction (axial direction) of the zinc liquid tank. The maximum allowable axial temperature gradient of the zinc liquid tank is the axial gradient threshold defined by the process specification. The target ratio is the ratio of the axial component to the maximum allowable gradient. The axial gradient weight factor is a normalized parameter that combines the target ratio and the heat conduction response time, which is used to quantify the axial heat conduction efficiency. The dynamic thermal balance index is an indicator that reflects the anti-disturbance ability of the thermal field, ranging from 0 to 1. The higher the value, the stronger the stability. The temperature fluctuation coefficient is the standard deviation of the temperature data, which characterizes the fluctuation amplitude of the thermal field. The radial offset attenuation coefficient is a parameter calculated by an exponential attenuation model, ranging from 0 to 1, which is used to suppress radial (width direction) heat conduction deviation.

[0103] Optionally, the first exponential decay model is α=I×e-σT / τ, where I is a dynamic thermal balance index, σT is a temperature fluctuation coefficient, and τ is a preset decay time constant.

[0104] In an embodiment of the present application, the first parameter set is input into the first process parameter intelligent optimization model. First, the axial component of the temperature gradient parameter in the first parameter set is extracted, and its ratio to the maximum allowable axial temperature gradient of the zinc liquid tank is used as the target ratio. The target ratio is then multiplied by the inverse of the heat conduction response time to obtain the axial gradient weight factor. At the same time, based on the dynamic thermal balance index and temperature fluctuation coefficient in the first parameter set, the first exponential attenuation model is used, in which the attenuation constant is calibrated by historical data, to calculate the radial offset attenuation coefficient.

[0105] Step a22: construct a dynamic thermal field characteristic vector consisting of an axial gradient weight factor and a radial offset attenuation coefficient.

[0106] Among them, the dynamic thermal field characteristic vector is a two-dimensional vector composed of the axial gradient weight factor and the radial offset attenuation coefficient, which is used to characterize the dynamic characteristics of the thermal field.

[0107] In an embodiment of the present application, the output axial gradient weight factor and the radial offset attenuation coefficient are subjected to multi-dimensional data fusion to construct a dynamic thermal field feature vector. The vector is compressed in dimension through principal component analysis or feature dimensionality reduction algorithm and is used to characterize the coupling relationship between the axial and radial thermal field stability of the zinc liquid tank.

[0108] Step a23: Calculate the galvanizing time correction value and the temperature distribution compensation vector through the heat conduction dynamic response function according to the dynamic thermal field characteristic vector and the galvanizing time fluctuation coefficient.

[0109] Among them, the galvanizing time fluctuation coefficient is the deviation rate between the historical galvanizing time and the actual coating thickness.

[0110] In an embodiment of the present application, based on the dynamic thermal field characteristic vector and the galvanizing time fluctuation coefficient, the galvanizing time correction value and the temperature distribution compensation vector are calculated through the heat conduction dynamic response function, wherein the temperature distribution compensation vector optimizes the power distribution ratio of each heating zone through the gradient back propagation algorithm.

[0111] Continuing with the above example, when an automobile factory produces galvanized door panels, it calculates a target ratio of 1.25 based on the axial temperature gradient component of 10°C / m and the maximum allowable gradient of 8°C / m in the first parameter set. This ratio, combined with the heat conduction response time of 14 seconds, generates an axial gradient weight factor of 0.089. Simultaneously, using the dynamic thermal balance index of 0.93 and the temperature fluctuation coefficient of 2.3%, an exponential decay model is used to calculate the radial offset attenuation coefficient of 0.92. Step a22 constructs the dynamic thermal field feature vector [0.089, 0.92]. Step a23, based on this vector and the galvanizing time fluctuation coefficient of 1.2 seconds, outputs a galvanizing time correction of -3 seconds and a temperature distribution compensation vector through the heat conduction dynamic response function. After execution, the temperature in zone D rises from 635°C to 650°C, the edge coating thickness drops to 21 μm, and the weld defect rate simultaneously drops to 1.5%.

[0112] By executing steps a21 to a23, the embodiment of the present application achieves precise matching of the multi-dimensional characteristics of the thermal field and the process parameters: the axial gradient weight factor suppresses the coating thickness deviation in the length direction of the zinc liquid tank, the radial offset attenuation coefficient improves the uniformity of the zinc liquid flow in the edge area, the galvanizing time correction driven by the dynamic thermal field characteristic vector effectively reduces the discreteness of the coating thickness distribution, and the temperature distribution compensation vector optimizes the power distribution ratio of the heating zone, synergistically improving the coating uniformity and reducing the instability of the welding heat affected zone, ultimately optimizing the coating quality and welding strength of the galvanized steel sheet, and stably achieving the preset process standards for the comprehensive quality indicators.

[0113] In one possible embodiment, step b3, inputting the second parameter set into the second process parameter intelligent optimization model, and generating the electrode pressure correction amount and the resistance spot welding parameter compensation vector using the welding dynamic compensation function predefined in the second process parameter intelligent optimization model, includes:

[0114] Step b31, input the second parameter set into the second process parameter intelligent optimization model, the second process parameter intelligent optimization model uses the weight factor calculation formula to obtain the axial gradient dynamic weight factor according to the temperature fluctuation amplitude and the axial component of the temperature gradient parameter in the second parameter set, and uses the second exponential attenuation model to calculate the impedance change attenuation coefficient according to the electrode pressure deviation and the resistance spot welding fluctuation coefficient in the second parameter set.

[0115] The axial gradient dynamic weight factor combines temperature fluctuations and axial gradients to reflect the dynamic influence of heat conduction on the weld zone. The electrode pressure deviation is the percentage deviation between the actual electrode pressure and the theoretical value. The resistance spot welding fluctuation coefficient is the welding current fluctuation rate. The impedance variation attenuation coefficient is a parameter calculated based on the electrode pressure deviation and current fluctuations, ranging from 0 to 1, and is used to suppress the effects of uneven resistance in the zinc coating.

[0116] Step b32: construct a welding heat affected characteristic vector consisting of an axial gradient dynamic weight factor and an impedance change attenuation coefficient.

[0117] The welding heat affected characteristic vector is a two-dimensional vector used to quantify the combined effects of heat conduction and resistance change on the welding zone.

[0118] Step b33: Calculate the electrode pressure correction amount and the resistance spot welding parameter compensation vector using the welding dynamic compensation function according to the welding heat influence characteristic vector and the resistance spot welding fluctuation amplitude.

[0119] The welding dynamic compensation function is a decision-making model based on reinforcement learning. It takes as input the feature vector and the resistance spot welding fluctuation amplitude, and outputs the electrode pressure correction and the spot welding parameter compensation vector. The resistance spot welding fluctuation amplitude is used to quantify the stability of the current during welding, reflecting the impact of changes in the zinc coating resistance or equipment control accuracy on welding quality.

[0120] Continuing with the above example, in the production scenario of galvanized door panels in an automotive steel plate plant, the second parameter set is input into the second process parameter intelligent optimization model. The weight factor calculation formula is combined with the second exponential decay model to calculate the axial gradient dynamic weight factor of 2.3% and the impedance change attenuation coefficient of 0.28. In step b32, these two are constructed into a welding heat affected characteristic vector [2.3%, 0.28] to characterize the dynamic impact of heat conduction anomalies on welding impedance. Based on this characteristic vector and the resistance spot welding fluctuation amplitude, the welding dynamic compensation function outputs an electrode pressure correction amount of +30N and a resistance spot welding parameter compensation vector. After execution, the electrode pressure is increased from 400N to 430N, and the welding pulse is adjusted from 12ms to 15ms. The heat affected uniformity of the weld zone is improved, the false weld rate is significantly reduced, and the coating edge thickness is optimized to the target range.

[0121] By executing steps b31 to b33, the embodiment of the present application effectively solves the problems of cold welding and insufficient welding strength caused by uneven heat conduction and resistance fluctuations through dynamic optimization control. By quantifying the dynamic weight factor of the heat conduction effect and the attenuation coefficient for suppressing resistance unevenness, the model accurately identifies the synergistic relationship between the thermal field distribution and the resistance characteristics of the welding area, and optimizes the electrode pressure parameters and spot welding process parameters in a targeted manner. After optimization, the cold welding rate is significantly reduced to the point where the failure of welds caused by thermal deformation of the galvanized layer is almost eliminated; the welding strength is comprehensively improved and stably exceeds the requirements of industry standards; the diameter of the weld nugget is significantly increased and fully meets the threshold of the process specification. At the same time, the fluctuation rate of the welding current is greatly reduced, and the influence of resistance changes on the welding quality is effectively suppressed; the temperature fluctuation range of the zinc liquid tank-related area is significantly narrowed, and the thermal field stability is significantly enhanced. The welding cycle efficiency is improved and the overall production capacity is optimized; by adjusting the pulse timing strategy, the unit welding energy consumption is significantly reduced; the rework cost is greatly reduced due to the improvement of the cold welding rate, and the production cost is effectively controlled.

[0122] In a possible embodiment, S12, performing thickness detection on different areas of the coating formed on the surface of the galvanized steel sheet to obtain final coating thickness distribution data, includes:

[0123] Step 121: Divide the surface of the galvanized steel plate into several detection areas according to a preset process grid, where the surface of the galvanized steel plate is a coating.

[0124] The preset process grid divides the galvanized steel sheet surface into regular rectangular grid cells at fixed intervals (e.g., 10 cm x 10 cm). The detection area is the coating detection area corresponding to each grid cell, which is used to independently collect spectral data.

[0125] Step 122: trigger the spectrum acquisition module of each detection area through the multi-spectral sensor array to obtain the original data of the spectrum reflection intensity of each detection area.

[0126] The multispectral sensor array is a matrix composed of multiple spectral sensors that covers all detection areas. The raw data of spectral reflection intensity is the intensity value of reflected light at different wavelengths (such as visible light and near-infrared).

[0127] Exemplarily, the multispectral sensor array is composed of near-infrared sensors of 10 bands, and the spectral reflection intensity of each detection area is calibrated by the reflectivity within the wavelength range of 550nm-850nm.

[0128] Step 123 : Based on the original data of the spectral reflection intensity of each detection area, dynamically calibrate the coating thickness of each detection area to generate initial coating thickness distribution data.

[0129] Among them, dynamic calibration is based on a pre-trained relationship model between spectral reflection intensity and coating thickness, and calculates the thickness of each area in real time.

[0130] Step 124 : interpolating and correcting the thickness jump abnormal points in the initial coating thickness distribution data by using the reflection intensity difference gradient between adjacent detection areas to generate corrected coating thickness distribution data.

[0131] The reflection intensity difference gradient is the absolute value of the change rate of the reflection intensity of adjacent regions (such as the reflection intensity difference between regions A1 and A2 / the spacing). The thickness jump abnormal point is the mutation point where the thickness difference between adjacent regions exceeds a threshold (such as ±5μm).

[0132] Step 125 : Perform regional consistency verification on the corrected coating thickness distribution data to obtain verified coating thickness data.

[0133] Regional consistency verification verifies the continuity of the thickness distribution through cluster analysis and eliminates outliers. Verified coating thickness data refers to highly reliable coating thickness distribution data obtained through multiple steps of testing, correction, and verification during the galvanized steel sheet production process.

[0134] Step 126 : spatially superimpose the verified coating thickness data with the original data of the spectral reflection intensity of all detection areas to generate final coating thickness distribution data.

[0135] Among them, spatial superposition is to map the verified thickness data with the original spectral reflection intensity according to coordinates to generate a distribution map with spectrum-thickness correlation.

[0136] Continuing with the above example, in the production scenario of galvanized door panels in an automobile steel plate factory, the surface of the galvanized steel plate is divided into rectangular process grid units according to a fixed spacing rule, and each grid serves as an independent detection area; a multispectral sensor array is deployed along the steel plate moving track, and the spectrum acquisition module of each grid unit is triggered in turn to obtain the original reflection intensity data of each area at a specific wavelength; based on the pre-calibrated relationship curve between reflection intensity and coating thickness, the thickness of each grid unit is dynamically calibrated to generate initial coating thickness distribution data; the reflection intensity difference gradient between adjacent grids is detected, and the abnormal points where the thickness jump exceeds the process threshold are corrected using a bilinear interpolation algorithm to generate smooth corrected thickness distribution data; local outliers are eliminated through a regional consistency verification algorithm to ensure data continuity; the verified thickness data is spatially superimposed and fused with the spectral reflection intensity to generate a final coating thickness distribution map that integrates thermodynamic properties, which is used to guide galvanizing process optimization and welding parameter adjustment.

[0137] By executing steps 121 to 126, the embodiment of the present application realizes high-precision global quantitative analysis of coating thickness through systematic detection and data processing: process grid division ensures detection coverage, dynamic calibration of spectral reflection intensity improves thickness calculation efficiency, interpolation correction and consistency verification eliminate local data anomalies, and spatial superposition and fusion enhance the multi-dimensional correlation of data. The coating thickness distribution data finally generated provides a reliable basis for process optimization, significantly improves coating uniformity and reduces the defect rate of subsequent welding processes.

[0138] In a possible embodiment, S13, generating a heat conduction efficiency correction parameter for each heating zone of the zinc bath according to the temperature distribution data and the final coating thickness distribution data, includes:

[0139] Step 131: Divide the temperature distribution data into a temperature gradient feature set according to the axial and radial distributions of the zinc bath, and divide the final coating thickness distribution data into a thickness difference feature set according to a preset process grid.

[0140] The temperature gradient feature set is the temperature distribution data along the axial (length direction) and radial (width direction) directions within the zinc bath, including the temperature value of each area and the temperature difference between adjacent areas. The thickness difference feature set is the coating thickness data divided by the preset process grid, including the thickness value of each grid area and the thickness difference between adjacent areas.

[0141] Step 132: Based on the axial temperature gradient mean corresponding to the temperature gradient feature set and the regional thickness standard deviation corresponding to the thickness difference feature set, a dynamic heat conduction feature vector consisting of an axial gradient weight factor and a thickness standard deviation compensation coefficient is constructed.

[0142] The thickness standard deviation compensation coefficient is a normalized compensation parameter calculated based on the percentage difference between the thickness standard deviation and the target value, and ranges from 0 to 1. The axial gradient weight factor quantifies the degree of deviation between the actual axial gradient and the maximum allowable gradient, and typically ranges from 0 to 1. Values ​​closer to 1 indicate that the heat transfer efficiency is closer to criticality and requires priority adjustment.

[0143] Step 133: Input the dynamic heat conduction characteristic vector into a predefined heat conduction efficiency correction function, and generate a heat conduction efficiency correction parameter through the exponential decay term of the radial temperature offset amplitude and the piecewise linear correction term of the thermal field fluctuation coefficient in the heat conduction efficiency correction function.

[0144] Among them, the radial temperature offset amplitude is the maximum temperature difference between different radial areas at the same axial position (such as a left-right temperature difference of 10°C). The thermal field fluctuation coefficient in the tank is the standard deviation of the overall temperature of the zinc liquid tank (such as ±3°C). The piecewise linear correction term of the thermal field fluctuation coefficient is to adjust the power compensation ratio according to the thermal field fluctuation coefficient interval. The exponential decay term of the radial temperature offset amplitude is a nonlinear correction term based on the radial offset amplitude, which suppresses local thermal deviations. The thermal conduction efficiency correction function can realize piecewise linear correction, for example: K = w1×e -ΔTr +w2×max(0,σ T -T0), where K is the heat conduction efficiency correction parameter, ΔTr is the radial temperature offset amplitude, T0 is the maximum temperature fluctuation reference value allowed by the process, σ T is the thermal field fluctuation coefficient in the tank, w1 and w2 are weight factors of the corresponding items. In addition, the embodiment of the present application does not specifically limit the specific expression of the heat conduction efficiency correction function.

[0145] Continuing with the above example, when a certain automobile factory produces galvanized door panels, it decomposes the zinc bath temperature data into an axial gradient feature set and a thickness difference feature set. The axial gradient weight factor (8÷10=0.8) and the thickness standard deviation compensation coefficient (3.5×0.3=1.05) are calculated to construct a dynamic heat conduction feature vector ([0.8,1.05]). Using the heat conduction efficiency correction function, the exponential decay term (5°C×e^(-0.2×1.05)≈4.1) and the piecewise linear correction term (0.8×1.2=0.96) generate a power improvement factor of 1.12, increasing the power of the heating zone D from 12kW to 13.44kW. After optimization, the edge coating thickness is reduced from 25μm to 21μm, and the weld defect rate is simultaneously reduced to 1.5%.

[0146] By executing steps 131 to 133, the embodiment of the present application realizes precise control of the heat conduction efficiency of the zinc liquid tank through multi-dimensional feature fusion and dynamic correction: the axial gradient weight factor suppresses excessive thickness of the coating in the length direction, the thickness standard deviation compensation coefficient reduces the discreteness of the thickness distribution, and the power distribution optimization driven by the heat conduction efficiency correction function significantly improves the uniformity of the coating, while reducing the heat influence effect of the welding zone, and ultimately the coating quality and welding strength of the galvanized steel sheet are coordinated to meet the standards.

[0147] Figure 2 A schematic diagram of the structure of an intelligent optimization system for process parameters of galvanized steel sheets provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the system includes:

[0148] The acquisition module 21 is used to obtain the galvanizing time and the temperature distribution data of different areas in the zinc bath during the galvanized steel sheet production process.

[0149] The detection module 22 is used to detect the thickness of different areas of the coating formed on the surface of the galvanized steel sheet to obtain coating thickness distribution data.

[0150] The calculation module 23 is used to generate heat conduction efficiency correction parameters of each heating zone of the zinc bath according to the temperature distribution data and the coating thickness distribution data.

[0151] The optimization module 24 is used to correct the parameters according to the heat conduction efficiency of all heating zones of the zinc liquid tank, optimize the galvanizing time and temperature distribution data using a pre-trained first process parameter intelligent optimization model, and optimize the electrode pressure parameters and resistance spot welding parameters in the welding stage using a pre-trained second process parameter intelligent optimization model, so that the quality estimation value of the galvanized steel sheet is greater than a preset quality threshold.

[0152] Figure 2 The intelligent optimization system for process parameters of galvanized steel sheets can be executed Figure 1 The implementation principle and technical effects of the intelligent optimization method for galvanized steel sheet process parameters described in the embodiment are not described in detail here. The specific manner in which each module and unit performs operations in the intelligent optimization system for galvanized steel sheet process parameters in the above embodiment has been described in detail in the embodiment of the method and will not be elaborated on here.

[0153] In one possible design, Figure 2 The intelligent optimization system for process parameters of galvanized steel sheets in the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32 .

[0154] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0155] The processing component 32 is used to: obtain the first galvanizing time and the temperature distribution data of different heating zones in the zinc bath in the galvanized steel sheet production process. Perform thickness detection on different areas of the coating formed on the surface of the galvanized steel sheet to obtain final coating thickness distribution data. Generate thermal conductivity efficiency correction parameters for each heating zone of the zinc bath based on the temperature distribution data and the final coating thickness distribution data. Based on the thermal conductivity efficiency correction parameters of all heating zones of the zinc bath, a pre-trained first process parameter intelligent optimization model is used to optimize the galvanizing time and temperature distribution data, and a pre-trained second process parameter intelligent optimization model is used to optimize the electrode pressure parameters and resistance spot welding parameters in the welding stage, so that the quality estimate of the galvanized steel sheet is greater than a preset quality threshold.

[0156] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0157] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as random access memory (RAM), static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0158] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0159] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0160] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0161] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0162] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is an intelligent optimization method for process parameters of galvanized steel sheets.

[0163] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0165] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0166] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent optimization method for process parameters of galvanized steel sheets, characterized in that: include: In the galvanized steel sheet production process, obtain the temperature distribution data of the first galvanizing time and different heating zones in the zinc bath; Conduct thickness testing on different areas of the coating formed on the surface of the galvanized steel sheet to obtain the final coating thickness distribution data; generating heat conduction efficiency correction parameters for each heating zone of the zinc bath according to the temperature distribution data and the final coating thickness distribution data; According to the heat conduction efficiency correction parameters of all heating zones in the zinc liquid tank, a pre-trained first process parameter intelligent optimization model is used to optimize the galvanizing time and temperature distribution data, and a pre-trained second process parameter intelligent optimization model is used to optimize the electrode pressure parameters and resistance spot welding parameters in the welding stage, so that the quality estimation value of the galvanized steel sheet is greater than the preset quality threshold.

2. The method according to claim 1, characterized in that The method of modifying the heat conduction efficiency parameters of all heating zones in the zinc bath and optimizing the galvanizing time and temperature distribution data using a pre-trained first process parameter intelligent optimization model includes: According to the heat conduction efficiency correction parameters of all heating zones of the zinc liquid tank, temperature gradient parameters, heat conduction response time and dynamic heat balance index are calculated respectively to generate a first parameter set for characterizing the thermal field stability of the zinc liquid tank; Inputting the first parameter set into the first process parameter intelligent optimization model, and generating a galvanizing time correction value and a temperature distribution compensation vector through a heat conduction dynamic response function predefined in the first process parameter intelligent optimization model; The galvanizing time is optimized according to the galvanizing time correction amount to obtain a second galvanizing time, and the power distribution ratio of each heating zone of the zinc liquid tank is dynamically adjusted according to the temperature distribution compensation vector to optimize the temperature distribution data.

3. The method according to claim 1, characterized in that The method of using a pre-trained second process parameter intelligent optimization model to optimize electrode pressure parameters and resistance spot welding parameters in the welding stage so that the quality estimation value of the galvanized steel sheet is greater than a preset quality threshold includes: Determine the welding area of ​​the galvanized steel sheet, and select the heat conduction efficiency correction parameter of the target heating area from the heat conduction efficiency correction parameters of all heating areas of the zinc bath, wherein the target heating area is the area in the zinc bath corresponding to the welding area in the galvanized steel sheet production process; Calculating the temperature fluctuation amplitude, electrode pressure deviation, and resistance spot welding fluctuation coefficient according to the heat conduction efficiency correction parameter of the corresponding heating zone to generate a second parameter set for characterizing the heat influence effect of the welding zone; Inputting the second parameter set into the second process parameter intelligent optimization model, and generating an electrode pressure correction value and a resistance spot welding parameter compensation vector using a welding dynamic compensation function predefined in the second process parameter intelligent optimization model; The electrode pressure parameters in the welding stage are optimized according to the electrode pressure correction amount, and the welding pulse duration and interval time are dynamically adjusted according to the resistance spot welding parameter compensation vector to optimize the resistance spot welding parameters so that the quality estimation value of the galvanized steel sheet is greater than the preset quality threshold.

4. The method according to claim 2, characterized in that The step of inputting the first parameter set into the first process parameter intelligent optimization model and generating a galvanizing time correction value and a temperature distribution compensation vector through a heat conduction dynamic response function predefined in the first process parameter intelligent optimization model includes: Inputting the first parameter set into the first process parameter intelligent optimization model, the first process parameter intelligent optimization model uses the ratio of the axial component of the temperature gradient parameter in the first parameter set to the maximum allowable axial temperature gradient of the zinc liquid tank as a target ratio, combining the target ratio with the heat conduction response time to obtain an axial gradient weight factor, and calculating the radial offset attenuation coefficient using a first exponential attenuation model based on the dynamic thermal balance index and the temperature fluctuation coefficient in the first parameter set; Constructing a dynamic thermal field characteristic vector composed of the axial gradient weight factor and the radial offset attenuation coefficient; According to the dynamic thermal field characteristic vector and the galvanizing time fluctuation coefficient, the galvanizing time correction amount and the temperature distribution compensation vector are calculated through the heat conduction dynamic response function.

5. The method according to claim 3, characterized in that Inputting the second parameter set into the second process parameter intelligent optimization model, and generating an electrode pressure correction amount and a resistance spot welding parameter compensation vector using a welding dynamic compensation function predefined in the second process parameter intelligent optimization model, includes: The second parameter set is input into the second process parameter intelligent optimization model. The second process parameter intelligent optimization model obtains an axial gradient dynamic weight factor using a weight factor calculation formula based on the temperature fluctuation amplitude and the axial component of the temperature gradient parameter in the second parameter set. The impedance change attenuation coefficient is calculated using a second exponential decay model based on the electrode pressure deviation and the resistance spot welding fluctuation coefficient in the second parameter set. Constructing a welding heat affected characteristic vector composed of the axial gradient dynamic weight factor and the impedance change attenuation coefficient; According to the welding heat influence characteristic vector and the resistance spot welding fluctuation amplitude, the electrode pressure correction amount and the resistance spot welding parameter compensation vector are calculated through the welding dynamic compensation function.

6. The method according to claim 1, wherein The thickness detection of different areas of the coating formed on the surface of the galvanized steel sheet is performed to obtain the final coating thickness distribution data, including: Divide the surface of the galvanized steel sheet into a number of inspection areas according to a preset process grid, wherein the surface of the galvanized steel sheet is the coating; The spectrum acquisition module of each detection area is triggered by the multi-spectral sensor array to obtain the original data of the spectral reflection intensity of each detection area; Based on the original data of the spectral reflection intensity of each detection area, dynamically calibrate the coating thickness of each detection area to generate initial coating thickness distribution data; Interpolating and correcting thickness jump abnormal points in the initial coating thickness distribution data by using the reflection intensity difference gradient between adjacent detection areas to generate corrected coating thickness distribution data; Performing a regional consistency check on the corrected coating thickness distribution data to obtain checked coating thickness data; The verified coating thickness data is spatially superimposed with the original data of spectral reflection intensity of all detection areas to generate final coating thickness distribution data.

7. The method according to claim 1, characterized in that Generating heat conduction efficiency correction parameters of each heating zone of the zinc bath according to the temperature distribution data and the final coating thickness distribution data includes: Dividing the temperature distribution data into a temperature gradient feature set according to the axial and radial distributions of the zinc bath, and dividing the final coating thickness distribution data into a thickness difference feature set according to a preset process grid; constructing a dynamic heat conduction feature vector consisting of an axial gradient weight factor and a thickness standard deviation compensation coefficient based on the axial temperature gradient mean corresponding to the temperature gradient feature set and the regional thickness standard deviation corresponding to the thickness difference feature set; The dynamic heat conduction characteristic vector is input into a predefined heat conduction efficiency correction function, and a heat conduction efficiency correction parameter is generated through the exponential decay term of the radial temperature offset amplitude and the piecewise linear correction term of the thermal field fluctuation coefficient in the slot in the heat conduction efficiency correction function.

8. An intelligent optimization system for process parameters of galvanized steel sheets, characterized in that: include: An acquisition module is used to obtain the first galvanizing time and the temperature distribution data of different heating zones in the zinc bath during the galvanized steel sheet production process; The detection module is used to detect the thickness of different areas of the coating formed on the surface of the galvanized steel sheet to obtain the final coating thickness distribution data; a calculation module, configured to generate a heat conduction efficiency correction parameter for each heating zone of the zinc bath according to the temperature distribution data and the final coating thickness distribution data; An optimization module is used to correct the parameters according to the heat conduction efficiency of all heating zones of the zinc liquid tank, optimize the galvanizing time and the temperature distribution data using a pre-trained first process parameter intelligent optimization model, and optimize the electrode pressure parameters and resistance spot welding parameters in the welding stage using a pre-trained second process parameter intelligent optimization model, so that the quality estimate of the galvanized steel sheet is greater than a preset quality threshold.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an intelligent optimization method for galvanized steel plate process parameters as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, an intelligent optimization method for process parameters of a galvanized steel sheet as described in any one of claims 1 to 7 is implemented.

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