Dynamic parameter fine-tuning method for wave-soldering by fusing infrared thermal imaging monitoring and simulation model comparison

Abstract

The application discloses a wave-soldering dynamic parameter fine adjustment method fusing infrared thermal imaging monitoring and simulation model comparison, and particularly relates to the technical field of wave-soldering dynamic parameter fine adjustment. The method converts factors such as the thermal decomposition evolution of a flux and the radiation characteristic difference of different material regions into quantifiable emissivity disturbance characteristics, introduces a simulation model to perform partition correction on ideal heat distribution, makes the simulation result closer to the infrared observation condition, effectively reduces systematic deviation caused by inconsistent temperature measurement mechanisms between simulation and measurement, constructs and distinguishes the thermal behavior residual error between the equivalent simulation heat distribution and the infrared measured heat distribution, realizes effective distinction of real welding process abnormalities and apparent measurement uncertainty, avoids false adjustment caused by temperature measurement noise, and dynamically fine adjusts process parameters according to real heat deviation.

Description

Technical Field

[0001] This invention relates to the field of wave soldering dynamic parameter fine-tuning technology, and more specifically, to a wave soldering dynamic parameter fine-tuning method that integrates infrared thermal imaging monitoring and simulation model comparison. Background Technology

[0002] In wave soldering, soldering quality is highly dependent on the transient heating state of the PCB board under the influence of solder waves. Infrared thermography, as a non-contact temperature monitoring method, has been introduced for online monitoring of the soldering process due to its fast response speed and high spatial resolution. However, infrared temperature measurement results essentially depend on the emissivity characteristics of the measured surface. In the actual wave soldering environment, the surface condition of the PCB board continuously changes during heating: on the one hand, the flux decomposes, volatilizes, and carbonizes during preheating and wave soldering, and its coverage and chemical composition constantly evolve, resulting in significant temporal dynamic changes in the surface emissivity of the pads and surrounding areas; on the other hand, the differences in material properties and surface morphology of the pad metal, solder mask, and component housings in different areas make their infrared radiation characteristics inherently inconsistent. Under the combined effect of these factors, even if the actual solder joints are under the same or similar real temperature conditions, the infrared thermographic image may still show local false hot spots or cold areas with apparent temperature differences, thus introducing measurement uncertainties that are difficult to eliminate. When the measured infrared thermal distribution is used to compare with the welding process simulation model and as the basis for dynamic parameter fine-tuning, the aforementioned emissivity fluctuations will directly amplify the deviation between simulation and measurement, making it difficult to distinguish between real process anomalies and measurement noise sources when adjusting parameters. This limits the potential for improving the accuracy and stability of the thermal imaging closed-loop control method in complex wave soldering scenarios. Summary of the Invention

[0003] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for fine-tuning dynamic parameters of wave soldering by integrating infrared thermal imaging monitoring and simulation model comparison, in order to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A method for fine-tuning dynamic parameters of wave soldering by integrating infrared thermal imaging monitoring and simulation model comparison includes the following steps: Infrared thermal imaging data of the PCB board in the preheating zone and wave soldering stage are acquired, and the pad type, solder mask distribution and flux coverage information of the corresponding area are acquired simultaneously to construct infrared observation partitions corresponding to the board area and surface condition. Based on the infrared observation zones, the characteristics of the change of infrared apparent temperature over time in each zone are analyzed, the apparent temperature distortion characteristics are extracted, and the emissivity perturbation feature set is obtained. The emissivity perturbation feature set is introduced into the wave soldering heat process simulation model, and the ideal heat distribution obtained from the simulation is corrected by partitioning to generate an equivalent simulated heat distribution that matches the infrared observation conditions. The thermal behavior residual is constructed based on the difference between the equivalent simulated thermal distribution and the infrared measured thermal distribution, and the thermal behavior residual is judged to distinguish between the actual thermal deviation and the apparent deviation. Based on the actual thermal deviation after discrimination, the adjustment offset of the wave soldering process parameters is calculated, and the wave soldering process parameters are dynamically fine-tuned according to the adjustment offset.

[0005] In a preferred embodiment, the process of acquiring infrared thermal imaging data of the PCB board in the preheating zone and wave soldering stage, and simultaneously acquiring information on the pad type, solder resist layer distribution, and flux coverage status of the corresponding areas, and constructing infrared observation zones corresponding to the board surface area and surface condition, is as follows: Infrared thermal imaging equipment is installed above the wave soldering preheating area and the solder wave contact area to perform continuous infrared imaging of the board surface during PCB board transport. A uniform sampling frequency is set for the infrared image sequence acquired by the infrared thermal imaging acquisition device, and a timestamp and location information are added to each frame of the infrared image; Simultaneously acquire PCB board design data, including pad type distribution, solder mask window area and flux spraying trajectory information, and map the design data to the actual spatial coordinate system of the PCB board to obtain a board surface structure feature distribution map; Based on the distribution map of the board surface structure features, the PCB board surface is divided into regions. Regions with the same pad type, solder mask state and flux coverage are divided into the same surface state sub-regions to obtain surface state regions. The pixels in the infrared thermal image are mapped to the corresponding surface state areas according to their spatial position in the PCB coordinate system. The infrared pixels in the same area are aggregated to obtain the apparent temperature sequence of each surface state area over time. Based on the apparent temperature sequence of each surface state region, an infrared observation partition set corresponding to the plate surface region and surface state is constructed.

[0006] In a preferred embodiment, the process of analyzing the time-varying characteristics of the apparent infrared temperature of each infrared observation zone, extracting apparent temperature distortion features, and obtaining the emissivity perturbation feature set according to the infrared observation zone is as follows: Obtain the apparent temperature sequence corresponding to each infrared observation zone, and establish the apparent temperature response curve as time for each infrared observation zone with apparent temperature as the vertical axis unit and time as the horizontal axis unit. The apparent temperature response curve is processed by time differentiation to extract the apparent heating rate characteristics of each infrared observation zone and identify potential emissivity perturbation segments under continuous transmission conditions. The apparent temperature offset is obtained by comparing the apparent temperatures of different infrared observation zones under the same transmission location conditions. Based on the anomalous heating characteristics within the potential emissivity perturbation zone and the apparent temperature shift characteristics in the spatial dimension, the degree of temperature measurement distortion in each infrared observation zone is comprehensively quantified to obtain the emissivity perturbation factor characterizing the intensity of emissivity change. The emissivity perturbation factors corresponding to each infrared observation zone are summarized to construct an emissivity perturbation feature set.

[0007] In a preferred embodiment, the process of introducing the emissivity perturbation feature set into the wave soldering thermal process simulation model, and performing partition correction on the ideal heat distribution obtained from the simulation to generate an equivalent simulated heat distribution that matches the infrared observation conditions is as follows: Obtain the ideal heat distribution output by the wave soldering heat process simulation model; map the ideal heat distribution into regions according to the same spatial division method as the infrared observation partition, so that each simulation unit in the simulation model corresponds to an infrared observation partition, and establish a one-to-one correspondence between the simulation unit and the infrared observation partition; Based on the emissivity perturbation factor corresponding to each infrared observation partition in the emissivity perturbation feature set, the ideal temperature of the corresponding region in the simulation model is corrected by partitioning to obtain the corrected equivalent simulation temperature. By combining the equivalent simulated temperatures corresponding to each infrared observation zone, an equivalent simulated heat distribution that matches the infrared observation conditions is obtained.

[0008] In a preferred embodiment, the process of constructing a thermal behavior residual based on the difference between the equivalent simulated thermal distribution and the infrared measured thermal distribution, and then judging the thermal behavior residual to distinguish between the actual thermal deviation and the apparent deviation, is as follows: After obtaining the equivalent simulated thermal distribution corresponding to each infrared observation zone, the equivalent simulated thermal distribution and the infrared measured thermal distribution of the corresponding zone are aligned on the time axis. Based on the time-aligned equivalent simulated thermal distribution and the infrared measured thermal distribution, the thermal behavior residuals of each infrared observation zone at different times are calculated. Statistical analysis was performed on the thermal behavior residuals over time to extract the residual variance; The residual variance is compared with a preset residual variance threshold. When the residual variance is less than or equal to the residual variance threshold, it is determined that the thermal behavior residual is in a stable state in the time dimension. When the residual variance is greater than the residual variance threshold, it is determined that the thermal behavior residual has abnormal fluctuations in the time dimension, and further judgment needs to be made by combining spatial continuity characteristics. When the thermal behavior residual exhibits abnormal fluctuations in the time dimension, the spatial continuity of the thermal behavior residual between adjacent infrared observation zones is analyzed, and the residual correlation coefficient between adjacent regions is calculated. The residual correlation coefficient is compared with a preset spatial correlation threshold. When the residual correlation coefficient is greater than or equal to the spatial correlation threshold, it is determined that the thermal behavior residual has continuous propagation characteristics in space. When the residual correlation coefficient is less than the spatial correlation threshold, it is determined that the thermal behavior residual does not have continuous propagation characteristics in space. Based on the combined results of the residual variance determination in the time dimension and the spatial continuity determination, when the residual variance is greater than the residual variance threshold and the residual correlation coefficient is greater than or equal to the spatial correlation threshold, it is determined that the thermal behavior residual originates from an abnormality in the actual welding process. When the residual variance is less than or equal to the residual variance threshold or the residual correlation coefficient is less than the spatial correlation threshold, it is determined that the thermal behavior residual originates from the apparent deviation caused by temperature measurement uncertainty.

[0009] In a preferred embodiment, the process of calculating the adjustment offset of the wave soldering process parameters based on the determined actual thermal deviation, and then dynamically fine-tuning the wave soldering process parameters according to the adjustment offset, is as follows: After completing the thermal behavior residual discrimination, the actual thermal deviations determined to be caused by the actual welding process abnormalities are extracted, and the actual thermal deviations are summarized according to the infrared observation partitions to obtain the actual thermal deviation distribution. Based on historical process data, the sensitivity coefficients of each process parameter to changes in plate surface temperature were extracted. Based on the actual thermal deviation and sensitivity coefficient, the adjustment offset of each wave soldering process parameter is calculated, and the wave soldering process parameters are dynamically fine-tuned.

[0010] The technical effects and advantages of this invention are as follows: 1. This invention introduces a deep fusion mechanism between infrared thermal imaging observation zones and thermal process simulation models during wave soldering. To address the problem that traditional infrared thermometry is susceptible to emissivity fluctuations and cannot accurately reflect the true heating state, this invention constructs a closed-loop parameter adjustment method that can explicitly characterize and compensate for the dynamic changes in surface radiation characteristics.

[0011] 2. This invention transforms factors such as the thermal decomposition and evolution of flux and the differences in radiation characteristics of different material regions into quantifiable emissivity perturbation characteristics. A simulation model is then introduced to correct the ideal heat distribution in different regions, making the simulation results closer to infrared observation conditions. This effectively reduces the systematic deviation caused by inconsistencies in temperature measurement mechanisms between simulation and actual measurement. By constructing and identifying the thermal behavior residual between the equivalent simulated heat distribution and the infrared measured heat distribution, it effectively distinguishes between actual welding process anomalies and apparent measurement uncertainties, avoiding misadjustments caused by temperature measurement noise. Based on the actual thermal deviation, key process parameters such as flux spraying amount, preheating temperature, conveying speed, and wave peak height are dynamically fine-tuned, significantly improving the accuracy and stability of thermal control in the wave soldering process. This invention is suitable for actual production scenarios with complex board structures, significant material differences, and drastic changes in flux state. Attached Figure Description

[0012] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] Example: Figure 1 This invention presents a method for fine-tuning dynamic parameters of wave soldering by integrating infrared thermal imaging monitoring and simulation model comparison, comprising the following steps: Infrared thermal imaging data of the PCB board in the preheating zone and wave soldering stage are acquired, and the pad type, solder mask distribution and flux coverage information of the corresponding area are acquired simultaneously to construct infrared observation partitions corresponding to the board area and surface condition. Based on the infrared observation zones, the changes in infrared apparent temperature over time in each zone are analyzed, and the apparent temperature distortion features caused by the thermal decomposition of flux and the differences in the radiation characteristics of different materials are extracted to obtain the emissivity perturbation feature set. The emissivity perturbation feature set is introduced into the wave soldering heat process simulation model, and the ideal heat distribution obtained from the simulation is corrected by partitioning to generate an equivalent simulated heat distribution that matches the infrared observation conditions. The thermal behavior residual is constructed based on the difference between the equivalent simulated thermal distribution and the infrared measured thermal distribution, and the thermal behavior residual is judged to distinguish between the actual thermal deviation caused by the abnormality of the actual welding process and the apparent deviation caused by the uncertainty of temperature measurement. Based on the actual thermal deviation after discrimination, the adjustment offset of the wave soldering process parameters is calculated, and the wave soldering process parameters are dynamically fine-tuned according to the adjustment offset.

[0015] In this embodiment of the invention, the process of acquiring infrared thermal imaging data of the PCB board in the preheating zone and wave soldering stage, and simultaneously acquiring information on the pad type, solder resist layer distribution, and flux coverage status of the corresponding areas, and constructing infrared observation zones corresponding to the board surface area and surface status is as follows: Infrared thermal imaging acquisition equipment is installed above the wave soldering preheating zone and the solder wave contact zone to continuously image the board surface with infrared during the PCB board transport process. The infrared thermal imaging acquisition equipment is set with multiple observation windows along the PCB transport direction to cover the key heating processes of the PCB board entering the preheating stage, fully preheating stage and passing the wave soldering stage. A uniform sampling frequency is set for the infrared image sequence acquired by the infrared thermal imaging acquisition device, and a timestamp and location information are added to each frame of infrared image to ensure that the infrared thermal imaging data is consistent in both the time dimension and the transmission location dimension. Preferably, the sampling frequency is determined based on the PCB board conveying speed and temperature rise rate to ensure that transient thermal changes during flux decomposition and rapid pad heating are captured. Simultaneously acquire PCB board design data, including pad type distribution, solder mask window area and flux spraying trajectory information, and map the design data to the actual spatial coordinate system of the PCB board to obtain a board surface structure feature distribution map; Based on the distribution map of the board surface structure features, the PCB board surface is divided into regions. Regions with the same pad type, solder mask state and flux coverage are divided into the same surface state sub-region, resulting in multiple surface state regions with clear physical meanings. The pixels in the infrared thermal image are mapped to the corresponding surface state areas according to their spatial position in the PCB coordinate system. The infrared pixels in the same area are aggregated to obtain the apparent temperature sequence of each surface state area over time. For example, the apparent temperature of the m-th surface state region can be expressed as: ,in, This represents the set of pixels corresponding to the m-th surface state region. This represents the number of pixels within the surface state region. The infrared temperature of pixel p at time t is measured. Based on the apparent temperature sequence of each surface condition region, an infrared observation partition set corresponding to the plate area and surface condition is constructed to describe the infrared thermal response characteristics under different material properties and flux conditions: The apparent temperature sequence of each surface state region is time-aligned, and the starting time of the PCB board entering the preheating zone is taken as the unified time zero point to form a continuous apparent temperature sequence covering the preheating, wave peak passing and wave peak leaving stages, which is used to characterize the thermal response evolution characteristics of different surface states in the complete soldering process. Based on the apparent temperature similarity between surface state regions, the same infrared observation partition is determined, and the surface state regions of the plate are aggregated according to the similarity determination results to construct an infrared observation partition set composed of multiple infrared observation partitions; For example, based on the apparent temperature similarity between surface state regions m and n The determination is made regarding the apparent temperature similarity. Defined as: ,in, Let m be the apparent temperature of the surface state region. Let n be the apparent temperature of the surface state region. This indicates the start time when the surface condition region enters the effective thermal analysis zone (i.e., the start time when the PCB board enters the preheating zone). Indicates the termination time of the welding heating process in the surface condition area; When the apparent temperature similarity is greater than the preset apparent temperature similarity threshold, it is determined that surface state regions m and n belong to the same infrared observation zone.

[0016] In this embodiment of the invention, based on the infrared observation zones, the process of analyzing the changes in the apparent infrared temperature of each zone over time, extracting the apparent temperature distortion characteristics caused by the thermal decomposition of flux and the differences in the radiation characteristics of different materials, and obtaining the emissivity perturbation feature set is as follows: Obtain the apparent temperature sequence corresponding to each infrared observation zone. With apparent temperature as the vertical axis and time as the horizontal axis, establish the apparent temperature response curve of each infrared observation zone as a function of time to characterize the overall thermal response trend of the zone in the preheating stage and the wave soldering stage. The apparent temperature response curve is processed by time differentiation to extract the apparent heating rate characteristics of each infrared observation zone and identify potential emissivity perturbation segments under continuous transmission conditions. The potential emissivity perturbation section is used to characterize the abrupt changes in infrared radiation characteristics caused by thermal decomposition, volatilization, or carbonization of flux. For example, for the i-th infrared observation zone, its apparent heating rate can be expressed as: ,in, Let be the apparent temperature of the i-th infrared observation zone at time t; For example, based on the apparent heating rate characteristics, anomaly variation intervals satisfying the following formula are identified: ,in, For the apparent heating rate, Indicates the reference heating rate. The abnormal change range is determined as a potential emissivity disturbance segment, based on a preset rate deviation threshold. The apparent temperatures of different infrared observation zones under the same transmission location conditions are compared laterally. The apparent temperature shift caused by the differences in material radiation characteristics of the solder pad metal area, solder resist layer covered area and flux covered area is analyzed to obtain the apparent temperature shift amount. For example, for the i-th and j-th infrared observation zones located at the same transmission position, their apparent temperature offset is... Defined as: ,in Let be the apparent temperature of the i-th infrared observation zone at time t. Let be the apparent temperature of the j-th infrared observation zone at time t; Based on the anomalous heating characteristics within the potential emissivity perturbation zone and the apparent temperature shift characteristics in the spatial dimension, the degree of temperature measurement distortion in each infrared observation zone is comprehensively quantified to obtain the emissivity perturbation factor characterizing the intensity of emissivity change. For example, the emissivity perturbation factor It can be represented as: ,in, and These are the preset proportional coefficients for abnormal temperature rise characteristics and apparent temperature deviation characteristics, respectively. and This refers to the start and end times of the heating phase. This represents the time average of the apparent temperature shift during the heating phase. ; It should be noted that in the formula for calculating the emissivity perturbation factor... This is an abnormal temperature rise characteristic. This is a characteristic of apparent temperature shift; The emissivity perturbation factors corresponding to each infrared observation zone are summarized to construct an emissivity perturbation feature set, which is used to describe the degree to which infrared thermometry results are affected by emissivity changes under different surface conditions. It should be noted that the physical meaning of the emissivity perturbation feature set is that it reflects the intensity of the apparent temperature deviation introduced by the flux state evolution and material radiation characteristics differences during infrared thermometry, rather than the change in the actual thermal behavior of the PCB board. This provides a basis for subsequent equivalent correction of the simulated thermal distribution and isolation of measurement uncertainties.

[0017] In this embodiment of the invention, the process of introducing the emissivity perturbation feature set into the wave soldering thermal process simulation model, and performing partition correction on the ideal heat distribution obtained from the simulation to generate an equivalent simulated heat distribution that matches the infrared observation conditions is as follows: Obtain the ideal heat distribution output from the wave soldering thermal process simulation model to characterize the actual temperature evolution of the PCB board at each heating stage; The ideal thermal distribution is mapped to regions using the same spatial division method as the infrared observation partition, so that each simulation unit in the simulation model corresponds to an infrared observation partition, and a one-to-one correspondence between the simulation unit and the infrared observation partition is established. Based on the emissivity perturbation factor corresponding to each infrared observation partition in the emissivity perturbation feature set, the ideal temperature of the corresponding region in the simulation model is partitioned and corrected to simulate the apparent temperature shift effect caused by emissivity change under infrared thermometry conditions. For example, for the simulation region corresponding to the i-th infrared observation zone, its equivalent simulation temperature can be expressed as: ,in, This represents the ideal temperature output by the simulation model. Indicates the emissivity perturbation factor; Based on the trend of the emissivity perturbation factor over time, the equivalent simulation temperature is corrected by time weighting to obtain the corrected equivalent simulation temperature, so as to avoid the amplified effect of short-term temperature fluctuations on the simulation-measurement comparison. For example, the time-weighted corrected equivalent simulation temperature It can be represented as: ,in, For the weighted time window length, As time weight, , ; The equivalent simulated temperatures corresponding to each infrared observation zone are combined to obtain an equivalent simulated heat distribution that matches the infrared observation conditions. This is used to compare and analyze the measured infrared heat distribution under the same emissivity perturbation background. It should be noted that the equivalent simulated heat distribution is not a re-estimation of the actual welding temperature, but rather an explicit introduction of emissivity perturbation into the simulation results to ensure that the simulation output and the infrared temperature measurement results are consistent in terms of observation conditions, thereby providing a unified reference benchmark for distinguishing between actual process thermal deviations and measurement side errors.

[0018] In this embodiment of the invention, the process of constructing a thermal behavior residual based on the difference between the equivalent simulated thermal distribution and the infrared measured thermal distribution, and then judging the thermal behavior residual to distinguish between the actual thermal deviation caused by abnormalities in the actual welding process and the apparent deviation caused by temperature measurement uncertainty, is as follows: After obtaining the equivalent simulated thermal distribution corresponding to each infrared observation zone, the equivalent simulated thermal distribution and the infrared measured thermal distribution of the corresponding zone are aligned on the time axis so that the two are comparable under the same transmission position or the same heating stage. Based on the time-aligned equivalent simulated thermal distribution and the infrared measured thermal distribution, the thermal behavior residuals of each infrared observation zone at different times are calculated to quantify the degree of deviation of the measured thermal behavior from the equivalent simulation benchmark. For example, for the i-th infrared observation partition, its thermal behavior residual can be expressed as: ,in, The apparent temperature is measured by infrared radiation. For equivalent simulation temperature; Statistical analysis was performed on the thermal behavior residuals over time to extract the residual variance, thereby characterizing the stability of the residuals as they evolve with the heating process. For example, the residual variance It can be represented as: ,in, This represents the Kth sampling time, where K is the sampling time index. Sampling time The thermal behavior residual, where N is the total number of sampling times used for statistical analysis within the same infrared observation partition. This represents the mean of the thermal behavior residuals for the i-th infrared observation zone; The residual variance Compared with the preset residual variance threshold When the residual variance is less than or equal to the residual variance threshold, it is determined that the thermal behavior residual is in a stable state in the time dimension. This is because the residual fluctuation amplitude is small, which conforms to the random disturbance characteristics caused by infrared thermometry noise or small fluctuations in emissivity. When the residual variance is greater than the residual variance threshold, it is determined that the thermal behavior residual has abnormal fluctuations in the time dimension, and further judgment is needed in combination with the spatial continuity characteristics. The reason is that a large residual variance means that the thermal deviation is not purely random measurement noise. When the thermal behavior residual exhibits abnormal fluctuations in the time dimension, the spatial continuity of the thermal behavior residual between adjacent infrared observation zones is analyzed, and the residual correlation coefficient between adjacent regions is calculated. For example, the residual correlation coefficient between the i-th and j-th adjacent partitions can be expressed as: ,in, This represents the covariance between the residuals of the thermal behavior of the two partitions; The residual correlation coefficient Spatial correlation threshold Comparison is made, and when the residual correlation coefficient is greater than or equal to the spatial correlation threshold, it is determined that the thermal behavior residual has continuous propagation characteristics in space. The reason is that real soldering process abnormalities usually cause cooperative temperature shifts in adjacent areas along the PCB conveying direction or heat conduction path. When the residual correlation coefficient is less than the spatial correlation threshold, it is determined that the thermal behavior residual does not have continuous propagation characteristics in space. The reason is that the apparent deviation caused by temperature measurement uncertainty usually manifests as local, discrete and random fluctuations without spatial correlation. Based on the combined results of the residual variance determination in the time dimension and the spatial continuity determination, when the residual variance is greater than the residual variance threshold and the residual correlation coefficient is greater than or equal to the spatial correlation threshold, it is determined that the thermal behavior residual originates from an abnormality in the actual welding process. When the residual variance is less than or equal to the residual variance threshold or the residual correlation coefficient is less than the spatial correlation threshold, it is determined that the thermal behavior residual originates from the apparent deviation caused by temperature measurement uncertainty. It should be noted that by introducing the dual constraints of residual variance threshold and spatial correlation threshold, the process of judging thermal deviation is transformed from empirical judgment into a quantifiable and reproducible threshold comparison process, thereby improving the reliability and stability of identifying real welding process anomalies.

[0019] In this embodiment of the invention, the process of calculating the adjustment offset of the wave soldering process parameters based on the determined actual thermal deviation, and then dynamically fine-tuning the wave soldering process parameters according to the adjustment offset, is as follows: After completing the thermal behavior residual discrimination, the actual thermal deviations determined to be caused by the actual welding process abnormalities are extracted, and the actual thermal deviations are summarized according to the infrared observation partitions to obtain the actual thermal deviation distribution that characterizes the actual insufficient heating and overheating degree of different plate areas. Based on the actual thermal deviation distribution, a response relationship between the actual thermal deviation and wave soldering process parameters is established to describe the influence of flux spraying amount, preheating temperature, conveying speed and wave height changes on the temperature field of the board surface. For example, for the i-th infrared observation zone, its true thermal deviation can be expressed as: Where q is the sampling time index and Q is the total number of time sampling points. The thermal behavior residual that is determined to be the true thermal deviation within the infrared observation zone; Based on historical process data, the sensitivity coefficients of each process parameter to changes in plate surface temperature are extracted to obtain the parameter temperature response matrix. For example, the sensitivity coefficient It can be represented as: ,in, This represents the wave soldering process parameter for the h-th term; Based on the actual thermal deviation and sensitivity coefficient, calculate the adjustment offset of each wave soldering process parameter to make the parameter adjustment direction consistent with the compensation direction of the actual thermal deviation. For example, the adjustment offset It can be represented as: ; Process safety constraints and rate of change limits are applied to the adjustment offset to avoid new process instabilities caused by rapid parameter changes; preferably, the safety constraints include upper and lower limits of the parameter and the maximum change per unit time. The constrained adjustment offset is applied to at least one of the flux spraying amount, preheating temperature, conveying speed and wave height to dynamically fine-tune the wave soldering process parameters. It should be noted that, through the above parameter adjustment process, the adjustment of wave soldering process parameters is based on the actual thermal deviation after eliminating the influence of temperature measurement uncertainty, thereby avoiding the misuse of apparent temperature fluctuations caused by changes in emissivity for process adjustment and improving the stability and reliability of the dynamic fine-tuning process.

[0020] This invention introduces a deep fusion mechanism between infrared thermal imaging observation zones and thermal process simulation models during wave soldering. To address the problem that traditional infrared thermometry is susceptible to emissivity fluctuations and cannot accurately reflect the true heating state, this invention constructs a closed-loop parameter adjustment method that can explicitly characterize and compensate for the dynamic changes in surface radiation characteristics.

[0021] This invention transforms factors such as the thermal decomposition and evolution of flux and the differences in radiation characteristics of different material regions into quantifiable emissivity perturbation features. It then introduces a simulation model to perform zonal correction of the ideal heat distribution, making the simulation results closer to infrared observation conditions. This effectively reduces the systematic deviation caused by inconsistencies in temperature measurement mechanisms between simulation and actual measurement. By constructing and identifying the thermal behavior residual between the equivalent simulated heat distribution and the infrared measured heat distribution, it effectively distinguishes between actual welding process anomalies and apparent measurement uncertainties, avoiding misadjustments caused by temperature measurement noise. Based on the actual thermal deviation, it dynamically fine-tunes key process parameters such as flux spraying amount, preheating temperature, conveying speed, and wave peak height, significantly improving the accuracy and stability of thermal control in wave soldering. This invention is suitable for actual production scenarios with complex board structures, significant material differences, and drastic changes in flux state.

[0022] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0023] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0024] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for fine-tuning dynamic parameters of wave soldering by integrating infrared thermal imaging monitoring and simulation model comparison, characterized in that: Includes the following steps: Infrared thermal imaging data of the PCB board in the preheating zone and wave soldering stage are acquired, and the pad type, solder mask distribution and flux coverage information of the corresponding area are acquired simultaneously to construct infrared observation partitions corresponding to the board area and surface condition. Based on the infrared observation zones, the characteristics of the change of infrared apparent temperature over time in each zone are analyzed, the apparent temperature distortion characteristics are extracted, and the emissivity perturbation feature set is obtained. The emissivity perturbation feature set is introduced into the wave soldering heat process simulation model, and the ideal heat distribution obtained from the simulation is corrected by partitioning to generate an equivalent simulated heat distribution that matches the infrared observation conditions. The thermal behavior residual is constructed based on the difference between the equivalent simulated thermal distribution and the infrared measured thermal distribution, and the thermal behavior residual is judged to distinguish between the actual thermal deviation and the apparent deviation. Based on the actual thermal deviation after discrimination, the adjustment offset of the wave soldering process parameters is calculated, and the wave soldering process parameters are dynamically fine-tuned according to the adjustment offset.

2. The wave soldering dynamic parameter fine-tuning method based on the fusion of infrared thermal imaging monitoring and simulation model comparison as described in claim 1, characterized in that: The process of acquiring infrared thermal imaging data of the PCB board in the preheating zone and wave soldering stage, and simultaneously acquiring information on the pad type, solder mask distribution, and flux coverage status of the corresponding areas, and constructing infrared observation zones corresponding to the board surface area and surface condition is as follows: Infrared thermal imaging equipment is installed above the wave soldering preheating area and the solder wave contact area to perform continuous infrared imaging of the board surface during PCB board transport. A uniform sampling frequency is set for the infrared image sequence acquired by the infrared thermal imaging acquisition device, and a timestamp and location information are added to each frame of the infrared image; Simultaneously acquire PCB board design data, including pad type distribution, solder mask window area and flux spraying trajectory information, and map the design data to the actual spatial coordinate system of the PCB board to obtain a board surface structure feature distribution map; Based on the distribution map of the board surface structure features, the PCB board surface is divided into regions. Regions with the same pad type, solder mask state and flux coverage are divided into the same surface state sub-regions to obtain surface state regions. The pixels in the infrared thermal image are mapped to the corresponding surface state areas according to their spatial position in the PCB coordinate system. The infrared pixels in the same area are aggregated to obtain the apparent temperature sequence of each surface state area over time. Based on the apparent temperature sequence of each surface state region, an infrared observation partition set corresponding to the plate surface region and surface state is constructed.

3. The wave soldering dynamic parameter fine-tuning method based on the fusion of infrared thermal imaging monitoring and simulation model comparison according to claim 2, characterized in that: Based on the infrared observation zones, the process of analyzing the time-varying characteristics of the apparent infrared temperature in each zone, extracting the apparent temperature distortion characteristics, and obtaining the emissivity perturbation feature set is as follows: Obtain the apparent temperature sequence corresponding to each infrared observation zone, and establish the apparent temperature response curve as time for each infrared observation zone with apparent temperature as the vertical axis unit and time as the horizontal axis unit. The apparent temperature response curve is processed by time differentiation to extract the apparent heating rate characteristics of each infrared observation zone and identify potential emissivity perturbation segments under continuous transmission conditions. The apparent temperature offset is obtained by comparing the apparent temperatures of different infrared observation zones under the same transmission location conditions. Based on the anomalous heating characteristics within the potential emissivity perturbation zone and the apparent temperature shift characteristics in the spatial dimension, the degree of temperature measurement distortion in each infrared observation zone is comprehensively quantified to obtain the emissivity perturbation factor characterizing the intensity of emissivity change. The emissivity perturbation factors corresponding to each infrared observation zone are summarized to construct an emissivity perturbation feature set.

4. The wave soldering dynamic parameter fine-tuning method based on the fusion of infrared thermal imaging monitoring and simulation model comparison according to claim 3, characterized in that: The process of introducing the emissivity perturbation feature set into the wave soldering thermal process simulation model, and performing partition correction on the ideal heat distribution obtained from the simulation to generate an equivalent simulated heat distribution that matches the infrared observation conditions is as follows: Obtain the ideal heat distribution output by the wave soldering heat process simulation model; map the ideal heat distribution into regions according to the same spatial division method as the infrared observation partition, so that each simulation unit in the simulation model corresponds to an infrared observation partition, and establish a one-to-one correspondence between the simulation unit and the infrared observation partition; Based on the emissivity perturbation factor corresponding to each infrared observation partition in the emissivity perturbation feature set, the ideal temperature of the corresponding region in the simulation model is corrected by partitioning to obtain the corrected equivalent simulation temperature. By combining the equivalent simulated temperatures corresponding to each infrared observation zone, an equivalent simulated heat distribution that matches the infrared observation conditions is obtained.

5. The wave soldering dynamic parameter fine-tuning method based on the fusion of infrared thermal imaging monitoring and simulation model comparison according to claim 4, characterized in that: The process of constructing thermal behavior residuals based on the difference between the equivalent simulated thermal distribution and the infrared measured thermal distribution, and then judging the thermal behavior residuals to distinguish between actual thermal deviations and apparent deviations is as follows: After obtaining the equivalent simulated thermal distribution corresponding to each infrared observation zone, the equivalent simulated thermal distribution and the infrared measured thermal distribution of the corresponding zone are aligned on the time axis. Based on the time-aligned equivalent simulated thermal distribution and the infrared measured thermal distribution, the thermal behavior residuals of each infrared observation zone at different times are calculated. Statistical analysis was performed on the thermal behavior residuals over time to extract the residual variance; The residual variance is compared with a preset residual variance threshold. When the residual variance is less than or equal to the residual variance threshold, it is determined that the thermal behavior residual is in a stable state in the time dimension. When the residual variance is greater than the residual variance threshold, it is determined that the thermal behavior residual has abnormal fluctuations in the time dimension, and further judgment is needed by combining spatial continuity characteristics. When the thermal behavior residual exhibits abnormal fluctuations in the time dimension, the spatial continuity of the thermal behavior residual between adjacent infrared observation zones is analyzed, and the residual correlation coefficient between adjacent regions is calculated. The residual correlation coefficient is compared with a preset spatial correlation threshold. When the residual correlation coefficient is greater than or equal to the spatial correlation threshold, it is determined that the thermal behavior residual has continuous propagation characteristics in space. When the residual correlation coefficient is less than the spatial correlation threshold, it is determined that the thermal behavior residual does not have continuous propagation characteristics in space. Based on the combined results of the residual variance determination in the time dimension and the spatial continuity determination, when the residual variance is greater than the residual variance threshold and the residual correlation coefficient is greater than or equal to the spatial correlation threshold, it is determined that the thermal behavior residual originates from an abnormality in the actual welding process. When the residual variance is less than or equal to the residual variance threshold or the residual correlation coefficient is less than the spatial correlation threshold, it is determined that the thermal behavior residual originates from the apparent deviation caused by temperature measurement uncertainty.

6. The wave soldering dynamic parameter fine-tuning method based on the fusion of infrared thermal imaging monitoring and simulation model comparison according to claim 5, characterized in that: The process of calculating the adjustment offset of wave soldering process parameters based on the determined actual thermal deviation, and then dynamically fine-tuning the wave soldering process parameters according to the adjustment offset, is as follows: After completing the thermal behavior residual discrimination, the actual thermal deviations determined to be caused by the actual welding process abnormalities are extracted, and the actual thermal deviations are summarized according to the infrared observation partitions to obtain the actual thermal deviation distribution. Based on historical process data, the sensitivity coefficients of each process parameter to changes in plate surface temperature were extracted. Based on the actual thermal deviation and sensitivity coefficient, the adjustment offset of each wave soldering process parameter is calculated, and the wave soldering process parameters are dynamically fine-tuned.

Images