Precise annealing control method and system for copper capillary tube

Abstract

The invention discloses a precise annealing control method and system for a copper capillary tube, and relates to the technical field of annealing control, and the method comprises the steps: connecting annealing treatment equipment, collecting historical data, and building an annealing process parameter library and an annealed copper tube feature influence library; acquiring tinning process information of the copper capillary, and training a tinning quality influence model corresponding to a tinning process flow; reading expected annealing performance parameters of the copper capillary tube; taking the expected annealing performance parameter as an optimization target, calling a tinning quality influence model, and performing annealing quality and tinning influence minimization optimization based on the annealing process parameter library and the annealed copper pipe characteristic influence library to generate an optimal annealing parameter; and carrying out annealing control on the copper capillary tube according to the optimal annealing parameter. The technical problem that in the prior art, coupling control over the annealing performance of the copper capillary and the follow-up tinning quality cannot be considered at the same time in the annealing process is solved, and the technical effect that the tinning quality consistency is improved while the annealing performance of the copper capillary reaches the standard is achieved.

Description

Technical Field

[0001] This invention relates to the field of annealing control technology, specifically to a method and system for precision annealing control of copper capillaries. Background Technology

[0002] Copper capillaries often require annealing during precision machining to improve their mechanical properties and crystal structure. The quality of annealing directly affects the subsequent tin plating process. In actual production, annealing process parameters are mainly set to optimize performance such as grain refinement and stress relief, while neglecting the impact of annealing results on the adhesion and uniformity of subsequent tin plating. This can lead to problems such as poor tin plating adhesion, insufficient gloss, or peeling even if the annealing performance meets the standards, making it difficult to achieve synergistic optimization of annealing performance and tin plating quality. Summary of the Invention

[0003] This application provides a method and system for precision annealing control of copper capillaries, which addresses the technical problem in the prior art that the annealing process cannot simultaneously take into account the annealing performance of copper capillaries and the subsequent tin plating quality through coupled control.

[0004] In view of the above problems, this application provides a method and system for precision annealing control of copper capillaries.

[0005] A first aspect of this application provides a method for precision annealing control of copper capillaries, the method comprising:

[0006] Connect the annealing equipment, collect historical data to establish an annealing process parameter library and an annealed copper tube characteristic influence library; obtain the tin plating process information of the copper capillary, and train the tin plating quality influence model corresponding to the tin plating process flow; read the expected annealing performance parameters of the copper capillary; take the expected annealing performance parameters as the optimization target, call the tin plating quality influence model, and perform optimization to minimize the annealing quality and tin plating influence based on the annealing process parameter library and the annealed copper tube characteristic influence library to generate the optimal annealing parameters; use the optimal annealing parameters to control the annealing of the copper capillary.

[0007] A second aspect of this application provides a precision annealing control system for copper capillaries, the system comprising:

[0008] The system includes a database establishment module for connecting to the annealing equipment and collecting historical data to establish an annealing process parameter library and an annealed copper tube characteristic influence library; a training module for acquiring tin plating process information of the copper capillary and training a tin plating quality influence model corresponding to the tin plating process flow; a parameter reading module for reading the desired annealing performance parameters of the copper capillary; an optimization module for using the desired annealing performance parameters as the optimization target, calling the tin plating quality influence model, and performing optimization to minimize the annealing quality and tin plating influence based on the annealing process parameter library and the annealed copper tube characteristic influence library to generate optimal annealing parameters; and an annealing control module for annealing control of the copper capillary using the optimal annealing parameters.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application connects to annealing equipment, collects historical data to establish an annealing process parameter library and an annealed copper tube characteristic influence library; obtains tin plating process information of the copper capillary, trains a tin plating quality influence model corresponding to the tin plating process flow; reads the desired annealing performance parameters of the copper capillary; uses the desired annealing performance parameters as the optimization target, calls the tin plating quality influence model, and performs optimization to minimize the annealing quality and tin plating influence based on the annealing process parameter library and the annealed copper tube characteristic influence library, generating optimal annealing parameters; and uses the optimal annealing parameters to control the annealing of the copper capillary. This invention solves the technical problem in the prior art where the annealing process cannot simultaneously consider the coupled control of copper capillary annealing performance and subsequent tin plating quality. By constructing an annealing process parameter library and an annealed copper tube characteristic influence library, and combining the tin plating quality influence model to optimize the annealing parameters, it achieves the technical effect of ensuring the copper capillary annealing performance meets standards while improving the consistency of tin plating quality. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A schematic diagram of a method for precision annealing control of copper capillaries provided in this application embodiment;

[0013] Figure 2 This is a schematic diagram of a precision annealing control system for copper capillary tubes, provided as an embodiment of this application.

[0014] Figure labeling: Database creation module 11, training module 12, parameter reading module 13, optimization module 14, annealing control module 15. Detailed Implementation

[0015] This application provides a method and system for precision annealing control of copper capillaries. It addresses the technical problem in the prior art that the annealing process cannot simultaneously take into account the coupled control of the annealing performance of copper capillaries and the subsequent tin plating quality. By constructing an annealing process parameter library and an annealed copper tube characteristic influence library, and combining the tin plating quality influence model, the optimal annealing parameters are optimized, thereby achieving the technical effect of improving the consistency of tin plating quality while ensuring that the annealing performance of copper capillaries meets the standards.

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0017] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0018] Example 1, as Figure 1 As shown, this application provides a method for precision annealing control of copper capillaries, the method comprising:

[0019] Step S100: Connect the annealing equipment, collect historical data to establish an annealing process parameter library and an annealing copper tube characteristic influence library.

[0020] In this embodiment, process control data during the operation of the annealing equipment is first obtained by connecting the equipment, including key parameters such as the annealing temperature setpoint and actual temperature curve, holding time, cooling method and cooling rate, and annealing tension. This data is categorized and organized to form an annealing process parameter library, used to record the specific numerical combinations of various annealing operating conditions. Subsequently, in the corresponding annealing batches, the copper capillaries are subjected to physical and structural performance tests, extracting key indicators such as surface condition (e.g., oxide film thickness after heat treatment, residual carbon content), crystal structure (e.g., grain size grade), and mechanical properties (e.g., tensile strength, residual stress). These indicators are then archived according to annealing conditions to construct a characteristic influence library for annealed copper tubes.

[0021] Furthermore, the method provided in the application embodiments also includes:

[0022] The annealing process parameter library includes multiple sets of annealing process parameters, and the annealed copper tube characteristic influence library includes multiple sets of annealed copper tube characteristic influence data corresponding to the multiple sets of annealing process parameters. Each set of annealed copper tube characteristic influence data includes at least surface state data, crystal structure data, and mechanical property data.

[0023] In this embodiment, multiple annealing tests were conducted on copper capillaries under different annealing conditions to form several sets of representative annealing process parameters. Each set of parameters includes elements such as annealing temperature, holding time, cooling method, and tension control, and is stored in an annealing process parameter library. For each set of annealing process parameters, the performance of the annealed copper capillaries was tested to obtain surface condition data, crystal structure data, and mechanical property data, which serve as the characteristic influence data of the annealed copper capillaries corresponding to that set of parameters. For example, surface condition data may include the thickness of the oxide film formed after heat treatment and the residual carbon level; crystal structure data reflects grain size or grain size grade; and mechanical property data includes residual stress or tensile strength. All these characteristic data are organized according to the process parameters to form the characteristic influence library of annealed copper capillaries.

[0024] Furthermore, the method provided in the application embodiments also includes:

[0025] Surface condition data includes surface oxidation parameters and residual carbon parameters after heat treatment; crystal structure data includes grain size grade after heat treatment; and mechanical property data includes residual stress parameters after heat treatment.

[0026] In this embodiment, surface state data refers to indicators used to characterize the surface chemical and physical state of copper capillary tubes after annealing, including surface oxidation parameters and residual carbon parameters. Surface oxidation parameters are determined by scanning electron microscopy (SEM) combined with energy dispersive spectroscopy (EDS) to quantify the thickness, compositional element ratio, and uniformity of the oxide film, expressed for example as oxide film thickness (nm) and oxygen content atomic percentage; residual carbon parameters are determined by infrared carbon-sulfur analyzer to reflect the carbon residue caused by incomplete decomposition or combustion of organic matter during annealing, characterized by residual carbon mass fraction (wt%).

[0027] Crystal structure data are obtained through electron backscatter diffraction or metallographic microscopy. Grain size grades represent the uniformity of crystallization and grain size distribution of the material, and can be divided into 1 to 10 levels. Higher grain size indicates finer grains and a denser microstructure. Mechanical property data focus on assessing the internal stress state of the material. Residual stress parameters are often measured using X-ray diffraction residual stress analysis or strain gauge methods, quantitatively representing the level of incompletely released stress within the material after heat treatment in MPa.

[0028] Step S200: Obtain the tin plating process information of the copper capillary and train the tin plating quality influence model corresponding to the tin plating process.

[0029] In this embodiment, the tin plating process information corresponding to the copper capillary is first obtained, including key parameters such as tin plating solution formulation, current density, tin plating temperature, and immersion time. Combined with historical tin plating process records, surface state data (such as oxide film thickness and residual carbon content), crystal structure data (such as grain size), and mechanical property data (such as residual stress parameters) of the annealed copper capillary are collected and analyzed against the measured tin plating quality indicators (such as adhesion, coating uniformity, and interface defect rate). By establishing surface state-tin plating quality influence curves, crystal structure-tin plating quality influence curves, and mechanical property-tin plating quality influence curves, a tin plating quality influence model is constructed and trained.

[0030] Furthermore, the method provided in the application embodiment, which involves obtaining tin plating process information of the copper capillary and training a tin plating quality influence model corresponding to the tin plating process flow, further includes:

[0031] Based on tin plating process information, historical tin plating process records are collected, and the influence relationship between surface state data, crystal structure data, and mechanical property data and tin plating quality is analyzed. The influence curves of surface state-tin plating quality, crystal structure-tin plating quality, and mechanical property-tin plating quality are output. Based on the influence curves of surface state-tin plating quality, crystal structure-tin plating quality, and mechanical property-tin plating quality, the tin plating quality influence model is trained.

[0032] In this embodiment, firstly, based on tin plating process information, historical tin plating process records for multiple batches are collected. These historical tin plating process records include key process parameters such as tin plating solution composition, current density, operating temperature, immersion time, and pretreatment method. This historical data is obtained by compiling on-site process logs and testing reports, and corresponds one-to-one with annealing process batches.

[0033] Subsequently, for each batch of copper capillary samples, key performance data after annealing were obtained as surface condition data, crystal structure data, and mechanical property data. Surface condition data included oxide film thickness measured by scanning electron microscopy (SEM) and residual carbon mass fraction measured by infrared carbon-sulfur analyzer; crystal structure data included average grain size obtained by electron backscatter diffraction and grain size grade evaluated according to GB / T6394 standard; mechanical property data included residual stress parameters determined by X-ray diffraction after heat treatment. Simultaneously, corresponding tin plating quality indicators were collected, including coating adhesion obtained by peel test, coating thickness uniformity measured by X-ray fluorescence analyzer, and interface defect rate statistically analyzed by SEM cross-sectional images.

[0034] After constructing a complete data comparison table, linear regression analysis was used to model the three types of annealing characteristic data with tin plating quality indicators, outputting surface state-tin plating quality influence curves, crystal structure-tin plating quality influence curves, and mechanical properties-tin plating quality influence curves. For example, the linear fitting results showed that when the oxide film thickness exceeded 100 nm, the coating adhesion decreased by more than 15%; when the grain size grade was lower than 6, the standard deviation of the coating thickness increased by more than 20%.

[0035] Finally, based on the dataset constructed from the three types of influence curves mentioned above, the Support Vector Regression (SVR) algorithm was used to train the data and generate a tin plating quality influence model. This model uses oxide film thickness, residual carbon mass fraction, grain size grade, and residual stress parameters as input variables, and coating adhesion, coating thickness uniformity, and bonding interface defect rate as output targets, forming a mapping relationship from annealing performance to tin plating quality.

[0036] Step S300: Read the desired annealing performance parameters of the copper capillary.

[0037] In this embodiment of the application, by calling the process design requirements, the performance target that the copper capillary should achieve after annealing is read, and a set of expected annealing performance parameters including surface state parameters, crystal structure parameters and mechanical property parameters is formed.

[0038] Step S400: Taking the desired annealing performance parameters as the optimization target, the tin plating quality influence model is called, and the annealing quality and tin plating influence are minimized based on the annealing process parameter library and the annealing copper tube characteristic influence library to generate the optimal annealing parameters.

[0039] In this embodiment, with the desired annealing performance parameters as the optimization objective, the tin plating quality influence model is invoked. When minimizing the annealing quality and tin plating influence based on the annealing process parameter library and the annealed copper tube characteristic influence library, different combinations of annealing parameters are first extracted from the annealing process parameter library. Combined with the annealed copper tube characteristic influence library, M sets of annealing process parameters that meet the desired annealing performance parameter requirements are selected, constructing a tin plating quality optimization space. Subsequently, within this optimization space, the tin plating quality influence model is invoked to evaluate and compare the tin plating quality influence corresponding to each parameter group, performing a tin plating influence minimization optimization, ultimately generating the optimal annealing parameters that satisfy annealing performance and have the best tin plating compatibility.

[0040] Furthermore, in the method provided in the application embodiment, taking the desired annealing performance parameters as the optimization objective, the method calls the tin plating quality influence model, and performs optimization to minimize the annealing quality and tin plating influence based on the annealing process parameter library and the annealing copper tube characteristic influence library, generating optimal annealing parameters, and further includes:

[0041] Extract any set of parameters from the annealing process parameter library and collect performance test records after copper tube annealing. Construct a tin plating quality optimization space with M sets of annealing process parameters whose performance meets the desired annealing performance parameters. In the tin plating quality optimization space, call the tin plating quality influence model to minimize the tin plating influence and generate the optimal annealing parameters.

[0042] In this embodiment, multiple sets of historical annealing process parameter combinations are first extracted sequentially from the annealing process parameter library. Each set of parameters includes annealing temperature, holding time, cooling method, and cooling rate. For each parameter combination, the corresponding post-annealing performance test data, including surface state data, crystal structure data, and mechanical property data, is obtained by searching the annealing copper tube characteristic influence library. Subsequently, a performance matching method is used to compare the extracted performance test data with the desired annealing performance parameters. Only parameter sets that meet all the requirements of the desired annealing performance parameters are retained, ultimately resulting in M ​​sets of annealing process parameters. These M sets of annealing process parameters together constitute the tin plating quality optimization space.

[0043] Next, within the tin plating quality optimization space, the tin plating quality influence model is invoked to minimize the tin plating impact. Specifically, firstly, within the tin plating quality optimization space, the tin plating quality influence model is used to analyze the quality impact of M groups of annealing process parameters, generating corresponding tin plating quality influence indices. Then, by performing virtual coordinate transformation on the M groups of parameters, their distribution positions in the parameter space are constructed, and the corresponding influence indices are mapped to this parameter distribution. Based on a preset tin plating quality influence threshold, the indices are classified, and excitation parameter density analysis and taboo parameter density analysis are conducted, generating excitation parameter density distributions and taboo parameter density distributions respectively. Finally, based on this, the parameter density of the excitation region is expanded, and further optimization to minimize the tin plating impact is performed based on the expansion results, ultimately determining the optimal annealing parameters.

[0044] Furthermore, in the method provided in the application embodiments, in the tin plating quality optimization space, the tin plating quality influence model is invoked to minimize the tin plating influence and generate the optimal annealing parameters, which further includes:

[0045] The tin plating quality influence model is used to analyze the tin plating quality influence of M sets of annealing process parameters in the tin plating quality optimization space, generating M tin plating quality influence indices. Virtual coordinate transformation is performed on the parameter distribution positions of the M sets of annealing process parameters to construct parameter distributions. The M tin plating quality influence indices are mapped to the parameter distributions, and excitation parameter density analysis is performed for tin plating quality influence indices that meet preset tin plating quality influence thresholds, and taboo parameter density analysis is performed for tin plating quality influence indices that do not meet preset tin plating quality influence thresholds, generating excitation parameter density distributions and taboo parameter density distributions. Based on the excitation parameter density distributions and taboo parameter density distributions, the parameter density of the excitation region is expanded, and the tin plating influence is minimized based on the expansion results to generate the optimal annealing parameters.

[0046] In this embodiment, firstly, within the established tin plating quality optimization space, a trained tin plating quality influence model is used to perform quality prediction analysis on each set of annealing process parameters. The model input consists of post-annealing performance data corresponding to each set of parameters, including oxide film thickness, residual carbon mass fraction, grain size grade, and residual stress parameters. The model output is a set of quantified tin plating quality influence indicators, such as predicted values ​​for coating adhesion, coating thickness uniformity, and bonding interface defect rate. After model calculation, M tin plating quality influence indicators corresponding to the M sets of annealing parameters are obtained.

[0047] Next, the virtual coordinate transformation of the parameter distribution positions of the M groups of annealing process parameters is performed to construct a parameter distribution under a unified dimension. This transformation process uses the Z-score normalization method to standardize variables such as annealing temperature, holding time, cooling method, and cooling rate in each group of annealing process parameters, and then uses principal component analysis to map the high-dimensional parameters to a two-dimensional or three-dimensional parameter coordinate space to obtain the virtual coordinate positions of each group of parameters in space, thus forming a visualized parameter distribution.

[0048] Subsequently, the M tin plating quality impact indicators are mapped to the parameter distribution, and the three corresponding quality indicators are labeled for each parameter position. Based on pre-determined tin plating quality impact thresholds, all parameter groups are classified. When all predicted quality indicators corresponding to a certain group of annealing process parameters meet their corresponding preset threshold standards (e.g., adhesion above the lower limit, thickness uniformity fluctuation less than the upper limit, defect rate below the maximum allowable value), the parameter group is classified as an excitation parameter; if any predicted quality indicator fails to meet its threshold requirement, the parameter group is classified as a prohibition parameter. After classification, the coordinate positions of the excitation and prohibition parameters in the parameter distribution map are analyzed using the kernel density estimation method. This method constructs the probability density distribution around each parameter point to obtain the dense regions of various parameters in space, ultimately forming the excitation parameter density distribution and the prohibition parameter density distribution. The former represents the concentrated distribution area of ​​parameter combinations with excellent performance in space, while the latter represents the distribution area of ​​parameters with obvious quality problems.

[0049] Finally, the parameter density of the excitation region is expanded based on the excitation parameter density distribution and the taboo parameter density distribution. The optimization of minimizing the tin plating impact is then performed based on the expansion results. In this process, firstly, density-consistent clustering is performed based on the excitation parameter density distribution to divide it into multiple excitation parameter distribution regions. Then, excitation coefficient analysis is performed on each excitation parameter distribution region based on the taboo parameter density distribution, resulting in multiple excitation coefficients. The excitation parameter distribution regions with excitation coefficients greater than a preset excitation threshold are selected as expansion regions. Parameter expansion is performed within these expansion regions, and the tin plating impact of the expanded parameter groups is analyzed. Finally, combining the analysis results before and after expansion, the annealing process parameters with the minimum tin plating quality impact index are selected as the final optimal annealing parameters.

[0050] Furthermore, in the method provided in the application embodiment, the parameter density of the excitation region is expanded based on the excitation parameter density distribution and the taboo parameter density distribution, and the optimal annealing parameters are generated by minimizing the tin plating influence based on the expansion result. The method further includes:

[0051] Based on the density distribution of the excitation parameters, density-consistent clustering is performed to generate multiple excitation parameter distribution regions. Excitation coefficient analysis is then performed on these regions based on the density distribution of the taboo parameters to generate multiple excitation coefficients. The regions with excitation coefficients greater than a preset excitation threshold are used as expansion regions. Parameter expansion is performed within these expansion regions. Tin plating impact analysis is conducted based on the expansion results. The annealing process parameters corresponding to the minimum tin plating quality impact index are determined by combining the tin plating impact analysis results before and after expansion, thus generating the optimal annealing parameters.

[0052] In this embodiment, the excitation parameters are first grouped using a density-based clustering method based on their density distribution, and then density-consistent clustering is performed to generate multiple excitation parameter distribution regions. For example, with a minimum sample size of 5 and a neighborhood radius of 0.4, several densely distributed clustering regions in the parameter space are obtained, each region corresponding to a high-quality cluster of a class of annealing process parameters.

[0053] Subsequently, based on the taboo parameter density distribution, statistical analysis is performed on the parameters within multiple excitation parameter distribution regions. The ratio of the excitation parameter to the taboo parameter in each region is calculated, generating the corresponding ratio coefficients. These ratio coefficients are then output as excitation coefficients.

[0054] Then, the distribution region of excitation parameters with excitation coefficients greater than a preset excitation threshold is selected as the expansion region, and parameter expansion is performed within this region. In this process, technical experts first set a preset excitation threshold (e.g., 0.85) and select all excitation parameter distribution regions that meet this condition as expansion targets. Then, within these regions, based on historical data on existing annealing parameters and performance results, a performance-parameter influence relationship between annealing performance parameters and annealing process parameters is constructed. Using the desired annealing performance parameters as constraints, the parameters within the expansion region are adjusted to generate new combinations of annealing process parameters, thus completing the parameter expansion.

[0055] Next, based on the expanded results, an impact analysis of tin plating is performed. The newly generated annealing process parameter set is input into the tin plating quality impact model, and the predicted values ​​of coating adhesion, coating thickness uniformity, and interface defect rate for each parameter set are output sequentially. Then, a weighted normalization method is used to standardize the three sub-indices, converting the predicted values ​​into normalized values. Weights are then set according to process priorities (e.g., adhesion 0.5, uniformity 0.3, defect rate 0.2) to obtain the final tin plating quality impact index, representing the performance of this parameter set in the overall tin plating performance.

[0056] Finally, combining the analysis results of the tin plating impact before and after the expansion, the annealing process parameters corresponding to the minimum tin plating quality impact index were determined, generating the optimal annealing parameters. This step employed a global minimum value screening method, searching for the set of parameters with the minimum tin plating quality impact index from the original M sets of annealing process parameters and the newly expanded parameter sets, while also ensuring that this set of parameters simultaneously met the desired annealing performance parameters. This set of parameters was ultimately determined as the optimal annealing parameters.

[0057] Furthermore, in the method provided in the application embodiments, the method further includes performing excitation coefficient analysis on the distribution regions of the plurality of excitation parameters based on the taboo parameter density distribution to generate a plurality of excitation coefficients, and also includes:

[0058] Based on the taboo parameter density distribution, the ratio of the excitation parameter to the taboo parameter is calculated for the multiple excitation parameter distribution regions to generate multiple ratio coefficients; the multiple excitation coefficients are then generated using the multiple ratio coefficients.

[0059] In this embodiment, when calculating the ratio of excitation parameters to taboo parameters for multiple excitation parameter distribution regions based on the taboo parameter density distribution, for each excitation parameter distribution region, the number density of excitation parameters covered in the parameter space is statistically analyzed, and the corresponding taboo parameter density within the same spatial range is retrieved simultaneously. By constructing the ratio relationship between the excitation parameter density and the taboo parameter density region by region, the ratio coefficient for each excitation parameter distribution region is obtained. Through this process, multiple ratio coefficients are obtained.

[0060] The obtained ratio coefficients are then used as multiple excitation coefficients.

[0061] Furthermore, in the method provided in the application embodiments, performing parameter expansion within the expanded region further includes:

[0062] Construct a performance-parameter influence relationship between annealing performance parameters and annealing process parameters; based on the performance-parameter influence relationship, and with the desired annealing performance parameters as constraints, perform parameter adjustment within the expanded region to complete parameter expansion.

[0063] In this embodiment, the performance-parameter influence relationship between annealing performance parameters and annealing process parameters is first constructed. This step employs a multiple linear regression method, using annealing process parameters (including annealing temperature, holding time, heating rate, and cooling method) as input variables and annealing performance parameters (including surface state parameters, crystal structure parameters, and mechanical property parameters) as output variables. The model is trained based on a large amount of historical annealing experimental data. The functional relationship between the parameters is established through least-squares fitting, thereby achieving quantitative prediction of performance indicators by annealing process parameters.

[0064] Next, during the parameter adjustment process within the expanded region, based on the performance-parameter influence relationship and using the desired annealing performance parameters as constraints, a particle swarm optimization algorithm is employed for target search. In this process, the annealing process parameters within the expanded region are represented as particle positions, and the minimum expected deviation of the annealing performance parameters is used as the fitness function to iteratively optimize the velocity and position of each particle in the particle swarm. By setting the initial population size, maximum number of iterations, and convergence conditions, the particle swarm is guided to search for the optimal combination of annealing process parameters within the expanded region.

[0065] Finally, the process parameter combinations that meet all annealing performance parameter requirements are selected from the PSO optimization results and used as valid parameter points within the expanded region. This process completes the parameter expansion within the expanded region and ensures that the expanded results not only are distributed in high excitation density regions, but also meet the expected quality standards in terms of surface state parameters, crystal structure parameters, and mechanical property parameters.

[0066] Step S500: The copper capillary is annealed using the optimal annealing parameters.

[0067] In this embodiment of the application, when the copper capillary is annealed using the optimal annealing parameters, the optimal annealing parameters are input into the control system of the annealing equipment to control key process parameters such as temperature, holding time and cooling method during the annealing process. This allows the copper capillary to complete grain regulation and stress release under controlled heat treatment conditions, ultimately achieving an annealing effect that meets the expected surface state parameters, crystal structure parameters and mechanical performance parameters.

[0068] In summary, the embodiments of this application have at least the following technical effects:

[0069] This application connects to annealing equipment, collects historical data to establish an annealing process parameter library and an annealed copper tube characteristic influence library; obtains tin plating process information of the copper capillary, trains a tin plating quality influence model corresponding to the tin plating process flow; reads the desired annealing performance parameters of the copper capillary; uses the desired annealing performance parameters as the optimization target, calls the tin plating quality influence model, and performs optimization to minimize the annealing quality and tin plating influence based on the annealing process parameter library and the annealed copper tube characteristic influence library, generating optimal annealing parameters; and uses the optimal annealing parameters to control the annealing of the copper capillary. This invention solves the technical problem in the prior art where the annealing process cannot simultaneously consider the coupled control of copper capillary annealing performance and subsequent tin plating quality. By constructing an annealing process parameter library and an annealed copper tube characteristic influence library, and combining the tin plating quality influence model to optimize the annealing parameters, it achieves the technical effect of ensuring the copper capillary annealing performance meets standards while improving the consistency of tin plating quality.

[0070] Example 2, based on the same inventive concept as the precision annealing control method for copper capillaries in the foregoing examples, such as... Figure 2 As shown, this application provides a precision annealing control system for copper capillaries. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0071] The database establishment module 11 is used to connect to the annealing equipment, collect historical data to establish an annealing process parameter library and an annealed copper tube feature influence library; the training module 12 is used to obtain the tin plating process information of the copper capillary and train the tin plating quality influence model corresponding to the tin plating process flow; the parameter reading module 13 is used to read the expected annealing performance parameters of the copper capillary; the optimization module 14 is used to use the expected annealing performance parameters as the optimization target, call the tin plating quality influence model, and perform optimization to minimize the annealing quality and tin plating influence based on the annealing process parameter library and the annealed copper tube feature influence library to generate the optimal annealing parameters; the annealing control module 15 is used to control the annealing of the copper capillary with the optimal annealing parameters.

[0072] Furthermore, the system is also used to implement the following functions:

[0073] The annealing process parameter library includes multiple sets of annealing process parameters, and the annealed copper tube characteristic influence library includes multiple sets of annealed copper tube characteristic influence data corresponding to the multiple sets of annealing process parameters. Each set of annealed copper tube characteristic influence data includes at least surface state data, crystal structure data, and mechanical property data.

[0074] Furthermore, the system is also used to implement the following functions:

[0075] Surface condition data includes surface oxidation parameters and residual carbon parameters after heat treatment; crystal structure data includes grain size grade after heat treatment; and mechanical property data includes residual stress parameters after heat treatment.

[0076] Furthermore, the system is also used to implement the following functions:

[0077] Based on tin plating process information, historical tin plating process records are collected, and the influence relationship between surface state data, crystal structure data, and mechanical property data and tin plating quality is analyzed. The influence curves of surface state-tin plating quality, crystal structure-tin plating quality, and mechanical property-tin plating quality are output. Based on the influence curves of surface state-tin plating quality, crystal structure-tin plating quality, and mechanical property-tin plating quality, the tin plating quality influence model is trained.

[0078] Furthermore, the system is also used to implement the following functions:

[0079] Extract any set of parameters from the annealing process parameter library and collect performance test records after copper tube annealing. Construct a tin plating quality optimization space with M sets of annealing process parameters whose performance meets the desired annealing performance parameters. In the tin plating quality optimization space, call the tin plating quality influence model to minimize the tin plating influence and generate the optimal annealing parameters.

[0080] Furthermore, the system is also used to implement the following functions:

[0081] The tin plating quality influence model is used to analyze the tin plating quality influence of M sets of annealing process parameters in the tin plating quality optimization space, generating M tin plating quality influence indices. Virtual coordinate transformation is performed on the parameter distribution positions of the M sets of annealing process parameters to construct parameter distributions. The M tin plating quality influence indices are mapped to the parameter distributions, and excitation parameter density analysis is performed for tin plating quality influence indices that meet preset tin plating quality influence thresholds, and taboo parameter density analysis is performed for tin plating quality influence indices that do not meet preset tin plating quality influence thresholds, generating excitation parameter density distributions and taboo parameter density distributions. Based on the excitation parameter density distributions and taboo parameter density distributions, the parameter density of the excitation region is expanded, and the tin plating influence is minimized based on the expansion results to generate the optimal annealing parameters.

[0082] Furthermore, the system is also used to implement the following functions:

[0083] Based on the density distribution of the excitation parameters, density-consistent clustering is performed to generate multiple excitation parameter distribution regions. Excitation coefficient analysis is then performed on these regions based on the density distribution of the taboo parameters to generate multiple excitation coefficients. The regions with excitation coefficients greater than a preset excitation threshold are used as expansion regions. Parameter expansion is performed within these expansion regions. Tin plating impact analysis is conducted based on the expansion results. The annealing process parameters corresponding to the minimum tin plating quality impact index are determined by combining the tin plating impact analysis results before and after expansion, thus generating the optimal annealing parameters.

[0084] Furthermore, the system is also used to implement the following functions:

[0085] Based on the taboo parameter density distribution, the ratio of the excitation parameter to the taboo parameter is calculated for the multiple excitation parameter distribution regions to generate multiple ratio coefficients; the multiple excitation coefficients are then generated using the multiple ratio coefficients.

[0086] Furthermore, the system is also used to implement the following functions:

[0087] Construct a performance-parameter influence relationship between annealing performance parameters and annealing process parameters; based on the performance-parameter influence relationship, and with the desired annealing performance parameters as constraints, perform parameter adjustment within the expanded region to complete parameter expansion.

[0088] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0089] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0090] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for precision annealing control of copper capillary tubes, characterized in that, include: Connect the annealing equipment, collect historical data, and establish an annealing process parameter library and an annealing copper tube characteristic influence library; Obtain the tin plating process information of the copper capillary and train the tin plating quality influence model corresponding to the tin plating process flow. Read the desired annealing performance parameters of the copper capillary; Taking the desired annealing performance parameters as the optimization target, the tin plating quality influence model is called, and the annealing quality and tin plating influence are minimized based on the annealing process parameter library and the annealing copper tube characteristic influence library to generate the optimal annealing parameters; The copper capillary is annealed using the optimal annealing parameters.

2. The method for precision annealing control of copper capillary tubes as described in claim 1, characterized in that, The annealing process parameter library includes multiple sets of annealing process parameters, and the annealed copper tube characteristic influence library includes multiple sets of annealed copper tube characteristic influence data corresponding to the multiple sets of annealing process parameters. Each set of annealed copper tube characteristic influence data includes at least surface state data, crystal structure data, and mechanical property data.

3. The method for precision annealing control of copper capillary tubes as described in claim 2, characterized in that, Surface condition data includes surface oxidation parameters and residual carbon parameters after heat treatment; crystal structure data includes grain size grade after heat treatment; and mechanical property data includes residual stress parameters after heat treatment.

4. The method for precision annealing control of copper capillary tubes as described in claim 3, characterized in that, Obtain the tin plating process information of the copper capillary, and train the tin plating quality influence model corresponding to the tin plating process flow, including: Based on tin plating process information, historical tin plating process records are collected, and the influence relationship between surface state data, crystal structure data, and mechanical property data and tin plating quality is analyzed. The surface state-tin plating quality influence curve, crystal structure-tin plating quality influence curve, and mechanical property-tin plating quality influence curve are output. The tin plating quality influence model is trained based on the surface state-tin plating quality influence curve, the crystal structure-tin plating quality influence curve, and the mechanical properties-tin plating quality influence curve.

5. The method for precision annealing control of copper capillary tubes as described in claim 1, characterized in that, Using the desired annealing performance parameters as the optimization objective, the tin plating quality influence model is invoked. Based on the annealing process parameter library and the annealing copper tube characteristic influence library, the optimization of minimizing the annealing quality and tin plating influence is performed to generate the optimal annealing parameters, including: Extract any set of parameters from the annealing process parameter library and collect performance test records after copper tube annealing. Construct a tin plating quality optimization space with M sets of annealing process parameters whose performance meets the desired annealing performance parameters. Within the tin plating quality optimization space, the tin plating quality influence model is invoked to minimize the tin plating influence and generate the optimal annealing parameters.

6. The method for precision annealing control of copper capillary tubes as described in claim 5, characterized in that, Within the tin plating quality optimization space, the tin plating quality influence model is invoked to minimize the tin plating influence, generating the optimal annealing parameters, including: The tin plating quality influence model is used to analyze the influence of M annealing process parameters in the tin plating quality optimization space on tin plating quality, and M tin plating quality influence indices are generated. The parameter distribution of the M groups of annealing process parameters is transformed using virtual coordinates to construct the parameter distribution. The M tin plating quality influence indicators are mapped to the parameter distribution. Excitation parameter density analysis is performed on the tin plating quality influence indicators that meet the preset tin plating quality influence threshold, and taboo parameter density analysis is performed on the tin plating quality influence indicators that do not meet the preset tin plating quality influence threshold, generating excitation parameter density distribution and taboo parameter density distribution. Based on the excitation parameter density distribution and the taboo parameter density distribution, the parameter density of the excitation region is expanded, and the tin plating influence is minimized according to the expansion result to generate the optimal annealing parameters.

7. The method for precision annealing control of copper capillary tubes as described in claim 6, characterized in that, Based on the excitation parameter density distribution and the taboo parameter density distribution, the parameter density of the excitation region is expanded. Based on the expansion result, the tin plating influence is minimized to generate the optimal annealing parameters, including: Based on the excitation parameter density distribution, density-consistent clustering is performed to generate multiple excitation parameter distribution regions; Based on the taboo parameter density distribution, the excitation coefficients of the multiple excitation parameter distribution regions are analyzed to generate multiple excitation coefficients; The distribution area of ​​excitation parameters with excitation coefficients greater than the preset excitation threshold is taken as the expansion area. The parameters in the expansion area are expanded. The tin plating influence analysis is performed based on the expansion results. The annealing process parameters corresponding to the minimum tin plating quality influence index are determined by combining the tin plating influence analysis results before and after expansion, and the optimal annealing parameters are generated.

8. The method for precision annealing control of copper capillary tubes as described in claim 7, characterized in that, Based on the taboo parameter density distribution, an excitation coefficient analysis is performed on the distribution regions of the multiple excitation parameters to generate multiple excitation coefficients, including: Based on the taboo parameter density distribution, the ratio of the excitation parameter to the taboo parameter is calculated for the multiple excitation parameter distribution regions, generating multiple ratio coefficients; The plurality of excitation coefficients are generated using the plurality of ratio coefficients.

9. A method for precision annealing control of copper capillary tubes as described in claim 7, characterized in that, Perform parameter expansion within the expanded region, including: Construct the performance-parameter influence relationship between annealing performance parameters and annealing process parameters; Based on the performance-parameter influence relationship, and with the desired annealing performance parameters as constraints, parameter adjustment is performed within the expanded region to complete parameter expansion.

10. A precision annealing control system for copper capillary tubes, characterized in that, The system is used to perform a precision annealing control method for copper capillaries as described in any one of claims 1-9, the system comprising: The database creation module is used to connect to the annealing equipment, collect historical data to create an annealing process parameter library and an annealed copper tube characteristic influence library; The training module is used to obtain the tin plating process information of the copper capillary and train the tin plating quality influence model corresponding to the tin plating process flow. The parameter reading module is used to read the desired annealing performance parameters of the copper capillary. The optimization module is used to take the desired annealing performance parameters as the optimization target, call the tin plating quality influence model, and perform optimization to minimize the annealing quality and tin plating influence based on the annealing process parameter library and the annealing copper tube characteristic influence library, and generate the optimal annealing parameters. The annealing control module is used to control the annealing of the copper capillary with the optimal annealing parameters.

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