A control method for optimizing the quality of carbon fiber to metal wire welding

Abstract

The application discloses a kind of control methods for optimizing carbon fiber and metal wire welding quality, it is related to industrial automation control technical field, including, plating current density data and plating time data are carried out thickness parameter mapping, generate plating process parameter set;According to coating thickness data, plating process parameter set is screened, and coating treatment parameter is generated;Real-time spectrum analysis is carried out to wideband acoustic emission signal, and soldering iron temperature adjustment data is output, and molten pool dynamic analysis is carried out to welding image sequence, and molten pool oscillation frequency data is output;Molten pool oscillation frequency data, soldering iron temperature adjustment data and coating treatment parameter are carried out multi-source information fusion analysis, and soldering iron control instruction is output;Based on soldering iron control instruction, the welding temperature of carbon fiber and metal wire is accurately regulated in real time, and welding quality report is generated.The application is synergized by multi-source information fusion analysis and real-time welding correction double mechanism, realizes the accurate control and continuous optimization of welding quality in whole process.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and in particular to a control method for optimizing the welding quality of carbon fiber and metal wire. Background Technology

[0002] In recent years, the methods for joining carbon fiber and metallic dissimilar materials have gradually evolved from manual processes relying on operator experience to automated control based on multi-parameter coordination. In the field of industrial joining technology, existing methods generally employ programmed electroplating equipment to achieve metallization of carbon fiber surfaces. Static parameters such as current density and electroplating time are set through programmable logic controllers to complete the coating preparation. In terms of welding process control, mainstream methods mainly use temperature sensors and PID controllers to form a closed loop to adjust the soldering iron power, or use machine vision to monitor the morphology of the molten pool in real time and determine thresholds, forming a basic automation framework with single-input single-output control as its core.

[0003] However, existing methods have shortcomings in the dynamic coupling optimization of process parameters and the deep fusion of multimodal sensing information. Currently, there is a lack of quantitative mapping between electroplating process parameters and coating quality indicators, making it difficult to adaptively adjust according to changes in material state. The tinning and soldering processes lack sufficient collaborative analysis capabilities for key physical signals such as acoustic emission spectrum characteristics and dynamic oscillations of the molten pool, resulting in an inability to accurately intervene in the interface reaction mechanism. The data silos in each process stage hinder the closed-loop optimization of quality information throughout the entire process, limiting the performance improvement of joints in terms of strength, consistency, and long-term reliability. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method for controlling the welding quality of carbon fiber and metal wire to solve the problems of insufficient dynamic coupling optimization of process parameters and deep fusion of multimodal sensing information.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides a method for optimizing the control quality of carbon fiber and metal wire welding. The method includes: collecting coating thickness data, electroplating current density data, electroplating time data, and electroplating thickness data; mapping the electroplating current density data and electroplating time data to a thickness parameter to generate a set of electroplating process parameters; filtering the electroplating process parameter set according to the coating thickness data, and electroplating copper onto the ends of the carbon fiber based on the filtering results to generate coating treatment parameters; acquiring a broadband acoustic emission signal, performing real-time spectrum analysis on the broadband acoustic emission signal, and outputting soldering iron temperature adjustment data; acquiring a welding image sequence, performing dynamic analysis of the weld pool on the welding image sequence, and outputting weld pool oscillation frequency data; performing multi-source information fusion analysis on the weld pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters to output high-temperature soldering iron control parameters, and performing real-time welding correction on the high-temperature soldering iron control parameters to output soldering iron control commands; performing real-time precise control of the welding temperature of the carbon fiber and metal wire based on the soldering iron control commands, outputting welding process status parameters, and performing welding quality detection, recording, and integration of the welding process status parameters to generate a welding quality report.

[0008] As a preferred embodiment of the method for controlling the welding quality of carbon fiber and metal wire described in this invention, the step of mapping electroplating current density data and electroplating time data to generate an electroplating process parameter set includes the following specific steps:

[0009] Linear regression analysis was used to perform parameter correlation analysis between electroplating current density data and electroplating time data to generate current-time correlation data.

[0010] The thickness parameter is mapped between the current-time correlation data and the electroplating thickness data, and the thickness-parameter mapping relationship is output.

[0011] Based on the thickness-parameter mapping relationship, electroplating current density data, electroplating time data, and electroplating thickness data are combined and encapsulated to generate an electroplating process parameter set.

[0012] As a preferred embodiment of the method for controlling the welding quality of carbon fiber and metal wire described in this invention, the steps include: screening the electroplating process parameter set according to the coating thickness data, and electroplating copper onto the ends of the carbon fiber based on the screening results to generate coating treatment parameters.

[0013] The coating thickness data is matched and filtered with the electroplating process parameter set to output the target electroplating parameter set.

[0014] The carbon fiber ends are electroplated with copper using the target electroplating parameter set, and the electroplating process parameters are output.

[0015] The electroplating process parameters are aligned and integrated with the target electroplating parameter set to generate coating treatment parameters.

[0016] As a preferred embodiment of the method for controlling the welding quality of carbon fiber and metal wire described in this invention, the steps of acquiring a broadband acoustic emission signal, performing real-time spectrum analysis on the broadband acoustic emission signal, and outputting soldering iron temperature adjustment data are as follows:

[0017] Wideband acoustic emission signals are acquired during the tinning process using an acoustic emission sensor;

[0018] Perform real-time spectrum analysis on broadband acoustic emission signals and output time-spectrum data;

[0019] Welding characteristic frequencies are extracted from the time-spectrum data, and a spectral feature vector is output.

[0020] The similarity statistics between the spectral feature vector and the preset welding state reference spectrum are performed to output the soldering iron temperature adjustment data.

[0021] As a preferred embodiment of the method for controlling the welding quality of carbon fiber and metal wire described in this invention, the steps of acquiring welding image sequences, performing dynamic analysis of the weld pool on the welding image sequences, and outputting weld pool oscillation frequency data are as follows:

[0022] Welding images of carbon fiber and metal wire are acquired using a high-speed camera and then integrated to generate a welding image sequence.

[0023] Dynamic tracking of the molten pool contour is performed on the welding image sequence, and the molten pool oscillation characteristic data is output.

[0024] Perform time-domain periodic statistics on the molten pool oscillation trajectory data and output the molten pool oscillation frequency data.

[0025] As a preferred embodiment of the control method for optimizing the welding quality of carbon fiber and metal wire described in this invention, the dynamic tracking of the molten pool contour is achieved by extracting the molten pool boundary pixels in the welding image sequence frame by frame using an edge detection algorithm, and establishing the molten pool contour motion trajectory based on the contour coordinate correlation of the molten pool boundary pixels between adjacent frames.

[0026] As a preferred embodiment of the method for controlling the welding quality of carbon fiber and metal wire described in this invention, the step of fusing and analyzing multi-source information such as molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters to output high-temperature soldering iron control parameters includes the following specific steps:

[0027] Hierarchical matching is performed on the molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters to generate a welding data set;

[0028] The welding data set is fused and analyzed to output a comprehensive set of control parameters;

[0029] The integrated control parameter set is converted into process parameters to output the high-temperature soldering iron control parameters.

[0030] As a preferred embodiment of the method for controlling the optimization of carbon fiber and metal wire welding quality according to the present invention, the specific steps of real-time welding correction of high-temperature soldering iron control parameters and outputting soldering iron control commands are as follows:

[0031] A PID control algorithm is used to perform real-time deviation compensation of the high-temperature soldering iron control parameters based on the molten pool oscillation frequency data, and output corrected control parameters.

[0032] The control parameters are modified and the control instructions are encapsulated to output soldering iron control instructions.

[0033] As a preferred embodiment of the control method for optimizing the welding quality of carbon fiber and metal wire according to the present invention, the specific steps of real-time and precise control of the welding temperature of carbon fiber and metal wire based on soldering iron control commands, and outputting welding process status parameters, are as follows:

[0034] Using soldering iron control commands, the welding temperature of carbon fiber and metal wire can be adjusted in real time, and real-time welding temperature data can be output.

[0035] The system analyzes the welding process status of real-time welding temperature data, outputs temperature characteristic data, and performs state parameter transformation on the temperature characteristic data to output welding process status parameters.

[0036] As a preferred embodiment of the method for controlling the welding quality of carbon fiber and metal wire as described in this invention, the specific steps for detecting, recording, and integrating welding process state parameters to generate a welding quality report are as follows:

[0037] Welding process state parameters are extracted using principal component analysis to output welding quality characteristic data.

[0038] Perform qualification matching verification and welding quality quantification on welding quality characteristic data, and output welding quality verification results;

[0039] The welding quality verification results, welding process status parameters, molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters are matched and integrated to generate a welding quality report.

[0040] The beneficial effects of this invention are as follows: Through the synergistic effect of multi-source information fusion analysis and real-time welding correction mechanisms, precise control and continuous optimization of welding quality throughout the entire process are achieved. By hierarchically matching and fusing molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters, a comprehensive control parameter set is output and converted into high-temperature soldering iron control parameters. This achieves deep integration and collaborative optimization of multi-dimensional process data, providing a precise benchmark for dynamic control. Based on molten pool oscillation frequency data, real-time deviation compensation and instruction encapsulation of high-temperature soldering iron control parameters are performed, enabling microsecond-level dynamic adjustment of welding heat input. This ensures stable and controllable interface reaction processes, establishing a complete data closed loop from material pretreatment and process monitoring to quality assessment, significantly improving the consistency and long-term reliability of the joint. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the 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.

[0042] Figure 1 A flowchart illustrating a method for optimizing the control of welding quality between carbon fiber and metal wire.

[0043] Figure 2 A flowchart for generating coating processing parameters.

[0044] Figure 3 This is a flowchart for outputting the molten pool oscillation frequency data.

[0045] Figure 4 A flowchart for generating a welding quality report. Detailed Implementation

[0046] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0047] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0049] Reference Figures 1-4 This is one embodiment of the present invention, which provides a method for optimizing the control quality of welding carbon fiber and metal wire, comprising the following steps:

[0050] S1. Collect plating thickness data, electroplating current density data, electroplating time data, and electroplating thickness data. Map the electroplating current density data and electroplating time data to the thickness parameter to generate a set of electroplating process parameters.

[0051] Data on coating thickness, electroplating current density, electroplating time, and electroplating thickness were collected. Linear regression analysis was used to perform parameter correlation analysis between electroplating current density data and electroplating time data to generate current-time correlation data.

[0052] Specifically, the coating thickness data for welding carbon fiber and metal wire is obtained from the user's welding requirement drawings; electroplating current density data is acquired in real time by the current and voltage sensors built into the electroplating power supply; electroplating time data is synchronously recorded and continuously accumulated by a high-precision timer in the industrial control system at the start of electroplating; electroplating thickness data is obtained by measuring the ends of the electroplated carbon fiber using a non-contact laser thickness gauge deployed at the outlet of the electroplating tank; linear regression analysis is used to perform parameter correlation analysis between electroplating current density data and electroplating time data, that is, using electroplating current density data as the independent variable and electroplating time data as the dependent variable to form a correspondence between electroplating current density data and electroplating time data, and the linear relationship between electroplating current density data and electroplating time data is statistically analyzed using the least squares method to determine the slope and intercept of the fitted line; according to the linear function relationship between independent and dependent variables in the linear regression analysis method, the electroplating current density data and electroplating time data are integrated to form a linear expression defined by the slope and intercept; based on the quantitative expression, each set of electroplating current density data and electroplating time data is mapped to generate current-time correlation data.

[0053] The thickness parameter is mapped between the current-time correlation data and the electroplating thickness data, and the thickness-parameter mapping relationship is output.

[0054] Specifically, each set of current-time correlation data is aligned with the corresponding collected electroplating thickness data by timestamp to form a triplet data structure; using current-time correlation data as input variables and electroplating thickness data as output variables, an interpolation fitting method is used to establish a monotonic correspondence between input and output variables; through piecewise linear fitting or polynomial fitting, the variation law of electroplating thickness data corresponding to different current-time correlation data is determined, and the variation law of electroplating thickness data is solidified in the form of a data table or function to output the thickness-parameter mapping relationship.

[0055] Based on the thickness-parameter mapping relationship, electroplating current density data, electroplating time data, and electroplating thickness data are combined and encapsulated to generate an electroplating process parameter set.

[0056] Specifically, the electroplating current density data, electroplating time data, and electroplating thickness data are paired according to the thickness-parameter mapping relationship to form a one-to-one correspondence of electroplating current density data, electroplating time data, and electroplating thickness data into a triplet data structure. The triplet data structure is then organized into a list or table with index keys using data structuring methods to ensure that each data point reflects the electroplating thickness data change pattern defined by the thickness-parameter mapping relationship. The integrated list or table is then encapsulated into an electroplating process parameter set.

[0057] S2. The electroplating process parameter set is screened according to the coating thickness data, and copper is electroplated on the ends of the carbon fiber based on the screening results to generate coating treatment parameters.

[0058] The coating thickness data is matched and filtered with the electroplating process parameter set to output the target electroplating parameter set.

[0059] Specifically, each triplet data structure in the electroplating process parameter set, consisting of electroplating current density data, electroplating time data, and electroplating thickness data, is read item by item. The electroplating thickness data in the triplet data structure is obtained, and the absolute difference between the electroplating thickness data and the coating thickness data is calculated. It is then determined whether the absolute difference is less than or equal to a preset thickness tolerance threshold. If it is less than or equal to the preset thickness tolerance threshold, the current triplet data structure is included in the candidate set. If the absolute difference is greater than the preset thickness tolerance threshold, the current triplet data structure is removed. After traversing all triplet data structures in the electroplating process parameter set, all triplet data structures in the candidate set that meet the preset thickness tolerance threshold are taken as the target electroplating parameter set.

[0060] Furthermore, the preset thickness tolerance threshold is set according to the requirements of the carbon fiber and metal wire welding process for coating uniformity and bonding strength, and is set to ±0.02mm. When the coating thickness deviation is controlled within ±0.02mm, the tinning wettability is good, and there is no poor soldering or peeling at the welding interface, while taking into account both electroplating efficiency and material cost. The thickness tolerance threshold can ensure the reliability of the subsequent tinning and welding process, and avoid damage to the carbon fiber matrix due to excessive pursuit of precision, such as excessively long electroplating time or excessively high current density.

[0061] Using the target electroplating parameter set, copper is electroplated onto the ends of carbon fibers, and the electroplating process parameters are output.

[0062] Specifically, a set of triplet data structures containing electroplating current density data, electroplating time data, and electroplating thickness data is selected from the target electroplating parameter set. All triplet data structures in the target electroplating parameter set are sorted in ascending order of electroplating time data. Under the premise of minimizing the deviation between electroplating thickness data and coating thickness data, the triplet data structure with the shortest electroplating time data is selected first to balance process efficiency and coating quality. The electroplating current density data is sent to the electroplating power supply as a current control command, and the electroplating time data is sent to a high-precision timer in the industrial control as the electroplating duration set value for the copper electroplating process. During the copper electroplating process, the actual applied current value, actual running time, and corresponding process status are recorded in real time to form electroplating process parameters.

[0063] The electroplating process parameters are aligned and integrated with the target electroplating parameter set to generate coating treatment parameters.

[0064] Specifically, the actual applied current value, actual running time, and corresponding process state in the electroplating process parameters are matched one-to-one with the electroplating current density data, electroplating time data, and electroplating thickness data in the selected triplet data structure of the target electroplating parameter set. The data of the corresponding fields are combined into a complete record containing the target set value and the actual executed value to form the coating processing parameters.

[0065] S3. Acquire broadband acoustic emission signals, perform real-time spectrum analysis on broadband acoustic emission signals, output soldering iron temperature adjustment data, acquire welding image sequences, perform dynamic analysis of the weld pool on the welding image sequences, and output weld pool oscillation frequency data.

[0066] Broadband acoustic emission signals during the tinning process are acquired using acoustic emission sensors. Real-time spectrum analysis of the broadband acoustic emission signals is performed, and time-spectrum data is output.

[0067] Specifically, an acoustic emission sensor is fixed at the soldering station to collect broadband acoustic emission signals generated in real time during the soldering process due to solder melting, flow, and wetting of the plating layer. These signals are then integrated to generate a discrete time series. The discrete time series is then processed by dividing it into frames. A window function (such as a Hanning window) is applied to each frame of data to reduce spectral leakage. A fast Fourier transform is performed on each frame of data after windowing to convert the time-domain signal into a frequency-domain representation, obtaining the amplitude-frequency spectrum corresponding to the current frame. The amplitude-frequency spectra of all frames are arranged sequentially according to the time of acquisition to form a two-dimensional time-spectrum data. In the time-spectrum data, the horizontal axis represents time, the vertical axis represents frequency, and the pixel intensity represents the energy magnitude of the corresponding frequency component.

[0068] Welding characteristic frequencies are extracted from the time-spectrum data, and the spectral feature vector is output.

[0069] Specifically, the frequency range corresponding to energy concentration regions (energy concentration regions refer to continuous regions in the time-spectrum data where the amplitude is higher than a preset energy threshold, which is set to 1.5 times the overall average amplitude of the time-spectrum data. Setting the preset energy threshold to 1.5 times the overall average amplitude of the time-spectrum data is to effectively distinguish the actual physical acoustic emission signal during welding from background noise. 1.5 times can suppress low-amplitude random interference while retaining key characteristic frequencies, taking into account both sensitivity and stability; during the determination, the average amplitude of all pixels in the time-spectrum data is first counted, and then the amplitude of each pixel is compared with 1.5 times the average amplitude. Pixels with amplitudes higher than the preset energy threshold are retained. Connectivity statistics are performed on adjacent high-amplitude pixels to form continuous regions, which are then identified as energy concentration regions) are determined to identify candidate feature frequency bands; local energy peak points are searched within the candidate feature frequency bands, and the frequency value and amplitude corresponding to each peak point are recorded; the frequency values ​​of all peak points are sorted in ascending order, and the amplitudes are normalized; the normalized amplitude sequence is combined with the corresponding frequency values ​​to form a numerical array, and the spectral feature vector is output.

[0070] The similarity statistics between the spectral feature vector and the preset welding state reference spectrum are performed to output the soldering iron temperature adjustment data.

[0071] Specifically, the standard spectral feature vector matching the current welding process conditions is retrieved from the preset welding state reference spectrum, and the cosine similarity between the spectral feature vector and the standard spectral feature vector is calculated. Based on the cosine similarity, the corresponding soldering iron temperature adjustment direction and adjustment amount are determined by querying the welding state reference spectrum. The adjustment direction and adjustment amount are combined into a numerical command, and the soldering iron temperature adjustment data is output.

[0072] Furthermore, the preset welding state reference spectrum is obtained by welding carbon fiber and metal wire under standard process conditions. It includes the spectral feature vectors corresponding to stable welding at different soldering iron temperatures and the associated optimal soldering iron temperature values. Each set of data records the acoustic emission characteristics of wetting, no false soldering, and stable molten pool. After normalization and clustering, a standard spectral feature vector set is formed, and a mapping relationship is established with the corresponding temperature adjustment direction and adjustment amount, which is then solidified into the welding state reference spectrum.

[0073] The formula for calculating cosine similarity is:

[0074] ;

[0075] in, The cosine similarity between the spectral eigenvector and the standard spectral eigenvector is represented. The component index number represents the difference between the spectral eigenvector and the standard spectral eigenvector. This represents the dimension of the spectral eigenvector compared to the standard spectral eigenvector. Represents the spectral eigenvector of the first The value of each component Represents the standard spectral eigenvector. The value of each component.

[0076] Welding images of carbon fiber and metal wire are acquired using a high-speed camera and then integrated to generate a welding image sequence.

[0077] Specifically, when acquiring welding images of carbon fiber and metal wire using a high-speed camera, the high-speed camera is aimed at the welding area and continuously captured at high speed during the welding process to obtain single-frame welding images that include the entire process of molten pool formation, expansion, and solidification. All single-frame welding images are arranged in order of acquisition time, and timestamps are added to generate a welding image sequence.

[0078] Dynamic tracking of the molten pool contour is performed on the welding image sequence, and the molten pool oscillation characteristic data is output.

[0079] Specifically, when performing dynamic tracking of the molten pool contour in a welding image sequence, an edge detection algorithm is used to extract boundary pixels for each frame of the welding image sequence, obtaining the coordinate set of the molten pool boundary pixels in the current frame. The coordinate set of the molten pool boundary pixels in the current frame is spatially proximity matched with the coordinate set of the molten pool boundary pixels in the previous frame, and a continuous molten pool contour motion trajectory is established based on the contour coordinate correlation of the molten pool boundary pixels between adjacent frames. The changes in molten pool area, molten pool centroid position, and boundary fluctuation amplitude over time are statistically analyzed according to the molten pool contour motion trajectory, and the molten pool oscillation feature data are output.

[0080] Perform time-domain periodic statistics on the molten pool oscillation trajectory data and output the molten pool oscillation frequency data.

[0081] Specifically, when performing time-domain periodic statistics on the molten pool oscillation trajectory data, a one-dimensional time series characterizing the change of the molten pool boundary fluctuation amplitude over time is obtained from the molten pool oscillation trajectory data. The one-dimensional time series is obtained by statistically analyzing the average Euclidean distance from the molten pool boundary pixel to the molten pool centroid in each frame of the welding image, and arranging the average Euclidean distances in frame order to form a numerical sequence that changes over time, i.e., the one-dimensional time series. Zero-point crossing detection or peak detection is performed on the one-dimensional time series to identify the time interval between adjacent peaks or adjacent zero-crossing points. The average value of all time intervals is calculated and the reciprocal is taken to integrate and output the molten pool oscillation frequency data.

[0082] S4. Perform multi-source information fusion analysis on the molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters, output high-temperature soldering iron control parameters, perform real-time soldering correction on the high-temperature soldering iron control parameters, and output soldering iron control commands.

[0083] Hierarchical matching is performed on the molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters to generate a welding data set.

[0084] Specifically, when performing hierarchical matching of molten pool oscillation frequency data, soldering iron temperature adjustment data, and plating treatment parameters, the plating treatment parameters are used as the first-level index to filter out molten pool oscillation frequency data and soldering iron temperature adjustment data corresponding to the same electroplating process conditions; the molten pool oscillation frequency data is used as the second-level index to match soldering iron temperature adjustment data with the same frequency band as the molten pool oscillation frequency data; the molten pool oscillation frequency data, soldering iron temperature adjustment data, and plating treatment parameters that satisfy the first-level index and the second-level index are combined into a welding record, and all matching welding records are collected to generate a welding data set.

[0085] The welding data set is fused and analyzed to output a comprehensive set of control parameters.

[0086] Specifically, the molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters in the welding dataset are normalized to eliminate dimensional differences. A weighted average method is then used to fuse the normalized molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters, with fixed weights: molten pool oscillation frequency data 40%, soldering iron temperature adjustment data 35%, and coating treatment parameters 25%. This weighting is determined based on the degree of influence of each parameter on the joint quality during carbon fiber and metal wire welding: the molten pool oscillation frequency directly reflects the stability of the molten pool and the dynamics of heat input, playing a dominant role in weld formation, hence it is given the highest weight; the soldering iron temperature adjustment data affects solder wettability and interfacial reaction rate, thus its importance is reduced; the coating treatment parameters determine the thickness and uniformity of the electroplated layer, although basic conditions, they are already solidified during the welding stage and change little, hence their lowest weight; the weighted average comprehensive control parameters are then organized into a structured parameter list, outputting a comprehensive control parameter set.

[0087] The formula for calculating the comprehensive control parameters is as follows:

[0088] ;

[0089] in, Indicates the comprehensive control parameters. This represents the value of the normalized molten pool oscillation frequency data. This represents the value of the normalized soldering iron temperature adjustment data. This represents the normalized coating treatment parameter values. The fixed weights represent the molten pool oscillation frequency data. This indicates the fixed weights of the soldering iron temperature adjustment data. This represents the fixed weights of the coating treatment parameters.

[0090] The integrated control parameter set is converted into process parameters to output the high-temperature soldering iron control parameters.

[0091] Specifically, the structured parameter list in the integrated control parameter set is converted item by item into process instructions executable by the high-temperature soldering iron according to the preset high-temperature soldering iron mapping rules (the preset high-temperature soldering iron mapping rules are obtained based on the carbon fiber and metal wire welding process window calibration, and a series of welding tests are conducted under different set temperatures, heating times and power levels (low, medium and high), the corresponding integrated control parameter set value range is recorded, and a one-to-one correspondence table between the normalized fusion value and the actual process parameters is established. The high-temperature soldering iron mapping rules ensure that the fusion value is linearly or piecewise linearly mapped to the actual temperature set value, heating time and power output level that the high-temperature soldering iron can execute within the [0,1] interval, thereby ensuring the process feasibility and execution accuracy of the control instructions). This includes denormalizing the normalized fusion value into the actual temperature set value, heating time and power output level; encapsulating the converted actual temperature set value, heating time and power output level, and outputting the high-temperature soldering iron control parameters.

[0092] A PID control algorithm is used to perform real-time deviation compensation on the control parameters of the high-temperature soldering iron based on the oscillation frequency data of the molten pool, and output corrected control parameters.

[0093] Specifically, the molten pool oscillation frequency data is compared with the target value of the molten pool oscillation frequency under stable welding conditions to obtain the current frequency deviation. The PID control algorithm performs proportional, integral, and derivative operations based on the frequency deviation. When performing proportional operations based on the frequency deviation, the PID control algorithm directly uses the frequency deviation at the current moment as the proportional term input. When performing integral operations, it continuously accumulates all frequency deviations from the start of welding to the current moment to form a historical deviation sum. When performing derivative operations, it calculates the rate of change of the frequency deviation at the current moment compared with the frequency deviation at the previous moment, and merges the results of proportional, integral, and derivative operations to generate a deviation compensation amount. The deviation compensation amount is then superimposed with the high-temperature soldering iron control parameters in real time to output the corrected control parameters.

[0094] The control parameters are modified and the control instructions are encapsulated to output soldering iron control instructions.

[0095] Specifically, the corrected actual temperature setpoint, heating time, and power output level in the corrected control parameters are encoded, including adding a start identifier, parameter field, check bit, and end identifier; the encoded corrected control parameters are packaged into digital signals that conform to the industrial controller interface, and soldering iron control commands are output.

[0096] S5. Based on the soldering iron control commands, the welding temperature of carbon fiber and metal wire is precisely controlled in real time, the welding process status parameters are output, the welding process status parameters are inspected and recorded and integrated to generate a welding quality report.

[0097] Using soldering iron control commands, the welding temperature of carbon fiber and metal wire is adjusted in real time, and real-time welding temperature data is output.

[0098] Specifically, the corrected actual temperature setpoint, heating time, and power output level in the soldering iron control command are sent to the high-temperature soldering iron. The high-temperature soldering iron dynamically adjusts the heating power and heating duration according to the received soldering iron control command, so that the soldering iron tip temperature tracks the corrected actual temperature setpoint in real time. The temperature sensor integrated inside the soldering iron tip continuously collects the current actual temperature, forming a temperature sampling sequence arranged in chronological order, which is the real-time soldering temperature data.

[0099] The system analyzes the welding process status of real-time welding temperature data, outputs temperature characteristic data, and performs state parameter transformation on the temperature characteristic data to output welding process status parameters.

[0100] Specifically, real-time welding temperature data is segmented chronologically to identify the heating, holding, and cooling stages. During the heating stage, the rate of temperature increase is recorded as the heating rate. The highest temperature reached during the entire welding process is recorded as the peak temperature. During the holding stage, the duration the temperature remains at the peak temperature is determined as the holding time. During the cooling stage, the rate of temperature decrease is calculated as the cooling rate. The heating rate, peak temperature, holding time, and cooling rate are combined to form temperature characteristic data. State parameter transformation is performed on the temperature characteristic data: the heating rate is divided into three levels (low, medium, and high) and mapped to corresponding state identifiers; the peak temperature is compared with the target temperature required by the process, and a corresponding deviation state identifier is generated based on the relative position of the peak temperature; the holding time is compared with the minimum effective time required by the process to determine whether the requirements are met and a compliance state identifier is generated; the cooling rate is divided into three cooling levels (slow cooling, normal, and rapid cooling) and mapped to corresponding state identifiers; the transformation results of each state parameter are combined into a structured vector to form the welding process state parameters.

[0101] Principal component analysis is used to extract the principal components of the welding process state parameters and output welding quality characteristic data.

[0102] Specifically, the welding process state parameters obtained during the welding process are used to construct a sample matrix, with each row corresponding to a structured vector of a single welding operation and each column corresponding to a state dimension. The sample matrix is ​​centered, and the covariance matrix is ​​calculated. The covariance matrix is ​​then decomposed into eigenvalues ​​to obtain eigenvalues ​​and their corresponding eigenvectors. The eigenvalues ​​are sorted from largest to smallest, and the eigenvectors corresponding to the principal components are selected to form a projection matrix. The welding process state parameters are then linearly transformed into the projection matrix to obtain the dimensionality-reduced principal component representation, and the welding quality feature data is output.

[0103] Perform qualification matching verification and welding quality quantification on welding quality characteristic data, and output welding quality verification results.

[0104] Specifically, the welding quality characteristic data is compared dimension-by-dimensionally with the qualified welding quality characteristic range (the qualified welding quality characteristic range is determined by performing principal component analysis on the welding process state parameters of historically stable welding batches, statistically analyzing the mean and standard deviation of each principal component, and using the mean ± 3 times the standard deviation as the upper and lower limits; the qualified welding quality characteristic range covers normal process fluctuations, effectively accommodating process variations while excluding abnormal welding events; using the qualified welding quality characteristic range ensures that the qualification judgment is neither too lenient, leading to missed defects, nor too strict, leading to misjudgments, thereby improving the robustness and reliability of welding quality verification) to determine whether each principal component of the welding quality characteristic data is within the qualified welding quality characteristic range; if all principal components are within the qualified welding quality characteristic range, it is judged as qualified; if any principal component is outside the qualified welding quality characteristic range, it is judged as unqualified; a comprehensive deviation index is calculated based on the degree of deviation of each principal component from the qualified welding quality characteristic range, which serves as the welding quality quantitative score; the qualified / unqualified judgment result is combined with the welding quality quantitative score to output the welding quality verification result.

[0105] The welding quality verification results, welding process status parameters, molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters are matched and integrated to generate a welding quality report.

[0106] Specifically, taking a single welding event as a unit, the welding quality verification results, welding process status parameters, molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters corresponding to the same welding event are organized into a structured record; the structured records corresponding to all welding events are arranged in chronological order to form a data table containing complete process chain information; the data table is processed into a report to generate a welding quality report.

[0107] This embodiment also provides a computer device applicable to the control method for optimizing the welding quality of carbon fiber and metal wire, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the control method for optimizing the welding quality of carbon fiber and metal wire as proposed in the above embodiment.

[0108] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0109] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the control method for optimizing the welding quality of carbon fiber and metal wire as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0110] In summary, this invention achieves precise control and continuous optimization of welding quality throughout the entire process through the synergistic effect of multi-source information fusion analysis and real-time welding correction. By hierarchically matching and fusing molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters, a comprehensive control parameter set is output and converted into high-temperature soldering iron control parameters. This enables deep integration and synergistic optimization of multi-dimensional process data, providing a precise benchmark for dynamic control. Based on molten pool oscillation frequency data, real-time deviation compensation and instruction encapsulation of high-temperature soldering iron control parameters are performed, achieving microsecond-level dynamic adjustment of welding heat input. This ensures stable and controllable interface reaction processes, and establishes a complete data closed loop from material pretreatment and process monitoring to quality assessment, significantly improving the consistency and long-term reliability of the joint.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling the welding quality of carbon fiber and metal wire, characterized in that: include, Collect coating thickness data, electroplating current density data, electroplating time data, and electroplating thickness data. Map the electroplating current density data and electroplating time data to the thickness parameter to generate a set of electroplating process parameters. Specifically, the coating thickness data for welding carbon fiber and metal wire is obtained from the user's welding requirement drawings; the electroplating thickness data is obtained by measuring the ends of the electroplated carbon fiber using a non-contact laser thickness gauge deployed at the outlet of the electroplating tank. The electroplating process parameter set is screened according to the coating thickness data, and copper is electroplated on the ends of the carbon fiber based on the screening results to generate coating treatment parameters. The specific steps are as follows: acquire a broadband acoustic emission signal, perform real-time spectrum analysis on the broadband acoustic emission signal, and output soldering iron temperature adjustment data. Wideband acoustic emission signals are acquired during the tinning process using an acoustic emission sensor; Perform real-time spectrum analysis on broadband acoustic emission signals and output time-spectrum data; Welding characteristic frequencies are extracted from the time-spectrum data, and a spectral feature vector is output. The similarity statistics between the spectral feature vector and the preset welding state reference spectrum are performed to output the soldering iron temperature adjustment data; Acquire welding image sequences, perform dynamic analysis of the molten pool in the welding image sequences, and output molten pool oscillation frequency data; The system integrates and analyzes multi-source information, including molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters, to output high-temperature soldering iron control parameters. It also performs real-time soldering correction on the high-temperature soldering iron control parameters and outputs soldering iron control commands. Based on the soldering iron control commands, the welding temperature of carbon fiber and metal wire is precisely controlled in real time, the welding process status parameters are output, the welding process status parameters are detected and recorded to integrate the welding quality, and a welding quality report is generated.

2. The method for controlling the welding quality of carbon fiber and metal wire as described in claim 1, characterized in that: The specific steps for mapping electroplating current density data and electroplating time data to thickness parameters to generate an electroplating process parameter set are as follows: Linear regression analysis was used to perform parameter correlation analysis between electroplating current density data and electroplating time data to generate current-time correlation data. The thickness parameter is mapped between the current-time correlation data and the electroplating thickness data, and the thickness-parameter mapping relationship is output. Based on the thickness-parameter mapping relationship, electroplating current density data, electroplating time data, and electroplating thickness data are combined and encapsulated to generate an electroplating process parameter set.

3. The method for controlling the welding quality of carbon fiber and metal wire as described in claim 1, characterized in that: The process involves filtering the electroplating process parameter set according to the coating thickness data, and then electroplating copper onto the ends of the carbon fiber based on the filtering results to generate coating treatment parameters. The specific steps are as follows: The coating thickness data is matched and filtered with the electroplating process parameter set to output the target electroplating parameter set. The carbon fiber ends are electroplated with copper using the target electroplating parameter set, and the electroplating process parameters are output. The electroplating process parameters are aligned and integrated with the target electroplating parameter set to generate coating treatment parameters.

4. The method for controlling the welding quality of carbon fiber and metal wire as described in claim 1, characterized in that: The steps for acquiring welding image sequences, performing dynamic analysis of the molten pool on the welding image sequences, and outputting molten pool oscillation frequency data are as follows: Welding images of carbon fiber and metal wire are acquired using a high-speed camera and then integrated to generate a welding image sequence. Dynamic tracking of the molten pool contour is performed on the welding image sequence, and the molten pool oscillation characteristic data is output. Perform time-domain periodic statistics on the molten pool oscillation trajectory data and output the molten pool oscillation frequency data.

5. The method for controlling the welding quality of carbon fiber and metal wire as described in claim 4, characterized in that: The dynamic tracking of the molten pool contour is achieved by extracting the boundary pixels of the molten pool in the welding image sequence frame by frame using an edge detection algorithm, and establishing the motion trajectory of the molten pool contour based on the contour coordinate correlation of the boundary pixels of the molten pool between adjacent frames.

6. The method for controlling the welding quality of carbon fiber and metal wire as described in claim 1, characterized in that: The specific steps involve fusing and analyzing multi-source information such as molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters to output high-temperature soldering iron control parameters. Hierarchical matching is performed on the molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters to generate a welding data set; The welding data set is fused and analyzed to output a comprehensive set of control parameters; The integrated control parameter set is converted into process parameters to output the high-temperature soldering iron control parameters.

7. The method for controlling the welding quality of carbon fiber and metal wire as described in claim 1, characterized in that: The specific steps for real-time soldering correction of the high-temperature soldering iron control parameters and output of soldering iron control commands are as follows: A PID control algorithm is used to perform real-time deviation compensation of the high-temperature soldering iron control parameters based on the molten pool oscillation frequency data, and output corrected control parameters. The control parameters are modified and the control instructions are encapsulated to output soldering iron control instructions.

8. The method for controlling the welding quality of carbon fiber and metal wire as described in claim 1, characterized in that: The process involves real-time and precise control of the welding temperature between carbon fiber and metal wire based on soldering iron control commands, and outputting welding process status parameters. The specific steps are as follows: Using soldering iron control commands, the welding temperature of carbon fiber and metal wire can be adjusted in real time, and real-time welding temperature data can be output. The system analyzes the welding process status of real-time welding temperature data, outputs temperature characteristic data, and performs state parameter transformation on the temperature characteristic data to output welding process status parameters.

9. The method for controlling the welding quality of carbon fiber and metal wire as described in claim 1, characterized in that: The specific steps for integrating and recording welding process status parameters to generate a welding quality report are as follows: Welding process state parameters are extracted using principal component analysis to output welding quality characteristic data. Perform qualification matching verification and welding quality quantification on welding quality characteristic data, and output welding quality verification results; The welding quality verification results, welding process status parameters, molten pool oscillation frequency data, soldering iron temperature adjustment data, and coating treatment parameters are matched and integrated to generate a welding quality report.

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