Insulator lead welding process for tantalum capacitor

Abstract

The invention discloses an insulator lead welding process for a tantalum capacitor. Comprising sensor data acquisition and monitoring, dynamic control algorithm execution and welding parameter adjustment, welding process adaptive adjustment, machine learning optimization and defect prediction and data fusion and Kalman filtering. By integrating various sensors and a real-time data feedback mechanism, key parameters such as temperature, pressure and time in the welding process are monitored in real time, defects possibly occurring in the welding process are predicted by applying a machine learning algorithm, and the welding process is dynamically adjusted. The self-adaptive system can cope with multiple dynamic changes such as temperature, humidity and material characteristics, the consistency and stability of welding quality are ensured, and the contradiction between welding efficiency and quality is effectively balanced. Compared with the prior art, a more accurate, real-time and intelligent welding control mode is provided, and the adaptability and stability under the complex environment and parameter change in the welding process are greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field related to welding control, and in particular to a welding process for insulator leads of tantalum capacitors. Background Art

[0002] Tantalum capacitors are widely used in electronic devices, and their welding quality directly affects the performance and stability of capacitors. During the welding process of tantalum capacitors, it is usually necessary to accurately control parameters such as temperature, pressure and time to ensure the quality of each welding point. However, due to the multiple dynamic factors involved in the welding process, the precise control of temperature, pressure and time has always been a technical problem in the industry.

[0003] In the prior art, the welding process is usually controlled by a simple temperature control system and pressure regulating equipment. These systems ensure the consistency and stability of the welding points by setting fixed welding parameters, but since the characteristics of the materials and environmental conditions may change during the welding process, fixed parameters often cannot adapt to complex working conditions. The main problems include: 1. It is difficult to find the best balance between temperature control and pressure control. Excessive temperature or pressure may cause poor welding or damage to the capacitor; 2. The temperature and pressure fluctuate greatly during the welding process, affecting the consistency of welding quality and resulting in a high rate of defective products; 3. As the equipment ages and environmental conditions change, the control of welding parameters needs to be adjusted in real time, and the prior art cannot achieve precise dynamic adjustment.

[0004] In order to deal with the above problems, the industry has adopted solutions to improve the accuracy of temperature control and pressure control, such as introducing advanced PID control algorithms and automatic feedback systems, but these systems are often costly and difficult to adapt to dynamic changes. Although these technologies have improved welding quality, they have not yet fully solved the problem of balancing temperature control and pressure control, especially under complex environmental conditions.

[0005] Therefore, how to improve welding quality and production efficiency through multi-parameter dynamic control and adaptive feedback mechanism has become the technical problem to be solved by the present invention. Summary of the invention

[0006] In order to solve the defects in the prior art, the present invention provides a welding process for insulator leads of tantalum capacitors.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] The present invention provides a welding process for insulator leads for tantalum capacitors, comprising the following steps:

[0009] Step 1: Sensor data collection and monitoring:

[0010] Real-time monitoring of temperature, pressure and humidity during welding by multiple sensors, wherein the sensors include at least one temperature sensor, one pressure sensor and one humidity sensor, and the data acquisition module of the sensors is connected to the central processing unit;

[0011] The data acquisition module transmits the real-time monitored temperature, pressure and humidity data to the central processing unit via the data bus;

[0012] Step 2, execution of dynamic control algorithm and adjustment of welding parameters:

[0013] The central processing unit uses the PID control algorithm and the fuzzy logic algorithm in the multi-parameter dynamic control algorithm to adjust the temperature, pressure and welding time in the welding process in real time according to the temperature, pressure and humidity data collected by the sensor;

[0014] The heating power adjustment formula in the PID control algorithm is: Where P is the heating power, Q is the heat transferred during the heating process, ΔT is the temperature change, t is the time, and the temperature change ΔT is calculated based on the difference between the real-time temperature and the set temperature; where the real-time temperature is measured by the sensor, the set temperature is the target temperature preset by the system, and the temperature change ΔT = real-time temperature - set temperature;

[0015] Fuzzy logic algorithms are used to optimize the interaction between temperature, pressure and welding time to improve welding quality and consistency;

[0016] Step 3: Adaptive adjustment of welding process:

[0017] The central processing unit dynamically adjusts the heating rate, cooling rate, welding time and pressure of the welding process according to the thermal conductivity, specific heat capacity, environmental conditions and real-time data changes of the tantalum capacitor lead wire during welding; wherein the environmental conditions include ambient temperature and humidity; the specific implementation methods are as follows:

[0018] Dynamic adjustment of heating rate: The heating power is adjusted according to the temperature difference measured in real time, that is, the difference between the real-time temperature and the set target temperature. The heating power P is dynamically controlled by the PID control algorithm to make the heating rate R heat To achieve the predetermined goal, the heating rate adjustment formula is:

[0019]

[0020] Where P is the heating power, m is the mass of the welding material, and C is the specific heat capacity of the welding material;

[0021] Dynamic adjustment of cooling rate: After welding is completed, the cooling rate R is controlled by adjusting the flow rate of the cooling medium or the opening degree of the cooling device. cool, the cooling rate adjustment formula is:

[0022]

[0023] Among them, T ambient is the ambient temperature, T weld is the welding area temperature, t cool For cooling time;

[0024] Dynamic adjustment of welding time: The welding time is dynamically adjusted according to the changes of real-time temperature and target temperature; set the target temperature T target When welding time t weld Calculated by the following formula:

[0025]

[0026] Among them, T current is the current temperature, T target To set the target temperature, R heat is the heating rate;

[0027] Dynamic adjustment of pressure: During welding, the applied pressure is adjusted by real-time feedback of pressure sensor data; real-time pressure value P sensor It is transmitted to the central processing unit, and based on the real-time data feedback, the pressure applied is adjusted by the pressure valve of the hydraulic system. The pressure adjustment formula is:

[0028] P pressure =f(T current , T target , P sensor )

[0029] Wherein, f is the pressure regulation function, which represents the temperature change, that is, the relationship between the difference between the real-time temperature and the set target temperature and the real-time pressure sensor feedback data;

[0030] Step 4: Machine learning optimization and defect prediction:

[0031] The central processing unit combines historical data with real-time data and uses a machine learning algorithm to predict and adjust welding defects that may occur during the welding process in real time;

[0032] The machine learning algorithm includes a support vector machine (SVM), a deep neural network (DNN), or a reinforcement learning algorithm. When training the model, the input data includes temperature, pressure, humidity, welding time, and historical records of welding defects;

[0033] During the training process, the weight coefficients used are fitted by a linear regression model based on historical welding data. The specific calculation method is as follows: the relationship between the welding defect probability and the welding parameters is obtained by fitting the historical data. The model formula is: y = α·X + β, where y is the welding defect probability, X is the welding parameter, and α and β are the regression coefficients obtained by fitting the historical data;

[0034] The predicted defect probability value output by the model is fed back to the central processing unit for real-time adjustment of welding parameters to ensure welding quality;

[0035] Step 5, data fusion and Kalman filtering:

[0036] The central processing unit optimizes welding parameters through Kalman filtering algorithm based on multi-sensor feedback data during welding, including temperature, pressure, humidity and welding quality data, using data fusion technology;

[0037] The prediction step formula of the Kalman filter algorithm is: k =F·x k-1 +B·u k , where x k is the current state estimate, F is the state transfer matrix, B is the control input matrix, u k is the control input;

[0038] The update steps are: K = P k|k-1 ·H T ·(H·P k|k-1 ·H T +R) -1 , where K is the Kalman gain, P is the covariance matrix, H is the measurement matrix, and R is the measurement noise covariance; the Kalman gain is used to calculate the temperature and pressure parameters in the welding process.

[0039] As a preferred technical solution of the present invention, the temperature control is implemented by a PID control algorithm, and the formula of the PID control algorithm is:

[0040]

[0041] Where, e(t) is the error between the current temperature and the set temperature, K p , K i , K d They are proportional, integral and differential constants respectively. The heating power can be adjusted in real time by adjusting these constants.

[0042] As a preferred technical solution of the present invention, the pressure adjustment is achieved through real-time feedback of pressure sensor data, and the pressure is dynamically controlled by adjusting the pressure valve of the hydraulic system.

[0043] As a preferred technical solution of the present invention, the central processing unit optimizes the nonlinear relationship between temperature, pressure and welding time during the welding process by introducing a fuzzy logic control algorithm, and adjusts the relationship between welding parameters in real time through fuzzy rules.

[0044] As a preferred technical solution of the present invention, the central processing unit predicts welding defects by combining historical data with real-time data and adopting a support vector machine (SVM) algorithm. The defect prediction results are used to adjust the temperature, pressure and time parameters during the welding process.

[0045] As a preferred technical solution of the present invention, the central processing unit generates a welding defect classification model based on a machine learning algorithm through a training data set, wherein the classification model includes a probability prediction of welding defects, and the relationship between the defect probability and the welding parameters is represented by a polynomial regression model.

[0046] As a preferred technical solution of the present invention, the Kalman filter algorithm recursively optimizes the temperature and pressure data and updates the system state at each sensor feedback to improve the accuracy and stability of temperature and pressure control.

[0047] As a preferred technical solution of the present invention, the heating / cooling rate during the welding process is calculated by a real-time physical model based on the thermal conductivity and specific heat capacity of the welding material. The physical model includes a heat conduction equation and a heat convection model for accurately calculating temperature changes.

[0048] As a preferred technical solution of the present invention, the real-time data during the welding process is uploaded to the cloud platform through the wireless communication module. The cloud platform further analyzes the data and generates a dynamic maintenance plan to guide the next welding operation.

[0049] As a preferred technical solution of the present invention, the welding defect prediction model quantifies the risk of welding defects by comparing historical welding defect data with current welding conditions using a weighted average method, and feeds back the quantified results to the central processing unit for real-time adjustment.

[0050] The beneficial effects of the present invention are:

[0051] 1. By integrating multiple sensors and real-time data feedback mechanisms, key parameters such as temperature, pressure, and time in the welding process are monitored in real time, and machine learning algorithms are used to predict possible defects in the welding process and dynamically adjust the welding process. The adaptive system can cope with multiple dynamic changes such as temperature, humidity, and material properties, ensure the consistency and stability of welding quality, and effectively balance the contradiction between welding efficiency and quality. Compared with existing technologies, it provides a more accurate, real-time, and intelligent welding control method, which greatly improves the adaptability and stability of the welding process under complex environments and parameter changes.

[0052] 2. By introducing real-time data feedback and automatic adjustment mechanisms in each link of the welding process, this technical solution can optimize the distribution of welding temperature, pressure and time, and significantly reduce quality fluctuations caused by equipment aging, environmental changes or material differences. The welding quality is guaranteed, and by reducing human intervention and equipment failures, this technology improves the overall efficiency of the production line, reduces the rate of defective products, and brings significant production cost savings.

[0053] 3. The present invention combines material properties and real-time environmental data to adjust various welding parameters using an adaptive feedback mechanism. The system can not only meet the welding needs of various types of materials, but also dynamically adjust welding conditions according to changes in ambient temperature and humidity, ensuring that the quality consistency of the welding points can be maintained regardless of whether it is in an environment with a large temperature difference or when welding different materials (such as tantalum capacitor leads, etc.).

[0054] 4. By integrating the PID control algorithm with the fuzzy logic control algorithm, the present invention can accurately adjust the temperature, pressure and time in a complex welding process, thereby avoiding common problems such as overheating, overpressure and uneven welding. The system is optimized based on real-time feedback so that each welding point meets high-standard quality requirements, thereby improving the long-term stability and reliability of the capacitor. This optimization process not only improves welding accuracy, but also effectively reduces defects caused by parameter fluctuations.

[0055] 5. Based on precise temperature and pressure control, this technical solution reduces energy waste during welding and reduces equipment energy consumption through real-time feedback mechanisms and optimization algorithms. In addition, automatic adjustment of welding parameters reduces the need for human intervention and avoids welding defects or material waste caused by improper operation, thereby further reducing overall production costs. This innovative technology not only improves production efficiency, but also shows significant advantages in energy saving and cost reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0057] Figure 1 It is a system architecture diagram of the present invention.

[0058] Figure 2 It is a welding process control flow chart of the present invention. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0060] The present invention provides a tantalum capacitor insulator lead welding process, comprising the following process steps:

[0061] Step 1: Sensor data collection and monitoring:

[0062] Real-time monitoring of temperature, pressure and humidity during welding by multiple sensors, wherein the sensors include at least one temperature sensor, one pressure sensor and one humidity sensor, and the data acquisition module of the sensors is connected to the central processing unit;

[0063] The data acquisition module transmits the real-time monitored temperature, pressure and humidity data to the central processing unit via the data bus;

[0064] Step 2, execution of dynamic control algorithm and adjustment of welding parameters:

[0065] The central processing unit adopts a multi-parameter dynamic control algorithm according to the temperature, pressure and humidity data collected by the sensor, wherein the multi-parameter dynamic control algorithm includes a PID control algorithm and a fuzzy logic algorithm for real-time adjustment of the temperature, pressure and welding time during the welding process;

[0066] The heating power adjustment formula in the PID control algorithm is: Where P is the heating power, Q is the heat transferred during the heating process, ΔT is the temperature change, t is the time, and the temperature change ΔT is calculated based on the difference between the real-time temperature and the set temperature; where the real-time temperature is measured by the sensor, the set temperature is the target temperature preset by the system, and the temperature change ΔT = real-time temperature - set temperature;

[0067] Fuzzy logic algorithms are used to optimize the interaction between temperature, pressure and welding time to improve welding quality and consistency;

[0068] Step 3: Adaptive adjustment of welding process:

[0069] The central processing unit dynamically adjusts the heating rate, cooling rate, welding time and pressure of the welding process according to the thermal conductivity, specific heat capacity, environmental conditions and real-time data changes of the tantalum capacitor lead wire during welding; wherein the environmental conditions include ambient temperature and humidity; the specific implementation method is as follows:

[0070] Dynamic adjustment of heating rate: The heating power is adjusted according to the temperature difference measured in real time, that is, the difference between the real-time temperature and the set target temperature. The heating power P is dynamically controlled by the PID control algorithm to make the heating rate R heat To achieve the predetermined goal, the heating rate adjustment formula is:

[0071]

[0072] Where P is the heating power, m is the mass of the welding material, and C is the specific heat capacity of the welding material; the difference between the real-time temperature and the set temperature determines the adjustment of the heating power, thereby controlling the heating rate. The heating rate can be further adjusted according to the thermal conductivity of the material to ensure uniform heating.

[0073] Dynamic adjustment of cooling rate: After welding is completed, the cooling rate R is controlled by adjusting the flow rate of the cooling medium or the opening degree of the cooling device. cool , the cooling rate adjustment formula is:

[0074]

[0075] Among them, T ambient is the ambient temperature, T weld is the welding area temperature, t cool For cooling time; real-time adjustment of cooling rate can optimize welding quality and avoid overheating or thermal cracks in solder joints.

[0076] Dynamic adjustment of welding time: The welding time is dynamically adjusted according to the changes of real-time temperature and target temperature; set the target temperature T target When welding time t weld Calculated by the following formula:

[0077]

[0078] Among them, T current is the current temperature, T target To set the target temperature, R heat is the heating rate; according to the relationship between the real-time temperature difference and the heating rate, the welding time is adjusted to achieve the best welding effect.

[0079] Dynamic adjustment of pressure: During welding, the applied pressure is adjusted by real-time feedback of pressure sensor data; real-time pressure value P sensorIt is transmitted to the central processing unit, and based on the real-time data feedback, the pressure applied is adjusted by the pressure valve of the hydraulic system. The pressure adjustment formula is:

[0080] P pressure =f(T current , T target , P sensor )

[0081] Among them, f is the pressure regulation function, which represents the temperature change, that is, the relationship between the difference between the real-time temperature and the set target temperature and the real-time pressure sensor feedback data; this function is obtained through experiments or data fitting, and its specific form is dynamically calculated based on the temperature difference and the real-time data feedback of the pressure sensor to ensure that the applied pressure meets the welding requirements.

[0082] Source of the function: Pressure regulation function f(T current , T target , P sensor ) is obtained by fitting historical experimental data and real-time sensor feedback. During the experiment, by measuring the pressure feedback and temperature changes under different welding conditions, the function is fitted using regression analysis or machine learning algorithms to ensure that the pressure applied during welding can be accurately adjusted according to the real-time feedback data.

[0083] Real-time regulation of applied pressure ensures uniform pressure during welding, thereby avoiding cracks or loose welds and further improving welding quality.

[0084] Step 4: Machine learning optimization and defect prediction:

[0085] The central processing unit combines historical data with real-time data and uses a machine learning algorithm to predict welding defects that may occur during the welding process and make real-time adjustments to avoid the occurrence of welding defects;

[0086] The machine learning algorithm includes a support vector machine (SVM), a deep neural network (DNN), or a reinforcement learning algorithm. When training the model, the input data includes temperature, pressure, humidity, welding time, and historical records of welding defects;

[0087] During the training process, the weight coefficients used are fitted by a linear regression model based on historical welding data. The specific calculation method is as follows: the relationship between the welding defect probability and the welding parameters is obtained by fitting the historical data. The model formula is: y = α·X + β, where y is the welding defect probability, X is the welding parameter, and α and β are the regression coefficients obtained by fitting the historical data;

[0088] The predicted defect probability value output by the model is fed back to the central processing unit for real-time adjustment of welding parameters to ensure welding quality;

[0089] Step 5, data fusion and Kalman filtering:

[0090] The central processing unit optimizes welding parameters through Kalman filtering algorithm based on multi-sensor feedback data during welding, including temperature, pressure, humidity and welding quality data, using data fusion technology;

[0091] The prediction step formula of the Kalman filter algorithm is: k =F·x k-1 +B·u k , where x_k is the current state estimate, F is the state transfer matrix, B is the control input matrix, and u_k is the control input;

[0092] The update steps are: K = P k|k-1 ·H T ·(H·P k|k-1 ·H T +R) -1 , where K is the Kalman gain, P is the covariance matrix, H is the measurement matrix, and R is the measurement noise covariance;

[0093] The Kalman gain is used to optimize the temperature and pressure parameters in the welding process and ensure the welding quality; and, in a further solution, system coordination and closed-loop control can be performed, such as the central processing unit realizes the coordinated work of each control module in the welding process based on the feedback of the multi-sensor data, control algorithm and machine learning model, forming a closed-loop control system to ensure the stability of welding quality; and temperature control is achieved through a PID control algorithm, and the formula of the PID control algorithm is:

[0094]

[0095] Where, e(t) is the error between the current temperature and the set temperature, K p , K i , K d They are proportional, integral and differential constants respectively. The heating power can be adjusted in real time by adjusting these constants.

[0096] Pressure adjustment is achieved through real-time feedback of pressure sensor data, and the pressure is dynamically controlled by adjusting the pressure valve of the hydraulic system to ensure the uniformity of applied pressure; the central processing unit optimizes the nonlinear relationship between temperature, pressure and welding time in the welding process by introducing a fuzzy logic control algorithm, and adjusts the relationship between welding parameters in real time through fuzzy rules; the central processing unit predicts welding defects by combining historical data with real-time data and using a support vector machine (SVM) algorithm, and the defect prediction results are used to adjust the temperature, pressure and time parameters in the welding process

[0097] The central processing unit generates a welding defect classification model through a training data set based on a machine learning algorithm, wherein the classification model includes a probability prediction of welding defects, and the relationship between the defect probability and the welding parameters is represented by a polynomial regression model; the Kalman filter algorithm recursively optimizes the temperature and pressure data, and updates the system state estimate at each sensor feedback to improve the accuracy and stability of temperature and pressure control; the heating / cooling rate during the welding process is calculated based on the thermal conductivity and specific heat capacity of the welding material through a real-time physical model, and the physical model includes a heat conduction equation and a thermal convection model for accurately calculating temperature changes; the real-time data during the welding process is uploaded to the cloud platform through a wireless communication module, and the cloud platform further analyzes the data and generates a dynamic maintenance plan to guide the next welding operation; the welding defect prediction model quantifies the risk of welding defects by comparing historical welding defect data with current welding conditions using a weighted average method, and feeds the quantified results back to the central processing unit for real-time adjustment.

[0098] Embodiment 1:

[0099] This embodiment uses the tantalum capacitor lead welding process as an application scenario to demonstrate the specific implementation of the technical solution of the present invention. In the traditional welding process, due to changes in temperature, pressure and time, the welding quality is easily affected by factors such as environmental changes and material differences, resulting in inconsistent welding point quality and affecting the reliability of the final product. In order to solve this problem, this embodiment adopts a dynamic control system based on sensor feedback, combined with a machine learning algorithm for optimization, to ensure high precision and high efficiency of the welding process.

[0100] In specific applications, the system monitors multiple parameters in the welding process in real time through integrated temperature, pressure and humidity sensors. When the welding environment or material properties change, the central processing unit receives sensor data and adjusts the welding temperature and pressure through the PID control algorithm. At this time, all variables in the welding process will be optimized through the adaptive adjustment mechanism, thereby avoiding the negative impact of overheating, overpressure and other problems on the welding point. At the same time, the machine learning algorithm uses historical data and real-time feedback data to predict possible defects in the welding process and automatically adjusts parameters, avoiding errors caused by manual intervention and ensuring the high quality of each welding point.

[0101] Specifically, the system can also adjust the welding parameters in real time according to the aging of the equipment and the dynamic changes of the external environment (such as humidity changes, ambient temperature fluctuations, etc.), thereby improving the adaptability of welding. For example, in an environment with high humidity, the system automatically extends the heating time and adjusts the pressure to ensure the consistency of welding quality. This technology effectively improves welding efficiency, reduces the occurrence of defective products, and significantly reduces production costs during the production process; through this embodiment, the technical solution demonstrates how to achieve a dual improvement in quality stability and production efficiency during the welding process through multi-parameter dynamic control and adaptive feedback mechanism, solving the problems of difficult control and high defect rate in traditional welding processes.

[0102] Embodiment 2:

[0103] This embodiment demonstrates the significant advantages of this technical solution in the welding process of tantalum capacitors through comparative experiments. In traditional welding processes, due to the large range of temperature and pressure changes, it is difficult to maintain consistent welding quality, and the incidence of welding defects is high. In order to verify the technical effect of the present invention, we designed a comparative experiment, using the traditional welding method and the multi-parameter dynamic control system of the present invention for welding, and tested and recorded various parameters during the welding process.

[0104] Experimental data show that when using traditional welding methods, the welding quality fluctuates greatly and the electrical performance of the welding points generally decreases. Specifically, when the ambient temperature changes greatly, the welding defect rate of the traditional method is 10.5%. In contrast, the welding system using this technical solution successfully reduces the defect rate to 3.2% by real-time monitoring of temperature, pressure and other parameters, and adjusting the welding conditions in real time according to feedback data, thereby improving the stability and consistency of welding quality.

[0105] In addition, this technical solution has significantly improved welding efficiency. By introducing machine learning algorithms to optimize welding parameter adjustment, this system not only reduces the generation of defective products, but also shortens the welding cycle by about 15%, thereby improving overall production efficiency. More importantly, with the application of the system's automatic adjustment function, a large amount of manual intervention is not required during the production process, which further reduces the error rate of operation and effectively reduces production costs.

[0106] In the actual production process, in an environment with large temperature fluctuations, such as welding, the temperature near the welding area fluctuates greatly. It is difficult for traditional welding methods to maintain consistency. This system ensures that each welding meets high quality standards by flexibly adjusting parameters. In addition, the system's adaptive adjustment mechanism for equipment aging also provides a guarantee for long-term stable operation.

[0107] Embodiment 3:

[0108] This embodiment describes in detail how to ensure accurate control of various parameters in the welding process to improve welding quality and consistency through dynamic adjustment of heating / cooling rate, welding time and pressure. At the same time, the details of the machine learning algorithm and the specific implementation path of the pressure adjustment function are further described.

[0109] This embodiment uses multiple sensors to monitor key parameters such as temperature, pressure, and time during the welding process in real time. These sensors include temperature sensors, pressure sensors, timers, etc., which can feed back data to the central processing unit in real time during the welding process. The temperature sensor monitors the temperature of the welding area in real time and feeds back the data to the central processing unit. Set the target temperature T target With real-time temperature T current The difference ΔT will be used to adjust the heating power. The pressure sensor monitors the pressure in the welding area in real time to ensure that the applied pressure is uniform. Through the feedback of pressure data, the central processing unit can adjust the pressure in real time to ensure the stability of the weld.

[0110] The adjustment of the heating rate is determined based on the real-time temperature difference ΔT and the thermal characteristics of the welding material. The central processing unit calculates the heating power P using the PID control algorithm based on the difference between the real-time temperature of the welding area and the set target temperature. The adjustment formula for the heating power P is: Where Q is the amount of heat transferred, t is the heating time, and ΔT is the temperature difference. This formula is based on the principles of thermodynamics and can accurately control the heating process. Heating rate R heat Calculated by the following formula: Where m is the mass of the welding material and C is the specific heat capacity of the welding material. The real-time temperature is combined with the thermal characteristics of the welding material to dynamically adjust the heating power, thereby achieving precise control.

[0111] Welding time t weld Dynamic adjustment based on temperature difference and heating rate. Based on the difference between the real-time temperature and the target temperature, the required welding time is calculated to ensure that the heat input during the welding process meets the predetermined standard; the welding time is calculated using the following formula: Among them, T target To set the target temperature, T current is the real-time temperature, R heat is the heating rate. This formula dynamically adjusts the welding time based on the relationship between the real-time temperature difference and the heating rate.

[0112] During welding, the applied pressure is adjusted by real-time feedback of the pressure sensor data. The pressure adjustment is based on the real-time temperature and pressure sensor data, using the function f(T current , T target , P sensor) to calculate the required applied pressure.

[0113] The pressure regulation function f is based on the real-time temperature difference ΔT and the real-time pressure sensor data P sensor Adjust the applied pressure. The function is as follows: P pressure =α·ΔT+β·P sensor +γ, where α, β, and γ are coefficients obtained by fitting historical experimental data, representing the influence of temperature changes and pressure sensor feedback data on the applied pressure. This formula is fitted by regression analysis or machine learning algorithms (such as support vector machines (SVM), neural networks (DNN), etc.) to ensure that the pressure applied under different welding conditions can be automatically adjusted according to real-time data. The machine learning model is trained by inputting historical experimental data and data from the real-time welding process to optimize the coefficients of the pressure regulation function. During the training process, the coefficients α, β, and γ are adjusted using the gradient descent method or other optimization algorithms to obtain the optimal pressure regulation function. After the model training is completed, the model is input with real-time welding data (such as temperature changes, pressure feedback, etc.) to calculate the required pressure value to ensure that the pressure applied during the welding process meets the predetermined standards, thereby improving the welding quality.

[0114] This embodiment also introduces a machine learning model (such as SVM, DNN, etc.) to optimize various parameters in the welding process. By training the historical data of the welding process, the model can predict welding defects and adjust parameters such as heating, pressure and time according to the prediction results. The optimization process is as follows: the machine learning model adjusts the welding parameters through real-time data input, reduces welding defects and improves the quality of solder joints. The training process of the model is carried out through historical experimental data to ensure that it can predict and adjust the welding process according to different welding conditions. The input of the model includes real-time data of the welding process such as temperature, pressure, time, etc., and the output is the adjustment parameters (such as heating power, welding time, applied pressure, etc.).

[0115] Embodiment 4:

[0116] This embodiment describes in detail how to optimize key parameters such as temperature, pressure, and welding time during the welding process of insulator leads for tantalum capacitors through data feedback and adaptive control technology, thereby further improving welding quality and production efficiency.

[0117] In this embodiment, multiple sensors (including temperature sensors, pressure sensors, humidity sensors, etc.) are used to monitor various parameters in the welding process in real time. The data acquisition modules of these sensors are connected to the central processing unit (CPU) to transmit the data fed back by the sensors to the central processing unit in real time. The data bus is used to ensure the efficiency and stability of the transmission.

[0118] The collected temperature, pressure and humidity data are processed by the central processing unit and used for subsequent parameter adjustment. The real-time and accuracy of the data directly affect the welding quality. Therefore, ensuring the high accuracy and stability of the data acquisition and transmission module is the prerequisite for implementing this technical solution.

[0119] In this embodiment, key parameters such as temperature, pressure and time in the welding process are dynamically adjusted by the central processing unit. The adjustment algorithm adopts a combination of PID control and fuzzy logic algorithm. Through the following specific implementation paths, such as temperature control: dynamically adjust the heating power through the PID control algorithm. The PID control formula is as follows:

[0120] P=Q·ΔT / t

[0121] Where P is the heating power, Q is the amount of heat transferred during the heating process, ΔT is the temperature change, and t is time. The difference (ΔT) between the real-time temperature and the set temperature is used to adjust the heating power P to ensure that the temperature reaches the predetermined target.

[0122] Welding time adjustment: Based on the difference between the real-time temperature and the target temperature, the welding time t weld is calculated by the following formula:

[0123] t weld =(T target -T current ) / R heat

[0124] Among them, T target is the target temperature, T current is the current temperature, R heat is the heating rate. Welding time t wold Dynamic adjustment ensures that the heat input meets the standard during welding, preventing overheating or insufficient welding.

[0125] Pressure control: The applied pressure is adjusted by real-time pressure sensor feedback data. The pressure adjustment formula is:

[0126] P pressure =f(T current , T target , P sensor )

[0127] Among them, T current and T target are the current temperature and the target temperature, P sensor It is the real-time pressure sensor data. Through this function, the pressure is adjusted in real time to ensure the stability and consistency of the welding area.

[0128] This embodiment further optimizes various parameters in the welding environment through an adaptive adjustment mechanism. According to the thermal conductivity, specific heat capacity and environmental conditions (such as ambient temperature, humidity, etc.) of the tantalum capacitor lead, the central processing unit dynamically adjusts the heating rate, cooling rate, welding time and applied pressure during the welding process.

[0129] Dynamic adjustment of heating rate: By measuring the temperature difference ΔT in real time, combined with the mass m and specific heat capacity C of the welding material, the heating power P is adjusted. Heating rate R heat Calculated by the following formula:

[0130] R heat =P / (m·C)

[0131] Where m is the mass of the welding material and C is the specific heat capacity of the welding material. By controlling the heating rate, the temperature change of the welding area can be accurately controlled to avoid excessively high or low temperatures.

[0132] Cooling rate control: After welding, the cooling rate R cool Adjusted by the following formula:

[0133] R cool =(T ambient -T weld ) / t cool

[0134] Among them, T ambient is the ambient temperature, T weld is the temperature of the welding area, t cool is the cooling time. This formula ensures that the solder joint can be cooled evenly after welding to prevent thermal cracks.

[0135] In this embodiment, the central processing unit also predicts defects in the welding process through a machine learning algorithm (such as a support vector machine SVM or a deep neural network DNN) and adjusts the welding parameters according to the prediction results.

[0136] The historical welding data and real-time feedback data are combined to fit the relationship between welding defect probability and welding parameters through a linear regression model. The specific formula is as follows:

[0137] y=α·X+β

[0138] Among them, y is the probability of welding defects, X is the welding parameters, and α and β are regression coefficients. The regression model obtained by fitting historical data can provide a basis for real-time adjustment to ensure welding quality.

[0139] And through data fusion technology, the central processing unit combines multi-sensor data (temperature, pressure, humidity, etc.) and uses the Kalman filter algorithm to optimize the welding parameters. The prediction formula of the Kalman filter is:

[0140] x k =F·x k-1 +B·u k

[0141] The update formula is:

[0142] K=P k|k-1 ·H T ·(H·P k|k-1 ·H T +R) -1

[0143] Among them, x k is the current state estimate, F is the state transfer matrix, B is the control input matrix, u k is the control input, K is the Kalman gain, P is the covariance matrix, H is the measurement matrix, and R is the measurement noise covariance. Through Kalman filtering, the control of temperature and pressure can be optimized more accurately.

[0144] Embodiment 5:

[0145] In order to further optimize the temperature, pressure and welding time control during the welding process of tantalum capacitors, this embodiment further improves the accuracy and adaptability of the welding process through an improved multi-parameter dynamic control system combined with a machine learning algorithm, thereby ensuring the stability and high quality of each welding point.

[0146] This embodiment combines a more accurate sensor feedback mechanism with an adaptive control algorithm to ensure that the various parameters in the welding process can still maintain high quality consistency under complex environmental conditions, such as:

[0147] Step 1: Sensor data acquisition and monitoring. Multiple sensors (temperature sensors, pressure sensors, humidity sensors) are used to monitor key parameters in the welding process in real time. The data acquisition module transmits the temperature, pressure and humidity data collected by the sensor to the central processing unit in real time. These real-time data are transmitted to the central processing unit through the data bus to ensure the efficiency and stability of data acquisition and transmission; the selection of sensors and their layout are optimized through experiments to ensure accurate real-time feedback under various working conditions. For example, during the welding process, the real-time temperature T measured by the temperature sensor is current As an important parameter, it is fed back to the central processing unit in real time and further used in calculations in the temperature control algorithm.

[0148] Step 2: Multi-parameter dynamic control and welding parameter adjustment; The central processing unit uses improved PID control algorithm and fuzzy logic control algorithm to adjust various parameters in the welding process according to real-time data; Heating power adjustment: According to temperature change ΔT = T current -Ttarget , the PID control algorithm is used to dynamically adjust the heating power P. The calculation formula for the heating power is:

[0149] P=Q·ΔT / t

[0150] Where Q is the transferred heat and t is the time, ensuring that the power control during the heating process is consistent with the welding requirements. ; Heating rate adjustment: According to the mass m and specific heat capacity C of the welding material, the heating rate R heat The dynamic adjustment formula is:

[0151]

[0152] Where P is the heating power, m is the mass of the welding material, and C is the specific heat capacity of the welding material. This formula can adjust the rate of the heating process in real time to avoid uneven welding caused by too fast or too slow temperature changes.

[0153] Step 3: Adaptively adjust the welding process; during the welding process, the central processing unit dynamically adjusts the heating rate, cooling rate, welding time and pressure according to the thermal conductivity, specific heat capacity and other material properties of the tantalum capacitor lead wire, combined with environmental conditions (such as humidity, temperature changes, etc.); Cooling rate adjustment: The cooling rate R during the cooling process cool The adjustment formula is:

[0154]

[0155] Among them, T ambient is the ambient temperature, T weld is the welding area temperature, t cool is the cooling time. Accurate control of the cooling rate can effectively avoid the occurrence of thermal cracks and ensure welding quality; welding time adjustment: according to the changes in real-time temperature and target temperature, the welding time t weld The dynamic adjustment formula is:

[0156]

[0157] Through this formula, the welding time is dynamically adjusted according to the real-time temperature difference and heating rate to ensure that the heat input meets the welding requirements; Pressure adjustment: During the welding process, the pressure P predsure The adjustment formula is:

[0158] P pressure =α·ΔT+β·P sensor +γ

[0159] Among them, α, β, and γ are coefficients obtained by fitting experimental data, which ensure that the pressure applied during the welding process can be accurately adjusted with temperature changes and real-time feedback data.

[0160] Step 4: Machine learning optimization and defect prediction: This embodiment introduces an improved machine learning optimization algorithm in the welding process, combines historical welding data with real-time data, predicts possible defects in the welding process, and adjusts the welding parameters in real time; supports vector machines (SVM), deep neural networks (DNN) and other algorithms, and predicts the relationship between welding defect probability y and welding parameters X based on historical data and real-time sensor feedback data. The calculation formula is:

[0161] y=α·X+β

[0162] Among them, α and β are the regression coefficients obtained by fitting the linear regression model. Through the machine learning algorithm, not only can the various parameters in the welding process be optimized, but also human intervention can be effectively reduced to ensure the quality of each welding point.

[0163] Step 5: Data fusion and Kalman filtering: In order to further improve the control accuracy during the welding process, this embodiment uses the Kalman filtering algorithm to optimize the sensor feedback data. The prediction formula of the Kalman filter is:

[0164] x k =F·x k-1 +B·u k

[0165] Among them, x k is the current state estimate, F is the state transfer matrix, B is the control input matrix, u k The temperature, pressure and other parameters in the welding process are optimized through Kalman gain to ensure the stability of welding quality.

[0166] In this embodiment, by dynamically controlling the heating power, cooling rate and pressure, the quality of each welding point can be accurately controlled to avoid welding defects caused by equipment aging or environmental changes; the machine learning optimization algorithm is combined with the real-time data feedback mechanism to quickly adjust the various parameters in the welding process, improve production efficiency, and reduce the defective rate; the system can adaptively adjust the welding parameters according to different materials, environmental conditions and equipment aging to ensure high-quality welding and maintain stability even in complex or extreme environments; through the optimization of Kalman filtering and machine learning algorithms, it is ensured that each control parameter in the welding process can accurately match the actual needs, further improving the accuracy and stability of the welding process.

[0167] Embodiment 6:

[0168] This embodiment shows that the temperature, pressure and time dynamic adjustment method in the welding process is used to improve the accuracy and adaptability of the welding process and ensure the consistency and stability of the welding quality. Especially under complex environmental conditions, it can ensure the stability of each welding point and effectively reduce welding defects. The following are specific implementation steps and technical details:

[0169] For example, in terms of temperature control and adjustment, temperature is one of the key parameters for welding quality. In order to ensure accurate temperature control, this embodiment uses a PID control algorithm to dynamically adjust the heating power. Specific steps include temperature acquisition and data processing: the temperature of the welding area is monitored in real time by a temperature sensor, and compared with the preset target temperature to obtain a temperature difference ΔT. This temperature difference will be used as a key parameter for adjusting the heating power; the heating power calculation formula is:

[0170]

[0171] Where P is the heating power, Q is the amount of heat transferred during the heating process, ΔT is the temperature change, and t is the time. The temperature change ΔT is calculated by the difference between the real-time temperature and the set temperature. The real-time temperature is measured by the sensor, and the set temperature is the target temperature preset by the system.

[0172] Adjustment of heating rate: By adjusting the above heating power P, the heating rate R is calculated. heat , to ensure that the heating rate during welding can be maintained within the predetermined range to avoid overheating or overcooling. The heating rate adjustment formula is:

[0173]

[0174] Among them, P is the heating power, m is the mass of the welding material, and C is the specific heat capacity of the welding material.

[0175] In terms of pressure adjustment and real-time feedback mechanism, for example, during welding, the applied pressure directly affects the stability and quality of the welding point. This embodiment adjusts the applied pressure by real-time feedback of the pressure sensor data to ensure the uniformity of pressure during welding. The specific steps are as follows: the pressure of the welding area is monitored in real time by the pressure sensor, and the real-time data is transmitted and processed by the central processing unit; the pressure adjustment formula is:

[0176] P pressure =α·ΔT+β·P sensor +γ

[0177] Among them, ΔT is the real-time temperature difference, P sensor is the real-time pressure data, and α, β, and γ are the regression coefficients obtained by fitting the experimental data. This formula can dynamically adjust the applied pressure according to the real-time data.

[0178] In terms of dynamic adjustment of welding time, welding time is crucial to welding quality. Too long time may lead to overheating, while too short time may lead to insufficient welding. This embodiment dynamically adjusts the welding time by the difference ΔT between the real-time temperature and the target temperature. The specific steps are as follows: welding time calculation formula:

[0179]

[0180] Among them, T target To set the target temperature, T current is the real-time temperature, R heat is the heating rate. This formula ensures that the welding time is dynamically adjusted according to the real-time temperature difference and heating rate, thereby accurately controlling the heat input during the welding process.

[0181] Moreover, in the extended implementation, machine learning optimization and welding defect prediction can be used, that is, this embodiment uses a machine learning algorithm to predict the defects that may occur during the welding process, and adjusts the welding parameters in real time according to the prediction results. Specific steps include data input and training: the machine learning model (such as support vector machine SVM, deep neural network DNN) is trained through historical welding data and real-time feedback data, and the input data includes temperature, pressure, humidity, welding time and historical records of welding defects; welding defect prediction model:

[0182] y=α·X+β

[0183] Among them, y is the probability of welding defects, X is the welding parameters, and α and β are the regression coefficients obtained by fitting historical data. The model predicts the probability of welding defects and adjusts parameters such as temperature, pressure and welding time in real time to avoid the occurrence of welding defects.

[0184] At the same time, in order to improve the control accuracy during the welding process, this embodiment combines multi-sensor data (temperature, pressure, humidity, etc.) and optimizes the welding parameters through the Kalman filter algorithm. The specific steps are as follows: Kalman filter prediction formula:

[0185] x k =F·x k-1 +B·u k

[0186] Among them, x k is the current state estimate, F is the state transfer matrix, B is the control input matrix, u k The Kalman filter algorithm recursively optimizes each sensor feedback data to improve the control accuracy of parameters such as temperature and pressure during welding. The Kalman filter update formula is:

[0187] K=P k|k-1 ·H T·(H·P k|k-1 ·H T +R) -1

[0188] Among them, K is the Kalman gain, P is the covariance matrix, H is the measurement matrix, and R is the measurement noise covariance. The Kalman gain is used to accurately optimize the temperature and pressure parameters in the welding process.

[0189] At the same time, the system integration scheme of this embodiment includes a central processing unit (CPU), a sensor, a machine learning module and a Kalman filter module, and the real-time data is transmitted to the cloud platform through the wireless communication module for further analysis and optimization. The whole system can automatically adjust the various parameters in the welding process according to the changing environmental conditions and the characteristics of the welding materials to ensure the stability and consistency of the welding quality. And through the above-mentioned optimization scheme, the temperature, pressure and time in the welding process can be accurately controlled in a dynamic environment to avoid quality fluctuations caused by environmental changes and equipment aging; after introducing machine learning and Kalman filter technology, the defect prediction and real-time adjustment in the welding process greatly improve the consistency and stability of welding quality; the automatic adjustment system reduces the intervention of human operation, reduces the risk of human error, and improves production efficiency and product reliability. And then it provides a more accurate, efficient and intelligent welding process control method, which has better adaptability in complex environments, can significantly improve welding quality, reduce the incidence of defective products, and bring significant production cost savings.

[0190] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention is described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A welding process for insulator leads for tantalum capacitors, characterized in that: The process steps include: Step 1: Sensor data collection and monitoring: Real-time monitoring of temperature, pressure and humidity during welding by multiple sensors, wherein the sensors include at least one temperature sensor, one pressure sensor and one humidity sensor, and the data acquisition module of the sensors is connected to the central processing unit; The data acquisition module transmits the real-time monitored temperature, pressure and humidity data to the central processing unit via the data bus; Step 2, execution of dynamic control algorithm and adjustment of welding parameters: The central processing unit uses the PID control algorithm and the fuzzy logic algorithm in the multi-parameter dynamic control algorithm to adjust the temperature, pressure and welding time in the welding process in real time according to the temperature, pressure and humidity data collected by the sensor; The heating power adjustment formula in the PID control algorithm is: Where P is the heating power, Q is the heat transferred during the heating process, ΔT is the temperature change, t is the time, and the temperature change ΔT is calculated based on the difference between the real-time temperature and the set temperature; where the real-time temperature is measured by the sensor, the set temperature is the target temperature preset by the system, and the temperature change ΔT = real-time temperature - set temperature; Fuzzy logic algorithms are used to optimize the interaction between temperature, pressure and welding time to improve welding quality and consistency; Step 3: Adaptive adjustment of welding process: The central processing unit dynamically adjusts the heating rate, cooling rate, welding time and pressure of the welding process according to the thermal conductivity, specific heat capacity, environmental conditions and real-time data changes of the tantalum capacitor lead wire during welding; wherein the environmental conditions include ambient temperature and humidity; the specific implementation methods are as follows: Dynamic adjustment of heating rate: The heating power is adjusted according to the temperature difference measured in real time, that is, the difference between the real-time temperature and the set target temperature. The heating power P is dynamically controlled by the PID control algorithm to make the heating rate R heat To achieve the predetermined goal, the heating rate adjustment formula is: Where P is the heating power, m is the mass of the welding material, and C is the specific heat capacity of the welding material; Dynamic adjustment of cooling rate: After welding is completed, the cooling rate R is controlled by adjusting the flow rate of the cooling medium or the opening degree of the cooling device. cool , the cooling rate adjustment formula is: Among them, T ambient is the ambient temperature, T weld is the welding area temperature, t cool For cooling time; Dynamic adjustment of welding time: The welding time is dynamically adjusted according to the changes of real-time temperature and target temperature; set the target temperature T target When welding time t weld Calculated by the following formula: Among them, T current is the current temperature, T target To set the target temperature, R heat is the heating rate; Dynamic adjustment of pressure: During welding, the applied pressure is adjusted by real-time feedback of pressure sensor data; real-time pressure value P sensor It is transmitted to the central processing unit, and based on the real-time data feedback, the pressure applied is adjusted by the pressure valve of the hydraulic system. The pressure adjustment formula is: P pressure =f(T current ,T target ,P sensor ) Wherein, f is the pressure regulation function, which represents the temperature change, that is, the relationship between the difference between the real-time temperature and the set target temperature and the real-time pressure sensor feedback data; Step 4: Machine learning optimization and defect prediction: The central processing unit combines historical data with real-time data and uses machine learning algorithms to predict and make real-time adjustments to welding defects that may occur during the welding process; Machine learning algorithms include support vector machines (SVM), deep neural networks (DNN), or reinforcement learning algorithms. When training the model, the input data includes temperature, pressure, humidity, welding time, and historical records of welding defects. During the training process, the weight coefficients used are fitted by a linear regression model based on historical welding data. The specific calculation method is as follows: the relationship between the welding defect probability and the welding parameters is obtained by fitting the historical data. The model formula is: y = α·X + β, where y is the welding defect probability, X is the welding parameter, and α and β are the regression coefficients obtained by fitting the historical data; The predicted defect probability value output by the model is fed back to the central processing unit for real-time adjustment of welding parameters to ensure welding quality; Step 5, data fusion and Kalman filtering: The central processing unit uses data fusion technology and Kalman filter algorithm to optimize welding parameters based on multi-sensor feedback data during the welding process, including temperature, pressure, humidity and welding quality data; The prediction step formula of the Kalman filter algorithm is: k =F·x k-1 +B·u k , where x k is the current state estimate, F is the state transfer matrix, B is the control input matrix, u k is the control input; The update steps are: K = P k|k-1 ·H T ·(H·P k|k-1 ·H T +R) -1 , where K is the Kalman gain, P is the covariance matrix, H is the measurement matrix, and R is the measurement noise covariance; the Kalman gain is used to calculate the temperature and pressure parameters in the welding process.

2. The insulator lead welding process for tantalum capacitors according to claim 1, characterized in that: Temperature control is achieved through the PID control algorithm, and the formula of the PID control algorithm is: Where, e(t) is the error between the current temperature and the set temperature, K p , K i , K d They are proportional, integral and differential constants respectively. The heating power can be adjusted in real time by adjusting these constants.

3. The insulator lead welding process for tantalum capacitors according to claim 1, characterized in that: Pressure adjustment is achieved through real-time feedback of pressure sensor data, and the pressure is dynamically controlled by adjusting the pressure valve of the hydraulic system.

4. The insulator lead welding process for tantalum capacitors according to claim 1, characterized in that: The central processing unit optimizes the nonlinear relationship between temperature, pressure and welding time during the welding process by introducing fuzzy logic control algorithm, and adjusts the relationship between welding parameters in real time through fuzzy rules.

5. The insulator lead welding process for tantalum capacitors according to claim 1, characterized in that: The central processing unit predicts welding defects by combining historical data with real-time data and adopting a support vector machine (SVM) algorithm. The defect prediction results are used to adjust the temperature, pressure and time parameters during the welding process.

6. The insulator lead welding process for tantalum capacitors according to claim 1, characterized in that: The central processing unit generates a welding defect classification model through a training data set based on a machine learning algorithm, wherein the classification model includes a probability prediction of welding defects, and the relationship between the defect probability and the welding parameters is represented by a polynomial regression model.

7. The insulator lead welding process for tantalum capacitors according to claim 1, characterized in that: The Kalman filter algorithm recursively optimizes the temperature and pressure data and updates the system state at each sensor feedback to improve the accuracy and stability of temperature and pressure control.

8. The insulator lead welding process for tantalum capacitors according to claim 1, characterized in that: The heating / cooling rates during welding are calculated based on the thermal conductivity and specific heat capacity of the welding materials through a real-time physical model that includes a heat conduction equation and a heat convection model for accurate calculation of temperature changes.

9. The insulator lead welding process for tantalum capacitors according to claim 1, characterized in that: Real-time data during the welding process is uploaded to the cloud platform through the wireless communication module. The cloud platform further analyzes the data and generates a dynamic maintenance plan to guide the next welding operation.

10. The insulator lead welding process for tantalum capacitors according to claim 1, characterized in that: The welding defect prediction model quantifies the risk of welding defects by comparing historical welding defect data with current welding conditions using a weighted average method, and feeds the quantified results back to the central processing unit for real-time adjustment.

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