An insulator lead welding process for tantalum capacitors

Abstract

The present invention discloses an insulator lead welding process for tantalum capacitors. It includes sensor data acquisition and monitoring, execution of dynamic control algorithms and adjustment of welding parameters, adaptive adjustment during the welding process, machine learning optimization and defect prediction, and data fusion and Kalman filtering. By integrating multiple sensors and a real-time data feedback mechanism, key parameters such as temperature, pressure, and time during the welding process are monitored in real time, and machine learning algorithms are used to predict possible defects during the welding process, dynamically adjusting the welding process. The adaptive system can cope with multiple dynamic changes such as temperature, humidity, and material properties, ensuring the consistency and stability of welding quality, and effectively balancing the contradiction between welding efficiency and quality. Compared with the prior art, it provides a more precise, real-time, and intelligent welding control method, greatly enhancing the adaptability and stability under complex environments and parameter changes during the welding process.

Description

Technical Field

[0001] The present invention relates to the technical field of welding control, and specifically to a welding process for the lead of an insulator used in tantalum capacitors. Background Art

[0002] Tantalum capacitors are widely used in electronic devices, and their welding quality directly affects the performance and stability of the capacitors. During the welding process of tantalum capacitors, it is usually necessary to precisely control parameters such as temperature, pressure, and time to ensure the quality of each welding point. However, due to the involvement of multiple dynamic factors 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 usually uses a simple temperature control system and pressure regulating equipment for control. These systems ensure the consistency and stability of the welding points by setting fixed welding parameters. However, due to the possible changes in the material properties and environmental conditions during the welding process, the fixed parameters often cannot adapt to complex working conditions. Its main problems include: 1. It is difficult to find the best balance between temperature control and pressure control. Excessive temperature or pressure may lead to poor welding or capacitor damage; 2. The temperature and pressure fluctuations during the welding process are relatively large, affecting the consistency of welding quality and resulting in a high defective rate; 3. With the aging of the equipment and the change of environmental conditions, the control of welding parameters needs to be adjusted in real time, and the prior art cannot achieve precise dynamic regulation.

[0004] To address 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. However, these systems are often costly and difficult to achieve adaptability to dynamic changes. Although these technologies have improved the welding quality, they still have not fully solved the balance problem between temperature control and pressure control, especially under complex environmental conditions.

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

[0006] To solve the defects existing in the prior art, the present invention provides a welding process for the lead of an insulator used in tantalum capacitors.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] A welding process for the lead of an insulator used in tantalum capacitors according to the present invention includes the following steps:

[0009] Step 1, sensor data acquisition and monitoring:

[0010] The temperature, pressure, and humidity during the welding process are monitored in real time through multiple sensors, where 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 temperature, pressure, and humidity data monitored in real time to the central processing unit through a data bus;

[0012] Step 2, Execution of the 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 during the welding process in real time according to the temperature, pressure, and humidity data collected by the sensors;

[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 calculation of the temperature change ΔT is based on the difference between the real-time temperature and the set temperature; among them, 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] The fuzzy logic algorithm is used to optimize the interaction among temperature, pressure, and welding time to improve welding quality and consistency;

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

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

[0018] Dynamic adjustment of the heating rate: According to the real-time measured temperature difference, that is, the difference between the real-time temperature and the set target temperature, adjust the heating power, and dynamically control the heating power P through the PID control algorithm to make the heating rate R heat reach the predetermined target, and the adjustment formula for the heating rate 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 the cooling rate: After welding is completed, control the cooling rate R by adjusting the flow rate of the cooling medium or the opening degree of the cooling devicecool , the adjustment formula for the cooling rate is:

[0022]

[0023] where, T ambient is the ambient temperature, T weld is the temperature of the welding area, and t cool is the cooling time;

[0024] Dynamic adjustment of the welding time: The welding time is dynamically adjusted according to the changes in the real-time temperature and the target temperature; when setting the target temperature T target , the welding time t weld is calculated by the following formula:

[0025]

[0026] where, T current is the current temperature, T target is the set target temperature, and R heat is the heating rate;

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

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

[0029] where, f is the pressure adjustment function, indicating 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 and real-time data, and uses machine learning algorithms to predict and adjust in real time the possible welding defects during the welding process;

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

[0033] During the training process, the weight coefficients used are fitted through a linear regression model based on historical welding data. The specific calculation method is as follows: The relationship between the welding defect probability and 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] Based on the multi-sensor feedback data during the welding process, including temperature, pressure, humidity, and welding quality data, the central processing unit uses data fusion technology to optimize the welding parameters through the Kalman filtering algorithm;

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

[0038] The update step is: 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 temperature and pressure parameters during the welding process are adjusted using the Kalman gain.

[0039] As a preferred technical solution of the present invention, the temperature control is achieved through the PID control algorithm. The formula of the PID control algorithm is:

[0040]

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

[0042] As a preferred technical solution of the present invention, the pressure adjustment is achieved through the real-time feedback of the 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 non-linear relationship among the temperature, pressure, and welding time during the welding process by introducing a fuzzy logic control algorithm, and adjusts the relationship of the welding parameters in real time through fuzzy rules.

[0044] As a preferred technical solution of the present invention, the central processing unit combines historical data and real-time data, and uses the support vector machine (SVM) algorithm to predict welding defects. 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. The classification model includes the 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 through 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 a 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 using the weighted average method through the comparison of historical welding defect data and current welding conditions, and feeds back the quantification 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 a real-time data feedback mechanism, key parameters such as temperature, pressure, and time during the welding process are monitored in real time, and machine learning algorithms are used to predict possible defects during the welding process, dynamically adjusting the welding process. The adaptive system can cope with multiple dynamic changes such as temperature, humidity, and material properties, ensuring the consistency and stability of welding quality, and effectively balancing the contradiction between welding efficiency and quality. Compared with the prior art, it provides a more accurate, real-time, and intelligent welding control method, greatly improving the adaptability and stability under complex environments and parameter changes during the welding process.

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

[0053] 3. The present invention combines material characteristics and real-time environmental data, and uses an adaptive feedback mechanism to adjust various welding parameters. This system can not only meet the welding requirements of various types of materials, but also dynamically adjust welding conditions according to changes in environmental temperature and humidity, ensuring the quality consistency of welding points whether in an environment with large temperature differences or when welding different materials (such as tantalum capacitor leads, etc.).

[0054] 4. By integrating the PID control algorithm and the fuzzy logic control algorithm, the present invention can accurately adjust temperature, pressure, and time during the complex welding process, thus avoiding common problems such as overheating, overpressure, and uneven welding. The system is optimized according to real-time feedback, making each welding point meet 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 control and pressure control, this technical solution reduces energy waste during the welding process and lowers equipment energy consumption through a real-time feedback mechanism and an optimization algorithm. In addition, the automatic adjustment of welding parameters reduces the need for human intervention, avoiding welding defects or material waste caused by improper operation, thereby further reducing the overall production cost. This innovative technology not only improves production efficiency but also shows significant advantages in energy conservation and cost reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The 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 to the present invention. In the drawings:

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

[0058] Figure 2 is the welding process control flow chart of the present invention. Specific Embodiments

[0059] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not intended to limit the present invention.

[0060] A welding process for the insulator leads of tantalum capacitors according to the present invention includes the following process steps:

[0061] Step 1, sensor data acquisition and monitoring:

[0062] The temperature, pressure, and humidity during the welding process are monitored in real time through multiple sensors, where the sensors include at least one temperature sensor, one pressure sensor, and one humidity sensor. The data acquisition module of the sensors is connected to the central processing unit;

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

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

[0065] The central processing unit adopts a multi-parameter dynamic control algorithm based on the temperature, pressure, and humidity data collected by the sensors. 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 calculation of the temperature change ΔT is based on the difference between the real-time temperature and the set temperature; among them, 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] The fuzzy logic algorithm is used to optimize the interaction among the temperature, pressure, and welding time to improve the welding quality and consistency;

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

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

[0070] Dynamic adjustment of the heating rate: According to the temperature difference measured in real time, that is, the difference between the real-time temperature and the set target temperature, adjust the heating power, and dynamically control the heating power P through the PID control algorithm to make the heating rate R heat reach the predetermined target. The adjustment formula for the heating rate 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 amount 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 the cooling rate: After welding is completed, control the cooling rate R by adjusting the flow rate of the cooling medium or the opening degree of the cooling device cool , and the adjustment formula for the cooling rate is:

[0074]

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

[0076] Dynamic adjustment of the welding time: The welding time is dynamically adjusted according to the changes in the real-time temperature and the target temperature; when setting the target temperature T target , the welding time t weld is calculated by the following formula:

[0077]

[0078] where T current is the current temperature, T target is the set target temperature, and R heat is the heating rate; adjust the welding time according to the relationship between the real-time temperature difference and the heating rate to achieve the optimal welding effect.

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

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

[0081] where f is the pressure adjustment function, representing the temperature change, that is, the relationship between the difference between the real-time temperature and the set target temperature and the feedback data of the real-time pressure sensor; 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: The pressure adjustment 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 change under different welding conditions, the function is fitted using regression analysis or machine learning algorithms to ensure that the pressure applied during the welding process can be precisely adjusted according to the real-time feedback data.

[0083] The real-time adjustment of the applied pressure ensures the uniformity of the pressure during the welding process, thereby avoiding cracks or looseness in the solder joints and further improving the welding quality.

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

[0085] The central processing unit combines historical data and real-time data, and uses machine learning algorithms to predict possible welding defects during the welding process and make real-time adjustments to avoid the occurrence of welding defects;

[0086] The machine learning algorithms include support vector machine (SVM), deep neural network (DNN) or 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 through a linear regression model based on historical welding data. The specific calculation method is: the relationship between the welding defect probability and welding parameters is obtained by fitting 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 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 the welding parameters by using data fusion technology through the Kalman filtering algorithm based on the multi-sensor feedback data during the welding process, including temperature, pressure, humidity, and welding quality data;

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

[0092] The update step is: 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] Optimize the temperature and pressure parameters during the welding process using the Kalman gain and ensure the welding quality; and, in a further solution, system coordination and closed-loop control can be performed. For example, the central processing unit realizes the coordinated work of each control module during 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 the welding quality; and the temperature control is achieved through the PID control algorithm. 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 are the proportional, integral, and differential constants respectively, and the heating power is adjusted in real time by adjusting these constants.

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

[0097] The central processing unit generates a welding defect classification model based on a machine learning algorithm through a training data set. The classification model includes the probability prediction of welding defects, and the relationship between the defect probability and welding parameters is represented by a polynomial regression model. The Kalman filter algorithm updates the system state estimation at each sensor feedback by recursively optimizing the temperature and pressure data to improve the accuracy and stability of temperature and pressure control. The heating / cooling rate during the welding process is calculated through 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. The real-time data during the welding process is uploaded to the cloud platform through a wireless communication module. 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 using the weighted average method by comparing historical welding defect data with the current welding conditions, and feeds the quantification result back to the central processing unit for real-time adjustment.

[0098] Example 1:

[0099] This example takes the lead welding process of tantalum capacitors as the application scenario and demonstrates the specific implementation of the technical solution of the present invention. In the traditional welding process, due to the 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. To solve this problem, this example adopts a dynamic control system based on sensor feedback and combines machine learning algorithms for optimization to ensure high precision and high efficiency in the welding process.

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

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

[0102] Example 2:

[0103] This embodiment demonstrates the significant advantages of the technical solution during the welding process of tantalum capacitors through a comparative experiment. In traditional welding processes, due to the large variation ranges of temperature and pressure, it is difficult to maintain consistent welding quality, and the incidence of welding defects is relatively high. To verify the technical effects of the present invention, we designed a comparative experiment, welding using the traditional welding method and the multi-parameter dynamic regulation system of the present invention respectively, and testing and recording various parameters during the welding process.

[0104] The experimental data shows that when using the traditional welding method, the welding quality fluctuates greatly, and the electrical performance of the welding points generally decreases. Specifically, in the case of large environmental temperature changes, the welding defect rate of the traditional method is 10.5%. In contrast, for the welding system adopting the technical solution, by real-time monitoring parameters such as temperature and pressure and adjusting the welding conditions in real time according to the feedback data, the defect rate is successfully reduced to 3.2%, improving the stability and consistency of welding quality.

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

[0106] During the actual production process, in an environment with large fluctuations in humidity and temperature, such as large temperature fluctuations near the welding area caused by welding, it is difficult for the traditional welding method to maintain consistency, while this system ensures that each welding meets the high-quality standard through flexible parameter adjustment. In addition, the adaptive adjustment mechanism of the system for equipment aging also provides a guarantee for long-term stable operation.

[0107] Example 3:

[0108] This embodiment details how to ensure precise control of various parameters during the welding process by dynamically adjusting the heating / cooling rate, welding time, and pressure, in order to improve welding quality and consistency. At the same time, it further explains the details of the machine learning algorithm part and the specific implementation path of the pressure adjustment function.

[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, time timers, etc., which can feedback 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 the data back to the central processing unit. Set the target temperature T target and the real-time temperature T current The difference ΔT between them will be used to adjust the heating power. The pressure sensor monitors the pressure of the welding area in real time to ensure uniform pressure application. Through the feedback pressure data, the central processing unit can adjust the magnitude of the pressure in real time to ensure the stability of the solder joint.

[0110] The adjustment of the heating rate is determined according to the real-time temperature difference ΔT and the thermal characteristics of the welding material. The central processing unit uses the PID control algorithm to calculate the heating power P 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 heat transferred, t is the heating time, and ΔT is the temperature difference. This formula is based on the principles of thermodynamics and can precisely control the heating process. The heating rate R heat is 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 combination of the real-time temperature and the thermal characteristics of the welding material dynamically adjusts the heating power, thereby achieving precise control.

[0111] The welding time t weld is dynamically adjusted according to the temperature difference and the 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 by the following formula: where T target is the set target temperature, T current is the real-time temperature, and 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 the welding process, the applied pressure is adjusted through the real-time feedback pressure sensor data. The pressure adjustment is based on the real-time temperature and the pressure sensor data, using the function f(Tcurrent , T target , P sensor ) to calculate the required applied pressure.

[0113] The pressure adjustment function f adjusts the applied pressure according to the real-time temperature difference ΔT and the real-time pressure sensor data P sensor The function form 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 change and pressure sensor feedback data on the applied pressure. This formula is fitted through regression analysis or machine learning algorithms (such as support vector machine (SVM), deep neural network (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 through historical experimental data and data input during the real-time welding process to optimize the coefficients of the pressure adjustment function. During the training process, the gradient descent method or other optimization algorithms are used to adjust the coefficients α, β, and γ to obtain the optimal pressure adjustment function. After the model training is completed, the required pressure value is calculated by inputting real-time welding data (such as temperature change, pressure feedback, etc.) into the model to ensure that the pressure applied during the welding process meets the predetermined standard, thereby improving the welding quality.

[0114] This embodiment also introduces a machine learning model (such as SVM, DNN, etc.) to optimize various parameters during the welding process. By training on historical data during the welding process, the model can predict welding defects and adjust parameters such as heating, pressure, and time according to the prediction results. Its 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 inputs of the model include real-time data such as temperature, pressure, and time during the welding process, and the outputs are adjusted parameters (such as heating power, welding time, applied pressure, etc.).

[0115] Embodiment 4:

[0116] This embodiment details the optimization of key parameters such as temperature, pressure, and welding time during the welding process of the insulator leads for tantalum capacitors through data feedback and adaptive control technology, further improving the 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 during the welding process in real time. The data acquisition modules of these sensors are connected to the central processing unit (CPU), and the data feedback from the sensors is transmitted 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] In this embodiment, the welding environment parameters are further optimized through an adaptive adjustment mechanism. According to the thermal conductivity, specific heat capacity of the tantalum capacitor leads, and environmental conditions (such as ambient temperature, humidity, etc.), the central processing unit dynamically adjusts the heating rate, cooling rate, welding time, and the magnitude of the applied pressure during the welding process.

[0129] Dynamic adjustment of the heating rate: By measuring the temperature difference ΔT in real time, and combining the mass m and specific heat capacity C of the welding material, the heating power P is adjusted. The heating rate R heat is 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 in the welding area can be precisely controlled, avoiding excessive or too low temperature.

[0132] Control of the cooling rate: After welding is completed, the cooling rate R cool is adjusted by the following formula:

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

[0134] where T ambient is the ambient temperature, T weld is the temperature of the welding area, and t cool is the cooling time. This formula ensures that the solder joints can cool evenly after welding, preventing the occurrence of thermal cracks.

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

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

[0137] y = α·X + β

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

[0139] And through data fusion technology, the central processing unit combines multi-sensor data (temperature, pressure, humidity, etc.) and uses the Kalman filtering 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] Where, x k is the current state estimate, F is the state transition 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 precisely.

[0144] Example 5:

[0145] To further optimize the control of temperature, pressure, and welding time during the tantalum capacitor welding process, in this example, an improved multi-parameter dynamic control system combined with a machine learning algorithm is used to further enhance the precision and adaptability of the welding process, ensuring the stability and high quality of each welding point.

[0146] This example combines a more precise sensor feedback mechanism with an adaptive control algorithm to ensure that the parameters during 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 sensor, pressure sensor, humidity sensor) are used to monitor the key parameters during the welding process in real time. The data acquisition module transmits the temperature, pressure, and humidity data collected by the sensors to the central processing unit in real time. Through the data bus, these real-time data are transmitted to the central processing unit to ensure the efficiency and stability of data acquisition and transmission; the selection and arrangement method of the sensors are optimized through experiments to ensure that accurate real-time feedback can be provided under various working conditions. For example, during the welding process, the real-time temperature T current measured by the temperature sensor is used as an important parameter and is fed back to the central processing unit in real time for further calculation in the temperature control algorithm.

[0148] Step 2: Multi-parameter dynamic control and welding parameter adjustment; the central processing unit adjusts the parameters during the welding process using an improved PID control algorithm and a fuzzy logic control algorithm based on the real-time data; Heating power adjustment: According to the temperature change ΔT = T current - Ttarget , the PID control algorithm is adopted to dynamically adjust the heating power P. The calculation formula of the heating power is:

[0149] P = Q·ΔT / t

[0150] where Q is the heat transferred, 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, avoiding uneven welding caused by too fast or too slow temperature changes.

[0153] Step 3: Adaptive adjustment of 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 material properties such as the thermal conductivity and specific heat capacity of the tantalum capacitor leads, 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] where T ambient is the ambient temperature, T weld is the temperature of the welding area, and t cool is the cooling time. Precise control of the cooling rate can effectively avoid the occurrence of thermal cracks and ensure the welding quality; Welding time adjustment: According to the change of the real-time temperature and the target temperature, the dynamic adjustment formula of the welding time t weld is:

[0156]

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

[0158] P pressure = α·ΔT + β·P sensor + γ

[0159] where α, β, and γ are coefficients obtained by fitting experimental data, ensuring that the pressure applied during the welding process can be precisely adjusted according to the temperature change and real-time feedback data.

[0160] Step 4: Machine learning optimization and defect prediction; In this embodiment, an improved machine learning optimization algorithm is introduced during the welding process. Combining historical welding data and real-time data, it predicts possible defects during the welding process and makes real-time adjustments to the welding parameters. Algorithms such as support vector machine (SVM) and deep neural network (DNN) are used to predict the relationship between the welding defect probability y and the welding parameters X according to historical data and real-time sensor feedback data. Its calculation formula is:

[0161] y = α·X + β

[0162] Among them, α and β are regression coefficients obtained by fitting through a linear regression model; Through machine learning algorithms, not only can 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 Kalman filtering 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 transition matrix, B is the control input matrix, and u k is the control input. The temperature, pressure and other parameters in the welding process are optimized through the Kalman gain to ensure the stability of the welding quality.

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

[0167] Example 6:

[0168] This embodiment demonstrates a method for dynamically adjusting temperature, pressure, and time during the welding process to improve the accuracy and adaptability of the welding process, ensuring the consistency and stability of welding quality. Especially under complex environmental conditions, it can ensure the stability of each welding point and effectively reduce welding defects. The following are the 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. To ensure precise temperature control, this embodiment uses a PID control algorithm to dynamically adjust the heating power. The specific steps are as follows: Temperature acquisition and data processing: The temperature of the welding area is monitored in real time through a temperature sensor and compared with the preset target temperature to obtain the temperature difference ΔT. This temperature difference value will be the key parameter for adjusting the heating power; Heating power calculation formula:

[0170]

[0171] where P is the heating power, Q is the heat transferred during the heating process, ΔT is the temperature change, and t is the time. The temperature change ΔT is calculated from 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 the heating rate: By adjusting the above heating power P, calculate the heating rate R heat , ensuring that the heating rate during the welding process can be maintained within a predetermined range to avoid overheating or overcooling. The adjustment formula for the heating rate is:

[0173]

[0174] 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.

[0175] In terms of pressure adjustment and real-time feedback mechanism, during the welding process, 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 data from the pressure sensor to ensure the pressure uniformity during the welding process. The specific steps are as follows: The pressure of the welding area is monitored in real time through the pressure sensor, and the real-time data is transmitted and processed by the central processing unit; Pressure adjustment formula:

[0176] P pressure =α·ΔT + β·P sensor +γ

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

[0178] In terms of the dynamic adjustment of the welding time, since the welding time is crucial for the welding quality, too long a time may cause overheating, while too short a time may lead to insufficient welding. In this embodiment, the welding time is dynamically adjusted based on the difference ΔT between the real-time temperature and the target temperature. The specific steps are as follows. The welding time calculation formula:

[0179]

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

[0181] Moreover, in the extended implementation, machine learning can be used to optimize and predict welding defects. That is, in this embodiment, machine learning algorithms are used to predict the possible defects during the welding process, and the welding parameters are adjusted in real time according to the prediction results. The specific steps are as follows. Data input and training: Machine learning models (such as Support Vector Machine SVM, Deep Neural Network DNN) are trained with historical welding data and real-time feedback data. The input data includes historical records of temperature, pressure, humidity, welding time, and welding defects; Welding defect prediction model:

[0182] y = α·X + β

[0183] where y is the probability of welding defects, X is the welding parameter, and α and β are regression coefficients obtained by fitting historical data. This 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] Meanwhile, 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 filtering algorithm. The specific steps are as follows. Kalman filtering prediction formula:

[0185] x k = F·x k-1 + B·u k

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

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

[0188] Wherein, K is the Kalman gain, P is the covariance matrix, H is the measurement matrix, and R is the measurement noise covariance. The temperature and pressure parameters in the welding process are precisely optimized using the Kalman gain.

[0189] Meanwhile, the system integration solution of this embodiment includes a central processing unit (CPU), sensors, a machine learning module, and a Kalman filtering module. Real-time data is transmitted to the cloud platform via a wireless communication module for further analysis and optimization. The entire system can automatically adjust various parameters in the welding process according to the changing environmental conditions and the characteristics of the welding materials, ensuring the stability and consistency of the welding quality. And through the above optimization solution, it is possible to precisely control the temperature, pressure, and time in the welding process in a dynamic environment, avoiding quality fluctuations caused by environmental changes and equipment aging; after introducing machine learning and Kalman filtering technologies, the defect prediction and real-time adjustment in the welding process greatly improve the consistency and stability of the welding quality; the automatic adjustment system reduces the intervention of manual operations, reduces the risk of human errors, and improves production efficiency and product reliability. Furthermore, it provides a more precise, efficient, and intelligent welding process control method, which has better adaptability in complex environments, can significantly improve the welding quality, reduce the incidence of unqualified products, and bring significant cost savings in production.

[0190] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within 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; The Kalman filter algorithm recursively optimizes the temperature and pressure data, and updates the system status 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, which includes a heat conduction equation and a thermal convection model for accurately calculating temperature changes; 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.

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: 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.

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