Online monitoring method for welding quality of joint of ultrasonic welding metal sheet

Abstract

The invention relates to the technical field of welding monitoring, and discloses an online monitoring method for the welding quality of a joint of an ultrasonic welding metal sheet, which comprises the following steps: S1, detecting the friction coefficient of a welding interface of the metal sheet in real time through a friction coefficient detection unit; s3, dividing the real-time friction coefficient detected in the step S1 into three stages, namely a pre-welding stage, a welding process stage and a post-welding cooling stage according to the welding process, and S4, performing statistical analysis on the real-time friction coefficient of each stage in the step S3 by adopting an average value and an extreme value, and during calculation, at least obtaining the real-time friction coefficient of each stage during five times of welding. The method has the following advantages and effects that by dynamically monitoring the friction coefficient of the interface of the ultrasonic welding metal sheet in real time, detecting the quality of a welding joint in real time and optimizing welding process parameters in time, the percent of pass of products can be greatly increased, and the operability and accuracy of quality control are remarkably improved.

Description

Technical Field

[0001] The invention relates to the technical field of welding monitoring, and in particular to an online monitoring method for the welding quality of ultrasonically welded metal sheet joints. Background Art

[0002] Pure electric vehicles have the advantages of no tail gas emissions, energy saving and environmental protection, and low cost of use. They have become an important direction for the development of the modern automobile industry. As the core component of pure electric vehicles, the quality and capacity of power lithium-ion battery packs determine the stability, power supply performance and safety of pure electric vehicles. There are a large number of welding joints in the assembly and manufacturing process of power lithium-ion battery packs. When the strength of the welding joints is insufficient, the internal resistance of the battery pack will increase and the power supply will not be effective. When the welding is excessive, the welding heat is too high and the battery core electrode cover is welded through, which is easy to cause electrolyte leakage and battery pack circuit short circuit. The battery pack may burn or explode, seriously affecting the safety of the user. Therefore, the welding quality plays a decisive role in the performance of the battery pack.

[0003] Ultrasonic welding is a process in which an ultrasonic generator converts a 50 Hz current into electrical energy of 15, 20, 30 or 40 kHz. The converted high-frequency electrical energy is converted again into mechanical vibrations of the same frequency through a transducer, and then the mechanical vibrations are transmitted to the welding head of the ultrasonic welding machine through a variable amplitude rod. The welding head transmits the received vibration energy to the position where the workpieces to be welded need to be welded, and the vibration energy is converted into frictional heat and deformation energy between the workpieces to be welded by friction. As the welding process proceeds, the temperature of the welding interface increases, the base material of the welding interface does not melt or melts a small amount, and the welding interface achieves metallurgical welding in a solid or small amount of liquid state.

[0004] As can be seen from the above, the friction coefficient of the welding interface is crucial to the quality of the welded joint. Even for the same batch of metal sheets, there are large or small differences in the crystal orientation, roughness, oxide type and thickness of the surface, which seriously affects the friction coefficient of the welding interface and thus affects the quality of the welded joint. The existing welding quality detection methods mainly rely on post-destructive detection or manual experience judgment, which cannot achieve real-time detection of the quality of the welded joint and have low detection efficiency. Therefore, in order to improve the quality of metal sheet welding, it is necessary to invent an online monitoring method for the joint welding quality of ultrasonically welded metal sheets. Summary of the invention

[0005] The purpose of the present invention is to provide an online monitoring method for the welding quality of ultrasonically welded metal sheet joints, which has the function of real-time and dynamic monitoring of the friction coefficient of the interface of ultrasonically welded metal sheets, real-time detection of the quality of the welding joints, which can greatly improve the qualified rate of products and significantly improve the operability and accuracy of quality control.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions: A method for online monitoring of the welding quality of ultrasonically welded metal sheet joints, comprising the following steps: S1. Real-time detection of the friction coefficient of the metal sheet welding interface by a friction coefficient detection unit; S2, receiving the real-time friction coefficient detected by S1 through the online monitoring system; S3, dividing the real-time friction coefficient detected by S1 into three stages according to the welding process: before welding, welding process, and cooling period after welding; S4, using average values ​​and extreme values ​​to statistically analyze the real-time friction coefficient of each stage of S3, when calculating, at least obtain the real-time friction coefficient of 5 weldings in each stage to ensure the accuracy of the average values ​​and extreme values, and calculate the average values ​​and extreme values ​​of the real-time friction coefficient of the three stages in S3 respectively; S5. Determine the weight of the influence of each stage on the quality of the welded joint in S3: the online monitoring system analyzes the data of the above three stages and obtains the average value and the upper and lower limits, and determines the influence weight according to the difference between the average value of each stage and the total average value of the three stages, and obtains which stage has a greater influence on the welding quality; S6, establishing a quantitative relationship model: according to the multiple values ​​of the friction coefficient recorded in S5, the real-time friction coefficient value is matched with the welding quality result one by one, and a quantitative relationship model between the real-time friction coefficient and the welding quality is established; S7. Determine the range value of the real-time friction coefficient when the quality of the welded joint is qualified: when the quality of the welded joint is qualified, record multiple values ​​of the real-time friction coefficient, and find out the maximum and minimum values. The value between the maximum and minimum values ​​is the range value of the real-time friction coefficient when the quality of the welded joint is qualified; S8. Determine the quality of welding according to the value of real-time friction coefficient: If the value of real-time friction coefficient is within the range determined in S7, the quality of welding joint is qualified and the online monitoring system gives a qualified signal; otherwise, it gives an unqualified signal.

[0007] The present invention is further configured as follows: in S1: the metal sheet includes an upper metal sheet and a lower metal sheet, the upper surface of the friction coefficient detection unit platform is provided with a base for production, the lower metal sheet is fixedly connected to the upper surface of the friction coefficient detection unit platform, and the base is located directly below the lower metal sheet.

[0008] The present invention is further configured as follows: in said S1: the ultrasonic welder applies welding pressure, welding time, welding amplitude and welding frequency to the upper metal sheet through the welding head.

[0009] The present invention is further configured as follows: in S1: the welding pressure range adopted is 0.1-0.6 MPa, the welding time is 0.1-1.2 s, the welding amplitude is 15-75 μm, and the welding frequency is 20-40 kHz.

[0010] The present invention is further configured as follows: in S1: the friction coefficient detection unit includes a sensor, a data acquisition module and a processing module, the sensor is used to detect the friction coefficient of the metal sheet welding interface in real time, the data acquisition module is used to collect the friction coefficient data output by the sensor, and the processing module is used to perform preliminary processing on the collected data and transmit it to the online monitoring system.

[0011] The present invention is further configured as follows: in said S1: the frequency of collecting the real-time friction coefficient is at least times per second, so as to ensure the real-time and accuracy of the data.

[0012] The present invention is further configured as follows: in S2: the online monitoring system includes a data receiving module, a statistical analysis module and an output module, the data receiving module is used to receive the real-time friction coefficient data transmitted by the processing module, the statistical analysis module is used to perform statistical analysis on the real-time friction coefficient data, and the output module is used to output a qualified or unqualified signal according to the statistical analysis results.

[0013] The present invention is further configured as follows: in said S7: said online monitoring system further comprises a storage module and a visualization interface, the storage module is used to store real-time friction coefficient data and its statistical analysis results, and the visualization interface is used to intuitively display the welding joint quality monitoring results.

[0014] The present invention is further configured as follows: in said S8: the output module of said online monitoring system has an alarm function, and automatically sends out an alarm signal when it is detected that the quality of the welding joint is unqualified.

[0015] The beneficial effects of the present invention are as follows: the present device adopts a statistical data analysis method, and through the statistical analysis of a large amount of data, a quantitative relationship model between the real-time friction coefficient and the welding quality is established. This model can accurately predict the quality of the welding joint and determine the range value of the real-time friction coefficient when the welding joint quality is qualified. This quantitative quality control method greatly improves the controllability and reliability of the welding quality, and can accurately determine the influence weight of each stage on the quality of the welding joint, and then optimize the process parameters such as welding pressure, welding time, welding amplitude and welding frequency, making the welding process more scientific and efficient. At the same time, the online monitoring system of the present invention can automatically analyze the friction coefficient data, and automatically judge the joint quality according to the preset qualified range value, and generate The invention can output qualified or unqualified signal, reduce manual intervention, improve the automation level of the production line, and meet the needs of modern welding. In summary, the present invention adopts statistical data analysis method to improve the accuracy and reliability of welding joint quality monitoring, and detects the quality of welding joints in real time and dynamically through real-time monitoring of the friction coefficient of the interface of ultrasonically welded metal sheets. By establishing a quantitative relationship model between the real-time friction coefficient and the welding quality, it can timely discover abnormal conditions in the welding process, timely optimize the welding process parameters, ensure the stability of the welding process, improve the welding quality and automation level, reduce the generation of unqualified products, greatly improve the qualified rate of products, and significantly improve the operability and accuracy of quality control. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 is a flow chart of the present invention; Figure 2 It is a structural schematic diagram of the online monitoring device of the present invention.

[0018] In the figure, 1. Friction coefficient detection unit; 2. Base; 3. Lower metal sheet; 4. Welding head; 5. Upper metal sheet; 6. Online monitoring system. DETAILED DESCRIPTION

[0019] Reference Figure 1-Figure 2, an online monitoring method for the welding quality of ultrasonically welded metal thin plates, comprising the following steps: S1, real-time detection of the friction coefficient of the metal thin plate welding interface by a friction coefficient detection unit 1, S2, receiving the real-time friction coefficient detected by S1 by an online monitoring system 6, S3, dividing the real-time friction coefficient detected by S1 into three stages according to the welding process: before welding, welding process, and cooling period after welding, S4, using average values ​​and extreme values ​​to statistically analyze the real-time friction coefficient of each stage of S3, and when calculating, at least obtain the real-time friction coefficient of 5 weldings in each stage to ensure the accuracy of the average value and extreme value, and respectively calculate the average value and extreme value of the real-time friction coefficient of the three stages in S3, S5, determine the real-time friction coefficient of each stage in S3 for welding Weight of influence on joint quality: Online monitoring system 6 analyzes the data of the above three stages and obtains the average value and upper and lower limits. The influence weight is determined by the difference between the average value of each stage and the total average value of the three stages, and it is found out which stage has a greater impact on the welding quality. S6. Establish a quantitative relationship model: According to the multiple values ​​of the friction coefficient recorded in S5, the value of the real-time friction coefficient is matched with the results of the welding quality one by one, and a quantitative relationship model between the real-time friction coefficient and the welding quality is established. S7. Determine the range of the real-time friction coefficient when the quality of the welding joint is qualified: When the quality of the welding joint is qualified, record multiple values ​​of the real-time friction coefficient, and find the maximum and minimum values. The value between the maximum and minimum values ​​is the real-time friction coefficient when the quality of the welding joint is qualified. Coefficient range value, S8, determine the quality of welding according to the value of real-time friction coefficient: if the value of real-time friction coefficient is within the range value determined in S7, the quality of welding joint is qualified, and the online monitoring system 6 gives a qualified signal, otherwise, it gives an unqualified signal. This device adopts statistical data analysis method, and establishes a quantitative relationship model between real-time friction coefficient and welding quality through statistical analysis of a large amount of data. This model can accurately predict the quality of welding joint and determine the range value of real-time friction coefficient when the quality of welding joint is qualified. This quantitative quality control method greatly improves the controllability and reliability of welding quality, and can accurately determine the influence weight of each stage on the quality of welding joint, thereby optimizing welding pressure, welding time, welding amplitude and welding The process parameters such as connection frequency make the welding process more scientific and efficient. At the same time, the online monitoring system of the present invention can automatically analyze the friction coefficient data, and automatically judge the joint quality according to the preset qualified range value, and send out qualified or unqualified signal signals. This automatic monitoring method reduces manual intervention, improves the automation level of the production line, and is suitable for modern welding needs. In summary, the present invention adopts a statistical data analysis method to improve the accuracy and reliability of welding joint quality monitoring, and detects the quality of welding joints in real time by real-time and dynamic monitoring of the friction coefficient of the interface of ultrasonically welded metal sheets. By establishing a quantitative relationship model between the real-time friction coefficient and the welding quality, it is possible to promptly discover abnormal conditions in the welding process and promptly optimize the welding process parameters.It ensures the stability of the welding process, improves the welding quality and automation level, reduces the production of unqualified products, greatly improves the product qualification rate, and significantly improves the operability and accuracy of quality control.

[0020] In S1, the metal sheet includes an upper metal sheet 5 and a lower metal sheet 3. The upper surface of the friction coefficient detection unit 1 is provided with a base 2 for production. The lower metal sheet 3 is fixedly connected to the upper surface of the friction coefficient detection unit 1. The base 2 is located directly below the lower metal sheet 3. The friction coefficient detection unit 1 has a base 2 for welding, which can adapt to the welding requirements of different types of metal sheets, enhances the versatility and compatibility of the equipment, and is suitable for various types of metal sheets.

[0021] In S1, the ultrasonic welder applies welding pressure, welding time, welding amplitude, and welding frequency to the upper metal sheet 5 through the welding head 4. By accurately applying welding pressure, welding time, welding amplitude, and welding frequency, the upper metal sheet 5 drives the lower metal sheet 3 to generate high-frequency motion, forming a relatively high-frequency friction motion between the upper metal sheet 5 and the lower metal sheet 3, and the welding process can be completed in a short time, thereby significantly improving the welding efficiency and quality.

[0022] In the S1, the welding pressure range is 0.1-0.6 MPa, the welding time is 0.1-1.2 s, the welding amplitude is 15-75 μm, and the welding frequency is 20-40 kHz. The welding process parameter range is clear, and the operator can adjust it according to the specific needs of different plates. The operation is simple and easy to master, and the parameters are highly controllable. It can effectively avoid welding quality problems caused by improper operation and improve the stability and reliability of the production process.

[0023] In the S1, the friction coefficient detection unit 1 includes a sensor, a data acquisition module and a processing module. The sensor is used to detect the friction coefficient of the metal sheet welding interface in real time. The data acquisition module is used to collect the friction coefficient data output by the sensor. The processing module is used to perform preliminary processing on the collected data and transmit it to the online monitoring system 6. By using the friction coefficient detection unit 1 to detect the friction coefficient of the metal sheet welding interface in real time and transmit the data to the online monitoring system 6, the key parameters of the welding process can be collected and processed in real time. This method avoids the lag of traditional post-detection and can promptly discover quality problems during the welding process, thereby significantly improving the quality stability of the welded joint.

[0024] In S1, the real-time friction coefficient is collected at a frequency of at least 10 times per second to ensure the real-time and accuracy of the data.

[0025] In S2, the online monitoring system 6 includes a data receiving module, a statistical analysis module and an output module. The data receiving module is used to receive the real-time friction coefficient data transmitted by the processing module, the statistical analysis module is used to perform statistical analysis on the real-time friction coefficient data, and the output module is used to output a qualified or unqualified signal according to the statistical analysis results. It can monitor and judge the joint quality in real time during the welding process, avoid subsequent processing and treatment of unqualified products, thereby reducing the waste of raw materials and production costs. In addition, it can also optimize the welding process parameters in real time and further improve production efficiency.

[0026] In the S7, the online monitoring system 6 also includes a storage module and a visualization interface. The storage module is used to store real-time friction coefficient data and its statistical analysis results, and the visualization interface is used to intuitively display the welding joint quality monitoring results. The storage module and the visualization interface are combined for storage and display to facilitate subsequent query and analysis.

[0027] In said S8, the output module of the online monitoring system 6 has an alarm function. When it is detected that the quality of the welding joint is unqualified, an alarm signal is automatically issued. The alarm signal is convenient for reminding the staff, and the staff can adjust the welding process parameters in time to ensure the stability of the welding process and further reduce the generation of unqualified products. The technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The online monitoring method for the quality of ultrasonic welded metal sheet joints in this embodiment mainly includes the following parts: Step 1: Preparation 1. Selection of plate material: The material of the lower metal plate 3 and the upper metal plate 5 is copper or aluminum.

[0029] 2. Determine the range of the metal sheet: The thickness of the lower metal sheet 3 and the upper metal sheet 5 ranges from 0.5 mm to 3.0 mm.

[0030] 3. Fix the lower metal plate 3 on the upper surface of the friction coefficient detection unit platform and ensure that it is in close contact with the base 2.

[0031] 4. Place the upper metal sheet 5 on top of the lower metal sheet 3 in preparation for welding.

[0032] Step 2: Soldering process 1. Start the ultrasonic welding machine and apply welding pressure, welding time, welding amplitude and welding frequency to the upper metal sheet 5 through the welding head 4.

[0033] 2. The welding pressure range is set to 0.1-0.6 MPa, the welding time is set to 0.1-1.2 s, the welding amplitude is set to 15-75 μm, and the welding frequency is set to 20-40 kHz.

[0034] 3. During the welding process, the upper metal sheet 5 drives the lower metal sheet 3 to generate high-frequency motion, forming a relatively high-frequency friction motion between the two.

[0035] Step 3: Friction coefficient test 1. The friction coefficient detection unit 1 detects the friction coefficient between the upper metal sheet 5 and the lower metal sheet 3 in real time.

[0036] 2. Transmit the detected real-time friction coefficient data to the online monitoring system 6.

[0037] Step 4: Data analysis and quality assessment 1. The online monitoring system 6 divides the received real-time friction coefficient data into three stages according to the welding process: before welding, during welding, and cooling period after welding.

[0038] 2. Perform statistical analysis on the friction coefficient data of each stage. The statistical analysis methods include variance, extreme value, mean value, standard deviation, and normal distribution analysis. This method adopts the statistical analysis method of mean value and extreme value. When the upper metal sheet 5 is welded to the lower metal sheet 3, a real-time friction coefficient value is randomly selected in each of the three stages before welding, during welding, and during the cooling period after welding. After five weldings, the average and extreme values ​​of the real-time friction coefficient in the three stages are calculated.

[0039] 3. Determine the weight of the impact of each stage on the quality of the welded joint: The online monitoring system 6 analyzes the data of the above three stages and obtains the average value and upper and lower limits. The impact weight is determined by the difference between the average value of each stage and the total average value of the three stages to determine which stage has a greater impact on the welding quality.

[0040] 4. Based on the multiple values ​​of the friction coefficient recorded in the above three stages, a quantitative relationship model between the real-time friction coefficient and the welding quality is established, and the value of the real-time friction coefficient corresponds to the result of the welding quality one by one.

[0041] 5. Determine the range of the real-time friction coefficient when the quality of the welding joint is qualified: When the quality of the welding joint is qualified, record multiple values ​​of the real-time friction coefficient, and find the maximum and minimum values. The value between the maximum and minimum values ​​is the range of the real-time friction coefficient when the quality of the welding joint is qualified.

[0042] Step 5: Quality determination 1. If the value of the real-time friction coefficient is within the above-mentioned qualified real-time friction coefficient range of the welding joint, the joint quality is judged to be qualified, and the online monitoring system 6 gives a qualified signal.

[0043] 2. If the value of the real-time friction coefficient is not within the specified range of the real-time friction coefficient of the qualified welding joint, the joint quality is judged to be unqualified, and the online monitoring system 6 gives an unqualified signal.

[0044] Implementation details Friction coefficient detection unit 1 The friction coefficient detection unit 1 uses a high-precision sensor, which can detect the friction coefficient of the metal sheet welding interface in real time and accurately. The output signal of the sensor is transmitted to the online monitoring system 6 in real time through the data transmission line.

[0045] Online monitoring system6 The online monitoring system 6 is responsible for receiving the friction coefficient data, performing statistical analysis, and judging the quality of the weld joint according to a preset model.

[0046] Statistical analysis methods During the statistical analysis, the following methods can be used: Analysis of variance: used to evaluate the degree of dispersion of the friction coefficient data.

[0047] Extreme value analysis: Determine the maximum and minimum values ​​of the friction coefficient.

[0048] Average value calculation: Calculate the average value of the friction coefficient at each stage.

[0049] Standard Deviation Calculation: Assess the volatility of your data.

[0050] Normal distribution analysis: Determine whether the friction coefficient data conforms to the normal distribution.

[0051] Through the above statistical analysis, a quantitative relationship model between the real-time friction coefficient and welding quality is established to ensure accurate judgment of the quality of the welding joint.

[0052] Action process description The system records the real-time friction coefficient data of the three stages respectively and analyzes the data. In each stage, the online monitoring system 6 updates the friction coefficient data in real time and performs statistical analysis to ensure real-time monitoring of welding quality.

[0053] In the present invention, the friction coefficient of the metal sheet welding interface is detected in real time by the friction coefficient detection unit 1. At the same time, the online monitoring system 6 is used to receive the real-time friction coefficient transmitted by the friction coefficient detection unit 1. Specifically, the device adopts a statistical data analysis method, and establishes a quantitative relationship model between the real-time friction coefficient and the welding quality through statistical analysis of a large amount of data. This model can accurately predict the quality of the welded joint and determine the range of the real-time friction coefficient when the welded joint quality is qualified. This quantitative quality control method greatly improves the controllability and reliability of the welding quality, and can accurately determine the influence weight of each stage on the quality of the welded joint, thereby optimizing the process parameters such as welding pressure, welding time, welding amplitude and welding frequency, making the welding process more scientific and efficient. At the same time, the online monitoring system of the present invention can automatically Dynamically analyze the friction coefficient data, and automatically judge the joint quality according to the preset qualified range value, and send out qualified or unqualified signals. This automated monitoring method reduces manual intervention, improves the automation level of the production line, and is suitable for modern welding needs. In summary, the present invention adopts a statistical data analysis method to improve the accuracy and reliability of welding joint quality monitoring, and dynamically monitors the friction coefficient of the ultrasonically welded metal sheet interface in real time to detect the quality of the welding joint in real time. By establishing a quantitative relationship model between the real-time friction coefficient and the welding quality, it is possible to promptly detect abnormal conditions in the welding process, promptly optimize the welding process parameters, ensure the stability of the welding process, improve the welding quality and automation level, reduce the generation of unqualified products, greatly improve the product qualification rate, and significantly improve the operability and accuracy of quality control.

[0054] In summary, this embodiment describes in detail the specific implementation method of the online monitoring method for the quality of ultrasonic welded metal sheet joints, and describes in detail the shape, structure and action process of each component in combination with the accompanying drawings to ensure that technical personnel in the relevant technical field can understand and implement it.

Claims

1. A method for online monitoring of the welding quality of ultrasonically welded metal sheet joints, characterized in that: The following steps are involved: S1, detecting the friction coefficient of the metal sheet welding interface in real time by means of a friction coefficient detection unit (1); S2, receiving the real-time friction coefficient detected by S1 through the online monitoring system (6); S3, dividing the real-time friction coefficient detected by S1 into three stages according to the welding process: before welding, welding process, and cooling period after welding; S4, using average values ​​and extreme values ​​to statistically analyze the real-time friction coefficient of each stage of S3, when calculating, at least obtain the real-time friction coefficient of 5 weldings in each stage to ensure the accuracy of the average values ​​and extreme values, and calculate the average values ​​and extreme values ​​of the real-time friction coefficient of the three stages in S3 respectively; S5, determining the weight of the influence of each stage on the quality of the welded joint in S3: the online monitoring system (6) analyzes the data of the above three stages and obtains the average value and the upper and lower limits, and determines the influence weight according to the difference between the average value of each stage and the total average value of the three stages, so as to determine which stage has a greater influence on the welding quality; S6, establishing a quantitative relationship model: according to the multiple values ​​of the friction coefficient recorded in S5, the real-time friction coefficient value is matched with the welding quality result one by one, and a quantitative relationship model between the real-time friction coefficient and the welding quality is established; S7. Determine the range value of the real-time friction coefficient when the quality of the welded joint is qualified: when the quality of the welded joint is qualified, record multiple values ​​of the real-time friction coefficient, and find out the maximum and minimum values. The value between the maximum and minimum values ​​is the range value of the real-time friction coefficient when the quality of the welded joint is qualified; S8. Determine the quality of welding according to the value of the real-time friction coefficient: If the value of the real-time friction coefficient is within the range determined in S7, the quality of the welding joint is qualified and the online monitoring system (6) gives a qualified signal; otherwise, it gives an unqualified signal.

2. The method for online monitoring of the welding quality of ultrasonically welded metal sheet joints according to claim 1, characterized in that: In S1: the metal sheet comprises an upper metal sheet (5) and a lower metal sheet (3); the upper surface of the friction coefficient detection unit (1) is provided with a base (2) for production; the lower metal sheet (3) is fixedly connected to the upper surface of the friction coefficient detection unit (1); and the base (2) is located directly below the lower metal sheet (3).

3. The method for online monitoring of the welding quality of ultrasonically welded metal sheet joints according to claim 2, characterized in that: In the S1: the ultrasonic welding machine's welding head (4) is applied to the upper metal sheet (5), and the welding pressure, welding time, welding amplitude, and welding frequency of the ultrasonic welding machine's welding head (4) are set.

4. The method for online monitoring of the welding quality of ultrasonically welded metal sheet joints according to claim 3 is characterized in that: In S1: the welding pressure range adopted is 0.1-0.6 MPa, the welding time is 0.1-1.2 s, the welding amplitude is 15-75 μm, and the welding frequency is 20-40 kHz.

5. The method for online monitoring of the welding quality of ultrasonically welded metal sheet joints according to claim 1, characterized in that: In S1: the friction coefficient detection unit (1) comprises a sensor, a data acquisition module and a processing module which are electrically connected to each other, the sensor is used to detect the friction coefficient of the metal sheet welding interface in real time, the data acquisition module is used to collect friction coefficient data output by the sensor, and the processing module is used to perform preliminary processing on the collected data and transmit it to the online monitoring system (6).

6. The method for online monitoring of the welding quality of ultrasonically welded metal sheet joints according to claim 5, characterized in that: In S1: the real-time friction coefficient is collected at a frequency of at least 10 times per second to ensure the real-time and accuracy of the data.

7. The method for online monitoring of the welding quality of ultrasonically welded metal sheet joints according to claim 1, characterized in that: In S2: the online monitoring system (6) comprises a data receiving module, a statistical analysis module and an output module, the data receiving module is used to receive the real-time friction coefficient data transmitted by the processing module, the statistical analysis module is used to perform statistical analysis on the real-time friction coefficient data, and the output module is used to output a qualified or unqualified signal according to the statistical analysis result.

8. The method for online monitoring of the welding quality of ultrasonically welded metal sheet joints according to claim 7, characterized in that: In S7: the online monitoring system (6) further includes a storage module and a visualization interface, the storage module is used to store real-time friction coefficient data and statistical analysis results thereof, and the visualization interface is used to intuitively display the welding joint quality monitoring results.

9. The method for online monitoring of the welding quality of ultrasonically welded metal sheet joints according to claim 7, characterized in that: In S8: the output module of the online monitoring system (6) has an alarm function, and automatically sends out an alarm signal when it is detected that the quality of the welding joint is unqualified.

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