Abnormal welding head detection method and template machine for ultrasonic welding

Abstract

The present application proposes a method and template machine for detecting abnormal welding heads in ultrasonic welding, the method comprising: obtaining the welding tasks and actual working data of each welding head to be observed; calculating the recommended welding parameters of the welding task based on the material information and size information; extracting the actual welding parameters corresponding to each welding task; screening out abnormal tasks by analyzing the actual welding parameters and the recommended welding parameters; screening out quasi-abnormal welding heads based on abnormal tasks, monitoring the temperature data of quasi-abnormal welding heads, and treating quasi-abnormal welding heads with abnormal temperature characteristics as abnormal welding heads. The present application conducts comprehensive welding head quality inspection through multi-dimensional data collection and analysis, which can quickly identify tasks that do not meet the standards, and then locate welds that may have faults, can more accurately identify and process abnormal welding heads, can optimize production efficiency, reduce costs, enhance safety, and improve the overall production management level while improving product quality.

Description

Technical Field

[0001] The present application relates to the field of ultrasonic welding, and more specifically, to an abnormal welding head detection method and template machine for ultrasonic welding. Background Art

[0002] Ultrasonic welding is a technology that uses ultrasonic waves generated by high-frequency mechanical vibrations to achieve processes such as material fusion, welding, and cutting. Its essence is the application of ultrasonic welding in the field of clothing. In clothing production, ultrasonic welding is also widely used, especially in some special materials, detail processing, and rapid production.

[0003] Ultrasonic welding can achieve needle-free welding by fusing two layers of fabric together at the molecular level under the action of ultrasound, thus replacing traditional sewing thread. In addition, ultrasonic welding can be used to cut fabrics and close the edges at the same time, preventing loose fibers in the fabric and effectively avoiding burrs. For some parts that require delicate processing, such as pockets, collars, cuffs, etc., ultrasonic welding can achieve precise stitching and fusion, and accurately cut out various complex patterns without the need for additional molds or cutters to ensure process quality. In some cases where multiple textile materials need to be combined into composite materials, ultrasonic welding can effectively connect them together to meet specific performance requirements.

[0004] Among them, the state of the welding head directly affects the welding quality. If the welding head is worn or damaged, it may cause problems such as uneven welding, incomplete fusion or excessive burning, which in turn affects the performance and appearance of the product. A good welding head state can ensure a stable welding process, reduce downtime and scrap rate caused by failures, and improve overall production efficiency. Traditional welding head abnormality detection mainly relies on the operator's experience and visual inspection, which is not only time-consuming but also easy to miss details. Summary of the invention

[0005] Based on the problems existing in the prior art, this application provides a method for detecting abnormal welding heads in ultrasonic welding and a template machine. The specific solution is as follows:

[0006] In the first part, the present application provides a method for detecting abnormal welding heads of ultrasonic welding, which is applicable to welding heads of ultrasonic welding. The method for detecting abnormal welding heads includes:

[0007] Obtain all welding tasks completed by each welding head to be observed within a preset period of time and actual work data when performing the corresponding welding tasks, and obtain material information and size information of the material to be welded from each welding task;

[0008] Calculate the recommended welding temperature, recommended welding time and recommended welding head frequency of the welding task according to the material information and size information; extract the actual welding temperature, actual welding head frequency and actual welding time corresponding to each welding task from the actual working data;

[0009] By analyzing the actual welding temperature and the recommended welding temperature, the actual welding head frequency and the recommended welding head frequency, and the actual welding time and the recommended welding time in each welding task, the welding tasks that meet the preset abnormal standards are screened out to obtain abnormal tasks;

[0010] Quasi-abnormal welding heads are screened out according to abnormal tasks, and the temperature data of the quasi-abnormal welding heads during operation is monitored. It is determined whether the temperature characteristics of the quasi-abnormal welding heads are abnormal according to the temperature data, and the quasi-abnormal welding heads with abnormal temperature characteristics are regarded as abnormal welding heads.

[0011] In some specific embodiments, obtaining the welding head to be observed includes:

[0012] Real-time monitoring of the sound data of each template machine welding head during operation;

[0013] Analyze the sound data, screen out welding heads with abnormal tones, and obtain welding heads with abnormal tones;

[0014] The reasons for the abnormal tone of the abnormal sound welding head are analyzed, and welding heads with abnormal tone due to non-welding head reasons are excluded from the abnormal sound welding heads to obtain welding heads to be observed.

[0015] In some specific embodiments, the acquisition of the welding head to be observed further includes:

[0016] Obtain image data of multiple template machine welding heads before, during and after work;

[0017] Analyze the appearance changes of the welding head before, during and after working according to the image data, as well as the appearance changes relative to a preset standard welding head image, so as to analyze whether the welding head has defects;

[0018] The influence of defects on the welding head on the processing effect is evaluated, and the welding head with an influence degree higher than the preset high influence degree is directly regarded as an abnormal welding head, and the welding head with an influence degree between the high influence degree and the preset low influence degree is regarded as a welding head to be observed.

[0019] In some specific embodiments, materials that can be welded by an ultrasonic welding device are classified in advance according to the melting point and melting characteristics of the materials to obtain three types of materials: high melting point materials, medium melting point materials, and low melting point materials; a comparison relationship table of welding speed ranges and welding temperature ranges of the three types of materials at preset standard thicknesses is constructed respectively;

[0020] The material type and its comparison table of the material to be welded are determined according to the material information, and then the recommended welding temperature, recommended welding time and recommended welding head frequency are obtained.

[0021] In some specific embodiments, the material to be welded is classified into one of three material types according to the material information, and a corresponding comparison relationship table is selected;

[0022] Acquire the welding length and welding thickness of the material to be welded according to the size information, and calculate the thickness difference between the welding thickness and the standard thickness;

[0023] Based on the comparison relationship table and referring to the thickness difference and the welding temperature and welding speed corresponding to the standard thickness, respectively determining a recommended welding temperature in the welding temperature range and determining a recommended welding speed in the welding speed range;

[0024] The recommended welding time is calculated according to the recommended welding speed and the welding length, and the recommended welding head frequency is selected according to the recommended welding time and the recommended welding temperature.

[0025] In some specific embodiments, the abnormality criteria include:

[0026] The actual welding head frequency is less than the recommended welding head frequency;

[0027] The actual welding temperature is higher than the recommended welding temperature;

[0028] The actual welding time is shorter than the recommended welding time;

[0029] The actual welding frequency is not less than the recommended welding frequency and the actual welding temperature is less than the recommended welding temperature;

[0030] The actual welding frequency is not less than the recommended welding frequency and the actual welding time is greater than the recommended welding time;

[0031] The actual welding temperature is not less than the recommended welding temperature and the actual welding time is greater than the recommended welding time.

[0032] In some specific embodiments, each welding head to be observed is scored abnormally based on the number and frequency of abnormal tasks, and / or whether it is an abnormal welding head, and / or the degree of influence of the defect on the welding head processing effect;

[0033] The welding heads to be observed whose abnormality scores do not meet the preset standard segments are regarded as quasi-abnormal welding heads.

[0034] In some specific embodiments, the temperature data including temperature change and temperature distribution of the quasi-abnormal welding head during operation is monitored by an infrared sensing device; if the quasi-abnormal welding head has uneven temperature distribution and / or uneven temperature change, the quasi-abnormal welding head is proved to be.

[0035] In the second part, the present application provides an abnormal welding head detection system for ultrasonic welding, which is applicable to the welding head of ultrasonic welding. The abnormal welding head detection system includes:

[0036] A data acquisition unit is used to acquire all welding tasks completed by each welding head to be observed within a preset period of time and actual working data when performing the corresponding welding tasks, and to acquire material information and size information of the material to be welded from each welding task;

[0037] A data analysis unit is used to calculate the recommended welding temperature, recommended welding time and recommended welding head frequency of the welding task according to the material information and size information; and extract the actual welding temperature, actual welding head frequency and actual welding time corresponding to each welding task from the actual working data;

[0038] An indicator judgment unit is used to screen out welding tasks that meet preset abnormal standards to obtain abnormal tasks by analyzing the actual welding temperature and the recommended welding temperature, the actual welding head frequency and the recommended welding head frequency, and the actual welding time and the recommended welding time in each welding task;

[0039] The abnormal screening unit is used to screen out quasi-abnormal welding heads according to abnormal tasks, monitor the temperature data of the quasi-abnormal welding heads during operation, and determine whether the temperature characteristics of the quasi-abnormal welding heads are abnormal according to the temperature data.

[0040] In the third part, the present application provides a template machine for implementing the abnormal welding head detection method of ultrasonic welding as described in any one of the first parts.

[0041] Beneficial effects: This application proposes a method and template machine for detecting abnormal welding heads of ultrasonic welding. Through multi-dimensional data collection and analysis, comprehensive welding head quality detection can be carried out, which can quickly identify tasks that do not meet the standards, and then locate welding heads that may have faults. It can more accurately identify and handle abnormal welding heads, and can optimize production efficiency, reduce costs, enhance safety, and improve the overall production management level while improving product quality. By comparing and analyzing the actual working data with the recommended parameters, tasks that do not meet the standards can be quickly identified, and then welding heads that may have faults can be located. By real-time monitoring of sound data, image data, and temperature data, not only can the welding heads be detected from multiple dimensions, the comprehensiveness and accuracy of abnormal detection are improved, but also problems can be discovered and measures can be taken in the first place, reducing downtime and improving the overall efficiency of the production line. Taking into account the number and frequency of abnormal tasks, abnormal sound conditions, and the degree of influence of defects, the state of the welding head can be more accurately evaluated to ensure the reliability of the detection results.

[0042] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 It is a schematic diagram of the abnormal welding head detection method of the present application;

[0045] Figure 2 is a schematic diagram of the hierarchical relationship between the features of this application;

[0046] Figure 3 It is a schematic diagram of the abnormal welding head detection system module of the present application.

[0047] Figure numerals: 1-data acquisition unit; 2-data analysis unit; 3-indicator judgment unit; 4-abnormal screening unit. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] This application proposes a method for detecting abnormal welding heads in ultrasonic welding. Through multi-dimensional data collection and analysis, comprehensive welding head quality detection can be performed, which can more accurately identify and process abnormal welding heads, thereby improving the stability and reliability of the entire production process. Figure 1 The screening principle of abnormal welding heads is shown in the attached Figure 2 As shown, the scheme is as follows:

[0050] A method for detecting an abnormal welding head of ultrasonic welding is applicable to the welding head of ultrasonic welding. The method for detecting an abnormal welding head comprises:

[0051] 101. Obtain all welding tasks completed by each welding head to be observed within a preset period of time and actual work data when performing the corresponding welding tasks, and obtain material information and size information of the material to be welded from each welding task;

[0052] 102. Calculate the recommended welding temperature, recommended welding time and recommended welding head frequency of the welding task according to the material information and the size information; extract the actual welding temperature, actual welding head frequency and actual welding time corresponding to each welding task from the actual working data;

[0053] 103. By analyzing the actual welding temperature and the recommended welding temperature, the actual welding head frequency and the recommended welding head frequency, and the actual welding time and the recommended welding time in each welding task, the welding tasks that meet the preset abnormal standards are screened out to obtain abnormal tasks;

[0054] 104. Screen out quasi-abnormal welding heads according to abnormal tasks, monitor the temperature data of the quasi-abnormal welding heads during the working process, and determine whether the temperature characteristics of the quasi-abnormal welding heads are abnormal according to the temperature data.

[0055] The ultrasonic welding head, also known as the welding head or mold, is the part of the ultrasonic welding equipment that directly contacts the material and transfers energy. It vibrates at ultrasonic frequencies, converting mechanical energy into heat energy, melting the surface of the material and bonding it together. The welding head experiences high-speed vibrations when working, which causes friction between the surface of the welding head and the material to be welded. If the frequency is not selected properly, this friction may increase, causing premature wear of the welding head. For example, using too high a frequency to process unsuitable materials may cause the local temperature of the welding head to be too high, accelerating the wear process. Conversely, if the frequency is too low, it may not be able to effectively stimulate the molecular motion inside the material, thereby extending the welding time, increasing the workload of the welding head, and also causing unnecessary wear. This application uses multi-dimensional data collection, parameter calculation, comparative analysis and real-time monitoring, and comprehensively evaluates the abnormal conditions of the welding head by parameters such as temperature, frequency, and duration, which can effectively identify and handle abnormal welding heads and improve welding quality and production efficiency.

[0056] In step 101, it involves obtaining all welding tasks completed by each welding head to be observed within a preset period of time and the actual working data when performing the corresponding welding tasks, and obtaining the material information and size information of the material to be welded from each welding task. In this application, it is necessary to establish a data acquisition system to collect the working data of the template machine, including sensors, production management systems and databases. Sensors include temperature sensors, frequency meters, time recorders, etc., which record various parameters of the welding head during the working process in real time. The tasks completed by each welding head and their related data are recorded through the production management system (such as the MES system). The collected data is stored in a central database for subsequent analysis and processing. In some embodiments, a structured database table is designed to store all relevant data, including fields such as welding head ID, task time, frequency, temperature, duration, material material and thickness. The collected data is inserted into the database through SQL statements to ensure the integrity and traceability of the data.

[0057] Determine the time period that needs to be monitored, such as one day, one week, or one month. The monitoring time period may be due to the frequent use of the welding head, which leads to performance degradation and needs to be evaluated. For example, if a certain template machine has produced products with quality problems in the recent period, it is necessary to obtain the production cycle in which the product has quality problems to determine whether it is caused by the welding head, and focus on checking the processing data of this cycle. It can also be daily quality monitoring, selecting the processing tasks of the latest processing cycle to determine whether the current processing performance of the welding head is abnormal. Material information can also be obtained through the database. For example, during the production process, material information is automatically read through a barcode scanner or RFID reading device, or the operator can manually enter material information. Associate the material information with the corresponding welding task and store it in the database to ensure that the working status and material information of each welding head are recorded in detail, which is convenient for subsequent analysis and problem tracing.

[0058] The actual welding head frequency, actual welding temperature and actual welding time are defined in this application as key parameters for evaluating the performance of ultrasonic welding heads. The actual welding head frequency, actual welding temperature and actual welding time are key parameters for evaluating the performance of ultrasonic welding heads. In practical applications, a frequency detection device is required to record the actual working frequency of the welding head in each task. The actual welding head frequency reflects the vibration frequency of the welding head in actual work, which is used for comparison with the recommended frequency. Frequency directly affects the efficiency of energy transfer. Higher frequencies are generally suitable for thinner or softer materials and can provide more delicate control; lower frequencies are suitable for thicker or harder materials and can provide greater energy input. Too high or too low frequencies may increase the wear of the welding head and related equipment and affect their service life. A temperature sensor is used to record the temperature data of the welding head in each task, including the temperature change of welding. The actual welding temperature reflects the temperature of the welding head in actual work, which is used for comparison with the recommended temperature. Temperature directly affects the melting degree of the material. The appropriate temperature can make the materials fully melt and combine together, while too high or too low temperature will cause welding quality problems. In addition, in some cases, temperature is closely related to welding speed. Too high a temperature may speed up the welding process, but may result in a weak weld; too low a temperature may slow down the welding process and affect production efficiency. Use a time recorder to record the time data for each welding task, including the total welding time. The actual welding time reflects the actual duration of each welding task and is used to compare with the recommended time. Appropriate welding time can ensure that the materials are fully melted and combined to form a strong joint. Insufficient time may result in a weak weld, while too long time may result in excessive melting or burning of the material. Reasonable welding time can improve production efficiency. Excessive welding time will not only reduce production speed and increase costs, but also affect the performance and service life of the welding head.

[0059] Step 102 involves calculating the recommended welding parameters (temperature, duration and frequency) based on the material and size information of the material, and extracting the actual working parameters corresponding to each welding task from the actual working data. In this application, the material information mainly includes the material of the material to be welded, and the size information mainly refers to the length and thickness of the material to be welded. The length affects the welding time, and the thickness affects the welding temperature and welding head frequency. Different materials have different melting points, thermal conductivity and mechanical properties, which directly affect the selection of welding parameters. The thickness and size of the material affect the energy transfer efficiency and welding time, and the welding parameters need to be adjusted according to the size.

[0060] The recommended welding parameters are calculated based on the material information and size information. Based on experience and experimental data, a recommended welding parameter comparison table corresponding to different materials and sizes can be established, or an existing parameter calculation model can be developed or used to input the material and size information of the material, and the recommended welding temperature, duration and frequency can be output. For example, in actual applications, through a large number of experiments, the welding effects of different materials and sizes under different parameters are recorded, a comprehensive comparison table is established, and the comparison table is supplemented and improved in combination with industry experience and expert knowledge. Use machine learning algorithms (such as regression analysis, neural networks, etc.) to train historical data and generate a prediction model. In this application, the recommended welding parameters represent the best welding parameters. Customizing the parameters requires comprehensive welding quality, service life of the welding head, processing cost and processing efficiency, but it is mainly based on the service life principle of the welding head and the product quality principle.

[0061] Step 103 involves screening out welding tasks that meet preset abnormal standards by analyzing the actual welding temperature and the recommended welding temperature, the actual welding head frequency and the recommended welding head frequency, and the actual welding time and the recommended welding time in each welding task.

[0062] In some specific embodiments, the acquisition of the welding head to be observed includes: real-time monitoring of the sound data of each template machine welding head during operation; analyzing the sound data, screening out welding heads with abnormal tones, and obtaining welding heads with abnormal tones; analyzing the reasons for the abnormal tones of the abnormal welding heads, and excluding welding heads with abnormal tones due to non-welding head reasons from the abnormal welding heads, to obtain welding heads to be observed. The welding heads to be observed are obtained by real-time monitoring of the sound data of the welding heads during operation, and welding heads that may have performance problems are quickly identified, and further analysis is performed to determine whether more detailed inspections are required, and abnormal tones caused by non-welding head reasons are excluded to avoid unnecessary inspections and maintenance. A sound sensor (such as a microphone) is installed near each welding head to capture sound signals during operation. A data acquisition system is set up to ensure that sound data can be recorded in real time. The collected sound data is transmitted to the central processing system in real time for analysis.

[0063] The sound data contains the vibration information generated during the operation of the welding head. Abnormal sound may indicate that the welding head is faulty or has degraded performance. Use spectrum analysis technology to process the sound data and extract frequency components. Machine learning or statistical methods can also be used to establish models of normal sound patterns and abnormal sound patterns, and the established models of normal sound patterns and abnormal sound patterns can be used to compare the current sound data. Or set thresholds for abnormal tones, and mark them as abnormal when the sound data exceeds these thresholds. Determine whether the sound data is abnormal based on the preset thresholds. If it exceeds the threshold, it is marked as an abnormal welding head. Spectral analysis can identify the main frequency components in the sound to determine whether there is an abnormality. Pattern recognition technology can automatically distinguish between normal and abnormal sounds to improve detection efficiency. Combined with other sensor data (such as temperature, frequency, etc.) and production environment information, exclude abnormal tones caused by external factors (such as environmental noise, mechanical vibration, etc.), analyze the causes of abnormal tones, and once it is finally confirmed that the abnormal tones are caused by the welding head itself, the welding head to be observed is obtained. In the process of analyzing the cause, other sensor data such as temperature and frequency at the same time point can be checked to determine whether there is a correlation, and other factors in the production environment can be considered, such as background noise, mechanical vibration, etc. After the above analysis, it is confirmed that the abnormal tone is indeed caused by the welding head itself, and it is marked as a welding head to be observed, so as to effectively identify and locate the welding head that may have performance problems, thereby improving welding quality and production efficiency.

[0064] In some specific embodiments, the acquisition of the welding head to be observed also includes: acquiring image data of welding heads of multiple template machines before, during and after work; analyzing the appearance changes of the welding heads before, during and after work, and the appearance changes relative to the preset standard welding head image according to the image data, so as to analyze whether the welding head has defects; evaluating the degree of influence of the defects on the welding head on the processing effect, and directly treating the welding head with an influence degree higher than the preset high influence degree as an abnormal welding head, and treating the welding head with an influence degree between the high influence degree and the preset low influence degree as the welding head to be observed. By acquiring and analyzing the image data of the welding head before, during and after work, the appearance changes and potential defects of the welding head can be further evaluated, and combined with visual inspection technology, the status of the welding head can be more comprehensively evaluated.

[0065] Specifically, a high-resolution camera is installed near each welding head, and the angle is adjusted to ensure that the key parts of the welding head are covered, so as to clearly capture the appearance of the welding head. The image data contains the appearance information of the welding head at different time points, which can intuitively reflect the state changes of the welding head. Use image processing technology (such as edge detection, contrast enhancement, etc.) to pre-process the image, such as removing noise in the image, enhancing the contrast and brightness of the image, and making the features more obvious. Extract the key features of the welding head, including: shape features such as the contour and size of the welding head, color distribution features of the welding head, and texture features on the surface of the welding head. By extracting key features, the state of the welding head can be described more accurately. Compare the images before, during and after work to analyze the changes in the appearance of the welding head. Compare the images before and after work to analyze the changes in the appearance of the welding head. Compare the images at different time points during work to analyze the change trend of the welding head. By comparing the images at different time points, subtle changes in the welding head can be found, and the defect position and type of the welding head can be accurately located.

[0066] In practical applications, a preset standard welding head image library can be established to mark key features in the standard image, such as shape, color, texture, etc. The actual collected image is compared with the standard image, and the difference between the actual image and the standard image, such as pixel value difference, shape difference, etc., is calculated. By comparing with the standard image, the defects of the welding head can be identified more accurately. Using machine learning or computer vision technology, the machine learning model is trained to identify the defect type (such as cracks, wear, deformation, etc.) and location (such as tip, side, etc.) of the welding head, so as to realize the automation of defect identification and improve efficiency. According to the type of defect (such as cracks, wear, deformation, etc.) and location (such as tip, side, etc.), the degree of influence on the welding effect is evaluated. Set preset high-impact standards (such as defects that cause serious problems such as loose welding and burning of materials) and low-impact standards (such as slight wear and defects that do not affect the welding effect) to distinguish defects with different degrees of influence. According to the evaluation results, the welding heads are classified as abnormal welding heads or welding heads to be observed. The welding heads with an impact degree higher than the preset high impact degree are directly marked as abnormal welding heads, and the welding heads with an impact degree between the high impact degree and the low impact degree are marked as welding heads to be observed.

[0067] Step 103 is the key link in the entire abnormal welding head detection method. It identifies welding tasks that may have problems by comparing and analyzing the actual welding parameters with the recommended parameters. The system compares the actual operating parameters of each welding task (such as actual welding temperature, actual welding head frequency, actual welding time) with the recommended operating parameters calculated based on material characteristics (recommended welding temperature, recommended welding head frequency, recommended welding time). Through this comparison, tasks that have significantly deviated from ideal conditions during operation can be identified.

[0068] The abnormal standard is set to capture those operating conditions that may cause product quality problems or equipment damage. In some specific embodiments, the abnormal standard includes: the actual welding head frequency is less than the recommended welding head frequency; the actual welding temperature is greater than the recommended welding temperature; the actual welding time is less than the recommended welding time; the actual welding head frequency is not less than the recommended welding head frequency and the actual welding temperature is less than the recommended welding temperature; the actual welding head frequency is not less than the recommended welding head frequency and the actual welding time is greater than the recommended welding time; the actual welding temperature is not less than the recommended welding temperature and the actual welding time is greater than the recommended welding time.

[0069] Specifically:

[0070] The actual welding head frequency is lower than the recommended welding head frequency: If the actual welding head frequency used is significantly lower than the recommended value, this may mean that the welding head does not achieve sufficient vibration intensity, thus affecting the quality of the weld, which may result in a weak weld or insufficient fusion of the materials.

[0071] The actual welding temperature is higher than the recommended welding temperature: When the actual temperature is much higher than the recommended temperature, it may cause overheating, burning or deformation of the material, and also increase the risk of wear on the welding head and other components.

[0072] The actual welding time is significantly shorter than the recommended welding time: If the actual welding time is much shorter than the recommended time, this may mean that the welding head is working too hard or is not set up properly, which may result in an inadequate weld and affect the durability and aesthetics of the product.

[0073] The actual welding head frequency is not less than the recommended welding head frequency and the actual welding temperature is less than the recommended welding temperature: In this case, the welding head works at a speed not less than the recommended speed, but the welding temperature is lower than the recommended value. This may cause the material to fail to fully melt and combine, thus affecting the welding quality, such as a weak joint.

[0074] The actual welding frequency is not less than the recommended welding frequency and the actual welding time is longer than the recommended welding time: Even if the welding head runs at or above the recommended frequency, it takes longer than expected to complete the task. Long welding time may cause overheating, deformation or even damage of the material, and also increase energy consumption and production costs.

[0075] The actual welding temperature is not less than the recommended welding temperature and the actual welding time is longer than the recommended welding time: When the welding temperature reaches or exceeds the recommended level and the time required is longer, it indicates that there may be other factors affecting the welding efficiency, such as poor surface condition of the welding head or poor thermal conductivity of the material itself. This situation may also cause overheating problems.

[0076] The above abnormal situations all point to abnormal conditions in the welding process, which may be caused by defects in the welding head itself, improper operation, or inappropriate equipment adjustment. By identifying and analyzing these abnormal situations, maintenance personnel can quickly locate potential fault points and take corresponding measures to correct or replace problematic parts, thereby ensuring the stability of the production line and product quality.

[0077] In addition, other criteria that may indicate problems with the weld head or welding process can be defined based on actual operating experience and equipment characteristics. These additional criteria can help to more fully evaluate the condition of the weld head and the quality of the weld.

[0078] In some embodiments, the exception criteria further include:

[0079] Large temperature fluctuations in the welding head: During the entire welding process, if the temperature of the welding head is monitored to fluctuate greatly, this may be caused by welding head damage, unstable power supply or control system failure. In this case, the consistency of welding will be affected.

[0080] Multiple consecutive abnormal tasks of the same type: If a machine exhibits the same abnormal pattern for multiple consecutive tasks (for example, the temperature is always too high), it is likely that there is a systemic problem with the equipment itself and further inspection and maintenance are required.

[0081] Welding head surface condition detection: Through visual inspection or other non-destructive testing methods, it is found that the welding head surface has obvious damage, cracks or contamination, which is an abnormal standard that directly reflects the welding head condition.

[0082] Decreased productivity: If you observe a consistent decrease in the productivity of a particular weld head, such as a gradual increase in the time required to complete the same task, even if other parameters are within the recommended range, this may be a sign that the weld head is about to fail or require cleaning / maintenance.

[0083] Increased product defect rates: If a product line that a particular weld head is responsible for begins to experience more quality issues such as breakage, cracking, etc., then that weld head may need to be inspected even if individual parameters appear normal.

[0084] Each abnormality standard is set to ensure the effectiveness and safety of the welding process. In practical applications, these standards can be flexibly adjusted according to specific application scenarios and technical requirements, and the detection methods can be continuously optimized and improved by combining historical data and expert experience.

[0085] Step 104 is a process of further monitoring and analyzing the quasi-abnormal welding heads screened out by step 103. This step aims to gain a deeper understanding of the specific performance of these welding heads during operation, especially their temperature characteristics, in order to determine whether there is a real fault or performance degradation. From step 103, the welding tasks that meet the preset abnormal standards have been identified. Next, it is necessary to determine the welding heads that perform these tasks, namely the quasi-abnormal welding heads. These quasi-abnormal welding heads are included in a special monitoring list for more detailed observation. Continuous temperature monitoring of quasi-abnormal welding heads. This usually involves installing temperature sensors or other types of thermal imaging equipment to obtain real-time temperature data during the operation of the welding head. The collected data will be recorded and stored in a database for subsequent analysis and processing. Analyze the temperature change trend of the welding head in different time periods. A normal welding head should have a relatively stable and expected working temperature curve; if it is found that the temperature fluctuates greatly, rises too fast or falls too slowly, it may be an abnormal performance. In practical applications, the actual monitored temperature data is compared with the historical normal operating data or the recommended operating temperature range to look for specific temperature patterns or characteristics that may indicate problems, such as excessively high temperature peaks, abnormal cooling rates, etc., to assess the degree of difference in the current state. Reasonable temperature thresholds can also be set based on empirical data and technical specifications to distinguish between normal and abnormal situations. Combined with the above analysis results, other factors (such as the age of the equipment, maintenance records, etc.) are considered comprehensively to ultimately determine whether the welding head actually has a problem. For welding heads that are confirmed to have problems, they should be stopped from use immediately and necessary repairs or replacements should be carried out; for suspected but uncertain situations, further diagnostic tests can be arranged.

[0086] Temperature is a very important parameter in the ultrasonic welding process, which directly affects the quality of welding. The temperature distribution map obtained by the infrared sensor device can intuitively show the temperature of the welding head surface, including whether there are local overheating or cold spots. Compared with relying solely on the average temperature or a single temperature point, the temperature distribution map can provide more comprehensive information. It can reveal possible hot spots or poor cooling on the welding head, helping technicians to quickly locate the problem. However, compared with sensing devices such as recording devices and oscilloscopes, temperature detection involves more complicated operations and maintenance, and the purchase cost is high. This application uses it as the last detection measure. Although thermal images are intuitive, it requires certain professional knowledge and technical experience to accurately interpret these images and identify potential problems. In addition, in order to ensure the accuracy of the measurement, the infrared sensor may need to be calibrated regularly, and the settings may need to be adjusted under different environmental conditions (such as different materials, working temperatures, etc.). In addition, reflections, radiation and other interference sources in the environment may affect the results of infrared measurements, so appropriate shielding measures are required to ensure the reliability of the data. Therefore, this application uses temperature detection as the last detection measure to reduce the overall difficulty of detection. Temperature detection will only be performed when all the above conditions are met, so as to save detection costs and maintenance costs as much as possible.

[0087] In some specific embodiments, the temperature data including temperature change and temperature distribution of the quasi-abnormal welding head during operation are monitored by an infrared sensing device; if the quasi-abnormal welding head has uneven temperature distribution and / or uneven temperature change, the quasi-abnormal welding head is proved. The infrared sensor can realize real-time monitoring of the temperature of the welding head, which enables potential problems to be discovered and handled in time during the production process, avoiding the accumulation of problems caused by long-term operation. As a non-contact measurement method, the infrared sensing device will not interfere with the normal operation of the welding head, which is conducive to maintaining the continuity and stability of the production process.

[0088] In some specific embodiments, materials that can be welded by an ultrasonic welding device are classified in advance according to the melting point and melting characteristics of the materials, and three types of materials are obtained, namely, high melting point materials, medium melting point materials, and low melting point materials; a comparison relationship table of welding speed intervals and welding temperature intervals of the three types of materials under a preset standard thickness is constructed respectively; the material type of the material to be welded and its comparison relationship table are determined according to the material information, and then the recommended welding temperature, recommended welding time, and recommended welding head frequency are obtained. By classifying different materials in advance and constructing corresponding comparison relationship tables, more accurate parameter recommendations can be provided for the ultrasonic welding process. Combined with material science and process control, it is ensured that each welding task can be performed under optimal conditions. High melting point materials usually require higher temperatures to reach a melting state. Low melting point materials can melt at lower temperatures. The melting point of medium melting point materials is between high melting point and low melting point. For each type of material, under a preset standard thickness, its suitable welding speed interval and welding temperature interval are determined. The data intervals in the comparison table are set based on experimental data, experience, and material properties, aiming to ensure welding quality while optimizing production efficiency.

[0089] In some specific embodiments, the material to be welded is classified into one of three material types according to the material information, and the corresponding comparison relationship table is selected; the welding length and welding thickness of the material to be welded are obtained according to the size information, and the thickness difference between the welding thickness and the standard thickness is calculated; based on the comparison relationship table, and referring to the thickness difference and the welding temperature and welding speed corresponding to the standard thickness, the recommended welding temperature is determined in the welding temperature range and the recommended welding speed is determined in the welding speed range respectively; the recommended welding time is calculated according to the recommended welding speed and welding length, and the recommended welding head frequency is selected according to the recommended welding time and recommended welding temperature. According to the material information of the material to be welded, it is classified into one of the above three material types. The corresponding comparison relationship table is selected, which will guide the selection of subsequent parameters. The welding length and actual welding thickness of the material to be welded are obtained from the information of the material to be welded. The difference (thickness difference) between the actual welding thickness and the standard thickness is calculated. According to the temperature range in the comparison relationship table, the thickness difference is adjusted. If the actual thickness is greater than the standard thickness, the temperature may need to be increased; otherwise, the temperature is reduced. Similarly, according to the speed range in the comparison relationship table, the most suitable welding speed is determined considering the influence of the thickness difference. According to the recommended welding time and recommended welding temperature, select an appropriate welding head frequency. Generally speaking, the choice of frequency will consider how to provide enough energy to complete the welding in a given time while avoiding overheating or underheating.

[0090] For example, there is a plastic part to be welded, which is made of a low-melting-point material. The recommended welding temperature range under standard thickness is 100°C to 120°C, and the welding speed range is 50mm / s to 70mm / s. If the actual thickness of the plastic part is slightly higher than the standard thickness, the recommended welding temperature may be adjusted to the range of 110°C to 120°C to ensure that the material can be fully fused. Assuming that the actual thickness is 10% higher than the standard thickness, the recommended welding speed may be appropriately reduced, such as adjusted to between 50mm / s and 60mm / s. If the welding length is 1 meter and the recommended welding speed is finally determined to be 55mm / s, the recommended welding time will be 1000mm / 55mm / s ≈ 18.18 seconds. Finally, based on this time and temperature, select a suitable welding head frequency, such as 20kHz, to ensure that sufficient ultrasonic energy is provided within the specified time.

[0091] In some specific embodiments, each welding head to be observed is scored for abnormality based on the number and frequency of abnormal tasks, and / or whether it is an abnormal sound welding head, and / or the degree of influence of defects on the welding head processing effect; and the welding heads to be observed whose abnormal scores do not meet the preset standard segmentation are regarded as quasi-abnormal welding heads. By comprehensively considering the number and frequency of abnormal tasks, whether it is an abnormal sound welding head (i.e., whether there is abnormal sound), and the degree of influence of defects on the welding head processing effect, each welding head to be observed can be scored for abnormality, thereby more comprehensively evaluating the status of the welding head and determining which welding heads need further attention or immediate action. The number of abnormal tasks performed by each welding head is recorded. The frequency can be expressed as a percentage by calculating the proportion of abnormal tasks to total tasks. These data can help identify welding heads with frequent problems. For example, if a welding head has had multiple abnormal tasks in the past month, it may be a potential problem point.

[0092] Abnormal sound is often an important indicator of mechanical failure or wear. If a welding head often makes abnormal sounds, it may indicate that it may have structural problems or need maintenance. Record the types and number of defects that may exist, and evaluate their impact on the performance of the final product based on the severity and frequency of the defects. For example, minor cosmetic defects may not seriously affect product quality, while severe insufficient welding strength may cause complete failure of the product. Develop a set of scoring criteria, including the number and frequency of abnormal tasks, the abnormal sound conditions, and the impact of the defects. Each factor can be assigned a certain weight. Scoring formula: Calculate the abnormality score for each welding head based on the above factors and their weights. Exemplary:

[0093] Abnormal task count score = (abnormal task count / total task count) * 0.4

[0094] Abnormal sound score = (number of abnormal sounds / total working time) * 0.3

[0095] Defect Impact Score = (Defect Severity * Defect Frequency) * 0.3

[0096] Total score = Abnormal task number score + Sound abnormality score + Defect impact score

[0097] According to empirical data and industry standards, set different levels of abnormality scoring thresholds, such as low risk, medium risk, and high risk. Compare the abnormality score of the welding head with the preset standard segment to determine its risk level. Welding heads with abnormality scores exceeding the preset standard segment are regarded as quasi-abnormal welding heads. These quasi-abnormal welding heads are inspected in more detail, including infrared temperature monitoring, detailed sound analysis, and other necessary diagnostic measures. Exemplary, there are the following scoring criteria and thresholds:

[0098] Abnormal task number score: 10 points will be added for each abnormal task.

[0099] Abnormal sound score: 5 points will be added for each abnormal sound.

[0100] Defect impact score: Minor defects add 5 points, moderate defects add 10 points, and serious defects add 20 points.

[0101] Total score: more than 30 points is high risk, 20-30 points is medium risk, and less than 20 points is low risk.

[0102] For a welding head, if it is found that it has 5 abnormal tasks, 3 abnormal sounds, 2 minor defects and 1 serious defect in the past month, the score is as follows:

[0103] Abnormal task number score = 5 * 10 = 50 points Abnormal sound score = 3 * 5 = 15 points Defect impact score = (2 * 5) + (1 * 20) = 10 + 20 = 30 points Total score = 50 + 15 + 30 = 95 points Since the total score of 95 points is far higher than the high-risk threshold of 30 points, the welding head is determined to be a quasi-abnormal welding head and requires further inspection and repair immediately.

[0104] The present application also provides an abnormal welding head detection system for ultrasonic welding, which is applicable to the welding head of ultrasonic welding. The module diagram of the system is shown in the attached Figure 3 As shown, the abnormal welding head detection system includes:

[0105] The data acquisition unit 1 is used to acquire all welding tasks completed by each welding head to be observed within a preset period of time and the actual working data when performing the corresponding welding tasks, and to acquire the material information and size information of the material to be welded from each welding task;

[0106] The data analysis unit 2 is used to calculate the recommended welding temperature, recommended welding time and recommended welding head frequency of the welding task according to the material information and the size information; and extract the actual welding temperature, actual welding head frequency and actual welding time corresponding to each welding task from the actual working data;

[0107] The indicator judgment unit 3 is used to screen out welding tasks that meet the preset abnormal standards to obtain abnormal tasks by analyzing the actual welding temperature and the recommended welding temperature, the actual welding head frequency and the recommended welding head frequency, and the actual welding time and the recommended welding time in each welding task;

[0108] The abnormal screening unit 4 is used to screen out quasi-abnormal welding heads according to abnormal tasks, monitor the temperature data of the quasi-abnormal welding heads during operation, and determine whether the temperature characteristics of the quasi-abnormal welding heads are abnormal according to the temperature data.

[0109] The present application also provides a template machine for implementing any of the above-mentioned methods for detecting abnormal welding heads in ultrasonic welding.

[0110] The present application provides a computer program product, which includes computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes a method for detecting abnormal horns of ultrasonic welding. An abnormal horn detection method for ultrasonic welding is applied to a computer program product for easy execution.

[0111] The present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the abnormal welding head detection method for ultrasonic welding are implemented as described above.

[0112] The computer storage medium of the present application may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. The present application applies a method for detecting abnormal welding heads of ultrasonic welding to a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the method steps provided in the present application are implemented, which is simple and fast, easy to store, and not easy to lose.

[0113] This application proposes a method and template machine for detecting abnormal welding heads of ultrasonic welding. Through multi-dimensional data collection and analysis, comprehensive welding head quality detection can be carried out, which can quickly identify tasks that do not meet the standards, and then locate welding heads that may have faults. It can more accurately identify and handle abnormal welding heads, and can optimize production efficiency, reduce costs, enhance safety, and improve the overall production management level while improving product quality. By comparing and analyzing the actual working data with the recommended parameters, tasks that do not meet the standards can be quickly identified, and then welding heads that may have faults can be located. By real-time monitoring of sound data, image data, and temperature data, not only can the welding heads be detected from multiple dimensions, the comprehensiveness and accuracy of abnormal detection are improved, but also problems can be found and measures can be taken in the first place, reducing downtime and improving the overall efficiency of the production line. Taking into account the number and frequency of abnormal tasks, abnormal sound conditions, and the degree of influence of defects, the state of the welding head can be more accurately evaluated to ensure the reliability of the detection results.

[0114] Those skilled in the art should understand that the modules of the present application described above can be implemented by a general-purpose computing system, they can be concentrated on a single computing system, or distributed on a network composed of multiple computing systems, and optionally, they can be implemented by program codes executable by a computer system, so that they can be stored in a storage system and executed by the computing system, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0115] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

[0116] The above disclosure only discloses several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be conceived by technicians in this field should fall within the scope of protection of the present application.

Claims

1. A method for detecting abnormal welding heads in ultrasonic welding, characterized in that: Suitable for ultrasonic welding welding heads, abnormal welding head detection methods include: Obtain all welding tasks completed by each welding head to be observed within a preset period of time and actual work data when performing the corresponding welding tasks, and obtain material information and size information of the material to be welded from each welding task; Calculate the recommended welding temperature, recommended welding time and recommended welding head frequency of the welding task according to the material information and size information; extract the actual welding temperature, actual welding head frequency and actual welding time corresponding to each welding task from the actual working data; By analyzing the actual welding temperature and the recommended welding temperature, the actual welding head frequency and the recommended welding head frequency, and the actual welding time and the recommended welding time in each welding task, the welding tasks that meet the preset abnormal standards are screened out to obtain abnormal tasks; Screen out quasi-abnormal welding heads according to abnormal tasks, monitor the temperature data of the quasi-abnormal welding heads during operation, determine whether the temperature characteristics of the quasi-abnormal welding heads are abnormal according to the temperature data, and regard the quasi-abnormal welding heads with abnormal temperature characteristics as abnormal welding heads; The materials that can be welded by the ultrasonic welding device are classified in advance according to the melting point and melting characteristics of the materials, and three types of materials are obtained: high melting point materials, medium melting point materials and low melting point materials; and a comparison relationship table of the welding speed range and the welding temperature range of the three types of materials under the preset standard thickness is constructed respectively; The material to be welded is classified into one of three material types according to the material information, and a corresponding comparison relationship table is selected; the welding length and welding thickness of the material to be welded are obtained according to the size information, and the thickness difference between the welding thickness and the preset standard thickness is calculated; based on the comparison relationship table, and with reference to the thickness difference and the welding temperature and welding speed corresponding to the preset standard thickness, a recommended welding temperature is determined in the welding temperature range and a recommended welding speed is determined in the welding speed range; a recommended welding time is calculated according to the recommended welding speed and the welding length, and a recommended welding head frequency is selected according to the recommended welding time and the recommended welding temperature.

2. The abnormal welding head detection method according to claim 1, characterized in that: The acquisition of the welding head to be observed includes: Real-time monitoring of the sound data of each template machine welding head during operation; Analyze the sound data, screen out welding heads with abnormal tones, and obtain welding heads with abnormal tones; The reasons for the abnormal tone of the abnormal sound welding head are analyzed, and welding heads with abnormal tone due to non-welding head reasons are excluded from the abnormal sound welding heads to obtain welding heads to be observed.

3. The abnormal welding head detection method according to claim 2, characterized in that: The acquisition of the welding head to be observed also includes: Obtain image data of multiple template machine welding heads before, during and after work; Analyze the appearance changes of the welding head before, during and after working according to the image data, as well as the appearance changes relative to a preset standard welding head image, so as to analyze whether the welding head has defects; The influence of defects on the welding head on the processing effect is evaluated, and the welding head with an influence degree higher than the preset high influence degree is directly regarded as an abnormal welding head, and the welding head with an influence degree between the high influence degree and the preset low influence degree is regarded as a welding head to be observed.

4. The abnormal welding head detection method according to claim 1, characterized in that: Exception criteria include: The actual welding head frequency is less than the recommended welding head frequency; The actual welding temperature is higher than the recommended welding temperature; The actual welding time is shorter than the recommended welding time; The actual welding frequency is not less than the recommended welding frequency and the actual welding temperature is less than the recommended welding temperature; The actual welding frequency is not less than the recommended welding frequency and the actual welding time is greater than the recommended welding time; The actual welding temperature is not less than the recommended welding temperature and the actual welding time is greater than the recommended welding time.

5. The abnormal welding head detection method according to claim 3, characterized in that: The number and frequency of abnormal tasks, and / or whether it is an abnormal welding head, and / or the degree of influence of defects on the welding head processing effect are comprehensively considered to give an abnormal score to each welding head to be observed; The welding heads to be observed whose abnormality scores do not meet the preset standard segments are regarded as quasi-abnormal welding heads.

6. The abnormal welding head detection method according to claim 1, characterized in that: The temperature data including temperature change and temperature distribution of the quasi-abnormal welding head during operation is monitored by an infrared sensor device; if the quasi-abnormal welding head has uneven temperature distribution and / or uneven temperature change, the quasi-abnormal welding head is confirmed.

7. A template machine, characterized in that: A method for detecting abnormal welding heads in ultrasonic welding for implementing any one of claims 1 to 6.

Images