High-temperature monitoring method and system for high-strength steel resistance spot welding process

Abstract

The application relates to the technical field of data monitoring, in particular to a high-temperature monitoring method and system for a high-strength steel resistance spot welding process. The method comprises the following steps: acquiring current instantaneous temperature data of a high-strength steel welding nugget area and key electrical parameters of a welding circuit; comparing the current instantaneous temperature data with a preset target temperature value to obtain a temperature comparison value; determining electrical change trajectory information according to the key electrical parameters of the welding circuit; comparing the electrical change trajectory information with preset change trajectory information to obtain an electrical comparison error; and obtaining a monitoring state of the current instantaneous temperature in the high-strength steel resistance spot welding process according to the temperature comparison value and the electrical comparison error. The application aims to solve the problem that a non-contact infrared temperature measurement sensor is prone to performance degradation in long-term use, leading to inaccurate monitoring of the instantaneous high temperature of the nugget area of high-strength steel resistance spot welding.

Description

Technical Field

[0001] This invention relates to the field of data monitoring technology, specifically to a method and system for high-temperature monitoring of the resistance spot welding process of high-strength steel. Background Technology

[0002] In modern industrial production, high-strength steel is widely used in key structural components of automobile manufacturing. Resistance spot welding equipment for high-strength steel undertakes the connection task of key structural components, and its operation is highly demanding, often requiring long-term uninterrupted operation. Under high-load conditions, extremely fine metal or microparticles are inevitably generated during the formation and cooling process of the weld nugget area. As the weak airflow around the welding area diffuses, the metal or microparticles gradually adhere to the surface of the optical lens of the non-contact infrared temperature sensor, forming a thin film. The transmittance of the thin film to infrared radiation will experience a slight but continuous decrease, resulting in a continuous weakening of the radiation signal intensity received by the infrared sensor, thus causing its output temperature reading to be systematically lower than the actual true temperature of the weld nugget.

[0003] Meanwhile, based on persistently low temperature readings, the welding control system, when executing its preset feedback adjustment function, may incorrectly determine that the weld nugget temperature has not reached the ideal target value set by the process. The control system will then systematically increase the welding energy input according to its built-in adjustment logic. This causes the metal inside the weld nugget to become unstable due to overheating, resulting in frequent and violent metal spatter. Frequent and violent metal spatter not only wastes energy and pollutes the environment, but the high-temperature radiation and physical impact it generates can also cause more serious damage to the optical lenses of the infrared sensor. This includes microscopic peeling of the anti-reflective coating on the lens surface, or microcracks or fogging phenomena inside the lens material caused by repeated thermal stress. This leads to performance degradation of the non-contact infrared temperature sensor over long-term use, resulting in errors in monitoring the instantaneous high temperature of the weld nugget area in high-strength steel resistance spot welding, and ultimately, inaccurate monitoring. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for high-temperature monitoring during the resistance spot welding process of high-strength steel, which solves the problem that non-contact infrared temperature sensors in the prior art are prone to performance degradation during long-term use, resulting in inaccurate instantaneous high-temperature monitoring of the molten core area in high-strength steel resistance spot welding.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for high-temperature monitoring during resistance spot welding of high-strength steel, comprising:

[0006] Obtain the current instantaneous temperature data of the weld nugget area of ​​high-strength steel and the key electrical parameters of the welding circuit;

[0007] The current instantaneous temperature data is compared with a preset target temperature value to obtain a temperature comparison value;

[0008] Based on the key electrical parameters of the welding circuit, determine the electrical change trajectory information;

[0009] The electrical change trajectory information is compared with preset change trajectory information to obtain the electrical comparison error;

[0010] Based on the temperature comparison value and the electrical comparison error, the current instantaneous temperature monitoring status during the high-strength steel resistance spot welding process is obtained.

[0011] Preferably, the step of determining the electrical change trajectory information based on the key electrical parameters of the welding circuit includes:

[0012] The key electrical parameters of the welding circuit were analyzed to obtain the resistance, current and voltage data of the welding circuit.

[0013] By using the resistance, current, and voltage data of the welding circuit, the trajectory changes of the welding circuit are processed to obtain electrical change trajectory information.

[0014] Preferably, the step of comparing the electrical change trajectory information with preset change trajectory information to obtain the electrical comparison error includes:

[0015] Based on the electrical change trajectory information, an electrical trajectory database is obtained;

[0016] Perform statistical analysis on the electrical change trajectory information to determine the type of change trajectory;

[0017] Using the aforementioned trajectory change type, preset trajectory change information for nucleus growth under current production conditions is selected from the electrical trajectory database;

[0018] The electrical change trajectory information is compared with the preset change trajectory information to obtain the electrical comparison error.

[0019] Preferably, the step of selecting preset change trajectory information for nucleus growth under current production conditions from the electrical trajectory database using the change trajectory type includes:

[0020] Determine the health assessment result for each electrical trajectory in the electrical trajectory database;

[0021] Based on each of the health assessment results, the change trajectory information in the electrical trajectory database is weighted to obtain each change trajectory information after weighting.

[0022] Using the aforementioned trajectory change type, each weighted trajectory change information is filtered to obtain preset trajectory change information.

[0023] Preferably, the step of determining the health assessment result of each electrical trajectory in the electrical trajectory database includes:

[0024] Based on the electrical change trajectory information, the high-strength steel parameter information and welding process information are determined;

[0025] Based on the high-strength steel parameter information and welding process information, the theoretical electrical trajectory information is confirmed;

[0026] The theoretical electrical trajectory information is compared with the electrical trajectory information in the electrical trajectory database to obtain the health assessment result of each electrical trajectory in the electrical trajectory database.

[0027] Preferably, the step of comparing the theoretical electrical trajectory information with each electrical trajectory information in the electrical trajectory database to obtain the health assessment result of each electrical trajectory in the electrical trajectory database includes:

[0028] Confirm the set of key feature points for the theoretical electrical trajectory information and the set of key feature points for each electrical trajectory information in the electrical trajectory database;

[0029] By aligning the set of key feature points of theoretical electrical trajectory information with the set of key feature points of each electrical trajectory information in time and then comparing them, the health assessment result of each electrical trajectory in the electrical trajectory database is obtained.

[0030] Preferably, the steps for obtaining the current instantaneous temperature data of the weld nugget region of high-strength steel and the key electrical parameters of the welding circuit include:

[0031] Obtain the raw data of the current instantaneous temperature in the weld nugget area of ​​high-strength steel and the key electrical parameters of the welding circuit;

[0032] The current instantaneous temperature raw data and key electrical raw parameters are preprocessed to obtain the preprocessed current instantaneous temperature raw data and key electrical raw parameters;

[0033] The preprocessed raw data of current instantaneous temperature and key electrical parameters are verified to obtain the current instantaneous temperature data of the high-strength steel welding fusion zone and the key electrical parameters of the welding circuit.

[0034] Preferably, the step of obtaining the monitoring status of the current instantaneous temperature during the resistance spot welding of high-strength steel based on the temperature comparison value and the electrical comparison error includes:

[0035] The temperature comparison value and the electrical comparison error are numerically judged to obtain the monitoring judgment result;

[0036] The monitoring and judgment results are analyzed to obtain the current instantaneous temperature monitoring status during the resistance spot welding process of high-strength steel.

[0037] Preferably, after the step of monitoring and analyzing the monitoring and judgment results to obtain the monitoring status of the current instantaneous temperature during the resistance spot welding of high-strength steel, the method further includes:

[0038] Determine the type of anomaly in the monitoring status of the current instantaneous temperature during the resistance spot welding process of high-strength steel;

[0039] If the anomaly type indicates that there is an anomaly in the current instantaneous temperature data, then the current instantaneous temperature data will be compensated and adjusted.

[0040] If the anomaly type indicates that the current instantaneous temperature data is normal but the key electrical parameters are abnormal, then the welding energy input should be adjusted.

[0041] This invention also provides a high-temperature monitoring system for the resistance spot welding process of high-strength steel, the system comprising:

[0042] The data acquisition module is used to acquire the current instantaneous temperature data of the weld nugget area of ​​high-strength steel and the key electrical parameters of the welding circuit;

[0043] The numerical comparison module is used to compare the current instantaneous temperature data with a preset target temperature value to obtain a temperature comparison value;

[0044] The trajectory determination module is used to determine the electrical change trajectory information based on the key electrical parameters of the welding circuit;

[0045] An error comparison module is used to compare the electrical change trajectory information with preset change trajectory information to obtain the electrical comparison error;

[0046] The status monitoring module is used to obtain the current instantaneous temperature monitoring status during the resistance spot welding process of high-strength steel based on the temperature comparison value and the electrical comparison error.

[0047] Compared with the prior art, the high-temperature monitoring method and system for high-strength steel resistance spot welding process of the present invention has the following advantages:

[0048] This invention acquires the instantaneous temperature data of the weld nugget area in high-strength steel welding and key electrical parameters of the welding circuit. It compares the temperature data with a preset target temperature value and simultaneously compares the electrical change trajectory information with preset change trajectory information. Finally, based on the temperature comparison value and the electrical comparison error, it obtains the monitoring status of the current instantaneous temperature during high-strength steel resistance spot welding. This invention avoids the inaccuracy caused by the performance degradation of non-contact infrared temperature sensors over long-term use. By comprehensively utilizing temperature data and electrical parameters, this invention overcomes the limitations of single sensor failure or accuracy degradation, improving the accuracy and reliability of high-temperature monitoring, and ensuring the welding quality of high-strength steel resistance spot welding and the safety of structural components. Attached Figure Description

[0049] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0050] Figure 1 This is a flowchart of a high-temperature monitoring method for the resistance spot welding process of high-strength steel according to the present invention.

[0051] Figure 2 This is a structural block diagram of a high-temperature monitoring system for resistance spot welding of high-strength steel according to the present invention.

[0052] In the diagram: 210, Data Acquisition Module; 220, Numerical Comparison Module; 230, Trajectory Determination Module; 240, Error Comparison Module; 250, Status Monitoring Module.

[0053] The implementation and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0054] The following drawings disclose several embodiments of the present invention. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not essential. Furthermore, for the sake of simplicity, some conventional structures and components will be shown in the drawings in a simple schematic manner.

[0055] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0056] Furthermore, in this invention, the use of terms such as "first" and "second" is for descriptive purposes only and does not specifically refer to any order or sequence, nor is it intended to limit the invention. They are merely used to distinguish components or operations described using the same technical terms, and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but only if they are feasible for those skilled in the art. If a combination of technical solutions is contradictory or impossible to implement, such a combination should be considered nonexistent and not within the scope of protection claimed by this invention.

[0057] To further understand the content, features, and effects of this invention, the following embodiments are provided, and detailed descriptions are given below in conjunction with the accompanying drawings:

[0058] Please see Figure 1 This invention provides a method for high-temperature monitoring during the resistance spot welding process of high-strength steel, comprising the following steps:

[0059] S100. Acquire the current instantaneous temperature data of the weld nugget area and key electrical parameters of the welding circuit for high-strength steel welding. The current instantaneous temperature data of the weld nugget area refers to the temperature value of the nugget area collected in real time by sensors during the welding process. Key electrical parameters of the welding circuit typically include welding current, voltage, and resistance, which reflect the energy input and nugget formation state during the welding process. This step can acquire the current instantaneous temperature data using non-contact or contact sensors such as infrared thermometers or thermocouples. Simultaneously, key electrical parameters such as current and voltage of the welding circuit are collected in real time using devices such as current sensors and voltage sensors.

[0060] S200. Compare the current instantaneous temperature data with the preset target temperature value to obtain a temperature comparison value. The preset target temperature value is an ideal temperature range pre-set based on welding process requirements and material properties. This step can directly determine the difference between the current instantaneous temperature data and the preset target temperature value, or calculate its percentage deviation. The comparison value can intuitively reflect the degree of deviation between the current temperature and the ideal temperature.

[0061] S300. Based on the key electrical parameters of the welding circuit, determine the electrical change trajectory information. This electrical change trajectory information refers to the curves or patterns of key electrical parameters changing over time during the welding process. This step allows for time-series analysis of the collected electrical parameters such as current, voltage, and resistance, generating curves showing how these parameters change over time. These curves reflect the dynamic processes of weld nugget formation, growth, and cooling during the welding process.

[0062] S400. Compare the electrical change trajectory information with preset change trajectory information to obtain the electrical comparison error. The preset change trajectory information is established based on a large amount of experimental data or theoretical models and serves as a benchmark for evaluating the electrical parameter changes in the welding process. This step involves superimposing the actual electrical change trajectory curve with the preset ideal trajectory curve and quantifying the comparison error by calculating the area difference and root mean square error between the two curves. The preset change trajectory information can be derived from accumulated data of historical successful welding cases or from ideal trajectories obtained through physical model simulation.

[0063] S500. Based on the temperature comparison value and the electrical comparison error, the current instantaneous temperature monitoring status during the high-strength steel resistance spot welding process is obtained. This step can be configured with judgment rules. When both the temperature comparison value and the electrical comparison error are within a preset normal range, the current instantaneous temperature monitoring status is considered normal; if either or both exceed the normal range, it may indicate a temperature abnormality or a welding process abnormality.

[0064] This invention acquires the instantaneous temperature data of the weld nugget region in high-strength steel welding and compares it with a preset target temperature value to obtain a temperature comparison value, providing a basis for preliminary judgment of the temperature status. Furthermore, this invention also determines the electrical change trajectory information based on key electrical parameters of the welding circuit and compares it with preset change trajectory information to obtain the electrical comparison error. The change trajectory of electrical parameters can sensitively reflect the formation, growth, and cooling process of the weld nugget. Even if the temperature sensor has a deviation, the normality or abnormality of the electrical parameters can provide independent and reliable evidence of the health status of the welding process. By comprehensively analyzing the temperature comparison value and the electrical comparison error, the monitoring status of the current instantaneous temperature during high-strength steel resistance spot welding can be obtained. Through dual verification, the accuracy and robustness of monitoring are significantly improved. For example, when a temperature sensor malfunctions, causing abnormal temperature data, if the electrical parameter trajectory is normal, it can be determined that the problem is with the temperature sensor rather than the welding quality; conversely, if the temperature data is normal but the electrical parameter trajectory is abnormal, it may indicate a problem with the welding energy input or weld nugget formation. Therefore, this invention can more comprehensively and accurately assess the instantaneous temperature state during the resistance spot welding process of high-strength steel, thereby effectively guiding the adjustment of the welding process, ensuring welding quality, and improving production efficiency and product reliability.

[0065] In some embodiments of this application described above, the step of determining the electrical change trajectory information based on the key electrical parameters of the welding circuit includes:

[0066] The key electrical parameters of the welding circuit are analyzed to obtain resistance, current, and voltage data. This step involves extracting the resistance, current, and voltage data of the welding circuit from the raw or pre-processed electrical parameters obtained from sensors or data acquisition equipment through calculation or conversion. These data are fundamental physical quantities characterizing the electrical state of the welding circuit. For example, key electrical parameters may include welding current, welding voltage, and electrode pressure. The instantaneous resistance of the welding circuit can be derived using physical relationships such as Ohm's law.

[0067] Using the resistance, current, and voltage data of the welding circuit, data processing is performed on the trajectory changes of the welding circuit to obtain electrical change trajectory information. This step involves integrating and processing the extracted resistance, current, and voltage data according to time series or other logical relationships to form trajectory information that reflects the dynamic changes in the electrical characteristics of the welding process. This trajectory information can be a multi-dimensional vector sequence, for example, a tuple corresponding to (resistance value, current value, and voltage value) at each time point, or a more concise trajectory representation can be formed through data dimensionality reduction and feature extraction. The purpose is to transform discrete electrical parameters into a continuous trajectory with time evolution characteristics for subsequent comparison and analysis.

[0068] This embodiment decomposes and analyzes key electrical parameters to obtain basic resistance, current, and voltage data, which are the fundamental elements constituting the electrical trajectory. Subsequently, data processing of trajectory changes is performed using this basic data to construct an electrical change trajectory information that comprehensively reflects the dynamic evolution of the electrical state during the welding process. This decomposition and reconstruction approach makes the generation process of the electrical trajectory clearer and more controllable, providing a structured data foundation for subsequent trajectory comparison.

[0069] In some embodiments of this application described above, the step of comparing the electrical change trajectory information with preset change trajectory information to obtain the electrical comparison error includes:

[0070] Based on the electrical change trajectory information, an electrical trajectory database is obtained. Specifically, the electrical trajectory database can be understood as a data set storing a large number of historical welding process electrical parameter change trajectories. This data is typically collected under different welding conditions, different material batches, and different equipment states, and is used to establish a comprehensive reference benchmark. Obtaining this database aims to provide rich reference samples for subsequent comparisons.

[0071] The electrical change trajectory information is subjected to type statistical analysis to determine the trajectory type. This type statistical analysis involves feature extraction and classification of the acquired electrical change trajectory information. For example, based on features such as trajectory shape, peak value, and rate of change, it can be categorized into different types such as normal weld nugget growth trajectory, spatter trajectory, adhesive weld trajectory, and cold weld trajectory. The purpose of determining the trajectory type is to enable the selection of the most matching preset trajectory information from the database during subsequent comparisons, thereby improving the accuracy and relevance of the comparisons.

[0072] Using the aforementioned trajectory type, preset trajectory information for weld nugget growth under current production conditions is selected from the electrical trajectory database. This step determines the type of the current electrical trajectory and then filters out preset trajectory information from the electrical trajectory database that matches the current production conditions (e.g., welding current, welding time, electrode pressure, and material thickness) and belongs to the same trajectory type. This ensures that the comparison benchmark is highly relevant to the actual situation, avoiding inappropriate references.

[0073] The electrical change trajectory information is compared with preset change trajectory information to obtain the electrical comparison error. This comparison step can employ various mathematical methods, such as curve fitting, feature point matching, and distance calculation (e.g., Euclidean distance or DTW distance), to quantify the difference between the two. The resulting electrical comparison error intuitively reflects the degree of deviation between the current welding process's electrical trajectory and the ideal or normal trajectory.

[0074] This embodiment first obtains an electrical trajectory database based on electrical change trajectory information, providing a rich reference sample for comparison. Furthermore, by performing type statistical analysis on the electrical change trajectory information, the characteristics of the current trajectory can be accurately identified, thereby determining its corresponding change trajectory type. Thus, using the determined change trajectory type, preset change trajectory information matching the current production conditions and trajectory type can be accurately selected from the electrical trajectory database. This ensures that the comparison benchmark is highly relevant, avoiding errors that may result from blind comparison. Finally, comparing the current electrical change trajectory information with the optimized preset change trajectory information allows for a more accurate and reliable determination of the electrical comparison error, providing a solid data foundation for subsequent monitoring status judgment.

[0075] The above technical solution enables refined management of the comparison process between electrical trajectory information and preset trajectory information. Specifically, by establishing and utilizing an electrical trajectory database, combined with type statistical analysis and a conditional selection mechanism, the comparison process becomes more targeted and accurate. As a result, the obtained electrical comparison error can more realistically reflect the actual state of the welding process, effectively avoiding misjudgments caused by reference trajectory mismatch, thereby improving the reliability and accuracy of high-temperature monitoring in the high-strength steel resistance spot welding process.

[0076] In some embodiments of this application described above, the step of selecting preset change trajectory information for melt nucleus growth under current production conditions from the electrical trajectory database using the change trajectory type includes:

[0077] Determine the health assessment result for each electrical trajectory in the electrical trajectory database. This involves quantitatively evaluating the quality, reliability, and representativeness of each historical electrical trajectory stored in the database. The aim is to identify and distinguish high-quality, high-confidence trajectory data, avoiding interference from low-quality or anomalous data in the selection of preset trajectories.

[0078] Based on each health assessment result, the changed trajectory information in the electrical trajectory database is weighted to obtain each weighted changed trajectory. This step involves assigning different weights to each changed trajectory in the electrical trajectory database according to the health assessment results. Trajectories with higher health are assigned higher weights, and vice versa. The purpose is to ensure that high-quality trajectory information has a greater impact on the generation of preset changed trajectory information during subsequent screening, thereby improving the accuracy and representativeness of the preset trajectories.

[0079] Using the aforementioned trajectory variation types, each weighted trajectory variation information is filtered to obtain preset trajectory variation information. This step refers to selecting trajectory information that matches the current production conditions and weld nugget growth stage from the weighted electrical trajectory database based on the pre-determined trajectory variation types. The purpose is to ensure that the selected preset trajectory variation information not only has high quality but also closely matches the characteristics of the actual welding process.

[0080] Specifically, when selecting preset trajectory information from the electrical trajectory database using trajectory type changes, firstly, a health assessment can be performed on each historical electrical trajectory in the database. For example, the health assessment can be based on multiple dimensions, including trajectory integrity, noise level, deviation from the theoretical model, and the corresponding welding quality results in actual production, assigning each trajectory a health score between 0 and 1. Next, each trajectory in the electrical trajectory database is weighted according to its health score; for example, a higher health score gives a greater weight in subsequent screening. Finally, when selecting preset information for a specific trajectory type, trajectory information matching that type and with higher weights is prioritized from the weighted database. These selected trajectories can then be averaged or fitted to generate the final preset trajectory information. For example, if the health assessment results show significant anomalies or noise in a historical trajectory, its weight will be significantly reduced, or it may even be excluded from the generation of preset trajectories, thus ensuring the purity and representativeness of the preset trajectories.

[0081] This embodiment incorporates health assessment results to classify the historical data in the electrical trajectory database, identifying more reliable and representative trajectories. Furthermore, by weighting the trajectory information based on the health assessment results, high-quality trajectories dominate the subsequent selection, effectively reducing the negative impact of low-quality or abnormal data on the accuracy of the preset trajectory information. Finally, by filtering the weighted information based on the trajectory type, it ensures that the selected preset trajectory information is not only of high quality but also highly matches the melt nucleus growth characteristics under current actual production conditions, thus providing a more accurate benchmark for subsequent electrical comparison error calculation.

[0082] In some embodiments of this application described above, the step of determining the health assessment result of each electrical trajectory in the electrical trajectory database includes:

[0083] Based on the electrical change trajectory information, the high-strength steel parameter information and welding process information are determined. The electrical change trajectory information refers to the curves or sequences of data reflecting the dynamic changes in weld nugget growth, obtained through analysis and data processing of key electrical parameters (such as resistance, current, and voltage data) in the welding circuit during resistance spot welding. The high-strength steel parameter information includes physical and chemical properties related to the high-strength steel material to be welded, such as the steel grade, thickness, surface condition (e.g., coating type and thickness), conductivity, coefficient of thermal expansion, and melting point. These parameters directly affect resistance heating and weld nugget formation during welding. This information can be obtained by consulting material databases, conducting material testing, or from the production management system, depending on the actual type of workpiece being welded. The welding process information refers to the specific process parameters used in resistance spot welding, such as welding current, welding time, electrode pressure, electrode type, and cooling method. These parameters collectively determine the input and distribution of welding energy, thus affecting the weld nugget growth process. This information can be obtained from the welding equipment controller, process recipe database, or operator settings.

[0084] Based on the high-strength steel parameter information and welding process information, the theoretical electrical trajectory information is confirmed. This theoretical electrical trajectory information refers to the electrical change trajectory corresponding to the weld nugget growth under ideal conditions, obtained through physical models, simulation calculations, or fitting of a large amount of experimental data, given specific high-strength steel parameter information and welding process information. This theoretical trajectory represents the electrical response that a healthy or standard welding process should have under specific conditions. This theoretical electrical trajectory information can be confirmed through calculation using a pre-established simulation model, or by performing multiple welds under standard experimental conditions and taking the average value.

[0085] The theoretical electrical trajectory information is compared with each electrical trajectory in the electrical trajectory database to obtain the health assessment result of each electrical trajectory in the database. Specifically, the electrical trajectory database stores a large collection of electrical change trajectory information collected during historical welding processes. These historical trajectories may originate from welding processes in different batches, with different equipment, or under different operating conditions. The health assessment result is an indicator that quantitatively evaluates the quality or reliability of each historical electrical trajectory in the database. A trajectory with high health indicates that it is closer to the theoretical ideal trajectory and better represents a normal weld nugget growth process; while a trajectory with low health may indicate anomalies in the welding process or errors in data acquisition. The specific comparison process involves performing curve similarity analysis between the theoretical electrical trajectory information and each electrical trajectory in the electrical trajectory database. For example, methods such as Euclidean distance, dynamic time warping (DTW) algorithms, or correlation coefficients can be used to measure the similarity between two trajectories. The higher the similarity, the better the health assessment result.

[0086] This embodiment can determine the health assessment result of each electrical trajectory in the electrical trajectory database because it introduces theoretical electrical trajectory information as a reference benchmark. When selecting preset variation trajectory information, historical data may be relied upon directly. However, historical data may contain abnormal trajectories caused by equipment failure, material batch differences, or process fluctuations. Without proper screening, unhealthy trajectories may be incorrectly selected as preset trajectories, leading to inaccurate subsequent electrical comparison errors and ultimately affecting the reliability of high-temperature monitoring. First, the electrical variation trajectory information of the current welding is used to infer or confirm the high-strength steel parameters and welding process information of the current workpiece. Based on the determined parameters and process information, the theoretical electrical trajectory information can be accurately confirmed. This theoretical trajectory represents the electrical response that an ideal, defect-free weld nugget growth process should have under the current specific conditions. Subsequently, the accurate theoretical electrical trajectory information is compared with each historical electrical trajectory information stored in the electrical trajectory database. This comparison quantifies the deviation of each historical trajectory from the ideal state, thereby obtaining its health assessment result. Trajectories with high health are considered reliable, while those with low health may be abnormal. This provides a scientific basis for subsequent screening and weighted processing of preset trajectory information from the database, ensuring the accuracy and representativeness of the selected preset trajectories.

[0087] In some embodiments of this application described above, the step of comparing the theoretical electrical trajectory information with each electrical trajectory information in the electrical trajectory database to obtain the health assessment result of each electrical trajectory in the electrical trajectory database includes:

[0088] This step involves identifying the set of key feature points for the theoretical electrical trajectory information and the set of key feature points for each electrical trajectory in the electrical trajectory database. This step refers to extracting discrete points or intervals from the electrical trajectory data that represent its core characteristics and trends. For example, key feature points may include, but are not limited to, the trajectory's starting point, peak points, valley points, inflection points, points with the largest slope changes, or specific threshold intersections. These feature points effectively capture the trajectory's morphological characteristics, reduce data redundancy, and provide a foundation for subsequent accurate comparison. The aim is to simplify trajectory representation, highlight the core information of the trajectory, and provide high-value reference points for subsequent alignment and comparison.

[0089] The key feature point set of the theoretical electrical trajectory information is time-aligned with the key feature point set of each electrical trajectory information before comparison, resulting in a health assessment result for each electrical trajectory in the electrical trajectory database. This step involves adjusting the relative positions of the two trajectories on the time axis using an algorithm before comparison to eliminate or reduce the impact of time offset. For example, Dynamic Time Warping (DTW), cross-correlation function methods, or alignment methods based on feature point matching can be used. After time alignment, the aligned key feature point sets are compared for similarity or difference, such as calculating Euclidean distance, Manhattan distance, cosine similarity, or correlation coefficient. The purpose is to ensure that corresponding parts of the trajectories can be accurately compared during comparison, thereby improving the accuracy and robustness of the comparison.

[0090] Specifically, in resistance spot welding of high-strength steel, it is necessary to evaluate the health of the electrical trajectory information of an actual welding process compared to the theoretical electrical trajectory information. First, key feature point sets are extracted from the theoretical electrical trajectory information; for example, identifying the inflection points, peak points, and inflection points of the current rise and fall phases. Simultaneously, corresponding key feature point sets are extracted from each electrical trajectory information to be evaluated in the electrical trajectory database. Next, the Dynamic Time Warping (DTW) algorithm is used to time-align the key feature point sets of the theoretical electrical trajectory information with the key feature point sets of each electrical trajectory information. Specifically, the DTW algorithm calculates the optimal matching path between the two time series to minimize their cumulative distance, thus achieving non-linear time alignment. After time alignment, Euclidean distance is calculated on the aligned key feature point sets to quantify their similarity or difference. For example, if the Euclidean distance between the aligned key feature point sets is less than a preset threshold, the electrical trajectory is considered to have high health; otherwise, it is considered to have low health. This allows us to obtain the health assessment results for each electrical trajectory in the electrical trajectory database, providing an accurate basis for subsequent screening of preset change trajectory information.

[0091] This embodiment effectively avoids errors caused by time shifts and local deformations that may exist in traditional direct comparisons by introducing the confirmation of key feature point sets and time alignment processing. First, by confirming the key feature point set, the complex electrical trajectory information is abstracted into more representative discrete points, which not only reduces the complexity of data processing but also allows the comparison process to focus on the core changing features of the trajectory. Second, time alignment before comparison can compensate for deviations on the time axis caused by various factors, ensuring that the theoretical trajectory and the actual trajectory achieve optimal temporal matching. Due to the preprocessing and refined comparison mechanism, subsequent comparison processing can more accurately reflect the true differences between the two trajectories, thus providing a more reliable basis for the assessment of the health of the electrical trajectory.

[0092] In some embodiments of this application, the steps for obtaining the current instantaneous temperature data of the weld nugget region of high-strength steel and the key electrical parameters of the welding circuit include:

[0093] This step involves acquiring the raw, instantaneous temperature data of the weld nugget region in high-strength steel welding and the key electrical parameters of the welding circuit. This means collecting unprocessed initial data in real time using appropriate sensors and measuring equipment. For example, the raw, instantaneous temperature data of the weld nugget region in high-strength steel welding can be acquired in real time using non-contact or contact sensors such as infrared thermometers or thermocouples; key electrical parameters of the welding circuit, such as current, voltage, and resistance, can be monitored and acquired in real time using devices integrated into the welding circuit, such as current sensors and voltage sensors. The raw data is unprocessed, initial information obtained directly from the physical world.

[0094] The raw data of the current instantaneous temperature and the raw parameters of key electrical parameters are preprocessed to obtain preprocessed raw data of the current instantaneous temperature and the raw parameters of key electrical parameters. The purpose of this step is to eliminate noise, interference, and outliers in the raw data to improve data quality. Specifically, preprocessing may include filtering (e.g., using median filtering or Kalman filtering to remove random noise), smoothing, and detrending. For temperature data, background radiation compensation may be required to correct for environmental influences; for electrical parameters, high-frequency noise filtering may be required to ensure signal purity. Through preprocessing, the raw data is transformed into a more stable and reliable form, laying the foundation for subsequent data verification.

[0095] The preprocessed raw data of the current instantaneous temperature and the raw parameters of key electrical circuits are verified to obtain the current instantaneous temperature data of the weld nugget region of high-strength steel and the key electrical parameters of the welding circuit. This step aims to further ensure the accuracy and validity of the data. Data verification may include checking the data range (e.g., determining whether the temperature value is within a reasonable physical range, and whether the current and voltage meet the requirements of the welding process settings), checking data consistency (e.g., the correlation and logic between data from different sensors), and identifying and processing missing or abnormal data. Through rigorous data verification, data that does not meet the requirements can be eliminated, ultimately yielding high-quality current instantaneous temperature data and key electrical parameters of the welding circuit that can be used for subsequent monitoring and analysis.

[0096] This embodiment processes the raw temperature and electrical data in stages. First, it acquires the unprocessed raw data. Then, it preprocesses the raw data to eliminate noise and interference. Finally, it verifies the data to further ensure its accuracy and validity. This ensures the quality of the data input into subsequent monitoring algorithms from the source, avoiding deviations in monitoring results due to inaccurate or abnormal data. This provides a reliable data foundation for accurate high-temperature monitoring of high-strength steel resistance spot welding. Furthermore, it effectively improves the acquisition quality and reliability of temperature data and electrical parameters during high-strength steel resistance spot welding. Specifically, preprocessing the raw data significantly reduces the impact of environmental noise and sensor errors. Data verification promptly identifies and removes abnormal or invalid data, preventing erroneous data from interfering with subsequent monitoring and judgment. This ensures the accuracy and validity of the current instantaneous temperature data and key electrical parameters used for high-temperature monitoring, laying a solid foundation for achieving high-precision, high-reliability high-temperature monitoring of the welding process, thereby improving the overall quality control level of the welding process.

[0097] In some embodiments of this application described above, the step of obtaining the monitoring status of the current instantaneous temperature during the resistance spot welding of high-strength steel based on the temperature comparison value and the electrical comparison error includes:

[0098] The temperature comparison value and the electrical comparison error are numerically judged to obtain monitoring and judgment results. This step refers to evaluating the magnitude and trend of the temperature comparison value and the electrical comparison error according to preset thresholds or judgment rules. For example, an upper and lower limit for temperature deviation can be set; when the temperature comparison value exceeds this range, it is judged as a temperature anomaly. Similarly, an allowable range for electrical comparison error can be set; when the electrical comparison error exceeds this range, it is judged as an electrical parameter anomaly. Thus, monitoring and judgment results for temperature and electrical parameters can be obtained, such as discrete or continuous judgment results like temperature normal or abnormal, and electrical parameters normal or abnormal.

[0099] The monitoring and judgment results are analyzed to obtain the current instantaneous temperature monitoring status during the high-strength steel resistance spot welding process. This step refers to comprehensively judging the overall monitoring status of the instantaneous temperature during the current high-strength steel resistance spot welding process based on the above-obtained temperature and electrical parameter monitoring and judgment results, through preset logic rules or decision models. For example, if the temperature judgment result is normal and the electrical judgment result is normal, the monitoring status can be judged as normal; if the temperature judgment result is abnormal and the electrical judgment result is normal, the monitoring status can be judged as abnormal temperature; if the temperature judgment result is normal and the electrical judgment result is abnormal, the monitoring status can be judged as abnormal electrical parameters; if both are abnormal, the monitoring status can be judged as comprehensively abnormal.

[0100] This embodiment refines the process of determining the monitoring status into two stages: numerical judgment and monitoring analysis. This allows for a more systematic and accurate assessment of the high-temperature state during the resistance spot welding of high-strength steel. First, by independently judging the temperature comparison value and electrical comparison error, the individual situations of temperature deviation and electrical parameter anomalies can be clearly identified. Then, based on the independent judgment results, a comprehensive monitoring analysis is performed, avoiding the limitations of single-parameter judgment and thus reflecting the actual state of the welding process more comprehensively and accurately. This makes the monitoring results more interpretable and helpful for subsequent fault diagnosis and process control. Furthermore, by decomposing the process of determining the monitoring status into numerical judgment and monitoring analysis, the monitoring logic becomes clearer and more structured. This not only improves the accuracy and reliability of the monitoring results but also provides a solid foundation for subsequent anomaly identification and targeted intervention. For example, when the monitoring status indicates an anomaly, the detailed results from the numerical judgment stage can quickly pinpoint whether the problem lies with the temperature itself or with the electrical parameters, thereby achieving more efficient and precise welding process control and quality assurance.

[0101] In some embodiments of this application described above, after the step of monitoring and analyzing the monitoring and judgment results to obtain the monitoring status of the current instantaneous temperature during the resistance spot welding of high-strength steel, the method further includes:

[0102] Determine the type of anomaly in the monitoring status of the current instantaneous temperature during high-strength steel resistance spot welding. This step involves classifying the detected anomalies based on the monitoring results and detailed monitoring analysis. For example, anomaly types may include abnormal temperature data (such as sensor malfunction and measurement error), abnormal electrical parameters (such as welding current fluctuations, voltage instability), or both. Anomaly type determination can be performed using a pre-set rule base, machine learning model, or expert system, with the aim of providing clear guidance for subsequent corrective measures.

[0103] If the anomaly type indicates an anomaly in the current instantaneous temperature data, then the current instantaneous temperature data is compensated and adjusted. This compensation and adjustment involves correcting damaged or inaccurate temperature data to restore its accuracy. Specifically, methods such as data interpolation, filtering, and predictive correction based on historical data or relevant parameters can be used, with the aim of ensuring that the temperature data used for subsequent decisions is accurate and reliable.

[0104] If the anomaly type indicates that the current instantaneous temperature data is normal but the key electrical parameters are abnormal, then the welding energy input is adjusted. Specifically, adjusting the welding energy input involves dynamically adjusting parameters such as welding current, welding time, or welding pressure based on the degree and type of electrical parameter anomaly. For example, the welding current can be increased or decreased, and the welding time can be extended or shortened. The purpose is to guide the welding process back to a normal state and avoid welding defects caused by abnormal electrical parameters.

[0105] Specifically, during the resistance spot welding of high-strength steel, the monitoring system determines that the current instantaneous temperature monitoring status is abnormal after numerical judgment and monitoring analysis of temperature comparison values ​​and electrical comparison errors. At this point, the type of anomaly is further determined. For example, if analysis reveals periodic fluctuations in the signal output by the temperature sensor that do not match the physical changes in the actual welding process, the anomaly type is determined to be an anomaly in the current instantaneous temperature data. In this case, the temperature data compensation module is automatically activated, using historical data models or data from adjacent sensors to correct the current instantaneous temperature data in real time, providing more accurate temperature information. Alternatively, if the monitoring status is also abnormal, but analysis reveals that the current instantaneous temperature data itself is within the normal range, while the key electrical parameters of the welding circuit (such as current data) remain consistently below a preset threshold, the anomaly type is determined to be normal for the current instantaneous temperature data but abnormal for the key electrical parameters. In this case, an instruction is immediately sent to the welding controller, for example, increasing the input power of the welding current by 5% to compensate for insufficient current, ensuring normal growth of the weld nugget and thus avoiding defects such as incomplete penetration due to insufficient welding energy. In this way, the solution proposed in this application can intelligently adopt different coping strategies according to different abnormal scenarios, thereby achieving precise control and optimization of the high-strength steel resistance spot welding process.

[0106] This embodiment effectively solves the problem of only providing monitoring status without a follow-up processing mechanism by further identifying the anomaly type and taking targeted corrective measures after receiving the monitoring status. When an anomaly is identified, it no longer simply issues a warning, but can intelligently determine the specific nature of the anomaly. For example, if the problem lies in the temperature data itself (such as sensor drift), compensation adjustments ensure the accuracy of the data, avoiding incorrect judgments based on erroneous data. When the temperature data is normal but the electrical parameters deviate, the welding process is directly intervened by adjusting the welding energy input, correcting potential welding defects at the source and ensuring welding quality. Due to the intelligent anomaly type judgment and feedback control, the entire high-temperature monitoring system transforms from passive monitoring to active intervention, significantly improving the system's practicality and reliability.

[0107] Based on any of the above embodiments, a high-temperature monitoring method for the resistance spot welding process of high-strength steel is provided. Please refer to [link to relevant documentation]. Figure 2 The present invention also provides a high-temperature monitoring system for the resistance spot welding process of high-strength steel, which includes a data acquisition module 210, a numerical comparison module 220, a trajectory determination module 230, an error comparison module 240, and a status monitoring module 250.

[0108] The data acquisition module 210 is used to acquire the current instantaneous temperature data of the high-strength steel welding fusion zone and the key electrical parameters of the welding circuit.

[0109] The numerical comparison module 220 is used to compare the current instantaneous temperature data with the preset target temperature value to obtain a temperature comparison value.

[0110] The trajectory determination module 230 is used to determine the electrical change trajectory information based on the key electrical parameters of the welding circuit.

[0111] The error comparison module 240 is used to compare the electrical change trajectory information with preset change trajectory information to obtain the electrical comparison error.

[0112] The status monitoring module 250 is used to obtain the current instantaneous temperature monitoring status during the resistance spot welding process of high-strength steel based on the temperature comparison value and the electrical comparison error.

[0113] In this embodiment, the data acquisition module 210 acquires the current instantaneous temperature data of the weld nugget area of ​​high-strength steel and the key electrical parameters of the welding circuit. The numerical comparison module 220 performs a preliminary evaluation of the temperature data, while the trajectory determination module 230 and the error comparison module 240 perform an in-depth analysis of the change trajectory of the electrical parameters. Even if the temperature sensor experiences performance degradation, resulting in a certain deviation in the temperature data, the normal change trajectory of the electrical parameters can still provide independent and reliable evidence of the health status of the welding process. For example, when the temperature data indicates abnormality but the electrical parameter trajectory is normal, the status monitoring module 250 can more accurately determine that it is a temperature sensor malfunction rather than an actual welding quality problem; conversely, if the temperature data is normal but the electrical parameter trajectory is abnormal, it may indicate a potential problem with the welding energy input or the formation of the weld nugget. Through comprehensive judgment, this system can more comprehensively and accurately evaluate the instantaneous temperature state during the resistance spot welding process of high-strength steel, thereby effectively guiding the adjustment of the welding process, ensuring welding quality, and improving production efficiency and product reliability.

[0114] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention specification.

Claims

1. A method for high-temperature monitoring during resistance spot welding of high-strength steel, characterized in that, include: Obtain the current instantaneous temperature data of the weld nugget area of ​​high-strength steel and the key electrical parameters of the welding circuit; The current instantaneous temperature data is compared with a preset target temperature value to obtain a temperature comparison value; The key electrical parameters of the welding circuit were analyzed to obtain the resistance, current and voltage data of the welding circuit. Using the resistance, current, and voltage data of the welding circuit, data processing is performed on the trajectory changes of the welding circuit to obtain electrical change trajectory information; Based on the electrical change trajectory information, an electrical trajectory database is obtained; Perform statistical analysis on the electrical change trajectory information to determine the type of change trajectory; Based on the electrical change trajectory information, the high-strength steel parameter information and welding process information are determined; Based on the high-strength steel parameter information and welding process information, the theoretical electrical trajectory information is confirmed; Confirm the set of key feature points for the theoretical electrical trajectory information and the set of key feature points for each electrical trajectory information in the electrical trajectory database; The key feature point set of theoretical electrical trajectory information is time-aligned with the key feature point set of each electrical trajectory information, and then compared to obtain the health assessment result of each electrical trajectory in the electrical trajectory database. Based on each of the health assessment results, the change trajectory information in the electrical trajectory database is weighted to obtain each change trajectory information after weighting. Using the aforementioned trajectory change type, each weighted trajectory change information is filtered to obtain preset trajectory change information; The electrical change trajectory information is compared with preset change trajectory information to obtain the electrical comparison error; Based on the temperature comparison value and the electrical comparison error, the current instantaneous temperature monitoring status during the high-strength steel resistance spot welding process is obtained.

2. The method for high-temperature monitoring during resistance spot welding of high-strength steel according to claim 1, characterized in that, The steps for obtaining the current instantaneous temperature data of the weld nugget region in high-strength steel welding and the key electrical parameters of the welding circuit include: Obtain the raw data of the current instantaneous temperature in the weld nugget area of ​​high-strength steel and the key electrical parameters of the welding circuit; The current instantaneous temperature raw data and key electrical raw parameters are preprocessed to obtain the preprocessed current instantaneous temperature raw data and key electrical raw parameters; The preprocessed raw data of current instantaneous temperature and key electrical parameters are verified to obtain the current instantaneous temperature data of the high-strength steel welding fusion zone and the key electrical parameters of the welding circuit.

3. The high-temperature monitoring method for high-strength steel resistance spot welding process according to claim 1, characterized in that, The steps for obtaining the monitoring status of the current instantaneous temperature during the resistance spot welding of high-strength steel based on the temperature comparison value and the electrical comparison error include: The temperature comparison value and the electrical comparison error are numerically judged to obtain the monitoring judgment result; The monitoring and judgment results are analyzed to obtain the current instantaneous temperature monitoring status during the resistance spot welding process of high-strength steel.

4. The high-temperature monitoring method for high-strength steel resistance spot welding process according to claim 3, characterized in that, After analyzing the monitoring and judgment results to obtain the monitoring status of the current instantaneous temperature during the resistance spot welding of high-strength steel, the method further includes: Determine the type of anomaly in the monitoring status of the current instantaneous temperature during the resistance spot welding process of high-strength steel; If the anomaly type indicates that there is an anomaly in the current instantaneous temperature data, then the current instantaneous temperature data will be compensated and adjusted. If the anomaly type indicates that the current instantaneous temperature data is normal but the key electrical parameters are abnormal, then the welding energy input should be adjusted.

5. A high-temperature monitoring system for the resistance spot welding process of high-strength steel, applied to the high-temperature monitoring method for the resistance spot welding process of high-strength steel as described in claim 1, characterized in that, The system includes: The data acquisition module is used to acquire the current instantaneous temperature data of the weld nugget area of ​​high-strength steel and the key electrical parameters of the welding circuit; The numerical comparison module is used to compare the current instantaneous temperature data with a preset target temperature value to obtain a temperature comparison value; The trajectory determination module is used to determine the electrical change trajectory information based on the key electrical parameters of the welding circuit; An error comparison module is used to compare the electrical change trajectory information with preset change trajectory information to obtain the electrical comparison error; The status monitoring module is used to obtain the current instantaneous temperature monitoring status during the resistance spot welding process of high-strength steel based on the temperature comparison value and the electrical comparison error.

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