Welding seam monitoring system based on electric welding machine

Abstract

The invention discloses a welding seam monitoring system based on an electric welding machine, and relates to the technical field of electric welding, an initial setting module is used for setting a normal fluctuation threshold range of each working parameter by collecting working information of the electric welding machine in a fault-free state, and the working information at least comprises welding current, voltage and welding speed; when it is determined that one or more pieces of working parameter information collected in real time exceed the preset normal fluctuation threshold range, whether short-term fluctuation or fault fluctuation exists is judged by judging the change of the parameters, and therefore parameter adjustment caused by misjudgment of the electric welding machine in the short-term fluctuation process can be prevented; and the quality defect of the welding seam is caused.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric welding, and specifically relates to a weld monitoring system based on an electric welding machine. Background Art

[0002] A weld is the part formed by the combination of workpieces during the welding process. According to the combination form, it can be divided into butt welds, fillet welds, plug welds, and slot welds, etc.; according to the macroscopic shape, it can be divided into straight and curved shapes; according to the spatial position, it can be divided into flat, vertical, horizontal, and overhead welds. In the actual application process, when the electric welding machine is working, the stability of the parameters of the electric welding machine plays a key role in the weld quality. However, in actual operation, the parameters of the electric welding machine may change due to various factors during the working process. How to accurately distinguish whether these changes are normal fluctuations or deviations caused by faults has become a problem to be solved. If it cannot be accurately judged in time, at this time, the electric welding machine may adjust the parameters, which may lead to weld quality defects, increase production costs, and reduce production efficiency. The existing discrimination methods often rely too much on manual experience, lack systematicness and accuracy, and are difficult to meet the requirements of modern automated welding production. Summary of the Invention

[0003] The purpose of the present invention is to provide a weld monitoring system based on an electric welding machine, which solves the technical problem in the prior art that it is impossible to distinguish whether the parameters of the electric welding machine are normal fluctuations or deviations caused by faults when the parameters change.

[0004] A weld monitoring system based on an electric welding machine includes: An initial setting module, which sets the normal fluctuation threshold range of each working parameter by collecting the working information of the electric welding machine in a fault-free state. The working information at least includes welding current, voltage, and welding speed. A data comparison and processing module, which uses relevant sensors to collect the working parameter information of the electric welding machine in real time during the welding process of the target part and determines the fluctuating parameters, and compares the fluctuating parameters during the welding process of the electric welding machine for the target part with the corresponding normal fluctuation threshold range. A control module, when the collected working parameter information exceeds the normal fluctuation threshold range in real time, further determines whether the parameter change is a short-term fluctuation or a deviation caused by a fault. A parameter self-adjustment module, which is used to send corresponding signals according to the judgment result. If it is a normal fluctuation, no parameter adjustment is performed. If it is a deviation caused by a fault, an alarm signal is sent, and a parameter adjustment instruction is generated according to the fault deviation situation to adjust the working parameters of the electric welding machine.

[0005] As a further scheme of the present invention: The specific way for the control module to further determine whether the parameter change is a short-term fluctuation or a deviation caused by a fault is: Collect multiple parameter data in the welding process in real time, wherein the parameters include at least welding current, welding voltage, welding speed and molten pool temperature; Based on multiple parameter data, a correlation model between parameters is established through the Pearson correlation coefficient or mutual information algorithm to calculate the real-time correlation between any two parameters; Parameters are screened according to a preset correlation threshold, and parameters with correlations lower than the threshold are excluded from interfering with subsequent judgments; For the remaining highly correlated parameters, the normal fluctuation range of each parameter and the allowable deviation range of linkage changes between parameters are set based on the historical data of the parameters; When a parameter is detected to fluctuate, other parameters with high correlation with it are analyzed simultaneously to see whether they show changes that conform to the linkage law within the corresponding allowable deviation range; If the fluctuations of all related parameters are within the allowable deviation range and conform to the linkage law, they are judged as short-term fluctuations. If the fluctuation of a parameter exceeds its normal range and the related parameters do not show the expected linkage changes, they are judged as fault fluctuations.

[0006] As a further solution of the present invention: it also includes an adaptive prediction module, and the specific working steps are: S1. Based on the parameter data collected in real time, predict the parameter change trend within a specified short period of time in the future; S2. When the real-time parameter approaches the boundary of the normal fluctuation threshold range, if the prediction result shows that the parameter will continue to change in the direction exceeding the threshold, and the change range is expected to exceed the set warning range, it is determined in advance that there is a risk of failure, a warning signal is issued, and the pre-preparation mechanism for parameter adjustment is started; S3. If the predicted parameter will fluctuate within the threshold range or return to the normal range, it is determined to be a normal fluctuation.

[0007] As a further solution of the present invention: the specific implementation of step S2 is: Expand a preset buffer area outside the normal fluctuation range threshold to form an early warning trigger area; Collect multiple parameter data and target parameters in the welding process in real time. When any parameter enters the preset buffer, the multi-parameter joint prediction process is triggered; Based on the results of the multi-parameter joint prediction process, if the prediction results show that the target parameter continues to extend beyond the threshold and the prediction deviation exceeds the warning range, and the associated parameters do not show the expected linkage pattern, it is judged as a fault risk and a warning signal is triggered.

[0008] As a further solution of the present invention: the working steps of the multi-parameter joint prediction process are: Extract the parameter data at the current moment and in the previous N cycles, including the target parameter and its highly correlated parameters; Use an LSTM neural network or an ARIMA model to model the dynamic coupling relationship and time series trend among the parameters, and output the predicted values of parameter changes in the next M cycles.

[0009] As a further solution of the present invention: The step of determining the risk of failure is specifically as follows: If the predicted values of the target parameter in the continuous K cycles in the prediction result continuously extend outside the threshold, and the associated parameters do not show the expected linkage law, it is determined as a risk trend; Calculate the difference between the predicted value and the threshold. If the maximum predicted deviation exceeds the warning range, and the process risk probability corresponding to the exceeded part is higher than the preset safety threshold, a warning is triggered.

[0010] As a further solution of the present invention: After the S2 step, it further includes: Retrieve historical successful adjustment cases to build a case library, and retrieve pre-adjustment strategies matching the current risk scenario; Pre-load adjustment parameters according to the prediction trend to shorten the response delay; Push adjustment suggestions to the operator and allow manual confirmation or modification of the pre-adjustment parameters.

[0011] As a further solution of the present invention: When retrieving the pre-adjustment strategy matching the current risk scenario, it further includes: Based on historical maintenance data and the difficulty of parameter adjustment, divide the welding parameters into low-cost adjustment parameters and high-cost adjustment parameters; Establish a parameter adjustment cost level table and assign a cost level to each parameter; Prioritize selecting the pre-adjustment strategy involving low-cost adjustment parameters and having the lowest total cost level as the execution plan.

[0012] As a further solution of the present invention: It further includes: Set an adjustment frequency threshold for each parameter. When the actual adjustment frequency exceeds the adjustment frequency threshold, dynamically increase the cost level of a single adjustment according to the proportion of the historical adjustment frequency exceeding; According to the dynamically updated cost level, re-evaluate the comprehensive cost of the pre-adjustment strategy, and prioritize selecting the parameter combination with the lowest marginal cost.

[0013] As a further solution of the present invention: It further includes: Real-time monitor the parameter fluctuations of each welding machine in the cluster. When a single welding machine triggers a pre-adjustment requirement, analyze whether there is a reverse fluctuation trend in other welding machines; If it exists, a cross-device collaborative adjustment strategy is generated based on the historical adjustment frequency, remaining life of components and parameter adjustment cost level of each welding machine, and the adjustment task is preferentially performed by low-cost equipment or low-loss equipment to minimize the overall adjustment cost of the cluster.

[0014] Compared with the prior art, the present invention has the following beneficial effects: (1) When the present invention determines that one or more of the working parameter information collected in real time exceeds a preset normal fluctuation threshold, it determines whether the change in the parameter is a short-term fluctuation or a fault fluctuation, thereby preventing the welding machine from adjusting the parameters due to misjudgment during short-term fluctuations, resulting in quality defects in the weld.

[0015] (2) The present invention forms an early warning trigger zone by extending a certain proportion outside the threshold boundary of the normal fluctuation range, which reserves a buffer space for discovering potential risks in advance, helps to avoid misjudgment caused by triggering an alarm as soon as the parameters fluctuate, and starts a multi-parameter joint prediction process when one or more parameters enter the early warning trigger zone; and based on the prediction results, if the target parameter continues to extend beyond the threshold, the prediction deviation exceeds the warning range, and the related parameters are not linked as expected, it is determined that there is a fault risk and triggers a warning signal. Comprehensive multi-dimensional judgment helps to improve the accuracy of fault identification, thereby identifying risk trends before the parameters exceed the limit, which is conducive to avoiding welding defects caused by sudden faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the system framework structure of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 The present application provides a weld monitoring system based on an electric welder, comprising: The initial setting module sets the normal fluctuation threshold range of various working parameters by collecting the working information of the electric welder in a fault-free state, and the working information at least includes welding current, voltage and welding speed; The data comparison processing module uses relevant sensors to collect various working parameter information of the welding machine in the process of welding the target part in real time and determine the parameters that fluctuate, and compares the parameters that fluctuate during the welding process of the welding machine with the corresponding normal fluctuation threshold range; The control module, when the working parameter information collected in real time exceeds the normal fluctuation threshold range, further determines whether the parameter change is a short-term fluctuation or a deviation caused by a fault; The parameter self-adjustment module is used to send corresponding signals according to the judgment result. If it is a normal fluctuation, no parameter adjustment is performed. If it is a deviation caused by a fault, an alarm signal is sent, and a parameter adjustment instruction is generated according to the fault deviation situation to adjust the working parameters of the welding machine; Among them, high-precision sensors (such as Hall current sensors, voltage transformers, encoders) are used to collect different parameters of the welding machine under the fault-free calibration working conditions. For example, welding current, voltage, and welding speed, etc.; Among them, the normal fluctuation threshold range setting algorithm is based on the 3σ principle (normal distribution): calculate the mean value (μ) and standard deviation (σ) of the fault-free data, and the normal fluctuation threshold is set to [μ - 2σ, μ + 2σ], covering 95% of the process fluctuations. For special welding processes (such as stainless steel and aluminum alloy welding), engineers are allowed to fine-tune the threshold through the human-machine interface (HMI). For example, the current threshold range is narrowed to 1.5σ to improve the sensitivity.

[0019] Among them, the current / voltage sensor is connected in series / parallel to the welding circuit according to actual needs, and the sampling frequency ≥ 100Hz to ensure capturing high-frequency interference. The speed sensor: is linked with the wire feeding mechanism of the welding machine through an encoder, or indirectly measures the moving speed of the welding torch using a vision sensor (such as a laser displacement sensor). The acquisition methods of other parameters are not listed one by one.

[0020] Furthermore, the sliding window difference method is used to calculate the mean value of the real-time data in a 50ms window, compare it with the mean value of the previous window, and a difference exceeding 10% triggers a fluctuation warning. Filter out electromagnetic interference (such as IGBT switching noise) during the welding process to avoid misjudgment. For example, decompose the voltage signal into a high-frequency noise layer and a low-frequency trend layer, and only perform fluctuation analysis on the trend layer. And based on the above, generate the "Welding Parameter Reference Library", which contains parameter thresholds corresponding to different workpiece materials and welding methods.

[0021] Among them, when it is determined that one or more pieces of working parameter information collected in real time exceed the preset normal fluctuation threshold range, at this time, it is necessary to judge the change of this parameter, because during the process of parameter fluctuation, the welding machine needs to perform parameter self-adaptation adjustment to prevent the occurrence of quality defects in the weld due to parameter fluctuation. When it is judged to be a short-term fluctuation, the welding machine does not need to be processed to prevent the occurrence of quality defects in the weld due to the welding machine adjusting the parameters. However, if it is judged to be a deviation caused by a fault, at this time, the welding machine needs to adjust the parameters, which is conducive to preventing the occurrence of quality defects in the weld due to the failure to adjust the corresponding parameters in time after a fault occurs; Furthermore, the alarm signal issued for the deviation caused by the fault contains information such as parameters that need to be adjusted, and a parameter adjustment instruction is generated based on the alarm signal, so that the electric welder adjusts the parameters according to the parameter adjustment instruction, which is beneficial for the working parameters of the electric welder to be within a normal range, thereby facilitating the quality of the weld obtained by welding to be within the preset standards.

[0022] As an optional embodiment, the control module further determines whether the parameter change is a short-term fluctuation or a deviation caused by a fault in a specific manner: Real-time collection of multiple parameter data during the welding process, the parameters at least include welding current, welding voltage, welding speed and molten pool temperature; It should be understood that the molten pool temperature is acquired in real time by an infrared thermal imaging sensor and serves as a core correlation parameter.

[0023] Based on multiple parameter data, a correlation model between parameters is established through the Pearson correlation coefficient or mutual information algorithm to calculate the real-time correlation between any two parameters; The steps of building the correlation model include: After the time offset compensation of the molten pool temperature parameters, the mutual information value is calculated with the current and voltage parameters; The welding stage self-identification algorithm is used to dynamically adjust the correlation threshold, and the threshold is set to 0.8 in the arc initiation stage and 0.7 in the stable stage; The FPGA hardware acceleration module is used to realize the correlation calculation of all parameters within a 50ms period.

[0024] For example, when the temperature of the molten pool suddenly rises, its correlation with the current and voltage is immediately recalculated. If the correlation drops sharply from 0.85 to 0.6 (lower than the threshold value of 0.7), a sensor fault warning is triggered.

[0025] Parameters are screened according to a preset correlation threshold, and parameters with correlations lower than the threshold are excluded from interfering with subsequent judgments; It should be understood that after the correlation is calculated by the Pearson correlation coefficient or the mutual information algorithm, the parameters below the correlation threshold are excluded to prevent subsequent interference with the data.

[0026] For the remaining highly correlated parameters, the normal fluctuation range of each parameter and the allowable deviation range of linkage changes between parameters are set based on the historical data of the parameters; among them, the parameters with a correlation higher than the preset threshold are highly correlated; It should be understood that the high correlation parameters are determined by calculating the Pearson correlation coefficient or mutual information value between the parameters in real time, and the correlation matrix is ​​updated every 50 ms to exclude the interference of parameters with correlation below 0.7 on subsequent analysis.

[0027] Furthermore, the allowable deviation range for linkage is determined through Statistical Process Control (SPC). The specific steps are as follows: 1. Collect 1000 sets of normal welding data, and calculate the mean μ and standard deviation σ of the current-voltage linkage; 2. Set the allowable deviation range as μ±2σ (covering 95.4% of the normal fluctuations); Example: When the current increases by 10A, if the voltage does not drop by more than (μ - 2σ) within 200ms, it is determined as "voltage not linked as expected", and an early warning is triggered even if the current does not exceed the threshold.

[0028] When a fluctuation in a certain parameter is detected, synchronously analyze whether other parameters with a high degree of correlation show changes that conform to the linkage law within the corresponding allowable deviation range; Furthermore, the expected linkage law is obtained through historical data statistics. For example: when the change in welding current exceeds 5%, the welding voltage should show a linkage change of -3% to +2% within 100ms, otherwise it is determined that the associated parameter is abnormal.

[0029] If the fluctuations of all associated parameters are within the allowable deviation range and conform to the linkage law, it is determined as short-term fluctuation. If the fluctuation of a certain parameter exceeds its normal range and the associated parameters do not show the expected linkage change, it is determined as fault fluctuation; Among them, in the actual welding process, the judgment of the parameter fluctuation of the welding machine generally only relies on a single parameter threshold (such as determining a fault when the current exceeds the limit), or simply setting a fixed parameter combination (such as a fixed ratio of current-voltage), which may lead to misjudgment; To further explain the above solution, it is elaborated through cases: Example 1. Application scenario: The surface of the welded part is slightly uneven. The current rises from 200A to 208A (not exceeding the normal upper limit of 210A), the voltage rises synchronously to 25.3V (normal range 24 - 26V), and the molten pool temperature is 1850℃ (normal 1800 - 1900℃); Basis for judgment: Parameter range: All parameters are within the normal fluctuation range; Linkage law: The current increases by 4%, and the voltage changes in the same direction by 1.2%, conforming to the historical linkage trend of "current increase - voltage slight increase" (not triggering a strict threshold but with a reasonable direction); Conclusion: It is determined as short-term fluctuation and no alarm is issued (caused by external instantaneous interference, and the system automatically adapts).

[0030] Example 2. Application scenario: The power supply module is aging. The current suddenly rises to 220A (exceeding the normal upper limit of 210A), the voltage only rises to 25.1V (within the normal range but not reaching the expected increase), and the molten pool temperature is 2000℃ (exceeding the normal upper limit of 1900℃).

[0031] Judgment basis: Parameter range: current and molten pool temperature are beyond the normal range; Linkage rule: The current changes by 10% (>5% trigger threshold), but the voltage changes by only 0.4% (falling short of the expected +2% minimum increase), violating the mandatory rule that “current increase-voltage should be significantly linked”.

[0032] Conclusion: It is determined to be a fault fluctuation, triggering an alarm and shutting down the machine (internal equipment fault causes parameter imbalance).

[0033] As an optional embodiment, an adaptive prediction module is also included, and the specific working steps are: S1. Based on the parameter data collected in real time, predict the parameter change trend within a specified short period of time in the future; S2. When the real-time parameter approaches the boundary of the normal fluctuation threshold range, if the prediction result shows that the parameter will continue to change in the direction exceeding the threshold, and the change range is expected to exceed the set warning range, it is determined in advance that there is a risk of failure, a warning signal is issued, and the pre-preparation mechanism for parameter adjustment is started; S3. If the predicted parameter will fluctuate within the threshold range or return to the normal range, it is determined to be a normal fluctuation.

[0034] As an optional embodiment, the specific implementation of step S2 is: Expand a preset buffer area outside the normal fluctuation range threshold to form an early warning trigger area; Collect multiple parameter data and target parameters in the welding process in real time. When any parameter enters the preset buffer, the multi-parameter joint prediction process is triggered; Based on the results of the multi-parameter joint prediction process, if the prediction results show that the target parameter continues to extend beyond the threshold and the prediction deviation exceeds the warning range, and the associated parameters do not show the expected linkage pattern, it is judged as a fault risk and a warning signal is triggered.

[0035] It should be understood that in the above example, whether it is a fault fluctuation is determined by calculating the correlation degree and the fluctuation of each associated parameter. However, in the actual application process, if the parameter is adjusted after exceeding the limit, it may cause a sudden fault and a welding defect; Therefore, the parameter results within a specified short period of time are predicted based on the parameter data collected in real time, wherein the short period of time is set based on the actual situation; First, a certain proportion is expanded outside the threshold boundary of the normal fluctuation range to form an early warning trigger zone. This reserves buffer space for early detection of potential risks and helps avoid misjudgment caused by triggering alarms as soon as parameters fluctuate. Key parameters such as welding current, voltage, speed, and molten pool temperature are collected in real time. Once one or more parameters enter the early warning trigger zone, the multi-parameter joint prediction process is started; And based on the prediction results, if the target parameter continues to extend beyond the threshold, the prediction deviation exceeds the warning range, and the related parameters do not work together as expected, it is determined that there is a fault risk and a warning signal is triggered. Comprehensive multi-dimensional judgment helps to improve the accuracy of fault identification, thereby identifying risk trends before the parameters exceed the limit, which is conducive to avoiding welding defects caused by sudden failures.

[0036] As an optional embodiment, the working steps of the multi-parameter joint prediction process are: Extract parameter data of the current moment and N historical periods, including target parameters and their highly correlated parameters; The LSTM neural network or ARIMA model is used to model the dynamic coupling relationship between parameters and time series trends, and the predicted values ​​of parameter changes in the next M periods are output.

[0037] It should be understood that by extracting parameter data of the current and historical N periods, focusing on the target parameters and their highly correlated parameters, and reducing the interference of irrelevant data, it is beneficial to improve the pertinence of prediction, and adopting LSTM neural network or ARIMA model. The former is good at processing long-term dependencies in time series data, and the latter is suitable for linear time series prediction. The two can be selected according to actual needs to model the relationship between parameters and output the predicted values ​​of the next M periods; Furthermore, before using the LSTM neural network or ARIMA model for prediction, the model is trained based on historical welding data, and the prediction error tolerance is set, such as the root mean square error ≤ 3%, to ensure the reliability and accuracy of the model prediction.

[0038] As an optional embodiment, the steps of determining a failure risk are specifically as follows: If the predicted values ​​of the target parameter for K consecutive periods in the prediction results continue to extend beyond the threshold, and the associated parameters do not show the expected linkage pattern, it is determined to be a risk trend; Among them, the manufacturer of the electric welding machine usually gives the recommended working range of each parameter according to the design specifications, performance parameters and standard requirements of the welding process of the equipment. For example, the range of welding current should take into account the rated output current of the welding machine, the characteristics of the welding material and the requirements of the welding joint, etc. The boundary values ​​of these recommended working ranges can be used as the initial threshold settings or by collecting a large amount of parameter data from previous successful welding cases to analyze the actual distribution and fluctuation range of the parameters.

[0039] Calculate the difference between the predicted value and the threshold. If the predicted maximum deviation exceeds the warning range, and the process risk probability corresponding to the exceeded part is higher than the preset safety threshold, a warning is triggered.

[0040] Among them, the preset safety threshold is obtained by collecting a large amount of historical welding data, including welding results and various problems that occur under different parameter combinations, such as burn-through, incomplete penetration, pores, etc. For each parameter value or parameter interval, the ratio of the number of occurrences of the corresponding process risk to the total number of welds is statistically calculated as an estimated value of the process risk probability under this parameter condition, or a relationship model between the process risk and welding parameters is established using machine learning or statistical analysis methods.

[0041] It should be understood that when the predicted values of the target parameter continuously extend outside the threshold for K consecutive cycles, and the associated parameters do not show the expected linkage law, it is determined as a risk trend. Potential failures are captured from the trend perspective, and by calculating the difference between the predicted value and the threshold, if the deviation exceeds the warning range, and the process risk probability corresponding to the exceeded part is higher than the preset safety threshold, a warning is triggered to further confirm the failure from the aspects of amplitude and risk probability.

[0042] As an optional embodiment, after step S2, it further includes: Retrieve historical successful adjustment cases to build a case library, and retrieve pre-adjustment strategies that match the current risk scenario; Among them, the case data in the data comes from daily welding and simulation experiments, and is structured and stored according to abnormal features, adjustment strategies, effects, and scenario labels. Extract the current abnormal features, and through similarity calculation and multi-dimensional matching screening (main parameters, scenarios, etc.), comprehensively score and select the optimal cases, and finally store them in the case library, and retrieve the cases that meet the current risk scenario.

[0043] Pre-load adjustment parameters according to the prediction trend to shorten the response delay; Push adjustment suggestions to the operator and allow manual confirmation or modification of the pre-adjustment parameters.

[0044] It should be understood that after the warning signal is triggered, retrieve the matching pre-adjustment strategy based on the historical successful adjustment case library. Further, after matching the matching case, adjust the parameters to be adjusted in a pre-loaded manner, and by starting the adjustment algorithm warm-up (such as activating the PID controller feed-forward compensation module to calculate the initial adjustment amount in advance), it is beneficial to shorten the response delay, reduce the occurrence of welding failures, and finally, push adjustment suggestions to the operator and allow manual intervention to achieve active adjustment before the failure occurs.

[0045] As an optional embodiment, when retrieving the pre-adjustment strategy that matches the current risk scenario, it further includes: Based on historical maintenance data and the difficulty of parameter adjustment, divide the welding parameters into low-cost adjustment parameters and high-cost adjustment parameters; Among them, low-cost adjustment parameters refer to parameters that can be adjusted by directly configuring software programs, with no direct loss or extremely low loss to the device hardware. For example: Wire feeding speed: It can be adjusted through software instructions for controlling the motor speed without hardware calibration; Gas flow rate: It can be adjusted by adjusting the electrical signal parameters of the proportional valve, with low adjustment cost and fast response; High-cost adjustment parameters refer to parameters that require triggering a hardware compensation mechanism, involve calibration of precision components, or may cause device loss. For example: Hardware parameters of power output: Such as the compensation of the hardware drive circuit for welding current. Frequent adjustment over a long period may cause aging of the power module; Calibration parameters of the molten pool temperature sensor: It requires hardware feedback verification relying on high-precision sensors. The adjustment process requires shutdown for calibration, which is time-consuming and laborious.

[0046] Establish a parameter adjustment cost level table and assign a cost level to each parameter; The method for establishing the level table is as follows: Based on the historical maintenance records of the welding machine (such as component replacement frequency, calibration time consumption, hardware cost), combined with the experience of process engineers, assign a cost level to each parameter. For example: Low-cost parameter - Wire feeding speed - Level 1 - Software adjustment, no hardware loss; Low-cost parameter - Gas flow rate - Level 1 - Electrical signal control, adjustment cost negligible; Medium-cost parameter - Welding voltage - Level 2 - Require software + hardware collaborative feedback adjustment; High-cost parameter - Power module output compensation - Level 3 - Frequent adjustment of the hardware drive circuit is prone to aging; High-cost parameter - Calibration value of the molten pool sensor - Level 3 - Require shutdown for calibration, time consumption ≥ 10 minutes; Furthermore, Level 1 is for equal cost and Level 3 is for high cost.

[0047] Furthermore, when the adjustment of a certain parameter causes equipment failure or shortens the maintenance cycle, its cost level is automatically increased. For example, if the wire feeding motor is worn due to frequent high-speed adjustment, its cost level is increased from Level 1 to Level 2; Priority selection of adjustment strategies: When multiple pre-adjustment strategies match the current risk scenario, the following options are preferred: ① Adjustment strategies that only include low-cost parameters (Level 1), such as only adjusting the wire feeding speed to compensate for current fluctuations; ② If high-cost parameters (Level 3) must be involved, then select the strategy with the fewest high-cost parameters and the smallest adjustment range, such as calibrating the sensor only once instead of multiple times.

[0048] Preferentially select the pre-adjustment strategy that involves low-cost adjustment parameters and has the lowest total cost level as the implementation plan.

[0049] It should be understood that by preferentially using low-cost parameter adjustment, the equipment loss cost of a single adjustment can be reduced, and at the same time, it is beneficial to avoid downtime caused by frequent hardware calibration; reduce unnecessary adjustment of high-cost components (such as power modules and precision sensors), and extend the life of the core components of the equipment.

[0050] As an optional embodiment, it further includes: Set an adjustment frequency threshold for each parameter. When the actual adjustment frequency exceeds the adjustment frequency threshold, dynamically increase the cost level of a single adjustment according to the proportion of the historical adjustment frequency exceeding the threshold; According to the dynamically updated cost level, re-evaluate the comprehensive cost of the pre-adjustment strategy, and preferentially select the parameter combination with the lowest marginal cost.

[0051] In the above embodiment, it is described that by selecting the adjustment method with the lowest cost for adjustment, the cost can be saved and the equipment loss cost can be reduced. However, under the above selection method, it may cause cumulative loss of low-cost parameters (such as wire feeding speed) due to frequent adjustment, which instead increases the long-term maintenance cost. Therefore, it is necessary to make a balanced selection when selecting parameters for adjustment, and there is a problem in how to balance this selection; By setting an adjustment frequency threshold for each parameter (such as the number of wire feeding speed adjustments per month ≤ 50 times), when the actual adjustment frequency exceeds the adjustment frequency threshold, trigger a cost increment mechanism: Among them, the cost of a single adjustment = basic cost level + proportion of historical adjustment frequency exceeding the threshold × loss coefficient.

[0052] Among them, the loss coefficient is preset according to the parameter type (such as the loss coefficient of software parameters = 0.5, and that of hardware parameters = 1.0).

[0053] Furthermore, the monthly adjustment frequency of each parameter is statistically calculated in real time, and the marginal cost level is automatically updated on the 1st of each month. The monthly adjustment frequency of each parameter is statistically calculated in real time, and the marginal cost level is automatically updated on the 1st of each month.

[0054] It should be further noted that by incorporating the historical adjustment frequency of the parameter into the cost assessment, when a certain parameter is adjusted too frequently, the cost level of a single adjustment of it is automatically increased. When selecting the pre-adjustment strategy, it is no longer limited to the direct cost of a single adjustment, but preferentially selects the parameter combination with the lowest long-term cost, balancing the short-term adjustment convenience and long-term equipment loss, so as to be beneficial to preventing motor wear caused by over-reliance on low-cost parameters (such as frequent adjustment of wire feeding speed), and being able to dynamically balance the economy and reliability of parameter adjustment according to the actual usage scenario.

[0055] As an optional embodiment, it further includes: Monitor the parameter fluctuations of each welding machine in the cluster in real time. When a single welding machine triggers a pre-adjustment requirement, analyze whether there is a reverse fluctuation trend in other welding machines; If there is, generate a cross-device collaborative adjustment strategy based on the historical adjustment frequency, remaining life of components, and parameter adjustment cost level of each welding machine. Prioritize low-cost devices or low-loss devices to undertake the adjustment task to minimize the overall adjustment cost of the cluster.

[0056] Furthermore, as described in the above embodiment, by evenly selecting adjustment parameters to reduce the loss of the device. However, in long-term use, the adjustment of each parameter will gradually tend to be "saturated". Therefore, in the actual application process, in the case of multiple welding machines working synchronously, this embodiment realizes the optimal cost adjustment through cross-device collaboration. Specifically, monitor the parameter fluctuations of all welding machines in the cluster in real time. When a certain welding machine (such as welding machine A) triggers a pre-adjustment requirement (such as an abnormal increase in current) due to a change in welding requirements, immediately scan the parameter trends of other welding machines to determine whether there is a reverse fluctuation (such as a synchronous decrease in the current of welding machine B); Furthermore, if there is a reverse fluctuation, it indicates that the condition for collaborative adjustment is met. Subsequently, the system quantitatively evaluates the adjustment situation of each welding machine in the cluster based on the historical adjustment frequency, remaining life of components, and parameter adjustment cost level of each welding machine; Furthermore, prioritize the allocation of adjustment tasks to low-load devices with a low recent adjustment frequency and a long remaining life of components, or low-cost devices with a lower cost level when performing the same adjustment action; For example, if welding machine A needs to perform a high-cost current hardware adjustment, and welding machine C has not performed such an adjustment recently and has sufficient remaining life, welding machine C will share part of the adjustment task to reduce the device loss of A. In this way, the adjustment requirements of a single welding machine are optimized from the perspective of the overall cluster, avoiding premature aging caused by excessive adjustment of a single device and reducing the repeated execution of high-cost adjustment actions, ultimately minimizing the overall adjustment cost of the cluster.

[0057] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. A weld monitoring system based on an electric welder, characterized in that: include: An initial setting module sets a normal fluctuation threshold range of various working parameters by collecting working information of the electric welder in a fault-free state, wherein the working information at least includes welding current, voltage and welding speed; The data comparison processing module uses relevant sensors to collect various working parameter information of the welding machine in the process of welding the target part in real time and determine the parameters that fluctuate, and compares the parameters that fluctuate during the welding process of the welding machine with the corresponding normal fluctuation threshold range; The control module further determines whether the parameter change is a short-term fluctuation or a deviation caused by a fault when the working parameter information collected in real time exceeds the normal fluctuation threshold range; The parameter self-adjustment module is used to send out corresponding signals according to the judgment results. If it is a normal fluctuation, no parameter adjustment is performed. If it is a deviation caused by a fault, an alarm signal is issued, and a parameter adjustment instruction is generated according to the fault deviation to adjust the working parameters of the welding machine.

2. A weld monitoring system based on an electric welder according to claim 1, characterized in that: The control module further determines whether the parameter change is a short-term fluctuation or a deviation caused by a fault in the following specific manner: Collect multiple parameter data in the welding process in real time, wherein the parameters include at least welding current, welding voltage, welding speed and molten pool temperature; Based on multiple parameter data, a correlation model between parameters is established through the Pearson correlation coefficient or mutual information algorithm to calculate the real-time correlation between any two parameters; Parameters are screened according to a preset correlation threshold, and parameters with correlations lower than the threshold are excluded from interfering with subsequent judgments; For the remaining highly correlated parameters, the normal fluctuation range of each parameter and the allowable deviation range of linkage changes between parameters are set based on the historical data of the parameters; When a parameter is detected to fluctuate, other parameters with high correlation with it are analyzed simultaneously to see whether they show changes that conform to the linkage law within the corresponding allowable deviation range; If the fluctuations of all related parameters are within the allowable deviation range and conform to the linkage law, they are judged as short-term fluctuations. If the fluctuation of a parameter exceeds its normal range and the related parameters do not show the expected linkage changes, they are judged as fault fluctuations.

3. A weld monitoring system based on an electric welder according to claim 1, characterized in that: It also includes an adaptive prediction module, and the specific working steps are: S1. Based on the parameter data collected in real time, predict the parameter change trend within a specified short period of time in the future; S2. When the real-time parameter approaches the boundary of the normal fluctuation threshold range, if the prediction result shows that the parameter will continue to change in the direction exceeding the threshold, and the change range is expected to exceed the set warning range, it is determined in advance that there is a risk of failure, a warning signal is issued, and the pre-preparation mechanism for parameter adjustment is started; S3. If the predicted parameter will fluctuate within the threshold range or return to the normal range, it is determined to be a normal fluctuation.

4. A weld monitoring system based on an electric welder according to claim 3, characterized in that: The specific implementation of step S2 is as follows: Expand a preset buffer area outside the normal fluctuation range threshold to form an early warning trigger area; Collect multiple parameter data and target parameters in the welding process in real time. When any parameter enters the preset buffer, the multi-parameter joint prediction process is triggered; Based on the results of the multi-parameter joint prediction process, if the prediction results show that the target parameter continues to extend beyond the threshold and the prediction deviation exceeds the warning range, and the associated parameters do not show the expected linkage pattern, it is judged as a fault risk and a warning signal is triggered.

5. A weld monitoring system based on an electric welder according to claim 4, characterized in that: The working steps of the multi-parameter joint prediction process are: Extract parameter data of the current moment and N historical periods, including target parameters and their highly correlated parameters; The LSTM neural network or ARIMA model is used to model the dynamic coupling relationship between parameters and time series trends, and the predicted values ​​of parameter changes in the next M periods are output.

6. A weld monitoring system based on an electric welder according to claim 4, characterized in that: The steps of determining the failure risk are specifically as follows: If the predicted values ​​of the target parameter for K consecutive periods in the prediction results continue to extend beyond the threshold, and the associated parameters do not show the expected linkage pattern, it is determined to be a risk trend; The difference between the predicted value and the threshold is calculated. If the maximum predicted deviation exceeds the warning range and the process risk probability corresponding to the excess part is higher than the preset safety threshold, an early warning is triggered.

7. A weld monitoring system based on an electric welder according to claim 3, characterized in that: The step S2 further includes: Retrieve historical successful mediation cases to build a case library and retrieve pre-adjustment strategies that match the current risk scenario; Preload adjustment parameters based on predicted trends to shorten response delays; Push adjustment suggestions to operators and allow manual confirmation or modification of pre-adjustment parameters.

8. A weld monitoring system based on an electric welder according to claim 7, characterized in that: The calling of the pre-adjustment strategy matching the current risk scenario also includes: Based on historical maintenance data and parameter adjustment difficulty, welding parameters are divided into low-cost adjustment parameters and high-cost adjustment parameters; Establish a parameter adjustment cost level table and assign a cost level to each parameter; The pre-adjustment strategy involving low-cost adjustment parameters and having the lowest total cost level is preferentially selected as the execution plan.

9. A weld monitoring system based on an electric welder according to claim 8, characterized in that: Also includes: Set an adjustment frequency threshold for each parameter. When the actual adjustment frequency exceeds the adjustment frequency threshold, the cost level of a single adjustment is dynamically increased according to the historical adjustment frequency excess ratio. According to the dynamically updated cost level, the comprehensive cost of the pre-adjustment strategy is re-evaluated, and the parameter combination with the lowest marginal cost is preferentially selected.

10. A weld monitoring system based on an electric welder according to claim 1, characterized in that: Also includes: Monitor the parameter fluctuations of each welding machine in the cluster in real time. When a single welding machine triggers the pre-adjustment demand, analyze whether other welding machines have a reverse fluctuation trend. If it exists, a cross-device collaborative adjustment strategy is generated based on the historical adjustment frequency, remaining life of components and parameter adjustment cost level of each welding machine, and the adjustment task is preferentially performed by low-cost equipment or low-loss equipment to minimize the overall adjustment cost of the cluster.

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