Ultra-long cantilever type friction stir welding track offset correction method and system, product and medium

Abstract

The invention discloses a super-long cantilever type friction stir welding track offset correction method and system, a product and a medium. The method comprises the steps that in the welding process, acoustic signals of a welding head are obtained in real time, acoustic feature data are extracted, the acoustic feature data are compared with an acoustic basic feature library constructed through no-load operation, and the acoustic difference degree is calculated. And when the acoustic difference exceeds a preset threshold value, identifying as acoustic abnormity and recording abnormal frequency characteristics. And according to the abnormal frequency characteristics, the rotating speed of the welding head is adjusted according to the fine adjustment amplitude and the fine adjustment time in direct proportion to the acoustic difference degree, and the rotating speed is recovered after fine adjustment is finished. Meanwhile, the friction force, the relative speed and the acoustic signal after fine adjustment are obtained, and friction energy in unit time is calculated. And when the friction energy fluctuation coefficient exceeds the threshold value and the instantaneous value is lower than the preset value, or the acoustic abnormity continuously exists and the welding head deviates from the preset track, the welding head is adjusted to the device track. By implementing the technical scheme provided by the invention, the track precision of super-long cantilever friction stir welding is improved.

Description

Technical Field

[0001] The present application relates to the field of welding, and particularly to a method, a system, a product and a medium for correcting the trajectory deviation of friction stir welding of ultra-long cantilever type. Background Art

[0002] At present, with the rapid development of advanced manufacturing industries such as aerospace, rail transit and new energy, the processing and assembly technologies of ultra-long cantilever type structural parts have been widely applied. As an efficient and green solid-state joining process, friction stir welding has important application value in these fields, and the joining quality of the friction stir welding process is directly related to the strength, durability and overall performance of the components.

[0003] At present, in the related technologies, in order to ensure the trajectory accuracy of friction stir welding, a vision sensor, a laser tracking system or a displacement sensor is usually used to monitor the welding path in real time. These methods directly detect the relative position between the welding head and the welding path, identify the trajectory deviation, and adjust the position or posture of the welding head through the control system, so as to realize the correction of the trajectory and ensure the weld quality.

[0004] However, in the related technologies, the detection and compensation methods directly relying on geometric deviation have problems of response lag and insufficient adaptability. When local micro-resonances or flutter are caused by welding heat input, periodic forging pressure or structural characteristics in ultra-long cantilever components, these high-frequency dynamic changes are usually difficult to be accurately captured by vision or geometric sensors. If the correction system is too sensitive to these small deviation changes, new disturbances may be introduced due to frequent high-frequency corrections, destroying the plastic flow state of the material during the friction stir welding process, resulting in welding defects (such as tunnel defects, surface ripples) or a decrease in process stability; if the correction system ignores the cumulative effect of these dynamic changes, the weld may gradually deviate from the ideal center line, ultimately affecting the welding accuracy and component strength. Therefore, how to effectively identify and compensate the trajectory deviation caused by the dynamic characteristics of the component during the welding process, while avoiding the damage of the correction action itself to the welding quality and process stability, is still a problem difficult to solve in the related technologies. Summary of the Invention

[0005] The present application provides a method, a system, a product and a medium for correcting the trajectory deviation of friction stir welding of ultra-long cantilever type, which are used to improve the trajectory accuracy of friction stir welding of ultra-long cantilever type.

[0006] In the first aspect of the present application, a method for correcting the trajectory deviation of friction stir welding of ultra-long cantilever type is provided, and the method includes: During the welding process, the acoustic signal of the welding head is obtained in real time, and the real-time acoustic feature data of the acoustic signal is extracted; the real-time acoustic feature data is compared with the acoustic basic feature library, and the acoustic difference degree is calculated; when the acoustic difference degree exceeds the preset acoustic difference threshold, it is identified as an acoustic anomaly and the abnormal frequency feature of the acoustic anomaly is recorded; according to the abnormal frequency feature, the rotation speed of the welding head is adjusted according to the fine-tuning amplitude and the fine-tuning time, and after the fine-tuning time ends, the welding head is controlled to return to the rotation speed before fine-tuning; the friction force, relative speed and real-time acoustic signal after the fine-tuning time are obtained in real time, and the friction energy within the preset unit time length is calculated; when the fluctuation coefficient of the friction energy exceeds the preset fluctuation coefficient threshold and the instantaneous value of the friction energy is lower than the preset friction energy threshold, or there is still an acoustic anomaly in the real-time acoustic signal, and the shortest distance between the real-time position of the welding head and the preset device trajectory exceeds the preset offset threshold, the position of the welding head is adjusted to the device trajectory.

[0007] In the above embodiment, by obtaining the acoustic signal of the welding head in real time, extracting the acoustic feature data and comparing it with the acoustic basic feature library, the high-frequency dynamic anomalies caused by periodic forging pressure, micro-resonance or flutter during the welding process are identified, overcoming the defects of the traditional geometric offset detection method with lagging response and difficulty in capturing high-frequency dynamic changes. At the same time, by recording the frequency characteristics of the acoustic anomaly and implementing the rotation speed adjustment of the fine-tuning amplitude and the fine-tuning time, the authenticity and severity of the anomaly are verified, avoiding the disturbance damage caused by frequent high-frequency corrections. In addition, through the multi-dimensional analysis of the friction force, relative speed and friction energy fluctuation, when the anomaly persists or the offset exceeds the threshold, the position of the welding head is dynamically adjusted to the preset trajectory, thereby suppressing the cumulative effect of dynamic changes and avoiding the weld seam deviating from the center line. This solution improves the real-time performance and adaptability of trajectory compensation, avoids the generation of welding defects such as defects and surface ripples, and at the same time ensures the welding path accuracy and process stability, improving the trajectory accuracy of friction stir welding of ultra-long cantilever type.

[0008] Combined with some embodiments of the first aspect, in some embodiments, according to the abnormal frequency feature, the rotation speed of the welding head is adjusted according to the fine-tuning amplitude and the fine-tuning time, and after the fine-tuning time ends, the welding head is controlled to return to the rotation speed before fine-tuning, specifically including: The heat flux density in the area of ​​the preset heat flux monitoring area size under the welding head is imaged in real time, and the rotation speed of the welding head is adjusted according to the fine-tuning amplitude and fine-tuning time, and the adjustment transient response characteristics of the heat flux density at the beginning of the fine-tuning time are recorded. After the fine-tuning time ends, the welding head is controlled to return to the rotation speed before fine-tuning; the speed adjustment data and the adjustment transient response characteristics are input into the preset ideal heat generation and heat transfer dynamic mathematical model of stir friction welding to obtain the actual heat generation efficiency and the actual heat flux density distribution; the speed adjustment data is the difference between the speed values ​​before and after the adjustment; when the heat generation efficiency deviation value exceeds the preset heat generation efficiency deviation value threshold or the heat flux density distribution deviation value exceeds the preset heat flux density distribution deviation threshold, an interface friction state instability warning information is issued, the axial force of the welding head is adjusted to the preset axial force optimization range, and the tool inclination angle of the welding head is adjusted according to the adjustment transient response characteristics.

[0009] In the above embodiment, by real-time imaging of the heat flux density of the preset heat flux monitoring area under the welding head, and combining the fine-tuning amplitude and fine-tuning time to adjust the welding head rotation speed in stages, the transient response characteristics of the heat flux density adjustment can be recorded, and it is input into the ideal heat generation and heat transfer dynamic mathematical model of stir friction welding to calculate the actual heat generation efficiency and heat flux density distribution. When the heat generation efficiency deviation value or the heat flux density distribution deviation value exceeds the preset threshold, the axial force of the welding head is adjusted to the optimization range, and the tool inclination angle is dynamically adjusted to restore the stability of the interface friction state. This method keenly captures the friction instability problem caused by uneven heat input or abnormal movement of the welding head through real-time heat flow monitoring, and optimizes the heat input distribution and material flow behavior by adjusting the axial force and tool inclination angle in a linked manner, avoiding the defects of delayed response to the dynamic changes of local heat diffusion and single adjustment means. Finally, the heat input uniformity and process stability of the welding process are improved, ensuring the consistency and reliability of the weld quality under complex working conditions, especially in ultra-long cantilever stir friction welding, significantly improving the accuracy of the welding trajectory.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the speed adjustment data and the adjustment transient response characteristics are input into a preset ideal heat generation and heat transfer dynamic mathematical model of stir friction welding, the method further includes: When it is found that the adjusted transient response characteristics are related to the rotation period of the welding head and match the preset acoustic-thermal flux coupling fingerprint of specific defects of the welding head, by analyzing the adjusted transient response characteristics, the heat flux fluctuation frequency, heat flux fluctuation phase, and heat flux fluctuation amplitude of the periodic heat flux fluctuation are obtained; according to the correlation between the heat flux fluctuation amplitude, heat flux fluctuation phase, and the abnormal spectrum characteristics corresponding to the heat flux fluctuation amplitude and heat flux fluctuation phase and the acoustic anomaly, by comparing with the preset welding head defect fingerprint library, the preliminary defect type and preliminary defect position of the welding head are judged; according to the ratio of the heat flux fluctuation amplitude to the preset fluctuation amplitude deviation threshold, the rotation speed adjustment amplitude of the welding head is calculated, and based on the heat flux fluctuation phase, the adjustment phase point of the rotation speed adjustment is calculated. At the adjustment phase point, the rotation speed of the welding head is secondarily adjusted according to the rotation speed adjustment amplitude; the second heat flux fluctuation amplitude and the second heat flux fluctuation phase distribution characteristics before and after the second adjustment are obtained, and the real-time offset of the welding head on the welding track is calculated; based on the real-time offset, the offset compensation path of the welding head is generated by using the multi-point interpolation method; the movement track of the welding head is adjusted again according to the offset compensation path.

[0011] In the above embodiment, by analyzing the correlation between the adjusted transient response characteristics and the rotation period of the welding head in real time and comparing it with the preset acoustic-thermal flux coupling fingerprint library of specific defects of the welding head, the frequency, phase, and amplitude of the periodic heat flux fluctuation are identified, so as to capture potential anomalies caused by periodic defects or dynamic changes, and preliminarily judge the defect type and position of the welding head. According to the ratio of the heat flux fluctuation amplitude to the preset deviation threshold, the rotation speed adjustment amplitude of the welding head is calculated, and the optimal adjustment phase point is determined in combination with the heat flux fluctuation phase information, and the rotation speed of the welding head is secondarily adjusted at the optimal timing of the dynamic response. This regulation method based on periodic characteristics not only verifies the authenticity of the defects, but also optimizes the dynamic behavior of the welding head, avoiding new disturbances caused by adjustment delays or over-adjustments. After the rotation speed adjustment is completed, by analyzing the heat flux fluctuation amplitude and phase distribution characteristics before and after the second adjustment, the offset of the welding head on the welding track is calculated in real time, and a smooth offset compensation path is generated in combination with the multi-point interpolation method to dynamically adjust the movement track of the welding head to ensure the stability of the welding process and the accuracy of the track. Finally, through the combination of multi-dimensional acoustic and heat flux characteristic analysis and dynamic adjustment, the problems of lagging response to periodic defects and insufficient path compensation accuracy of traditional methods are overcome, the accuracy of the welding track and the weld quality are improved, especially in friction stir welding of ultra-long cantilever type, ensuring the stability and reliability of the welding process under complex working conditions.

[0012] Combined with some embodiments of the first aspect, in some embodiments, after comparing the real-time acoustic feature data with the acoustic basic feature library and calculating the acoustic difference degree, it further includes: When the welding path is detected to be a long-distance straight line area, the vibration characteristics of the welding head are monitored in real time, the vibration cumulative amount and the vibration change trend are calculated, and at the same time, the thermal input gradient distribution of the welding area is monitored; when the change rate of the vibration cumulative amount, the vibration change trend or the thermal input gradient distribution exceeds the corresponding preset threshold and there is no acoustic anomaly in the acoustic difference degree, the rotation speed of the welding head is adjusted according to the preset fine-tuning parameters.

[0013] In the above embodiment, when the welding path is detected to be a long-distance straight line area, the vibration characteristics of the welding head are monitored in real time and the vibration cumulative amount is calculated, and at the same time, the thermal input gradient distribution of the welding area is monitored. When the change rate of the vibration cumulative amount, the vibration change trend or the thermal input gradient distribution exceeds the corresponding preset threshold, and no anomaly is detected in the acoustic difference degree, the rotation speed of the welding head is adjusted according to the preset fine-tuning range to balance the vibration state and optimize the thermal input distribution. This method can identify and suppress the early abnormal trends caused by vibration accumulation or uneven thermal input through the linkage monitoring and dynamic adjustment of vibration and thermal input, avoiding the limitation that only relying on acoustic detection may ignore non-acoustic anomalies. Compared with the traditional method, this solution overcomes the defects of lagging response and insufficient adjustment accuracy to the change of thermal input gradient and vibration accumulation in long-distance straight welding through the real-time analysis and regulation of multi-dimensional parameters, improves the uniformity of the weld seam and the accuracy of the straight welding path, and at the same time ensures the stability of the welding process. Especially in the friction stir welding of ultra-long cantilever type, the accuracy of the welding trajectory and the process adaptability are further improved.

[0014] Combined with some embodiments of the first aspect, in some embodiments, when the welding path is detected to be a long-distance straight line area, after the vibration characteristics of the welding head are monitored in real time, the vibration cumulative amount and the vibration change trend are calculated, and at the same time, the thermal input gradient distribution of the welding area is monitored, it further includes: When the vibration change trend in the vibration characteristics exceeds the preset change trend threshold, the fine-tuning parameters are adjusted according to the vibration frequency and vibration amplitude in the vibration characteristics; according to the thermal input gradient distribution in the welding path direction, the rotation speed of the welding head corresponding to the area with too high thermal input is reduced, and the axial pressure of the welding head is increased in the area with too high thermal input.

[0015] In the above embodiments, when the vibration change trend in the vibration characteristics exceeds the preset change trend threshold, the fine-tuning amplitude of the welding head is dynamically adjusted according to the vibration frequency and vibration amplitude; at the same time, according to the heat input gradient distribution in the welding path direction, the rotational speed of the welding head corresponding to the area with too high heat input is reduced, and the axial pressure of the welding head in this area is increased to achieve balanced heat input and optimized material flow state. This method can quickly respond to the abnormal vibration cumulative effect or the abnormality caused by too high local heat input by capturing the abnormal vibration change trend in real time and combining with the heat input characteristics for linkage adjustment, avoiding problems such as weld quality decline and process instability. Compared with the traditional method that only relies on single adjustment of rotational speed or has a lag in response to vibration abnormality, this solution overcomes the defect of insufficient adaptability to dynamic changes through multi-dimensional collaborative control of vibration characteristics and heat input distribution, improving the accuracy of the welding path and the consistency of the weld. Finally, this solution improves the accuracy of the welding trajectory and the stability of the process in friction stir welding of ultra-long cantilever type, meeting the requirements of high-precision welding under complex working conditions.

[0016] In combination with some embodiments of the first aspect, in some embodiments, before obtaining the friction force, relative velocity, and real-time acoustic signal after the fine-tuning time in real time and calculating the friction energy within a preset unit time length, it further includes: When it is predicted that the inherent low-frequency modal vibration mode of the unwelded path section is activated by analyzing historical welding data and the CAD model of the current workpiece, according to the predicted vibration frequency and vibration amplitude, select the corresponding predicted anti-instability parameter combination from the preset anti-instability welding parameter library; when the welding head reaches the high-risk section corresponding to the activation of the inherent low-frequency modal vibration mode, apply the predicted anti-instability parameter combination with a preset probing length, and monitor the probing acoustic signal and the probing dynamic stability of the welding head in real time; if the probing dynamic stability is within the preset stability threshold range and the probing acoustic signal is within the preset acoustic signal threshold range, apply the predicted anti-instability parameter combination to complete the welding of the high-risk section; if the probing dynamic stability is not within the preset stability threshold range or the probing acoustic signal is not within the preset acoustic signal threshold range, then superimpose a preset small perturbation signal on the control instruction of the welding head.

[0017] In the above embodiments, by analyzing historical welding data and the CAD model of the current workpiece, the inherent low-frequency modal vibration modes that may be activated in the unwelded path section are predicted, and according to the predicted vibration frequency and vibration amplitude, the corresponding predicted anti-instability parameter combination is selected from the preset anti-instability welding parameter library. In the high-risk section where the inherent low-frequency modal vibration mode is activated, the predicted anti-instability parameter combination is applied with a preset tentative length, and the tentative acoustic signal and the tentative dynamic stability of the welding head are monitored in real time to judge the stability and reliability of the welding process. If both the tentative dynamic stability and the tentative acoustic signal are within the preset threshold range, the predicted anti-instability parameter combination is directly applied to complete the welding of the high-risk section; if the threshold requirements are not met, a small disturbance signal is superimposed on the control command of the welding head to dynamically optimize the vibration state and suppress the adverse effects of the low-frequency modal vibration mode. By combining the prediction ability of historical welding data and CAD model analysis, as well as the dynamic adjustment of tentative parameter verification and small disturbance signals, the welding instability problems caused by low-frequency modal vibration modes in high-risk sections can be identified and addressed in advance. In the friction stir welding of ultra-long cantilever type, through prediction and real-time control, the accuracy of the welding trajectory and the weld quality in high-risk sections are improved, and at the same time, the stability and adaptability of the welding process are significantly enhanced, meeting the requirements for high-precision welding under complex working conditions.

[0018] Combined with some embodiments of the first aspect, in some embodiments, after comparing the real-time acoustic feature data with the acoustic basic feature library and calculating the acoustic difference degree, it further includes: When the acoustic difference degree continuously remains high and there is no acoustic anomaly in the acoustic difference degree, identify the high-baseline, high-reproducibility features of the acoustic difference degree and the corresponding workpiece type and welding section; retrieve the preset digital twin model of the workpiece type, and simulate the equivalent thermal-mechanical load applied during the friction stir welding process in the digital twin model for the corresponding welding section; through simulation calculation, analyze the inherent structural acoustic response characteristics generated by the welding section; if the inherent structural acoustic response characteristics match the real-time acoustic feature data, replace the inherent structural acoustic response characteristics into the acoustic basic feature library.

[0019] In the above embodiments, when the acoustic difference degree remains high and there is no acoustic anomaly, by identifying the high baseline and high reproducibility characteristics of the acoustic difference degree and their corresponding workpiece types and welding sections, the preset digital twin model of the workpiece type is retrieved, and the equivalent thermal-mechanical load during the friction stir welding process of the welding section is simulated in the model. By analyzing the inherent structural acoustic response characteristics of the welding section through simulation calculation, if the simulation results match the real-time acoustic characteristic data, the inherent structural acoustic response characteristics are replaced into the acoustic basic characteristic library to update and optimize the applicability of the characteristic library. By combining the acoustic anomaly with the digital twin model, the acoustic high baseline phenomenon caused by the inherent structural characteristics of the workpiece can be identified, and its rationality can be verified through simulation, avoiding the risk of misjudging the inherent acoustic characteristics of the workpiece as welding anomalies. By optimizing the acoustic basic characteristic library, the monitoring accuracy and reliability are improved. By accurately identifying and dynamically updating the acoustic basic characteristic library, the adaptability and accuracy of acoustic monitoring during the friction stir welding process are improved. In the ultra-long cantilever type welding, unnecessary adjustments or misoperations caused by misjudging the inherent acoustic characteristics are avoided, further improving the accuracy of the welding trajectory and the stability of the process, meeting the requirements of high-precision welding under complex working conditions.

[0020] In a second aspect, an embodiment of the present application provides a trajectory offset correction system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the trajectory offset correction system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions, when the above computer program product runs on the trajectory offset correction system, causing the above trajectory offset correction system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, when the above instructions run on the trajectory offset correction system, causing the above trajectory offset correction system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the trajectory offset correction system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the ultra-long cantilever type friction stir welding trajectory offset correction method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application obtains the acoustic signal of the welding head in real time, extracts the acoustic feature data and compares it with the acoustic basic feature library to identify high-frequency dynamic anomalies caused by periodic forging pressure, small resonance or flutter during welding, thereby overcoming the defects of the traditional detection method that relies on geometric offset, which is response lag and difficult to capture high-frequency dynamic changes. By recording the frequency characteristics of the acoustic anomaly and implementing the speed adjustment of the fine-tuning amplitude and fine-tuning time, the authenticity and severity of the anomaly are verified, while avoiding the disturbance damage caused by frequent high-frequency correction. Combined with the multi-dimensional analysis of friction force, relative speed and friction energy fluctuations, when the anomaly persists or the offset exceeds the threshold, the welding head position is dynamically adjusted to the preset trajectory, thereby suppressing the cumulative effect of dynamic changes and preventing the weld from deviating from the center line. This solution improves the real-time and adaptability of trajectory compensation, avoids the generation of welding defects such as defects and surface ripples, and ensures the welding path accuracy and process stability, and improves the trajectory accuracy of ultra-long cantilever stir friction welding.

[0025] 2. This application can record the transient response characteristics of the heat flux density adjustment by real-time imaging of the heat flux density in the preset heat flux monitoring area under the welding head, and combine the fine-tuning amplitude and fine-tuning time to adjust the welding head rotation speed in stages, and input it into the ideal heat generation and heat transfer dynamic mathematical model of stir friction welding to calculate the actual heat generation efficiency and heat flux density distribution. When the heat generation efficiency deviation value or the heat flux density distribution deviation value exceeds the preset threshold, the axial force of the welding head is adjusted to the optimization range, and the tool inclination angle is dynamically adjusted to restore the stability of the interface friction state. This method keenly captures the friction instability problem caused by uneven heat input or abnormal movement of the welding head through real-time heat flow monitoring, and optimizes the heat input distribution and material flow behavior by adjusting the axial force and tool inclination angle in a linked manner, avoiding the defects of delayed response to the dynamic changes of local heat diffusion and single adjustment means. Finally, the heat input uniformity and process stability of the welding process are improved, ensuring the consistency and reliability of the weld quality under complex working conditions, especially in ultra-long cantilever stir friction welding, significantly improving the accuracy of the welding trajectory.

[0026] 3. This application analyzes and adjusts the correlation between the transient response characteristics and the welding head rotation period in real time, and compares it with a preset acoustic-thermal flux coupling fingerprint library for specific welding head defects to identify the frequency, phase, and amplitude of periodic heat flux fluctuations, thereby capturing potential anomalies caused by periodic defects or dynamic changes, and preliminarily judging the type and location of the welding head defects. According to the ratio of the heat flux fluctuation amplitude to the preset deviation threshold, the adjustment amplitude of the welding head rotation speed is calculated, and the optimal adjustment phase point is determined in combination with the heat flux fluctuation phase information to perform the second adjustment of the welding head rotation speed at the optimal timing of the dynamic response. This control method based on periodic characteristics not only verifies the authenticity of the defects but also optimizes the dynamic behavior of the welding head, avoiding new disturbances caused by adjustment delays or over-adjustments. After completing the rotation speed adjustment, by analyzing the heat flux fluctuation amplitude and phase distribution characteristics before and after the second adjustment, the offset of the welding head on the welding track is calculated in real time, and a smooth offset compensation path is generated in combination with the multi-point interpolation method to dynamically adjust the movement track of the welding head to ensure the stability of the welding process and the accuracy of the track. Finally, through the combination of multi-dimensional acoustic and heat flux characteristic analysis and dynamic adjustment, the problems of lagging response to periodic defects and insufficient path compensation accuracy in traditional methods are overcome, improving the accuracy of the welding track and the weld quality. Especially in the friction stir welding of ultra-long cantilever types, the stability and reliability of the welding process under complex working conditions are ensured. Brief Description of the Drawings

[0027] Figure 1 is a schematic flow chart of a method for correcting the offset of a friction stir welding track of an ultra-long cantilever type in an embodiment of the present application; Figure 2 is another schematic flow chart of a method for correcting the offset of a friction stir welding track of an ultra-long cantilever type in an embodiment of the present application; Figure 3 is a schematic diagram of an exemplary hardware structure of a track offset correction system in an embodiment of the present application. Detailed Embodiments

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above", "said", "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more of the listed items.

[0029] Hereinafter, the terms "first" and "second" are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0030] In the related art, in order to ensure the trajectory accuracy of friction stir welding, the welding path is usually monitored in real time by means of a vision sensor, a laser tracking system, a displacement sensor, etc. These methods directly detect the relative position between the welding head and the welding path, identify the trajectory deviation, and adjust the position or motion posture of the welding head through the control system, thereby correcting the welding trajectory and ensuring the weld quality. However, these methods have obvious limitations when dealing with high-frequency dynamic changes caused by welding heat input, periodic forging pressure or workpiece structural characteristics. It is difficult for vision or geometric sensors to accurately capture the small resonances or vibrations that occur in the welding process of ultra-long cantilever components. These dynamic changes may cause the weld to gradually deviate from the ideal trajectory. In addition, the correction method directly relying on geometric deviation has a lag in response speed and cannot quickly adapt to high-frequency changes; and if the correction system is too sensitive to small deviation changes, frequent high-frequency corrections may cause damage to the plastic flow state of the material, further leading to welding defects (such as tunnel defects, weld ripples) or a decrease in process stability. Therefore, how to accurately identify welding dynamic anomalies during the welding process and avoid the dual effects of trajectory deviation and correction disturbance through appropriate compensation remains a difficult problem to solve.

[0031] In the embodiments of the present application, by acquiring the acoustic signal of the welding head in real time, extracting the acoustic feature data and comparing it with the acoustic basic feature library, calculating the acoustic difference degree, and identifying the acoustic anomalies caused by periodic forging pressure, small resonances or vibrations during the welding process. The acoustic basic feature library is constructed by the no-load acoustic signal, ensuring the reliability of the difference degree calculation. When the acoustic difference degree exceeds the preset threshold, according to the abnormal frequency characteristics, the rotation speed of the welding head is adjusted by the fine-tuning amplitude and the fine-tuning time, and the rotation speed is restored after the fine-tuning time ends, so as to verify the authenticity of the anomaly and avoid new disturbances caused by frequent corrections. At the same time, by acquiring the friction force, relative speed and friction energy in real time, the stability of the welding interface can be dynamically quantified, and when the friction energy fluctuation coefficient or the real-time acoustic signal is abnormal, the position of the welding head is adjusted to the preset trajectory. By combining the multi-dimensional dynamic analysis of the acoustic signal, friction energy and the position of the welding head, this solution overcomes the response lag problem of traditional geometric deviation detection, avoids the damage to the welding quality and stability caused by frequent corrections, improves the real-time performance and adaptability of trajectory compensation, ensures the weld quality and the accuracy of the welding path, and especially improves the process stability and the accuracy of the welding trajectory in the friction stir welding of ultra-long cantilever type.

[0032] Figure 1 It is a schematic flowchart of a process using the method for correcting the offset of the friction stir welding trajectory of an ultra-long cantilever type in the embodiments of the present application, including the following steps: S101. During the welding process, the acoustic signal of the welding head is acquired in real time, and the real-time acoustic feature data of the acoustic signal is extracted.

[0033] Specifically, acquiring the acoustic signal of the welding head in real time and extracting the acoustic feature data during the welding process are completed by high-sensitivity acoustic sensors (such as piezoelectric sensors or MEMS microphones). These sensors are installed near the welding head or the workpiece and can capture the vibration and sound signals generated at the contact interface between the welding head and the workpiece. These signals are collected in analog form and converted into digital signals through analog-to-digital conversion (ADC).

[0034] The acquired acoustic signal contains time-domain data and frequency-domain data. The signal is subjected to spectral analysis through fast Fourier transform (FFT) to extract its frequency features (such as main frequency, harmonic frequency, amplitude) and energy features (such as total energy and energy distribution).

[0035] S102. Compare the real-time acoustic feature data with the acoustic basic feature library and calculate the acoustic difference degree.

[0036] Specifically, the construction of the acoustic basic feature library is based on the acoustic signals collected when the welding head is in the no-load running state. In the no-load state, the welding head is not in contact with the workpiece, and its acoustic signal is mainly composed of the normal vibration and running noise of the equipment itself. Through high-sensitivity acoustic sensors, the acoustic signals are continuously collected during the no-load running process and recorded in the time domain, and then converted into frequency-domain data through signal processing methods. Usually, the fast Fourier transform (FFT) is used to decompose the original signal into different frequency components, and characteristic values such as main frequency, harmonic frequency, frequency amplitude, and energy distribution are extracted. After multiple samplings of these characteristic values, statistical methods (such as calculating the average value, standard deviation, etc.) are used to form a set of parameter sets that can represent the acoustic characteristics of the equipment in the no-load state and store them as the acoustic basic feature library.

[0037] Then, when comparing the real-time acoustic feature data with the acoustic basic feature library, the corresponding frequency features in the real-time signal and the basic feature library are compared one by one. Compare the difference between the main frequency of the real-time signal and the main frequency in the basic feature library, the amplitude difference of the harmonic frequency, etc., to quantify the deviation degree between the real-time signal and the no-load signal. To accurately evaluate this deviation, a common method is to use the Euclidean distance formula to take the average value after squaring the difference between the real-time feature and the basic feature. Other algorithms (such as cosine similarity) can also be combined to quantify the overall similarity of the signal.

[0038] Finally, the acoustic difference calculation result is a quantitative value that describes the degree to which the real-time signal deviates from the basic feature library. This difference value can be used to determine the amplitude of the change of the acoustic signal during the welding process.

[0039] In some embodiments, when it is detected that the welding path is a long-distance straight line area, the welding parameters can be dynamically adjusted by real-time monitoring of the vibration characteristics and heat input gradient distribution of the welding head and combining data such as vibration accumulation, vibration change trend and acoustic signal to further optimize the welding stability and weld quality.

[0040] First, after detecting the long-distance straight welding path, the vibration characteristics of the welding head are monitored in real time, including vibration amplitude, vibration frequency and vibration accumulation. The vibration accumulation is obtained by integrating the vibration amplitude per unit time and is used to quantify the cumulative effect of vibration during the welding process. When the vibration change trend exceeds the preset change trend threshold, the fine-tuning amplitude of the welding head is dynamically adjusted according to the vibration characteristics (such as vibration frequency and vibration amplitude). The fine-tuning amplitude is proportional to the vibration change trend, that is, the more drastic the vibration change, the larger the fine-tuning amplitude. This operation can quickly respond to vibration anomalies and suppress the instability of the welding interface caused by vibration accumulation.

[0041] Secondly, the heat input gradient distribution of the welding area is monitored in real time. The heat input data in the welding path direction is obtained through thermal sensors or infrared imaging technology, and its mean and gradient distribution are calculated. When the heat input gradient distribution in a certain area exceeds the mean, the area will be judged as an area with excessive heat input. In this case, the rotation speed of the welding head is reduced to reduce the heat input, and the axial pressure of the welding head is increased to improve the plastic flow state of the material. By dynamically controlling the heat input and pressure distribution, weld defects caused by overheated welding areas, such as material burnout or internal pores, can be prevented.

[0042] Then, when the vibration accumulation, vibration change trend or change rate of heat input gradient distribution exceeds the corresponding preset threshold, and there is no abnormality in the acoustic difference, the welding head speed is adjusted according to the preset fine-tuning amplitude. This operation combines the dynamic monitoring results of vibration characteristics and heat input, and further refers to the stability of acoustic characteristics to ensure the comprehensive balance of various parameters during the welding process, which helps to maintain process stability in a dynamically changing environment.

[0043] Finally, to ensure the effectiveness of the adjustment, the vibration characteristics, heat input distribution and acoustic signal changes of the welding head are dynamically monitored throughout the process, and the response effect of the adjusted parameters is evaluated in real time. Through the closed-loop control system, the parameter adjustment of the welding head will be continuously optimized to avoid the interference of secondary disturbances on the welding interface, thereby ensuring the stability and accuracy of the welding path in the long-distance area.

[0044] In summary, by real-time monitoring of vibration characteristics and the distribution of heat input gradient, and combining with the comprehensive analysis of vibration cumulative amount, change trend and acoustic signal, the problems of abnormal vibration and uneven heat input in the straight welding path can be solved. The adjustment of vibration characteristics can suppress the vibration cumulative effect and prevent the instability of the welding interface; the control of heat input gradient balances the heat distribution, reduces the incidence of weld defects, thereby improving the welding quality and efficiency, and enhancing the adaptability and reliability of the welding system.

[0045] S103. When the acoustic difference degree exceeds the preset acoustic difference threshold, it is identified as an acoustic anomaly and the abnormal frequency characteristics of the acoustic anomaly are recorded.

[0046] Specifically, when the calculated acoustic difference degree in real time exceeds the preset acoustic difference threshold, it is identified as an acoustic anomaly. At this time, the acoustic signal is further analyzed to extract the abnormal frequency characteristics. Specifically, the frequency characteristics of the acoustic anomaly are extracted by performing spectral analysis (such as fast Fourier transform, FFT) on the real-time acoustic signal. Through spectral analysis, key information such as the main frequency, harmonic frequency and their amplitudes can be identified in the abnormal signal, and these characteristic values are recorded to describe the characteristics of the acoustic anomaly.

[0047] During the processing, the dynamic changes of the abnormal frequency characteristics also need to be considered. If the amplitude or frequency distribution of the abnormal frequency changes over time, continuous sampling of the signal will be performed, and the abnormal frequency characteristics at a series of time points will be recorded to form a description of the dynamic characteristics of the abnormal signal.

[0048] In addition, in order to avoid misjudgment, noise filtering processing will be carried out. For example, if there is background noise in the environment, which may cause fluctuations in the acoustic difference degree in a short period of time, the influence of the noise can be eliminated through filtering algorithms (such as band-pass filtering or wavelet transform) to ensure that the recorded abnormal frequency characteristics accurately reflect the dynamic state of welding.

[0049] S104. According to the abnormal frequency characteristics, adjust the rotation speed of the welding head according to the fine-tuning amplitude and fine-tuning time, and control the welding head to return to the rotation speed before fine-tuning after the fine-tuning time ends.

[0050] Specifically, both the calculation of the fine-tuning amplitude and the fine-tuning time are proportional to the acoustic difference degree. The acoustic difference degree is the quantitative deviation value between the real-time acoustic signal and the acoustic basic feature library, reflecting the degree of dynamic change during the welding process. The fine-tuning amplitude is calculated by the formula fine-tuning amplitude = k1 × acoustic difference degree, where k1 is the adjustment sensitivity coefficient, used to control the rotation speed adjustment amplitude; the fine-tuning time is calculated by the formula fine-tuning time = k2 × acoustic difference degree, where k2 is the time adjustment coefficient, ensuring that the fine-tuning time matches the intensity and duration of the acoustic anomaly.

[0051] According to the calculation results, the rotation speed of the welding head will be adjusted to the sum of the original rotation speed and the fine-tuning amplitude (the adjustment direction depends on the type of abnormality, and the rotation speed is usually reduced during resonance), and this adjusted state will be maintained within the fine-tuning time. After the fine-tuning time ends, the rotation speed of the welding head will be gradually restored to the original rotation speed before fine-tuning in a smooth transition manner to avoid new dynamic instabilities caused by instantaneous speed changes.

[0052] In the above steps, by dynamically adjusting the rotation speed of the welding head according to the abnormal frequency characteristics of the acoustic abnormality, the response ability and adaptability to high-frequency dynamic changes (such as resonance or chatter) during the welding process are improved. Different from the prior art that relies on geometric offset correction and has risks of response lag and perturbation, in this step, the fine-tuning amplitude and time proportional to the acoustic difference degree are calculated, targeted rotation speed adjustment is performed, and the original rotation speed is smoothly restored after the fine-tuning time ends, avoiding the damage to the welding interface caused by frequent compensation. The cumulative effect of dynamic changes is suppressed, the weld seam is prevented from deviating from the ideal trajectory, and at the same time, the plastic flow state of the material during the welding process is protected, reducing the risk of occurrence of welding defects (such as tunnel defects, surface ripples). Finally, the stability of the welding process and the weld quality are improved, avoiding the damage to the welding quality and process stability caused by the frequent correction actions themselves, and providing a more reliable technical guarantee for improving the welding accuracy.

[0053] S105. Real-time obtain the friction force, relative speed, and real-time acoustic signal after the fine-tuning time, and calculate the friction energy within a preset unit time length.

[0054] Specifically, the friction force at the contact interface between the welding head and the workpiece is collected in real time through a sensor. The friction force is usually detected by a force sensor installed on the welding device, and its tangential component is the friction force; the relative speed is obtained by combining the encoder of the welding head driving device and the workpiece displacement sensor, representing the actual movement speed at the contact interface between the welding head and the workpiece. In addition, the acoustic signal is captured in real time by an acoustic sensor to assist in judging the change of the friction state. After these real-time parameters are collected, they are calculated through the friction energy formula: friction energy = ∫(friction force × relative speed)dt, and discretized within the unit time length as friction energy = Σ(friction force × relative speed × unit time), quantifying the energy generated at the friction interface due to friction through real-time integral calculation.

[0055] In some embodiments, due to the high flexibility and complex dynamic characteristics of the structural members, the contact interface during the welding process may be affected by resonance or thermal expansion, resulting in a sudden increase in friction force, manifested as abnormal contact pressure. Such abnormalities can trigger a significant increase in frictional energy, accompanied by the appearance of high-frequency acoustic noise (such as high-amplitude signals at abnormal frequencies). If not dealt with in a timely manner, such dynamic changes may disrupt the stability of the contact interface between the welding head and the workpiece, disturbing the plastic flow state of the interface material, and further leading to welding defects (such as uneven welds, tunnel defects) or a reduction in the trajectory accuracy of the welding. To address the above problems, according to the change amplitude of the frictional energy and the characteristic frequency of the acoustic signal, the rotational speed of the welding head is dynamically adjusted to reduce the peak friction force caused by resonance or thermal expansion; at the same time, according to the duration of the abnormal signal, the pressure between the welding head and the workpiece is adjusted to restore the stability of the contact interface. Throughout the process, the changes in frictional energy and acoustic signals are continuously monitored through closed-loop control.

[0056] Through the above technical steps, it is possible to alleviate the pressure abnormality caused by resonance or thermal expansion, restore the stability of the welding interface, prevent the further development of welding defects, enhance the dynamic adaptability of the welding process, and meet the process requirements under complex working conditions.

[0057] In other embodiments, when analyzing historical welding data and the CAD model of the current workpiece predicts that there may be a situation where the inherent low-frequency modal vibration mode is activated in the un-welded path section, the welding stability can be dynamically evaluated and the welding parameters can be optimized by selecting a suitable combination of anti-instability welding parameters and performing a trial welding, so as to suppress the welding defects caused by low-frequency modal vibration and ensure the welding quality and process stability of high-risk sections.

[0058] First, by analyzing historical welding data and the CAD model of the current workpiece, it is possible to predict whether there is a risk of activation of the inherent low-frequency modal vibration mode in the un-welded path section. Specifically, in combination with the CAD model of the current workpiece, dynamic characteristic parameters such as the geometric characteristics, material characteristics, and structural support conditions of the un-welded path section are extracted, and similarity comparison is performed in combination with historical welding data. When the dynamic characteristic parameters of the path are similar to the historical data, the vibration frequency and vibration amplitude in the similar un-welded path section are predicted through the corresponding historical vibration frequency and historical vibration amplitude in the historical welding data. If the deviation between the predicted vibration frequency and the inherent modal vibration frequency is less than the preset activation frequency threshold, and the vibration amplitude exceeds the preset activation multiple of the historical average vibration amplitude, it is determined that the inherent low-frequency modal vibration mode may be activated. According to the predicted vibration frequency and vibration amplitude, an anti-instability parameter combination matching this dynamic characteristic is selected from the anti-instability welding parameter library to suppress the occurrence of modal activation during the welding process.

[0059] Secondly, after identifying the high-risk sections where the inherent low-frequency modal shapes are activated, apply the selected predicted anti-instability parameter combinations with a preset trial length, and monitor the trial acoustic signals and the trial dynamic stability of the welding head in real time. The setting of the trial length is for short-time dynamic verification within the high-risk sections to determine whether the selected parameter combinations can suppress the low-frequency modal vibrations. The trial acoustic signals monitored in real time include the changes in spectral characteristics, signal intensity, and abnormal frequencies, which are used to evaluate the acoustic stability of the welding interface; the trial dynamic stability is judged by monitoring the operating state of the welding head (such as vibration amplitude and motion trajectory).

[0060] Next, if the dynamic stability is within the preset stability threshold range during the trial process, and the trial acoustic signals are within the preset acoustic signal threshold range, confirm that the selected predicted anti-instability parameter combinations meet the requirements, and apply this parameter combination to complete the welding of the corresponding high-risk sections. This parameter application strategy can ensure the reliability of the welding process based on verification, and avoid the decline of welding quality caused by directly applying unverified parameters.

[0061] Finally, if the dynamic stability is not within the preset stability threshold range or the trial acoustic signals exceed the acoustic signal threshold range during the trial process, superimpose a preset small perturbation signal on the control command of the welding head to further optimize the welding parameter combination. The role of the small perturbation signal is to break the possible vibration resonance state and help the system explore a new stable welding state. In this way, the welding parameters can be dynamically adjusted under complex working conditions, gradually improving the welding stability and ensuring the welding quality.

[0062] In summary, by analyzing the historical welding data and the CAD model of the current workpiece, predicting and dynamically verifying the welding parameters of the high-risk sections can cope with the activation risk of the low-frequency modal shapes. The application of trial welding and small perturbation signals ensures the reliability and adaptability of parameter adjustment, thereby suppressing the vibration instability problem, improving the stability of the welding process and the quality consistency of the welds, and providing an important guarantee for high-precision welding under complex working conditions.

[0063] S106. When the fluctuation coefficient of the friction energy exceeds the preset fluctuation coefficient threshold and the instantaneous value of the friction energy is lower than the preset friction energy threshold, or there is still an acoustic anomaly in the real-time acoustic signal, and the shortest distance between the real-time position of the welding head and the preset device trajectory exceeds the preset offset threshold, adjust the position of the welding head to the device trajectory.

[0064] Specifically, during the welding process, when the following conditions are met, the position adjustment of the welding head will be triggered to realign it with the preset device trajectory. First, the dynamic anomaly conditions need to be satisfied simultaneously, that is, the fluctuation coefficient of the friction energy exceeds the preset fluctuation coefficient threshold and the instantaneous value of the friction energy is lower than the preset friction energy threshold, or there is still an acoustic anomaly in the real-time acoustic signal, that is, the acoustic difference degree exceeds the preset acoustic difference threshold. This indicates that there may be an unstable friction state, insufficient pressure, or resonance problem at the welding interface. Second, the trajectory deviation condition also needs to be satisfied, that is, the shortest distance between the real-time position of the welding head and the preset device trajectory exceeds the preset offset threshold, indicating that the welding head has deviated from the ideal path, which may lead to a decrease in weld quality or an increase in welding defects. Only when both the dynamic anomaly and trajectory deviation conditions are met simultaneously will the position adjustment be triggered.

[0065] When the position adjustment is triggered, the welding head is repositioned to the preset trajectory through a position adjustment mechanism (such as a servo system or a multi-axis motion control system). The adjustment process includes path planning and closed-loop control. First, the position error between the real-time position of the welding head and the target trajectory point is collected, and the optimal path and target position for adjustment are calculated using a motion control algorithm (such as the shortest path planning or smooth curve fitting). When performing the adjustment, the movement of the welding head is controlled in real time by a servo motor, and at the same time, the friction energy and acoustic signal are continuously monitored to ensure that no new disturbances are generated during the adjustment process. The closed-loop control mechanism compares the real-time position of the welding head with the target position and continuously reduces the position error to ensure the adjustment accuracy. In addition, to avoid secondary disturbances to the welding interface caused by the adjustment action, a flexible transition mechanism is adopted to gradually decelerate when approaching the target position, so that the welding head can smoothly transition onto the trajectory.

[0066] In the above embodiment, by obtaining the acoustic signal of the welding head in real time, extracting the acoustic feature data and comparing it with the acoustic basic feature library, the high-frequency dynamic anomalies caused by the periodic forging pressure, micro-resonance, or chatter during the welding process are identified, overcoming the defects of the traditional geometric offset detection method with a lagging response and difficulty in capturing high-frequency dynamic changes. By recording the frequency characteristics of the acoustic anomalies and implementing the rotational speed adjustment of the fine-tuning amplitude and fine-tuning time, the authenticity and severity of the anomalies are verified, and at the same time, the disturbance damage caused by frequent high-frequency corrections is avoided. Combining the multi-dimensional analysis of the friction force, relative speed, and friction energy fluctuation, when the anomaly persists or the offset exceeds the threshold, the position of the welding head is dynamically adjusted to the preset trajectory, thereby suppressing the cumulative effect of dynamic changes and preventing the weld from deviating from the center line. This solution improves the real-time performance and adaptability of trajectory compensation, avoids the generation of welding defects such as defects and surface ripples, and at the same time ensures the welding path accuracy and process stability, improving the trajectory accuracy of friction stir welding for ultra-long cantilever types.

[0067] In some other embodiments of the present application, when the acoustic difference degree remains relatively high during the welding process but there are no abnormal conditions, it may lead to monitoring misjudgment due to the acoustic feature shift of the inherent structural characteristics of the workpiece. By adopting the ultra-long cantilever type friction stir welding trajectory offset correction method provided by the present application, the digital twin model of the workpiece can be retrieved, the equivalent thermal-mechanical load can be simulated, and the inherent structural acoustic response characteristics can be analyzed. By comparing it with the real-time acoustic data, the acoustic offset caused by the inherent characteristics of the workpiece can be identified, and the acoustic basic feature library can be dynamically updated to improve the monitoring accuracy.

[0068] As Figure 2 shown, it is another process schematic diagram of the ultra-long cantilever type friction stir welding trajectory offset correction method provided by the embodiment of the present application, including the following steps: S201. During the welding process, the acoustic signal of the welding head is obtained in real time, and the real-time acoustic feature data of the acoustic signal is extracted.

[0069] S202. The real-time acoustic feature data is compared with the acoustic basic feature library, and the acoustic difference degree is calculated.

[0070] S203. When the acoustic difference degree remains relatively high and there is no acoustic abnormality in the acoustic difference degree, the high baseline, high reproducibility characteristics and the corresponding workpiece type and welding section are identified.

[0071] Specifically, by long-term monitoring the change curve of the acoustic difference degree, its high baseline characteristics are identified. The data of the acoustic difference degree is processed in segments according to a fixed time window, and its mean value and fluctuation amplitude are calculated within each time segment. If the fluctuation amplitude of the mean value of the acoustic difference degree in multiple time segments is small and the central value remains within a specific relatively high range, it is determined that it has high baseline characteristics. In addition, time series analysis methods (such as weighted moving average or exponential smoothing method) are used to further confirm whether the trend of the acoustic difference degree is stable throughout the welding cycle, excluding the influence of short-term fluctuations. The high baseline characteristic refers to the continuous stable state of the acoustic difference degree within a relatively high range, manifested as a small fluctuation amplitude and the central value approaching a certain fixed range. By identifying the high baseline characteristics, it can be judged whether the relatively high acoustic difference degree belongs to the normal state, avoiding misjudgment and unnecessary adjustments.

[0072] Meanwhile, extract the acoustic difference records under the same or similar working conditions in historical welding tasks, and compare their variation patterns in different tasks. Quantify the similarity between different tasks by calculating the correlation of the mean acoustic difference (such as the Pearson correlation coefficient). If the correlation coefficient is higher than the set threshold, it indicates that the acoustic difference has a high degree of consistency under the same conditions. In addition, establish a reproducibility model using machine learning algorithms (such as support vector machines or clustering analysis), and associate the high reproducibility features with factors such as workpiece type and welding section. In this way, identify the inherent rules of high acoustic difference and provide a basis for optimizing welding parameters. Calculate the reproducibility of acoustic features based on the acoustic data during multiple repeated welding processes. High reproducibility features indicate that the high state of acoustic difference has strong consistency under the same or similar welding conditions, and this consistency may stem from the acoustic response characteristics of specific workpiece materials or the unique geometric structure of the welding area. By combining the material type of the workpiece, the local characteristics of the welding path, and the welding history data, locate the reasons for the high acoustic difference, such as the heat input characteristics of the welding section or the acoustic wave propagation mode inside the material.

[0073] In some embodiments, due to the presence of various curvatures, sharp corners, depressions, or irregular boundaries on the material surface, sound waves may be reflected, superimposed, or interfered with in local areas during the welding process, resulting in a temporarily high state of acoustic difference. This high state may be due to changes in the sound wave propagation path caused by specific geometric shapes, or it may be caused by a mismatch between the welding parameters and the local structural characteristics of the workpiece. If this phenomenon is not identified and processed, it may be misjudged as an abnormal situation, thus triggering unnecessary parameter adjustments and affecting the stability of the welding process and the quality of the weld.

[0074] To solve this problem, first, monitor the change trend of the acoustic difference in real time, and judge whether the high state belongs to a temporary disturbance through baseline analysis. The specific methods include calculating the mean and fluctuation amplitude of the acoustic difference within a time window and judging its stability by combining time series analysis. Second, analyze the reproducibility characteristics of the acoustic difference in combination with historical data. Through multi-task comparison and correlation calculation, identify whether this high state is related to the workpiece geometry. If the high state is determined to be related to geometric characteristics and has high reproducibility, optimize the welding path or adjust the welding parameters (such as welding head speed, heat input, or pressure distribution) based on the analysis results to reduce the sound wave reflection effect and improve the welding dynamic stability.

[0075] Through the above technical steps, it is possible to distinguish the short-term perturbations and stable characteristics of the acoustic difference degree, and identify the correlation between its source and the workpiece geometry. False judgments of the normal high state are avoided, and at the same time, by optimizing the welding path or parameters, the adverse effects of acoustic wave reflection on the welding process are reduced, ensuring the stability of the welding process and improving the consistency and reliability of the weld quality.

[0076] S204. Retrieve the preset digital twin model of the workpiece type, and simulate the equivalent thermal-mechanical load applied to the corresponding welding section during the friction stir welding process in the digital twin model.

[0077] Specifically, during the welding process, retrieving the preset digital twin model corresponding to the workpiece type and simulating the equivalent thermal-mechanical load applied to the welding section during the friction stir welding process in this model can provide prediction and guidance for welding parameter optimization and process control.

[0078] The digital twin model is a high-precision virtual model constructed based on the workpiece CAD data, material properties, and welding process parameters, and can dynamically map the thermal-mechanical state of the real welding process. By inputting the geometric features, material properties, and process parameters of the welding section into the digital twin model, the finite element analysis (FEA) or other physical field simulation methods can be used to calculate the heat input distribution, stress-strain distribution, and material flow behavior generated during the friction stir welding process, and predict the influence of the equivalent thermal-mechanical load on the welding area.

[0079] Retrieve the digital twin model of the workpiece according to the workpiece type. The model contains the geometry of the workpiece, material properties (such as thermal conductivity, coefficient of thermal expansion, elastic modulus, etc.), and welding path information. Then, input the welding process parameters (such as the rotational speed of the welding head, axial pressure, welding speed) into the model, and apply the equivalent thermal-mechanical load in the simulation environment. The thermal load is simulated by the frictional heat generated between the welding head and the workpiece surface during the welding process, and the force load is calculated from the axial pressure of the welding head and the plastic flow of the material caused by the welding movement. Through numerical calculation, the temperature distribution, stress distribution, and material deformation of the welding section are obtained, and the possible defect positions and types (such as overheating, uneven welds, or residual stress concentration) are predicted.

[0080] S205. Analyze the inherent structural acoustic response characteristics generated in the welding section through simulation calculations.

[0081] Specifically, using the digital twin model or CAD model of the workpiece, extract the geometric features and material properties (such as density, elastic modulus, Poisson's ratio, etc.) of the welding section, and divide the local mesh according to the welding path. On this basis, calculate the natural modal parameters of the welding section through modal analysis, including natural frequency, modal shape, and modal participation factor. The core of modal analysis is to establish the dynamic equation of the workpiece and obtain its natural modal characteristics by solving the eigenvalue problem. Then, apply equivalent thermal-mechanical loads to the welding section, and combine thermal-mechanical coupling field simulation to calculate the stress distribution and deformation behavior of the material during welding, so as to correct the dynamic response characteristics of the welding section.

[0082] Simulate the acoustic behavior of the welding section under specific excitation through frequency response analysis. The excitation source can be the frictional force generated at the contact interface between the welding head and the workpiece, or the sound wave caused by the local material vibration during welding. By solving the dynamic equation, the response amplitude and phase distribution of the welding section under different frequency excitations can be obtained, and then the sound wave propagation path and sound pressure distribution characteristics can be calculated.

[0083] In some embodiments, there may be features such as multi-curvature, concavity, sharp corners, or irregular boundaries on the surface of the workpiece. These geometric complexities will significantly affect the propagation behavior of sound waves. Sound waves may undergo reflection, scattering, and interference phenomena in these regions, resulting in abnormal elevation of local sound pressure or non-uniform distribution of acoustic response. This abnormal acoustic behavior may cause dynamic instability of the welding section, affect the operating state of the welding head, and may lead to a decrease in weld quality or the generation of welding defects, such as material overheating, incomplete fusion, or uneven welding interface. To solve this problem, first, extract the geometric features of the workpiece through high-precision mesh division, and define the propagation path and reflection characteristics of sound waves in different regions based on the boundary conditions. Secondly, use acoustic finite element analysis or boundary element method to calculate the sound pressure distribution characteristics of sound waves in complex regions and identify the high sound pressure regions caused by local sound wave superposition or interference. Finally, combine welding process parameters and material properties to optimize the sound wave propagation conditions by adjusting parameters such as welding head rotation speed, welding path, or heat input, so as to reduce the impact of abnormal acoustic response on the welding process.

[0084] Through the above technical steps, it is possible to identify the problems caused by abnormal sound wave propagation in complex geometric regions and avoid acoustic instability by optimizing welding parameters. This not only improves the accuracy of the simulation results, but also enhances the stability of the welding process, ensuring the consistency of weld quality and the reliability of the process.

[0085] S206. If the natural structural acoustic response characteristics match the real-time acoustic characteristic data, replace the natural structural acoustic response characteristics into the acoustic basic feature library.

[0086] Specifically, the inherent structural acoustic response characteristics of the welding section are obtained through simulation calculations, including information such as natural frequencies, modal vibration modes, and sound pressure distributions. At the same time, the acoustic characteristic data recorded by the acoustic sensor during the welding process are collected in real time, such as frequency spectra, sound pressure amplitudes, phase changes, etc., and their key characteristic parameters are extracted through data processing. Feature matching algorithms (such as Pearson correlation coefficient, dynamic time warping algorithm, etc.) are used to perform similarity analysis on the simulation features and real-time features. If the similarity index is higher than the set threshold, it is determined that the two sets of feature data match. The acoustic features calculated by the simulation are used as new reference features to replace or supplement the acoustic basic feature library. The update process of the feature library is usually carried out in an incremental manner, that is, new features are added on the basis of retaining historical data to ensure the comprehensiveness and dynamic adaptability of the feature library.

[0087] S207. When the acoustic difference exceeds the preset acoustic difference threshold, it is identified as an acoustic anomaly and the abnormal frequency characteristics of the acoustic anomaly are recorded.

[0088] S208. Real-time imaging of the heat flux density within a region of a preset heat flux monitoring area size below the welding head, adjusting the rotation speed of the welding head according to the fine-tuning amplitude and fine-tuning time, recording the transient response characteristics of the heat flux density adjustment at the start of the fine-tuning time, and controlling the welding head to return to the rotation speed before fine-tuning after the fine-tuning time ends.

[0089] Specifically, a preset heat flux monitoring area is set below the key area of the welding path, and real-time imaging of the heat flux density in this area is performed through an infrared thermal imager or a thermopile sensor. The heat flux density imaging captures the thermal radiation generated during the welding process and calculates the heat flux distribution at each time point in combination with the heat conduction model. The results of the real-time imaging are presented in the form of a high-resolution heat flux density map and are continuously updated to reflect the dynamic heat input state of the welding section.

[0090] Taking the fine-tuning amplitude and fine-tuning time as parameters, the rotation speed of the welding head is adjusted to implement a short-term perturbation. The fine-tuning of the rotation speed is carried out with small amplitude changes and is maintained within the preset fine-tuning time range. The fine-tuning of the rotation speed directly affects the friction heat generation rate at the contact between the welding head and the workpiece, thereby causing changes in the heat flux density. Through this perturbation process, the transient response characteristics of the heat flux density adjustment are observed and recorded, including the amplitude of the heat flux density change, the response time, and the recovery process curve.

[0091] After the fine-tuning operation is completed, the rotation speed of the welding head is controlled to return to the set value before fine-tuning to ensure the overall stability and continuity of the welding process. The smooth transition of the rotation speed recovery is achieved by the PID control algorithm to avoid secondary effects on the welding quality caused by large parameter jumps.

[0092] S209. Input the rotational speed adjustment data and the adjustment transient response characteristics into a preset dynamic mathematical model of ideal heat generation and heat transfer in friction stir welding to obtain the actual heat generation efficiency and the actual heat flux density distribution.

[0093] Specifically, record the rotational speed values before and after the adjustment of the welding head, calculate the rotational speed adjustment data (i.e., the rotational speed difference) as the input variable. At the same time, capture the adjustment transient response characteristics through real-time heat flux imaging technology, including the change amplitude, response time, and distribution state of the heat flux density. These transient characteristic data reflect the transfer and diffusion laws of heat input in the welding section.

[0094] Next, input the above data into the dynamic mathematical model of ideal heat generation and heat transfer in friction stir welding. The dynamic mathematical model of ideal heat generation and heat transfer in friction stir welding is established based on the principles of thermodynamics and heat transfer, comprehensively considering the frictional heat generation between the welding head and the workpiece surface, the heat conduction characteristics of the material, and the dynamic heat diffusion law during the welding process. The model mainly consists of the following parts: the frictional heat generation model, which calculates the heat power generated at the interface through the rotational speed of the welding head, the axial pressure, and the material friction coefficient; the heat transfer model, which uses the Fourier heat conduction equation to simulate the diffusion behavior of heat flux inside the workpiece and considers the thermal conductivity, density, and specific heat capacity of the material; the dynamic response model, which couples the heat source model and the conduction model and describes the change of heat flux density in time and space with partial differential equations. By numerically solving these equations, the transient heat flux distribution and heat generation efficiency in the welding section can be predicted, providing a theoretical support for welding process optimization. By solving the model, the actual heat generation efficiency and heat flux density distribution in the welding section are obtained.

[0095] In some embodiments, after inputting the rotational speed adjustment data and the adjustment transient response characteristics into the preset dynamic mathematical model of ideal heat generation and heat transfer in friction stir welding, if it is found that the adjustment transient response characteristics are related to the rotation period of the welding head and match the preset acoustic heat flux coupling fingerprint of specific defects of the welding head, the potential defect type and location of the welding head can be further judged by analyzing the heat flux fluctuation characteristics, and the stability of the welding process and the weld quality can be optimized by dynamically adjusting the rotational speed and movement trajectory of the welding head.

[0096] By analyzing the adjustment transient response characteristics, identify the heat flux fluctuation characteristics related to the rotation period of the welding head, and lock its heat flux fluctuation frequency, fluctuation phase, and fluctuation amplitude. This process uses the real-time heat flux monitoring data, combines the kinematic characteristics of the welding head, and separates the periodic fluctuations from the complex heat flux density signal through frequency domain analysis or time domain signal processing methods, can capture the heat flux anomalies caused by the periodic movement of the welding head, identify possible mechanical defects or interface instability problems, and provide basic data for subsequent defect judgment.

[0097] Based on the correlation relationship between the locked heat flux fluctuation amplitude, fluctuation phase, and their abnormal spectral characteristics corresponding to acoustic anomalies, these characteristics are compared with a preset welding head defect fingerprint library to determine the type and location of the defects existing in the welding head. The defect fingerprint library is established based on a large number of experiments and theoretical analyses, covering the characteristic information of common defects (such as wear, imbalance, or cracks). Through comparative analysis, the defects can be quickly located and their nature can be clarified, which can provide targeted guidance for subsequent optimization of welding parameters and path adjustment.

[0098] Calculate the adjustment amplitude of the welding head's rotational speed according to the ratio of the heat flux fluctuation amplitude to the preset fluctuation amplitude deviation threshold; at the same time, determine the adjustment phase point based on the heat flux fluctuation phase. This step is to perform rotational speed adjustment at the optimal moment of the welding head's periodic motion, thereby minimizing the interference of the adjustment to the welding process and ensuring the dynamic balance of heat input. Through time and amplitude control, the thermal stability and process controllability of the welding process can be improved.

[0099] At the determined adjustment phase point, perform the second adjustment of the welding head's rotational speed according to the calculated rotational speed adjustment amplitude, and obtain the heat flux fluctuation amplitude and fluctuation phase distribution characteristics before and after the adjustment in real time. The purpose of this adjustment is to further verify and optimize the heat flux distribution by changing the motion state of the welding head, and at the same time provide high-precision input data for subsequent dynamic trajectory adjustment. This method can reduce the abnormal amplitude of heat flux fluctuation during the welding process and improve the uniformity of welding heat input.

[0100] By analyzing the heat flux fluctuation amplitude and fluctuation phase distribution characteristics after the second adjustment, calculate the real-time offset of the welding head on the welding trajectory, that is, the deviation value between the center point of the welding head and the center point of the target trajectory. Subsequently, based on the change trend of the offset, use the multi-point interpolation method to generate the offset compensation path of the welding head. The compensation path is calculated by fitting the change trend of the real-time offset, which can correct the motion trajectory of the welding head and prevent problems such as offset accumulation or weld non-straightness during the welding process.

[0101] Adjust the motion trajectory of the welding head according to the calculated offset compensation path, and ensure the continuity and smoothness of the welding path through the smooth path algorithm. This operation avoids problems such as uneven welds or surface defects caused by trajectory adjustment, and at the same time ensures the overall stability of the welding process and the weld quality.

[0102] Through the above steps, the potential defects of the welding head can be identified and located in real time, the rotational speed and trajectory of the welding head can be dynamically optimized, and the thermal input stability of the welding process and the flatness of the weld can be ensured. This technical step reduces the risk of welding defects, improves the consistency of welding quality, and provides a strong technical guarantee for friction stir welding under complex working conditions.

[0103] S210. When the heat generation efficiency deviation value exceeds the preset heat generation efficiency deviation value threshold or the heat flux density distribution deviation value exceeds the preset heat flux density distribution deviation threshold, an interface friction state instability warning message is issued, the axial force of the welding head is adjusted to the preset axial force optimization range, and the tool inclination angle of the welding head is adjusted according to the transient response characteristics of the adjustment.

[0104] Specifically, the absolute value of the difference between the actual heat generation efficiency and the preset theoretical heat generation efficiency is calculated by real-time monitoring as the heat generation efficiency deviation value. At the same time, the real-time imaging technology of heat flux density captures the actual heat flux density distribution, compares it with the preset theoretical heat flux density distribution, and calculates the absolute value of the difference between the two as the heat flux density distribution deviation value. The preset thresholds are determined based on the welding material, workpiece geometry, and process conditions, and there are a heat generation efficiency deviation threshold and a heat flux density distribution deviation threshold respectively. If any deviation value exceeds the threshold range, it is determined that the interface friction state is unstable, and a warning message is issued in real time.

[0105] Next, to restore the stable state, first, the axial force of the welding head is adjusted to the preset axial force optimization range. The axial force is a key parameter affecting the frictional heat generation between the welding head and the workpiece surface, and the main basis for adjustment is the deviation amplitude of the actual heat generation efficiency. By increasing or decreasing the axial force, the frictional heat generation rate can be adjusted, thereby restoring the stability of the interface heat input. In addition, according to the transient response characteristics of the adjusted heat flux density, the tool inclination angle of the welding head is dynamically optimized. The adjustment of the tool inclination angle will change the force distribution and material flow direction at the contact interface between the welding head and the workpiece, thereby affecting the spatial distribution of the heat flux density. By adjusting the inclination angle, the problem of uneven heat flux density distribution can be improved.

[0106] In some embodiments, due to the high thermal conductivity characteristics or large heat capacity of the material, during the welding process, heat rapidly diffuses or is absorbed inside the workpiece, resulting in insufficient local heat input in the welding section and an excessive heat generation efficiency deviation value. In this case, the interface friction state may become unstable, which in turn affects the temperature field and material flow state in the welding section, and may ultimately lead to weld quality problems, such as incomplete welding, insufficient material fusion, or insufficient weld strength. To solve the above problems, first, the axial force of the welding head is increased to the preset axial force optimization range to improve the interface frictional heat generation efficiency and supplement the heat input in the welding section; at the same time, according to the transient response characteristics of the heat flux density, the tool inclination angle of the welding head is dynamically adjusted to optimize the material flow behavior and improve the spatial distribution of the heat flux density. By adjusting the axial force and the tool inclination angle in a coordinated operation, it is possible to dynamically adapt to the complex heat diffusion environment of the workpiece and ensure uniform and sufficient heat input in the welding section.

[0107] Through the above technical steps, the heat input stability of the welding section can be restored and the heat flow distribution can be optimized, ensuring the weld quality. This solution significantly reduces the risk of welding defects caused by insufficient heat input under special working conditions, while improving the adaptability of the welding process and the welding efficiency, providing reliable technical support for high-quality welding of complex materials and thick-walled workpieces.

[0108] S211. Obtain the friction force, relative velocity, and real-time acoustic signal after the fine-tuning time in real time, and calculate the friction energy within a preset unit time length.

[0109] S212. When the fluctuation coefficient of the friction energy exceeds the preset fluctuation coefficient threshold and the instantaneous value of the friction energy is lower than the preset friction energy threshold, or there is still acoustic anomaly in the real-time acoustic signal, and the shortest distance between the real-time position of the welding head and the preset device trajectory exceeds the preset offset threshold, adjust the position of the welding head to the device trajectory.

[0110] Steps S201 - S202, S207, S211 - S212 are similar to Figure 1 Steps S101 - S103, S105 - S106 in the illustrated embodiment. Refer to the descriptions in Steps S101 - S103, S105 - S106, and details will not be repeated here.

[0111] In the above embodiment, when the acoustic difference degree remains high and there is no anomaly, identify its high baseline and high reproducibility characteristics, call the digital twin model in combination with the workpiece type and the welding section, simulate the equivalent thermal-mechanical load applied during the friction stir welding process in the welding section, and analyze the inherent structural acoustic response characteristics through simulation calculation. If the simulation results match the real-time acoustic feature data, replace the inherent structural acoustic response characteristics into the acoustic basic feature library, thereby dynamically optimizing the adaptability of the basic feature library. This solution overcomes the problems of lagging response and insufficient dynamic adaptability of traditional geometric detection methods through multi-dimensional linkage analysis of acoustic signals, heat flux density, and friction energy, ensuring the smoothness of the welding path and the trajectory accuracy. Finally, this method significantly improves the welding quality and process stability of ultra-long cantilever type friction stir welding, meeting the requirements of high-precision welding under complex working conditions.

[0112] Next, an exemplary trajectory offset correction system 300 provided by an embodiment of the present application will be introduced. Figure 3 It is an exemplary hardware structure diagram of the trajectory offset correction system 300 provided by an embodiment of the present application.

[0113] In some embodiments, the trajectory offset correction system 300 is a computer device or the trajectory offset correction system 300 includes a computer device. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers through a network connection. In some embodiments, the network interface may be a wired network interface, and in some embodiments, the network interface may also be a wireless network interface. The computer program, when executed by the processor, implements the method in the embodiments of the present application.

[0114] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0115] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements 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 application.

[0116] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as "if..." or "after..." or "in response to determining..." or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if detecting (the stated condition or event)" can be interpreted as "if determining..." or "in response to determining..." or "when detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)".

[0117] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive), etc.

[0118] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware with a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: various media that can store program codes such as ROM or random access memory RAM, magnetic disks, or optical discs.

Claims

1. A method for correcting the trajectory deviation of friction stir welding of an ultra-long cantilever type, characterized in that Including: During the welding process, acoustical signals of the welding head are acquired in real time, and real-time acoustical feature data of the acoustical signals are extracted; The real-time acoustical feature data are compared with an acoustical basic feature library, and an acoustical difference degree is calculated; the acoustical basic feature library is constructed by acquiring no-load acoustical signals during no-load operation and extracting the feature data of the no-load acoustical signals; When the acoustical difference degree exceeds a preset acoustical difference threshold, it is identified as acoustical abnormality and the abnormal frequency feature of the acoustical abnormality is recorded; According to the abnormal frequency feature, the rotation speed of the welding head is adjusted by a fine-tuning amplitude and a fine-tuning time, and after the fine-tuning time ends, the welding head is controlled to return to the rotation speed before fine-tuning; both the fine-tuning amplitude and the fine-tuning time are proportional to the acoustical difference degree; Friction force, relative speed and real-time acoustical signals after the fine-tuning time are acquired in real time, and friction energy within a preset unit time length is calculated; The friction energy is the energy generated due to the friction effect at the contact interface between the welding head and the workpiece; When the fluctuation coefficient of the friction energy exceeds a preset fluctuation coefficient threshold and the instantaneous value of the friction energy is lower than a preset friction energy threshold, or the real-time acoustical signal still has the acoustical abnormality and the shortest distance between the real-time position of the welding head and a preset device track exceeds a preset offset threshold, the position of the welding head is adjusted to the device track; The fluctuation coefficient is a quantitative value of the stability of the friction energy within the unit time; 2. The method according to claim 1, characterized in that The adjusting the rotation speed of the welding head by a fine-tuning amplitude and a fine-tuning time according to the abnormal frequency feature and controlling the welding head to return to the rotation speed before fine-tuning after the fine-tuning time ends specifically includes: Performing real-time imaging on the heat flux density within a region with a preset heat flux monitoring region size below the welding head, adjusting the rotation speed of the welding head by a fine-tuning amplitude and a fine-tuning time, recording the adjustment transient response feature of the heat flux density at the start of the fine-tuning time, and controlling the welding head to return to the rotation speed before fine-tuning after the fine-tuning time ends; Inputting the rotation speed adjustment data and the adjustment transient response feature into a preset ideal heat generation and heat transfer dynamic mathematical model for friction stir welding to obtain an actual heat generation efficiency and an actual heat flux density distribution; the rotation speed adjustment data is the difference value of the rotation speed values recorded before and after adjustment; When the heat generation efficiency deviation value exceeds a preset heat generation efficiency deviation value threshold or the heat flux density distribution deviation value exceeds a preset heat flux density distribution deviation threshold, a warning information of unstable interface friction state is issued, the axial force of the welding head is adjusted to a preset axial force optimization range, and the tool tilt angle of the welding head is adjusted according to the adjustment transient response feature; the heat generation efficiency deviation value is the absolute value of the difference between the actual heat generation efficiency and the preset theoretical heat generation efficiency; the heat flux density distribution deviation value is the absolute value of the difference between the actual heat flux density distribution and the preset theoretical heat flux distribution; 3. The method according to claim 2, wherein After inputting the rotation speed adjustment data and the adjustment transient response feature into the preset ideal heat generation and heat transfer dynamic mathematical model for friction stir welding, it further includes: When it is found that the adjusted transient response characteristic is related to the rotation period of the welding head and matches the preset acoustic-thermal flux coupling fingerprint of specific defects of the welding head, by analyzing the adjusted transient response characteristic, the heat flux fluctuation frequency, heat flux fluctuation phase, and heat flux fluctuation amplitude of the periodic heat flux fluctuation are obtained; According to the correlation relationship between the heat flux fluctuation amplitude, the heat flux fluctuation phase, and the abnormal spectrum characteristic corresponding to the acoustic anomaly, by comparing with the preset welding head defect fingerprint library, the preliminary defect type and preliminary defect position of the welding head are judged; According to the ratio of the heat flux fluctuation amplitude to the preset fluctuation amplitude deviation threshold, the rotation speed adjustment amplitude of the welding head is calculated, and based on the heat flux fluctuation phase, the adjustment phase point of the rotation speed adjustment is calculated. At the adjustment phase point, the rotation speed of the welding head is secondarily adjusted according to the rotation speed adjustment amplitude; Obtain the second heat flux fluctuation amplitude and the second heat flux fluctuation phase distribution characteristics before and after the second adjustment, and calculate the real-time offset of the welding head on the welding trajectory; the real-time offset is the deviation value between the center point of the welding head and the center point of the target trajectory; Based on the real-time offset, use the multi-point interpolation method to generate the offset compensation path of the welding head; the compensation path is calculated by fitting the change trend of the real-time offset; Adjust the movement trajectory of the welding head again according to the offset compensation path.

4. The method according to claim 1, wherein After comparing the real-time acoustic feature data with the acoustic basic feature library and calculating the acoustic difference degree, it further includes: When it is detected that the welding path is a long-distance straight line area, the vibration characteristics of the welding head are monitored in real time, the vibration accumulation amount and the vibration change trend are calculated, and the heat input gradient distribution in the welding area is monitored at the same time; When the change rate of the vibration accumulation amount, the vibration change trend, or the heat input gradient distribution exceeds the corresponding preset threshold and there is no such acoustic anomaly in the acoustic difference degree, the rotation speed of the welding head is adjusted according to the preset fine-tuning parameters.

5. The method according to claim 4, wherein After monitoring the vibration characteristics of the welding head in real time, calculating the vibration accumulation amount and the vibration change trend, and monitoring the heat input gradient distribution in the welding area when it is detected that the welding path is a long-distance straight line area, it further includes: When the vibration change trend in the vibration characteristics exceeds the preset change trend threshold, according to the vibration frequency and vibration amplitude in the vibration characteristics, the fine-tuning parameters are adjusted; the fine-tuning parameters are proportional to the vibration change trend; According to the heat input gradient distribution in the welding path direction, reduce the rotation speed of the welding head corresponding to the area with too high heat input, and increase the axial pressure of the welding head in the area with too high heat input; the area with too high heat input is the area where the heat input gradient distribution exceeds the average value.

6. The method according to claim 1, characterized in that, Before obtaining the friction force, relative speed, and real-time acoustic signal after the fine-tuning time in real time and calculating the friction energy within the preset unit time length, it further includes When it is predicted, by analyzing historical welding data and the CAD model of the current workpiece, that there is an inherent low-frequency modal vibration mode activated in the unwelded path section, according to the predicted vibration frequency and predicted vibration amplitude, a corresponding predicted anti-instability parameter combination is selected from a preset anti-instability welding parameter library; the activation of the inherent low-frequency modal vibration mode means that the deviation between the vibration frequency and the natural modal vibration frequency is less than a preset activation frequency threshold and the vibration amplitude exceeds a preset activation multiple of the historical average vibration amplitude. When the welding head reaches the high-risk section corresponding to the activation of the inherent low-frequency modal vibration mode, the predicted anti-instability parameter combination is applied with a preset probing length, and the probing acoustic signal and the probing dynamic stability of the welding head are monitored in real time. If the probing dynamic stability is within the preset stability threshold range and the probing acoustic signal is within the preset acoustic signal threshold range, the predicted anti-instability parameter combination is applied to complete the welding of the high-risk section. If the probing dynamic stability is not within the preset stability threshold range or the probing acoustic signal is not within the preset acoustic signal threshold range, a preset small perturbation signal is superimposed on the control instruction of the welding head.

7. The method according to claim 1, wherein After comparing the real-time acoustic feature data with the acoustic basic feature library and calculating the acoustic difference degree, it further includes: When the acoustic difference degree remains high and there is no such acoustic anomaly in the acoustic difference degree, identify the high baseline, high reproducibility features of the acoustic difference degree and the corresponding workpiece type and welding section. Retrieve the preset digital twin model of the workpiece type, and simulate the equivalent thermal-mechanical load applied during the friction stir welding process to the corresponding welding section in the digital twin model. Through simulation calculation, analyze the inherent structural acoustic response characteristics generated by the welding section. If the inherent structural acoustic response characteristics match the real-time acoustic feature data, replace the inherent structural acoustic response characteristics into the acoustic basic feature library.

8. A trajectory offset correction system, characterized in that, The trajectory offset correction system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the trajectory offset correction system to execute the method according to any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that, When the computer program product runs on the trajectory offset correction system, it enables the trajectory offset correction system to execute the method according to any one of claims 1-7.

10. A computer-readable storage medium, comprising instructions, characterized in that, When the instruction runs on the trajectory offset correction system, it enables the trajectory offset correction system to execute the method according to any one of claims 1-7.

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