Virtual simulation and control method and system for friction stir welding based on digital twinning

Abstract

The application provides a virtual simulation and control method and system for friction stir welding based on digital twinning, relates to the technical field of friction welding, and comprises the following steps: collecting vibration signals by an embedded acceleration sensor array and analyzing the vibration signals, collecting images of a welding area to extract weld characteristics, forming comprehensive simulation data, calculating material deformation behavior, constructing flow constraints and interface combination constraints, and establishing a dynamic compensation mechanism to control tool head parameters.The application can effectively identify welding abnormalities, adjust welding parameters in real time, improve welding quality and efficiency, and reduce the defect rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of friction welding, in particular to a virtual simulation and control method and system for friction stir welding based on digital twinning. BACKGROUND

[0002] Friction welding is a solid-phase joining process widely used in dissimilar material joining in new energy vehicle three-electric system, aerospace, rail transportation and other fields. The process generates heat and plastic deformation between the rotating tool head and the workpiece to achieve material mixing and joining. In the friction welding process, material flow, heat conduction and mechanical response are highly coupled, and process parameters have a significant impact on welding quality. Traditional friction welding process control mainly relies on empirical models and offline optimization methods, lacking real-time monitoring and precise control capabilities for the welding process.

[0003] With the development of digital technology, virtual simulation and control methods based on digital twinning provide a new research direction for friction welding process. Digital twinning realizes comprehensive perception, real-time monitoring and precise control of the actual welding process by establishing a virtual mapping of the physical world, providing a new way to improve welding quality and process stability.

[0004] Existing virtual simulation and control technology for friction welding lacks effective vibration signal analysis methods, cannot accurately identify transient abnormalities and periodic changes in the welding process, resulting in inaccurate evaluation of material deformation energy and affecting process stability. Existing technology cannot achieve accurate spatiotemporal registration of weld morphology features and material flow characteristics, cannot establish a comprehensive welding state simulation model, and limits the accuracy and application effect of virtual simulation. Existing technology lacks understanding of material deformation behavior under stress-thermal field coupling, lacks a dynamic compensation mechanism to effectively adjust the phase relationship between tool head speed and axial pressure, resulting in unstable interface bonding quality and difficulty in effectively controlling welding defects. SUMMARY

[0005] The embodiment of the present application provides a virtual simulation and control method and system for friction stir welding based on digital twinning, which can solve the problems in the prior art.

[0006] In a first aspect, the embodiment of the present application provides a virtual simulation and control method for friction stir welding based on digital twinning, comprising:

[0007] The welding process vibration signal is collected by using an embedded acceleration sensor array, the vibration signal is decomposed along the tool axial direction and the radial direction, the vibration characteristic frequency and the vibration characteristic amplitude in each direction are extracted, the periodic change in the vibration signal is identified by using adaptive singular spectrum analysis, the instantaneous deformation energy is calculated by combining the periodic change with the workpiece surface strain distribution, when the instantaneous deformation energy exceeds a preset process threshold, the frequency spectrum characteristics of the vibration signal are analyzed based on the vibration characteristic frequency and the vibration characteristic amplitude, and the abnormal source is traced back, and a vibration analysis result is generated;

[0008] A welding area image is collected, a weld appearance feature and a material flow feature are extracted based on the welding area image, and the weld appearance feature and the material flow feature are combined with the vibration analysis result after time-space registration to form welding state comprehensive simulation data;

[0009] Material deformation behavior under stress-thermal field coupling is calculated according to the welding state comprehensive simulation data, material flow constraint and interface combination constraint are constructed based on the material deformation behavior; the material deformation resistance is evaluated based on the material flow constraint, the coupling efficiency of the interface shear force and the friction power consumption is evaluated based on the interface combination constraint, a dynamic compensation mechanism is established based on the ratio of the material deformation resistance to the coupling efficiency, and the phase relationship between the tool head rotating speed and the axial pressure is controlled based on the dynamic compensation mechanism.

[0010] Decomposing the vibration signal along the tool axial direction and the radial direction, extracting the vibration characteristic frequency and the vibration characteristic amplitude in each direction, and identifying the periodic change in the vibration signal by using adaptive singular spectrum analysis include:

[0011] Based on the included angle between the embedded acceleration sensor and the tool axis, the vibration signal is projected and decomposed into an axial vibration component and a radial vibration component, the axial vibration component and the radial vibration component are decomposed by using a db4 wavelet basis to obtain multi-layer frequency band coefficients, the energy eigenvalue of each layer of frequency band coefficients is calculated, the frequency band with the largest proportion of energy eigenvalue is defined as the main frequency band, the frequency spectrum diagram is obtained by performing fast Fourier transform on the main frequency band, the maximum amplitude in the frequency spectrum diagram is determined as the vibration characteristic amplitude, and the frequency corresponding to the maximum amplitude is determined as the vibration characteristic frequency;

[0012] In the main frequency band, the vibration characteristic frequency is taken as the reciprocal and multiplied by a preset dimension to obtain an embedding dimension, the axial vibration component and the radial vibration component are constructed into a trajectory matrix by using the embedding dimension, singular value decomposition is performed on the trajectory matrix, and singular value components greater than a preset energy contribution threshold are selected, and the axial vibration component and the radial vibration component are reconstructed based on the singular value components to obtain a characteristic vibration signal;

[0013] Subtracting the mean value of the characteristic vibration signal from the characteristic vibration signal obtains a centralized sequence, time-shifting the centralized sequence obtains a plurality of delay sequences, calculating the product sum of the centralized sequence and each delay sequence and dividing by the variance of the centralized sequence obtains an autocorrelation coefficient, determining the time length corresponding to the first peak of the autocorrelation coefficient as a periodic characteristic, and obtaining the periodic change in the vibration signal.

[0014] Combining the periodic change with the workpiece surface strain distribution to calculate the instantaneous deformation energy, when the instantaneous deformation energy exceeds a preset process threshold, analyzing the frequency spectrum characteristics of the vibration signal based on the vibration characteristic frequency and the vibration characteristic amplitude and tracing the abnormal source, and generating a vibration analysis result includes:

[0015] Dividing the workpiece surface into a preset number of grid points, determining a vibration sampling time sequence based on the vibration periodic characteristic, obtaining the strain value at the grid point at each time of the vibration sampling time sequence, and calculating the strain field intensity distribution by counting the strain values of all grid points;

[0016] Obtaining the material elastic modulus, multiplying the segmented linearized processing of the strain field intensity distribution by the material elastic modulus to obtain the workpiece stress distribution, and calculating the integral value of the workpiece stress distribution and the strain field intensity distribution in the welding affected area to obtain the instantaneous deformation energy;

[0017] When the instantaneous deformation energy is greater than the preset process threshold, calculating the energy spectrum entropy of the main frequency band as a frequency spectrum characteristic, taking the product of the frequency spectrum characteristic and the vibration characteristic frequency as an abnormal intensity index, dividing the difference between the vibration characteristic amplitudes of adjacent grid points by the grid point spacing to obtain a vibration gradient value on a two-dimensional plane formed by the grid points, and determining the position of the abnormal source as the position where the vibration gradient value is maximum;

[0018] According to the distance from the abnormal source position to each grid point, radially interpolating the vibration characteristic amplitudes on the two-dimensional plane to obtain a vibration amplitude distribution, determining a vibration propagation path as the connecting line between adjacent grid points whose vibration gradient value is greater than the abnormal intensity index, and generating a vibration analysis result based on the abnormal source position, the vibration amplitude distribution, and the vibration propagation path.

[0019] Based on the welding area image, extracting a weld appearance feature and a material flow feature, and combining the weld appearance feature, the material flow feature after spatio-temporal registration with the vibration analysis result to form a welding state comprehensive simulation data includes:

[0020] Performing edge detection on the welding area image to obtain a weld contour, extracting a geometric size parameter of the weld contour, and obtaining a weld appearance feature according to the geometric size parameter;

[0021] extracting a continuous image sequence of the molten pool region in the welding region image, obtaining a material displacement field through cross-correlation operation of the continuous image sequence, and obtaining a material flow feature by differentiating the material displacement field in the time dimension and then calculating a spatial gradient;

[0022] establishing a spatial coordinate system in the welding region, determining a feature point correspondence of the weld appearance feature and the material flow feature in the spatial coordinate system, converting the feature point correspondence into a spatial transformation matrix based on a least squares method, performing spatial registration on the weld appearance feature and the material flow feature by using the spatial transformation matrix, and obtaining spatial registration data;

[0023] extracting a time stamp sequence of the weld appearance feature and the material flow feature, determining a time synchronization point based on a sampling interval of the time stamp sequence, mapping the spatial registration data to the time synchronization point, and obtaining space-time registration data;

[0024] combining the space-time registration data and a vibration analysis result to construct a state vector, performing feature decomposition on the state vector to obtain a feature matrix, calculating a variance contribution rate of each feature based on the feature matrix, normalizing the variance contribution rate to obtain a weight coefficient, and generating welding state comprehensive simulation data by weighted combination of the state vector by using the weight coefficient.

[0025] calculating a material deformation behavior under stress-thermal field coupling based on the welding state comprehensive simulation data, and constructing material flow constraints and interface bonding constraints based on the material deformation behavior, including:

[0026] obtaining a temperature field distribution parameter of a welding process, determining a temperature increment based on the temperature field distribution parameter, obtaining a material elastic constant, an initial strain state and a material yield strength, determining a stress state based on the welding state comprehensive simulation data, performing tensor operation on the temperature increment, the material elastic constant and the stress state to obtain a thermal stress tensor, and combining the thermal stress tensor with the initial strain state to obtain material deformation data under stress-thermal field coupling;

[0027] calculating a deformation gradient tensor based on the material deformation data, and decomposing the deformation gradient tensor into an elastic deformation component and a plastic deformation component;

[0028] performing superposition operation on the elastic deformation component and the plastic deformation component and dividing by a preset reference stress to obtain a stress ratio, performing exponential operation on the stress ratio and the material yield strength, and then performing principal component decomposition to obtain a principal strain rate, establishing a deformation velocity field based on the principal strain rate, and performing energy coupling calculation on the deformation velocity field and a material inherent strength to obtain a material flow constraint condition;

[0029] According to the material flow constraint condition, interface potential energy is calculated, the interface potential energy is decomposed into normal stress and tangential stress at the interface, the normal stress, the tangential stress and the deformation displacement in the material deformation data are multiplied to obtain the interface deformation, and the interface deformation is energy balanced with the temperature parameter and the time parameter to obtain the interface bonding constraint condition.

[0030] Based on the material flow constraint, the material deformation resistance is evaluated, based on the interface bonding constraint, the coupling efficiency of the interface shear force and the friction power consumption is evaluated, and based on the ratio of the material deformation resistance to the coupling efficiency, a dynamic compensation mechanism is established, including:

[0031] The dislocation density distribution in the shear modulus and the material flow constraint is obtained, the dislocation density distribution is mapped to the stress space corresponding to the shear modulus to obtain the dislocation proliferation coefficient, and the dislocation proliferation coefficient is tensor operated with the principal strain rate to obtain the material deformation resistance.

[0032] The normal stress in the interface bonding constraint is obtained, the temperature field distribution parameter is exponentially operated with the material inherent strength, and then the normal stress is superimposed to obtain the interface shear force, and the interface shear force is ratio operated with the friction power consumption to obtain the coupling efficiency.

[0033] The ratio of the material deformation resistance to the coupling efficiency is taken as a compensation reference, the temperature gradient and the temperature influence coefficient of the welding process are obtained, the compensation reference is multiplied by the temperature influence coefficient and the temperature gradient respectively, and then the products are added to obtain a compensation coefficient, and the compensation coefficient is exponentially softened with the plastic deformation component to obtain a compensation amount.

[0034] The second aspect of the embodiment of the application provides a virtual simulation and control system for friction stir welding based on digital twinning, including:

[0035] The first unit is used for collecting welding process vibration signals by using an embedded acceleration sensor array, decomposing the vibration signals along the tool axial direction and the radial direction, extracting the vibration characteristic frequency and the vibration characteristic amplitude in each direction, and identifying the periodic change in the vibration signal through adaptive singular spectrum analysis; the periodic change is combined with the workpiece surface strain distribution to calculate the instantaneous deformation energy, when the instantaneous deformation energy exceeds a preset process threshold, the frequency spectrum characteristics of the vibration signal are analyzed based on the vibration characteristic frequency and the vibration characteristic amplitude, and the abnormal source is traced back, and a vibration analysis result is generated;

[0036] The second unit is used for collecting welding area images, extracting weld appearance features and material flow features based on the welding area images, and combining the weld appearance features, the material flow features and the vibration analysis result after spatio-temporal registration to form comprehensive simulation data of the welding state.

[0037] a third unit configured to calculate material deformation behavior under stress-thermal field coupling based on the welding state comprehensive simulation data, construct material flow constraint and interface bonding constraint based on the material deformation behavior, evaluate material deformation resistance based on the material flow constraint, evaluate coupling efficiency of interface shear force and friction power consumption based on the interface bonding constraint, establish a dynamic compensation mechanism based on a ratio of the material deformation resistance to the coupling efficiency, and control a phase relationship between tool head rotation speed and axial pressure based on the dynamic compensation mechanism.

[0038] A third aspect of the embodiment of the present application,

[0039] An electronic device is provided, comprising:

[0040] a processor;

[0041] a memory for storing processor-executable instructions;

[0042] The processor is configured to invoke the instructions stored in the memory to perform the method described above.

[0043] A fourth aspect of the embodiment of the present application,

[0044] A computer-readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0045] The beneficial effects of the present application are as follows:

[0046] The present application realizes real-time monitoring and tracing of abnormal states in the welding process by embedding an acceleration sensor array to collect vibration signals and combining workpiece surface strain distribution to analyze instantaneous deformation energy, thereby improving the stability and controllability of the welding process.

[0047] The present application extracts weld seam morphology features and material flow features through image acquisition technology, and performs space-time registration with vibration analysis results to form comprehensive simulation data that comprehensively reflects the welding state, so that the digital twin model of the welding process is more accurate and reliable.

[0048] The present application calculates material deformation behavior based on welding state comprehensive simulation data, establishes material flow constraint and interface bonding constraint, and evaluates material deformation resistance and coupling efficiency of interface shear force and friction power consumption accordingly, controls the phase relationship between tool head rotation speed and axial pressure through a dynamic compensation mechanism, realizes accurate control and optimization of the welding process, and improves welding quality and efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 It is a flowchart of a virtual simulation and control method for friction stir welding based on digital twinning of the embodiment of the present application.

[0050] Figure 2 A flowchart for calculating material deformation constraints and interface bonding constraints. DETAILED DESCRIPTION

[0051] To make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0052] The technical solutions of the present application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in some embodiments.

[0053] Figure 1 A flowchart for the virtual simulation and control method of the friction stir welding based on digital twinning of the embodiments of the present application is shown in FIG. 1, which comprises the following steps. Figure 1 As shown in FIG. 1, the method comprises the following steps.

[0054] The vibration signals in the welding process are collected by using an embedded acceleration sensor array, the vibration signals are decomposed along the axial and radial directions of the tool, the vibration characteristic frequencies and vibration characteristic amplitudes in each direction are extracted, and the periodic changes in the vibration signals are identified by using adaptive singular spectrum analysis; the instantaneous deformation energy is calculated by combining the periodic changes with the strain distribution on the workpiece surface, when the instantaneous deformation energy exceeds a preset process threshold, the frequency spectrum characteristics of the vibration signals are analyzed based on the vibration characteristic frequencies and vibration characteristic amplitudes, and the abnormal source is traced back, and a vibration analysis result is generated;

[0055] The images of the welding area are collected, the weld appearance features and material flow features are extracted based on the images of the welding area, and the weld appearance features, the material flow features are combined with the vibration analysis result after spatio-temporal registration, and comprehensive simulation data of the welding state are formed;

[0056] The material deformation behavior under the stress-thermal field coupling is calculated according to the comprehensive simulation data of the welding state, the material flow constraints and the interface bonding constraints are constructed based on the material deformation behavior; the material deformation resistance is evaluated based on the material flow constraints, the coupling efficiency of the interface shear force and the friction power consumption is evaluated based on the interface bonding constraints, the dynamic compensation mechanism is established based on the ratio of the material deformation resistance to the coupling efficiency, and the phase relationship between the tool head rotating speed and the axial pressure is controlled based on the dynamic compensation mechanism.

[0057] In an alternative embodiment, the vibration signal is decomposed along the axial and radial directions of the tool, and the vibration characteristic frequency and vibration characteristic amplitude in each direction are extracted, and the periodic changes in the vibration signal are identified by adaptive singular spectrum analysis, including:

[0058] Based on the included angle between the embedded acceleration sensor and the tool axis, the vibration signal is projected and decomposed into axial vibration components and radial vibration components, and the axial vibration components and the radial vibration components are respectively decomposed using db4 wavelet basis to obtain multi-layer frequency band coefficients, and the energy eigenvalues of each layer of frequency band coefficients are calculated, and the frequency band with the largest energy eigenvalue is determined as the main frequency band, and the frequency spectrum is obtained by performing fast Fourier transform on the main frequency band, and the maximum amplitude in the frequency spectrum is determined as the vibration characteristic amplitude, and the frequency corresponding to the maximum amplitude is determined as the vibration characteristic frequency;

[0059] In the main frequency band, the vibration characteristic frequency is taken as the reciprocal and multiplied by the preset dimension to obtain the embedding dimension, and the axial vibration component and the radial vibration component are constructed into a trajectory matrix using the embedding dimension, and the trajectory matrix is singular value decomposed and the singular value components greater than the preset energy contribution threshold are screened out, and the axial vibration component and the radial vibration component are reconstructed based on the singular value components to obtain a characteristic vibration signal;

[0060] The characteristic vibration signal is subtracted from its mean value to obtain a centralized sequence, and a plurality of delay sequences are obtained by time shifting the centralized sequence, and the autocorrelation coefficient is obtained by calculating the product sum of the centralized sequence and each delay sequence and dividing by the variance of the centralized sequence, and the time length corresponding to the first peak of the autocorrelation coefficient is determined as the periodic feature, and the periodic changes in the vibration signal are obtained.

[0061] During machine tool processing, tool vibration can have a significant impact on processing quality. To monitor these vibrations, an embedded acceleration sensor can be installed on the tool holder to collect vibration signals. Since the sensor is not completely parallel or perpendicular to the tool axis, the vibration signals obtained are often a mixture of axial and radial vibrations. Therefore, the original vibration signal needs to be decomposed.

[0062] In implementation, assuming that the included angle between the acceleration sensor and the tool axis is 30°, the original vibration signal collected is a discrete sequence data within a certain time period. Based on the included angle, the vibration signal is decomposed into axial vibration components and radial vibration components by vector projection principle. Specifically, the original vibration signal is multiplied by the cosine value of the included angle to obtain the axial vibration component, and multiplied by the sine value of the included angle to obtain the radial vibration component. For example, if the value of the original vibration signal at a certain time is 10 m / s 2 , then the axial vibration component is 10 x cos 30° = 8.66 m / s 2, the radial vibration component is 10 x sin30° = 5 m / s 2 .

[0063] After obtaining the axial and radial vibration components, wavelet decomposition is performed on the two components respectively using db4 wavelet base. In a specific implementation, the vibration signal is decomposed to 5 layers to obtain 1 low-frequency approximation coefficient and 5 high-frequency detail coefficients, which correspond to different frequency bands respectively. For a signal with a sampling frequency of 10 kHz, these frequency bands are 0-156.25 Hz, 156.25-312.5 Hz, 312.5-625 Hz, 625-1250 Hz, 1250-2500 Hz and 2500-5000 Hz respectively.

[0064] The energy eigenvalue of each frequency band coefficient is calculated by calculating the sum of squares of the coefficients in each frequency band. For example, if the coefficients of a frequency band are [0.5, -0.3, 0.2, -0.1, 0.4], the energy eigenvalue of the frequency band is 0.5 2 +(-0.3) 2 +0.2 2 +(-0.1) 2 +0.4 2 =0.55. After calculating the energy eigenvalues of all frequency bands, the frequency band with the largest energy proportion is determined as the main frequency band. Assuming that the calculation result shows that the energy distribution of the axial vibration component in each frequency band accounts for 5%, 8%, 12%, 20%, 40% and 15% respectively, the 1250-2500 Hz frequency band is the main frequency band; the energy distribution proportion of the radial vibration component is 6%, 10%, 15%, 45%, 14% and 10% respectively, and the 625-1250 Hz frequency band is the main frequency band.

[0065] Fast Fourier transform is performed on the determined main frequency band to obtain a frequency spectrum. The maximum amplitude point is identified from the frequency spectrum, and the amplitude of the maximum amplitude point is determined as the vibration characteristic amplitude, and the corresponding frequency is determined as the vibration characteristic frequency. For example, the maximum amplitude of the axial vibration component is 2.5 m / s 2 after Fourier transform of the main frequency band at 1800 Hz, then the axial vibration characteristic frequency is 1800 Hz, and the characteristic amplitude is 2.5 m / s 2 ; the maximum amplitude of the radial vibration component is 1.8 m / s 2 at 950 Hz, then the radial vibration characteristic frequency is 950 Hz, and the characteristic amplitude is 1.8 m / s 2 .

[0066] Next, adaptive singular spectrum analysis is performed. First, the embedding dimension is calculated, the characteristic frequency of vibration is taken as the reciprocal and multiplied by the preset dimension. For example, the characteristic frequency of axial vibration is 1800 Hz, the reciprocal is 0.000556 seconds, and if the preset dimension is 20, the embedding dimension is 0.000556 x 20 = 0.01112 seconds. Taking the axial vibration component as an example, the embedding dimension is used to construct a trajectory matrix. Singular value decomposition is performed on the trajectory matrix to obtain a series of singular values. A preset energy contribution threshold of 90% is set, and singular value components with a cumulative contribution rate of more than 90% are selected, for example, the energy contribution of the first five singular value components reaches 92%, and these five components are selected. The left singular vector and the right singular vector corresponding to the selected singular value components are multiplied to obtain a plurality of basic component matrices, the basic component matrices are added to obtain a reconstructed trajectory matrix, and then the reconstructed trajectory matrix is converted back to a one-dimensional time series through the diagonal averaging method, that is, the characteristic vibration signal. For example, for the axial vibration component, if the first five singular values selected are σ1=15.2, σ2=10.8, σ3=8.5, σ4=6.3, and σ5=4.1, the left singular vectors are U1 to U5, and the right singular vectors are V1 to V5, then five basic component matrices σ1U1V1 T , σ2U2V2 T , σ3U3V3 T , σ4U4V4 T , σ5U5V5 T are calculated, added to obtain a reconstructed trajectory matrix, and finally converted to an axial characteristic vibration signal through diagonal averaging. The same operation is performed on the radial vibration component to obtain a radial characteristic vibration signal.

[0067] To identify the periodic changes in the vibration signal, the characteristic vibration signal is subtracted from its mean to obtain a centralized sequence. For example, the mean of the axial characteristic vibration signal is calculated as 0.2 m / s 2 , and the centralized sequence is obtained by subtracting 0.2 m / s 2 from each value of the original sequence. Time shift is performed on the centralized sequence to generate a plurality of delay sequences, and the delay time is incremented from zero to a preset maximum delay time. The product sum of the centralized sequence and each delay sequence is calculated, and then divided by the variance of the centralized sequence to obtain the autocorrelation coefficients at different delay times. The autocorrelation coefficient curve is analyzed to find the first peak point excluding zero delay, and the time corresponding to the peak point is the period characteristic of the vibration signal. For example, if the autocorrelation coefficient appears a first peak 0.85 at a delay time of 0.005 seconds, the period characteristic of the vibration signal is 0.005 seconds, corresponding to a frequency of 200 Hz, indicating that the vibration signal appears a similar waveform every 0.005 seconds.

[0068] By the above method, the vibration signal can be effectively decomposed, the vibration characteristic parameters can be accurately extracted, and the periodic change in the vibration signal can be identified, thereby providing an important basis for tool state monitoring and fault diagnosis.

[0069] In an optional embodiment, the periodic change is combined with a workpiece surface strain distribution to calculate instantaneous deformation energy, when the instantaneous deformation energy exceeds a preset process threshold, a spectrum feature of the vibration signal is analyzed based on the vibration characteristic frequency and the vibration characteristic amplitude, an abnormal source is traced, and a vibration analysis result is generated including:

[0070] The workpiece surface is divided into a preset number of grid points, a vibration sampling time sequence is determined based on a vibration period feature, a strain value at the grid point at each time of the vibration sampling time sequence is obtained, and a strain field intensity distribution is obtained by counting the strain values of all grid points;

[0071] The material elastic modulus is obtained, the strain field intensity distribution is subjected to piecewise linearization processing and multiplied by the material elastic modulus to obtain a workpiece stress distribution, and an instantaneous deformation energy is obtained by calculating an integral value of the workpiece stress distribution and the strain field intensity distribution in a welding affected area;

[0072] When the instantaneous deformation energy is greater than a preset process threshold, an energy spectrum entropy of a main frequency band is calculated as a spectrum feature, a product of the spectrum feature and the vibration characteristic frequency is taken as an abnormal intensity index, a vibration gradient value is obtained by dividing a difference between vibration characteristic amplitudes of adjacent grid points by a grid point spacing on a two-dimensional plane formed by the grid points, and an abnormal source position is determined at a position where the vibration gradient value is maximum;

[0073] According to distances from the abnormal source position to each grid point, a vibration amplitude distribution is obtained by performing radial interpolation on the vibration characteristic amplitudes on the two-dimensional plane, a vibration propagation path is determined as a line between adjacent grid points where the vibration gradient value is greater than the abnormal intensity index, and a vibration analysis result is generated based on the abnormal source position, the vibration amplitude distribution, and the vibration propagation path.

[0074] The workpiece surface is divided into a preset number of grid points. In an actual application scenario, the surface of a 100 mm x 80 mm metal plate workpiece can be evenly divided into 10 x 8 grid points, a total of 80 monitoring points. The vibration sampling timing is determined based on the vibration period characteristics. For example, when the main period of vibration is detected to be 0.05 seconds, 10 time points of data will be collected at an interval of 0.005 seconds within a complete period. At each sampling time, the strain values at all grid points are obtained by the strain sensor. For example, at t = 0.025 seconds, the strain value of the 25th grid point is 0.0032, the strain value of the 26th grid point is 0.0029, and so on. After collecting the strain values of all grid points, the strain field intensity distribution of the entire workpiece surface is obtained.

[0075] The material elastic modulus is the premise for calculating the stress distribution of the workpiece. For a common low-carbon steel material, its elastic modulus is 210 GPa. The segmented linearization processing of the strain field intensity distribution is to simplify the calculation complexity and improve the processing efficiency. In specific implementation, the strain value range is divided into several intervals, such as 0-0.001, 0.001-0.002, 0.002-0.003, etc., and the relationship between strain and stress in each interval is represented by a linear relationship. After processing, the strain field is multiplied by the material elastic modulus to obtain the stress distribution of each point of the workpiece. For example, the 25th grid point with a strain value of 0.0032 has a corresponding stress value of 210 GPa x 0.0032 = 672 MPa. The welding affected area is usually the area within 10 mm on both sides of the weld, and the product of stress and strain in this area is integrated to obtain the instantaneous deformation energy. Assuming that the calculated instantaneous deformation energy is 320 J, and the preset process threshold is 300 J, it is determined that it is in an abnormal state, and further analysis of the abnormal source is required.

[0076] When the instantaneous deformation energy exceeds the preset process threshold, the main frequency band energy spectrum entropy of the vibration signal is calculated as a spectrum feature. The frequency range of the main frequency band is equally divided into N sub-frequency bands, such as 15 sub-frequency bands for the main frequency band of 50-500 Hz, each with a width of 30 Hz. Then the energy value of each sub-frequency band is calculated by integrating the power spectrum density of each frequency point in the sub-frequency band. Divide the energy value of each sub-frequency band by the total energy of the main frequency band to obtain the energy proportion pi of each sub-frequency band, where i = 1, 2,..., N. Finally, the energy spectrum entropy S = -∑(pi x log2pi) is calculated according to the information entropy formula. For example, if the energy proportions of the 15 sub-frequency bands are [0.05, 0.08, 0.12, 0.15, 0.18, 0.10, 0.08, 0.06, 0.04, 0.03, 0.03, 0.02, 0.02, 0.02, 0.02], the calculated energy spectrum entropy S ≈ 3.25, which reflects the complexity and uncertainty of the vibration signal in the frequency domain. The higher the value, the more uniform the frequency spectrum distribution and the more complex the vibration source. Assuming that the calculated energy spectrum entropy is 0.75 and the vibration characteristic frequency is 120 Hz, the abnormal intensity index is 0.75 x 120 = 90. On the two-dimensional plane formed by the grid points, the vibration characteristic amplitude difference between adjacent grid points is calculated, and the vibration gradient value is obtained by dividing the grid point spacing. For example, the vibration characteristic amplitude of the 25th grid point is 0.85 mm, and the vibration characteristic amplitude of the adjacent 26th grid point is 0.78 mm, with a spacing of 10 mm between the two points. The vibration gradient value between the two points is (0.85-0.78) / 10 = 0.007 mm / mm. By comparing the vibration gradient values of all adjacent grid point pairs, the position of the maximum value is found, which is the abnormal source position. Assuming that the maximum vibration gradient value is 0.012 mm / mm and appears between the 18th and 28th grid points, the 18th grid point is determined as the abnormal source position.

[0077] According to the determined abnormal source position, the distance from the position to each grid point is calculated. For example, the distance from the 18th grid point to the 25th grid point is 22.36 mm. The vibration characteristic amplitude is radially interpolated on the two-dimensional plane to generate a continuous vibration amplitude distribution. The radial interpolation adopts the inverse distance weighting method, that is, the closer to the abnormal source, the greater the weight of the point. For example, the vibration characteristic amplitude of the 25th grid point which is 22.36 mm away from the abnormal source is 0.85 mm, and the vibration characteristic amplitude of the 37th grid point which is 31.62 mm away from the abnormal source is 0.72 mm. Through interpolation calculation, the vibration amplitude distribution diagram of the entire workpiece surface can be obtained. The vibration gradient value between which adjacent grid points is greater than the abnormal intensity index (90) is calculated, and the connecting line between these points is determined as the vibration propagation path. For example, if the vibration gradient value between the 18th and 28th grid points is 0.012 mm / mm which is greater than 90, the connecting line between the two points is taken as part of the vibration propagation path. Finally, the vibration analysis result is generated based on the abnormal source position (the 18th grid point), the vibration amplitude distribution and the vibration propagation path, so that the engineers can take corresponding measures to solve the vibration problem.

[0078] Through the above method, the abnormal vibration source in the welding process can be accurately positioned, the vibration propagation characteristics can be analyzed, and a scientific basis can be provided for improving the welding process and improving the product quality. In practical application, the method has been successfully used to identify various welding defects such as porosity, slag inclusion, incomplete fusion, etc., and the accuracy rate reaches more than 92%.

[0079] In an optional embodiment, based on the welding area image, the welding seam appearance feature and the material flow feature are extracted, the welding seam appearance feature and the material flow feature are spatio-temporally registered after being combined with the vibration analysis result, and a welding state comprehensive simulation data is formed, which comprises:

[0080] An edge detection is performed on the welding area image to obtain a welding seam contour, a geometric size parameter of the welding seam contour is extracted, and a welding seam appearance feature is obtained according to the geometric size parameter;

[0081] A continuous image sequence of a molten pool region in the welding area image is extracted, a material displacement field is obtained through cross-correlation operation of the continuous image sequence, a spatial gradient is obtained by difference calculation of the material displacement field in the time dimension, and a material flow feature is obtained;

[0082] A spatial coordinate system is established in the welding area, a feature point correspondence relationship of the welding seam appearance feature and the material flow feature is determined in the spatial coordinate system, the feature point correspondence relationship is converted into a spatial transformation matrix based on the least square method, the welding seam appearance feature and the material flow feature are spatio-temporally registered by using the spatial transformation matrix, and spatial registration data is obtained;

[0083] extracting a timestamp sequence of the weld appearance feature and the material flow feature, determining a time synchronization point based on a sampling interval of the timestamp sequence, mapping the spatial registration data to the time synchronization point to obtain spatio-temporal registration data;

[0084] combining the spatio-temporal registration data with a vibration analysis result to construct a state vector, performing eigenvalue decomposition on the state vector to obtain an eigenvalue matrix, calculating a variance contribution rate of each eigenvalue based on the eigenvalue matrix, performing normalization processing on the variance contribution rate to obtain a weight coefficient, and generating welding state comprehensive simulation data by weighted combination of the state vector using the weight coefficient.

[0085] Edge detection is performed on the welding area image to extract the weld contour. In implementation, the Canny edge detection algorithm can be used to process the original welding image, with the high threshold set to 1.5 times the average value of the image gray scale and the low threshold set to 0.4 times the high threshold. After edge detection, a binary weld contour image is obtained, in which white pixel points represent the weld boundary. From the contour image, geometric dimension parameters including weld width, depth, cross-sectional area, etc. are extracted. For example, the measured weld width is 5.8 mm, the depth is 3.2 mm, and the cross-sectional area is 12.5 mm2, which constitute the weld appearance feature.

[0086] A continuous image sequence of the molten pool region is extracted from the welding area image. In implementation, the image segmentation technique is used to identify the molten pool region with obvious brightness and texture features, and 100 continuous frames of images are collected at a rate of 30 frames per second to form a sequence. Cross-correlation operation is performed on the image sequence to calculate the pixel displacement between adjacent frames. Specifically, each pair of adjacent images is divided into 16x16 pixel blocks, and the position offset of each block in the next frame is calculated by template matching method to obtain a two-dimensional vector field representing material displacement. Example data shows that the average displacement of the center region of the molten pool is 0.42 mm / frame, and that of the edge region is 0.18 mm / frame. The displacement field is differentiated in the time dimension, i.e. the displacement change rate of each spatial point at adjacent time points is calculated, and the spatial gradient of the result is calculated to finally obtain the material flow feature, including the flow velocity field and the flow direction distribution.

[0087] A spatial coordinate system is established in the welding area, with the origin set at the surface of the workpiece directly below the center of the welding torch, the x-axis along the welding direction, the y-axis perpendicular to the welding direction and on the surface of the workpiece, and the z-axis upward perpendicular to the surface of the workpiece. In this coordinate system, the correspondence between the feature points of the weld appearance features and the material flow features is determined. In practice, 20 feature points are selected on the weld contour, and 20 corresponding position points are selected in the material flow field to form a set of feature point pairs. Based on the least squares method, a spatial transformation matrix is obtained by solving an optimization problem. The matrix is a 4x4 homogeneous transformation matrix containing rotation and translation components. The obtained transformation matrix is applied to transform the material flow features into the same coordinate system as the weld appearance features, achieving spatial registration and obtaining spatial registration data.

[0088] The timestamp sequences of the weld appearance features and the material flow features are extracted. The collection time interval of the weld appearance features is 100 milliseconds, and the collection time interval of the material flow features is 33.3 milliseconds. Based on these two time sequences, common time synchronization points are determined, and the synchronization interval is selected as 100 milliseconds. Through linear interpolation method, the material flow feature data is mapped to these synchronization time points, and time alignment is performed with the weld appearance features to obtain spatio-temporal registration data.

[0089] The spatio-temporal registration data is combined with the vibration analysis results to construct a state vector. The vibration analysis results include the vibration spectral features in the welding process obtained from the acceleration sensor, such as the main frequency of 32 Hz and the amplitude of 0.75 mm. The state vector is composed of weld geometry parameters, material flow parameters, and vibration parameters, with a dimension of 25. Feature decomposition is performed on the state vector, and principal component analysis method is used to obtain a feature matrix with a dimension of 25x25. According to the feature matrix, the variance contribution rates of each feature are calculated, and the variance contribution rates of the first five principal components are 42%, 28%, 15%, 8%, and 4%, respectively. These variance contribution rates are normalized to obtain an array of weight coefficients, for example, the weight coefficients of the first five principal components are 0.42, 0.28, 0.15, 0.08, and 0.04, respectively. The state vector is weighted and combined using these weight coefficients to generate the final welding state comprehensive simulation data.

[0090] In practical applications, this method collects 200 groups of welding state comprehensive simulation data in the welding process of an aluminum alloy sheet. By comparing with the welding quality detection results, it is found that the abnormal patterns in the welding state comprehensive simulation data have a correlation of 93.5% with the welding defects, proving the effectiveness of this method in welding quality monitoring. This method integrates weld appearance, material flow, and vibration features, providing technical support for comprehensive characterization and analysis of the welding process.

[0091] In one optional implementation, the material deformation behavior under stress-thermal field coupling is calculated based on the comprehensive simulation data of the welding state, and the material flow constraints and interface bonding constraints are constructed based on the material deformation behavior, including:

[0092] The temperature field distribution parameters of the welding process are obtained, the temperature increment is determined based on the temperature field distribution parameters, the material elastic constant, initial strain state and material yield strength are obtained, the stress state is determined based on the comprehensive simulation data of the welding state, the thermal stress tensor is obtained by tensor operation of the temperature increment, the material elastic constant and the stress state, and the thermal stress tensor is combined with the initial strain state to obtain the material deformation data under the stress-thermal field coupling action.

[0093] The deformation gradient tensor is calculated based on the material deformation data, and the deformation gradient tensor is decomposed into elastic deformation components and plastic deformation components.

[0094] The stress ratio is obtained by superimposing the elastic deformation component and the plastic deformation component and dividing it by a preset reference stress. The stress ratio is then exponentially calculated with the yield strength of the material and decomposed into principal components to obtain the principal strain rate. A deformation velocity field is established based on the principal strain rate. The deformation velocity field is then energy-coupled with the inherent strength of the material to obtain the material flow constraint condition.

[0095] The interfacial potential energy is calculated based on the material flow constraint conditions. The interfacial potential energy is then decomposed at the interface to obtain the normal stress and tangential stress. The normal stress and the tangential stress are multiplied by the deformation displacement in the material deformation data to obtain the interfacial deformation. The interfacial deformation is then used to perform energy balance calculations with temperature parameters and time parameters to obtain the interfacial bonding constraint conditions.

[0096] like Figure 2 As shown, the method includes:

[0097] When calculating the material deformation behavior under stress-thermal field coupling based on the comprehensive simulation data of the welding state, it is necessary to obtain the temperature field distribution parameters of the welding process. Specifically, measuring points are arranged on the surface of the welded workpiece using measuring equipment such as thermocouples or infrared thermal imagers to record the temperature changes at each measuring point during the welding process, forming temperature-time curve data. For example, in the welding process of steel plates, 10 measuring points can be arranged at the weld and its surrounding area. The temperature at the center of the weld is measured to be 1450℃, the temperature at 10mm from the weld is 800℃, and the temperature at 20mm from the weld is 500℃. The temperature field distribution of the entire workpiece can be obtained through interpolation calculation. Based on the temperature data of two adjacent time points, the temperature increment is calculated. For example, if the temperature at a certain point is 300℃ 5 seconds after the start of welding, and the temperature rises to 500℃ 10 seconds later, then the temperature increment for that time period is 200℃.

[0098] Meanwhile, the material elastic constant, initial strain state and material yield strength need to be obtained. Take low carbon steel as an example, the elastic modulus E is 210 GPa, the Poisson's ratio is 0.3, and the initial strain state can be determined by measuring the initial deformation of the workpiece, for example, the strain of a certain region of the workpiece before welding is 0.001, and the yield strength of the material at room temperature is 350 MPa. According to the comprehensive simulation data of the welding state, the stress state is determined, including the normal stress and shear stress components in each direction. The thermal stress tensor is obtained by tensor operation of temperature increment, material elastic constant and stress state. For example, the temperature increment of a certain point is 200℃, the thermal expansion coefficient is 1.2×10 -5 / ℃, and the main component of the calculated thermal stress tensor is -50 MPa. The material deformation data under the coupling effect of stress-thermal field is obtained by combining the thermal stress tensor with the initial strain state.

[0099] The deformation gradient tensor is calculated according to the material deformation data. The deformation gradient tensor describes the change of the position relationship of the material points before and after deformation. By measuring the displacement of the corresponding points of the workpiece before and after deformation, the deformation gradient tensor is constructed. For example, the displacement of a certain point in x, y, z directions caused by welding is 0.5mm, 0.3mm and 0.1mm respectively, and the corresponding deformation gradient tensor can be represented as a corresponding 3×3 matrix. The deformation gradient tensor is decomposed into elastic deformation component and plastic deformation component. Elastic deformation is recoverable deformation, and plastic deformation is permanent deformation that cannot be recovered. In the high-temperature welding area, plastic deformation is dominant, while in the low-temperature area away from the weld, elastic deformation is dominant.

[0100] The elastic and plastic deformation components are superimposed and divided by a preset reference stress to obtain a stress ratio. The preset reference stress can be the yield strength of the material at room temperature, such as 350 MPa. The stress ratio is exponentially operated with the material yield strength to obtain the principal strain rate. For example, the calculated principal strain rates are 0.005 / s, 0.003 / s and -0.008 / s. The velocity vector field expression v(x, y, z) of the material point in space is constructed, where each component is linearly combined by the product of the principal strain rate and the coordinate. For example, vx=ε1·x+c1, vy=ε2·y+c2, vz=ε3·z+c3, where ε1, ε2, ε3 are the principal strain rates, and c1, c2, c3 are integral constants, which can be determined by the boundary conditions. In actual calculation, mesh division is established for the welding area, and the velocity vector at each grid node is calculated to form the overall deformation velocity field. The deformation velocity field is energy coupled with the material inherent strength to obtain the material flow constraint condition: the deformation power density W=σ:D is calculated, where σ is the stress tensor, D is the deformation rate tensor (composed of the gradient of the deformation velocity field), and ":" represents the tensor contraction operation. For example, the principal components of the stress tensor of a certain point are [200, 150, 100] MPa, and the corresponding principal components of the deformation rate tensor are [0.005, 0.003, -0.008] / s, then the deformation power density W=200×0.005+150×0.003+100×(-0.008)=0.45 MPa / s. The total deformation power is integrated over the entire deformation region, compared with the material inherent strength (yield strength or flow stress varying with temperature), and the material flow constraint condition is determined. The material inherent strength varies with temperature, such as the strength of low carbon steel at 1000°C can be reduced to about 20% of the room temperature strength.

[0101] The interface potential energy is calculated according to the material flow constraint condition. The interface potential energy represents the energy required for interface deformation, and is related to the interface bonding strength and deformation amount. The interface potential energy is decomposed into stress at the interface to obtain the normal stress and the tangential stress. For example, the calculated interface normal stress is 25 MPa, and the tangential stress is 15 MPa. The normal stress, the tangential stress and the deformation displacement amount in the material deformation data are multiplied to obtain the interface deformation amount. For example, if the deformation displacement amount of a point on the interface is 0.2 mm, then the interface deformation amount is 5 MPa·mm and 3 MPa·mm. The interface deformation amount is subjected to energy balance calculation with the temperature parameter and the time parameter to obtain the interface bonding constraint condition: an interface energy balance equation Edef=Ebond+Etherm is established, wherein Edef is the interface deformation energy, which is composed of the normal deformation energy and the tangential deformation energy, Edef=∫(σn·δn+τ·δτ)dA, wherein σn is the normal stress, δn is the normal displacement, τ is the tangential stress, and δτ is the tangential displacement, and the integral range is the entire interface area; Ebond is the interface bonding energy, which is related to the interface area and the unit area bonding energy γ, Ebond=γ·A; Etherm is the thermal energy contribution, which is related to the temperature T and the holding time t, Etherm=k·T·t, wherein k is the thermal energy conversion coefficient. The interface bonding constraint condition is obtained by solving the equation, which is expressed as a critical stress or an interface strength. For example, under the condition of T=1200℃ and t=30s, the calculated interface bonding strength is 120 MPa.

[0102] In practical applications, the heat input amount can be affected by adjusting welding parameters such as current, voltage, welding speed, etc., so as to change the temperature field distribution, and then control the material deformation behavior and the interface bonding quality. For example, increasing the welding current from 200 A to 250 A can increase the weld temperature from 1450℃ to 1550℃, accelerate the material flow, but at the same time, it will also increase the heat affected zone, resulting in greater residual stress and deformation. By establishing the above material flow constraint and interface bonding constraint model, the welding quality under different welding parameters can be predicted, and theoretical guidance can be provided for welding process optimization.

[0103] In an alternative embodiment, the material deformation resistance is evaluated based on the material flow constraint, the coupling efficiency of the interface shear force and the friction power consumption is evaluated based on the interface bonding constraint, and the dynamic compensation mechanism is established based on the ratio of the material deformation resistance to the coupling efficiency, which includes:

[0104] The dislocation density distribution in the shear modulus and the material flow constraint is obtained, the dislocation density distribution is mapped to the stress space corresponding to the shear modulus to obtain a dislocation proliferation coefficient, and the material deformation resistance is obtained by tensor operation of the dislocation proliferation coefficient and the principal strain rate;

[0105] The normal stress in the interface bonding constraint is obtained, the temperature field distribution parameter and the material inherent strength are subjected to exponential operation, and then the normal stress is subjected to superposition operation to obtain the interface shear force, and the interface shear force and the friction power consumption are subjected to ratio operation to obtain the coupling efficiency;

[0106] The ratio of the material deformation resistance and the coupling efficiency is taken as a compensation reference, the temperature gradient and the temperature influence coefficient of the welding process are obtained, the compensation reference is multiplied by the temperature influence coefficient and the temperature gradient respectively, and then the products are added to obtain a compensation coefficient, and the compensation coefficient is subjected to exponential softening operation with the plastic deformation component to obtain a compensation amount.

[0107] Shear modulus data in the welding process is obtained by a high-precision sensor, and the data is stored in a matrix form. For example, in the aluminum alloy FSW welding, the shear modulus matrix is [28 GPa, 27.5 GPa, 27 GPa]. At the same time, the dislocation density model is used to collect the dislocation density distribution in the material flow constraint, which is recorded as a density matrix, and the typical value range is 10 12 -10 14 / m 2 . The dislocation density distribution is converted to the stress space corresponding to the shear modulus through a stress mapping function to obtain a dislocation multiplication coefficient. The mapping process is realized by using Taylor relationship. For 6061 aluminum alloy, the typical value of the dislocation multiplication coefficient is 3.2×10 -5 mm 2 . The dislocation multiplication coefficient and the principal strain rate are subjected to tensor operation, specifically, the two are subjected to point multiplication and then tensor contraction, so as to obtain the material deformation resistance. Under standard working conditions, the deformation resistance of 6061 aluminum alloy is about 135 MPa.

[0108] For the interface bonding constraint, the normal stress distribution of the welding interface is obtained by the pressure sensor array, and the data sampling frequency is set to 200 Hz to ensure the capture of transient changes. Under the typical FSW process parameters (rotation speed 1000 rpm, feed speed 100 mm / min), the average value of the interface normal stress is about 80 MPa. The temperature field distribution parameters during welding are collected, including the temperature gradient and peak temperature of the heat affected zone. For aluminum alloy welding, the peak temperature is usually 480℃, and the temperature gradient is 40℃ / mm. The temperature field distribution parameters and the inherent strength of the material (320 MPa for aluminum alloy) are exponentially operated. The specific implementation is to divide the temperature parameter by the melting point of the material (660℃ for aluminum alloy) and take the inverse, and then multiply it by the inherent strength of the material to obtain the temperature correction coefficient 0.72. The temperature correction coefficient and the normal stress are superimposed to obtain the interface shear force. For aluminum alloy welding under the given parameters, the interface shear force is about 57.6 MPa. The friction power consumption during welding is measured, which is calculated by the product of the torque value recorded by the torque sensor and the rotation speed. The typical value is 2.3kW. The interface shear force and the friction power consumption are operated by ratio to obtain the coupling efficiency, which is dimensionless and about 0.68 under the given conditions.

[0109] The establishment of the dynamic compensation mechanism is based on the ratio of the material deformation resistance to the coupling efficiency. The material deformation resistance (135 MPa) is divided by the coupling efficiency (0.68) to obtain the compensation reference 198.5 MPa. The temperature gradient during welding is obtained in real time by an infrared thermal imager, with an accuracy of ±2℃. For aluminum alloy welding, the typical value of the measured temperature gradient is 40℃ / mm. Based on the thermal physical properties of the material, the temperature influence coefficient is calculated, which reflects the influence of temperature change on the plastic behavior of the material. For aluminum alloy, at a working temperature of 480℃, the temperature influence coefficient is 0.85. The compensation reference is multiplied by the temperature influence coefficient and the temperature gradient respectively, and the product is 169 MPa and 7940 MPa·℃ / mm. The sum of the two products is the compensation coefficient 8109 MPa·℃ / mm.

[0110] In the plastic deformation processing link, the plastic deformation component during welding is measured by a strain sensor. For aluminum alloy FSW, the typical plastic deformation value is 0.35. The compensation coefficient is exponentially softened with the plastic deformation component. The calculation method is to take the plastic deformation component as the index and the compensation coefficient as the base number for power operation, and then multiply it by the softening coefficient 0.12 to finally obtain the compensation amount 36.5 MPa. This compensation amount is used to adjust the welding parameters. For example, when the compensation amount exceeds 30 MPa, the rotation speed is automatically increased by 50 rpm or the feed speed is reduced by 5 mm / min to ensure the welding quality.

[0111] In practical applications, the dynamic compensation mechanism is used to control the friction stir welding of 6061 aluminum alloy, and under the conditions of 1000 rpm rotational speed and 100 mm / min feeding speed, the tensile strength of the weld is increased by 12.5%, reaching 285 MPa, close to 95% of the strength of the base material, the hardness distribution of the weld is more uniform, the standard deviation is reduced from 12HV to 5HV, the forming quality of the weld is significantly improved, there is no obvious defect, and the joint efficiency is improved to 0.92, which is significantly better than the joint efficiency of 0.81 without using the technical scheme.

[0112] The embodiment of the application is a virtual simulation and control system for friction stir welding based on digital twinning, and the system comprises:

[0113] The first unit is used for collecting welding process vibration signals by using an embedded acceleration sensor array, decomposing the vibration signals along the tool axial and radial directions, extracting vibration characteristic frequencies and vibration characteristic amplitudes in each direction, identifying periodic changes in the vibration signals through adaptive singular spectrum analysis, calculating instantaneous deformation energy by combining the periodic changes with workpiece surface strain distribution, analyzing the frequency spectrum characteristics of the vibration signals and tracing abnormal sources based on the vibration characteristic frequencies and vibration characteristic amplitudes when the instantaneous deformation energy exceeds a preset process threshold, and generating a vibration analysis result.

[0114] The second unit is used for collecting welding area images, extracting weld appearance features and material flow features based on the welding area images, combining the weld appearance features, the material flow features, and the vibration analysis result after spatio-temporal registration, and forming comprehensive simulation data of a welding state.

[0115] The third unit is used for calculating material deformation behavior under stress-thermal field coupling based on the comprehensive simulation data of the welding state, constructing material flow constraints and interface bonding constraints based on the material deformation behavior, evaluating material deformation resistance based on the material flow constraints, evaluating the coupling efficiency of interface shear force and friction power consumption based on the interface bonding constraints, establishing a dynamic compensation mechanism based on the ratio of the material deformation resistance to the coupling efficiency, and controlling the phase relationship between the tool head rotational speed and the axial pressure based on the dynamic compensation mechanism.

[0116] In the third aspect of the embodiment of the application, an electronic device is provided, comprising:

[0117] a processor;

[0118] a memory for storing processor-executable instructions;

[0119] The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0120] In a fourth aspect, the present application provides a computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method described above.

[0121] The present application can be a method, an apparatus, a system, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for performing various aspects of the present application.

[0122] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A virtual simulation and control method for friction welding based on digital twins, characterized in that, include: Vibration signals during the welding process are acquired using an embedded accelerometer array. The vibration signals are decomposed along the tool axis and radial direction to extract the characteristic frequencies and amplitudes of vibration in each direction. Periodic variations in the vibration signals are identified through adaptive singular spectrum analysis. The periodic variations are combined with the strain distribution on the workpiece surface to calculate the instantaneous deformation energy. When the instantaneous deformation energy exceeds a preset process threshold, the spectral characteristics of the vibration signal are analyzed based on the characteristic frequencies and amplitudes to trace the source of the anomaly and generate vibration analysis results. Images of the welding area are acquired, and weld morphology features and material flow features are extracted based on the images. The weld morphology features and material flow features are spatiotemporally registered and combined with the vibration analysis results to form comprehensive simulation data of the welding state. Based on the comprehensive simulation data of the welding state, the material deformation behavior under stress-thermal field coupling is calculated, and material flow constraints and interface binding constraints are constructed based on the material deformation behavior. The deformation resistance of the material is evaluated based on the material flow constraints, and the coupling efficiency of the interface shear force and frictional power consumption is evaluated based on the interface bonding constraints. A dynamic compensation mechanism is established based on the ratio of the material deformation resistance to the coupling efficiency, including: obtaining the shear modulus and dislocation density distribution in the material flow constraints; mapping the dislocation density distribution to the stress space corresponding to the shear modulus to obtain the dislocation multiplication coefficient; performing tensor operation on the dislocation multiplication coefficient and the principal strain rate to obtain the material deformation resistance; obtaining the normal stress in the interface bonding constraints; performing exponential operation on the temperature field distribution parameters and the material's inherent strength, and then superimposing it on the normal stress to obtain the interface shear force; performing a ratio operation on the interface shear force and frictional power consumption to obtain the coupling efficiency; using the ratio of the material deformation resistance to the coupling efficiency as a compensation benchmark; obtaining the temperature gradient and temperature influence coefficient of the welding process; multiplying the compensation benchmark by the temperature influence coefficient and the temperature gradient respectively, and then adding the products to obtain the compensation coefficient; and performing an exponential softening operation on the compensation coefficient and the plastic deformation component to obtain the compensation amount. The dynamic compensation mechanism is used to control the phase relationship between the tool head rotation speed and the axial pressure.

2. The method according to claim 1, characterized in that, The vibration signal is decomposed along the tool's axial and radial directions to extract the characteristic frequencies and amplitudes of vibration in each direction. Adaptive singular spectrum analysis is then used to identify periodic variations in the vibration signal, including: Based on the angle between the embedded accelerometer and the tool axis, the vibration signal is decomposed into axial vibration components and radial vibration components. The axial vibration components and the radial vibration components are decomposed using the db4 wavelet basis to obtain multi-level frequency band coefficients. The energy characteristic value of each level of frequency band coefficient is calculated. The frequency band with the largest proportion of the energy characteristic value is defined as the main frequency band. The main frequency band is subjected to a fast Fourier transform to obtain a spectrum. The largest amplitude value in the spectrum is determined as the vibration characteristic amplitude. The frequency corresponding to the largest amplitude value is determined as the vibration characteristic frequency. In the main frequency band, the reciprocal of the vibration characteristic frequency is multiplied by a preset dimension to obtain the embedding dimension. The axial vibration component and the radial vibration component are constructed into a trajectory matrix using the embedding dimension. The trajectory matrix is ​​subjected to singular value decomposition, and singular value components with a value greater than a preset energy contribution threshold are selected. The axial vibration component and the radial vibration component are reconstructed based on the singular value components to obtain the characteristic vibration signal. The mean of the characteristic vibration signal is subtracted to obtain a centered sequence. The centered sequence is time-shifted to obtain multiple delayed sequences. The sum of the products of the centered sequence and each delayed sequence is calculated and divided by the variance of the centered sequence to obtain the autocorrelation coefficient. The duration corresponding to the first peak of the autocorrelation coefficient is determined as the periodic feature, thus obtaining the periodic change in the vibration signal.

3. The method according to claim 1, characterized in that, The instantaneous deformation energy is calculated by combining the periodic changes with the strain distribution on the workpiece surface. When the instantaneous deformation energy exceeds a preset process threshold, the spectral characteristics of the vibration signal are analyzed based on the vibration characteristic frequency and vibration characteristic amplitude to trace the abnormal source and generate vibration analysis results, including: The workpiece surface is divided into a preset number of grid points. The vibration sampling sequence is determined based on the vibration period characteristics. The strain value at each grid point is obtained at each moment of the vibration sampling sequence. The strain value at all grid points is statistically analyzed to obtain the strain field intensity distribution. The elastic modulus of the material is obtained, and the stress distribution of the workpiece is obtained by multiplying the piecewise linearized strain field intensity distribution with the elastic modulus of the material. The instantaneous deformation energy is obtained by calculating the integral value of the workpiece stress distribution and the strain field intensity distribution in the welding influence region. When the instantaneous deformation energy is greater than the preset process threshold, the energy spectrum entropy of the main frequency band is calculated as the spectrum feature, and the product of the spectrum feature and the vibration feature frequency is used as the abnormal intensity index. On the two-dimensional plane formed by the grid points, the difference of the vibration feature amplitude of the adjacent grid points is divided by the grid point spacing to obtain the vibration gradient value, and the location of the maximum vibration gradient value is determined as the location of the abnormal source. Based on the distance from the location of the anomaly source to each grid point, the vibration characteristic amplitude is radially interpolated on the two-dimensional plane to obtain the vibration amplitude distribution. The line connecting adjacent grid points where the vibration gradient value is greater than the anomaly intensity index is determined as the vibration propagation path. Vibration analysis results are generated based on the location of the anomaly source, the vibration amplitude distribution, and the vibration propagation path.

4. The method according to claim 1, characterized in that, Based on the weld area image, weld morphology features and material flow features are extracted. These features are then spatiotemporally registered and combined with the vibration analysis results to form comprehensive welding state simulation data, including: Edge detection is performed on the welding area image to obtain the weld contour, the geometric dimension parameters of the weld contour are extracted, and the weld morphology features are obtained based on the geometric dimension parameters; Extract a continuous image sequence of the molten pool region from the welding area image, obtain the material displacement field through cross-correlation operation of the continuous image sequence, and obtain the material flow characteristics by calculating the spatial gradient after differential calculation of the material displacement field in the time dimension. A spatial coordinate system is established in the welding area. The correspondence between the feature points of the weld morphology and the material flow features is determined in the spatial coordinate system. The correspondence between the feature points is converted into a spatial transformation matrix based on the least squares method. The spatial transformation matrix is ​​used to spatially register the weld morphology and the material flow features to obtain spatial registration data. Extract the timestamp sequences of the weld morphology features and the material flow features, determine the time synchronization point based on the sampling interval of the timestamp sequences, and map the spatial registration data to the time synchronization point to obtain spatiotemporal registration data; The spatiotemporal registration data and vibration analysis results are combined to construct a state vector. The state vector is decomposed into a feature matrix. The variance contribution rate of each feature is calculated based on the feature matrix. The variance contribution rate is normalized to obtain a weight coefficient. The state vector is weighted and combined using the weight coefficient to generate comprehensive simulation data of welding state.

5. The method according to claim 1, characterized in that, Based on the comprehensive simulation data of the welding state, the material deformation behavior under stress-thermal field coupling is calculated. Based on the material deformation behavior, material flow constraints and interface bonding constraints are constructed, including: The temperature field distribution parameters of the welding process are obtained, the temperature increment is determined based on the temperature field distribution parameters, the material elastic constant, initial strain state and material yield strength are obtained, the stress state is determined based on the comprehensive simulation data of the welding state, the thermal stress tensor is obtained by tensor operation of the temperature increment, the material elastic constant and the stress state, and the thermal stress tensor is combined with the initial strain state to obtain the material deformation data under the stress-thermal field coupling action. The deformation gradient tensor is calculated based on the material deformation data, and the deformation gradient tensor is decomposed into elastic deformation components and plastic deformation components. The stress ratio is obtained by superimposing the elastic deformation component and the plastic deformation component and dividing it by a preset reference stress. The stress ratio is then exponentially calculated with the yield strength of the material and decomposed into principal components to obtain the principal strain rate. A deformation velocity field is established based on the principal strain rate. The deformation velocity field is then energy-coupled with the inherent strength of the material to obtain the material flow constraint condition. The interfacial potential energy is calculated based on the material flow constraint conditions. The interfacial potential energy is then decomposed at the interface to obtain the normal stress and tangential stress. The normal stress and the tangential stress are multiplied by the deformation displacement in the material deformation data to obtain the interfacial deformation. The interfacial deformation is then used to perform energy balance calculations with temperature parameters and time parameters to obtain the interfacial bonding constraint conditions.

6. A virtual simulation and control system for friction welding based on digital twins, used to implement the method as described in any one of claims 1-5, characterized in that, include: The first unit is used to collect vibration signals during the welding process using an embedded accelerometer array, decompose the vibration signals along the tool axis and radial direction, extract the vibration characteristic frequencies and vibration characteristic amplitudes in each direction, identify periodic changes in the vibration signals through adaptive singular spectrum analysis, calculate the instantaneous deformation energy by combining the periodic changes with the workpiece surface strain distribution, and when the instantaneous deformation energy exceeds a preset process threshold, analyze the spectral characteristics of the vibration signal based on the vibration characteristic frequencies and vibration characteristic amplitudes and trace the abnormal source to generate vibration analysis results. The second unit is used to acquire images of the welding area, extract weld morphology features and material flow features based on the images of the welding area, and combine the weld morphology features and material flow features with the vibration analysis results after spatiotemporal registration to form comprehensive simulation data of the welding state. The third unit is used to calculate the material deformation behavior under stress-thermal field coupling based on the comprehensive simulation data of the welding state, and to construct material flow constraints and interface binding constraints based on the material deformation behavior. The deformation resistance of the material is evaluated based on the material flow constraints, the coupling efficiency of the interface shear force and frictional power consumption is evaluated based on the interface combination constraints, a dynamic compensation mechanism is established based on the ratio of the material deformation resistance to the coupling efficiency, and the phase relationship between the tool head rotation speed and the axial pressure is controlled based on the dynamic compensation mechanism.

7. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

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