Wire straightening process simulation method based on digital twinning

Abstract

The invention provides a wire straightening process simulation method based on digital twinning, and the method comprises the steps: collecting the characteristics of an original acoustic emission signal, calculating the initial distribution range of the internal crack propagation of a wire, and obtaining a crack propagation parameter; according to the crack propagation parameters, simulating internal stress distribution of the wire under the action of the straightening force, the straightening speed and the straightening angle by adopting a finite element analysis method, and determining the position of a stress concentration area; the bending deformation degree of the stress concentration area position under the influence of the repeated bending effect and the stretching effect is calculated, and the change trend of crack propagation is judged in combination with acoustic emission signal characteristics; according to the parameter adjustment demand quantity, the straightening force is dynamically adjusted, and the stress distribution data after adjustment is determined by iteratively calculating the change of internal stress distribution; and verifying the stability of internal stress distribution and bending deformation degree according to the target straightening angle and the adjusted straightening speed and straightening force to obtain a dynamic balance state in the wire straightening process.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and particularly to a method for simulating the wire straightening process based on digital twin. Background Art

[0002] The safe operation and maintenance of the distribution network in the power system is a crucial research direction in the energy field, which is directly related to the stability of the power grid and the reliability of power supply. Among them, the straightening process of the outgoing wires of the distribution transformer bridge occupies a core position. During long-term operation, internal cracks may occur in the wires due to environmental stress or manufacturing defects. If the straightening process is not properly controlled, it may induce crack propagation or even fracture, seriously threatening the safety of the line. Therefore, it is of great practical significance to study the damage evolution and control methods during the wire straightening process. Currently, wire straightening mainly relies on traditional mechanical methods, and the force and speed are adjusted based on manual experience or fixed parameters. However, these methods generally lack the ability to real-time monitor internal defects of the wire and cannot dynamically sense the crack propagation state, resulting in strong blindness in setting straightening parameters. Especially when facing complex stress effects, it is difficult to effectively avoid the aggravation of damage. The core challenge in this field is how to accurately characterize the dynamic evolution of internal cracks in the wire during the straightening process and achieve intelligent adjustment of process parameters. Specifically, crack propagation is directly affected by the straightening force, speed, and angle, but it is difficult for existing technologies to quantify the coupling relationship between these parameters and the internal stress distribution and bending deformation. At the same time, the lack of effective on-line detection means makes it difficult to timely capture the critical state of crack propagation, resulting in lagging process optimization and incomplete elimination of fracture risk. These unresolved technical factors have given rise to problems that urgently need to be solved, such as how to find a balance between real-time and accuracy and seamlessly connect the damage mechanism with process control. Therefore, how to construct a damage model of the wire based on fracture mechanics, use the finite element method to simulate the internal stress distribution and bending deformation during the straightening process, and combine the acoustic emission sensing technology to predict the acoustic emission signal characteristics of crack propagation, so as to realize the automatic optimization of the straightening force, speed, and angle, has become the key problem that this research urgently needs to solve. Summary of the Invention

[0003] The present invention provides a method for simulating the wire straightening process based on digital twin, which mainly includes: Collect the original acoustic emission signal characteristics, calculate the initial distribution range of the internal crack propagation of the wire, and obtain the crack propagation parameters; According to the crack propagation parameters, use the finite element analysis method to simulate the internal stress distribution of the wire under the action of the straightening force, straightening speed, and straightening angle, and determine the position of the stress concentration area; Calculate the degree of bending deformation of the position of the stress concentration area under the influence of repeated bending and tensile actions, and combine the acoustic emission signal characteristics to judge the change trend of crack propagation; Obtain the change amplitude of the acoustic emission signal characteristics according to the change trend of crack propagation, and combine with the pre-established acoustic emission model to predict the mapping relationship between crack propagation and straightening parameters, so as to obtain the parameter adjustment requirement; According to the parameter adjustment requirement, dynamically adjust the straightening force, and determine the adjusted stress distribution data by iteratively calculating the change of the internal stress distribution; Combined with the degree of bending deformation and the adjusted stress distribution data, judge whether the straightening speed parameter needs to be adjusted synchronously. If adjustment is required, reduce the straightening speed, and calculate the coupling relationship between the degree of bending deformation and the crack propagation direction after reducing the straightening speed to determine the target straightening angle; According to the target straightening angle, the adjusted straightening speed and straightening force, verify the stability of the internal stress distribution and the degree of bending deformation, and obtain the dynamic balance state of the wire straightening process; Obtain the acoustic emission signal characteristics in the dynamic balance state, and for the real-time change of crack propagation, repeatedly execute the acoustic emission model prediction and parameter adjustment steps to determine the continuously optimized parameter combination in the wire straightening process.

[0004] Furthermore, collect the original acoustic emission signal characteristics, calculate the initial distribution range of crack propagation inside the wire, and obtain crack propagation parameters, including: obtain the wire stress distribution data according to the wire internal state measurement device, collect stress wave signals by evenly arranging at least three groups of acoustic emission sensor arrays on the wire surface, and arrange one sensor every three wire diameters along the axial direction of the wire for each acoustic emission sensor array. For the collected stress wave signals, perform preprocessing using a Butterworth high-pass filter to filter out low-frequency interference signals to obtain the acoustic emission signal, and the acoustic emission signal includes four time-domain characteristic parameters: amplitude, rise time, duration, and ring count. Perform wavelet transform on the acoustic emission signal, select the three-layer wavelet decomposition coefficient as the frequency-domain characteristic parameter, calculate the frequency range of the acoustic emission signal according to the acoustic emission characteristic curve of the wire material, calculate the signal amplitude attenuation law using an exponential decay model, and calibrate the signal characteristic parameters in combination with the acoustic emission sensor sensitivity curve. Calculate the sound source position coordinates using the time difference positioning method, and the time difference positioning method is based on the time difference of arrival of the acoustic emission signals received by at least three sensors and the sound wave propagation speed in the wire, and establish a sound source coordinate calculation equation set to solve for the three-dimensional coordinates of the sound source. Calculate the sound source density in unit volume according to the three-dimensional coordinates of the sound source to obtain the sound source density distribution, establish the corresponding relationship between the sound source density and the crack propagation range in combination with the wire stress distribution data, and calculate the crack size through the calibration curve of the acoustic emission signal amplitude and the crack size. Construct a wire internal crack propagation parameter data set based on the sound source density distribution and the crack size, and the data set includes three characteristic quantities: crack position coordinates, crack size, and density. Use the least squares method to fit to obtain the initial distribution range of crack propagation.

[0005] Furthermore, according to the crack propagation parameters, the finite element analysis method is used to simulate the internal stress distribution of the wire under the action of straightening force, straightening speed and straightening angle, and to determine the location of the stress concentration area, including: constructing a three-dimensional solid model of the wire according to the basic parameters of crack propagation, the wire material properties including three parameters of elastic modulus, Poisson's ratio and yield strength, using hexahedral second-order elements to mesh the wire solid model, setting a refined area with a mesh size less than one-tenth of the crack size in the crack propagation area, and judging the mesh quality according to the standard that the element distortion degree is less than 0.3. Applying three load parameters of straightening force, straightening speed and straightening angle to the boundary of the wire solid model, constructing a wire deformation control equation based on the principle of minimum total potential energy, using the incremental iteration method to solve the deformation equations to obtain the displacement field, and calculating the wire deformation amount through the geometrically nonlinear strain tensor. Establishing a strain-displacement relationship matrix according to the deformation amount, calculating the stress components using four-point Gaussian integration, calculating the equivalent stress based on the von Mises yield criterion, and using the tangent modulus iteration method to calculate the plastic stress increment when the equivalent stress exceeds the material yield strength. Constructing a stress gradient field for the stress components, calculating the stress change rate through the second-order stress derivative, determining the stress concentration point when the stress change rate exceeds the preset threshold, and calculating the stress value of the stress concentration point using the stress extrapolation method. Constructing a stress concentration area according to the stress concentration points, clustering the stress concentration points by the spatial distance weighting method, calculating the spatial distribution range and the maximum stress value of the stress concentration area, and determining the position coordinates of the stress concentration area.

[0006] Furthermore, calculate the degree of bending deformation of the stress concentration area under the influence of repeated bending and tensile actions, and combine the characteristics of acoustic emission signals to judge the change trend of crack propagation, including: calculating the bending deformation amount according to the coordinate of the stress concentration area, numerically integrating the bending deformation differential equation using the fourth-order Runge-Kutta method, the differential equation includes the bending moment term under repeated bending load and the axial force term under tensile load, and calculating the combined deformation field distribution through the linear superposition principle of load and strain. The deformation calculation of the wire under repeated bending and tensile actions uses the fourth-order Runge-Kutta method, which discretizes the bending differential equation into four sub-step lengths, and calculates the function value of the current step through the slope of the previous step. Arrange an acoustic emission sensor array for the combined deformation field distribution to collect acoustic emission signals, perform three-layer decomposition on the acoustic emission signals using wavelet transform to extract spectral characteristics, and calculate the acoustic emission event density according to the number of times the amplitude of the acoustic emission signal exceeds the preset threshold per unit time. Calculate the crack opening displacement through the combined deformation field distribution data, decompose the displacement field using the eighth-order Fourier series to obtain the main harmonic components, calculate the stress intensity factor at the crack front based on the strain energy density method, and determine crack propagation when the stress intensity factor exceeds the material fracture toughness. Establish a crack propagation driving force function using fracture mechanics parameters for the crack propagation state, the parameters include three quantities: stress intensity factor, strain energy release rate, and crack opening displacement, and determine the crack propagation direction through the maximum principal stress criterion. Construct a feature vector according to the acoustic emission event density and the crack propagation driving force function, use a support vector regressor to predict the crack propagation rate, and judge the crack propagation trend through three characteristic quantities: acoustic emission signal amplitude, frequency, and duration.

[0007] Furthermore, the change amplitude of the acoustic emission signal characteristics is obtained based on the change trend of crack propagation, and the mapping relationship between crack propagation and straightening parameters is predicted by combining with the pre-established acoustic emission model to obtain the parameter adjustment demand, including: calculating the change amount of acoustic emission signal characteristics according to the crack propagation trend, using the central difference method to calculate the change rates of three characteristic quantities of the acoustic emission signal amplitude, frequency, and duration, performing weighted summation based on the importance of the characteristic quantities to allocate weight coefficients, and performing maximum-minimum normalization processing on the weighted result to obtain the acoustic emission signal change index. For the acoustic emission signal change index, training samples containing the corresponding relationship between signal characteristics and crack states are extracted from the pre-established acoustic emission database, and the database records the acoustic emission signal characteristics in different crack propagation stages. A mapping relationship between acoustic emission signal characteristics and crack propagation states is constructed using a deep residual network, and the network includes five convolutional layers and three fully connected layers. The prediction error is calculated through a cross-entropy loss function, and the network parameters are optimized using the stochastic gradient descent method. According to the mapping relationship, the influence degree of straightening parameters on crack propagation is calculated, the main characteristic components of three parameters, namely straightening force, straightening speed, and straightening angle, are extracted using the principal component regression method, and the parameter sensitivity is determined by calculating the grey correlation coefficient between the parameter sequence and the reference sequence. A response function between straightening parameters and crack propagation states is established for the parameter sensitivity, a mapping relationship matrix is constructed using the cubic spline interpolation method, and the straightening parameter adjustment demand is obtained by solving the nonlinear equation system based on the steepest descent method.

[0008] Furthermore, according to the parameter adjustment demand, the straightening force is dynamically adjusted, and the change of the internal stress distribution is determined through iterative calculation to obtain the adjusted stress distribution data, including: setting the adjustment interval of the straightening force according to the parameter adjustment demand, using the adaptive step method with a step factor of 0.1 to perform segmented adjustment on the straightening force, calculating the strain increment at each adjustment step through the elastoplastic constitutive equation, and calculating the stress increment corresponding to the elastic strain based on Hooke's law. An iterative calculation process is constructed for the stress increment, and the nonlinear stress equation system is solved using the Newton iteration method. When the equivalent stress of the element exceeds the yield strength, the tangent modulus is used to calculate the plastic stress increment, and the convergence of the stress field is verified through the force balance equation and the geometric compatibility equation. A stress distribution function is constructed based on the iterative calculation result, the stress field is meshed using tetrahedral elements, the stress value is calculated using the shape function at the element nodes, and a stress distribution function is constructed through linear interpolation to obtain a piecewise continuous stress field. For the stress field, the three principal stress components are calculated, and the accuracy of the stress field is judged using the first invariant and the second invariant of the stress tensor. When the stress error is less than the preset threshold, the stress field is determined to converge. An equivalent stress distribution is constructed based on the stress field, the internal stress of the element is calculated using the Gaussian integration method, and the stress field is smoothed using the residual least squares method to obtain the adjusted stress distribution data.

[0009] Furthermore, in combination with the degree of bending deformation and the adjusted stress distribution data, it is determined whether the straightening speed parameter needs to be adjusted synchronously. If adjustment is required, the straightening speed is reduced, and the coupling relationship between the degree of bending deformation and the crack propagation direction after reducing the straightening speed is calculated to determine the target straightening angle, including: calculating the stress change rate using the five-point difference format based on the degree of bending deformation and the adjusted stress distribution data, calculating the speed influence coefficient through the linear correlation function between the stress change rate and the straightening speed, and determining that the straightening speed parameter needs to be adjusted when the speed influence coefficient exceeds the critical value. A speed adjustment function is constructed for the speed influence coefficient, the straightening speed is reduced based on a proportional-integral controller, the strain increment corresponding to the speed reduction amount is calculated using the Euler method, and the degree of bending deformation after speed reduction is calculated through the deformation curvature equation. The principal stress distribution is calculated based on the degree of bending deformation, the crack propagation direction is determined using the maximum shear stress criterion, and the crack propagation angle is determined when the shear stress exceeds the critical stress of the material. The strain energy density distribution is calculated for the crack propagation angle, the element strain energy is calculated using the four-point Gaussian quadrature method, and the coupling relationship between the degree of bending deformation and the crack propagation direction is established through the strain energy density matrix. A target function is constructed based on the coupling relationship, the genetic algorithm is used to optimize the straightening angle parameter, the angle parameter sensitivity is calculated through the partial derivative of the strain energy density with respect to the angle, and the target straightening angle is obtained by solving the optimization equation based on the principle of minimum strain energy.

[0010] Furthermore, collect the bending deformation data and crack propagation data during the straightening process of the wire to obtain the state information, compare it with the preset threshold, and judge the interaction intensity. If the interaction intensity exceeds the preset threshold, analyze the coupling characteristics of the bending deformation and crack propagation, obtain the dynamic change trend, calculate the interaction relationship during the wire straightening process, predict the critical point of the equilibrium state, obtain the adjustment parameters during the straightening process, generate the wire straightening control instruction, and obtain the dynamic equilibrium state, including: collect the wire deformation and crack data according to the strain sensor and acoustic emission sensor, preprocess the deformation signal and crack signal by using a band-pass filter, calculate the correlation coefficient of the two signals through the cross-correlation function, and judge the interaction intensity based on the correlation coefficient threshold of 0.8. For the said correlation coefficient, adopt the singular value decomposition method to extract the main feature vectors of the bending deformation and crack propagation, and the feature vectors include four components: deformation amplitude, deformation frequency, crack length, and crack propagation rate. Construct a double-layer recursive neural network according to the feature vectors, set 4 neurons in the network input layer and 8 neurons in the hidden layer, and train the network parameters through the time series backpropagation algorithm to obtain the dynamic change trend of the deformation and crack propagation. Establish the state equation of the wire straightening process for the said dynamic change trend, and the state equation includes three state variables: deformation amount, stress component, and crack length, and use the quadratic Lyapunov function to judge the stability of the state equation. According to the stability analysis result of the state equation, calculate the critical value of the state variable through the bifurcation theory, and the critical value corresponds to the equilibrium point of the state equation, and generate the adjustment amounts of three parameters: straightening force, straightening speed, and straightening angle based on the critical value. Construct a control instruction sequence for the said adjustment amount, smooth the control instruction by using a Butterworth filter, and adjust the wire straightening parameters in real time through a proportional-integral controller. When the ratio of the deformation rate to the crack propagation rate is stable within the preset range, it is determined that the dynamic equilibrium state is reached.

[0011] Furthermore, based on the target straightening angle, the adjusted straightening speed, and the straightening force, verify the stability of the internal stress distribution and the degree of bending deformation to obtain the dynamic equilibrium state of the wire straightening process, including: constructing a three-dimensional parameter space according to the target straightening angle, straightening speed, and straightening force, generating parameter combination points using the orthogonal design method, calculating the stress distribution corresponding to each parameter combination by the finite element method, and calculating the bending deformation amount based on the nodal displacement. Calculate the von Mises stress of each element for the stress distribution data, construct a stress field stability criterion using the global stress average value and standard deviation, judge the uniformity of the stress field by calculating the stress gradient, and evaluate the stability of the stress field based on the Lyapunov exponent. Establish a phase space according to the stress field stability index, reconstruct the dynamic characteristics of the stress field in the three-dimensional phase space, and judge the steady-state characteristics of the stress field by calculating the convergence of the phase space trajectory. Establish a deformation stability evaluation index for the steady-state characteristics of the stress field, calculate the adaptive thresholds of the deformation amount and stress amount using the mean shift method, and predict the deformation trend through a Kalman filter. Construct a Fourier spectrum according to the deformation trend, calculate the main frequency component of the deformation signal using the spectrum energy distribution, and determine that the deformation reaches a stable state when the energy ratio of the main frequency component exceeds a preset threshold. Construct a stability prediction function for the deformation stable state using the recursive least squares method, judge the prediction accuracy by the root mean square error of the prediction residual, and determine that the wire straightening process reaches the dynamic equilibrium state when the prediction error is less than the set threshold.

[0012] Furthermore, acquire the acoustic emission signal characteristics in the dynamic equilibrium state. In response to the real-time changes in crack propagation, repeatedly execute the acoustic emission model prediction and parameter adjustment steps to determine the continuously optimized parameter combination during the wire straightening process, including: collect the acoustic emission signal according to the dynamic equilibrium state, perform three-layer decomposition on the signal through wavelet transform to extract the low-frequency approximation coefficient and high-frequency detail coefficient, calculate the energy distribution of the signal in different frequency bands using Fourier transform, and construct a normalized feature vector based on the signal amplitude, main frequency, and duration. Establish a long short-term memory neural network for the feature vector, set the number of neurons in the input layer to the dimension of the feature vector, set the number of neurons in the hidden layer to twice that of the input layer, and perform sliding prediction using a time window with a fixed length of 100 sampling points. Calculate the root mean square value of the prediction error according to the prediction result, update the network parameters through the backpropagation algorithm, and determine that the prediction result converges when the root mean square value of the error for 50 consecutive predictions is less than the preset threshold. Construct a parameter optimization objective function for the prediction result, where the objective function includes a crack propagation rate term and a parameter change amount term, and use the particle swarm algorithm to optimize the straightening parameters. Calculate the parameter change amount according to the optimization result, judge the parameter optimization direction through the linear correlation coefficient between the change amount and the crack propagation rate, and determine that the parameter optimization converges when the absolute value of the correlation coefficient is greater than the preset threshold. Perform real-time adjustment on the converged parameter combination using a proportional-integral controller, update the straightening parameters through the controller output, and judge the parameter adjustment effect based on the change trend of the acoustic emission signal characteristics to determine the continuously optimized parameter combination.

[0013] The technical solution provided by the embodiment of the present invention may include the following beneficial effects: The present invention discloses a method for simulating the wire straightening process based on digital twin. This method collects the initial distribution range of crack propagation inside the wire through an acoustic emission sensor, and combines finite element analysis to simulate the internal stress distribution of the wire during the straightening process. According to the bending deformation degree and acoustic emission signal characteristics in the stress concentration area, analyze the crack propagation trend and predict the mapping relationship with the straightening parameters. The present invention dynamically adjusts the straightening force, speed, and angle to achieve continuous optimization of the wire straightening process. By iteratively calculating the change in the internal stress distribution and verifying the stability of the bending deformation degree, the dynamic equilibrium state is determined. Finally, the present invention realizes real-time monitoring of crack propagation and parameter optimization during the wire straightening process, effectively improving the quality and production efficiency of wire straightening. Description of the Drawings

[0014] Figure 1 It is a flowchart of a method for simulating the wire straightening process based on digital twin of the present invention. Detailed Embodiments

[0015] Next, the technical solutions in the embodiments of the present invention will be clearly and detailedly described in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only a part of the embodiments of the present invention.

[0016] As Figure 1 , a method for simulating the wire straightening process based on digital twin in this embodiment may specifically include: S101. Obtain the initial state data of the wire, collect the original acoustic emission signal characteristics through an acoustic emission sensor, calculate the initial distribution range of the crack propagation inside the wire, and obtain the crack propagation parameters.

[0017] S1011. Uniformly arrange an acoustic emission sensor array on the wire surface to collect stress wave signals. Use a Butterworth high-pass filter to preprocess the signals to filter out low-frequency interference, and obtain acoustic emission signals containing four time-domain characteristics: amplitude, rise time, duration, and ring count. Perform wavelet transform on the signals to extract three-layer decomposition coefficients as frequency-domain characteristics, calibrate the characteristic parameters in combination with the sensor sensitivity curve, use the time difference positioning method to calculate the three-dimensional coordinates of the sound source based on the time difference of arrival of signals received by at least three sensors, and then calculate the sound source density distribution according to the sound source coordinates and construct a crack propagation parameter dataset in combination with the crack size. Obtain the initial distribution range of crack propagation through least squares fitting. In the embodiments of the present invention, the sensor array adopts a circular arrangement scheme, with a sensor set every 120 degrees along the circumference of the wire cross-section and a group of arrays arranged every 300 mm axially to ensure full-range monitoring of internal defects. The working frequency range of the acoustic emission sensor is from 100 kHz to 1 MHz, with a sensitivity higher than 55 dB, and it is tightly connected to the wire surface through a couplant to ensure the signal transmission efficiency. The cut-off frequency of the Butterworth high-pass filter is set to 80 kHz to filter out vibration interference. The wavelet transform selects the db4 basis function and decomposes to three layers to cover the frequency band from 125 kHz to 1 MHz. The signal attenuation follows an exponential law and is related to the acoustic impedance of the wire. The time difference positioning method is based on the longitudinal wave velocity of 6320 m / s and the transverse wave velocity of 3080 m / s of the aluminum wire, and combines the time difference to solve the sound source coordinates. The sound source density is statistically calculated through a 10-mm grid division. When the density exceeds 200 per cubic centimeter, it is determined as a crack area. The 55-dB signal corresponds to a crack size of 0.1 mm, and the fitting uses a quadratic polynomial with a goodness of fit greater than 0.95.

[0018] S1012. Obtain the initial stress distribution data through the internal state measurement device of the wire. Use at least three groups of acoustic emission sensor arrays to collect stress wave signals at intervals of three times the wire diameter along the axial direction of the wire. After preprocessing with a Butterworth high-pass filter, obtain the acoustic emission signals. Extract four time-domain features: amplitude, rise time, duration, and ring count. Perform wavelet transform on the signals to obtain three-layer frequency-domain feature coefficients. Calculate the frequency range according to the acoustic emission characteristic curve of the wire material and calibrate the amplitude attenuation law in combination with the exponential decay model. Use the time difference positioning method to calculate the source position coordinates. Establish a corresponding relationship between the source density and the stress distribution. Calculate the crack size in combination with the calibration curve of amplitude and crack size. Construct a crack propagation parameter dataset including position coordinates, size, and density, and fit the initial distribution range by the least squares method. In the embodiment of the present invention, the acoustic emission characteristic curve is obtained through the fracture experiment of standard specimens. The amplitude reflects the energy release, the rise time characterizes the propagation rate, the duration reflects the process persistence, and the ring count represents the number of times the waveform crosses the threshold. The dataset provides input for stress simulation, and the sensor layout density can be adjusted according to the actual scenario.

[0019] S1013. Calculate the source density distribution through three-dimensional grid division. Combine the stress distribution data and the crack size calibration result to determine the basic parameters of crack propagation, including position, size, and density, laying a foundation for subsequent finite element analysis. The fitting result of the initial crack distribution range can be further optimized by adjusting the grid size or the number of sensors, without excessive limitation.

[0020] In the embodiment of the present invention, by combining the acoustic emission sensor with numerical analysis, the initial state of the internal crack of the wire can be accurately characterized, providing reliable data support for the dynamic simulation of the straightening process.

[0021] In the embodiment of the present invention, the simulation method of the wire straightening process based on digital twin combines acoustic emission technology with finite element analysis to achieve accurate simulation and optimal control of the internal stress distribution of the wire.

[0022] S102. According to the crack propagation parameters, use the finite element analysis method to simulate the internal stress distribution of the wire under the action of straightening force, speed, and angle, and determine the position of the stress concentration area.

[0023] S1021. Construct a three-dimensional solid model of the conductor based on the basic parameters of crack propagation, use hexahedral second-order units for meshing, set a mesh size of less than one-tenth of the crack size in the crack propagation area to improve the calculation accuracy, apply straightening force, speed and angle as boundary load conditions, establish the deformation control equation based on the principle of minimum total potential energy, use the incremental iteration method to solve the displacement field and calculate the deformation of the conductor, and then calculate the stress component through geometric nonlinear strain tensor and four-point Gaussian integral method, construct the stress gradient field and analyze the stress change characteristics. The material properties of the conductor solid model include elastic modulus, Poisson's ratio and yield strength. For example, the elastic modulus of aluminum alloy conductor is set to 69000 MPa, Poisson's ratio is 0.33, and yield strength is 160 MPa. 20-node hexahedral units are used for meshing, and the mesh size of the crack area is controlled at 0.2 mm to ensure accurate capture of the stress field at the crack tip. The mesh quality is judged by the unit distortion, and the mesh quality is considered qualified when the ratio of the longest side to the shortest side is less than 0.3. The load parameter range is set to straightening force 100 to 500 Newtons, speed 0.5 to 2 meters per second, and angle 5 to 15 degrees, reflecting the actual working conditions. When solving with the incremental iteration method, the displacement increment convergence threshold is set to 0.001 mm to ensure calculation stability. The four-point Gaussian integral sets 16 integration points in each unit. When calculating the stress component, if the yield strength is exceeded, the tangent modulus iteration method is introduced to calculate the plastic stress increment. The tangent modulus decreases with the increase of plastic strain, reflecting the hardening characteristics of the material. The stress gradient field calculates the local rate of change through the second-order stress derivative, laying the foundation for stress concentration analysis.

[0024] S1022, construct a stress gradient field for stress components, calculate the stress change rate by the second-order stress derivative, and determine the stress concentration point when the change rate exceeds a preset threshold value such as 50 MPa per millimeter. The stress extrapolation method is used to calculate the exact value of the stress concentration point and the location of the stress concentration area is determined by clustering through the spatial distance weighted method. In an embodiment of the present invention, the stress extrapolation method selects the stress values ​​of 8 nodes around the stress concentration point, and the precise stress is calculated by fitting a quadratic polynomial. The clustering radius is set to 2 mm. If the distance between two stress concentration points is less than this value, they are classified into the same area. The spatial distribution range of the stress concentration area is determined by calculating the maximum stress value in the area, which is usually 2 to 3 times the nominal stress. For example, for a wire with a length of 1000 mm, 3 to 5 stress concentration areas may be detected. The construction of the stress gradient field can reflect the local drastic change characteristics of the stress field and provide a basis for crack propagation trend analysis.

[0025] When simulating the stress distribution through finite element analysis, if the equivalent stress exceeds the yield strength of the material, based on the von Mises yield criterion, it is determined that the wire enters the plastic deformation stage, and the tangent modulus iteration method is used to calculate the plastic stress increment to ensure the accuracy of stress calculation. The von Mises criterion quantifies whether plastic deformation occurs in the material by comparing the equivalent stress with the yield strength. For example, when the equivalent stress exceeds 160 MPa, plastic calculation is triggered. The determination of the stress concentration area not only depends on the change rate threshold but also combines the spatial clustering method to improve the rationality of area division, providing reliable data support for the subsequent optimization of straightening parameters.

[0026] In the embodiments of the present invention, the mesh generation and load application method of the wire solid model can be adjusted according to the actual wire size and material properties. For example, for wires with different diameters, the mesh size in the encryption area can be scaled proportionally, without excessive limitation. The identification of the stress concentration area through the combination of numerical simulation and algorithms can effectively capture the dynamic changes in the stress distribution during the straightening process, laying a foundation for the prediction and control of crack propagation.

[0027] S1023. Based on the distribution characteristics of the stress gradient field and stress concentration points, calculate the maximum stress value and spatial coordinates of each stress concentration area. By analyzing the corresponding relationship between the stress concentration area and the basic parameters of crack propagation, input data is provided for the analysis of bending deformation and crack evolution. The calculation of the maximum stress value combines the extrapolation method and the Gaussian integration result to ensure accuracy. The spatial distance weighted clustering method optimizes the area division through the weighted distance formula. For example, using the reciprocal of the distance as the weight to enhance the physical meaning of the clustering result.

[0028] In the embodiments of the present invention, through the above steps, the accurate simulation of the internal stress distribution of the wire is achieved, which can effectively identify the key areas that may cause crack propagation during the straightening process. The position of the obtained stress concentration area provides a scientific basis for parameter adjustment, ensuring the stability and safety of the straightening process.

[0029] The method for simulating the wire straightening process based on digital twin combines acoustic emission signals and numerical analysis techniques to achieve accurate prediction and evaluation of the crack propagation trend of the wire during the straightening process.

[0030] S103. Calculate the degree of bending deformation of the stress concentration area under repeated bending and stretching, and combine the characteristics of the acoustic emission signal to analyze the change trend of crack propagation.

[0031] In the embodiments of the present invention, according to the position coordinates of the stress concentration region, the fourth-order Runge-Kutta method is used to numerically integrate the bending deformation differential equation to calculate the combined deformation field distribution of the wire under repeated bending and tensile loads, and the signal characteristics are collected by an acoustic emission sensor to judge the crack propagation state and trend. The differential equation includes a bending moment term and an axial force term, corresponding to bending and tensile actions respectively, and the deformation amount is solved by the principle of linear superposition. The fourth-order Runge-Kutta method divides the integration process into four sub-step lengths, and each sub-step length is recursively calculated based on the slope of the previous step to ensure accuracy. For example, when the wire bears a bending load of 200 Newtons and a tensile load of 500 Newtons, the integration step length is set to 0.1 mm, and the deformation amount accuracy can reach 0.01 mm. The obtained combined deformation field distribution reflects the deformation characteristics of the wire under complex loads.

[0032] S1031. Based on the combined deformation field distribution, an acoustic emission sensor array is arranged to collect signals. The acoustic emission signals are decomposed into three layers by wavelet transform to extract spectral characteristics, the acoustic emission event density is calculated, and the stress intensity factor at the crack front is calculated by the strain energy density method to determine the crack propagation state. The acoustic emission sensor is of the resonance type, with a sensitivity of 55 dB and a sampling frequency of 2 MHz. The wavelet transform uses the db4 basis function and is decomposed into the frequency band of 125 kHz to 1 MHz. The number of signals with an amplitude exceeding 45 dB is counted, and the acoustic emission event density per unit time is calculated. The typical value can reach 500 times per second during crack propagation. The strain energy density method calculates the crack opening displacement through the deformation field, combines the eighth-order Fourier series decomposition to extract the main harmonic components, retains the harmonics with an amplitude greater than 1%, and calculates the stress intensity factor. For example, the fracture toughness of an aluminum alloy wire is 25 MPa·√m. If the stress intensity factor exceeds the standard, it is determined that the crack propagates. The increase in the acoustic emission event density reflects the intensification of crack activity and provides real-time data for trend analysis.

[0033] S1032. According to the crack propagation state, a crack propagation driving force function is constructed. The crack propagation direction is determined by the maximum principal stress criterion. A feature vector is constructed by combining the acoustic emission event density and fracture mechanics parameters. A support vector regressor is used to predict the crack propagation rate and judge the change trend. The driving force function includes three parameters: the stress intensity factor, the strain energy release rate, and the crack opening displacement. Among them, the stress intensity factor characterizes the stress concentration degree at the crack tip, the strain energy release rate reflects the expansion energy condition, and the range is usually 0.1 to 0.5 J / m². The crack opening displacement describes the separation state of the crack surface. The maximum principal stress criterion analyzes the stress field at the crack tip and determines that the crack propagates along the direction perpendicular to the maximum principal stress. The feature vector fuses the amplitude, main frequency, and duration of the acoustic emission signal, as well as the fracture mechanics parameters. The support vector regressor uses a radial basis kernel function, and the parameters are optimized through cross-validation. The predicted crack propagation rate is between 0.01 and 0.1 mm per cycle, showing an accelerating trend with damage accumulation.

[0034] The characteristics of acoustic emission signals change significantly with crack propagation. The amplitude increases, and the main frequency shifts to lower frequencies, reflecting the dynamic process of crack propagation. Through the above method, the deformation and crack evolution law of the wire during the straightening process can be effectively captured.

[0035] The calculation of the degree of bending deformation not only depends on numerical integration but also improves the accuracy through Fourier decomposition. The spectral analysis of acoustic emission signals is combined with fracture mechanics parameters to ensure that the prediction of the crack propagation trend has high reliability. The obtained trend data can be adjusted according to the actual load conditions. For example, when the load is increased, the verification frequency is accelerated, and there are no excessive limitations.

[0036] In the embodiment of the present invention, through the deformation analysis under repeated bending and stretching, a scientific basis can be provided for crack control during the wire straightening process. The combination of acoustic emission signals and numerical simulation enhances the visualization and quantification of the crack propagation trend.

[0037] The wire straightening process simulation method based on digital twin realizes the intelligent optimization of straightening parameters by analyzing the correlation between the crack propagation trend and the characteristics of acoustic emission signals.

[0038] S104. Calculate the change amplitude of the acoustic emission signal characteristics according to the change trend of crack propagation, combine with the pre-established acoustic emission model to predict the mapping relationship between crack propagation and straightening parameters, and then determine the adjustment demand of the straightening parameters to optimize the straightening process.

[0039] S1041. Use the central difference method to calculate the change rates of the amplitude, frequency, and duration of the acoustic emission signal. Through weighted summation and normalization processing of the importance weights of the characteristic quantities, an acoustic emission signal change index is obtained, and training samples are extracted from the pre-established acoustic emission database to construct a prediction model. The central difference method is based on a sampling interval of 0.5 milliseconds, and the change rates of the characteristic quantities are calculated by taking two sampling points before and after. For example, the amplitude change rate is usually between 10 and 50 decibels per second, the frequency change rate is between 50 and 200 kilohertz per second, and the duration change rate is between 0.1 and 0.5 milliseconds per second. According to the sensitivity of each characteristic quantity to crack propagation, the weight coefficients are assigned as 0.4, 0.35, and 0.25 respectively. After weighted summation, the change index is normalized to the range of 0 to 1 through maximum-minimum normalization. The acoustic emission database contains 10,000 groups of samples, recording the corresponding relationship between the acoustic emission signal characteristics and crack states of the wire under different loads. The signal characteristics include an amplitude range of 35 to 85 decibels, a main frequency range of 100 kilohertz to 1 megahertz, and a duration range of 0.1 to 2 milliseconds. The crack states cover information such as position, propagation rate, and direction. These samples provide a rich data basis for subsequent model training.

[0040] S1042. A mapping relationship between acoustic emission signal features and crack propagation states is constructed using a deep residual network. The main feature components of the straightening parameters are extracted by the principal component regression method, and the parameter adjustment requirements are calculated by combining the grey correlation coefficient and the cubic spline interpolation method. The deep residual network consists of five convolutional layers and three fully connected layers. The convolutional layers use 3×3 convolutional kernels, and the number of output channels is 64, 128, 256, 512, and 512 in sequence. Batch normalization and the ReLU activation function are combined to enhance the feature extraction ability. The number of neurons in the fully connected layers is 1024, 512, and 256 respectively, and a dropout layer is added to avoid overfitting. The network evaluates the prediction error through the cross-entropy loss function, optimizes the parameters using the stochastic gradient descent method, sets the learning rate to 0.001, the batch size to 64, and converges after 100 rounds of training. The principal component regression analysis shows that the contribution rates of the straightening force, speed, and angle to crack propagation are 45%, 35%, and 20% respectively, and the extracted main feature components reflect the influence weights of the parameters. The grey correlation coefficient calculates the sensitivity coefficients of the straightening force, speed, and angle to be 0.82, 0.75, and 0.63 respectively by comparing the correlation between the parameter sequence and the reference sequence. The cubic spline interpolation method constructs a mapping relationship matrix within the range of the straightening force from 100 to 500 Newtons, the speed from 0.5 to 2 meters per second, and the angle from 5 to 15 degrees. When solving the non-linear equations by the steepest descent method, the initial point is the current parameter value, the convergence accuracy is 0.001, and the iteration upper limit is 100 times, and the adjustment requirements are obtained, such as the force change is 50 to 100 Newtons, the speed change is 0.2 to 0.5 meters per second, and the angle change is 2 to 5 degrees.

[0041] In the embodiment of the present invention, the calculation of the change index of the acoustic emission signal can reflect the dynamic process of crack propagation in real time. By combining the deep residual network and the principal component regression method, the control effect of the straightening parameter adjustment on crack propagation is accurately predicted. The adjusted parameters are verified by the acoustic emission signal. For example, the amplitude is reduced by 10 to 20 decibels, and the event rate is reduced by 200 to 300 times per second, indicating that the crack propagation is inhibited.

[0042] Based on the mapping relationship between the crack propagation trend and the acoustic emission signal features, the pre-established acoustic emission model is used to predict the parameter adjustment requirements to ensure the stability and safety of the straightening process. The model training and parameter optimization can be flexibly adjusted according to the actual wire state.

[0043] In the embodiment of the present invention, the full-process control from acoustic emission signal analysis to straightening parameter optimization is realized through the above method. The change trend of the acoustic emission signal features is highly correlated with the crack propagation state, providing a reliable basis for parameter adjustment. The optimized parameters can effectively reduce the crack propagation risk and improve the process quality of wire straightening.

[0044] The simulation method of the wire straightening process based on digital twin optimizes the stress distribution to reduce the risk of crack propagation by dynamically adjusting the straightening parameters and combining numerical calculations.

[0045] S105. Dynamically adjust the straightening force according to the parameter adjustment requirement, analyze the change trend of the internal stress distribution through iterative calculation, and determine the adjusted stress distribution data.

[0046] Set the adjustment range of the straightening force according to the parameter adjustment requirement, segmentally adjust the straightening force by the adaptive step method, calculate the strain increment through the elastoplastic constitutive equation and combine Hooke's law and Newton's iteration method to solve the stress increment, then construct the stress distribution function and obtain the optimized stress distribution through Gaussian integration and smoothing processing. The adaptive step method is based on a step factor of 0.1 to ensure that the adjustment amplitude each time does not exceed 10% of the current force. For example, when the initial force is 300 N, the single adjustment does not exceed 30 N. This progressive method avoids stress mutation. The elastoplastic constitutive equation is based on the material properties of the aluminum alloy wire, with an elastic modulus of 69000 MPa, a Poisson's ratio of 0.33, and a yield strength of 160 MPa, and reflects the behavior of the material in the elastic and plastic stages through the calculation of the strain increment. Hooke's law is used for elastic strain calculation, and when Newton's iteration method solves the nonlinear stress equations, the convergence accuracy is set to 0.001 to ensure the reliability of the results. If the equivalent stress of the element exceeds 160 MPa, it enters the plastic stage, and the tangent modulus is used to calculate the plastic stress increment. The tangent modulus decreases with the increase of the plastic strain, reflecting the work hardening characteristic.

[0047] S1051. Use tetrahedral elements for mesh division of the stress increment data, calculate the nodal stress through the shape function and construct the stress distribution function using linear interpolation. Combine the force balance equation and the geometric compatibility equation to verify the convergence of the stress field and ensure the rationality of the adjusted stress distribution. The size of the tetrahedral elements is set to 2 mm in the area with a large stress gradient and 5 mm in the uniform area. It has strong geometric adaptability and calculates the stress inside the element through linear shape function interpolation of four nodes to ensure piecewise continuity. The force balance equation verifies that the stress field satisfies static equilibrium, and the geometric compatibility equation ensures deformation compatibility. If the stress error is less than 0.1 MPa, the stress field is considered to converge. The stress distribution function reflects the spatial change of the stress field after the adjustment of the straightening force and provides a basis for analysis.

[0048] S1052. Calculate the internal stress of the element using the Gaussian quadrature method based on the stress distribution function, smooth the stress field by the residual least squares method, analyze the principal stress components and invariants to evaluate the stress distribution accuracy, and obtain the adjusted stress distribution data. The Gaussian quadrature method selects 4 integration points in each tetrahedral element to calculate the equivalent stress to ensure accuracy. The residual least squares method eliminates the jump phenomenon caused by mesh discretization by minimizing the stress difference between adjacent elements, and the stress difference is controlled within 5 MPa. The principal stress component analysis combines the first invariant, i.e., the hydrostatic pressure, and the second invariant, i.e., the shear stress intensity, to evaluate the overall characteristics of the stress field. For example, before adjustment, the maximum equivalent stress is 180 MPa, and obvious plastic deformation occurs in the concentrated area. After adjustment, it drops to 140 MPa, the first invariant decreases from 60 MPa to 45 MPa, and the second invariant decreases from 85 MPa to 65 MPa, indicating that the stress distribution is more uniform and the overall level decreases.

[0049] By dynamically adjusting the straightening force and iteratively calculating the stress distribution, the internal stress state of the wire can be effectively optimized. The obtained stress distribution data reflects the characteristics of the adjusted stress field, provides reliable support for subsequent process verification, and the step size or mesh density can be flexibly adjusted according to the actual working conditions.

[0050] The wire straightening process simulation method based on digital twin optimizes the straightening speed and angle parameters by analyzing the interaction between bending deformation and stress distribution, and realizes dynamic balance control by combining real-time state monitoring.

[0051] S106. Combine the degree of bending deformation and the adjusted stress distribution data to judge whether it is necessary to synchronously adjust the straightening speed. If adjustment is required, reduce the speed and analyze its coupling relationship with the bending deformation and crack propagation direction, optimize the straightening angle to determine the target parameters, and dynamic balance can also be achieved by generating control instructions through state monitoring.

[0052] S1061. Calculate the stress change rate using the five-point difference format based on the degree of bending deformation and the adjusted stress distribution data. Determine the velocity influence coefficient through a linear correlation function and calculate the reduction in the straightening speed using a proportional-integral controller. Then, evaluate the degree of bending deformation after speed reduction through the deformation curvature equation. The five-point difference format is based on a time interval of 0.1 milliseconds, taking two points before and after to calculate the stress change rate. For example, under normal conditions, the stress change rate is approximately 1000 megapascals per second. If it exceeds 2000 megapascals per second, it indicates that the speed is too high. The velocity influence coefficient is calculated through a linear regression model of the stress change rate and the straightening speed, with a critical value set at 1.5. If the coefficient exceeds the standard, the speed needs to be adjusted. The proportional-integral controller uses a proportional coefficient of 0.8 and an integral time constant of 0.5 seconds. For example, when the coefficient is 1.8, the output reduction in speed is 0.4 meters per second. The deformation curvature equation combines the Euler method to calculate the strain increment, with a time step of 0.01 seconds. After speed reduction, the curvature change range is between 0.002 and 0.005 per millimeter, reflecting the direct impact of speed adjustment on deformation.

[0053] S1062. Calculate the principal stress distribution based on the degree of bending deformation after speed reduction. Determine the crack propagation direction using the maximum shear stress criterion. Calculate the element strain energy through the four-point Gaussian integration method and construct the strain energy density matrix. Analyze the coupling relationship between bending deformation and the crack propagation direction. Then, optimize the straightening angle using the genetic algorithm. The principal stress distribution is derived from the deformation data. The maximum shear stress criterion is based on the critical shear stress of 80 megapascals for aluminum alloy to calculate the crack propagation angle, usually between 60 and 80 degrees, which is related to the material microstructure. Four-point Gaussian integration sets 4 integration points within the element to calculate the stress energy, with a density range of 0.1 to 0.5 joules per cubic millimeter. The eigenvalues and vectors of the strain energy density matrix respectively characterize the coupling strength and direction. When optimizing the angle using the genetic algorithm, the population size is 100, the crossover probability is 0.8, and the mutation probability is 0.1. Evaluate the sensitivity through the partial derivative of the strain energy with respect to the angle. For example, when the angle is adjusted from 15 degrees to 8 degrees, the strain energy density decreases by 40%, and the crack propagation rate is halved.

[0054] In the embodiment of the present invention, the bending deformation and crack propagation data during the wire straightening process are collected. The signals are preprocessed by a band-pass filter and the cross-correlation coefficient is calculated. Feature vectors are extracted and a recurrent neural network is used to predict the dynamic trend. Adjustment parameters are generated based on bifurcation theory and control is implemented. The band-pass filter separates the deformation signals in the frequency band of 1 to 100 Hz and the crack signals in the frequency band of 100 kHz to 1 MHz. The cross-correlation coefficient rises from 0.3 to 0.85, and when it exceeds the threshold of 0.8, it indicates a significant interaction. Singular value decomposition is used to extract feature vectors, including a deformation amplitude of 0.1 to 0.5 mm, a frequency of 5 to 20 Hz, a crack length of 0.2 to 2 mm, and a propagation rate of 0.01 to 0.1 mm per cycle. A double-layer recurrent neural network is constructed with 4 input neurons and 8 hidden-layer neurons and trained with 1000 sets of samples by temporal backpropagation, and the error converges to 0.01. The state equation takes the deformation amount, stress components, and crack length as variables. The Lyapunov function is used to analyze the stability, and bifurcation theory is used to calculate critical values such as a force of 350 N, a speed of 1.2 m / s, and an angle of 10°. The adjustment amount is generated and smoothed by a Butterworth filter before control is implemented. When the ratio of the deformation rate to the crack rate is stable at 2 to 3, dynamic equilibrium is achieved.

[0055] Through the collaborative optimization of speed and angle, the promoting effect of bending deformation on crack propagation is reduced. Real-time state monitoring ensures the accuracy of parameter adjustment, and the algorithm parameters can be adjusted according to the wire characteristics.

[0056] In the embodiment of the present invention, the above method realizes the coupled control of deformation and crack propagation during the straightening process. The optimized parameters significantly improve the process stability. For example, the deformation amount is controlled below 0.2 mm, and the crack propagation rate is reduced to 0.02 mm per cycle.

[0057] The wire straightening process simulation method based on digital twin verifies the stability of the adjusted parameters to ensure that the wire straightening process reaches dynamic equilibrium.

[0058] S107. Verify the stability of the internal stress distribution and the degree of bending deformation according to the target straightening angle, the adjusted straightening speed, and the straightening force. By constructing a three-dimensional parameter space and combining numerical analysis and prediction methods, determine the dynamic equilibrium state of the wire straightening process.

[0059] S1071. Construct a three-dimensional parameter space based on the target straightening angle, straightening speed, and straightening force. Use orthogonal design to generate parameter combinations and calculate the nodal displacement and element stress distribution through the finite element method, and then analyze the stress field uniformity and steady-state characteristics. The orthogonal design selects combination points in the range of 5 to 15 degrees for the angle, 0.5 to 2 m / s for the speed, and 100 to 500 N for the force. The finite element calculation obtains the von Mises stress distribution. For example, the stress concentration area reaches 180 MPa, and the uniform area is about 60 MPa. The calculation of the global stress average value and standard deviation shows that when the standard deviation is less than 20% of the average value, the stress distribution is uniform. The stress gradient exceeds 100 MPa / mm in the concentrated area and is lower than 10 MPa / mm in the uniform area. The stability is evaluated through the Lyapunov exponent. When the negative value and its absolute value are greater than 0.5, it indicates that the stress field tends to be stable.

[0060] S1072. Reconstruct the stress field characteristics in the three-dimensional phase space based on the stress distribution data, judge the steady-state characteristics through the convergence of the phase space trajectory, use the mean shift method to calculate the adaptive threshold and combine the Kalman filter to predict the deformation trend, and evaluate the deformation stability. The three-dimensional phase space is reconstructed with a delay time 10 times the sampling period. The trajectory converges to an area with a diameter less than 0.2 mm, and the convergence time is about 2 s, reflecting the steady state of the stress field. The mean shift method uses 3 times the standard deviation of the deformation amount as the initial threshold, dynamically updates it, and combines the Kalman filter to predict the deformation. The error is less than 0.05 mm, and the confidence interval coverage rate is 95%, ensuring the prediction accuracy.

[0061] In the embodiment of the present invention, Fourier spectrum analysis is performed on the deformation trend, the energy ratio of the main frequency component is calculated, and a stability prediction function is constructed through the recursive least squares method to judge whether the wire straightening process reaches dynamic balance. The spectrum shows that the energy is concentrated in the range of 0 to 20 Hz, and the low-frequency components below 5 Hz account for 80%. If this ratio remains stable for 5 s, the deformation is stable. The recursive least squares method predicts with a 20th-order model. The root mean square error of the residuals decreases with iteration. When the error is lower than 0.02 mm and the change rate is less than 1% for 50 consecutive times, dynamic balance is confirmed. At this time, the deformation amount is controlled below 0.2 mm, and the stress non-uniformity is less than 15%.

[0062] Through the above verification method, the coordinated stability of stress distribution and deformation is ensured. The parameter range or algorithm step size can be adjusted according to the wire material characteristics to adapt to different working conditions.

[0063] In the embodiment of the present invention, the realization of the dynamic balance state benefits from the combination of parameter optimization and real-time prediction, significantly improving the reliability and safety of the straightening process and providing technical support for practical applications.

[0064] The wire straightening process simulation method based on digital twin analyzes the acoustic emission signal characteristics in the dynamic balance state and continuously optimizes the straightening parameters to control crack propagation. The following steps elaborate on the implementation process.

[0065] S108. Obtain the acoustic emission signal characteristics in the dynamic balance state, and repeatedly execute the prediction and adjustment steps for the real-time changes of crack propagation. Determine the continuously optimized parameter combination during the wire straightening process through signal analysis, neural network prediction, and parameter optimization.

[0066] S1081. Collect the acoustic emission signals in the dynamic balance state, perform three-layer decomposition using wavelet transform to extract low-frequency approximation coefficients and high-frequency detail coefficients, calculate the energy distribution in frequency bands through Fourier transform, and construct a normalized feature vector to provide input data for subsequent prediction. The wavelet transform uses the db4 basis function. The low-frequency coefficients cover 0 to 125 kHz to reflect the crack propagation trend, and the high-frequency coefficients cover 125 kHz to 1 MHz to capture local changes. Fourier transform analyzes the energy distribution of the signal with an amplitude of 35 to 85 dB, a main frequency of 100 to 800 kHz, and a duration of 0.1 to 2 ms. The normalized feature vector is standardized to the range of 0 to 1 based on these features to ensure data consistency.

[0067] S1082. Input the feature vector into a long short-term memory neural network for sliding prediction. Optimize the network parameters through the backpropagation algorithm until the prediction error converges. Construct an objective function and use the particle swarm optimization algorithm to optimize the straightening parameters. Combine a proportional-integral controller to adjust the parameters in real time and verify the effect. The input layer of the neural network has 6 neurons corresponding to the components of the feature vector, the hidden layer has 12 long short-term memory units, the time window is 100 sampling points, the step size is 10 points, and the prediction is made every 0.5 seconds. The mean squared error loss function is used for training, the learning rate is 0.001, and the initial error of 0.2 is reduced to less than 0.05 after 50 iterations. The objective function is constructed with a weight of 0.6 for the crack propagation rate and a weight of 0.4 for the parameter change amount. The population size of the particle swarm optimization algorithm is 50, and it iterates 100 times. After optimization, the force change is less than 50 N, the speed change is less than 0.2 m / s, and the angle change is less than 2 degrees. The proportional-integral controller adjusts the parameters with a proportional coefficient of 0.8 and an integral time constant of 0.5 s, with a period of 1 s. After adjustment, the signal amplitude decreases by 15 dB, the event rate decreases by 250 times per second, and the main frequency shifts to a lower frequency, indicating that the crack is under control.

[0068] In the embodiment of the present invention, through the repeated execution of acoustic emission signal feature analysis and parameter optimization, the crack propagation changes can be tracked in real time and the parameters can be dynamically adjusted. Correlation analysis shows that the linear correlation coefficients between the straightening force, speed, and angle and the crack propagation rate are -0.85, -0.72, and -0.65 respectively. When the absolute value exceeds 0.6, the optimization converges, ensuring the correct adjustment direction.

[0069] The continuously optimized parameter combination remains stable under dynamic balance, and the window length or algorithm parameters can be adjusted according to the wire state to improve adaptability.

[0070] In the embodiments of the present invention, the above method combines signal feature extraction with intelligent optimization to achieve adaptive control of the straightening process, significantly reducing the risk of crack propagation and improving process reliability.

[0071] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for simulating the wire straightening process based on digital twin, characterized in that, The method includes: Collect the characteristics of the original acoustic emission signal, calculate the initial distribution range of the crack propagation inside the wire, and obtain the crack propagation parameters; According to the crack propagation parameters, use the finite element analysis method to simulate the internal stress distribution of the wire under the action of straightening force, straightening speed, and straightening angle, and determine the position of the stress concentration area; Calculate the degree of bending deformation of the position of the stress concentration area under the influence of repeated bending and tensile actions, and combine the characteristics of the acoustic emission signal to judge the change trend of crack propagation; Obtain the change amplitude of the acoustic emission signal characteristics according to the change trend of crack propagation, and combine the pre-established acoustic emission model to predict the mapping relationship between crack propagation and straightening parameters, and obtain the required parameter adjustment; According to the required parameter adjustment, dynamically adjust the straightening force, and determine the adjusted stress distribution data by iteratively calculating the change of the internal stress distribution; Combine the degree of bending deformation and the adjusted stress distribution data to judge whether the straightening speed parameter needs to be adjusted synchronously. If adjustment is required, reduce the straightening speed, and calculate the coupling relationship between the degree of bending deformation and the crack propagation direction after reducing the straightening speed, and determine the target straightening angle.

2. The method according to claim 1, wherein The collecting the characteristics of the original acoustic emission signal, calculating the initial distribution range of the crack propagation inside the wire, and obtaining the crack propagation parameters includes: Collect stress wave signals according to the acoustic emission sensor array uniformly arranged on the wire surface. The stress wave signals are processed by a Butterworth high-pass filter to obtain acoustic emission signals, and the acoustic emission signals include four time-domain characteristic parameters: amplitude, rise time, duration, and ring count; Perform wavelet transform on the acoustic emission signal to obtain three-layer wavelet decomposition coefficients as frequency-domain characteristic parameters, and calibrate the frequency-domain characteristic parameters in combination with the acoustic emission sensor sensitivity curve; Use the time difference positioning method to calculate the three-dimensional coordinates of the sound source according to the time difference of arrival of the acoustic emission signal. The time difference positioning method is based on signals received by at least three sensors; Calculate the sound source density distribution according to the three-dimensional coordinates of the sound source, and construct a data set of crack propagation parameters by combining the sound source density distribution and the crack size.

3. The method according to claim 1, wherein The according to the crack propagation parameters, using the finite element analysis method to simulate the internal stress distribution of the wire under the action of straightening force, straightening speed, and straightening angle, and determining the position of the stress concentration area includes: Use hexahedral second-order elements to perform mesh division on the wire solid model. According to the mesh division result of the wire solid model, solve the deformation control equation by the incremental iteration method to obtain the displacement field; Establish a strain-displacement relationship matrix for the displacement field, calculate the stress components using four-point Gaussian integration, and use the tangent modulus iteration method to calculate the plastic stress increment when the stress components exceed the material yield strength; Construct a stress gradient field according to the stress components, calculate the stress change rate through the second-order stress derivative, determine the stress concentration point when the stress change rate exceeds the preset threshold, and cluster the stress concentration points using the spatial distance weighting method to obtain the stress concentration area.

4. The method according to claim 1, characterized in that, The calculating the degree of bending deformation of the position of the stress concentration area under the influence of repeated bending and tensile actions, and combining the characteristics of the acoustic emission signal to judge the change trend of crack propagation includes: The fourth-order Runge-Kutta method is used to numerically integrate the differential equation of bending deformation to obtain the distribution data of the combined deformation field; An acoustic emission sensor array is arranged according to the distribution data of the combined deformation field, and the acoustic emission signal is decomposed into three layers by wavelet transform to obtain the acoustic emission event density; The stress intensity factor at the crack front is calculated for the acoustic emission event density. If the stress intensity factor exceeds the material fracture toughness, the crack propagation state is determined; A feature vector is constructed based on the crack propagation state and the acoustic emission event density, and the crack propagation rate is obtained through a support vector regressor to judge the crack propagation trend.

5. The method according to claim 1, wherein The change amplitude of the acoustic emission signal features is obtained according to the change trend of crack propagation, and the mapping relationship between crack propagation and straightening parameters is predicted by combining with a pre-established acoustic emission model to obtain the parameter adjustment requirement, including: The central difference method is used to calculate the change rates of three feature quantities, namely the amplitude, frequency and duration of the acoustic emission signal, and the acoustic emission signal change index is obtained by weighted summation with the importance weight coefficients of the feature quantities; Training samples are extracted from a pre-established acoustic emission database according to the acoustic emission signal change index, and the database records the acoustic emission signal features at different crack propagation stages; A mapping relationship between the acoustic emission signal features and the crack propagation state is constructed by using a deep residual network for the training samples, and the network calculates the prediction error through a cross-entropy loss function; According to the mapping relationship, the main feature components of three parameters, namely the straightening force, straightening speed and straightening angle, are extracted by using the principal component regression method, and the straightening parameter adjustment requirement is obtained by calculating the grey correlation coefficient between the parameter sequence and the reference sequence.

6. The method according to claim 1, characterized in that According to the parameter adjustment requirement, the straightening force is dynamically adjusted, and the change of the internal stress distribution is determined by iterative calculation. It includes: The straightening force is adjusted in segments by using the adaptive step method according to the straightening parameters, and the strain increment data is calculated by the elastoplastic constitutive equation; Hooke's law is used to calculate the elastic strain for the strain increment data, and the stress increment data is obtained by solving the nonlinear stress equations by the Newton iteration method. If the equivalent stress of the element exceeds the yield strength, the tangent modulus is used to calculate the plastic stress increment; The tetrahedral element is used for mesh division for the stress increment data, the stress value is calculated at the element nodes, the stress distribution function is constructed by linear interpolation, the internal stress of the element is calculated by the Gaussian integration method, and the adjusted stress distribution data is obtained by smoothing the stress field by the residual least squares method.

7. The method according to claim 1, wherein Combined with the bending deformation degree and the adjusted stress distribution data, it is judged whether the straightening speed parameter needs to be adjusted synchronously. If adjustment is required, the straightening speed is reduced, and the coupling relationship between the bending deformation degree and the crack propagation direction after reducing the straightening speed is calculated to determine the target straightening angle, including: The stress change rate is calculated by using the five-point difference format according to the bending deformation degree and the adjusted stress distribution data, and the speed influence coefficient is obtained through the linear correlation function between the stress change rate and the straightening speed; A proportional-integral controller is used to calculate the reduction in the straightening speed for the speed influence coefficient, and the degree of bending deformation after speed reduction is obtained through the reduction in the straightening speed and the deformation curvature equation; The principal stress distribution data is calculated based on the degree of bending deformation, and the crack propagation direction is obtained by using the maximum shear stress criterion for the principal stress distribution data; The four-point Gaussian integration method is used to calculate the element strain energy for the crack propagation direction, and the coupling relationship between the degree of bending deformation and the crack propagation direction is obtained by establishing a strain energy density matrix through the element strain energy, and the target straightening angle is determined according to the coupling relationship; A target function is constructed according to the coupling relationship, and the genetic algorithm is used to optimize the straightening angle parameters. The sensitivity of the angle parameters is calculated through the partial derivative of the strain energy density with respect to the angle, and the optimization equation is solved based on the principle of minimum strain energy to obtain the target straightening angle.

8. The method according to claim 7, wherein It further includes: Collecting the bending deformation data and crack propagation data during the straightening process of the wire to obtain the state information, comparing it with the preset threshold to judge the interaction intensity. If the interaction intensity exceeds the preset threshold, then analyze the coupling characteristics of the bending deformation and crack propagation, obtain the dynamic change trend, calculate the interaction relationship during the wire straightening process, predict the critical point of the equilibrium state, obtain the adjustment parameters during the straightening process, generate the wire straightening control instruction, and obtain the dynamic equilibrium state, specifically including: Using a band-pass filter to preprocess the deformation signal collected by the strain sensor and the crack signal collected by the acoustic emission sensor, and calculating the correlation coefficient between the deformation signal and the crack signal through the cross-correlation function; according to the correlation coefficient, using the singular value decomposition method to extract the eigenvectors of the deformation signal and the crack signal, and the eigenvectors include four components: deformation amplitude, deformation frequency, crack length, and crack propagation rate; constructing a two-layer recurrent neural network for the eigenvectors, and the recurrent neural network is trained through the time series backpropagation algorithm to obtain the dynamic change trend of the deformation and crack propagation; establishing a state equation for the wire straightening process according to the dynamic change trend, calculating the critical value of the state equation through the bifurcation theory, and generating the adjustment amounts of three parameters: straightening force, straightening speed, and straightening angle based on the critical value; Constructing a control instruction sequence for the adjustment amounts, using a Butterworth filter to smooth the control instructions, and adjusting the wire straightening parameters in real time through a proportional-integral controller. When the ratio of the deformation rate and the crack propagation rate is stable within the preset range, it is determined that the dynamic equilibrium state is reached.

9. The method according to claim 1, characterized in that The method further includes: verifying the stability of the internal stress distribution and the degree of bending deformation according to the target straightening angle and the adjusted straightening speed and straightening force, and obtaining the dynamic equilibrium state of the wire straightening process, specifically including: Constructing a three-dimensional parameter space according to the straightening angle, straightening speed, and straightening force, and obtaining the node displacement and element stress distribution data through finite element calculation; Calculating the global stress average value and standard deviation for the element stress distribution data, and obtaining the stress field uniformity criterion through stress gradient calculation; Reconstruct the stress field characteristics in the three-dimensional phase space according to the stress field uniformity criterion, and obtain the steady-state characteristics of the stress field through the calculation of the convergence of the phase space trajectory. Calculate the adaptive threshold using the mean shift method for the steady-state characteristics of the stress field, and obtain the deformation trend data through Kalman filter prediction.

10. The method according to claim 1, wherein The method further includes: obtaining the acoustic emission signal characteristics under the dynamic equilibrium state, and for the real-time changes of crack propagation, cyclically executing the acoustic emission model prediction and parameter adjustment steps to determine the continuously optimized parameter combination during the wire straightening process, specifically including: Perform three-layer decomposition on the acoustic emission signal using wavelet transform to obtain the low-frequency approximation coefficient and high-frequency detail coefficient, calculate the energy distribution of the low-frequency approximation coefficient and high-frequency detail coefficient in different frequency bands through Fourier transform, and obtain the normalized feature vector. Input the feature vector into the long short-term memory neural network, and the neural network uses a fixed-length time window for sliding prediction, and updates the network parameters through the backpropagation algorithm until the root mean square value of the prediction error is less than the preset threshold. Construct a parameter optimization objective function for the predicted result, and the objective function includes a crack propagation rate term and a parameter change amount term, and obtain the optimized straightening parameters through the particle swarm algorithm. Perform real-time adjustment using a proportional-integral controller according to the optimized straightening parameters, output the updated straightening parameters through the controller, and judge the parameter adjustment effect based on the change trend of the acoustic emission signal characteristics to determine the continuously optimized parameter combination.

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