Real-time monitoring method for detecting dynamic defects of welds by acoustic emission

Abstract

The present application relates to the technical field of nondestructive testing, and discloses a real-time monitoring method for acoustic emission detection of weld seam dynamic defects, which comprises the following steps: firstly, acquiring multiple acoustic emission signals and welding gun position information in the welding process, establishing a weld seam traveling coordinate system, mapping the acoustic emission signals from the time parameter domain to the weld seam arc length domain, and performing fixed arc length window segmentation; reconstructing the local signal segment by delay embedding phase space, constructing a multi-channel joint phase space point set; generating a simple complex on the point set and calculating a topological invariant to form a topological signature sequence arranged along the weld seam traveling direction; mapping the mutation section to the weld seam spatial position in the workpiece coordinate system, and constructing a topological evolution sequence for combined analysis. The present application can directly give the position range of the defect generating section in the weld seam geometric coordinate by mapping the acoustic emission signal from the time parameter domain to the weld seam arc length domain and introducing the weld seam traveling coordinate system for space re-labeling of the signal, thereby achieving the purpose of real-time monitoring of the weld seam dynamic defects.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, and in particular to a method for real-time monitoring of dynamic defects in welds using acoustic emission testing. Background Technology

[0002] Welded structures are widely used in aerospace, rail transportation, pressure vessels, and engineering equipment during manufacturing and service. The quality of the weld directly affects the load-bearing capacity and safety of the structure. Due to the high-temperature melting, solidification, and phase transformation of the metal structure involved in the welding process, dynamic defects such as cracks, lack of fusion, and porosity are prone to occur in the weld area. Therefore, online monitoring of the weld condition and defect location during the welding process has become an important technical direction for welding quality control.

[0003] Existing acoustic emission monitoring technologies for welding typically use time as the independent variable, analyzing the amplitude, energy, spectrum, or statistical characteristics of the acquired acoustic emission signals, and combining this with threshold judgment or pattern recognition methods to evaluate the welding status. Some technical solutions also introduce welding torch position information or process parameters such as welding current and voltage as auxiliary quantities to mark or segment the acoustic emission signals on the time axis. However, these methods are still mainly based on signal analysis within the time parameter domain, and the acoustic emission event and the geometric position of the weld are usually indirectly correlated through timestamps. When there are fluctuations in welding speed, changes in the curvature of the trajectory, or acceleration / deceleration of the welding torch, the linear relationship between the time scale and the weld spatial scale is no longer maintained, resulting in inconsistent weld arc lengths within the same time interval. This makes it difficult to stably map acoustic emission characteristics to specific weld spatial locations.

[0004] In their research and practice, the inventors of this application discovered that existing acoustic emission weld monitoring methods based on the time parameter domain have the following technical problems: there is a lack of a unified spatial calibration mechanism with weld arc length as a parameter between the acoustic emission signal and the weld geometric position, which makes it difficult for defect-related acoustic emission features to form a stable correspondence in the weld travel direction. Summary of the Invention

[0005] To overcome the above shortcomings, this invention provides a method for real-time monitoring of dynamic defects in welds by acoustic emission detection. It aims to improve the existing technology by addressing the lack of a unified spatial calibration mechanism with weld arc length as a parameter, which makes it difficult for defect-related acoustic emission features to form a stable correspondence in the direction of weld travel.

[0006] This invention provides the following technical solution: a method for real-time monitoring of dynamic defects in welds using acoustic emission detection, comprising the following steps:

[0007] S1. Arrange multiple acoustic emission sensors around the weld to synchronously collect the original acoustic emission signals corresponding to each sensor during the welding process;

[0008] S2. Obtain the continuous position information of the welding torch in the workpiece coordinate system, establish the weld travel coordinate system based on the welding torch trajectory, and establish the correspondence between the welding torch time parameters and the weld arc length coordinate.

[0009] S3. Based on the weld travel coordinate system, the original acoustic emission signal is mapped from the time parameter domain to the weld arc length domain, and segmented within the weld arc length domain according to a fixed arc length window to form a local acoustic emission signal segment corresponding to the weld position.

[0010] S4. Delay embedding phase space reconstruction is performed on each local acoustic emission signal segment, and the phase space states of multiple acoustic emission signals are combined to form a multi-channel joint phase space point set.

[0011] S5. Construct a simple complex structure based on the multi-channel joint phase space point set, and calculate the corresponding topological invariants at each weld arc length position to form a topological signature sequence arranged along the weld travel direction.

[0012] S6. Establish a topological transport recursion relationship between adjacent weld arc length positions, perform transport prediction on the topological signature of the previous position, and compare the structural consistency with the actual topological signature of the next position to determine the position of topological structural abrupt change.

[0013] S7. Map the topological abrupt change position to the weld space segment in the workpiece coordinate system, and perform combined analysis on the topological signature evolution sequence along the weld travel direction to form the discrimination result of the weld dynamic defect evolution process.

[0014] Preferably, in step S2, the step of establishing the correspondence between the welding torch time parameters and the weld arc length coordinates includes:

[0015] Obtain the spatial coordinate sequence of the welding torch at consecutive time points;

[0016] The instantaneous motion direction of the welding torch is calculated based on the coordinate difference between adjacent moments.

[0017] The instantaneous movement direction of the welding torch is used as the reference axis for the weld travel direction;

[0018] A local coordinate system is constructed based on the reference axis, which moves with the welding torch, and the welding torch position information is projected onto the weld arc length coordinate axis to form a correspondence between the welding torch time parameters and the weld arc length coordinates.

[0019] Preferably, in step S3, the step of segmenting the weld arc length domain according to a fixed arc length window to form a local acoustic emission signal segment corresponding to the weld position includes:

[0020] Based on the correspondence between welding torch time parameters and weld arc length coordinates, the original acoustic emission signals collected at each moment are recalibrated.

[0021] The recalibrated acoustic emission signals are reordered into a signal sequence that varies with the weld arc length.

[0022] Set sliding windows with equal arc length intervals within the arc length domain of the weld;

[0023] The acoustic emission signal samples within each sliding window are extracted to form local acoustic emission signal segments that match the corresponding weld section.

[0024] Preferably, in step S4, the step of combining the phase space states of the multiple acoustic emission signals to form a multi-channel joint phase space point set includes:

[0025] For each local acoustic emission signal segment, a multidimensional state vector sequence is constructed according to a uniform delay time and embedding dimension;

[0026] The state vector sequences corresponding to the same sensor channel are used to form the phase space trajectory point set of that channel;

[0027] The phase space trajectory point sets of different sensor channels are mapped to the same phase space coordinate system to form a multi-channel joint phase space point set.

[0028] Preferably, in step S5, the step of calculating the corresponding topological invariants at each weld arc length position to form a topological signature sequence arranged along the weld travel direction includes:

[0029] Calculate the distance relationship between any two points in the multi-channel joint phase space point set;

[0030] Establish an adjacency network between point sets based on a preset adjacency scale;

[0031] A simple complex structure is generated based on the adjacency network.

[0032] Homology analysis is performed on the simple complex to obtain the number of connected branches and the number of void structures at the corresponding weld arc length position, which are used as the topological signature composed of topological invariants.

[0033] Preferably, in step S6, the step of performing transport prediction on the topology signature at the previous position and comparing its structural consistency with the actual topology signature at the next position includes:

[0034] The topological signature sequence is arranged sequentially according to the arc length of the weld.

[0035] A topological signature recursive relationship is established based on the continuity between the arc length positions of adjacent welds;

[0036] The topological signature of the previous weld arc length position is used to generate the predicted topological signature of the next weld arc length position.

[0037] The predicted topological signature is compared with the actual calculated topological signature to identify whether the topological structure has changed.

[0038] Preferably, in step S6, the step of determining the location of the topological abrupt change includes:

[0039] Mark the weld arc length positions in the topology signature sequence where the predicted topology signature is inconsistent with the actual topology signature;

[0040] The locations where inconsistencies occur consecutively are merged into weld arc length segments;

[0041] The weld arc length segment is recorded as a topological abrupt change segment.

[0042] Preferably, in step S7, the step of mapping the topological abrupt change location to the weld space segment in the workpiece coordinate system includes:

[0043] Based on the correspondence between the weld arc length coordinates and the welding torch position information, the topological abrupt change section is converted into a spatial coordinate section in the workpiece coordinate system;

[0044] The spatial coordinate segment is matched with the weld geometry model to obtain the corresponding spatial location range of the weld.

[0045] Preferably, in step S7, the step of performing combined analysis on the topological signature evolution sequence along the weld travel direction includes:

[0046] Extract the topological signatures corresponding to several weld arc length windows before and after the abrupt topological change section.

[0047] The topological signatures are arranged sequentially according to the direction of weld seam travel.

[0048] A topological evolution sequence describing the changes in local weld structure with the direction of travel is formed.

[0049] Preferably, in step S7, the step of forming the discrimination result of the dynamic defect evolution process of the weld includes:

[0050] The connectivity structure change patterns, hole structure generation and disappearance patterns, and connectivity domain splitting persistence patterns in the topological evolution sequence are grouped and described.

[0051] Different topological change patterns are combined in order of weld arc length to form a sequence of topological patterns corresponding to the dynamic defect evolution process of the weld.

[0052] The present invention has the following beneficial effects:

[0053] 1. This invention maps the acoustic emission signal from the time parameter domain to the weld arc length domain and introduces the weld travel coordinate system to spatially recalibrate the signal, so that each segment of acoustic emission data establishes a stable correspondence with the specific weld position, thereby directly giving the position range of the defect-generating section in the weld geometric coordinates during the actual welding process.

[0054] 2. This invention employs delayed embedding phase space reconstruction and constructs a multi-channel joint phase space point set. Then, it models the phase space structure through simple complex and topological invariants, expanding the acoustic emission signal from a single amplitude or spectral feature into a topological signature sequence that reflects the dynamic state structure of the system. During the welding process, it can characterize the state space structure changes caused by molten pool morphology changes, crack initiation, and defect propagation, enhancing the ability to structurally describe the internal evolution process of the weld.

[0055] 3. This invention establishes a topological transport recursive relationship between adjacent weld arc length positions and determines the location of topological structural abrupt change by comparing the structural consistency between the predicted topological signature and the actual topological signature. This transforms the defect judgment criterion from a single-moment abnormal signal to an objective criterion of the disruption of topological continuity. Thus, in the actual welding process, the topological evolution continuity in the weld travel direction can be used as the basis for judgment to stably identify and segment the dynamic defect generation section. Attached Figure Description

[0056] Figure 1 This is a flowchart of the real-time monitoring method for dynamic defects in welds using acoustic emission detection proposed in this invention.

[0057] Figure 2 A flowchart for establishing the weld travel coordinate system in the acoustic emission detection method for real-time monitoring of dynamic defects in welds proposed in this invention.

[0058] Figure 3 This is a flowchart of signal segmentation and phase space reconstruction for the acoustic emission detection method for real-time monitoring of dynamic defects in welds proposed in this invention. Detailed Implementation

[0059] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Reference Figures 1-3 This invention provides a method for real-time monitoring of dynamic defects in welds using acoustic emission detection, comprising the following steps:

[0061] S1. Arrange multiple acoustic emission sensors around the weld to synchronously collect the original acoustic emission signals corresponding to each sensor during the welding process;

[0062] S2. Obtain the continuous position information of the welding torch in the workpiece coordinate system, establish the weld travel coordinate system based on the welding torch trajectory, and establish the correspondence between the welding torch time parameters and the weld arc length coordinate.

[0063] S3. Based on the weld travel coordinate system, the original acoustic emission signal is mapped from the time parameter domain to the weld arc length domain, and segmented within the weld arc length domain according to a fixed arc length window to form a local acoustic emission signal segment corresponding to the weld position.

[0064] S4. Delay embedding phase space reconstruction is performed on each local acoustic emission signal segment, and the phase space states of multiple acoustic emission signals are combined to form a multi-channel joint phase space point set.

[0065] S5. Construct a simple complex structure based on the multi-channel joint phase space point set, and calculate the corresponding topological invariants at each weld arc length position to form a topological signature sequence arranged along the weld travel direction.

[0066] S6. Establish a topological transport recursion relationship between adjacent weld arc length positions, perform transport prediction on the topological signature of the previous position, and compare the structural consistency with the actual topological signature of the next position to determine the position of topological structural abrupt change.

[0067] S7. Map the topological change location to the weld space segment in the workpiece coordinate system, and perform combined analysis on the topological signature evolution sequence along the weld travel direction to form the discrimination result of the weld dynamic defect evolution process.

[0068] Specifically, the original acoustic emission signal is acquired by the i-th acoustic emission sensor at time t, denoted as . Where M represents the number of acoustic emission sensors, and t represents the time parameter under a unified time base. The spatial position of the welding torch in the workpiece coordinate system is represented as: ;in, , , These represent the position components of the welding torch along the three orthogonal coordinate axes of the workpiece coordinate system. The weld arc length coordinates are established based on the welding torch trajectory; the weld arc length parameter is defined as follows: ;in, The welding start time, Let be the integration variable, and ∥∥ denote the Euclidean norm of the vector. This establishes the correspondence between the time parameter t and the weld arc length parameter s.

[0069] Based on the above correspondence, the acoustic emission signal is mapped from the time parameter domain to the weld arc length domain to obtain the arc length domain signal representation: ;in, To perform an inverse function mapping from arc length parameters to time parameters, a fixed arc length window of length L is set within the arc length domain of the weld, and the center position of the window is denoted as . The arc length interval corresponding to the j-th window is represented as Within this interval, the arc length domain signal of each channel is extracted. Forming local acoustic emission signal segments ,in This represents the local arc length parameter relative to the center of the window.

[0070] For each local acoustic emission signal segment, a delay embedding process is performed to construct a phase space state vector: ;in, Indicates the embedding dimension. This represents the arc-length domain delay interval. The multi-channel joint phase space point set is composed of the state vector sets of each channel. ; in the joint phase space point set A simple complex structure is constructed, and the distance relationship between any two points is calculated based on a preset adjacency scale ε to generate the simple complex. By performing homology analysis on this simple complex, the set of topological invariants at the corresponding weld arc length position is obtained: ;in, Indicates the number of connected components. Indicates the number of holes / holes. Let represent the rank of the k-th homology group.

[0071] The topological invariants at each window position are sequentially arranged along the weld arc length to form a topological signature sequence. At adjacent arc length positions and Establish a topological transport recursion relationship between them, and construct a predicted topological signature based on the topological signatures of the previous position and its adjacent historical positions. ; and compare it with the actual calculations. Perform a structural consistency comparison. If there exists a dimension k that satisfies... When the topological change occurs, record the corresponding weld arc length position as the topological change location. Then, record the arc length parameter corresponding to the topological change location. Substituting the welding torch trajectory mapping relationship, we obtain its spatial position in the workpiece coordinate system. This determines the spatial segment of the weld. Further, the topological signatures of several arc-length windows before and after this segment are extracted, and a topological evolution sequence is constructed according to the weld travel direction. This sequence is used to characterize the structured evolution process of the weld's local dynamic state as it changes with the arc-length direction.

[0072] Furthermore, in step S2, the step of establishing the correspondence between the welding torch time parameters and the weld arc length coordinates includes:

[0073] Obtain the spatial coordinate sequence of the welding torch at consecutive time points;

[0074] The instantaneous motion direction of the welding torch is calculated based on the coordinate difference between adjacent moments.

[0075] The instantaneous movement direction of the welding torch is used as the reference axis for the weld travel direction;

[0076] A local coordinate system is constructed based on the reference axis, which moves with the welding torch, and the position information of the welding torch is projected onto the weld arc length coordinate axis to form a correspondence between the welding torch time parameters and the weld arc length coordinate.

[0077] Specifically, the spatial trajectory of the welding torch during the welding process is continuously collected by the position measurement unit, forming a spatial coordinate sequence arranged in chronological order. ;in, This represents the k-th sampling time. This represents the spatial position vector of the welding torch in the workpiece coordinate system at that moment. By performing a difference operation on the welding torch position vectors at adjacent moments, the displacement vector of the welding torch at discrete moments is obtained. Therefore, the welding torch at time [time] is calculated. instantaneous motion direction unit vector ; where ∥∥ represents the Euclidean norm of the vector, This represents the unit tangent vector of the welding torch along the direction of weld travel at that moment.

[0078] unit tangent vector Using the weld seam travel direction as the reference axis, a local coordinate system is constructed on top of it, moving with the welding torch. The local coordinate system has its origin at the current position of the welding torch, and its coordinates are... As the first coordinate axis, the remaining coordinate axes are orthogonal to the workpiece coordinate system or determined according to preset rules, thus forming a weld seam travel coordinate system that is continuously updated with the welding torch trajectory.

[0079] In the weld travel coordinate system, the welding torch position information is projected along the travel direction to obtain the weld arc length parameter. In discrete form, the arc length increment at the k-th time moment is expressed as... By accumulating the arc length increments, the welding torch can be obtained from the moment welding begins. At any time weld arc length coordinates ; This establishes the time parameter Coordinates of weld arc length The correspondence between them.

[0080] Based on the above correspondence, acoustic emission sampling data at any given moment can be directly correlated with the weld arc length coordinates, achieving spatial calibration of the acoustic emission signal in the weld travel direction. This correspondence serves as a unified spatial index basis in subsequent arc length domain segmentation, phase space reconstruction, and topology evolution analysis, ensuring that each processing step uses the weld travel direction as the reference coordinate, thereby guaranteeing that the acoustic emission signal, welding torch trajectory information, and topology analysis results are aligned and correlated within the same weld spatial parameter system.

[0081] Furthermore, in step S3, the step of segmenting the weld arc length domain according to a fixed arc length window to form a local acoustic emission signal segment corresponding to the weld position includes:

[0082] Based on the correspondence between welding torch time parameters and weld arc length coordinates, the original acoustic emission signals collected at each moment are recalibrated.

[0083] The recalibrated acoustic emission signals are reordered into a signal sequence that varies with the weld arc length.

[0084] Set sliding windows with equal arc length intervals within the arc length domain of the weld;

[0085] The acoustic emission signal samples within each sliding window are extracted to form local acoustic emission signal segments that match the corresponding weld section.

[0086] Specifically, based on the correspondence between the time parameter t and the weld arc length parameter s established in step S2, the acoustic emission channels are sampled at discrete sampling times. The acquired raw signal Perform spatial recalibration, mapping it to the signal value at the corresponding weld arc length position. ;

[0087] in, Indicates time The corresponding weld arc length coordinates This indicates the position of the i-th sensor at the arc length. The amplitude of the acoustic emission signal at the location. All sampling points are calculated using arc length coordinates. The acoustic emission signals are sorted from smallest to largest to form a monotonically arranged arc-length domain acoustic emission signal sequence that follows the direction of weld movement. This allows the original multi-channel acoustic emission data, which had time as the independent variable, to be reconstructed into a spatial sequence data structure with weld arc length as the independent variable.

[0088] A sliding window of fixed length L is set within the arc length domain of the weld, and the center position of the window is denoted as L. The corresponding arc length interval is defined as: ; Sliding step size set to The centers of adjacent windows satisfy This creates a sequence of analysis windows with equal arc length intervals along the weld arc length axis. For each arc-length window Searching for signals in the arc-length domain that satisfy... All sampling points will form the corresponding multi-channel acoustic emission signal sample set. Extract and combine the segments to form the j-th local acoustic emission signal segment, denoted as . ;in, With weld arc length section Correspondingly, and simultaneously associated with its center arc length coordinates in the data structure. and the corresponding welding torch spatial position .

[0089] Through the aforementioned recalibration, sorting, and sliding window segmentation, the original acoustic emission time series is converted into a sequence of local spatial segments arranged according to the weld travel direction. Each local acoustic emission signal segment corresponds to a specific arc-length segment on the weld, maintaining the synchronization of multi-channel signals within the same arc-length window. This processing method enables subsequent phase space reconstruction, topology modeling, and topology transport analysis to be based on the weld arc-length coordinate system, thereby achieving continuous organization and segmented representation of acoustic emission data in the spatial location of the weld, providing a unified parameter basis for the spatial localization and evolution analysis of dynamic defects in the weld.

[0090] Furthermore, in step S4, the step of combining the phase space states of the multiple acoustic emission signals to form a multi-channel joint phase space point set includes:

[0091] For each local acoustic emission signal segment, a multidimensional state vector sequence is constructed according to a uniform delay time and embedding dimension;

[0092] The state vector sequences corresponding to the same sensor channel are used to form the phase space trajectory point set of that channel;

[0093] The phase space trajectory point sets of different sensor channels are mapped to the same phase space coordinate system to form a multi-channel joint phase space point set.

[0094] Specifically, for the local acoustic emission signal segment corresponding to the j-th weld arc length window obtained in step S3... It contains discrete sampling sequences of each sensor channel within that arc length segment. Where M is the number of acoustic emission sensor channels, This represents the weld arc length coordinates corresponding to the k-th sampling point within the j-th arc length window. This represents the number of sampling points within the window. The local signal sequence of each acoustic emission channel is processed according to a uniformly set delay interval. and embedding dimension Perform delayed embedding to construct a multidimensional state vector: ;in, This indicates the position of the i-th sensor at the arc length. The corresponding phase space state vector.

[0095] For the same sensor channel in the window All constructible state vectors within the channel are collected to form the phase space trajectory point set of the channel: ;in, To satisfy the delayed embedding condition, the effective number of state vectors is determined. To achieve a unified description of multi-channel information in the same dynamic state space, the trajectory point sets of each channel are jointly represented in the same coordinate system, forming a multi-channel joint phase space point set: In the joint point set, the state vectors of each phase space all use the same embedding dimension m and delay interval. Including coordinate dimensions, thus ensuring that the phase space states of different sensor channels have a consistent geometric measurement basis. At the data structure level, a channel identifier and arc length position index are added to each state vector, so that the joint phase space point set not only contains phase space coordinate information, but also retains the index relationship of its source channel and the corresponding weld arc length segment, which is used to subsequently construct cross-channel topological adjacency relationships and simple complex structures.

[0096] Through the aforementioned delayed embedding and multi-channel joint processing, local acoustic emission signal segments are converted into multi-channel state point clouds distributed in a unified spatial coordinate system. This enables the acoustic emission information collected by different spatial locations and different sensor channels during the welding process to be structurally represented in the same dynamic state space, providing a unified geometric and topological analysis object for subsequent simple complex construction and topological invariant calculation.

[0097] Furthermore, in step S5, the step of calculating the corresponding topological invariants at each weld arc length position to form a topological signature sequence arranged along the weld travel direction includes:

[0098] Calculate the distance relationship between any two points in the multi-channel joint phase space point set;

[0099] Establish an adjacency network between point sets based on a preset adjacency scale;

[0100] Generating simple complex structures based on adjacency networks;

[0101] Homology analysis is performed on the simple complex to obtain the number of connected branches and the number of void structures at the corresponding weld arc length position, which are used as the topological signature composed of topological invariants.

[0102] Specifically, for step S4 at the weld arc length position The multi-channel joint phase space point set obtained at [location] ;in, This represents the k-th phase space state vector, where m is the embedding dimension. This represents the number of state vectors within the arc-length window. First, a Euclidean distance metric is defined for this point set to characterize the geometric distance between any two phase space state vectors: This allows us to construct the distance matrix of the point set in phase space. Based on the distance matrix, a preset adjacency scale parameter is introduced. When satisfied At that time, it is considered that the phase space point and There exists an adjacency relationship between them, and an undirected edge is established between them. Thus, an adjacency network can be constructed on the set of points in the joint phase space. Among them, the vertex set edge set Based on the adjacency relationship network, simple complex structures are further generated according to the simple complex construction rules.

[0103] When any q+1 phase space points When all pairs of simplexes satisfy the adjacency condition, a corresponding q-dimensional simplex is introduced into the simplex complex, thus obtaining a simplex at the scale... Simple complex For simple complexes Perform homology analysis, calculate the rank of each homology group, and obtain the set of topological invariants: ;in, Indicates the number of connected components. This represents the number of one-dimensional hole structures. Topological invariants are assigned according to arc length position. Combine them to form a topological signature vector: The center positions of each arc-length window obtained along the weld travel direction. Calculate the topological signature vector sequentially They are arranged in order of arc length to form a topological signature sequence. ;

[0104] Through the above processing, the multi-channel phase space point cloud corresponding to each position on the weld is converted into a topological signature sequence characterized by the number of connected structures and the number of void structures. This allows the structural differences in the phase space geometric distribution to be organized and recorded along the weld travel direction in the form of discrete topological invariants, providing a unified structured input for the subsequent establishment of topological transport recursion relationships and the determination of abrupt changes in topological structure.

[0105] Furthermore, in step S6, the steps of performing transport prediction on the topology signature at the previous position and comparing its structural consistency with the actual topology signature at the next position include:

[0106] The topological signature sequence is arranged sequentially according to the arc length of the weld.

[0107] A topological signature recursive relationship is established based on the continuity between the arc length positions of adjacent welds;

[0108] The topological signature of the previous weld arc length position is used to generate the predicted topological signature of the next weld arc length position.

[0109] The predicted topological signature is compared with the actual calculated topological signature to identify whether the topological structure has changed.

[0110] In step S6, the step of determining the location of the topological abrupt change includes:

[0111] Mark the weld arc length positions in the topology signature sequence where the predicted topology signature is inconsistent with the actual topology signature;

[0112] The locations where inconsistencies occur consecutively are merged into weld arc length segments;

[0113] The arc length section of the weld is recorded as a topological abrupt change section.

[0114] Specifically, based on the topological signature sequence obtained in step S5 ;in, This indicates the center position of the j-th arc-length window. This represents the topological invariant vector at that location. First, the topological signatures are sorted in ascending order of arc length coordinates to maintain a spatial order consistent with the weld travel direction. At adjacent arc length positions... Based on the spatial continuity of the dynamic state during weld seam travel, a recursive prediction model for topological signatures is constructed. This model takes historical topological signatures as input and forms a structural prediction expression for the topological signature at the current position. ;in, Indicates position based on arc length and Topological evolution trend on position The predicted topological signature.

[0115] After obtaining the predicted topological signature, it is compared with the actual topological signature obtained through simple complex isohomology analysis. To perform a dimension-by-dimensional structural consistency comparison, the topological difference vector is defined as follows: When any dimension satisfies At that time, determine the position of the arc length. The topology at this point does not satisfy the transport recursion relation. Perform the above recursive prediction and consistency comparison process on the entire topological signature sequence, marking all sequences that satisfy the recursive relation on the arc-length coordinate axis. The arc length position points. For adjacent positions on the arc length coordinate system that show consecutive inconsistent markers, segments are merged to form a set of arc length segments with abrupt topological changes: ;in, This indicates the start and end positions of the q-th continuous phase transformation segment within the weld arc length domain.

[0116] Through the aforementioned processing flow based on the topological transport recursive model and structural consistency determination, the topological signature sequence of the weld along the travel direction is divided into segments that satisfy continuous transport relations and segments that do not. The latter are recorded as topological abrupt change segments in the form of arc-length segments. This processing clearly distinguishes the continuous evolution and local abrupt changes of the weld dynamic state in space at the level of topological invariants, providing structured and indexable arc-length segment data for subsequent weld spatial segment location and topological evolution sequence construction.

[0117] Furthermore, in step S7, the step of mapping the topological abrupt change location to the weld space segment in the workpiece coordinate system includes:

[0118] Based on the correspondence between the weld arc length coordinates and the welding torch position information, the topological abrupt change section is converted into a spatial coordinate section in the workpiece coordinate system;

[0119] By matching the spatial coordinate segments with the weld geometry model, the corresponding spatial location range of the weld is obtained.

[0120] In step S7, the step of performing combined analysis on the topological signature evolution sequence along the weld seam travel direction includes:

[0121] Extract the topological signatures corresponding to several weld arc length windows before and after the abrupt topological change section.

[0122] Arrange the topological signatures in chronological order according to the direction of weld seam travel;

[0123] A topological evolution sequence describing the changes in local weld structure with the direction of travel is formed.

[0124] In step S7, the steps for forming the discrimination result of the dynamic defect evolution process of the weld include:

[0125] The patterns of connectivity structure change, hole structure generation and disappearance, and connectivity domain splitting persistence in the topological evolution sequence are grouped and described.

[0126] Different topological change patterns are combined in order of weld arc length to form a sequence of topological patterns corresponding to the dynamic defect evolution process of the weld.

[0127] Specifically, based on the set of arc length segments of topological mutation obtained in step S6 ;in, This represents the start and end positions of the q-th topological abrupt change segment within the weld arc length domain. The correspondence between the welding torch time parameters and the weld arc length coordinates established in step S2 is utilized. By inversely mapping the arc length parameters at the endpoints of the segment, the corresponding time parameter intervals are obtained. ; and further derived from the welding torch trajectory function Calculate its spatial coordinate segment in the workpiece coordinate system: This forms a set of weld space coordinate segments corresponding to each arc length segment of abrupt topological changes. .

[0128] The spatial coordinate segments are matched with the weld geometry model. The weld geometry model stores the weld centerline and its normal information in the form of parametric curves or discrete point sets. This information is obtained by retrieving the coordinates from the weld model. The nearest weld centerline point to each trajectory point determines the corresponding spatial location range of the topological abrupt change section in the weld model, realizing a unified spatial index among the arc length section, the welding torch trajectory, and the weld geometry model.

[0129] After obtaining the spatial location of the topologically abrupt change segment, from the topological signature sequence Extract the segment located in Center position of several arc-length windows The corresponding topological signatures constitute a local topological signature subsequence. Arranging this subsequence in ascending order of weld arc length forms a topological evolution sequence describing the change of the weld's topological structure within this spatial segment as it travels: ;

[0130] For each topological evolution sequence We analyze the combination patterns of topological invariants varying with arc length parameters. The sequence of changes in the number of connected components is considered the connectivity structure change pattern; the occurrence, disappearance, and persistence of the number of void structures are considered void structure generation and disappearance patterns; and the sequence of long-term splitting or merging of connected components is considered the connectivity domain splitting persistence pattern. These three types of patterns are combined in arc length order to form a topological pattern sequence: ;in, Indicates the pattern of changes in the connected structure. This indicates the evolution pattern of the pore structure. This indicates a persistent mode of connected component splitting.

[0131] By mapping the arc length segment of the topological abrupt change to the weld space segment in the workpiece coordinate system, and performing pattern combination and structured description of the evolution sequence of the topological signature before and after the segment, a unified expression of the local dynamic state change of the weld in both spatial position and topological structure dimensions is achieved. This enables the formation and evolution process of weld dynamic defects to be output synchronously in the form of arc length parameters, spatial coordinates and topological pattern sequences.

[0132] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for real-time monitoring of dynamic defects in welds using acoustic emission detection, characterized in that, Includes the following steps: S1. Arrange multiple acoustic emission sensors around the weld to synchronously collect the original acoustic emission signals corresponding to each sensor during the welding process; S2. Obtain the continuous position information of the welding torch in the workpiece coordinate system, establish the weld travel coordinate system based on the welding torch trajectory, and establish the correspondence between the welding torch time parameters and the weld arc length coordinate. S3. Based on the weld travel coordinate system, the original acoustic emission signal is mapped from the time parameter domain to the weld arc length domain, and segmented within the weld arc length domain according to a fixed arc length window to form a local acoustic emission signal segment corresponding to the weld position. S4. Delay embedding phase space reconstruction is performed on each local acoustic emission signal segment, and the phase space states of multiple acoustic emission signals are combined to form a multi-channel joint phase space point set. S5. Construct a simple complex structure based on the multi-channel joint phase space point set, and calculate the corresponding topological invariants at each weld arc length position to form a topological signature sequence arranged along the weld travel direction. S6. Establish a topological transport recursion relationship between adjacent weld arc length positions, perform transport prediction on the topological signature of the previous position, and compare the structural consistency with the actual topological signature of the next position to determine the position of topological structural abrupt change. S7. Map the topological abrupt change position to the weld space segment in the workpiece coordinate system, and perform combined analysis on the topological signature evolution sequence along the weld travel direction to form the discrimination result of the weld dynamic defect evolution process.

2. The method for real-time monitoring of dynamic defects in welds using acoustic emission detection according to claim 1, characterized in that, In step S2, the step of establishing the correspondence between the welding torch time parameters and the weld arc length coordinates includes: Obtain the spatial coordinate sequence of the welding torch at consecutive time points; The instantaneous direction of the welding torch is calculated based on the coordinate difference between adjacent moments. The instantaneous movement direction of the welding torch is used as the reference axis for the weld travel direction; A local coordinate system is constructed based on the reference axis, which moves with the welding torch, and the welding torch position information is projected onto the weld arc length coordinate axis to form a correspondence between the welding torch time parameters and the weld arc length coordinate.

3. The method for real-time monitoring of dynamic defects in welds using acoustic emission detection according to claim 1, characterized in that, In step S3, the step of segmenting the weld arc length domain according to a fixed arc length window to form a local acoustic emission signal segment corresponding to the weld position includes: Based on the correspondence between the welding torch time parameters and the weld arc length coordinates, the original acoustic emission signals collected at each moment are recalibrated. The recalibrated acoustic emission signals are reordered into a signal sequence that varies with the weld arc length. Set sliding windows with equal arc length intervals within the arc length domain of the weld; The acoustic emission signal samples within each sliding window are extracted to form local acoustic emission signal segments that match the corresponding weld section.

4. The method for real-time monitoring of dynamic defects in welds using acoustic emission detection according to claim 1, characterized in that, In step S4, the step of combining the phase space states of the multiple acoustic emission signals to form a multi-channel joint phase space point set includes: For each local acoustic emission signal segment, a multidimensional state vector sequence is constructed according to a uniform delay time and embedding dimension; The state vector sequences corresponding to the same sensor channel are used to form the phase space trajectory point set of that channel; The phase space trajectory point sets of different sensor channels are mapped to the same phase space coordinate system to form a multi-channel joint phase space point set.

5. The method for real-time monitoring of dynamic defects in welds using acoustic emission detection according to claim 1, characterized in that, In step S5, the step of calculating the corresponding topological invariants at each weld arc length position to form a topological signature sequence arranged along the weld travel direction includes: Calculate the distance relationship between any two points in the multi-channel joint phase space point set; Establish an adjacency network between point sets based on a preset adjacency scale; A simple complex structure is generated based on the adjacency network. Homology analysis is performed on the simple complex to obtain the number of connected branches and the number of void structures at the corresponding weld arc length position, which are used as the topological signature composed of topological invariants.

6. The method for real-time monitoring of dynamic defects in welds using acoustic emission detection according to claim 1, characterized in that, In step S6, the step of performing transport prediction on the topology signature at the previous position and comparing its structural consistency with the actual topology signature at the next position includes: The topological signature sequence is arranged sequentially according to the arc length of the weld. A topological signature recursive relationship is established based on the continuity between the arc length positions of adjacent welds; The topological signature of the previous weld arc length position is used to generate the predicted topological signature of the next weld arc length position. The predicted topological signature is compared with the actual calculated topological signature to identify whether the topological structure has changed.

7. The method for real-time monitoring of dynamic defects in welds using acoustic emission detection according to claim 1, characterized in that, In step S6, the step of determining the location of the topological abrupt change includes: Mark the weld arc length positions in the topology signature sequence where the predicted topology signature is inconsistent with the actual topology signature; The locations where inconsistencies occur consecutively are merged into weld arc length segments; The weld arc length segment is recorded as a topological abrupt change segment.

8. The method for real-time monitoring of dynamic defects in welds using acoustic emission detection according to claim 1, characterized in that, In step S7, the step of mapping the abrupt change in topology to the weld space segment in the workpiece coordinate system includes: Based on the correspondence between the weld arc length coordinates and the welding torch position information, the topological abrupt change section is converted into a spatial coordinate section in the workpiece coordinate system; The spatial coordinate segment is matched with the weld geometry model to obtain the corresponding spatial location range of the weld.

9. The method for real-time monitoring of dynamic defects in welds using acoustic emission detection according to claim 1, characterized in that, In step S7, the step of performing combined analysis on the topological signature evolution sequence along the weld travel direction includes: Extract the topological signatures corresponding to several weld arc length windows before and after the abrupt topological change section. The topological signatures are arranged sequentially according to the direction of weld seam travel. A topological evolution sequence describing the changes in local weld structure with the direction of travel is formed.

10. The method for real-time monitoring of dynamic defects in welds using acoustic emission detection according to claim 9, characterized in that, In step S7, the step of forming the discrimination result of the dynamic defect evolution process of the weld includes: The connectivity structure change patterns, hole structure generation and disappearance patterns, and connectivity domain splitting persistence patterns in the topological evolution sequence are grouped and described. Different topological change patterns are combined in order of weld arc length to form a sequence of topological patterns corresponding to the dynamic defect evolution process of the weld.

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