An OCT penetration depth defect detection method for laser welding
By acquiring workpiece data through an OCT penetration depth testing device, filtering and meshing are performed, solving the low efficiency problem of existing testing methods and achieving efficient and reliable penetration depth defect detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- WUHAN NEWLAZ INTELLIGENT TECH CO LTD
- Filing Date
- 2025-06-24
- Publication Date
- 2026-06-19
Smart Images
Figure CN120778730B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of laser welding processing, and specifically relates to an OCT method for detecting penetration defects in laser welding. Background Technology
[0002] With the rapid development of science and technology and industry in my country, the demand for laser welding in the industrial sector is increasing, and the quality requirements for laser welding are also becoming higher. Among them, the weld penetration depth is a key indicator for measuring the quality of the weld, which directly affects the connection strength and sealing performance.
[0003] Currently, existing methods for detecting penetration defects (porosity, cold welds, etc.) rely on metallographic sections and ultrasonic testing, which have problems such as being destructive, contact-based, having high delays, and low real-time performance (waiting for the high-temperature molten pool to cool down), resulting in low efficiency of OCT penetration defect detection. Summary of the Invention
[0004] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides an OCT penetration defect detection method for laser welding. Its purpose is to obtain penetration data in real time by tracing the workpiece, and to achieve accurate detection by filtering and meshing. It has high reliability, avoids the use of contact or destructive detection methods, and improves production efficiency.
[0005] To achieve the above objectives, the present invention provides an OCT penetration defect detection method for laser welding, the OCT penetration defect detection method comprising:
[0006] The workpiece is scanned by a penetration depth detection device to obtain penetration depth data. The penetration depth data includes multiple primary data points, which are located in a plane and each corresponds to a plane coordinate.
[0007] Noise is removed from the melt depth data using filtering to determine the corresponding filtered melt depth data. The filtered melt depth data includes multiple secondary data points. For multiple primary data points at the edges of the melt depth data, a mirror filling method is used to supplement them during filtering.
[0008] The regions corresponding to multiple secondary data points in the plane are divided into grids, and the sparsity of secondary data points in each grid region is calculated. Each grid region has at least one secondary data point. If the sparsity of a grid region is greater than a first threshold, it is determined to be an abnormal grid region; otherwise, it is determined to be a normal grid region. If the number of abnormal grid regions is greater than a second threshold, the workpiece is determined to have unqualified melt depth quality; otherwise, the workpiece is determined to have qualified melt depth quality.
[0009] Optionally, the step of dividing the region corresponding to the multiple secondary data points in the plane into a grid includes:
[0010] Determine the maximum and minimum values of multiple secondary data points in the X and Y directions;
[0011] The total grid area is formed by drawing axes in the plane based on the maximum and minimum values of multiple secondary data points in the X and Y directions;
[0012] The total grid area is divided into multiple grid areas.
[0013] Optionally, the division of the total grid area into multiple grid areas is achieved using the following formula:
[0014] N=(X Max -X Min ) / X Step ;
[0015] M = (Y Max -Y Min ) / Y Step ;
[0016] Among them, X Max and X Min Let X and Y be the maximum and minimum values in the X direction, respectively, among multiple secondary data points. Max and Y Min Let X be the maximum and minimum values in the Y direction among multiple secondary data points. Step and Y Step The length and width of the respective grid cells.
[0017] Optionally, the calculation of the sparsity of secondary data points in each grid region is achieved using the following formula:
[0018]
[0019] Where z is the sparsity of the current grid region; s is the number of secondary data points in the current grid region; α is the neighborhood weight coefficient; t is the total number of secondary data points in multiple grid regions corresponding to the current grid region in the circumferential direction; m is the maximum number of secondary data points generated in each grid region based on the minimum distance between secondary data points; and n is the number of multiple grid regions corresponding to the current grid region in the circumferential direction.
[0020] Alternatively, n can be 8 or 4.
[0021] Optionally, 1 ≤ α ≤ 5.
[0022] Optionally, the step of filtering noise from the melt depth data includes:
[0023] The filter window is determined to be a cross-shaped window. The filter window is then slid down row by row and column by column, starting from the maximum value in the Y direction among multiple primary data points, until the entire image is covered.
[0024] During the movement of the cross-shaped window, the position of each secondary data point after filtering is determined through filtering calculations.
[0025] Optionally, determining the position of each secondary data point after filtering through filtering calculation during the movement of the cross-shaped window includes:
[0026] In the Y direction, determine the total number of primary data points in a unit area. If the total number of primary data points is greater than the third threshold, the mean filtering method is used to determine the position of each secondary data point after filtering; otherwise, the median filtering method is used to determine the position of each secondary data point after filtering.
[0027] Optionally, the third threshold is 1000-2000.
[0028] Optionally, the first threshold is 0.7-0.8, and the second threshold is 20-100.
[0029] The aforementioned improved technical features can be combined with each other as long as they do not conflict with each other.
[0030] In summary, the beneficial effects of the above-described technical solutions conceived by this invention compared with the prior art include:
[0031] For the OCT penetration defect detection method for laser welding provided in this embodiment of the invention, when detecting penetration defects in a workpiece, firstly, the workpiece is scanned by a penetration detection device to obtain penetration data of the workpiece. The penetration data includes multiple primary data points, which are located in a plane and each corresponds to a plane coordinate, thereby completing the acquisition of data points. Each data point corresponds to a plane coordinate, which facilitates subsequent filtering and mesh generation.
[0032] Then, noise is removed from the melt depth data using filtering to determine the corresponding filtered melt depth data. This filtered melt depth data includes multiple secondary data points. For the primary data points at the edges of the melt depth data, a mirror-fill method is used during filtering to supplement them. This mirror-fill method for the primary data points at the edges of the melt depth data addresses the issue of the filtering window exceeding the effective data range when using filtering on boundary data. This ensures that the filter can still utilize reasonable contextual information for calculation at the boundaries, effectively filtering noise while avoiding information loss.
[0033] Finally, the regions corresponding to multiple secondary data points in the plane are meshed, and the sparsity of secondary data points in each mesh region is calculated. Each mesh region contains at least one secondary data point. If the sparsity of a mesh region is greater than a first threshold, it is considered an abnormal mesh region; otherwise, it is considered a normal mesh region. If the number of abnormal mesh regions is greater than a second threshold, the workpiece's weld depth quality is considered unqualified; otherwise, the workpiece's weld depth quality is considered qualified. For each mesh region, the greater the sparsity, the higher the risk of the mesh region being an abnormal mesh region, and therefore it is considered an abnormal mesh region; conversely, it is considered a normal mesh region. Correspondingly, when the number of abnormal mesh regions corresponding to a certain weld depth is large, the possibility of defects in that weld depth is greater, thus ultimately achieving weld depth defect detection.
[0034] In other words, the OCT penetration defect detection method for laser welding provided by the embodiments of the present invention can obtain penetration data in real time by tracing the workpiece, and achieve accurate detection by filtering and meshing. It has high reliability, avoids the use of contact or destructive detection methods, and improves production efficiency. Attached Figure Description
[0035] Figure 1 This is a flowchart of an OCT penetration defect detection method for laser welding provided in an embodiment of the present invention;
[0036] Figure 2 This is a schematic diagram showing the distribution of multiple primary data points corresponding to the workpiece melting depth provided in an embodiment of the present invention;
[0037] Figure 3 This is a schematic diagram showing the distribution of multiple secondary data points corresponding to the workpiece melting depth provided in an embodiment of the present invention;
[0038] Figure 4 This is a schematic diagram of the mesh division of multiple secondary data points corresponding to the workpiece melting depth provided in an embodiment of the present invention. Detailed Implementation
[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.
[0040] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.
[0041] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.
[0042] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.
[0043] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.
[0044] Example:
[0045] Figure 1 This is a flowchart of an OCT penetration defect detection method for laser welding provided by an embodiment of the present invention, as shown below. Figure 1 As shown, this OCT penetration defect detection method includes:
[0046] S1. The workpiece's weld depth data is obtained by scanning it with a weld depth detection device (i.e., by scanning after analyzing the laser interference signal using OCT technology, which is real-time and a conventional technique in this field). The weld depth data includes multiple primary data points, which are located in a plane and each corresponds to a plane coordinate (see...). Figure 2 ).
[0047] S2. Use filtering to remove noise from the melt depth data and determine the corresponding filtered melt depth data (see...). Figure 3 The filtered melt depth data includes multiple secondary data points. For multiple primary data points at the edges of the melt depth data, a mirror filling method is used to supplement them during filtering.
[0048] S3. Divide the region corresponding to multiple secondary data points in the plane into a grid (see...). Figure 4 The sparsity of secondary data points in each grid region is calculated, wherein each grid region has at least one secondary data point. If the sparsity of a grid region is greater than the first threshold, it is determined to be an abnormal grid region; otherwise, it is determined to be a normal grid region. If the number of abnormal grid regions is greater than the second threshold, the workpiece melt depth quality is determined to be unqualified; otherwise, the workpiece melt depth quality is determined to be qualified.
[0049] For the OCT penetration defect detection method for laser welding provided in this embodiment of the invention, when detecting penetration defects in a workpiece, firstly, the workpiece is scanned by a penetration detection device to obtain penetration data of the workpiece. The penetration data includes multiple primary data points, which are located in a plane and each corresponds to a plane coordinate, thereby completing the acquisition of data points. Each data point corresponds to a plane coordinate, which facilitates subsequent filtering and mesh generation.
[0050] Then, noise is removed from the melt depth data using filtering to determine the corresponding filtered melt depth data. This filtered melt depth data includes multiple secondary data points. For multiple primary data points at the edges of the melt depth data, a mirror-fill method is used to supplement them during filtering. Since there are discrete points among the primary data points, the improved filtering (using mirror-fill to supplement primary data points at the edges) avoids problems such as inability to handle edge filtering effects effectively. It also effectively filters out discrete points caused by noise (hardware, sample effects, environmental interference, etc.), causing these discrete points to move to the positions of normal data points based on the filtering process. Furthermore, processing these discrete points facilitates subsequent mesh generation, avoiding interference from these discrete points in the subsequent mesh generation.
[0051] Finally, the regions corresponding to multiple secondary data points in the plane are meshed, and the sparsity of secondary data points in each mesh region is calculated. Each mesh region contains at least one secondary data point. If the sparsity of a mesh region is greater than a first threshold, it is considered an abnormal mesh region; otherwise, it is considered a normal mesh region. If the number of abnormal mesh regions is greater than a second threshold, the workpiece's weld depth quality is considered unqualified; otherwise, the workpiece's weld depth quality is considered qualified. For each mesh region, the greater the sparsity, the higher the risk of the mesh region being an abnormal mesh region, and therefore it is considered an abnormal mesh region; conversely, it is considered a normal mesh region. Correspondingly, when the number of abnormal mesh regions corresponding to a certain weld depth is large, the possibility of defects in that weld depth is greater, thus ultimately achieving weld depth defect detection.
[0052] In other words, the OCT penetration defect detection method for laser welding provided by the embodiments of the present invention can obtain penetration data in real time by tracing the workpiece, and achieve accurate detection by filtering and meshing. It has high reliability, avoids the use of contact or destructive detection methods, and improves production efficiency.
[0053] For example, the first threshold is 0.7-0.8, and the second threshold is 20-100. Preferably, the first threshold is 0.7, and the second threshold is 20.
[0054] It should be noted that the specific values of the first threshold and the second threshold are related to the actual application scenario and the data size, and this invention does not impose any limitations on them.
[0055] In step S1, noise is filtered out from the melt depth data, including:
[0056] a. Determine that the filtering window is a cross-shaped window, and slide the filtering window row by row and column by column starting from the maximum value in the Y direction among multiple primary data points until it covers the entire image.
[0057] b. During the movement of the cross-shaped window, the position of each secondary data point after filtering is determined by filtering calculation.
[0058] It's easy to understand that if the filtering window is a rectangular window, its shape encompasses all directions, resulting in significant damage to the original data after filtering and impacting subsequent analysis. In this embodiment, however, an (n*n) shaped cross-shaped window is used, effectively avoiding this drawback and filtering out both horizontal and vertical noise points. This also greatly reduces the amount of data point calculation. For example, in a 3*3 cross-shaped window, the center point is (i,j), and the coordinates of the remaining points are (i-1,j), (i+1,j), (i,j-1), and (i,j+1).
[0059] Preferably, the size of the cross window is selected as 9 (i.e., n is 9) to achieve a balance between noise filtering and data integrity preservation. In addition, the sliding step size is 1.
[0060] It should be noted that the specific size of the cross window and the sliding step size are related to the actual application scenario and the amount of data, and this invention does not impose any limitations on them.
[0061] Furthermore, for step b above, the positions of each secondary data point after filtering are determined through filtering calculations during the movement of the cross-shaped window, including:
[0062] In the Y direction, determine the total number of primary data points in a unit area. If the total number of primary data points is greater than the third threshold, the mean filtering method is used to determine the position of each secondary data point after filtering; otherwise, the median filtering method is used to determine the position of each secondary data point after filtering.
[0063] In the above implementation, median filtering is used for regions that may have noise (i.e., the total number of primary data points in a unit region is small and less than the third threshold), while mean filtering is used for regions with flat data (i.e., the total number of primary data points in a unit region is large) to reduce the sorting calculation overhead caused by median filtering, thereby reducing computation and improving efficiency.
[0064] For example, the method for determining whether a unit region is a noise region is as follows: determine the number of primary data points within the window range (j-1, j+1) as N. If N is less than the third threshold, then the unit region is determined to be a noise region, and median filtering is used for calculation; otherwise, the unit region is a non-noise region, and mean filtering is used for calculation.
[0065] For example, the third threshold is 1000-2000, and preferably, the third threshold is 1000.
[0066] In this embodiment, step S3, which involves dividing the region corresponding to multiple secondary data points in the plane into a grid, includes:
[0067] a. Determine the maximum and minimum values of multiple secondary data points in the X and Y directions.
[0068] b. Draw axes in the plane based on the maximum and minimum values of multiple secondary data points in the X and Y directions to form the total grid area.
[0069] c. Divide the total grid area into multiple grid areas.
[0070] In the above implementation, by determining the maximum and minimum values in the X and Y directions, rapid mesh generation can be performed, and the risk of blank mesh areas can be reduced.
[0071] Furthermore, in step c, the total grid area is divided into multiple grid areas using the following formula:
[0072] N=(X Max -X Min ) / X Step (1)
[0073] M = (Y Max -Y Min ) / Y Step (2)
[0074] Among them, X Max and X Min Let X and Y be the maximum and minimum values in the X direction, respectively, among multiple secondary data points. Max and Y Min Let X be the maximum and minimum values in the Y direction among multiple secondary data points. Step and Y Step The length and width of the respective grid cells.
[0075] Preferably, X Step Y is 10 Step The value is 10 to achieve a balance between effective grid division and analysis efficiency. The specific value depends on the actual application scenario and the data size.
[0076] In one implementation of the present invention, in step S3, the sparsity of secondary data points in each grid region is calculated using the following formula:
[0077]
[0078] Where z is the sparsity of the current grid region; s is the number of secondary data points in the current grid region; α is the neighborhood weight coefficient; t is the total number of secondary data points in multiple grid regions corresponding to the current grid region in the circumferential direction; m is the maximum number of secondary data points generated in each grid region based on the minimum distance between secondary data points; and n is the number of multiple grid regions corresponding to the current grid region in the circumferential direction.
[0079] Grid 1 Grid 2 Grid 3 Grid 4 Grid 5 Grid 6 Grid 7 Grid 8 Grid 9
[0080] The table above shows the arrangement of multiple grid regions when n=8. Grid 5 is the current grid whose sparsity is to be calculated, and grids 1-4 and 6-9 are the multiple grid regions corresponding to the current grid region in the circumferential direction.
[0081] It is easy to understand that if only the current grid is considered when calculating grid sparsity, while ignoring the circumferential grid, it cannot fully reflect whether the grid is an abnormal grid region (i.e., the high density of a single grid cannot represent the overall density of the region, and vice versa), resulting in large errors and strong randomness, and thus low reliability of the sparsity. Therefore, formula (3) combines not only the current grid but also the circumferential grid, so that the calculation of the grid sparsity can reflect the overall density of the region, and the calculated sparsity is more reliable.
[0082] For the numerator in formula (3): s+α·t is the total number of secondary data points in the 9 grids in the table above;
[0083] The denominator in formula (3), m·(1+α·n), is used for normalization to ensure that the sparsity result is within the range of [0,1], where (1+α·n) is the normalization factor.
[0084] For example, n is 8 (i.e., eight neighborhoods, considering the surrounding 8 grids (including the top, bottom, left, right and diagonal directions)) or 4 (i.e., four neighborhoods, considering the surrounding 4 grids, ignoring diagonal sparsity).
[0085] It's easy to understand that α is the neighborhood weight coefficient, used to adjust the strength of the influence of neighboring grids on the sparsity of the current grid. When α = 0, neighborhood information can be completely ignored; when α > 0, the number of neighborhood points will increase the effective density of the current grid, that is, the number of neighborhood points will affect the judgment of the grid sparsity; when α approaches infinity, the influence of the neighborhood dominates the calculation results.
[0086] For example, 1≤α≤5.
[0087] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. An OCT method for detecting penetration defects in laser welding, characterized in that, The OCT penetration depth defect detection method includes: The workpiece is scanned by a penetration depth detection device to obtain penetration depth data. The penetration depth data includes multiple primary data points, which are located in a plane and each corresponds to a plane coordinate. Noise is removed from the melt depth data using filtering to determine the corresponding filtered melt depth data. The filtered melt depth data includes multiple secondary data points. For multiple primary data points at the edges of the melt depth data, a mirror filling method is used to supplement them during filtering. The regions corresponding to multiple secondary data points in the plane are divided into grids, and the sparsity of secondary data points in each grid region is calculated. Each grid region has at least one secondary data point. If the sparsity of a grid region is greater than a first threshold, it is determined to be an abnormal grid region; otherwise, it is determined to be a normal grid region. If the number of abnormal grid regions is greater than a second threshold, the workpiece is determined to have unqualified melt depth quality; otherwise, the workpiece is determined to have qualified melt depth quality. The sparsity of secondary data points in each grid region is calculated using the following formula: ； Where z is the sparsity of the current grid region; s is the number of secondary data points in the current grid region; α is the neighborhood weight coefficient; t is the total number of secondary data points in multiple grid regions corresponding to the current grid region in the circumferential direction; m is the maximum number of secondary data points that can be accommodated by the grid region generated based on the minimum distance of secondary data points in each grid region; and n is the number of multiple grid regions corresponding to the current grid region in the circumferential direction.
2. The OCT weld penetration flaw detection method for laser welding of claim 1, wherein, The step of dividing the region corresponding to multiple secondary data points in the plane into a grid includes: Determine the maximum and minimum values of multiple secondary data points in the X and Y directions; The total grid area is formed by drawing axes in the plane based on the maximum and minimum values of multiple secondary data points in the X and Y directions; The total grid area is divided into multiple grid areas.
3. The OCT penetration defect detection method for laser welding according to claim 2, characterized in that, The division of the total grid area into multiple grid areas is achieved using the following formula: N=(X Max -X Min ) / X Step ; M = (Y Max - Y Min ) / Y Step ; Among them, X Max and X Min Let X and Y be the maximum and minimum values in the X direction, respectively, among multiple secondary data points. Max and Y Min Let X be the maximum and minimum values in the Y direction among multiple secondary data points. Step and Y Step The length and width of the respective grid cells.
4. The OCT penetration defect detection method for laser welding according to claim 1, characterized in that, n is either 8 or 4.
5. The OCT weld penetration flaw detection method for laser welding of claim 1, wherein, 1≤α≤5。 6. The OCT weld penetration flaw detection method for laser welding of claim 1, wherein, The noise removal of the melt depth data using filtering includes: The filter window is determined to be a cross-shaped window. The filter window is then slid down row by row and column by column, starting from the maximum value in the Y direction among multiple primary data points, until the entire image is covered. During the movement of the cross-shaped window, the position of each secondary data point after filtering is determined through filtering calculations.
7. The OCT weld penetration flaw detection method for laser welding of claim 6, wherein, The process of determining the position of each secondary data point after filtering through filtering calculations during the movement of the cross-shaped window includes: In the Y direction, determine the total number of primary data points in a unit area. If the total number of primary data points is greater than the third threshold, the mean filtering method is used to determine the position of each secondary data point after filtering; otherwise, the median filtering method is used to determine the position of each secondary data point after filtering.
8. The OCT penetration defect detection method for laser welding according to claim 7, characterized in that, The third threshold is 1000-2000.
9. The OCT weld penetration flaw detection method for laser welding according to any one of claims 1-8, characterized in that, The first threshold is 0.7-0.8, and the second threshold is 20-100.