Automatic measurement method and system for elongation of stainless steel laser weld

Abstract

This invention provides an automatic measurement method and system for the elongation of stainless steel laser welds. The automatic measurement method for stainless steel laser weld elongation includes: sample preparation and image acquisition; performing DIC analysis on the acquired image sequence to obtain a full-field strain cloud map, and obtaining the full-field displacement field and longitudinal strain field of the sample during the tensile process based on the full-field strain cloud map; weld region identification and width measurement to obtain the weld width; extracting strain from multiple virtual gauge lengths to form multiple sets of data; establishing a linear relationship between the overall elongation and the proportion of weld width; performing linear fitting on the multiple sets of data based on the linear relationship to obtain a coefficient proportional to the weld elongation; and obtaining the weld elongation based on the proportional coefficient. By preparing micron-level random speckle patterns and using a sub-pixel DIC algorithm to perform high-precision full-field strain analysis on the weld region, the method achieves high-efficiency, high-precision, and automated measurement of millimeter-level ultra-narrow weld elongation.

Description

Technical Field

[0001] This invention belongs to the field of material mechanical property testing and welding quality inspection technology, and in particular relates to an automatic measurement method and system for the elongation of stainless steel laser welds. Background Technology

[0002] Due to its high energy density and low heat input, laser welding of stainless steel typically produces welds with widths of only 0.8–1.2 mm, classifying them as ultra-narrow welds. Weld elongation is a core mechanical indicator for evaluating weld plasticity, fracture toughness, and joint service reliability; its accurate measurement is crucial for high-end manufacturing in fields such as commercial aerospace (reusable rockets), aerospace, nuclear power, and medical devices. However, due to the extremely small weld size, existing elongation measurement techniques are difficult to apply, becoming a recognized technical bottleneck in the industry.

[0003] Existing technologies include the contact gauge length method and the generalized digital image correlation (DIC) method. The contact gauge length method measures weld elongation by marking a fixed gauge length (typically ≥25 mm) on the specimen, measuring the overall elongation using an extensometer or physical fixture, and then estimating the weld contribution using empirical formulas. This method is still applicable to wide welds (such as arc welding), but it completely fails in 1 mm-level laser welds for the following reasons: 1. If the gauge length / weld ratio is too large (greater than or equal to 20), the weld deformation will be "submerged" by the base metal, and the weld elongation will be seriously underestimated (measured error > 60%). 2. The fixture alignment error (±0.1mm) is close to the weld width, making it impossible to guarantee that the weld is centered on the gauge length, and the measurement repeatability is <70%. 3. Multiple clamping operations with different gauge lengths are required, which amplifies the cumulative error, and the time required for a single test is >2 hours.

[0004] While the existing general digital image correlation (DIC) method can achieve non-contact full-field strain measurement, it cannot meet the high-precision and automated measurement of millimeter-level ultra-narrow weld elongation. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention provides an automatic measurement method and system for the elongation of stainless steel laser welds, which at least partially solves the problem that the prior art cannot measure the elongation of millimeter-level ultra-narrow welds with high precision and automation.

[0006] In a first aspect, embodiments of this disclosure provide an automatic method for measuring the elongation of stainless steel laser welds, including: Sample preparation and image acquisition steps: Micron-level random speckle patterns are prepared on the surface of the tensile specimen of the stainless steel laser weld. The specimen is clamped into the tensile testing machine, and the image sequence of the specimen surface is acquired through an optical acquisition device during the tensile process. Full-field strain analysis steps: Perform DIC analysis on the acquired image sequence to obtain the full-field strain cloud map, and obtain the full-field displacement field and longitudinal strain field of the specimen during the tensile process based on the full-field strain cloud map; Weld area identification and width measurement steps: Identify the weld centerline and weld boundary based on the longitudinal strain field, and measure the weld width based on the weld centerline and weld boundary; Multi-virtual gauge length strain extraction steps: Within the gauge length range of the specimen, at least three sets of virtual gauge lengths of different lengths are generated. The overall elongation corresponding to each set of virtual gauge lengths is calculated based on the full-field displacement field. Multiple sets of data are formed based on the virtual gauge lengths and the overall elongation. The regression separation model calculation steps are as follows: establish a linear relationship between the overall elongation and the proportion of weld width, perform linear fitting on multiple sets of data based on the linear relationship to obtain the coefficient that is proportional to the weld elongation, and obtain the weld elongation based on the coefficient that is proportional.

[0007] Optionally, the overall elongation corresponding to each set of virtual gauge lengths is calculated based on the full-field displacement field, using the following formula: ,in, For the overall elongation rate, For the first The elongation of the virtual gauge length during the stretching process. The gauge length of the virtual gauge length. It is obtained based on the full-field displacement field.

[0008] Optionally, the linear relationship formula is: , , in, The average elongation of the sample base material. A coefficient proportional to the weld elongation. The width of the weld. This represents the proportion of weld width. For weld elongation, This represents the equivalent conversion function after normalization based on the equivalent length of the weld.

[0009] Optionally, the particle size of the micron-scale random speckle pattern is 5~10 μm, the resolution of the optical acquisition device is not less than 12 million pixels, and the sample surface image sequence is acquired at a frame rate of not less than 100 fps.

[0010] Optionally, the DIC analysis uses a small subset with a size of 9×13 to 13×13 pixels for subpixel matching calculation to ensure strain resolution for millimeter-level weld areas.

[0011] Optionally, the gauge length of the virtual gauge length is distributed within the range of 5 mm to 50 mm.

[0012] Optionally, the linear fitting of multiple sets of data based on linear relationships includes: The least squares method, weighted regression method, or confidence interval analysis method were used for fitting, and the goodness of fit R² ≥ 0.98.

[0013] Secondly, embodiments of this disclosure also provide an automatic measurement system for implementing the measurement method according to any one of the first aspects of the claims, comprising: A speckle construction device for preparing micron-scale random speckle patterns on the surface of a sample; A tensile testing machine is used to apply tensile loads to a specimen. An optical acquisition device is used to acquire image sequences of the sample surface during the tensile process; A synchronous triggering device connects the tensile testing machine and the optical acquisition device to achieve synchronization between load application and image acquisition; And an industrial control computer, which includes a DIC analysis module, a weld identification and width measurement module, a multi-gauge strain fusion module, and a regression separation model module; wherein: The DIC analysis module is used to process image sequences and output the full-field displacement and strain fields. The weld identification and width measurement module is used to automatically identify weld areas and measure weld width. The multi-gauge-length strain fusion module is used to generate multiple sets of virtual gauge lengths and calculate the corresponding overall elongation. The regression separation model module is used to calculate the weld elongation rate based on the overall elongation rate and the weld width ratio using a linear regression model.

[0014] Optionally, the optical acquisition device includes an industrial camera, a lens, and a fixed bracket. The lens is mounted on the industrial camera, and the industrial camera is mounted on the fixed bracket. The pixel size of the camera is no greater than 3.5 μm, and the focal length of the lens is 35~75 mm.

[0015] Optionally, the weld seam recognition and width measurement module can automatically locate and measure the weld seam based on grayscale gradient, texture difference, structural morphology, or deep learning model.

[0016] The present invention provides an automatic method for measuring the elongation of stainless steel laser welds. This method involves preparing micron-level random speckle patterns on the sample surface, utilizing a sub-pixel DIC algorithm to perform high-precision full-field strain analysis on the weld region, and achieving effective decoupling of the weld elongation from the base material based on a multi-virtual gauge length fusion and linear regression separation model. From speckle construction, synchronous image acquisition, and DIC calculation, this scheme can be fully automated, eliminating human error and significantly improving the accuracy and efficiency of measuring the elongation of millimeter-level ultra-narrow laser welds. This achieves the goal of high-efficiency, high-precision, and automated measurement of the elongation of millimeter-level ultra-narrow welds. Attached Figure Description

[0017] The above and other objects, features and advantages of this disclosure will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0018] Figure 1 A flowchart of an automatic measurement method for the elongation of stainless steel laser weld seams provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of the automatic measurement system provided in the embodiments of this disclosure; Figure 3 Virtual gauge length division and full-field strain cloud map provided for embodiments of this disclosure; Figure 4 This is a schematic diagram of automatic weld width recognition provided in an embodiment of the present disclosure; Figure 5 This is a schematic diagram of the linear regression fitting results provided in the embodiments of this disclosure. Detailed Implementation

[0019] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0020] It should be understood that the following specific examples illustrate the implementation of this disclosure, and those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0021] It should be noted that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice method. Furthermore, this device and / or practice method can be implemented using other structures and / or functionalities besides one or more of the aspects set forth herein.

[0022] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The illustrations only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0023] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0024] Existing general-purpose digital image correlation methods are mainly designed for full-field strain measurement of macroscopic structures (typical size > 10 mm), and are not optimized for ultra-narrow weld seams of millimeter scale and below, exhibiting significant failures in weld seam regions of millimeter scale and below. The statistical scale of conventional speckle and DIC algorithms is larger than the weld width. When the weld width is much smaller than the statistical feature size of conventional speckle, it leads to local texture degradation, decreased correlation, and insufficient spatial resolution. To maintain the stability of correlation calculations, traditional DIC typically requires a large subset size, resulting in insufficient spatial resolution and difficulty in resolving high-gradient strain within the weld. Furthermore, existing DIC methods lack automatic identification and width measurement capabilities for weld regions. They lack closed-loop integration from image acquisition and processing to local elongation calculation, relying on manual intervention, resulting in low efficiency and high subjectivity. Existing methods also fail to establish a mathematical relationship between weld width proportion and weld elongation, i.e., they do not establish a quantitative mapping relationship between micro-area deformation, weld elongation, and weld performance, making it impossible to obtain weld elongation parameters.

[0025] Based on the above problems, the technical problem to be solved in this embodiment is: to overcome the difficulty of achieving high-precision and automated measurement of weld elongation rate by existing contact gauge length methods and general DIC technology, given the ultra-narrow width of stainless steel laser welds (only 0.8~1.2mm). Specifically, this includes: 1. Solve the problem that an excessively large gauge length / weld ratio leads to weld deformation being "diluted" by the base metal and a severely underestimated weld elongation (error > 60%); 2. Solve the problems of large centering error of physical fixtures, systematic error introduced by multiple clamping, and poor measurement repeatability; 3. Solve the problems of excessively large speckle size, loss of correlation in the weld zone, and insufficient spatial resolution in general-purpose DIC speckle patterns; 4. Solve the problem of lacking an automatic measurement model for weld width and a regression separation model for elongation rate, and being unable to output weld elongation rate; 5. This solution addresses the issues of broken automation links, excessive manual intervention, and low efficiency in the image acquisition and result output process. It proposes a non-contact, high-resolution, fully automated method and system for measuring weld elongation at the 1mm level ultra-narrow laser weld seam, reducing measurement error from over 60% to less than 0.5%, with a single test duration of <5 minutes and an R² > 0.99 performance index.

[0026] like Figure 1 As shown in the figure, this embodiment discloses an automatic method for measuring the elongation of stainless steel laser welds, characterized by comprising: Sample preparation and image acquisition steps: Micron-level random speckle patterns are prepared on the surface of the tensile specimen of the stainless steel laser weld. The specimen is clamped into the tensile testing machine, and the image sequence of the specimen surface is acquired through an optical acquisition device during the tensile process. Full-field strain analysis steps: Perform DIC analysis on the acquired image sequence to obtain the full-field strain cloud map, and obtain the full-field displacement field and longitudinal strain field of the specimen during the tensile process based on the full-field strain cloud map; Weld area identification and width measurement steps: Identify the weld centerline and weld boundary based on the longitudinal strain field, and measure the weld width based on the weld centerline and weld boundary; Multi-virtual gauge length strain extraction steps: Within the gauge length range of the specimen, at least three sets of virtual gauge lengths of different lengths are generated. The overall elongation corresponding to each set of virtual gauge lengths is calculated based on the full-field displacement field. Multiple sets of data are formed based on the virtual gauge lengths and the overall elongation. The regression separation model calculation steps are as follows: establish a linear relationship between the overall elongation and the proportion of weld width, perform linear fitting on multiple sets of data based on the linear relationship to obtain the coefficient that is proportional to the weld elongation, and obtain the weld elongation based on the coefficient that is proportional.

[0027] The overall elongation corresponding to each virtual gauge length is calculated based on the full-field displacement field, and the formula is as follows: ,in, For the overall elongation rate, For the first The elongation of the virtual gauge length during the stretching process. The gauge length of the virtual gauge length. It is obtained based on the full-field displacement field. The full-field displacement field describes the displacement of each pixel coordinate point on the sample surface in two directions (such as horizontal and vertical). The calculation is based on the full-field displacement field. Specifically, the starting and ending displacements of the virtual gauge length are obtained from the full-field displacement field. The result is obtained by subtracting the starting displacement from the ending displacement. .

[0028] The formula for the linear relationship is: , , in, The average elongation of the sample base material. A coefficient proportional to the weld elongation. The width of the weld. This represents the proportion of weld width. For weld elongation, This represents the equivalent conversion function after normalization based on the equivalent length of the weld.

[0029] The particle size of the micron-level random speckle pattern is 5~10 μm, and the resolution of the optical acquisition device is not less than 12 million pixels, acquiring sample surface image sequences at a frame rate of not less than 100 fps.

[0030] The DIC analysis uses a small subset with a size of 9×13 to 13×13 pixels for subpixel matching calculation to ensure strain resolution for millimeter-level weld areas.

[0031] The gauge length of the virtual gauge length is distributed between 5 mm and 50 mm.

[0032] The linear fitting of multiple sets of data based on linear relationships includes: The least squares method, weighted regression method, or confidence interval analysis method were used for fitting, and the goodness of fit R² ≥ 0.98.

[0033] like Figure 2 As shown, this embodiment also discloses an automatic measurement system, including: A speckle construction device for preparing micron-scale random speckle patterns 7 on the surface of sample 5; Tensile testing machine 4 is used to apply tensile load to specimen 5, apply uniaxial loading to specimen 5 and record force-displacement data; Optical acquisition device 8 is used to acquire image sequences of the surface of sample 5 during the tensile process; A synchronous triggering device is connected to the tensile testing machine 4 and the optical acquisition device 8 to realize the synchronization of load application and image acquisition. The synchronous triggering device can use a TTL synchronous triggering line 3. The system also includes an industrial control computer 2, which applies the DIC algorithm and regression separation model to control the data acquisition and processing flow. A synchronous triggering device is installed between the optical acquisition device 8 and the tensile testing machine 4, electrically connecting them. The industrial control computer 2 is electrically connected to the optical acquisition device 8 via wires. The system also includes a display 1, which is electrically connected to the industrial control computer 2 via wires. The display 1 is used to display the DIC cloud map, weld identification results, and weld elongation calculation results. The electrical connection in this implementation is not limited to wire connections; wireless connections or other communication methods can also be used.

[0034] The specimen 5 in this embodiment is a stainless steel test piece containing a laser weld 6, divided into a weld area and a base material area. The laser weld 6 is the micro-area to be tested, with a target width of approximately 1 mm. Micron-level random speckle pattern 7: A speckle pattern of 5~10 μm in size is constructed on the surface of the tensile specimen 5 using a speckle construction device to ensure stable texture matching in the weld area. Optical acquisition device 8 includes an industrial camera, lens, and mounting bracket. The camera resolution is ≥12 megapixels, with a pixel size ≤3.5 μm, and can acquire image sequences of the specimen 5 surface at ≥100fps during the tensile process. The lens focal length can be 35~75 mm, supporting a field-of-view spatial resolution of ≥0.05 mm.

[0035] The software installed on the industrial control computer 2 performs multi-gauge coupling solution of the overall elongation based on DIC full-field data, and obtains the weld elongation through a mathematical separation model. The software includes a DIC analysis module, a weld identification and width measurement module, a multi-gauge strain fusion module, and a regression separation model module. The DIC analysis module is used to process image sequences and output the full-field displacement and strain fields; this module performs sub-pixel matching solutions on continuous images.

[0036] A small subset correlation mechanism is employed, with the subset size preferably between 9×9 and 13×13 pixels, to maintain effective correlation in narrow weld seam regions. The DIC analysis module outputs: displacement fields u(x, y) and v(x, y) and longitudinal strain field. .

[0037] The weld identification and width measurement module is used to automatically identify weld areas and measure weld width; the module outputs the weld centerline, heat-affected zone boundary, and weld width (actual weld width). With an accuracy of ≤0.01mm, it automatically determines the start and end positions of the weld seam, which is used for gauge length generation and regression separation model construction.

[0038] The multi-gauge-length strain fusion module is used to generate multiple sets of virtual gauge lengths and calculate the corresponding overall elongation; it automatically generates N sets of virtual gauge lengths (N≥3) within a 5-gauge-length range of the specimen, with gauge lengths... It is distributed in the range of 5~30mm.

[0039] The overall elongation corresponding to each gauge length is calculated by integrating the displacement field. : , The multi-gauge strain fusion module automatically eliminates local noise and generates multiple sets of data. , ).

[0040] The regression separation model module is used to calculate the weld elongation rate based on the overall elongation rate and the weld width ratio using a linear regression model. (Based on weld width...) proportion of gauge length Establish overall extension rate With weld elongation Linear relationship: , The average elongation of the base material; To match the weld elongation A proportional coefficient.

[0041] The weld elongation is obtained by the following formula: ,in This represents the equivalent conversion function after normalization based on the equivalent length of the weld.

[0042] The industrial computer 2 software also includes a result output module for outputting weld elongation. elongation of the parent material It supports multiple output methods, including multi-gauge fitting curves and residuals, weld width images, full-field displacement and strain cloud maps, and supports multiple output methods such as CSV, PDF, and graphical interface.

[0043] In a specific application scenario, black and white matte paint is sprayed onto the surface of a standard tensile specimen 5 of stainless steel laser welding to form micron-sized random speckles 7 with a particle size of 5~10μm. The specimen 5 is clamped in the tensile testing machine 4, and the high-speed camera achieves hardware synchronous acquisition with the testing machine through the TTL synchronous trigger line 3. DIC analysis was performed using the ZNCC9 (Zero-mean Normalized Cross-Correlation) algorithm with a subset of 15×15 pixels and a step size of 5 pixels to obtain the full-field strain contour map, as shown below. Figure 3 As shown; Define at least 3 sets of virtual gauge lengths in the strain contour map. (5~50mm), calculate the overall elongation of each gauge length. ; The software automatically calculates the weld width. Measurement, such as Figure 4 As shown; The proportion of welds x = For independent variable, Perform linear regression on the dependent variable, such as Figure 5 As shown, R² = 0.9916, extrapolating x = 1 yields the weld elongation. ; Output results and PDF reports with a single click through the graphical user interface.

[0044] In this embodiment, the measured weld width was 1.05 mm. =80.6%, =30.1%, R²=0.997, error <0.5%, single measurement <5 minutes.

[0045] The optical acquisition device 8 in this embodiment can use a stereo dual-camera system to calculate the three-dimensional deformation field, thereby further improving the measurement accuracy of the weld surface.

[0046] The DIC algorithm can sample global DIC or AI-assisted subset optimization algorithms, adapting to large deformation or fracture scenarios.

[0047] Generating virtual gauges can enhance the robustness of regression models by dynamically generating multiple sets of microgauges and selecting a subset of well-fitting models.

[0048] Weld width measurement can be achieved by using deep learning segmentation methods to automatically identify welds and heat-affected zones, thus enabling high-precision width measurement.

[0049] The regression separation model can use weighted regression or confidence interval calculation to enhance the extraction of local contributions of weld seams.

[0050] The system architecture supports cloud computing, embedded or mobile devices, enabling deployment in multiple scenarios.

[0051] The output module can be integrated with 3D strain visualization or mobile push notifications to improve the readability of results and diagnostic efficiency.

[0052] In the system disclosed in this embodiment, the speckle construction device, optical acquisition device, DIC analysis module, weld identification and width measurement module, multi-gauge strain fusion module, regression separation model module, and result output module constitute a fully automated closed loop from image acquisition to weld elongation output.

[0053] The full-field deformation measurement capability based on DIC technology disclosed in this embodiment achieves coupled modeling of overall elongation and weld width through microscale speckle structure, sub-pixel displacement solution, multi-gauge strain extraction, and automatic weld geometry identification. It also constructs a model of the ratio of overall elongation to weld width and gauge length. The linear relationship is used to obtain the weld elongation rate.

[0054] This invention solves the problems of large errors in traditional contact extensometers, insufficient resolution of universal DIC, degraded texture in weld areas, inability to automatically identify welds, and inability to separate welds. Overcoming technical bottlenecks, achieving high-precision, non-contact, and automated measurement of weld elongation at the millimeter level.

[0055] This embodiment has the following effects: Significantly improved measurement accuracy: Through micron-level speckle and sub-pixel DIC algorithms, high-precision measurement of weld elongation is achieved, meeting the requirements for 1mm-level ultra-narrow laser weld inspection; Significantly improved spatial resolution: Micron-level speckle and optimized subset algorithms enable precise capture of deformation gradients within the weld seam; Enhanced reliability of the fitting model: A linear regression separation algorithm is introduced to achieve effective mathematical separation of the weld elongation and the base metal elongation. Measurement efficiency is significantly improved: multiple gauge length measurements can be completed in a single tensile test, significantly reducing test time and operational complexity; Reduced material and labor costs: The number of test samples and operators required for the experiment is significantly reduced, thus lowering the overall cost; Automation is significantly improved: From speckle spraying, image acquisition, DIC analysis to result output, the entire process can be completed automatically, supporting batch processing and report generation; Equipment adaptability and economic optimization: The system supports a variety of hardware and software combinations, making it flexible to use and relatively low in cost, resulting in significantly improved long-term economic efficiency.

[0056] In summary, this embodiment breaks through the industry bottleneck of 1mm-level laser weld elongation measurement, achieving significant results in multiple dimensions such as accuracy, spatial resolution, efficiency, automation, and economy.

[0057] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0058] In this disclosure, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The block diagrams of devices, apparatuses, devices, and systems involved in this disclosure are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as "comprising," "including," "having," etc., are open-ended terms meaning "including but not limited to," and are used interchangeably with them. The terms "or" and "and" as used herein refer to the terms "and / or," and are used interchangeably with them unless the context clearly indicates otherwise. The term "such as" as used herein refers to the phrase "such as but not limited to," and is used interchangeably with it.

[0059] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0060] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0061] Various changes, substitutions, and modifications can be made to the techniques described herein without departing from the teachings defined in this embodiment. Furthermore, the scope of this embodiment is not limited to the specific aspects of the processes, machines, manufacturing processes, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufacturing processes, events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein can be utilized. Therefore, this embodiment includes such processes, machines, manufacturing processes, events, means, methods, or actions within its scope.

[0062] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0063] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. An automatic method for measuring the elongation of stainless steel laser welds, characterized in that, include: Sample preparation and image acquisition steps: Micron-level random speckle patterns are prepared on the surface of the tensile specimen of the stainless steel laser weld. The specimen is clamped into the tensile testing machine, and the image sequence of the specimen surface is acquired through an optical acquisition device during the tensile process. Full-field strain analysis steps: Perform DIC analysis on the acquired image sequence to obtain the full-field strain cloud map, and obtain the full-field displacement field and longitudinal strain field of the specimen during the tensile process based on the full-field strain cloud map; Weld area identification and width measurement steps: Identify the weld centerline and weld boundary based on the longitudinal strain field, and measure the weld width based on the weld centerline and weld boundary; Multi-virtual gauge length strain extraction steps: Within the gauge length range of the specimen, at least three sets of virtual gauge lengths of different lengths are generated. The overall elongation corresponding to each set of virtual gauge lengths is calculated based on the full-field displacement field. Multiple sets of data are formed based on the virtual gauge lengths and the overall elongation. The regression separation model calculation steps are as follows: establish a linear relationship between the overall elongation and the proportion of weld width, perform linear fitting on multiple sets of data based on the linear relationship to obtain the coefficient that is proportional to the weld elongation, and obtain the weld elongation based on the coefficient that is proportional.

2. The automatic measurement method for elongation of stainless steel laser welds according to claim 1, characterized in that, The overall elongation corresponding to each virtual gauge length is calculated based on the full-field displacement field, and the formula is as follows: ,in, For the overall elongation rate, For the first The elongation of the virtual gauge length during the stretching process. The gauge length of the virtual gauge length. It is obtained based on the full-field displacement field.

3. The automatic measurement method for elongation of stainless steel laser weld seam according to claim 2, characterized in that, The formula for the linear relationship is: ， ， in, The average elongation of the sample base material. A coefficient proportional to the weld elongation. The width of the weld. This represents the proportion of weld width. For weld elongation, This represents the equivalent conversion function after normalization based on the equivalent length of the weld.

4. The automatic measurement method for elongation of stainless steel laser weld seam according to claim 1, characterized in that, The particle size of the micron-level random speckle pattern is 5~10 μm, and the resolution of the optical acquisition device is not less than 12 million pixels, acquiring sample surface image sequences at a frame rate of not less than 100 fps.

5. The automatic measurement method for elongation of stainless steel laser welds according to claim 1, characterized in that, The DIC analysis uses a small subset with a size of 9×13 to 13×13 pixels for subpixel matching calculation to ensure strain resolution for millimeter-level weld areas.

6. The automatic measurement method for elongation of stainless steel laser welds according to claim 1, characterized in that, The gauge length of the virtual gauge length is distributed between 5 mm and 50 mm.

7. The automatic measurement method for elongation of stainless steel laser weld seam according to claim 1, characterized in that, The linear fitting of multiple sets of data based on linear relationships includes: The least squares method, weighted regression method, or confidence interval analysis method were used for fitting, and the goodness of fit R² ≥ 0.

98.

8. An automatic measurement system for implementing the measurement method according to any one of claims 1-7, characterized in that, include: A speckle construction device for preparing micron-scale random speckle patterns on the surface of a sample; A tensile testing machine is used to apply tensile loads to a specimen. An optical acquisition device is used to acquire image sequences of the sample surface during the tensile process; A synchronous triggering device connects the tensile testing machine and the optical acquisition device to achieve synchronization between load application and image acquisition; And an industrial control computer, which includes a DIC analysis module, a weld identification and width measurement module, a multi-gauge strain fusion module, and a regression separation model module; wherein: The DIC analysis module is used to process image sequences and output the full-field displacement and strain fields. The weld identification and width measurement module is used to automatically identify weld areas and measure weld width. The multi-gauge-length strain fusion module is used to generate multiple sets of virtual gauge lengths and calculate the corresponding overall elongation. The regression separation model module is used to calculate the weld elongation rate based on the overall elongation rate and the weld width ratio using a linear regression model.

9. The automatic measurement system according to claim 8, characterized in that, The optical acquisition device includes an industrial camera, a lens, and a fixed bracket. The lens is mounted on the industrial camera, and the industrial camera is set on the fixed bracket. The pixel size of the camera is no greater than 3.5 μm, and the focal length of the lens is 35~75 mm.

10. The automatic measurement system according to claim 8, characterized in that, The weld seam recognition and width measurement module achieves automatic weld seam positioning and measurement based on grayscale gradient, texture difference, structural morphology, or deep learning model.

Images