Quality inspection method for electron beam welding of metal composite materials

Abstract

The present disclosure relates to a method for detecting the quality of electron beam welding of metal composite materials. The method comprises: mechanically processing a first metal material and a second metal material so that the composite interface of the first metal material and the second metal material has a consistent size; assembling the first metal material and the second metal material in a composite interface-to-composite interface manner to obtain a metal composite material assembly; subjecting the metal composite material assembly to solid-state vacuum treatment, and then subjecting the metal composite material assembly to electron beam welding to obtain a welded metal composite material body; and performing welding quality detection on the welded metal composite material body. In this way, it is possible to intelligently determine whether the welding quality meets the predetermined standards. Furthermore, welding detection of the welded metal composite material body can detect defects such as cracks, pores, and incomplete penetration in the weld, thereby avoiding structural failure caused by these welding defects.

Description

Technical Field

[0001] The present disclosure relates to the technical field of electron beam welding, and in particular to a method for detecting the quality of electron beam welding of metal composite materials. Background Art

[0002] Metal composites are widely sought after for high-performance structural components due to their unique combination of properties, such as high strength, high stiffness, and good wear and corrosion resistance. These materials are typically composed of two or more metals with different physical and chemical properties, hoping to achieve a combination of properties that are difficult to achieve with a single material.

[0003] Traditional welding techniques, such as laser welding and arc welding, present numerous challenges when processing metal composites. For example, differences in the thermophysical properties of different metals can lead to uneven thermal stresses during welding, causing cracks, deformation, and other problems. Furthermore, certain materials in composite materials can be very sensitive to heat treatment, resulting in performance degradation.

[0004] Electron beam welding (EBW) is a precision welding technique that uses a high-energy electron beam to bombard the surface of a material, achieving localized melting and joining. While EBW offers many advantages, it also presents some challenges and drawbacks. During EBW, defects such as weld porosity, cracks, weld depression, and undercutting can occur. These defects can affect the quality and performance of the welded joint.

[0005] Therefore, an optimized electron beam welding quality inspection method for metal composite materials is expected. Summary of the Invention

[0006] This summary is provided to briefly introduce concepts that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] In a first aspect, the present disclosure provides a method for detecting the quality of electron beam welding of a metal composite material, the method comprising:

[0008] Mechanically processing the first metal material and the second metal material so that a composite interface of the first metal material and the second metal material has uniform dimensions;

[0009] Assembling the first metal material and the second metal material in a composite interface-to-composite interface manner to obtain a metal composite material assembly;

[0010] After solid-state vacuum treatment is performed on the metal composite material assembly, electron beam welding is performed on the metal composite material assembly to obtain a welded metal composite material body;

[0011] The welding quality of the welded metal composite material blank is inspected.

[0012] Optionally, the welding quality inspection of the welded metal composite material blank includes: obtaining a welding area image captured by a camera; performing grayscale processing on the welding area image to obtain a welding area grayscale image; performing welding state feature extraction and foreground mapping visualization on the welding area grayscale image to obtain a foreground-salient welding area state feature map; and determining the inspection result based on the foreground-salient welding area state feature map.

[0013] Optionally, welding state feature extraction and foreground mapping visualization are performed on the welding area grayscale image to obtain a foreground-salient welding area state feature map, including: passing the welding area grayscale image through a welding area state feature extractor based on a DenseNet model to obtain a welding area state feature map; performing feature dispersion along the channel dimension on the welding area state feature map to obtain a set of welding area pixel channel feature vectors; passing the set of welding area pixel channel feature vectors through a welding area pixel-level semantic association encoder based on a Transformer layer to obtain a set of context welding area pixel channel feature vectors; performing feature aggregation on the set of context welding area pixel channel feature vectors along a feature dispersion pattern to obtain an optimized welding area state feature map; and passing the optimized welding area state feature map through a foreground mapping network based on an attention mechanism to obtain the foreground-salient welding area state feature map.

[0014] Optionally, the optimized welding area state feature map is passed through a foreground mapping network based on an attention mechanism to obtain the foreground-salient welding area state feature map, including: calculating the welding area spatial attention feature matrix of the optimized welding area state feature map; masking the welding area spatial attention feature matrix based on a predetermined threshold to obtain a masked welding area spatial attention feature matrix; and fusing the masked welding area spatial attention feature matrix and the optimized welding area state feature map to obtain the foreground-salient welding area state feature map.

[0015] Optionally, calculating the welding area spatial attention feature matrix of the optimized welding area state feature map includes: calculating the global mean pooling of the optimized welding area state feature map along the channel dimension to obtain a welding state pooling feature matrix; and passing the welding state pooling feature matrix through an activation unit based on a sigmoid function to obtain the welding area spatial attention feature matrix.

[0016] Optionally, the welding area spatial attention feature matrix is masked based on a predetermined threshold to obtain a masked welding area spatial attention feature matrix, including: setting the eigenvalues in the welding area spatial attention feature matrix that are less than or equal to the predetermined threshold to zero to obtain the masked welding area spatial attention feature matrix.

[0017] Optionally, the masked welding area spatial attention feature matrix and the optimized welding area state feature map are fused to obtain the foreground significant welding area state feature map, including: calculating the position point multiplication between each feature matrix of the optimized welding area state feature map and the masked welding area spatial attention feature matrix to obtain the foreground significant welding area state feature map.

[0018] Optionally, determining the detection result based on the foreground-salient welding area state characteristic map includes: passing the foreground-salient welding area state characteristic map through a classifier-based welding quality detector to obtain the detection result, and the detection result is used to indicate whether the welding quality meets the predetermined standard.

[0019] Optionally, a training step is also included: training the welding area state feature extractor based on the DenseNet model, the welding area pixel-level semantic association encoder based on the Transformer layer, the foreground mapping network based on the attention mechanism, and the welding quality detector based on the classifier.

[0020] Optionally, the training step includes: obtaining a training welding area image captured by a camera, and a true value of whether the welding quality meets a predetermined standard; performing grayscale processing on the training welding area image to obtain a training welding area grayscale image; passing the training welding area grayscale image through the welding area state feature extractor based on the DenseNet model to obtain a training welding area state feature map; performing feature dispersion along the channel dimension on the training welding area state feature map to obtain a set of training welding area pixel channel feature vectors; passing the set of training welding area pixel channel feature vectors through the welding area pixel-level semantic association encoder based on the Transformer layer to obtain a set of training context welding area pixel channel feature vectors; and performing feature aggregation on the set of training context welding area pixel channel feature vectors along the feature dispersion pattern. To obtain a training optimized welding area state feature map; pass the training optimized welding area state feature map through the foreground mapping network based on the attention mechanism to obtain a training foreground salient welding area state feature map; pass the training foreground salient welding area state feature map through the classifier-based welding quality detector to obtain a detection result; calculate the cross entropy loss function value between the detection result and the true value to obtain a classification loss function value; based on the classification loss function value, train the welding area state feature extractor based on the DenseNet model, the welding area pixel-level semantic association encoder based on the Transformer layer, the foreground mapping network based on the attention mechanism and the welding quality detector based on the classifier, and further introduce a noise distribution loss function when updating the model parameters through gradient back propagation based on the classification loss function.

[0021] The above technical solution involves mechanically processing a first metal material and a second metal material so that the composite interface between the first and second metal materials has consistent dimensions; assembling the first and second metal materials in a composite interface-to-composite manner to obtain a metal composite material assembly; subjecting the metal composite material assembly to solid-state vacuum treatment, and then electron beam welding the metal composite material assembly to obtain a welded metal composite material body; and performing welding quality testing on the welded metal composite material body. This allows for intelligent determination of whether the welding quality meets predetermined standards. Furthermore, welding testing of the welded metal composite material body can identify defects such as cracks, pores, and incomplete penetration in the weld, thereby preventing structural failure caused by these welding defects.

[0022] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other features, advantages and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale. In the drawings:

[0024] Figure 1 The present invention is a flow chart showing a method for detecting the quality of electron beam welding of a metal composite material according to an exemplary embodiment.

[0025] Figure 2 is based on Figure 1 The illustrated embodiment shows a flow chart of step S104 of a method for detecting quality of electron beam welding of a metal composite material.

[0026] Figure 3 FIG. 4 is a schematic diagram of penetration after welding according to an exemplary embodiment.

[0027] Figure 4 The figure is a block diagram of an electron beam welding system for metal composite materials according to an exemplary embodiment.

[0028] Figure 5 It is a block diagram of an electronic device according to an exemplary embodiment.

[0029] Figure 6 This is a diagram showing an application scenario of a method for detecting the quality of electron beam welding of a metal composite material according to an exemplary embodiment. DETAILED DESCRIPTION

[0030] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0031] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0032] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0033] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0034] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0035] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0036] In order to solve the above problems, the present disclosure provides a method for detecting the quality of electron beam welding of metal composite materials, which comprises mechanically processing a first metal material and a second metal material so that the composite interface of the first metal material and the second metal material has a consistent size, wherein the first metal material and the second metal material are both solid metals and remain solid at the vacuum treatment temperature of electron beam welding; assembling the first metal material and the second metal material in a composite interface-to-composite interface manner to obtain a metal composite material assembly; after the metal composite material assembly is subjected to solid-state vacuum treatment, electron beam welding is performed on the metal composite material assembly to obtain a welded metal composite material body; and welding quality detection is performed on the welded metal composite material body. In this way, it is possible to intelligently determine whether the welding quality meets the predetermined standard. Furthermore, welding detection of the welded metal composite material body can detect defects such as cracks, pores, and incomplete penetration in the weld, thereby avoiding structural failure caused by these welding defects.

[0037] The specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0038] Figure 1 is a flow chart showing a method for detecting the quality of electron beam welding of a metal composite material according to an exemplary embodiment. Figure 1 As shown, the method includes:

[0039] Step 101: Mechanically process a first metal material and a second metal material so that a composite interface of the first metal material and the second metal material has consistent dimensions;

[0040] Step 102: Assemble the first metal material and the second metal material in a composite interface-to-composite interface manner to obtain a metal composite material assembly;

[0041] Step 103: After performing solid-state vacuum treatment on the metal composite material assembly, electron beam welding is performed on the metal composite material assembly to obtain a welded metal composite material body;

[0042] Step 104: Perform welding quality inspection on the welded metal composite material blank.

[0043] It should be understood that in the actual application of this application, weld inspection of welded metal composite materials can detect defects such as cracks, pores, and lack of penetration in the weld seam, thereby preventing structural failure caused by these welding defects. However, some existing inspection technologies may not be able to detect all types of defects. For example, minor defects may be difficult to accurately detect. In addition, some inspection technologies require highly skilled operators, and such welding quality inspection methods have high requirements for the operator's experience and skill level.

[0044] In response to the above technical problems, the technical concept of this application is to introduce deep learning algorithms and computer vision technology in the process of welding quality inspection, extract image features of the welding area image to capture the state characteristics of the welding area, and highlight the significant features in the welding area through the attention mechanism, so as to intelligently judge whether the welding quality meets the predetermined standards.

[0045] Figure 2 is based on Figure 1 The embodiment shown is a flow chart of step S104 of a method for detecting the quality of electron beam welding of a metal composite material. Figure 2 The step 104 of performing welding quality inspection on the welded metal composite material blank comprises:

[0046] Step 1041: Acquire a welding area image captured by a camera;

[0047] Step 1042: grayscale processing is performed on the welding area image to obtain a welding area grayscale image;

[0048] Step 1043: performing welding state feature extraction and foreground mapping visualization on the welding area grayscale image to obtain a foreground-visualized welding area state feature map;

[0049] Step 1044: Determine a detection result based on the foreground-salient welding area state feature map.

[0050] Specifically, in step S104, the weld quality inspection of the welded metal composite material blank includes: first, acquiring a weld area image captured by a camera. The weld area image contains rich information about the weld area, such as the shape and size of the weld. This rich information is important for identifying weld anomalies such as cracks, pores, and undercuts.

[0051] The weld area image is then grayscale processed to obtain a weld area grayscale image. Grayscale processing converts color or multi-band images into grayscale images. In a grayscale image, each pixel is represented by a single brightness value, typically ranging from 0 (black) to 255 (white). Grayscale images are single-channel images and, compared to color images, contain only brightness information, not color information. Grayscale processing removes color information from the weld area image, retaining only key structural information within the weld area image, which helps simplify subsequent processing.

[0052] In one embodiment of the present disclosure, welding state feature extraction and foreground mapping visualization are performed on the welding area grayscale image to obtain a foreground-salient welding area state feature map, including: passing the welding area grayscale image through a welding area state feature extractor based on a DenseNet (Densely Connected Convolutional Networks) model to obtain a welding area state feature map; performing feature discretization along the channel dimension on the welding area state feature map to obtain a set of welding area pixel channel feature vectors; passing the set of welding area pixel channel feature vectors through a welding area pixel-level semantic association encoder based on a Transformer layer to obtain a set of context welding area pixel channel feature vectors; performing feature aggregation on the set of context welding area pixel channel feature vectors along a feature discretization pattern to obtain an optimized welding area state feature map; and passing the optimized welding area state feature map through a foreground mapping network based on an attention mechanism to obtain the foreground-salient welding area state feature map.

[0053] Next, the weld area grayscale image is passed through a weld area state feature extractor based on the DenseNet model to obtain a weld area state feature map. DenseNet is a deep convolutional neural network that improves the network's information flow and parameter efficiency by introducing direct connections between each layer, making it particularly suitable for image feature extraction. Specifically, DenseNet's connection method allows the network to fuse features from all previous layers during forward propagation, which helps capture richer and more semantically robust feature representations. Because the weld area grayscale image may contain a variety of complex structural information, including different defect types and background noise, DenseNet's powerful feature extraction capabilities can handle this complexity.

[0054] Considering that the weld area state feature map is limited by the convolution kernel during the feature extraction process, it may only extract the state features of the local neighborhood pixel space of the weld area grayscale image, while ignoring the contextual connections between local area blocks. Therefore, in the technical solution of the present application, the weld area state feature map is feature-diversified along the channel dimension to obtain a set of weld area pixel channel feature vectors; and the set of weld area pixel channel feature vectors is passed through a weld area pixel-level semantic association encoder based on the Transformer layer to obtain a set of context weld area pixel channel feature vectors. In this way, the spatial association relationship and contextual semantic information of the weld area state feature map are extracted and integrated to understand the complex weld defect feature pattern.

[0055] Since there may be a large amount of background information in the welding area image, this background information may be retained when the welding area image is subjected to feature extraction, and may be deepened and amplified as the network layer deepens. In other words, there may be a large amount of noise interference feature information in the set of context welding area pixel channel feature vectors. This noise interference feature information is unimportant for the learning and recognition of defect feature patterns and may even interfere with the target task. Therefore, in the technical solution of the present application, the set of context welding area pixel channel feature vectors is further feature aggregated along the feature dispersion pattern to obtain an optimized welding area state feature map; and the optimized welding area state feature map is passed through a foreground mapping network based on an attention mechanism to obtain a foreground salient welding area state feature map.

[0056] In one embodiment of the present disclosure, the optimized welding area state feature map is passed through a foreground mapping network based on an attention mechanism to obtain the foreground salient welding area state feature map, including: calculating the welding area spatial attention feature matrix of the optimized welding area state feature map; masking the welding area spatial attention feature matrix based on a predetermined threshold to obtain a masked welding area spatial attention feature matrix; and fusing the masked welding area spatial attention feature matrix and the optimized welding area state feature map to obtain the foreground salient welding area state feature map.

[0057] Specifically, in one embodiment of the present disclosure, calculating the welding area spatial attention feature matrix of the optimized welding area state feature map includes: calculating the global mean pooling of the optimized welding area state feature map along the channel dimension to obtain a welding state pooling feature matrix; and passing the welding state pooling feature matrix through an activation unit based on a sigmoid function to obtain the welding area spatial attention feature matrix.

[0058] More specifically, in one embodiment of the present disclosure, the welding area spatial attention feature matrix is masked based on a predetermined threshold to obtain a masked welding area spatial attention feature matrix, including: setting the eigenvalues in the welding area spatial attention feature matrix that are less than or equal to the predetermined threshold to zero to obtain the masked welding area spatial attention feature matrix.

[0059] More specifically, in one embodiment of the present disclosure, the masked welding area spatial attention feature matrix and the optimized welding area state feature map are fused to obtain the foreground salient welding area state feature map, including: calculating the position point multiplication between each feature matrix of the optimized welding area state feature map and the masked welding area spatial attention feature matrix to obtain the foreground salient welding area state feature map.

[0060] Here, feature aggregation is used to aggregate the discretely distributed pixel channel feature vectors of the context weld region into the optimized weld region state feature map. The attention-based foreground mapping network, which introduces a spatial attention mechanism and a masking mechanism, is used to highlight the importance of foreground information in the optimized weld region state feature map. Specifically, the attention-based foreground mapping network utilizes the high-level image semantic feature information in the optimized weld region state feature map to extract corresponding foreground target feature information of interest, such as important defect areas. Masking is then used to mark and purify the foreground target feature information of interest and filter background information, thereby enabling the network module to focus on the foreground target in the weld region image.

[0061] Then, the foreground-salient welding area state feature map is passed through a classifier-based welding quality detector to obtain a detection result, and the detection result is used to indicate whether the welding quality meets a predetermined standard.

[0062] In one embodiment of the present disclosure, a detection result is determined based on the foreground-salient welding area state feature map, including: passing the foreground-salient welding area state feature map through a classifier-based welding quality detector to obtain the detection result, wherein the detection result is used to indicate whether the welding quality meets a predetermined standard.

[0063] Furthermore, in one embodiment of the present disclosure, the electron beam welding quality detection method for metal composite materials further includes a training step: training the welding area state feature extractor based on the DenseNet model, the welding area pixel-level semantic association encoder based on the Transformer layer, the foreground mapping network based on the attention mechanism, and the welding quality detector based on the classifier. The training step includes: obtaining a training welding area image captured by a camera, and a true value of whether the welding quality meets a predetermined standard; grayscale processing the training welding area image to obtain a training welding area grayscale image; passing the training welding area grayscale image through the welding area state feature extractor based on the DenseNet model to obtain a training welding area state feature map; performing feature dispersion along the channel dimension on the training welding area state feature map to obtain a set of training welding area pixel channel feature vectors; passing the set of training welding area pixel channel feature vectors through the welding area pixel-level semantic association encoder based on the Transformer layer to obtain a set of training context welding area pixel channel feature vectors; and performing feature aggregation along the feature dispersion pattern on the set of training context welding area pixel channel feature vectors to obtain to the training optimized welding area state feature map; the training optimized welding area state feature map is passed through the foreground mapping network based on the attention mechanism to obtain a training foreground salient welding area state feature map; the training foreground salient welding area state feature map is passed through the welding quality detector based on the classifier to obtain a detection result; the cross entropy loss function value between the detection result and the true value is calculated to obtain a classification loss function value; based on the classification loss function value, the welding area state feature extractor based on the DenseNet model, the welding area pixel-level semantic association encoder based on the Transformer layer, the foreground mapping network based on the attention mechanism and the welding quality detector based on the classifier are trained, and when the model parameters are updated through gradient back propagation based on the classification loss function, a noise distribution loss function is further introduced.

[0064] In a preferred example, when updating the model parameters by gradient back propagation based on the classification loss function, a noise distribution loss function is further introduced, and the calculation of the noise distribution loss function includes the following steps:

[0065] Obtaining a class probability value of the training foreground significant welding area state feature map through a classifier and calculating a value minus the class probability value to obtain an inverse probability value;

[0066] Calculating the feature mean and feature variance of the training foreground significant welding area state feature map;

[0067] Calculate the product term difference between a first product term of each feature value in the training foreground significant welding area state feature map and the class probability value and a second product term of the inverse probability value and the feature mean;

[0068] The first difference term of the noise distribution loss function is obtained by summing the squares of the product term differences corresponding to each eigenvalue in the training foreground significant welding area state feature map and dividing the sum by the scale of the training foreground significant welding area state feature map, where the scale of the training foreground significant welding area state feature map is the product of the width and height of each feature matrix of the training foreground significant welding area state feature map and the number of channels of the training foreground significant welding area state feature map.

[0069] Calculating a square sum of the class probability value and the inverse probability value and multiplying the sum by the feature variance to obtain a second difference term of the noise distribution loss function;

[0070] A difference between the first difference term and the second difference term is calculated to obtain the noise distribution loss function.

[0071] Here, in order to address the problem that after the dense image semantic feature representation of the training welding area grayscale image is associated through channel context, the local image semantic feature spatial distribution focusing causes noise outside the image semantic distribution of the training foreground significant welding area state feature map in the foreground mapping network based on the attention mechanism, which further leads to the overall class distribution noise of the training foreground significant welding area state feature map, thereby affecting the accuracy of the classification results, the applicant of this application further introduces the above-mentioned noise distribution loss function.

[0072] Specifically, the noise distribution loss function sets the approximate variance expectation characteristics of the out-of-distribution noise of the feature set of the training foreground significant welding area state feature map relative to the Bernoulli sampling instances of the feature set by the class probability Bernoulli distribution characteristics expressed by the class probability value and inverse probability value of the classifier based on the training foreground significant welding area state feature map.

[0073] Therefore, the noise distribution loss function is used to measure the distribution feature noise based on the class probability distribution characteristics expressed by the class probability value and the inverse probability value through statistical noise distribution. The possible sampling noise features can be degraded based on the probability cross random selection characteristics of the variance expectation of each feature value of the training prospect significant welding area state feature map in the classification process, thereby realizing the class distribution noise reduction of self-supervised learning in the process of model parameter updating, so as to improve the accuracy of the detection results of the training prospect significant welding area state feature map obtained by the classifier-based welding quality detector.

[0074] In summary, the above scheme is adopted. By introducing deep learning algorithms and computer vision technology in the process of welding quality inspection, image features of the welding area image are extracted to capture the state characteristics of the welding area, and the significant features in the welding area are highlighted through the attention mechanism, so as to intelligently judge whether the welding quality meets the predetermined standards.

[0075] Furthermore, with the increasingly urgent demand for large-scale equipment manufacturing and diversified functions, ultra-thick steel plates and heterogeneous metal composite materials are mostly produced by explosive welding, large ingot casting, riveting and other processes. There have always been low-magnification defects such as unstable composite interface quality, low yield, component segregation and central porosity.

[0076] The "solid-state vacuum treatment + electron beam welding" process, combined with a subsequent rolling process, enables the production of high-quality metal composites, achieving the following objectives: ① Composite materials of the same material to meet the current process limitations for rolling thick steel plates from continuous casting billets (compression ratio ≥ 3). ② Determine optimal solid-state vacuum treatment and electron beam welding process parameters based on the physical properties of different metals, such as thermal conductivity, linear expansion coefficient, and melting point, to produce composite materials of dissimilar materials such as carbon steel + stainless steel, carbon steel + nickel-based alloy, steel + aluminum, copper + steel, and titanium + steel. ③ Establish an evaluation mechanism and testing methods for the interface quality and performance of composite materials.

[0077] Specifically, in one embodiment of the present disclosure, the process of non-oxidation surface machining of the pseudo-composite metal material → organic solvent surface treatment → solid-state vacuum treatment of the composite material → vacuum chamber electron beam welding → weld non-destructive testing → subsequent rolling and pressure processing to produce the composite material (plate, strip, tube, rod) → interface performance testing is adopted.

[0078] Among them, in the non-oxidation surface machining step of metal materials, the "low-speed milling machine + local inert gas protection of the machined surface" process is adopted to carry out non-oxidation processing of the metal composite interface, so that the thickness of the composite interface oxide layer, surface flatness and surface roughness meet the composite requirements.

[0079] During the solid-state vacuum treatment step, composite materials undergo a solid-state vacuum treatment to prevent the presence of impurities such as air, water vapor, and dust in the composite interface during composite welding, which could affect the quality of the composite interface. The core process parameters include the treatment vacuum level, vacuum hold time, vacuum level at the start of welding, and weld cooling rate under vacuum conditions.

[0080] In the electron beam welding process research steps, based on the metal properties of different composite metal materials such as thermal conductivity, linear expansion coefficient, melting point, melting rate, thickness storage, etc., reasonable surface focusing method, welding beam current, and welding speed are determined to achieve the best match between the penetration depth, penetration width, weld surface quality and internal quality of the composite material, thereby ensuring the quality of subsequent processing.

[0081] Establish scientific composite interface quality evaluation and testing methods within the evaluation mechanism and testing procedures for composite interface quality and performance. Based on relevant standards, a series of methods, including ultrasonic testing (UT), interface tensile testing, interface torsion delamination testing, and interface shear testing, will be used to evaluate composite effects.

[0082] Furthermore, the "low-speed milling machine + local inert gas protection of the machined surface" process is used to carry out oxidation-free processing of the solid-state metal composite interface, and the composite material is subjected to solid-state vacuum treatment and then electron beam welding. By studying the focusing method, welding beam, welding speed, etc. of the composite interface surface, the penetration depth, penetration width, weld surface quality and internal quality of the composite material after welding are optimally matched, thereby ensuring the subsequent rolling process and making the metal material high-quality composite. Based on this type of research and practice, an evaluation mechanism and testing method for the quality and performance of the composite interface of composite materials are established.

[0083] In another embodiment of the present disclosure, a vacuum welding method for asymmetric composite of ferritic stainless steel and carbon steel is provided, wherein a composite interface is machined without beveling, and electron beam welding is used, so that the weld has a high aspect ratio and strength.

[0084] Vacuum electron beam welding is to machine the continuous casting billet and the stainless steel plate to make the composite interface size consistent, then assemble the stainless steel and carbon steel as shown in the figure above, put them into the vacuum chamber and start evacuating. After the chamber reaches a high vacuum, the welding process and parameters are set according to the chemical and physical properties of the materials. After welding, the penetration depth can reach as follows: Figure 3 As shown, the thickness is approximately 50 mm. Vacuum electron beam welding requires no beveling and utilizes mechanical processing, resulting in high production efficiency. Compared to symmetrical lamination, vacuum electron beam welding eliminates the need for release agents and seals. Compared to explosive lamination, it overcomes the problems of low efficiency, high pollution, and small product size associated with explosive lamination. Therefore, vacuum electron beam welding offers the advantages of high production efficiency, low cost, high yield, and superior weld quality and performance.

[0085] Specifically, vacuum electron beam welding uses a high-speed electron beam to bombard metal, converting kinetic energy into heat, melting the metal being welded. The weld is then formed after rapid cooling and solidification. The blanks are assembled as required using a centering assembly device. Welding is performed under high vacuum, with process parameters set according to material properties and composition. After welding, the blanks are rolled to achieve a flat profile and meet performance standards.

[0086] In this way, the working time of beveling can be reduced. Compared with symmetrical composite welding, no isolation agent is required and welding is performed in a high vacuum environment, which reduces working time, improves efficiency, reduces costs, and reduces material consumption.

[0087] Figure 4 FIG. 1 is a block diagram of an electron beam welding system for metal composite materials according to an exemplary embodiment. Figure 4 As shown, the electron beam welding system 200 includes:

[0088] A machining module 201 is configured to perform machining on a first metal material and a second metal material so that a composite interface between the first metal material and the second metal material has consistent dimensions;

[0089] An assembly module 202 is configured to assemble the first metal material and the second metal material in a composite interface-to-composite interface manner to obtain a metal composite material assembly;

[0090] an electron beam welding module 203 for performing electron beam welding on the metal composite material assembly after performing solid-state vacuum treatment on the metal composite material assembly to obtain a welded metal composite material body;

[0091] The welding quality detection module 204 is used to perform welding quality detection on the welded metal composite material blank.

[0092] Reference below Figure 5 , which shows a schematic structural diagram of an electronic device 600 suitable for implementing the embodiments of the present disclosure. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0093] like Figure 5As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the electronic device 600 are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0094] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 5 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0095] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present disclosure are performed.

[0096] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0097] In some embodiments, the client and server can communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0098] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0099] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0100] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0101] The modules described in the embodiments of the present disclosure may be implemented in software or hardware. In some cases, the name of a module does not necessarily limit the module itself. For example, a test parameter acquisition module may also be described as a "module for acquiring device test parameters corresponding to a target device."

[0102] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0103] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0104] Figure 6 This is an application scenario diagram of a method for detecting the quality of electron beam welding of a metal composite material according to an exemplary embodiment. Figure 6 As shown, in this application scenario, first, the welding area image captured by the camera is obtained (for example, Figure 6 Then, the obtained welding area image is input to a server (for example, Figure 6 In S) as shown in , the server is capable of processing the welding area image based on the electron beam welding algorithm of the metal composite material to determine the detection result.

[0105] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

[0106] In addition, although each operation is described in a specific order, this should not be understood as requiring these operations to be performed in the specific order shown or in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details have been included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

Claims

1. A method for detecting the quality of electron beam welding of metal composite materials, characterized in that: include: Mechanically processing a first metal material and a second metal material so that a composite interface of the first metal material and the second metal material has consistent dimensions, wherein the first metal material and the second metal material are both solid metals and remain solid at a vacuum processing temperature of electron beam welding; Assembling the first metal material and the second metal material in a composite interface-to-composite interface manner to obtain a metal composite material assembly; After solid-state vacuum treatment is performed on the metal composite material assembly, electron beam welding is performed on the metal composite material assembly to obtain a welded metal composite material body; Performing welding quality inspection on the welded metal composite material blank; The welding quality inspection of the welded metal composite material blank comprises: Acquire the welding area image captured by the camera; performing grayscale processing on the welding area image to obtain a welding area grayscale image; Performing welding state feature extraction and foreground mapping visualization on the welding area grayscale image to obtain a foreground-visualized welding area state feature map; Determining a detection result based on the foreground-salient welding area state characteristic map; The step of extracting welding state features and performing foreground mapping visualization on the grayscale image of the welding area to obtain a foreground-visualized welding area state feature map includes: Passing the welding area grayscale image through a welding area state feature extractor based on a DenseNet model to obtain a welding area state feature map; Performing feature dispersion along the channel dimension on the welding area state feature map to obtain a set of welding area pixel channel feature vectors; Passing the set of weld area pixel channel feature vectors through a weld area pixel-level semantic association encoder based on a Transformer layer to obtain a set of context weld area pixel channel feature vectors; Performing feature aggregation on the set of pixel channel feature vectors of the context welding area along a feature dispersion pattern to obtain an optimized welding area state feature map; Passing the optimized welding area state feature map through a foreground mapping network based on an attention mechanism to obtain the foreground salient welding area state feature map; The optimized welding area state feature map is passed through a foreground mapping network based on an attention mechanism to obtain the foreground-salient welding area state feature map, including: Calculating a welding area spatial attention feature matrix of the optimized welding area state feature map; performing masking processing on the welding region spatial attention feature matrix based on a predetermined threshold to obtain a masked welding region spatial attention feature matrix; fusing the masked welding region spatial attention feature matrix and the optimized welding region state feature map to obtain the foreground salient welding region state feature map; Calculating the welding area spatial attention feature matrix of the optimized welding area state feature map includes: Calculating the global mean pooling of the optimized welding area state feature map along the channel dimension to obtain a welding state pooling feature matrix; The welding state pooling feature matrix is passed through an activation unit based on a sigmoid function to obtain the welding area spatial attention feature matrix; The step of performing masking processing on the welding region spatial attention feature matrix based on a predetermined threshold to obtain a masked welding region spatial attention feature matrix comprises: Setting eigenvalues in the welding region spatial attention feature matrix that are less than or equal to the predetermined threshold to zero to obtain the masked welding region spatial attention feature matrix; The step of fusing the masked welding region spatial attention feature matrix and the optimized welding region state feature map to obtain the foreground salient welding region state feature map includes: The foreground-salient welding region state feature map is obtained by calculating the position-wise multiplication between each feature matrix of the optimized welding region state feature map and the masked welding region spatial attention feature matrix.

2. The method for detecting the quality of electron beam welding of metal composite materials according to claim 1, characterized in that: Determining a detection result based on the foreground-salient welding area state feature map includes: The foreground-salient welding area state feature map is passed through a classifier-based welding quality detector to obtain the detection result, which is used to indicate whether the welding quality meets a predetermined standard.

3. The method for detecting the quality of electron beam welding of metal composite materials according to claim 2, wherein: It also includes a training step: training the welding area state feature extractor based on the DenseNet model, the welding area pixel-level semantic association encoder based on the Transformer layer, the foreground mapping network based on the attention mechanism, and the welding quality detector based on the classifier.

4. The method for detecting the quality of electron beam welding of metal composite materials according to claim 3, wherein: The training step comprises: Obtaining a training welding area image captured by a camera, and a true value of whether the welding quality meets a predetermined standard; performing grayscale processing on the training welding area image to obtain a training welding area grayscale image; Passing the training welding area grayscale image through the welding area state feature extractor based on the DenseNet model to obtain a training welding area state feature map; Performing feature dispersion along the channel dimension on the training welding area state feature map to obtain a set of training welding area pixel channel feature vectors; Passing the set of training weld area pixel channel feature vectors through the weld area pixel level semantic association encoder based on the Transformer layer to obtain a set of training context weld area pixel channel feature vectors; Performing feature aggregation on the set of pixel channel feature vectors of the training context welding area along a feature dispersion pattern to obtain a training optimized welding area state feature map; Passing the training optimized welding area state feature map through the foreground mapping network based on the attention mechanism to obtain a training foreground salient welding area state feature map; Passing the training foreground salient welding area state feature map through the classifier-based welding quality detector to obtain a detection result; Calculating a cross entropy loss function value between the detection result and the true value to obtain a classification loss function value; Based on the classification loss function value, the welding area state feature extractor based on the DenseNet model, the welding area pixel-level semantic association encoder based on the Transformer layer, the foreground mapping network based on the attention mechanism and the welding quality detector based on the classifier are trained. When the model parameters are updated through gradient back propagation based on the classification loss function, a noise distribution loss function is further introduced.

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