Metallographic phase automatic detection device and method

Abstract

The automatic metallographic detection device comprises a metallographic sample placed on an automatic sample table, a microscopic image of the metallographic sample is obtained through a metallographic microscope, and then image acquisition, processing, identification, data analysis and report generation are automatically carried out on the microscopic image in a computer through an image data transmission system. According to the method, the microstructure of the metal material can be rapidly detected, the microstructure and performance of the metal material can be efficiently and accurately analyzed, and then reliable technical guarantee is provided for the performance and use safety of the material. The method has wide application prospects in the fields of material science, manufacturing industry and scientific research, and the metallographic detection efficiency and accuracy can be remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of material science, and in particular relates to an automatic metallographic detection device and method. Background Art

[0002] In recent years, with the increasing widespread use of various new metal materials in various fields of production and life, the problems of material manufacturing and failure have become increasingly prominent. Various accidents are prone to occur during the manufacturing and use processes, so the microstructure inspection of metal materials is very important. Metallographic inspection includes low-magnification inspection, non-metallic inclusions, grain size, microstructure, decarburization layer, structural aging rating, phase structure, phase composition, fracture macroscopic and microscopic examinations, micro-phase composition in micro-regions, and stainless steel grain boundary corrosion testing. Metallographic inspection can prevent manufacturing and premature failure accidents caused by improper material selection, poor quality, welding defects, etc. Metallographic inspection plays an important role in quality control, performance evaluation, failure analysis, new material research and development optimization, and safety assessment. It is a key means to ensure material performance and safety in use. Therefore, by analyzing the structure and performance of metal materials through metallographic inspection, an automatic metallographic detection device has been invented to provide reliable technical guarantees for material performance and safety in use. Summary of the Invention

[0003] The present invention aims to provide an efficient and accurate automatic metallographic inspection device and method. By placing a metallographic sample on an automatic sample stage and obtaining a microscopic image of the sample using a metallographic microscope, the device then automatically performs image acquisition, processing, identification, data analysis, and report generation on a computer via an image data transmission system. This device and method can rapidly inspect the microstructure of metal materials, efficiently and accurately analyzing the structure and properties of metal materials, thereby providing reliable technical assurance for material performance and safety.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions: A metallographic automatic detection device, comprising: Automatic sample stage, an automatic sample stage for placing metallographic samples; Metallographic microscope, used to obtain microscopic images of metallographic samples; A computer is used to automatically acquire, process, identify, analyze data and generate reports for the microscopic images of metallographic samples through an image data transmission system.

[0005] A further improvement of the present invention is that the automatic sample stage can automatically move the metallographic sample to achieve multi-region and multi-angle image acquisition and detection.

[0006] The further improvement of the present application is that the image data transmission system automatically captures the microscopic image of the metallographic sample through the optical sensor of the metallographic microscope lens and digitizes it.

[0007] The further improvement of the present application is that after the image acquisition, the image data transmission system is input into the computer, and then the acquired image is automatically preprocessed.

[0008] The further improvement of the present application is that the image preprocessing includes denoising, enhancement and segmentation.

[0009] The further improvement of the present application is that the computer is provided with a deep learning model for automatically classifying, identifying and quantitatively analyzing the microscopic image of the metallographic sample, including grain size, phase distribution, aging level and defect detection.

[0010] The further improvement of the present application is that the deep learning model is a software model of the microscopic image of the metallographic sample of various different materials under different temperatures and different use states, and can replace different microscopic images of the metallographic sample confirmed in real time.

[0011] The further improvement of the present application is that the computer is used to automatically generate a report including quantitative data and images of the metallographic structure.

[0012] A metallographic automatic detection method, comprising: placing a metallographic sample on an automatic sample stage; acquiring a microscopic image of the metallographic sample through a metallographic microscope; the computer automatically acquires, processes, identifies, analyzes data and generates a report of the microscopic image of the metallographic sample through an image data transmission system.

[0013] The further improvement of the present application is that the computer is provided with a deep learning model to automatically classify, identify and quantitatively analyze the microscopic image of the metallographic sample, including grain size, phase distribution, aging level and defect detection.

[0014] Compared with the prior art, the present application has at least the following beneficial technical effects: The present application provides a metallographic automatic detection device and method, which includes a metallographic sample placed on an automatic sample stage, and a microscopic image of the metallographic sample acquired through a metallographic microscope, and then automatically acquires, processes, identifies, analyzes data and generates a report of the microscopic image in the computer through an image data transmission system. The microscopic structure of the metal material can be quickly detected, and the metal material structure and performance can be efficiently and accurately analyzed, thereby providing reliable technical guarantee for material performance and use safety. This device has wide application prospects in the fields of material science, manufacturing and scientific research, and can significantly improve the efficiency and accuracy of metallographic detection.

[0015] Furthermore, the present invention utilizes deep learning models, particularly deep convolutional neural networks (CNNs), to automatically extract features from images. The model is trained using a large amount of annotated metallographic image data, enabling it to learn the metallographic structural characteristics of different materials and under different conditions, thereby enabling automatic classification, recognition, and quantitative analysis of new images. To adapt the deep learning model to the needs of analyzing microscopic images of metallographic samples of different materials at different temperatures and under different usage conditions, a real-time update mechanism is required. On the one hand, the model is continuously trained and optimized by continuously collecting new metallographic image data, improving its adaptability and accuracy. On the other hand, when new materials or processes emerge, corresponding microscopic images of metallographic samples are promptly produced and confirmed, incorporated into the training dataset, and the model is updated and replaced to ensure that it consistently meets actual analysis needs.

[0016] In summary, the automatic metallographic detection device and method provided by the present invention achieve rapid and accurate detection of the microstructure of metal materials through the deep integration of high-precision hardware and deep learning models. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 The figure is a schematic structural diagram of an automatic metallographic detection device of the present invention.

[0019] Description of reference numerals: 1. Metallographic sample; 2. Automatic sample stage; 3. Metallographic microscope; 4. Image data transmission system; 5. Computer. DETAILED DESCRIPTION

[0020] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0021] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0022] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0023] In the present invention, unless otherwise expressly specified or limited, terms such as "mounted," "connected," "connect," and "fixed" should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection, electrical connection, or communication; direct connection or indirect connection through an intermediate medium; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0024] In the present invention, unless otherwise expressly specified or limited, a first feature being "above" or "below" a second feature may include the first and second features being in direct contact, or may include the first and second features being in contact not directly but through another feature between them. Furthermore, a first feature being "above," "above," and "above" a second feature may include the first feature being directly above or obliquely above the second feature, or may simply mean that the first feature is higher in level than the second feature. A first feature being "below," "below," and "below" a second feature may include the first feature being directly above or obliquely above the second feature, or may simply mean that the first feature is lower in level than the second feature.

[0025] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0026] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0028] The accompanying drawings illustrate various schematic diagrams of the structures of the embodiments disclosed herein. These figures are not drawn to scale; for the purpose of clarity, some details are exaggerated and some details may be omitted. The shapes of the various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary and may deviate in practice due to manufacturing tolerances or technical limitations. Those skilled in the art may design regions / layers with different shapes, sizes, and relative positions as needed.

[0029] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0030] Example 1 like Figure 1 As shown, the present invention provides an automatic metallographic detection device, including an automatic sample stage 2 for placing a metallographic sample 1; a metallographic microscope 3 for obtaining a microscopic image of the metallographic sample; and a computer 5 for automatically performing image acquisition, processing, recognition, data analysis, and report generation on the microscopic image of the metallographic sample through an image data transmission system 4.

[0031] The present invention automates the entire testing process, from sample placement (via an automated sample stage), acquisition of metallographic sample microscopic images (using a metallographic microscope), to image acquisition, processing, recognition, data analysis, and report generation (using a computer and image data transmission system). This reduces manual intervention, improves testing efficiency, minimizes errors caused by human factors, and ensures the consistency and accuracy of test results. The present invention integrates key components such as the automated sample stage, metallographic microscope, image data transmission system, and computer into a single testing device, forming a complete metallographic testing system. This integrated design makes the device compact and easy to operate and manage, while also reducing the time required to connect and debug devices, improving the stability and reliability of the overall system. The computer automatically processes, recognizes, and analyzes the metallographic sample microscopic images through the image data transmission system, enabling rapid and accurate extraction of key information from the images and in-depth data mining and analysis. This facilitates the discovery of microscopic defects, structural changes, and other characteristics in metallographic samples, providing strong support for material quality assessment, performance prediction, and process improvement. The present device can automatically generate test reports, presenting test results to the user in a clear and intuitive manner. This not only saves time and energy in manually compiling reports, but also improves the standardization and accuracy of reports, making it easier for users to make subsequent decisions and management.

[0032] In this embodiment, the automatic sample stage 2 can automatically move the metallographic sample 1 to achieve multi-region and multi-angle image acquisition and detection. The automatic sample stage can accurately control the movement of the metallographic sample, allowing the microscope to capture images of different areas of the sample. This ensures that the microstructure of the entire sample surface can be detected, avoids omissions due to manual operation, and improves the comprehensiveness and accuracy of the detection. By automatically adjusting the position and angle of the sample, the automatic sample stage allows the microscope to observe the metallographic sample from multiple perspectives. This is particularly important for detecting materials with complex three-dimensional structures or anisotropic properties, and can reveal more hidden microscopic features and defects.

[0033] In this embodiment, the image data transmission system 4 automatically captures and digitizes microscopic images of metallographic samples using the optical sensor in the lens of the metallographic microscope 3. Using the optical sensor (such as a CMOS or CCD sensor) in the lens of the metallographic microscope, the image data transmission system is able to capture microscopic images of the metallographic sample surface with extremely high sensitivity. The sensor's high resolution (e.g., millions of pixels) ensures clear image detail, accurately capturing even the smallest grain structures or defects. The seamless connection between the optical sensor and the image data transmission system enables real-time image acquisition. This feature is particularly important for scenarios requiring rapid detection or dynamic observation, such as monitoring real-time changes in material structure during heat treatment.

[0034] In this embodiment, after image acquisition, the image is input to a computer 5 via an image data transmission system 4. The acquired image then undergoes automatic image preprocessing, which includes denoising, enhancement, and segmentation. This preprocessing process significantly improves the quality of metallographic images, providing reliable data for subsequent analysis (such as grain size measurement and defect classification).

[0035] In this embodiment, a computer deep learning model is used to automatically classify, identify, and quantitatively analyze microscopic images of metallographic samples, such as grain size, phase distribution, aging level, and defect detection.

[0036] In this embodiment, the deep learning model is a software model of metallographic sample microscopic images of various materials at different temperatures and different usage conditions, and can replace the microscopic images of different metallographic samples that are produced and confirmed in real time.

[0037] Deep learning models, particularly deep convolutional neural networks (CNNs), can automatically extract features from images, such as grain boundaries, phase boundaries, and defects. The models are trained using a large amount of annotated metallographic image data, enabling them to learn the metallographic structural characteristics of different materials and conditions, thereby enabling automatic classification, recognition, and quantitative analysis of new images. For example, for grain size measurement, the model can identify the boundaries of each grain and calculate the area or diameter of the grain, thereby deriving the grain size distribution. For phase distribution analysis, the model can distinguish different phases and calculate the volume fraction of each phase. To ensure that deep learning models can adapt to the needs of analyzing microscopic images of metallographic samples of different materials at different temperatures and under different usage conditions, a real-time update mechanism is necessary. This requires continuous training and optimization of the model through the continuous collection of new metallographic image data, improving its adaptability and accuracy. Furthermore, as new materials or processes emerge, corresponding microscopic images of metallographic samples are promptly generated and confirmed, incorporated into the training dataset, and the model is updated and replaced to ensure that the model consistently meets actual analysis requirements.

[0038] In this embodiment, the computer 5 is used to automatically generate a report based on the test results, including quantitative data and images of the metallographic structure. Through the automated report generation function, the computer 5 integrates the quantitative data and visual images of the metallographic test into a standardized report, significantly improving the efficiency of the test and the value of the data.

[0039] Example 2 like Figure 1 As shown, the present invention provides an automatic metallographic detection device, comprising: Place the metallographic sample 1 on the automatic sample stage 2; Acquire a microscopic image of the metallographic sample through a metallographic microscope 3; The computer 5 automatically performs image acquisition, processing, recognition, data analysis and report generation on the microscopic images of the metallographic samples through the image data transmission system 4 .

[0040] In this embodiment, the computer 5 is provided with a deep learning model to automatically classify, identify and quantitatively analyze the microscopic images of metallographic samples, including grain size, phase distribution, aging level and defect detection.

[0041] Example 3 like Figure 1 As shown, the present invention provides an automatic metallographic detection method, comprising the following steps: First, the prepared metallographic sample is placed on an automatic sample stage. The metallographic microscope and image data transmission system are then connected to a computer and powered on. The metallographic sample is automatically rotated and moved by the automatic sample stage. The image acquisition system then begins to automatically capture the metallographic sample microscopic image through the metallographic microscope lens and sensor, digitizes it, and then inputs it into the computer through the data transmission system. The captured image is automatically preprocessed (such as denoising, enhancement, segmentation, etc.). Finally, the computer's deep learning model is used to automatically classify, identify, and quantitatively analyze the metallographic sample microscopic image, such as grain size, phase distribution, aging level, defect detection, etc. The test results are automatically generated into a report including quantitative data and images of the metallographic structure. The present invention provides a metallographic automatic detection method that achieves efficient, accurate, and intelligent metallographic detection through an automated sample stage, high-precision image acquisition, deep learning analysis, and standardized report generation. This method not only improves detection efficiency and accuracy, but also provides strong support for materials science research and industrial production.

[0042] In summary, the present invention provides an automatic metallographic inspection device and method, which includes placing a metallographic sample on an automatic sample stage, obtaining a microscopic image of the metallographic sample using a metallographic microscope, and then automatically performing image acquisition, processing, recognition, data analysis, and report generation on a computer via an image data transmission system. This device can rapidly inspect the microstructure of metal materials, efficiently and accurately analyze the structure and properties of metal materials, and thus provide reliable technical assurance for material performance and safety. This device has broad application prospects in materials science, manufacturing, and scientific research, and can significantly improve the efficiency and accuracy of metallographic inspection.

[0043] The foregoing merely illustrates the principles of the application and application of its leading features. This application is not limited to the exact details shown above and described herein, and obvious modifications will occur to those skilled in the art upon reading the foregoing description. Therefore, the scope of the application is not to be determined by the specific examples shown above, but only by the claims below. Any reference signs in the claims should not be construed as limiting the scope of the claims.

[0044] Furthermore, it should be appreciated that although the present specification describes particular embodiments, each of which contain only a single independent technology, the specification makes no implication that the application is limited to such. Rather, the specification using such terms as "in one embodiment" or "in an embodiment" is to establish as many independent embodiments as can be explicitly or implicitly disclosed. The mere inclusion of such terms, however, does not limit those embodiments but rather, the broadest possible interpretation of the specification is intended. The specification is not intended to be limited to the embodiments described herein, but rather the claims should be accorded the full scope consistent with the claims for which the support is recognized in the art. No language in the specification should be construed as indicating any non-claimed element as essential. The foregoing merely illustrates the principles of the application and application of its leading features. This application is not limited to the exact details shown above and described herein, and obvious modifications will occur to those skilled in the art upon reading the foregoing description. Therefore, the scope of the application is not to be determined by the specific examples shown above, but only by the claims below. Any reference signs in the claims should not be construed as limiting the scope of the claims.

[0044] Furthermore, it should be appreciated that although the present specification describes particular embodiments, each of which contain only a single independent technology, the specification makes no implication that the application is limited to such. Rather, the specification using such terms as "in one embodiment" or "in an embodiment" is to establish as many independent embodiments as can be explicitly or implicitly disclosed. The mere inclusion of such terms, however, does not limit those embodiments but rather, the broadest possible interpretation of the specification is intended. The specification is not intended to be limited to the embodiments described herein, but rather the claims should be accorded the full scope consistent with the claims for which the support is recognized in the art. No language in the specification should be construed as indicating any non-claimed element as essential.

Claims

1. A metallographic automatic detection device, characterized in that: include: An automatic sample stage (2), an automatic sample stage (2) for placing a metallographic sample (1); Metallographic microscope (3), used to obtain microscopic images of metallographic samples; The computer (5) is used for automatically collecting, processing, identifying, analyzing data and generating reports on the microscopic images of the metallographic samples through the image data transmission system (4).

2. The automatic metallographic detection device according to claim 1, characterized in that: The automatic sample stage (2) can automatically move the metallographic sample (1) to achieve multi-region and multi-angle image acquisition and detection.

3. The automatic metallographic detection device according to claim 1, characterized in that: The image data transmission system (4) automatically captures the microscopic image of the metallographic sample through the optical sensor of the metallographic microscope lens and digitizes it.

4. The automatic metallographic detection device according to claim 1, characterized in that: After the image is collected, it is input into a computer (5) through an image data transmission system (4), and then the collected image is automatically pre-processed.

5. The automatic metallographic detection device according to claim 4, characterized in that: Image preprocessing includes denoising, enhancement and segmentation.

6. The automatic metallographic detection device according to claim 1, characterized in that: The computer (5) is equipped with a deep learning model for automatically classifying, identifying and quantitatively analyzing the microscopic images of metallographic samples, including grain size, phase distribution, aging level and defect detection.

7. The automatic metallographic detection device according to claim 6, characterized in that: The deep learning model is a software model for microscopic images of metallographic samples of various materials at different temperatures and under different usage conditions, and can replace microscopic images of different metallographic samples that are produced and confirmed in real time.

8. The automatic metallographic detection device according to claim 1, characterized in that: The computer (5) is used to automatically generate a report based on the test results, including quantitative data and images of the metallographic structure.

9. A metallographic automatic detection method, characterized in that: include: Place the metallographic sample (1) on the automatic sample stage (2); Obtaining a microscopic image of the metallographic sample through a metallographic microscope (3); The computer (5) automatically performs image acquisition, processing, recognition, data analysis and report generation on the microscopic image of the metallographic sample through the image data transmission system (4).

10. The metallographic automatic detection method according to claim 1, characterized in that: The computer (5) is equipped with a deep learning model to automatically classify, identify and quantitatively analyze the microscopic images of metallographic samples, including grain size, phase distribution, aging level and defect detection.

Images