Method for image analysis by combining metallographic microscope and AI

Abstract

According to the technical scheme, the method for image analysis through combination of the metallographic microscope and the AI comprises the steps of hardware preparation, software development, data acquisition and processing, model training and optimization, system integration and testing and application and optimization. According to the system integration and test, a microscope, an automatic platform, image acquisition and processing software, an AI model and the like are integrated into a complete system, smooth communication and cooperative work among components are ensured, and comprehensive system test is carried out. The method has the advantages that the AI is used for automatically identifying and analyzing the microscopic image, the manual operation time is greatly shortened, meanwhile, the AI model can provide a high-precision and consistent analysis result through mass data training, a large amount of microscopic image data is processed and analyzed through a big data platform, and the accuracy of the analysis result is improved. Valuable rules and modes can be mined from mass data, and the depth and breadth of material analysis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of metallographic microscopes, and in particular to a method for image analysis combining a metallographic microscope with AI. Background Art

[0002] Metallographic microscope is an important tool for studying the microstructure of metal materials. Several key technologies of metallographic microscope are: optical microscope, which uses visible light and optical lens system to magnify the microstructure of samples and observe the microstructure of metal samples. Scanning electron microscope, which uses electron beam to scan the surface of samples to form high-resolution images and observe the fine structure of metal samples. Sample preparation technology, through cutting, inlaying, grinding, polishing and corrosion, obtains a flat and pollution-free sample surface for microscopic observation.

[0003] Images acquired by a microscope need to be processed and analyzed to extract useful information. However, traditional metallographic analysis has a low degree of automation, is time-consuming, and is easily affected by human factors, resulting in inaccurate results. In addition, traditional methods find it difficult to process and analyze large amounts of microscopic image data, resulting in a low depth and breadth of material analysis. Summary of the invention

[0004] In view of the shortcomings of the prior art, the object of the present invention is to provide a method for image analysis by combining a metallographic microscope with AI.

[0005] In order to achieve the above object, the technical solution of the present invention is as follows:

[0006] The method for image analysis combining a metallographic microscope and AI comprises the following steps:

[0007] S1. Hardware preparation: choose a high-resolution optical microscope or scanning electron microscope equipped with a high-definition imaging sensor;

[0008] S2. Software development: develop or select appropriate software to control microscopes and automation platforms, develop image preprocessing modules to perform operations such as image noise reduction, contrast enhancement, and artifact removal, develop image segmentation and feature extraction modules, use algorithms to segment images, and extract useful image features;

[0009] S3, data acquisition and processing, prepare the metal samples according to the standard process, and then use the microscope and automated platform to fully scan and capture images of the samples;

[0010] S4, model training and optimization, annotate the collected microscopic images, mark the areas of interest such as grains, phase boundaries, defects, etc., form a training data set, select a suitable deep learning model, and use the annotated data for model training, use data enhancement technology to increase the diversity of training data, adjust the model's hyperparameters, optimize the network structure, use the validation data set to evaluate the model performance, and make adjustments and improvements based on the results;

[0011] S5, system integration and testing, integrating the microscope, automation platform, image acquisition and processing software, AI model, etc. into a complete system, ensuring smooth communication and collaboration between components, and conducting comprehensive system testing;

[0012] S6. Application and optimization: Apply the system to actual metallographic analysis work, perform actual operations and data analysis, collect user feedback and usage data, and continuously optimize AI models and system functions based on data and feedback from actual applications, and continuously update and expand sample data sets.

[0013] Preferably, the microscope is equipped with an autofocus and autoexposure system and an integrated motorized stage.

[0014] Preferably, deep learning algorithms such as convolutional neural networks are used to perform feature extraction, automatic segmentation, classification and recognition on microscopic images.

[0015] Preferably, based on the pre-trained model, transfer learning is used to adapt to specific microscopic image analysis tasks.

[0016] Preferably, multiple deep learning models are combined for integrated learning.

[0017] Preferably, a big data processing platform such as Hadoop and Spark is used to process and analyze large-scale microscopic image data.

[0018] Preferably, sensors and AI technologies are integrated.

[0019] Preferably, AI and virtual reality technology are combined to simulate microscope operation.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. The present invention combines AI technology to realize a fully automated process from sample preparation, image acquisition, preprocessing to analysis, greatly reducing manual operation time. At the same time, the AI ​​model is trained with a large amount of data and can provide high-precision and consistent analysis results;

[0022] 2. The present invention uses a big data platform to process and analyze a large amount of microscopic image data, which can mine valuable laws and patterns from massive data and improve the depth and breadth of material analysis;

[0023] 3. The present invention integrates an electric stage, an autofocus system and an automatic scanning system to achieve fully automated operation. It also uses AI to analyze image data in real time and dynamically adjust microscope parameters to achieve real-time monitoring and optimization of samples.

[0024] 4. The present invention can automatically detect and classify tiny defects in microscopic images through AI, improve detection accuracy, and achieve efficient quality control in large-scale production through automation and intelligent means;

[0025] 5. The present invention can quickly process and analyze large amounts of data through AI technology, accelerate the scientific research process, and discover new laws and characteristics from data through AI, supporting innovation and breakthroughs in the fields of materials science and engineering;

[0026] 6. The present invention combines AI and virtual reality technology to simulate microscope operation, improve training efficiency, and provide real-time AI feedback and guidance to help novices quickly master microscope operation and image analysis skills. DETAILED DESCRIPTION

[0027] It is easy to understand that according to the technical solution of the present invention, without changing the essential spirit of the present invention, a person skilled in the art can propose a variety of interchangeable structural modes and implementation modes. Therefore, the following specific implementation modes and drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as the whole of the present invention or as a limitation or restriction to the technical solution of the present invention.

[0028] The method of combining the metallographic microscope with AI for image analysis of the present invention includes hardware preparation, software development, data acquisition and processing, model training and optimization, system integration and testing, and application and optimization. First, hardware preparation should be carried out, and a high-resolution optical microscope or scanning electron microscope (SEM) should be selected to ensure that it has functions such as autofocus and automatic scanning, is equipped with a high-definition imaging sensor to capture clear microscopic images, is equipped with an electric stage to realize automatic movement and precise positioning of samples, and has an integrated autofocus system to ensure image clarity.

[0029] The second step is software development, which involves developing or selecting appropriate software to control the microscope and automation platform, realize automatic image acquisition and management, and implement autofocus, autoexposure, and autoscanning functions to improve image acquisition efficiency. The image preprocessing module is developed to perform operations such as image denoising, contrast enhancement, and artifact removal. The image segmentation and feature extraction module is developed to use algorithms to segment images and extract useful image features.

[0030] The third step is data collection and processing. Metal samples are prepared according to standard procedures, including cutting, inlaying, grinding, polishing and corrosion, to ensure the surface quality of the samples in order to obtain clear microscopic images. Microscopes and automated platforms are then used to comprehensively scan and capture images of the samples to obtain a large amount of high-quality microscopic image data. Databases or big data platforms are used to store and manage the collected microscopic images and their metadata to ensure data security and accessibility. Big data processing platforms such as Hadoop and Spark are used to efficiently process and analyze large-scale microscopic image data. Combined with AI technology, valuable information and patterns can be extracted from big data to improve data utilization.

[0031] The fourth step is model training and optimization. The collected microscopic images are annotated to mark the areas of interest such as grains, phase boundaries, defects, etc. to form a training data set, select a suitable deep learning model (such as convolutional neural network, CNN), and use the annotated data for model training. Data enhancement technology is used to increase the diversity of training data, improve the generalization ability of the model, adjust the hyperparameters of the model, optimize the network structure, and improve the accuracy of the model. The model performance is evaluated using a validation data set, and adjustments and improvements are made based on the results. CNN is used to extract features, classify and segment microscopic images, and complex microstructures and defects can be identified. On the basis of the pre-trained model, transfer learning is used to adapt to specific microscopic image analysis tasks, reduce training data requirements, improve the generalization ability of the model, and combine multiple deep learning models to improve the accuracy and robustness of image analysis.

[0032] The fifth step is system integration and testing, which integrates the microscope, automation platform, image acquisition and processing software, AI model, etc. into a complete system to ensure smooth communication and collaboration between components, and conducts comprehensive system testing, including functional testing, performance testing, and reliability testing, to ensure the stability and accuracy of the system. Debug and optimize according to the test results, solve the problems found, integrate sensors and AI technology, monitor the microscope operation and sample status in real time, provide instant feedback, and dynamically adjust microscope parameters such as focus, exposure, and scanning path based on real-time analysis results to optimize the operating process. Use AI technology to automatically detect defects in microscopic images, classify and label them, and combine AI and automation equipment to achieve fully automated quality inspection and control.

[0033] The sixth step is application and optimization. The system is applied to actual metallographic analysis work, actual operation and data analysis are carried out, user feedback and usage data are collected, system performance and user experience are further optimized, and AI models and system functions are continuously optimized based on data and feedback from actual applications. Sample data sets are continuously updated and expanded to improve the adaptability and accuracy of the system. AI and virtual reality technologies are combined to simulate microscope operations and improve training efficiency. AI technology can quickly process and analyze large amounts of data to support scientific researchers in discovering new patterns and characteristics. The intelligent training system helps novices quickly master microscope operations and image analysis skills. The intelligent training system improves training effectiveness and shortens the learning curve.

[0034] The technical scope of the present invention is not limited to the contents in the above description. Those skilled in the art can make various deformations and modifications to the above embodiments without departing from the technical idea of ​​the present invention, and these deformations and modifications should all fall within the protection scope of the present invention.

Claims

1. A method for image analysis using a metallographic microscope combined with AI, characterized in that: The following steps are involved: S1. Hardware preparation: choose a high-resolution optical microscope or scanning electron microscope equipped with a high-definition imaging sensor; S2. Software development: develop or select appropriate software to control microscopes and automation platforms, develop image preprocessing modules to perform operations such as image noise reduction, contrast enhancement, and artifact removal, develop image segmentation and feature extraction modules, use algorithms to segment images, and extract useful image features; S3, data acquisition and processing, prepare the metal samples according to the standard process, and then use the microscope and automated platform to fully scan and capture images of the samples; S4, model training and optimization, annotate the collected microscopic images, mark the areas of interest such as grains, phase boundaries, defects, etc., form a training data set, select a suitable deep learning model, and use the annotated data for model training, use data enhancement technology to increase the diversity of training data, adjust the model's hyperparameters, optimize the network structure, use the validation data set to evaluate the model performance, and make adjustments and improvements based on the results; S5, system integration and testing, integrating the microscope, automation platform, image acquisition and processing software, AI model, etc. into a complete system, ensuring smooth communication and collaboration between components, and conducting comprehensive system testing; S6. Application and optimization: Apply the system to actual metallographic analysis work, perform actual operations and data analysis, collect user feedback and usage data, and continuously optimize AI models and system functions based on data and feedback from actual applications, and continuously update and expand sample data sets.

2. The method for image analysis using a metallographic microscope combined with AI according to claim 1, characterized in that: The microscope is equipped with autofocus and autoexposure systems and has an integrated motorized stage.

3. The method for image analysis using a metallographic microscope combined with AI according to claim 2, characterized in that: Deep learning algorithms such as convolutional neural networks are used to perform feature extraction, automatic segmentation, classification and recognition of microscopic images.

4. The method for image analysis using a metallographic microscope combined with AI according to claim 3, characterized in that: Based on the pre-trained model, it is adapted to specific microscopic image analysis tasks through transfer learning.

5. The method for image analysis using a metallographic microscope combined with AI according to claim 4, characterized in that: Combine multiple deep learning models for ensemble learning.

6. The method for image analysis using a metallographic microscope combined with AI according to claim 5, characterized in that: Use Hadoop, Spark and other big data processing platforms to process and analyze large-scale microscopic image data.

7. The method for image analysis using a metallographic microscope combined with AI according to claim 6, characterized in that: Integrated sensors and AI technology.

8. The method for image analysis using a metallographic microscope combined with AI according to claim 7, characterized in that: Combine AI and virtual reality technology to simulate microscope operation.