Nickel-based high-temperature alloy microstructure image diffusion generation method, system, equipment, medium and product

Abstract

The present application discloses a method, system, device, medium and product for diffusion generation of nickel-based high-temperature alloy microstructure images, which relates to the field of microstructure reconstruction of high-temperature alloys. The method includes using a forward diffusion process of a denoising diffusion generation network to gradually diffuse the original training image into a Gaussian noise-like image, and a reverse diffusion process to gradually denoise to obtain a nickel-based high-temperature alloy microstructure-like image; using a geometric structure feature loss penalty mechanism to obtain a trained denoising diffusion generation network based on geometric feature differences; using the reverse diffusion process of the trained denoising diffusion generation network to obtain a virtual microstructure image, and then screening to obtain the final microstructure image. The present application solves the problems faced by data samples, and the cumbersome, time-consuming and costly acquisition of the internal microstructure of the alloy, and intuitively reproduces similar microstructure images, realizing the intelligent generation of virtual microstructures.

Description

Technical Field

[0001] The present application relates to the field of microstructure reconstruction of high-temperature alloys, and in particular to a method, system, equipment, medium and product for diffusion generation of microstructure images of nickel-based high-temperature alloys. Background Art

[0002] Nickel-based superalloys, with their exceptional high-temperature strength, corrosion resistance, and thermal stability, are widely used in the aerospace and new energy industries. They are the primary raw material for critical hot-end components such as aircraft engine turbine disks and combustion chamber blades. These components are subjected to long-term high temperatures, high pressures, and high rotational speeds. Fatigue can cause changes in the material's microstructure, gradually progressing from minor defects to macroscopic defects, ultimately leading to failure. Therefore, structural changes are the root cause of damage and failure.

[0003] A material's microstructure is inextricably linked to its macroscopic properties. Accurately characterizing this structure is crucial for ensuring quality control throughout the lifecycle of critical components. However, existing experimental characterization techniques for microstructure characterization can be detrimental to critical components, resulting in long testing cycles and high costs. Furthermore, microstructure simulation techniques require large computational resources and inaccurate model predictions. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, equipment, medium and product for diffusion generation of microstructure images of nickel-based high-temperature alloys, which can solve the problems faced by the existing technology, such as scarcity of data samples and cumbersome, time-consuming and costly acquisition of the internal microstructure of the alloy. It can intuitively reproduce similar microstructure images, bringing convenience to the study of material properties.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for generating a diffusion image of a nickel-based high-temperature alloy microstructure, comprising:

[0007] Acquire multiple original samples of nickel-based superalloys to form a sample set; each original sample of the nickel-based superalloys includes: an original training image and geometric structure feature information corresponding to the original training image; the original training image is an image of the microstructure of the nickel-based superalloy; the geometric structure feature information includes grain size, grain roundness, and grain major-minor axis ratio;

[0008] The original training image is gradually diffused into a Gaussian noise-like image using a forward diffusion process of a denoising diffusion generative network;

[0009] The Gaussian noise image is gradually denoised using a reverse diffusion process of a denoising diffusion generation network to obtain a microstructure image of a nickel-based high-temperature alloy;

[0010] Determining geometric structural feature information of the nickel-based high-temperature alloy microstructure image, and determining a geometric feature difference based on the geometric structural feature information corresponding to the original training image and the geometric structural feature information of the nickel-based high-temperature alloy microstructure image;

[0011] Determining whether the geometric feature difference is zero;

[0012] When the geometric feature difference is not zero, the geometric feature difference is introduced into the denoising diffusion model as a geometric structure feature loss penalty term to obtain a trained denoising diffusion generation network;

[0013] When the geometric feature difference is zero, zero is introduced into the denoising diffusion model as a geometric structure feature loss penalty term to obtain a trained denoising diffusion generation network;

[0014] Acquire an original image of a nickel-based high-temperature alloy specimen; the original image of the nickel-based high-temperature alloy specimen is a microstructure image containing Gaussian noise;

[0015] Generate a virtual microstructure image based on the original image of the nickel-based high-temperature alloy specimen using a reverse diffusion process of a trained denoising diffusion generation network;

[0016] The virtual microstructure image is screened using the grain boundary integrity principle to obtain a microstructure image of the nickel-based high-temperature alloy specimen.

[0017] Optionally, the forward diffusion process of the denoising diffusion generation network is used to gradually diffuse the original training image into a Gaussian noise-like image, specifically including:

[0018] The forward diffusion process of the denoising diffusion generation network is adopted to add Gaussian noise to the original training image according to a set time step to obtain the Gaussian noise-like image.

[0019] Optionally, the forward diffusion process of the denoising diffusion generation network is a Markov process.

[0020] Optionally, the Gaussian noise-like image is gradually denoised using a reverse diffusion process of a denoising diffusion generation network to obtain a microstructure image of a nickel-based high-temperature alloy, specifically comprising:

[0021] Inputting the Gaussian noise image and the set time step into a U-Net prediction network to obtain a noise-added noise;

[0022] The reverse diffusion process of the denoising diffusion generation network is adopted to remove the noise in the Gaussian noise-like image according to the set time step to obtain the microstructure image of the nickel-based high-temperature alloy.

[0023] Optionally, determining the geometric structural feature information of the nickel-based high-temperature alloy microstructure image specifically includes:

[0024] Determining the area of ​​grains in the nickel-based high-temperature alloy microstructure image using a contour area calculation method, and determining the grain size of individual grains based on the grain area;

[0025] Determining the grain length in the nickel-based high-temperature alloy microstructure image using a contour length calculation method, and determining the roundness of a single grain based on the grain area and the grain length;

[0026] Determining the major axis and minor axis of the grains in the nickel-based high-temperature alloy microstructure image using an ellipse focus coordinate calculation method, and determining the major-minor axis ratio of a single grain based on the major axis and minor axis of the grain;

[0027] Obtaining an average grain size, an average roundness, and an average aspect ratio based on the grain size of a single grain, the roundness of a single grain, and the aspect ratio of a single grain;

[0028] The average grain size, average roundness and average major-minor axis ratio are used as geometric structural feature information of the microstructure image of the nickel-based high-temperature alloy.

[0029] Optionally, the virtual microstructure image is screened using the grain boundary integrity principle to obtain the microstructure image of the nickel-based high-temperature alloy specimen, specifically comprising:

[0030] performing binarization processing on the virtual microscopic tissue structure image to obtain a binarized image;

[0031] By means of image traversal, each pixel in the binary image is traversed, and an 8-neighborhood detection method is used to detect whether there is a 0 value near each 0-value pixel in the binary image; the vicinity of each 0-value pixel refers to an area with a set value extending from the 0-value pixel as the starting point;

[0032] When there is no 0 value near each 0-value pixel, it indicates that the grain boundary line is broken, and the virtual microstructure image is deleted;

[0033] When there is a 0 value near each 0-value pixel, it indicates that the grain boundaries in the entire microstructure are intact, and the virtual microstructure image is retained, and the retained virtual microstructure image is used as the microstructure image of the nickel-based high-temperature alloy specimen.

[0034] In a second aspect, the present application provides a system for generating a diffusion image of a nickel-based high-temperature alloy microstructure, comprising:

[0035] A data acquisition module is configured to acquire a plurality of original samples of nickel-based superalloys to form a sample set, and to acquire original images of nickel-based superalloy specimens; each original sample of the nickel-based superalloys includes: an original training image and geometric structure feature information corresponding to the original training image; the original training image is a microstructure image of the nickel-based superalloy; the geometric structure feature information includes grain size, grain roundness, and grain aspect ratio; the original image of the nickel-based superalloy specimen is a microstructure image containing Gaussian noise;

[0036] A forward diffusion module, configured to gradually diffuse the original training image into a Gaussian noise-like image using a forward diffusion process of a denoising diffusion generation network;

[0037] a reverse diffusion module, configured to gradually denoise the Gaussian noise image using a reverse diffusion process of a denoising diffusion generation network to obtain a microstructure image of a nickel-based high-temperature alloy;

[0038] a geometric feature difference determination module, configured to determine geometric feature information of the nickel-based high-temperature alloy microstructure image, and determine the geometric feature difference based on the geometric feature information corresponding to the original training image and the geometric feature information of the nickel-based high-temperature alloy microstructure image;

[0039] a geometric structure feature loss penalty module, configured to determine whether the geometric feature difference is zero; when the geometric feature difference is not zero, introducing the geometric feature difference as a geometric structure feature loss penalty term into the denoising diffusion model to obtain a trained denoising diffusion generation network; and when the geometric feature difference is zero, introducing zero as a geometric structure feature loss penalty term into the denoising diffusion model to obtain a trained denoising diffusion generation network;

[0040] a microstructure generation module, configured to generate a virtual microstructure image based on the original image of the nickel-based high-temperature alloy specimen by using a reverse diffusion process of a trained denoising diffusion generation network;

[0041] The microstructure image generation module is used to screen the virtual microstructure image using the grain boundary integrity principle to obtain the microstructure image of the nickel-based high-temperature alloy specimen.

[0042] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the above-mentioned method for generating diffusion images of the microstructure of nickel-based high-temperature alloys.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the diffusion generation method for the microstructure image of the nickel-based high-temperature alloy provided above is implemented.

[0044] In a fifth aspect, the present application provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, the diffusion generation method of the microstructure image of the nickel-based high-temperature alloy provided above is implemented.

[0045] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0046] The present application provides a method, system, device, medium, and product for diffusion generation of microstructure images of nickel-based high-temperature alloys. By adopting the reverse diffusion process of a trained denoising diffusion generation network, a virtual microstructure image is generated based on the original image of the nickel-based high-temperature alloy specimen. The corresponding virtual microstructure image can be obtained based on the real-time original image, solving the problems faced by the prior art of scarce data samples and the cumbersome, time-consuming, and costly process of obtaining the internal microstructure of the alloy. The virtual microstructure image is screened using the grain boundary integrity principle to obtain a microstructure image of the nickel-based high-temperature alloy specimen. This allows for intuitive reproduction of similar microstructure images, enabling intelligent generation of virtual microstructures and facilitating the study of material properties. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 This is an application environment diagram of a method for generating a diffusion-based image of a nickel-based high-temperature alloy microstructure in one embodiment of the present application;

[0049] Figure 2 A flow chart of a method for generating a diffusion-based image of a nickel-based high-temperature alloy microstructure according to an embodiment of the present application;

[0050] Figure 3 A flowchart of a method for generating and visualizing the diffusion of microstructures of GH4169 provided in another embodiment of the present application;

[0051] Figure 4 A schematic diagram of a method for generating and visualizing the diffusion of microstructures of GH4169 is provided for another embodiment of the present application;

[0052] Figure 5 For another embodiment of the present application, a microstructure diagram of GH4169 obtained by optical microscopy after a specified solution aging treatment is provided;

[0053] Figure 6 A flowchart for calculating the geometric characteristics of a microstructure provided by another embodiment of the present application;

[0054] Figure 7 A schematic diagram of the functional modules of a nickel-based high-temperature alloy microstructure image diffusion generation system provided in another embodiment of the present application;

[0055] Figure 8 A system structure diagram of a microstructure diffusion generation and two-dimensional visualization method for GH4169 provided in another embodiment of the present application;

[0056] Figure 9 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0058] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0059] The nickel-based high-temperature alloy microstructure image diffusion generation method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the sample set and the original image of the nickel-based high-temperature alloy specimen to the server 104. After the server 104 receives the sample set and the original image of the nickel-based high-temperature alloy specimen, for the sample set and the original image of the nickel-based high-temperature alloy specimen, the server 104 uses the forward diffusion process of the denoising diffusion generation network to gradually diffuse the original training image into a Gaussian noise image; uses the reverse diffusion process of the denoising diffusion generation network to gradually denoise the Gaussian noise image to obtain a nickel-based high-temperature alloy microstructure image; determines the geometric structure characteristics of the nickel-based high-temperature alloy microstructure image The method further comprises the steps of: first, determining a geometric feature difference based on the geometric feature information corresponding to the original training image and the geometric feature information of the nickel-based superalloy microstructure image; second, determining whether the geometric feature difference approaches zero using a geometric feature loss penalty mechanism; and finally, generating a trained denoising diffusion generation network using a reverse diffusion process of the trained denoising diffusion generation network to generate a virtual microstructure image based on the original image of the nickel-based superalloy specimen; and finally, filtering the virtual microstructure image using the grain boundary integrity principle to obtain a microstructure image of the nickel-based superalloy specimen. The server 104 can provide feedback of the resulting microstructure image to the terminal 102. In addition, in some embodiments, the method for generating the diffusion image of the microstructure of the nickel-based high-temperature alloy can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform image diffusion and generation processing on the original images of the sample set and the nickel-based high-temperature alloy specimen, or the server 104 can obtain the original images of the sample set and the nickel-based high-temperature alloy specimen from the data storage system, and perform image diffusion and generation processing on the original images of the sample set and the nickel-based high-temperature alloy specimen.

[0060] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0061] In an exemplary embodiment, Figure 2As shown, a method for generating a diffusion image of a nickel-based high-temperature alloy microstructure is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps 200 to 209.

[0062] Step 200: Acquire multiple original samples of nickel-based superalloys to form a sample set. Each original sample of nickel-based superalloys includes an original training image and geometric structural feature information corresponding to the original training image. The original training image is an image of the microstructure of the nickel-based superalloy. The geometric structural feature information includes grain size, grain roundness, and grain major-minor axis ratio.

[0063] Step 201: The original training image is gradually diffused into a Gaussian noise-like image using the forward diffusion process of the denoising diffusion generation network.

[0064] Step 202: The Gaussian noise image is gradually denoised using the reverse diffusion process of the denoising diffusion generation network to obtain a microstructure image of the nickel-based high-temperature alloy.

[0065] Step 203: Determine the geometric structure feature information of the nickel-based high-temperature alloy microstructure image, and determine the geometric feature difference based on the geometric structure feature information corresponding to the original training image and the geometric structure feature information of the nickel-based high-temperature alloy microstructure image.

[0066] Step 204: Determine whether the geometric feature difference is zero.

[0067] Step 205: When the geometric feature difference is not zero, the geometric feature difference is introduced into the denoising diffusion model as a geometric structure feature loss penalty term to obtain a trained denoising diffusion generation network.

[0068] Step 206: When the geometric feature difference is zero, zero is introduced into the denoising diffusion model as a geometric structure feature loss penalty term to obtain a trained denoising diffusion generation network.

[0069] Step 207: Acquire an original image of the nickel-based superalloy specimen. The original image of the nickel-based superalloy specimen is a microstructure image containing Gaussian noise.

[0070] Step 208: Generate a virtual microstructure image based on the original image of the nickel-based high-temperature alloy specimen using the reverse diffusion process of the trained denoising diffusion generation network.

[0071] Step 209: Using the grain boundary integrity principle to screen the virtual microstructure image, a microstructure image of the nickel-based high-temperature alloy specimen is obtained.

[0072] In actual applications, multiple original samples of nickel-based high-temperature alloys and original images of nickel-based high-temperature alloy specimens can be obtained using an optical microscope.

[0073] Implementing the above steps 200 to 209 can solve the problems faced by the existing technology, such as the scarcity of data samples and the cumbersome, time-consuming and costly process of obtaining the internal microstructure of the alloy. It can intuitively reproduce similar microstructure images, bringing convenience to the study of material properties.

[0074] In another exemplary embodiment of the present application, in order to fully simulate the diffusion process of noise, the implementation process of the above-mentioned step 201 can be: using the forward diffusion process of the denoising diffusion generation network, adding Gaussian noise to the original training image according to the set time step to obtain a Gaussian noise-like image.

[0075] For example, the implementation process of step 201 given above is described by taking the Markov process as the forward diffusion process of the denoising diffusion generation network as an example, wherein:

[0076] Noise is added to the original training image in a Markov process. t |x t-1 ) is a conditional probability that defines the probability distribution of the image at step t where the noise is large, given the image at step t-1 where the noise is small. That is, starting from the microstructure image x0 that follows the data distribution q(x), the data x with small noise is t-1 The calculation formula is as follows:

[0077]

[0078] in, represents the weight of the original training image to the noisy microstructure image after adding noise, N represents Gaussian distribution, and I represents the unit matrix. After reparameterization, the noisy microstructure image x t It can be written as:

[0079]

[0080] Where ε represents the added Gaussian noise.

[0081] After adding noise for T time steps, a Gaussian noise image is obtained.

[0082] In another exemplary embodiment of the present application, in order to gradually denoise the Gaussian noise image and restore an image similar to the original training microstructure image using the reverse diffusion process of the denoising diffusion generation network, the implementation process of step 202 may be:

[0083] Step 2021: Input the Gaussian noise-like image and the set time step into the U-Net prediction network to obtain the added noise (i.e., the Gaussian noise to be added). For example, the U-Net network is used to predict the added noise for the Gaussian noise-like image.

[0084] Step 2022: Using the reverse diffusion process of the denoising diffusion generation network, remove the noise in the Gaussian noise-like image according to the set time step to obtain the microstructure image of the nickel-based high-temperature alloy. For example, using the Bayesian theorem, the denoising formula for restoring the image to a less noisy version is as follows:

[0085]

[0086] Where μ θ (x t ,t) is the predicted mean, is the variance diffusion table, I represents the unit matrix, p θ (x t-1 |x t ) represents the reverse diffusion kernel.

[0087] Among them, the predicted mean μ θ (x t ,t) can be obtained by adding noise, and the calculation formula is as follows:

[0088]

[0089] Where, β t represents the variance, α t =1-β t Represented as a decreasing sequence, α t and β t The growth direction is opposite, ε θ (x t ,t) represents the added noise.

[0090] The Gaussian noise image is iteratively denoised by the predicted noise addition noise to restore an image similar to the original training microstructure image, that is, a nickel-based high-temperature alloy microstructure image.

[0091] Based on the above description, the implementation principle architecture of the diffusion generation method of the nickel-based high-temperature alloy microstructure image provided by this application is as follows: Figure 4 shown.

[0092] In another exemplary embodiment of the present application, in the process of using the geometric structure feature loss penalty mechanism to determine whether the geometric feature difference is zero, image processing methods can be used to obtain the geometric structure feature information of the original training image and the nickel-based high-temperature alloy microstructure image, and then the geometric feature difference is determined based on the obtained geometric structure feature information. Based on this, Figure 6 As shown in Figure 2, the process of determining geometric structure feature information can be described as:

[0093] (1) The grain area Area is determined by the contour area calculation method, and the grain size of a single grain is determined based on the grain area. The grain size is recorded as D. The calculation formula of the grain size D is:

[0094] (2) The grain length Len is determined by the contour length calculation method, and the roundness of a single grain is determined based on the grain area and grain length, which is recorded as M. The calculation formula for roundness M is:

[0095] (3) The ellipse focus coordinate calculation method is used to determine the long axis (denoted as a) and short axis (denoted as b) of the grain in the microstructure image of the nickel-based high-temperature alloy. Based on the long axis and short axis of the grain, the long-short axis ratio of a single grain is determined, denoted as R. The calculation formula for the long-short axis ratio R is:

[0096] (4) The average grain size, average roundness and average aspect ratio are obtained based on the grain size, roundness and aspect ratio of individual grains.

[0097] (5) The average grain size, average roundness and average major-minor axis ratio are used as geometric structure feature information.

[0098] Furthermore, in this embodiment, the mean square error MSE calculation formula is used Calculate the geometric feature difference, that is, the mean square error is used as the geometric feature difference. i and y i Represents the geometric structural feature information of the original training image and the microstructure image of the nickel-based high-temperature alloy.

[0099] In another exemplary embodiment of the present application, the virtual microscopic tissue structure image obtained in step 208 is obtained through training of a forward diffusion process and a backward diffusion process, which includes:

[0100] Randomly generate a Gaussian noise image of the specified size.

[0101] The Gaussian noise image is input into the trained denoising diffusion generation network for the reverse diffusion process.

[0102] Output the required virtual microstructure image, which is a two-dimensional image of the microstructure of the nickel-based high-temperature alloy.

[0103] In another exemplary embodiment of the present application, the virtual microstructure image can be screened using the grain boundary integrity principle in the real microstructure. The grain boundary integrity principle states that the grains in the microstructure must be closed intervals. The closed intervals of the grains are identified by line break detection using image processing. Based on this, the implementation process of the above step 209 can be:

[0104] (1) Binarization is performed on the virtual microstructure image to obtain a binary image. For example, the virtual microstructure pixels are only 0 and 255, where 0 represents the black boundary line and 255 represents the interior of the grain.

[0105] (2) By means of image traversal, each pixel in the binary image is traversed, and the 8-neighborhood detection method is used to detect whether there is a 0 value near each 0-value pixel in the binary image. The vicinity of each 0-value pixel refers to the area with a set value extending from the 0-value pixel as the starting point.

[0106] (3) When there is no 0 value near each 0-value pixel, it indicates that there is a break in the grain boundary line, and the virtual microstructure image is deleted.

[0107] (4) When there is a 0 value near each 0-value pixel, it indicates that the grain boundary in the entire microstructure is intact, and the virtual microstructure image is retained, and the retained virtual microstructure image is used as the microstructure image of the nickel-based high-temperature alloy specimen.

[0108] In another exemplary embodiment of the present application, the microstructure image of GH4169 obtained by the diffusion generation method of the microstructure image of the nickel-based high-temperature alloy provided above is used as an example for description, wherein:

[0109] (1) Obtain the microstructure image of GH4169 and the corresponding geometric structure feature information through optical microscopy, and evaluate its effectiveness.

[0110] Optical microscope was used to collect microstructure images of GH4169 under different heat treatment processes.

[0111] The quality of the acquired GH4169 microstructure images was evaluated, and the microstructure images with poor performance were eliminated.

[0112] (2) Preprocessing the microstructure image obtained in step (1) to eliminate interference such as grain boundary blur, precipitated phase and polishing impurities.

[0113] (3) Using image grayscale conversion, image brightness and contrast adjustment, grain boundary extraction and enhancement, etc., a pure and clear microstructure image is obtained and its geometric structure characteristics are calculated.

[0114] (4) The microstructure image obtained in step (3) is weightedly combined with Gaussian noise to obtain a noisy microstructure image.

[0115] The noisy microstructure image is continuously weighted combined with Gaussian noise, and the combination time step is T times, and the weight of Gaussian noise increases with the increase of the time step.

[0116] (5) Noise prediction is performed on the noisy microstructure image that has undergone T times of noise addition.

[0117] The noisy microstructure image and the current time step T are used as the input of the U-Net prediction network to predict the noise.

[0118] (6) The predicted noise is removed from the noisy microstructure image to restore the original noise-free microstructure image.

[0119] (7) Using image processing methods, calculate the geometric structure features inside the restored microstructure image, and calculate the difference between the geometric structure features and the input original microstructure geometric features.

[0120] (8) A geometric structure feature loss penalty mechanism is used to determine whether the geometric feature difference approaches (or is equal to) zero. If the geometric feature difference does not approach zero, the difference is introduced into the loss penalty term of the denoising diffusion model.

[0121] (9) Randomly generate a Gaussian noise image of a specified size and input it into the reverse diffusion process of the trained denoising diffusion generation network to generate a virtual GH4169 microstructure image.

[0122] (10) The grain boundaries in the generated virtual GH4169 microstructure image are closed and the microstructure image with complete grain boundaries is selected as the final output.

[0123] As a specific example, Figure 3 As shown, the specific implementation process is as follows:

[0124] K1: Original sample collection and validity assessment, including:

[0125] K11: The microstructure images of GH4169 were obtained using an optical microscope under different heat treatment processes. For example, a solution treatment was performed at 960°C, the holding time was 60 minutes, followed by air cooling with a temperature gradient of 20°C. Then the heat treatment was continued at 720°C for 8 hours, followed by furnace cooling to 620°C at a rate of 50°C / h, and finally the heat treatment was continued for 8 hours before air cooling. The following images were obtained using an optical microscope: Figure 5 Microstructure images are shown.

[0126] K12: Evaluate the quality of the acquired GH4169 microstructure images and eliminate microstructure images with poor performance.

[0127] K2: Preprocessing of original microstructure images, including:

[0128] K21: Use image grayscale processing to convert the original image into an 8-bit image, and adjust the brightness and contrast to obtain an enhanced image.

[0129] K22: Select appropriate parameters for grain region of interest (ROI) selection and segmentation, and compare the original metallographic image to selectively ignore the edge grains and misselected extremely small grains in order to improve the accuracy of grain identification, and finally obtain a pure and clear microstructure image.

[0130] K23: Calculate the geometric structure characteristics of the segmented grains separately to obtain the geometric structure characteristics of the overall image.

[0131] K3: The original microstructure image is subjected to noise processing to be converted into a noisy microstructure image, including:

[0132] K31: The noisy microstructure image continues to be weightedly combined with Gaussian noise for T time steps, and the weight of the Gaussian noise increases with the increase of the time step.

[0133] K32: Noise prediction for a noisy microstructure image that has undergone T noise additions.

[0134] K4: U-Net network predicts noise and restores the noisy microstructure image to a noise-free microstructure image, including:

[0135] K41: Take the noisy microstructure image and the current time step T as the input of the U-Net prediction network to predict the noise.

[0136] K42: The original noise-free microstructure image is restored by removing the predicted noise from the noisy microstructure image.

[0137] K5: Calculates the geometric features of the noise-free microstructure image and uses the geometric features to determine the loss penalty, including:

[0138] K51: Use image processing, for details, refer to Figure 6 , calculate the geometric structure features inside the restored microstructure image, and calculate the difference between the geometric structure features and the input original microstructure geometric features (ie, the geometric feature difference).

[0139] K52: A geometric structure feature loss penalty mechanism is used to determine whether the geometric feature difference is zero. If the geometric feature difference is not zero, the difference is introduced into the denoising diffusion model as a geometric structure feature loss penalty. If the geometric feature difference is zero, the geometric structure feature loss penalty is zero.

[0140] K6: Use the trained denoising diffusion generation network (including geometric structure feature loss penalty) to obtain virtual microstructure images, including:

[0141] K61: Randomly generates Gaussian noise images of specified size.

[0142] K62: A Gaussian noise image is input into the reverse diffusion process of the trained denoising diffusion generation network to generate a virtual two-dimensional image of the microstructure of a nickel-based high-temperature alloy.

[0143] K7: Perform grain boundary closure detection on the generated microstructure image, including:

[0144] K71: Binarize the generated microstructure image.

[0145] K72: Use image traversal to traverse every pixel in the microstructure image.

[0146] K73: An 8-neighborhood detection method is used to detect whether there is one or more zero values ​​near each zero-value pixel. If not, it indicates that the boundary line is broken. Otherwise, the grain boundaries in the entire microstructure are intact.

[0147] Based on the same inventive concept, embodiments of the present application also provide a nickel-based superalloy microstructure image diffusion generation system for implementing the aforementioned nickel-based superalloy microstructure image diffusion generation method. The solution provided by this system is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the nickel-based superalloy microstructure image diffusion generation system provided below can be found in the limitations of the nickel-based superalloy microstructure image diffusion generation method described above and will not be repeated here.

[0148] In an exemplary embodiment, Figure 7 As shown, a nickel-based high-temperature alloy microstructure image diffusion generation system is provided, including:

[0149] The data acquisition module is used to acquire multiple original samples of nickel-based superalloys to form a sample set, and to obtain original images of the nickel-based superalloy specimens. Each original sample of the nickel-based superalloy includes an original training image and geometric structural feature information corresponding to the original training image. The original training image is an image of the nickel-based superalloy microstructure. The geometric structural feature information includes grain size, grain roundness, and grain major-minor axis ratio. The original image of the nickel-based superalloy specimen is a microstructure image containing Gaussian noise.

[0150] The forward diffusion module is used to gradually diffuse the original training image into a Gaussian noise-like image using the forward diffusion process of the denoising diffusion generation network.

[0151] The reverse diffusion module is used to gradually denoise the Gaussian noise image by using the reverse diffusion process of the denoising diffusion generation network to obtain the microstructure image of the nickel-based high-temperature alloy.

[0152] The geometric feature difference determination module is used to determine the geometric structure feature information of the nickel-based high-temperature alloy microstructure image, and determine the geometric feature difference based on the geometric structure feature information corresponding to the original training image and the geometric structure feature information of the nickel-based high-temperature alloy microstructure image.

[0153] The geometric feature loss penalty module is used to determine whether the geometric feature difference is zero. If the geometric feature difference is not zero, the geometric feature difference is introduced as a geometric feature loss penalty term into the denoising diffusion model, resulting in a trained denoising diffusion generative network. If the geometric feature difference is zero, zero is introduced as a geometric feature loss penalty term into the denoising diffusion model, resulting in a trained denoising diffusion generative network.

[0154] The microstructure generation module is used to generate a virtual microstructure image based on the original image of the nickel-based high-temperature alloy specimen by using the reverse diffusion process of the trained denoising diffusion generation network.

[0155] The microstructure image generation module is used to screen the virtual microstructure image by adopting the grain boundary integrity principle to obtain the microstructure image of the nickel-based high-temperature alloy specimen.

[0156] As an optional implementation, in practical applications, such as Figure 8 As shown, the nickel-based high-temperature alloy microstructure image diffusion generation system may also include:

[0157] The data collection and evaluation module is used to collect GH4169 microstructure images and corresponding geometric structure feature information, and to evaluate the validity of all acquired data and eliminate microstructure images with poor performance.

[0158] The microstructure image preprocessing module is used to eliminate interference such as grain boundary blur, precipitated phase and polishing impurities to obtain pure and clear microstructure images.

[0159] The microstructure image noise addition module destroys the structural features of the original image by adding Gaussian noise to obtain a noisy microstructure image.

[0160] The noise prediction and denoising module predicts the added noise through the U-Net network, and uses the added noise to remove the noise inside the noisy microstructure image to obtain a noise-free microstructure image.

[0161] The geometric feature penalty module is used to determine whether there is a difference between the geometric structure features of the restored noise-free microstructure image and the geometric structure features of the original training microstructure. If there is a difference, it indicates that the generation effect is poor and the model needs to be penalized.

[0162] The microstructure generation module is used to generate a virtual two-dimensional image of the microstructure after the training is completed.

[0163] The closure detection module is used to determine whether the internal grains of the generated two-dimensional microstructure image conform to objective laws and whether the grain boundaries of the internal grains are closed small areas.

[0164] In this embodiment, the data collection and evaluation module can collect microstructure images of nickel-based superalloys obtained using an optical microscope under different heat treatment processes, evaluate the quality of the obtained microstructure images, and eliminate microstructure images with poor performance.

[0165] The microstructure image preprocessing module eliminates interference such as grain boundary blur, precipitated phases, and polishing impurities. Image grayscale processing is used to adjust brightness and contrast, resulting in an enhanced image. Comparing the original metallographic image, it selectively ignores edge grains and misselected extremely small grains to improve grain identification accuracy, ultimately producing a pure and clear microstructure image. The geometric structural characteristics of each identified grain are calculated to obtain the geometric structural characteristics of the overall image.

[0166] The microstructure image noise module generates random Gaussian noise and transforms the original microstructure image into a noisy microstructure image through linear weighting. The noisy microstructure image is then weighted with the Gaussian noise for T time steps, with the weight of the Gaussian noise increasing as the time step increases.

[0167] The noise prediction and denoising module can be used in the U-Net network for noise prediction to predict the added noise in the noisy process, obtain the distribution of the denoised microstructure image with the help of the Bayesian formula, and restore the original noise-free microstructure image through sampling.

[0168] The geometric feature penalty module can use the microstructure geometric feature calculation formula to obtain the geometric structure features of the restored microstructure image, calculate the mean square error between the geometric structure features and the geometric structure features corresponding to the input original microstructure image, and use the mean square error as one of the loss penalties of the prediction network U-Net to optimize the weight of the prediction network.

[0169] The microstructure generation module can randomly generate a Gaussian noise image of the same size as the microstructure image to be generated, and use the Gaussian noise image as the input of the denoising process, and gradually denoise it to obtain a virtual microstructure image.

[0170] The closure detection module can perform binarization processing on the virtual microstructure image generated by the microstructure generation module, detect whether there are unclosed broken lines at the grain boundaries inside it, and screen out microstructure images containing unclosed broken lines.

[0171] In summary, the present application obtains optical microstructure images of nickel-based high-temperature alloys and evaluates the validity of all acquired original sample data; then pre-processes the above microstructure images to eliminate interference such as grain boundary blur, precipitated phases and polishing impurities, and calculates the corresponding geometric structure features; uses a forward diffusion process to add noise to the original microstructure; denoises the noisy microstructure image through a reverse diffusion process, and uses the mean square error between the geometric structure features of the denoised microstructure image and the geometric structure features of the input original microstructure as the loss penalty term of the model to optimize the prediction model; reconstructs the virtual microstructure through the trained model; performs closure detection on the acquired virtual microstructure image to screen out microstructure images that do not meet the requirements. This application addresses the problem of difficulty in obtaining existing microstructures, and by introducing a deep learning model, it is possible to achieve convenient and fast microstructure image acquisition.

[0172] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 9 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store image diffusion and generate processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for generating diffusion images of a microstructure of a nickel-based high-temperature alloy is realized.

[0173] Those skilled in the art will understand that Figure 9 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of the above-mentioned method embodiments when executing the computer program.

[0174] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0175] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0176] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0177] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0178] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0179] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0180] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for generating a diffusion image of a nickel-based high-temperature alloy microstructure, characterized in that: The method for generating a diffusion-based image of a nickel-based high-temperature alloy microstructure comprises: Acquire multiple original samples of nickel-based superalloys to form a sample set; each original sample of the nickel-based superalloys includes: an original training image and geometric structure feature information corresponding to the original training image; the original training image is an image of the microstructure of the nickel-based superalloy; the geometric structure feature information includes grain size, grain roundness, and grain major-minor axis ratio; The original training image is gradually diffused into a Gaussian noise-like image using a forward diffusion process of a denoising diffusion generative network; The Gaussian noise image is gradually denoised using a reverse diffusion process of a denoising diffusion generation network to obtain a microstructure image of a nickel-based high-temperature alloy; Determining geometric structural feature information of the nickel-based high-temperature alloy microstructure image, and determining a geometric feature difference based on the geometric structural feature information corresponding to the original training image and the geometric structural feature information of the nickel-based high-temperature alloy microstructure image; Determining whether the geometric feature difference is zero; When the geometric feature difference is not zero, the geometric feature difference is introduced into the denoising diffusion generation network as a geometric structure feature loss penalty term to obtain a trained denoising diffusion generation network; When the geometric feature difference is zero, zero is introduced into the denoising diffusion generation network as a geometric structure feature loss penalty term to obtain a trained denoising diffusion generation network; Acquire an original image of a nickel-based high-temperature alloy specimen; the original image of the nickel-based high-temperature alloy specimen is a microstructure image containing Gaussian noise; Generate a virtual microstructure image based on the original image of the nickel-based high-temperature alloy specimen using a reverse diffusion process of a trained denoising diffusion generation network; The virtual microstructure image is screened using the grain boundary integrity principle to obtain a microstructure image of the nickel-based high-temperature alloy specimen.

2. The method for generating a diffusion image of a nickel-based high-temperature alloy microstructure according to claim 1, characterized in that: The forward diffusion process of the denoising diffusion generation network is used to gradually diffuse the original training image into a Gaussian noise-like image, specifically including: The forward diffusion process of the denoising diffusion generation network is adopted to add Gaussian noise to the original training image according to a set time step to obtain the Gaussian noise-like image.

3. The method for generating a diffusion image of a nickel-based high-temperature alloy microstructure according to claim 2, characterized in that: The forward diffusion process of the denoising diffusion generation network is a Markov process.

4. The method for generating a diffusion image of a nickel-based high-temperature alloy microstructure according to claim 2, wherein: The Gaussian noise image is gradually denoised using the reverse diffusion process of the denoising diffusion generation network to obtain a microstructure image of the nickel-based high-temperature alloy, specifically including: Inputting the Gaussian noise image and the set time step into a U-Net prediction network to obtain a noise-added noise; The reverse diffusion process of the denoising diffusion generation network is adopted to remove the noise in the Gaussian noise-like image according to the set time step to obtain the microstructure image of the nickel-based high-temperature alloy.

5. The method for generating a diffusion image of a nickel-based high-temperature alloy microstructure according to claim 1, wherein: Determining geometric structural feature information of the nickel-based high-temperature alloy microstructure image specifically includes: Determining the area of ​​grains in the nickel-based high-temperature alloy microstructure image using a contour area calculation method, and determining the grain size of individual grains based on the grain area; Determining the grain length in the nickel-based high-temperature alloy microstructure image using a contour length calculation method, and determining the roundness of a single grain based on the grain area and the grain length; Determining the major axis and minor axis of the grains in the nickel-based high-temperature alloy microstructure image using an ellipse focus coordinate calculation method, and determining the major-minor axis ratio of a single grain based on the major axis and minor axis of the grain; Obtaining an average grain size, an average roundness, and an average aspect ratio based on the grain size of a single grain, the roundness of a single grain, and the aspect ratio of a single grain; The average grain size, average roundness and average major-minor axis ratio are used as geometric structural feature information of the microstructure image of the nickel-based high-temperature alloy.

6. The method for generating a diffusion image of a nickel-based high-temperature alloy microstructure according to claim 1, wherein: The virtual microstructure image is screened using the grain boundary integrity principle to obtain the microstructure image of the nickel-based high-temperature alloy specimen, specifically comprising: performing binarization processing on the virtual microscopic tissue structure image to obtain a binarized image; By means of image traversal, each pixel in the binary image is traversed, and an 8-neighborhood detection method is used to detect whether there is a 0 value near each 0-value pixel in the binary image; the vicinity of each 0-value pixel refers to an area with a set value extending from the 0-value pixel as the starting point; When there is no 0 value near each 0-value pixel, it indicates that the grain boundary line is broken, and the virtual microstructure image is deleted; When there is a 0 value near each 0-value pixel, it indicates that the grain boundaries in the entire microstructure are intact, and the virtual microstructure image is retained, and the retained virtual microstructure image is used as the microstructure image of the nickel-based high-temperature alloy specimen.

7. A nickel-based high-temperature alloy microstructure image diffusion generation system, characterized in that: The nickel-based high-temperature alloy microstructure image diffusion generation system includes: A data acquisition module is configured to acquire a plurality of original samples of nickel-based superalloys to form a sample set, and to acquire original images of nickel-based superalloy specimens; each original sample of the nickel-based superalloys includes: an original training image and geometric structure feature information corresponding to the original training image; the original training image is a microstructure image of the nickel-based superalloy; the geometric structure feature information includes grain size, grain roundness, and grain aspect ratio; the original image of the nickel-based superalloy specimen is a microstructure image containing Gaussian noise; A forward diffusion module, configured to gradually diffuse the original training image into a Gaussian noise-like image using a forward diffusion process of a denoising diffusion generation network; a reverse diffusion module, configured to gradually denoise the Gaussian noise image using a reverse diffusion process of a denoising diffusion generation network to obtain a microstructure image of a nickel-based high-temperature alloy; a geometric feature difference determination module, configured to determine geometric feature information of the nickel-based high-temperature alloy microstructure image, and determine the geometric feature difference based on the geometric feature information corresponding to the original training image and the geometric feature information of the nickel-based high-temperature alloy microstructure image; a geometric structure feature loss penalty module, configured to determine whether the geometric feature difference is zero; when the geometric feature difference is not zero, introducing the geometric feature difference as a geometric structure feature loss penalty term into the denoising diffusion generation network to obtain a trained denoising diffusion generation network; and when the geometric feature difference is zero, introducing zero as a geometric structure feature loss penalty term into the denoising diffusion generation network to obtain a trained denoising diffusion generation network; a microstructure generation module, configured to generate a virtual microstructure image based on the original image of the nickel-based high-temperature alloy specimen by using a reverse diffusion process of a trained denoising diffusion generation network; The microstructure image generation module is used to screen the virtual microstructure image using the grain boundary integrity principle to obtain the microstructure image of the nickel-based high-temperature alloy specimen.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for generating a diffusion-based image of a nickel-based high-temperature alloy microstructure according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for generating a diffusion-based image of a nickel-based high-temperature alloy microstructure according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for generating a diffusion-based image of a nickel-based high-temperature alloy microstructure according to any one of claims 1 to 6 is implemented.

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