Display method of DD432 alloy structure

Abstract

The invention relates to the technical field of metal material structure display, in particular to a DD432 alloy structure display method which comprises the following steps: S1, pretreating the surface of a DD432 alloy sample to obtain a to-be-detected surface; s2, scanning the surface of the sample by adopting a laser-induced plasma spectrum technology, and continuously controlling a laser beam scanning path by utilizing a three-dimensional mobile platform; s3, plasma emission spectrum data are collected, and characteristic spectral lines of Ni, Cr, Co, Mo, Al and Ti elements are identified; s4, converting the spectral data into structure characteristic information of grain size, precipitated phase distribution and grain boundary morphology through a machine learning algorithm; and S5, reconstructing a microstructure image based on the microstructure feature information. According to the method provided by the invention, the LIBS technology is adopted, complicated sample pretreatment is not needed, tissue information can be quickly obtained, and the sample is not damaged.

Description

Technical Field

[0001] This invention relates to the field of metal material microstructure display technology, and in particular to a method for displaying the microstructure of DD432 alloy. Background Technology

[0002] DD432 alloy is a high-performance nickel-based superalloy widely used in key components such as turbine blades for aerospace engines and gas turbines. Its microstructure characteristics, such as grain size, precipitate distribution, and grain boundary morphology, directly affect the material's mechanical properties, fatigue life, and high-temperature oxidation resistance. Therefore, rapidly and accurately displaying and analyzing the microstructure characteristics of DD432 alloy is of great significance for material performance optimization and quality control.

[0003] Traditional methods for displaying the microstructure of DD432 alloy mainly include metallographic etching, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). While these methods can provide high-resolution microstructure images, they have the following drawbacks: A) Complex operation: requiring cumbersome sample preparation processes such as cutting, polishing, and etching. B) Time-consuming: from sample preparation to image acquisition, it typically takes several hours or even longer. C) Destructive to the sample: sample preparation and etching can damage the sample surface or internal structure. D) Limitations: it is difficult to achieve large-scale, continuous microstructure feature analysis.

[0004] Therefore, how to display the microstructure of DD432 alloy is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] The main objective of this invention is to propose a method for displaying the microstructure of DD432 alloy, aiming to solve the aforementioned technical problems.

[0006] To achieve the above objectives, this invention proposes a method for displaying the microstructure of DD432 alloy, comprising the following steps:

[0007] S1. Pre-treat the surface of the DD432 alloy sample to obtain the surface to be tested;

[0008] S2. Laser-induced plasma spectroscopy is used to scan the sample surface, and a three-dimensional moving platform is used to continuously control the laser beam scanning path.

[0009] S3. Collect plasma emission spectrum data and identify the characteristic spectral lines of Ni, Cr, Co, Mo, Al, and Ti elements;

[0010] S4. Use machine learning algorithms to transform spectral data into microstructural features such as grain size, precipitate distribution, and grain boundary morphology.

[0011] S5. Reconstruct a microscopic tissue image based on the tissue feature information.

[0012] Preferably, in step S1, the surface of the DD432 alloy sample is pretreated by mechanical polishing and ultrasonic cleaning to remove the oxide layer and contaminant layer.

[0013] Preferably, the preprocessing in step S1 includes:

[0014] S101. Cut the sample into specimens with dimensions of 10mm × 10mm × 5mm;

[0015] S102. Mechanically polish the surface using a diamond polishing slurry with a particle size of 1-3μm until the surface finish Ra ≤ 0.1μm;

[0016] S103. Perform ultrasonic cleaning with anhydrous ethanol for 10 minutes at a frequency of 40kHz.

[0017] Preferably, in step S2, the laser parameters include: wavelength 1064nm, pulse energy 10-100mJ, pulse width 5-20ns, and repetition frequency 1-20Hz.

[0018] Furthermore, in step S2, the laser pulse energy is 50 mJ, the pulse width is 10 ns, and the repetition frequency is 10 Hz.

[0019] Preferably, in step S2, the three-dimensional moving platform scans a 5mm×5mm area at a step distance of 50μm, and the scanning trajectory is a serpentine path.

[0020] Preferably, in step S3, a spectrometer is used to collect spectral data, and the spectrometer resolution is ≤0.1nm.

[0021] Preferably, in step S3, the characteristic spectral line identification is based on a preset database, which contains the spectral line mapping relationships of Ni, Cr, Co, Mo, Al, and Ti.

[0022] Preferably, in step S4, the machine learning algorithm is a convolutional neural network or a support vector machine.

[0023] Preferably, in step S5, the reconstructed microstructure image is generated using the Richardson-Lucy deconvolution algorithm with 15-20 iterations; the microstructure image output in step S5 is processed with pseudo-color to enhance the contrast between precipitates and grain boundaries.

[0024] Due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows:

[0025] (1) Fast and non-destructive: Using LIBS technology, tissue information can be obtained quickly without complicated sample pretreatment and without damaging the sample.

[0026] (2) High spatial resolution: This invention achieves micron-scale tissue feature display by optimizing laser parameters and using a high-resolution spectrometer.

[0027] (3) Simultaneous analysis of multiple elements: LIBS technology can analyze multiple elements at the same time. Therefore, this invention can simultaneously display multiple microstructure characteristics, such as grain size and precipitate distribution.

[0028] (4) High degree of automation: Machine learning algorithms and image processing algorithms are used to realize the automatic extraction of tissue features and image reconstruction, which significantly improves the efficiency of analysis.

[0029] (5) Highly targeted: The chemical composition and microstructure of DD432 alloy were optimized, which significantly improved the accuracy and applicability of the analysis. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0031] Figure 1 This is a flowchart of the method for displaying the microstructure of DD432 alloy provided by the present invention. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0033] Combination Figure 1 As shown, a method for displaying the microstructure of DD432 alloy includes the following steps:

[0034] S1. Pre-treat the surface of the DD432 alloy sample to obtain the surface to be tested;

[0035] S2. Laser-induced plasma spectroscopy is used to scan the sample surface, and a three-dimensional moving platform is used to continuously control the laser beam scanning path.

[0036] S3. Collect plasma emission spectrum data and identify the characteristic spectral lines of Ni, Cr, Co, Mo, Al, and Ti elements;

[0037] S4. Use machine learning algorithms to transform spectral data into microstructural features such as grain size, precipitate distribution, and grain boundary morphology.

[0038] S5. Reconstruct a microstructure image based on the described tissue feature information, including grain morphology, precipitate distribution, and grain boundary morphology.

[0039] In step S1, the surface of the DD432 alloy sample is pretreated by mechanical polishing and ultrasonic cleaning to remove the oxide layer and contaminant layer. Specifically, the pretreatment includes:

[0040] S101. Cut the sample into specimens with dimensions of 10mm × 10mm × 5mm;

[0041] S102. Mechanically polish the surface using a diamond polishing slurry with a particle size of 1-3μm until the surface finish Ra ≤ 0.1μm;

[0042] S103. Perform ultrasonic cleaning with anhydrous ethanol for 10 minutes at a frequency of 40kHz.

[0043] In step S2, an Nd:YAG pulsed laser is used. The laser beam is focused onto the sample surface to generate plasma. The laser parameters include: wavelength 1064 nm, pulse energy 10-100 mJ, pulse width 5-20 ns, and repetition frequency 1-20 Hz. Specifically, the laser pulse energy is 50 mJ, the pulse width is 10 ns, and the repetition frequency is 10 Hz. During scanning, the three-dimensional moving platform scans a 5 mm × 5 mm area at 50 μm steps, and the scanning trajectory is a serpentine path.

[0044] In step S3, spectral data is acquired using a spectrometer with a resolution ≤0.1nm.

[0045] In step S3, the characteristic spectral line identification is performed based on a preset database, which contains spectral line mapping relationships for Ni, Cr, Co, Mo, Al, and Ti. Specifically, the characteristic spectral lines include:

[0046] Ni: 352.4nm;

[0047] Cr: 425.4nm;

[0048] Co: 345.3nm;

[0049] Al: 394.4nm;

[0050] Ti: 334.9nm.

[0051] In step S4, when the Al / Ti spectral line intensity ratio is greater than 1.2, it is marked as a γ' strengthening phase enrichment region.

[0052] In step S4, the machine learning algorithm is a convolutional neural network or a support vector machine. The convolutional neural network structure includes: an input layer: a spectral intensity matrix; 5 convolutional layers; and an output layer: a grain size distribution map and a thermographic map of precipitated phase coordinates.

[0053] In step S5, the reconstructed microstructure image is generated using the Richardson-Lucy deconvolution algorithm, with 15-20 iterations. The microstructure image output in step S5 is processed with pseudo-color to enhance the contrast between precipitates and grain boundaries.

[0054] Specifically, the false color processing is as follows:

[0055] Grain boundaries are displayed in blue (RGB:0,0,255);

[0056] The γ' precipitate appears as red (RGB:255,0,0);

[0057] The substrate is displayed in green (RGB:0,255,0).

[0058] Additionally, in step S5, the final image output parameters include:

[0059] Grain size distribution histogram, statistical interval 5-200μm;

[0060] γ' phase area fraction, with an accuracy of ±0.5%;

[0061] Grain boundary coherence index CI = L total / L linear L total The total grain boundary length, L linear The length is linearized.

[0062] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for displaying the microstructure of DD432 alloy, characterized in that the steps include... include: S1. Pre-treat the surface of the DD432 alloy sample to obtain the surface to be tested; S2. Laser-induced plasma spectroscopy is used to scan the sample surface, and a three-dimensional moving platform is used to continuously control the laser beam scanning path. S3. Collect plasma emission spectrum data and identify the characteristic spectral lines of Ni, Cr, Co, Mo, Al, and Ti elements; S4. Use machine learning algorithms to transform spectral data into microstructural features such as grain size, precipitate distribution, and grain boundary morphology. S5. Reconstruct a microscopic tissue image based on the tissue feature information.

2. The method for displaying the microstructure of DD432 alloy as described in claim 1, characterized in that, In step S1, the surface of the DD432 alloy sample is pretreated by mechanical polishing and ultrasonic cleaning to remove the oxide layer and contaminant layer.

3. The method for displaying the microstructure of DD432 alloy as described in claim 2, characterized in that, The preprocessing in step S1 includes: S101. Cut the sample into specimens with dimensions of 10mm × 10mm × 5mm; S102. Mechanically polish the surface using a diamond polishing slurry with a particle size of 1-3μm until the surface finish Ra ≤ 0.1μm; S103. Perform ultrasonic cleaning with anhydrous ethanol for 10 minutes at a frequency of 40kHz.

4. The method for displaying the microstructure of DD432 alloy as described in claim 1, characterized in that, In step S2, the laser parameters include: wavelength 1064nm, pulse energy 10-100mJ, pulse width 5-20ns, and repetition frequency 1-20Hz.

5. The method for displaying the microstructure of DD432 alloy as described in claim 4, characterized in that, In step S2, the laser pulse energy is 50 mJ, the pulse width is 10 ns, and the repetition frequency is 10 Hz.

6. The method for displaying the microstructure of DD432 alloy as described in claim 1, characterized in that, In step S2, the three-dimensional moving platform scans a 5mm×5mm area at a step distance of 50μm, and the scanning trajectory is a serpentine path.

7. The method for displaying the microstructure of DD432 alloy as described in claim 1, characterized in that, In step S3, a spectrometer with a resolution ≤0.1nm is used to collect spectral data.

8. The method for displaying the microstructure of DD432 alloy as described in claim 1, characterized in that, In step S3, the characteristic spectral line identification is performed based on a preset database, which contains the spectral line mapping relationships of Ni, Cr, Co, Mo, Al, and Ti.

9. The method for displaying the microstructure of DD432 alloy as described in claim 1, characterized in that, In step S4, the machine learning algorithm is a convolutional neural network or a support vector machine.

10. The method for displaying the microstructure of DD432 alloy as described in claim 1, characterized in that, In step S5, the reconstructed microstructure image is generated using the Richardson-Lucy deconvolution algorithm, with 15-20 iterations. The microstructure image output in step S5 is processed with pseudo-color to enhance the contrast between precipitates and grain boundaries.

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