Method and system for generating high-throughput data of hydrogen-induced plasticity loss of ultra-high strength steel material

Abstract

The application provides a high-throughput data generation method and system for hydrogen-induced plasticity loss of ultrahigh-strength steel material, and belongs to the field of material genome engineering. The method first determines key trace alloying elements in the ultrahigh-strength steel material and regulates the concentration gradient, prepares the ultrahigh-strength steel material with the corresponding element concentration gradient, and arranges a rhombic indentation array on the surface to realize accurate positioning; the digital composition atlas of element distribution is obtained by using electron probe microanalysis, and a microcolumn array is formed by combining three-stage focused ion beam fine processing; the microcolumn is filled with hydrogen and then placed in liquid nitrogen, and in-situ compression testing is performed, and continuous load-displacement data streams are synchronously collected to obtain the hydrogen-induced plasticity loss rate data of the microcolumn before and after hydrogen filling; finally, various experimental data are integrated and preprocessed to generate hydrogen-induced plasticity loss data of the ultrahigh-strength steel material. The application realizes rapid and systematic production of hydrogen-induced plasticity loss data, and provides data support for related material design and performance evaluation.

Description

Technical fields:

[0001] This invention belongs to the field of materials genome engineering, and specifically relates to a method and system for generating high-throughput data on hydrogen-induced plasticity loss of ultra-high strength steel materials. Background technology:

[0002] With the rapid development of hydrogen energy equipment, rail transportation, and marine engineering, ultra-high-strength steel, as a structural material, possesses excellent strength-toughness matching characteristics and is widely used in high-pressure hydrogen storage containers, ultra-high-strength automotive steel, and deep-sea oil production equipment. However, these materials are exposed to hydrogen environments during service, thus exhibiting hydrogen embrittlement sensitivity, which seriously threatens the safety and reliability of engineering equipment and has become a key bottleneck restricting the long-term service performance of materials. Hydrogen embrittlement sensitivity not only limits the application range of ultra-high-strength steel materials in harsh environments but also poses a significant challenge to the large-scale development of the hydrogen energy economy. To address this issue, traditional research methods often employ discrete experimental modes with single-component samples. This not only results in lengthy experimental cycles and limited data dimensions but also makes it difficult to systematically reveal the complex interaction mechanisms between material composition, microstructure, and hydrogen-induced plasticity loss behavior, severely restricting the design and optimization process of hydrogen embrittlement-resistant materials.

[0003] In existing technologies, with the rise of the data-driven scientific paradigm, machine learning technology offers a new research path for the design of materials to combat hydrogen embrittlement. By constructing efficient and accurate predictive models, machine learning can extract key features from massive amounts of experimental data and theoretical calculation results, establishing quantitative relationships between material composition, process parameters, and performance. Especially with the support of high-throughput computing and experimental techniques, machine learning can overcome the limitations of traditional "trial and error" methods, enabling rapid prediction and optimized design of material properties. However, despite the enormous potential of machine learning in materials design, its practical application still faces significant limitations in areas such as data acquisition.

[0004] First, high-quality materials datasets are severely lacking. Existing experimental data often suffer from insufficient sample size, uneven data distribution, and inconsistent testing conditions, making it difficult to meet the training requirements of machine learning models. Second, acquiring materials performance data is costly, especially experiments involving complex failure behaviors such as hydrogen embrittlement, which require a significant amount of time and resources. Existing data acquisition methods exhibit significant fragmentation, and the discrete experimental data severely restricts the practical application of machine learning in the design of hydrogen embrittlement-resistant materials, highlighting the urgent need to develop efficient and rapid methods for acquiring high-throughput data. Summary of the Invention:

[0005] To address the aforementioned issues, this invention provides a high-throughput data generation method and system for hydrogen-induced plasticity loss in ultra-high-strength steel materials. This method utilizes techniques not limited to magnetron sputtering, 3D printing, and diffusion multi-node technology to achieve gradient-controlled distribution of the composition of ultra-high-strength steel materials. It also combines a micropillar compression array to construct a multi-dimensional dataset of composition-hydrogen-induced plasticity loss, overcoming the limitations of traditional single-point test data acquisition, improving data acquisition efficiency, reducing experimental costs, providing high-quality, systematic training data for machine learning models, and offering fundamental data for the intelligent design of hydrogen-resistant materials.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows:

[0007] In a first aspect, embodiments of the present invention provide a method for generating high-throughput data on hydrogen-induced plasticity loss in ultra-high-strength steel materials, the method comprising the following steps:

[0008] Step S1: Identify the key trace alloying elements in ultra-high strength steel materials; control the concentration gradient of key trace alloying elements, and design at least 1000 sets of element combinations covering the concentration range of 0.1-0.6 wt.% through orthogonal experiments to prepare ultra-high strength steel materials with corresponding element combination concentration gradients.

[0009] Step S2: In the composition gradient region of the ultra-high strength steel material, a diamond-shaped indentation array is arranged on the surface to achieve precise positioning;

[0010] Step S3: For each indentation in the indentation array, a digital compositional map of elemental distribution is established using an electron probe microanalyzer (EPMA).

[0011] Step S4: Based on EPMA data, a three-stage ion beam refinement process is carried out using a dual-beam focused ion beam system FIB-SEM to obtain a micropillar array; the micropillars have an aspect ratio of 2.0±0.1, a height of 10.0±0.2μm, and a taper of less than 3°.

[0012] Step S5: Place the ultra-high strength steel material with micropillar array in a constant temperature electrolytic cell, and fill the micropillars with hydrogen using an electrochemical method. After completion, store the sample in liquid nitrogen.

[0013] Step S6: In-situ compression test of micropillar in liquid nitrogen is performed. An in-situ nano-testing system equipped with a high-resolution field emission scanning electron microscope and a diamond flat indenter is used to perform three-stage compression on the micropillar. Continuous load-displacement data stream is acquired simultaneously, and the evolution of surface slip bands is captured by an in-situ imaging system to obtain comparative data on micropillar compression before and after hydrogen filling.

[0014] Step S7: Calculate the hydrogen-induced plasticity loss rate using the load-displacement data obtained from the micro-column compression experiment; integrate the data into a table according to the order of element type, content, hydrogen charging conditions, and hydrogen-induced plasticity loss rate to achieve composition-performance mapping.

[0015] Step S8: Preprocess the dataset and use the preprocessed dataset as data on hydrogen-induced plasticity loss of ultra-high strength steel.

[0016] In a preferred embodiment of the present invention, step S7 calculates the hydrogen-induced plasticity loss rate using the following formula:

[0017]

[0018] In equation (1), D0 represents the displacement of the micropillar before hydrogen filling, D1 represents the displacement of the micropillar after hydrogen filling, and HEI represents the hydrogen-induced plasticity loss rate.

[0019] In a preferred embodiment of the present invention, the trace alloying elements and their concentration ranges mentioned in step S1 include Cr: 0-0.6±0.2wt.%, V: 0-0.5±0.1wt.%, Mo: 0-0.6±0.05wt.%, Ti: 0-0.3±0.1wt.%, and Nb: 0-0.6±0.05wt.%.

[0020] In a preferred embodiment of the present invention, during the indentation operation in step S2, a diamond-shaped indentation array is positioned in the composition gradient region using a Vickers hardness tester with a load of HV5 and a holding time of 15s, and the indentation spacing is 100±5μm.

[0021] As a preferred embodiment of the present invention, step S4, the three-stage ion beam refinement, includes:

[0022] The first stage uses a 30keV / 5nA beam for rough processing to prepare... Positioning trench and reserving Thick column;

[0023] The second stage involves switching to a 1nA beam for column refinement, obtaining... Column diameter; the third stage uses an ultra-low beam current of 0.1 nA for surface finishing, with an incident angle of 5°, and circular milling to ensure the diameter of the top of the column. And the taper is less than 2°;

[0024] The internal pressure of the equipment is maintained at 100±5Pa by a dynamic gas injection system (GIS), and a low current is used to ensure that the micro-column taper is less than 3°.

[0025] In a preferred embodiment of the present invention, step S5, the hydrogen charging process, is carried out in an electrolytic cell at a constant temperature of 25±0.5℃. The potential is precisely controlled using electrochemical hydrogen charging, and hydrogen charging is performed for 24 hours using a 0.1M NaOH + 0.25g / L thiourea solution, maintaining a constant voltage of 5mA / cm². 2 Constant current.

[0026] In a preferred embodiment of the present invention, in step S6, the vacuum degree of the high-resolution field emission scanning electron microscope is 5 × 10⁻⁶. -4 Pa; the diameter of the diamond flat indenter is 10±0.2μm, and the surface roughness Ra<5nm.

[0027] In a preferred embodiment of the present invention, step S6 involves a three-stage compression of the micropillar, including: preloading to 5mN at a rate of 5nm / s → holding the load for 30s → formal compression at a rate of 20±1nm / s.

[0028] In a preferred embodiment of the present invention, the parameters for acquiring the continuous data stream in step S6 are a load resolution of 0.1 μN and a displacement resolution of 0.2 nm.

[0029] Secondly, embodiments of the present invention also provide a high-throughput data generation system for hydrogen-induced plasticity loss of ultra-high-strength steel materials. The system includes: a trace alloying element determination module, an ultra-high-strength steel material preparation device, an indentation device, a digital composition mapping module, a micro-pillar array refinement device, a hydrogen charging device, a liquid nitrogen storage device, an in-situ compression testing device, a data acquisition module, a data preprocessing module, and a data storage module; wherein...

[0030] The trace alloying element determination module is used to identify the key trace alloying elements in ultra-high strength steel materials.

[0031] The ultra-high strength steel material preparation device is used to prepare ultra-high strength steel materials with corresponding element combination concentration gradients by designing at least 1,000 sets of element combinations covering the concentration range of 0.1-0.6 wt.% through orthogonal experimental design.

[0032] The indentation device is used to arrange a diamond-shaped indentation array on the surface of ultra-high strength steel material in the composition gradient region to achieve precise positioning;

[0033] The digital composition map drawing module is used to create a digital composition map of the elemental distribution for each indentation in the indentation array using an electron probe microanalyzer (EPMA).

[0034] The micropillar array refinement device is used to perform a three-stage ion beam refinement process based on EPMA data using a dual-beam focused ion beam system (FIB-SEM) to obtain a micropillar array; the micropillars have an aspect ratio of 2.0±0.1, a height of 10.0±0.2μm, and a taper of less than 3°.

[0035] The hydrogen charging device is used to place ultra-high strength steel material with micro-column array in a constant temperature electrolytic cell and charge the micro-columns with hydrogen by electrochemical means.

[0036] The liquid nitrogen storage device is used to preserve the hydrogen-filled sample in liquid nitrogen;

[0037] The in-situ compression testing device is used to perform in-situ compression testing on microcolumns in liquid nitrogen. It employs an in-situ nano-testing system equipped with a high-resolution field emission scanning electron microscope and a diamond flat indenter to perform three-stage compression on the microcolumns.

[0038] The data acquisition module is used to synchronously acquire continuous load-displacement data streams and capture the evolution of surface slip zones through an in-situ imaging system, thereby obtaining comparative data on micropillar compression before and after hydrogen filling. The hydrogen-induced plasticity loss rate is calculated based on the load-displacement data obtained from the micropillar compression experiment. The data is then integrated into a table according to the order of element type, content, hydrogen filling conditions, and hydrogen-induced plasticity loss rate, serving as a dataset to achieve composition-performance mapping.

[0039] The data preprocessing module is used to preprocess the dataset and use the preprocessed dataset as data on the hydrogen-induced plasticity loss of ultra-high strength steel.

[0040] The data storage module is used to save data on the hydrogen-induced plasticity loss of ultra-high strength steel materials.

[0041] The solutions of the embodiments of the present invention have the following beneficial effects:

[0042] The data generation method and system for hydrogen-induced plasticity loss of ultra-high strength steel provided in this invention realizes the generation and collection of data on hydrogen-induced plasticity loss of ultra-high strength steel based on high-throughput experiments. This is beneficial for guiding the intelligent design and rational optimization of high-strength hydrogen-resistant steel, and is particularly suitable for the study of the mechanism of hydrogen-induced plasticity loss of ultra-high strength steel and the prediction of material properties.

[0043] Of course, implementing any product or method of the present invention does not necessarily require achieving all of the advantages described above at the same time. Attached image description:

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a flowchart of the data generation method for hydrogen-induced plasticity loss of ultra-high strength steel materials according to an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of the combination of multiple micro-alloying elements in an embodiment of the present invention;

[0047] Figure 3 This is a distribution diagram of the micropillar array processed within the elemental gradient range in this embodiment of the invention;

[0048] Figure 4 This is a graph showing the performance of the microcolumns before and after hydrogen filling, obtained by microcolumn compression of the ultra-high strength steel material with the first component combination in this embodiment of the invention.

[0049] Figure 5 This is a graph showing the performance of the ultra-high strength steel material with the second component combination in this embodiment of the invention before and after hydrogen filling, obtained by micro-column compression.

[0050] Figure 6 This is a graph showing the performance of the ultra-high strength steel material with the third component combination in this embodiment of the invention before and after hydrogen filling, obtained by microcolumn compression. Detailed implementation method:

[0051] 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 them. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. It should be noted that, without conflict, the embodiments and features in the embodiments of the present invention can also be combined with each other.

[0052] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In the description of this invention, the terms "first," "second," "third," "fourth," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0053] To address the issue of hydrogen-induced plasticity loss in ultra-high-strength steel, this invention proposes a high-throughput data generation method for this problem from the perspective of materials genome engineering. This method employs magnetron sputtering, 3D printing, and diffusion multi-junction techniques to control the concentration gradient of microalloying elements, achieving a gradient distribution of microalloying elements affecting hydrogen-induced plasticity loss performance in ultra-high-strength steel. A multimodal localization and layered processing strategy is used to construct a micropillar array system with precise composition-performance correlation, enabling localized processing and composition matching of the micropillar array. Subsequently, a comparative micropillar compression experiment before and after hydrogen filling is conducted in an in-situ testing system. Data is cleaned according to the order of element type, content, hydrogen filling conditions, and hydrogen-induced plasticity loss rate. For element content below the detection value and missing element content values, the minimum value in the dataset is used to replace them. Outliers are replaced with values ​​obtained from curve fitting. This invention enables the evaluation of hydrogen-induced plasticity loss performance of ultra-high strength steel materials based on high-throughput experiments, which is beneficial for guiding the intelligent design and rational optimization of high-strength hydrogen-resistant steel. It is particularly suitable for the study of the hydrogen-induced plasticity loss mechanism and the prediction of material properties of ultra-high strength steel materials.

[0054] like Figure 1 As shown, the high-throughput data generation method for hydrogen-induced plasticity loss of ultra-high-strength steel includes the following steps:

[0055] Step S1: Identify the key trace alloying elements in ultra-high strength steel materials; control the concentration gradient of key trace alloying elements, and prepare ultra-high strength steel materials with corresponding element combination concentration gradients by designing at least 1000 sets of element combinations covering the concentration range of 0.1-0.6 wt.% through orthogonal experiments.

[0056] In this step, the trace alloying elements and their concentration ranges include Cr (0-0.6±0.2wt.%), V (0-0.5±0.1wt.%), Mo (0-0.6±0.05wt.%), Ti (0-0.3±0.1wt.%), and Nb (0-0.6±0.05wt.%).

[0057] High-throughput experiments were employed to design concentration gradients in order to prepare ultra-high-strength steel materials with corresponding elemental combination concentration gradients. The high-throughput orthogonal experiments used included, but were not limited to, magnetron sputtering, 3D printing, and diffusion multi-element junctions.

[0058] like Figure 2 As shown, taking the combination of Cr and Mo elements as an example, the prepared ultra-high strength steel material contains two trace alloying elements, chromium and molybdenum; the concentration gradients of the two alloying elements were designed and controlled to be Cr (0-0.6%) and Mo (0-0.6%), respectively. Figure 2Within the 800-micrometer range shown, multiple micropillar fabrication sites with different Cr and Mo elements can be selected simultaneously (in... Figure 2 By drawing a perpendicular line from any point on the X-axis, a set of elemental compositions coupled with Cr and Mo can be obtained. This composition can be realized in actual gradient composition steel samples.

[0059] Step S2: In the composition gradient region of the ultra-high strength steel material, a diamond-shaped indentation array is arranged on the surface to achieve precise positioning.

[0060] In this step, during the indentation operation, a diamond-shaped indentation array (spacing 100±5μm) is positioned in the composition gradient region using a Vickers hardness tester (HV5 load, hold for 15s).

[0061] Step S3: For each indentation in the indentation array, a digital compositional map of elemental distribution is established using an electron probe microanalyzer (EPMA, beam spot 1μm, step size 10μm).

[0062] Step S4: Based on EPMA data, a three-stage ion beam refinement process is carried out using a dual-beam focused ion beam system (FIB-SEM) to obtain a micropillar array; the aspect ratio of the micropillar is 2.0±0.1 (height 10.0±0.2μm), and the micropillar taper is less than 3°.

[0063] In this step, the three-stage ion beam finishing includes: the first stage using a 30keV / 5nA beam current for rough processing to prepare... Positioning trench and reserving The first stage involves coarse column preparation; the second stage switches to a 1nA beam for fine column finishing, yielding... Column diameter; the third stage uses an ultra-low beam current of 0.1nA for surface finishing (incident angle 5°, ring milling) to ensure the diameter of the top of the column. Furthermore, the taper is <2°. The column is specially designed with an aspect ratio of 2.0±0.1 (height 10.0±0.2μm), and the internal pressure of the equipment is maintained at 100±5Pa through a dynamic gas injection system (GIS). Low current (100pa) is used to ensure that the taper of the microcolumn is less than 3°.

[0064] Through the above-mentioned multimodal localization and hierarchical processing, a micropillar array system with precise component-performance correlation is constructed, and the localization and processing of the micropillar array is completed.

[0065] Subsequently, EPMA technology was used to perform point scanning quantitative analysis on the prepared micropillars to determine the detailed composition of the micropillars, thereby achieving point-to-point matching between the micropillar positions and the corresponding microalloy components.

[0066] Step S5: Place the ultra-high strength steel material with micropillar array in a constant temperature electrolytic cell (25±0.5℃), and electrochemically charge the micropillars with hydrogen. After completion, store the sample in liquid nitrogen.

[0067] In this step, the hydrogen charging process is carried out in a constant-temperature electrolytic cell (25±0.5℃). The potential is precisely controlled by electrochemical hydrogen charging. Hydrogen charging is carried out for 24 hours using a 0.1M NaOH + 0.25g / L thiourea solution, maintaining a voltage of 5mA / cm² during the process. 2 Constant current. Immediately after hydrogen priming, the sample is placed in liquid nitrogen for storage.

[0068] Step S6: In-situ compression test of the microcolumn in liquid nitrogen is performed using a high-resolution field emission scanning electron microscope (vacuum degree 5×10⁻⁶). -4 An in-situ nanocomputing system using a diamond flat indenter (diameter 10±0.2μm, surface roughness Ra<5nm) was employed to perform three-stage compression on the micropillar: preloading to 5mN (rate 5nm / s) → holding for 30s → formal compression (rate 20±1nm / s); continuous load-displacement data streams with a load resolution of 0.1μN and a displacement resolution of 0.2nm were acquired simultaneously, and the evolution of surface slip bands was captured using an in-situ imaging system, thus obtaining comparative data on micropillar compression before and after hydrogen filling.

[0069] In this step, preferably, when collecting data after compression, each combination is tested three times, and two sets of valid values ​​are retained in the final data. The in-situ nano-testing system is a closed-loop system for the entire process of hydrogen charging, storage, and testing.

[0070] like Figures 4 to 6 As shown, a combined technique of dynamic hydrogen regulation and in-situ characterization was employed. In-situ compression tests of the micropillars were conducted to obtain the performance of the micropillars before and after hydrogen filling. These samples represent three micropillars with different couplings of Cr, V, and Mo elemental compositions in the microalloy. Compression experiments were performed on micropillars with different compositions before and after hydrogen filling, allowing for the rapid acquisition of corresponding load-displacement curves. Figure 4 CrVMo1-Air is a load-displacement curve obtained by compressing a component (containing Cr: 0.3%, V: 0.2%, and Mo: 0.2%) in air, while CrVMo1-H is a load-displacement curve obtained by compressing a micropillar with the same composition after hydrogen filling. Figure 5 (Contents were Cr: 0.2%, V: 0.1%, Mo: 0.2%) and Figure 6(Contents: Cr: 0.2%, V: 0.15%, Mo: 0.1%) The following are load-displacement curves obtained after compression of two other groups of micropillars with different compositions. CrVMo2-Air and CrVMo3-Air represent load-displacement curves obtained by compression in air, while CrVMo2-H and CrVMo3-H represent load-displacement curves obtained by compression after hydrogen purging. The load-displacement curves allow for rapid acquisition of the sample's strength and plasticity characteristics, and the hydrogen-induced plasticity loss rate can be calculated using Formula 1 below. The comparison shows that the CrVMo3 micropillar exhibits better compressive plasticity and resistance to hydrogen embrittlement than the CrVMo1 and CrVMo2 micropillars because it has a longer displacement during compression, and the displacement change after hydrogen purging is minimal, indicating minimal plasticity loss after hydrogen purging.

[0071] Step S7: The load-displacement data obtained from the micro-column compression experiment are processed using the formula:

[0072]

[0073] In equation (1), D0 represents the displacement of the micro-pillar before hydrogen filling, D1 represents the displacement of the micro-pillar after hydrogen filling, and HEI represents the hydrogen-induced plasticity loss rate. The elements are integrated into a table according to the order of element type, content, hydrogen filling conditions, and hydrogen-induced plasticity loss rate, which serves as a dataset to realize the composition-performance mapping.

[0074] Step S8: Preprocess the dataset and use the preprocessed dataset as data on hydrogen-induced plasticity loss of ultra-high strength steel.

[0075] In this step, the preprocessing includes cleaning and optimizing the data. Specifically, for element content below the detected value and for values ​​with missing element content, the minimum value in the dataset is used to replace them. For outliers, values ​​obtained through curve fitting are used to replace the outlier values.

[0076] Based on the same idea, this invention also provides a high-throughput data generation system for hydrogen-induced plasticity loss in ultra-high-strength steel materials. The system includes: a trace alloying element determination module, an ultra-high-strength steel material preparation device, an indentation device, a digital composition mapping module, a micro-pillar array refinement device, a hydrogen filling device, a liquid nitrogen storage device, an in-situ compression testing device, a data acquisition module, a data preprocessing module, and a data storage module.

[0077] The trace alloying element determination module is used to identify the key trace alloying elements in ultra-high strength steel materials.

[0078] The ultra-high strength steel material preparation device is used to prepare ultra-high strength steel materials with corresponding element combination concentration gradients by designing at least 1,000 sets of element combinations covering the concentration range of 0.1-0.6 wt.% through orthogonal experimental design.

[0079] The indentation device is used to arrange a diamond-shaped indentation array on the surface of ultra-high strength steel material in the composition gradient region to achieve precise positioning;

[0080] The digital composition map drawing module is used to create a digital composition map of the elemental distribution for each indentation in the indentation array using an electron probe microanalyzer (EPMA).

[0081] The micropillar array refinement device is used to perform a three-stage ion beam refinement process based on EPMA data using a dual-beam focused ion beam system (FIB-SEM) to obtain a micropillar array; the micropillars have an aspect ratio of 2.0±0.1, a height of 10.0±0.2μm, and a taper of less than 3°.

[0082] The hydrogen charging device is used to place ultra-high strength steel material with micro-column array in a constant temperature electrolytic cell and charge the micro-columns with hydrogen by electrochemical means.

[0083] The liquid nitrogen storage device is used to preserve the hydrogen-filled sample in liquid nitrogen;

[0084] The in-situ compression testing device is used to perform in-situ compression testing on microcolumns in liquid nitrogen. It employs an in-situ nano-testing system equipped with a high-resolution field emission scanning electron microscope and a diamond flat indenter to perform three-stage compression on the microcolumns.

[0085] The data acquisition module is used to synchronously acquire continuous load-displacement data streams and capture the evolution of surface slip zones through an in-situ imaging system, thereby obtaining comparative data on micropillar compression before and after hydrogen filling. The hydrogen-induced plasticity loss rate is calculated based on the load-displacement data obtained from the micropillar compression experiment. The data is then integrated into a table according to the order of element type, content, hydrogen filling conditions, and hydrogen-induced plasticity loss rate, serving as a dataset to achieve composition-performance mapping.

[0086] The data preprocessing module is used to preprocess the dataset and use the preprocessed dataset as data on the hydrogen-induced plasticity loss of ultra-high strength steel.

[0087] The data storage module is used to save data on the hydrogen-induced plasticity loss of ultra-high strength steel materials.

[0088] In this embodiment, each module is implemented using a processor, with additional memory added as needed for storage. The processor can be, but is not limited to, a microprocessor (MPU), a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components, etc. The memory can include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory can also be at least one storage device located remotely from the aforementioned processor.

[0089] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0090] It should also be noted that the data generation system for hydrogen-induced plasticity loss of ultra-high strength steel materials described in this embodiment corresponds to the data generation method for hydrogen-induced plasticity loss of ultra-high strength steel materials. The description and limitations of the method also apply to the system, and will not be repeated here.

[0091] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed, and is not intended to limit the scope of the claimed invention, but merely to illustrate preferred embodiments of the invention. Those skilled in the art should understand that the scope of the invention is not limited to the specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

Claims

1. A method for generating high-throughput data on hydrogen-induced plasticity loss in ultra-high-strength steel, characterized in that, The method includes the following steps: Step S1: Identify the key trace alloying elements in ultra-high strength steel materials; control the concentration gradient of key trace alloying elements, and design at least 1000 sets of element combinations covering the concentration range of 0.1-0.6 wt.% through orthogonal experiments to prepare ultra-high strength steel materials with corresponding element combination concentration gradients. Step S2: In the composition gradient region of the ultra-high strength steel material, a diamond-shaped indentation array is arranged on the surface to achieve precise positioning; Step S3: For each indentation in the indentation array, a digital compositional map of elemental distribution is established using an electron probe microanalyzer (EPMA). Step S4: Based on EPMA data, a three-stage ion beam refinement process is carried out using a dual-beam focused ion beam system FIB-SEM to obtain a micropillar array; the micropillars have an aspect ratio of 2.0±0.1, a height of 10.0±0.2μm, and a taper of less than 3°. Step S5: Place the ultra-high strength steel material with micropillar array in a constant temperature electrolytic cell, and fill the micropillars with hydrogen using an electrochemical method. After completion, store the sample in liquid nitrogen. Step S6: In-situ compression test of micropillar in liquid nitrogen is performed. An in-situ nano-testing system equipped with a high-resolution field emission scanning electron microscope and a diamond flat indenter is used to perform three-stage compression on the micropillar. Continuous load-displacement data stream is acquired simultaneously, and the evolution of surface slip bands is captured by an in-situ imaging system to obtain comparative data on micropillar compression before and after hydrogen filling. Step S7: Calculate the hydrogen-induced plasticity loss rate using the load-displacement data obtained from the micro-column compression experiment; integrate the data into a table according to the order of element type, content, hydrogen charging conditions, and hydrogen-induced plasticity loss rate to achieve composition-performance mapping. Step S8: Preprocess the dataset and use the preprocessed dataset as data on hydrogen-induced plasticity loss of ultra-high strength steel.

2. The method for generating high-throughput data on hydrogen-induced plasticity loss of ultra-high-strength steel materials according to claim 1, characterized in that, Step S7 calculates the hydrogen-induced plasticity loss rate using the following formula: In equation (1), D0 represents the displacement of the micropillar before hydrogen filling, D1 represents the displacement of the micropillar after hydrogen filling, and HEI represents the hydrogen-induced plasticity loss rate.

3. The method for generating high-throughput data on hydrogen-induced plasticity loss of ultra-high-strength steel materials according to claim 1, characterized in that, The trace alloying elements and concentration ranges mentioned in step S1 include Cr: 0-0.6±0.2wt.%, V: 0-0.5±0.1wt.%, Mo: 0-0.6±0.05wt.%, Ti: 0-0.3±0.1wt.%, and Nb: 0-0.6±0.05wt.%.

4. The method for generating high-throughput data on hydrogen-induced plasticity loss of ultra-high-strength steel materials according to claim 1, characterized in that, In step S2, during the indentation operation, a diamond-shaped indentation array is positioned in the composition gradient region using a Vickers hardness tester with a load of HV5 and a holding time of 15s. The indentation spacing is 100±5μm.

5. The method for generating high-throughput data on hydrogen-induced plasticity loss of ultra-high-strength steel materials according to claim 1, characterized in that, Step S4, the three-stage ion beam refinement, includes: The first stage uses a 30keV / 5nA beam for rough processing to prepare... Positioning trench and reserving Thick column; The second stage involves switching to a 1nA beam for column refinement, obtaining... Column diameter; the third stage uses an ultra-low beam current of 0.1 nA for surface finishing, with an incident angle of 5°, and circular milling to ensure the diameter of the top of the column. And the taper is less than 2°; The internal pressure of the equipment is maintained at 100±5Pa by a dynamic gas injection system (GIS), and a low current is used to ensure that the micro-column taper is less than 3°.

6. The method for generating high-throughput data on hydrogen-induced plasticity loss of ultra-high-strength steel materials according to claim 1, characterized in that, Step S5, the hydrogen charging process, was carried out in an electrolytic cell at a constant temperature of 25±0.5℃. The potential was precisely controlled using electrochemical hydrogen charging, and hydrogen charging was carried out for 24 hours using a 0.1M NaOH + 0.25g / L thiourea solution, maintaining a constant voltage of 5mA / cm². 2 Constant current.

7. The method for generating high-throughput data on hydrogen-induced plasticity loss of ultra-high-strength steel materials according to claim 1, characterized in that, In step S6, the vacuum level of the high-resolution field emission scanning electron microscope is 5 × 10⁻⁶. -4 Pa; the diameter of the diamond flat indenter is 10±0.2μm, and the surface roughness Ra<5nm.

8. The method for generating high-throughput data on hydrogen-induced plasticity loss of ultra-high-strength steel materials according to claim 7, characterized in that, In step S6, the micropillars are compressed in three stages, including: preloading to 5mN at a rate of 5nm / s → holding the load for 30s → formal compression at a rate of 20±1nm / s.

9. The method for generating high-throughput data on hydrogen-induced plasticity loss of ultra-high-strength steel materials according to claim 8, characterized in that, Step S6 acquires continuous data streams with a load resolution of 0.1 μN and a displacement resolution of 0.2 nm.

10. A high-throughput data generation system for hydrogen-induced plasticity loss in ultra-high-strength steel, characterized in that, The system includes: a trace alloying element determination module, an ultra-high strength steel material preparation device, an indentation device, a digital composition map drawing module, a micro-column array refinement device, a hydrogen filling device, a liquid nitrogen storage device, an in-situ compression testing device, a data acquisition module, a data preprocessing module, and a data storage module; wherein... The trace alloying element determination module is used to identify the key trace alloying elements in ultra-high strength steel materials. The ultra-high strength steel material preparation device is used to prepare ultra-high strength steel materials with corresponding element combination concentration gradients by designing at least 1,000 sets of element combinations covering the concentration range of 0.1-0.6 wt.% through orthogonal experimental design. The indentation device is used to arrange a diamond-shaped indentation array on the surface of ultra-high strength steel material in the composition gradient region to achieve precise positioning; The digital composition map drawing module is used to create a digital composition map of the elemental distribution for each indentation in the indentation array using an electron probe microanalyzer (EPMA). The micropillar array refinement device is used to perform a three-stage ion beam refinement process based on EPMA data using a dual-beam focused ion beam system (FIB-SEM) to obtain a micropillar array; the micropillars have an aspect ratio of 2.0±0.1, a height of 10.0±0.2μm, and a taper of less than 3°. The hydrogen charging device is used to place ultra-high strength steel material with micro-column array in a constant temperature electrolytic cell and charge the micro-columns with hydrogen by electrochemical means. The liquid nitrogen storage device is used to preserve the hydrogen-filled sample in liquid nitrogen; The in-situ compression testing device is used to perform in-situ compression testing on microcolumns in liquid nitrogen. It employs an in-situ nano-testing system equipped with a high-resolution field emission scanning electron microscope and a diamond flat indenter to perform three-stage compression on the microcolumns. The data acquisition module is used to synchronously acquire continuous load-displacement data streams and capture the evolution of surface slip zones through an in-situ imaging system, thereby obtaining comparative data on micropillar compression before and after hydrogen filling. The hydrogen-induced plasticity loss rate is calculated based on the load-displacement data obtained from the micropillar compression experiment. The data is then integrated into a table according to the order of element type, content, hydrogen filling conditions, and hydrogen-induced plasticity loss rate, serving as a dataset to achieve composition-performance mapping. The data preprocessing module is used to preprocess the dataset and use the preprocessed dataset as data on the hydrogen-induced plasticity loss of ultra-high strength steel. The data storage module is used to save data on the hydrogen-induced plasticity loss of ultra-high strength steel materials.

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