High-throughput data generation method and system for hydrogen-induced plasticity loss of ultrahigh-strength steel material

Abstract

The invention provides a high-throughput data generation method and system for hydrogen-induced plasticity loss of an ultrahigh-strength steel material, and belongs to the field of material genome engineering. The method comprises the steps that firstly, key trace alloy elements in the ultrahigh-strength steel material are determined, the concentration gradient is regulated and controlled, the ultrahigh-strength steel material with the corresponding element concentration gradient is prepared, and a rhombic indentation array is arranged on the surface to achieve accurate positioning; the method comprises the following steps: acquiring a digitized component spectrum of element distribution by utilizing microscopic analysis of an electronic probe, and forming a micro-column array in combination with fine processing of a three-stage focused ion beam; charging hydrogen into the micro-column, then putting the micro-column into liquid nitrogen, carrying out in-situ compression test, and synchronously collecting continuous load-displacement data flow to obtain hydrogen-induced plastic loss rate data of the micro-column before and after hydrogen charging; and finally, integrating and preprocessing various experimental data to generate hydrogen-induced plasticity loss data of the ultrahigh-strength steel material. According to the invention, rapid and systematic production of hydrogen-induced plasticity loss data is realized, and data support is provided for design and performance evaluation of related materials.

Description

Technical field:

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

[0002] With the rapid development of hydrogen energy equipment, rail transportation, marine engineering and other fields, ultra-high strength steel materials, as a type of structural material, have excellent strength-toughness matching characteristics and are widely used in high-pressure hydrogen storage containers, ultra-high strength automotive steel, deep-sea oil production equipment and other fields. However, these materials will be exposed to the hydrogen environment during service, thereby exposing 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 scope of ultra-high strength steel materials in harsh environments, but also poses a major challenge to the large-scale development of the hydrogen energy economy. To address this problem, traditional research methods mostly use discrete experimental models of single-component specimens. Not only are the experimental cycles lengthy and the data dimensions limited, but it is also difficult to systematically reveal the complex interaction mechanism between material composition, microstructure and hydrogen-induced plastic loss behavior, which seriously restricts the design and optimization process of hydrogen embrittlement-resistant materials.

[0003] With the rise of the data-driven scientific paradigm, machine learning offers a new research path for the design of hydrogen-embrittlement-resistant materials. By building efficient and accurate predictive models, machine learning can extract key features from massive amounts of experimental data and theoretical calculations, establishing quantitative relationships between material composition, process parameters, and performance. In particular, supported by 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 its significant potential in materials design, its practical application still faces significant limitations, particularly in data collection.

[0004] First, there is a severe shortage of high-quality material datasets. Existing experimental data often have problems such as insufficient sample size, uneven data distribution, and inconsistent testing conditions, which make it difficult to meet the training needs of machine learning models. Second, the cost of obtaining material performance data is high, especially for experiments involving complex failure behaviors such as hydrogen embrittlement, which require a lot of time and resources. The existing data acquisition mode has significant fragmentation characteristics. The discrete experimental data seriously restricts the actual application effect of machine learning in the design of hydrogen embrittlement-resistant materials, and also highlights the urgent need to develop efficient and rapid methods to obtain high-throughput data. Summary of the invention:

[0005] In order to solve the above problems, the present invention provides a high-throughput data generation method and system for hydrogen-induced plastic loss of ultra-high-strength steel materials. The composition gradient control distribution of ultra-high-strength steel materials is achieved through technologies such as magnetron sputtering, 3D printing, and diffusion multi-nodes. A multi-dimensional data set of composition-hydrogen-induced plastic loss is constructed in combination with a micro-pillar compression array. This breaks through the collection limitations of single-point test data of traditional methods, improves data collection efficiency, reduces experimental costs, provides high-quality and systematic training data for machine learning models, and provides basic data for the intelligent design of anti-hydrogen embrittlement materials.

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

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

[0008] Step S1, identifying key trace alloying elements in the ultra-high-strength steel material; regulating the concentration gradient of the key trace alloying elements, and preparing ultra-high-strength steel materials with corresponding element combination concentration gradients by orthogonal experimental design covering at least 1,000 combinations of single, binary, ternary, and multiple elements in the concentration range of 0.1-0.6 wt.%;

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

[0010] Step S3, for each indentation in the indentation array, using an electron probe microanalyzer EPMA to create a digital composition map of element distribution;

[0011] Step S4, based on the EPMA data, a three-stage ion beam refinement process is performed 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±0.2 μm, and a micropillar taper of less than 3°;

[0012] Step S5: placing the ultra-high-strength steel material with the micropillar array in a constant-temperature electrolytic cell, electrochemically charging the micropillars with hydrogen, and then storing the sample in liquid nitrogen;

[0013] Step S6: Performing an in-situ compression test on the micropillars in liquid nitrogen using an in-situ nanometer testing system equipped with a high-resolution field emission scanning electron microscope and a diamond flat indenter. Three stages of compression are applied to the micropillars. A continuous load-displacement data stream is simultaneously acquired, and the evolution of the surface slip band is captured using an in-situ imaging system. This yields comparative compression data for the micropillars before and after hydrogen charging.

[0014] Step S7, calculating the hydrogen-induced plastic loss rate using the load-displacement data obtained from the micropillar compression test; integrating the data into a table in the order of element type, content, hydrogen charging conditions, and hydrogen-induced plastic loss rate as a data set to achieve composition-performance mapping;

[0015] Step S8: preprocessing the data set, and using the preprocessed data set as data on hydrogen-induced plasticity loss of ultra-high strength steel material.

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

[0017]

[0018] In formula (1), D0 represents the displacement of the microcolumn before hydrogen charging, D1 represents the displacement of the microcolumn after hydrogen charging, and HEI represents the hydrogen-induced plastic loss rate.

[0019] As a preferred embodiment of the present invention, the trace alloying elements and their concentration ranges 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] As a preferred embodiment of the present invention, during the indentation operation in step S2, a Vickers hardness tester is used to perform diamond indentation array positioning in the composition gradient region with a load of HV5 and a load holding parameter of 15s, and the indentation spacing is 100±5μm.

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

[0022] The first stage uses 30keV / 5nA beam current for rough processing and preparation Position the groove and reserve thick column;

[0023] In the second stage, the beam current was switched to 1nA for column refinement, and the Pillar diameter; In the third stage, 0.1nA ultra-low beam current is used for surface final treatment, with an incident angle of 5° and annular milling to ensure the top diameter of the pillar and the taper is <2°;

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

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

[0026] As 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 -4 Pa; the diameter of the diamond flat indenter is 10±0.2μm, and the surface roughness is Ra<5nm.

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

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

[0029] In a second aspect, an embodiment of the present invention further provides a high-throughput data generation system for hydrogen-induced plasticity loss of ultra-high-strength steel materials, the system comprising: 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-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 determine 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 orthogonal experimental design covering at least 1,000 sets of single, binary, ternary and multi-element combinations in the concentration range of 0.1-0.6wt.%.

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

[0033] The digital composition map drawing module is used to establish a digital composition map of element distribution for each indentation in the indentation array using an electron probe microanalyzer EPMA;

[0034] The micropillar array finishing device is used to perform a three-stage ion beam finishing 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±0.2 μm, and a micropillar taper of less than 3°;

[0035] The hydrogen charging device is used to place the ultra-high strength steel material with the micro-pillar array in a constant temperature electrolytic cell and charge the micro-pillars with hydrogen using an electrochemical method;

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

[0037] The in-situ compression test device is used to perform in-situ compression testing on micropillars in liquid nitrogen, using 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 micropillars;

[0038] The data acquisition module is used to synchronously collect continuous load-displacement data streams and capture the evolution of surface slip bands through an in-situ imaging system, thereby obtaining comparative data on micropillar compression before and after hydrogen charging. The load-displacement data obtained from the micropillar compression experiment are used to calculate the hydrogen-induced plastic loss rate. The data are integrated into a table in the order of element type, content, hydrogen charging conditions, and hydrogen-induced plastic loss rate as a data set to achieve composition-performance mapping.

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

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

[0041] The solution of the embodiment of the present invention has the following beneficial effects:

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

[0043] Of course, it is not necessary to achieve all of the advantages described above simultaneously in order to implement any product or method of the present invention. Description of the drawings:

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

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

[0047] Figure 3 is a distribution diagram of a micropillar array processed within an element gradient range in an embodiment of the present invention;

[0048] Figure 4 Graphs of the performance of micro-pillars before and after hydrogen charging are obtained by micro-pillar compression of the ultra-high strength steel material of the first composition combination in an embodiment of the present invention;

[0049] Figure 5 This is a micro-column performance diagram before and after hydrogen charging obtained by micro-column compression of the ultra-high strength steel material of the second composition combination in the embodiment of the present invention;

[0050] Figure 6 This is a micro-column performance diagram before and after hydrogen charging obtained by micro-column compression of the ultra-high strength steel material with the third component combination in the embodiment of the present invention. Specific implementation method:

[0051] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations. It should be noted that the embodiments of the present invention and the features in the embodiments can also be combined with each other in the absence of conflict.

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

[0053] Based on the problem of hydrogen-induced plasticity loss in ultra-high-strength steel materials, the present invention proposes a high-throughput data generation method for hydrogen-induced plasticity loss in ultra-high-strength steel materials from the perspective of material genome engineering. The concentration gradient of microalloying elements is regulated by magnetron sputtering technology, 3D printing technology, diffusion multi-node technology and other methods, so that the microalloying elements that affect the hydrogen-induced plasticity loss performance in ultra-high-strength steel materials are distributed in a gradient. A multimodal positioning and layered processing strategy is adopted to construct a micropillar array system with precise composition-performance correlation, and the positioning processing and composition matching of the micropillar array are achieved. Subsequently, a micropillar compression comparison test before and after hydrogen charging is carried out in an in-situ test system, and the data are cleaned in the order of element type, content, hydrogen charging conditions, and hydrogen-induced plasticity loss rate. The minimum value in the data set is used to replace the element content lower than the detection value and the missing value of some element content. The abnormal value is replaced by the value obtained by curve fitting. The present invention can realize the performance evaluation of hydrogen-induced plasticity loss of ultra-high-strength steel materials based on high-throughput experiments, which is conducive to guiding the intelligent design and rational optimization of high-strength hydrogen-resistant steels, and is particularly suitable for the study of the hydrogen-induced plasticity loss mechanism of ultra-high-strength steel materials and the prediction of material properties.

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

[0055] Step S1, identifying the key trace alloying elements in the ultra-high-strength steel material; regulating the concentration gradient of the key trace alloying elements, and preparing an ultra-high-strength steel material with a corresponding element combination concentration gradient by orthogonal experimental design covering at least 1,000 sets of element combinations of one, two, three, and multiple elements in the concentration range of 0.1-0.6wt.%.

[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 are used to design concentration gradients to produce ultra-high-strength steel materials with corresponding element combination concentration gradients. The high-throughput orthogonal experiments used include but are not limited to magnetron sputtering, 3D printing, and diffusion multi-element junctions.

[0058] like Figure 2 As shown in the figure, taking the combination of Cr and Mo 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 are designed and regulated to be Cr (0-0.6%) and Mo (0-0.6%) respectively. Figure 2Within the 800 μm range shown, multiple micropillar preparation positions with different Cr and Mo elements can be selected simultaneously (in Figure 2 A set of coupled elemental components of Cr and Mo can be obtained by drawing a vertical line at any point on the X-axis), which can be realized in actual gradient composition steel samples.

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

[0060] In this step, during the indentation operation, a Vickers hardness tester (HV5 load, load holding 15s) was used to perform diamond indentation array positioning (spacing 100±5 μm) in the composition gradient region.

[0061] Step S3 : For each indentation in the indentation array, an electron probe microanalyzer (EPMA, beam spot size 1 μm, step size 10 μm) is used to establish a digital composition map of element distribution.

[0062] In step S4, based on the EPMA data, a three-stage ion beam refinement process is performed using a dual-beam focused ion beam scanning electron microscope (FIB-SEM) to obtain a micropillar array; the micropillars have an aspect ratio of 2.0±0.1 (height 10.0±0.2 μm) and a micropillar taper of less than 3°.

[0063] In this step, the three-stage ion beam refinement includes: the first stage uses 30keV / 5nA beam current for rough processing, and prepares Position the groove and reserve In the second stage, the beam current was switched to 1nA for column refinement. Pillar diameter; In the third stage, 0.1nA ultra-low beam current is used for surface final treatment (incident angle 5°, annular milling) to ensure that the top diameter of the pillar The taper is <2°. The specially designed pillars have an aspect ratio of 2.0±0.1 (height 10.0±0.2μm). A dynamic gas injection system (GIS) maintains an internal pressure of 100±5Pa, using a low current (100Pa) to ensure the micropillar taper is less than 3°.

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

[0065] Subsequently, the prepared micropillars were subjected to point scanning quantitative analysis using EPMA technology to determine the detailed composition of the micropillars, thereby achieving point-to-point matching of the micropillar positions with the corresponding microalloy components.

[0066] In step S5, the ultra-high-strength steel material with the micropillar array is placed in a constant-temperature electrolytic cell (25±0.5° C.), and the micropillars are charged with hydrogen by electrochemical means. After completion, the sample is stored in liquid nitrogen.

[0067] In this step, the hydrogen charging process was carried out in a constant temperature electrolytic cell (25 ± 0.5 ° C), and the potential was precisely controlled by electrochemical hydrogen charging. The hydrogen was charged for 24 h using 0.1 M NaOH + 0.25 g / L thiourea solution, and the current was maintained at 5 mA / cm 2 After the hydrogen filling is completed, the sample is immediately placed in liquid nitrogen for storage.

[0068] Step S6: Perform in-situ compression testing on the microcolumns in liquid nitrogen using a high-resolution field emission scanning electron microscope (vacuum degree 5×10 -4 The in-situ nano-testing system, equipped with a diamond flat indenter (diameter 10±0.2μm, surface roughness Ra<5nm), subjected the micropillars to three-stage compression: preloading to 5mN (rate 5nm / s) → holding the load for 30s → formal compression (rate 20±1nm / s). A continuous load-displacement data stream with a load resolution of 0.1μN and a displacement resolution of 0.2nm was simultaneously collected. The evolution of the surface slip band was captured using an in-situ imaging system, thereby obtaining comparative data on the micropillar compression before and after hydrogen charging.

[0069] In this step, preferably, when collecting data after compression is completed, each combination is tested three times, and the final data retains two sets of valid values. The in-situ nanometer testing system is a closed-loop system with a full process of hydrogen charging, storage, and testing.

[0070] like Figures 4 to 6 As shown in the figure, the dynamic hydrogen regulation and in-situ characterization technology is combined to obtain the microcolumn performance before and after hydrogen charging through in-situ testing of microcolumn compression. Three microcolumn samples with different microalloy Cr, V, and Mo element components are coupled. By performing compression tests on microcolumns with different components before and after hydrogen charging, the corresponding load-displacement curves can be quickly obtained. Figure 4 CrVMo1-Air is a load-displacement curve obtained by compressing a component (content: Cr: 0.3%, V: 0.2%, Mo: 0.2%) in air, while CrVMo1-H is a load-displacement curve obtained by compressing a microcolumn with the same composition after being filled with hydrogen. Figure 5 (contents are Cr: 0.2%, V: 0.1%, Mo: 0.2%) and Figure 6The load-displacement curves for two other groups of micropillars with different compositions (containing 0.2% Cr, 0.15% V, and 0.1% Mo, respectively) are obtained after micropillar compression. CrVMo2-Air and CrVMo3-Air represent the load-displacement curves obtained by compression in air, while CrVMo2-H and CrVMo3-H represent the load-displacement curves obtained by compression after hydrogen charging. The load-displacement curves can be used to quickly obtain the strength and plasticity characteristics of the sample, and the hydrogen-induced plasticity loss rate can be calculated using the following formula 1. By comparison, it can be seen that the micropillars with CrVMo3 composition have better compressive plasticity and hydrogen embrittlement resistance than the CrVMo1 and CrVMo2 micropillars because they have a longer displacement during compression and the displacement does not change much after hydrogen charging, that is, the plasticity loss after hydrogen charging is not obvious.

[0071] Step S7: The load-displacement data obtained from the micro-pillar compression test are calculated using the formula:

[0072]

[0073] In formula (1), D0 represents the displacement of the microcolumn before hydrogen charging, D1 represents the displacement of the microcolumn after hydrogen charging, and HEI represents the hydrogen-induced plastic loss rate. The elements, content, hydrogen charging conditions, and hydrogen-induced plastic loss rate are integrated into a table in the order of element type, content, hydrogen charging conditions, and hydrogen-induced plastic loss rate to realize composition-performance mapping as a data set.

[0074] Step S8: preprocessing the data set, and using the preprocessed data set as data on hydrogen-induced plasticity loss of ultra-high strength steel material.

[0075] In this step, the preprocessing includes cleaning and optimizing the data. For element contents below the detection value and missing values ​​of some element contents, the minimum value in the data set is used to replace them. For outliers, the value obtained by curve fitting is used to replace the abnormal value.

[0076] Based on the same idea, an embodiment of the present invention also provides a high-throughput data generation system for hydrogen-induced plasticity loss of ultra-high-strength steel materials. The system includes: a trace alloy element determination module, an ultra-high-strength steel material preparation device, an indentation device, a digital composition map drawing 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,

[0077] The trace alloying element determination module is used to determine 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 orthogonal experimental design covering at least 1,000 sets of single, binary, ternary and multi-element combinations in the concentration range of 0.1-0.6wt.%.

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

[0080] The digital composition map drawing module is used to establish a digital composition map of element distribution for each indentation in the indentation array using an electron probe microanalyzer EPMA;

[0081] The micropillar array finishing device is used to perform a three-stage ion beam finishing 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±0.2 μm, and a micropillar taper of less than 3°;

[0082] The hydrogen charging device is used to place the ultra-high strength steel material with the micro-pillar array in a constant temperature electrolytic cell and charge the micro-pillars with hydrogen using an electrochemical method;

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

[0084] The in-situ compression test device is used to perform in-situ compression testing on micropillars in liquid nitrogen, using 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 micropillars;

[0085] The data acquisition module is used to synchronously collect continuous load-displacement data streams and capture the evolution of surface slip bands through an in-situ imaging system, thereby obtaining comparative data on micropillar compression before and after hydrogen charging. The load-displacement data obtained from the micropillar compression experiment are used to calculate the hydrogen-induced plastic loss rate. The data are integrated into a table in the order of element type, content, hydrogen charging conditions, and hydrogen-induced plastic loss rate as a data set to achieve composition-performance mapping.

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

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

[0088] In this embodiment, each module is implemented by a processor, and a memory is appropriately added when storage is required. The processor may 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 may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk storage. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0089] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of 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, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

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

[0091] The above description is only a preferred embodiment of the present invention and an explanation of the technical principles used. It is not intended to limit the scope of the invention to be protected, but merely represents a preferred embodiment of the present invention. Those skilled in the art should understand that the scope of the invention involved in the present invention is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the inventive concept. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative work shall fall within the scope of protection of the present invention.

Claims

1. A high-throughput data generation method for hydrogen-induced plasticity loss of ultra-high-strength steel materials, characterized in that: The method comprises the following steps: Step S1, identifying key trace alloying elements in the ultra-high-strength steel material; regulating the concentration gradient of the key trace alloying elements, and preparing ultra-high-strength steel materials with corresponding element combination concentration gradients by orthogonal experimental design covering at least 1,000 combinations of single, binary, ternary, and multiple elements in the concentration range of 0.1-0.6 wt.%; Step S2, arranging a diamond-shaped indentation array on the surface of the composition gradient region of the ultra-high-strength steel material to achieve precise positioning; Step S3, for each indentation in the indentation array, using an electron probe microanalyzer EPMA to create a digital composition map of element distribution; Step S4, based on the EPMA data, a three-stage ion beam refinement process is performed 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±0.2 μm, and a micropillar taper of less than 3°; Step S5: placing the ultra-high-strength steel material with the micropillar array in a constant-temperature electrolytic cell, electrochemically charging the micropillars with hydrogen, and then storing the sample in liquid nitrogen; Step S6: Performing an in-situ compression test on the micropillars in liquid nitrogen using an in-situ nanometer testing system equipped with a high-resolution field emission scanning electron microscope and a diamond flat indenter. Three stages of compression are applied to the micropillars. A continuous load-displacement data stream is simultaneously acquired, and the evolution of the surface slip band is captured using an in-situ imaging system. This yields comparative compression data for the micropillars before and after hydrogen charging. Step S7, calculating the hydrogen-induced plastic loss rate using the load-displacement data obtained from the micropillar compression test; integrating the data into a table in the order of element type, content, hydrogen charging conditions, and hydrogen-induced plastic loss rate as a data set to achieve composition-performance mapping; Step S8: preprocessing the data set, and using the preprocessed data set as data on hydrogen-induced plasticity loss of ultra-high strength steel material.

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

3. The high-throughput data generation method for hydrogen-induced plasticity loss of ultra-high-strength steel material according to claim 1, characterized in that: The trace alloying elements and their concentration ranges 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 high-throughput data generation method for hydrogen-induced plasticity loss of ultra-high-strength steel materials according to claim 1, characterized in that: During the indentation operation in step S2, a Vickers hardness tester is used to perform diamond indentation array positioning in the composition gradient region with a load of HV5 and a load holding parameter of 15 seconds, and the indentation spacing is 100±5 μm.

5. The high-throughput data generation method for hydrogen-induced plasticity loss of ultra-high-strength steel material according to claim 1, characterized in that: The three-stage ion beam refinement in step S4 includes: The first stage uses 30keV / 5nA beam current for rough processing and preparation Position the groove and reserve thick column; In the second stage, the beam current was switched to 1nA for column refinement, and the Pillar diameter; In the third stage, 0.1nA ultra-low beam current is used for surface final treatment, with an incident angle of 5° and annular milling to ensure the top diameter of the pillar and the taper is <2°; The internal pressure of the device was maintained at 100±5Pa by a dynamic gas injection system (GIS), and low current was used to ensure that the taper of the micropillars was less than 3°.

6. The high-throughput data generation method for hydrogen-induced plasticity loss of ultra-high-strength steel material according to claim 1, characterized in that: The hydrogen charging process in step S5 is carried out in an electrolytic cell at a constant temperature of 25±0.5℃. The potential is precisely controlled by electrochemical hydrogen charging. The hydrogen is charged for 24 hours using 0.1M NaOH+0.25g / L thiourea solution, and the current is maintained at 5mA / cm 2 Constant current.

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

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

9. The high-throughput data generation method for hydrogen-induced plasticity loss of ultra-high-strength steel material according to claim 8, characterized in that: The parameters for collecting the continuous data stream in step S6 are 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 of ultra-high strength steel materials, characterized in that: The system includes: a trace alloy element determination module, an ultra-high strength steel material preparation device, an indentation device, a digital composition map drawing module, a micro-pillar array refinement device, a hydrogen charging device, a liquid nitrogen storage device, an in-situ compression test device, a data acquisition module, a data preprocessing module and a data storage module; wherein, The trace alloying element determination module is used to determine 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 orthogonal experimental design covering at least 1,000 sets of single, binary, ternary and multi-element combinations in the concentration range of 0.1-0.6wt.%. The indentation device is used to arrange a diamond-shaped indentation array on the surface of the ultra-high-strength steel material in a composition gradient area to achieve precise positioning; The digital composition map drawing module is used to establish a digital composition map of element distribution for each indentation in the indentation array using an electron probe microanalyzer EPMA; The micropillar array finishing device is used to perform a three-stage ion beam finishing 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±0.2 μm, and a micropillar taper of less than 3°; The hydrogen charging device is used to place the ultra-high strength steel material with the micro-pillar array in a constant temperature electrolytic cell and charge the micro-pillars with hydrogen using an electrochemical method; The liquid nitrogen storage device is used to store the hydrogen-charged sample in liquid nitrogen; The in-situ compression test device is used to perform in-situ compression testing on micropillars in liquid nitrogen, using 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 micropillars; The data acquisition module is used to synchronously collect continuous load-displacement data streams and capture the evolution of surface slip bands through an in-situ imaging system, thereby obtaining comparative data on micropillar compression before and after hydrogen charging. The load-displacement data obtained from the micropillar compression experiment are used to calculate the hydrogen-induced plastic loss rate. The data are integrated into a table in the order of element type, content, hydrogen charging conditions, and hydrogen-induced plastic loss rate as a data set to achieve composition-performance mapping. The data preprocessing module is used to preprocess the data set and use the preprocessed data set as data on hydrogen-induced plasticity loss of ultra-high strength steel materials; The data storage module is used to store data on hydrogen-induced plasticity loss of ultra-high strength steel materials.

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