Saturation magnetic induction prediction, composition design and ultra-high saturation magnetic induction iron-based amorphous / nanocrystalline alloy

Abstract

The application discloses a kind of saturation magnetic induction prediction of iron-based amorphous alloy, component design and super-high saturation magnetic induction iron-based amorphous / nanocrystalline alloy. Through machine learning model XGBoost combined with SHAP analysis reveals the core role of ferromagnetic element content, alloy mixing enthalpy and electronegativity difference, while maintaining high ferromagnetic element content, without deteriorating amorphous forming ability, after annealing can obtain very low coercivity, realize high saturation magnetic induction and amorphous forming ability, the synergistic optimization of coercivity. The parameters used in the composition design criteria proposed by the application do not require experimental data and can be calculated directly from the alloy composition. Combined with machine learning model, high-performance components can be quickly screened, significantly reducing the development cycle and cost of traditional trial-and-error method, providing systematic theoretical guidance for the development of high-performance soft magnetic materials and improving industrial feasibility.

Description

Technical Field

[0001] This invention relates to the field of iron-based amorphous / nanocrystalline alloys, specifically to the prediction of saturation magnetic induction, composition design, and ultra-high saturation magnetic induction iron-based amorphous / nanocrystalline alloys. Background Technology

[0002] Soft magnetic materials play a crucial role in energy transfer and conversion in electrical and electronic systems. Faced with increasingly severe global energy challenges, modern electronic devices are shifting towards miniaturization, higher operating frequencies, and greater energy efficiency. In this context, developing materials with excellent soft magnetic properties is essential to driving these technological advancements. In particular, there is an urgent market demand for soft magnetic materials that combine high saturation magnetic induction and low coercivity, which is crucial for meeting the dual requirements of high power density, low loss, and device miniaturization.

[0003] For decades, iron-based amorphous / nanocrystalline alloys have been the most promising candidate materials for addressing this challenge due to their excellent soft magnetic properties. However, the saturation magnetic induction of iron-based amorphous / nanocrystalline alloys is typically between 1.2 and 1.7 T, lower than that of silicon steel (1.8-2.0 T), which limits their application in high power density devices. Furthermore, existing high-saturation magnetic alloys (such as Nanomet and Hitperm) have high coercivity (7-100 A / m) or stringent requirements for the quality of amorphous precursors and heat treatment processes, resulting in high industrial production costs and low yields. In addition, traditional methods for developing amorphous alloys by controlling composition lack systematic theoretical guidance, leading to low efficiency, long R&D cycles, high costs, and difficulty in balancing the contradiction between high saturation magnetic induction and poor amorphous forming ability and high coercivity.

[0004] The development of iron-based amorphous / nanocrystalline soft magnetic alloys began in the 1960s, with the core objective of achieving a comprehensive performance balance between high saturation magnetic induction, low coercivity, and amorphous forming capability through material design. Historically, various systems of iron-based amorphous / nanocrystalline soft magnetic alloys have been developed both domestically and internationally. The Fe-Si-B system (such as METGLAS2605SA1, saturation magnetic induction ≈ 1.56T) has become the mainstream material for power transformers, but its saturation magnetic induction is still lower than that of silicon steel (1.8-2.0T). The Fe-Si-B-Cu-Nb system (Finemet, saturation magnetic induction ≈ 1.24T) significantly improves permeability and reduces coercivity to 0.5A / m. Subsequently, systems such as NANOPERM (Fe-Zr-B) and NANOMET (Fe-Si-BP-Cu) emerged, continuously improving saturation magnetic induction, but facing the challenge of balancing amorphous forming capability with magnetic properties.

[0005] Annealing is a key step in controlling the structure of nanocrystals. Traditional processes employ a two-stage annealing process: the first stage eliminates internal stress, and the second stage induces the precipitation of α-Fe(Si) nanocrystals. Annealing under an external magnetic field can optimize magnetic domain orientation and reduce coercivity.

[0006] Because the correlation between the composition and magnetic properties of amorphous alloys lacks theoretical guidance, traditional alloy design based on compositional control aims to balance amorphous forming ability and magnetic properties through synergistic effects of elements. For example, adding B / Si can enhance amorphous forming ability, but excessive amounts can reduce saturation magnetization; adding Cu / Nb acts as a nucleating agent to suppress grain coarsening; adding Co / Ni enhances the magnetic moment but increases cost. Such methods are highly dependent on experience and require extensive trial-and-error experiments. In recent years, the application of machine learning in materials science has rapidly emerged, driving a transformation in this field. Machine learning has great potential in data-driven approaches, providing a more efficient way to accelerate the discovery of new materials, especially in the field of complex and disordered materials such as amorphous alloys, where it has significant advantages.

[0007] Chinese patent application CN116364205A discloses a machine learning-based prediction method for iron-based nanocrystalline alloys, achieving synergistic optimization of high magnetic induction and low coercivity through a data-driven model. The technical solution first constructs a database containing the composition, process parameters, and performance data of 348 alloy combinations. Quenched amorphous samples are screened, and redundant data is cleaned, retaining 20 key features such as annealing temperature, electron concentration (VEC), and nonferromagnetic electron concentration (VEC1). Using a random forest model and forward selection, the core features of Bs are determined to be VEC1, Fe content, and electronegativity; the core features of coercivity are VEC, annealing temperature, and electronegativity. A prediction framework is established using an XGBoost model. Further multi-objective optimization is performed using the NSGA-II genetic algorithm, revealing the design criteria that when VEC1≤0.78, the saturation magnetic induction increases as it decreases, and when VEC≤7.12, the coercivity decreases as it decreases. The optimal Fe composition for the Fe-Si-B-Cu-P-Nb system is then selected. 81.4 Si 6.8 B 8.6 Cu 0.7 P 1.9 Nb 0.6 After melt quenching (roller speed 50m / s) and annealing, the alloy has a saturation magnetic induction of 1.72T and a coercivity as low as 3.8A / m.

[0008] Chinese patent application CN117457117A discloses a method and system for synthesizing high-frequency nanocrystalline alloys. The technical solution first constructs a dataset containing 393 alloy compositions, process parameters, and performance indicators, covering 15 key elements (Fe, Co, Ni, Si, B, etc.) and 22 characteristic parameters (such as electron concentration VEC, nonferromagnetic electron concentration VEC1, crystallization temperature range ΔT, etc.). An XGBoost algorithm is used to train an amorphous forming ability prediction model. Combined with interpretability analysis, VEC1, VEC, B content, and ΔT are selected as core influencing factors, and design criteria are established: VEC1 > 0.71, VEC < 7.29, ΔT > 121℃, and B content 6.85–11.54 at%. Based on these criteria, novel alloys (such as Fe) are developed. 72.8 Si 12.7 B 9.9 Cu 1.5 Nb1Al 0.52 Mn 0.8 Ga 0.8 Ultrathin amorphous strips of 14–16 μm were prepared by melting and single-roll quenching. After nanocrystallization at 560℃ and magnetic field annealing, a composite structure of α-Fe(Si) nanocrystals and an amorphous matrix was formed. The alloy exhibits a coercivity as low as 0.5 A / m, a loss of 2.3 W / kg at 0.5 T / 10 kHz, and a surface roughness Ra < 0.8 μm.

[0009] Traditional techniques for preparing high-saturation magnetic induction iron-based amorphous alloys have the following main drawbacks:

[0010] 1. It is difficult to achieve both high saturation magnetization and low coercivity. Traditional methods improve saturation magnetization by increasing the Fe content (>80 at%), but high iron content leads to grain coarsening (grain size >30 nm), increasing domain wall pinning effect and thus significantly increasing coercivity. For example, Fe... 85 The Si1B9P3Cu1 alloy can achieve a saturation magnetic induction of up to 1.8T, but its coercivity is as high as 7-100 A / m. There is a strong negative correlation between high saturation magnetic induction and coercivity, making it difficult to optimize them simultaneously.

[0011] 2. Limited amorphous formation capability. High iron content reduces the alloy's amorphous formation capability, leading to the easy formation of crystalline phases during rapid solidification, especially the surface crystalline layer, which deteriorates soft magnetic properties. This limits the flexibility of alloy composition design, making it difficult to further improve saturation magnetism by adjusting the Fe content.

[0012] 3. Composition design relies on experience, resulting in low R&D efficiency. Traditional methods rely on trial and error to optimize compositions, lacking quantitative analysis of key physical parameters, leading to long R&D cycles, high R&D costs, and difficulty in quickly screening high-performance alloy compositions.

[0013] Although the aforementioned patent applications utilize machine learning methods to develop novel soft magnetic alloys, they still have shortcomings. The technical solution provided in CN116364205A only considers the characteristics of the alloy composition that affect saturation magnetic induction, without taking into account amorphous formation capability. The design criteria proposed in the technical solution of CN117457117A require that the crystallization temperature range ΔT cannot be obtained from the alloy composition and must be measured experimentally beforehand, which is not conducive to more efficient development of new alloys. Furthermore, the saturation magnetic induction of the alloys designed in these two patent applications has not exceeded 1.8T, limiting the widespread application of the alloys. Summary of the Invention

[0014] This invention provides prediction of saturation magnetic induction, composition design, and ultra-high saturation magnetic induction iron-based amorphous / nanocrystalline alloys.

[0015] This invention provides a method for predicting saturation magnetic induction and a compositional design criterion for iron-based amorphous soft magnetic alloys with high saturation magnetic induction through machine learning-based data mining. A series of novel iron-based amorphous alloys were designed based on this criterion. These alloys, after annealing under an applied magnetic field, exhibit both ultra-high saturation magnetic induction (1.85-1.92T) and extremely low coercivity (1.2-6A / m), while also considering amorphous formation capability, significantly improving the practicality and industrial feasibility of the materials.

[0016] This invention employs a machine learning method to establish a quantitative relationship between ferromagnetic element content, alloy mixing enthalpy, electronegativity difference, and saturation magnetic induction. The alloy mixing enthalpy considers the amorphous forming ability of the alloy, while the ferromagnetic element content and electronegativity difference limit the types and proportions of ferromagnetic elements and dopants. The parameters in the prediction method and composition design criteria proposed in this invention can be directly obtained from the alloy composition without requiring any prior experimental data. The iron-based amorphous alloy developed in this invention achieves a saturation magnetic induction of 1.85-1.92T, while also exhibiting extremely low coercivity of 1.2-6 A / m. Its superior performance enables its wider application in various electronic devices requiring miniaturization and high energy density.

[0017] The specific technical solution is described below:

[0018] [1] A method for predicting the saturation magnetic induction of iron-based amorphous alloys, comprising:

[0019] A dataset of iron-based amorphous alloy composition and corresponding saturation magnetic induction was established. Based on the iron-based amorphous alloy composition, the following six key physical characteristics were calculated: ferromagnetic element content, alloy mixing enthalpy, electronegativity difference, average atomic radius, mixing entropy, and average electronegativity.

[0020] An XGboost machine learning model was established and trained. The input of the XGboost machine learning model included the proportion of each element in the iron-based amorphous alloy composition and the corresponding 6 key physical characteristics. The output was saturated magnetic induction.

[0021] The composition of the iron-based amorphous alloy to be predicted is obtained, its six key physical characteristics are calculated, and the saturation magnetic induction is predicted using a trained XGboost machine learning model.

[0022] The method for predicting the saturation magnetic induction of iron-based amorphous alloys, wherein the dataset may contain any one or more of the following elements used to compose iron-based amorphous alloys: Fe, B, Si, C, P, Co, Nb, Mo, Ni, Ga, Cu, Zr, Al, Dy, Cr, Y, Hf, Nd, Gd, W.

[0023] The saturation magnetic induction prediction method for iron-based amorphous alloys, wherein the ferromagnetic element may be Fe, or Fe and Co.

[0024] The formula for calculating the ferromagnetic element content can be: C Fe =N Fe / N total C Fe Indicates the content of ferromagnetic elements, N Fe N represents the number of ferromagnetic element atoms in the composition of iron-based amorphous alloys. total This indicates the total number of atoms in the composition of an iron-based amorphous alloy.

[0025] The formula for calculating the enthalpy of mixing of the alloy can be: Where ΔH mix This represents the enthalpy of mixing of the alloy, where i and j are element labels in the composition of the iron-based amorphous alloy, and C. i =N i / N total C j =N j / N total Represent the content of element i and element j in the iron-based amorphous alloy, respectively, and N i N j Let i and j represent the number of atoms of element i and element j in the iron-based amorphous alloy, respectively. The enthalpy of mixing between elements i and j can be obtained by looking up a table or by calculation using methods known in the art.

[0026] The expression for calculating the electronegativity difference can be: Where δ χ Indicates poor electronegativity, χ i Indicates the electronegativity of element i. It indicates average electronegativity.

[0027] The expression for calculating the average atomic radius can be: in r represents the average atomic radius. i This represents the atomic radius of element i.

[0028] The expression for calculating the mixed entropy can be: ΔS mix =∑-RC i ln(C i ), where ΔS mix Let represent the entropy of mixing, and R represent the gas constant, which has a value of 8.314 J / (mol·K).

[0029] The expression for calculating the average electronegativity can be:

[0030] [2] A method for designing the composition of a high-saturation magnetic induction iron-based amorphous alloy, wherein the composition of the iron-based amorphous alloy is designed according to the following criteria:

[0031] C Fe >75%, C Fe C represents the content of ferromagnetic elements in iron-based amorphous alloys. Fe =N Fe / N total N Fe N represents the number of ferromagnetic element atoms in the composition of iron-based amorphous alloys. total This indicates the total number of atoms in the composition of an iron-based amorphous alloy;

[0032] -18.7 kJ / mol < ΔH mix <-14kJ / mol, ΔH mix Indicates the enthalpy of alloy mixing. i and j are element labels in the composition of the iron-based amorphous alloy, C i =N i / N total C j =N j / N total Represent the content of element i and element j in the iron-based amorphous alloy, respectively, and N i N j Let i and j represent the number of atoms of element i and element j in the iron-based amorphous alloy, respectively. The enthalpy of mixing between element i and element j can be obtained by looking up a table or by calculation using methods known in the art.

[0033] δ χ <0.07, δ χ Indicates poor electronegativity. χ i Indicates the electronegativity of element i. Indicates average electronegativity.

[0034] Of the above criteria, C Fe A saturation magnetic induction is significantly enhanced when the saturation magnetic induction is greater than 75%, with -18.7 kJ / mol < ΔH. mix At a balance between high saturation magnetism and amorphous formation ability at <-14 kJ / mol, δ χ A value <0.07 can increase the number of unpaired electrons in iron atoms in the alloy, thereby increasing the magnetic moment and resulting in an iron-based amorphous alloy composition with high saturation magnetic induction.

[0035] The above component design criteria can be obtained by performing SHAP analysis on the machine learning model described in [1].

[0036] The composition design method for the high saturation magnetic induction iron-based amorphous alloy allows the elements constituting the iron-based amorphous alloy to be selected from: Fe, B, Si, C, P, Co, Nb, Mo, Ni, Ga, Cu, Zr, Al, Dy, Cr, Y, Hf, Nd, Gd, and W.

[0037] The ferromagnetic element may be Fe, or Fe and Co.

[0038] [3] An iron-based amorphous / nanocrystalline alloy, with the following chemical formula based on atomic ratio:

[0039] Fe 70-x Co 16 Ni x Si3B 11 Where x ranges from 0 to 1; or,

[0040] (Fe 100-x Co x ) 85.5 Ni 1.5 B 9-y P3C1Mo y Where x ranges from 18 to 25 (e.g., 20), and y ranges from 0 to 1 (e.g., 0.5); or,

[0041] (Fe 100-x Co x ) 85.5 Ni 1.5 B 9-y P3C1V y The range of x is 18 to 25 (e.g., 20), and the range of y is 0 to 1 (e.g., 0.5).

[0042] Furthermore, based on atomic ratio, the chemical expression of the iron-based amorphous / nanocrystalline alloy can be any of the following:

[0043] Fe 69 Co 16 Ni1Si3B11 ,

[0044] (Fe 80 Co 20 ) 85.5 Ni 1.5 B 8.5 P3C1Mo 0.5 ,

[0045] (Fe 75 Co 25 ) 85.5 Ni 1.5 B 8.5 P3C1V 0.5 ,

[0046] (Fe 75 Co 25 ) 85.5 Ni 1.5 B9P3C1,

[0047] (Fe 80 Co 20 ) 85.5 Ni 1.5 B9P3C1,

[0048] (Fe 82 Co 18 ) 85.5 Ni 1.5 B9P3C1.

[0049] The coercivity of the iron-based amorphous / nanocrystalline alloy is less than 6 A / m, for example 1.2 A / m, 1.3 A / m, 1.7 A / m, 1.8 A / m, 2.3 A / m, 2.4 A / m, 3.2 A / m, 3.3 A / m, 3.4 A / m, 3.5 A / m, 3.6 A / m, 3.9 A / m, 4.0 A / m, 5.5 A / m, 5.6 A / m, etc., and further not exceeding 5.6 A / m.

[0050] The saturation magnetic induction of the iron-based amorphous / nanocrystalline alloy is not less than 1.85T, such as 1.87T, 1.89T, 1.90T, 1.92T, 1.95T, etc.

[0051] [4] The preparation method of the iron-based amorphous / nanocrystalline alloy according to [3] includes:

[0052] Amorphous precursors were prepared according to the chemical formula described above;

[0053] The amorphous precursor is annealed in an external magnetic field at 340–420°C, preferably 360–380°C. The preferred annealing temperature can further reduce coercivity.

[0054] The amorphous precursor can be a strip material, which can be prepared by melt quenching.

[0055] The direction of the applied magnetic field is preferably aligned with the length direction of the amorphous precursor, which helps to reduce coercivity.

[0056] The magnitude of the applied magnetic field can be 100 to 10000 A / m, for example, 400 A / m.

[0057] The annealing process can take 10 to 20 minutes, for example, 15 minutes.

[0058] Compared with the prior art, the beneficial effects of this invention are as follows:

[0059] The method for predicting the saturation magnetic induction of iron-based amorphous alloys in this invention can accurately predict the saturation magnetic induction of iron-based amorphous alloys solely based on their alloy composition.

[0060] The key features and innovations of the composition design criteria of this invention are as follows: the three selected parameters do not require any experimental measurement and can all be obtained by simple calculation through alloy composition; the selection range of alloy mixing enthalpy allows the amorphous forming ability of the composition to be considered simultaneously when designing the alloy, which is beneficial to meeting the requirements of production and preparation.

[0061] The ultra-high saturation magnetic induction iron-based amorphous / nanocrystalline alloy of the present invention can be composed of 5-7 elements from Fe, Co, Ni, Si, B, P, C, Mo, V, etc., satisfying the parameter range of the composition design criteria of the present invention.

[0062] This invention, through the XGBoost machine learning model combined with SHAP analysis, reveals the core roles of ferromagnetic element content, alloy mixing enthalpy, and poor electronegativity. By controlling these parameters, while maintaining high ferromagnetic element content, the amorphous forming ability is not deteriorated, grain coarsening is suppressed, and the magnetic domain pinning effect is reduced. This achieves a balance between ultra-high saturation magnetization (1.85-1.92T) and extremely low coercivity (1.2-6A / m) after annealing, breaking through the bottleneck of traditional alloys where these two aspects are difficult to achieve simultaneously, and realizing the synergistic optimization of high saturation magnetization, amorphous forming ability, and coercivity.

[0063] The parameters used in the composition design criteria proposed in this invention (ferromagnetic element content, alloy mixing enthalpy, and electronegativity difference) do not require experimental data and can be directly calculated from the alloy composition. Combining machine learning models to rapidly screen high-performance components significantly reduces the R&D cycle and cost of traditional trial-and-error methods, providing systematic theoretical guidance for the development of high-performance soft magnetic materials and improving industrial feasibility. Attached Figure Description

[0064] Figure 1 This is a diagram showing the range of iron content determined by SHAP analysis in a specific implementation method.

[0065] Figure 2 This diagram illustrates the range of alloy mixing enthalpy determined by SHAP analysis in a specific implementation.

[0066] Figure 3 This is a diagram showing the range of electronegativity differences determined by SHAP analysis in a specific implementation.

[0067] Figure 4 The X-ray diffraction (XRD) patterns of six amorphous precursor ribbons in a specific implementation embodiment are shown.

[0068] Figure 5 The image shows the differential scanning calorimetry (DSC) curves of six amorphous precursor strips in a specific embodiment.

[0069] Figure 6 The figure shows the coercivity variation curves of six amorphous precursor strips after being treated at different annealing temperatures in a specific implementation.

[0070] Figure 7 Magnetization curves for six alloy examples with optimal annealing temperatures in specific implementation embodiments. Detailed Implementation

[0071] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Operating methods not specifically specified in the following embodiments are generally performed under conventional conditions or as recommended by the manufacturer.

[0072] A method for predicting the saturation magnetic induction of iron-based amorphous alloys includes:

[0073] A dataset of iron-based amorphous alloy compositions and corresponding saturation magnetizations was established. This example collected literature data on 536 different iron-based amorphous alloys, including the 20 elements that make up iron-based amorphous alloys: Fe, B, Si, C, P, Co, Nb, Mo, Ni, Ga, Cu, Zr, Al, Dy, Cr, Y, Hf, Nd, Gd, and W. The saturation magnetization ranged from 0.33 to 1.92 T. Based on the iron-based amorphous alloy compositions, the following six key physical characteristics were calculated: ferromagnetic element content, alloy mixing enthalpy, electronegativity difference, average atomic radius, mixing entropy, and average electronegativity.

[0074] The ferromagnetic elements are Fe, or Fe and Co.

[0075] The formula for calculating the content of ferromagnetic elements is: C Fe =N Fe / N total C Fe Indicates the content of ferromagnetic elements, N FeN represents the number of ferromagnetic element atoms in the composition of iron-based amorphous alloys. total This indicates the total number of atoms in the composition of an iron-based amorphous alloy.

[0076] The formula for calculating the enthalpy of alloy mixing is: Where ΔH mix This represents the enthalpy of mixing of the alloy, where i and j are element labels in the composition of the iron-based amorphous alloy, and C. i =N i / N total C j =N j / N total Represent the content of element i and element j in the iron-based amorphous alloy, respectively, and N i N j Let i and j represent the number of atoms of element i and element j in the iron-based amorphous alloy, respectively. The enthalpy of mixing between elements i and j can be obtained by looking up a table or by calculation using methods known in the art.

[0077] The expression for calculating the electronegativity difference is: Where δ χ Indicates poor electronegativity, χ i Indicates the electronegativity of element i. It indicates average electronegativity.

[0078] The expression for calculating the average atomic radius is: in r represents the average atomic radius. i This represents the atomic radius of element i.

[0079] The expression for calculating the entropy of mixture is: ΔS mix =∑-RC i ln(C i ), where ΔS mix Let represent the entropy of mixing, and R represent the gas constant, which has a value of 8.314 J / (mol·K).

[0080] The expression for calculating average electronegativity is:

[0081] An XGboost machine learning model is established and trained. The input of the XGboost machine learning model includes the proportion of each element in the iron-based amorphous alloy composition (in this example, the proportion of 20 elements, and 0 for elements that are not present) and the corresponding 6 key physical features. The output is saturated magnetic induction.

[0082] The composition of the iron-based amorphous alloy to be predicted is obtained, its six key physical characteristics are calculated, and the saturation magnetic induction is predicted using a trained XGboost machine learning model, thus achieving accurate prediction of the saturation magnetic induction.

[0083] Based on the above XGboost machine learning model, SHAP analysis shows that C Fe ΔH mix δ χ It is the core factor affecting saturation magnetization. C Fe Significantly improves saturation magnetism when >75% ( Figure 1 -18.7 kJ / mol < ΔH mix The equilibrium between high saturation magnetism and amorphous formation ability is at <-14 kJ / mol. Figure 2 ). δ χ A value <0.07 can increase the number of unpaired electrons in iron atoms in the alloy, thereby increasing the magnetic moment. Figure 3 Therefore, the composition design criteria for iron-based amorphous alloys with high saturation magnetic induction are as follows:

[0084] C Fe >75%, C Fe C represents the content of ferromagnetic elements in iron-based amorphous alloys. Fe =N Fe / N total N Fe N represents the number of ferromagnetic element atoms in the composition of iron-based amorphous alloys. total This indicates the total number of atoms in the composition of an iron-based amorphous alloy;

[0085] -18.7 kJ / mol < ΔH mix <-14kJ / mol, ΔH mix Indicates the enthalpy of alloy mixing. i and j are element labels in the composition of the iron-based amorphous alloy, C i =N i / N total C j =N j / N total Represent the content of element i and element j in the iron-based amorphous alloy, respectively, and N i N j Let i and j represent the number of atoms of element i and element j in the iron-based amorphous alloy, respectively. The enthalpy of mixing between element i and element j can be obtained by looking up a table or by calculation using methods known in the art.

[0086] δ χ <0.07, δ χ Indicates poor electronegativity. χ i Indicates the electronegativity of element i. Indicates average electronegativity.

[0087] Using the above criteria, the composition design of iron-based amorphous alloys with high saturation magnetic induction can be carried out.

[0088] Based on the above composition design method, Co was further introduced to enhance the Fe-Co ferromagnetic exchange interaction, and the following system (by atomic ratio) was designed:

[0089] Fe 70-x Co 16 Ni x Si3B 11 Where x ranges from 0 to 1; or,

[0090] (Fe 100-x Co x ) 85.5 Ni 1.5 B 9-y P3C1Mo y Where x ranges from 18 to 25 (e.g., 20), and y ranges from 0 to 1 (e.g., 0.5); or,

[0091] (Fe 100-x Co x ) 85.5 Ni 1.5 B 9-y P3C1V y The range of x is 18 to 25 (e.g., 20), and the range of y is 0 to 1 (e.g., 0.5).

[0092] Specific alloy examples include (by atomic ratio): Fe 69 Co 16 Ni1Si3B 11 , (Fe 80 Co 20 ) 85.5 Ni 1.5 B 8.5 P3C1Mo 0.5 , (Fe 75 Co 25 ) 85.5 Ni 1.5 B 8.5 P3C1V 0.5 , (Fe 75 Co 25 ) 85.5 Ni 1.5 B9P3C1, (Fe 80 Co 20 ) 85.5 Ni 1.5 B9P3C1, (Fe 82 Co 18 ) 85.5 Ni 1.5 B9P3C1.

[0093] The raw materials used to prepare the six alloy examples above were all commercially available high-purity raw materials, including pure iron, pure cobalt, pure nickel, pure silicon, iron-phosphorus alloy, pure carbon, pure vanadium, and pure molybdenum. The raw materials conforming to the above alloy compositions were vacuum melted to prepare a master alloy ingot. The resulting master alloy ingot was then processed into amorphous precursor strips using a melt rapid quenching method. The amorphous precursor strips were annealed using a magnetic field heat treatment method, with an external 400 A / m longitudinal magnetic field applied (i.e., the direction of the applied magnetic field was consistent with the length direction of the amorphous precursor strip) to reduce magnetic anisotropy and coercivity. The annealing temperature range was 340-420℃, and the annealing time was 15 min. After holding at this temperature, the strips were cooled to room temperature to obtain amorphous / nanocrystalline soft magnetic alloys.

[0094] The XRD patterns of these alloy amorphous precursor ribbons are as follows: Figure 4 As shown, all XRD patterns exhibit a diffusion peak at 2θ = 44-45°, indicating a predominantly amorphous structure. Figure 5 DSC curves of six amorphous precursor ribbons were shown, with crystallization temperatures ranging from approximately 360°C to 380°C. Therefore, the subsequent annealing temperature was set between 340°C and 420°C, and the annealing time was 15 minutes. The prepared amorphous precursor ribbons were annealed in an external magnetic field. Annealing can alleviate the magnetic anisotropy energy fluctuations caused by internal stress, thereby making the locally induced anisotropy more uniform. With increasing annealing temperature, the coercivity of the alloy showed a trend of first decreasing and then increasing, indicating the existence of an optimal annealing temperature. At this temperature, the coercivity significantly decreased from 13–27 A / m to 1.2–5.6 A / m. Figure 6 As shown. The coercivity is much lower than that of crystalline silicon steel. The magnetization curve of the annealed sample with the lowest coercivity is shown in the figure. Figure 7 As shown in Table 1, the coercivity performance of the alloy examples at different annealing temperatures is expressed in A / m.

[0095] Table 1

[0096]

[0097] Performance parameters of saturation magnetic induction for each alloy example at the optimal annealing temperature:

[0098] Fe 69 Co 16 Ni1Si3B 11 1.92T, optimal annealing temperature 380℃;

[0099] (Fe 80 Co 20 ) 85.5 Ni 1.5 B 8.5 P3C1Mo 0.5 1.87T, optimal annealing temperature 360℃;

[0100] (Fe 75 Co 25 ) 85.5 Ni 1.5 B 8.5 P3C1V 0.5 1.90T, optimal annealing temperature 360℃;

[0101] (Fe 75 Co 25 ) 85.5 Ni 1.5 B9P3C1 1.85T, optimal annealing temperature 380℃;

[0102] (Fe 80 Co 20 ) 85.5 Ni 1.5 B9P3C1 1.89T, optimal annealing temperature 380℃;

[0103] (Fe 82 Co 18 ) 85.5 Ni 1.5 B9P3C1 1.92T, optimal annealing temperature 380℃.

[0104] Furthermore, it should be understood that after reading the above description of the present invention, those skilled in the art can make various alterations or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims.

Claims

1. A method for predicting the saturation magnetic induction of iron-based amorphous alloys, characterized in that, include: A dataset of iron-based amorphous alloy composition and corresponding saturation magnetic induction was established. Based on the iron-based amorphous alloy composition, the following six key physical characteristics were calculated: ferromagnetic element content, alloy mixing enthalpy, electronegativity difference, average atomic radius, mixing entropy, and average electronegativity. An XGboost machine learning model was established and trained. The input of the XGboost machine learning model included the proportion of each element in the iron-based amorphous alloy and the corresponding 6 key physical characteristics. The output was saturated magnetic induction. The composition of the iron-based amorphous alloy to be predicted is obtained, its six key physical characteristics are calculated, and the saturation magnetic induction is predicted using a trained XGboost machine learning model.

2. The method for predicting the saturation magnetic induction of iron-based amorphous alloys according to claim 1, characterized in that, The dataset contains any one or more of the following elements used to compose iron-based amorphous alloys: Fe, B, Si, C, P, Co, Nb, Mo, Ni, Ga, Cu, Zr, Al, Dy, Cr, Y, Hf, Nd, Gd, W.

3. The method for predicting the saturation magnetic induction of iron-based amorphous alloys according to claim 1, characterized in that, The ferromagnetic element is Fe, or Fe and Co; The formula for calculating the ferromagnetic element content is as follows: ,in Indicates the content of ferromagnetic elements. This indicates the number of ferromagnetic element atoms in the composition of an iron-based amorphous alloy. This indicates the total number of atoms in the composition of an iron-based amorphous alloy; The formula for calculating the enthalpy of mixture of the alloy is: ,in Indicates the enthalpy of alloy mixing. , These are element labels for the composition of iron-based amorphous alloys. , These represent the elements in iron-based amorphous alloys. Content, elements content, , These represent the elements in iron-based amorphous alloys. ,element The number of atoms, Represents element and elements Mixture enthalpy; The expression for calculating the electronegativity difference is: ,in Indicates poor electronegativity. Represents element electronegativity, Indicates average electronegativity; The expression for calculating the average atomic radius is as follows: ,in Indicates the average atomic radius. Represents element atomic radius; The expression for calculating the mixed entropy is: ,in Represents the mixed entropy, Represents the gas constant; The expression for calculating the average electronegativity is as follows: .

4. A method for designing the composition of a high-saturation magnetic induction iron-based amorphous alloy, characterized in that, The composition of iron-based amorphous alloys should be designed according to the following criteria: >75%, This indicates the content of ferromagnetic elements in iron-based amorphous alloys. , This indicates the number of ferromagnetic element atoms in the composition of an iron-based amorphous alloy. This indicates the total number of atoms in the composition of an iron-based amorphous alloy; -18.7 kJ / mol< <-14 kJ / mol, Indicates the enthalpy of alloy mixing. , , These are element labels for the composition of iron-based amorphous alloys. , These represent the elements in iron-based amorphous alloys. Content, elements content, , These represent the elements in iron-based amorphous alloys. ,element The number of atoms, Represents element and elements Mixture enthalpy; <0.07, Indicates poor electronegativity. , Represents element electronegativity, Indicates average electronegativity. .

5. The method for designing the composition of a high-saturation magnetic induction iron-based amorphous alloy according to claim 4, characterized in that, The elements that make up iron-based amorphous alloys are selected from: Fe, B, Si, C, P, Co, Nb, Mo, Ni, Ga, Cu, Zr, Al, Dy, Cr, Y, Hf, Nd, Gd, W; The ferromagnetic element is Fe, or Fe and Co.

6. A type of iron-based amorphous / nanocrystalline alloy, characterized in that, Based on atomic ratio, the chemical expression is: Fe 70-x Co 16 Ni x Si3B 11 Where x ranges from 0 to 1; or, (Fe 100-x Co x ) 85.5 Ni 1.5 B 9-y P3C1Mo y Where x ranges from 18 to 25, and y ranges from 0 to 1; or, (Fe 100-x Co x ) 85.5 Ni 1.5 B 9-y P3C1V y , where x ranges from 18 to 25 and y ranges from 0 to 1.

7. The iron-based amorphous / nanocrystalline alloy according to claim 6, characterized in that, Based on atomic ratio, the chemical formula of the iron-based amorphous / nanocrystalline alloy is any one of the following: Fe 69 Co 16 Ni1Si3B 11 , (Faith 80 Co 20 ) 85.5 Nose 1.5 B 8.5 P3C1Mo 0.5 , (Fe 75 What 25 ) 85.5 There is no 1.5 B 8.5 P3C1V 0.5 , (Want 75 Co 25 ) 85.5 In 1.5 B9P3C1, (Want 80 Co 20 ) 85.5 In 1.5 B9P3C1, (Want 82 Co 18 ) 85.5 In 1.5 B9P3C1.

8. The iron-based amorphous / nanocrystalline alloy according to claim 6 or 7, characterized in that, The coercivity of the iron-based amorphous / nanocrystalline alloy is less than 6 A / m; The saturation magnetic induction of the iron-based amorphous / nanocrystalline alloy is not less than 1.85 T.

9. The iron-based amorphous / nanocrystalline alloy according to claim 8, characterized in that, The coercivity of the iron-based amorphous / nanocrystalline alloy does not exceed 5.6 A / m.

10. The method for preparing iron-based amorphous / nanocrystalline alloys according to any one of claims 6 to 9, characterized in that, include: Amorphous precursors were prepared according to the chemical formula described above; The amorphous precursor is annealed at 340~420℃ in an external magnetic field.

11. The preparation method according to claim 10, characterized in that, The amorphous precursor is annealed at 360~380℃ in an external magnetic field.

12. The preparation method according to claim 10, characterized in that, The amorphous precursor is a strip material, prepared by melt quenching. The direction of the applied magnetic field is consistent with the length direction of the amorphous precursor; The magnitude of the applied magnetic field is 100~10000 A / m; The annealing process takes 10 to 20 minutes.

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