Acoustic emission material life prediction method and system based on stress wave frequency

Abstract

The application discloses a kind of acoustic emission material life prediction methods based on stress wave frequency, belong to material nondestructive testing and structural health monitoring field.The application takes the peak frequency of acoustic emission signal as the direct and quantifiable physical dimension of characterizing material internal damage.Obtain the peak frequency of acoustic emission signal of initial state of the material to be measured;Damage sensitive coefficient is calculated based on the inherent damage distribution factor and geometric structure factor of material;In the process of material use, the peak frequency of its acoustic emission signal is monitored in real time;The shift of frequency is directly and quantitatively converted into the micro-damage value in material by establishing model, the time when material reaches critical damage threshold is predicted by fitting the evolution curve of damage value, so as to determine its remaining life.The application reveals the physical nature of crystal bond fracture-frequency shift in material damage process, realizes the life prediction from atomic bond scale to macro failure, with the advantages of quantitative precision, strong anti-interference force.

Description

Technical Field

[0001] This invention belongs to the field of material damage analysis technology, specifically relating to a method and system for predicting the lifetime of acoustic emission materials based on stress wave frequency. Background Technology

[0002] Acoustic emission (AE) technology, as a dynamic and real-time non-destructive testing method, offers the possibility of assessing material damage state by capturing transient elastic waves generated by the release of internal energy during material stress. Traditional AE techniques mainly rely on parameter and waveform analysis of the AE signals. However, these methods have inherent limitations in revealing the physical nature of the material, quantifying damage, and accurately predicting lifetime.

[0003] Existing methods for material damage analysis, including some machine learning models, are essentially "black box" or "grey box" models. While they can uncover statistical correlations between damage and signal characteristics from massive amounts of data, they often lack clear physical drivers. These models cannot clearly answer the microscopic physical mechanisms behind acoustic emission events, leading to doubts about their extrapolation and reliability. When materials, processes, or service environments change, models based on pure data fitting may quickly fail.

[0004] Meanwhile, existing acoustic emission techniques struggle to establish a quantitative bridge between microscopic damage mechanisms and macroscopic acoustic responses. While traditional spectral analysis can provide frequency information, it typically stops at interpreting the signal's appearance and fails to correlate observed frequency characteristics with the material's inherent crystal structure, chemical bond strength, and other essential properties.

[0005] In summary, existing acoustic emission technology remains largely an analytical tool based on appearance and experience, failing to fundamentally link the characteristics of acoustic emission signals directly and quantitatively with the invisible damage evolution within materials. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the fact that the existing acoustic emission material damage analysis methods cannot achieve effective damage analysis from the physical characterization analysis of the acoustic emission damage signal of the material, thereby providing a method and system for predicting the lifetime of acoustic emission materials based on stress wave frequency.

[0007] A method for predicting the lifetime of acoustic emission materials based on stress wave frequency includes the following steps:

[0008] Step S1:

[0009] For the initial test material, acoustic emission signals are acquired using an acoustic emission sensor, and the initial peak frequency f0 is obtained based on the acoustic emission signals.

[0010] The damage distribution factor S_damage and the geometric structure factor G_structure are obtained based on the characteristics of the material under test.

[0011] The damage sensitivity coefficient α is calculated based on the damage distribution factor S_damage and the geometric structure factor G_structure of the material under test.

[0012] Step S2:

[0013] For the material under test, acoustic emission signals are acquired using an acoustic emission sensor, and the peak frequency f(t) is obtained based on the acoustic emission signals.

[0014] Step S3:

[0015] Based on the initial peak frequency f0 and the peak frequency f(t), the damage value D of the material under test is calculated, where the damage value D is the ratio of the number of broken crystal bonds to the initial number of crystal bonds in the material under test.

[0016] f(t) = f0[1 - αD];

[0017] Step S4:

[0018] Predict the lifetime of the material under test based on the damage value D;

[0019] Based on steps S1-S3, multiple damage values ​​D of the material under test are obtained. A time curve of the damage values ​​D of the material under test is fitted based on the damage values ​​D. The corresponding time of the preset damage threshold is calculated based on the time curve to obtain the lifespan of the material under test.

[0020] Furthermore, the following methodological steps are also included:

[0021] The method for calculating the damage sensitivity coefficient α is expressed as follows:

[0022] α = (1 / 2) × S_damage × G_structure;

[0023] Where S_damage represents the damage distribution factor and G_structure represents the geometric structure factor;

[0024] The calculation method for the geometric structure factor G_structure is expressed as follows:

[0025] G_structure = √(Z_eff / Z_max);

[0026] Where Z_eff represents the coordination number of each atom in the crystal structure of the material under test, and Z_max represents the maximum coordination number of the atom.

[0027] Furthermore, the following methodological steps are also included:

[0028] For the test material with an FCC crystal structure, the calculation method for the damage distribution factor S_damage is expressed as follows:

[0029] S_FCC = 1 / (M × √Z_eff);

[0030] Where M represents the Taylor factor and Z_eff represents the coordination number of each atom in the crystal structure.

[0031] Furthermore, the following methodological steps are also included:

[0032] For the test material with a BCC crystal structure, the damage distribution factor S_damage is calculated as follows:

[0033] S_BCC = (1 / √Z_eff) × localization factor × (1 / M);

[0034] Where Z_eff represents the coordination number of each atom in the crystal structure, the localization factor is a preset value for the crystal structure, and M represents the Taylor factor.

[0035] Furthermore, the following methodological steps are also included:

[0036] For the test material with an HCP crystal structure, the damage distribution factor S_damage is calculated as follows:

[0037] S_HCP = (1 / √Z_eff) × CRSS_anisotropy × (1 / M);

[0038] Where Z_eff represents the coordination number of each atom in the crystal structure, CRSS_anisotropy represents the critical anisotropic shear stress factor, and M represents the Taylor factor.

[0039] Furthermore, the following methodological steps are also included:

[0040] The damage sensitivity coefficient α is proportionally corrected according to the stress concentration factor K, which is related to the crystal structure of the material under test.

[0041] For the test material with an FCC crystal structure, the calculation factors for the stress concentration factor include those expressed as follows:

[0042] K_FCC = √(E_cleavage / E_dislocation);

[0043] E_dislocation = Gb² / 2;

[0044] E_cleavag = 2γ_s;

[0045] Where E_dislocation represents dislocation emission energy, E_cleavage represents cleavage energy, G represents shear modulus, b represents Burgers vector of dislocation, and γ_s represents surface energy;

[0046] For the test material with a BCC crystal structure, the calculation factors for the stress concentration factor include those expressed as follows:

[0047] K_BCC = 1 + β×(T / T_BDT)

[0048] Where T_BDT represents the brittle-ductile transition temperature, β represents the material parameter, and T represents the temperature;

[0049] For the test material with an HCP crystal structure, the calculation factors for the stress concentration factor include those expressed as follows:

[0050] K_HCP = 1 + (c / a - κ_ideal) × α_sens;

[0051] Where a represents the side length of the basal plane of the cell of the material under test, c represents the height of the cell of the material under test, κ_ideal is the ideal close-packed c / a axis ratio, and α_sens represents the brittleness sensitivity coefficient.

[0052] Furthermore, predicting the lifetime of the material under test based on the damage value D includes the following method steps:

[0053] Step S4.1: Extract time series data of damage values;

[0054] Step S4.2: Based on the damage evolution model, the damage evolution equation of the current material under test is obtained by fitting the time series data of the damage value;

[0055] Step S4.3: Preset a critical damage threshold, integrate the damage evolution equation from the current state to the critical state to obtain an analytical expression for the remaining time; obtain the latest damage state measurement value, and calculate the lifetime based on the analytical expression for the remaining time.

[0056] Furthermore, the damage evolution model is a power-law model in the form of Paris's law, expressed as:

[0057] dD / dt = C × D^m;

[0058] Where D represents the damage value, t represents time, C represents the damage coefficient, and m represents the damage index.

[0059] A lifetime prediction system for acoustic emission materials based on stress wave frequency, used to implement the aforementioned lifetime prediction method for acoustic emission materials, includes:

[0060] Acoustic emission sensor, acquires acoustic emission signals, and obtains peak frequency based on acoustic emission signals;

[0061] The damage sensitivity coefficient calculation module is used to obtain the damage distribution factor and geometric structure factor based on the characteristics of the material under test, and to calculate the damage sensitivity coefficient based on the damage distribution factor and geometric structure factor.

[0062] The damage value calculation module calculates the damage value of the material under test based on the initial peak frequency and the peak frequency;

[0063] A lifetime prediction module is used to predict the lifetime of the material under test based on the damage value.

[0064] A lifetime prediction module is used to predict the lifetime of the material under test based on the peak frequency.

[0065] Furthermore, it also includes:

[0066] The propagation effect correction module is used to obtain the signal distance and calculate the corrected peak frequency based on the peak frequency and the distance.

[0067] Beneficial effects:

[0068] This invention establishes a deterministic physical model of the peak frequency offset to the proportion of microcrystalline bond breakage. For the first time, this invention transforms invisible material damage into a continuously and accurately measurable frequency signal. As a fundamental physical quantity, frequency measurement results are not significantly affected by sensor position, coupling state, or the magnitude of external load. This achieves a fundamental shift in damage assessment from indirect and relative to direct and absolute, making damage assessment results in different scenarios directly comparable.

[0069] This invention introduces a damage distribution factor and a geometric structure factor to reflect the natural frequency selectivity of the material's crystal structure, and integrates them into a damage sensitivity coefficient. This allows the method to fully consider individual differences in the microstructure and macroscopic shape of materials, achieving truly targeted and personalized lifetime prediction with strong universality.

[0070] This invention provides a complete physical picture, directly linking the breaking of crystal bonds at the atomic / lattice scale to the macroscopic remaining lifetime of a component through frequency variations. This makes it possible to directly invert the fundamental mechanical properties of materials and predict their remaining lifetime using acoustic emission signals. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a schematic flowchart of the method steps of the present invention. Detailed Implementation

[0073] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0074] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0075] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0076] The principle of stress wave displacement under acoustic emission measurement: When acoustic emission measurement is performed on a material, the crystal bonds of the material break and recombine. Atoms rely on crystal bonds to maintain their relative positions. When a pair of atoms loses its original balance due to the breaking of crystal bonds, the remaining crystal bonds have to bear the force of the broken bonds. At this time, due to the displacement of atoms, their adjacent atoms will also be displaced. The broken crystal bonds will bring new stretching forces to the other crystal bonds that are maintaining normality. The recombination of the structure will lead to the high-speed rearrangement and oscillation of the crystal itself, forming stress waves.

[0077] Specifically, when crystal bonds break, the bond energy is 10. - ¹ 5 The energy is released as an elastic wave packet within a second, with a frequency component of f = E / h. At this time, the energy is transmitted outward at the highest frequency that can be transmitted, and the signal is down-frequencyd to f_acoustic≈ 100-800 kHz through phonon interaction.

[0078] After a crystal bond breaks, the number of effective crystal bonds decreases. Therefore, when the crystal bonds break again, the number of crystal bonds is even smaller, the time to reach a steady state is longer, and the frequency will be slightly shifted downwards in sync.

[0079] Before a single crystal bond breaks, k_single bond = dF / dx = d²U / dx²; after it breaks, the k value of the bond becomes 0, and the adjacent crystal bonds bear the additional load, so the local equivalent stiffness is reduced by about 1 / N, where N represents the coordination number.

[0080] Here, we take the Fe-Fe bond in steel as an example. For a cluster of broken bonds, its original frequency is expressed as: f0 = (1 / 2π)√(ΣK_intact / M_equivalent) = (1 / 2π)√(N×k_bond / M). After n bonds break, f_new = (1 / 2π)√((Nn)×k_bond / M) = f0×√(1 - n / N). When the damage is small (n / N<<1), f_new ≈ f0×(1 - n / (2N)).

[0081] For a completely new material, S(f) = A × f^(-1) × exp(-f / f_cutoff), its peak frequency is f_peak = f_cutoff / 2; after some of its crystal bonds break, the new cutoff frequency f'_cutoff = f_cutoff × √(1 - damage), and the new peak frequency f'_peak = f_peak × √(1 - damage).

[0082] Therefore, for materials with different crystal structures, their damage values ​​can be calculated based on the shift in their peak frequency, according to their different characteristics. Based on this, the present invention proposes a method for predicting the lifetime of acoustic emission materials based on stress wave frequency.

[0083] Example 1:

[0084] Reference Figure 1 As shown, this embodiment provides a method for predicting the lifetime of acoustic emission materials based on stress wave frequency, including the following steps:

[0085] Step S1: For the initial test material, the acoustic emission signal is acquired through the acoustic emission sensor, and the initial peak frequency f0 is obtained based on the acoustic emission signal; the initial test material is the new test material, that is, the state of the test material when it has not been damaged by crystal bond breakage at the beginning.

[0086] The damage distribution factor S_damage and the geometric structure factor G_structure are obtained based on the characteristics of the material under test.

[0087] In some embodiments of this example, the damage distribution factor S_damage and the geometric structure factor G_structure are only related to the crystal structure of the material under test.

[0088] The damage sensitivity coefficient α is calculated based on the damage distribution factor S_damage and the geometric structure factor G_structure of the material under test.

[0089] In this embodiment, for the test material with an FCC crystal structure, the calculation method of the damage distribution factor S_damage is expressed as follows:

[0090] S_FCC = 1 / (M × √Z_eff);

[0091] Where M represents the Taylor factor, which ranges from 3.0 to 3.1, Z_eff represents the coordination number of each atom in the crystal structure, and FCC is 12.

[0092] For the test material with a BCC crystal structure, the damage distribution factor S_damage is calculated as follows:

[0093] S_BCC = (1 / √Z_eff) × localization factor × (1 / M);

[0094] Where Z_eff represents the coordination number of each atom in the crystal structure, BCC is 8, the localization factor is a preset value for the crystal structure, ranging from 1.2 to 1.8, and M represents the Taylor factor, ranging from 2.8 to 3.0.

[0095] For the test material with an HCP crystal structure, the damage distribution factor S_damage is calculated as follows:

[0096] S_HCP = (1 / √Z_eff) × CRSS_anisotropy × (1 / M);

[0097] Where Z_eff represents the coordination number of each atom in the crystal structure, HCP is 6, CRSS_anisotropy represents the critical anisotropic shear stress factor with a value of 2.0-3.0, and M represents the Taylor factor with a value of 2.0-2.5.

[0098] Specifically, the method for calculating the damage sensitivity coefficient α is expressed as follows:

[0099] α = (1 / 2) × S_damage × G_structure;

[0100] Where S_damage represents the damage distribution factor and G_structure represents the geometric structure factor;

[0101] The calculation method for the geometric structure factor G_structure is expressed as follows:

[0102] G_structure = √(Z_eff / Z_max);

[0103] Where Z_eff represents the coordination number of each atom in the crystal structure of the material under test, and Z_max represents the maximum coordination number of the atom.

[0104] In other embodiments of this example, the damage sensitivity coefficient α is obtained by looking up a table, wherein for the FCC structure, α = 0.20-0.30; for the BCC structure, α = 0.30-0.40; and for the HCP structure, α = 0.40-0.50.

[0105] Step S2: For the material to be tested, acquire the acoustic emission signal through the acoustic emission sensor, and obtain the peak frequency f(t) based on the acoustic emission signal;

[0106] Specifically, step S2 includes the following method steps:

[0107] S2.1: Acoustic emission signal acquisition: Acoustic emission signal u(t) is acquired through an acoustic emission sensor during the service life of the material under test;

[0108] S2.2: Spectrum analysis, performing Fast Fourier Transform (FFT) on the acoustic emission signal to calculate the power spectral density PSD(f);

[0109] S2.3: Peak frequency extraction: f(t) = argmax{PSD(f)};

[0110] As a further improvement to this embodiment, step S2.4 is also included: multi-band feature extraction, which extracts multiple frequency peaks including the main peak frequency and the secondary peak frequency, and extracts auxiliary features such as spectral bandwidth and frequency centroid; multi-band feature extraction can improve prediction reliability.

[0111] As a further improvement to this embodiment, it also includes:

[0112] S2.5: Correction for propagation effect of peak frequency;

[0113] First, the longitudinal wave velocity of the material to be tested is obtained as a reference for wave propagation velocity. In this embodiment, the longitudinal wave velocities of steel, aluminum, and titanium are 5960 m / s, 6320 m / s, and 6070 m / s, respectively.

[0114] Based on the empirical formula α(f) = α0×f n The material's corresponding α0 and exponent n are determined, where α is the attenuation coefficient and f is the frequency. In this embodiment, α0 is 0.01 Np / (m²) for steel. (MHz), aluminum is 0.005 Np / (m (MHz), the exponent n is 1.5 for steel and 1.3 for aluminum.

[0115] For thin plate materials under test with thickness h < 10 × wavelength, the critical frequency f_critical = c / (2h) is calculated as the threshold for determining whether it enters the dispersion region, where c represents the longitudinal wave velocity and h represents the material thickness.

[0116] Considering the faster attenuation of high-frequency components during wave propagation, the amplitude attenuation is calculated as follows: Attenuation in decibels = α0 × (measurement frequency / 1e6) n × Propagation distance × 8.686, where 8.686 is the conversion factor between neperts and decibels. Calculate the frequency offset: delta_f_attenuation = -0.05 × measurement frequency × (α0 × measurement frequency) n × (propagation distance), the negative sign indicates a shift to lower frequencies. Here, because high-frequency attenuation is more significant, the peak frequency will shift to lower frequencies.

[0117] Based on the relationship between the measured frequency and the critical frequency, calculate the dispersion effect of wave propagation in the thin plate:

[0118] If the measured frequency is greater than 0.5 × critical frequency, then the region of dispersion is entered. The group velocity (energy propagation speed) is not equal to the phase velocity (longitudinal wave velocity). The group velocity is calculated as v_group = c × √[1 - (critical frequency / measured frequency)²]. Dispersion will cause frequency broadening. The dispersion frequency offset is calculated as delta_f_dispersion = 0.1 × (measured frequency - critical frequency).

[0119] If the measured frequency is ≤ 0.5 × critical frequency, when entering the low dispersion region, the group velocity is approximately equal to the longitudinal wave velocity, and the dispersion frequency shift is 0.

[0120] Subtract the frequency offset caused by attenuation and the frequency offset caused by dispersion from the measured frequency to obtain the corrected measured frequency: f_source = measured frequency - delta_f_attenuation - delta_f_dispersion.

[0121] The greater the distance, the more significant the effects of attenuation and dispersion become, and the lower the confidence level of the results. The confidence level formula is 1.0 / (1 + 0.1 × propagation distance).

[0122] Step S3: Based on the initial peak frequency f0 and the peak frequency f(t), calculate the damage value D of the material under test, where the damage value D is the ratio of the number of broken crystal bonds to the initial number of crystal bonds in the material under test;

[0123] f(t) = f0[1 - αD(t)];

[0124] In this embodiment, f(t) = f0[1 - αD] is a small damage linearization approximation of the Taylor expansion of √(1-D) ≈ 1 - D / 2. When the damage D < 20%, the error is < 5%, and the calculation is simplified by the above formula.

[0125] In this embodiment, the method further includes: checking whether the damage value meets the physical constraint, wherein the physical constraint is 0 ≤ D(t) ≤ 1. If the physical constraint is not met, the damage value D of the obtained material to be tested is discarded. If multiple frequency peaks are extracted in step S3, the damage values ​​are calculated separately and a consistency check is performed.

[0126] D1 = [1 - f1(t) / f 01 ] / α1;

[0127] D2 = [1 - f2(t) / f 02 ] / α2;

[0128] …

[0129] The average of multiple frequency peaks is taken as the final damage value.

[0130] In some implementations of this embodiment, the final damage value is also calculated using a consistency coefficient:

[0131] CV = σ_D / mean(D).

[0132] Step S4: Predict the lifetime of the material under test based on the damage value D;

[0133] Based on steps S1-S3, multiple damage values ​​D of the material under test are obtained. A time curve of the damage values ​​D of the material under test is fitted based on the damage values ​​D. The corresponding time of the preset damage threshold is calculated based on the time curve to obtain the lifespan of the material under test.

[0134] Specifically, it includes the following steps:

[0135] Step S4.1: Extract time series data of damage values:

[0136] {(t1, D1), (t2, D2), ..., (t n D n )};

[0137] Among them, t n D represents the nth time point. n This represents the damage value at the nth time point;

[0138] Step S4.2: Based on the damage evolution model, the damage evolution equation of the current material under test is obtained by fitting the time series data of the damage value;

[0139] In this embodiment, the damage evolution model is a power-law model in the form of Paris's law, expressed as:

[0140] dD / dt = C × D^m;

[0141] Where D represents the damage value, t represents time, C represents the damage coefficient, and m represents the damage index.

[0142] The parameters C and m are fitted based on the time series data of the damage values ​​using the least squares method or other fitting methods.

[0143] Step S4.3: Preset the critical damage threshold D_critical, integrate the damage evolution equation from the current state to the critical state to obtain the analytical expression of the remaining time; obtain the latest damage state measurement value, and calculate the lifetime based on the analytical expression of the remaining time.

[0144] For tough materials, the critical damage threshold D_critical ranges from 0.20 to 0.30; for brittle materials, the critical damage threshold D_critical ranges from 0.10 to 0.15; and for high-toughness materials, the critical damage threshold D_critical ranges from 0.40 to 0.50.

[0145] Integrating the damage evolution equation from the current state D_current to the critical state D_critical:

[0146] t_remaining = ∫_{D_current}^{D_critical} dD / (C × D^m);

[0147] The analytical solution is (when m ≠ 1):

[0148] t_remaining = [D_critical^(1-m) - D_current^(1-m)] / [(1-m) × C];

[0149] The special case is (when m = 1):

[0150] t_remaining = ln(D_critical / D_current) / C; This yields the lifetime of the material under test.

[0151] The remaining life and total life of the material under test are obtained by calculating the corresponding time of the preset damage threshold based on the time curve.

[0152] As a further improvement to this embodiment, the damage sensitivity coefficient α is proportionally corrected according to the stress concentration factor K, which is related to the crystal structure of the material under test.

[0153] For the test material with an FCC crystal structure, the calculation factors for the stress concentration factor include those expressed as follows:

[0154] K_FCC = √(E_cleavage / E_dislocation);

[0155] E_dislocation = Gb² / 2;

[0156] E_cleavag = 2γ_s;

[0157] Where E_dislocation represents dislocation emission energy, E_cleavage represents cleavage energy, G represents shear modulus, b represents Burgers vector of dislocation, and γ_s represents surface energy;

[0158] For the test material with a BCC crystal structure, the calculation factors for the stress concentration factor include those expressed as follows:

[0159] K_BCC = 1 + β×(T / T_BDT)

[0160] Where T_BDT represents the brittle-ductile transition temperature, a material-related constant, approximately 200K for steel; β represents the material parameter, approximately 0.7 for steel; and T represents the temperature, i.e., the actual measured operating temperature of the material.

[0161] Specifically, in steel materials operating at room temperature (300K):

[0162] K_BCC = 1 + 0.7×(300 / 200) = 2.05;

[0163] In steel materials operating at low temperatures (100K):

[0164] K_BCC = 1 + 0.7×(100 / 200) = 1.35;

[0165] For the test material with an HCP crystal structure, the calculation factors for the stress concentration factor include those expressed as follows:

[0166] K_HCP = 1 + (c / a - κ_ideal) × α_sens;

[0167] K_HCP is determined by the unit cell assembly parameters and is independent of temperature. Here, 'a' represents the edge length of the basal plane of the unit cell of the material under test, 'c' represents the height of the unit cell, κ_ideal is the ideal close-packed c / a axis ratio, and α_sens is the brittleness sensitivity coefficient, a material-dependent constant; for an ideal hexagonal close-packed structure, κ_ideal = 1.633.

[0168] Example 2:

[0169] This embodiment provides a lifetime prediction system for acoustic emission materials based on stress wave frequency, used to implement the lifetime prediction method for acoustic emission materials described in Embodiment 1, including:

[0170] Acoustic emission sensor, acquires acoustic emission signals, and obtains peak frequency based on acoustic emission signals;

[0171] The damage sensitivity coefficient calculation module is used to obtain the damage distribution factor and geometric structure factor based on the characteristics of the material under test, and to calculate the damage sensitivity coefficient based on the damage distribution factor and geometric structure factor.

[0172] The damage value calculation module calculates the damage value of the material under test based on the initial peak frequency and the peak frequency;

[0173] A lifetime prediction module is used to predict the lifetime of the material under test based on the damage value.

[0174] The propagation effect correction module is used to obtain the signal distance and calculate the corrected peak frequency based on the peak frequency and the distance. The propagation effect correction is as described in Embodiment 1.

[0175] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0176] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for predicting the lifetime of acoustic emission materials based on stress wave frequency, characterized in that, include: For the initial test material, acoustic emission signals are acquired using an acoustic emission sensor, and the initial peak frequency is obtained based on the acoustic emission signals; Damage distribution factor and geometric structure factor are obtained based on the characteristics of the material under test; For the test material with an FCC crystal structure, the calculation method for the damage distribution factor S_damage is expressed as follows: S_FCC = 1 / (M × ); Where M represents the Taylor factor and Z_eff represents the coordination number of each atom in the crystal structure; For the test material with a BCC crystal structure, the damage distribution factor S_damage is calculated as follows: S_BCC = (1 / ) × Localization factor × (1 / M); Where Z_eff represents the coordination number of each atom in the crystal structure, the localization factor is a preset value for the crystal structure, and M represents the Taylor factor; For the test material with an HCP crystal structure, the damage distribution factor S_damage is calculated as follows: S_HCP = (1 / ) × CRSS_anisotropy × (1 / M); Where Z_eff represents the coordination number of each atom in the crystal structure, CRSS_anisotropy represents the critical anisotropic shear stress factor, and M represents the Taylor factor; The calculation method for the geometric structure factor G_structure is expressed as follows: G_structure = ; Where Z_eff represents the coordination number of each atom in the crystal structure of the material under test, and Z_max represents the maximum coordination number of the atom; The damage sensitivity coefficient is calculated based on the damage distribution factor and geometric structure factor. The method for calculating the damage sensitivity coefficient is expressed as follows: α = (1 / 2) × S_damage × G_structure; Where S_damage represents the damage distribution factor and G_structure represents the geometric structure factor; For the material under test, acoustic emission signals are acquired using acoustic emission sensors, and the peak frequency is obtained based on the acoustic emission signals. Based on the initial peak frequency and the peak frequency, the damage value of the material under test is calculated. The damage value is the ratio of the number of broken crystal bonds in the material under test to the initial number of crystal bonds, expressed as: f(t) = f0[1 - αD(t)]; Where f0 represents the initial peak frequency, f(t) represents the peak frequency, α represents the damage sensitivity coefficient, and D(t) represents the damage value; Predicting the lifespan of the material under test based on the damage values ​​includes: acquiring multiple damage values ​​of the material under test, fitting a time curve of the damage values ​​of the material under test based on the damage values, calculating the corresponding time of a preset damage threshold based on the time curve, and obtaining the lifespan of the material under test.

2. The method for predicting the lifetime of acoustic emission materials based on stress wave frequency according to claim 1, characterized in that, The damage sensitivity coefficient α is proportionally corrected according to the stress concentration factor K, which is related to the crystal structure of the material under test. For the test material with an FCC crystal structure, the calculation factors for the stress concentration factor include those expressed as follows: K_FCC = ; E_dislocation = Gb² / 2; E_cleavag = 2γ_s; Where E_dislocation represents dislocation emission energy, E_cleavage represents cleavage energy, G represents shear modulus, b represents Burgers vector of dislocation, and γ_s represents surface energy; For the test material with a BCC crystal structure, the calculation factors for the stress concentration factor include those expressed as follows: K_BCC = 1 + β×(T / T_BDT) Where T_BDT represents the brittle-ductile transition temperature, β represents the material parameter, and T represents the temperature; For the test material with an HCP crystal structure, the calculation factors for the stress concentration factor include those expressed as follows: K_HCP = 1 + (c / a - κ_ideal) × α_sens; Where a represents the side length of the basal plane of the cell of the material under test, c represents the height of the cell of the material under test, κ_ideal is the ideal close-packed c / a axis ratio, and α_sens represents the brittleness sensitivity coefficient.

3. The method for predicting the lifetime of acoustic emission materials based on stress wave frequency according to claim 1, characterized in that, Predicting the lifetime of the material under test based on the damage value includes: Extract time-series data of damage values; Based on the damage evolution model, the damage evolution equation of the material under test is obtained by fitting the time series data of the damage value. A critical damage threshold is preset, and the damage evolution equation is integrated from the current state to the critical state to obtain an analytical expression for the remaining time; the latest damage state measurement value is obtained, and the lifetime is calculated based on the analytical expression for the remaining time.

4. The method for predicting the lifetime of acoustic emission materials based on stress wave frequency according to claim 3, characterized in that, The damage evolution model is a power-law model in the form of Paris's law, expressed as: dD / dt = C × D^m; Where D represents the damage value, t represents time, C represents the damage coefficient, and m represents the damage index.

5. A system for predicting the lifetime of acoustic emission materials based on stress wave frequency, used to implement the method for predicting the lifetime of acoustic emission materials based on stress wave frequency as described in any one of claims 1-4, characterized in that, include: Acoustic emission sensor, acquires acoustic emission signals, and obtains peak frequency based on acoustic emission signals; The damage sensitivity coefficient calculation module is used to obtain the damage distribution factor and geometric structure factor based on the characteristics of the material under test, and to calculate the damage sensitivity coefficient based on the damage distribution factor and geometric structure factor. The damage value calculation module calculates the damage value of the material under test based on the initial peak frequency and the peak frequency; A lifetime prediction module is used to predict the lifetime of the material under test based on the damage value.

6. The acoustic emission material lifetime prediction system based on stress wave frequency according to claim 5, characterized in that, Also includes: The propagation effect correction module is used to obtain the signal distance and calculate the corrected peak frequency based on the peak frequency and the distance.

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