Fiber-reinforced thermosetting thermoplastic composite structure interface quality detection method and system

Abstract

The invention relates to the technical field of composite material detection, in particular to a fiber-reinforced thermosetting thermoplastic composite structure interface quality detection method and system.The method comprises the steps that vibration excitation is applied to a target area of the surface of a to-be-detected sample, and the amplitude of the excitation is lower than the damage critical value of a weak bonding interface; a dynamic response signal perpendicular to the interface is collected in a vibration field non-diffusion area of the excitation point of the target area; performing frequency domain transformation on the dynamic response signal, and separating a fundamental frequency component and at least one high-order harmonic frequency component; according to the nonlinear enhancement effect of the high-order harmonic frequency component relative to the fundamental wave frequency component, whether the weak bonding defect exists in the target area or not is judged. According to the method, the technical bottleneck that the dynamic evolution process of the weak bonding defect of the composite material interface is difficult to capture in real time is effectively solved, sensitive response to the microdefect is realized through a nonlinear harmonic characteristic enhancement effect, and the recognition precision of traditional ultrasonic detection is improved.

Description

Technical Field

[0001] This invention relates to the field of composite material testing technology, and in particular to a method and system for testing the interface quality of fiber-reinforced thermosetting and thermoplastic composite structures. Background Technology

[0002] Fiber-reinforced thermosetting resin and thermoplastic resin composite structures have been widely used in high-end equipment fields such as aerospace and new energy vehicles due to their comprehensive performance advantages. The core load-bearing capacity of such structures is highly dependent on the interfacial bonding quality between the thermosetting resin matrix and the thermoplastic resin matrix. However, due to the inherent differences between the two types of resins in terms of molecular structure, thermal expansion characteristics and surface energy, a special type of defect—weak adhesion—is easily formed at their interface. This defect is characterized by a significant reduction in the interfacial molecular bonding force, but no macroscopic separation or porosity occurs, which is almost impossible to identify by traditional non-destructive testing methods.

[0003] Current mainstream nondestructive testing methods have fundamental limitations in detecting this type of weak bond defect: ultrasonic testing is difficult to effectively extract features because the acoustic impedance difference at the weak bond interface is very small, and the change in echo signal is lower than the system signal-to-noise ratio; X-ray and industrial CT technologies rely on density difference imaging, but there is no density change in the weak bond area, so no discernible contrast can be generated; infrared thermography is not sensitive to small changes in the thermal conductivity of the interface, and the resulting temperature field disturbance is lower than the equipment resolution threshold. Although existing nonlinear ultrasonic technology is sensitive to the nonlinear effects of the interface, the high-energy excitation it relies on can directly damage the weak bond interface itself, leading to distorted test results; destructive mechanical testing can quantify the interface strength, but it cannot be used for full component inspection and in-service evaluation. In engineering practice, weak bond defects are extremely difficult to detect. Components that pass routine quality inspection can rapidly expand into macroscopic debonding under dynamic loads or environmental aging, causing sudden structural failure. Summary of the Invention

[0004] This invention provides a method and system for detecting the interface quality of fiber-reinforced thermosetting and thermoplastic composite structures, thereby effectively solving the problems pointed out in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for testing the interface quality of fiber-reinforced thermosetting-thermoplastic composite structures includes:

[0007] Vibrational excitation is applied to the target area of ​​the surface of the test sample, wherein the amplitude of the excitation is lower than the damage threshold of the weakly bonded interface;

[0008] Within the non-diffusion region of the vibration field at the excitation point in the target area, a dynamic response signal perpendicular to the interface is acquired.

[0009] The dynamic response signal is subjected to frequency domain transformation to separate the fundamental frequency component and at least one higher-order harmonic frequency component.

[0010] Based on the nonlinear enhancement effect of the higher-order harmonic frequency components relative to the fundamental frequency components, it is determined whether there is a weak bonding defect in the target area.

[0011] Furthermore, the amplitude of the excitation satisfies:

[0012]

[0013] Where, σ ex Let F be the stress amplitude, F be the transient force peak value, A be the effective contact area between the exciter and the interface under test, and E be the stress amplitude. int Let ε be the elastic modulus of the interface layer. y This represents the yield strain of the interface layer.

[0014] Furthermore, within the non-diffusion region of the vibration field at the excitation point in the target area, a dynamic response signal perpendicular to the interface is acquired, including:

[0015] Based on the excitation frequency and the material sound velocity parameters, the coherence length boundary of the vibration field is calculated, and a non-diffusion region with a radius smaller than the coherence length and centered on the excitation point is determined.

[0016] Within the non-diffusion region, particle vibration displacement signals along the interface normal direction are continuously acquired;

[0017] The acquired signals are processed to suppress interference from non-interface normal directions.

[0018] Furthermore, the dynamic response signal is subjected to frequency domain transformation to separate the fundamental frequency component and at least one higher-order harmonic frequency component, including:

[0019] A windowing function is used to perform frequency domain transformation on the dynamic response signal to generate a joint time-frequency distribution map.

[0020] Based on the time-frequency joint distribution map, the fundamental frequency component corresponding to the excitation frequency is extracted, and the higher harmonic frequency components that conform to the energy intensity are screened out.

[0021] Based on the fundamental wave amplitude value and the integer-order higher harmonic amplitude values, a nonlinear calculation is performed to obtain the enhancement factor.

[0022] Furthermore, the calculation formula for the nonlinear calculation is as follows:

[0023]

[0024] Where, β nis the nth nonlinear enhancement factor, where n is the harmonic order and its value ranges from n=2,3,4,5. ||S(nf0)|| is the extracted fundamental component amplitude, and ||S(f0)|| is the extracted nth harmonic component amplitude.

[0025] Furthermore, based on the nonlinear enhancement effect of the higher-order harmonic frequency components relative to the fundamental frequency components, it is determined whether a weak bonding defect exists in the target region, including:

[0026] The sequence of the enhancement factors is mapped to a three-dimensional feature space to generate real-time dynamic feature trajectories.

[0027] The real-time dynamic feature trajectory is compared with the pre-established typical defect evolution map library for similarity.

[0028] When the matching similarity between the real-time dynamic feature trajectory and any type of defect in the typical defect evolution map library exceeds a preset critical confidence level, the corresponding quality detection result is output.

[0029] Furthermore, the vibration excitation is a composite excitation, with the frequency of the main vibration component covering the entire frequency band of the standing wave effect generated by the interface layer, and the auxiliary vibration component maintaining a fixed phase difference with the main vibration component.

[0030] Furthermore, the real-time dynamic feature trajectory is compared with a pre-established typical defect evolution map library for similarity, including:

[0031] Calculate the morphological similarity parameter between the real-time dynamic feature trajectory and the standard defect trajectory in the typical defect evolution map library;

[0032] Calculate the similarity parameter of the evolution rate between the real-time dynamic feature trajectory and the standard defect trajectory;

[0033] The morphological detail parameter, evolution rate, and similarity parameter are input into the confidence fusion decision-maker to determine the defect type.

[0034] A fiber-reinforced thermosetting-thermoplastic composite structure interface quality testing system includes:

[0035] The vibration excitation application module applies vibration excitation to the target area on the surface of the test sample, wherein the amplitude of the excitation is lower than the damage threshold of the weak bonding interface.

[0036] The response signal acquisition module acquires dynamic response signals perpendicular to the interface within the non-diffusion region of the vibration field at the excitation point in the target area.

[0037] The signal frequency domain transformation module performs frequency domain transformation on the dynamic response signal to separate the fundamental frequency component and at least one higher-order harmonic frequency component.

[0038] The weak bonding defect judgment module determines whether a weak bonding defect exists in the target area based on the nonlinear enhancement effect of the higher-order harmonic frequency components relative to the fundamental frequency components.

[0039] Furthermore, the signal frequency domain transformation module includes:

[0040] The distribution map generation unit uses a windowing function to perform frequency domain transformation on the dynamic response signal to generate a time-frequency joint distribution map.

[0041] The frequency component extraction unit extracts the fundamental frequency component corresponding to the excitation frequency based on the time-frequency joint distribution map, and filters out the higher-order harmonic frequency components that conform to the energy intensity.

[0042] The nonlinear calculation unit performs nonlinear calculations based on the fundamental wave amplitude value and the integer-order higher harmonic amplitude values ​​to obtain the enhancement factor.

[0043] The technical solution of this invention can achieve the following technical effects:

[0044] This technology effectively solves the technical bottleneck of the difficulty in real-time capture of the dynamic evolution process of weak bonding defects at composite material interfaces. It achieves a sensitive response to micro-defects through the nonlinear harmonic characteristic enhancement effect, thereby improving the identification accuracy of traditional ultrasonic testing. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating a method for testing the interface quality of fiber-reinforced thermosetting and thermoplastic composite structures.

[0047] Figure 2 A flowchart illustrating the process of acquiring dynamic response signals perpendicular to the interface;

[0048] Figure 3 This is a flowchart illustrating the frequency domain transformation of a dynamic response signal.

[0049] Figure 4 A flowchart illustrating the process for determining whether a target area has weak bonding defects;

[0050] Figure 5 This is a flowchart illustrating the process of comparing real-time dynamic feature trajectories with a library of typical defect evolution maps. Detailed Implementation

[0051] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0053] Example 1

[0054] like Figure 1 As shown, this invention provides a method for detecting the interface quality of fiber-reinforced thermosetting-thermoplastic composite structures, the method comprising:

[0055] S1: Apply vibration excitation to the target area of ​​the test sample surface, and the amplitude of the excitation is lower than the damage threshold of the weak bonding interface.

[0056] Specifically, a piezoelectric transducer is used to apply precisely controlled mechanical vibration excitation to the target area on the sample surface. The excitation amplitude is rigorously calibrated and stably maintained below the damage threshold of the weakly bonded interface, remaining in a subcritical state. This operation, by precisely suppressing the excitation energy level, ensures that no new damage to the interface is caused while selectively activating the unique microscopic nonlinear dynamic effects of the weakly bonded region, typically manifested as nonlinear contact acoustic phenomena. In contrast, the intact interface only produces a conventional linear vibration response. The original vibration signal obtained in this way retains the sensitive characteristics of the interface defects while avoiding external interference, laying the foundation for the extraction of higher harmonic aberration components and the construction of a dynamic identification model in subsequent steps.

[0057] S2: In the non-diffusion region of the vibration field at the excitation point in the target area, collect the dynamic response signal perpendicular to the interface;

[0058] Specifically, in the non-diffused wave field region near the vibration excitation point, a high-precision laser Doppler vibrometer is used to directionally acquire dynamic response signals perpendicular to the bonding interface. This operation focuses on the near-field region where the vibration energy has not yet dissipated, and utilizes the spatial resolution capability of the vibrometer to capture the normal vibration component directly related to the mechanical state of the interface. The acquired time-domain signal completely preserves the high-order harmonic characteristics generated by the contact nonlinearity effect in the weak bonding region, while avoiding the problems of far-field signal attenuation and mode mixing. This high signal-to-noise ratio raw data provides key input for the subsequent nonlinear feature extraction steps.

[0059] S3: Perform frequency domain transformation on the dynamic response signal to separate the fundamental frequency component and at least one higher-order harmonic frequency component.

[0060] Specifically, time-frequency joint decoupling analysis is performed on the dynamic response signal to accurately separate and purify the fundamental component and high-order harmonic components with significant energy that are strictly consistent with the excitation frequency. By applying a window function and short-time Fourier transform, the time-domain signal is transformed into a time-frequency energy distribution spectrum. Based on a preset energy intensity threshold, physically real integer harmonic components are screened. Finally, the decoupled fundamental component (characterizing the linear response of the system) and harmonic component set (characterizing the nonlinear response of the system) are output. This step fundamentally solves the component aliasing and noise interference problems existing in the traditional spectrum analysis method in non-stationary signal processing. It builds a frequency domain input data foundation with clear physical meaning and sufficient noise suppression for subsequent nonlinear parameter calculation. Moreover, its technical operation strictly terminates at the completion of component separation operation and does not involve the direct analysis and diagnosis of nonlinear characteristics.

[0061] S4: Based on the nonlinear enhancement effect of higher-order harmonic frequency components relative to the fundamental frequency components, determine whether there are weak bonding defects in the target area.

[0062] Specifically, this step establishes a causal relationship between abnormal harmonic energy amplification and weak bonding defects at the interface by quantitatively analyzing the energy enhancement characteristics of higher-order harmonic components relative to the fundamental component (i.e., nonlinear enhancement effect). This allows for a physical diagnosis of the bonding quality of the target area structure at the frequency domain. The technical operation is strictly limited to using the separated fundamental and harmonic component data and generating a defect existence judgment result based on a preset nonlinear enhancement threshold. It does not involve the specific location and quantification of defects, nor does it extend to the analysis of material-level failure mechanisms, thus providing a preliminary defect screening basis for subsequent precision testing.

[0063] This invention effectively solves the technical bottleneck of the difficulty in real-time capture of the dynamic evolution process of weak bonding defects at composite material interfaces. It achieves a sensitive response to micro-defects through the nonlinear harmonic characteristic enhancement effect, thereby improving the identification accuracy of traditional ultrasonic testing.

[0064] As a preferred embodiment of the above, the amplitude of the excitation satisfies:

[0065]

[0066] Where, σ ex Let F be the stress amplitude, F be the transient force peak value, A be the effective contact area between the exciter and the interface under test, and E be the stress amplitude. int Let ε be the elastic modulus of the interface layer. y This represents the yield strain of the interface layer.

[0067] Specifically, the core significance of this formula lies in setting a safety window for the nonlinear excitation intensity in nondestructive testing based on the intrinsic properties of the material. This achieves a precise balance between exciting effective defect responses and ensuring structural integrity. By constraining the excitation stress amplitude to not exceed half the yield strength of the interface layer material, it fundamentally solves the contradiction in selecting excitation intensity in traditional testing: if the excitation is too weak, the micro-slip nonlinear effect of weak bonding defects cannot be excited, resulting in insignificant high-order harmonic signals and missed detections; if the excitation is too strong, it may cause interface plastic damage or intrinsic nonlinear interference of the material, generating false harmonics and misjudging defects, or even causing irreversible damage due to the testing operation. The safety factor "2" in the formula is not a theoretical derivation, but a distillation of engineering experience and the nonlinear threshold of the material—most materials enter the nonlinear elastic deformation zone when the stress reaches 50% of the yield strength. At this time, the nonlinear mechanisms such as frictional energy dissipation and opening and closing oscillations of interface microcracks are fully activated, while non-defect-related nonlinear responses such as lattice slip have not yet occurred significantly. This preferred condition transforms the material's yield strength (defined by the product of elastic modulus and yield strain) into a directly operable excitation force threshold (controlled by F / A), enabling the detection process to capture the subtle nonlinear characteristics of micron-level weak bonding defects while strictly ensuring that the interface is within the elastic safety zone, thereby achieving ultra-high sensitivity detection under non-destructive conditions.

[0068] As a preferred embodiment of the above, such as Figure 2 As shown, step S2 involves acquiring a dynamic response signal perpendicular to the interface within the non-diffusion region of the vibration field at the excitation point in the target area, including:

[0069] S21: Based on the excitation frequency and material sound velocity parameters, calculate the coherence length boundary of the vibration field and determine the non-diffusion region with a radius smaller than the coherence length centered on the excitation point;

[0070] S22: In the non-diffusion region, continuously collect particle vibration displacement signals along the interface normal direction;

[0071] S23: Process the acquired signal to suppress interference from non-interface normal directions.

[0072] Specifically, in the near-field region of the excitation point, the coherence length of the vibration field is first calculated based on the excitation frequency and the longitudinal wave velocity of the material. The measurement range is strictly limited to a non-diffusion region with a radius smaller than this length and the excitation center as the origin. Under this spatial constraint, an array of sensors is deployed to collect vibration signals along the interface normal direction. The sensor normal deflection angle is controlled by precise positioning, and an appropriate pre-clamping force is applied. A sampling rate significantly higher than the excitation frequency is used during signal acquisition to ensure the integrity of the nonlinear spectrum. At the same time, a sufficient single acquisition duration is set according to the target defect scale. The raw signal undergoes third-order physical purification processing: stray wave components deviating from the normal are filtered out using the array wavenumber response; the normal displacement is accurately separated from the multi-axis data through vector projection; and finally, multi-channel coherent averaging is performed based on the cross-correlation algorithm to suppress noise. When the workpiece is geometrically constrained to be aligned with the normal, motion compensation or mathematical reconstruction equivalent schemes are activated. The entire process, through the synergistic effect of spatial gating and directional filtering, extracts micro-slip nonlinear characteristics while ensuring the effectiveness of acoustic principles. Its parameters can be adapted to engineering requirements within a reasonable range of theoretical optimal values. The ultimate goal is to reliably separate the nonlinear response caused by defects from the noise substrate.

[0073] As a preferred embodiment of the above, such as Figure 3 As shown, step S3 involves performing a frequency domain transformation on the dynamic response signal to separate the fundamental frequency component and at least one higher-order harmonic frequency component, including:

[0074] S31: A windowing function is used to perform frequency domain transformation on the dynamic response signal to generate a joint time-frequency distribution map;

[0075] S32: Based on the time-frequency joint distribution map, extract the fundamental frequency component corresponding to the excitation frequency, and screen out the higher harmonic frequency components that conform to the energy intensity.

[0076] S33: Based on the fundamental wave amplitude value and the integer order higher harmonic amplitude values, nonlinear calculations are performed to obtain the enhancement factor.

[0077] Specifically, in the dynamic response signal analysis stage, the original signal is first processed using an optimized windowed Fourier transform technique. A carefully selected window function effectively suppresses spectral leakage, generating a time-frequency joint distribution map containing three-dimensional information of time, frequency, and energy. Based on this map, a dual-track feature extraction is performed: on the one hand, the fundamental component strictly corresponding to the excitation source frequency is precisely located, and its amplitude peak value is accurately recorded; on the other hand, the system scans the frequency axis, screening for higher-order harmonic components that satisfy the relationship that the frequency is an integer multiple of the fundamental frequency and whose energy intensity significantly exceeds the statistical level of background noise. Typical extraction targets include effective components such as the second and third harmonics. Subsequently, the nonlinear quantization stage begins, where the peak amplitude of the fundamental wave and the peak amplitudes of each harmonic are substituted into the normalization calculation formula. A specific form of amplitude ratio calculation eliminates the influence of excitation intensity fluctuations, ultimately outputting an enhancement factor index characterizing the nonlinear strength of the material contact.

[0078] As a preferred embodiment of the above, the calculation formula for nonlinear calculation is:

[0079]

[0080] Where, β n is the nth nonlinear enhancement factor, where n is the harmonic order and its value ranges from n=2,3,4,5. ||S(nf0)|| is the extracted fundamental component amplitude, and ||S(f0)|| is the extracted nth harmonic component amplitude.

[0081] Specifically, the numerator ||S(nf0)|| captures the actual intensity of the harmonic energy at the nth harmonic, and the denominator ||S(f0)|| n A normalized benchmark based on the fundamental amplitude was constructed. Its core significance lies in establishing a quantitative correlation between harmonic order and amplitude attenuation through exponential operations. When contact defects exist within the material, the nonlinear vibration of the micro-interface will disrupt the harmonic attenuation law of the linear system, leading to an abnormal increase in the numerator harmonic amplitude while the denominator remains stable, ultimately causing the enhancement factor to exhibit a step-like increase. This design achieves three core functions: first, it eliminates the interference of excitation source intensity differences through power-law normalization of amplitude, ensuring the comparability of test results across operating conditions; second, the introduction of the exponential term n accurately quantifies the differences in sensitivity of different order harmonics to nonlinear effects; and third, it transforms the vibration energy redistribution characteristics caused by micro-damage into quantifiable indicators, establishing mathematical criteria with clear physical meaning for defect diagnosis.

[0082] As a preferred embodiment of the above, such as Figure 4 As shown, step S4, based on the nonlinear enhancement effect of higher-order harmonic frequency components relative to the fundamental frequency component, determines whether there is a weak bonding defect in the target region, including:

[0083] S41: Map the sequence of enhancement factors to a three-dimensional feature space to generate real-time dynamic feature trajectories;

[0084] S42: Compare the real-time dynamic feature trajectory with the pre-established typical defect evolution map library for similarity.

[0085] S43: When the matching similarity between the real-time dynamic feature trajectory and any type of defect in the typical defect evolution map library exceeds the preset critical confidence level, the corresponding quality detection result is output.

[0086] Specifically, the continuously acquired enhancement factor sequence is first mapped to a three-dimensional feature space with harmonic order as the coordinate axis, generating a real-time dynamic trajectory characterizing the evolution of the material state. The trajectory of healthy samples is distributed in a compact spherical shape near the origin, while weak adhesion defects extend divergently along a specific directional vector. The contraction and expansion of the radius of curvature of their trajectories directly reflect the accumulation process of micro-damage. Then, the real-time trajectory is compared with a pre-built typical defect evolution map library for multimodal similarity. A weighted fusion algorithm is used to comprehensively evaluate three indicators: dynamic time warping distance, evolution direction cosine similarity, and spatial distribution variance difference. When the comprehensive similarity exceeds the preset critical confidence level and meets the condition of positive growth of time-varying derivative, the system triggers a confidence grading mechanism and simultaneously outputs defect type code, location grid coordinates, and probabilistic quality report. This method significantly improves the detection specificity of hidden defects such as composite material delamination and metal-polymer interface peeling through joint analysis of spatiotemporal evolution features.

[0087] As a preferred embodiment of the above, the vibration excitation is a composite excitation, the frequency of the main vibration component covers the entire frequency band of the standing wave effect generated by the interface layer, and the auxiliary vibration component maintains a fixed phase difference with the main vibration component.

[0088] Specifically, this composite vibration excitation scheme precisely excites the interface defect response through the synergistic effect of the main and auxiliary frequencies. The main vibration component adopts a swept-frequency excitation mode, and its frequency range completely covers the characteristic frequency band of the standing wave effect generated by the interface layer. The output of the high-voltage piezoelectric actuator ensures the establishment of a stable sound field inside the material. The auxiliary vibration component maintains a constant phase difference with the main vibration through a precision phase-locked loop circuit. Its frequency is set to a specific ratio value to avoid resonance interference, and the output amplitude is strictly controlled below 30% of the main vibration. The two are superimposed to form a composite waveform with regular amplitude modulation, triggering a dual physical effect at the defect interface: the main vibration induces a high-intensity cavitation bubble group in the debonding region, significantly improving the transmission efficiency of the third harmonic signal; the auxiliary vibration excites the nonlinear mixing response of the microcrack through a phase synchronization mechanism, generating a difference frequency signal with defect fingerprint characteristics.

[0089] As a preferred embodiment of the above, such as Figure 5As shown, step S42 involves comparing the real-time dynamic feature trajectory with a pre-established library of typical defect evolution maps, including:

[0090] S421: Calculate the morphological similarity parameter between the real-time dynamic feature trajectory and the standard defect trajectory in the typical defect evolution map library;

[0091] S422: Calculate the similarity parameter of the evolution rate between the real-time dynamic feature trajectory and the standard defect trajectory;

[0092] S423: Input the morphological detail parameter, evolution rate, and similarity parameter into the confidence fusion decision-maker to determine the defect type.

[0093] Specifically, the morphological similarity parameters between the real-time trajectory and the standard defect trajectory are first extracted. An improved dynamic time warping algorithm is used to calculate the matching degree of the trajectory curvature sequence, and the eigenvalue similarity of the spatial distribution covariance matrix is ​​introduced to jointly characterize the consistency of the geometric topology. Then, the evolution rate similarity parameter is quantified, the first derivative correlation of the trajectory expansion vector is calculated through a sliding time window, and the matching degree of the damage development dynamic is evaluated by combining the KL divergence of the acceleration power spectrum. Finally, after inputting the above parameters into the confidence fusion decision-maker, the defect type probability and evolution stage diagnosis conclusion are output.

[0094] Example 2

[0095] Based on the same inventive concept as the fiber-reinforced thermosetting-thermoplastic composite structure interface quality testing method in the foregoing embodiments, the present invention also provides a fiber-reinforced thermosetting-thermoplastic composite structure interface quality testing system, the system comprising:

[0096] The vibration excitation application module applies vibration excitation to the target area on the surface of the test sample. The amplitude of the excitation is lower than the damage threshold of the weak bonding interface.

[0097] The response signal acquisition module acquires dynamic response signals perpendicular to the interface within the non-diffusion region of the vibration field at the excitation point in the target area.

[0098] The signal frequency domain transformation module performs frequency domain transformation on the dynamic response signal to separate the fundamental frequency component and at least one higher-order harmonic frequency component.

[0099] The weak bonding defect detection module determines whether a weak bonding defect exists in the target area based on the nonlinear enhancement effect of higher-order harmonic frequency components relative to the fundamental frequency component.

[0100] The detection system described above in this invention can effectively realize the interface quality detection method of fiber-reinforced thermosetting and thermoplastic composite structures, and the technical effects it can achieve are as described in the above embodiments, which will not be repeated here.

[0101] The distribution map generation unit uses a windowing function to perform frequency domain transformation on the dynamic response signal to generate a joint time-frequency distribution map.

[0102] The frequency component extraction unit extracts the fundamental frequency component corresponding to the excitation frequency based on the time-frequency joint distribution map, and selects the higher-order harmonic frequency components that conform to the energy intensity.

[0103] The nonlinear calculation unit performs nonlinear calculations based on the fundamental wave amplitude value and the integer-order higher harmonic amplitude values ​​to obtain the enhancement factor.

[0104] Similarly, the above-mentioned optimization schemes for the system can also achieve the optimization effects corresponding to the methods in Embodiment 1, which will not be repeated here.

[0105] Although this application has been described in conjunction with specific features and embodiments, it will be apparent that various modifications and combinations can be made therein without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of the application as defined herein, and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this application.

[0106] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for detecting the interface quality of fiber-reinforced thermosetting-thermoplastic composite structures, characterized in that, include: Vibrational excitation is applied to the target area of ​​the surface of the test sample, wherein the amplitude of the excitation is lower than the damage threshold of the weakly bonded interface; Within the non-diffusion region of the vibration field at the excitation point in the target area, a dynamic response signal perpendicular to the interface is acquired. The dynamic response signal is subjected to frequency domain transformation to separate the fundamental frequency component and at least one higher-order harmonic frequency component. Based on the nonlinear enhancement effect of the higher-order harmonic frequency components relative to the fundamental frequency components, it is determined whether there is a weak bonding defect in the target area.

2. The method for detecting the interface quality of fiber-reinforced thermosetting-thermoplastic composite structures according to claim 1, characterized in that, The amplitude of the excitation satisfies: Where, σ ex Let F be the stress amplitude, F be the transient force peak value, A be the effective contact area between the exciter and the interface under test, and E be the stress amplitude. int Let ε be the elastic modulus of the interface layer. y This represents the yield strain of the interface layer.

3. The method for detecting the interface quality of fiber-reinforced thermosetting-thermoplastic composite structures according to claim 1, characterized in that, Within the non-diffusion region of the vibration field at the excitation point in the target area, a dynamic response signal perpendicular to the interface is acquired, including: Based on the excitation frequency and the material sound velocity parameters, the coherence length boundary of the vibration field is calculated, and a non-diffusion region with a radius smaller than the coherence length and centered on the excitation point is determined. Within the non-diffusion region, particle vibration displacement signals along the interface normal direction are continuously acquired; The acquired signals are processed to suppress interference from non-interface normal directions.

4. The method for detecting the interface quality of fiber-reinforced thermosetting-thermoplastic composite structures according to claim 1, characterized in that, The dynamic response signal is subjected to frequency domain transformation to separate the fundamental frequency component and at least one higher-order harmonic frequency component, including: A windowing function is used to perform frequency domain transformation on the dynamic response signal to generate a joint time-frequency distribution map. Based on the time-frequency joint distribution map, the fundamental frequency component corresponding to the excitation frequency is extracted, and the higher harmonic frequency components that conform to the energy intensity are screened out. Based on the fundamental wave amplitude value and the integer-order higher harmonic amplitude values, a nonlinear calculation is performed to obtain the enhancement factor.

5. The method for detecting the interface quality of fiber-reinforced thermosetting-thermoplastic composite structures according to claim 4, characterized in that, The calculation formula for the nonlinear calculation is as follows: Where, β n is the nth nonlinear enhancement factor, where n is the harmonic order and its value ranges from n=2,3,4,5. ||S(nf0)|| is the extracted fundamental component amplitude, and ||S(f0)|| is the extracted nth harmonic component amplitude.

6. The method for detecting the interface quality of fiber-reinforced thermosetting-thermoplastic composite structures according to claim 1, characterized in that, Based on the nonlinear enhancement effect of the higher-order harmonic frequency components relative to the fundamental frequency components, the determination of whether a weak bonding defect exists in the target region includes: The sequence of the enhancement factors is mapped to a three-dimensional feature space to generate real-time dynamic feature trajectories. The real-time dynamic feature trajectory is compared with the pre-established typical defect evolution map library for similarity. When the matching similarity between the real-time dynamic feature trajectory and any type of defect in the typical defect evolution map library exceeds a preset critical confidence level, the corresponding quality detection result is output.

7. The method for detecting the interface quality of fiber-reinforced thermosetting-thermoplastic composite structures according to claim 1, characterized in that, The vibration excitation is a composite excitation, with the frequency of the main vibration component covering the entire frequency band of the standing wave effect generated by the interface layer, and the auxiliary vibration component maintaining a fixed phase difference with the main vibration component.

8. The method for detecting the interface quality of fiber-reinforced thermosetting-thermoplastic composite structures according to claim 6, characterized in that, The real-time dynamic feature trajectory is compared with a pre-established library of typical defect evolution maps for similarity, including: Calculate the morphological similarity parameter between the real-time dynamic feature trajectory and the standard defect trajectory in the typical defect evolution map library; Calculate the similarity parameter of the evolution rate between the real-time dynamic feature trajectory and the standard defect trajectory; The morphological detail parameter, evolution rate, and similarity parameter are input into the confidence fusion decision-maker to determine the defect type.

9. A system for detecting the interface quality of fiber-reinforced thermosetting-thermoplastic composite structures, characterized in that, include: The vibration excitation application module applies vibration excitation to the target area on the surface of the test sample, wherein the amplitude of the excitation is lower than the damage threshold of the weak bonding interface. The response signal acquisition module acquires dynamic response signals perpendicular to the interface within the non-diffusion region of the vibration field at the excitation point in the target area. The signal frequency domain transformation module performs frequency domain transformation on the dynamic response signal to separate the fundamental frequency component and at least one higher-order harmonic frequency component. The weak bonding defect judgment module determines whether a weak bonding defect exists in the target area based on the nonlinear enhancement effect of the higher-order harmonic frequency components relative to the fundamental frequency components.

10. The fiber-reinforced thermosetting-thermoplastic composite structure interface quality testing system according to claim 9, characterized in that, The signal frequency domain transformation module includes: The distribution map generation unit uses a windowing function to perform frequency domain transformation on the dynamic response signal to generate a time-frequency joint distribution map. The frequency component extraction unit extracts the fundamental frequency component corresponding to the excitation frequency based on the time-frequency joint distribution map, and filters out the higher-order harmonic frequency components that conform to the energy intensity. The nonlinear calculation unit performs nonlinear calculations based on the fundamental wave amplitude value and the integer-order higher harmonic amplitude values ​​to obtain the enhancement factor.

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