A composite material defect detection system and method

Abstract

The application provides a composite material defect detection system and method. It relates to the field of nondestructive testing technology. It is used to solve the problem of inaccurate detection of small defects under low power excitation. It comprises: a multi-excitation signal generation module that provides different excitation modes, each excitation mode selectively enhances the response of a specific type of defect in the composite material under test; output excitation signals matching target instructions; an ultrasonic excitation module that receives excitation signals and converts them into mechanical vibrations; the mechanical vibrations are transmitted to the composite material under test in the form of symmetrical surface contact to excite the internal defect area of the composite material under test to form a temperature difference relative to the non-defect area; an infrared thermal image acquisition module that acquires thermal image data of the composite material under test through a thermal imaging technology matching the excitation signal; a synchronous control and signal processing module that receives thermal image data and obtains defect positions based on the thermal image data.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, and in particular to a system and method for detecting defects in composite materials. Background Technology

[0002] Composite materials, especially carbon fiber reinforced resin matrix composites, are widely used in critical fields such as aerospace, wind power generation, and rail transportation due to their superior properties such as high specific strength and high specific modulus. However, during molding, assembly, and service, they are susceptible to low-velocity impacts or fatigue loads, often resulting in various internal defects invisible to the surface—such as delamination, debonding, cracks, porosity or voids, and fiber breakage. These hidden defects significantly weaken the load-bearing capacity and impact resistance of composite materials, directly threatening structural safety. Therefore, developing efficient and reliable non-destructive testing technologies to promptly identify and locate different types of defects is crucial for ensuring the integrity and safe use of composite material structures.

[0003] In existing technologies, active infrared thermal imaging is a non-destructive testing method. Its basic principle is to apply external excitation, such as ultrasound, halogen lamp, or flash lamp, to the test piece, causing an abnormal temperature field to be generated at the defect due to the difference in thermodynamic parameters. Then, an infrared thermal imager is used to capture and analyze the distribution of this temperature field, thereby realizing the visualization of the defect.

[0004] However, the above method cannot form a sufficiently significant and stable local thermal response at the interface of small defects under low power excitation conditions, resulting in weak abnormal temperature field signals at the small defects that are submerged in background noise, ultimately leading to inaccurate detection of small defects. Summary of the Invention

[0005] Therefore, it is necessary to provide a composite material defect detection system and method to address the above-mentioned technical problems, so as to solve the shortcomings of insufficient sensitivity and inaccurate detection results of small defects under low power excitation in the existing technology, and to achieve accurate identification of small defects in composite materials under low power conditions.

[0006] The following technical solution is adopted in this specification:

[0007] This specification provides a composite material defect detection system, including:

[0008] A multi-excitation signal generation module is used to provide different excitation modes, each of which selectively enhances the response of a specific type of defect in the composite material under test; it outputs an excitation signal that matches the target command; the different excitation modes specifically include:

[0009] Low-power pulse excitation mode, configured to output a square wave excitation signal with adjustable duty cycle to excite a broadband thermal wave response;

[0010] The low-power phase-locked loop excitation mode is configured to output a frequency-adjustable sine wave or modulated continuous wave excitation signal to excite a steady-state thermal response at the same frequency.

[0011] Low-power step excitation mode, configured to output a step power signal with adjustable duration to excite transient thermal diffusion response;

[0012] The ultrasonic excitation module is used to receive excitation signals and convert them into mechanical vibrations; it is used to transmit mechanical vibrations to the composite material under test in the form of symmetrical surface contact, so as to excite the formation of temperature differences between the defective regions and the non-defective regions inside the composite material under test.

[0013] The infrared thermal imaging acquisition module is used to acquire thermal image data of the composite material under test through thermal imaging technology matched with the excitation signal.

[0014] The synchronization control and signal processing module is used to receive thermal image data and obtain the defect location based on the thermal image data: It performs binarization and morphological processing on the thermal image data to obtain a target binary image; it constructs a distance map based on the Euclidean distance from each foreground pixel to the nearest background pixel in the target binary image; it selects the pair of candidate endpoints with the greatest Euclidean distance as the start and end points, using each foreground pixel in the target binary image as a graph node, and defines the edge weight between adjacent foreground pixel nodes as the reciprocal of the distance value of the adjacent nodes in the distance map to construct a weighted graph; it searches for the minimum weight path between the start and end points on the weighted graph to obtain the defect location.

[0015] Furthermore, the synchronization control and signal processing module is also used to output target instructions that match the material properties, geometric features, and estimated defect types of the composite material under test; the estimated defect types are determined based on the material properties, geometric features, and service condition parameters of the composite material under test, and the degree of matching with the corresponding data in the preset historical defect database; the historical defect database stores typical defect types and defect feature data corresponding to different material properties, geometric features, and service condition parameters.

[0016] Furthermore, the synchronization control and signal processing module is electrically connected to the multi-excitation signal generation module, the ultrasonic excitation module, and the infrared thermal imaging acquisition module, and is used for:

[0017] Output target instructions to the multi-excitation signal generation module to trigger the generation of excitation signals that match the material properties, geometric features and predicted defect types of the composite material under test;

[0018] A synchronous trigger signal is output to the ultrasonic excitation module to control the timing of mechanical vibration application;

[0019] Output acquisition signals to the infrared thermal imaging acquisition module to achieve synchronous acquisition of thermal image data and excitation signals;

[0020] It also receives status signals from each module to coordinate the working timing of each module in a closed loop.

[0021] Furthermore, the ultrasonic excitation module includes:

[0022] An ultrasonic transducer is used to receive trigger signals to control the timing of mechanical vibration application; it is used to convert excitation signals into mechanical vibration.

[0023] Clamping structure for clamping the composite material to be tested in a symmetrical plane contact manner;

[0024] The ultrasonic transducer is positioned at the end of the gripper of the clamping structure, and its contact surface with the composite material under test is a symmetrical planar contact surface, enabling the coupled transmission of mechanical vibration; or,

[0025] The ultrasonic transducer is positioned in the non-grip region of the clamping structure, and indirectly transmits mechanical vibrations to the composite material under test in the form of symmetrical planar contact through the clamping structure.

[0026] Furthermore, the acquisition of thermal image data of the composite material under test using thermal imaging technology matched with the excitation signal specifically includes:

[0027] In low-power pulse excitation mode, pulsed thermal imaging technology is used to acquire transient thermal diffusion sequence thermal image data;

[0028] In low-power phase-locked excitation mode, phase-locked thermal imaging technology is used to acquire phase-locked thermal wave response thermal image data;

[0029] In the low-power step excitation mode, step thermal imaging technology is used to acquire quasi-steady-state thermal distribution thermal image data.

[0030] Furthermore, obtaining the defect location based on thermal image data specifically includes:

[0031] The thermal image data is processed by noise filtering, thermal contrast enhancement and defect feature extraction, and the defect detection location is output.

[0032] This specification provides a method for detecting defects in composite materials, including:

[0033] Obtain target instructions that match the material properties, geometric features, and predicted defect types of the composite material under test, and trigger excitation signals that match the target instructions; including:

[0034] Low-power pulse excitation mode, configured to output a square wave excitation signal with adjustable duty cycle to excite a broadband thermal wave response;

[0035] The low-power phase-locked loop excitation mode is configured to output a frequency-adjustable sine wave or modulated continuous wave excitation signal to excite a steady-state thermal response at the same frequency.

[0036] Low-power step excitation mode, configured to output a step power signal with adjustable duration to excite transient thermal diffusion response;

[0037] The excitation signal is converted into mechanical vibration, which is transmitted to the composite material under test in the form of symmetrical surface contact, so as to excite the formation of temperature difference between the defect region and the non-defect region inside the composite material under test.

[0038] The temperature difference between the internal defect region and the non-defect region is converted into thermal image data by thermal imaging technology that matches the excitation signal.

[0039] Binarization and morphological processing are performed on the thermal image data to obtain a binary image of the target.

[0040] A distance map is constructed based on the Euclidean distance from each foreground pixel to the nearest background pixel in the target binary image;

[0041] The local maximum point in the distance graph is used as the candidate endpoint, and the pair of candidate endpoints with the greatest Euclidean distance is selected as the start and end points.

[0042] Using each foreground pixel in the target binary image as a graph node, the edge weight between foreground pixel nodes adjacent to the graph node is defined as the reciprocal of the distance value of the adjacent node in the distance graph, and a weighted graph is constructed.

[0043] The defect location is obtained by searching for the minimum weight path between the start and end points on the weighted graph.

[0044] Furthermore, before performing binarization and morphological processing on the thermal image data, filtering and linear stretching operations are also performed on the thermal image data.

[0045] Furthermore, the binarization and morphological processing of the thermal image data to obtain the target binary image specifically includes:

[0046] The thermal image data is adaptively binarized using the Sauvola local thresholding algorithm to obtain the first binary image.

[0047] Mark the connected components in the first binary graph, and delete the connected components in the first binary graph that are smaller than a preset area threshold to obtain the second binary graph;

[0048] A morphological method based on elliptical structuring elements is used to perform a closing operation on the second binary image to obtain the third binary image;

[0049] A thinning algorithm is used to generate a temporary skeleton of the third binary image, and the target binary image is obtained.

[0050] Furthermore, the search for the minimum weighted path between the start and end points on the weighted graph is achieved using Dijkstra's algorithm.

[0051] The above-mentioned technical solutions adopted in this specification can achieve the following beneficial effects:

[0052] This invention integrates three differentiated low-power excitation modes—low-power pulse excitation, low-power phase-locked excitation, and low-power step excitation—through a multi-excitation signal generation module. This allows for the targeted excitation of broadband thermal wave responses, steady-state thermal responses at the same frequency, and transient thermal diffusion responses. This achieves selective enhancement of responses to different types of micro-defects in composite materials, overcoming the limitation of single excitation modes in evoking significant and stable local thermal responses under low-power conditions. Simultaneously, the symmetric plane coupling transmission mechanism ensures uniform distribution of mechanical vibration energy across the test sample, effectively avoiding surface damage, vibration energy scattering, and transmission blind zones caused by localized stress concentration. Furthermore, symmetric plane coupling enables the defect region to achieve [further enhancement] under low-power conditions. Sufficient energy deposition, especially at minute defects, forms a more concentrated and significant thermal response signal. Combined with thermal imaging technology that precisely matches the excitation signal, the thermal signal of the defect can be captured and extracted in a targeted manner. Then, through a refined signal processing flow using synchronous control and signal processing modules, including binarization, morphological processing, distance map construction, candidate endpoint screening, and minimum weight path search of the weighted graph, background noise is effectively suppressed and minute defect features are accurately extracted from weak thermal image data. This significantly improves the thermal response intensity and signal-to-noise ratio of minute defects under low-power excitation conditions, enabling accurate localization of minute defects and improving the detection accuracy of minute defects under low-power constraints. Attached Figure Description

[0053] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0054] Figure 1 This is one of the structural schematic diagrams of a composite material defect detection system provided in this specification;

[0055] Figure 2 This specification provides a schematic diagram of a pulse excitation signal.

[0056] Figure 3 This is a schematic diagram of a phase-locked loop excitation signal provided in this specification;

[0057] Figure 4 This specification provides a schematic diagram of a step excitation signal.

[0058] Figure 5This is the second structural schematic diagram of a composite material defect detection system provided in this specification;

[0059] Figure 6 This is a flowchart illustrating a method for detecting defects in composite materials provided in this specification.

[0060] Figure 7 This is a schematic diagram of thermal image data provided in this specification;

[0061] Figure 8 This is a schematic diagram of a defect provided in this specification. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this specification clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without creative effort are within the scope of protection of this application.

[0063] This invention can be applied to nondestructive testing of composite materials based on ultrasonic excitation. Ultrasonic excitation sources have concentrated energy and strong penetrating power, making them particularly suitable for detecting crack-like defects in composite materials. However, traditional ultrasonic excitation methods have the following drawbacks: 1. High ultrasonic excitation power: To generate sufficient thermal effect to visualize defects, high-power ultrasonic generators of hundreds of watts or even kilowatts are typically required. This not only results in bulky equipment and high energy consumption but also easily causes potential thermal or mechanical damage to intact areas of the composite material due to excessive energy. 2. Bulky system, insufficient portability and on-site testing capabilities: Traditional high-power ultrasonic infrared detection devices contain necessary components such as high-power power supplies and forced cooling units, resulting in a large overall size, heavy weight, and difficulty in movement. Their inherent non-portability severely restricts the application of this technology in scenarios requiring on-site operations, high-altitude inspections, or nondestructive evaluation of large, immovable structures. 3. The excitation method is a single frequency sweep excitation, limiting its ability to detect composite materials with different materials and defect types. To address the aforementioned issues, the developed low-power multi-excitation method requires extensive experimental verification on composite materials and various types of defects such as cracks, debonding, and porosity. This is to demonstrate that even with reduced power, it still possesses detection capabilities comparable to or even superior to traditional high-power methods. This process is time-consuming and labor-intensive, and the results are uncertain. Therefore, this invention provides a composite material defect detection system and method.

[0064] The composite material defect detection system of the present invention will now be described in detail with reference to the accompanying drawings.

[0065] Figure 1This is a structural schematic diagram of a composite material defect detection system provided in this specification, as shown below. Figure 1 As shown, the system includes a multi-excitation signal generation module, an ultrasonic excitation module, an infrared thermal imaging acquisition module, and a synchronization control and signal processing module.

[0066] A multi-excitation signal generation module is used to provide different excitation modes, each of which selectively enhances the response of a specific type of defect in the composite material under test; and outputs an excitation signal that matches the target command.

[0067] For example, in this embodiment of the application, the multi-excitation signal generation module is connected to the synchronization control and signal processing system to provide three switchable modulation signal modes: pulse excitation, phase-locked loop excitation, and step excitation. It selects and switches different excitation modes according to instructions from the synchronization control and signal processing system. By integrating these three switchable modes, the optimal excitation method can be selected based on the sample's material, structure, and expected defect characteristics, achieving a balance between detection efficiency and accuracy. For instance, shallow defects can be quickly located using pulses, deep defects can be accurately detected using phase-locked loops, and medium-sized defects can be detected using step excitations, taking into account both speed and depth information.

[0068] The low-power pulse excitation mode is configured such that the pulse excitation signal is controlled by the excitation time, with the excitation time ranging from 0.1 to 5 seconds. Its characteristics are short-duration high energy, wide frequency, and observation of transient response. It is suitable for near-surface, large lateral size, and high thermal contrast defects, and can quickly locate and resolve defects. However, it is not suitable for deep, small, and low-contrast defects. Figure 2 This is a schematic diagram of a pulse excitation signal provided in this specification. The pulse energy is dispersed in the frequency domain. When detecting deep defects, the low-frequency component amplitude is small, resulting in insufficient signal-to-noise ratio. Local temperature disturbances in small-sized defects are easily smoothed out by surrounding heat diffusion. The transient amplitude is small and difficult to distinguish. Furthermore, pulse measurement depends on the absolute amplitude of the transient curve, which is easily affected by uneven excitation and changes in surface emissivity, reducing the reliability of detection.

[0069] The low-power phase-locked loop excitation mode is configured with a constant frequency square wave or sine wave periodic signal with a duty cycle range of 40%-60% to excite a steady-state thermal response at the same frequency. The frequency range of the sine wave or modulated continuous wave excitation signal is 20-40kHz, with a frequency step of no more than 100Hz. Its characteristics are that it uses sine or modulated continuous excitation in conjunction with phase-sensitive detection, selectable frequency, focuses on specific depth response, has a high signal-to-noise ratio, and is suitable for deep, small-sized, low-contrast defects. However, its advantages are not obvious in obvious shallow defect scenarios where only rapid inspection is required, and its lateral resolution effect for extremely shallow large defects is weak. Figure 3This is a schematic diagram of a phase-locked loop (PLL) excitation signal provided in this specification. PLL requires stable long-term modulation and multi-cycle averaging, which is time-consuming. When using low frequency to increase penetration depth for shallow defects, it is easily affected by the system time constant and phase measurement accuracy. Furthermore, low-frequency excitation will intensify the lateral thermal diffusion of shallow layers, resulting in blurred defect edges.

[0070] The low-power step excitation mode is configured to output a step power signal with adjustable duration to excite transient thermal diffusion response. The step excitation signal continues throughout the detection process, with the duration being the total number of frames acquired / acquisition frequency. Its characteristics are that the excitation energy rises rapidly from zero to a constant value and remains there, observing the transient to quasi-steady-state process of the thermal response that gradually develops over time. It is suitable for defects of medium depth or size, and the defect depth can be estimated by utilizing the evolution of surface temperature over time. It is easier to implement than pulse excitation and faster than phase-locked loop excitation, but it is not suitable for very shallow scenarios that require extremely high lateral resolution. For very deep or extremely low-contrast defects, phase-locked loop multi-cycle detection is still recommended. Figure 4 This is a schematic diagram of a step excitation signal provided in this specification. The transient process of step excitation is relatively long, and the lateral resolution is lower than that of pulse. For extremely deep or extremely low contrast defects, phase-locked loop multi-cycle detection is still recommended.

[0071] The ultrasonic excitation module is used to receive excitation signals and convert them into mechanical vibrations; it is used to transmit mechanical vibrations to the composite material under test in the form of symmetrical surface contact, so as to excite the formation of temperature differences between the defective regions and the non-defective regions inside the composite material under test.

[0072] For example, in this embodiment of the application, the ultrasonic excitation module includes a drive circuit and an ultrasonic transducer. The drive circuit is connected to a multi-excitation signal generation module and is used to receive a modulation signal and output an electrical signal to the ultrasonic transducer; the ultrasonic transducer is used to receive a trigger signal to control the application time of the mechanical vibration and convert the electrical signal into mechanical vibration. For example, the center frequency of the ultrasonic transducer is 20kHz-40kHz, the output power is 10-80 watts, and the adjustable output amplitude range is 0-100%.

[0073] The ultrasonic excitation module also includes a clamping structure, which can be a U-shaped clamp used to clamp the composite material under test in a symmetrical planar contact manner. The U-shaped clamp is made of lightweight metal material, with an elastic material layer attached to its inner surface to efficiently transfer the vibration energy generated by the ultrasonic transducer to the surface of the composite material under test, while avoiding scratching the sample surface. Specifically, the ultrasonic transducer can be configured in two ways on the clamping structure. One implementation is that the ultrasonic transducer is positioned at the end of the gripper of the clamping structure, with the contact surface with the composite material under test being a symmetrical planar contact surface, achieving direct coupling transmission of mechanical vibration. The other implementation is that the ultrasonic transducer is positioned in the non-gripper area of ​​the clamping structure, indirectly transmitting mechanical vibration to the composite material under test in a symmetrical planar contact manner through the clamping structure.

[0074] During operation, the ultrasonic transducer converts electrical signals into mechanical vibrations, which are coupled to the surface of the test sample through a U-shaped clamp, exciting the sample to generate thermoelastic or frictional heating effects. The ultrasonic excitation module is configured with low power, less than 100 watts, ensuring sufficient excitation intensity while avoiding secondary damage to the sample, making it suitable for non-destructive testing of heat-sensitive or fragile materials.

[0075] In infrared thermal imaging, low-power excitation is essential to avoid damaging the test piece. However, when minute defects are located deep within the test piece, low-power energy under conventional unilateral excitation methods often fails to effectively penetrate and reach the defect area. To achieve the detection objective, it is often necessary to increase the excitation power. However, excessively high power can exceed the material's tolerance range, leading to localized stress concentration, surface burns, or even internal interlayer damage in the excitation area, ultimately damaging the test material. In contrast, the bilateral symmetrical transmission design adopted in this application does not require increased excitation power. It enables energy to be transmitted synchronously from both sides at low power levels, forming in-phase superposition and resonance enhancement in the defect area, thus generating a more significant and stable thermal effect. From a working principle perspective, symmetrical surface contact enables large-area, highly consistent mechanical vibration coupling, allowing the excitation wave to form a uniform and stable steady-state stress field within the composite material. This ensures that the excitation energy is evenly transmitted throughout the entire test area without local distortion, avoiding problems such as stress concentration, wavefront distortion, energy reflection and scattering, asymmetrical propagation paths, and deep detection blind zones caused by point contact, line contact, or unilateral asymmetrical contact.

[0076] Existing ultrasonic infrared testing devices often employ high-power ultrasonic excitation ranging from hundreds of watts to kilowatts to enhance detection capabilities. This poses risks such as sample surface damage, material overheating, or piezoelectric transducer overload, making them particularly unsuitable for critical components sensitive to thermal and mechanical forces, such as aerospace composite materials and microelectronic components. This embodiment employs an optimized low-power drive design, combined with U-shaped clamp coupling and multiple excitation modes, ensuring sufficient excitation of defect thermal effects while fundamentally avoiding potential physical or thermal damage to the sample during the testing process. This allows the device to be safely applied to precision, valuable, or heat-sensitive components that traditional high-power equipment cannot detect, expanding the application scope of non-destructive testing, solving the problem of potential damage to the composite material matrix caused by high-power ultrasonic excitation, and achieving safer non-destructive testing.

[0077] The infrared thermal imaging acquisition module is used to acquire thermal image data of the composite material under test through thermal imaging technology matched with the excitation signal.

[0078] For example, in this embodiment of the application, the infrared thermal imaging acquisition module is positioned facing the detection area of ​​the composite material to be tested, acquiring the temperature field distribution changes on the sample surface and uploading the thermal image data to the synchronous control and signal processing module. The infrared thermal imaging acquisition module can be configured with different acquisition timing sequences, frame accumulation methods, and data processing algorithms to adapt to thermal imaging strategies corresponding to different excitation modes: in low-power pulse excitation mode, pulse thermal imaging technology is used to acquire transient thermal diffusion sequence thermal image data; in low-power phase-locked loop excitation mode, phase-locked loop thermal imaging technology is used to acquire phase-locked thermal wave response thermal image data, extracting thermal wave responses at the same frequency as the excitation to improve the signal-to-noise ratio of defect signals; in low-power step excitation mode, step thermal imaging technology is used to acquire quasi-steady-state thermal distribution thermal image data. In a specific embodiment, the infrared thermal imaging acquisition module is a high-resolution infrared thermal imaging device with a room temperature resolution of not less than 0.05K, a spatial resolution of 1024×768 pixels, a frame rate of 15-30Hz, and a spectral response range of 7.5-14μm.

[0079] This ensures a precise match between the acquisition strategy and the characteristics of the excitation signal, guaranteeing a high degree of synergy between the thermal image acquisition process and the generation patterns of the defect's thermal response. Consequently, the temperature contrast between the defect signal and the normal region can be maximized under pulse, phase-locked, or step excitation, significantly improving the sensitivity and accuracy of defect identification. This step efficiently transforms the physical effects generated in the first two steps into high-quality digital images.

[0080] The synchronization control and signal processing module is used to receive thermal image data and obtain the defect location based on the thermal image data.

[0081] For example, in this embodiment of the application, the synchronization control and signal processing module is used to perform binarization and morphological processing on the thermal image data to obtain a target binary image; construct a distance map based on the Euclidean distance from each foreground pixel to the nearest background pixel in the target binary image; select the local maximum point in the distance map as candidate endpoints and select the pair of candidate endpoints with the farthest Euclidean distance as start and end points; use each foreground pixel in the target binary image as a graph node, define the edge weight between foreground pixel nodes adjacent to the graph node as the reciprocal of the distance value of the adjacent node in the distance map, and construct a weighted graph; search for the minimum weight path between the start and end points on the weighted graph to obtain the defect location.

[0082] The synchronous control and signal processing module is electrically connected to the multi-excitation signal generation module, the ultrasonic excitation module, and the infrared thermal imaging acquisition module, and is used for closed-loop coordination of the working timing of each module. Specifically, it is used to output target commands to the multi-excitation signal generation module to trigger the generation of excitation signals that match the material properties, geometric features, and predicted defect types of the composite material under test; to output synchronous trigger signals to the ultrasonic excitation module to control the timing of mechanical vibration application; to output acquisition signals to the infrared thermal imaging acquisition module to achieve synchronous acquisition of thermal image data and excitation signals; and to receive status signals from each module.

[0083] The synchronous control and signal processing module is also used to output target commands that match the material properties, geometric features, and estimated defect types of the composite material under test. The estimated defect types are determined based on the matching degree between the material properties, geometric features, and service condition parameters of the composite material under test and corresponding data in a pre-set historical defect database. Specifically, material properties include fiber volume fraction, fiber layup angle, resin matrix type, and interfacial bonding strength; geometric features include radius of curvature, thickness gradient, stiffener layout, and opening location; service condition parameters include load type, load amplitude, number of cycles, ambient temperature, and humidity level; the historical defect database contains defect morphology, location distribution, and critical size data of similar composite materials under corresponding service conditions. The matching degree is obtained through quantitative calculation using a similarity algorithm, which includes feature vector matching based on cosine similarity or defect pattern classification based on support vector machines. The estimated defect types include delamination, debonding, fiber breakage, and matrix cracking.

[0084] The pre-built historical defect database constructed in this application establishes a mathematical and logical relationship between material properties and excitation parameters based on data analysis methods. This enables the output of the most matching target command according to the specific properties of the composite material to be tested, reducing the probability of failure to induce defect response due to parameter mismatch or expansion of small defects due to energy overload during manual debugging.

[0085] In the process of judging defect detection results, defects are defined in two ways: large near-surface defects and difficult-to-detect defects. Large near-surface defects are defined as: defects with a depth ≤ 0.5 × thermal diffusion scale or a depth ≤ 0.5 × thermal diffusion length, and a lateral dimension ≥ 1.0 × thermal diffusion scale or a lateral dimension ≥ 2 × thermal diffusion scale. These defects produce a significant localized surface temperature difference due to their lateral dimension being on the same order of magnitude or larger than the ambiguity caused by thermal diffusion, making them easy to detect. Difficult-to-detect defects are defined as: defects with a depth ≥ 1.5–2.0 × thermal diffusion length or a depth ≥ 2 × thermal diffusion scale, and a lateral dimension ≤ 0.5 × thermal diffusion scale or a lateral dimension ≤ 0.5 × thermal diffusion length. These defects have low thermal contrast. If the surface temperature difference generated by the defect is < 1–2 × noise standard deviation, or the signal-to-noise ratio is < 2, it is considered a difficult-to-detect defect. Examples include narrow cracks, deep small-volume pores, extremely narrow fissures along the thickness direction, or minute damage with thermal conductivity similar to the substrate.

[0086] The synchronous control and signal processing system also includes a display terminal. The display terminal is connected to the synchronous control and signal processing system and is used to display thermal image data, defect location distribution maps, defect size information, and system operating parameters, enabling intuitive output of detection results.

[0087] Figure 5 This is the second structural schematic diagram of a composite material defect detection system provided in this specification, as shown below. Figure 5 As shown, the system comprises a power supply, a control unit, a 120W / 40KHz driver board, a U-shaped clamp, an ultrasonic transducer, a test sample, an infrared thermal imager, a junction box, and a computer. The power supply is connected to the control unit and the 120W / 40KHz driver board. The control unit is connected to the computer and the junction box. The 120W / 40KHz driver board is connected to the ultrasonic transducer mounted on the U-shaped clamp, which is in direct contact with the test sample. The infrared thermal imager is positioned facing the detection area of ​​the test sample and is connected to the junction box and the computer. All modules are electrically connected and transmit signals through the junction box, forming a complete detection system for detecting internal defects in the test sample. This embodiment achieves electrical connection between modules by setting up a junction box and wiring connection system. It integrates the multi-excitation signal generation module, ultrasonic excitation module, infrared thermal image acquisition module, and synchronous control and signal processing module into an integrated portable or fixed detection device. The connection of each module through the junction box and wiring further enhances the integrated effect of the system, ensuring the convenience of rapid detection of planar composite material samples.

[0088] Based on the above-mentioned modular integration and junction box connection technical solution, this invention specifically addresses three core problems in the prior art: First, it solves the problem of limited detection capability of single excitation methods for composite material defects. By using a control unit to perform multimodal excitation on the ultrasonic transducer, it adapts to different types and depths of composite material defects. Simultaneously, it overcomes the shortcomings of existing ultrasonic excitation infrared thermal imaging detection technologies, such as low detection safety and poor portability due to high power and bulky equipment, providing a safer, more sensitive, and more suitable low-power ultrasonic multi-excitation device for field conditions. Second, it solves the problem of limited field application of high-power equipment. By optimizing the excitation mode, it significantly reduces system power consumption and... While reducing size, it ensures and improves detection efficiency, freeing the equipment from the constraints of traditional high-power systems that are bulky, heavy, and dependent on fixed power supplies. It transforms the equipment into a mobile and easily deployable field detection platform, expanding the application scenarios and practicality of ultrasonic infrared composite detection technology. Thirdly, it solves the problems of separate excitation, acquisition, and control modules, complex connections, poor portability, and low field detection efficiency of existing similar equipment. By integrating multiple excitation signal sources, low-power drives, synchronous control units, and data acquisition and processing systems into one unit, and using a junction box to achieve fast and reliable connection, it simplifies the operation process, improves system stability, facilitates the carrying of the equipment to the field for rapid detection, lowers the usage threshold, and improves detection efficiency.

[0089] Figure 6 This is a flowchart illustrating a method for detecting defects in composite materials provided in this specification, as shown below. Figure 6 As shown, after inputting an infrared image, the process first enters an interactive optimization loop. During this process, parameters can be adjusted and the binarized image / skeleton can be previewed in real time. The parameters are adjusted based on the preview effect. Once the parameters are determined, they are saved. Then, the image undergoes final binarization morphological processing. Subsequently, relevant endpoint information is obtained through distance transform endpoint detection. Path extraction is then performed until endpoint detection is successful. After that, the path is converted into a skeleton. Dijkstra's dynamic programming algorithm is then run on the weighted graph to search for the optimal path. The optimal path obtained is fed back to the interactive optimization loop to adjust the parameters. Finally, the skeleton map is output. Figure 7 This is a schematic diagram of thermal image data provided in this specification; Figure 8 This is a schematic diagram of a defect provided in this specification. (In conjunction with...) Figure 7 Thermal image data and Figure 8 The actual defects were examined, verifying the accuracy and effectiveness of the detection method for identifying defects and extracting the skeleton in composite materials. The following describes the composite material defect detection method provided by this invention, which includes:

[0090] S1. Obtain the target instruction that matches the material properties, geometric features and predicted defect types of the composite material to be tested, and trigger the excitation signal that matches the target instruction.

[0091] For example, the method for obtaining the predicted defect type includes: collecting the material properties, geometric features, and service condition parameters of the composite material to be tested; constructing a historical defect database, which stores typical defect types and defect feature data corresponding to different material properties, geometric features, and service condition parameters; performing multi-dimensional matching between the material properties, geometric features, and service condition parameters of the composite material to be tested and the data in the preset historical defect database to obtain the matching degree; and taking the defect type with the highest matching degree in the historical defect database as the predicted defect type.

[0092] For example, after receiving the target instruction, the excitation mode is adaptively selected according to the estimated defect type: if the estimated defect type is a large near-surface defect, the multi-excitation signal generation module is controlled to select a low-power pulse excitation mode to quickly locate the defect using transient thermal waves; if the estimated defect type is a deep or small defect, the multi-excitation signal generation module is controlled to select a low-power phase-locked loop excitation mode and set the corresponding modulation frequency to extract phase information at a specific depth; if the estimated defect type is a medium-depth defect or requires quantitative depth analysis, the multi-excitation signal generation module is controlled to select a low-power step excitation mode to observe the time evolution of thermal diffusion.

[0093] S2. The excitation signal is converted into mechanical vibration, and the mechanical vibration is transmitted to the composite material under test in the form of symmetrical surface contact, so as to excite the formation of temperature difference between the defect area and the non-defect area in the internal defect area of ​​the composite material under test.

[0094] S3. The temperature difference between the internal defect region and the non-defect region is converted into thermal image data by using thermal imaging technology matched with the excitation signal.

[0095] S4. Perform filtering and linear stretching operations on the thermal image data.

[0096] For example, the thermal image is first converted to a grayscale image. Then, median filtering is used to remove isolated salt-and-pepper noise from the image and protect the crack edges. Finally, by calculating the 2% and 98% quantiles of the image grayscale, the pixel values ​​are linearly mapped to the range of 0~255 to achieve linear contrast stretching, thereby enhancing the overall contrast of the image and preventing the target skeleton from shifting due to subsequent operations.

[0097] S5. Adaptively binarize the thermal image data using the Sauvola local thresholding algorithm to obtain a first binary image; mark the connected components in the first binary image and delete connected components in the first binary image that are smaller than a preset area threshold to obtain a second binary image; perform a closing operation on the second binary image using a morphological method based on elliptical structuring elements to obtain a third binary image; use a thinning algorithm to generate a temporary skeleton of the third binary image to obtain the target binary image.

[0098] For example, for each pixel (x, y) in the image, calculate the mean within its neighborhood window. and standard deviation The segmentation threshold is determined according to the preset threshold formula:

[0099] ;

[0100] in, This refers to the window size.

[0101] Since a dark line detection mode is used and cracks are represented by dark areas, areas with pixel values ​​less than a threshold are identified as cracks and a first binary image is generated.

[0102] ;

[0103] in, For the initial pixels, The pixels are those of the first binary image.

[0104] In binarized images, besides the target crack area, there are often small, isolated pixel clusters (i.e., noise) caused by factors such as image acquisition noise, uneven lighting, and threshold selection deviations. These noises interfere with the recognition of the true crack morphology and need to be removed. First, a connected component analysis algorithm is used to mark all foreground regions in the binary image and count the number of pixels in each connected component (i.e., the area of ​​the connected component). Then, a reasonable area threshold MIN_AREA is set (this threshold can be adaptively adjusted according to the actual crack size, image resolution, and noise level). All isolated connected components with an area smaller than MIN_AREA are identified as noise and deleted. The target crack connected components with the required area are retained, thereby effectively purifying the image background and highlighting the main crack.

[0105] Even after noise removal, the crack area may still exhibit discontinuous crack morphology due to image blurring, minor breaks or gaps within the crack itself, affecting the accuracy of subsequent skeleton extraction. Therefore, a closing operation from mathematical morphology is used to process the cleaned binary image. This closing operation consists of two consecutive operations: dilation followed by erosion. This effectively fills the tiny pores inside the crack and connects the minor breaks, without significantly altering the overall outline and size of the crack. During the operation, an elliptical structuring element with a radius of CLOSE_RADIUS is selected (elliptical structuring elements, compared to rectangular or circular structuring elements, better fit the slender shape of the crack, reducing excessive modification of the crack edges). This structuring element is applied to the binary image; dilation fills crack gaps and expands connected regions, while erosion restores the original crack width, ultimately connecting the minor breaks in the crack and resulting in a binary image of the crack with a coherent morphology and clear outline.

[0106] Skeleton extraction is a core step in crack morphology analysis and size quantization. Its purpose is to simplify the continuous crack region into a single-pixel-wide centerline, preserving the crack's topological structure and orientation information. After noise removal and closing operations, the Zhang-Suen thinning algorithm (also known as the fast parallel thinning algorithm) is used to generate a temporary skeleton for the binary image. This algorithm employs a parallel iterative approach, using two alternating boundary pixel deletion operations to gradually peel away the boundary pixels of the crack region without disrupting crack connectivity or generating redundant branches, until a single-pixel-wide skeleton structure is obtained. Compared to other thinning algorithms, the Zhang-Suen algorithm is computationally efficient, produces uniform thinning results, and effectively preserves the key morphological features of the crack. The generated temporary skeleton can serve as the basis for subsequent centerline optimization and crack parameter calculation.

[0107] S6. Construct a distance map based on the Euclidean distance from each foreground pixel to the nearest background pixel in the target binary image.

[0108] For the binarized crack region image, each foreground pixel is traversed, and its Euclidean distance to the nearest background pixel in the image is calculated. The distance value corresponding to each pixel is used as a grayscale value to generate a distance map D(x,y) with the same size as the original binary image. In the distance map, the pixel value is positively correlated with the distance from the point to the crack edge. The larger the distance value, the farther the foreground pixel is from the crack edge and the closer it is to the crack centerline; the smaller the distance value, the closer the pixel is to the crack boundary. This distance map can effectively characterize the geometric center distribution features of the crack region, providing a basic metric for subsequent crack centerline extraction, width quantization, and morphological analysis.

[0109] S7. Take the local maximum point in the graph as the candidate endpoint and select the pair of candidate endpoints with the greatest Euclidean distance as the start and end points.

[0110] To accurately locate the endpoints of cracks and provide a basis for subsequent crack length calculation, direction analysis, and morphological integrity assessment, endpoint filtering is performed on each crack connected region based on the distance map constructed earlier. The specific steps are as follows: First, for each crack connected region retained after cleaning, all foreground pixels within the region are traversed. Combining the pixel value distribution of the distance map D(x,y), local maximum points in the distance map are found within each connected region. These local maximum points correspond to the positions farthest from the crack edge within the connected region, and are likely to be crack endpoints or key feature points. These are marked as candidate endpoints. Then, the maximum distance value in the entire distance map is calculated, and a filtering threshold of 30% of the maximum distance value in the entire map is set. All candidate endpoints are filtered: if the distance value corresponding to a candidate endpoint (i.e., the pixel value of that point in the distance map) is greater than the filtering threshold, the point is determined to be a valid crack endpoint and retained; if it is less than or equal to the threshold, it is determined to be an invalid endpoint and discarded. After the above screening process, the final set of all effective crack endpoints E={e1,e2,…,em} is obtained, where m is the number of effective endpoints, and each endpoint ei corresponds to a specific coordinate in the image, which can be directly used for further analysis of crack morphology.

[0111] S8. Using each foreground pixel in the target binary graph as a graph node, the edge weight between foreground pixel nodes adjacent to the graph node is defined as the reciprocal of the distance value of the adjacent node in the distance graph, and a weighted graph is constructed.

[0112] Treat all foreground pixels of the target binary image as graph nodes. For each graph node... Foreground nodes in its 8-neighborhood Establish directed edges → The edge weight is defined as:

[0113] ;

[0114] in, To prevent division by zero for extremely small constants, This represents the distance from the foreground node to the crack edge. The larger the distance to the target node, the smaller the edge cost, thus guiding the path through the central region with the larger distance value.

[0115] S9. Search for the minimum weight path between the start and end points on the weighted graph to obtain the defect location.

[0116] If the number of crack endpoints is greater than 2, the pair of endpoints with the farthest Euclidean distance is selected as the start and end points of the main crack to ensure that the main crack is the longest. If there are only two endpoints, they are directly used as the start and end points. Then, Dijkstra's algorithm is run on the weighted graph to find the minimum weight path from the start point to the end point, which is the path with the minimum total cost. Since the weight is inversely proportional to the distance value, the path will travel along the "ridge" of the distance field and be on the crack center line. Then, the path to skeleton conversion operation is performed, all pixels on the path are assigned a value of 1 and the rest are assigned a value of 0 to obtain the initial skeleton. Then, isolated short branches with a length of less than 3 pixels are deleted by the isolated short branch removal operation to reduce noise and spikes. Finally, the processed skeleton matrix is ​​multiplied by 255 and saved as a PNG image with white lines and a black background.

[0117] 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.

Claims

1. A composite material defect detection system, characterized by, include: A multi-excitation signal generation module is used to provide different excitation modes, each of which selectively enhances the response of a specific type of defect in the composite material under test; Output an excitation signal that matches the target command; the different excitation modes specifically include: Low-power pulse excitation mode, configured to output a square wave excitation signal with adjustable duty cycle to excite a broadband thermal wave response; The low-power phase-locked loop excitation mode is configured to output a frequency-adjustable sine wave or modulated continuous wave excitation signal to excite a steady-state thermal response at the same frequency. Low-power step excitation mode, configured to output a step power signal with adjustable duration to excite transient thermal diffusion response; The ultrasonic excitation module is used to receive excitation signals and convert them into mechanical vibrations; it is used to transmit mechanical vibrations to the composite material under test in the form of symmetrical surface contact, so as to excite the formation of temperature differences between the defective regions and the non-defective regions inside the composite material under test. The infrared thermal imaging acquisition module is used to acquire thermal image data of the composite material under test through thermal imaging technology matched with the excitation signal. The synchronization control and signal processing module is used to receive thermal image data and obtain the defect location based on the thermal image data: It performs binarization and morphological processing on the thermal image data to obtain a target binary image; it constructs a distance map based on the Euclidean distance from each foreground pixel to the nearest background pixel in the target binary image; it selects the pair of candidate endpoints with the greatest Euclidean distance as the start and end points, using each foreground pixel in the target binary image as a graph node, and defines the edge weight between adjacent foreground pixel nodes as the reciprocal of the distance value in the distance map, thus constructing a weighted graph; it searches for the minimum weight path between the start and end points on the weighted graph to obtain the defect location. The synchronization control and signal processing module is also used to output target instructions that match the material properties, geometric features and estimated defect types of the composite material under test. The estimated defect types are determined based on the material properties, geometric features and service condition parameters of the composite material under test, and the degree of matching with the corresponding data in the preset historical defect database. The historical defect database stores typical defect types and defect feature data corresponding to different material properties, geometric features and service condition parameters. It is also used to adaptively select the excitation mode according to the estimated defect type: if the estimated defect type is a large near-surface defect, the multi-excitation signal generation module is controlled to select a low-power pulse excitation mode to quickly locate the defect using transient thermal waves; if the estimated defect type is a deep or small defect, the multi-excitation signal generation module is controlled to select a low-power phase-locked loop excitation mode and set the corresponding modulation frequency to extract phase information at a specific depth; if the estimated defect type is a medium-depth defect or requires quantitative depth analysis, the multi-excitation signal generation module is controlled to select a low-power step excitation mode to observe the time evolution of thermal diffusion.

2. The composite material defect detection system of claim 1, wherein, The synchronization control and signal processing module is electrically connected to the multi-excitation signal generation module, the ultrasonic excitation module, and the infrared thermal imaging acquisition module, and is used for: Output target instructions to the multi-excitation signal generation module to trigger the generation of excitation signals that match the material properties, geometric features and predicted defect types of the composite material under test; A synchronous trigger signal is output to the ultrasonic excitation module to control the timing of mechanical vibration application; Output acquisition signals to the infrared thermal imaging acquisition module to achieve synchronous acquisition of thermal image data and excitation signals; It also receives status signals from each module to coordinate the working timing of each module in a closed loop.

3. The composite material defect detection system of claim 2, wherein, The ultrasonic excitation module includes: An ultrasonic transducer is used to receive trigger signals to control the timing of mechanical vibration application; it is used to convert excitation signals into mechanical vibration. Clamping structure for clamping the composite material to be tested in a symmetrical plane contact manner; The ultrasonic transducer is positioned at the end of the gripper of the clamping structure, and its contact surface with the composite material under test is a symmetrical planar contact surface, enabling the coupled transmission of mechanical vibration; or, The ultrasonic transducer is positioned in the non-grip region of the clamping structure, and indirectly transmits mechanical vibrations to the composite material under test in the form of symmetrical planar contact through the clamping structure.

4. The composite material defect detection system of claim 1, wherein, The acquisition of thermal image data of the composite material under test through thermal imaging technology matched with the excitation signal specifically includes: In low-power pulse excitation mode, pulsed thermal imaging technology is used to acquire transient thermal diffusion sequence thermal image data; In low-power phase-locked excitation mode, phase-locked thermal imaging technology is used to acquire phase-locked thermal wave response thermal image data; In the low-power step excitation mode, step thermal imaging technology is used to acquire quasi-steady-state thermal distribution thermal image data.

5. A method of defect detection using the composite material defect detection system according to any one of claims 1 to 4, characterized by, Includes the following steps: Obtain target instructions that match the material properties, geometric features, and predicted defect types of the composite material under test, and trigger excitation signals that match the target instructions; include: Low-power pulse excitation mode, configured to output a square wave excitation signal with adjustable duty cycle to excite a broadband thermal wave response; The low-power phase-locked loop excitation mode is configured to output a frequency-adjustable sine wave or modulated continuous wave excitation signal to excite a steady-state thermal response at the same frequency. Low-power step excitation mode, configured to output a step power signal with adjustable duration to excite transient thermal diffusion response; The excitation signal is converted into mechanical vibration, which is transmitted to the composite material under test in the form of symmetrical surface contact, so as to excite the formation of temperature difference between the defect region and the non-defect region inside the composite material under test. The temperature difference between the internal defect region and the non-defect region is converted into thermal image data by thermal imaging technology that matches the excitation signal. Binarization and morphological processing are performed on the thermal image data to obtain a binary image of the target. A distance map is constructed based on the Euclidean distance from each foreground pixel to the nearest background pixel in the target binary image; The local maximum point in the distance graph is used as the candidate endpoint, and the pair of candidate endpoints with the greatest Euclidean distance is selected as the start and end points. Using each foreground pixel in the target binary image as a graph node, the edge weight between foreground pixel nodes adjacent to the graph node is defined as the reciprocal of the distance value of the adjacent node in the distance graph, and a weighted graph is constructed. The defect location is obtained by searching for the minimum weight path between the start and end points on the weighted graph.

6. The defect detection method of claim 5, wherein Before performing binarization and morphological processing on the thermal image data, the process also includes filtering and linear stretching of the thermal image data.

7. The defect detection method of claim 5, wherein The process of binarizing and morphologically processing the thermal image data to obtain the target binary image specifically includes: The thermal image data is adaptively binarized using the Sauvola local thresholding algorithm to obtain the first binary image. Mark the connected components in the first binary graph, and delete the connected components in the first binary graph that are smaller than a preset area threshold to obtain the second binary graph; A morphological method based on elliptical structuring elements is used to perform a closing operation on the second binary image to obtain the third binary image; A thinning algorithm is used to generate a temporary skeleton of the third binary image, and the target binary image is obtained.

8. The defect detection method of claim 5, wherein, The search for the minimum weighted path between the start and end points on the weighted graph is achieved using Dijkstra's algorithm.

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