System and method for detecting thermal stress of aero-engine blade

Abstract

The invention relates to a system and a method for detecting thermal stress of an aero-engine blade. The system comprises a green laser, a near-infrared laser, and an optical splitter, a scanning control unit, a signal receiver and a processing module which are connected in sequence, the green laser and the near-infrared laser are respectively connected with the optical splitter; the green laser is used for outputting a first laser beam to the engine blade; the near-infrared laser is used for outputting a second laser beam to the engine blade; the optical splitter is used for combining the first laser beam and the second laser beam to obtain a combined beam; the scanning control unit is used for controlling the combined light beam to scan engine blades; the signal receiver is used for receiving an ultrasonic signal and a photoacoustic signal generated by the engine blade based on the combined light beam; and the processing module is used for processing the ultrasonic signal and the photoacoustic signal to generate a detection result. According to the invention, an accurate and efficient three-dimensional visual solution can be provided for the detection of the thermal stress of the aero-engine blade.

Description

Technical Field

[0001] The present disclosure relates to the technical field of aero-engine blade manufacturing and detection, and in particular to a system and method for detecting thermal stress of an aero-engine blade. Background Art

[0002] Aircraft engine blades will generate thermal cycle fatigue internal stress under long-term high temperature, high pressure and high speed working environment. This internal stress is mainly manifested as the superposition effect of thermal stress, residual stress and phase change stress. The release of internal stress can lead to many problems. For example, the release of internal stress during the electroplating process can cause uneven metal ion deposition and lead to large-scale shedding of the coating. Therefore, it is necessary to detect the thermal stress of engine blades.

[0003] In related technologies, the thermal stress of engine blades is usually detected by laser detection technology. However, the detection technology in related technologies can only detect thermal stress information of a single mode, and the detection depth and detection efficiency in related technologies are limited, which cannot meet the detection requirements of thermal stress of aircraft engine blades under complex working conditions. Summary of the Invention

[0004] The present disclosure provides a system and method for detecting thermal stress of an aero-engine blade.

[0005] According to a first aspect of the present disclosure, a system for detecting thermal stress of an aircraft engine blade is provided, the system comprising: a green laser, a near-infrared laser, and a spectrometer, a scanning control unit, a signal receiver, and a processing module connected in sequence; the green laser and the near-infrared laser are respectively connected to the spectrometer;

[0006] The green laser is used to output a first laser beam to the engine blades;

[0007] The near-infrared laser is used to output a second laser beam to the engine blade;

[0008] The beam splitter is configured to combine the first laser beam and the second laser beam to obtain a composite beam;

[0009] The scanning control unit is used to control the synthetic light beam to scan the engine blade;

[0010] The signal receiver is used to receive the ultrasonic signal and the photoacoustic signal generated by the engine blade based on the synthetic light beam;

[0011] The processing module is used to process the ultrasonic signal and the photoacoustic signal to generate a detection result; wherein the detection result at least includes: the maximum stress value, crack coordinates and safety factor of the engine blade.

[0012] Further, the scanning control unit comprises a two-dimensional galvanometer and a six-axis mechanical arm;

[0013] The two-dimensional galvanometer is configured to reflect the combined light beam to generate a reflected light beam;

[0014] The six-axis mechanical arm is configured to drive the two-dimensional galvanometer to move, so that the reflected light beam scans the engine blade according to a preset scanning track.

[0015] According to a second aspect of the present disclosure, a method for detecting thermal stress of an engine blade is provided, which is applied to the above-mentioned system for detecting thermal stress of an engine blade, and the method comprises:

[0016] controlling a green laser to output a first laser beam to the engine blade, and controlling a near-infrared laser to output a second laser beam to the engine blade;

[0017] merging the first laser beam and the second laser beam to obtain a combined light beam;

[0018] controlling the combined light beam to scan the engine blade;

[0019] receiving an ultrasonic signal and an optoacoustic signal generated by the engine blade based on the combined light beam;

[0020] processing the ultrasonic signal and the optoacoustic signal to generate a detection result; wherein the detection result at least includes a maximum stress value, a crack coordinate and a safety factor of the engine blade.

[0021] Further, the processing the ultrasonic signal and the optoacoustic signal to generate a detection result comprises:

[0022] obtaining an implicit field feature of the engine blade based on the ultrasonic signal and the optoacoustic signal; wherein the implicit field feature is used to represent the physical state of stress, temperature and material response of each point inside the engine blade;

[0023] determining a stress tensor of the engine blade based on the implicit field feature; wherein the stress tensor is used to represent the stress size, direction and thermal coupling state of any point inside the engine blade in three-dimensional space;

[0024] establishing a three-dimensional stress field distribution cloud chart of the engine blade based on the stress tensor, and determining a detection result based on the three-dimensional stress field distribution cloud chart.

[0025] Further, the obtaining an implicit field feature of the engine blade based on the ultrasonic signal and the optoacoustic signal comprises:

[0026] determine an ultrasonic wave propagation time delay based on the ultrasonic wave signal;

[0027] calculate an elastic modulus and a strain rate sensitivity coefficient of the engine blade;

[0028] obtain a temperature gradient of the engine blade based on the photoacoustic signal, and calculate a thermal expansion coefficient based on the temperature gradient;

[0029] input the ultrasonic wave propagation time delay, the temperature gradient, the elastic modulus, the thermal expansion coefficient, and the strain rate sensitivity coefficient into a dynamic hash coding network model to obtain an implicit neural radiated field; wherein the implicit neural radiated field is used to represent a spatio-temporal evolution process of a stress, temperature, and strain multi-physical field coupling state of the engine blade in a three-dimensional space;

[0030] determine an implicit field feature of the engine blade based on the implicit neural radiated field.

[0031] Further, the determination of the stress tensor of the engine blade based on the implicit field feature comprises:

[0032] input the implicit field feature into a multi-layer perception network and embed a physical constraint equation in the multi-layer perception network to obtain a modified multi-layer perception network; wherein the physical constraint equation at least includes a momentum conservation equation and an energy conservation equation;

[0033] determine the stress tensor of the engine blade based on the modified multi-layer perception network.

[0034] Further, the establishment of the three-dimensional stress field distribution cloud chart of the engine blade based on the stress tensor comprises:

[0035] map the stress tensor to a continuous three-dimensional stress field function through differentiable interpolation;

[0036] optimize the continuous three-dimensional stress field function through a loss function to obtain an optimized three-dimensional stress field function;

[0037] establish a three-dimensional stress field distribution cloud chart based on the optimized three-dimensional stress field function.

[0038] Further, the determination of the detection result based on the three-dimensional stress field distribution cloud chart comprises:

[0039] perform interactive interface dynamic rendering on the three-dimensional stress field distribution cloud chart to obtain a visual rendering result; wherein the visual rendering result is a color gradient display of the stress gradient of the engine blade;

[0040] A detection result is determined based on the visual rendering result; wherein the detection result includes at least: a maximum stress value, crack coordinates, and a safety factor of the engine blade.

[0041] Furthermore, after establishing a three-dimensional stress field distribution cloud map of the engine blade based on the stress tensor, the method further includes:

[0042] determining the local stress intensity of the engine blade based on the three-dimensional stress field distribution cloud map;

[0043] updating a dynamic hash resolution of the dynamic hash coding network model based on the local stress intensity to obtain an updated hash coding network model;

[0044] An updated three-dimensional stress field distribution cloud map is determined based on the updated hash coding network model.

[0045] Furthermore, the method further comprises:

[0046] Processing the reference stress of the engine blade, the stress field at the current moment, the strain rate, and the process random noise through a long short-term memory network to obtain the stress field of the engine blade at the next moment;

[0047] determining a gradient distribution of the engine blade based on the stress field at the next moment;

[0048] The scanning instructions of the six-axis robotic arm are determined based on the gradient distribution; wherein, the scanning instructions are used to drive the six-axis robotic arm to drive the two-dimensional galvanometer to move, so that the reflected light beam performs high-density scanning on the high stress gradient area of ​​the engine blade.

[0049] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the program.

[0050] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above method of the present disclosure is implemented.

[0051] According to a fifth aspect of the present disclosure, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, it implements the above method of the present disclosure.

[0052] The system comprises a green laser, a near-infrared laser, and a light splitter, a scanning control unit, a signal receiver and a processing module connected in sequence; the green laser and the near-infrared laser are connected with the light splitter; the green laser is used for outputting a first laser beam to the engine blade; the near-infrared laser is used for outputting a second laser beam to the engine blade; the light splitter is used for merging the first laser beam and the second laser beam to obtain a composite beam; the scanning control unit is used for controlling the composite beam to scan the engine blade; the signal receiver is used for receiving ultrasonic signals and photoacoustic signals generated by the engine blade based on the composite beam; and the processing module is used for processing the ultrasonic signals and the photoacoustic signals to generate a detection result; wherein the detection result at least comprises a maximum stress value, a crack coordinate and a safety factor of the engine blade.

[0053] As can be known from the above description, the detection system for the thermal stress of the engine blade provided by the technical scheme of the present disclosure generates a composite beam through the cooperation of the green laser and the near-infrared laser and the light splitter, the composite beam can realize precision scanning of a complex curved surface after being controlled by the scanning control unit, the signal receiver synchronously captures ultrasonic signals and photoacoustic signals, the processing module can further analyze and process the ultrasonic signals and the photoacoustic signals, and finally a detection result containing the maximum stress value, the crack coordinate and the safety factor of the engine blade can be generated. The detection system for the thermal stress of the engine blade provided by the technical scheme of the present disclosure breaks through the limitation of single modal detection in the traditional technology, realizes the synchronous acquisition of ultrasonic signals and photoacoustic signals, makes the detection depth cover the full thickness of the blade, avoids the shortcoming that the traditional technology can only detect the surface layer, and the system can adapt to the real-time monitoring requirements of complex working conditions such as the start and stop of the aero-engine and the variable load of the aero-engine, so as to improve the service life of the aero-engine blade and the flight safety guarantee capability. BRIEF DESCRIPTION OF DRAWINGS

[0054] The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings. The drawings provided in the specification and the contents of the specification are provided to further understand the embodiments of the present disclosure and to constitute a part of the specification, which are used to explain the present disclosure, and do not constitute a limitation on the present disclosure. In the drawings, the same reference numerals generally represent the same components or steps.

[0055] Figure 1 A structural schematic diagram of a detection system for the thermal stress of an engine blade provided by an exemplary embodiment of the present disclosure is shown in the figure;

[0056] Figure 2 A structural schematic diagram of a detection system for the thermal stress of an engine blade provided by another exemplary embodiment of the present disclosure is shown in the figure;

[0057] Figure 3 Flowchart of a method for detecting thermal stress of an aeroengine blade according to an exemplary embodiment of the present disclosure;

[0058] Figure 4 Flowchart of a method for detecting thermal stress of an aeroengine blade according to another exemplary embodiment of the present disclosure;

[0059] Figure 5 Flowchart of a method for detecting thermal stress of an aeroengine blade according to another exemplary embodiment of the present disclosure;

[0060] Figure 6 Overall flowchart of a method for detecting thermal stress of an aeroengine blade according to an exemplary embodiment of the present disclosure;

[0061] Figure 7 Structural block diagram of an electronic device according to an exemplary embodiment of the present disclosure;

[0062] Figure 8 Structural block diagram of a computer system according to an exemplary embodiment of the present disclosure;

[0063] Figure 9 Structural block diagram of a computer program product according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0064] Embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings. While several embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein, but rather should be construed to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure. It is to be understood that the drawings and the embodiments are only for illustrative purposes and should not be construed as limiting the scope of protection of the present disclosure.

[0065] It is to be understood that the various steps of the method embodiments of the present disclosure can be performed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit performing the steps shown. The scope of the present disclosure is not limited in this respect.

[0066] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0067] It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0068] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0069] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0070] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the operation requested will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operations of the disclosed technical solution based on the prompt message.

[0071] As an optional but non-limiting implementation method, in response to receiving the user's active request, the method of sending a prompt message to the user can be, for example, a pop-up window, and the prompt message can be presented in the form of text in the pop-up window. In addition, the pop-up window can also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation method of the present disclosure. Other methods that meet relevant laws and regulations can also be applied to the implementation method of the present disclosure.

[0072] In one embodiment, Figure 1As shown, a detection system for thermal stress of an aircraft engine blade is provided, comprising: a green laser 2, a near-infrared laser 1, and a spectrometer 3, a scanning control unit 9, a signal receiver 10, and a processing module 8 connected in sequence. The green laser 2 and the near-infrared laser 1 are respectively connected to the spectrometer 3, wherein the green laser 2 is used to output a first laser beam to the engine blade, the near-infrared laser 1 is used to output a second laser beam to the engine blade, the spectrometer 3 is used to combine the first laser beam and the second laser beam to obtain a composite beam, the scanning control unit 9 is used to control the composite beam to scan the engine blade, the signal receiver 10 is used to receive an ultrasonic signal and a photoacoustic signal generated by the engine blade based on the composite beam, and the processing module 8 is used to process the ultrasonic signal and the photoacoustic signal to generate a detection result, wherein the detection result at least includes: a maximum stress value, crack coordinates, and a safety factor of the engine blade.

[0073] In one embodiment, Figure 2 As shown, the scanning control unit 9 includes: a two-dimensional galvanometer 4 and a six-axis robotic arm 5, wherein the two-dimensional galvanometer 4 is used to reflect the synthetic light beam to generate a reflected light beam, and the six-axis robotic arm 5 is used to drive the two-dimensional galvanometer 4 to move so that the reflected light beam scans the engine blades according to a preset scanning trajectory, and the signal receiver 10 includes: a laser interferometer 7 and an infrared thermal imager 6, wherein the laser interferometer 7 is used to receive the ultrasonic signal generated by the engine blade based on the first laser beam, and the infrared thermal imager 6 is used to receive the photoacoustic signal generated by the engine blade based on the second laser beam.

[0074] In this embodiment, the operator can control the green laser 2 and the near-infrared laser 1 to start at the same time. The spectrometer combines the first laser beam emitted by the green laser 2 and the second laser beam emitted by the near-infrared laser 1 into a composite beam. After being reflected by the two-dimensional galvanometer 4, the composite beam is scanned on the surface of the engine blade under the drive of the six-axis robotic arm 5. The green laser 2 excites the engine blade to generate an ultrasonic signal, which is received by the laser interferometer 7. The near-infrared laser 1 regulates the material stress of the engine blade, and the generated photoacoustic signal is received by the infrared thermal imager 6. The processing module 8 processes the acoustic signal and the photoacoustic signal to generate a detection result.

[0075] In a possible embodiment, the wavelength of the green laser 2 can be 532 nm, which has a high photon energy and is prone to effectively generating photoacoustic effect with the surface of a metal material, and can efficiently excite ultrasonic surface waves, and is used for high-sensitivity surface residual stress detection. The wavelength of the near-infrared laser 1 can be 1064 nm, which has strong penetration ability and is suitable for regulating thermal stress in the interior of the engine blade material and exciting deep photoacoustic signals, and is convenient for realizing the detection of internal stress of thick-walled or complex structures. It should be noted that the above embodiment only exemplarily illustrates the wavelength selection of the near-infrared laser 1 and the green laser 2, and in actual application, the wavelength of the near-infrared laser 1 and the green laser 2 can be flexibly adjusted according to the material of the engine blade, which is not limited herein.

[0076] The system provided by the method for detecting the thermal stress of the aero-engine blade comprises a green laser, a near-infrared laser, and a light splitter, a scanning control unit, a signal receiver and a processing module connected in sequence. The green laser and the near-infrared laser are connected with the light splitter. The green laser is configured to output a first laser beam to the engine blade. The near-infrared laser is configured to output a second laser beam to the engine blade. The light splitter is configured to combine the first laser beam and the second laser beam to obtain a combined beam. The scanning control unit is configured to control the combined beam to scan the engine blade. The signal receiver is configured to receive ultrasonic signals and photoacoustic signals generated by the engine blade based on the combined beam. The processing module is configured to process the ultrasonic signals and the photoacoustic signals to generate a detection result. The detection result at least comprises a maximum stress value, a crack coordinate and a safety factor of the engine blade.

[0077] As can be known from the above description, the system for detecting the thermal stress of the aero-engine blade provided by the technical scheme of the present disclosure generates a combined beam through the green laser and the near-infrared laser in cooperation with the light splitter. The combined beam can realize precision scanning of a complex curved surface after being controlled by the scanning control unit. The signal receiver synchronously captures ultrasonic signals and photoacoustic signals. The processing module can further analyze and process the ultrasonic signals and the photoacoustic signals. Finally, a detection result containing a maximum stress value, a crack coordinate and a safety factor of the engine blade can be generated. The system for detecting the thermal stress of the aero-engine blade provided by the technical scheme of the present disclosure breaks through the limitation of single modal detection in the traditional technology, realizes synchronous acquisition of ultrasonic signals and photoacoustic signals, makes the detection depth cover the full thickness of the blade, avoids the shortcoming that the traditional technology can only detect the surface layer, and the system can adapt to the real-time monitoring requirements of complex working conditions such as starting and stopping of the aero-engine and variable load, so as to improve the service life of the aero-engine blade and the flight safety guarantee capability.

[0078] In one embodiment, as Figure 3As shown, this embodiment relates to a method for detecting thermal stress of an aircraft engine blade used in the aircraft engine blade thermal stress detection system of any of the above embodiments, and the method comprises the following steps:

[0079] Step 301: Control a green laser to output a first laser beam to an engine blade, and control a near-infrared laser to output a second laser beam to the engine blade.

[0080] In a possible embodiment, the operator can control the green laser 2 and the near-infrared laser 1 to start at the same time, wherein the green laser 2 outputs a first laser beam to the engine blades, and the first laser beam can excite the engine blades to generate ultrasonic signals, and the near-infrared laser 1 outputs a second laser beam to the engine blades, and the second laser beam can excite the engine blades to generate photoacoustic signals.

[0081] Step 302: Combine the first laser beam and the second laser beam to obtain a composite beam.

[0082] In a possible embodiment, the beam splitter 3 can combine the first laser beam output by the green laser 2 to the engine blade and the second laser beam output by the near-infrared laser 1 to the engine blade, and finally obtain a composite beam.

[0083] Step 303: Control the synthesized light beam to scan the engine blades.

[0084] In a possible embodiment, after the composite light beam is reflected by the two-dimensional galvanometer 4 , the operator can control the six-axis robotic arm 5 to drive the composite light beam to scan the surface of the engine blade.

[0085] Step 304: Receive the ultrasonic signal and the photoacoustic signal generated by the engine blade based on the synthesized light beam.

[0086] In a possible embodiment, the laser interferometer 7 receives the ultrasonic signal generated by the engine blade based on the synthetic light beam, and the infrared thermal imager 6 receives the photoacoustic signal generated by the engine blade based on the synthetic light beam.

[0087] Step 305: Process the ultrasonic signal and the photoacoustic signal to generate a detection result.

[0088] In a possible embodiment, the laser interferometer 7 and the infrared thermal imager 6 respectively send the received ultrasonic signal and photoacoustic signal to the processing module, and the processing module 8 processes the ultrasonic signal and the photoacoustic signal to generate a detection result, wherein the detection result includes at least: the maximum stress value of the engine blade, the crack coordinates and the safety factor.

[0089] In one possible embodiment, Figure 4As shown, the ultrasonic signal and the photoacoustic signal are processed to generate a detection result, which also includes the following steps:

[0090] Step 3051: Acquire the implicit field characteristics of the engine blade based on the ultrasonic signal and the photoacoustic signal.

[0091] Here, the processing module can obtain the implicit field characteristics of the engine blade based on the ultrasonic signal and the photoacoustic signal, wherein the implicit field characteristics are used to characterize the physical state of stress, temperature and material response at each point inside the engine blade.

[0092] In a possible embodiment, obtaining implicit field characteristics of an engine blade based on ultrasonic signals and photoacoustic signals includes the following steps:

[0093] determining an ultrasonic propagation delay based on the ultrasonic signal;

[0094] Calculate the elastic modulus and strain rate sensitivity coefficient of engine blades;

[0095] Acquiring the temperature gradient of the engine blade based on the photoacoustic signal and calculating the thermal expansion coefficient based on the temperature gradient;

[0096] The ultrasonic propagation delay, temperature gradient, elastic modulus, thermal expansion coefficient, and strain rate sensitivity coefficient are input into the dynamic hash coding network model to obtain the implicit neural radiation field.

[0097] Determine the implicit field characteristics of engine blades based on implicit neural radiation field.

[0098] Specifically, first, the processing module determines the ultrasonic propagation delay based on the ultrasonic signal, and then calculates the elastic modulus and strain rate sensitivity coefficient of the engine blade. After that, the processing module obtains the temperature gradient of the engine blade based on the photoacoustic signal, and calculates the thermal expansion coefficient based on the temperature gradient. After that, the processing module inputs the ultrasonic propagation delay, temperature gradient, elastic modulus, thermal expansion coefficient, and strain rate sensitivity coefficient into the dynamic hash coding network model to obtain an implicit neural radiation field, wherein the implicit neural radiation field is used to characterize the spatiotemporal evolution process of the stress, temperature and strain multi-physical field coupling state of the engine blade in three-dimensional space. Finally, the processing module determines the implicit field characteristics of the engine blade based on the implicit neural radiation field.

[0099] In one possible embodiment, the near-infrared laser 1 and the green laser 2 excite the engine blade to generate ultrasonic signals and temperature field changes. The laser interferometer 7 detects and records the ultrasonic response signal, which is the ultrasonic signal. The processing module 8 determines the ultrasonic propagation delay t through the ultrasonic signal and obtains the elastic modulus C of the engine blade by calculation. ijkl(T) and the strain rate sensitivity coefficient β, the infrared thermal imager 6 captures the temperature distribution of the engine blade in real time and obtains a photoacoustic signal. The processing module 8 obtains the temperature gradient ▽T of the engine blade through the photoacoustic signal and calculates the thermal expansion coefficient α based on the temperature gradient ▽T T .

[0100] The processing module 8 obtains the ultrasonic propagation delay t, temperature gradient ▽T, elastic modulus C ijkl (T), thermal expansion coefficient α T , strain rate sensitivity coefficient β, then t, C ijkl (T), α T and β are used as the basis for characterizing the local material properties and spatiotemporal evolution of the engine blades, and are input into the dynamic hash coding network model to obtain the implicit neural radiation field. It should be noted that the input of the traditional neural radiation field algorithm is the geometric position and viewing direction. The spatial radiation field is implicitly modeled through the multi-layer perceptron (MLP). The calculation formula is:

[0101]

[0102] Where γ(.) is the hash code output value, σ is the volume density, and c is the color feature. It can be seen that the traditional algorithm only relies on geometric position and viewing direction, cannot integrate multiple physical field parameters, and lacks physical conservation constraints. This embodiment makes the following improvements and uses dynamic hash coding to output the implicit neural radiation field. The calculation formula is:

[0103]

[0104] Among them, H(x,y,z,t) is the hash code output value, MLP is the neural network function, (x,y,z,t) and l d is a dynamic level parameter, Temperature gradient, λ s is the precision control parameter, M is the characteristic space size, and the implicit neural radiation field is used to characterize the spatiotemporal evolution process of the coupled state of multiple physical fields of stress, temperature and strain of the engine blades in three-dimensional space. It should be noted that, in this embodiment, the acoustic parameters, i.e., the ultrasonic excitation characteristics, and the thermodynamic parameters, i.e., the temperature gradient, are jointly encoded as implicit field inputs, breaking through the limitation of the traditional neural radiation field algorithm that only relies on geometric position.

[0105] After obtaining the implicit neural radiation field, the processing module 8 determines the implicit field characteristics of the engine blade based on the implicit neural radiation field. The processing module 8 splices the sound velocity tensor and the temperature gradient and inputs them into the MLP to generate the implicit field characteristics. The calculation formula is:

[0106]

[0107] in, is the output of the implicit neural radiation field, i.e., the implicit field feature, C ijkl (x, y, z, t) is the elastic modulus. It should be noted that, through implicit neural field mapping, the processing module 8 fuses the acoustic-elastic and thermal information to generate implicit field features, providing efficient and continuous spatial feature representation for the subsequent differentiable rendering field, thereby realizing physically driven and smooth three-dimensional stress field reconstruction representation.

[0108] Step 3052: Determine the stress tensor of the engine blade based on the implicit field characteristics.

[0109] Here, after the processing module 8 obtains the implicit field characteristics of the engine blade based on the ultrasonic signal and the photoacoustic signal, it can determine the stress tensor of the engine blade based on the implicit field characteristics, wherein the stress tensor is used to characterize the stress magnitude, direction and thermal-mechanical coupling state of any point inside the engine blade in three-dimensional space.

[0110] In a possible embodiment, determining the stress tensor of an engine blade based on implicit field characteristics includes the following steps:

[0111] The implicit field features are input into the multi-layer perceptron network, and the physical constraint equations are embedded in the multi-layer perceptron network to obtain a modified multi-layer perceptron network;

[0112] Determine the stress tensor of the engine blade based on the modified multilayer perceptron network.

[0113] Specifically, first, the processing module 8 inputs the implicit field features into the multi-layer perceptron network, and embeds the physical constraint equations in the multi-layer perceptron network to obtain a modified multi-layer perceptron network, wherein the physical constraint equations include at least: momentum conservation equation and energy conservation equation. Then, the processing module 8 determines the stress tensor of the engine blade based on the modified multi-layer perceptron network.

[0114] It should be noted that traditional data-driven models are usually based on local data fitting and ignore the multi-field coupling effect. The traditional calculation formula for the stress tensor of an engine blade is:

[0115]

[0116] Among them, ω i (x, y, z) is the weight function based on local features, f i (t) is the time-series stress response function. According to the above formula, it can be observed that although the traditional data-driven model can quickly predict stress through simple mathematical expressions, it is limited by single-factor input and static constitutive assumptions, and lacks explicit modeling of mechanical and thermal conservation laws, making it difficult to apply to the actual working conditions of aircraft engine blades.

[0117] In this embodiment, the processing module 8 inputs the implicit field feature into the multi-layer perceptron network, and embeds the physical constraint equation in the multi-layer perceptron network to obtain a modified multi-layer perceptron network, the physical constraint equation at least includes: momentum conservation equation and energy conservation equation. Specifically, the processing module 8 constructs the constitutive relation through the elastic modulus C ijkl (T), the thermal expansion coefficient a kl , and the strain rate sensitive coefficient b, and applies the momentum conservation and energy conservation double physical constraints. The formula of the modified multi-layer perceptron network is:

[0118]

[0119] Where, s ij is the stress tensor, d ij is the Kronecker function, is the equivalent strain rate.

[0120] The momentum conservation equation embedded in the multi-layer perceptron network is:

[0121]

[0122] Where, p is the material density, and u is the displacement field.

[0123] The energy conservation equation embedded in the multi-layer perceptron network is:

[0124]

[0125] Where, C p is the specific heat capacity, k is the thermal conductivity, is the strain rate tensor, and Q is the heat source.

[0126] It should be noted that in this embodiment, the processing module 8 explicitly fuses the material constitutive relation (stress-strain-temperature coupling) with the Navier-Stokes (momentum) and Fourier (energy) equations, realizes the physical driven multi-scale stress field evolution, and ensures the physical consistency of the numerical and neural field results, and improves the reliability of the results.

[0127] Step 3053, based on the stress tensor, a three-dimensional stress field distribution cloud chart of the engine blade is established, and a detection result is determined based on the three-dimensional stress field distribution cloud chart.

[0128] Here, the processing module 8 can establish a three-dimensional stress field distribution cloud chart of the engine blade based on the stress tensor, and determine a detection result based on the three-dimensional stress field distribution cloud chart.

[0129] In one possible embodiment, establishing a three-dimensional stress field distribution cloud chart of the engine blade based on the stress tensor includes the following steps:

[0130] The stress tensor is mapped into a continuous three-dimensional stress field function through differentiable interpolation;

[0131] The continuous three-dimensional stress field function is optimized by the loss function to obtain the optimized three-dimensional stress field function;

[0132] A three-dimensional stress field distribution cloud map is established based on the optimized three-dimensional stress field function.

[0133] Specifically, first, the processing module 8 maps the stress tensor into a continuous three-dimensional stress field function through differentiable interpolation. Then, the processing module 8 optimizes the continuous three-dimensional stress field function through a loss function to obtain an optimized three-dimensional stress field function. Finally, the processing module 8 establishes a three-dimensional stress field distribution cloud map based on the optimized three-dimensional stress field function.

[0134] In a possible embodiment, the processing module 8 maps the stress tensor into a continuous three-dimensional stress field function through differentiable interpolation. The differentiable interpolation formula is:

[0135]

[0136] Where σ(p) is the stress value at point p, ω k is the weight coefficient of the key point k, and the weight can be dynamically adjusted through hash coding, p k is the coordinate of the discrete sampling point, ω is the weight vector, σ k The true stress value at discrete sampling point k.

[0137] The processing module 8 optimizes the continuous three-dimensional stress field function through the loss function, wherein the loss function of the differentiable interpolation formula is:

[0138]

[0139] Among them, W i is the weight factor of each position error, is the actual stress value, and λ is the regularization parameter used to suppress weight noise.

[0140] After the processing module 8 obtains the optimized three-dimensional stress field function, it establishes a three-dimensional stress field distribution cloud map based on the optimized three-dimensional stress field function, which can realize high-precision continuous differentiable stress field visualization rendering of the engine blade based on physical constraints.

[0141] In a possible embodiment, determining the detection result based on the three-dimensional stress field distribution cloud map includes the following steps:

[0142] Perform interactive interface dynamic rendering on the three-dimensional stress field distribution cloud map to obtain visual rendering results;

[0143] The detection results are determined based on the visual rendering results.

[0144] Specifically, after the processing module 8 establishes the three-dimensional stress field distribution cloud map, the processing module 8 first performs interactive interface dynamic rendering on the three-dimensional stress field distribution cloud map to obtain a visual rendering result, wherein the visual rendering result is a color gradient display of the stress gradient of the engine blade. Then, the processing module 8 determines the detection result based on the visual rendering result, wherein the detection result includes at least: the maximum stress value of the engine blade, the crack coordinates and the safety factor.

[0145] In a possible embodiment, the processing module 8 can dynamically render the three-dimensional stress field distribution cloud map constructed by the above embodiment through an interactive 3D interface, combine OpenGL technology to realize stress gradient color gradient display, and support rotation, zoom and translation operations.

[0146] In one possible embodiment, processing module 8 can use the MarchingCubes algorithm to extract isosurfaces corresponding to stress thresholds from a three-dimensional stress field distribution cloud map, visually presenting the morphology and distribution range of stress concentration areas. Processing module 8 can also automatically mark crack hotspots using LSTM prediction residual data, indicating the direction of expansion with three-dimensional arrows. Furthermore, processing module 8 can simultaneously overlay infrared thermal imaging temperature fields and laser interferometer strain field data to implement multi-physics field spatiotemporal coupling analysis. Through this processing, processing module 8 can ultimately generate a test report that complies with national standards and includes key parameters such as the maximum stress value, crack coordinates, and safety factor for the engine blade.

[0147] In this embodiment, first, the processing module obtains the implicit field characteristics of the engine blade based on the ultrasonic signal and the photoacoustic signal. Then, the processing module determines the stress tensor of the engine blade based on the implicit field characteristics. Finally, the processing module establishes a three-dimensional stress field distribution cloud map of the engine blade based on the stress tensor, and determines the detection result based on the three-dimensional stress field distribution cloud map.

[0148] As can be seen from the above description, this embodiment sequentially acquires implicit field characteristics, determines stress tensors, establishes three-dimensional stress field distribution cloud maps, and generates test results through the processing module, achieving deep fusion of multi-physics field parameters and high-precision stress characterization under physical constraints. Specifically, this embodiment uses parameters such as propagation delay and temperature gradient extracted from ultrasonic and photoacoustic signals to construct a dynamic hash coding network model, breaking through the limitation of traditional neural radiation fields that rely solely on geometric position and integrating acoustic and thermodynamic parameters into implicit field characteristics. This embodiment embeds momentum conservation and energy conservation equations in a multi-layer perceptron network to achieve strong coupling modeling of stress-strain-temperature fields, addressing the lack of physical conservation in traditional data-driven models. This embodiment uses differentiable interpolation and loss function optimization to map stress tensors into continuous three-dimensional stress field functions. Combined with interactive 3D rendering and multi-physics field overlay analysis, this embodiment intuitively presents stress concentration areas and crack hotspots, and ultimately generates a test report containing key parameters such as maximum stress value and crack coordinates. This provides a solution for thermal stress detection of aircraft engine blades that combines physical accuracy with visualization efficiency, effectively improving the comprehensiveness and reliability of stress assessment under complex operating conditions.

[0149] In a possible embodiment, after establishing a three-dimensional stress field distribution cloud map of the engine blade based on the stress tensor, the following steps are further included:

[0150] Determine the local stress intensity of engine blades based on the three-dimensional stress field distribution cloud map;

[0151] updating the dynamic hash resolution of the dynamic hash coding network model based on the local stress intensity to obtain an updated hash coding network model;

[0152] The updated three-dimensional stress field distribution cloud map is determined based on the updated hash coding network model.

[0153] Specifically, after the processing module 8 establishes a three-dimensional stress field distribution cloud map of the engine blade based on the stress tensor, the processing module 8 determines the local stress intensity of the engine blade based on the three-dimensional stress field distribution cloud map, and updates the dynamic hash resolution of the dynamic hash coding network model based on the local stress intensity, that is, the updated hash coding network model is obtained through the hash resolution adaptive algorithm. The calculation process of the hash resolution adaptive algorithm is as follows:

[0154]

[0155] Among them, L h is the dynamic hash resolution, L0 is the initial resolution, β is the stress sensitivity coefficient, σ vM is the VM equivalent stress, σ refFor reference to the baseline stress, after obtaining the updated hash code network model, the processing module 8 may determine an updated three-dimensional stress field distribution cloud map based on the updated hash code network model.

[0156] From the above description, it can be seen that after the processing module in this embodiment establishes a three-dimensional stress field distribution cloud map based on the stress tensor, it dynamically updates the resolution of the hash coding network model by determining the local stress intensity, thereby realizing adaptive optimization of the detection accuracy. Specifically, the local stress intensity calculated by the processing module based on the three-dimensional stress field distribution cloud map can drive the nonlinear adjustment of the dynamic hash resolution, so that the system automatically increases the sampling density in the stress concentration area and accurately captures the details of the stress peak, while reducing the resolution in the stress flat area to balance the calculation load. This adaptive mechanism breaks through the limitations of traditional fixed-resolution detection, which not only ensures the detection accuracy of key areas, but also improves the overall computing efficiency, enabling the system to more efficiently cope with the dynamic changes of the stress field under extreme working conditions such as the start-stop and variable load of aircraft engine blades, providing a more intelligent technical solution for the accurate assessment of blade thermal stress.

[0157] In one embodiment, Figure 5 As shown, the method for detecting thermal stress of an aero-engine blade further includes the following steps:

[0158] Step 501: Process the reference stress of the engine blade, the stress field at the current moment, the strain rate, and the process random noise through a long short-term memory network to obtain the stress field of the engine blade at the next moment.

[0159] In one possible embodiment, the processing module 8 processes the reference stress of the engine blade, the stress field at the current moment, the strain rate, and the process random noise through a long short-term memory network to obtain the stress field of the engine blade at the next moment. The calculation formula is:

[0160]

[0161] Among them, σ t+1 is the stress field at the next moment, LSTM is the long short-term memory network, σ t is the stress field at the current moment, Strain rate, n t is the random noise of the process.

[0162] Step 502: Determine the gradient distribution of the engine blade based on the stress field at the next moment.

[0163] In a possible embodiment, the processing module 8 obtains the stress field σ of the engine blade at the next moment: t+1 Finally, the gradient distribution of thermal stress in the engine blade can be determined.

[0164] Step 503: Determine a scanning instruction for the six-axis robotic arm based on the gradient distribution.

[0165] In a possible embodiment, the processing module 8 can determine the scanning instructions of the six-axis robotic arm 5 based on the gradient distribution, wherein the scanning instructions are used to drive the six-axis robotic arm 5 to drive the two-dimensional galvanometer 4 to move, so that the reflected light beam performs high-density scanning on the high stress gradient area of ​​the engine blade.

[0166] From the above description, it can be seen that in this embodiment, the processing module 8 performs time series processing on the reference benchmark stress, current stress field, strain rate and process random noise through the long short-term memory network (LSTM), accurately predicts the stress field at the next moment, and then dynamically generates six-axis robotic arm scanning instructions based on the stress gradient distribution, realizes intelligent optimization of the scanning path, and further improves the reliability of the detection method of thermal stress of aircraft engine blades.

[0167] In one embodiment, Figure 6 As shown, all the above embodiments are combined to provide an overall process of a method for detecting thermal stress of an aircraft engine blade.

[0168] In this embodiment, the overall process of the detection method of thermal stress of aircraft engine blades includes seven stages: data input, nonlinear dynamic topology encoding, implicit neural field mapping, multi-physics field strong coupling modeling constrained by conservation laws, differentiable rendering, LSTM-Hash collaborative resolution control, multi-modal spatiotemporal coupling stress field adaptive visualization and crack analysis.

[0169] In the "data input" stage, the laser interferometer 7 and the infrared thermal imager 6 input the received ultrasonic signals and photoacoustic signals into the processing module 8, respectively. In the "nonlinear dynamic topology coding" stage, the processing module 8 uses the nonlinear dynamic system theory to code the input ultrasonic signals and photoacoustic signals, extracts the nonlinear dynamic topology features such as stress-strain and temperature-deformation inside the blade by analyzing the dynamic correlation between the ultrasonic propagation time delay and temperature gradient, i.e., constructs a phase space model of the data, mines the topology structure of different physical quantities changing with time and space, and converts the original signals into coded features containing dynamic correlation information. In the "implicit neural field mapping" stage, the processing module 8 builds an implicit neural radiance field (NeRF) model, inputs the nonlinear dynamic topology coded features, and maps them to an implicit space. The model learns the implicit representation of the blade stress, temperature, strain and other physical fields in three-dimensional space through a multi-layer perceptron (MLP), realizing unified modeling of multiple physical quantities. In the "multi-physical field strongly coupled modeling constrained by conservation laws" stage, the processing module 8 embeds the constraint of physical conservation laws based on the implicit neural field mapping, introduces the momentum conservation equation, and combines with the blade material constitutive relation to correct the implicit neural field results, ensuring that the modeling conforms to the real physical laws and realizes strong coupling of multiple physical fields. In the "differentiable rendering" stage, the processing module 8 uses the differentiable rendering algorithm to convert the abstract physical field into intuitive visual results. The processing module 8 maps the stress field, temperature field and other physical quantities into three-dimensional cloud maps, isosurface and other visual forms through the construction of a differentiable rendering pipeline, while retaining the differential characteristics to support subsequent backpropagation optimization. In the "LSTM-Hash collaborative resolution regulation" stage, the processing module 8 uses the long short-term memory network (LSTM) to learn the time evolution law of the blade stress field, predicts the stress gradient distribution at different times, and combines the Hash algorithm to construct an efficient spatial index. According to the stress gradient distribution predicted by the LSTM, the resolution of differentiable rendering and subsequent analysis is dynamically regulated. The resolution is increased in the stress concentration and large gradient areas (such as the leading edge and tenon of the blade) to accurately capture details, and the resolution is reduced in the stress flat area to balance the calculation efficiency and accuracy. In the "multi-modal spatio-temporal coupled stress field adaptive visualization and crack analysis" stage, the processing module 8 performs crack identification and evaluation on the stress field visualization results after adaptive resolution regulation. The Marching Cubes algorithm is used to extract the stress threshold isosurface to present the stress concentration area morphology. The LSTM is used to predict the crack hotspots and indicate the expansion direction, and the multi-physical field data is superimposed for spatio-temporal coupled analysis. Finally, a detection report is generated, which contains key parameters such as maximum stress value, crack coordinates, safety factor, etc.

[0170] In a possible embodiment, in order to verify the superiority of the thermal stress detection system for aircraft engine blades in the above embodiment, thermal stress detection is performed on TC4 titanium alloy aircraft engine blades through experiments. The experimental results show that the thermal stress detection system for aircraft engine blades in the above embodiment achieves technical indicators of a spatial resolution of 0.3mm and an absolute error of thermal stress detection of ±18MPa in the detection of TC4 titanium alloy aircraft engine blades, with a crack detection rate of 95%. In the blade root area with a thickness of 3.2mm, an internal stress gradient of 18MPa / mm is detected. However, traditional surface detection can only reflect a surface gradient of ≤5MPa / mm.

[0171] Through the above experiments, it can be seen that the method for detecting thermal stress of air engine blades provided by the above embodiment has achieved a multi-dimensional technological breakthrough compared with the traditional X-ray diffraction method (XRD): in terms of detection depth, it can penetrate the entire thickness, overcoming the limitation of XRD that is limited to the surface layer of 0.05-0.1mm, and the spatial resolution is improved to 0.3mm. An adaptive mechanism driven by stress gradient is also established; in terms of dynamic detection, it supports real-time tracking of transient thermal stress, achieving an order of magnitude leap compared to the static measurement of XRD, while breaking through the limitation of destructive sample preparation, realizing in-situ non-destructive testing through multi-modal laser collaboration, and integrating differential-algebraic dual conservation constraints to construct a three-dimensional stress field distribution model, which completely solves the distortion problem of traditional two-dimensional surface stress characterization, and finally expands the adaptability of working conditions from the laboratory to extreme industrial environments, with a detection speed of 200mm2 / s, meeting the needs of online monitoring of all working conditions of aircraft engines.

[0172] From the above description, it can be seen that the technical solution disclosed in the present invention has achieved a systematic breakthrough in the thermal stress detection of aircraft engine blades through the innovative fusion of multi-modal laser detection and dynamic neural radiation field algorithm: the technical solution disclosed in the present invention adopts nonlinear dynamic topology coding to construct an implicit radiation field, and for the first time jointly models the acoustic elastic parameters and temperature gradients, breaking through the limitation of traditional algorithms relying on geometric positions; the technical solution disclosed in the present invention realizes the strict physical consistency evolution of the stress-strain-temperature three fields through a multi-physics field strong coupling model constrained by conservation laws, significantly improving the reliability of the results; the technical solution disclosed in the present invention combines the LSTM-Hash collaborative resolution control mechanism to dynamically optimize the scanning path and adaptively improve the accuracy of stress concentration areas to meet the real-time monitoring needs of extreme working conditions; the technical solution disclosed in the present invention ultimately constructs a penetrating three-dimensional dynamic stress field visualization system, which supports sub-micron precision detection of complex surfaces and internal stress gradient analysis, and simultaneously realizes crack hotspot positioning and multi-physics field spatiotemporal coupling diagnosis, thereby providing a high-precision, non-contact, full-condition adaptable intelligent non-destructive testing solution for the life prediction of key components of aircraft engines and flight safety assurance.

[0173] An embodiment of the present disclosure further provides an electronic device, comprising: at least one processor; a memory for storing instructions executable by the at least one processor; wherein the at least one processor is configured to execute the instructions to implement the above method disclosed in the embodiment of the present disclosure.

[0174] Figure 7 This is a schematic diagram of the structure of an electronic device provided by an exemplary embodiment of the present disclosure. Figure 7 As shown, the electronic device 700 includes at least one processor 701 and a memory 702 coupled to the processor 701. The processor 701 can execute corresponding steps in the above method disclosed in the embodiment of the present disclosure.

[0175] The processor 701 can also be referred to as a central processing unit (CPU), which can be an integrated circuit chip with signal processing capabilities. Each step in the method disclosed in the embodiment of the present disclosure can be completed by hardware integrated logic circuits in the processor 701 or software instructions. The processor 701 can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiment of the present disclosure can be directly implemented as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in the memory 702, such as a random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor 701 reads the information in the memory 702 and, in conjunction with its hardware, completes the steps of the method.

[0176] In addition, when various operations / processes according to the present disclosure are implemented by software and / or firmware, they can be transmitted from a storage medium or a network to a computer system having a dedicated hardware structure, such as Figure 8 The computer system 800 shown is installed with the programs constituting the software. When the various programs are installed, the computer system can perform various functions, including the functions described above. Figure 8 A structural block diagram of a computer system provided by an exemplary embodiment of the present disclosure.

[0177] Computer system 800 is intended to represent various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic equipment can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0178] like Figure 8 As shown, the computer system 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the computer system 800 can also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0179] Multiple components within computer system 800 are connected to I / O interface 805, including an input unit 806, an output unit 807, a storage unit 808, and a communication unit 809. Input unit 806 can be any type of device capable of inputting information into computer system 800. Input unit 806 can receive input numeric or character information and generate key input signals related to user settings and / or function control of an electronic device. Output unit 807 can be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 808 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 809 allows computer system 800 to exchange information / data with other devices over a network, such as the Internet, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0180] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above. For example, in some embodiments, the above-mentioned method disclosed in the embodiments of the present disclosure may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 700 via the ROM 802 and / or the communication unit 809. In some embodiments, the computing unit 801 may be configured to perform the above-mentioned method disclosed in the embodiments of the present disclosure by any other appropriate means (e.g., by means of firmware).

[0181] An embodiment of the present disclosure further provides a computer-readable storage medium, wherein, when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the above method disclosed in the embodiment of the present disclosure.

[0182] The computer-readable storage medium in the embodiments of the present disclosure can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. The above-mentioned computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specifically, the above-mentioned computer-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0183] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0184] Figure 9 A computer program product 900 is provided as an exemplary embodiment of the present disclosure. The computer program product 900 includes a computer program 901 , wherein the computer program 901 implements the above method disclosed in the embodiment of the present disclosure when executed by a processor.

[0185] Computer program code for carrying out operations of the present disclosure can be written in any one or more of a variety of programming languages or combinations of languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0186] The flow diagrams and the block diagrams in the drawings are meant as possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams and the block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0187] The modules, components or units described in the embodiments of the present disclosure can be implemented by software or by hardware. In some cases, the name of the module, component or unit does not constitute a limitation on the module, component or unit itself.

[0188] The functions described above in the detailed description of embodiments of the present disclosure can be implemented in one or more hardware logic components or by computer instructions that are executed in a hardware logic component. For example, and without limitation, illustrative hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Application-specific Integrated Circuits (ASICs), Application-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0189] The above descriptions are merely some embodiments of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present disclosure.

[0190] Although some specific embodiments of the present disclosure have been described in detail by way of examples, those skilled in the art will appreciate that the above examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Those skilled in the art will appreciate that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A system for detecting thermal stress of an aero-engine blade, characterized in that: The system includes: a green laser, a near-infrared laser, and a spectrometer, a scanning control unit, a signal receiver, and a processing module connected in sequence; the green laser and the near-infrared laser are respectively connected to the spectrometer; The green laser is used to output a first laser beam to the engine blades; The near-infrared laser is used to output a second laser beam to the engine blade; The beam splitter is configured to combine the first laser beam and the second laser beam to obtain a composite beam; The scanning control unit is used to control the synthetic light beam to scan the engine blade; The signal receiver is used to receive the ultrasonic signal and the photoacoustic signal generated by the engine blade based on the synthetic light beam; The processing module is used to process the ultrasonic signal and the photoacoustic signal to generate a detection result; wherein the detection result at least includes: the maximum stress value, crack coordinates and safety factor of the engine blade.

2. The system according to claim 1, wherein: The scanning control unit includes: a two-dimensional galvanometer and a six-axis robotic arm; The two-dimensional galvanometer is used to reflect the composite light beam to generate a reflected light beam; The six-axis robotic arm is used to drive the two-dimensional galvanometer to move, so that the reflected light beam scans the engine blade according to a preset scanning trajectory.

3. A method for detecting thermal stress of an aero-engine blade, characterized in that: A method for detecting thermal stress of an aerospace engine blade applied to any one of claims 1 to 2 includes: controlling a green laser to output a first laser beam toward an engine blade, and controlling a near-infrared laser to output a second laser beam toward the engine blade; combining the first laser beam and the second laser beam to obtain a composite beam; controlling the synthetic light beam to scan the engine blade; receiving an ultrasonic signal and a photoacoustic signal generated by the engine blade based on the synthetic light beam; The ultrasonic signal and the photoacoustic signal are processed to generate a detection result; wherein the detection result at least includes: a maximum stress value, crack coordinates and a safety factor of the engine blade.

4. The method according to claim 3, characterized in that The processing of the ultrasonic signal and the photoacoustic signal to generate a detection result includes: Obtaining implicit field characteristics of the engine blade based on the ultrasonic signal and the photoacoustic signal; wherein the implicit field characteristics are used to characterize the physical state of stress, temperature, and material response at each point inside the engine blade; Determining a stress tensor of the engine blade based on the implicit field characteristics; wherein the stress tensor is used to characterize the stress magnitude, direction, and thermal-mechanical coupling state borne by any point inside the engine blade in three-dimensional space; A three-dimensional stress field distribution cloud map of the engine blade is established based on the stress tensor, and a detection result is determined based on the three-dimensional stress field distribution cloud map.

5. The method according to claim 4, characterized in that The acquiring of the implicit field characteristics of the engine blade based on the ultrasonic signal and the photoacoustic signal includes: determining an ultrasonic wave propagation delay based on the ultrasonic wave signal; Calculating the elastic modulus and strain rate sensitivity coefficient of the engine blade; acquiring a temperature gradient of the engine blade based on the photoacoustic signal, and calculating a thermal expansion coefficient based on the temperature gradient; The ultrasonic propagation delay, the temperature gradient, the elastic modulus, the thermal expansion coefficient, and the strain rate sensitivity coefficient are input into a dynamic hash coding network model to obtain an implicit neural radiation field; wherein the implicit neural radiation field is used to characterize the spatiotemporal evolution process of the coupled state of the stress, temperature, and strain multi-physics fields of the engine blade in three-dimensional space; An implicit field characteristic of the engine blade is determined based on the implicit neural radiation field.

6. The method according to claim 4, characterized in that The determining of the stress tensor of the engine blade based on the implicit field characteristics includes: Inputting the implicit field features into a multilayer perceptron network, and embedding physical constraint equations in the multilayer perceptron network to obtain a modified multilayer perceptron network; wherein the physical constraint equations include at least: a momentum conservation equation and an energy conservation equation; The stress tensor of the engine blade is determined based on the modified multi-layer perceptron network.

7. The method according to claim 4, characterized in that The step of establishing a three-dimensional stress field distribution cloud map of the engine blade based on the stress tensor includes: Mapping the stress tensor into a continuous three-dimensional stress field function through differentiable interpolation; Optimizing the continuous three-dimensional stress field function by using a loss function to obtain an optimized three-dimensional stress field function; A three-dimensional stress field distribution cloud map is established based on the optimized three-dimensional stress field function.

8. The method according to claim 4, characterized in that Determining the detection result based on the three-dimensional stress field distribution cloud map includes: Performing interactive interface dynamic rendering on the three-dimensional stress field distribution cloud map to obtain a visual rendering result; wherein the visual rendering result is a color gradient display of the stress gradient of the engine blade; A detection result is determined based on the visual rendering result; wherein the detection result includes at least: a maximum stress value, crack coordinates, and a safety factor of the engine blade.

9. The method according to claim 4, characterized in that After establishing a three-dimensional stress field distribution cloud map of the engine blade based on the stress tensor, the method further includes: determining the local stress intensity of the engine blade based on the three-dimensional stress field distribution cloud map; updating a dynamic hash resolution of the dynamic hash coding network model based on the local stress intensity to obtain an updated hash coding network model; An updated three-dimensional stress field distribution cloud map is determined based on the updated hash coding network model.

10. The method according to claim 4, characterized in that The method further comprises: Processing the reference stress of the engine blade, the stress field at the current moment, the strain rate, and the process random noise through a long short-term memory network to obtain the stress field of the engine blade at the next moment; determining a gradient distribution of the engine blade based on the stress field at the next moment; The scanning instructions of the six-axis robotic arm are determined based on the gradient distribution; wherein, the scanning instructions are used to drive the six-axis robotic arm to drive the two-dimensional galvanometer to move, so that the reflected light beam performs high-density scanning on the high stress gradient area of ​​the engine blade.

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