Thermal shock failure prediction and laser repair device for thermal barrier coating and application method

Abstract

The invention discloses a thermal shock failure prediction and laser repair device for a thermal barrier coating and an application method. The device comprises a multi-physics field coupling simulation analysis module, a thermal shock experiment loading module, an online damage monitoring and failure diagnosis module, a robot laser repair execution module and a central control and data processing core unit. A thermal-mechanical-chemical coupling model is established to predict a coating failure behavior, a high-frequency induction heating and gas quenching system is adopted to simulate an extreme thermal shock environment, a multi-band thermal infrared imager, an ultrasonic probe and a digital image related system are integrated to monitor damage evolution in real time, and in-situ repair of a damaged area is realized based on a six-degree-of-freedom industrial robot. According to the method, the whole-process closed-loop control from failure prediction to repair regeneration is realized, the problems of single function and lack of multi-field coupling analysis and real-time repair capability in the prior art are solved, and the service reliability evaluation precision and the remanufacturing efficiency of the thermal barrier coating are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of high-temperature protective coating service performance evaluation and remanufacturing, and particularly relates to a thermal barrier coating multi-field coupling thermal shock failure prediction and laser repair device. The device can realize the closed-loop integrated system of accurate prediction of the failure behavior of the thermal barrier coating under extreme thermal mechanical load and in-situ repair of the damage area. BACKGROUND

[0002] In the fields of aerospace and energy equipment, thermal barrier coatings are widely used on the surfaces of hot end components such as gas turbine blades and combustion chambers to ensure the reliable service of the components in extreme environments through their excellent heat insulation performance and high-temperature oxidation resistance. However, in the process of repeated start-up and shutdown of thermal cycles, the coating system faces thermal shock loads caused by rapid temperature changes, which leads to a large thermal stress between the coating and the substrate due to the mismatch of the thermal expansion coefficients, and further causes the initiation and propagation of micro-cracks in the coating and the thickening of the interfacial oxidation layer, ultimately resulting in coating spalling and component failure. The traditional thermal barrier coating performance evaluation method relies on offline detection and destructive sampling analysis, which cannot monitor the damage evolution of the coating in the thermal shock process in real time, and it is even more difficult to achieve predictive maintenance and in-situ repair. The existing thermal shock experimental devices mostly have single function, can only simulate thermal load or mechanical load, lack the ability of multi-physical field coupling loading, and the damage detection means is limited to offline characterization, which cannot provide comprehensive data support for coating failure mechanism research and life prediction. Therefore, the development of an integrated equipment that integrates thermal shock experiment, online monitoring, failure prediction and intelligent repair has great significance for breaking through the service life bottleneck of thermal barrier coatings and improving the reliability of high-end equipment.

[0003] The published patent CN114770900A discloses a thermal barrier coating thermal shock experimental device, which realizes thermal cycling through resistance heating and forced air cooling, and uses an acoustic emission sensor to monitor coating cracks; however, this device lacks multi-physical field coupling analysis capability, cannot simulate the interaction of thermal-mechanical-chemical fields in actual working conditions, and does not integrate online damage quantitative diagnosis function, only providing a simple crack alarm signal. CN113275301A proposes a method for laser repair of thermal barrier coatings, which uses a robot to carry a laser head for cladding repair, but this method is independent of the coating state monitoring system, the repair process lacks real-time damage data support, and can only perform standardized operations according to the preset program, and cannot realize adaptive repair for specific damage forms. CN112730299A introduces a thermal barrier coating defect detection system based on an infrared thermal imager, but this system is only suitable for offline or static detection, cannot capture dynamic damage evolution in real time during thermal shock, and does not form a closed-loop system with the repair equipment. These existing technologies focus on single function implementation, lack of system-level integration and multi-module cooperation, resulting in insufficient prediction accuracy, unsatisfactory repair effect and low overall efficiency.

[0004] In the face of the urgent demand for long service life and high reliability of thermal barrier coatings for high-temperature components, existing technical means have been difficult to meet the requirements of early warning and rapid self-repair of coating failure in engineering practice. Therefore, it is urgent to develop an integrated equipment that can integrate multi-physical field simulation, online monitoring, intelligent prediction and accurate repair. The present application precisely predicts the coating failure critical point by building a thermal-mechanical-chemical coupled numerical model, obtains damage data in real time by combining high-frequency induction thermal shock loading and multi-sensor online monitoring, and generates a repair strategy based on autonomous decision-making to drive the robot laser repair system to perform in-situ repair, forming a full-process closed-loop control from failure prediction to repair regeneration. This integrated device not only deepens the multi-physical field coupling cognition of the failure mechanism of thermal barrier coatings, but also greatly improves the service safety and maintainability of key hot end components, and has important scientific research significance and engineering application value. SUMMARY

[0005] The present application relates to a thermal barrier coating thermal shock failure prediction and laser repair device, which can realize accurate prediction of thermal barrier coating failure behavior and in-situ repair of damage area under simulated extreme thermal mechanical loading environment.

[0006] The device forms a complete prediction-monitoring-repair closed-loop system by integrating a multi-physical field coupling simulation analysis module, a thermal shock experiment loading module, an online damage monitoring and failure diagnosis module, a robot laser repair execution module, and a central control and data processing core unit.

[0007] The multi-physical field coupling simulation analysis module first establishes a constitutive model of the thermal barrier coating based on thermal-mechanical-chemical coupling theory, and calculates the temperature gradient distribution, thermal stress field evolution and phase change behavior of the coating under thermal shock loading using the finite element method.

[0008] The simulation analysis module runs the transient heat conduction equation based on the first and second laws of thermodynamics, and calculates the three-dimensional temperature field in combination with the Fourier heat conduction model and the boundary radiation heat transfer condition.

[0009] Preferably, the mechanical analysis part adopts the incremental plasticity theory and the creep constitutive equation, considers the thermal expansion mismatch effect between the coating and the substrate, and calculates the interfacial shear stress and normal stress distribution during thermal cycling.

[0010] The chemical module introduces oxidation kinetics equation and phase change dynamics model to predict the formation rate of thermally grown oxide layer and the porosity change caused by sintering of ceramic layer.

[0011] The chemical module solves the control equations of temperature field, stress field and material diffusion field by coupling to realize thermal-mechanical-chemical three-field two-way coupling, and uses adaptive grid technology to improve the calculation accuracy of crack tip.

[0012] The simulation results are outputted in the form of temperature cloud, stress contour and damage parameter evolution curve, which provide the theoretical load spectrum and failure threshold for the experimental module.

[0013] Preferably, the thermal-mechanical coupling algorithm adopts a sequential coupling strategy, which first solves the transient temperature field and inputs the thermal expansion strain as the initial stress condition into the mechanical field;

[0014] The heat conduction control equation considers the anisotropy and temperature-dependent thermal physical parameters of the material, and solves the non-steady-state temperature distribution through an explicit difference format;

[0015] The mechanical field calculation adopts the Jaumann stress rate constitutive relation, considering the brittle fracture behavior of the ceramic layer and the cyclic plastic strain accumulation of the metal matrix;

[0016] The interface damage model uses cohesive elements to simulate the debonding process of the coating and substrate interface, and describes the interface strength degradation through a bilinear traction-separation law;

[0017] The oxidation kinetics module uses the Wagner oxidation theory to calculate the oxygen diffusion flux, and couples with the stress field through a stress-dependent diffusion coefficient model;

[0018] All control equations are solved by the Newton-Raphson iteration method, and convergence test is performed at each time step to ensure calculation accuracy.

[0019] The thermal shock experiment loading module uses a high-frequency induction heating system to simulate extreme thermal cycling conditions, generates eddy current heating in the metal matrix through electromagnetic induction principle, and uses a closed-loop temperature control algorithm to heat the sample surface to above 1600K at a rate of 100-500K per second.

[0020] The cooling subsystem realizes a cooling rate of more than 1000K per second through high-pressure inert gas injection or water mist quenching, accurately simulating the thermal shock environment faced by turbine blades of aircraft engines.

[0021] The mechanical loading part uses a hydraulic servo fatigue testing machine to apply constant tensile stress or alternating mechanical stress to the sample through a clamp, reproducing thermal mechanical fatigue conditions.

[0022] The thermal shock experiment loading module also includes a vacuum environment chamber, which can control the oxygen partial pressure to simulate high-temperature oxidation atmosphere, and record the thermal shock response process of the coating surface through an observation window combined with a high-speed camera.

[0023] Preferably, the high-frequency induction heating system consists of a three-phase full-bridge inverter circuit and an LC resonance network, and the output power frequency is adjustable through pulse width modulation technology, adjustable from 10-100KHz.

[0024] The temperature measurement adopts an S-shaped platinum rhodium-platinum thermocouple combined with an infrared colorimetric temperature measuring instrument, with a temperature measurement range of 300-2000 Kelvin and an accuracy of plus or minus 1.5 percent;

[0025] The gas quenching system controls the flow and pressure of argon and nitrogen mixed gas through a proportional valve, and the cooling rate is adjusted by changing the arrangement angle and jet distance of the nozzle array;

[0026] The mechanical loading unit is equipped with a hydraulic servo actuator, with a maximum load of 50KN and a frequency range of 0.01-100Hz, and can generate various load spectra such as sine wave, triangle wave and square wave through a waveform generator;

[0027] The protective cover is made of 316L stainless steel, with a limit vacuum degree of 10-3Pa, equipped with a quartz observation window and an electrical feedthrough interface, to ensure the safety and controllability of the high-temperature experiment process.

[0028] The online damage monitoring and failure diagnosis module integrates a multi-band infrared thermal imager, an ultrasonic probe and a digital image correlation system to collect coating surface temperature field, acoustic emission signal and full-field strain data in real time.

[0029] The infrared thermal imager inverses the temperature distribution based on the Stefan-Boltzmann law by measuring the infrared radiation intensity of the coating surface, with a spatial resolution of 0.1mm and a sampling frequency of 1000Hz.

[0030] The ultrasonic detection system uses a piezoelectric transducer to emit 5-20MHz longitudinal and transverse waves, and identifies internal micro-cracks and delamination defects by analyzing the amplitude attenuation and time delay changes of the echo signals.

[0031] The digital image correlation system calculates the full-field thermal strain distribution and crack opening displacement by comparing the displacement field of the coating surface speckle pattern before and after thermal shock.

[0032] The acoustic emission sensor array captures the stress wave signals released by the coating cracking based on the piezoelectric effect, and distinguishes crack types by wavelet transform analysis of frequency characteristics.

[0033] The monitoring data is transmitted synchronously to the diagnosis computer through a data acquisition card, and a machine learning algorithm is used to establish a mapping relationship between damage characteristics and failure modes to realize real-time evaluation of the remaining life of the coating.

[0034] Preferably, the infrared thermal imager covers the 3-5 micron mid-wave infrared band using an indium gallium arsenide detector, with a noise equivalent temperature difference of less than 25mK;

[0035] The ultrasonic detection system uses a phased array probe array, which can realize beam deflection and focusing through electronic scanning, with a defect detection sensitivity of 0.1mm;

[0036] The digital image correlation system uses two 500 million pixel CMOS cameras to form a stereo vision system, uses speckle preparation technology to make an alumina-based high-temperature speckle pattern on the coating surface, and has a displacement measurement accuracy of 0.01 pixels;

[0037] The acoustic emission system is equipped with a positioning array composed of 6 broadband sensors, uses a time difference positioning algorithm to determine the crack source coordinates, and has a positioning error of not more than 1 mm;

[0038] The data diagnosis software integrates feature extraction algorithms, including short-time Fourier transform, Hilbert-Huang transform and principal component analysis, and can automatically identify 17 typical damage signal patterns.

[0039] The robot laser repair execution module uses a six-degree-of-freedom industrial robot to carry a coaxial powder feeding laser head, and automatically plans the path according to the failure prediction results.

[0040] The execution module includes a six-degree-of-freedom articulated industrial robot, which has a repeat positioning accuracy of 0.05 mm and a load capacity of not less than 20 Kg. The laser processing head integrates a 2-4KW fiber laser, a powder feeding system and a high-temperature vision sensor, and the laser focusing spot diameter is continuously adjustable from 0.3 to 1.2 mm.

[0041] The powder feeding system uses a carrier gas type powder conveying device to accurately control the conveying rate and focusing position of yttrium-stabilized zirconia ceramic powder.

[0042] Preferably, during the repair process, the robot divides the damage area into multiple cladding tracks through a layered slicing algorithm according to the three-dimensional repair path generated by the central control unit, and the laser beam melts the powder under a protective atmosphere and forms a metallurgical bonded repair layer on the substrate surface.

[0043] The real-time monitoring system analyzes the melt pool radiation spectrum through a UV-visible spectrometer, and adjusts the laser power and scanning speed in feedback to ensure the consistency of the chemical composition and microstructure of the ceramic layer.

[0044] Preferably, the industrial robot uses absolute encoder feedback and friction compensation algorithms, and has a pose stability error of less than 0.1 mm in a high-temperature environment;

[0045] The fiber laser has an output wavelength of 1070 nm and a beam quality factor M2 of less than 1.1, and is transmitted to the processing head through fiber coupling;

[0046] The powder feeding system uses a four-channel powder feeding nozzle, and the matching error between the powder focusing diameter and the laser spot is less than 5%;

[0047] The visual system is equipped with a high-temperature CCD camera with a narrow-band filter, which eliminates the interference of molten pool glow by active illumination technology, and monitors the molten pool morphology and wetting angle in real time.

[0048] The process database stores a combination of repair parameters for different damage types, including laser power 800-2000W, scanning speed 2-10mm / s, powder feeding rate 5-20g / min, etc. The optimized parameter set ensures that the repair layer forms a metallurgical bond with the substrate and the hardness reaches more than 95% of the original coating.

[0049] The central control and data processing core unit adopts a distributed system architecture, including a multi-core processor array and a parallel computing platform with a graphics processing unit.

[0050] The data fusion algorithm integrates simulation data and experimental monitoring data based on Kalman filtering theory, and corrects the thermal conductivity coefficient and constitutive model parameters through a bias compensation mechanism.

[0051] The adaptive prediction model uses deep neural network technology, taking thermal history curve, stress distribution and damage characteristics as input, and outputting crack propagation rate and critical failure temperature.

[0052] The decision system assesses the severity of damage based on fuzzy logic rules and generates a three-level repair strategy:

[0053] For microcracks, laser remelting is used, for macrocracks, multilayer cladding repair is used, and for large area peeling, full area reprocessing is started.

[0054] The human-machine interaction interface provides three-dimensional visual monitoring, real-time display of thermal shock experiment progress, damage evolution graph and repair quality evaluation report.

[0055] The central control and data processing core unit coordinates the synchronous operation of each module, uses multi-sensor data fusion technology to compare simulation and experimental results, dynamically corrects the prediction model and generates a repair strategy, and finally forms a closed-loop control system for thermal shock failure prediction and repair.

[0056] The application workflow of the integrated device is as follows:

[0057] First, the multi-physics field coupling simulation analysis module predicts the failure behavior of the coating under specific thermal shock conditions, generates a theoretical load spectrum and failure threshold;

[0058] Then the thermal shock experiment loading module sets the experimental parameters according to the simulation results, implements the thermal shock experiment and collects data in real time during the process;

[0059] The online damage monitoring and failure diagnosis module monitors the entire experiment process, identifies damage characteristics and evaluates the coating state;

[0060] When the critical damage is detected, the central control unit generates a repair strategy and drives the robot laser repair execution module to perform in-situ repair;

[0061] After the repair is completed, the system can automatically start a new round of thermal shock experiment to verify the repair effect, forming a complete closed-loop control.

[0062] During the whole process, each module shares data and works collaboratively through the central control unit, ensuring high consistency between prediction and repair.

[0063] Advantages

[0064] Compared with the prior art, the present application has the following advantages and technical effects:

[0065] The present application adopts modular design, and each functional module can not only run independently but also work collaboratively, greatly improving the flexibility and application range of the device. All sensors and actuators are strictly calibrated to ensure the accuracy of measurement data and the reliability of the repair process. The device is also equipped with a perfect safety protection system, including overheat protection, air pressure monitoring and emergency stop functions, to ensure the safe operation of the experiment.

[0066] The present application combines multi-physical field simulation, experimental monitoring and robot repair organically, realizing the whole process automation from failure prediction to repair regeneration. Compared with traditional methods, the device not only improves the accuracy of thermal barrier coating performance evaluation, but also greatly improves the repair efficiency and quality. Through real-time data feedback and model correction, the device can continuously optimize the prediction accuracy and repair effect, providing strong technical support for the application of thermal barrier coating in aerospace and other fields.

[0067] The multi-sensor data fusion technology and adaptive prediction algorithm adopted by the present application can effectively process a large amount of real-time data in complex environments and accurately identify the damage state of the coating. The path planning algorithm and process parameter optimization strategy of the robot repair module ensure that the quality and performance of the repaired layer reach or even exceed the original coating level. The integrated design and intelligent control method of the whole system represent the latest development direction of thermal barrier coating evaluation and repair technology.

[0068] The present application can be used for reliability evaluation and remanufacturing of thermal barrier coating of high-end equipment such as aircraft engines and gas turbines, and has important engineering application value and scientific research significance. BRIEF DESCRIPTION OF DRAWINGS

[0069] The drawings constituting a part of this application are used to provide further understanding of the application, the illustrative embodiments of the application and their descriptions serve to explain the application, and do not constitute an improper limitation on the application. In the drawings:

[0070] Figure 1A schematic diagram of the overall structure of the thermal barrier coating multi-physical field coupling thermal shock failure prediction and robot laser repair integrated device of the embodiment of the present application;

[0071] Figure 2 A schematic diagram of the structure of the thermal shock experiment loading module of the embodiment of the present application;

[0072] Figure 3 A schematic diagram of the structure of the online monitoring and diagnosis module of the embodiment of the present application;

[0073] Figure 4 A schematic diagram of the structure of the robot laser repair module of the embodiment of the present application;

[0074] Figure 5 A flowchart of the multi-modal detection and intelligent evaluation of the embodiment of the present application;

[0075] Wherein, 1, thermal shock failure prediction and robot laser repair integrated device experimental table protective cover; 2, observation window; 3, protective cover right side; 4, thermal shock experiment loading platform; 5, cylindrical sample clamp; 6, horizontal sample clamp; 7, sample; 8, high-precision three-dimensional motion horizontal workbench; 9, liquid cooling liquid pipeline; 10, pump; 11, cooling liquid return pipeline; 12, oil tank to cooling pump pipeline; 13, cooling liquid oil tank; 14, cooling gas pipe liftable support; 15, cooling gas pipe; 16, thermal shock module gas pipe; 17, gas pipe middle road; 18, air compressor; 19, three-way valve; 20, thermal shock failure prediction and robot laser repair integrated device experimental table; 21, field bus; 22, electric control cabinet; 23, central control and processing data core unit; 24, online damage monitoring and failure diagnosis module; 25, multi-physical field simulation analysis module; 26, man-machine interface; 27, protective cover left side; 28, robot laser repair execution module; 29, cylindrical sample rotation motor; 30, gas quenching nozzle 1; 31, high-frequency induction heating coil; 32, gas quenching nozzle 2; 33, gas quenching nozzle 3; 34, thermal barrier coating sample; 35, sample clamp; 36, three-dimensional motion platform X axis; 37, three-dimensional motion platform Z axis; 38, three-dimensional motion platform Y axis; 39, horizontal base; 40, gas quenching nozzle 6; 41, gas quenching nozzle 4; 41, gas quenching nozzle 5; 42, data acquisition and processing system; 43, ultrasonic probe; 44, infrared thermal imager; 45, DIC camera 1; 46, DIC camera 2; 47, thermal barrier coating sample; 48, acoustic emission sensor array; 49, powder feeding laser head; 50, thermal barrier coating sample; 51, workbench; 52, laser repair execution robot shaft 6; 53, laser repair execution robot shaft 5; 54, laser repair execution robot shaft 4; 55, laser repair execution robot shaft 3; 56, laser repair execution robot shaft 2; 57, laser repair execution robot shaft 1; 58, powder feeding system raw material bin 1; 59, powder feeding system raw material bin 2; 60, powder feeding system. DETAILED DESCRIPTION

[0076] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0077] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a group of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in different order.

[0078] This invention aims to address the shortcomings of existing technologies by proposing an integrated device and method for multiphysics-coupled thermal shock failure prediction and robotic laser repair of thermal barrier coatings. It addresses the limitations of existing thermal shock testing devices, such as limited functionality, lack of multiphysics coupling analysis capabilities, and inability to achieve online quantitative damage diagnosis and intelligent repair. The invention introduces advanced multi-sensor data fusion technology and adaptive prediction algorithms. This invention enables closed-loop control of the entire process from failure prediction to repair and regeneration, offering advantages such as multi-module collaboration, high prediction accuracy, and good repair results. It accurately assesses the failure behavior of thermal barrier coatings under extreme thermomechanical loads and achieves in-situ repair of damaged areas, thereby improving the service safety and maintainability of critical hot-end components.

[0079] To achieve the above objectives, the present invention provides the following solution:

[0080] The integrated device for multi-physics field coupled thermal shock failure prediction and robotic laser repair of thermal barrier coatings described in this invention, such as... Figures 1 to 5 As shown, the device includes an experimental platform 20 for integrated thermal shock failure prediction and robotic laser repair, a central control and data processing core unit 23, a multiphysics simulation analysis module 25, a thermal shock experiment loading module 4, an online damage monitoring and failure diagnosis module 24, a robotic laser repair execution module 28, and related auxiliary systems and connecting components.

[0081] The experimental platform 20 of the integrated thermal shock failure prediction and robotic laser repair device constitutes the basic platform of the entire system. A thermal shock test loading platform 4 is set on its upper part. The platform is equipped with a cylindrical sample clamp 5 and a horizontal sample clamp 6 for fixing samples 7 of different shapes.

[0082] The high-precision three-dimensional motion horizontal worktable 8 provides the sample with precise positioning in the XYZ directions, with a positioning accuracy of 0.01mm.

[0083] The experimental platform 20 integrates a liquid cooling system, including a coolant pipeline 9, a pump 10, a coolant return pipeline 11, an oil tank to coolant pump pipeline 12, and a coolant oil tank 13, to ensure effective heat dissipation of the system during high-temperature experiments.

[0084] The gas cooling system consists of a liftable support for the cooling air pipe 14, a cooling air pipe 15, a thermal shock module air pipe 16, a central air pipe 17, an air compressor 18, and a three-way valve 19, providing controllable gas cooling capacity.

[0085] The experimental platform 20 is equipped with a protective cover 1, an observation window 2, a right-side door 3, and a left-side door 27 to ensure the safety and observability of the experimental process.

[0086] The field bus 21 connects each module with the electric control cabinet 22, realizing fast transmission of data and centralized control.

[0087] The central control and processing data core unit 23 adopts a distributed system architecture, including a multi-core processor array and a parallel computing platform of a graphics processing unit.

[0088] The central control and processing data core unit runs a data fusion algorithm based on Kalman filtering theory, integrates the prediction data of the multi-physical field simulation analysis module 25 and the experimental monitoring data of the online damage monitoring and failure diagnosis module 24, and modifies the heat conduction coefficient and the constitutive model parameters in real time through a bias compensation mechanism.

[0089] Preferably, the adaptive prediction model adopts deep neural network technology, taking the thermal history curve, stress distribution and damage characteristics as input, and outputting the crack propagation rate and critical failure temperature.

[0090] Preferably, the decision system evaluates the damage severity based on fuzzy logic rules, and generates a three-level repair strategy, including micro-crack laser remelting treatment, macro-crack multi-layer cladding repair and large-area peeling full-area re-manufacturing program.

[0091] The human-computer interaction interface 26 provides three-dimensional visualization monitoring function, real-time display of thermal shock experiment process, damage evolution graph and repair quality evaluation report, supporting parameter setting and process monitoring of operating personnel.

[0092] The multi-physical field simulation analysis module 25 establishes the constitutive model of thermal barrier coating based on the theory of thermal-mechanical coupling, and calculates the temperature gradient distribution, thermal stress field evolution and phase change behavior of the coating under thermal shock load through the finite element method.

[0093] The multi-physical field simulation analysis module includes a high-performance computing unit, which runs a transient heat conduction equation based on the first and second laws of thermodynamics, and calculates a three-dimensional temperature field in combination with a Fourier heat conduction model and boundary radiation heat transfer conditions.

[0094] Preferably, the mechanical analysis part adopts incremental plasticity theory and creep constitutive equation, considers the thermal expansion mismatch effect between the coating and the substrate, and calculates the interfacial shear stress and normal stress distribution during thermal cycle.

[0095] Preferably, the chemical module introduces oxidation kinetics equation and phase change kinetics model to predict the formation rate of thermally grown oxide layer and the porosity change caused by sintering of ceramic layer.

[0096] Preferably, the simulation module solves the control equations of temperature field, stress field and material diffusion field through coupling, realizes thermal-mechanical three-field bidirectional coupling, and adopts adaptive grid technology to improve the calculation accuracy of crack tip.

[0097] The simulation results are outputted in visual form, such as temperature cloud map, stress contour and damage parameter evolution curve, to provide theoretical load spectrum and failure threshold for the experimental module.

[0098] The thermal shock experiment loading module 4 is composed of a high-frequency induction heating system, which generates eddy current heating in the metal matrix through electromagnetic induction principle.

[0099] Preferably, the high-frequency induction heating system is composed of a three-phase full-bridge inverter circuit and an LC resonance network, and the output power is adjusted by pulse width modulation technology, and the frequency is adjustable in the range of 10-100 kHz.

[0100] The temperature measurement adopts a combination of S-type platinum-rhodium-platinum thermocouple and infrared colorimetric temperature instrument, with a temperature measurement range of 300-2000 K and an accuracy of ±1.5%.

[0101] The cooling subsystem achieves a cooling rate of more than 1000 K per second through high-pressure inert gas injection, and the gas quenching system controls the flow and pressure of argon-nitrogen mixed gas through a proportional valve, and the cooling rate is adjusted by changing the arrangement angle and injection distance of the nozzle array.

[0102] The mechanical loading unit is equipped with a hydraulic servo actuator with a maximum load of 50 kN and a frequency range of 0.01-100 Hz, and can generate various load spectra such as sine wave, triangle wave and square wave through a waveform generator.

[0103] The protective cover is made of 316L stainless steel with a limit vacuum degree of 10-3 Pa, equipped with a quartz observation window and an electrical feedthrough interface to ensure safety and controllability during high-temperature experiments.

[0104] The cylindrical sample rotating motor 29 provides sample rotation function to achieve uniform heating and cooling.

[0105] The online damage monitoring and failure diagnosis module 24 integrates a multi-band infrared thermal imager 44, an ultrasonic probe 43 and a digital image correlation system.

[0106] The infrared thermal imager 44 uses indium gallium arsenide detectors to cover the 3-5 μm mid-wave infrared band, with a noise equivalent temperature difference of less than 25 mK, a spatial resolution of 0.1 mm, a sampling frequency of 1000 Hz, and a temperature distribution inversion based on the Stefan-Boltzmann law by measuring the infrared radiation intensity of the coating surface.

[0107] The ultrasonic detection system uses a phased array probe array to emit 5-20 MHz longitudinal and transverse waves, and achieves beam deflection and focusing through electronic scanning, with a defect detection sensitivity of 0.1 mm, and identifies internal micro-cracks and delamination defects by analyzing the amplitude attenuation and time delay changes of the echo signals.

[0108] The digital image correlation system uses two 500 million pixel CMOS cameras to form a stereo vision system, uses speckle preparation technology to make an alumina-based high-temperature speckle pattern on the coating surface, the displacement measurement accuracy is 0.01 pixels, and the full-field thermal strain distribution and crack opening displacement are calculated by comparing the displacement field of the speckle pattern on the coating surface before and after thermal shock.

[0109] The acoustic emission system is equipped with a positioning array composed of 6 broadband sensors, uses a time difference positioning algorithm to determine the crack source coordinates, the positioning error is not more than 1 mm, captures the stress wave signals released by the coating cracking based on the piezoelectric effect, and distinguishes the crack types by wavelet transform analysis of frequency characteristics.

[0110] The monitoring data is transmitted to the data acquisition and processing system 42 through the data acquisition card, a machine learning algorithm is used to establish the mapping relationship between damage characteristics and failure modes, and the real-time evaluation of the remaining life of the coating is realized.

[0111] The robot laser repair execution module 28 uses a six-degree-of-freedom articulated industrial robot, which includes laser repair execution robot axes 1 to 6, and the repeatability is 0.05 mm, and the load capacity is not less than 20 kg.

[0112] The laser processing head 49 integrates a 2-4 kW fiber laser with an output wavelength of 1070 nm and a beam quality factor M2 less than 1.1, which is transmitted to the processing head through an optical fiber, and the laser focusing spot diameter is continuously adjustable from 0.3 to 1.2 mm.

[0113] The powder feeding system uses a four-channel powder feeding nozzle, the powder focusing diameter and the laser spot matching error is less than 5%, the powder feeding system includes powder feeding system raw material bins 58 and 59, and uses a carrier gas type powder conveying device to accurately control the conveying rate and focusing position of yttrium-stabilized zirconia ceramic powder.

[0114] The vision system is equipped with a high-temperature CCD camera with a narrow-band filter, which eliminates the molten pool glow interference through active illumination technology, and monitors the molten pool morphology and wetting angle in real time.

[0115] The real-time monitoring system analyzes the molten pool radiation spectrum through an ultraviolet-visible spectrometer, and adjusts the laser power and scanning speed to ensure the consistency of the chemical composition and microstructure of the ceramic layer.

[0116] The process database stores repair parameter combinations for different damage types, including laser power 800-2000W, scanning speed 2-10mm / s, powder feeding rate 5-20g / min, etc. The optimized parameter set ensures that the repair layer forms a metallurgical bond with the substrate and the hardness is more than 95% of the original coating.

[0117] The operation process of the thermal barrier coating multi-physical field coupling thermal shock failure prediction and robot laser repair integrated device is carried out according to the following steps:

[0118] S1, start the thermal barrier coating multi-physical field coupling thermal shock failure prediction and robot laser repair integrated device, carry out system initialization and self-checking, and enter the standby state after confirming that the states of each module are normal.

[0119] S2, multi-modal data synchronous acquisition, the surface temperature field distribution of the coating is monitored by the infrared thermal imager 44 at a sampling frequency of 1000Hz, the internal defects are detected by the ultrasonic probe 43 emitting 5-20MHz sound waves, the full-field strain data is collected by the digital image correlation system, the crack propagation stress wave signal is captured by the acoustic emission sensor array 48, and all data is synchronously transmitted to the central control and processing data core unit 23 through the high-speed data acquisition card.

[0120] S3, multi-sensor data fusion processing, Kalman filter algorithm is used to integrate infrared temperature data, ultrasonic echo signal, DIC strain field and acoustic emission characteristics, multi-source data fusion is realized through time synchronization and space registration technology, thermal history curve, stress distribution and damage characteristic parameters are extracted, and a unified data representation model is established.

[0121] S4, damage feature extraction and identification, short-time Fourier transform is used to analyze the time-frequency characteristics of acoustic emission signals, Hilbert-Huang transform is used to process non-stationary temperature signals, principal component analysis is used to reduce dimension processing of multi-dimensional monitoring data, and deep learning algorithm is used to automatically identify 17 kinds of typical damage modes such as coating spalling, cracking and sintering.

[0122] S5, damage degree evaluation, based on fuzzy logic rules and deep neural network model, the damage characteristic parameters and historical data are comprehensively analyzed, the current state of the coating is evaluated and the remaining life is predicted, and the damage degree is divided into three grades of slight damage, moderate damage and severe damage.

[0123] S6, according to the damage level, the corresponding repair strategy is executed, for slight damage, laser remelting is used for micro-cracks, when the crack length is less than 0.5 mm and the depth is less than 0.1 mm, 800-1200 W laser power is used for crack area remelting treatment, the scanning speed is 5-8 mm / s, the coating material is re-melted to heal the micro-cracks through local heating, and the original coating structure integrity is maintained. For moderate damage, multi-layer cladding is used for macro-cracks, when the crack length is 0.5-2 mm and the depth is 0.1-0.3 mm, 1200-1600 W laser power is used for multi-layer cladding repair, the scanning speed is 3-6 mm / s, the powder feeding rate is 10-15 g / min, YSZ ceramic material is deposited by coaxial powder feeding to fill the cracks layer by layer and ensure metallurgical bonding with the substrate. For severe damage, large-area peeling is used for re-preparation, when the peeling area is greater than 10 square millimeters and the crack depth is greater than 0.3 mm, 1600-2000 W laser power is used for full-area re-preparation, the scanning speed is 2-4 mm / s, the powder feeding rate is 15-20 g / min, after completely removing the damaged coating, a new thermal barrier coating is deposited to ensure that the repair layer thickness and performance meet the original standard.

[0124] S7, robot laser repair is executed, a six-degree-of-freedom industrial robot automatically generates a three-dimensional repair path according to the repair strategy, the damage area is decomposed into multiple cladding tracks by a layered slicing algorithm, the laser head accurately executes the repair process in a protective atmosphere, the molten pool state is monitored in real time and the laser parameters are adjusted in real time to ensure that the repair quality meets the requirements.

[0125] S8, repair effect evaluation, ultrasonic phased array is used to detect the internal quality of the repair layer, infrared thermal imager is used to analyze the uniformity of the temperature field, digital image correlation system is used to measure the residual stress distribution, the bonding strength, hardness and thermal insulation performance of the repair layer are comprehensively evaluated, and it is judged whether the repair effect meets the performance indicators of more than 95% of the original coating.

[0126] S9, the whole process of thermal barrier coating thermal shock failure prediction and repair is completed, detailed detection report and repair record are generated, including damage evaluation results, repair parameters, performance verification data and process record, which provides complete data support for coating life prediction and maintenance decision.

[0127] The whole device realizes the collaborative work and data sharing of each module through the central control and processing data core unit 23, forming a complete closed-loop control system from failure prediction to repair verification. The device not only deepens the multi-physical field coupling cognition of the failure mechanism of thermal barrier coating, but also greatly improves the service safety and maintainability of key hot end components, which has important scientific research significance and engineering application value.

[0128] The application integrates multi-physical field coupling simulation analysis and robot laser repair technology in the whole process of thermal barrier coating thermal shock failure prediction and repair to realize accurate prediction of coating failure behavior and in-situ regeneration repair of damage area under extreme conditions. The application comprises a multi-physical field coupling simulation analysis module, which predicts the coating failure critical value through bidirectional coupling calculation of thermal, mechanical and chemical fields; a thermal shock experiment loading module integrating high-frequency induction heating and gas quenching system, which accurately simulates the thermal shock environment of turbine blades of an aero-engine; an online damage monitoring system comprising a multi-band infrared thermal imager, an ultrasonic phased array probe and a digital image correlation system, which captures the coating damage evolution process in real time; a laser repair execution module based on a six-degree-of-freedom industrial robot, which automatically executes a three-level repair strategy according to the damage evaluation result. The device realizes closed-loop control from failure prediction to repair verification through a central control and data processing core unit, forming a complete intelligent evaluation and remanufacturing system. The application can not only be used for reliability evaluation and life prediction of thermal barrier coatings of hot end parts of an aero-engine, but also can be popularized to state monitoring and intelligent maintenance of high-temperature parts such as turbine blades and combustion chambers of a gas turbine. The technology has the advantages of multi-modal data fusion, high prediction accuracy and good repair effect, and has important engineering value for prolonging the life and improving the reliability of key parts of high-end equipment.

[0129] The above is only a preferred specific embodiment of the application, but the protection scope of the application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the application, which should be covered in the protection scope of the application. Therefore, the protection scope of the application should be subject to the protection scope of the claims.

Claims

1. A thermal barrier coating (TBC) thermal shock failure prediction and laser repair device, characterized in that, it comprises an integrated system platform composed of a multi-physical field coupling simulation analysis module, a thermal shock experiment loading module, an online damage monitoring and failure diagnosis module, a robot laser repair execution module, and a central control and data processing core unit; the multi-physical field coupling simulation analysis module establishes a constitutive model of the TBC based on thermal-mechanical-chemical coupling theory, calculates the temperature gradient distribution, thermal stress field evolution and phase change behavior of the coating under thermal shock loading by finite element method, and predicts the crack initiation position and propagation path of the coating; the thermal shock experiment loading module simulates extreme thermal cycle conditions by using high-frequency induction heating or laser transient heat source, realizes rapid heating and cooling through an accurate temperature control system, and is equipped with a mechanical restraint device to apply specific thermal mechanical load; the online damage monitoring and failure diagnosis module integrates a high-resolution infrared thermal imager, an ultrasonic probe and a digital image correlation system to real-time collect coating surface temperature field, acoustic emission signal and full-field strain data, and identify damage modes such as coating spalling, cracking and sintering through feature extraction algorithms; the robot laser repair execution module uses a six-degree-of-freedom industrial robot to carry a coaxial powder feeding laser head, automatically plans a path according to the failure prediction results, and realizes in-situ repair of the damaged area by layer-by-layer deposition of ceramic materials through laser cladding technology; the central control and data processing core unit coordinates the synchronous operation of each module, compares the simulation and experimental results by using multi-sensor data fusion technology, dynamically corrects the prediction model and generates a repair strategy, and finally forms a closed-loop control system for thermal shock failure prediction and repair.

2. The apparatus of claim 1, wherein, the multi-physical field coupling simulation analysis module comprises a high-performance computing unit that runs a transient heat conduction equation based on the first and second laws of thermodynamics, combines a Fourier heat conduction model and boundary radiation heat transfer conditions to calculate a three-dimensional temperature field; the mechanical analysis part uses incremental plasticity theory and creep constitutive equation to calculate the interfacial shear stress and normal stress distribution during thermal cycle by considering the thermal expansion mismatch effect between the coating and the substrate; the chemical module introduces oxidation kinetics equation and phase change kinetics model to predict the formation rate of thermally grown oxide layer and the porosity change caused by ceramic layer sintering; the simulation module solves the control equations of temperature field, stress field and material diffusion field to realize thermal-mechanical-chemical three-field bidirectional coupling, and uses adaptive mesh technology to improve the calculation accuracy of crack tip; the simulation results are output in the form of temperature cloud map, stress contour and damage parameter evolution curve with time, which provide theoretical load spectrum and failure threshold for the experimental module.

3. The apparatus of claim 1, wherein, the thermal shock experiment loading module is composed of a high-frequency induction heating system that generates eddy current heating inside the metal substrate through electromagnetic induction principle, uses a closed-loop temperature control algorithm to make the sample surface temperature rise at a rate of 100-500 K / s to above 1600 K, and realizes a cooling rate of more than 1000 K / s through high-pressure inert gas injection or water mist quenching to accurately simulate the thermal shock environment faced by turbine blades of an aero-engine. ​ The mechanical loading part adopts a hydraulic servo fatigue testing machine to apply constant tensile stress or alternating mechanical stress to the specimen through a clamp to reproduce the thermal mechanical fatigue condition; The thermal shock experiment loading module also contains a controllable oxygen partial pressure simulation high-temperature oxidation atmosphere, and the thermal shock response process of the coating surface is recorded through a high-speed camera through an observation window.

4. The apparatus of claim 1, wherein, The online damage monitoring and failure diagnosis module is composed of a multi-band infrared thermal imager. Based on the Stefan-Boltzmann law, the temperature distribution is inversely calculated by measuring the infrared radiation intensity of the coating surface. The spatial resolution reaches 0.1mm, and the sampling frequency is 1000Hz; The ultrasonic detection system uses a piezoelectric transducer to emit 5-20MHz longitudinal and transverse waves. By analyzing the amplitude attenuation and time delay changes of the echo signals, internal micro-cracks and delamination defects are identified. The digital image correlation system calculates the full-field thermal strain distribution and crack opening displacement by comparing the displacement field of the speckle pattern on the coating surface before and after thermal shock. The acoustic emission sensor array captures the stress wave signals released by the coating cracking based on the piezoelectric effect, and distinguishes crack types by wavelet transform analysis of frequency characteristics. All monitoring data are transmitted synchronously to the diagnosis computer through the data acquisition card, and the mapping relationship between damage characteristics and failure modes is established by using machine learning algorithms to realize real-time evaluation of the remaining life of the coating.

5. The apparatus of claim 1, wherein, The robot laser repair execution module contains a six-degree-of-freedom articulated industrial robot with a repeat positioning accuracy of 0.05mm and a load capacity of not less than 20Kg; The laser processing head integrates a 2-4kW fiber laser, a powder feeding system, and a high-temperature visual sensor. The laser focusing spot diameter is continuously adjustable from 0.3 to 1.2mm. The powder feeding system uses a carrier gas type powder conveying device to accurately control the delivery rate and focusing position of yttria-stabilized zirconia ceramic powder. During the repair process, the robot follows the three-dimensional repair path generated by the central control unit, and the damage area is decomposed into multiple cladding tracks by layering and slicing algorithms. The laser beam melts the powder in a protective atmosphere and forms a metallurgically bonded repair layer on the substrate surface. The real-time monitoring system analyzes the melt pool radiation spectrum by ultraviolet-visible spectrometer to feedback adjust the laser power and scanning speed to ensure the consistency of the chemical composition and microstructure of the ceramic layer.

6. The apparatus of claim 1, wherein, The central control and data processing core unit adopts a distributed system architecture, including a multi-core processor array and a graphics processing unit parallel computing platform; The data fusion algorithm integrates simulation data and experimental monitoring data based on Kalman filter theory, and corrects the thermal conductivity coefficient and constitutive model parameters through a bias compensation mechanism; The adaptive prediction model uses deep neural network technology, taking the thermal history curve, stress distribution, and damage characteristics as input, and outputs the crack propagation rate and critical failure temperature; The decision system evaluates the damage severity based on fuzzy logic rules and generates a three-level repair strategy: laser remelting for micro-cracks, multi-layer cladding repair for macro-cracks, and full-area re-manufacturing for large-area peeling. The human-computer interaction interface provides three-dimensional visual monitoring, real-time display of thermal shock experiment process, damage evolution diagram and repair quality evaluation report.

7. The apparatus of claim 2, wherein, The thermal-mechanical coupling algorithm of the multi-physical field coupling simulation analysis module adopts a sequential coupling strategy, solves the transient temperature field first and inputs the thermal expansion strain as the initial stress condition into the mechanical field; The heat conduction control equation considers the anisotropy and temperature-dependent thermal physical parameters of the material, and solves the unsteady temperature distribution through an explicit difference format; The mechanical field calculation adopts the Jaumann stress rate constitutive relation, considering the brittle fracture behavior of the ceramic layer and the cyclic plastic strain accumulation of the metal matrix; The interface damage model simulates the debonding process of the coating / substrate interface by using a cohesive element, and describes the interface strength degradation through a bilinear traction-separation law; The oxidation kinetics module calculates the oxygen diffusion flux by using the Wagner oxidation theory, and realizes the coupling with the stress field through a stress-dependent diffusion coefficient model; The control equations are solved by the Newton-Raphson iteration method, and the convergence is checked at each time step to ensure the calculation accuracy.

8. The apparatus of claim 3, wherein, The high-frequency induction heating system of the thermal shock experiment loading module is composed of a three-phase full-bridge inverter circuit and an LC resonance network, and the output power frequency is adjustable through pulse width modulation technology, adjustable from 10 kHz to 100 kHz; The temperature measurement adopts an S-shaped platinum-rhodium-platinum thermocouple combined with an infrared colorimetric thermometer, with a temperature measurement range of 300-2000 K and an accuracy of ±1.5% or more; The gas quenching system controls the flow and pressure of argon / nitrogen mixed gas through a proportional valve, and adjusts the cooling rate by changing the arrangement angle and injection distance of the nozzle array; The mechanical loading unit is equipped with a hydraulic servo actuator, with a maximum load of 50 kN and a frequency range of 0.01-100 Hz, and can generate various load spectra such as sine wave, triangle wave and square wave through a waveform generator; The protective cover is made of 316L stainless steel, equipped with a quartz observation window and an electrical feedthrough interface, ensuring the safety and controllability of the high-temperature experiment process.

9. The apparatus of claim 4, wherein, The infrared thermal imager of the online damage monitoring and failure diagnosis module uses an indium gallium arsenide detector to cover the 3-5 μm mid-wave infrared band, with a noise equivalent temperature difference of less than 25 mK; The ultrasonic detection system uses a phased array probe array, which can realize beam deflection and focusing through electronic scanning, with a defect detection sensitivity of 0.1 mm; The digital image correlation system uses two 5 million pixel CMOS cameras to form a stereo vision system, uses speckle preparation technology to make an aluminum oxide-based high-temperature speckle pattern on the coating surface, and has a displacement measurement accuracy of 0.01 pixels; The acoustic emission system is equipped with a positioning array composed of 6 broadband sensors, and uses a time difference positioning algorithm to determine the crack source coordinates, with a positioning error of not more than 1 mm; The data diagnosis software integrates feature extraction algorithms, including short-time Fourier transform, Hilbert-Huang transform and principal component analysis, and can automatically identify 17 typical damage signal patterns.

10. The apparatus of claim 5, wherein, The industrial robot of the robot laser repair execution module adopts absolute encoder feedback and friction compensation algorithm, and the pose stability error is less than 0.1mm in high temperature environment; The fiber laser outputs wavelength 1070nm, and beam quality factor M2<1.1, and is transmitted to the machining head through fiber coupling; The powder feeding system adopts a four-channel powder feeding nozzle, and the matching error of the powder focusing diameter and the laser spot is less than 5%; The visual system is equipped with a high-temperature CCD camera with a narrow-band filter, and the molten pool glow interference is eliminated through active illumination technology to monitor the molten pool morphology and wetting angle in real time; the process database stores the repair parameter combinations of different damage types, including the optimized parameter set of laser power 800-2000W, scanning speed 2-10mm / s, and powder feeding rate 5-20g / min, to ensure that the repair layer and the substrate form metallurgical bonding and the hardness reaches more than 95% of the original coating.

11. The method of using the device of claim 1, wherein, The process is as follows: First, the multi-physical field coupling simulation analysis module predicts the failure behavior of the coating under specific thermal shock conditions, generates a theoretical load spectrum and a failure threshold; Then, the thermal shock experiment loading module sets the experimental parameters according to the simulation results, implements the thermal shock experiment and collects data in real time during the process; The online damage monitoring and failure diagnosis module monitors the whole process, identifies damage characteristics and evaluates the coating state; When the critical damage is detected, the central control unit generates a repair strategy and drives the robot laser repair execution module to repair in situ; After the repair is completed, the system automatically starts a new round of thermal shock experiment to verify the repair effect, forming a complete closed-loop control; During the whole process, each module realizes data sharing and collaborative work through the central control unit, ensuring the high consistency of prediction and repair.

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