CN117147028B - In-situ terahertz detection device and detection method for residual stress of thermal barrier coating - Google Patents
In-situ terahertz detection device and detection method for residual stress of thermal barrier coating Download PDFInfo
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- 239000012530 fluid Substances 0.000 claims description 31
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 28
- 238000013527 convolutional neural network Methods 0.000 claims description 17
- 238000010438 heat treatment Methods 0.000 claims description 15
- 239000007788 liquid Substances 0.000 claims description 14
- 229910052757 nitrogen Inorganic materials 0.000 claims description 14
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- 238000001328 terahertz time-domain spectroscopy Methods 0.000 claims description 3
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L5/00—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes
- G01L5/0047—Apparatus for, or methods of, measuring force, work, mechanical power, or torque, specially adapted for specific purposes measuring forces due to residual stresses
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- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L1/00—Measuring force or stress, in general
- G01L1/24—Measuring force or stress, in general by measuring variations of optical properties of material when it is stressed, e.g. by photoelastic stress analysis using infrared, visible light, ultraviolet
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Abstract
The invention discloses an in-situ terahertz detection device and a detection method for residual stress of a thermal barrier coating, wherein the device comprises the following components: in-situ magnetic control indentation module: the method is used for obtaining an indentation depth-load relation curve and calculating residual stress of the thermal barrier coating; multi-degree-of-freedom terahertz module: the method is used for acquiring terahertz time-domain spectrum data in the indentation process and representing the change of residual stress of the thermal barrier coating; machine learning evaluation module: the method is used for establishing an evaluation model based on indentation data of the in-situ magnetic control indentation module, terahertz time-domain spectrum data and residual stress of the thermal barrier coating under different temperature gradients and combining a machine learning algorithm, and analyzing the relation between terahertz signals and the residual stress. The method can comprehensively and accurately evaluate the residual stress of the thermal barrier coating under different temperature gradients and monitor the change condition of the thermal barrier coating in real time.
Description
Technical Field
The invention relates to the technical field of layer detection, in particular to an in-situ terahertz detection device and method for residual stress of a thermal barrier coating.
Background
The thermal barrier coating (Thermal barrier coatings, TBCs) is a surface protection coating commonly used in the fields of high temperature engineering such as aerospace, gas turbines and the like. Under the high-temperature environment, the thermal barrier coating can effectively reduce the heat loss of the substrate material and improve the heat resistance of the material, thereby prolonging the service life of the equipment. However, under the thermal cycle working condition and different thermal gradient conditions, the thermal expansion of the thermal barrier coating is caused by the influence of mechanical load, so that the residual stress is generated and accumulated, and the thermal barrier coating has the problems of cracking, peeling and the like. The presence of residual stresses may affect the performance and reliability of the thermal barrier coating, and therefore accurate assessment and monitoring of the residual stresses of the thermal barrier coating is critical.
The traditional residual stress measurement method mainly comprises an X-ray diffraction method, a neutron diffraction method, a grating method and the like, and the methods have certain precision, but usually require off-line measurement and are not applicable to real-time monitoring and in-situ evaluation of the residual stress.
In order to overcome the limitations of the traditional method, the in-situ terahertz detection technology is gradually becoming a new means for evaluating the residual stress of the thermal barrier coating. Terahertz is an electromagnetic wave with a band between 0.1 and 10 THz. As a new nondestructive testing technology, the infrared radiation-based infrared radiation detection device has strong penetrability in the frequency range between infrared and microwave, can penetrate through some nonmetallic materials, and can detect the electromagnetic wave response of the materials. Therefore, the method is an ideal tool for detecting the residual stress of the thermal barrier coating, and the interaction of the terahertz wave and the substance mainly comprises the processes of absorption, scattering, refraction and the like, and the changes can be used for representing the physical properties and the residual stress of the material. By detecting the intensity, frequency spectrum, phase and other characteristics of terahertz radiation, the residual stress condition inside the material can be deduced.
Therefore, the invention aims to provide an in-situ terahertz detection device and method for residual stress of a thermal barrier coating, and the residual stress of the thermal barrier coating is accurately and comprehensively evaluated by combining a terahertz technology with an indentation method. The device can detect terahertz signals and indentation data in real time in the thermal cycle process, and introduces a machine learning algorithm to establish an evaluation model, so that the prediction and evaluation of residual stress of the thermal barrier coating under different temperature gradients are realized. The device and the method can be used for more comprehensively knowing the residual stress distribution and the change rule of the thermal barrier coating, providing reliable data support for material design and engineering application, and having wide application prospects in the fields of aerospace, energy sources and the like.
Disclosure of Invention
The invention aims to solve the defects of the prior art, provides an in-situ terahertz detection device and method for residual stress of a thermal barrier coating, is an innovative improvement of traditional fixed terahertz equipment, and skillfully combines multi-degree-of-freedom terahertz equipment with an indentation method to accurately evaluate the residual stress of the thermal barrier coating.
In order to achieve the above object, the present invention provides an in-situ terahertz detection device for residual stress of a thermal barrier coating, including:
in-situ magnetic control indentation module: the method is used for obtaining an indentation depth-load relation curve and calculating residual stress of the thermal barrier coating;
Multi-degree-of-freedom terahertz module: the method is used for acquiring terahertz time-domain spectrum data in the indentation process and representing the change of residual stress of the thermal barrier coating;
Machine learning evaluation module: the method is used for establishing an evaluation model based on indentation data of the in-situ magnetic control indentation module, terahertz time-domain spectrum data and residual stress of the thermal barrier coating under different temperature gradients and combining a machine learning algorithm, and analyzing the relation between terahertz signals and the residual stress.
Preferably, the detection device further comprises a temperature control module, wherein the temperature control module is used for controlling the temperature gradient of the thermal barrier coating sample.
Preferably, the temperature control module includes:
Resistance belt heating device: for heating the sample at high temperature;
Liquid nitrogen cooling device: for rapid cooling of the sample;
temperature sensor: the method is used for monitoring the temperature of the thermal barrier coating sample in real time.
Preferably, the controlling of the temperature gradient of the thermal barrier coating sample comprises:
And carrying out temperature gradient loading on the thermal barrier coating sample, introducing different stress state changes, and providing controllable temperature load.
Preferably, the in-situ magnetic control indentation module comprises:
Magnetorheological fluid hydraulic piston: the device is used for controlling the current intensity of the electrified coil to change the state distribution of magnetorheological fluid, controlling the lifting of the piston disc and further controlling the micro-lifting and descending processes of the indentation round head;
a pressure sensor: the pressure load monitoring device is used for monitoring the pressure load in the movement process of the magnetorheological fluid hydraulic piston;
DIC imaging device: the method is used for calibrating the contact position of the round head of the indentation and the surface of the thermal barrier coating and measuring the indentation depth.
Preferably, the method for calculating the residual stress of the thermal barrier coating comprises the following steps:
Wherein σ i represents the residual stress of the ith indentation, R represents the spherical radius of the round head of the indentation, namely the radius of the indenter, E represents the elastic modulus of the thermal barrier coating, mu represents the Poisson's ratio, h i represents the ith indentation depth, P i represents the load of the ith indentation, and n represents the number of times of indentation.
Preferably, the multi-degree-of-freedom terahertz module includes: the three-dimensional positioning camera device, the full-angle moving mechanism, the terahertz transmitter and the terahertz receiver;
the three-dimensional positioning camera device is used for identifying the space position and transmitting the adjustment information to the full-angle moving mechanism;
The full-angle moving mechanism is used for synchronously adjusting terahertz equipment based on the spatial position; wherein the spatial locations include locations of a thermal barrier coating and an indenter;
the terahertz transmitter and the terahertz receiver are used for receiving light reflected by the surface of the thermal barrier coating and acquiring terahertz time-domain spectrum information; the terahertz time-domain spectrum information is converted into terahertz time-domain spectrum data through a computer and displayed.
Preferably, after the terahertz time-domain spectroscopy data is acquired, the method further includes:
Performing noise removal, data cleaning and feature extraction on the terahertz time-domain spectrum data;
Carrying out Fourier transform on the terahertz time-domain spectrum data to obtain a frequency domain spectrum and a phase spectrum, extracting a peak value of the frequency domain spectrum as a first terahertz characteristic parameter, unwrapping the phase spectrum, and calculating the slope of a curve after removing phase jump as a second terahertz characteristic parameter; the first terahertz characteristic parameter and the second terahertz characteristic parameter are used together as characteristic parameters for representing the full-period variation of the residual stress.
Preferably, the machine learning evaluation module comprises a convolutional neural network unit, wherein the convolutional neural network unit is used for establishing a CNN regression prediction model and optimizing the CNN regression prediction model through a cross verification method.
On the other hand, in order to achieve the above purpose, the invention also provides a detection method of the in-situ terahertz detection device for residual stress of the thermal barrier coating, which comprises the following steps:
acquiring an indentation depth-load relation curve based on an in-situ magnetic control indentation module, and calculating residual stress of the thermal barrier coating;
acquiring terahertz time-domain spectrum data in the indentation process by a multi-degree-of-freedom terahertz module, and representing the change of residual stress of the thermal barrier coating;
Based on the indentation data of the in-situ magnetic control indentation module, the terahertz time-domain spectrum data and the residual stress of the thermal barrier coating under different temperature gradients, an evaluation model is established by combining a machine learning algorithm, and the relation between terahertz signals and the residual stress is analyzed.
Compared with the prior art, the invention has the following advantages and technical effects:
(1) The invention can realize high-precision nondestructive evaluation: residual stress of the thermal barrier coating under different temperature gradients can be comprehensively and accurately estimated through in-situ indentation experiments and terahertz time-domain spectrum data acquisition, and the change condition of the thermal barrier coating can be monitored in real time; realizing full-cycle characteristic parameters: by combining the terahertz characteristic parameters and the thermal barrier coating material parameters through a machine learning algorithm, an evaluation model is constructed, the full-period change of the residual stress of the thermal barrier coating can be represented, and the evaluation precision is improved; realizing detection automation and intellectualization: the application of the multi-degree-of-freedom terahertz equipment and the advanced magnetic control indentation meter improves the detection efficiency and the detection precision.
(2) The method is used for realizing in-situ terahertz detection of the residual stress of the thermal barrier coating, comprehensively and accurately evaluating the residual stress of the thermal barrier coating by combining a plurality of parameters and a machine learning algorithm, and provides an efficient and reliable method for evaluating the performance and researching the materials of the thermal barrier coating.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 is a schematic diagram of the overall structure of an in-situ terahertz detection device for residual stress of a thermal barrier coating in an embodiment of the invention;
FIG. 2 is a schematic diagram of a resistive band heating module assembly according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a liquid nitrogen heat dissipation module assembly according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an in-situ magnetic control indentation mechanism according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a terahertz time-domain spectroscopy assembly according to an embodiment of the present invention;
FIG. 6 is a flowchart of a method for using an in-situ terahertz detection device for residual stress of a thermal barrier coating according to an embodiment of the invention;
Wherein, the device comprises a 1-adjustable foot pad, a 2-reinforcing rib, a 3-liquid nitrogen heat radiating device, a 4-rotary object placing plate, a 5-fixer, a 6-receiver sliding sleeve, a 7-first sliding chute, an 8-magnetorheological fluid hydraulic piston, a 9-digital display meter, a 10-upper top plate, a 11-control integrator, a 12-terahertz equipment bracket, a 13-first three-dimensional positioning camera, a 14-terahertz transmitter, a 15-thermal barrier coating sample to be tested, a 16-computer, a 17-fixed rotating screw, a 18-rotating shaft sleeve, a 19-heating component, a 20-resistance belt, a 21-lower bottom plate, a 22-heat conduction metal rod, a 23-radiating fin clamp and a 24-liquid nitrogen radiating fin, the device comprises a 25-first motor component, a 26-DIC camera, a 27-second sliding chute, a 28-X axis moving block, a 29-pressure sensor, a 30-first telescopic rod, a 31-second telescopic rod, a 32-indentation round head, a 33-first magnetorheological fluid cavity, a 34-second magnetorheological fluid cavity, a 35-energizing coil, a 36-one-way valve, a 37-spring, a 38-piston lifting plate, a 39-second three-dimensional positioning camera, a 40-terahertz receiver, a 41-circular turntable fixed rod, a 42-terahertz equipment bracket fastener, a 43-circular turntable, a 44-second motor component and a 45-emitter sliding sleeve.
Detailed Description
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
The invention provides an in-situ terahertz detection device for residual stress of a thermal barrier coating, as shown in fig. 1, comprising:
in-situ magnetic control indentation module: the method is used for obtaining an indentation depth-load relation curve and calculating residual stress of the thermal barrier coating;
Multi-degree-of-freedom terahertz module: the method is used for acquiring terahertz time-domain spectrum data in the indentation process and representing the change of residual stress of the thermal barrier coating;
Machine learning evaluation module: the method is used for establishing an evaluation model based on indentation data of the in-situ magnetic control indentation module, terahertz time-domain spectrum data and residual stress of the thermal barrier coating under different temperature gradients and combining a machine learning algorithm, and analyzing the relation between terahertz signals and the residual stress.
The detection device also comprises a temperature control module for controlling the temperature gradient of the thermal barrier coating sample.
The temperature control module comprises a resistor belt heating device, a liquid nitrogen cooling device and a temperature sensor;
Resistive band heating device (as in fig. 2): for heating the sample at high temperature; adjusting a fixed rotary screw, fixing a sample to be measured in a heating assembly through a fixer, wherein the sample to be measured is relatively fixed with a rotary object placing plate and is contacted with a heat conduction metal rod;
Liquid nitrogen cooling device (as in fig. 3): for rapid cooling of the sample; the liquid nitrogen cooling fin and the heat conduction metal rod are fixed on the cooling fin clamp, and cooling and heat dissipation are realized through direct contact of the liquid nitrogen and the heat conduction metal rod;
Temperature sensor: the temperature monitoring device is used for monitoring the temperature of the sample in real time, is integrated in the resistor strip heating device and the liquid nitrogen cooling device, and can monitor the temperature in real time.
Controlling the temperature gradient of the thermal barrier coating sample comprises the following steps:
and carrying out temperature gradient loading on the thermal barrier coating sample, introducing different stress state changes, and providing controllable temperature load. Specifically, a thermal barrier coating sample is heated from room temperature of 25 ℃ to 1100 ℃; heating to 215 ℃ every 40min and continuously preserving heat for 30min, wherein 70 min is a gradient criterion, and continuously heating for 5 times to 1100 ℃ when the in-situ magnetic control indentation meter is loaded; when the in-situ magnetic control indentation meter is unloaded, the temperature is cooled to 215 ℃ every 40min, the temperature is kept for 30min,70 min is a gradient criterion, and the temperature is continuously cooled for 5 times to 25 ℃.
The in-situ magnetic control indentation module comprises a magnetorheological fluid hydraulic piston, a pressure sensor, a DIC camera device, a digital display meter, an indentation meter and an indentation round head;
Magnetorheological fluid hydraulic piston: the device is used for controlling the current intensity of the electrified coil to change the state distribution of magnetorheological fluid, controlling the lifting of the piston disc and further controlling the micro-lifting and descending processes of the indentation round head; the upper part of the magnetorheological fluid hydraulic piston is fixed on the X-axis moving block, and the lower part of the magnetorheological fluid hydraulic piston is connected with the pressure sensor;
a pressure sensor: the pressure load monitoring device is used for monitoring the pressure load in the movement process of the magnetorheological fluid hydraulic piston;
DIC imaging device: the DIC camera device is fixed beside the pressure sensor and used for calibrating the contact position of the round head of the indentation and the surface of the thermal barrier coating and measuring the indentation depth;
Digital display table: the digital display meter is used for displaying the current value of the electrified coil and the load value of the pressure sensor, and is fixed relative to the X-axis moving block;
Indentation meter: the device comprises a first telescopic rod, a second telescopic rod and an indentation round head, wherein the first telescopic rod, the second telescopic rod and the indentation round head are fixed on the lower surface of a pressure sensor and are used for acquiring an indentation depth-load relation curve, and the indentation depth-load relation curve is displayed through a computer;
Indentation button head: and the surface of the thermal barrier coating to be measured is contacted or pressed under the action of load, so as to obtain indentation pits on the surface.
The magnetorheological fluid flows through the first magnetorheological fluid cavity and the second magnetorheological fluid cavity through the one-way valve, and the state distribution of the magnetorheological fluid is changed by controlling the current intensity of the electrified coil, so that the lifting of the piston disc is controlled, and the micro-lifting and descending processes of the indentation round head are accurately controlled; the magnetic field strength is regulated by changing the current of the electrified coil, so that the arrangement state of magnetic particles in the magnetorheological fluid is changed.
When current flows through the coil, a magnetic field is generated, particles in the magnetorheological fluid are aligned, and the hydraulic piston is caused to rise under the action of pressure. When the current is disconnected, the magnetic field disappears, particles in the magnetorheological fluid recover to a disordered arrangement state, and the hydraulic piston is lowered under the action of external pressure; the fixing device is used for fixing the position of a sample to be detected by 4 rotary screws which are arranged in a 90-degree circumferential distribution mode.
Further, the residual stress of the thermal barrier coating is calculated by a mathematical model based on indentation data:
Wherein sigma i represents the residual stress of the ith indentation, R represents the spherical radius of the round head of the indentation, namely the radius of the pressing head, E represents the elastic modulus of the thermal barrier coating, mu represents the Poisson ratio, h i represents the ith indentation depth, P i represents the load of the ith indentation, and n represents the number of times of indentation.
The radius of the pressing head, the elastic modulus and the poisson ratio are basic physical parameters of the thermal barrier coating, and the indentation depth and the indentation load are data obtained through experiments. Load data under different indentation depths can be obtained through multiple indentation experiments, and residual stress is calculated by using the data.
The multi-degree-of-freedom terahertz module can acquire full-period terahertz time-domain spectrum data of an indentation unloading process under a temperature gradient and is used for representing the change degree of residual stress of the thermal barrier coating.
The multi-degree-of-freedom terahertz module comprises a three-dimensional positioning camera device, a full-angle moving mechanism, a terahertz transmitter and a terahertz receiver;
Three-dimensional positioning camera device: the device is respectively arranged on the terahertz transmitter and the terahertz receiver, can identify the space position and transmit the adjustment information to the full-angle moving mechanism, so as to realize the positioning function;
Full angle moving mechanism: the terahertz equipment can be synchronously adjusted according to the positions of the thermal barrier coating and the indentation meter, circumferential coarse adjustment is performed on the annular turntable, and the sliding sleeve can rotate to drive the terahertz equipment to perform fine adjustment so as to ensure that the terahertz transmitter and the terahertz receiver are detected at a proper angle;
The terahertz transmitter and the terahertz receiver are fixed on the sliding sleeve of the annular turntable, can be used for multi-angle and multi-azimuth adjustment of the incident light rays emitted from different angles, receiving the reflected light rays from the surface of the thermal barrier coating, and displaying and acquiring terahertz time-domain spectrum data on a computer.
Further, terahertz time-domain spectrum data can represent residual stress in the indentation unloading process under different temperature gradients;
preprocessing and feature extraction of terahertz time-domain data comprise the following steps:
And carrying out noise removal and data cleaning on the terahertz time-domain data, and extracting features. Carrying out Fourier transform on the terahertz time-domain signal to obtain a frequency domain spectrum and a phase spectrum, extracting a peak value of the frequency domain spectrum as a first terahertz characteristic parameter, and carrying out unwrapping treatment on the phase spectrum to remove phase jump and then calculating the slope of a curve as a second terahertz characteristic parameter; can be used as a characteristic parameter for representing the full period change of the residual stress.
The machine learning evaluation module comprises a convolutional neural network unit, wherein the convolutional neural network unit is used for establishing a CNN regression prediction model, and optimizing the CNN regression prediction model through a cross verification method.
Based on indentation data of an in-situ magnetic control indentation module, terahertz time-domain spectrum data acquired by a multi-degree-of-freedom terahertz module, and residual stress data of the thermal barrier coating under different temperature gradients, a convolutional neural network (Convolutional Neural Networks, CNN) is combined; and taking the terahertz first characteristic parameter, the second terahertz characteristic parameter, the indentation area, the elastic modulus and the poisson ratio as inputs, taking residual stress as outputs, establishing a CNN regression prediction model, and optimizing the model by methods such as cross validation and the like to ensure that the model has better generalization capability and prediction accuracy.
The embodiment also provides a detection method of the in-situ terahertz detection device for the residual stress of the thermal barrier coating, which comprises the following steps:
acquiring an indentation depth-load relation curve based on an in-situ magnetic control indentation module, and calculating residual stress of the thermal barrier coating;
acquiring terahertz time-domain spectrum data in the indentation process by a multi-degree-of-freedom terahertz module, and representing the change of residual stress of the thermal barrier coating;
Based on the indentation data of the in-situ magnetic control indentation module, the terahertz time-domain spectrum data and the residual stress of the thermal barrier coating under different temperature gradients, an evaluation model is established by combining a machine learning algorithm, and the relation between terahertz signals and the residual stress is analyzed.
The embodiment also provides a use method of the in-situ terahertz detection device for residual stress of the thermal barrier coating, as shown in fig. 6, which comprises the following steps:
s1, placing a thermal barrier coating sample and fixing the thermal barrier coating sample through a fixer, and adjusting the positions of an in-situ magnetic control indentation meter, a terahertz transmitter and a terahertz receiver according to the position to be measured of the coating sample;
S2, starting a resistor belt heating device and a liquid nitrogen cooling device, and loading a temperature gradient; the in-situ magnetic control indentation meter works, the input current controls the hydraulic piston of the magnetorheological fluid to lift and drive the indentation round head to obtain an indentation depth-load curve in the unloading stage; meanwhile, the terahertz transmitter transmits pulse signals, the pulse signals are received by the terahertz receiving device after being reflected at a position to be detected, and terahertz time-domain data are displayed through a computer;
S3, establishing a mathematical model based on indentation data to calculate residual stress, and extracting terahertz first characteristic parameters and second characteristic parameters through the terahertz time-domain data;
S4, establishing a thermal barrier coating residual stress evaluation model based on a CNN algorithm, and optimizing the model through cross verification.
The working principle of the in-situ terahertz detection device for the residual stress of the thermal barrier coating is as follows:
As shown in fig. 1, the adjustable foot pad 1 is first adjusted so that the entire device is in a horizontal state. And placing a thermal barrier coating sample 15 to be measured on the rotary object placing plate 4, and adjusting fixed rotary screws 17 on the fixing device 5 to position the thermal barrier coating sample 15 to be measured. The second runner 27 and X-axis moving block 28, which are fixed to the control integrator 11, are then adjusted so that the indentation domes 32 are aligned with the area to be measured. And then the first chute 7 is adjusted to move to a proper position in the Y-axis direction, and the angles of the terahertz transmitter 14 and the terahertz receiver 40 are adjusted by combining the position identification of the first three-dimensional positioning camera 13 and the second three-dimensional positioning camera 39, so that the reflected signals of the transmitted terahertz signals passing through the thermal barrier coating can be received by the terahertz receiver 40.
As shown in fig. 2, the rotary shaft sleeve 18 is adjusted, the fixer 5 is adjusted, and the rotary screw 17 is fixed to fix the thermal barrier coating sample 15 to be measured. The heating component 19 is started, the temperature is raised through the resistance belt 20, and the resistance belt is matched with the liquid nitrogen heat dissipation device 3 to form a temperature gradient.
As shown in fig. 3, the liquid nitrogen heat sink 3 is fixed to the lower plate 21, and conducts heat dissipation by the heat conduction metal rod 22. The liquid nitrogen heat sink 24 is fixed to the heat sink jig 23.
As shown in fig. 4, the in-situ magnetic control indentation module comprises that the first motor assembly 25 is used for adjusting the position of the magnetorheological fluid hydraulic piston 8 in the second chute 27 and adjusting the position of the X-axis moving block 28 for preliminary positioning. And then the current is applied to the electrified coil 35 to enable the second magnetorheological fluid cavity 34 to be full of a magnetic field, the magnetorheological fluid moves between the second magnetorheological fluid cavity 34 and the first magnetorheological fluid cavity 33 through the one-way valve 36, the position of the adjustable piston lifting plate 38 is realized, loading and unloading are further realized, and the spring 37 plays a role in buffering.
As shown in fig. 5, the position of the terahertz device bracket 12 on the Y axis is initially adjusted by adjusting the position of the terahertz device bracket fastener 42 in the first chute 7 by the second motor assembly 44. The annular turntable 43 is fixed to the terahertz device bracket 12 by an annular turntable fixing lever 41. By combining the positioning information of the first three-dimensional positioning camera 13 and the second three-dimensional positioning camera 39 and adjusting the circumferential positions of the transmitter sliding sleeve 45 and the receiver sliding sleeve 6, the terahertz signal transmitted by the terahertz transmitter 14 can be received by the terahertz receiver 40 after passing through the reflected signal of the thermal barrier coating sample 15 to be detected.
The present application is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present application are intended to be included in the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims (9)
1. The utility model provides an normal position terahertz detection device of thermal barrier coating residual stress which characterized in that includes:
in-situ magnetic control indentation module: the method is used for obtaining an indentation depth-load relation curve and calculating residual stress of the thermal barrier coating;
Wherein, the normal position magnetic control indentation module includes:
Magnetorheological fluid hydraulic piston: the device is used for controlling the current intensity of the electrified coil to change the state distribution of magnetorheological fluid, controlling the lifting of the piston disc and further controlling the micro-lifting and descending processes of the indentation round head;
a pressure sensor: the pressure load monitoring device is used for monitoring the pressure load in the movement process of the magnetorheological fluid hydraulic piston;
DIC imaging device: the method comprises the steps of calibrating the contact position of an indentation round head and the surface of the thermal barrier coating and measuring the indentation depth;
Multi-degree-of-freedom terahertz module: the method is used for acquiring terahertz time-domain spectrum data in the indentation process and representing the change of residual stress of the thermal barrier coating;
Machine learning evaluation module: the method is used for establishing an evaluation model based on indentation data of the in-situ magnetic control indentation module, terahertz time-domain spectrum data and residual stress of the thermal barrier coating under different temperature gradients and combining a machine learning algorithm, and analyzing the relation between terahertz signals and the residual stress.
2. The in-situ terahertz detection device of thermal barrier coating residual stress according to claim 1, further comprising a temperature control module for controlling a temperature gradient of a thermal barrier coating sample.
3. The in-situ terahertz detection device of thermal barrier coating residual stress according to claim 2, wherein the temperature control module comprises:
Resistance belt heating device: for heating the sample at high temperature;
Liquid nitrogen cooling device: for rapid cooling of the sample;
temperature sensor: the method is used for monitoring the temperature of the thermal barrier coating sample in real time.
4. The in-situ terahertz detection device of thermal barrier coating residual stress according to claim 3, wherein the thermal barrier coating sample is subjected to temperature gradient control, comprising:
And carrying out temperature gradient loading on the thermal barrier coating sample, introducing different stress state changes, and providing controllable temperature load.
5. The in-situ terahertz detection device of thermal barrier coating residual stress according to claim 1, wherein the method for calculating the thermal barrier coating residual stress is as follows:
Wherein sigma i represents the residual stress of the ith indentation, R represents the spherical radius of the round head of the indentation, namely the radius of the pressing head, E represents the elastic modulus of the thermal barrier coating, mu represents the Poisson ratio, h i represents the ith indentation depth, P i represents the load of the ith indentation, and n represents the number of times of indentation.
6. The in-situ terahertz detection device of thermal barrier coating residual stress according to claim 1, wherein the multi-degree-of-freedom terahertz module comprises: the three-dimensional positioning camera device, the full-angle moving mechanism, the terahertz transmitter and the terahertz receiver;
the three-dimensional positioning camera device is used for identifying the space position and transmitting the adjustment information to the full-angle moving mechanism;
The full-angle moving mechanism is used for synchronously adjusting terahertz equipment based on the spatial position; wherein the spatial locations include locations of a thermal barrier coating and an indenter;
the terahertz transmitter and the terahertz receiver are used for receiving light reflected by the surface of the thermal barrier coating and acquiring terahertz time-domain spectrum information; the terahertz time-domain spectrum information is converted into terahertz time-domain spectrum data through a computer and displayed.
7. The in-situ terahertz detection device of residual stress of a thermal barrier coating according to claim 6, further comprising, after acquiring the terahertz time-domain spectroscopy data:
Performing noise removal, data cleaning and feature extraction on the terahertz time-domain spectrum data;
Carrying out Fourier transform on the terahertz time-domain spectrum data to obtain a frequency domain spectrum and a phase spectrum, extracting a peak value of the frequency domain spectrum as a first terahertz characteristic parameter, unwrapping the phase spectrum, and calculating the slope of a curve after removing phase jump as a second terahertz characteristic parameter; the first terahertz characteristic parameter and the second terahertz characteristic parameter are used together as characteristic parameters for representing the full-period variation of the residual stress.
8. The in-situ terahertz detection device of thermal barrier coating residual stress according to claim 1, wherein the machine learning evaluation module comprises a convolutional neural network unit, and the convolutional neural network unit is used for establishing a CNN regression prediction model and optimizing the CNN regression prediction model through a cross validation method.
9. The detection method of the in-situ terahertz detection device for the residual stress of the thermal barrier coating is characterized by comprising the following steps of:
acquiring an indentation depth-load relation curve based on an in-situ magnetic control indentation module, and calculating residual stress of the thermal barrier coating;
acquiring terahertz time-domain spectrum data in the indentation process by a multi-degree-of-freedom terahertz module, and representing the change of residual stress of the thermal barrier coating;
Based on the indentation data of the in-situ magnetic control indentation module, the terahertz time-domain spectrum data and the residual stress of the thermal barrier coating under different temperature gradients, an evaluation model is established by combining a machine learning algorithm, and the relation between terahertz signals and the residual stress is analyzed;
Wherein, the normal position magnetic control indentation module includes:
Magnetorheological fluid hydraulic piston: the device is used for controlling the current intensity of the electrified coil to change the state distribution of magnetorheological fluid, controlling the lifting of the piston disc and further controlling the micro-lifting and descending processes of the indentation round head;
a pressure sensor: the pressure load monitoring device is used for monitoring the pressure load in the movement process of the magnetorheological fluid hydraulic piston;
DIC imaging device: the method is used for calibrating the contact position of the round head of the indentation and the surface of the thermal barrier coating and measuring the indentation depth.
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| CN113820053A (en) * | 2021-08-27 | 2021-12-21 | 天津大学 | Method for determining stress optical coefficient of dielectric material |
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| Publication number | Priority date | Publication date | Assignee | Title |
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Non-Patent Citations (4)
| Title |
|---|
| Characterization of thermal barrier coatings microstructural features using terahertz spectroscopy;Dongdong Ye 等;Surface and Coatings Technology;20200504;1-10 * |
| 温度梯度下热障涂层残余应力的解析模型研究;白玉梅;徐颖强;孟宏君;;热加工工艺(第20期);119-122 * |
| 热障涂层太赫兹无损检测技术研究进展;叶东东;王卫泽;;表面技术;20201020(第10期);135-146 * |
| 白玉梅 ; 徐颖强 ; 孟宏君 ; .温度梯度下热障涂层残余应力的解析模型研究.热加工工艺.(20), * |
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