Thermal barrier coating spraying method based on acoustic emission online monitoring

Abstract

The invention discloses a thermal barrier coating spraying method based on acoustic emission on-line monitoring, which comprises the following steps of: acquiring acoustic signals in a spraying process in real time through an acoustic emission sensor, and extracting characteristic values related to key quality indexes such as coating bonding strength, porosity and internal stress by combining wavelet analysis and a machine learning classification algorithm; a closed-loop feedback control loop is constructed to adjust spraying parameters, the technical problem that in the prior art, accurate online monitoring and real-time feedback control over the coating quality are difficult to achieve is solved, real-time and accurate control over the coating quality in the spraying process is achieved, coating quality fluctuation caused by manual experience adjustment is avoided, and the spraying quality is improved. The stability and the consistency of the coating quality are improved, and the coating is prevented from falling off too early.

Description

Technical Field

[0001] This invention relates to the field of thermal barrier coating technology, and in particular to a thermal barrier coating spraying method based on online acoustic emission monitoring. Background Technology

[0002] Thermal barrier coatings (TBCs) are ceramic coatings applied to the surfaces of high-temperature components. They possess excellent high-temperature resistance, thermal insulation, and oxidation resistance, effectively reducing the surface temperature of the substrate and extending the service life of high-temperature components. Currently, TBCs are widely used in key components such as aero-engine turbine blades and gas turbine blades. Plasma spraying, due to its advantages such as high preparation efficiency, low cost, and good controllability of coating thickness, has become one of the most commonly used TBC preparation technologies in industrial production.

[0003] However, during plasma spraying, the quality of the coating formation is affected by various spraying parameters (such as spray gun power, powder feed rate, spraying distance, etc.). Key quality indicators such as coating bonding strength, porosity, and internal stress are difficult to control accurately online and mainly rely on the operator's experience and post-process inspection, resulting in unstable coating quality, large fluctuations in coating performance, and premature peeling and cracking failures during use, which seriously affect the safe operation of the equipment. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing thermal barrier coating spraying technologies, such as the difficulty in accurately controlling the coating bonding strength and internal stress online, leading to premature coating detachment. This invention provides a thermal barrier coating spraying method based on online acoustic emission detection and feedback control. This method uses acoustic emission sensors to collect acoustic signals in real time during the spraying process, combines signal processing techniques to extract coating quality characteristic values, and constructs a closed-loop feedback control circuit to adjust spraying parameters. This achieves online monitoring and precise control of coating quality, improves coating quality stability, and prevents premature coating detachment.

[0005] Another objective of this invention is to provide a smart spraying system for thermal barrier coatings that implements the above-described method.

[0006] The objective of this invention is achieved through the following technical solutions.

[0007] A method for spraying a thermal barrier coating based on online acoustic emission monitoring, characterized by comprising the following steps:

[0008] S1. Acoustic signal acquisition: During the thermal barrier coating spraying process, at least one acoustic emission sensor is used to acquire the acoustic emission signals generated when the plasma jet and / or sprayed particles hit the substrate in real time.

[0009] S2. Feature extraction: Feature values ​​related to coating quality are extracted using signal processing methods, including wavelet analysis and machine learning classification algorithms.

[0010] S3. Generate a deviation signal: Compare the feature value with a preset threshold value for the corresponding coating quality index to generate a deviation signal;

[0011] S4. Adjusting spraying process parameters: Based on the deviation signal, implement feedback control to adjust the spraying parameters so that the characteristic value approaches the preset threshold, thereby achieving online closed-loop control of coating quality.

[0012] Furthermore, the spraying parameters are at least one of the following: spraying power, powder feeding rate, spraying distance, working gas flow rate, spray gun moving speed, and substrate preheating temperature.

[0013] Furthermore, the acoustic emission sensor is disposed on the substrate or spraying equipment and is capable of detecting acoustic signals in the frequency range of 20kHz-1MHz.

[0014] Furthermore, the wavelet analysis employs multi-scale wavelet decomposition technology to extract the energy distribution characteristics of different frequency bands of the acoustic emission signal.

[0015] Furthermore, the machine learning classification algorithm employs support vector machine, neural network, or random forest algorithms to classify and identify the coating quality status; by training a large amount of acoustic emission data with known quality status, a correspondence model between acoustic emission features and coating quality status is established.

[0016] Furthermore, the characteristic values ​​include porosity-related characteristics, crack propagation characteristics, bond strength characteristics, and internal stress state characteristics.

[0017] Furthermore, the feedback control employs an adaptive PID control algorithm to achieve real-time adjustment of the spraying parameters.

[0018] Furthermore, the spraying parameters are adjusted within the following ranges: spraying power 10-50kW, powder feeding rate 10-100g / min, spraying distance 50-200mm, spray gun moving speed 50mm / s-200mm / s, and plasma gas flow rate: Ar gas 15L / min-30L / min and H2 gas 5L / min-15L / min.

[0019] Furthermore, it also includes establishing a database of the correspondence between acoustic emission signal characteristics and coating quality parameters.

[0020] Furthermore, a smart thermal barrier coating spraying system for implementing a thermal barrier coating spraying method based on online acoustic emission monitoring includes:

[0021] Plasma spraying equipment: including spray gun, powder feeding mechanism, plasma power supply and gas supply unit, used to spray coating materials onto the substrate surface;

[0022] Acoustic emission detection module: includes at least one acoustic emission sensor, a signal amplifier and a data acquisition card, used to acquire and preprocess acoustic signals generated by plasma jets and particle impacts on the matrix;

[0023] Signal processing module: Receives and analyzes the acoustic signal transmitted by the acoustic emission detection module, extracts its features, and obtains coating quality feature values;

[0024] Feedback control module: Adjusts the spraying parameters of the plasma spraying device based on the comparison results between the coating quality characteristic value and the preset threshold.

[0025] This invention uses an acoustic emission sensor to collect acoustic signals in real time during the spraying process. It combines wavelet analysis and machine learning classification algorithms to extract feature values ​​related to key quality indicators such as coating bonding strength, porosity, and internal stress. A closed-loop feedback control loop is then constructed to adjust the spraying parameters. This solves the technical problem of achieving accurate online monitoring and real-time feedback control of coating quality in existing technologies. It enables real-time and precise control of coating quality during the spraying process, avoids coating quality fluctuations caused by manual experience adjustments, improves the stability and consistency of coating quality, and prevents premature coating peeling.

[0026] This invention employs wavelet analysis to denoise the acoustic signal, effectively removing interference factors such as environmental noise, equipment vibration, and strong plasma jet light, and accurately extracting the effective signal that is only related to coating quality. Compared with online detection technologies such as optical imaging and infrared thermal imagers, the acoustic emission detection method of this invention has stronger anti-interference capabilities and higher detection accuracy in complex plasma spraying environments. Attached Figure Description

[0027] Figure 1 This is a flowchart of a thermal barrier coating spraying method based on online acoustic emission monitoring according to the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0029] like Figure 1 As shown, a thermal barrier coating spraying method based on online acoustic emission monitoring is described. The method operates within a thermal barrier coating intelligent spraying system, and its overall process is as follows: Figure 1 As shown, it includes the following steps.

[0030] Step 1: Acoustic signal acquisition.

[0031] Based on the coating material and substrate material, set the initial spraying parameters within the following range: spraying power 10-50kW, powder feed rate 10-100g / min, spraying distance 50-200mm, spray gun moving speed 50mm / s-200mm / s, plasma gas flow rate (Ar gas 15L / min-30L / min, H2 gas)

[0032] 5L / min-15L / min.

[0033] Meanwhile, the preamplifier gain is set to 40dB-60dB and the sampling frequency of the audio signal data acquisition card is set to 1MHz-2MHz to ensure the sensitivity and accuracy of audio signal acquisition.

[0034] An acoustic emission sensor is installed on the spray gun, and at least one acoustic emission sensor is also arranged on the back or side of the substrate to be sprayed by magnetic attraction or adhesive. The sensor frequency response range is 20kHz-1MHz and the sensitivity is better than -60dB.

[0035] During the spraying process, multiple acoustic emission sensors of the acoustic emission detection module work simultaneously: the sensor installed on the outside of the spray gun nozzle mainly collects the acoustic signals generated by the plasma jet during combustion and expansion; the sensor installed on the back or side of the substrate mainly collects the acoustic signals generated when molten coating particles collide with the substrate surface at high speed.

[0036] The acquired acoustic signals are amplified by a preamplifier, filtered by a signal conditioner, and then converted into digital signals by a data acquisition card, which transmits them to the signal processing module in real time. To ensure the continuity of signal acquisition, the data acquisition card uses a circular buffer mode to store the acoustic signal data of the most recent 10 minutes in real time, avoiding data loss.

[0037] Step 2: Extract feature values.

[0038] Eight-level wavelet decomposition was performed using the db8 wavelet basis function to obtain wavelet coefficients at different scales. The energy proportion of the wavelet coefficients at each scale was calculated. High-frequency wavelet coefficients with an energy proportion below 0.5% were considered noise coefficients and set to zero, while effective wavelet coefficients with an energy proportion above 0.5% were retained. The denoised acoustic signal was reconstructed through inverse wavelet transform to remove environmental noise and equipment vibration interference.

[0039] The noise-reduced acoustic signal is analyzed in the time and frequency domains to extract time-domain feature parameters such as peak amplitude, energy count, and duration. The time-domain acoustic signal is then converted into a frequency-domain signal using a fast Fourier transform to extract frequency-domain feature parameters such as peak frequency, center frequency, spectral entropy, and frequency band energy ratio.

[0040] The extracted feature parameters are input into a pre-defined machine learning classification model, such as a random forest model or a support vector machine model, which is trained using previous experimental data. Based on the correlation between the feature parameters and coating quality indicators, the model outputs corresponding coating quality feature values, including bonding strength feature values, internal stress feature values, porosity feature values, and crack distribution feature values.

[0041] Step 3: Generate the deviation signal.

[0042] The coating quality feature values ​​output by the above machine learning classification model are compared with the corresponding thresholds in the preset threshold database. The preset thresholds are determined according to the design quality requirements of thermal barrier coatings.

[0043] The feedback control module determines whether the current coating quality meets the standard based on the comparison results: if all feature values ​​are within the corresponding preset threshold range, the coating quality is determined to meet the standard, and the current spraying parameters remain unchanged; if any feature value exceeds the preset threshold range (higher than the upper threshold or lower than the lower threshold), the coating quality is determined to be substandard, a deviation signal is generated, and the spraying parameter adjustment stage is entered.

[0044] Step 4: Adjust the spraying process parameters.

[0045] When the coating quality fails to meet standards, the feedback control module generates parameter adjustment commands according to the deviation between the characteristic value and the preset threshold, following preset parameter adjustment rules. These commands are then sent to the plasma spraying device to adjust the spraying parameters in real time, bringing the characteristic value closer to the preset threshold, thus achieving online closed-loop control of coating quality. The feedback control employs an adaptive PID control algorithm with a proportional coefficient Kp = 0.8, an integral coefficient Ki = 0.1, a derivative coefficient Kd = 0.05, and a control cycle of 100ms to ensure real-time response.

[0046] The parameter adjustment rules were formulated based on the previous experimental data. For different types of deviation of characteristic values, the corresponding spraying characteristic parameters were determined and adjusted, as shown in Table 1 below.

[0047] Table 1: Coating monitoring quality characteristics and spraying process parameter adjustments.

[0048]

[0049]

[0050] The above-mentioned thermal barrier coating spraying method based on online acoustic emission monitoring is specifically implemented through the following intelligent thermal barrier coating spraying system.

[0051] A smart spraying system for thermal barrier coatings mainly includes the following components.

[0052] Plasma spraying equipment includes a spray gun, a powder feeding mechanism, a plasma power supply, and a gas supply unit. It is used to spray coating materials onto a substrate surface to form a thermal barrier coating. The spray gun generates a plasma jet; the powder feeding mechanism delivers coating powder to the spray gun nozzle at a stable rate; the plasma power supply provides the electrical energy required for arc discharge and regulates the spray gun power; the gas supply unit provides the plasma working gas (such as Ar or H2) and the powder carrier gas (such as N2) and regulates the gas flow rate.

[0053] Acoustic emission detection module: Includes at least two acoustic emission sensors, a preamplifier, a signal conditioner, and a data acquisition card. The acoustic emission sensors are mounted on the outside of the spray gun nozzle and on the side or back of the substrate, respectively collecting acoustic signals generated by the plasma jet and by coating particles impacting the substrate surface. The preamplifier amplifies the weak acoustic signals collected by the sensors, with a gain range of 40dB-60dB. The signal conditioner filters the amplified acoustic signals to remove environmental noise and equipment vibration interference. The data acquisition card converts the analog acoustic signals into digital signals and transmits them to the signal processing module, with a sampling frequency of no less than 1MHz to ensure the capture of the high-frequency characteristics of the acoustic signals.

[0054] Signal processing module: Electrically connected to the data acquisition card of the acoustic emission detection module, it is used to perform noise reduction and feature extraction on the digital acoustic signal to obtain feature values ​​related to coating quality. This module adopts a signal processing method combining wavelet analysis and machine learning classification.

[0055] Feedback control module: Electrically connected to both the signal processing module and the plasma spraying device, this module serves as the system's control core, adjusting spraying parameters based on coating quality characteristic values. It incorporates a built-in database of preset thresholds and parameter adjustment rules. First, it compares the characteristic values ​​output by the signal processing module with the preset thresholds to determine if the current coating quality meets the standards. If the characteristic values ​​are within the threshold range, the current spraying parameters remain unchanged. If the characteristic values ​​exceed the threshold range, a corresponding parameter adjustment command is generated based on the degree of deviation and sent to the plasma spraying device to adjust the spraying parameters in real time, achieving closed-loop feedback control of the spraying process.

[0056] The technical features of the above-described embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A thermal barrier coating spraying method based on acoustic emission online monitoring, characterized in that, The method comprises the following steps: S1. Acquiring acoustic signals: acquiring acoustic emission signals generated when plasma jets and / or sprayed particles impact the substrate in real time during thermal barrier coating spraying by using at least one acoustic emission sensor; S2. Extracting characteristic values: extracting characteristic values related to coating quality by a signal processing method, which comprises wavelet analysis and a machine learning classification algorithm; S3. Generating deviation signals: comparing the characteristic values with preset threshold values of corresponding coating quality indicators to generate deviation signals; S4. Adjusting spraying process parameters: based on the deviation signals, implementing feedback control to adjust spraying parameters so that the characteristic values tend to approach the preset threshold values, thereby realizing online closed-loop control of coating quality.

2. The thermal barrier coating spraying method based on acoustic emission online monitoring according to claim 1, characterized in that: The spraying parameters are at least one of spraying power, powder feeding rate, spraying distance, working gas flow, spray gun moving speed, and substrate preheating temperature.

3. The thermal barrier coating spraying method based on acoustic emission online monitoring according to claim 1, characterized in that: The acoustic emission sensor is arranged on the substrate or the spraying equipment and can detect acoustic signals with a frequency range of 20 kHz-1 MHz.

4. The thermal barrier coating spraying method based on acoustic emission online monitoring according to claim 1, characterized in that: The wavelet analysis adopts a multi-scale wavelet decomposition technique to extract energy distribution characteristics of acoustic emission signals in different frequency bands.

5. The thermal barrier coating spraying method based on acoustic emission online monitoring according to claim 1, characterized in that: The machine learning classification algorithm adopts a support vector machine, a neural network, or a random forest algorithm to classify and identify coating quality states.

6. The thermal barrier coating spraying method based on acoustic emission online monitoring according to claim 1, characterized in that: The characteristic values include porosity-related characteristics, crack propagation characteristics, bonding strength characteristics, and internal stress state characteristics.

7. The thermal barrier coating spraying method based on acoustic emission online monitoring according to claim 1, characterized in that: The feedback control adopts an adaptive PID control algorithm to realize real-time adjustment of spraying parameters.

8. The thermal barrier coating spraying method based on acoustic emission online monitoring according to claim 1, characterized in that, The spraying parameter adjustment range is: spraying power 10-50 kW, powder feeding rate 10-100 g / min, spraying distance 50-200 mm, spray gun moving speed 50 mm / s-200 mm / s, plasma gas flow Ar gas 15 L / min-30 L / min, and H2 gas 5 L / min-15 L / min.

9. The thermal barrier coating spraying method based on acoustic emission online monitoring according to claim 1, characterized in that: A database of the corresponding relationship between acoustic emission signal characteristics and coating quality parameters is also established.

10. A thermal barrier coating intelligent spraying system for realizing the thermal barrier coating spraying method based on acoustic emission online monitoring according to any one of claims 1-9, characterized in that, The method comprises: A plasma spraying device: including a spray gun, a powder feeding mechanism, a plasma power supply, and a gas supply unit, used for spraying coating materials to the substrate surface; An acoustic emission detection module: including at least one acoustic emission sensor, a signal amplifier, and a data acquisition card, used for acquiring and preprocessing acoustic signals generated when plasma jets and particles impact the substrate; A signal processing module: receiving and analyzing acoustic signals transmitted by the acoustic emission detection module and extracting coating quality characteristic values therefrom; A feedback control module: adjusting spraying parameters of the plasma spraying device according to the comparison results of coating quality characteristic values and preset threshold values.

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