Laser shock thermal imaging composite coating spraying method based on vision guidance

Abstract

The present application proposes a method and device for spraying a composite coating using laser shock thermal imaging based on vision guidance, wherein the method comprises: generating a transient thermal response on the coating surface by laser shock, and using a high-speed infrared thermal imager to collect the temperature field distribution on the coating surface in real time; identifying the crack position according to the temperature field distribution on the coating surface, and obtaining the thermal response characteristics of the crack area on the coating surface, wherein the thermal response characteristics include the real-time temperature and temperature gradient data of the crack area; establishing a nonlinear mapping model between the crack thermal response characteristics and the crack size, and using a deep neural network for training, inputting the thermal response characteristics after data preprocessing into the trained deep neural network, and predicting the size and spatial position of the crack in real time; setting the target spraying parameters based on the predicted crack size information, adjusting the spraying process parameters in real time through a self-feedback PID control system, and using a multi-channel powder feeding system to dynamically adjust the material ratio to achieve the formation of a gradient composite coating.

Description

Technical Field

[0001] The present application relates to the technical field of plasma spraying of high-entropy ceramic coatings, and in particular to a method and device for spraying a composite coating using laser shock thermal imaging based on vision guidance. Background Art

[0002] Controlling crack initiation and propagation during the plasma spraying of high-entropy ceramic coatings remains a challenge. Due to the complex interplay between thermal stress, material properties, and spraying parameters, cracks are prone to forming on the coating surface. These cracks not only affect the coating's mechanical properties, such as strength and toughness, but also severely degrade its overall performance, limiting its application in high-temperature and complex operating conditions.

[0003] Furthermore, traditional spray coating technologies generally lack effective real-time monitoring and feedback mechanisms. Most spray coating processes rely on preset spray parameters (such as current, gas flow, and powder feed rate), making it impossible to monitor crack formation in real time during the spraying process. This prevents timely detection of cracks and the implementation of control measures, resulting in the problem not being effectively addressed at its source. Furthermore, cracks during the spraying process often expand, affecting the final coating quality.

[0004] Furthermore, existing coatings often have limited performance, primarily because most spray coating techniques utilize a single material. This single material cannot achieve optimal gradients in the coating, resulting in an inability to achieve an optimal balance between strength and toughness under complex operating conditions such as high temperature and high pressure, limiting the coating's effectiveness in practical applications.

[0005] In summary, existing technologies have the following shortcomings: First, there is a lack of real-time monitoring, which prevents the timely detection and treatment of coating cracks after they form; second, there is a delay in adjustment, with crack detection and repair performed only after spraying, making it impossible to effectively control problems in the early stages of coating application; third, the coating has a single performance characteristic, making it difficult to optimize coating performance according to the needs of different parts, and unable to meet the comprehensive coating requirements under complex working conditions. Summary of the Invention

[0006] The present application aims to solve one of the technical problems in the related art at least to a certain extent.

[0007] To this end, the first purpose of this application is to propose a laser shock thermal imaging composite coating spraying method based on vision guidance.

[0008] The second purpose of this application is to propose a laser shock thermal imaging composite coating spraying device based on vision guidance.

[0009] The third objective of this application is to provide an electronic device.

[0010] The fourth object of this application is to provide a computer-readable storage medium.

[0011] A fifth object of this application is to provide a computer program product.

[0012] To achieve the above objectives, the first embodiment of the present application proposes a method for spraying a composite coating using laser shock imaging based on vision guidance, comprising:

[0013] The laser shock generates transient thermal response on the coating surface, and a high-speed infrared thermal imager is used to collect the temperature field distribution on the coating surface in real time.

[0014] Identifying the crack location based on the temperature field distribution on the coating surface and obtaining thermal response characteristics of the crack area on the coating surface, wherein the thermal response characteristics include real-time temperature and temperature gradient data of the crack area;

[0015] A nonlinear mapping model between crack thermal response characteristics and crack size is established and trained using a deep neural network. The thermal response characteristics after data preprocessing are input into the trained deep neural network to predict the size and spatial position of the crack in real time.

[0016] The target spraying parameters are set based on the predicted crack size information, the spraying process parameters are adjusted in real time through the self-feedback PID control system, and the material ratio is dynamically adjusted using a multi-channel powder feeding system to achieve the formation of a gradient composite coating.

[0017] Optionally, the method of identifying the crack location based on the temperature field distribution on the coating surface and obtaining the thermal response characteristics of the crack area on the coating surface, wherein the thermal response characteristics include real-time temperature and temperature gradient data of the crack area, includes:

[0018] Based on the temperature field distribution of the coating surface, the temperature data in the time series are averaged to obtain the average temperature field. The calculation expression is:

[0019]

[0020] Where, is the average temperature field at position (x,y), T(x,y,t i ) is the position (x, y) and time point t i The temperature value of , N is the total time points in the time series;

[0021] Finding the area with the highest temperature in the average temperature field as a heat source area, and removing the heat source area from the original temperature field;

[0022] The processed temperature field is gradient calculated, and the gradient image is threshold segmented using threshold A to segment the image into crack area and non-crack area;

[0023] Check whether the crack area after threshold segmentation is within the preset range. If not, adjust the threshold A to A±0.02 and re-perform threshold segmentation until the crack area after threshold segmentation is within the preset range to determine the crack location;

[0024] For each frame of the thermal image at the crack location, the ambient temperature is subtracted to obtain the real-time temperature and temperature gradient data of the crack area.

[0025] Optionally, the data preprocessing process of the thermal response characteristics includes:

[0026] The thermal response characteristics are normalized and noise filtered.

[0027] Optionally, the spraying process parameters include spraying current, gas flow rate, feed speed and multi-channel powder feeding rate.

[0028] Optionally, the method of dynamically adjusting the material ratio by using a multi-channel powder feeding system includes:

[0029] Multiple powder feeding channels are used to supply high entropy alloy powder and ceramic powder respectively;

[0030] Dynamically adjust the powder feeding rate of each channel according to coating performance requirements and real-time crack information;

[0031] In areas prone to cracking, high entropy alloy is added as a toughening phase material.

[0032] Optionally, also include:

[0033] After each round of spraying, the spraying strategy is automatically adjusted according to the working condition changes and coating quality feedback, and the next round of spraying is carried out according to the adjusted spraying strategy to ensure the optimal performance of the coating at different locations.

[0034] To achieve the above objectives, the second embodiment of the present application proposes a laser shock thermal imaging composite coating spraying device based on vision guidance, comprising:

[0035] An acquisition module is used to generate a transient thermal response on the coating surface through laser shock and use a high-speed infrared thermal imager to collect the temperature field distribution on the coating surface in real time;

[0036] An acquisition module, configured to identify crack locations based on the temperature field distribution on the coating surface and acquire thermal response characteristics of the crack region on the coating surface, the thermal response characteristics including real-time temperature and temperature gradient data of the crack region;

[0037] The prediction module is used to establish a nonlinear mapping model between the crack thermal response characteristics and the crack size, and uses a deep neural network for training. The thermal response characteristics after data preprocessing are input into the trained deep neural network to predict the size and spatial position of the crack in real time;

[0038] The spraying module is used to set target spraying parameters based on the predicted crack size information, adjust the spraying process parameters in real time through a self-feedback PID control system, and dynamically adjust the material ratio using a multi-channel powder feeding system to achieve the formation of a gradient composite coating.

[0039] To achieve the above-mentioned purpose, a third embodiment of the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0040] The memory stores computer-executable instructions;

[0041] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0042] To achieve the above-mentioned purpose, the fourth embodiment of the present application proposes a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0043] To achieve the above-mentioned objectives, the fifth embodiment of the present application proposes a computer program product, which implements any one of the methods in the first aspect when executed by a processor.

[0044] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0045] This application integrates laser shock thermography technology, a neural network model, and a self-feedback PID control system to achieve real-time monitoring and control of coating cracks during the plasma spraying process. By detecting and predicting crack size in real time, crack expansion and its impact on coating performance are avoided, thereby improving the strength and toughness of the coating and meeting the gradient performance requirements of the coating under complex working conditions. In addition, closed-loop control and adaptive spraying further improve the accuracy and stability of the spraying process, ensuring the consistency and optimization of coating quality.

[0046] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0048] Figure 1 A schematic flow chart of a method for spraying a composite coating using laser shock thermography based on vision guidance provided in an embodiment of the present application;

[0049] Figure 2 A flowchart of a method for spraying a composite coating using laser shock imaging based on vision guidance provided in an embodiment of the present application;

[0050] Figure 3 A schematic diagram of a process for obtaining thermal response characteristics provided in an embodiment of the present application;

[0051] Figure 4 Schematic diagram of the gradient composite coating provided in an embodiment of the present application;

[0052] Figure 5 This is a schematic structural diagram of a vision-guided laser shock thermal imaging composite coating spray device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0054] In response to the problems of crack generation and expansion in the current high-entropy ceramic coating preparation process, the embodiment of the present application provides a laser shock thermal imaging composite coating spraying method based on vision guidance. By real-time detection of coating cracks, prediction of crack size and automatic adjustment of spraying parameters, a closed-loop feedback system is formed, thereby achieving optimized control of coating quality.

[0055] Figure 1 A schematic flow chart of a method for spraying a composite coating using laser shock thermography based on vision guidance provided in an embodiment of the present application.

[0056] In the embodiment of the present application, the laser shock thermal imaging composite coating spraying method based on vision guidance is to Figure 2 The system architecture shown is implemented.

[0057] like Figure 1 As shown, the method includes the following steps:

[0058] Step 101: generating a transient thermal response on the coating surface by laser shock, and using a high-speed infrared thermal imager to collect the temperature field distribution on the coating surface in real time.

[0059] In the embodiment of the present application, laser shock technology is used to generate a transient thermal response on the coating surface. Specifically, when the laser beam is irradiated to the coating surface, the high energy density of the laser causes the coating surface to heat up rapidly and produces temperature fluctuations in a short period of time. This thermal response is instantaneous and is closely related to the physical properties of the coating surface (such as thickness, thermal conductivity, etc.) and the presence of cracks. Cracks or discontinuities often lead to abnormal heat flow, which manifests as different temperature gradients.

[0060] To accurately capture this process, a high-speed infrared thermal imager is used to capture the temperature distribution of the coating surface in real time. By sensing the infrared radiation emitted by the coating surface, the infrared thermal imager can accurately measure the surface temperature distribution and quickly scan to obtain a complete temperature field image. Due to the infrared thermal imager's high-speed imaging capability, it can capture temperature changes in a very short time, even at the millisecond to microsecond level, which is critical for real-time monitoring of crack initiation and growth.

[0061] It's clear that by acquiring temperature data on the coating surface, it's possible to analyze temperature gradients in different regions, thereby inferring the location, size, and potential growth trends of cracks. This thermal response data provides the foundation for subsequent crack size prediction and adaptive adjustments to the spraying process, enabling more precise and real-time control of the entire spraying process, preventing crack growth and optimizing coating performance.

[0062] Step 102 : Identify the crack location based on the temperature field distribution on the coating surface, and obtain thermal response characteristics of the crack region on the coating surface. The thermal response characteristics include real-time temperature and temperature gradient data of the crack region.

[0063] In the embodiment of the present application, by Figure 2 The thermal response data acquisition unit shown is used to obtain the thermal response characteristics of the crack area on the coating surface.

[0064] Figure 3 A schematic flow chart of the thermal response feature acquisition process provided in an embodiment of the present application.

[0065] Reference Figure 3 First, based on the temperature field distribution of the coating surface, the temperature data in the time series are averaged to obtain a smooth temperature field. The average temperature field is obtained by summing and averaging the temperature values ​​at different positions on the coating surface at multiple time points. The calculation expression of the average temperature field is:

[0066]

[0067] Where, is the average temperature field at position (x,y), T(x,y,ti ) is the position (x, y) and time point t i The temperature value of , N is the total time points in the time series.

[0068] After obtaining the average temperature field, find the area with the highest temperature. This is usually the heat source area, which corresponds to the crack or other coating discontinuity. By removing the heat source area from the original temperature field, the error caused by the heat source interference can be eliminated.

[0069] Next, the temperature field gradient is calculated after removing the heat source area. The temperature gradient reflects the rate of temperature change, and the crack area usually exhibits different gradient characteristics than the normal area.

[0070] Then, a preset threshold A is used to segment the temperature gradient image, dividing the image into crack area and non-crack area, and checking whether the area of ​​the crack area after threshold segmentation is within the preset range. If not, adjust the threshold A to A±0.02 and re-perform the threshold segmentation until the area of ​​the crack area after threshold segmentation is within the preset range to determine the location of the crack.

[0071] Once the crack location is determined, each frame of the thermal image at the crack location is processed and the ambient temperature is subtracted to obtain real-time temperature and temperature gradient data for the crack area. This data accurately reflects the temperature changes and thermal response characteristics of the crack, providing key information for subsequent crack size prediction and spray process adjustments.

[0072] Step 103: Establish a nonlinear mapping model between the crack thermal response characteristics and the crack size, and use a deep neural network for training. Input the thermal response characteristics after data preprocessing into the trained deep neural network to predict the size and spatial position of the crack in real time.

[0073] In the embodiment of the present application, before inputting the thermal response features into the deep neural network, the data needs to be preprocessed to improve the prediction accuracy and stability of the model. The preprocessing mainly includes two aspects:

[0074] (1) Normalization: Through normalization, feature data of different scales are converted to a uniform range to avoid the influence of large numerical features on neural network training. The commonly used normalization method is to scale the data to the interval [0, 1] or standardize it using the mean and standard deviation to make the distribution of each feature more uniform, which helps to accelerate the convergence of the network.

[0075] (2) Noise Filtering: Due to various interference factors during the acquisition process, the thermal response data may contain noise. Using a filtering algorithm (such as median filtering or Gaussian filtering) to remove noise helps to clearly extract the true crack characteristics and avoid unnecessary interference with model training.

[0076] In the present embodiment, pre-processed thermal response characteristic samples are fed into a deep neural network, which is trained to learn the nonlinear relationship between crack thermal response characteristics and crack dimensions (e.g., width and depth). The deep neural network can automatically extract complex features and patterns from a large amount of training data, thereby establishing an effective mapping model. This model can accurately map the coating's thermal response data to the size and location of the crack.

[0077] Specifically, a labeled training data set (including known crack size and thermal response characteristics) is used to train a deep neural network. During the training process, the deep neural network continuously adjusts its internal parameters to minimize the error between the predicted results and the actual values. Common optimization algorithms include gradient descent, which aims to continuously optimize the network weights through the backpropagation algorithm, so that the network's prediction accuracy on new data gradually improves. This application does not specifically limit or explain this.

[0078] The trained deep neural network can receive new thermal response feature data in real time and use the model to infer the size (width and depth) of cracks and their location on the coating surface. This process can be carried out in real time during the spraying process, ensuring that cracks can be detected and controlled in a timely manner, thereby ensuring coating quality.

[0079] Finally, the thermal response characteristics after data preprocessing are input into the trained deep neural network to predict the size and spatial position of the crack in real time.

[0080] Step 104 , target spraying parameters are set based on the predicted crack size information, spraying process parameters are adjusted in real time through a self-feedback PID control system, and material ratios are dynamically adjusted using a multi-channel powder feeding system to achieve the formation of a gradient composite coating.

[0081] In an embodiment of the present application, target spraying parameters (such as spraying current, gas flow, feed speed and multi-channel powder feeding rate) are further set based on the crack size information predicted by the deep neural network, and the spraying parameters are dynamically adjusted according to the real-time crack data through a self-feedback PID control system to ensure the accuracy and stability of the spraying process.

[0082] The spray current controls the energy density of the plasma and is a key parameter affecting the melting state of the sprayed particles. A higher current fully melts the particles, improving the coating's bonding strength; a lower current is suitable for spraying thin coatings or fine features. The gas flow rate regulates the morphology and stability of the plasma jet. A high flow rate enhances the jet's penetration, facilitating the spraying of thick coatings or materials with high melting points; a low flow rate is suitable for spraying thin coatings with high uniformity requirements. The feed rate adjusts the speed at which the spray gun moves relative to the workpiece, directly affecting the thickness and uniformity of the coating. Faster speeds are suitable for rapid spraying of large areas, while slower speeds can enhance the thickness and strength of localized coatings. Multi-channel powder feed rates precisely control the feed ratio of different powders (such as high-entropy alloys and ceramic powders). By dynamically adjusting the powder feed rate of each channel, gradient optimization can be achieved in different areas of the coating, such as increasing the amount of high-entropy alloy to improve toughness or increasing the amount of ceramic powder to improve wear resistance.

[0083] After adjusting the spraying process parameters, multiple powder feeding channels are used during the spraying process to supply high-entropy alloy powder and ceramic powder respectively, providing multiple material sources for coating formation, and dynamically adjusting the rate of each powder feeding channel according to the coating performance requirements and real-time crack information.

[0084] Furthermore, the embodiment of the present application also adds high entropy alloy as a toughening phase material in areas prone to cracking, thereby improving the toughness of the coating and preventing crack expansion and further damage.

[0085] It should be noted that after each round of spraying is completed, the embodiment of the present application adjusts the spraying strategy based on the real-time feedback of the working condition changes and the coating quality. These feedback information may include changes in the crack area, the uniformity of the coating thickness, and the evaluation results of the performance indicators. Then, based on the adjusted spraying strategy, the next round of spraying process is entered to ensure that the performance of the coating in different areas is optimally configured. For example, in areas with greater stress, the proportion of toughness material can be increased, while in areas with higher wear resistance requirements, ceramic powder can be increased.

[0086] Thus, the entire process forms a closed-loop system. Through the process of real-time crack detection - size prediction - parameter adjustment, the spraying parameters are continuously optimized to ensure the dynamic adaptability of coating quality and crack control.

[0087] In one possible embodiment, the generated gradient composite coating is shown in FIG. Figure 5 As shown, HEC is a ceramic and HEA is a high entropy alloy. This schematic shows the gradual change in composition and properties of the coating from the surface to the substrate.

[0088] In order to implement the above embodiment, the present application also proposes a laser shock thermal imaging composite coating spraying device based on vision guidance. Figure 5This is a schematic diagram of the structure of a laser shock thermal imaging composite coating spraying device 10 based on visual guidance provided in an embodiment of the present application. Figure 5 As shown, the device includes:

[0089] The acquisition module 100 is used to generate a transient thermal response on the coating surface by laser shock and to acquire the temperature field distribution on the coating surface in real time using a high-speed infrared thermal imager;

[0090] An acquisition module 200 is used to identify the crack location based on the temperature field distribution on the coating surface and obtain the thermal response characteristics of the crack area on the coating surface, wherein the thermal response characteristics include real-time temperature and temperature gradient data of the crack area;

[0091] The prediction module 300 is used to establish a nonlinear mapping model between the crack thermal response characteristics and the crack size, and uses a deep neural network for training. The thermal response characteristics after data preprocessing are input into the trained deep neural network to predict the size and spatial position of the crack in real time;

[0092] The spraying module 400 is used to set target spraying parameters based on the predicted crack size information, adjust the spraying process parameters in real time through a self-feedback PID control system, and dynamically adjust the material ratio using a multi-channel powder feeding system to achieve the formation of a gradient composite coating.

[0093] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0094] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0095] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0096] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0097] It is important to note that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold beyond these legitimate uses. Furthermore, such collection / sharing should be conducted only after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes the relevant user information before using the feature. Furthermore, any necessary steps must be taken to safeguard and secure access to such personal information and ensure that others with access to personal information comply with its privacy policy and procedures.

[0098] This application contemplates providing implementations that allow users to selectively block the use or access of personal information data. Specifically, this disclosure contemplates providing hardware and / or software to prevent or block access to such personal information data. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0099] In the descriptions of the foregoing embodiments, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are mutually inconsistent.

[0100] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0101] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0102] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.

[0103] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0104] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0105] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0106] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

[0107] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved. This is not limited herein.

[0108] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for spraying composite coatings using laser shock thermal imaging based on vision guidance, characterized in that: The following steps are involved: The laser shock generates transient thermal response on the coating surface, and a high-speed infrared thermal imager is used to collect the temperature field distribution on the coating surface in real time. Identifying the crack location based on the temperature field distribution on the coating surface and obtaining thermal response characteristics of the crack area on the coating surface, wherein the thermal response characteristics include real-time temperature and temperature gradient data of the crack area; A nonlinear mapping model between crack thermal response characteristics and crack size is established and trained using a deep neural network. The thermal response characteristics after data preprocessing are input into the trained deep neural network to predict the size and spatial position of the crack in real time. The target spraying parameters are set based on the predicted crack size information. The spraying process parameters are adjusted in real time through a self-feedback PID control system. The material ratio is dynamically adjusted using a multi-channel powder feeding system to achieve the formation of a gradient composite coating. The method of identifying the crack location based on the temperature field distribution on the coating surface and obtaining the thermal response characteristics of the crack area on the coating surface, wherein the thermal response characteristics include real-time temperature and temperature gradient data of the crack area, includes: Based on the temperature field distribution of the coating surface, the temperature data in the time series are averaged to obtain the average temperature field. The calculation expression is: Where, For the location The average temperature field, For the location and time point The temperature value, is the total time points in the time series; Finding the area with the highest temperature in the average temperature field as a heat source area, and removing the heat source area from the original temperature field; The gradient of the processed temperature field is calculated and the threshold is used Perform threshold segmentation on the gradient image to divide the image into crack area and non-crack area; Check whether the crack area after threshold segmentation is within the preset range. If not, adjust the threshold. to Then, the threshold segmentation is performed again until the crack area after the threshold segmentation is within the preset range, and the crack location is determined; For each frame of the thermal image at the crack location, the ambient temperature is subtracted to obtain the real-time temperature and temperature gradient data of the crack area.

2. The method according to claim 1, characterized in that The data preprocessing process of the thermal response characteristics includes: The thermal response characteristics are normalized and noise filtered.

3. The method according to claim 2, characterized in that The spraying process parameters include spraying current, gas flow rate, feed speed and multi-channel powder feeding rate.

4. The method according to claim 3, characterized in that The method of dynamically adjusting the material ratio by using a multi-channel powder feeding system includes: Multiple powder feeding channels are used to supply high entropy alloy powder and ceramic powder respectively; Dynamically adjust the powder feeding rate of each channel according to coating performance requirements and real-time crack information; In areas prone to cracking, high entropy alloy is added as a toughening phase material.

5. The method according to any one of claims 1 to 4, characterized in that Also includes: After each round of spraying, the spraying strategy is automatically adjusted according to the working condition changes and coating quality feedback, and the next round of spraying is carried out according to the adjusted spraying strategy to ensure the optimal performance of the coating at different locations.

6. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 5 when executed by a processor.

8. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 5 when executed by a processor.

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