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

Abstract

The invention provides a laser shock thermal imaging composite coating spraying method and device based on visual guidance, and the method comprises the steps: generating a transient thermal response on the surface of a coating through laser shock, and collecting the temperature field distribution of the surface of the coating in real time through a high-speed thermal infrared imager; the crack position is identified according to the temperature field distribution of the coating surface, thermal response characteristics of a crack area on the coating surface are obtained, and the thermal response characteristics comprise 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, training by adopting a deep neural network, inputting the thermal response characteristics subjected to data preprocessing into the trained deep neural network, and predicting the size and the spatial position of the crack in real time; and target spraying parameters are set based on predicted crack size information, spraying process parameters are adjusted in real time through a self-feedback PID control system, the material ratio is dynamically adjusted through a multi-channel powder feeding system, and the gradient composite coating is formed.

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] In the prior art, the control of crack generation and expansion has always been a difficult problem during the plasma spraying process of high entropy ceramic coatings. Due to the complex interaction between thermal stress, material properties and spraying parameters, cracks are easily generated on the coating surface. The formation of these cracks not only affects the mechanical properties of the coating, such as strength and toughness, but also seriously reduces the overall performance of the coating, limiting its application under high temperature and complex working conditions.

[0003] In addition, traditional spraying technology generally lacks effective real-time monitoring and feedback mechanisms. Most spraying processes rely on preset spraying parameters (such as current, gas flow rate and powder feeding speed), and cannot monitor the formation of cracks in real time during the spraying process. As a result, cracks cannot be discovered in time and measures cannot be taken to control them, resulting in the problem not being effectively solved at the source, and cracks in the spraying process often expand, affecting the final coating quality.

[0004] In addition, the coating performance in the prior art is often limited, mainly because most spraying technologies use a single material for coating spraying. The use of such a single material cannot achieve gradient optimization of the coating, so when facing complex working conditions such as high temperature and high pressure, the strength and toughness of the coating cannot achieve the best balance, thus limiting the effect of the coating in practical applications.

[0005] In summary, the existing technology has the following shortcomings: first, there is a lack of real-time monitoring, which results in the inability to detect and deal with coating cracks in time after they are generated; second, the adjustment is delayed, and crack detection and repair are performed after spraying, which cannot effectively control the problem in the early stage of coating; third, the coating performance is single, and it is difficult to optimize the coating performance according to the needs of different parts, and it cannot meet the comprehensive requirements of the coating 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 some extent.

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

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

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

[0010] A fourth objective of the present application is to provide a computer-readable storage medium.

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

[0012] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a laser shock thermal imaging composite coating spraying method 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] Identify the crack position according to 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;

[0015] A nonlinear mapping model between crack thermal response characteristics and crack size is established, and a deep neural network is used 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.

[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 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 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] In the formula, 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 the 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 location of the crack;

[0024] For each frame of the thermal image at the crack position, 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 changes in working conditions and coating quality feedback, and the next round of spraying is carried out according to the adjusted spraying strategy to ensure that the performance of the coating at different locations is optimized.

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

[0035] The acquisition module is used to generate transient thermal response on the coating surface through laser impact, 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, used to identify the crack position according to the temperature field distribution on the coating surface, and acquire 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;

[0037] The prediction module is used to establish a nonlinear mapping model between the crack thermal response characteristics and the crack size, and adopt 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 the target spraying parameters based on the predicted crack size information, adjust the spraying process parameters in real time through the 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, the third aspect of the present application provides an electronic device, including: 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 aspect embodiment of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer execution instructions, and when the computer execution 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 purpose, the fifth aspect of the present application proposes a computer program product, which implements any method in the first aspect when executed by a processor.

[0044] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0045] This application realizes real-time monitoring and control of coating cracks during plasma spraying by integrating laser shock thermal imaging technology, neural network model and self-feedback PID control system. By real-time detection and prediction of crack size, crack expansion and the 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, through closed-loop control and adaptive spraying, the accuracy and stability of the spraying process are further improved, 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 the 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 diagram of a process of spraying a composite coating using laser shock thermal imaging based on vision guidance provided in an embodiment of the present application;

[0049] Figure 2 A flowchart of a laser shock thermal imaging composite coating spraying method 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 A schematic diagram of a gradient composite coating provided in an embodiment of the present application;

[0052] Figure 5 A schematic structural diagram of a laser shock thermal imaging composite coating spray device based on vision guidance provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] Embodiments of the present application are described in detail below, and examples of the embodiments 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 preparation process of high-entropy ceramic coatings, an embodiment of the present application provides a laser shock thermal imaging composite coating spraying method based on vision guidance, which forms a closed-loop feedback system by real-time detection of coating cracks, prediction of crack size and automatic adjustment of spraying parameters, 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 thermal imaging 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 Figure 2 The system architecture shown is implemented.

[0057] like Figure 1 As shown, the method comprises 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 embodiments of the present application, laser shock technology is used to produce transient thermal response on the coating surface. Specifically, when the laser beam is irradiated to the coating surface, due to the high energy density of the laser, the coating surface will be rapidly heated and temperature fluctuations will occur 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] In order to accurately capture this process, a high-speed infrared thermal imager is used to collect the temperature field distribution of the coating surface in real time. By sensing the infrared radiation emitted by the coating surface, the infrared thermal imager can measure the temperature distribution of the surface with high precision and obtain a complete temperature field image through rapid scanning. Due to the high-speed imaging capability of the infrared thermal imager, it can capture temperature changes in a very short time, even at the level of milliseconds to microseconds, which is critical for real-time monitoring of crack generation and expansion.

[0061] It is understandable that by acquiring the temperature data of the coating surface, the temperature gradient of different areas can be analyzed, and then the location, size and possible expansion trend of the crack can be inferred. The thermal response data provides basic data for subsequent crack size prediction and adaptive adjustment of the spraying process, so that the entire spraying process can be controlled more accurately and in real time, avoiding crack expansion and optimizing coating performance.

[0062] Step 102, 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 real-time temperature and temperature gradient data of the crack area.

[0063] In the present application embodiment, 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 on the coating surface, the temperature data in the time series are averaged to obtain a smooth temperature field, which 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] In the formula, 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 after removing the heat source area is gradient calculated. The temperature gradient reflects the rate of temperature change, and the crack area usually shows gradient characteristics different from 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 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, the thermal image of each frame at the crack location is processed, and the ambient temperature is subtracted to obtain the real-time temperature and temperature gradient data of the crack area. These data can accurately reflect the temperature change of the crack and its thermal response characteristics, providing key information for subsequent crack size prediction and adjustment of the spraying process.

[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, and predict the size and spatial position of the crack in real time.

[0073] In the embodiment of the present application, before the thermal response characteristics are input into the deep neural network, the data need 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 unified 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 [0,1] interval, or use the mean and standard deviation for standardization, so that the distribution of each feature is more uniform, which helps to accelerate the convergence of the network.

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

[0076] In the embodiment of the present application, the thermal response feature samples after data preprocessing are input into a deep neural network, and the deep neural network is trained to learn the nonlinear relationship between the thermal response features of the crack and the crack size (such as width and depth). The deep neural network can automatically extract complex features and patterns from a large amount of training data to establish an effective mapping model. This model can accurately map the thermal response data of the coating to the size and position of the crack.

[0077] Specifically, a labeled training data set (including known crack size and thermal response characteristics) is used to train the deep neural network. During the training process, the deep neural network will continuously adjust its internal parameters to minimize the error between the predicted result and the actual value. Common optimization algorithms include gradient descent, the goal of which is to continuously optimize the network weights through the back propagation algorithm, so that the prediction accuracy of the network on new data is gradually improved. This application does not make specific limitations and descriptions on this.

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

[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 rate, feed speed and multi-channel powder feeding rate) are further set according to 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] Among them, the spraying current is used to control the energy density of the plasma and is a key parameter that affects the melting state of the sprayed particles. A higher current can fully melt the particles and improve the bonding strength of the coating; a lower current is suitable for spraying thin coatings or fine features. The gas flow rate is used to adjust the morphology and stability of the plasma jet. A high flow rate enhances the penetration of the jet and helps spray thick coatings or high melting point materials; a low flow rate is suitable for spraying thin coatings with high uniformity requirements. The feed speed is used to adjust the movement speed of the spray gun relative to the workpiece, which directly affects the thickness and uniformity of the coating. A faster speed is suitable for rapid spraying of a large area, while a slower speed can enhance the thickness and strength of the local coating. The multi-channel powder feeding rate is used to accurately control the supply ratio of different powders (such as high entropy alloys and ceramic powders). By dynamically adjusting the powder feeding rate of each channel, gradient optimization can be achieved in different areas of the coating, such as increasing high entropy alloys to improve toughness, or increasing ceramic powders 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 embodiments of the present application also add 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 according to 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 tough materials can be increased, and in areas with higher wear resistance requirements, ceramic powder can be increased.

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

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

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

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

[0090] An acquisition module 200 is used to identify the crack position according to the temperature field distribution on the coating surface, and to acquire 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;

[0091] The prediction module 300 is used to establish a nonlinear mapping model between the crack thermal response characteristics and the crack size, and adopt a deep neural network for training, input the thermal response characteristics after data preprocessing into the trained deep neural network, and 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 should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0098] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0099] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means 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 representations 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, without contradiction.

[0100] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0101] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment 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 not be performed in 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 technicians in the technical field to which the embodiments of the present application belong.

[0102] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, 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 in other suitable ways if necessary, and then stored in a computer memory.

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

[0104] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0105] In addition, each functional unit in each embodiment 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 one 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 can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

[0107] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0108] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A laser shock thermal imaging composite coating spraying method 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; Identify the crack position according to 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; A nonlinear mapping model between crack thermal response characteristics and crack size is established, and a deep neural network is used 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. 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.

2. The method according to claim 1, characterized in that The method of 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 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: In the formula, 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; 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 processed temperature field is gradient calculated, and the gradient image is threshold segmented using threshold A to segment the image into crack areas and non-crack areas; 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 location of the crack; For each frame of the thermal image at the crack position, the ambient temperature is subtracted to obtain the real-time temperature and temperature gradient data of the crack area.

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

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

5. The method according to claim 4, 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.

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

7. A laser shock thermal imaging composite coating spraying device based on vision guidance, characterized in that: include: The acquisition module is used to generate transient thermal response on the coating surface through laser impact, and use a high-speed infrared thermal imager to collect the temperature field distribution on the coating surface in real time; An acquisition module, used to identify the crack position according to the temperature field distribution on the coating surface, and acquire 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; The prediction module is used to establish a nonlinear mapping model between the crack thermal response characteristics and the crack size, and adopt 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; The spraying module is used to set the target spraying parameters based on the predicted crack size information, adjust the spraying process parameters in real time through the 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.

8. 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 6.

9. 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 6 when executed by a processor.

10. 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 6 when being executed by a processor.

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